Input: Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: Because habitat fragmentation was historically used as an umbrella term to describe studies within the discipline of landscape ecology, this term is still used in a very broad sense with often confusing meanings. In a seminal review published in 2003, Fahrig showed the multiple, and sometimes contradictory, uses for the concept, and suggested that instead researchers should focus on studying habitat fragmentation per se, which is the breaking apart of habitats while controlling for changes in habitat amount. According to Fahrig, while habitat loss always has negative consequences on biodiversity, habitat fragmentation per se can have variable results that are often positive. These findings spurred a multitude of studies looking to set apart the relative importance of habitat loss versus fragmentation, and as of 2020, a full consensus has not yet been reached (but see The context-specific effects of habitat transformation). Habitat degradation involves changes to the quality of a habitat without any significant changes to its amount or fragmentation. Habitat degradation is caused by processes such as burning or selective logging, as well as by the gradual changes that occur in ecological communities over time as a result of unequal disruptions to the birth and death rates of different species (Figure 1). Figure 1 Habitat loss, fragmentation, and degradationShow full captionThe three components of habitat transformation are causally related and difficult to separate in many contexts. Habitat loss often drives fragmentation, and vice versa, and both these habitat changes lead to degradation through edge effects and disturbance to species composition and ecosystem functioning. Perhaps one the greatest challenges of studying, and setting apart, the relative influences of habitat loss, fragmentation, and degradation is that the three types of habitat transformation are often confounded. Although theoretically habitat loss, fragmentation, and degradation can occur independently of each other, in real landscapes, they are usually causally related to each other. This is clearly illustrated by land-use change in the Amazon, where researchers have been able to carefully track habitat changes with satellites. The series of cascading modifications to the habitat usually start with the construction of a road. This fragments the forest and can reduce the movement of species but does so with minimal habitat loss. However, subsequent deforestation occurs within 50 km of main roads and adjacent to networks of secondary roads, ultimately leading to more widespread loss and simultaneous fragmentation. Increased luminosity along the new forest edges can desiccate the forest floor, and a drier microclimate can cause trees to shed their leaves, together providing the conditions for surface fires.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: e. , the matrix of stacked together principal directions (Figures 3–6c). Using the decomposition X~=∑ϕX~ϕ, we can split the fraction of explained variance into additive contributions from different marginalizations: R2=∑ϕ∥𝐗~ϕ∥2-∥𝐗~ϕ-𝐅𝐃𝐗~ϕ∥2∥𝐗~∥2. We used this decomposition to produce the bar plots in Figures 3–6d, showing how the explained variance of each single dPCA component is split between marginalizations. Following the approach of Machens et al. (2010), we note that our PSTH estimates 𝐗~ must differ from the 'true' underlying PSTHs due to the finite amount of recorded trials. Hence, some fraction of the total variance of 𝐗~ is coming from this residual noise. We can estimate this fraction as follows. Our estimate of the noise variance of the n-th neuron is given by C~nn, the n-th diagonal element of 𝐂~noise. There are on average K~n=1SQ∑Knsd trials being averaged to compute the PSTHs for this neuron. So a reasonable estimate of the residual noise variance of the n-th neuron is C~nn/K~n. Accordingly, we define the total residual noise sum of squares as Θ=SQT⋅∑nC~nnK~n. In turn, the fraction of total signal variance is computed as 1-Θ/∥𝐗~∥2 which is the dashed line shown in Figures 3–6c. Note that each component likewise has contributions from both signal and noise variance, and hence the fraction of total signal variance does not constitute an upper bound on the number of components. The residual noise variance is not split uniformly across marginalizations: the fraction falling into marginalization ϕ is proportional to the respective number of degrees of freedom, Kϕ.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Early colonizing Amphinema and Wilcoxina may have out-competed other species, including less competitive Piloderma spp. (Erland and Söderström, 1991; Erland and Finlay, 1992; Wu et al. , 1999). Differences in fungal nutritional requirements might also influence colonization. For example, Cortinarius spp. avoid nutrient-limited sand hyphal traps (Kjøller, 2006; Berner et al. , 2012). These EMF may have similarly avoided the sand barrier in the current study, as Cortinarius were more abundant in hyphal traps without a sand barrier. novel microcosms of sterile, FH substrate surrounded by a barrier of sand, we determined that both oxidative and hydrolytic enzyme activities in EMF-dominated substrates are comparable to or greater than that exhibited by microcosms enriched in saprotrophic hyphae. Collectively, our data supports a substantial role for EMF hyphae in the turnover of both recalcitrant and labile SOM in sub-boreal spruce stands. Furthermore, it expands our ability to predict feedbacks between elevated atmospheric CO2 and soil C fluxes. study was funded by a Council of Grant. The preliminary study development, additional field and lab assistance was provided by and Jennifer Walker. We thank NetPak of Coquitlam, British Columbia, Australia for donating the plastic containers used to construct the hyphal traps with sand barriers. We acknowledge of the genotyping platform of the Mcand Génome Québec for their technical assistance. Accession codes Accessions GenBank/EMBL//EMBL/022584 Competing interests The authors declare no conflict of interest. 10. 1111/j. 1469-8137. 2009. 03160. x. 10. 4137/EBO. S6271. 10. 1007/s005720100108. 10. 1007/s10533-006-9046-2. 10. 1016/j. soilbio. 2008. 10. 032. 10. 1093/nar/25. 17. 3389. 10. 3410/B3-16. 10. 1016/S0275-0287(08)80004-5. 10. 1007/s00442-012-2562-y. 10. 1093/nar/gkq1079. 10.

Output: corroborate


Input: Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: These, in turn, lead to changes in aboveground biomass and forest structure, the hallmarks of habitat degradation. While there are specific situations where habitat loss can be independently separated from either fragmentation or degradation, in the vast majority of human-modified landscapes, this is not possible. The context-specific effects of habitat transformationThe effects of habitat loss, fragmentation, and degradation are inherently scale and species dependent as well as context specific. This means that two studies investigating the effects of habitat loss on the same species can obtain very different results if they are conducted at different spatial or temporal scales. The effects of fragmentation on species will also be very different if one study is investigating different aspects of fragmentation—e. g. , one examining “number of patches” and the other “edge density”—despite the fact that the two variables are often used interchangeably to describe fragmentation. The effects of habitat degradation also vary among species and in different parts of the world, with biodiversity being less negatively impacted by selective logging in Borneo than in South-America, for instance. For these reasons, we still have very poor predictability about how biodiversity en masse will be impacted by habitat changes in specific locations and at specific times. Despite the intrinsically complex nature of studying habitat loss, fragmentation, and degradation, two recent studies have shed light on potential reasons behind such extreme context-dependency. These studies provided evidence to show that species’ responses to habitat changes at local scales are often modulated by evolutionary and biogeographic processes occurring at large scales. For instance, species are more negatively affected by habitat fragmentation in the tropics and other areas where there have been relatively few disturbances over evolutionary time such as storms, fires, and glaciations. Temperate species, by contrast, have a longer evolutionary history of dealing with disturbance and are more resilient to habitat changes. This explains some of the controversy regarding the importance of habitat fragmentation across studies and species. But even the responses of an individual species are not immutable: it is now known that at local scales habitat loss, which is still portrayed in the literature as always having negative effects, can impact populations of the same species in different ways—depending on their distance from the species’ geographic range edge. Populations occurring near their range edge are negatively affected by habitat loss, while populations of the same species occurring far from the range edge tend to either not be affected or can even be positively affected by habitat loss.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: In other datasets these numbers are similar, and the same differences were significant in all cases. Angles between dPa detailed protocol 3–6e, stars mark the pairs of components whose encoding axes 𝐟1 and 𝐟2 are significantly and robustly non-orthogonal. These were identified as follows: space of N dimensions, two random unit vectors (from a uniform distribution on the unit sphere) have dot product (cosine of the angle between them) distributed with mean zero and standard deviation N-1/2. For large N the distribution is approximately Gaussian. To avoid the problems inherent to multiple comparisons, we chose a conservative significance level of p<0. 001, which means that two axes are significantly non-orthogonal if |f1⋅f2|>3. 3/N1/2. Coordinates of 𝐟1 quantify how much this component contributes to the activity of each neuron. Hence, if cells exhibiting one component also tend to exhibit another, the dot product between the axes f1⋅f2>0 is positive (note that 𝐟1⋅𝐟2 is approximately equal to the correlation between the coordinates of 𝐟1 and 𝐟2). Sometimes, however, the dot product has large absolute value only due to several outlying cells. To ease interpretation, we marked with stars only those pairs of axes for which the Kendall (robust) correlation was significant at p<0. 001 level (in addition to the above criterion on 𝐟1⋅𝐟2). Experimental data Request a detailed protocol Brief descriptions of experimental paradigms are provided in the Results section and readers are referred to the original publications for all further details. Here we describe the selection of animals, sessions, and trials for the present manuscript. In all experiments neural recordings were obtained in multiple sessions, so most of the neurons were not recorded simultaneously. All four datasets used in this manuscript have been made available at (Romo et al. , 2016; Constantinidis et al. , 2016; Feierstein et al. , 2016; Uchida et al. , 2016). Somatosensory working memory task in monkeys (Romo et al. , 1999; Brody et al. , 2003).

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: 1006463107. 10. 1111/j. 1461-0248. 2011. 01611. x. 10. 1007/s00572-011-0379-x. 10. 5194/bg-9-2099-2012. 10. 1016/j. soilbio. 2006. 04. 038. 2. Ecol Lett. 2012;15:1042–1049 10. 1111/j. 1461-0248. 2012. 01827. x. 10. 1007/s11104-008-9766-2. 10. 1016/j. mimet. 2004. 04. 001. 10. 1111/j. 1574-6941. 2007. 00375. x. 45. 10. 1111/j. 1462-2920. 2012. 02736. x. 10. 1186/1471-2148-8-50. 10. 1016/S0038-0717(02)00074-3. 10. 1093/forestry/50. 2. 113. 10. 1128/AEM. 01541-09. 10. 1038/nature10386. 10. 1038/ismej. 2012. 11. 10. 1016/j. soilbio. 2009. 10. 014. 10. 1111/j. 1461-0248. 2008. 01245. x. 10. 1016/S0929-1393(03)00002-7. 10. 1111/j. 1574-6941. 2012. 01343. x. 10. 1111/j. 1365-2435. 2008. 01402. x. 10. 1016/j. soilbio. 2012. 10. 004. 10. 1111/j. 1469-8137. 2012. 04217. x. 10. 1007/s00374-010-0440-5. 10. 1016/j. soilbio. 2005. 09. 006. 10. 1007/s00572-011-0416-9. 10. 1111/1574-6976. 12001. 10. 1007/s00374-009-0413-8. 10. 1016/j. apsoil. 2012. 02. 017. 10. 1111/j. 1469-8137. 2010. 03324. x. 10.

