Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Recycling cocoa pod husk is important for C and nutrient retentionThe recorded cocoa yields of 559 kg ha−1 yr−1 were higher than the 477 kg ha−1yr−1 reported for the mid zone of Ghana (Abdulai et al. 2020), which has similar climatic conditions as Suhum. The average annual nutrient stocks of 19 kg N ha−1, 3 kg P ha−1, 30 kg K ha−1, 4 kg Ca ha−1 and 4 kg Mg ha−1 in the cocoa fruit (beans and pods) falls within the range of 19–29 kg N ha−1, 4–5 kg P ha−1, 15–51 kg K ha−1 (Hartemink 2005), 3–6 kg Ca ha−1, and 6–10 kg Mg ha−1 (Fontes et al. 2014) for other cocoa agroforests across the world. Farmers could reduce C export by 43% and nutrient export from harvest by 33% for N and 25% for P, if they spread the cocoa pod husks in their farms after harvest. This is even more important for nutrients such as K and Ca, where 81% and 90%, respectively, can be retained after harvest if cocoa pod husk were returned. However, the spreading cocoa pod husk is often avoided to prevent the spread of diseases from disease infested pods (Opoku-Ameyaw et al. 2010). Composting of cocoa pod husk could be an option to control disease pathogens (Opoku-Ameyaw et al. 2010) and return nutrients to the cocoa trees. Cocoa farmers in the study area may therefore benefit from training on composting to be able to use cocoa pod husk for improvement of their internal nutrient cycling. ConclusionsOur findings show that litterfall follows a seasonal pattern with peaks at the onset of the dry season. Seasonality affected litterfall K and N concentrations, but organic versus conventional management had no effect on annual litterfall. We found that management had no significant effect on decomposition and nutrient release rates of N, P, K, and Ca. Only the Mg release rate under conventional management was 51% greater than under organic management, which may be attributed to the chelating capacity of herbicides used.

Output: corroborate


Input: Claim: Baobab trees can live for over 1,000 years and store large amounts of water in their trunks. [SEP] Text: The corresponding calibrated ages are 120 ± 25 and 150 ± 25 years. The deepest segment 13x (20 cm) originating from stem II, at a height of 1. 20 m above ground, had a radiocarbon date of 170 ± 22 bp, with a corresponding calibrated age of 250 ± 25 years. 3. 5 Growth rates of the stemsCalibrated ages and positions of segments 11x and 12x, which originate from samples collected from the exterior of stem I, were used to derive the mean radial increase. Thus, stem I had grown by 3. 75 × 10−3 m year−1 over the past ca. 120 years to the height of 1. 34 m, and by 3. 40 × 10−3 m year−1 over the past ca. 150 years to the height of 2. 75 m. Such high values indicate that the very large stem I is still growing very fast. However, as reported elsewhere (Patrut et al. 2010b), the mean radial increase expresses only an apparent growth rate of trunk/stems. Trees grow in three dimensions and their size is accurately expressed by the wood volume of the trunk/stems, which is in proportion to the cross-sectional areas at different heights. Consequently, the area increase and volume increase are much better estimates of the growth rate than the radial increase. If one considers in this respect the dating results and positions of all six samples/segments collected from the cavity and the exterior of stem I, one can state that stem I accelerated its growth over the past ca. 150 years. This result corresponds to our previous research on African baobabs (Patrut et al. 2010b), as well to other research done on giant tree species (Sillett et al. 2010). The calibrated age and position of segment 13x, which originates from the sample collected from the exterior of stem II, shows that this stem had grown by only 0. 80 × 10−3 m year−1 over the past ca. 250 years to the height of 1. 20 m. This low value demonstrates that the smaller and older stem II slowed down its growth considerably and that it could be close to the final stage of the African baobab's life cycle, when trees almost stop growing. 3.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Cluster centres are characterized by high local density and large distance to the point of even higher density; they should appear as outliers in the upper-right corner of the plot (see Rodriguez and Laio, 2014, for details). In each case, there is only one such outlier (bigger dot), indicating a single cluster. selectivity of neurons in higher cortical areas has been increasingly recognized as a problem for the analysis of neurophysiological recordings, with many different approaches suggested to deal with it (Brody et al. , 2003; Machens et al. , 2010; Machens, 2010; Brendel et al. , 2011; Rigotti et al. , 2013; Pagan and Rust, 2014; Park et al. , 2014; Raposo et al. , 2014; Cunningham and Yu, 2014). The main strength and the main novelty of the method suggested here (dPCA) is that it offers a unified and principled way of analyzing such data. Demixed PCA combines the strengths of existing supervised and unsupervised approaches to neural population data analysis (Table 1, see also the first section of the Results). Supervised methods can characterize population tuning to various parameters of interest but often do not faithfully represent the whole dataset. Unsupervised methods can capture the overall variance but are not informed by task parameters. Our method yields components that capture almost as much variance as PCA does, but are demixed. We view both properties as equally important. On one hand, demixing can greatly simplify visualization and interpretation of neural population data. Indeed, in all cases presented here, all the major aspects of the population activity that had previously been reported are directly visible on the dPCA summary figure. On the other hand, faithful representation of the population activity (i. e. 'capturing variance') avoids that a particular interpretation distorts characteristic features of the data. The latter feature is particularly important for the development of theoretical models, which otherwise may inherit an interpretation bias without being aware of it. Apart from being a useful tool for analyzing any particular dataset, dPCA highlights common features of neural activity when applied to several datasets, allowing to adopt a comparative approach to study population activity. Table 1 Demixed PCA in comparison with existing methods. Columns: 'Signif. ' refers to the method of counting significantly tuned cells, as shown in Figure 1c–e.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: in through your institution issue search term Advance 10. 1038/ismej. 2013. 195 Published: 31 October 2013 10. 1038/ismej. 2013. 195 Select format. ris (Mendeley, Papers, Zotero). enw (EndNote). bibtex (BibTex). txt (Medlars, RefWorks) Download citation issue search term issue search term of northern temperate and boreal forests represent a large terrestrial carbon (C) sink. The fate of this C under elevated atmospheric CO2 and climate change is still uncertain. A fundamental knowledge gap is the extent to which ectomycorrhizal fungi (EMF) and saprotrophic fungi contribute to C cycling in the systems by soil organic matter (SOM) decomposition. In this study, we used a novel approach to generate and compare enzymatically active EMF hyphae-dominated and saprotrophic hyphae-enriched communities under field conditions. Fermentation-humus (FH)-filled mesh bags, surrounded by a sand barrier, effectively trapped EMF hyphae with a community structure comparable to that found in the surrounding FH layer, at both trophic and taxonomic levels. In contrast, over half the sequences from mesh bags with no sand barrier were identified as belonging to saprotrophic fungi. The EMF hyphae-dominated systems exhibited levels of hydrolytic and oxidative enzyme activities that were comparable to or higher than saprotroph-enriched systems. The enzymes assayed included those associated with both labile and recalcitrant SOM degradation. Our study shows that EMF hyphae are likely important contributors to current SOM turnover in sub-boreal systems. Our results also suggest that any increased EMF biomass that might result from higher below-ground C allocation by trees would not suppress C fluxes from sub-boreal soils. temperate and boreal forests represent a large carbon (C) sink, with an estimated half a gigatonne of C sequestered in above ground biomass every year (Myneni et al. , 2001). Much of this C is eventually transferred below ground, either after the death or annual senescence of vegetation or directly via photosynthetic transfer to root systems and associated ectomycorrhizal fungi (EMF).

Output: corroborate


Input: Claim: Baobab trees can live for over 1,000 years and store large amounts of water in their trunks. [SEP] Text: Moreover, important size differences between specimens growing on the same site were reported; these dissimilarities were considered to be mainly of genetic origin (Breitenbach 1985; Wickens and Lowe 2008). Our study on the Platland tree demonstrates, once again, how questionable the size–age relation is and the large errors it can generate. According to complete measurements and dating results, the very large stem I is considerably younger than the smaller stem II (overall wood. 5 vs. 136. 7 m3; age 750 vs. 1,060 years). A preliminary genetic research (Catana and Patrut, unpublished results) suggested that the two stems of Platland tree possess identical DNA and belong to a single individual. The two stems have also grown on the same site. In this case, we need an explanation for the unexpected finding that a stem which is almost three times bigger is, however, over 300 years younger than the smaller stem of the same tree. Researchers of tall and large tree species around the world learned from their field experience that the largest specimens are usually not also the oldest. Typically, the largest specimens are those which had grown very fast when they were young and, eventually, continued their rapid growth (Hartesveldt et al. 1975). The research reported here demonstrates that such statements might be also valid for the African baobab. Our research on radiocarbon dating of baobabs (Patrut et al. 2010c), as well dating results presented by other researchers (Swart 1963; Woodborne et al. 2010) show that very large specimens are not necessarily among the oldest trees and that medium-sized individuals can also be very old. If one considers that the largest baobabs are those which grew very fast over their first ca. 100 years of life and maintained their rapid growth, we may have an explanation for our results on the two stems of Platland tree. In this case, stem I had probably grown much faster and is also considerably bigger, as the climate conditions in the area over its first century of life (ca. ad 1260–1360) were probably much more favorable for baobabs than over the first century of stem II (ca. ad 950–1050). 4. 3 tree systemAccording to dating results, the present trunk of the Platland tree comprises two stems of different ages. Therefore, morphologically, it can be considered a double tree.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: TDR refers to the 'targeted dimensionality reduction' of Mante et al. (2013) shown in Figure 1f–h. LDA stands for linear discriminant analysis, but this column applies to any classification method (e. g. support vector machine, ordinal logistic regression, etc. ). All classification methods can be used to summarize population tuning via a time-dependent classification accuracy (e. g. Meyers et al. , 2012). PCA stands for principal component analysis, as shown in Figure 1i–k. FA stands for factor analysis, GPFA for Gaussian process factor analysis (Yu et al. , 2009), LDS for hidden linear dynamical system (Buesing et al. , 2012a; 2012b), jPCA is the method introduced in Churchland et al. (2012). Some of the existing methods can be extended to become more general, but here we refer to how these methods are actually used in the original research. Rows: The first two rows are the two defining goals of dPCA. Following rows highlight notable features of other methods. Signif. task parameters into account & provides summary statistics of population tuning✓✓✓✓Allows to reconstruct neural firing (captures variance)✓✓✓✓✓Based on dynamical model✓✓Based on probabilistic model✓✓✓Takes spike trains as input✓✓ Relationship to other methods, including our earlier work The method presented here is conceptually based on our previous work (Machens, 2010; Machens et al. , 2010; Brendel et al. , 2011), but is technically very different. The original approach from Machens et al. (2010) only works for two parameters of interest, such as time and stimulus. Machens (2010) suggested a partial generalization to multiple parameters and Brendel et al. (2011) introduced the full covariance decomposition and developed a probabilistic model. However, all of them imposed orthogonality on the decoder/encoder axes (and as a result did not distinguish them), a constraint that cannot be easily relaxed. While we have previously argued that orthogonality is a desirable feature of the decomposition, we now believe that it is better not to impose it upfront.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: These soils store up to three times the amount of C as is stored in the above ground vegetation, primarily in the form of soil organic matter (SOM) (Schmidt et al. , 2011). sinks have the potential to become C sources under the projected global climate change (IPCC, 2007). Although some climate models assume increased C storage in temperate forest soils because of the increased plant allocation of photosynthetic C to roots and symbiotic fungi (Drigo et al. , 2010; Orwin et al. , 2011; Clemmensen et al. , 2013), there is a high degree of uncertainly in this assumption (Higgins and Harte, 2012). allocation with elevated CO2 may not only be short-lived (Norby et al. , 2010), but concomitant increases in mycorrhizal and mycorrhizosphere activity may stimulate decomposition of previously recalcitrant SOM (Cheng et al. , 2012; Phillips et al. , 2012b). Fundamental gaps in our understanding of terrestrial C cycling, including the relative contributions of EMF and other fungi to the release of SOM-C (Bargett, 2011), limit the predictive capability of current models (Von Lützow and Kögel-Knabner, 2009; Hayes et al. , 2012). Fungi, especially saprotrophic fungi, are thought to dominate organic matter turnover in temperate forest soils (Baldrian, 2008). Certain groups of saprotrophic fungi, specifically the white rot fungi, have unique abilities to degrade wood because they possess numerous copies of class II peroxidases (Floudas et al. , 2012). Both saprotrophic and EMF species, however, produce a range of hydrolytic and oxidative enzymes that have the potential to break down C-containing compounds and mobilize nutrients from SOM (Courty et al. , 2010; Floudas et al. , 2012). Although photosynthates are likely the primary source of C used by EMF under ideal conditions (Treseder et al. , 2006; Wolfe et al. , 2012), current research suggests that these fungi may also directly (Vaario et al. , 2012) or indirectly (Rineau et al. , 2012) access SOM-C pools.

