Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The eye position signal that modulates the gain fields in LIP could come from the representation of eye position in area 3a (Zhang et al. , 2008). It is mathematically trivial to calculate target position in supraretinal coordinates from retinotopic gain fields (Zipser and Andersen, 1988; Salinas and Abbott, 1997; Pouget and Sejnowski, 1994). Although gain fields are inaccurate immediately after a saccade (Xu et al. , 2012), they could contribute to a late, accurate, supraretinal representation that develops when the eye position signal is accurate, as one would expect if the eye position signal arose from proprioception. The neurons that we studied did not have eye position modulation of their visual responses – such neurons often have a phasic and tonic postsaccadic response that arises from the eye position signal itself (Wang et al. ). Indeed, because the memory of the stimulus was established well before the saccade that evoked the memory, the supraretinal representation of the remembered stimulus could easily be implicitly encoded in the gain-field responses of LIP neurons (Zipser and Andersen, 1988). Data from patients with parietal lesions, such as the previously described results from Bisiach and Luzzatti, suggest that although LIP might calculate target position in retinotopic coordinates, the parietal cortex does not store the results of that calculation. Instead, a supraretinal representation of space could be stored elsewhere, possibly in the medial temporal lobe. O’Keefe discovered that neurons in the rat hippocampus (the ‘place cells’) discharge when rats enter spatial location that they have seen before (O'Keefe and Dostrovsky, 1971). Other studies have duplicated this result in the monkey hippocampus (Ono et al. , 1993; Rolls et al. , 1989). Grid cells in entorhinal cortex are thought to be the precursors of hippocampal place cells (Moser et al. , 2008) and these have been found in the monkey (Buffalo, 2015) as well as in humans (Jacobs et al. , 2013). It is known that there is a direct, reciprocal connection between the parahippocampal gyrus and LIP (Suzuki and Amaral, 1994; Baizer et al. , 1991).

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Full size imageTo validate the value of using prior information, we tested with non-informative prior distributions (i. e. we assigned equal prior probability for all variables to be included in the variable selection; see Methods for further details) and compared the results to those obtained with informative priors (Fig. 3a, green curve; Additional file 1, Fig. S1, green curves; Table S1). This clearly showed that the use of relevant prior information significantly improves the classification accuracy of (Bayesian P-value < 0. 001, related to the curves in Fig. 3a). The dramatic improvement was due to the fact that the prior information focused the model to include relevant genes in the signature, thus reducing the risk of detecting spurious relationships and overfitting the model. Analogously, the use of prior information enforced consistency in signature selection across disparate datasets: 15. 2% of the genes were selected all five times with informative priors was fit, which may be compared to 1% when noninformative priors were used, and 0% among the original signatures. The median of the pairwise overlap between two different model fits was 27. 5%, compared to 3% among the original signatures and 12% reported in Chuang et al. [6]. We also assessed the advantage of using multiple datasets by using only single datasets for training (with informative priors); results are shown in Fig. 3a (blue curve), Additional file 1, Fig. S1 (blue curves) and Table S1. A significant improvement was found when multiple datasets were used for model training compared to when only a single dataset was used (Bayesian P-value < 0. 001, related to the curves in Fig. 3a). In a final comparison, we modified to use only the expression data that corresponded to the respective gene signatures derived in Wang et al. [13], Miller et al. [14], Sotiriou et al. [15], and Pawitan et al. [16], together with noninformative priors (only the genes printed on the HG-U133A were used for the Miller et al. [14] and Pawitan et al. [16] signatures). Again, we found that (with informative priors) performed significantly better than when the signatures from Wang et al. [13], Miller et al. [14], Sotiriou et al. [15], and Pawitan et al.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: The common method to produce lactic acid from starchy biomass involves the pretreatment for gelatinisation and hydrolysis (liquefaction and saccharification). The liquefaction of the starch is carried out at high temperatures of 90–130 °C for 15 min followed by enzymatic saccharification to glucose and subsequent conversion of glucose to lactic acid by fermentation [22, 1]. This two-step process involving consecutive enzymatic hydrolysis and fermentation makes it economically unattractive. The bioconversion of carbohydrate materials to lactic acid can be made much more effective by coupling the enzymatic hydrolysis of carbohydrate substrates and microbial fermentation of the derived glucose into a single step. This has been successfully employed for lactic acid production from raw starch materials with many representative bacteria including Lactobacillus and Lactococcus species [23, 20, 24, 21]. Because at industrial scale, the use of glucose addition is an expensive alternative, there is interest in the use of a cheaper source of carbon, such as starch, the most abundantly available raw material on earth next to cellulose. This, in combination with amylolytic lactic acid bacteria may help to decrease the cost of the overall fermentation process. Amylolytic lactic acid bacteria can convert the starch directly into lactic acid. Development of production strains which ferment starch to lactic acid in a single step is necessary to make the process economical. Very few bacteria have been reported so far for direct fermentation of starch to lactic acid [1, 25, 26] Approximately 3. 5 billion tonnes of agricultural residues are produced per annum in the world [27]. The use of a specific carbohydrate feedstock depends on its price, availability, and purity. Although agro-industrial residues are rich in carbohydrates, their utilization is limited [27]. Different food/agro-industrial products or residues form the cheaper alternatives to refined sugars as substrates for lactic acid production. Sucrose-containing materials such as molasses are commonly exploited raw materials for lactic acid production. Starch produced from various plant products is a potentially interesting raw material based on cost and availability. Laboratory-scale fermentations have been reported for lactic acid production from starch by Lactobacillus amylophilus GV6 [20], L.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Thus, LIP could send an eye-position modulated visual signal to the medial temporal lobe, which could serve as a building block for a supraretinal memory. The medial temporal lobe could then use this activity to generate the supraretinal component of place cell activity and send this supraretinal signal back to LIP. LIP itself would only be activated when the spatial location of the vanished stimulus can be described by a retinotopic vector from the center of gaze to the receptive field of the neuron. Thus, LIP has access to a supraretinal representation, but expresses only a retinotopic representation. It is important to emphasize that LIP is part of a spatial network that includes the FEF and the superior colliculus. Although neuropsychological studies suggest that parietal damage affects spatial memory, we do not know if damage to other parts of the network has a similar effect. Although early models posited that the oculomotor system uses a supraretinal representation (Zee et al. , 1976), it is clear that the parietal cortex sends a spatially accurate retinotopic signal to the oculomotor (Pare and Wurtz, 1997) and skeletomotor (Batista et al. , 1999) systems. Even auditory stimuli, which are initially coded in craniotopic coordinates, are converted to retinotopic coordinates for the oculomotor (Jay and Sparks, 1987b; Jay and Sparks, 1987a) and skeletal motor system (Grunewald et al. , 1999; Cohen and Andersen, 2000) in parietal cortex, and the oculomotor system of the superior colliculus (Jay and Sparks, 1984). We suggest that the environmental memory signal in LIP is an example of the conversion of a supraretinal signal to a retinotopic signal for memory and spatial attention as well as for motor control. Materials and methods Care and at and the Institute approved all of the animal protocols in this study as complying with the guidelines established in the for the Care and Use of (protocol NYSPI-1225-C). We used one female and two male rhesus monkeys (Macaca mulatta) with weights between 6 and 13 kg. We first trained monkeys to sit in a primate chair using a pole and collar technique.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: [16] were used. Results are displayed in Additional file 1, Fig. S4 and Table S1. To derive a "final" gene signature, we fitted using all five datasets and informative priors. Some genes were included in this signature that a priori had been regarded as non-relevant to breast cancer; these genes were strongly supported by the data and may be of interest for future experimental investigations. The genes included in the signature are shown in Additional file 1, Table S2, stratified into genes that were relevant a priori as well as a posteriori, and genes that were regarded non-relevant a priori but relevant a posteriori. In order to further demonstrate the eScience-Bayes approach, we used it to create a model that predicted the probability of distant metastasis development as a function of time. This model would provide finer granularity in the prognosis predictions of future patients compared to the current practice of stratifying patients into only two classes (metastases or no metastases within five years). Patient stratification into two classes discards information by discretizing a continuous variable (time to development of distant metastasis) into an arbitrarily defined binary variable (yes/no development of distant metastasis within five years). Because not all patients had developed distant metastases before the last follow-up in the studies, this required a consideration of the censoring of the data (Methods). We created a multilevel Bayesian accelerated failure time (AFT) (Methods; Sha et al. [21]) model with the expression data as explanatory variables and the informative prior distributions (Model II, see Methods for details). Analogously to the assessment of Models I, we assessed Model II by fitting it five times, each time using three of the datasets as a training set and the remaining two datasets as test sets to assess the performance of Model II. The results implied that we indeed could provide more fine-grained prognoses predictions, compared to the binary classification afforded with (Fig. 3c). In this example, we provided evidence for that adopting the eScience-Bayes approach can significantly improve consistency in gene signature selection and in the predictive performance of the constructed models. In contrast to previous studies, our results were presented together with Bayesian confidence intervals, which permit assessment of the level of accuracy in the results [4].