Output: pass


Input: Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: Correction 1 Jul 2015: (2015) Correction: for in Peninsular Spain. 10(7): e0132199. View correction Figures AbstractEcological theory predicts that fragmentation aggravates the effects of habitat loss, yet empirical results show mixed evidences, which fail to support the theory instead reinforcing the primary importance of habitat loss. Fragmentation hypotheses have received much attention due to their potential implications for biodiversity conservation, however, animal studies have traditionally been their main focus. Here we assess variation in species sensitivity to forest amount and fragmentation and evaluate if fragmentation is related to extinction thresholds in forest understory herbs and ferns. Our expectation was that forest herbs would be more sensitive to fragmentation than ferns due to their lower dispersal capabilities. Using forest cover percentage and the proportion of this percentage occurring in the largest patch within UTM cells of 10-km resolution covering Peninsular Spain, we partitioned the effects of forest amount versus fragmentation and applied logistic regression to model occurrences of 16 species. For nine models showing robustness according to a set of quality criteria we subsequently defined two empirical fragmentation scenarios, minimum and maximum, and quantified species’ sensitivity to forest contraction with no fragmentation, and to fragmentation under constant forest cover. We finally assessed how the extinction threshold of each species (the habitat amount below which it cannot persist) varies under no and maximum fragmentation. Consistent with their preference for forest habitats probability occurrences of all species decreased as forest cover contracted. On average, herbs did not show significant sensitivity to fragmentation whereas ferns were favored. In line with theory, fragmentation yielded higher extinction thresholds for two species. For the remaining species, fragmentation had either positive or non-significant effects. We interpret these differences as reflecting species-specific traits and conclude that although forest amount is of primary importance for the persistence of understory plants, to neglect the impact of fragmentation for some species can lead them to local extinction. Citation: -íguez MÁ (2015) for in Peninsular Spain. 10(5): e0126424. Editor: Marco Júnior, de Goiás, : October 11, 2014; Accepted: April 1, 2015; Published: May 15, 2015Copyright: © 2015 Rueda et al. This is an open access 8. Fontqueria 17: 1-36.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Note that recordings from one of the rats (N1) were included in both this and previous datasets; when we excluded it from either of the datasets, the results stayed qualitatively the same (data not shown). We excluded from the analysis all trials that were aborted by rats before reward delivery (or before waiting 0. 3 s at the reward port for the error trials). Selection of neurons Request a detailed protocol For our analysis, we only selected neurons which had been recorded in each possible condition (combination of parameters), which avoids the missing data problems explained above. Additionally, we required that in each condition there were at least Kmin>1 trials, to reduce the standard error of the mean when averaging over trials, and also for cross-validation purposes. The cutoff was set to Kmin=5 for both working memory datasets, and to Kmin=2 for both olfactory datasets (due to less neurons available). We have further excluded very few neurons with mean firing rates over 50 Hz, as such neurons can bias the variance-based analysis. Firing rates above 50 Hz were atypical in all datasets (number of excluded neurons for each dataset: 5 2 1 0). This exclusion had a minor positive effect on the components. We did not apply any variance-stabilizing transformations, but if the square-root transformation was applied, the results stayed qualitatively the same (data not shown). No other pre-selection of neurons was used. This procedure left 832 neurons (230 602 for individual animals, order as above) in the somatosensory working memory dataset, 956 neurons (182 774) in the visuospatial working memory dataset, 437 neurons in the olfactory discrimination dataset (166 30 9 106 126), and 214 neurons in the olfactory categorization dataset (67 38 109). Preprocessing of the neural data Request a detailed protocol The spike trains were filtered with a Gaussian kernel (σ=50 ms) and sampled at 100 Hz to produce single-trial instantaneous firing rates. In the visuospatial working memory dataset we identified the preferred location of each neuron as the location that evoked maximum mean firing rate in the 500 ms time period while the first stimulus was shown. The neural tuning was shown before to have a symmetric bell shape (Qi et al. , 2011; Meyer et al. , 2011), with each neuron having its own preferred location.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Create a new account user Can't sign in? Forgot your username? Enter your email address below and we will send you your username Email* the address matches an existing account you will receive an email with instructions to retrieve your username close | or * Password* Forgot password? Reset it here Keep me logged in All fields with * are mandatory Email* All fields with * are mandatory Already have an account? Login here password must have 8 characters or more and contain 3 of the following: a lower case character, an upper case character, a special character or a digit password has been changed Can't sign in? Forgot your password? Enter your email address below and we will send you the reset instructions Email* the address matches an existing account you will receive an email with instructions to reset your password the verification code Cancel Congrats! has been verified close 10. 1139/x83-110 Get AccessContentsof Forest Research, Number 5October 19831. 8, 2. 8, and 2. 0 times less than in the corresponding deciduous forest horizons, respectively. and F horizons had respiration rates 11. 5 times higher than the corresponding black spruce layers. Weight losses in birch L, F, and H horizons were 6, 3, and 2 times higher, respectively, than in the corresponding black spruce substrates. Substrates had a quality-dependent decay rate which did not change when they were relocated within or between sites indicating that measured field climatic differences were not as influential on decay rates as substrate quality components. Fungal biomass was significantly correlated with the quantity of organic matter in all sites (n = 15, r = 0. 62) but correlations were better for deciduous (n = 9, r = 0. 89), and evergreen (n = 6, r = 0. 82) forests separately. Strong correlations exist also between grams of organic matter decayed per square metre per year and fungal biomass (n = 13, r = 0. 86), and fungal biomass and grams of N and P mineralized per square metre per year (n = 14, r = 0. 95) and (n = 11, r = 0. 94, respectively). Seedlings on mineral-deficient substrates produced more tannins than the controls, and seedlings on substrates with widening C/N ratios had successively less tissue with lower N content, and proportionally more roots.

Output: corroborate


Input: Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: Villar L, Sesé JA, Ferrández JV, (1997-2001) Atlas de la flora del Pirineo Aragonés. 2 Vols. Consejo de Protección de la Naturaleza de Aragón e Instituto de Estudios Altoaragoneses, Huesca. Bolòs O (1998) Atlas corològic de la flora vascular dels Països Catalans. Primera compilació general. 2 Vols. Institut d’Estudis Catalans, Barcelona. Anthos project: www. anthos. es. SIVIM project: www. sivim. info. Funding: This study was sponsored by a research grant from the Ministry of Economy and Competitiveness of Spain (CGL2010-22119 to MÁR). MR was funded by the for Education and Science (BVA-2010-0596). IM-C was funded by the of IC&DT (No. 1//ALENT-07- 0224-FEDER-001755). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist. IntroductionKey issues concerning conservation related to the anthropogenic alteration of natural habitats include the amount of habitat needed to achieve conservation goals and the importance of habitat fragmentation [1]. Habitat fragmentation is literally the ‘breaking apart’ of habitat and is usually defined as a landscape-scale process that differs from how much habitat there is [2, 3]. However, the term habitat fragmentation has often been used loosely to illustrate habitat destruction by humans even though habitat might be removed without increasing fragmentation [4]. This has generated a misunderstanding of the impacts of habitat amount and fragmentation on biodiversity [3]. Nowadays, habitat loss is still the biggest single source of pressure on biodiversity worldwide [5], and there is controversy about the effects of habitat fragmentation per se (i. e. independent of habitat loss effects). Conceptually, the debate extends to distinguishing habitat loss per se from habitat fragmentation per se [2, 3], or to their interdependence, recognizing that fragmentation is usually a consequence of habitat loss [6].

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: We then re-sorted the trials (separately for each neuron) such that only five distinct stimuli were left: preferred location, 45°, 90°, 135°, and 180° away from the preferred location. In both olfactory datasets trials were self-paced. Accordingly, trials last different amounts of time, and firing rates cannot simply be averaged over trials. We used the following time warping (re-stretching) procedure to equalize the length of all trials and to align several events of interest (Figure 10) separately in each dataset. We defined five alignment events: odor poke in, odor poke out, water poke in, reward delivery, and water poke out. First, we aligned all trials on odor poke in (T1=0) and computed median times of the four other events Ti,i=2…5 (for the time of reward delivery, we took the median over all correct trials). Second, we set ΔT to be the minimal waiting time between water port entry and reward delivery across the whole experiment (ΔT=0. 2 s for the olfactory discrimination task and ΔT=0. 3 s for the olfactory categorization task). Finally, for each trial with instantaneous firing rate x(t) we set ti,i=1…5, to be the times of alignment events on this particular trial (for error trials we took t4=t3+ΔT), and stretched x(t) along the time axis in a piecewise-linear manner to align each ti with the corresponding Ti. Figure 10 Download asset Open asset Re-stretching (time warping) procedure. We defined several alignment events (such as odour poke in, odour poke out, etc. ) and for each trial found the times ti of these events. After aligning all trials on t1=0 (left) we computed median times Ti for all other events. Then for each trial we re-stretched the firing rate on each interval [ti,ti+1] to align it with [Ti,Ti+1] (right). After such re-stretching, all events are aligned and the trials corresponding to one condition can be averaged.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Nitrogen content of litter fall in increasingly nitrogen-poor forest floors was correspondingly lower. Nitrogen content of litter fall on N rich forest floors and N fertilized forest floors was proportionately higher. Nitrogen withdrawal in leaves at senescence was inversely correlated with grams N mineralized per square metre per year in forest floors. Fertilization did not influence microbial processes in the field, though lab studies indicated a negative influence of NH4, P, and K on microbial respiration. Glucose added in the laboratory and field markedly increased forest-floor microbial respiration. In vitro glucose-induced increases in respiration were not influenced by addition of ammonium nitrate and were significantly depressed by addition of P and K. In the field, fertilization had no effect on either glucose-induced respiration or microbial biomass. RésuméLes auteurs ont étudié l'effet des propriétés chimiques de la couverture morte sur le taux de décomposition de la matière organique et le cycle des éléments dans plusieurs forêts de conifères et d'espèces décidues de la taïga de l'intérieur de l'Alaska, sur une période de 5 ans. Au moyen des techniques de sac à litière et de respirométrie, ils ont mesuré les pertes de poids et la consommation d'oxygène d'échantillons de la couverture morte, soit sans traitement ou avec addition d'engrais N, P et K, et de glucose comme source de carbone. En utilisant des échantillons de couverture morte possédant un éventail de rapports C/N, y compris des échantillons carences en azote, on a pu mesurer l'influence de la nature du substrat sur la croissance de semis et leur teneur en éléments et en tannins.