Output: corroborate


Input: Claim: Jupiter is so massive that it doesn’t orbit the Sun directly but instead orbits a point just outside the Sun called the barycenter. [SEP] Text: 닫기 논문명 참고문헌 신청 검색조건추가 참고문헌 신청이 불가능한 논문이 있습니다. 입력된 URL을 확인해 주세요. 취소 다음 확인 이용수. . . 닫기 최근3년 최근1년 최근6개월 연도 월 이용수 정기구독 정책 구독 상품을 부정한 방법으로 이용시 구독 서비스 제공을 중단할 수 있습니다. 하나의 계정으로 중복로그인 한 경우 먼저 로그인 되어있던 계정은 자동 로그아웃 됩니다.

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: First, by looking across many datasets, we have learnt that encoding subspaces can sometimes be highly non-orthogonal (Figures 3–6e) and hence not demixable under orthogonality constraints. Second, by not imposing orthogonality, we can easier identify components that are truly orthogonal. Third, removing the orthogonality constraint allowed us to obtain a simple analytical solution in terms of singular value decompositions (see Materials and methods) and hence to avoid local minima, convergence issues, and any additional optimization-related hyperparameters. To demonstrate these advantages, we ran the algorithm of Brendel et al. (2011), dPCA-2011, on all our datasets. The resulting components were similar to the components presented here, with the amount of variance captured by the first 15 components being very close; but the achieved demixing was worse. For each component we defined a demixing index (see Materials and methods) that is equal to 1 if the component is perfectly demixed. For all datasets, these indices were significantly higher with our current dPCA-2015 method than with dPCA-2011. Moreover, dPCA-2011 failed to find some weak components at all. For comparison, see Figure 14 in the Materials and methods. Another method, called 'targeted dimensionality reduction' (TDR) has recently been suggested for neural data analysis and is similar in spirit to dPCA in that it looks for demixing linear projections (Mante et al. , 2013). As mentioned above, the original application of this method yields only one component per task parameter and ignores the condition-independent components. While TDR can be extended in various ways to yield more components, no principled way of doing it has been suggested so far. Comparison of dPCA with TDR on our datasets shows that dPCA demixes the task-parameter dependencies better than TDR (see Figure 14 in the Materials and methods). For an in-depth discussion of the relationship between dPCA and LDA/we refer the reader to the Methods. Briefly, LDA is a one-way technique, meaning that only one parameter (class id) is associated with each data point. Therefore, LDA cannot directly be applied to the demixing problem.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Recalcitrant SOM, including lignin and humic complexes, is predominantly found in soil horizons dominated by EMF mycelial networks (Lindahl et al. , 2007). These mycelia are present at up to 600 kg of hyphae per hectare (Wallander et al. , 2001; Hendricks et al. , 2006) and account for up to one-third of the total microbial biomass in coniferous forests (Högberg and Högberg, 2002; Cairney, 2012). Although EMF mats are known to increase total soil CO2 respiration (Phillips et al. , 2012a), the full contribution of these fungi, and their extensive mycelia, to decomposition processes remains unknown (van der Wal et al. , 2012). Given the current interest in the C sequestration potential of boreal and temperate forests (and Boisvenue, 2012), as well as the influence of any increased C allocation below ground by plants, the role of EMF mycelia in SOM-C turnover warrants increased investigation (Talbot et al. , 2008; Ekblad et al. , 2013). Current research to estimate the extent and activity of EMF mycelia rely on methods to exclude roots and most saprotrophic fungi from a given zone in the soil profile. Hyphal in-growth cores or bags are commonly used (Wallander et al. , 2001). These hyphae traps are usually filled with an inert substrate, such as sand, that minimizes colonization by saprotrophic fungi (Korkama et al. , 2007; Lindahl et al. , 2007; Wallander et al. , 2010). In some cases, additional nutrient sources are added to ensure sufficient hyphal colonization or to evaluate the impact of limiting nutrients on EMF biomass production (Hagerberg and Wallander, 2002; Hagerberg et al. , 2003; Hedh et al. , 2008; Potila et al. , 2009; Berner et al. , 2012). However, the captured extramatrical hyphae may not accurately represent the composition or abundance of EMF communities in the surrounding soil (Hendricks et al. , 2006; Kjøller, 2006). Further, enzyme expression and activity is downregulated when no nutrients are present (Wright et al. , 2005).

Output: corroborate


Input: Claim: Saturn’s rings are composed mostly of ice particles, with some rock and dust mixed in. [SEP] Text: The maximum and minimum of the measurements at each level are shown by black lines. Contributions from the seven tagged sources in CTRL are shown by colored dashed lines. dust mass concentrations are derived from measured calcium and sodium concentrations. The measurement data are processed using the same method as Breider et al. (2014). Briefly, we assume a calcium to dust mass ratio of 6. 8 % and further correct the calcium concentrations for sea salt by assuming a calcium to sodium ratio of 4 %. Only measurements obtained north of 60∘ N are used for the analyses. The low-altitude observations near Fairbanks, Barrow, and are removed. Also, data from below 1 km on 1, 4, 5, and 9 July are removed to exclude the influence of wildfire. flight campaign was conducted in 2008, while the modeled vertical profiles are averaged for each April and July from 2007 to 2011, respectively. Zwaaftink et al. (2016), the simulation profiles are averaged for the regions north of 60∘ N and 170 to 35∘ W in April and 135 to 35∘ W in July. Also, the observations have a cutoff size of 4 µm and are thus only compared with simulated dust concentrations in the same size range. The pink shading in each panel represents the standard deviation with respect to time and space for the simulated total dust concentrations. 5Comparison of seasonal -retrieved (Luo et al. , 2015a, b; Yang et al. , 2022) (black solid line; with gray shading representing uncertainty) and model-simulated (pink solid line; with pink shading representing year-to-year variability) dust extinction vertical profiles in the Arctic (above 60∘ N). Contributions from seven tagged sources are shown by colored dashed lines. retrievals are for the year 2007 to 2009, while the model results are averaged over the same years. The uncertainties of the retrievals are assumed to be 20 % following Yang et al. (2022). Download Finally, we evaluate the simulated dust extinction against the Cloud– () retrieval (Luo et al. , 2015a, b; Yang et al. , 2022), which includes nighttime dust extinction for the period of 2007 to 2009. This dataset has improvements in dust separation from other aerosol types and thin dust layer detection in the Arctic compared to the standard Cloud–with () level 2 product (Winker et al. , 2013).

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: While LDA could be generalized to deal with several parameters in a systematic way, such a generalization has not been used for dimensionality reduction of neural data and does not have an established name in the statistical literature (we call it factorial LDA). We believe that for the purposes of dimensionality reduction, dPCA is a superior approach since it combines a reasonably high class separation with low reconstruction error, whereas LDA only optimizes class separation without taking the (potential) reconstruction error into account (see Figure 2). on the other hand, is a statistical test closely related to LDA that deals with multiple parameters. However, it deals with isolating the contribution of each parameter from residual noise rather than from the other parameters, and is therefore not suited for demixing. Limitations and future work While we believe that dPCA is an easy-to-use method of visualizing complex data sets with multiple task parameters, several limitations should be kept in mind. First, dPCA as presented here works only with discrete parameters, and all possible parameter combinations must be present in the data. This limitation is the downside of the large flexibility of the method: apart from the demixing constraint, we do not impose any other constraints on the latent variables and their estimation remains essentially non-parametric. In order to be able to treat continuous parameters or missing data (missing parameter combinations), we would need to further constrain the estimation of these latent variables, using e. g. a parametric model. One simple possibility is to directly use a parametric model for the activity of the single neurons, such as the linear model used in Mante et al. (2013), in order to fill in any missing data points, and then run dPCA subsequently. Second, the number of neurons needs to be sufficiently high in order to obtain reliable estimates of the demixed components. In our datasets, we found that at least ∼100 neurons were needed to achieve satisfactory demixing. The number is likely to be higher if more than three task parameters are to be demixed, as the number of interaction terms grows exponentially with the number of parameters. This trade-off between model complexity and demixing feasibility should be kept in mind when deciding how many parameters to put into the dPCA procedure.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: In order to estimate the contribution of EMF to the terrestrial C cycle, the potential SOM-degrading enzyme activity in locally abundant EMF hyphae needs to be determined. To investigate the relative contribution of EMF mycelia to enzyme activities in a sub-boreal spruce forest, we buried mesh bags filled with sterilized organic material with or without a surrounding sand barrier. Bags with a sand barrier were served to trap enzymatically active EMF hyphae while excluding obligately saprotrophic hyphae. Filling the mesh bags with fermentation-humic (FH) substrate taken from the same soil in which the traps were to be buried enabled a direct comparison between the EMF hyphae in the mesh bags and the surrounding FH layer. Based on our previous research, which showed that EMF root tips in these ecosystems had high hydrolytic and oxidative enzyme activities (Jones et al. , 2010, 2012), we hypothesized that EMF-dominated communities would have a SOM-degradation potential comparable to that of saprotroph-dominated communities. High-throughput sequencing, which combined with comprehensive measures of enzymes involved in SOM turnover, allowed us to evaluate the relative abundance and structure of fungal communities and to elucidate the potential role of EMF fungi in the soil C cycle. Materials and methods Field site The field site was part of a Ministry of Forests study that investigated the impact of fertilization on forest productivity and below-ground ecosystem processes (Brockley and Simpson, 2004). The current study occurred at the site, established in 1994 near Houston, British Columbia, Australia (54° 20′ 126° 17′) in the sub-boreal spruce biogeoclimatic zone, on Brunisol to Orthic Humo-soils. Each treatment within the site consisted 0. 164 hectare plots replicated three times. The current study utilized the non-fertilized control and two fertilized (annually and every 6 years) treatments; for further site information refer to Brockley and Simpson (2004). During the season of this study, the FH layer averaged 33% total C, 1. 06% total N, 64% organic matter, NH4-N, NO3-N and available P of 46, 2. 9 and 346 mg kg−1, respectively, and pH(CaCl2) 4. 35 (Ministry of ).