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: amylophilus B4437 [28], Lactobacillus amylovorus [29, 23, Lactococcus lactis combined with Aspergillus awamorii [30] and Rhizopus arrhizus [31]. L. amylophilus NRRL B4437 [32], L. amylovorus [17] and L. amylophilus GV6 are exceptions that have been described to actively ferment starch to lactic acid and this may lead to alternative process of industrial lactic acid production [23, 20]. To make the process cost effective in terms of substrate, various groups have worked on acid/enzyme hydrolysis of starchy substrates followed by Lactobacillus fermentation or simultaneous saccharification and fermentation by co-culture/mixed culture fermentations. It has been reported that starch is used as substrate in two steps [1]. 2. 2. Thermostable amylasesAmylases are among the most important enzymes and are of great significance in present-day biotechnology. Although they can be derived from several sources, such as plants, animals and microorganisms; enzymes from microbial sources generally meet industrial demands. The spectrum of amylase application has widened in many other fields, such as clinical, medical and analytical chemistries, as well as their widespread application in starch saccharification and in the textile, food, brewing and distilling industries. Thermostability is one of the main features of many enzymes sold for bulk industrial usage. Thermostable α-amylases are of interest because of their potential industrial applications. They have extensive commercial applications in starch liquefaction, brewing, sizing in textile industries, paper and detergent manufacturing processes. [33,34, 35]. The advantages of using thermostable amylases in industrial processes include the decreased risk of contamination and cost of external cooling, a better solubility of substrates, a lower viscosity allowing accelerated mixing and pumping [36]. Several thermostable α-amylase have been purified from Bacillus sp. and the factors influencing their thermostability have been investigated, but the thermostability of amylases from lactic acid bacteria have attracted very few scientific attention.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: All PSTHs were normalized to baseline activity (block 1) and were constructed from 2 ms spike bins, smoothed using a 10 ms sliding causal filter. Baseline calculations were made in the −426 to −226 ms (pre-saccade onset) window. To quantify the decay, perisaccadic memory and visual responses, we selected the time point of peak activity in the window from 125 ms before the saccade onset (to include any predictive remapping) to 500 ms after. We then looked at the median of raw spike count over a window extending 100 ms before and after this point to calculate response latencies, as described next. To obtain a latency value of the memory response, we used a median threshold-crossing and sustained response method. In short, this analysis compared the median activity in the baseline and response windows. We derived a ‘cut off’ value by determining the spike count values which would fall above at least the 75% percentile of the baseline median. By comparing the median spike counts in the response window to this ‘cut off’ value, we could determine when a minimum of five bins (of 2 ms each) contained a greater spike count than the ‘cut off’ value. We took the first of these bins as the response latency, and verified visually by looking at the corresponding time on the PSTH. Since the baseline activity can vary between different trials in the same experiment, we first chose a window within the time from fixation to −226 ms before saccade onset. Within this wide window, we made 200 ms wide sliding windows, in 1 ms steps. By finding the median spike count within these smaller windows, we picked the window closest to the start of the response window (but still had the same median as the population of these several 200 ms wide windows). This median was chosen to look at the response window in which a threshold crossing for at least 20 ms was picked as the response latency. We could not calculate latencies for a few neurons using this method, because the baseline (pre-saccadic) memory block response was as high as post-saccadic activity. There were also a few other units whose latencies were automatically picked by the aforementioned median method but a more precise time point could be picked by doing yet another median-based analysis.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Proteomics data: Analyzing PDZ domain-peptide interactionsPDZ domains mediate protein-protein interactions and have been extensively studied over the last fifteen years. Recently, a large-scale PDZ domain-peptide interaction dataset was published in Stiffler et al. [22] and follow-up studies were later described in Chen et al. [23]. The combined data from these two publications comprise 2306 observations of interactions between peptides and PDZ domains from mouse, C. elegans, and D. melanogaster. We reanalyzed these data using the eScience-Bayes approach. To this end, we modeled the PDZ domain-peptide interactions using a multilevel Bayesian probit model with physicochemical properties of the PDZ domains and peptides as explanatory variables and a binary variable as response, indicating whether or not a PDZ domain and a peptide bound to each other (Model III). We derived informative prior distributions from 3D structural information using the by Structure [24] (SAS), a tool for annotating a protein sequence with structural information based on all solved 3D structures of the proteins in the Bank [25] (PDB). Using the SAS Web service [26] (WSsas) we estiamted the number of contacts made between residues in each PDZ domain and in the peptide ligands. Similarly to the breast cancer demonstration, we used the informative prior distributions to "guide" the variable selection in order to reduce the dimensionality of the model based on the assumption that the importance of a PDZ domain residue for the interaction is reflected in the number of contacts it makes with the ligand (see Methods for details). Fig. 4 shows the work flow for the prior elicitation and modeling. Fig. 5a summarizes the predictive performance of Model III in a receiver operating characteristics (ROC, see Methods) curve based on a 10-fold cross-validation, and shows a comparison with the predictive performance when noninformative prior distributions were used (i. e. equal prior probability for all variables to be included in the variable selection; see Methods). We observed a nonsignificant (Bayesian P-value = 0. 11) improvement in the predictive performance with informative compared to noninformative priors (Fig. 5a). Figure 4eScience-Bayes applied to protein-protein interaction data.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Lactobacillus amylovorus, Lactobacillus plantarum, Lactobacillus manihotivorans, and Lactobacillus fermentum are some of the lactic acid bacteria exhibiting amylolytic activity which have been studied [37, 10, 38, 5, 39, 40]. However, most of α-amylase from these bacteria presented weak thermostability compared to those of genus Bacillus. Owing to the important acidification of fermenting medium by most lactic acid bacteria, the production of thermostable amylase by a lactic acid bacterium under submerged or solid-state fermentation can help to reduce the risk of contamination caused by undesirable micro-organisms during the process [41, 42]. Another advantage is the non-pathogen character of the genus Lactobacillus that allows their utilization in food fermentation processes. 2. 3. Lactic acidLactic acid a water soluble and highly hygroscopic aliphatic acid is present in humans, animals and microorganisms. It is the first biotechnologically produced multi-functional versatile organic acid having wide range of applications [1], namely as a preservative in many food products. It can be produce by LAB trough fermentation or synthetically from lactonitrile [43]. It is non-volatile, odorless organic acid and is classified as GRAS (As Safe) for use as a general purpose food additive. The lactic acid consumption market is dominated by the food and beverage sector since 1982 [1]. More than 50% of lactic acid produced is used as emulsifying agent in bakery products [44]. It is used as acidulant/flavoring/pH buffering agent or inhibitor of bacterial spoilage in a wide variety of processed foods, such as candy, breads and bakery products, soft drinks, soups, sherbets, dairy products, beer, jams and jellies, mayonnaise, and processed eggs, often in conjunction with other acidulants. Lactic acid or its salts are used in the disinfection and packaging of carcasses, particularly those of poultry and fish, where the addition of aqueous solutions during processing increased shelf life and reduced microbial spoilage. The esters of calcium and sodium salts of lactate with longer chain fatty acids have been used as very good dough conditioners and emulsifiers in bakery products.