Output: corroborate


Input: Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: Statistical differences between the extinction thresholds found for each fragmentation scenario were again established based on bootstrapped 95% CIs. ResultsOf the 16 forest understory species modeled, robust models meeting all three goodness of fit criteria were obtained for nine, comprising five herbs (Monotropa hypopitys, Hepatica nobilis, Teucrium scorodonia, Poa nemoralis and Deschampsia flexuosa), and four ferns (Polystichum aculeatum, Dryopteris filix-mas, Athyrium filix-femina and Blechnum spicant) (Table 2). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 2. Goodness-of-fit statistics for the forest cover and fragmentation logistic models generated for ten herbs and six ferns inhabiting forest understories in Peninsular Spain. Sensitivity to forest amount per se and fragmentation per se As expected for forest understory plants, all species responded negatively to forest cover contraction in absence of fragmentation (a negative Ωj,cover with 95% CIs not including zero) (Fig 2A). In contrast, for the scenario of increased fragmentation, only two species showed negative Ωj,fragm values with 95% CIs not overlapping zero (Fig 2B). These species were thus significantly negatively affected by fragmentation and comprised one herb (M. hypopitys) and one fern (P. aculeatum), although the latter had a weaker response as indicated by the close proximity of its upper 95% CI to zero. Additionally, five species, the herbs P. nemoralis and D. flexuosa, and three ferns (D. filix-mas, A. filix-femina, and B. spicant) responded positively and, hence, are more likely to be found in more fragmented cells. Again, D. flexuosa had a weaker response as indicated by the close proximity of its lower 95% CI to zero. The occurrence of two herbs was not significantly affected by fragmentation (H. nobilis, and T. scorodonia). As groups, herbs and ferns were equally affected by forest contraction (Fig 2C). Ferns were, on average, favored by fragmentation, and herbs did not show sensitivity to fragmentation different from zero (Fig 2D).

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: We made sure that time warping did not introduce any artifacts by considering an alternative procedure, where short ( ± 450 ms) time intervals around each ti were cut out of each trial and concatenated together; this procedure is similar to the pooling of neural data performed in the original studies (Feierstein et al. , 2006; Kepecs et al. , 2008). The dPCA analysis revealed qualitatively similar components (data not shown). Cross-validation to select regularization parameter Request a detailed protocol As noted above, we renormalized the regularization parameter μ=(λ∥𝐗∥)2, and then used cross-validation to find the optimal value of λ for each dataset. To separate the data into training and testing sets, we held out one random trial for each neuron in each condition as a set of SQ test 'pseudo-trials' 𝐗test (as the neurons were not recorded simultaneously, we do not have recordings of all N neurons in any actual trial). Remaining trials were averaged to form a training set of PSTHs 𝐗~train and an estimate of the noise covariance matrix 𝐂~train. Note that 𝐗test and 𝐗~train have the same dimensions. We then performed dPCA on 𝐗~train for various values of λ between 10-7 and 10-3 (on a logarithmic grid). For each λ, we selected ten components in each marginalization (i. e. 40 components in total) to obtain 𝐅ϕ(λ) and 𝐃ϕ(λ), and computed the normalized reconstruction error LCV(λ) on the test set (see below). We repeated this procedure ten times for different train-test splittings and averaged the resulting functions LCV(λ). In all cases the average function L¯CV(λ) had a clear minimum (Figure 11) that we selected as the optimal λ. The values of λ selected for each dataset were 2. 6⋅10-6 5. 8⋅10-6 5. 8⋅10-6 5. 8⋅10-6.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: La biomasse microbienne (bactéries et champignons) fut mesurée pour un éventail de types forestiers, de même que le pH, la saturation en bases, l'ampleur des réserves de N et P, et la minéralisation de la matière organique. Pour des conditions identiques de température et d'humidité, les taux moyens de respiration des substrats L, F et H de couverture morte sous couvert résineux étaient respectivement 1,8, 2,8 et 2,0 fois moindre que les taux moyens de respiration des horizons correspondants sous couvert feuillu. Le taux de respiration des horizons L et F de forêt de bouleau était 11,5 fois plus élevé que celui des horizons correspondants de forêt d'épinette noire. Les pertes de poids des horizons L, F et H de forêt de bouleau étaient 6, 3 et 2 fois plus élevées que celles des substrats correspondants de forêt d'épinette noire. La décomposition des substrats s'est avérée influencée davantage par les caractères intrinsèques de ces derniers que par les conditions climatiques de terrain. La biomasse fongique était corrélce à la quantité de matière organique en considérant tous les sites (n = 15, r = 0,62), mais les corrélations étaient meilleures pour les sites décidus (n = 9, r = 0,89), et résineux (n = 6, r = 0,82) pris séparément.

Output: corroborate


Input: Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: Apart from supporting the fragmentation threshold hypothesis, these results again suggest a stronger sensitivity to increased forest loss and fragmentation for M. hypopitys, as this species first reached the 0. 5 probability at a higher forest cover in each fragmentation scenario and also showed significantly different occurrence probabilities between both scenarios at all forest covers except for the highest (PFC = 85%, see Fig 3A). In contrast, for P. aculeatum these significant differences only occurred at intermediate PFC values (see Fig 3F). On the other hand and consistent with the sensitivity analysis, there were two herbs that showed no significantly different extinction thresholds between both fragmentation scenarios, H. nobilis and T. scorodonia (see Fig 3B and 3C), and five species that exhibited the opposite response to that predicted by the fragmentation hypothesis (i. e. a significantly higher extinction threshold under no fragmentation): the herbs D. flexuosa and P. nemoralis, and the ferns A. filix-femina, D. filix-mas and B. spicant (Fig 3D,3E and 3G–3I). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFig 3. Occurrence probability of the five herbs and four ferns under the two scenarios of forest fragmentation. The nine predictive values of forest cover (PFC) range from 5% to 85%. White circles represent occurrence probabilities in the maximum fragmentation scenario, while black circles represent occurrence probabilities in the minimum fragmentation scenario. Confidence intervals at 95% are given. Note that PFC decreases to the right. together, these results suggest that support for the fragmentation threshold hypothesis is rare among forest understory plant species, and also that some of these species may even react positively to increased fragmentation. Moreover, herbs do not show negative responses to fragmentation, i. e. , our expectation based on lower dispersal abilities of forest herbs was not supported. DiscussionThe array of responses to fragmentation showed by the modeled species underscore the complexity that interacting ecological processes associated with fragmentation may have on specific species [13]. Lack of support for our expectation based on the low dispersal capability of forest herbs could indicate that other factors contribute to species’ responses to fragmentation [28]. We suggest that variation in shade tolerance across species, and hence, in their respective possibilities to survive in more open, fragmented habitats (e. g.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: We used 100 iterations of stratified leave-group-out cross-validation, where on each iteration we held out one trial for each neuron in each condition as a set of SQ test 'pseudo-trials' 𝐗test and averaged over remaining trials to form a training set 𝐗~train (see above). After running dPCA on 𝐗~train, we used decoding axes of the first three stimulus/decision/interaction dPCs as a linear classifier to decode stimulus/decision/condition respectively. Consider e. g. the first stimulus dPC: first, for each stimulus, we computed the mean value of this dPC separately for every time-point. Then we projected each test trial on the corresponding decoding axis and classified it at each time-point according to the closest class mean. The proportion of test trials (out of SQ) classified correctly resulted in a time-dependent classification accuracy, which we averaged over 100 cross-validation iterations. Note that this is a stratified procedure: even though in reality some conditions have many fewer trials than others, here we classify exactly the same number of 'pseudo-trials' per condition. At the same time, as the coordinates of individual data points in each pseudo-trial are pooled from different sessions, the influence of noise correlations on the classification accuracies is neglected, similar to Meyers et al. (2012). We then used 100 shuffles to compute the distribution of classification accuracies expected by chance. On each iteration and for each neuron, we shuffled all available trials between conditions, respecting the number of trials per condition (i. e. all ∑sdKnsd trials were shuffled and then randomly assigned to the conditions such that all values Knsd stayed the same). Then exactly the same classification procedure as above (with 100 cross-validation iterations) was applied to the shuffled dataset to find mean classification accuracy for the first stimulus, decision, and interaction components. All 100 shuffling iterations resulted in a set of 100 time-dependent accuracies expected by chance. The time periods when actual classification accuracy exceeded all 100 shuffled decoding accuracies in at least ten consecutive time bins are marked by black lines on Figures 3–6. Components without any periods of significant classification are not shown. 12 for classification accuracies in each dataset.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: L'étude révèle l'existence de corrélations étroites entre le taux de décomposition de la matière organique et la biomasse fongique (n = 13, r = 0,86), et entre la biomasse fongique et la quantité de N et P minéralisé (n = 14, r = 0,95; n = 11, r = 0,94, respectivement). Les semis cultivés sur substrat carencé en éléments ont produit plus de tannins que les témoins, et les semis cultivés sur substrat classé par ordre croissant du rapport C/N ont formé moins de matière sèche, avec un contenu moindre en azote et avaient proportionnellement une masse racinaire plus élevée. Parallèlement, la teneur en azote de la litière s'atténuait avec l'augmentation du rapport C/N des substrats. La résorption de l'azote des feuilles à la sénescence était corrélée inversement avec la quantité de N minéralisé par mètre carré et par an. La fertilisation n'a pas eu d'influence sur les processus microbiens au champ, malgré que les études en laboratoire indiquaient une influence négative de NH4, P et K sur la respiration microbienne. L'addition de glucose, tant au champ qu'en laboratoire, a augmenté fortement la respiration. L'augmentation de respiration induite par le glucose invitro ne fut pas influencée par l'addition de nitrate d'ammonium, mais elle fut réduite significativement par l'addition de Pet K. La fertilisation au champ fut sans effet sur la respiration induite par le glucose, non plus que sur la biomasse microbienne. [Traduit par le journal]Get full access to this 1983. Nutrient cycling in relation to decomposition and organic-matter quality in taiga ecosystems. of Forest Research.

