Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: In a small data set, one can use the elbow test or similar methods to detect the changing shape for the decay of the spectrum of SVD to determine the layout dimension. In a large data set, one feasible approach is to use stochastic methods by cross-validation to measure the layout dimension [11]. MethodsWe first describe SC-MDS using a simple case, and for convenience we use the classical MDS (CMDS) as the default MDS method to show how we can improve it. Assume that x i ∈ Rpare the coordinates of data points for i = 1,. . . ,N and p <<N. We define di,j= ||x i - x j ||2 as the Euclidean distance between x i and x j. N is large such that applying the CMDS technique is impractical. We split the points into two overlapping sets S1 and S2, and the intersection of these two sets contains more than p points. The main idea is to apply MDS to each individual set to get the configuration, and to use the information of the overlapping points to combine the two sets into one. There are two problems that need to be solved: (1) how to combine two sets into one and (2) what is the sufficient condition for this solution to be equivalent to that obtained by working directly with the full set? The solutions to these two problems are proposed in the next subsections. Combination MethodWhen we split the whole set of data points into two overlapping subsets of equal sizes, the combined size of the two distance matrices for the two subsets is less than that for the whole set. Assume that the configurations of these two subsets obtained from MDS are xi,1and xi,2and the dimensions of the two configurations are the same. We can fix the coordinates of the data points in the first set and use the overlapping data points to find an affine mapping U(·) + b such that for each intersection point xk,1∈ S1, xk,1= Uxj,2+ b, where xj,2∈ S2 for some j. Note that the matrix U of the affine mapping is a unitary matrix, which is a volume-preserving operator. The affine mapping can be found as follows.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: 75 x more fermentation products per each mol of mannitol utilized (Yfermentation, Table 1). The culture medium pH was also significantly lower than under OC (Figure 5B), a result which may indicate that EET conferred higher levels of acid stress on L. plantarum, and therefore, reductions in cell viability, despite EET leading to higher cell numbers overall (as measured by dry cell weight) (Figure 4A–B). A similar acidification of the medium was observed for ΔpplA, but not for Δndh2, when an anode was present as electron acceptor, indicating that ndh2-dependent EET is needed to decrease the pH (Figure 3—figure supplement 2B). When much lower, sub-physiological levels of DHNA were supplied (0. 01 μg/mL), a smaller but significant decrease in the pH of the medium was also observed (Figure 1—figure supplement 3C). Overall, these results show that EET allows L. plantarum to ferment to ~1. 75 x greater extent and to acidify the medium to a greater extent as well. Figure 5 with 2 supplements see all Download asset Open asset Fermentation fluxes are increased when an anode is provided as an extracellular electron acceptor. Results are from the same set of experiments as the current density plot shown in Figure 3C. (A) Metabolic yields of L. plantarum end-fermentation products under open circuit conditions (OC) and current circulating conditions (EET) in mCDM supplemented with 20 μg/mL DHNA. (B) pH measurements and (C) mannitol, (D) lactate, (E) acetate, and (F) ethanol concentrations over time under OC and EET conditions. (G) Schematic of proposed model for NADH regeneration during fermentation of mannitol in the presence of an anode as electron sink for L. plantarum. The avg ± stdev of three biological replicates is shown. Significant differences were determined by one-way ANOVA with Dunn-Sidak post-hoc (n = 3), ** p < 0. 01. See also Figure 5—figure supplement 1 and Figure 5—figure supplement 2 and related data in Figure 5—source data 1. Figure 5—source data 1 Raw data of Figure 5A–F.

Output: corroborate


Input: Claim: Dinosaurs were all warm-blooded and behaved like modern birds. [SEP] Text: In certain nonavian maniraptorans, long pennaceous feathers attach to the distal part of the tail, either in a fan-like fashion or giving the tail the frond-like appearance common to primitive birds such as Archaeopteryx (Fig. 1a). Long pennaceous feathers also attach to the tip of the forelimbs of some of these maniraptorans, and in the case of the peculiar dromaeosaurid Microraptor (Norell and Xu 2005), they form a wing of essentially modern design. Despite the evidence of plumage being restricted to a handful of nonavian dinosaurs, the fact that these fossils span a large portion of the family tree of theropods and display a great diversity of sizes, appearances, and lifestyles, hints at a much larger and yet undocumented diversity (Fig. 3)—even the colossal T. rex may have been covered with a cloak of feathers at some early stage of its life. It is an amazing experience to gaze at the entirely modern feathers of animals, whereas their skeletal characteristics are so unquestionable dinosaurian. Fig. 3Genealogical relationships of feathered nonavian theropods. Current evidence supports the hypothesis that filamentous and vaned feathers evolved with the divergence of coelurosaurs and maniraptorans, respectivelyFull size imageAn important corollary of these discoveries is that feathers did not evolve in the context of flight. With the sole exception of Microraptor, it is certain that none of these feathered dinosaurs were able to take to the air. The forelimbs and their feathers are both much shorter than in flying birds and their bodies are larger. The evolutionary transition toward birds and the origin of their flight involved a dramatic reduction in body size. These feathered dinosaurs indicate that, at their onset, feathers must have had a different function, perhaps insulating the bodies of animals that had metabolically diverged from their cold-blooded, reptilian ancestors. My research has suggested that vaned feathers may have originated in the context of thrust, evolving in running nonavian theropods that by flapping their feathered arms were able to increase their running speed (Burgers and Chiappe 1999). In the end, however, we simply do not have an answer for what was the original function of feathers; nonetheless, we have been able to eliminate flight as an option.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Assume X1 and X2 are matrices in which the columns are the two coordinates of the overlapping points obtained by applying MDS to two data sets, and X ¯ 1 MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae@2E42@ and X ¯ 2 MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae@2E44@ are the means of columns of X1 and X2, respectively.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Download elife-70684-fig5-data1-v2. xlsx We also observed that EET led to higher cellular metabolic fluxes, that is, higher changes in metabolites per cell per unit time. Although the final OD600nm and dry cell weight were not significantly different (Supplementary file 1), L. plantarum utilized mannitol and produced acetate and lactate more rapidly under EET than OC conditions (Figure 5C–F). Cells performing EET were ~2 fold faster at consuming mannitol (Figure 5C) between days 1 and 3. Mannitol consumption increased between day 1 and day 2, approximately when the cells transitioned to higher current density (Figure 3C), suggesting that increased EET drove that increased consumption. The overall rates of acetate and lactate production also increased 3. 4 and 3. 6 times (Figure 5D–E), respectively. Measurements of metabolites produced by ΔpplA and Δndh2 strains confirmed that, like for current production to an anode, the EET-associated increased metabolic flux in L. plantarum requires the presence of Ndh2, but not PplA (Figure 3—figure supplement 2C). Overall, these data indicate that ndh2-dependent EET increases both the flux and final yield of fermentation in L. plantarum. Because the production of acetate yields ATP, these results also suggested that the increase in ATP generation under EET conditions may be due to substrate-level phosphorylation. To probe whether EET-associated increase in fermentative flux could account for the changes in ATP generation, we calculated fermentation balances (Table 1). Our measurements account for 80% of the carbon under EET conditions (see Supplementary file 1), leaving a maximum of ~20% systematic uncertainty in these calculations. The concentrations of fermentation products detected (Supplementary file 1) were used to estimate the total ATP in the presence and absence of EET. The estimated ATP was 3-fold higher under EET conditions than OC conditions (Table 1), a result that is consistent with the ~2. 5 fold higher accumulation of ATP measured at maximum current density (Figure 4D). Overall, this quantitative analysis shows that the vast majority of the increased energy conservation under EET conditions can be accounted for by an increase in fermentation yield and substrate-level phosphorylation. EET shifts how L.

Output: corroborate


Input: Claim: All brain functions can be mapped with current technology. [SEP] Text: The position and orientation of each 2D slice within the embryo is indicated on the Nissl-stained two photon reference image to the right of the panel. Scale bar: 100 μm. (A) the fezf2 expression domain is highly disorganized in multiple regions of the diencephalon and telencephalon. (B) Vmat2 expression is largely unaffected in DA clusters of the telencephalon (arrow). (C) Expression of glyt1, a marker of astrocytic and glycinergic neuronal differentiation, is upregulated in the ventricular zone of the dorsal telencephalon (arrow). (D) Telencephalic expression of the GABAergic marker gad1b undergoes a shift away from the midline in mutant embryos (asterisk) but is not significantly reduced. (E) Expression of tbr1b, a transcription factor that plays an essential role in specifying glutamatergic pyramidal neurons, is significantly reduced. (F) Expression of emx3, a transcription factor that is broadly expressed within the telencephalon, is significantly reduced. (G) Expression of the neural progenitor marker eomesa is substantially reduced. (H) The neural progenitor marker neurog1 undergoes an anterior shift in regions of the diencephalon that closely neighbor the telencephalon (arrow). (I) The neural progenitor marker zic2a is shifted ventrally and possibly expanded in the dorsal telencephalon and surrounding regions. Figure 5—figure supplement 2 Download asset Open asset 3D segmentations of telencephalic expression domains. 3D segmentations of gene expression domains within and immediately adjacent to telencephalon at 2 dpf are generated in a semi-automated manner using adaptive thresholding. The gray background segmentation represents the union of telencephalic segmentations for all probes in the library and serves as an estimation for the outline for the telencephalon. Segmentations were generated from averaged 3D reconstructions. All segmentations are shown in front view (dorsal is to the top) and side view (anterior is to the left) for both wild-type and fezf2 mutant embryos. Scale bar: 100 μm. Figure 6 with 1 supplement Download asset Open asset Segmentation and volume measurements.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: In order to use the same orthogonal basis to represent these vertices, we apply QR factorization to X 1 − X ¯ 1 1 ˙ T MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae1aa@33EF@ and X 2 − X ¯ 2 1 ˙ T MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae1aa@33F3@ so that X 1 − X ¯ 1 1 ˙ T = Q 1 R 1 MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae1aa2da9iabdgfarnaa1aaSbaaSqaaiabigdaXaqabaaaaa@3999@ and X 2 − X ¯ 2 1 ˙ T = Q 2 R 2 MathType@MTEF@5@5@+=