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: In a second round, we shuffled the object positions and repeated the task. Here three of the subjects made a mistake, presumably owing to interference with the memory formed on the previous round. Sighted subjects who inspected the scene visually performed similarly on the recall task (Figure 2—figure supplement 1). These experiments suggest that active exploration of object voices builds an effective mental representation of the scene that supports subsequent recall and orientation in the environment. Figure 2 with 1 supplement see all Download asset Open asset Spatial memory task. (A) Five objects are arranged on a half-circle; the subject explores the scene, then reports the recalled object identities and locations. (B) Recall performance during blocks 1 (left) and 2 (right). Recalled target angle potted against true angle. Shaded bar along the diagonal shows the 30 deg width of each object; data points within the bar indicate perfect recall. Dotted lines are linear regressions. (C) Slope and (D) correlation coefficient for the regressions in panel (B). Direct navigation Here, the subject was instructed to walk to a virtual chair, located 2 m away at a random location (Figure 3A). mode, the chair called out its name on every clicker press. All subjects found the chair after walking essentially straight-line trajectories (Figure 3B–C, Figure 3—figure supplement 1). Most users followed a two-phase strategy: first localize the voice by turning in place, then walk swiftly toward it (Figure 3—figure supplement 1D–E). On rare occasions (~5 of 139 trials), a subject started walking in the opposite direction, then reversed course (Figure 3—figure supplement 1C), presumably owing to ambiguities in azimuthal sound cues (McAnally and Martin, 2014). Subject seven aimed consistently to the left of the target (just as in the task of Figure 1) and thus approached the chair in a spiral trajectory (Figure 3C). Regardless, for all subjects the average trajectory was only 11–25% longer than the straight-line distance (Figure 3E, Figure 3—figure supplement 1A). Figure 3 with 2 supplements see all Download asset Open asset Direct navigation task. (A) For each trial, a target chair is randomly placed at one of four locations.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Modeling of the PDZ domain-peptide interaction data with the eScience-Bayes approach. (Color-coding is as in Fig. 1. ) (A) All PDZ and peptide sequences and interaction data were retrieved from the supplementary material of Chen et al. [23] via PubMed Central. The PDZ sequences were aligned using the service. Prior information was retrieved using the SAS Web service. SAS was used to determine the number of contacts made between PDZ domain residues and ligand residues from the 3D complexes available in the Bank. (B) The aligned PDZ domain and peptide sequences were characterized by numbers capturing their physicochemical properties. The characterizations of the PDZ domain-peptide interactions were correlated to a 1 or a 0 in a binary response variable, which indicated whether a PDZ domain and a peptide could bind or not to each other. Analogous to the breast cancer demonstration, we reduce the dimensionality of the model guided by the prior information derived in (A) (see Methods). (C) The models were fit on an HPC resource. Full size imageFigure 5Model III results. (A) The ROC curves for Model III; the red and blue lines show the results when the prior information was or was not used, respectively. Solid lines denote the mode of the ROC curve distribution, and the dotted lines the 95% Bayesian confidence intervals. The area under the curves (AUC) are 0. 92 (0. 89;0. 93) (red) and 0. 90 (0. 87;0. 92) (blue). (B) Trends in differences between different PDZ domains were obtained by allowing the regression coefficients to vary with PDZ domains, and then subjecting the resulting matrix of regression coefficients to a principal component analysis (PCA) and plotting the PDZ domains in the space of the first three principal components. It should be noted that the regression coefficients are evenly distributed, which supports the relaxation of the canonical PDZ classes proposed in e. g. Stiffler et al. [22] and Tonikian et al. [37]. The regression coefficients from interactions in C. elegans (green dots) and D. melanogaster (blue dots) cluster together in the outskirts of the mouse interactions (red dots). (C) Differences in predicted networks between different PDZ domains.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: SamplesTwenty-eight samples of soils were collected from main geographic zones of Cameroon in four localities: (Ngaoundere, Yaounde, Bafoussam and Mbouda) at the factories where starchy wastes are frequently submitted to natural fermentation. Four kinds of factories were investigated: “gari” factories, corn and cassava mills, cassava plantation after harvesting and treatment of tubers and flour markets. At the site where degradation of starchy material was remarkable and visible, one to five grams of soils were collected and transferred to polyethylene aseptic bag, the factory age recorded; the samples were finally transported to the laboratory and analyzed in the same week. 3. 2. Screening of thermostable amylases and lactic acid producing bacteriaThe starch degrading amylolytic lactic acid bacterial strains were isolated from different samples of soil. Amylolytic micro-organisms were firstly enriched by introduction of 1 g of soil sample in 100 ml Erlenmeyer flasks containing 50 ml of enrichment liquid medium, composed of (gram per litre): 5 g soluble starch, 5 g peptone, 5 g yeast extract 0. 5 g MgSO4. 7H2O, 0. 01g FeSO4. 7H2O, 0. 01 g NaCl. Enrichment of thermostable amylases producing bacteria was carried out by heating Erlenmeyer flasks at 90°C for 5 min followed by incubation in an alternative shaker at 37-40°C and speed of 150 oscillations per minute for 24 h. Amylases producing bacteria strains were screened on agar plate, containing (gram per liter): 10 g soluble starch, 5 g peptone, 5 g yeast extract, 0. 5 g MgSO4. 7H2O, 0. 01g FeSO4. 7H2O, 0. 01 g NaCl, 15 g agar. Incubation at 37-40°C was carried out for 48 h, after which the plates were stained with lugol solution (Gram iodine solution: 0. 1% I2 and 1% KI). The colonies with the largest halo forming zone were pre-selected and tested for Gram staining and catalase activity. Preliminary tests were carried out to determine the heat stability of the amylase of each isolate as we described previously [47, 48].

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Using CARA, subjects found the key efficiently (Figure 5D). Using vOICe none of the subjects were able to locate the key (Figure 5D), although two of them encountered it once by accident (Figure 5—figure supplement 1). These experiments illustrate the use of a standardized testing environment. Naive subjects performed well on these real-world tasks using CARA, but not using vOICe. It should be said that interpreting the vOICe sounds is very non-intuitive. Our subjects received the basic instructions offered on the vOICe web site and the ‘Exercise’ mode of vOICe (Sound, 2018), followed by a short period of practice. Extensive training with vOICe confers blind subjects with some ability to distinguish high contrast shapes on a clean background (Auvray et al. , 2007; Striem-Amit et al. , 2012). Conceivably an experienced vOICe user might perform better on the tests described here. Other investigators can attempt to demonstrate this using our published code and specifications (Liu and Meister, 2018; copy archived at Discussion ‘Seeing is knowing what is where by looking’ (Marr, 1982). The cognitive assistant CARA enables exactly this process while replacing the eye and early visual system. CARA conveys ‘what’ by the names of objects and ‘where’ by the location from where each object calls. ‘Looking’ occurs when the user actively requests these calls. The principal reason sighted people rely on vision much more than audition is that almost all objects in the world emit useful light signals almost all the time, whereas useful sound signals from our surroundings are few and sporadic. CARA can change this calculus fundamentally, such that all the relevant objects emit easily interpretable sounds. It is conceivable that the extended use of such an intuitive modality eventually produces subjective qualia that resemble the feeling of seeing. The use of spatialized sound in assistive devices has been considered for some time (Spagnol et al. , 2018), although to date there are few practical implementations with test results. For the purpose of outdoor navigation, Loomis et al. (2005) tested a system that guides the user along a path by virtual sounds emitted from way points.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Five residues (spacefilled) located outside the binding pocket that were estimated with Model III to be allosterically linked to residues in the binding peptide (light green) in representative PDZ domains: (i) mouse α 1-syntrophin(1/1), (ii) D. melanogaster Lap4(2/4), and (iii) C. elegans Lin7(1/1). The network residues are color coded according to the physicochemical property that is estimated to be important for the network: blue = size; red = polarity. Full size imageIn this example, the main advantage of adopting an eScience-Bayes approach was the increase in information that we obtained. For example, Model III enabled an analysis of the differences between PDZ domains. By allowing the regression coefficients to vary between PDZ domains (Methods), we found differences in the estimated nonzero coefficients for different PDZ domains (Fig. 5b). This suggests that there may be variations among the PDZ domains in the networks of residues that are important for interacting with ligands [27]. Interestingly, some of these residues are located far away from the binding site. Although the tertiary structures of PDZ domains are very similar, their primary structures vary substantially [28]. This may cause differences in the intramolecular interactions within different PDZ domains. Networks of such intramolecular interaction have previously been reported for PDZ domains [28, 29]. The results from studies by Gianni et al. [29] and Chi et al. [30] support the notion that the networks may differ among PDZ domains; for example, a network present in a PDZ domain from mouse tyrosine phosphatase BL was not found in the human PSD-95 PDZ3. The eScience-Bayes approach allowed analysis of these differences across a large set of PDZ domains. Model III further indicated that the PDZ domains from C. elegans and D. melanogaster may form two clusters (Fig. 5b). Given the small datasets from C. elegans and D. melanogaster, which contain only seven PDZ domains each (Fig. 4), the clustering might be a sampling or an experimental artifact. Nevertheless, it is tantalizing to hypothesize that there are general trends in the intramolecular interaction networks in PDZ domains that differ between species.