Output: corroborate


Input: Claim: Habitat fragmentation is one of the leading causes of species extinction worldwide. [SEP] Text: seeds dispersed by ants [28], and absence of clonal growth or seed bank [45]) make this species a candidate for negative impacts of fragmentation, it has been observed that increased shade from trees may impair its flowering and cause considerable declines in its populations, and also that the species is able to survive in open wooded meadows [46]. This may explain why this species shows null responses to fragmentation (Fig 3B). Teucrium scorodonia and Deschampsia flexuosa have also been described in hedges, heaths and grasslands—that probably owe their origin to the clearance of original woodland—in Britain [47, 48] and the [49, 50]. Also, germination of both species is indeed less successful in the shade/closed canopy compared with sunny conditions [48, 51]. Further, Poa nemoralis, one of the species clearly favored by forest fragmentation (Fig 3E), has a germination rate of up to 60% in the sun in contrast to 7% in the shade [52], and when this species occurs in the densest canopy forest cover is in the form of scattered and sterile individuals [51]. Also, this species is effectively dispersed in animal fur or in deer dung [53, 54], which would facilitate the colonization of forest fragments [55]. All of these characteristics suggest that, although able to survive in dense canopy forests, these herbs grow and reproduce better in forests with sparse canopy, forest gaps or even forest edges. Together with the typical problems of empirical studies, e. g. to use a general definition of habitat for all species (forest), our analysis entails several limitations that might make unclear the response of the species in some cases. Our database comprises species presence/absence in 10 km x 10 km grid cells, which can be too coarse a resolution to detect some fragmentation effects. Thus, we lack information about the processes acting at the landscape level, namely demographic processes such as source-sink dynamics that are not captured by presence/absence data. However, using species densities, Montoya et al. [23] only confirmed the extinction threshold hypothesis for one out of eight tree species, which may suggest that, in general, forest species may show a weak response to fragmentation due to their own idiosyncrasy (e. g. long-lived perennials). Additionally, from the wealth of existing metrics quantifying habitat configuration and landscape matrix characteristics (e. g.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Carlo computations took ∼8 hr for each of the larger datasets on a 6 core 3. 2 i7-3930K processor. Figure 12 Download asset Open asset Cross-validated time-dependent classification accuracies of linear classifiers (black lines) given by the first three stimulus/decision/interaction dPCs (columns) in each dataset (rows). Shaded gray regions show distribution of classification accuracies expected by chance as estimated by 100 iterations of shuffling procedure. Implementation of classical approaches (Figure 1) Request a detailed protocol The two-way ANOVA shown in Figure 1c–e was performed as follows. The two factors were stimulus (with six levels) and decision (with two levels), the interaction term was included, and a separate ANOVA was run for the firing rate of each neuron at each time point. Significance level was set at α=0. 05. Effect size was defined as partial omega squared with a sign given by the sign of the correlation coefficient between firing rate and the corresponding parameter. It can take values between -1 and 1, with 0 meaning no effect. For one-way ANOVA with a single two-level factor (which is a t-test), it would reduce to the signed R2 between firing rate and factor level. 1f–g, we ran linear regressions for the firing rate of each neuron at t=0. 25 s and t=3. 75 s, taking F1 stimulus value in Hz as one predictor, decision as another one, and including an interaction effect. Predictors were standardized (so regression coefficients in Figure 1g are standardized coefficients). The components shown in Figure 1f were constructed following the 'targeted dimensionality reduction' method presented in Mante et al. (2013) (see below for more details). To compute the proportion of explained PSTH variance, we arranged the two components (obtained by either method) into a matrix 𝐙 of 2×SQT size. Both the PSTH data matrix 𝐗~ and 𝐙 were centered by subtracting row means. Linear regression was used to find reconstruction weights 𝐁=𝐗~𝐙⊤(𝐙𝐙⊤)-1 minimizing reconstruction error ∥𝐗~-𝐁𝐙∥2.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Influence of the N and P status of plant material and of added N and P on the mineralization of C from 14C-labelled ryegrass in soil Go to 231. of to 232. Interactions between 14C-labelled atrazine and the soil microbial biomass in relation to herbicide degradation Go to 233. in the Go to 234. Decomposition of birch leaf litters with varying C-to-N ratios Go to 235. Changes in nutrient availability and forest floor characteristics in relation to stand age and forest composition in the southern part of the boreal forest of northwestern to 236. An assessment of the possible impact of expansion of native woodland cover on the chemistry of Scottish freshwaters Go to 237. Soil carbon cycling in a Japanese cedar (Cryptomeria japonica) plantation Go to 238. Development patterns in young conifer‐hardwood forests of interior to 239. Direct and of on in Go to 240. Nutrient cycling strategies Go to 241. Nitrogen cycling and dynamic analysis of man made larch forest ecosystem Go to 242. Nutrient cycling strategies Go to 243. Nitrogen cycling and dynamic analysis of man made larch forest ecosystem Go to 244. Removal of nitrogen during needle senescence in Scots pine (Pinus sylvestris L. ) Go to 245. Decomposition of tree leaf litters grown under elevated CO2: Effect of litter quality Go to 246. Influence of herbicide applications on the decomposition, microbial biomass, and microbial activity of pasture shoot and root litter Go to 247. Microsite and regional variation in the potential decay rate of Sphagnum magellanicum in south Swedish raised bogs Go to 248. The influence of different forest organic matter on the growth of one-year old planted Norway spruce seedlings in a greenhouse experiment Go to 249. Decomposition and nutrient dynamics of litter in long‐term optimum nutrition experiments. 11. Nutrient concentrations in decomposing Picea abies needle litter Go to 250. Changes in the forest floor of Canadian southern boreal forest after disturbance Go to 251. Phosphorus and nitrogen dynamics during field incubations in forest and fallow subarctic soils Go to 252. Forest responses to CO2 enrichment and climate warming Go to 253. Moose herbivory, browse quality, and nutrient cycling in an Alaskan treeline community Go to 254.

Output: corroborate


Input: Claim: The human brain contains approximately 86 billion neurons, which communicate through electrical and chemical signals. [SEP] Text: (c) Quantification of CB+, CR+, SST+, and PV+ cells among the TUJ-1+ cells. Upper, H9-01 LHX6 OE; below, ihtc-03 LHX6 OE group. (d) SST and PV expression in the ihtc-03 LHX6 OE group. (e) Neurons expressing SST and PV interneurons showed a characteristic morphological structure with more than five secondary branches. Figure 3 with 1 supplement Download asset Open asset Generation of SST and PV subtypes by overexpressing LHX6 and ventral patterning. (a) Schematic showing the differentiation of ventral cells from transgenic hPSC lines after treatment with SAG. V-CON, ventral control group (−dox); V-LHX6 OE, ventral LHX6 OE group (+dox). (b) The mRNA expression levels of ventral transcriptional markers in three transgenic hPSC lines and in each control at 17 days post-differentiation; n ≥ 3 biological replicates. (c) The proportions of LHX6+ cells in the control and LHX6 OE groups. The NKX2. 1, -1 groups all displayed high expression of LHX6 in both the H9-01 and ihtc-03 cell lines with or without dox. Scale bar, 50 μm. (d–e) Quantification of NKX2. 1+ cells (d) and GINs (e). (f) Percentage of GINs in the ventral ihtc-03 LHX6 OE group. At least 1500 cells were counted from random selected fields in each cell line, n ≥ 3 for each cell line. (g) Immunostaining of GIN subtypes in the ventral ihtc-03 control (V-CON) and LHX6 OE groups. (h) Representative tracing images of three different types PV+ neurons (type I, 130/369; type II, 137/369; type III multipolar cells, 102/369). Figure 3—figure supplement 1 Download asset Open asset GIN progenitors identity and subtype distribution.

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Then the proportion of explained variance was computed as R2=1-∥𝐗~-𝐁𝐙∥2/∥𝐗~∥2. The PCA on Figure 1i–k was done on the centered PSTH data matrix 𝐗~. Let its singular value decomposition be 𝐗~=𝐔𝐒𝐕⊤. Then each subplot on Figure 1j is a histogram of elements of one column of 𝐔 and each subplot on Figure 1j is one column of 𝐕. Comparison of dPCA to PCA in each marginalization Request a detailed protocol To understand the differences of dPCA with respect to other (demixing) methods, we will make several explicit comparisons. The first method we will consider is performing a series of standard PCAs in each marginalization separately. This procedure can be understood in two ways: after performing PCA on 𝐗ϕ and obtaining the matrix 𝐔ϕ for the k leading principal directions, we can use this matrix to project either the marginalized data or the full data. In the first case we obtain the principal components of the corresponding marginalization, 𝐔ϕ𝐗ϕ. However, while these components provide a particular decomposition or visualization of the data, they do not constitute readouts of the neural activity, since they are based on projecting the marginalized data. One particular advantage of the dPCA formulation is that it operates on the raw data, so that the decoders (and encoders) can actually be used on single trials. In turn, the visualization of the data found through dPCA also provides insights into the utility of the respective population code for the brain. In the second case we obtain 𝐔ϕ𝐗 components from the full data, so that these components could be obtained by projecting single-trial activities. However, now there is no guarantee that these components will be demixed. For a simple counter-example, consider Figure 13: the stimulus marginalization 𝐗s consists of three points (one for each stimulus) located roughly on a horizontal axis, and so the first principal axis of 𝐗s is roughly horizontal.

Output: pass


Input: Claim: Certain fungi can decompose organic matter, contributing to nutrient cycling. [SEP] Text: Response of soil microbial biomass and plant litter decomposition to weed management strategies in maize and asparagus cropping systems Go to 255. to Co2 Enrichment and Go to 256. A review of internal cycling of nitrogen within trees in relation to soil fertility Go to 257. Tropical soils could dominate the short-term carbon cycle feedbacks to increased global temperatures Go to 258. Decomposition in forest and fallow subarctic soils Go to 259. Mechanisms controlling soil respiration (CO2 and CH4) in southern peatlands Go to 260. Effects of plant species on nutrient cycling Go to 261. Aspects of the interaction between vegetation and soil under global change Go to 262. to 263. Litter production and decomposition dynamics in moist deciduous forests of the in Go to 264. for Aspen and in the Go to 265. Abiotic and litter quality control during the decomposition of different plant parts in dry tropical bamboo savanna in to 266. Immobilization and availability of N and P in the forest floors of fertilized coniferous forests Go to 267. Environmental and physiological factors governing nutrient resorption efficiency in barley Go to 268. Aspects of the Vegetation and Go to 269. Line in a Go to 270. Controls over Production by to 271. Responses of Soils to Go to 272. Comparison of atmospheric carbon dioxide concentration and metabolic activity in ecosystems Go to 273. Seasonal and annual carbon fluxes in a boreal forest landscape Go to 274. Atmosphere‐biosphere exchange of carbon dioxide in boreal forests Go to 275. Leaf demography and the seasonal internal cycling of nitrogen in sycamore ( Acer pseudoplatanus L. ) seedlings in relation to nitrogen supply Go to 276. Decomposition of water hyacinth detritus in eutrophic lake water Go to 277. in Taiga Forests: State-Go to 278. Plant litter: Its dynamics and effects on plant community structure Go to 279. Release of mineral nitrogen from plant root nodules Go to 280. Relationships between fungal and bacterial substrate-induced respiration, biomass and plant residue decomposition Go to 281. Effects of Stresses on to 282. Decomposition and nutrient dynamics of litter in long‐term optimum nutrition experiments Go to 283. Species effects on nitrogen cycling: a test with perennial grasses Go to 284.