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: plantarum uses electron acceptors and converts ATP into biomass Thus far, our results provided an unusual picture of the energy metabolism of L. plantarum under EET conditions; while EET significantly shifted the intracellular redox state to a more respiratory-like balance, its increased ATP yield was mainly accounted for by an increased fermentative yield. Another major difference in fermentation and anaerobic respiration is the use of the endogenous versus exogenous electron acceptors. To more deeply understand how L. plantarum uses organic molecules and the anode as electron acceptors when performing EET, the electron balances under EET and OC conditions were calculated (Table 1 and Supplementary file 1). We estimated the NADH produced using two different methods (see Supplementary file 1 for methodology) and the NADH re-oxidized through the reduction of the anode (measured as current) and via substrate-level phosphorylation. This allowed us to obtain a global balance of the NAD+/NADH ratio. Under OC conditions between 35% and 66% of the NADH produced from the oxidation of mannitol to pyruvate (a range is given using the two methods used) was re-oxidized to NAD+ (5. 5 mM NADH consumed, Table 1), qualitatively agreeing with the low NAD+/NADH ratios measured (Figure 4E). In contrast, electron balance calculations showed that between 77% and 96% of the NADH produced under EET conditions was re-oxidized (17 mM NADH consumed, Table 1), a result that is consistent with the significantly higher NAD+/NADH ratios measured (Figure 4E). Interestingly, these calculations estimate that 55–69% of the total NADH generated was oxidized through fermentation, while 21–28% of the NADH was oxidized using the electrode as a terminal electron acceptor (Table 1). Thus, L. plantarum growing under EET conditions achieves a more oxidized intracellular redox balance by more completely fermenting mannitol to lactate and ethanol and by using the electrode as a terminal electron acceptor (Figure 5G). These observations reinforce that the energy metabolism of L. plantarum under EET conditions utilizes elements of both fermentation and anaerobic respiration. In rapidly dividing cells, energy conservation, a catabolic process, is associated with growth, an anabolic process.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: However, in contrast to previous suggestions, we additionally observed a connectivity difference along the anteroposterior axis (repeated-measures ANOVA: main effect of network, F(1,21) = 13. 2, p = 0. 001) and post-hoc t-tests showed that the anterior parts of EC connected more with the AT compared to the PM network (T(21) = 2. 7: p = 0. 01; posterior EC: T(21) = −0. 49, p = 0. 63). Table 1 Selection of regions associated with the posterior-medial (PM) and the anterior-temporal (AT) system (Libby et al. , 2012). The coordinates of the PM system reflect peak voxel coordinates of a seed-based connectivity contrast of right parahippocampal cortex > right perirhinal cortex connectivity reported by Libby et al. (Libby et al. , 2012). The coordinates of the AT system reflect peak voxel coordinates of a seed-based connectivity contrast of right perirhinal cortex > right parahippocampal cortex connectivity. Coordinates are in MNI space.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae1aa2da9iabdgfarnaa1aaSbaaSqaaiabikdaYaqabaaaaa@39A1@.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: However, catabolism need not be coupled with anabolism (Russell and Cook, 1995). To determine how catabolic and anabolism are connected under EET conditions, the ATP requirements to grow biomass (YATP) were estimated using the calculated ATP and the measured dry weight. Under OC conditions, the YATP obtained (8. 06 ± 0. 8 g dw/mol ATP) for L. plantarum was similar to that observed previously (10. 9 g-dw/mol ATP) (Dirar and Collins, 1972). Hence, without EET, the ATP generated from fermentation was converted into biomass nearly at the expected efficiency. In contrast, a significantly lower YATP was reached for L. plantarum performing EET (3. 07 ± 0. 35 g dw/mol ATP) (Table 1). This observation indicates that under EET conditions, either more ATP is required to produce biomass or more ATP is utilized by other functions such as for PMF-generation and intracellular pH regulation (Russell and Cook, 1995). EET conditions also resulted in 79% more ATP per mol of fermented mannitol (Ymannitol). Consequently, molar biomass yields (g-dw/mol-mannitol) under EET conditions were significantly lower (Table 1), in agreement with previous observations in respiratory electroactive species (Esteve-Núñez et al. , 2005). These calculations show that when L. plantarum performs EET, anabolism and catabolism processes are differently coupled than under OC conditions. ATP is produced more efficiently, but this it is less efficiently utilized to make biomass. Overall, these results show an intriguing pattern of coupling between anabolism and catabolism, indicative of a novel energy metabolism in L. plantarum during EET. EET is active in vegetable fermentations Our results inspired us to explore whether EET could occur in a physiological niche of LAB such as fermented foods. LAB are necessary for the making of many fermented fruit and vegetable foods and the properties of those foods depend on the metabolic diversity of the LAB strains present (Gänzle, 2015). Plant tissues also contain a much wider variety of carbon substrates and potential electron acceptors than the CDM used in our prior experiments.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Both model-based and data-driven connectivity analyses, as well as sensitivity to non-spatial vs spatial stimuli provide evidence for an anterior-lateral and a PM localisation of the homologues of the rodent LEC and MEC, respectively. Maass et al confirmed these findings in a study with two high-resolution, high-field fMRI datasets by focusing on local connectivity between regions of the medial temporal lobes. They found preferential connectivity of PRC and proximal subiculum to anterior-lateral parts of the EC, whereas posterior-medial parts of the EC were more connected to PHC and distal subiculum. This corresponds well with our findings (Figure 2—figure supplement 1). In line with the present study, Maass et al (Maass et al. , 2015) report local connectivity fingerprints of the human anterior-lateral and posterior-medial EC that mimicked those of the rodent LEC and MEC, respectively. In addition to the change in functional connectivity from PM to anterior-lateral, our data-driven connectivity analysis also revealed a second organisation structure approximately perpendicular to the first (Figure 3—figure supplement 1), which might reflect bands of intra-entorhinal projections that are known to cross the LEC/MEC boundary in a roughly orthogonal orientation in rodents (Canto et al. , 2008) and in primates (Chrobak and Amaral, 2007). The selective sensitivity to spatial and non-spatial information, or ‘context vs content’ more broadly (Knierim et al. , 2013), points towards fundamental difference in computations of the LEC and MEC. How to characterise those differences most accurately remains an open question (Knierim et al. , 2013), but our results can help to inform future studies on the role of the human alEC and pmEC in higher-level cognition. Notably, the present findings confirm three out of four complementary criteria for the definition of cortical areas that have traditionally been advocated (Van Essen, 1985), namely topographic organization, connectivity and functional properties (the fourth one being cyto- and myeloarchitectonic organization). Previous neuroimaging studies in humans reported differences between medial and lateral aspects of EC that mimicked differences between the rodent MEC and LEC and assumed that both subregions are present on coronal slices of the EC (Schultz et al.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Since these two coordinates represent the same points, the triangular matrices R1 and R2 should be identical when there is no rounding error from computing the QR factorization in X1 and X2. The positive and negative signs of columns of Q i could be arbitrarily assigned in the computation of QR factorization. Hence, the signs of columns of Q i should be adjusted according to the corresponding diagonal elements of R i so that the signs of diagonal elements of R1 and R2 become the same. After the signs of columns of Q i are modified, we conclude Q 1 T ( X 1 − X ¯ 1 1 ˙ T ) = Q 2 T ( X 2 − X ¯ 2 1 ˙ T ).