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: The gas production from glucose, growth at different temperature (10, 40, 45 °C) as well as the ability to grow in different concentration of NaCl was determined as described by Schillinger and Lucke [49] and Dykes et al [50]. The isolates which were Gram positive and catalase negative, non-motile and producing heat stable amylase and lactic acid were finally selected and identified using API 50 CH test kit (bioMerieux, France). database identification was used to interpret the results. 3. 3. Microbial growth, amylase and lactic acid productionIn order to study microbial growth, amylase and lactic acid production, the microorganism was propagated at 40°C for 70 h in 50 ml of a basal medium containing: soluble starch, 1% (w/v); yeast extract, 0. 5 % (w/v) placed in 100 ml Erlenmeyer flask with shaking at 150 oscillations per minute in an alternative shaker (Kotterman, Germany). The initial pH of the medium was adjusted to 6. 5 using 0. 1 M HCl. After removal of cells by centrifugation (8000xg, 30 min, 4°C) in centrifugator (Heraeus, Germany), the supernatant was considered as the crude enzyme solution and was also used for lactic acid evaluation. 3. 4. Optimisation of raw starch degrading thermostable amylase and lactic acid productionThe amylase and lactic acid production was optimized by studying the effect of cultural and environmental variables (carbohydrate and nitrogen sources, metal salts and surfactants) individually and simultaneously. The effect of carbohydrate sources was studied by replacing soluble starch in basal medium with different sugars, gelatinized and raw natural crude starch sources (glucose, fructose, maltose, amylose, amylopectine, cassava, corn, rice tapioca, and sorghum flours at final concentration of 1% (w/v)). Nitrogen sources were tested by replacing yeast extract with various nitrogen sources (peptone, tryptone, beef extract, soyabean meal, ammonium sulphate, and urea at final concentration of 1. 5% (w/v)). The effect of metal salts was studied by adding individually various metal salts (CaCl2. 2H2O, MgSO4.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Natural spatialized sound is computed based on a generic head-related transfer function (Wenzel et al. , 1993); nothing about the software was customized to individual users. Object names and guide commands are translated into English using the text-to-speech engine from HoloToolkit. The user provides input by moving the head to point at objects, pressing a wireless Clicker, using hand gesture commands or English voice commands. In addition to instructions shown in the main body of the 4. Task design Task 1, object localization (Figure 1): In each trial, a single target is placed 1 m from the subject at a random azimuth angle drawn from a uniform distribution between 0 and 360 degrees. To localize the target, the subject presses the Clicker to hear a spatialized call from the target. After aiming the face at the object the subject confirms via a voice command (‘Target confirmed’). When the location is successfully registered, the device plays a feedback message confirming the voice command and providing the aiming error. The subject was given 10–15 practice trials to learn the interaction with CARA, followed by 21 experimental trials. To estimate the upper limit on performance in this task, two sighted subjects performed the task with eyes open: this produced a standard deviation across trials of 0. 31 and 0. 36 degrees, and a bias of 0. 02 and 0. 06 degrees. That includes instrumentation errors as well as uncertainties in the subject’s head movement. Note that these error sources are insignificant compared to the accuracy and bias reported in Figures 1 and 2. Task 2, spatial memory (Figure 2): This task consists of an exploration phase in which the subject scans the scene, followed by a recall phase with queries about the scene. Five objects are placed two meters from the subject at azimuth angles of −60°, −30°, 0°, 30°, 60° from the subject’s initial orientation. Throughout the experiment, a range between −7. 5° and 7. 5° in azimuth angle is marked by ‘sonar beeps’ to provide the subject a reference orientation. During the 60 s exploration phase, the subject uses ‘Spotlight Mode’: This projects a virtual spotlight cone of 30° aperture around the direction the subject is facing and activates object voices inside this spotlight. Typically subjects scan the virtual scene repeatedly, while listening to the voices.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: c X thus represents the c-values of the tciz-coded residues r1,. . . ,r67 and p1,. . . ,p10 incremented by 1. The c-values were incremented by 1 to ensure that all variables in H have a chance of being selected (i. e. including those with c-values equal to zero). Model IIIWe modeled the interaction between PDZ domains and peptides using a Bayesian hierarchical probit model according to: (6) where is the i th row in (the rows of H corresponding to PDZ domain ϕ). d j is the j th value in the row vector cX, H(X). Hence, the a priori probability of a variable to be selected is larger, the larger the number of contacts the PDZ residue it is derived from makes with the ligand (the assumption being that the more contact a PDZ residue makes with the ligand, the greater our a priori belief that the residue is important for the interaction). The other denotations are analogous to the ones used in Model I. The prior for m, i. e. the parameter controlling the number of selected variables, was chosen so that roughly one twentieth of the total number of variables was selected (i. e. so that = 11935/20, where E denotes the expectation operator). The mean of the prior distribution for m was estimated using cross-validation on the training data. Model III was also modified to assess the use of the prior information about which residues that are important for governing the interaction between PDZ domains and peptides. We then set p = 1/11935. Residue coupling depicted in Fig. 5cThe coupling was estimated by studying the interaction terms in H formed between tciz scales from one residue in the PDZ domain and one in the peptide as outlined in Prusis et al. [38]. The figure displays the five such interaction terms with the largest (absolute-valued) modes of the regression coefficient's distributions. The network residues are color coded according to the physicochemical property that is estimated to be important for the network; residue size is shown in blue and residue polarity in red.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: After a time interval of 6 h, the reducing sugars liberated in the reaction mixtures were determined by dinitrosalicyclic acid method [58]. Light microscopy was used for the examination of the effect of enzyme on raw starch granules using Olympus microscope BH-2. Advertisement 4. Results and discussion4. 1. Biochemical properties of amylolytic LAB isolatedFrom the 28 samples of soil collected from different localities of Cameroon, 90 amylolytic isolates were screened but only 9 isolates (04BBA15, 04BBA19, 05BBA22, 05BBA23, 14BYA42, 20BBA60, 17BNG51, 23BYA21, 26BMB81) presented very high amylolytic power (≥15mm) and were qualified as amylase overproducing isolates. The amylolytic power was defined as the average diameter (mm) of starch hydrolysis halo (Fig. 1. ) provoked by a strain after its inoculation in micro-well on MRS-starch agar plate for 48 h incubation at optimum temperature of growth for three assays. The amylolytic power is an expression of the capacity of an isolate to degrade starch during the culture. Among the amylase overproducing isolates, two (04BBA19, 26BMB81) were aero-anaerobic non spore forming, gram positive and catalase negative bacteria; this characteristic is proper to lactic acid bacteria. Microscopic observation showed rod cells. Biochemical characteristics of these isolates were carried out using API 50 CH kit bioMerieux system, the results are summarized in Table 1, the isolates were tested for their possibility to ferment 50 carbohydrates, and this fermentation profile was use for their numerical identification. According to their biochemical profile, 04BBA19 and 26BMB81 were respectively identified as Lactobacillus fermentum and Lactobacillus plantarum. The strain 04BBA19 (Lactobacillus fermentum) presented a very high amylolytic power, as it was able to cause a starch hydrolysis halo of 45 ±1. 5mm on MRS-starch agar plate after 48 h of incubation at 40 °C; consequently it was selected for further studies.