Output: corroborate


Input: Claim: Mirror neurons are involved in empathy and allow individuals to understand others’ actions and emotions. [SEP] Text: Like any other skill, to be used successfully, empathy needs to be practised and developed over time. When used correctly, empathy provides a way to access the minds of others, helping us to understand their thoughts and actions, and enabling us to interact with them. Although empathy plays an important role in connecting with others, more seems to be needed for full understanding. To some people, ‘understanding’ means knowing why someone else is feeling a certain way, while for others, it requires a shared personal experience. “For example, we struggle to empathise with people who commit hideous crimes,” explains Thomas. “In consequence, we can fail to understand them, even though we might know their motives. ” Empathy and understanding, therefore, appear to be heavily linked, but full understanding likely requires more than just empathy. ? Thomas is a member of a research group investigating this connection between empathy and interpersonal understanding. Philosophers research a topic by checking reality (known as ‘phenomena’) against a specific concept. “We are concerned with conceptual problems and try to develop terminology related to these topics that is clear and unambiguous,” says Thomas. For example, Thomas may ask whether ‘emotional contagion’, such as our ability to pick up the mood of a room full of people, is really a form of empathy. “Does this phenomenon fall under the concept of empathy? ” Thomas is not only concerned with theoretical concepts. “We want to use findings from developmental psychology to reveal the reality of empathy,” he says. Hopefully, this will lead him to an empirically informed theory of empathy in relation to interpersonal understanding. “We want to explain how empathy works. ” today’s world, it often seems like we are getting worse at understanding each other. Thomas believes that psychological mechanisms such as biases and poor emotion regulation are hindering our performance of empathy. And our increasing interaction through technology is not helping, as we lose so many social cues such as tone of voice and body language when we do not communicate face-to-face. When no personal connection exists, it is hard to ‘read’ someone, and so therefore harder to empathise with them. We also struggle to grasp what understanding actually means, and what a successful version of empathy looks like.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: It is easy to see that the projection of the full data onto this axis will be not only stimulus-, but also time-dependent. Figure 13 Download asset Open asset Toy example illustrating the pseudo-inverse intuition. (a) Firing rate trajectories of two neurons for three different stimuli. (b) Same data with dPCA decoding and encoding axes. The encoding axes are approximately equivalent to the axes of the principal components in this case. Nonetheless, we can obtain a reasonable approximation to the dPCA solution using PCA in each marginalization. Namely, 𝐔ϕ can be taken to constitute the encoders 𝐅ϕ. In turn, the decoders 𝐃ϕ are obtained by a pseudo-inverse 𝐃=𝐔+, where 𝐔 is a matrix with 4k columns obtained by joining together all 𝐔ϕ. We found that this procedure provides a close approximation of the actual decoder and encoder matrices, provided one chooses a reasonable value of k: choosing k too small results in poor demixing, and choosing k too large results in overfitting. In our datasets, k=10 provides a good trade-off. This approximate solution highlights the conditions under which dPCA will work well, i. e. , result in well-demixed components that capture most of the variance of the data: the main principal axes of different marginalizations 𝐗ϕ need to be non-collinear. In other words, principal subspaces of different marginalizations should not overlap. Comparison of dPCA with targeted dimensionality reduction Request a detailed protocol Next, we compare dPCA with 'targeted dimensionality reduction' (TDR), the method proposed by Mante et al. (2013). Briefly, the algorithm underlying TDR works as follows: Perform PCA of the trial-average neural data 𝐗~ and define a 'denoising' matrix 𝐊 as a linear projector on the space spanned by the leading principal axes. Here we used 20 principal axes: 𝐊=𝐔20𝐔20⊤.

Output: pass


Input: Claim: Fossilized plants provide insights into ancient climates and ecosystems. [SEP] Text: The two lineages are characterized by a largely well-differentiated spatial distribution along an altitudinal gradient, and tend to differ with respect to flowering phenology and the morphologies of leaves and fruits. Most hybrids, which are probably later generation of F1 individuals, tend to be distributed at intermediate altitudes between those of the two parental lineages. Collectively, these results would tend to provide evidence of a reproductive barrier between the two lineages, whereas interfertile hybrids occur incidentally. Ecological niche divergence has evolved between the NEA and SEA lineages NEA and SEA lineages in the DHS were found to show a clear pattern of spatial isolation, with NEA-DHS tending to be predominantly distributed at altitudes greater than 670 m, whereas SEA-DHS were found to be scattered at lower altitudes, with rare overlap between the two lineages. Nevertheless, despite the limited size of our study population, we believe our findings to be reasonably representative of the regional population, given that we sampled all accessible individuals along a 5-km-long, 20-m-wide transect, with only four or five inaccessible individuals being excluded from sampling. It is unconvinced whether inclusion of these latter unsampled individuals would change the established spatial genetic pattern. Moreover, consistent with the findings of the present study, 100 trees sampled during a preliminary survey undertaken in an area near the transect at an altitude of less than 650 m all proved to be SEA-DHS individuals (unpublished data). In addition, the trees of both lineages, as well their hybrids, appear to have a similar age structure, as indicated by DBH measurements, which for most individuals ranged between 26 and 45 cm (Figure S5), thereby tending to indicate a long persistence of the observed spatial pattern. We speculate that spatial isolation along altitudinal gradients may be common feature of Acer populations in North China. According to the findings of a previous phylogeographical study, the NEA and SEA lineages meet at an area to the North of Mt. Taihang and in the (Fig. 1b) 14. However, most Acer populations in comprise exclusively NEA or SEA lineages, with only two populations near the DHS showing clear evidence of more than one lineage. We believe that the apparent absence of any substantial overlap at the local scale can be ascribed to two overriding factors.

Output: pass


Input: Claim: Mirror neurons are involved in empathy and allow individuals to understand others’ actions and emotions. [SEP] Text: We often fail to understand what is good for others, meaning empathic decisions might not lead us to morally sound conclusions. And barriers to fully understanding someone are always going to exist. “It is a common feature of empathy that we cannot ever fully succeed in empathising, because we cannot ever be the other person,” says Thomas. Download the 10. 33424/185 Link to the activity sheet 0 +1 Pinterest 0 LinkedIn 0 speaking at the Council. The website of Thomas’s research project, it Feel? The cover of Forms of Fellow Feeling, a book about empathy that Thomas co-edited. Thomas with Einstein at Madame Tussauds. This is Anfield! When not pondering philosophical questions, Thomas’s passion is football. – the ability to share the perspectives and feelings of another being – to gain appreciative knowledge of thoughts and feelings of others – the feeling of pity or sorrow for someone else – the study of foundational questions regarding all aspects of human and natural being, including knowledge, the mind, ethics and existence – the scientific study of how human thought and behaviour change over a lifetime – the ability to manage and respond to our emotions Many of us find it hard to understand the decisions that other people make. We may be concerned that our actions are not understood by others, we get frustrated when our friends and family cannot see something from our point-of-view, and we struggle to comprehend how someone could commit an atrocious act like murder. But why is it so hard to understand each other, and what prevents us from putting ourselves in someone else’s shoes? Empathy is a skill that enables us to take the perspective of other beings and feel what they feel. Schramme, a philosopher at the University of Liverpool, is using philosophical discussion and psychological findings to develop a theory relating empathy to interpersonal understanding. In a world that is becoming increasingly politically and culturally divided, this theory will aid future discussions by increasing our knowledge of how we understand each other. “Empathy is not itself a moral skill,” says Thomas, “but a mode of gaining understanding of another being. ” For example, some con artists are very good at ‘getting into the heads of others’, and so are good at empathy, but they are not caring or moral, rather they use their skill in empathy to cheat others.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: We found that we obtain best results if we select this order ad hoc for each dataset; the orders we used were stimulus → decision → interaction for the somatosensory working memory task, stimulus → interaction → decision for the visuospatial working memory task, and interaction → decision → stimulus for both olfactory tasks. The vector 𝜷o(t) was always used last. We applied TDR to all our datasets and observed that dPCA consistently outperforms it in terms of capturing variance and demixing task parameters. First, unlike dyields only one component per task parameter. Second, even this component tends to retain more mixed selectivity than the corresponding dPCA component. Some representative components are shown in Figure 14. Figure 14 Download asset Open asset Some demixed components as given by three different demixing methods (rows) in various datasets and marginalizations (columns). Empty subplots mean that the corresponding method did not find any components. All projections were z-scored to make them of the same scale. Barplots on the right show fractions of variance in each marginalization for each component (stimulus in blue, decision in red, interaction in purple, condition-independent in gray): ∥𝐝𝐗~ϕ∥2/∥𝐝𝐗~∥2. Barplots consisting of a single colour correspond to perfect demixing. Comparison of dPCA with LDA Request a detailed protocol Analysis (LDA) is usually understood as a one-way technique: there is only one parameter (class id) associated with each data point, whereas in this manuscript we dealt with three parameters simultaneously. Therefore, LDA in its standard form cannot directly be applied to the demixing problem. We can, however, use the same data and covariance decomposition X=∑ϕXϕ+XnoiseC=∑ϕCϕ+Cnoise that dPCA is using and construct a separate LDA for each marginalization ϕ. To the best of our knowledge, this framework does not have an established name, so we call it factorial LDA. Let us first consider the case of finding demixed components for marginalization 𝐗ϕ.

Output: pass


Input: Claim: The evolution of flowering plants had a significant impact on terrestrial ecosystems. [SEP] Text: Жизнедеятельность преимущественно микроскопических, но многочисленных наземных организмов неизбежно оказывала существенное воздействие на первичные минеральные субстраты, разрушая их твёрдую минеральную основу, обогащая её органическим материалом и создавая предпосылки для появления первых архаичных почв. Этот процесс способствовал формированию условий для проникновения на сушу высших растений.