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: To study the physiological and biotechnological relevance of EET in food fermentations, kale juice was fermented using L. plantarum as a starter culture (Figure 6A). The fermentation of kale juice was measured under EET conditions (a polarized anode with or without DHNA), and an OC control (a nonpolarized anode with DHNA) was used to separate the role of DHNA and electron flow to the anode on the fermentation process. An additional bioreactor without cells, but with DHNA, was operated to identify any possible electrochemical-driven conversion of substrates. When L. plantarum was added to the prepared kale juice, approximately 10-fold more current was generated during EET conditions with DHNA (EET+ DHNA) as compared to abiotic and biological non-EET promoting conditions (no DHNA) (Figure 6B). This current was comparable to the current generated in laboratory medium (Figure 3C), indicating that robust EET by LAB is possible in the complex physiological conditions of a food fermentation. Figure 6 with 1 supplement see all Download asset Open asset EET in a kale juice increases the production of fermentation end products. (A) Preparation of kale juice medium used for fermentation in bioelectrochemical reactors. (B) Current density production measured from kale juice medium over time in the presence of L. plantarum and 20 μg/mL DHNA, no DHNA, or under abiotic conditions with addition of 20 μg/mL DHNA. The anode polarization was maintained at 0. 2 VAg/AgCl. (C) Normalized total quantities of the metabolites detected per cell (CFUmax used for calculations). (D) pH measurements over time under different conditions tested on a second set of kale juice fermentations performed under the same conditions. (E) Production rate per viable cell, r, of lactate, acetate, and succinate. The avg ± stdev of three biological replicates is shown. See also Figure 6—figure supplement 1 and related data in Figure 6—source data 1. Figure 6—source data 1 Raw data of Figure 6B–E. Download elife-70684-fig6-data1-v2. xlsx We next investigated the impact of EET on L. plantarum growth and metabolism in the kale juice fermentation.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Correction to: of on Nonverbal Memory: Meta-regression of Stimulus- and Task-related. Bentvelzen1,2, Roy P. C. Kessels3,4, Nicholas A. Badcock5,6 & …Greg Savage2 Show authors 1710 Accesses 1 all metrics 10. 1007/s11065-021-09514-3 In the original published paper, it was belatedly discovered that there was a data entry error in coding one test-related factor for the (BVRT) only, for which the factor Format was incorrectly coded as Recognition, when it should have been coded as Recall. While only a minor error in isolation this had widespread ramifications for the paper since a key unique aspect of the analysis was comparing the relative impact of stimulus- and test-related factors on the association between nonverbal memory tests and identifying right (versus left) temporal lobe pathology. In addition, there were k = 10 papers on the BVRT, which is enough that it possibly meant that this could impact statistically on the results and therefore the interpretation and conclusions. The section within the Results entitled “Meta‑regression of Type and Test‑related Moderators” effectively required a complete rewrite. It was therefore considered essential to write this erratum. The remaining tests were thoroughly checked, and thankfully this type of error was not replicated elsewhere. In addition to the edits due to the coding error, there were some additional minor errors in the manuscript I have taken the opportunity to also correct. 1 The sentences. “Stimulus type significantly moderated the size of the right-lateralization effect (faces > designs) for postsurgical patients, test format moderated the size of the right-lateralization effect for presurgical-postsurgical change (recognition > recall) but learning format and test delay had no right-lateralization effect for either sample. For presurgical patients, none of the task-related factors significantly increased right-lateralization effects. ” are replaced by: “For presurgical patients the size of the right-lateralization effect was significantly moderated by stimulus type (faces > designs), testing format (recall > recognition), and its interaction with the learning format (repeated trials more affected by format effect than single trials) of the nonverbal memory tests.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xI8qi9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaGaemyuae1aa0baa09aqpcq1aa6caUaaa@48C5@ (5) Furthermore, we can obtain X 1 = Q 1 Q 2 T X 2 − Q 1 Q 2 T ( X ¯ 2 1 ˙ T ) + X ¯ 1 1 ˙ T.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Significant changes in the pH and fermentation products were detected under EET conditions (Figure 6C–D). These differences occurred in the absence of significant changes in viable cell numbers (Figure 6—figure supplement 1A) at the time points measured. As previously observed using laboratory culture media, an approximately 2-fold greater accumulation of total end-fermentation products per cell was obtained when cells interacted with an anode in the presence of DHNA (Figure 6C). In the kale juice fermentation, EET+ DHNA conditions enhanced both lactate and acetate production per cell without changing the distribution of metabolites (Figure 6E and Figure 6—figure supplement 1B). Thus, when DHNA was provided, EET enhanced the overall yield of fermentation end-products and their production rates per cell, mimicking our observations in laboratory medium (Figure 5C–D). EET also led to a significantly higher acidification of the kale juice compared to OC conditions, and the presence of DHNA dramatically enhanced this pH drop (Figure 6D). In general, when no DHNA was supplied but an anode was present as an electron acceptor, the fermentation process was very similar to OC conditions. This means in kale juice, a source of quinones is essential to support L. plantarum EET activity. Overall, these results show that EET under physiological conditions impacts cellular metabolism in L. plantarum by increasing metabolic flux which ultimately can affect the flavor profile of fermented foods (Chen et al. , 2017). in fermentation and energy conservation from EET have important bioenergetic implications for the mainly fermentative LAB. We showed that L. plantarum and other diverse LAB species perform EET if riboflavin and quinones are present. L. plantarum EET activity requires an NADH dehydrogenase (Ndh2) and conditionally requires an extracellular, flavin-binding reductase (PplA). EET in L. plantarum generates a high NAD+/NADH ratio, increases fermentation yield and flux, shortens lag phase, and increases ATP production. Thus, EET in L. plantarum is a hybrid energy metabolism that contains metabolic features of fermentation, redox features of anaerobic respiration, and predominately uses substrate-level phosphorylation to conserve energy. This pathway is active in L.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The results for Format were notable as Format was not significant in the additive model when the Stimulus factor was present (note that a Stimulus x Format interaction could not, by definition, be included in the interactive model as there were no Faces-Recall tests to complete all cross-combinations of Stimulus x Format). We therefore considered it possible that tests with design stimuli may be disproportionately explaining the right-lateralized Recall effect. To explore these results further, contrasts with Bonferroni-correction to account for multiple comparisons (i. e. , p = 0. 05/2 = 0. 025) showed that the right-lateralization effect for Faces-Recognition was not significantly larger than for Designs-Recognition, and the right-lateralization effect for Designs-Recall was not significantly larger than for Designs-Recognition (see Fig. 3D), together suggesting that the Stimulus effect (right-lateralized Faces) and Format effect (right-lateralized Recall) were independent. Bonferroni contrasts to explore the Learning x Format effect (i. e. , p = 0. 05/3 = 0. 017) showed a right-lateralization effect of Format (Recall > Recognition) that was stronger for tests with repeated learning trials than for tests with single learning trials (d = -0. 10, p =. 004), while the Format effect was not significant within individual learning trial (i. e. , each of single and repeated learning trial types; see Fig. 3E). Figure 3C shows the results for specific moderator levels. Taken together, for presurgical patients, the small-sized performance decrement on nonverbal memory tests for RTLE versus LTLE patients was significantly moderated by both stimulus type and task-based factors (testing format on its own and interacting with learning format) of the nonverbal memory tests. Fig. 4A shows that for the additive meta-regression model, the moderators were significant as a group, QM = 20. 43, p < 0. 001, but there was only a borderline significant trend for the Stimulus effect on right-lateralization (Faces > Designs, d = -0. 25, p = 0. 052). No other moderators were significant. Figure 4B shows the moderators were collectively significant for the interactive model, QM = 20. 15, p = 0.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xI8qi9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae1aa9aqpcq1aa0baa4ka@4BAC@ (6) That is, the unitary operator is U = Q 1 Q 2 T MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae0Jaemyuae1aa@33DA@ and the shifting operator is b = − Q 1 Q 2 T X ¯ 2 + X ¯ 1 MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae0JaeyOeI0Iaemyuae1aa@3AB3@. In practice, one problem with the X1 and X2 obtained by CMDS from a distance matrix is that some of the eigenvalues of the double centered distance matrix can be negative.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: plantarum with physiologically relevant DHNA concentrations and in a food fermentation and results in an increased metabolic flux and acidification rate. The combined EET fermentation hybrid metabolism is distinct from anaerobic respiration, fermentation, and other energy conservation strategies When performing EET, the metabolism of L. plantarum, a primarily fermentative bacterial species, is fundamentally different from EET-driven, anaerobic respiration of metal-reducing bacteria. Although aspects of EET in L. plantarum and metal-reducing Geobacter spp. are similar, such as the upregulation of NADH dehydrogenase, the reduction rate of extracellular electron acceptors, and the high NAD+/NADH ratio, other aspects of energy metabolism during EET in these two organisms are starkly different (see comparison in Supplementary file 2). Geobacter spp. direct their metabolic flux through the TCA cycle, rely almost exclusively on extracellular electron acceptors to regenerate NADH, and produce ATP exclusively through oxidative phosphorylation. In contrast, L. plantarum regenerates a substantial fraction of its NADH by directing metabolic flux through fermentative pathways. Additionally, oxidative phosphorylation is not a major mechanism of energy conservation in L. plantarum during EET, as supported by three lines of evidence: the marginal metabolic flux through the reduced branch of TCA cycle, no involvement of known PMF-generating proteins, and that increased ATP levels can be accounted for by increased substrate-level phosphorylation. While additional data are required to eliminate the possibility that oxidative phosphorylation is occurring in L. plantarum during EET, we can qualitatively state that substrate-level phosphorylation is the major mechanism for ATP generation. Comparing EET and respiratory metabolism in LAB also reveals substantial differences in these metabolisms (Supplementary file 2). While both metabolisms require quinones, respiration also requires exogenous heme. Our findings and similar findings in E. faecalis (Pankratova et al. , 2018) confirm that heme is not required for EET. Moreover, EET also differs from respiration in LAB because it occurs at the start of or prior to exponential phase growth, does not change the final cell density, and increases fermentation with no effect on the resultant proportions of lactate, acetate, and ethanol (Duwat et al. , 2001).