Output: corroborate


Input: Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: The fairly low number of endemics in the might be attributed to their land-bridge characteristics, as is also the case for the Islands [24]. However, the recent isolation of the might also be a determining factor explaining the low richness in endemic species. Islands were isolated from the mainland during the Pleistocene, or more recently, whereas the palaeogeographic evolution of the started about 23–12 million years ago [15]. Thus, there has been less time for the speciation and evolution of new species on the Ionian Islands. Most of the Ionian endemics have probably evolved before the isolation of the islands, resulting also to the low single island endemic species richness observed in the Ionian Islands. The low number of regional endemics in the small could be attributed to both increased competition and increased stochastic extinction risks. In contrast, endemics are also found on islands of the Aegean that are three orders of magnitude smaller than in the Ionian. However, the isolation and size of the islands only partially explain the difference in the number of endemics between the two archipelagos, because Greek endemics are absent from small compared to similar sized islands in the Aegean. Furthermore, if recent isolation is the only mechanism responsible for this difference, the absence of Greek endemics would not be expected. An alternative explanation might be the role of competition among species serving as a limiting factor. Precipitation levels are higher in the Ionian compared to the Aegean. Consequently, vegetation cover is higher and much denser in the Ionian compared to the Aegean. For instance, small islets are typically barren in the Aegean, whereas similarly sized islets in the Ionian are densely vegetated. This dense vegetation is primarily dominated by a few, common woody species that competitively exclude more ‘ecologically fragile’ and marginal endemic species. Consequently, most Ionian endemics tend to persist on rocky cliffs, which are areas of low interspecific competition. Another explanation for the difference in endemism levels between the two archipelagos might be based on the availability and diversity of suitable habitats for endemics. The habitats available to endemics is more restricted in the Ionian compared to the Aegean. The maximum elevation of islands hosting regional and single island endemic species is much lower in the compared to the Ionian ones (Table 1).

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: In cases when there are many task parameters of interest, dPCA is likely to be less useful than the more standard parametric single-unit approaches (such as linear regression). As a trivial example, imagine that only N=1 neuron has been recorded; it might have strong and significant tuning to various parameters of interest, but there is no way to demix (or decode) these parameters from the recorded 'population. ' Third, even with a large number of neurons, a dataset may be non-demixable, in which case dPCA would fail. For instance, if the high-variance directions of the stimulus and the decision parts of the neural activities fully overlap, then there is no linear decoder that can demix the two parameters. Finally, dPCA components corresponding to the same parameter (e. g. successive stimulus components) are here chosen to be orthogonal, similarly to PCA. This can make successive components difficult to interpret (e. g. the second and the third stimulus components in Figure 3). To make them more interpretable, the orthogonality constraint could be replaced with some other constraints, such as e. g. requiring each component to have activity 'localized' in time. This problem may be addressed in future work. Materials and methods We will first explain the dPCA algorithm in the most well-behaved case of simultaneously recorded and fully balanced data. A dataset with categorical predictors is called balanced when there is the same number of data points for each combination of predictors; in our case this means that there is the same number of trials for each combination of task parameters. This scenario is unlikely in most practical applications where the experimenter often does not have full control over some of the task parameters (such as e. g. animal’s decisions). Our suggestion for unbalanced datasets is to use what amounts to a 're-balancing' procedure as explained below. Finally, we will deal with the case of sequentially recorded neurons (all datasets analyzed in this manuscript fall into this category). Mathematical notation Request a detailed protocol In each of the datasets analyzed in this manuscript, trials can be labeled with two parameters: 'stimulus' and 'decision'. Note that a 'reward' label is not needed, because its value can be deduced from the other two due to the deterministic reward protocols in all tasks.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Hyphal trap bags were buried at the site on 5 June 2009. Each set of bags (with or without sand barriers, +SB and –SB, respectively) were placed in the FH layer of the treatment plots from which the substrates were originally taken. One pair of +SB and –SB bags was buried at five random spots within each plot, 60 cm from the base of a tree and 60 cm from each other. All bags and an FH sample (10 × 10 cm) from between each set were harvested on 1 October 2009. Upon return to the lab, the five +SB, −SB or FH sub-samples from individual plots were composited, so that a single representative +SB, −SB and FH sample was obtained per plot. Sand from the +S mesh bags was also composited on a plot basis. Half of each composited sample was frozen at −20 °C for subsequent molecular analysis and half was stored at 4 °C overnight for use in enzyme assays. Enzyme assays All +SB, −SB, FH, sand and residual-activity control samples, as well as EMF tips selected from the FH samples, were assessed for their organic matter breakdown potential. The assessed enzymes included β-1,4-glucosidase (EC 3. 2. 1. 21), cellobiohydrolase (EC 3. 2. 1. 91), β-1,4-xylosidase (EC 3. 2. 1. 37), peroxidase (EC 1. 11. 1. 7), laccase (EC 1. 10. 3. 2), phenol oxidase (EC 1. 14. 18. 1 and EC 1. 10. 3. 2), β-1,4-N-acetylglucosaminidase (EC 3. 2. 1. 52), acid phosphatase (EC 3. 1. 3. 2), leucine aminopeptidase (EC 3. 4. 11. 1) and sulfatase (EC 3. 1. 6. 1).

Output: corroborate


Input: Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: IntroductionFungi are an essential functional component of terrestrial ecosystems as decomposers, symbionts and pathogens (Mueller et al. 2007) and they represent one of the most biodiverse groups of organisms on earth (Hawksworth 1991, 2001). However, our knowledge of their diversity and ecological function in Neotropical lowland forests is limited. The ecological interaction of macrofungi with other organisms in these forests is poorly understood due to the largely unexplored, but likely huge, fungal diversity, as well as the cryptic and ephemeral nature of many fungal species. Incomplete information on the biodiversity of macrofungi from such ecosystems is only available from scattered sources (Lodge and Cantrell 1995; Lodge 1997; Jiménez-Valverde and Hortal 2003; Piepenbring 2007; Schmit and Mueller 2007; Swapna et al. 2008). A major part of the global but unknown fungal biodiversity is assumed to occur in tropical regions, where the diversity of fungi may be higher than in temperate regions, because of more favorable environmental conditions throughout the year, a higher diversity of vascular plants that create niches and microhabitats for fungi, and the presence of many ecotones (Hawksworth 2001; Kark 2007). The diversity of macrofungi in tropical forests as assessed by Lodge et al. (1995) showed that the highest diversity in the Neotropics occurred in the Amazon basin with aphyllophoralean, pyrenomycetous, xylariaceous and hyphomycetous fungi being most species rich. rainforest is arguably the most species-rich terrestrial ecosystem in the world (Hoorn et al. 2010). Biodiversity studies in Amazon forests have focused mainly on plants, especially tree species (Gentry 1988a; Duivenvoorden 1996; Pitman et al. 2001; Condit et al. 2002) and revealed that these forests hold a very high number of plant species (Gentry 1988a; Valencia et al. 1994; Rudas and Prieto 1998; et al. 2003; Duque 2004; Hoorn et al. 2010). Despite this extensive plant and animal biodiversity, the region is not yet recognized as a biodiversity hotspot (Myers et al. 2000) ( December 2009). The study of fungal biodiversity in tropical lowland rain forests is urgent as these are rapidly decreasing due to deforestation (Brown et al.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Let us define the following marginalized averages: x¯=⟨xtsdk⟩tsdk=x¯⋅⋅⋅⋅x¯t=⟨xtsdk−x¯⟩sdk=x¯t⋅⋅⋅−x¯⋅⋅⋅⋅x¯s=⟨xtsdk−x¯⟩tdk=x¯⋅s⋅⋅−x¯⋅⋅⋅⋅x¯d=⟨xtsdk−x¯⟩tsk=x¯⋅⋅d⋅−x¯⋅⋅⋅⋅x¯ts=⟨xtsdk−x¯−x¯t−x¯s−x¯d⟩dk=x¯ts⋅⋅−x¯t⋅⋅⋅−x¯⋅s⋅⋅+x¯⋅⋅⋅⋅x¯td=⟨xtsdk−x¯−x¯t−x¯s−x¯d⟩sk=x¯t⋅d⋅−x¯t⋅⋅⋅−x¯⋅⋅d⋅+x¯⋅⋅⋅⋅x¯sd=⟨xtsdk−x¯−x¯t−x¯s−x¯d⟩tk=x¯⋅sd⋅−x¯⋅s⋅⋅−x¯⋅⋅d⋅+x¯⋅⋅⋅⋅x¯tsd=⟨xtsdk−x¯−x¯t−x¯s−x¯d−x¯ts−x¯td−x¯sd⟩k=x¯tsd⋅−x¯ts⋅⋅−x¯⋅sd⋅−x¯t⋅d⋅=+x

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Data are provided as means (n=9) with s. d. in brackets. EMF tips, matching sequences belonged to unidentified ectomycorrhizal tips. aThe most common (>0. 5% relative abundance) fungal groups are given at the genus level. Fungal functional diversity The presence of a sand barrier had a significant impact on fungal groups colonizing the hyphal traps. Hyphal traps with a sand barrier were primarily colonized by EMF, whereas hyphal traps without a sand barrier were equally colonized by saprotrophs and EMF (Figure 2a). As previously noted, −SB traps were heavily colonized by Mortierella spp. , which comprised the majority of the identified saprotrophs. The trophic groups that colonized the +SB traps were highly comparable to those in the surrounding FH layer (Figure 2b), with the two groups clustering at ∼98% similarity. Both of these communities contained significantly lower populations of known saprotrophs than the −SB community (Figure 2c). Figure 2Fungal functional group diversity in the different hyphal trap bags, sand barriers and FH layers of the forest floor. −SB, hyphal trap bags with no sand barrier; FH, in situ fermentation-humic layer; +SB, hyphal trap bags with sand barrier; Sand, sand barrier; n=9 each. (a) The relative abundance of the different ecological functional groups in each compartment. The total number of contributing sequence reads is indicated next to each histogram. (b) Cluster analysis of functional group diversity, indicating the percent similarity (based on Pearson’s r correlation) of communities in the different compartments. (c) Differences in the abundance of each functional group. Communities in the same row with different letters are different at the indicated significance level; NS, not significant. Other mycorrhizae: ericoid, arbutoid, monitropoid and arbuscular mycorrhizae; mixed functional groups: belong to more than one functional group. Unknown fungi indicates that the best matching sequence belonged to the kingdom Fungi. Open in new tabDownload slide Ectomycorrhizal tip diversity A total of 226 EMF tips were selected, based on abundant morphotypes, from the FH layer adjacent to the hyphal trap bags.