Output: corroborate


Input: Claim: Mirror neurons are involved in empathy and allow individuals to understand others’ actions and emotions. [SEP] Text: Empathy is also often confused with sympathy, which is the feeling of sadness or pity you may feel if someone else is sad, whereas empathy involves a broader understanding of others. Like any other skill, to be used successfully, empathy needs to be practised and developed over time. When used correctly, empathy provides a way to access the minds of others, helping us to understand their thoughts and actions, and enabling us to interact with them. Although empathy plays an important role in connecting with others, more seems to be needed for full understanding. To some people, ‘understanding’ means knowing why someone else is feeling a certain way, while for others, it requires a shared personal experience. “For example, we struggle to empathise with people who commit hideous crimes,” explains Thomas. “In consequence, we can fail to understand them, even though we might know their motives. ” Empathy and understanding, therefore, appear to be heavily linked, but full understanding likely requires more than just empathy. ? Thomas is a member of a research group investigating this connection between empathy and interpersonal understanding. Philosophers research a topic by checking reality (known as ‘phenomena’) against a specific concept. “We are concerned with conceptual problems and try to develop terminology related to these topics that is clear and unambiguous,” says Thomas. For example, Thomas may ask whether ‘emotional contagion’, such as our ability to pick up the mood of a room full of people, is really a form of empathy. “Does this phenomenon fall under the concept of empathy? ” Thomas is not only concerned with theoretical concepts. “We want to use findings from developmental psychology to reveal the reality of empathy,” he says. Hopefully, this will lead him to an empirically informed theory of empathy in relation to interpersonal understanding. “We want to explain how empathy works. ” today’s world, it often seems like we are getting worse at understanding each other. Thomas believes that psychological mechanisms such as biases and poor emotion regulation are hindering our performance of empathy. And our increasing interaction through technology is not helping, as we lose so many social cues such as tone of voice and body language when we do not communicate face-to-face. When no personal connection exists, it is hard to ‘read’ someone, and so therefore harder to empathise with them.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: We will denote the remaining part of the data matrix as 𝐗-ϕ=𝐗-𝐗ϕ and the remaining part of the covariance matrix as 𝐂-ϕ=𝐂-𝐂ϕ. In turn, the goal of LDA will be to find linear projections that have high variance in 𝐂ϕ and low variance in 𝐂-ϕ. In LDA, these matrices are usually called between-class and within-class covariance matrices (Hastie et al. , 2009). The standard treatment of LDA is to maximize the multivariate signal-to-noise ratio tr(DCϕD⊤[DC−ϕD⊤]−1), where 𝐃 is the matrix with discriminant axes in rows. The well-known solution is that 𝐃LDA is given by the leading eigenvectors of 𝐂-ϕ-1𝐂ϕ (stacked together as rows), or, equivalently, as eigenvectors of 𝐂-1𝐂ϕ. More useful for our purposes is the reformulation of LDA as a reduced-rank regression problem (Izenman, 2008; De la Torre, 2012). When classes are balanced, it can be formulated as LLDA=∥𝐆ϕ-𝐅𝐃𝐗∥2, where 𝐆ϕ is a class indicator matrix. This matrix has as many rows as there are possible values of parameter ϕ and specifies which data point is labeled with which parameter value: Gij=1 if the j-th data point belongs to class i (has i-th value of the parameter ϕ) and Gij=0 otherwise. In the toy example shown in Figure 2, there are three classes with five points each, and so 𝐆ϕ will be a 3×15 matrix of zeros and ones. In this reformulation of LDA, the main interest is in the decoder matrix 𝐃, whereas the encoder matrix 𝐅, which serves to map the low-dimensional representation onto the class indicator matrix, plays only an auxiliary role.

Output: pass


Input: Claim: The evolution of flowering plants had a significant impact on terrestrial ecosystems. [SEP] Text: Уже в раннем девоне наземная растительность состояла из растений разного систематического положения (псилофиты или риниофиты, печёночники, прапапоротники, плауновидные, членистостебельные, а также целый ряд растений, таксономическое положение которых не ясно). Возможность широкой эволюционной радиации5 первых наземных высших растений в условиях суши уже неоднократно отмечалась ранее [26].

Output: corroborate


Input: Claim: Tornadoes are most common in the United States, particularly in a region known as Tornado Alley. [SEP] Text: To determine if the 104 tornado reports were plausible, first possible duplicate reports were eliminated, resulting in as few as 58 tornadoes to as many as 90. Second, the number of possible parent misovortices that may have spawned tornadoes is estimated from model output. The number of plausible tornado reports in the 200-m grid-spacing domain was 22 and as many as 44, whereas the model simulation was used to estimate 30 possible parent misovortices within this domain. These results suggest that a number of 90 reports was plausible. Access content. a Additional affiliation: Christ’s College, University of Cambridge, Cambridge, United Kingdom. Corresponding author address: Prof. David M. Schultz, Centre for Atmospheric Science, School of Earth, Atmospheric and Environmental Sciences, University of 13 9PL, United Kingdom. E-mail: david. schultz@manchester. ac. uk Keywords: Circulation/ Dynamics; Mesoscale processes; Tornadogenesis; Atm/Ocean Structure/ Phenomena; Cold fronts; Tornadoes; Forecasting; Mesoscale forecasting; Models and modeling; Mesoscale models1. IntroductionOn 23 November 1981, a strong cold front swept across the United Kingdom, producing an unprecedented 104 reports of tornadoes (Fig. 1). Called Britain’s greatest tornado outbreak (Rowe and Meaden 1985), this event had many more tornadoes than the next largest event just a month earlier on 20 October 1981, which spawned only 29 tornadoes (Turner et al. 1986; Rowe 2016). For comparison, even modest outbreaks by U. S. standards are relatively uncommon in the United Kingdom. Specifically, over 90% of U. K. tornado days between 1980 and 2012 had fewer than eight tornadoes (Fig. 14 in Mulder and Schultz 2015). 1981 outbreak is so exceptional that it distorts the historical record and climatologies of tornadoes in the and Europe. For example, in the U. K. tornado climatology by Mulder and Schultz (2015), several figures had to be plotted with the outbreak excluded (e. g. , their Figs. 5–7, 9, 12, and 13), and, in their review of tornadoes across Europe, Antonescu et al. (2016) found that the large number of reports produced a bias in their synthesized results and capped the total number of reports from this outbreak at 58.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: In contrast, the dPCA loss function is LdPCA=∥𝐗ϕ-𝐅𝐃𝐗∥2, where 𝐗ϕ is the matrix of the same size as 𝐆ϕ with j-th column being the class centroid of the class to which the j-th point belongs. This comparison highlights the difference between the two methods: LDA looks for decoders that allow to reconstruct class identity (as encoded by 𝐆ϕ) whereas dPCA looks for decoders that allow to reconstruct class means (as encoded by 𝐗ϕ). Figure 2b,f,h provides a toy example of a situation when these two goals yield very different solutions: the LDA projection separates the three classes better than the dPCA projection, but the dPCA projection preserves the information about the distance between classes. Using the explicit solution for reduced-rank regression, one can show that LLDA does indeed have eigenvectors of 𝐂-1𝐂ϕ as a solution 𝐃LDA for decoder (see Section 8. 5. 3 in Izenman, 2008). Following the similar logic for LdPCA, one can derive the corresponding expression for the dPCA decoder: 𝐃dPCA is given by the eigenvectors of 𝐂-1𝐂ϕ2 (personal communication with Maneesh Sahani). A statistical test known as can be seen as another possible factorial generalization of LDA. Given the same data and covariance decomposition, tests if the effect of ϕ is statistically significant by analyzing eigenvalues of 𝐂noise-1𝐂ϕ. The eigenvectors of this matrix can in principle serve as decoders, but these projections are optimized to separate the contribution of ϕ from noise, not from the contributions of noise and other parameters. is not the appropriate method for demixing purposes. While the toy example of Figure 2 illustrates that dPCA and LDA will in principle have very different solutions, we note that in all datasets considered here factorial LDA and dPCA yielded very similar components.

Output: pass


Input: Claim: The evolution of flowering plants had a significant impact on terrestrial ecosystems. [SEP] Text: С этого момента в истории Земли низменные участки суши были окончательно колонизованы высшими растениями, однако возвышенности и тем более водоразделы оставались безжизненными до появления первых настоящих семян у примитивных птеридоспермов конца девонского периода.

Output: corroborate


Input: Claim: Tornadoes are most common in the United States, particularly in a region known as Tornado Alley. [SEP] Text: The next seven tornado reports occurred near Aughton (near Liverpool) at 1100 UTC, and there were four more in greater Manchester at 1200 UTC. A further 11 reports in the early afternoon came from Birmingham, Nottinghamshire, and the (east of Birmingham), where 20 large caravans (camper vans) were blown over (Rowe and Meaden 1985). Seven tornado reports clustered near Hull in the early afternoon. Many of the reports (43), however, came from (northeast of Cambridge) between 1300 and 1600 UTC. The last tornadoes occurred in southeast Essex (east-northeast of London) just before 1600 UTC. Despite the large number of reports and despite occurring in conjunction with an extensive southwest–northeast-oriented cold front advancing southeastward across nearly the entirety of the United Kingdom, these tornado reports only occurred along a narrow swath 200–250 km wide and 400 km long (Fig. 1). Thus, the first goal of this 0. 3 and 4 km long. Where estimated, damage tracks are believed to have had widths of 10–20 m. The direction of travel was reported in 16 cases, with 14 coming from between west and north-northwest, consistent with the movement of the front; the other 2 came from the south and southwest. Ninety-nine of the 104 reports were assigned values on the or T scale (Meaden 1983; Meaden et al. 2007). Compared to the Fujita (F) scale, the T scale has twice as many classifications. Conversion between the F and T scales can be performed using the equation F ≈ 0. 5T and rounding down to the nearest integer (Brooks and Doswell 2001; Meaden et al. 2007). Figure 2 shows that most (96, or 97%) of the tornadoes were T0–T3 (or F0–F1); three tornadoes, however, reached T4 (or F2). This distribution is similar to the national distribution from the Kirk (2014) and Mulder and Schultz (2015) climatologies in which 94%–95% were between T0 and T3 (or F0–F1). Size Fig. 2. Distribution on the T scale of intensities of the 99 tornado reports on 23 Nov 1981 associated with an intensity rating from the TORRO database. Citation: Weather and Forecasting 31, 3; 10.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: This may reflect several pecularities of the data: for instance, the population activity for different values of the same parameter was spaced rather evenly, and all decisions were binary. Nevertheless, we emphasize that dPCA is better suited for (demixed) dimensionality reduction due to its focus on reconstructing the original data, as explained and discussed in the Results (Figure 2). Comparison of dPCA with previous versions Request a detailed protocol Demixed PCA as presented here is conceptually based on our previous work. Machens et al. (2010) suggested a demixing method called difference of covariances (DOC) that can only handle two parameters, e. g. stimulus s and time t. Given PSTHs 𝐱(s,t), DOC first constructs stimulus-dependent and time-dependent marginalizations 𝐱¯(s)=⟨𝐱(s,t)⟩t and 𝐱¯(t)=⟨𝐱(s,t)⟩s, and then computes the difference between the stimulus and time covariance matrices 𝐂s=⟨𝐱¯(s)𝐱¯(s)⊤⟩ and 𝐂t=⟨𝐱¯(t)𝐱¯(t)⊤⟩, 𝐒=𝐂s-𝐂t. Eigenvectors of S with maximum (positive) eigenvalues correspond to directions with maximum stimulus variance and minimum decision variance. Vice versa, eigenvectors with minimum (negative) eigenvalues correspond to directions with maximum decision variance and minimum stimulus variance. In the toy example presented in Figure 2 DOC finds the axis that is very close to the first PCA axis of class centroids (which is also very close to the dPCA encoder axis shown on the figure), providing worse demixing than both LDA and dPCA. A possible extension of DOC to more than two parameters is described in Machens (2010). Here the PSTHs are assumed to depend on M parameters, and the method constructs M marginalizations by averaging over all parameters except one.