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: 003, with a significant effect of Format (Recognition > Recall, d = -0. 38, p < 0. 001), but no other moderators were significant. Fig. 4 Standardized effect sizes (Cohen’s d) and 95% confidence intervals of lateralization (left minus right TLR performance) for postsurgical patients, by type of moderator analysis and individual moderator levels. Negative effect sizes indicate poorer performance for right than left TLR patients. Sample size (k, studies) inferential statistics, heterogeneity and fail-safe N measures are in 10. Contrasts used mean differences and t-statistic For the same reasons as described in the section above for the presurgical sample, we used Bonferroni-corrected contrasts (i. e. , p = 0. 05/2 = 0. 025) to determine whether this Format effect for the right TLR group was influenced by the borderline significant effect of Stimulus (see Fig. 4D). These showed the right-lateralization effect for Designs-Recognition was significantly larger than for Designs-Recall, while the effect size for Faces-Recognition was not significantly larger than for Designs-Recognition (see Fig. 4D). Therefore, when taken together, for postsurgical patients the lateralization effects (right < left performance) were affected by testing format (recognition > recall) that appeared to overshadow and explain the effects of stimulus type (faces > designs). For postsurgical change, the additive meta-regression model showed that for left TLR patients the moderators were significant as a group, QM = 12. 95, p = 0. 01, with a significant effect of Format (Recognition more left-lateralized than Recall, d = 0. 22, p = 0. 4), but no other moderators were significant (see Fig. 5A). The interactive model (Fig. 5B) was also significant, QM = 15. 15, p = 0. 01, again with a significant effect of Format only (Recognition more left-lateralized than Recall, d = 0. 25, p = 0. 007). Fig.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: This has been mentioned in [3], where the information of both the neighborhood and afar is used for the spring model. If one puts only the neighboring points into the same group, the rotation effect will hamper the performance of the low dimensional layout (see Fig. 1d later). These two conditions are sufficient to guarantee good performance in a low dimension layout. The layout of the correct approximation solution of our method is outlined (see Fig. 1b). Figure 12D simulation results. a. The 2D CMDS projection of 2000 simulation points. b. The 2D layout of an approximate solution by SC-MDS with no dimension loss (N i = 30, N g = 200). The points in a group were chosen randomly. c. The 2D layout of the approximate solution by SC-MDS with a loss of 12 dimensions (N I = 5, N g = 200). The points in a group were chosen randomly. d. The 2D layout of the approximate solution by SC-MDS with a loss of 12 dimensions (N I = 5, N g = 200). The points in a group were chosen by the neighboring method. Full size imageReductionWe now show how SC-MDS reduces the computational complexity of CMDS from O(N3) to O(N) when p <<N. Assume that there are N points in a data set, N I is the number of points in each intersection region, and N g is the number of points in each group.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Thus, EET in LAB diverges from respiration in metal-reducing bacteria or LAB in its metabolic pattern and energetic consequences. In addition, while EET provokes a shift in fermentative metabolism in other bacteria upon the addition of artificial mediators (Vassilev et al. , 2021; Emde and Schink, 1990), L. plantarum EET is active upon the presence of a mediator present in a complex food system. This EET mechanism is also a novel energy conservation strategy compared to known fermentative metabolisms in LAB (comparison in Supplementary file 2). L. plantarum and other LAB, reduce alternative intracellular electron acceptors like citrate, fructose, and phenolic acids, resulting in increased intracellular NAD+/NADH ratios (Hansen, 2018). This metabolic activity is especially important for heterofermentative LAB in order to synthesize additional ATP through acetate kinase (Gänzle, 2015). Unlike these examples, however, the reduction of extracellular Fe3+ or an anode by EET requires a respiratory protein (Ndh2) and the shuttling of electrons outside of the cell. In addition, the reduction of the oxygen and organic compounds for cofactor regeneration by LAB leads to a metabolic shift toward acetate production and altered metabolic end-product ratios (Gänzle, 2015), which does not occur during EET. These differences show how the hybrid metabolism under EET conditions is distinct from other pathways that alleviate reduced intracellular conditions in LAB. Previous studies have reported a simultaneous use of fermentation and electron transport elements, such as in respiro-fermentation in Saccharomyces cerevisiae (Blom et al. , 2000). However, respiro-fermentation produces ATP and maintains intracellular redox balance through substrate-level phosphorylation and/or oxidative phosphorylation using separate pathways. Our data strongly suggests a single pathway is responsible for both ATP generation and intracellular redox balance. This hybrid fermentation mode is also different from the electron bifurcating mechanism, in which the extra ATP generation is driven by the creation of a H+ or Na+ potential from the oxidation of a ferredoxin (Buckel and Thauer, 2013).

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Sample size (k, studies) inferential statistics, heterogeneity and fail-safe N measures are in 13. Contrasts used mean differences and t-statistic In summary, for postsurgical change, there was an effect of testing format in favor of improved recognition over recall for left TLR patients, and when comparing left and right TLR the lateralization effects (left > right improvement) were affected by testing format (recognition > recall) that appeared to overshadow and explain the effects of stimulus type (faces > designs). 12 For clarity we replace the entire second and third paragraph concerning the moderating effects of stimulus-related and task-related factors, and the initial section of the following paragraph, corrected version: This study revealed several novel findings. Taking all nonverbal memory tests together, patients with RTLE performed slightly worse than patients with LTLE, in both the presurgical and postsurgical patient groups, with larger differences for the postsurgical group compared to the presurgical groups. For presurgical patients, lateralization effects (right < left) were consistently small and differed by stimulus type (faces > designs) and testing format (recall > recognition), in a manner that interacted with learning type, i. e. , larger effect size change for repeated than single learning trials). For postsurgical patients, lateralization effects (right < left) also differed by format but in the opposite direction (recognition > recall), that appeared to overshadow and explain the borderline non-significant effects of stimulus type (faces > designs). Change in nonverbal memory performance following TLR showed an overall pattern of mild improvement in left TLR patients and no change in right TLR patients. In left TLR patients, the degree of improvement differed by format (recognition > recall) that appeared to overshadow and explain the borderline non-significant effects of stimulus type (faces > designs). In right TLR patients, the effect size was not significant and did not differ for any of the moderators. This advantage for recognition over recall was maintained when comparing postsurgical change for left and right TLR patients and did not appear to differ by the type of stimulus. Patient age (at the time of the assessment) had no significant effect on nonverbal memory performance in any patient sample, while patients with left TLR performed worse on nonverbal memory tests following surgery when the age of epilepsy onset was younger.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: When we split N points into K overlapping groups, we have KN g - (K-1)N I = N, and we have K = ( N − N I ) ( N g − N I ~ O ( N ) MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaGaem4saSKaeyypa0tcfa4aa40aa6eaonaa+b@410C@. For each group, we apply CMDS to compute the coordinates of the group data, which costs O ( N g 3 ) MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaGaem4ta8KaeiikaGIaemOta40aa0baa@3255@ computation time.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Unlike in this example, EET in LAB does not involve PMF creating elements and EET drives ATP generation through substrate-level phosphorylation. Another poorly understood example of the use of substrate-level phosphorylation and electron transport chains to balance intracellular redox state is found in the non-fermentative bacterium S. oneidensis. Although this species is a respiratory bacterium, it relies predominately on substrate-level phosphorylation to grow anaerobically with the exogenous electron acceptor fumarate (Hunt et al. , 2010). In this scenario, it is unclear if there are changes in intracellular redox state or metabolism in this species. In contrast to and expanding upon these studies, our work elucidates a qualitatively and quantitatively different blending of fermentation and respiration. L. plantarum EET-associated metabolism contains features of both fermentation (e. g. substrate-level phosphorylation, high fermentation product yields) and respiratory metabolisms (e. g. NAD+/NADH ratios, NADH dehydrogenase required) (Supplementary file 2). Quantitatively, this hybrid metabolism leads to an overall ~1. 75x-more efficient and ~1. 75x-faster energy conservation (increased Ymannitol, mannitol flux), but an overall ~1. 5 fold weaker coupling between anabolism and catabolism (lower YATP). Additionally, the increased NAD+/NADH ratio arises from using an ~2:1 ratio of endogenous to extracellular electron acceptors. Thus, to our knowledge, the hybrid strategy that L. plantarum uses to generate ATP performing EET constitutes a novel mode of energy conservation in a primarily fermentative microorganism. Different mechanisms of EET appear to be widespread in LAB Based on our observations and others, we propose that EET is widespread in LAB and occurs by different mechanisms. Besides L. plantarum, we showed that L. lactis is able to generate current despite lacking pplA. Current generation by L. lactis was observed previously, found to be riboflavin dependent, and resulted in a small metabolic shift (yet to be defined) in which the flux through NADH-oxidizing pathways was reduced and ATP generating pathways were increased (Freguia et al. , 2009; Masuda et al. , 2010).

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Our meta-analysis shows that the testing format moderates the capacity of nonverbal memory tests to reveal memory decline following surgery (i. e. , recognition > recall) for right TLR patients and that recognition tests better discriminate postsurgical change of left and right TLR patients. This suggests that a recognition format may more specifically test memory consolidation and storage functions linked to the medial temporal lobe than a recall format (Lowndes & Savage, 2007; Mayes et al. , 2007). In contrast, other test attributes including stimulus type, learning format and test delay did not impact the right-lateralization effect for the postsurgical change group (cf. Jones-Gotman et al. , 2000; Majdan et al. , 1996). For presurgical patients there was a more complex pattern that indicated a slight advantage of recall over recognition that depended on an interaction with learning type. The interpretation of this finding is less clear but suggests that for presurgical patients tests with particular combinations of learning and format type (e. g. , learning with repeated trials, tested via recall) may on average slightly outperform tests with other combinations. Our findings support the recommendations of the (ILAE; Wilson et al. , 2015) report regarding the use of recognition testing alongside recall measures for comparison, from the same tests where applicable. Page 13 For the fifth paragraph under the heading “Limitations and Further Research”, we changed the original version of the first sentence: Given the modest effect sizes of most tests examined and the lack of moderating effects of test-related factors on the capacity of nonverbal memory tests to indicate impairment in RTLE presurgical samples, there remains a clear and urgent need for improved tests to predict the effects of right temporal lobe pathology. To the corrected version: Given the modest effect sizes of most tests examined particularly for presurgical patients, there remains a clear and urgent need for improved tests to predict the effects of right temporal lobe pathology. For the first sentence under “Conclusion” we changed: “This comprehensive meta-analysis has shown the value of nonverbal recognition measures in detecting postsurgical change in right TLR patients. ” To the corrected version: This comprehensive meta-analysis has shown the value of nonverbal recall measures in detecting presurgical right TLE, and the value of recognition measures in detecting postsurgical performance in right TLR patients and postsurgical improvement in left TLR patients. The original 10.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: At each overlapping region, we apply QR factorization to compute the affine transform, which costs O ( N I 3 ) MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaGaem4ta8KaeiikaGIaemOta40aa0baaSqaaiabdMeajbqaaiabiodaZaaakiabcMcaPaaa@3219@ computation time. Since the lower bound of N I is p+1, we can assume that N g = αp for some constant α. Then the total computation time is about N − p ( α − 1 ) p O ( α 3 p 3 ) + N − α p ( α − 1 ) p O ( p 3 ) ~ O ( p 2 N ). MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xI8qi9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaqcfa4aa4mamdaaOGaeiykaKIaey4kaSscfa4aa6ha+jabd+eapjabc4KaeiykaKIaeiOla4caaa@5AB4@ (7) Thus, when p <<N, the computation time of the SC-MDS becomes O(p2N).