Output: corroborate


Input: Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: Country-level endemism showed that Loja and Guayas had most endemics in Ecuador (12 and 11 species, respectively), and Tumbes (9 species) in Peru. The ratio woody SDF endemics versus total vascular plant endemics showed that Tumbes had a substantial percentage of the endemics reported for that department in the SDF vegetation. Woody SDF endemics per 1,000 km2 of the study area were highest in Loja, Tumbes and (Table 3). Collection intensity, i. e. , the number of collections per species of woody plants in the SDFs, has been highest in Guayas (ca. eight collections per species), Tumbes (ca. six collections per species) and Manabí (ca. five collections per species). Lowest collection intensity was in the SDFs of Piura. Table 3 Species and endemism numbers for provinces and departments with seasonally dry forests in western Ecuador and northwestern PeruFull size table DiscussionPatterns of species richness, endemism and distributionIn the first comprehensive review of the floristics of neotropical SDF (1995) noted that SDF ecosystems were less species rich and contained only a subset of the plant diversity found in the more humid forests. The lower diversity in the SDFs is clearly due to the low levels of diversity within families and genera. A notable exception is Leguminosae. This family showed high levels of diversity at the generic (34 genera, 19% of the total), specific (70 species, 22% of the total) and endemic species level (15 endemics, 21% of the total). This is not surprising since several studies have shown that this family is among the most, if not the most, prominent members of SDF in the Neotropics (Gentry 1995; Pennington et al. 2006). Malvaceae, on the contrary, are not necessarily regarded as important constituents of tropical dry forest communities (Pennington et al. 2006).

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: ¯t⋅⋅⋅+x¯⋅s⋅⋅+x¯⋅⋅d⋅−x¯⋅⋅⋅⋅ϵtsdk=xtsdk−⟨xtsdk⟩k=xtsdk−x¯tsd⋅.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Sequencing of the complete ITS region confirmed EMF tip identity at the following relative abundances: Cenococcum (24%), Piloderma (24%), Amphinema (17%), Tylospora (12%), Cortinarius (12%), Hygrophorus (5%) and Hysterangium (3%) spp. Approximately half of the Cenococcum tips exhibited multiple banding patterns after PCR amplification of the ITS region. Band-specific sequencing confirmed that these tips contained Cenococcum sequences in association with Phialocephala, Piloderma, Mycena or Cadophora spp. sequences (S4). For the purposes of enzyme activity assays (below), these tips were considered as Cenococcum based on the distinctive mantle formed by this fungus. Activity of enzymes associated with SOM breakdown The hydrolytic and oxidative enzyme activity potentials were assessed for all components of the hyphal trap systems, the adjacent FH layer and residual-activity controls (Table 3), and for EMF tips sampled from the FH layer (Table 4). Residual enzyme activity was detected for most enzymes in the gamma-irradiated control soils, which were kept under dry and sterile conditions in the lab. This residual activity was generally lower than that in the field samples, however, β-1,4-xylosidase and cellobiohydrolase activities were not statistically different from the hyphal traps (Table 3). Exploratory PCA analysis showed that activity patterns in the control samples were distinct from those in the field samples (S5), indicating that active fungi had a dominant influence on enzyme activity under the study conditions. These differences in magnitude and pattern, combined with the likelihood that residual enzymes would have been degraded under non-sterile field conditions (Allison, 2006), informed the decision not to subtract the control from field enzyme activities. Table 3 Open in new tab Potential organic matter breakdown activity in the hyphal trap bags, associated sand, adjacent forest floor and residual-activity controls Enzyme. Enzyme activity (μmol h−1 g−1). . General role. −SB. FH. +SB. Sand. Control. Acid phosphatasePhosphate mobilization3. 06 (0. 51) b5. 11 (1. 06) a2. 53 (0. 80) b<0. 01 (0.

Output: corroborate


Input: Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: Tocher’s method partially agreed with the UPGMA method (Figure 1), since group II generated by the former had the same composition as group V obtained by UPGMA. Tocher’s group I included all other groups formed by the UPGMA method (groups and IV); however, if the average distance of 0. 78 had been used as a cutoff point, there would be 100% agreement between the two clustering methods. Both methods have been widely used in studies of populations; records of similarities between both have been reported by several authors (Büttow et al. , 2010Büttow. Caracterização Molecular de Populações de (Arecaceae) do Sul do Brasil através de Marcadores AFLP. De Fruticultura, 2010; 32:230-239. ; Campos et al. , 2010. Avaliação de Acessos de Mandioca do Banco de Germoplasma da Unemat Cáceres - Mato Grosso. Revista Trópica - Ciências Agrárias e Biológicas, 2010; 4(2): 44-54. ; Bertan et al. , 2014da, et al. Comparação de Métodos de Agrupamento na Representação da Distância Morfológica entre Genótipos de Trigo. Technology, 2014; 12(3): 279-286. ). Many specimens from the sampled area are drying and dying as a consequence of the drastic change in the natural environment of the species, which causes it to not adapt to the new microclimate conditions created after the disturbance. Forest fragmentation, which is a direct and inevitable consequence of deforestation, can affect the Amazon biome in many ways, altering the diversity and communities of the formed fragments, as well as changing ecological processes such as pollination, nutrient cycling and carbon stocking (Laurence & Vasconcelos, 2009). According to Tabarelli et al. (2012. A conversão da floresta atlântica em paisagens antrópicas: lições para a conservação da diversidade biológica das florestas tropicais. Interciencia, 2012; 37(2): 88-92.

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Here x¯ is simply the overall mean firing rate of our neuron, x¯t is the average time-varying firing rate once the overall mean has been subtracted, etc.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: 79 (0. 47) a<0. 01 (0. 00) b0. 21 (0. 40) bβ-1,4-GlucosidaseCellulose degradation2. 23 (0. 69) b3. 05 (0. 91) ab3. 44 (1. 12) a0. 01 (0. 00) c0. 70 (0. 56) cCellobiohydrolaseCellulose degradation0. 77 (0. 58) ab0. 87 (0. 56) ab1. 07 (0. 61) a<0. 01 (0. 00) c0. 36 (0. 20) bcβ-1,4-XylosidaseHemicellulose degradation0. 12 (0. 06) ab0. 22 (0. 18) a0. 19 (0. 10) a<0. 01 (0. 00) b0. 10 (0. 05) abLaccaseaLignin degradation13. 3 (12. 5) ab19. 0 (11. 3) a5. 4 (8. 3) bND4. 13 (4. 6) bPeroxidaseaLignin degradation19. 2 (15. 4) c39. 2 (18. 4) ab48. 9 (16. 5) aND27. 5 (12. 6) bcPhenol oxidaseaLignin degradation0. 5 (1. 3) b0. 7 (1. 1) b3. 5 (2. 6) aND1. 54 (1. 00) abAbbreviations: control, residual enzyme activity in irradiated controls (irradiated control bags not placed in the field); FH, in situ fermentation-humic layer; ND, no activity detected; −SB, hyphal trap bags with no sand barrier; +SB, hyphal trap bags with sand barrier; Sand, sand barrier. Data are provided as means (n=9) with s. d. in brackets. Data in the same row with different letters are significantly different at P<0. 01. aActivities given as mmol h−1 g−1. Table 4 Open in new tab Potential organic matter breakdown activity of ectomycorrhizal tips Enzyme. Enzyme activity (pmol min−1 mm−2). .

Output: corroborate


Input: Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: The first large worm lizard described from Angola was Lepidostsernon (Phractogonus) anchietae Bocage 1873, from ‘Humbe, dans l’intérior de Mossamedes’, later transferred to the genus Monopeltis (Boulenger 1885). Broadley et al. (1976) relegated Monard’s (1937) species M. okavangensis and M. devisi to M. anchietae, which is now known to have a wider range in northern Namibia and adjacent Botswana. Monopeltis vanderysti vilhenai, described by Laurent (1954) from Dundo, Angola, was not recognised by Broadley et al. (1976) and returned to M. vanderysti, which is widely distributed in the Congo region. specimen remains the only Angolan record of the species. Monopeltis capensis in Angola was first recorded by Bocage (1873), and later by Monard (1937). Although provisionally placed in the M. capensis capensis (Broadley et al. 1976), with a wide range through the Kalahari region (Northern Cape, South Africa, through Botswana to southern Angola), it was later treated as a separate species, M. infuscata Broadley 1997a. Monard’s (1937) species Monopeltis granti kuanyamarum, described from a single specimen from Mupanda, was transferred to Dalophia pistillum (Broadley et al. 1976). The only Angolan specimen of Dalophia ellenbergeri was collected whilst trench digging during hostilities at (Branch and McCartney 1992; Broadley 1997b). Gray (1865) described Dalophia welwitschii from Pungo Andongo, and this has not been rediscovered. It is the type species for the genus Dalophia, and Gans (2005) was obviously in error when treating it as M. welwitschii and yet still continuing to recognise Dalophia. A phylogeny of African amphisbaenids (Measey and Tolley 2013), albeit based on poor taxon sampling, recovered Monopeltis and Dalophia as monophyletic clades, supporting the use of Dalophia welwitschii.