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: In the recall phase, ‘Spotlight Mode’ is turned off and the subject performs four recall trials. For each recall trial, the subject presses the Clicker, then a voice instruction specifies which object to turn to, the subject faces in the recalled direction, and confirms with a voice command (‘Target confirmed’). The entire task was repeated in two blocks that differed in the arrangement of the objects. The object sequence from left to right was ‘piano’, ‘table’, ‘chair’, ‘lamp’, ‘trash bin’ (block 1), and ‘trash bin’, ‘piano’, ‘table’, ‘chair’,‘ lamp’ (block 2). The center object is never selected as a recall target because 0° is marked by sonar beeps and thus can be aimed at trivially. Task 3, direct navigation (Figure 3): In each trial, a single chair is placed at 2 m from the center of the arena at an azimuth angle randomly drawn from four possible choices: 0°, 90°, 180°, 270°. To start a trial, the subject must be in a starting zone of 1 m diameter in the center. During navigation, the subject can repeatedly press the Clicker to receive a spatialized call from the target. The trial completes when the subject arrives within 0. 5 m of the center of the target. Then the system guides the subject back to the starting zone using spatialized calls emanating from the center of the arena, and the next trial begins. Subjects performed 19–21 trials. All blind subjects moved freely without cane or guide dog during this task. To measure performance on a comparable search without CARA, each subject performed a single trial with audio feedback turned off. A real chair is placed at one of the locations previously used for virtual chairs. The subject wears the HoloLens for tracking and uses a cane or other walking aid as desired. The trial completes when the subject touches the target chair with a hand. All blind subjects used a cane during this silent trial. Task 4, long range guided navigation (Figure 4): The experimenter defined a guide path of ~36 m length from the first-floor lobby to the second-floor office by placing nine waypoints in the pre-scanned environment.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Note that the same PDZ domain 3D structure (α 1-syntrophin accession number 2PDZ) has been used in all three pictures since the structures of Lap4(2/4) and Lin7(1/1) from C. elegans and D. melanogaster have not been solved. The residue labels refer to the amino acid at the given position in Lap4(2/4) and Lin7(1/1) and do thus not correspond to the structure of the depicted amino acids. Residue numbering is according to 2PDZ. pdb. Bayesian P-valuesTo explain the Bayesian P-values used in the 6026. 1259. 10. 1038/nbt1017216. 127. 10. 1186/1471-2105-7-1271965. 10. 1016/j. patrec. 2008. 06. 018539. 10. 1038/nbt1397140. 10. 1038/msb4100180125. 10. 1186/1471-2105-9-1252000. Book 1178. 10. 1038/nbt0904-11772007. 1985. 651. 10. 1038/451648a679. 3555. 10. 1073/pnas. 0506230102272. 10. 1093/jnci/djj05264. 10. 1186/bcr1325178. 10. 1093/bioinformatics/bth469492. 10. 1016/S0140-6736(05)17866-0210. 10. 1093/nar/30. 1. 2073214. 10. 1158/1078-0432. CCR-06-27652268. 10. 1093/bioinformatics/btl362369. 10. 1126/science. 11445921045. 10. 1038/nbt. 1489859. 10. 1093/protein/11. 10. 855242. 10. 1093/nar/28. 1. 2351194. 10. 1093/bioinformatics/btp116203. 10. 1126/science. 1169377299. 10. 1126/science. 286. 5438. 2951809. 10.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: The preliminary test of thermostability carried out on its crude extract amylase showed that it produce a very high thermostable enzyme and it was selected for an application on simultaneous production of thermostable amylase and lactic acid from starchy material. Figure 1. Plate assays for detection of amylase activity of lactic acid bacteria (04BBA19, 26BMB81) on MRS-starch agar plate medium. The diameter of hydrolysis halo was revealed by flooding the plates with Iodine solution (0. 1% I2+1% KI) after 48 h of culture at 40°CTest number1234567891011121314151617181920212223242526272829Strains codHeterofermentativeGaz productionOptimum temperature of growthGrowth at 10°CDextranAmmonia from ArginineNitrate reductionGlycerolErythritolD-arabinoseL-arabinoseRiboseD-xyloseL-xyloseAdonitolß methyl-D-α-Methyl-D-mannosideα-Methyl-D-glucosideN-Acetyl-Glucosamine04BBA19++45°C--+-----+----++++---nc-----26BMB81-+40°C-------++----++++----++-++Test number303132333435363738394041424344454647484950515253545556Strains codß GentiobioseD-turanoseD-lyxoseD-tagatoseD-fucoseL-fucoseD-arabitolL-arabitolGluconate2-gh-Gluconate5-Keto-GluconateIdentification species (API 50CHL) 04BBA19--nc--++++---++----------+--Lactobacillus fermentum 26BMB81++++++++++nc++++-++------+--Lactobacillus plantarumTable 1. Biochemical characteristics of amylases overproducing Lactobacillus isolated from soils4. 2. Amylase and lactic acid productionIn the presence of starch as carbon source at 40°C, L.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: This approach, which the authors call 'targeted dimensionality reduction' (TDR), also yields one component per task parameter: in our example, we obtain one component for the stimulus and one for the decision (Figure 1h; see Materials and methods for details). Both of these approaches are supervised, meaning that they are informed by the task parameters. At the same time, they do not seek to faithfully represent the whole dataset and are prone to losing some information about the neural activities. Indeed, the two components from Figure 1e explain only 23% of the total variance of the population firing rates and the two components from Figure 1h explain only 22% (see Materials and methods). Consequently, a naive observer would not be able to infer from the components what the original neural activities looked like. While such supervised approaches can be extended in various ways to produce more components and capture more variance, a more direct way to avoid this loss of information is to resort to unsupervised methods such as principal component analysis (PCA). This method extracts a set of principal components (PCs) that are linear combinations of the original PSTHs, just as the population averages above. However, the weights to form these linear combinations are chosen so as to maximize the amount of explained variance (first six components explain 69% of variance, see Figure 1i–k). The principal components can be thought of as 'building blocks' of neural activity: PSTHs of actual neurons are given by linear combinations of PCs, with the first PCs being more informative than the later ones. However, since PCA is an unsupervised method, information about stimuli and decisions is not taken into account, and the resulting components can retain mixed selectivity and therefore fail to highlight neural tuning to the task parameters. The most striking observation when comparing supervised and unsupervised approaches is how different the results look. Indeed, PCA paints a much more complex picture of the population activity, dominated by strong temporal dynamics, with several stimulus- and decision-related components. At the same time, none of the methods can fully demix the stimulus and decision information: even the supervised methods show decision-related activity in the stimulus components and stimulus-related activity in the decision components (Figure 1e,h).

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Skip to main content Explore journals Get published About BMC My account Search all BMC 69. 92 the features "snoRNA," "scRNA," "snRNA," and "misc_RNA" were replaced by the new feature "ncRNA" and its mandatory qualifier "ncRNA_class. " The qualifier defines the kind of "ncRNA," and its value set is defined in an extensible form [11]. This suggests that further development in the area of non-coding RNA may be expected in the future. As new insights into MB are gained, the domain and its concepts quite often have to be viewed from a new perspective; sometimes new experimental or theoretical results shift even fundamental concepts. The term "concepts" means the notable entities in the domain, such as different kinds of molecules (and proteins) or special regions of molecules (genes or promoter). Software systems for MB must be sufficiently flexible to accommodate these changes in a reasonable amount of time; otherwise, the software soon becomes obsolete or its value diminishes [12]. Previous studies investigated extensibility as an important requirement to achieve flexibility for MB software; e. g. Jones and Paton [13]. Besides extensibility, changeability is equally required to achieve flexibility [14]. An approach often used to deal with the problem of flexibility is to consider only a small, well-defined area of the domain. By this means, knowledge changes are scarcer and, due to the lower program complexity, easier to implement. The drawback is an isolated domain understanding of these programs, which is very much dependent on the particular program's domain view. Furthermore, these programs very often use proprietary data formats. The explorative manner of biologists work is not well supported, and interoperability and data exchange are difficult to achieve. Performing large-scale "in-silico" analyses using several programs often requires an enormous amount of time and resources for interoperability. The challenge in developing software for MB is to satisfy both requirements appropriately: interoperability to support explorative work in MB, and flexibility to accommodate new knowledge in the domain. To meet this challenge, we approach the problem from the fundamental level of computer program development; namely, the domain model, also known as "conceptual model. " In object-oriented (OO) software development [15, 16], the domain model is a systematic description of entities in the subject domain and their relationships [14, 17].