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: L. lactis can also perform EET by reduction of tetrazolium violet and this activity depends on the presence of both quinones and an NADH dehydrogenase (NoxAB) (Tachon et al. , 2009). E. faecalis is another LAB that performs EET, and similar to L. plantarum, it requires quinones (Pankratova et al. , 2018) and a type-dehydrogenase (Ndh3) (Hederstedt et al. , 2020) for Fe3+ reduction. In contrast to this mechanistic similarity, E. faecalis performs EET using matrix-associated iron resulting in both increased final cell biomass and intracellular iron (Keogh et al. , 2018). Moreover, unlike L. plantarum and L. lactis, the presence of PplA is not necessary for anode reduction or Fe3+ reduction (Hederstedt et al. , 2020). The conditional need for PplA in EET may be explained by the different prior growth conditions used and/or related to the existence of different mechanisms and proteins depending on the redox potential of the extracellular electron acceptor. Other flavin-binding, extracellular reductases amongst Gram-positive organisms, such as FrdA (acting on fumarate) have been identified in L. monocytogenes and UrdA (acting on urocanate) in Enterococcus rivorum (Light et al. , 2019). Thus, there may exist a yet unidentified extracellular reductase in L. plantarum and L. lactis required for anode reduction. Thus, our findings elucidate a new pattern of metabolic changes associated with EET. It seems likely that these many mechanisms reflect the ability of EET to alleviate constraints of intracellular redox balance in fermentative metabolism across LAB. EET has important implications for ecology and biotechnology of LAB Conservation of the FLEET locus among different LAB species supports the premise that this hybrid fermentation with EET provides an important metabolic strategy for these bacteria in their natural habitats. LAB with a complete FLEET locus are homofermentative, thus underscoring the distinct ways homofermentative and heterofermentative LAB have evolved for energy conservation (Salvetti et al. , 2013). L.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: multivariate autoregressive models have failed to adequately account for the complexity of neural signals, researchers have predominantly relied on non-parametric methods when studying the relations between brain and behavior. Using medial temporal lobe (MTL) recordings from 96 neurosurgical patients, we show that time series models with volatility described by a multivariate stochastic latent-variable process and lagged interactions between signals in different brain regions provide new insights into the dynamics of brain function. The implied volatility inferred from our process positively correlates with high-frequency spectral activity, a signal that correlates with neuronal activity. We show that volatility features derived from our model can reliably decode memory states, and that this classifier performs as well as those using spectral features. Using the directional connections between brain regions during complex cognitive process provided by the model, we uncovered perirhinal-hippocampal desynchronization in the MTL regions that is associated with successful memory encoding. advances in neuroscience have enabled researchers to measure brain function with both high spatial and temporal resolution, leading to significant advances in our ability to relate complex behaviors to underlying neural signals. Because neural activity gives rise to electrical potentials, much of our knowledge concerning the neural correlates of cognition derive from the analyses of multi-electrode recordings, which yield a multivariate time series of voltage recorded at varying brain locations (denoted here as 𝐲t). Such signals may be measured non-invasively, using scalp electroencephalography (EEG), or invasively, using subdural grids or intraparenchymal depth electrodes in human neurosurgical patients. In recent years, intracranially recorded (iEEG) signals have yielded detailed information on correlations between time-series measures and a wide range of behaviors including perception, attention, learning, memory, language, problem solving and decision making (Jacobs and Kahana, 2010). Whereas other fields that grapple with complex multivariate time series have made effective use of parametric models such as economics and engineering (Kim et al. , 1998; Blanchard and Simon, 2001; West, 1996), neuroscientists largely ceded early parametric approaches (e. g. linear autoregressive models) in favor of non-parametric spectral decomposition methods, as a means of uncovering features of neural activity that may correlate with behavior.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Discretize θ into N-1 intervals and let θ i = 2π + idθ, d θ = 3 π N − 1 MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae0tcfa4aa@3633@. At each θ i k points are constructed with noise.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: plantarum and other LAB with FLEET systems such as L. casei are genetically and metabolically diverse and grow in a variety of nutrient rich environments including dairy and plant foods and the digestive tract (Cai et al. , 2009; Martino et al. , 2016; Siezen et al. , 2010a). Those environments also are rich sources of sources of quinones, flavins, and extracellular electron acceptors such as iron (Cataldi et al. , 2003; Fenn et al. , 2017; Kim, 2017; Roughead and McCormick, 1990; Walther et al. , 2013). Increased organic acid production and environmental acidification by LAB with this hybrid metabolism would provide an effective mechanism to inhibit competing microorganisms and confer a competitive advantage for growth. The increased ATP relative to biomass generation observed during growth on mannitol might also give sufficient readiness for using this energy later on to outcompete neighboring organisms (Russell and Cook, 1995). These effects of EET may be particularly important on plant tissues and intestinal environments, wherein LAB tend to be present in low numbers. Besides our observation that L. plantarum performs EET in kale juice, the FLEET pathway is important for intestinal colonization by both L. monocytogenes (Light et al. , 2018) and E. faecalis (Lam et al. , 2019), and L. plantarum FLEET genes including ndh2 and pplA were highly induced in the small intestine of rhesus macaques (Golomb et al. , 2016). The hybrid fermentation metabolism of LAB also has technological relevance. For many LAB food fermentations, acidification of the food matrix is required to prevent the growth of undesired microorganisms and result in a more consistent and reproducible product (Marco et al. , 2021). Starter cultures are frequently selected based on their capacity for rapid growth and acid production (Bintsis, 2018). In the presence of an anode, exposure of L. plantarum to EET conditions during kale juice fermentation increased the acidification rate. Thus, this shows that EET metabolism is active in complex nutritive environments such as kale leaf tissues that contain other potential electron acceptors besides the anode and diverse electron donors (glucose, fructose, sucrose) (Thavarajah et al. , 2016).

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: We employed a Bayesian approach to estimate the latent volatility series and the parameters of the MSV model using the forward filtering backward sampling and algorithms. We validated the MSV model in a unique dataset comprising depth-electrode recordings from 96 neurosurgical patients. These patients volunteered to participate in a verbal recall memory task while they were undergoing clinical monitoring to localize the epileptogenic foci responsible for seizure onset. Our analyses focused on the subset of electrodes (n=718) implanted in medial temporal lobe (MTL) regions, including hippocampus, parahippocampal cortex, entorhinal cortex and perirhinal cortex. We chose to focus on these regions given their prominent role in the encoding and retrieval of episodic memories (Davachi et al. , 2003; Kirwan and Stark, 2004; Kreiman et al. , 2000; Sederberg et al. , 2007). We show that the MSV model, which allows for interactions between regions, provides a substantially superior fit to MTL recordings than univariate stochastic volatility (SV) models. The implied volatility in these models positively correlates with non-parametric estimates of spectral power, especially in the gamma frequency band. We demonstrate the utility of our method for decoding cognitive states by using a penalized logistic regression classifier trained on the implied volatility data across MTL electrodes to predict which studied items will be subsequently recalled. We find that the MSV-derived features outperform spectral features in decoding cognitive states, supporting the value of this model-based time-series analysis approach to the study of human cognition. Furthermore, using the MSV model to construct a directional MTL connectivity network, we find that significant bidirectional desynchronization between the perirhinal cortex and the hippocampus predicts successful memory encoding. Volatility models for iEEG Volatility of iEEG is stochastic Previous studies have shown that variance of EEG recordings is time-varying (Wong et al. , 2006; Galka et al. , 2010). Klonowski et al. (2004) demonstrated a striking similarity between the timeseries of the index during economic recessions (big crashes) and the timeseries of iEEG during epileptic seizures. These timeseries typically possess 'big spikes' that are associated with abrupt changes in the variance of the measured signal.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The following steps generate the data:Construct two raw vectors q1 and q2, q 1 i = 2 θ ⌊ i k ⌋ cos θ ⌊ i k ⌋ + n i q 2 i = 2 θ ⌊ i k ⌋ sin θ ⌊ i k ⌋ + n i MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xI8qi9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae0JaeGOmaiJaeqiUde3aa4la8Iaem45+4daabeaakiGbcogaJjabc+gaVjabcohaZjabeI7aXnaaBaaaleaadaGbdaqaaiabdMgaPjabc+caViabdUgaRbGaayj84laawUp+aaqaba0JaeGOmaiJaeqiUde3aa4la8Iaem45+4daabeaakiGbcohaZjabcMgaPjabc6gaUjabeI7aXnaaBaaaleaadaGbdaqaaiabdMgaPjabc+caViabdUgaRbGaayj84laawUp+aaqaba@760C@ where n i is a random variable with normal distribution n i ~ N(0,1), for i = 1⋯kN. Then we add some p-2 dimensional randomness into the coordinate matrix as follows. = ⟨α j ui,j⟩, ui,j~ N(0,1), for i = 1⋯kN and j = 1⋯p - 2, where α j is the parameter to control the standard deviation of random variables. The final reference coordinates matrix X isX = [q1, q2, Z]T= [x1, x2,. . .