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The right-hand side shows the same averaging procedure in the more explicit form using ANOVA-style notation, in which averages of x over everything apart from the explicitly mentioned parameters, e. g. , the stimulus s, are denoted by terms of the form x¯⋅s⋅⋅. One can directly see that the original neural activities are given by the sum of all marginalizations: xtsdk=x¯+x¯t+x¯s+x¯d+x¯ts+x¯td+x¯ds+x¯tsd+ϵtsdk. This decomposition is identical to the one used in factorial ANOVA (Rutherford, 2001; Christensen, 2011) where task parameters are called factors. literature uses a slightly different notation with task parameters (t,s,d,k) replaced by indices (i,j,k,l) and with Greek letters designating individual terms: xijkl=μ+αi+βj+γk+δij+ζjk+ηik+θijk+ϵijkl. We will use our notation, though, to keep the connection with the task parameters more explicit. For the purposes of demixing neural signals in the context of our datasets, we combine some of these terms together. Indeed, demixing a time-independent pure stimulus term x¯s from a stimulus-time interaction term x¯ts makes little sense because we expect all neural components to change with time. Hence, we group the terms as follows (without changing the notation): xtsdk=x¯+x¯t+x¯s+x¯ts⏟x¯ts+x¯d+x¯td⏟x¯td+x¯sd+x¯tsd⏟x¯tsd+ϵtsdk. Here the first term on the right-hand side is the mean firing rate, the last term is the trial-to-trial noise, and we call the other terms condition-independent term, stimulus term, decision term, and stimulus-decision interaction term. This decomposition is illustrated in Figure 8 for several exemplary neurons (we only show the decomposition of the PSTH part, leaving out the noise term). Figure 8 Download asset Open asset Marginalization procedure.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: 05) enzyme activities associated with cellulose and hemicellulose degradation than did Piloderma or Cenococcum tips. The hydrolytic enzyme activity of these EMF tips was highly correlated with that of the FH layer from which they were sampled (Table 5). Both the average tip activity and the individually assessed activities of Amphinema and Piloderma spp. were positively associated with cellobiohydrolase, glucosidase and xylosidase activities within the surrounding FH layer. Table 5 Open in new tab Direct correlation (Spearmans r) between enzyme activities of the ectomycorrhizal tips selected from the FH layer of the forest floor and the activities of the FH layer. Enzyme. EMF tipsa. Amphinema. Piloderma. FH layerXylosidase0. 850**0. 729*NSFH layerCellobiohydrolase0. 883**0. 712*0. 750*FH layerβ-Glucosidase0. 833**0. 797*NS. Enzyme. EMF tipsa. Amphinema. Piloderma. FH layerXylosidase0. 850**0. 729*NSFH layerCellobiohydrolase0. 883**0. 712*0. 750*FH layerβ-Glucosidase0. 833**0. 797*NSAbbreviation: FH, in situ fermentation-humic layer. Correlations are significant at *P<0. 05, **P<0. 01. Note that only enzymes and EMF tips that were significantly related to FH are shown. aEMF tips, correlation with the average activity of all ectomycorrhizal tips. Table 5 Open in new tab Direct correlation (Spearmans r) between enzyme activities of the ectomycorrhizal tips selected from the FH layer of the forest floor and the activities of the FH layer. Enzyme. EMF tipsa. Amphinema. Piloderma. FH layerXylosidase0. 850**0. 729*NSFH layerCellobiohydrolase0. 883**0. 712*0. 750*FH layerβ-Glucosidase0. 833**0. 797*NS. Enzyme. EMF tipsa.

Output: corroborate


Input: Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: It would appear that perhaps as many as 75+ new lizard species await discovery in Angola, and that many of these will be endemic. Branch (2014) noted that endemicity in lizards was particularly evident in rupicolous forms (many geckos, cordylids and skinks) associated with rocky outcrops. Rock exposures may form an archipelago of ‘sky islands’ on which isolation inhibits gene flow and thus leads to speciation. It is the lizard families containing large numbers of rupicolous species, i. e. Gekkonidae, Cordylidae and Scincidae, already show the greatest levels of endemicity among Angolan reptiles, and in which recent surveys have already identified numerous cryptic taxa (Stanley et al. 2016; Branch et al. 2017). Fig. 13. 1Species accumulation curves for reptile discoveries in Angola (blue) and southern Africa (red) showing the relative stagnation of Angolan reptile species descriptions during the twentieth century. By the end of the nineteenth century 67. 4% of Angolan reptiles had already been described, in contrast to less than half (47. 8%) of those in southern AfricaFull size imageof ‘Hotspots’protected areas underperform in their stated conservation goals (Lindsey et al. 2014; Bowker et al. 2017). It is now generally accepted that modern national and internationally co-ordinated networks of protected area should be designed to cover important biodiversity hotspots and also protect habitats essential for the maintenance of ecosystem services such as water flow and quality, nutrient transfer, etc. (NPAES 2010). Such a revised Angolan network was proposed many years ago (Huntley 1974), and initial biodiversity surveys to gain insight into biodiversity in potential sites were undertaken (Huntley 2009; Huntley and Francisco 2015). Recent studies have shown that protected reserves designed to protect mammals, birds and amphibians are effective in protecting snakes, but fare badly in protecting African lizard diversity (Lewin et al. 2016; Roll et al. 2017). Future systematic biodiversity surveys should be directed to unique habitats and landforms in undersampled regions. Some of the interesting species recorded on recent field surveys are illustrated in Fig. 13. 2. Fig. 13. 2Angolan reptiles. Top to bottom, left to right. Bogert’s (Afroedura cf.

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: PSTHs of three exemplary neurons from the somatosensory working memory task decomposed into marginalizations. We apply this marginalization procedure to every neuron, splitting the whole data matrix 𝐗 into parts. Assuming from now on that the data matrix is centered (i. e. x¯=0 for all neurons), we can write the decomposition in the matrix form X=Xt+Xts+Xtd+Xtsd+Xnoise=∑ϕXϕ+Xnoise. Here t, ts, td, and tsd are labels and not indices, and all terms are understood to be matrices of the same N×KSQT size, so e. g. 𝐗t is not an N×T sized matrix, but the full size N×KSQT matrix with N×T unique values replicated KSQ times. Crucially, the marginalization procedure ensures that all terms are uncorrelated and that the N×N covariance matrix 𝐂=𝐗𝐗⊤/(KSQT) is linearly decomposed into the sum of covariance matrices from each marginalization (see 2. In the remaining discussion, it will often be sufficient to focus on the individual loss functions Lϕ, in which case we will drop the indices ϕ on the decoder and encoder matrices for notational convenience, and simply write 𝐅 and 𝐃. Without any additional constraints, the decoder and encoder are only defined up to their product 𝐅𝐃 of rank q. To make the decomposition unique, we will assume that 𝐅 has orthonormal columns and that components are ordered such that their variance (row variance of 𝐃𝐗) is decreasing. The reason for this choice will become clear below. This loss function penalizes the difference between the marginalized data 𝐗ϕ and the reconstructed full data 𝐗, i. e. , the full data projected with the decoders 𝐃 onto a low-dimensional latent space and then reconstructed with the encoders 𝐅 (see Video 2).

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Amphinema. Piloderma. FH layerXylosidase0. 850**0. 729*NSFH layerCellobiohydrolase0. 883**0. 712*0. 750*FH layerβ-Glucosidase0. 833**0. 797*NSAbbreviation: FH, in situ fermentation-humic layer. Correlations are significant at *P<0. 05, **P<0. 01. Note that only enzymes and EMF tips that were significantly related to FH are shown. aEMF tips, correlation with the average activity of all ectomycorrhizal tips. Impact of fungal community structure on enzyme activity The relationships between fungal community structure and the activity of enzymes associated with organic matter breakdown were explored by PCA. Sand samples were not included as they exhibited little enzyme activity. Differences in enzyme activity patterns between the in situ FH layer and hyphal traps were best described by the first three PCA axes (Figure 3). Location along PCA axis 1 (PC1) was driven primarily by the activities of cellulose- and hemicellulose-degrading enzymes, which strongly overlapped in the FH layer and +SB traps. Activities in these communities were also strongly associated along PC3, reflecting similar patterns of lignin degradation (peroxidase) potential. The combined effects of the PC1- and PC3-loading factors resulted in a distinct separation of the −SB traps. Both FH and −SB differed from +SB along PC2, however, reflecting variation in enzymes associated with phosphorus release, lignin degradation (laccase versus phenol oxidase) and nitrogen release. Figure 3PCA ordination of potential enzyme activity in the hyphal trap bags and the FH layers of the forest floor. The proportion of variance explained by each axis is shown, and the contribution of each enzyme in the ordination is indicated by the plot vectors. −SB, hyphal trap bags with no sand barrier; FH, in situ fermentation-humic layer; +SB, hyphal trap bags with sand barrier.

Output: corroborate


Input: Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: Figure 5Distribution of endemic vascular plant species in general and of range-restricted endemic vascular plant species in Iran and its areas of endemism. Full size imageFigure 6Endemic vascular plant species of Iran in areas of endemism. Endemic vascular plant species restricted to an area of endemism are indicated in dark grey, whereas those shared with other areas of endemism are indicated in light grey, their numbers being given in the bars connecting pie charts corresponding to areas of endemism. Full size imageTable 5 Vascular plant endemism in the areas of endemism in Iran. Full size tableThe second-richest area is Alborz, which harbours 29% of all Iranian vascular plant endemics nearly half of them being restricted to Alborz (Figs 5, 6 and Table 5). If taking area size into account, Alborz is richer than other regions. This is particularly evident for range-restricted endemics (here species which are only in maximally three grid cells of 0. 5° × 0. 5°), where, compared to Zagros, Alborz has half the number of endemics on one fifth the area (Fig. 5; Table 5). Generally, has the highest concentration of endemics in the (Noroozi et al. 2018). Alborz is a narrow, but very high and contiguous east-west oriented mountain range (Fig. 1) that borders on lowland deserts in the south and on the in the north. A wide elevational range, high topographic complexity and strong environmental heterogeneity explain its high plant diversity and great richness of endemics19. Alborz connects Kopet Dagh-Khorassan mountains in the east with the and Zagros in the west (evident also in the high numbers of endemic species shared between Alborz and at least one of these regions; Fig. 6), thus acting as a corridor between and Caucasus plus Anatolian mountains. Therefore, Alborz not only harbours a high number of local endemics, but also many elements showing biogeographic connections east- and/or westwards. In the Azerbaijan Plateau, 21% of Iranian vascular plant endemics are found, half of which are endemic to this area (Figs 5, 6 and Table 5). Plateau has a fragmented orography caused by different tectonic and volcanic activities44.

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The loss function thereby favours variance in marginalization ϕ and punishes variance coming from all other marginalizations and from trial-to-trial noise. Given that the marginalized averages are uncorrelated with each other, we can make this observation clear by writing, Lϕ=∥Xϕ−FDX∥2=∥Xϕ−FDXϕ∥2+∥FD(X−Xϕ)∥2. Here the first term corresponds to the non-explained variance in marginalization ϕ and the second term corresponds to the variance coming from all other marginalizations and from trial-to-trial noise. The dPCA objective is to minimize both. Video 2 Download asset This video cannot be played in place because your browser does support HTML5 video. You may still download the video for offline viewing. Download as MPEG-4 Download as WebM Download as of the dPCA algorithm. Illustration of the dPCA algorithm using the somatosensory working memory task. We note that the loss function Lϕ is of the general form ∥𝐗ϕ-𝐀𝐗∥2, with 𝐀=𝐅𝐃. For an arbitrary N×N matrix 𝐀, minimization of the loss function amounts to a classical regression problem with the well-known ordinary least squares (OLS) solution, 𝐀OLS=𝐗ϕ𝐗(𝐗𝐗)⊤-1⊤. In our case, 𝐀=𝐅𝐃 is an N×N matrix of rank q, which we will make explicit by writing 𝐀q. The dPCA loss function therefore amounts to a linear regression problem with an additional rank constraint on the matrix of regression coefficients. This problem is known as reduced-rank regression (RRR) (Izenman, 1975; Reinsel and Velu, 1998; Izenman, 2008) and can be solved via the singular value decomposition. To see this, we write Xϕ−AqX=(Xϕ−AOLSX)+(AOLSX−AqX).