Output: pass


Input: Claim: Coral reefs support a diverse range of marine species. [SEP] Text: IntroductionMesophotic coral ecosystems (MCEs; light-dependent coral ecosystems found at depths between 30 to 150 m (Hinderstein et al. 2010; Kahng et al. 2014)), have received increasing attention from the scientific community over the past few decades. However, they are still largely underexplored compared to their shallow-water counterparts (Bongaerts and Smith 2019; Pyle and Copus 2019; Soares et al. 2020; Stefanoudis et al. 2022). The increasing interest in MCEs was initially driven by the potential refuge MCEs could offer to threatened shallow coral reefs, the so-called deep reef refugia hypothesis (DRRH) (Glynn 1996), and the evolution of technology, allowing exploration of these deeper, cooler waters (Bongaerts et al. 2010). More recently, the interest has been driven by the desire to investigate the distinct biological and environmental characteristics of MCEs compared to shallow reefs, and the functional role that MCEs play in coral reef ecosystems (Kramer et al. 2020; Carpenter et al. 2022; Pérez-Rosales, et al. 2022a). Recent research has revealed that MCEs are more biodiverse and widespread than previously expected, with 80% of all coral reef habitat falling within MCEs (based on depth alone) and they are an important component for maintaining overall reef biodiversity (Englebert et al. 2017; Shoham and Benayahu 2017; Muir and Pichon 2019; Pyle and Copus 2019; Pérez-Rosales et al. 2022). Yet the protection status of MCEs remains poor (Hernandez‐Agreda et al. 2022). In fact, despite meeting the international criteria used to identify priority areas for conservation policies, and hence being good candidates for future Area (MPA) designation (Soares et al. 2020), MCEs and deeper reefs (i. e. rariphotic zone 150–300 m, sensu Baldwin et al. (2018) are considered under-protected and under-represented in marine spatial planning (Rocha et al. 2018; Stefanoudis et al. 2022). Indeed, the few protected mesophotic and deeper reefs were often included incidentally due to geopolitical boundaries (e. g.

Output: corroborate


Input: Claim: Tornadoes are most common in the United States, particularly in a region known as Tornado Alley. [SEP] Text: 1175/1520-0469(2000)057<2246:>2. 0. CO;2. Lee, B. D. , and WilhelmsonR. B. , 2000: The numerical simulation of nonsupercell tornadogenesis. Part III: Parameter tests investigating the role of CAPE, vortex sheet strength, and boundary layer vertical shear. J. Atmos. Sci. , 57, 2246–2261, doi:10. 1175/1520-0469(2000)057<2246:>2. 0. CO;2. )| falseSearch 10. 1016/0169-8095(94)00093-S. Locatelli, J. D. , MartinJ. E. , and HobbsP. V. , 1995: Development and propagation of precipitation cores on cold fronts. Atmos. Res. , 38, 177–206, doi:10. 1016/0169-8095(94)00093-S. )| falseSearch 10. 1175/1520-0493(2002)130<1692:>2. 0. CO;2. Markowski, P. M. , StrakaJ. M. , and RasmussenE. N. , 2002: Direct surface thermodynamic observations within the rear-flank downdrafts of nontornadic and tornadic supercells. Mon. Wea. Rev. , 130, 1692–1721, doi:10. 1175/1520-0493(2002)130<1692:>2. 0. CO;2. )| falseSearch 185. Meaden, G. T. , 1983: tornado intensity scale. J. Meteor. , 8, 151–153. Meaden, G. T. , 1983: tornado intensity scale. J. Meteor. , 8, 151–153. )| falseSearch 10. 1016/j. atmosres. 2005. 11. 014. Meaden, G. T. , KochevS. , KolendowiczL. , Kosa-KissA. , MarcinonieneI. , SioutasM. , ToomingH. , and TyrrellJ. , 2007: Comparing the theoretical versions of the Beaufort scale, the T-scale and the Fujita scale. Atmos. Res.

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The respective covariance matrices 𝐂ϕ are then formed as above. The extension of DOC seeks to find the matrix of orthogonal directions 𝐔 such that L=∑ϕtr(Uϕ⊤CϕUϕ) is maximized subject to 𝐔⊤𝐔=𝐈 where 𝐔=[𝐔1…𝐔M]. For M=2 this can be shown to be equivalent to the original DOC. Note that Machens (2010) did not address the interaction terms. The connection between the current dPCA and the DOC approach can be made more explicit if we consider the full covariance decomposition C=∑ϕCϕ and introduce into the dPCA loss function an additional constraint that both encoder and decoder should be given by the same matrix with orthonormal columns: 𝐅ϕ=𝐃ϕ⊤=𝐔ϕ. Then ∥Xϕ−UϕUϕ⊤X∥2=∥Xϕ−UϕUϕ⊤Xϕ∥2+∥UϕUϕ⊤X−ϕ||2=∥Xϕ∥2−∥UϕUϕ⊤Xϕ||2+∥UϕUϕ⊤X−ϕ||2∼−tr(Uϕ⊤(Cϕ−C−ϕ)Uϕ), where the first equality follows from properties of the decomposition, the second equality from the properties of the orthonormal matrices 𝐔ϕ, and the third equality uses the definition of the covariance. This derivation shows that the difference of covariances 𝐂ϕ-𝐂-ϕ emerges from the dPCA loss function if the decoder and encoder are given by the same set of orthogonal axes. However, such axes 𝐔ϕ from different marginalizations ϕ will in general not be orthogonal to each other, whereas both DOC and its generalization insisted on orthogonal axes. Both the original DOC and its extension ignored interaction terms.

Output: pass


Input: Claim: Coral reefs support a diverse range of marine species. [SEP] Text: Secondly, the observed and extrapolated diversities at each site and depth zone were estimated by computing individual-based and sample-size based R/E curves for Hill numbers of q = 0, 1, 2; with extrapolation to a sampling effort equal to double the number of individuals (extrapolation beyond this is unreliable due to prediction bias; (Chao et al. 2014)). Thirdly, the observed and extrapolated Hill numbers were compared to assess undetected diversity and compare diversity between the depth zones and sites (Table S4). However, these results must be taken with caution as they were extracted prior to sample completeness standardisation. Finally, diversity estimates were calculated at a standardised coverage Cmax (e. g. equally complete samples) to statistically compare the benthic diversities along the depth gradient and between sites, which corresponds to the sample coverage of the depth zone exhibiting the lowest coverage when samples are extrapolated to double the number of samples per depth zone at a site (Table S5), as used in El-Khaled et al. (2022). Here, the Cmax value was 96. 4% (Fig. S1; Table S5), corresponding to the lowest sample completeness for q = 0, 1, 2. Beta diversity analysesBeta diversity was measured as the variation in morphospecies with depth and between sites. Jaccard’s dissimilarity was calculated, including its two components, turnover (i. e. species replacement) and nestedness (i. e. subset of a richer community), using the entire benthic community presence-absence data for each depth zone and site. Jaccard dissimilarity varies between 0 and 1, with values close to 1 revealing a high difference in the composition of benthic communities between two depth zones, while a low dissimilarity (close to 0) denotes similar benthic communities. These indices were calculated using the betapart package and plotted in a heatmap, as well as a barplot with ggplot2 package in R (Baselga and Orme 2012; Core 2020). ResultsAlpha diversityA total of 582 distinct morphospecies (OTUs) were identified from the benthic communities at Egmont Atoll, between 15 and 160 m, covering 17 higher taxa (ranging from phylum to genus; Table S2).

Output: corroborate


Input: Claim: Tornadoes are most common in the United States, particularly in a region known as Tornado Alley. [SEP] Text: , 2009: Impact of cloud microphysics on the development of trailing stratiform precipitation in a simulated squall line: Comparison of one- and two-moment schemes. Mon. Wea. Rev. , 137, 991–1007, doi:10. 1175/2008MWR2556. 1. )| falseSearch 2012. Mon. Wea. Rev. , 143, 2224–2240, doi:10. 1175/MWR-D-14-00299. 1. Mulder, K. J. , and SchultzD. M. , 2015: Climatology, storm morphologies, and environments of tornadoes in the British Isles: 1980–2012. Mon. Wea. Rev. , 143, 2224–2240, doi:10. 1175/MWR-D-14-00299. 1. )| falseSearch 279. Nolen, R. H. , 1959: A radar pattern associated with tornadoes. Bull. Amer. Meteor. Soc. , 40, 277–279. )| falseSearch 10. 1175/2010WAF2222368. 1. Potvin, C. K. , ElmoreK. L. , and WeissS. J. , 2010: Assessing the impacts of proximity sounding criteria on the climatology of significant tornado environments. Wea. Forecasting, 25, 921–930, doi:10. 1175/2010WAF2222368. 1. )| falseSearch 10. 1175/MWR-D-15-0104. 1. Púčik, T. , GroenemeijerP. , RývaD. , and KolářM. , 2015: Proximity soundings of severe and nonsevere thunderstorms in central Europe. Mon. Wea. Rev. , 143, 4805–4821, doi:10. 1175/MWR-D-15-0104. 1. )| falseSearch 10. 1175/1520-0434(1998)013<1148:>2. 0. CO;2. Rasmussen, E. N. , and BlanchardD. O. , 1998: A baseline climatology of sounding-derived supercell and tornado forecast parameters. Wea. Forecasting, 13, 1148–1164, doi:10.