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: This example also shows how electro-fermentation, the technological process by which fermentation is modulated using electrodes, can be used to control food fermentations (Moscoviz et al. , 2016; Schievano et al. , 2016; Vassilev et al. , 2021). Because L. plantarum also increased fermentation flux when the electrode was available as an electron sink, higher quantities of organic acid flavor compounds were formed. Therefore, by the manipulation of extracellular redox potential, food electro-fermentations may be used to control microbial growth. This would allow the creation of new or altered sensory profiles in fermented foods, such as through altered organic acid production and metabolism or synthesis of other compounds that alter food flavors, aromas, and textures. Final perspective We expect that our study will improve the current understanding of energy conservation in primarily fermentative microorganisms and contribute to establishing the ecological relevance of EET in lactic acid bacteria. This work will ultimately allow the use of EET to electronically modulate the flavor and textural profiles of fermented foods and expand the use of lactic acid bacteria in bioelectronics, biomedicine, and bioenergy (Moscoviz et al. , 2016). The identification of the precise components and full bioenergetics involved in L. plantarum EET will be key to unravel physiological and ecological questions and to develop other biotechnological applications. Materials and methods Key resources table Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional informationStrain, strain background (Lactiplantibacillus plantarum)NCIMB8826Dandekar, 2019 Strain information listed in Supplementary file 3Strain, strain background (Lactiplantibacillus plantarum)NCIMB8826-RYin et al.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Since spectral powers of neighboring frequencies exhibit high correlations, we utilized a Gaussian regression model (Rasmussen, 2004) to estimate the correlation between volatility and spectral power as a function of frequency, which allows for a non-linear relation. Figure 3 indicates that the correlation between volatility and spectral power is significantly positive across the spectrum and increasing in frequency. This illustrates the broadband nature of the volatility measure, but also suggests that volatility may more closely relate to previous neuroscientific findings observed for high-frequency as compared with low-frequency activity. Having established that the MSV model outperforms the more traditional SV approach, and having shown that the implied volatility of the series reliably correlates with high-frequency neural activity, we next asked whether we can use the model-derived time series of volatility to predict subjects’ behavior in a memory task. Figure 3 Download asset Open asset Correlation between volatility and spectral power over a frequency range from 3 to 180 Hz. We fit a Gaussian process model to estimate the functional form of the correlation function between volatility and spectral power (solid blue line). The 95% confidence bands were constructed from 96 subjects (dashed gray lines). The red line shows the null model. We observe a significantly positive correlation between volatility and spectral power, and the correlation increases with frequency. Classification of subsequent memory recall Extensive previous work on the electrophysiological correlates of memory encoding has shown that spectral power, in both the low-frequency (4–8 Hz) theta band and at frequencies about 40 Hz (so-called gamma activity), reliably predicts which studied words will be subsequently recalled or recognized (Sederberg et al. , 2003). Here, we ask whether the implied volatility derived from the MSV model during word encoding can also reliably predict subsequent recall. To benchmark our MSV findings, we conducted parallel analyses of wavelet-derived spectral power at frequencies ranging between 3 and 180 Hz. To aggregate across MTL electrodes within each subject we applied an L2-penalized logistic regression classifier using features extracted during the encoding period to predict subsequent memory performance (Ezzyat et al. , 2017; Ezzyat et al. , 2018). To estimate the generalization of the classifier, we utilized a nested cross-validation procedure in which we trained the model on N-1 sessions using the optimal penalty parameter selected via another inner cross-validation procedure on the same training data (see 0.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: ,x kN ],where x i ∈ Rp. So the distance matrix is D = ⟨di, j⟩ with di,j= ||x i - x j ||. In this simulation, p = 17 is used. Fig. 1a is the 2D projection obtained by applying CMDS to 2000 simulation points. Then, in the first application of SC-MDS, we set the number of points in each group to N g = 200 and the number of points in each intersection region to N I = 30 such that the number of the overlapping points is greater than the real dimension 17. All points in each group are chosen randomly. In order to measure the difference between classical MDS and SC-MDS, we use the (Kruskal's goodness of fit index) to compute the error between the distance matrixes.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: in through your institution issuesearch term Advance 10. 1093/femsre/fuaa014 Published: 18 May 2020 10. 1093/femsre/fuaa014 Select format. ris (Mendeley, Papers, Zotero). enw (EndNote). bibtex (BibTex). txt (Medlars, RefWorks) Download citation issuesearch term issuesearch term cocoa beans are obtained through a post-harvest, batchwise process of fermentation and drying carried out on farms in the equatorial zone. Fermentation of cocoa pulp-bean mass is performed mainly in heaps or boxes. It is made possible by a succession of yeast, lactic acid bacteria (LAB) and acetic acid bacteria (AAB) activities. Yeasts ferment the glucose of the cocoa pulp into ethanol, perform pectinolysis and produce flavour compounds, such as (higher) alcohols, aldehydes, organic acids and esters. LAB ferment the glucose, fructose and citric acid of the cocoa pulp into lactic acid, acetic acid, mannitol and pyruvate, generate a microbiologically stable fermentation environment, provide lactate as carbon source for the indispensable growth of AAB, and contribute to the cocoa and chocolate flavours by the production of sugar alcohols, organic acids, (higher) alcohols and aldehydes. AAB oxidize the ethanol into acetic acid, which penetrates into the bean cotyledons to prevent seed germination. Destruction of the subcellular seed structure in turn initiates enzymatic and non-enzymatic conversions inside the cocoa beans, which provides the necessary colour and flavour precursor molecules (hydrophilic peptides, hydrophobic amino acids and reducing sugars) for later roasting of the cured cocoa beans, the first step of the chocolate-making. acetic acid bacteria, chocolate, cocoa fermentation, flavour, lactic acid bacteria, yeasts of perishable raw materials originating from plant agriculture and animal husbandry is an ancient biotechnological process that is practiced to produce stable and safe fermented food products of high microbiological, organoleptic, nutritional and health-beneficial quality (Leroy and 2004; Bourdichon et al. 2012; Wolfe and Dutton 2015; Tamang, Watanabe and Holzapfel 2016; Marco et al. 2017; Rezac et al. 2018).

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: 53 (95% CI, from 0. 51 to 0. 55). AUCs reliably exceeded chance levels in 72 percent of subjects (30 out of 42 subjects who contributed at least 3 sessions of data). Figure 4 compares these findings against results obtained using wavelet-derived power. Here we see that implied volatility does as well as, or better than, spectral measures at nearly all frequencies. In order to capture the correlation between spectral powers (thus their corresponding classifiers’ performances), we fit a Gaussian regression model to test the functional form of ΔAUC. We find that the ΔAUC function is significantly different from the 0 function (χ112=42, P < 10-5) (Benavoli and Mangili, 2015), which indicates that on average volatility performs significantly better than spectral power in predicting subsequent memory recall. Figure 4 Download asset Open asset Classification of subsequent memory recall. (A) Average AUC of the classifier trained on spectral power across 42 subjects with at least three sessions of recording (blue). The red line indicates the average AUC of the classifier trained on volatility. (B) ΔAUC = AUCvol−AUCpower as a function of frequency estimated by using a Gaussian regression model (dashed gray lines indicate 95% confidence bands). The red line shows the null model. We observe that the classifier trained on volatility performs at least as well as the one trained on spectral power across the frequency spectrum. We find that functional form of ΔAUC is significantly different from the function (χ112=42, P < 10-5) using a Gaussian process model, suggesting that the difference in performance between the volatility classifier and the spectral power classifier is significant. Directional connectivity analysis Having established that volatility is predictive of subsequent memory recall, we now seek to identify directional connections between MTL subregions that are related to successful memory encoding. To investigate the intra-MTL directional connectivity patterns that correlate with successful memory encoding, we utilize a subsequent memory effect (SME) paradigm in which we compare the MTL directional connectivity patterns (regression coefficient matrix 𝜷) associated with recalled (R) word items to those associated with non-recalled (NR) items.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The formula of is = ∑ i j ( d i j − d ˜ i j ) 2 ∑ i j d i j 2 MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xI8qi9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaaeGaciGaaiaabeqaaeqabiWaaaGcbaGaem4uamLaemivaqLaemOuaiLaemyrauKaem4uamLaem4uamLaeyypa0tcfa4aaOaaaeaadaWcaaqaamaaqafabaGaeiikaGIaemizaq2aa5aaqaamaaqafabaGaemizaq2aa0baaeaacq@54CE@ where di,jrefers to the distance matrix of the original data and d ˜ i j MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xH8viVGI8Gi=hEeeu0xXdbba9frFj0xb9qqpG0dXdb9aspeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae@30F9@ refers to that for the SC-MDS reconstruction. In this spiral example, the of computing errors for SC-MDS is only 3. 93 × 10-14, and the for CMDS is only 1. 25 × 10-15. These errors are here considered as rounding errors. Thus, SC-MDS can reconstruct the configuration as does CMDS; the result of our method (Fig 1b) is similar to the 2D projection in Fig. 1a.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: This process is based on environmental inoculation of the raw materials (often referred to as natural or spontaneous fermentation), intentional inoculation through backslopping (use of a part of a finished fermented food product to inoculate a fresh batch of raw material), or after addition of a microbial starter culture (Leroy and 2004; Hutkins 2019). Whereas starter cultures are in use for the production of fermented dairy products (e. g. yoghurt and cheese), fermented meat products (e. g. fermented sausage), bread production (by means of bakers’ yeast) and fermented alcoholic beverages (e. g. beer and wine) since several decades, several processes for the fermentation of cereals (e. g. artisan sourdoughs), vegetables (e. g. sauerkraut), fruits (e. g. vinegar), fish (e. g. fish sauce), etc. are still mostly of a backslopping or spontaneous nature, but research on the use of starter cultures for these fermentation processes is ongoing (Hutkins 2019). Some of these spontaneous fermentation processes are of great economic importance, as in the case of the cash tree crop cocoa (Schwan, Fleet and Afoakwa 2015; Pereira, Soccol and Soccol 2016). They hence would likely take profit of the introduction of starter cultures to carry out accelerated and enhanced fermentation processes that lead to end-products of improved and standardized quality. Cocoa originated in the Amazon basin of (Zarrillo et al. 2018; Coe and Coe 2019). It was cultivated and consumed as a beverage constituent in since prehistoric times. Its consumption, first as cocoa drink and later on as chocolate, was first introduced into Europe in the 16th century. Nowadays, cocoa trees are cultivated on plantations in the equatorial zone globally. Their fruits have to undergo a rather complex and often uncontrolled, on-farm, post-harvest, batchwise process of fermentation and drying to be able to use the fermented dry cocoa beans for further manufacturing (Fig. 1; Schwan and Wheals 2004; Beckett 2009; Badrie et al. 2015; Afoakwa 2016; and Weckx 2016a,b; Figueroa-Hernández et al. 2019; Muñoz et al. 2019). and Brazil are the main cocoa-producing countries in the cocoa belt, some 20° north and south of the equator. Figure 1.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The SME paradigm has been widely used in the memory literature to study neural correlates (typically spectral power in a specific frequency band) that predict successful memory formation (Sederberg et al. , 2003; Long et al. , 2014; Burke et al. , 2014). The intra-MTL connectivity SME was constructed using the following procedure. First, we partitioned the word items into recalled and non-recalled items offline. Using the MSV model, we constructed an intra-MTL connectivity network for each memory outcome. We compared the distribution of the elements of these matrices across subjects. For the analysis, we considered four subregions of the MTL: hippocampus (Hipp), entorhinal cortex (EC), perirhinal cortex (PRC), and parahippocampal cortex (PHC). Each MTL subregion contains a different number of recording locations depending on the subject’s electrode coverage. We then computed the contrast between the two intra-MTL networks corresponding to recalled and non-recalled items for each ordered pair of subregions excluding the ones with fewer than 10 subjects contributing to the analysis. To compute the directional connectivity from region I to region J, we took the average of the lag-one 'influences’ that electrodes in region I have on electrodes in region J, where |I| denotes the number of electrodes in region I. We then computed the contrast between the two connectivity networks associated with recalled and non-recalled items: ΔI→J=CI→JR-CI→JNR. Finally, we averaged the contrast for each ordered pair of MTL subregions across sessions within a subject. From now on, we refer to this contrast as simply connectivity network. Figure 5 illustrates the intra-MTL connectivity SME for the left and right hemispheres. Directed connections between the left hippocampus and the left PRC reliably decrease (false-discovery-rate-corrected) during successful memory encoding (ΔHipp→PRC=-0. 04,t47=-3. 49, adj. P <0. 01 and ΔPRC→Hipp=-0. 06,t47=-2. 66, adj. P <0. 05).