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: fermentum 04BBA19 strain grew, exhibited amylolytic activity and produced lactic acid in the culture medium. The amylase production pattern in L. fermentum 04BBA19 (Fig. 2) indicates that the induction of amylase took place during the lag phase (after 10 h of incubation) in the presence of starch. The level of amylase production increased significantly during the exponential phase of growth. Lactic acid production became visible around 15 h after incubation and also increased considerably during the exponential phase of growth. Cell growth, amylase and lactic acid production reached maxima values at the same time (40 h of fermentation). The values of those maxima were 1. 1x109 cfu/ml, 107. 3±0. 5 U/ml, 8. 7±0. 5g/l for cell growth, amylase activity, and lactic acid production respectively. Such coincidence shows that amylase production by L. fermentum 04BBA19 was tightly linked to cell growth. These results are in agreement with the report of Goyal et al. [53], Liu and Xu [54] on the relationship between pattern of cell growth and amylase production. The decline of cell growth and amylase production after the peak occurred around 50 h of incubation and could be attributed to the rise of lactic acid concentration in fermented broth [6] or to the rise of protease levels [55]. The acidification was also expressed by the decrease of initial pH of culture broth (Fig. 2). The initial pH of culture broth declined significantly and reached a value of 3. 0 around 50 h of incubation and then remained constant. Figure 2. Time course of growth (○), pH (▽), α-amylase (▲) and lactic acid (□) production by L. fermentum 04BBA19 in 1% (w/v) soluble starch medium at 40°C, pH 6. 0. The data shown are averages of triplicates assays within 10% of the mean value. The study of cell growth and amylase production as a function of temperature (Fig. 3a) showed that L. fermentum 04BBA19 exhibited maximal growth and amylase activity at 45°C, confirming thus the strong relationships between cell growth and amylase production.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: Demixed principal component analysis (dPCA) To address these problems, we developed a modified version of PCA that not only compresses the data, but also demixes the dependencies of the population activity on the task parameters. We will first explain that these two goals generally constitute a trade-off, then suggest a solution to this trade-off for a single task parameter, and then generalize to multiple task parameters. The trade-off between demixing and compression is illustrated in Figure 2, where we compare linear discriminant analysis (LDA, Figure 2a,b), PCA (Figure 2c,d), and dPCA (Figure 2e–h). We will first focus on a single task parameter and seek to reduce the activity of N=2 neurons responding to three different stimuli. For each stimulus, the joint activity of the two neurons traces out a trajectory in the space of firing rates as time progresses (Figure 2b). The aim of 'demixing' in this simplified case is to find a linear mapping (decoder) of the neural activity that separates the different stimuli (Figure 2a) and ignores the time-dependency. We can use LDA in order to determine a projection of the data that optimally separates the three stimuli. However, LDA will generally not preserve the 'geometry' of the original neural activity: firing patterns for stimuli 1 and 2 are close to each other and far away from stimulus 3, whereas in the LDA projection all three stimuli are equally spaced (Figure 2b). More generally, decoding is always prone to distorting the data and therefore tends to impede a proper reconstruction of the original data from the reduced description. Figure 2 Download asset Open asset Linear dimensionality reduction. (a) Linear discriminant analysis maps the firing rates of individual neurons onto a latent component that allows us to decode a task parameter of interest. Shades of grey inside each neuron show the proportion of variance due to the various task parameters (e. g. stimulus, decision, and time), illustrating mixed selectivity. In contrast, the LDA component is maximally demixed. (b) At any moment in time, the population firing rate of N neurons is represented by a point in the N-dimensional space; here N=2. Each trial is represented by a trajectory in this space. Colors indicate different stimuli and dot sizes represent time.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: It reduces the gap between mental and software models, and it supports communication between domain experts and software developers [18]. The domain model is created in an early development phase, the analysis phase. It is the basis for data models created in later phases, such as data formats for data exchange and file storage, or the database model, or the class model for the domain layer. Changes of the domain model affect many parts of a software system; they are therefore costly and complicated and should be avoided. So, besides flexibility, stability is also very important for domain models in MB. A comprehensive domain model for MB could provide a shared domain understanding for a wide range of programs and therefore support interoperability. It is difficult to fulfill both requirements using commonly applied OO modeling approaches. In these approaches, domain concepts are modeled statically in class hierarchies with associations between classes. New or changed knowledge leads to changes in the domain model, which should be avoided, as mentioned above. Therefore, we present a different modeling approach, which has adapted the "Model" pattern [19]. This approach promises to fulfill both requirements of a comprehensive domain model for MB – flexibility and stability – in a balanced manner and therefore supports interoperability. ResultsApproachOverviewThe essential idea behind our approach is the separation of fundamental domain concepts from domain knowledge, as proposed by Beale [12]. Fundamental concepts define the scope of the subject domain, and hence the scope of the software. If fundamental concepts were to change, the subject of the domain and the software would have to change, too. Therefore, fundamental concepts constitute a stable core; changes would only affect domain knowledge. Following this idea, fundamental concepts were modeled in an abstract class model and domain knowledge was reflected in an object model. This approach utilizes the "Model" pattern [19], also known as "Model" [20]. This is a composed pattern and its core is the "Type Object" pattern [21]. We named abstract classes corresponding to fundamental concepts typed classes. Instances of typed classes get a more concrete meaning from a special attribute, called type attribute, which describes a concrete concept. In other words, instances of typed classes are parameterized by their type attributes. These type attributes constitute the model for the domain knowledge. They are instances of special classes, called type definition classes. The following example explains this approach: In classical OO models, different molecules such as and proteins are represented as classes in a class hierarchy (Figure 1a).

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: [63], Saxena et al. [4] who reported maximum amylase production when starch was used as carbohydrate source. In the presence of glucose and fructose, amylase production was almost nil; and that was a proof that glucose and fructose repressed amylase synthesis by L. fermentum 04BBA19. This observation is in agreement with the reports of Theodoro and Martin [64] showing that synthesis of carbohydrate degrading enzymes in some microbial species leads to catabolic repression by substrate such as glucose and fructose. Similar results were observed by Halsetine et al. [65] for the production of amylase by the hyperthemophilic archeon Sulfolobus solfataricus. According to them, glucose prevented α-amylase gene expression and not only secretion of performed enzyme. Since amylase yield is higher with amylose (92. 3 U/ml) as carbohydrate source than with amylopectin (50. 1 U/ml), the L. fermentum 04BBA19 amylase is more efficient for hydrolysis of alpha-1,4 linkages than those of alpha-1,6. The amylase production increased with the soluble starch concentration (Fig. 4), reaching a maximum (180. 5 ± 0. 3 U/ml) at the concentration range of 8-16 % (w/v). These optimum starch concentrations for amylase production by L. fermentum 04BBA19 are higher than that observed for amylase production in Bacillus sp. PN5 reported by Saxena et al. [4]. This microorganism presented an optimum soluble starch concentration of 0. 6% (w/v) for amylase production. The lactic acid production also increased with the soluble starch concentration, the optimum starch concentration for lactic acid production was achieved at the same range of concentration for amylase production. Figure 4. Effect of starch concentration on α-amylase (●) and lactic acid production () by L. fermentum 04BBA19. The data shown are averages of triplicate assays with SD within 10% of mean valueAmong the various gelatinized starchy sources tested, corn and sorghum flour were found to be the most suitable for α-amylase and lactic acid production by L.