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Cel, cellobiohydrolase; Glu, β-1,4-glucosidase; N-Ag, β-1,4-N-acetylglucosaminidase; Pho, acid phosphatase; Sul, sulfatase; peptidase, leucine aminopeptidase; Lac, laccase; perox, peroxidase; phenox, phenol oxidase PC1 is associated with Glu (r=0. 895), Cel (r=0. 599), Xyl (r=0. 851) and N-Ag (r=0. 865); PC2 is associated with Pho (r=-0. 799), peptidase (r=0. 650), Lac (r=-0. 695) and Phenox (r=0. 729); PC3 is associated with Sul (r=0. 798), peptidase (r=0. 484) and Perox (r=0. 702). All associations are significant at P<0. 05. Open in new tabDownload slideThe relative abundance of fungi in a given community was significantly associated (P<0. 05) with patterns of enzyme activity. Joint plot vectors in Figure 4 illustrate the strength and direction of significant correlations between fungal communities and enzyme-loading factors at three different levels of community structure. At the ecological function level, the abundance of saprotrophic fungi was strongly related to the patterns of enzyme activity that separated −SB communities from the other two communities along PC1 and PC3 (Figure 4a). This association continued at the higher (Figure 4b, that is, Fungi I. sedis) and lower taxonomic levels (Figure 4c, that is, Mortierella spp. ). By contrast, the enzyme-loading factors along axes PC1 and PC3 that influenced +SB and FH community clustering were generally correlated with the relative abundance of EMF and other mycorrhizal fungi. The abundance of specific EMF genera also strongly correlated with the enzyme-loading factors of PC2 (Figure 4c), which as mentioned above, largely reflects variations in enzymes associated with phosphorus and nitrogen release and lignin degradation.

Output: corroborate


Input: Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: (Noroozi et al. , unpublished data). A typical representative of the local endemic flora is Acantholimon (Plumbaginaceae; Fig. 4b), which is highly diverse in this area50,51. Additionally, the Yazd-Kerman harbours isolated occurrences of species otherwise distributed in Hindukush and mountains, especially in alpine regions30,49,52,53. Considerable differences can be observed among areas of endemism with respect to the richness of the 10 largest genera of the Iranian endemic vascular flora (Fig. 4b), as shall be illustrated with the following examples. Astragalus (Fabaceae), Allium (Alliaceae) and Centaurea (Asteraceae) are well represented in the Azerbaijan Plateau, but Acantholimon (Plumbaginaceae), Cousinia (Asteraceae) and Echinops (Asteraceae) are underrepresented in this area. Acantholimon, Echinops and Nepeta (Lamiaceae) are very well represented in Yazd-Kerman and Cousinia is very diverse in Kopet Dagh-Khorassan. Oxytropis (Fabaceae) is diverse in both Alborz and Kopet Dagh-Khorassan, but very poor in Zagros. The proportion of Silene (Caryophyllaceae) is high in Zagros, and Dionysia (Primulaceae) is a Zagros element with 29 endemic species in this mountain range, three species in Yazd-Kerman and only one species in Alborz. Elevational distribution of endemicsThe elevational distributions of surface area, non-endemic and endemic vascular plant species of Iran are displaced and peak at different elevations, as has already been found before on a taxonomically much smaller data set19. Despite a high proportion of surface area in lowlands (−26 to 1400 m), the proportions of non-endemics and especially of endemics in this elevation zone are low (Fig. 7). At mid elevations (1400 to 2800 m a. s. l. ), the proportion of both non-endemics and endemics are high. At high elevations (2800 to 5671 m a. s. l.

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The first term, 𝐗ϕ-𝐀OLS𝐗, consists of the regression residuals that cannot be accounted for by any linear transformation of 𝐗. It is straightforward to verify that these regression residuals, 𝐗ϕ-𝐀OLS𝐗, are orthogonal to 𝐗 (Hastie et al. , 2009, Section 3. 2) and hence also orthogonal to (𝐀OLS-𝐀q)𝐗. This orthogonality allows us to split the loss function into two terms, ∥Xϕ−AqX∥2=∥Xϕ−AOLSX∥2+∥AOLSX−AqX∥2, where the first term captures the (unavoidable) error of the least squares fit while the second term describes the additional loss suffered through the rank constraint. Since the first term does not depend on 𝐀q, the problem reduces to minimizing the second term. To minimize the second term, we note that the best rank-q approximation to 𝐀OLS𝐗 is given by its first q principal components (Eckart-Young-Mirsky theorem). Accordingly, if we write 𝐔q for the matrix of the q leading principal directions (left singular vectors) 𝐮i of 𝐀OLS𝐗, then the best approximation is given by 𝐔q𝐔q⊤𝐀OLS𝐗 and hence 𝐀q=𝐔q𝐔q⊤𝐀OLS. To summarize, the reduced-rank regression problem posed above can be solved in a three-step procedure: Compute the OLS solution 𝐀OLS=𝐗ϕ𝐗⊤(𝐗𝐗⊤)-1.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: These fungi included Amphinema, which was strongly associated with +SB enzyme patterns, and Piloderma, Cortinarius and Tylospora, which were strongly associated with FH enzyme patterns. Figure 4Relationship between fungal communities and the potential enzyme activities in hyphal trap bags and the FH layers of the forest floor. −SB, hyphal trap bags with no sand barrier; FH, in situ fermentation-humic layer; +SB, hyphal trap bags with sand barrier. Joint plots vectors indicate the strength and direction of significant (P<0. 05) correlations between enzyme PC-loading factors and fungal communities at the level of (a) ecological function, (b) phyla and (c) genus or closest identity. The direction and strength of enzyme correlations contributing to each PC axis (as in Figure 3) are indicated with the abbreviations Cel, cellobiohydrolase; Glu, β-1,4-glucosidase; N-Ag, β-1,4-N-acetylglucosaminidase; Pho, acid phosphatase; Sul, sulfatase; peptidase, leucine aminopeptidase; Lac, laccase; Perox, peroxidase. Open in new tabDownload slide large fraction of terrestrial ecosystem C pools, with estimates as high as 1700 PgC (and Boisvenue, 2012), are sequestered as SOM in the soils of temperate and boreal forests (Schmidt et al. , 2011). Increased temperature and atmospheric CO2 levels associated with climate change are predicted to increase the decomposition of this SOM-C pool (Fierer et al. , 2005; Conant et al. , 2008; Craine et al. , 2010). The majority of this SOM is found in the FH horizons dominated by EMF and not saprotrophic fungi (Lindahl et al. , 2007). Quantifying the contribution of EMF mycelia to SOM turnover is, therefore, of critical importance to Cmodels (Cairney, 2012). Potential contribution of EMF mycelia to soil organic C turnover It has generally been believed that saprotrophic fungi bear primary responsibility for recalcitrant SOM-C decomposition in forest soils (Baldrian, 2008). Recently, Talbot et al.

Output: corroborate


Input: Claim: Biodiversity hotspots, such as the Amazon rainforest, are regions with high species diversity and endemism. [SEP] Text: 2984 (2017). 10. 1016/j. cub. 2016. 09. 045 (2016). 10. 2307/2413466 (1994). 10. 1007/s00035-015-0160-4 (2016). 10. 1038/s41598-018-28504-9 (2018). 10. 1111/jbi. 12185 (2014). 10. 1111/j. 1095-8339. 2004. 00310. x (2004). 10. 1111/j. 1095-8339. 2007. 00707. x (2007). 10. 1017/S0269727000008976 (1986). 10. 11646/phytotaxa. 249. 1. 5 (2016). 10. 11646/phytotaxa. 249. 1. 4 (2016). 10. 1007/s10531-007-9246-7 (2008). 10. 1007/s10531-011-0029-9 (2011). 10. 1016/j. flora. 2006. 10. 004 (2007). 10. 3372/wi. 38. 38201 (2008). 10. 1007/s10531-016-1254-z (2017). 10. 1080/17550874. 2012. 698660 (2011). 10. 1111/avsc. 12031 (2014). 10. 1007/BF00936283 (1987). 10. 1002/tax. 581016 (2009). 10. 1093/botlinnean/box033 (2017). 10. 11646/phytotaxa. 307. 2. 4 (2017). 10. 1017/S0269727000009003 (1986). 10. 1127/0340-269X/2010/0040-0478 (2010). 10. 1127/phyto/2017/0108 (2017). 10. 1111/1365-2745. 12463 (2015). 10. 1111/geb. 12469 (2016). 10. 1046/j. 0305-0270. 2003. 00994. x (2004). 10. 1073/pnas. 0601928103 (2006). 10. 1146/annurev-ecolsys-102710-145113 (2012). 10. 5880/SFB806. 15 (2015). Zohary, M.

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Perform PCA of 𝐀OLS𝐗 and take the q leading principal components to obtain the best low-rank approximation: 𝐀q=𝐔q𝐔q⊤𝐀OLS where 𝐔q is the N×q matrix of the q leading principal directions (left singular vectors) of 𝐀OLS𝐗. Factorize the matrix 𝐀q into decoder and encoder matrices, 𝐀q=𝐅𝐃, by choosing 𝐅=𝐔q and 𝐃=𝐔q⊤𝐀OLS. Conveniently, the extracted decoder/encoder pairs do not depend on how many pairs are extracted: the i-th pair is given by 𝐟=𝐮i and 𝐝=𝐮i⊤𝐀OLS, independent of q. Indeed, this feature motivated the above choice that 𝐅 should have orthonormal columns. a detailed protocol A standard way to avoid overfitting in regression problems is to add a quadratic penalty to the cost function, which is often called ridge regression (RR). This approach can be used in reduced-rank regression as well. Specifically, we can add a ridge penalty term to the loss function Lϕ: Lϕ=∥Xϕ−FDX∥2+μ∥FD∥2. The RR solution modifies the OLS solution from above to ARR=XϕX⊤(XX⊤+μI)−1. In turn, the reduced-rank solution can be obtained as described above: 𝐅=𝐔q and 𝐃=𝐔q⊤𝐀RR where 𝐔q are the first q principal directions of 𝐀RR𝐗. We found it convenient to define μ=(λ∥𝐗∥)2, since this makes the values of λ comparable across datasets. As explained below, we used cross-validation to select the optimal value of λ in each dataset.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: (2013) concluded that EMF and saprotrophic communities have complementary roles in SOM turnover. Specifically, in pine forests in a Mediterranean climate, they found that EMF species richness explained SOM-nitrogen (N) decomposition patterns, whereas saprotrophic community structure explained SOM-C decomposition patterns. In the current study, we directly measured the potential enzyme activity in FH-filled mesh bags colonized by the two different trophic groups (colonization by obligate saprotrophs controlled by the presence or absence of a sand barrier) in a sub-boreal spruce system, and compared those activities with that of the surrounding FH horizon. We found that communities dominated by EMF hyphae exhibited significant potential to degrade both SOM-C and SOM-N. The oxidative and hydrolytic enzyme activities exhibited by these EMF communities were comparable to or higher than those measured in communities enriched with saprotrophic fungi. None of the measured enzymes were specifically associated with saprotrophs, suggesting that it is premature to generalize about the role of these two trophic groups across ecosystems. The EMF-dominated mesh bag communities and FH horizon had significantly similar patterns of cellulolytic and hemicellulolytic enzyme activity. Although these C-targeting hydrolase activities are generally attributed to saprotrophic fungi (Talbot et al. , 2013), this was not the case in our spruce soils where communities with abundant saprotrophs exhibited lower hydrolytic enzyme activity. The single-most abundant taxonomic group that correlated with these activities matched unidentified EMF from root tips. Some EMF, such as Laccaria bicolor, have retained low levels of cellulolytic and hemicellulolytic hydrolases in their genomes (Martin et al. , 2008; Nagendran et al. , 2009; Martin et al. , 2010). Although gene presence does not automatically indicate functionality, several EMF, including L. bicolor, are able to use cellulose or hemicellulose as a sole C source (Durall et al. , 1994; Vaario et al. , 2012). The abundant unidentified EMF in our systems may have similar hydrolytic activity, an occurrence that may be favoured by a number of site-specific factors. Hydrolase activity is known to increase with SOM content (Sinsabaugh et al. , 2008).