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Because this spiral example has only two essential dimensions, the 3rd to 17th dimensions are considered as random perturbations. If we reduce the number N I from 16 to 3 but keep N g = 200, we can see that the 2D SC-MDS representation keeps the shape of spiral but becomes more and more blurred as N I decreases. At the same time, the increases when N I decreases, as will be shown in Fig 2. In Fig. 1c, we set N g = 200 and N I = 5 and the points in each group are also chosen randomly. The performance is now slightly worse than CMD due to dimension loss; the here is 0. 0398. In Fig. 1d, we set N g = 200 and N I = 5, and group points neighboring to each other into the same group. It shows that the performance is totally bad – the spiral structure is lost and the increases to 0. 5232. Figure 2Computational error as a function of N I. The solid line refers to the case where the points in each group are chosen randomly, while the dash line refers to the case where the points in each group are chosen from neighbors. In all cases, N g is fixed to 200. Full size imageFigure 2 shows the correlation between and the number of points in the overlapping region N I. There are two lines. The solid line refers to points in each group that are chosen randomly, while the dash line refers to points in each group that are chosen from neighbors; all N g are fixed to 200. Points in each line are the average of 20 repeats of computed with different randomly choosing points in each group. We can see that the of each line decays as N I increases. I is larger than the real dimension of data, the is almost zero. I is smaller than the real dimension of data, the performance of solid line is better than the dash line. Without the afar information in each group, the performance is worse. That is, randomly choosing points in each group helps to reduce the error when N I is not large enough. In the same simulation example, we observed the relationship between error and the number of groups. I is larger than the dimension of the data, like N I = 20, we can reconstruct the configuration of the data well. The number of groups of our grouping method does not affect the and the average is ~10-12.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Besides the early review of Schwan and Wheals (2004), some recent reviews partially dealt with the potential of starter cultures to improve the cocoa fermentation process (Saltini, Akkerman and Frosch 2013; Pereira, Soccol and Soccol 2016; and Weckx 2016a,b; Ozturk and Young 2017; Castro-Alayo et al. 2019; Figueroa-Hernández et al. 2019; Muñoz et al. 2019). The present review provides a state-of-the-art overview of the microbiology and biochemistry of cocoa fermentation processes and the importance of controlled, starter culture-initiated fermentation processes to produce fermented dry cocoa beans. It will focus in particular on the functional role of yeasts, lactic acid bacteria (LAB) and acetic acid bacteria (AAB), thereby attempting to cover most of the studies published since 2000. Role of fermentation in cocoa curing Fermentation is an essential step in the post-harvest processing of cocoa pods, the big oval fibrous fruits of the cocoa tree (Theobroma cacao L. ) (Bartley 2005; Motamayor et al. 2008; Badrie et al. 2015). These fruits differ in size, shape and surface colour. After processing, their seeds, the cured cocoa beans, are used for chocolate production (Fig. 1; Schwan and Wheals 2004; and Weckx 2016a,b; Figueroa-Hernández et al. 2019; Muñoz et al. 2019). Morpho-geographically, distinction can be made between bulk (basic or ordinary) cocoa (Forastero variety; 95% of the world cocoa production) and fine (flavour) cocoa (Criollo and Trinitario varieties; 5%). However, most cocoa crops in use are hybrids of several varieties. The white to dark purple, fresh cocoa beans (the colour depends on their anthocyanin contents) are embedded in a white-grey, sticky, sour-sweet and viscous pulp, the cocoa pulp-bean mass, which is manually or mechanically scooped out of the cocoa pods after their harvest (Fig. 1). When applying good agricultural and farming practices, only mature, healthy and undamaged cocoa pods should be used. The fresh cocoa pulp-bean mass, ideally depleted from placentas and residual cocoa pod p2.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The difference between the directional connections between these two regions is not significant (t47=0. 53, p=0. 60). The decreases in the bi-directional connections within the left MTL are consistent with the findings in Solomon et al. (2017) which noted memory-related decreases in phase synchronization at high frequencies. We did not, however, find any other significant directional connections among the remaining regions (Figure 5, 0. 01) and the reverse directional connectivity is also significant (adj. P < 0. 05). ability to record electrophysiological signals from large numbers of brain recording sites has created a wealth of data on the neural basis of behavior and a pressing need for statistical methods suited to the properties of multivariate, neural, time-series data. Because neural data strongly violate variance-stationarity assumptions underlying standard approaches, such as Granger causality (Stokes and Purdon, 2017), researchers have generally eschewed these model-based approaches and embraced non-parameter data analytic procedures. The multivariate stochastic volatility framework that we propose allows for non-stationary variance in the signals. This framework allows us to explicitly model the time-varying variance of neural signals. Similar stochastic volatility models have been used extensively in the financial economics literature to characterize a wide range of phenomena. The MSV models proposed in this paper provide a new framework for studying multi-channel neural data and relating them to cognition. Using a large MTL intracranical EEG dataset from 96 neurosurgical patients while performing a free-recall task, we found that volatility of iEEG timeseries is correlated with spectral power across the frequency spectrum and the correlation increases with frequency. To further test the ability of the MSV model to link iEEG recordings to behavior, we asked whether MTL volatility features during encoding can predict subsequent memory recall as well as spectral power features. Our findings indicate that volatility features significantly outperform the spectral features in decoding memory process in the human brain, suggesting that volatility can serve as a reliable measure for understanding cognitive processes. A key strength of the MSV approach is its ability to identify directed interactions between brain regions without assuming variance-stationarity of neural signals. We therefore used this approach to determine the directional connections between MTL subregions that correlate with successful memory encoding.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Hence, SC-MDS gives accurate results if we carefully control the number of overlapping points and choose random points in each group. Next, we compare the speeds of non-metric and SC-MDS. We create random vectors in a 20-dimension space, with the sample size ranging from 50 to 2000. 3 the black line (non-metric MDS) and the blue line (CMDS) are produced by Matlab default scripts, mdscale. m and cmdscale. m. The red line refers to our SC-MDS method. We can see that the simulation result matches our theoretical analysis. Figure 3Speed comparison among non-metric and SC-MDS. The SC-MDS method is denoted by the red line, the CMDS method is denoted by the blue line, and the non-metric MDS method is denoted by the black line. The black and red lines are produced by average of 20 repeats. Full size imageBecause of the hardware limitation to process CMDS, we use only a set of 2000 sample points as the maximal sample size to demonstrate that SC-MDS performs as well as CMDS when the number of data points (N) is not very large. We give below an example of large N to show that SC-MDS works well in a large data set that cannot be handled by CMDS. MapGene correlation maps are used to represent the correlations of genes such that genes with similar biological functions or in the same biological pathway tend to be located in the same neighborhood. It provides a prior probability in many applications of genome research by Bayesian methods. U133A GeneChip is widely used in many studies, we used genes listed in this chip and GO descriptions on the website to create the gene correlation map. In this chip, there are 22,283 genes and 2168 GO terms are used in the list of these genes. Hence, we consider each gene a binary vector with length of 2168. If the i-th term of the vector is one, then this gene has the i-th GO description. There are 5781 genes without any GO description so that these genes are not used to compute the correlation with the genes with a GO description. Hence, the term-document matrix is reduced to the size of 16502 × 2168. To apply the CMDS to a distance matrix of 16502 × 16502 is impossible for current hardware.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Overview of the fermentation of the cocoa pulp-bean mass through the succession of an anaerobic phase, involving yeasts and lactic acid bacteria (LAB), and an aerobic phase, involving acetic acid bacteria (AAB), and the concomitant bioconversion reactions taking place inside the cocoa beans. Uptake of substrates and secretion of metabolites by the microbial cells are not represented. EMP, Embden-Meyerhoff-Parnas pathway. HeFe, heterofermentation. HoFe, homofermentation. PKP, phosphoketolase pathway. TCA, tricarboxylic acid cycle. Open in new tabDownload slide Microbial species diversity and community dynamics during cocoa fermentation The microbial ecology and succession of cocoa fermentation processes is complex (Schwan and Wheals 2004; and Weckx 2016a,b; Figueroa-Hernández et al. 2019; Muñoz et al. 2019). Cocoa fermentation processes differ in microbial species and counts as well as in substrate and metabolite compounds and concentrations over time, hence leading to variable fermentation quality and cocoa flavour. Moreover, cocoa fermentation processes are characterized by changing interactions with the environment and among the indigenous microorganisms (Adler et al. 2013, 2014; Moens, Lefeber and 2014; Illeghems, Weckx and 2015b; Moreno-Zambrano et al. 2018; Mota-Gutierrez et al. 2018). This variability not only depends on the cocoa variety, agricultural and farm practices, fermentation method and duration, etc. , but even occurs for fermentation processes carried out on the same farm, under the same conditions and with the same cocoa variety (Afoakwa et al. 2008; Cevallos-Cevallos et al. 2018; Rottiers et al. 2019). Nonetheless, as further outlined below, these complex successive microbial activities and diversity of the cocoa fermentation process also account for its robustness. The actual fermentation of the cocoa pulp-bean mass is carried out by a temporal succession of activities of yeasts, LAB and AAB, whose interactions are determined mainly by their ethanol, lactic acid and acetic acid concentrations produced as well as the temperature increase upon fermentation. A well-performed cocoa fermentation process, implying good agricultural and farm practices, is finished within 4–6 days (Schwan and Wheals 2004; Lima et al.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Using the regression coefficient matrix of the multivariate volatility process, we found that periods of decreased connectivity in the volatility network among MTL subregions predicted successful learning. Specifically, we found that the hippocampus and the perirhinal cortex in the left hemisphere desynchronize (exerting less influence on one another) during successful learning, which is consistent with the late-phase gamma decoupling noted in Fell et al. (2001). A more recent study by Solomon et al. (2019) also examined the association between intra-MTL connectivity and successful memory formation using phase-based measures of connectivity. Solomon et al. (2019) noted intra-MTL desynchronization at high frequencies during successful memory formation, aligning with the finding here that the volatility network tended to desynchronize. Furthermore, Solomon, et al. found broad increases in low-frequency connectivity, which did not appear to be captured by our stochastic model. This suggests that volatility features reflect neural processes that are also captured by high-frequency phase information. We further noted more statistically reliable changes in volatility networks in the left MTL compared to the right. This result is in line with a long history of neuroanatomical, electrophysiological, and imaging studies (e. g. Ojemann and Dodrill, 1985; Kelley et al. , 1998) that found an association between verbal memory and the left MTL. It is possible that the verbal nature of our memory task specifically engaged processing in the left MTL, resulting in a lateralization of observed volatility phenomena. Prior studies implicate the perirhinal cortex in judgement of familiarity and in recency discrimination system, while the hippocampus supports contextual binding (Eichenbaum et al. , 2007; Diana et al. , 2007; Hasselmo, 2005). These two systems play important roles in memory associative retrieval as suggested by animal studies (Brown and Aggleton, 2001), but it is still unclear how the hippocampus and PRC interact during memory processing. Fell et al. (2006) suggested that rhinal-hippocampal coupling in the gamma range is associated with successful memory formation. Our results show no evidence for such a phenomenon, but rather agree with more recent studies demonstrating memory-related overall high-frequency desynchronization in the MTL (Solomon et al. , 2019; Burke et al. , 2015).