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The LDA component for stimulus is given by the projection onto the LDA axis (black line); projections of all points are shown along this line. All three stimuli are clearly separated, but their geometrical relation to each other is lost. (c) Principal component analysis linearly maps the firing rates into a few principal components such that a second linear transformation can reconstruct the original firing rates. (d) The same set of points as in (b) is projected onto the first PCA axis. However, the stimuli are no longer separated. Rather, the points along the PCA axis have complex dependencies on stimulus and time (mixed selectivity). The PCA axis minimizes the distances between the original points and their projections. (e) Demixed principal component analysis also compresses and decompresses the firing rates through two linear transformations. However, here the transformations are found by both minimizing the reconstruction error and enforcing a demixing constraint on the latent variables. (f) The same set of points as in (b) projected onto the first dPCA decoder axis. The three stimuli are clearly separated (as in LDA), but some information about the relative distances between classes is preserved as well (as in PCA). (g) The same data as in (b) linearly decomposed into the time effect, the stimulus effect, and the noise. (h) The dPCA projection from (f) has to be mapped onto a different axis, given by the dPCA encoder, in order to reconstruct the stimulus class means (large colored circles). The decoder and encoder axes together minimize the reconstruction error between the original data and the stimulus class means. The aim of compression is to find a linear mapping (decoder) that reduces the dimensionality and preserves the original data as much as possible (Figure 2c,d). Using PCA, we determine a projection of the data that minimizes the reconstruction error between the projections and the original points. In contrast to seeks to preserve the geometry of the neural activity, and thereby yields the most faithful reduction of the data (Figure 2d). However, the PCA projection does not properly separate the stimuli and mixes the time-dependency with the stimulus-dependency. The wildly different projection axes for LDA (Figure 2b) and PCA (Figure 2d) seem to suggest that the goals of demixing and compression are essentially incompatible in this example.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: In contrast, the presented modeling approach uses classes as well as instances (Figure 1b): Molecule is a typed class and MoleculeType is a type definition class. Different molecule types are represented by instances of MoleculeType, not by subclasses of Molecule (see the object diagram in Figure 1b). These instances are type attributes of Molecule instances. By using this approach, new concepts can be introduced into the model without changing the class model. Figure 1Comparison of the classical and the proposed modeling approach. A) Classical OO-class model of different kinds of molecules. B) Model of different kinds of molecules following the proposed approach. On top, the class model with typed class Molecule and its type definition class MoleculeType. At the bottom, the object model that defines concrete kinds of molecules, instances of MoleculeType. They are used by instances of the typed class Molecule. Full size imageFollowing this approach, we realized that four models can be distinguished on different abstraction levels (Figure 2):Figure 2Four different models. The presented approach leads to four different models: operational model, meta model of the knowledge model (meta model), knowledge model, and information model. The operational model and meta model are OO-class models. The knowledge model consists of instantiated classes of the meta model. The information model consists of instantiated classes of the operational model that are parameterized by elements of the knowledge model. Elements of the operational and information model are shown in red. Elements of the meta model and knowledge model are depicted in blue. Instances are given in a lighter color. Full size image The operational model defines the scope of the domain model. It contains the fundamental abstract domain concepts. It is a classical OO-class model that consists of the typed classes. The knowledge model defines concrete concepts in the area of interest; these concepts and their relations are subject to changing knowledge. The elements of the knowledge model are instances of type definition classes that can be created and changed dynamically. They are used as type attributes in the operational model. The meta model of the knowledge model defines the structure of the knowledge model. It joins concrete concepts defined in the knowledge model with abstract concepts of the operational model. Classes in the meta model of the knowledge model are type definition classes. This model is therefore an OO-class model. The information model contains application data from the developed system.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: fermentum 04BBA19 while for the raw starchy sources tested, potato starch was most suitable (Table 2). On the other hand the level of lactic acid was more important when corn and sorghum flours were used. The good production of α-amylase and lactic acid when these starchy flours are used is based on their composition; they also contain proteins and vitamins which are required by lactic acid bacteria for their growth, enzymes and acids production [66]. Among nitrogen sources used in the present study, soya bean meal and yeast extract showed significant effect on α-amylase and lactic acid production. Soya bean meal, rich in protein is a potential nutrient for lactic acid fermentation. Similar results were obtained by several authors. Goyal et al. [53] reported that soybean meal presented a positive effect and was the best nitrogen source for raw starch digesting thermostable α-amylase production by the Bacillus sp I-3 strain. The yeast extract was also reported to be a potential nutrient for lactic acid fermentation, since it contains vitamins, amino acids [66]. Though all nitrogen sources are positively influencing enzyme production by L. fermentum 04BBA19, an inverse behaviour has been observed with other bacterial strains, for instance, Tanyildizi et al. [67] reported zero effect of yeast extract on amylase production by Bacillus sp. All metal salts tested in this study increased amylase and lactic acid production by L. fermentum 04BBA19, except CuSO4. 5H2O that acted as inhibitor. The inhibition of amylase production by CuSO4. 5H2O was also reported by Wu et al. [68] for the Bacillus sp CRP strain. Copper ion acted as poisonous compound for this strain and consequently inhibited amylase synthesis. The effect of CaCl2. 2H2O was the most important, and was in agreement with the observation of Gangadharan et al. [61] who described the rise of amylase production by B. amyloliquefaciens when CaCl2. 2H2O was supplemented to the culture medium. The supplementation of metal ions has been reported to provide good growth and also influence higher enzyme production.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: However, we can achieve both goals by assuming that the reconstruction of the original data works along a separate encoder axis (Figure 2f,h). Given this additional flexibility, we first choose a decoder axis that reconciles the decoding and compression objectives. Once projected onto this axis, all three stimuli are separated from each other, as in LDA, yet their geometrical arrangement is approximately preserved, as in PCA (Figure 2f). In turn, when reconstructed along the encoder axis, the projected data still approximates the original data (Figure 2h). To define these ideas more formally, we assume that we simultaneously recorded the spike trains of N neurons. Let 𝐗 be our data matrix with N rows, in which the i-th row contains the instantaneous firing rate (i. e. binned or smoothed spike train) of the i-th neuron for all task conditions and all trials (assumed to be centered, i. e. , with row means subtracted). Classical PCA compresses the data with a decoder matrix 𝐃. The resulting principal components can then be linearly de-compressed through an encoder matrix 𝐃⊤, approximately reconstructing the original data (Hastie et al. , 2009). The optimal decoder matrix is found by minimizing the squared error between the original data, 𝐗, and the reconstructed data, 𝐃⊤𝐃𝐗, given by LPCA=∥X−D⊤DX∥2. In the toy example of Figure 2, the data matrix 𝐗 is of size 2×15, and the decoder matrix 𝐃 is of size 1×2. Crucially, the information about task parameters does not enter the loss function and hence PCA neither decodes nor demixes these parameters. In our method, which we call demixed PCA (dPCA), we make two changes to this classical formulation. First, we require that the compression and decompression steps reconstruct not the neural activity directly, but the neural activity averaged over trials and over some of the task parameters.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: It is not developed in any phase of the software development process, but rather it originates during the execution of an implemented system. It consists of instances of classes from the operational model, which use type attributes. The names "operational model" and "knowledge model" are inspired by the terms "operational level" and "knowledge level," used by Fowler in [22]. Beale used the terms "knowledge level" and "information level" in [12]; the latter term was the inspiration behind the naming of the "information model. "Properties of knowledge model conceptsConcrete concepts usually define properties. For example, double-stranded DNA has a melting temperature and a GC ratio, and proteins can have annotations describing their function. In our approach, properties cannot be modeled as instance attributes of the typed class. This is because properties are not shared by all kinds of concepts; every concept has its own set of properties. To meet this challenge, we integrated properties using a generic property model (Figure 3). The basis of this model is the "Property" pattern [23]. Figure 3Modeling properties. Properties of concrete concepts are held by the typed class Property. Instances of its type definition class PropertyType (gcType, mtType, and funcType) specify the meaning of properties with their attribute name. The attributes valueType and cardinality restrict the data type and number of hold values. PropertyHolder and PropertyRegistry support the handling and definition of property sets. The PropertyHolder aDnaMol holds the Property gcProp of type gcType; the optional Property of type mtType is not set. Full size imageThe property model defines kinds of properties by instances of the type definition class PropertyType. These instances are elements of the knowledge model (the object diagram at the bottom right of Figure 3) and represent concrete kinds of properties (mtType, gcType, and funcType). Instances of the typed class Property (mtProp and funcProp) represent property values according to its PropertyType. Domain concepts usually have several properties; they have a property set. These sets are defined by instances of the type definition class PropertyRegistry (dnaPropReg and proteinPropReg in Figure 3). These instances define property sets for concrete concepts, they are also elements of the knowledge model.