Output: pass


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Brendel et al. (2011) introduced interaction terms and the full covariance splitting C=∑ϕCϕ as described in this manuscript, and developed a probabilistic dPCA model based on probabilistic PCA (PPCA); to remove ambiguity we call this method dPCA-2011. Similar to PPCA, dPCA-2011 assumes that the data are described by a linear model with Gaussian residuals, i. e. p(𝐱|𝐳)=𝒩(𝐖𝐳,σ2𝐈), but the prior over the components z is chosen such that the components are sparsely distributed over marginalizations. In other words, the prior is chosen such that those components are favored that have variance in only one marginalization. Under the constraint that decoding directions W are orthogonal, the model can be fit using the expectation-maximization algorithm. However, the probabilistic formulation of Brendel et al. (2011) still suffers from the orthogonality constraint. As explained in the Discussion, the orthogonality constraint is too rigid and can prevent successful demixing if parameter subspaces are sufficiently non-orthogonal. Indeed, we applied dPCA-2011 to all our datasets and observed that dPCA-2015 showed better demixing (Figure 14). Moreover, dPCA-2011 failed to find any decision components in the visuospatial working memory task. In addition, the formulation of dPCA in this manuscript is radically simplified compared to Brendel et al. (2011), features an analytic solution and is easier to compare with other linear dimensionality reduction techniques. 0. This can be seen from equation (⋆) because x¯ψ and x¯χ both consist of an even number of terms with alternating signs, so their product will also consist of an even number of terms with alternating signs, and after averaging over Ψ all terms will become equal to ± x¯2 and cancel each other. Third, from the definition of the noise term it follows that any marginalization x¯ψ is uncorrelated with the noise term: ⟨x¯ψx¯noise⟩Ψ=0.

Output: pass


Input: Claim: Coral reefs support a diverse range of marine species. [SEP] Text: All morphospecies identified can be consulted in the freely accessible catalogue created for the region (Diaz et al. , 2023b). Species richness (q = 0) was observed to decrease with depth at both sites, with lower diversity observed at 110–120 m and 150–160 m compared to the shallower depth zones (Fig. 2a). Although only conservative minimum estimates could be obtained for species richness (as individual-based R/E curves did not reach an asymptote; Fig. 2a), at the fixed sample coverage of Cmax = 96. 4%, benthic morphospecies richness was significantly lower for the two deepest zones compared to the shallower depth zones (Fig. S1c, Table S5; non-overlapping 95% confidence intervals (CIs) for the two deepest depth zones, overlapping CIs for 15 m to 90 m depth zones). Fig. 2Overview of alpha diversity analysis based on Hill numbers. a) individual-based Rarefaction/Extrapolation (R/E) curves for q = 0. b) individual-based R/E curves for q = 1. c) individual-based R/E curves for q = 2. Shaded areas represent the 95% confidence intervals for each curve. Shades of blue represent the different surveyed depth zones at Rats (IDR), and the shades of orange are the depth zones at (MA). Solid lines are the rarefaction curves while the dashed lines represent the extrapolation curves up to double the respective sample sizeFull size imageEstimates of Shannon and Simpson diversity reached asymptotes for all depths at both sites and overall showed significant decreases in diversity over the depth gradient, from 15 to 160 m (Fig. 2b, c; Fig. S1b, c; non-overlapping CIs). The exception was at IDR, where both Shannon and Simpson diversity were significantly higher at 30–40 m compared to 15–20 m (Fig. 2b, c). At both sites, Shannon diversity between 60–70 m and 150–160 m was significantly lower than observed at shallower depths; however, there was no significant difference in Shannon diversity between 80–90 m and 110–120 m (Fig. 2b). Simpson diversity differed between the two sites (Fig.

Output: pass


Input: Claim: Tornadoes are most common in the United States, particularly in a region known as Tornado Alley. [SEP] Text: On a per-tornado basis, for those states with more than 100 tornadoes, the top six states in order are Alabama, Arkansas, Mississippi, Georgia, Oklahoma, and Louisiana. Five of the six are in the deep South. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFig 7. Tornado energy by state. DiscussionThe amount of destruction varies widely from one tornado to the next. To better understand this variability this study estimates the per-tornado total kinetic energy (TKE) for the 8752 tornadoes in the SPC database over the period 2007–2013. The main idea is to distinguish individual tornadoes based on a variable of destruction that has physical units of energy. The method uses the fraction of the tornado path experiencing EF damage and the midpoint wind speed for each EF rating. The fraction of the path is obtained from a model developed for the NRC that combines theoretical considerations with empirical data. An earlier study showed estimates of TKE from the model matched well those computed from recent tornado observations. The TKE is validated as a useful metric of destruction by comparing it to other destructive indexes in the literature including the DPI and TDI. Additional validation is demonstrated by correlating it to casualties and loss estimates. TKE is highly correlated with DPI since they are both sensitive to path length and width in the same way (path area). As such TKE offers no statistical advantage over DPI as a single indicator of destruction for individual tornadoes. However, since TKE is based on physical theory and has units of energy it can be more easily understood by scientists outside of meteorology. Moreover, TKE has units of Joules (or more simply, J/kg) and can be directly compared with environmental energy variables like CAPE and helicity, which have the same units of J/kg. This is important toward the goal of improving understanding of the relationship between tornadoes and their parent-storm environment. Moreover, TKE can be spatially dis-aggregated. For example, if interest lies in the portion of energy within a county from a tornado passing through several counties, then the county-level TKE can be computed based on the contours by EF rating. In this way maps of TKE by area can be drawn accumulated by month, year, and decades.

Output: contrast


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The fact that all marginalizations and the noise are pairwise uncorrelated allows to segregate the variance of x (here we assume that x is centered, i. e. x¯=0): var[x]=⟨x2⟩Ψ=⟨(∑ψx¯ψ+xnoise)2⟩Ψ=∑ψ⟨x¯ψ⟩Ψ+⟨xnoise2⟩Ψ=∑ψvar[x¯ψ]+var[xnoise]. Turning now to the multivariate case, if we replace x with x∈RN, everything remains true but variances should be replaced by covariance matrices: C=⟨xx⊤⟩Ψ=∑ψCψ+Cnoise. Note that in ANOVA literature one usually talks about decomposing sums of squares ∑x2 and in literature about decomposing scatter matrices ∑𝐱𝐱⊤, because (co)variances of different terms are computed from these sums using different denominators (depending on the corresponding number of degrees of freedom) and do not add up. We do not make this distinction and prefer to talk about decomposing the (co)variance, i. e. all (co)variances here are defined with the same denominator equal to the total number of sample points. 3. Here the second and the third components are closely resembling the first and second temporal derivatives of the leading decision component. To illustrate why these components are likely to be artifacts of the underlying sampling process, consider a highly simplified example in which a population of N neurons is encoding a one-dimensional bell-shaped signal z(t) in the population vector 𝐚, i. e the population response is given by 𝐲(t)=𝐚z(t).

Output: pass


Input: Claim: Coral reefs support a diverse range of marine species. [SEP] Text: Using alpha and beta diversity measures, the results show that benthic communities within MCEs at host a high and unique biodiversity, highlighting the importance of considering MCEs within conservation and management plans. Here, diversity values were comparable to shallow-water coral reefs (Edgar et al. 2017), which are considered to be one of the most diverse ecosystems in the world (Bryant et al. 1998), and communities displayed a high species turnover along the depth gradient. Species richness values estimated at (calculated at Cmax and ranging from 57 to 220 across the depth gradient; see Methods and Table S5) were analogous to values from a global study of inshore reef richness for invertebrates, both in the (species richness of 120) and the wider tropical-subtropical regions (species richness ranged from 40 to 230) (Edgar et al. 2017). Furthermore, even the deepest zone (150–160 m) in the current study, displaying the lowest diversity values, had higher species richness than some shallow (~ 10 m) subtropical regions (i. e. 67. 59 for IDR and 56. 58 for MA compared to 40 in Edgar et al. (2017)). When accounting for scleractinian corals only, the species richness and Shannon index values calculated in this study were similar to those measured in the Red Sea, within shallow-water reefs and the upper mesophotic zone (Table S2; (Kramer et al. 2019)), but slightly lower than coral species richness values in (Montgomery et al. 2021). In addition, for one of our two study sites, total benthic biodiversity was higher at 30–40 m than 15–20 m (IDR, Fig. 2a, b, c). This trend has also been noted in the Caribbean for the entire benthic community (increase in species richness from 15 to 53 m, (Liddell and Ohlhorst 1988)), but also for scleractinian corals in (Pérez-Rosales, et al. 2022b) and octocorals in the Gulf of Aqaba (Shoham and Benayahu 2017). According to Pérez-Rosales et al.

Output: corroborate


Input: Claim: El Niño is a climate pattern that occurs when the Pacific Ocean warms, affecting global weather patterns. [SEP] Text: Though a scarcity of proxy records limits our ability to robustly reconstruct the mean state using our methods, if the late Pliocene climate primarily responded to GHG-forcing we might predict a La Niña-like pattern, at odds with the prevailing interpretation of an El Niño-like pattern. As the is often assumed to be a pseudo-analog for future warming34, understanding the tropical Pacific mean state of the Pliocene has key implications for future climate projections. Our study indicates that state-of-the-art climate models are unable to produce the LGM tropical Pacific surface cooling pattern observed in proxy data. Given that the spatial pattern of temperature change is known to affect climate sensitivity35, our finding has important implications for modeling and data assimilation studies that have attempted to constrain the upper bound of equilibrium climate sensitivity using the LGM36,37. As our results suggest that the magnitude of cooling in the EECT may be exaggerated in most model simulations, we hypothesize that this may lead to overly high estimates of climate sensitivity. Therefore, we propose that only models and data assimilation products that simulate higher SST cooling in the WPWP compared to the EECT (i. e. , an El Niño-like tropical Pacific sea surface cooling pattern) are capable of generating reliable estimates of climate sensitivity. Since the proxy-model mismatch in the reconstructed pattern of tropical Pacific SST cooling during the LGM can be mostly attributed to differences in reconstructed changes in the EECT region, we highlight that it is urgent to improve model skill in this region. Finally, we stress the need for additional proxy SubT data from the tropical Pacific, as we demonstrate that thermocline depth and zonal tilt indicators may be better suited for detecting El Niño- and La Niña-like tropical Pacific upper ocean configurations than indicators of sea surface conditions. MethodsArgo dataTo ground-truth the mean state indices commonly used in proxy-based paleoclimate reconstructions, we used gridded monthly mean Argo seawater temperature data from January 2005 to December 2020 interpolated on standard levels downloaded from We re-interpolated the data to an even vertical resolution of 5 m and generated September-November (SON) seasonal averages. We chose to use SON seasonal averages given that the Oceanic Niño Index values (i. e.

Output: contrast