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Full size imageBefore applying K-means clustering, we determine how many clusters we should use as the parameter in the K-means process. We search the number of clusters in the K-means process from 10 to 75. Since the clustering result of K-means depends on the initial guess of the centroids of the sets, we repeat 30 times the K-means process and use the Criterion (BIC) score [14] to pick up the best clustering index from the mean of these 30 clustering results. In each K-means process, if it does not reach convergence in 100 iterations, we reset the initial values of the cluster centers and re-run the K-means process until the process converges. We set the upper bound for re-running time to be 5. If the re-running time reaches this upper bound, we choose the final cluster index by the best cluster sets from the previous iteration results. The BIC score is computed by the following formula: B I C = ∑ i = 1 n log ( ∑ j = 1 k Pr ( x i | c j ) Pr ( c j ) ) − ( k + 1 ) p 2 log ( n ) MathType@MTEF@5@5@+=feaafiart1ev1aaat9MDH5MBPbIqV92AaeXatLxBI9g6xNi=xI8qi9v8qqaqFr0xc9vqFj0dXdbba91qpepeI8k8fiI+fsY=rqGqVepae9pg0db9vqaiVgFr0xfr=xfr=xc9adbaqaae4qamKaeyypa02da9iabigdaXaqaaiabd6gaUbqdcqGHris5a4naa4yam2aa0JaeGymaedabaGaem4@67C3@ where n is the number of data points, k is the number of clusters, c j is the j-th model and p is the length of data points. We assume that each cluster from the K-means procedure is a multivariate normal distribution. The first term of the formula is the log-likelihood and the second term is penalty.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: (2019)] and the metabolic potential of the microbial communities detected, including meta-pathway reconstruction [e. g. Illeghems, Weckx and (2015b) and Agyirifo et al. (2019)]. All these studies have brought deeper insights into the microbial community dynamics and species diversity of the complex cocoa fermentation process. Some of these studies have been accompanied with a metabolite target analysis of pulp and/or beans, unravelling the dynamics of the pulp substrate consumption and conversions as well as those of the microbial metabolite production and conversions in the pulp and/or beans, now also including volatile organic compounds of seed and microbial origin [e. g. Ardhana and Fleet (2003), Camu et al. (2007), Aculey et al. (2010) and Moreira et al. (2016)]. Recent multi-omic approaches and multivariate analyses have linked the microbiology and biochemistry of cocoa pulp-bean mass fermentation processes with the flavour profiles of the cured cocoa beans and chocolates produced thereof [e. g. Ho, Fleet and Zhao (2018) and Mota-Gutierrez et al. (2018)]. The actual cocoa fermentation process can be split into two progressive phases that differ in environmental conditions, in particular oxygen availability, which are further referred to as an anaerobic phase and an aerobic phase. Besides microbial activities in the pulp, different enzymatic activities take place inside the beans during these two cocoa fermentation process phases. Anaerobic yeast and lactic acid bacteria phase Yeasts and LAB Yeasts and LAB grow simultaneously for about 24–36 h of fermentation during the anaerobic phase of the cocoa fermentation process, which takes off at an environmental temperature of around 25–30°C under carbohydrate-rich and acidic conditions (Schwan and Wheals 2004; and Weckx 2016a,b; Figueroa-Hernández et al. 2019; Muñoz et al. 2019). These conditions are characterized by high glucose and fructose concentrations in the mature pulp (formed from sucrose through mainly pulp invertase activity and—to a lesser extent—yeast invertase activity) and a low pH of approximately 3. 0 (because of the presence of citric acid), respectively. This simultaneous growth of yeasts and LAB may be ascribed to their non-competitive interactions.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: effortlessly establish a gist-like memory of their environment whenever they enter a new place, a memory that can guide action even in the absence of vision. Neurons in the lateral intraparietal area (LIP) of the monkey exhibit a form of this environmental memory. These neurons respond when a monkey makes a saccade that brings the spatial location of a stimulus that appeared on a number of prior trials, but not on the present trial, into their receptive fields (RFs). The stimulus need never have appeared in the neuron’s RF. This memory response is usually weaker, with a longer latency than the neuron’s visual response. We suggest that these results demonstrate that LIP has access to a supraretinal memory of space, which is activated when the spatial location of the vanished stimulus can be described by a retinotopic vector from the center of gaze to the remembered spatial location. Introduction Humans, and presumably monkeys, effortlessly establish a gist-like memory of their environment whenever they enter a new place. They can then use this memory to guide action even in the absence of vision. The hallmark of this environmental memory is that the objects remembered need not be relevant to the subject’s current behavior. For example, although you may never be asked to point to the door with your eyes closed, you establish a memory of the door’s location automatically. Although normally humans have no trouble pointing to objects in the room with their eyes closed, patients with bilateral parietal lesions cannot do this even though they can easily locate and point to objects in the room with their eyes open (Levine et al. , 1985), suggesting an environmental memory impairment. Visually responsive neurons in the frontal eye field (FEF) exhibit a signal that could represent environmental memory (Umeno and Goldberg, 2001). After monkeys make a number of saccades that bring a task-irrelevant probe stimulus into the receptive field of a visually responsive FEF neuron, many neurons respond on trials when a saccade brings the spatial location of the stimulus into the receptive field, even though the probe stimulus did not appear on the current trial. However, because the investigators always used the same saccade both to establish and evaluate the memory response, it is not clear if the effect is a true spatial memory or merely a memory of receptive field stimulation associated with a saccade.

Output: contrast