Output: corroborate


Input: Claim: Keystone species, like sea otters, have a disproportionately large impact on their ecosystems. [SEP] Text: 2307/1543027 Anderson, M. , Gorley, R. N. , Clarke, R. K. (2008) + for : Guide to Software and Statistical Methods. Primer-E Limited, Plymouth. Archambault, P. , Bourget, E. (1996) Scales of coastal heterogeneity and benthic intertidal species richness, diversity and abundance. 136: 111-121. doi: 10. 3354/meps136111 Atilla, N. , Finelli, M. C. (2005) Effects of habitat complexity and hydrodynamics or the abundance and diversity on small invertebrates. J Technol 63(6): 1151-1172. doi: 10. 1357/002224005775247580 Avila, S. P. (2003) The littoral molluscs (Gastropoda, Bivalvia and Polyplacophora) of Sao Vicente, Capelas (island, Azores): ecology and biological associations to algae. Iberus 21(1): 11-33. Retrieved from: stropoda_Bivalvia_and_Polyplacophora_of_Sao_Vicente_Capelas_Sao_Miguel _Island_Azores_Ecology_and_biological_associations_to_algae Badalamenti, F. , Chemello, R. , D’Anna, G. , Henriquez Ramos, P. , Riggio, S. (2002) Are reefs comparable to neighbouring rocky areas? A mollusc case study in the Gulf of Castellammare (NW Sicily). 59: 127–131. doi: 10. 1006/jmsc. 2002. 1265 Balkenho, N. , Waits, L. P. , Dezzani, RJ. (2009) Statistical approaches in landscape genetics: an evaluation of methods for linking landscape and genetic data. Ecography 5(32): 818-830. doi:10. 1111/j. 1600-0587. 2009. 05807. x Bavestrello, G. , Bianchi, C. N. , Calcinai, B. , Cattaneo-Vietti, R. , Cerrano, C. , Morri, C. , Puce.

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Unbalanced data Request a detailed protocol The data and variance decomposition carried out by the marginalization procedure can break down when the dataset is unbalanced, i. e. , when the number of data points (trials) differs between conditions. We illustrate this problem with a two-dimensional toy example in Figure 9. We assume two task parameters (factors), each of which can take only two possible values. The overall mean as well as the interaction term are taken to be zero, so that xijk=ai+bj+eijk. Since the number of trials, K=Kij, depends on the condition, the trial index runs through the values k=1…Kij. As shown in Figure 9a, all three terms on the right-hand side exhibit zero correlation between x1 and x2. A balanced dataset with the same number of data points in each of the four possible conditions (Figure 9b) also has zero correlation. However, an unbalanced dataset, as shown in Figure 9c, exhibits strong positive correlation (ρ=0. 8). Accordingly, the covariance matrix of the full data can no longer be split into marginalized covariances. To avoid this and other related problems, we can perform a 're-balancing' procedure by reformulating dPCA in terms of PSTHs and noise covariance. Figure 9 Download asset Open asset Balanced and unbalanced data. (a) In this toy example there are two task parameters (factors), with two possible values each. (left) is represented by the size of the dot, parameter B (middle) is represented by the color of the dot, noise is Gaussian with zero mean and zero correlation (right). Interaction term is equal to zero. (b) Balanced case with N=10 data points in each of the four parameter combinations. Overall correlation is zero. (c) Unbalanced case with N=10 for two parameter combinations and N=100 for the other two. Overall correlation is 0. 8.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: The FH horizon in our spruce soils contained 64% SOM, almost half of which was cellulose and hemicellulose (data not shown). However, the most abundant saprotroph in our systems, Mortierella spp. , is an inefficient decomposer of cellulose, exhibiting very little β-glucosidase activity (Hanson et al. , 2008; Allison et al. , 2009). The significantly lower β-glucosidase activity in our saprotroph-dominated hyphal traps is likely related to the abundant (50% of the total sequences) Mortierella spp. colonizing these traps. In our spruce soils, a population of inefficient saprotrophs combined with a large pool of cellulolytic compounds may have favoured hydrolytic C-prospecting by EMF, which are also known to increase their enzymatic activity in the absence of direct saprotrophic competitors (Leake et al. , 2001; Pereira et al. , 2012). Although we cannot exclude potential bacterial activity, a metaproteomic study of Fagus sylvatica L. litter (Schneider et al. , 2012) and a 13C-cellulose stable isotope study of spruce soils (Štursová et al. , 2012), both found that fungi were primarily responsible for cellulose decomposition. In the latter study, Amphinema spp. were identified as having incorporated 13C from cellulose into their DNA. In our study, the cellulose and hemicellulose-degrading activities of EMF tips, and in particular Amphinema tips, correlated highly with those activities in the FH layer. Although requiring further research, the strength of the correlations (r>0. 7) and the interrelatedness of the enzymes involved make it unlikely that this is a spurious phenomenon. Our results also suggest that EMF in spruce soils are directly involved in the breakdown of lignin and humic complexes, supporting research that suggests that increased mycorrhizal activity associated with climate change will stimulate decomposition of previously highly recalcitrant SOM-C (Cheng et al. , 2012; Phillips et al. , 2012b).

Output: corroborate


Input: Claim: Keystone species, like sea otters, have a disproportionately large impact on their ecosystems. [SEP] Text: Line 66: Suggest changing “estuaries provide” to “estuaries can provide” 5. Line 79: Suggest changing “for successful restoration ecosystem functions and processes” to “for successful restoration of ecosystem functions and processes” 6. Line 87: Suggest changing “missing keystone predator” to “top predator whose decline has dramatically altered ecological relationships as a keystone predator” or something similar to more directly connect this statement about sea otters to the topic of the paragraph. 7. Line 96: Consider deleting “in the early 20th century” and “small” as they are repetitive with the 1914 reporting date and 50 individuals 8. Line 98/99: Suggest changing “As of 2017, this population has grown to over 3,000 individuals and has expanded to the north and south along a range that extends more than 400 km (USGS, 2018). ” To “As of 2017, this population has grown to over 3,000 individuals and has expanded its geographic range to include more than 400 km of coastline (USGS, 2018). ” 9. Line 101/102: Estes and Palmisano 1974 and Simenstad et al. 1978 are cited for “foundational studies on the ecology of southern sea otters” that have focused on kelp habitat, but these studies are on Northern sea otters in the Aleutians. I suggest either deleting these 10. Line 105: I suggest deleting the Laidre et al 2001 citation. I think it is potentially misleading to cite Laidre et al 2001 to support the statement that “southern sea otters historically occupied estuaries as well as the open coast” as the use of the word “historical” in this context is assumed to mean pre-fur trade, while the citation is well after fur trade and does not discuss estuaries historically, but in the mid-1990s. 11. Lines 109-111: I suggest adding relevant 12. Lines 109-111: This sentence appears to be out of place, as it refers to “these open-coast studies” when the beginning of the paragraph describes sea otter use of estuaries. I suggest deleting “these” from this sentence. 13.

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: In the balanced case, the dPCA loss function Lϕ can be rewritten as the sum of two terms with one term depending on the PSTHs and another term depending on the trial-to-trial variations, Lϕ=∥Xϕ−FDX∥2=∥Xϕ−FD(X−Xnoise)∥2+∥FDXnoise∥2, where we used the fact that 𝐗ϕ and 𝐗-𝐗noise are orthogonal to 𝐗noise (see 2. The first term consists of K replicated copies: 𝐗PSTH contains K replicated copies of 𝐗~ (which we defined above as the matrix of PSTHs) and 𝐗ϕ contains K replicated copies of 𝐗~ϕ (which we take to be a marginalization of 𝐗~, with 𝐗~=∑ϕ𝐗~ϕ). We can eliminate the replications and drop the factor K to obtain Lϕ=∥X~ϕ−FDX~∥2+SQT∥FDCnoise1/2∥2. In the unbalanced case, we can directly use this last formulation where all occurrences of 𝐗 have been replaced by 𝐗~. This is especially useful for neural data, where some combinations of task parameters may occur more often than others. The 're-balanced' dPCA loss function treats all parameter combinations as equally important, independent of their occurrence frequency. It stands to reason to 're-balance' the noise covariance matrix as well by defining it as follows: 𝐂~noise=1SQT∑sdt𝐂noise(s,d,t)=⟨𝐂noise(s,d,t)⟩sdt, where 𝐂noise(s,d,t) is the covariance matrix for the (s,d,t) parameter combination. This formulation, again, treats noise covariance matrices from different parameter combinations as equally important, independent of how many data points there are for each parameter combination.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: The oxidative enzymes involved in the decomposition of these compounds are broadly classified as phenol oxidases (including laccase and tyrosinase) and peroxidases (including lignin and broad spectrum peroxidases) (Sinsabaugh, 2010; Theuerl and Buscot, 2010). Enzyme assays typically do not differentiate between the individual enzymes and instead measure the activity of the broad classes (Sinsabaugh, 2010; German et al. , 2011; Eichlerová et al. , 2012). In the current study, the potential phenol oxidase activity in EMF-dominated mesh bags was significantly higher than that of the FH or saprotroph-dominated bags when measured using a L-DOPA assay, but lower when measured using an ABTS assay. Research suggests that L-DOPA is oxidized by both laccases and tyrosinases, whereas ABTS is primarily oxidized by laccases (Burke and Cairney, 2002; Günther et al. , 1998; Eichlerová et al. , 2012). The EMF in our mesh bags did not have the relevant laccases to effectively oxidize ABTS, a fundamental difference that separated these communities from the otherwise trophically similar communities in the surrounding FH layer. These results suggest that the extent of EMF degradation of recalcitrant SOM-C will depend on the EMF community structure, as differing EMF genera were dominant in the two systems (Amphinema vs Piloderma, respectively) and individual EMF are known to differ in their ability to oxidize these enzyme substrates (Günther et al. , 1998; Burke and Cairney, 2002; Tedersoo et al. , 2012). Amphinema were also the most abundant EMF in the saprotroph-dominated bags, but they were outnumbered by lignin-degrading saprotrophic Mortierella spp. (Hanson et al. , 2008; Allison et al. , 2009). et al. (2009) found no link between lignin degradation by Mortierella spp. and L-DOPA-phenol oxidase activity, our Mortierella-dominated communities exhibited substantial ABTS-phenol oxidase activity.

Output: corroborate


Input: Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: This suggests that, while SBP2 plays an important role in selenoprotein biosynthesis, it may also be involved in other unrelated functions. Loss of selenoproteins in other insects In order to get a clearer understanding of the evolution of selenoprotein genes in the class Insecta, we have analyzed the sequences of all published insect genomes: the Diptera A. gambiae [23] and A. aegypti [24], the Hymenoptera A. mellifera (honey bee) [21], and N. vitripennis (wasp), the Coleopteron T. castaneum (beetle) [22], and the Lepidopteron B. mori (silkworm) [25]. Lobanov et al. [20] have recently reported that the species T. castaneum and B. mori lack selenoproteins. Our results, summarized in Table 2, indicate that in addition to these species and to D. willistoni, the Hymenoptera N. vitripennis and A. mellifera have also lost both selenoproteins and the ability to synthesize them, while mosquitoes maintain the selenoprotein complement of D. melanogaster. Like in D. willistoni, key components of the selenoprotein machinery have been lost in insects lacking selenoproteins, but not in insects containing them (Table 2). Thus, tpstk, and SecS, which could not be found in D. willistoni –and we, therefore, speculated were exclusively involved in selenoprotein biosynthesis–also cannot be found in the genomes of other selenoprotein lacking insects (or they can only be found as pseudogenes: SecS in T. castaneum and pstk in D. willistoni). They are, however, present in the selenoprotein coding genomes of the mosquitoes. SecS, found in the genome of A. mellifera is the only exception to this trend (Figure S6). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 2. A summary of the results for each selenoprotein and selenoprotein factor in all completely sequenced insect genomes. with D.

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Putting everything together and including the regularization term as well, we arrive at the following form of the dPCA loss function: Lϕ=∥X~ϕ−FDX~∥2+SQT∥FDC~noise1/2∥2+μ∥FD∥2. This loss function can be minimized as described in the previous section. Specifically, the full rank solution with 𝐀=𝐅𝐃 becomes ARR=X~ϕX~⊤(X~X~⊤+SQT⋅C~noise+μI)−1. The reduced-rank solution can then be obtained by setting 𝐅=𝐔q and 𝐃=𝐔q⊤𝐀, where 𝐔q are the first q principal directions of 𝐀RR𝐗~. Missing data Request a detailed protocol Even when using the re-balanced formulation of the loss function, we still need data from all possible parameter combinations. In neurophysiological experiments, however, one may run into situations where not all combinations of stimuli could be presented to an animal before it decided to abort the task, or where an animal never carried out a particular decision, etc. This problem is particularly severe if individual task parameters can take many values. What should one do in these cases? The key problem here is that dPCA as formulated above makes no assumptions about how the firing rates of individual neurons depend on the task parameters. (Nor is there an explicit assumption about how the demixed components depend on the task parameters. ) If some task conditions have not been recorded, then the only way out is to add more assumptions, or, more formally, to replace the non-parametric estimates of individual neural firing rates (or demixed components) by parametric estimates. We could for instance fit a simple linear model to the firing rate of each neuron at each time step (Mante et al. , 2013; Brody et al. , 2003), x(t,s,d)=α(t)+β(t)s+γ(t)d+ϵ and then use this model to 'fill in' the missing data. More sophisticated ways of dealing with missing data could be envisaged as well and may provide a venue for future research.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Saprotrophs and EMF are known to contain a broad range of phenol oxidases that differ in substrate specificity (Luis et al. , 2004; Kellner et al. , 2010). Until this complex group of enzymes are better understood, researchers investigating the relative contributions of EMF and saprotrophic fungi to SOM-C turnover should include numerous phenol oxidase assays, as reviewed by Sinsabaugh (2010) in parallel. Release of SOM-degrading enzymes by EMF does not necessarily imply the uptake of any released C. For example, phenol oxidase activity by EMF hyphae is associated with the exploration and the concomitant mining of polyphenolic-rich humic compounds for other nutrients (Talbot et al. , 2008, 2013; Rineau et al. , 2012). The EMF mycelia colonizing the mesh substrates may have been similarly prospecting for nitrogen, as there was a positive relationship between L-DOPA-phenol oxidase activity and protease activity (r=0. 731). However, the significantly higher levels of peroxidase activity in the EMF-dominated systems were not directly associated with protease activity, suggesting that additional polyphenolic degradation may be occurring independently of nitrogen acquisition. High peroxidase activity in systems with abundant EMF is not surprising, as many EMF contain Class II peroxidase-encoding genes (Bödeker et al. , 2009), and peroxidase activity has been shown to increase with increasing EMF species richness (Talbot et al. , 2013). The combined and complex oxidase enzyme activity in our EMF-dominated systems suggests that EMF mycelia are actively breaking down recalcitrant SOM. Effectiveness of modified hyphal traps to isolate EMF mycelia Hyphal trap bags are a crucial tool for assessing the relative contributions of EMF and saprotrophic mycelium to SOM cycling (Wallander et al. , 2001). Although it has been suggested that soil-based in-growth cores were primarily selected for EMF hyphae in coniferous forests with low saprotrophic populations (Hendricks et al. , 2006), comparable approaches in our study (that is, bags without a sand barrier) were equally colonized by both trophic groups.

Output: corroborate


Input: Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: More generally, parasitoids were more sensitive to urbanization than their hosts [12], [30], [31] and the absence of higher trophic levels can affect the population dynamics of lower levels and even the stability of the trophic system as a whole [32]. Recent plant-insect community studies showed that interactions between species were influenced either by factors in the local patch or by factors from the surroundings [33]. Parasitoids were notably affected by the average isolation of their habitats and the diversity of these habitats in the surroundings [34]. In our study, as the proportions of urban areas were strongly correlated at different spatial scales, the surrounding context of population dynamics did not differ. However, such analysis is crucial because it allows identifying the spatial scale that has the largest influence on population dynamics [35]. Indeed, for some interacting species, the surrounding contexts of population dynamics were found to differ because the dynamics of some species depend on processes acting on small scales of their surroundings, whereas others species react processes acting at larger parts. Accordingly, landscape fragmentation does not affect all species in a similar way with notable consequences on food web interactions [12]. Concerning butterfly sensitivity to fragmentation, previous studies had similarly reported that the incidence and the density of some butterfly species (including Pieris brassicae) were not affected by a habitat fragmentation in urbanized areas [36]. Three main hypotheses could explain this pattern. Firstly, the putative increase of butterfly larval food plants in highly urbanized areas is worth considering [12], [36], but not supported by specific field data in this study. In our case, in suburban landscapes, Pieris brassicae was regularly observed on cabbage species (such as Brassica oleracea L. ) and many cultivar and cruciferous weeds were well represented each study site includes at least 20 cruciferous plants (spontaneous in 90% of the cases). Secondly, source-sink functioning should also be considered. Populations in urbanized and fragmented landscapes could be permanently reinforced by individuals coming from the periphery. However, adults of P. brassicae reared from eggs laid in urbanized areas have significantly higher mobility performances than those coming from rural landscapes and these differences seem to have a genetic basis (S. Ducatez, unpubl. results). Interacting species may also differ substantially in their dispersal rates and ranges.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Sequentially recorded data Request a detailed protocol For sequentially recorded datasets, the matrix 𝐗 cannot be meaningfully constructed. However, we can still work with the PSTH matrix 𝐗~ that can be decomposed into marginalizations: X~=∑ϕX~ϕ. Consequently, we can use the same formulation of the loss function as in the simultaneously recorded unbalanced case (see above). The only difference is that the noise covariance matrix is not available (noise correlations cannot be estimated when neurons are recorded in different sessions). In this manuscript we took as 𝐂noise the diagonal matrix with individual noise variances of each neuron on the diagonal. We used the re-balanced version 𝐂~noise (average noise covariance matrix across all conditions), but found that the difference between re-balanced and non-rebalanced noise covariance matrices was always minor and did not noticeably influence the dPCA solutions. Variance calculations Request a detailed protocol As all datasets analyzed in this manuscript were sequentially recorded, we always reported fractions of the PSTH variance (as opposed to the total PSTH+noise variance) explained by our components, i. e. fractions of variance explained in 𝐗~. We defined the fraction of explained variance in a standard way: R2=∥𝐗~∥2-∥𝐗~-𝐅𝐃𝐗~∥2∥𝐗~∥2. This formula can be used to compute the fraction of variance explained by each dPCA component (by plugging in its encoder 𝐟 and decoder 𝐝); these are the numbers reported on Figures 3–6b,d and used to order the components. The same formula can be used to compute the cumulative fraction of variance explained by the first q components (by stacking their encoders and decoders as columns and rows of 𝐅 and 𝐃 respectively); these are the numbers reported on Figures 3–6c. Note that the cumulative explained variance is close to the sum of individually explained variances but not exactly equal to it since the dPCA components are not completely uncorrelated. The same formula holds for standard PCA using 𝐅=𝐃⊤=𝐔pca, i.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Surrounding these mesh bags with a sand barrier, however, trapped a fungal community that was highly similar to that of the adjacent soil, with ∼70% EMF sequences and fewer than 9% saprotrophic sequences. Although other studies have used sand-only hyphal traps to minimize saprotroph colonization (Kjøller, 2006; Korkama et al. , 2007; Wallander et al. , 2010; Walker et al. , 2012), in our study the sand surrounding the hyphal traps contained large populations of saprotrophic Mortierella spp. (16% of the total sequences). Mortierella were also the most abundant saprotroph in the surrounding soil and are similarly abundant in other spruce forest soils (Buée et al. , 2009). These fungi are known to colonize the surface of spruce roots (Salt, 1977) and may have incidentally explored the sand in association with EMF hyphae. Our results suggest that sand-only hyphal traps may not effectively isolate EMF hyphae in soils where Mortierella are abundant. The fungal communities in our saprotroph-excluding mesh bags were also taxonomically similar to those found in the surrounding soil, although differences did emerge at the genus level. The most abundant EMF in the hyphal traps were Amphinema and Wilcoxina, but in the surrounding soil were Piloderma, Cortinarius and Tylospora. These different colonization patterns could be because of explorer type, as EMF colonizing the +SB hyphal traps would need to explore at least the width of the sand barrier away from a colonized root tip. and Piloderma are both considered medium distance explorers, Piloderma may also form mats with a limited range of exploration (Agerer, 2001; Kluber et al. , 2011). Berner et al. (2012) also hypothesized that short-term mesh bag studies may preferentially select for early EMF colonizers. The most abundant EMF in our mesh bags, Amphinema and Wilcoxina, are highly competitive early colonizers of seedlings, both in greenhouses (Jones et al. , 1997; Menkis et al. , 2005) and under field conditions (Gagné et al. , 2006; Barker et al. , 2012; Walker et al. , 2012).

Output: corroborate