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: Most α-amylases are metalloenzymes and in most cases, Ca2+ ions are required for maintaining the spatial conformation of the enzyme, thus play an important role in enzyme stability [61]. From the surfactants tested in this study, Tween-80 appeared to be the best surfactant sources for amylase production by L. fermentum 04BBA19. Similar results were obtained by Reddy et al. [69]. These authors reported that the supplementation of culture medium with Tween-80 resulted in a marked increase in the yields of thermostable β-amylase and pullullanase by C. thermosulfurogenes SV2, and that the stimulation of enzyme production was greater when the surfactants were added after 18 h of incubation of culture. Beside stimulation, the surfactants caused and increased secretion of the enzymes into extracellular fluid [59]. From various environmental factors tested for α-amylase and lactic acid production by L. fermentum 04BBA19, it has been observed that all factors that increase amylase synthesis also positively affect lactic acid production. The optimization of the basal medium by supplementation of all carbohydrate, nitrogen, mineral and surfactant sources (excepted CuSO4. 5H2O4) in culture medium resulted to a significant improvement of enzyme and lactic acid yield. In the optimized medium, amylase activity and lactic acid content reached 732. 4±0. 4 U/ml and 53. 2±0. 4 g/l respectively. ParametersEnzyme yield (U/ml)Lactic acid(g/l)Carbohydrate sources (1% w/v)Glucose0. 1±0. 0d*14. 3±0. 5aFructose0. 2±0. 0d12. 1±0. 5bMaltose0. 1±0. 0d12. 8±0. 4bAmylose92. 3±0. 1b12. 2±0. 1bAmylopectin50. 1±0. 5c10. 3±0. 5cSoluble starch107. 3±0. 5a8. 7±0. 5dNitrogen sources (1. 5% w/v)Yeast Extract107. 3±0. 5b8.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: For each neuron i, regress its firing rate at each time point t on stimulus, decision, and interaction between them: xi(t)=βio(t)+βis(t)s+βid(t)d+βisd(t)sd+ϵ, where d is any suitable parametrization of decision, e. g. d= ± 1, and s is stimulus value (e. g. actual stimulation frequency in the somatosensory working memory task in monkeys). values of βis(t) as defining an N-dimensional vector 𝜷s(t), and analogously define 𝜷d(t), 𝜷sd(t), and 𝜷o(t). Mante et al. (2013) did not use the condition-independent term 𝜷o(t) in their original study, but we treat it here on equal footing. Project each 𝜷ϕ(t) onto the space spanned by the leading PCA axes: 𝐊𝜷ϕ(t). For each of the three parameters select one vector 𝜷*ϕ=𝐊𝜷ϕ(t*ϕ), where t*ϕ is the time point at which the norm of 𝐊𝜷ϕ(t) is maximal. Finally, stack the obtained vectors to form a matrix 𝐁=[𝜷*s𝜷*d𝜷*sd𝜷*o] and perform QR decomposition 𝐁=𝐐𝐑 with 𝐐 orthonormal and 𝐑 upper triangular to find a set of three orthogonal demixing axes (as columns of 𝐐). If the individual vectors in 𝐁 are far from orthogonal, then the resulting axes will strongly depend on the order of stacking them into the matrix 𝐁.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: The corresponding typed class for PropertyRegistry is PropertyHolder, which is a container class to manage instances of Property according to the PropertyRegistry. The property model is integrated into our domain model using inheritance (see "Modeling properties of concrete concepts"). If inheritance might cause problems, delegation can be used instead: For example, when the class that should have properties is the subclass of another class, delegation would avoid problems that multiple inheritance could imply [24]. Associations between concrete conceptsDomain objects are often related to other domain objects. For example, a promoter can be involved in gene regulation. To support relationships, Riehle suggests the "Objects" [19] pattern. It is a combination of "Association Classes" [22, 25] (classes that describe associations) and the "Type Object" pattern. "Objects" are "Association Classes" that have a type attribute; in our terms, they are typed association classes. On the basis of "Objects," we have developed a model for associations between typed classes (Figure 4). Figure 4Modeling relations. A relation model realizes relations between concrete concepts defined in the knowledge model. Relation is a typed association class that holds 8. Figure 8Example of the usage of properties. The property framework is integrated into the operational and meta model by inheritance. This is exemplified here by the Molecule class. Full size imageOverview of the operational modelThe preceding sections explained parts of the operational and the meta model in combination. The next two sections will explain both models separately to clarify their content and responsibility. This section describes the operational model (Figure 9). Figure 9Operational model. Full size imageMolecules and special regions within these molecules are the relevant concepts in the domain. They define the scope and have to be contained in the operational model. Both are represented by typed classes, Molecule and MoleculeSegment. Relations between molecules and special regions are also in the focus. They are realized by the relation model described in "Associations between concrete concepts. " The operational model contains the typed class Relation for this reason. Instances of these typed classes have to hold type specific properties. Therefore, Molecule, MoleculeSegment, and Relation are subclasses of PropertyHolder. The linear molecule structure is represented by the class Sequence, while the class MoleculeElement represents the monomers. The class Location specifies the position of a special region within a molecule.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: 5bOptimized medium732. 3±0. 453. 2±0. 4aTable 2. Effect of different parameters on α-amylase and lactic acid production by L. fermentum 04BBA19 in submerged state fermentation at 45 °C and initial pH 6. 5. The basal medium contained soluble starch, 1% (w/v); yeast extract, 0. 5 % (w/v); while the optimized medium contained all parameters without CuSO4. 5H2O. The data shown are averages of triplicate assays with SD within 10% of mean value. For each group of parameters (Carbohydrate, Nitrogen, Mineral, Starchy sources, media), means with different superscripts within columns are significantly different (p<0. 05). 4. 4. Enzyme propertiesThe amylase produced by L. fermentum 04BBA19 showed high affinity toward cassava raw starch granules with 80% adsorption and brought about 79% hydrolysis of 1% (w/v) suspension of raw cassava starch. On the other hand, the enzyme was able to hydrolyze blocked p-nitro phenyl methyl heptaoside, releasing a yellow compound (p-nitro phenol) with maximum absorption at 530 nm. This result was a proof that amylase from L. fermentum 04BBA19 is an endo acting amylase (α-amylase), since the blocked p-nitro phenyl methyl heptaoside is known to be hydrolysed only by endo-acting amylases [38]. The enzyme exhibited maximum activity at 60-70°C and maintained 100% of its initial activity at 80°C for 30 min of heat treatment (Fig 5-a). When the enzyme was treated for the same time (30 min. ) at 90°C and 100°C, the remaining activities were 90 and 87% respectively. These results showed the thermophilic character and very high thermostability of α-amylase from L. fermentum 04BBA19. In general, most of lactic acid bacteria do not produce amylases. However, this property have been observed in some genera of lactic acid bacteria, especially in L. plantarum and L. amylovorus [37], L.

Output: corroborate


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

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Member holds 2. 3 [34, 38] and applies the notation "term-ID = term name" to reference ontology terms: The term "SO:0000110 = sequence_feature" is identified as a key concept in the ontology and is mapped to the class SegmentType. This term serves as an entry point, and according to this term, an instance of SegmentType is created and inserted into the knowledge model. The concept id "SO:0000110" is used as the name attribute for the new instance. All ontology terms with a direct or indirect "is-a" relation to the term "SO:0000110 = sequence_feature" are searched by reasoning. Every term contained in the result forces to insert a corresponding instance of SegmentType into the knowledge model. For example, the term "SO:0000001 = region" has a direct "is-a" relation to "SO:0000110 = sequence_feature" and is inserted into the knowledge model. The term "SO:0000833 = transcript_region" has an indirect "is-a" relation to "SO:0000110 = sequence_feature". This means, there is a path from "SO:0000110 = sequence_feature" over "SO:0000001 = region" to "SO:0000833 = transcript_region". Therefore, "SO:0000833 = transcript_region" is inserted as a SegmentType into the knowledge model. This strategy permits the derivation of the knowledge model from ontologies, and "is-a" relations (but also other relations) can now be expressed in the knowledge model by using reasoning. Current usage of ontologies and their relevance for deriving the knowledge modelOntologies are used extensively in current MB software and our approach can benefit from experiences made there. This section provides a short overview of current ontology usage, relevant to our approach. project [39] is an example for ontology usage to mediate between different domain models. A mediator component and several database wrappers use the ontology to provide transparent access to heterogeneous databases. strategy is to perform a mapping between the terms of the heterogeneous databases and the ontology terms. usage may be convenient for the user. However, due to continuous database development, the wrapper maintenance will be very labor intensive. On the other hand, the ontology proves the possibility of a comprehensive domain ontology.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: manihotivorans [70, 13], L. fermentum OGI E1 [38]. But amylases produced by these strains are not thermostable. Traditionally high thermostable and thermophiles amylases are found in Bacillus and Thermococcus genera as: B. amyloliquefaciens [71]; B. licheniformis [72]; B. stearothermophilus [73]; B. subtilis and T. aggreganes [74], T. profundus [75], Bacillus sp PN5 [4], B. cohnii US147 [35], Chromohalobacter sp. TVSP 101 [76]. Fig. 6 shows the thermostability pattern of α-amylase from L. fermentum 04BBA19 at 80°C, 90°C and 100°C when the time of heat treatment is beyond 30 min. Table 3 presents the thermal inactivation rate constant (ki) and half-life (T) at these temperatures. The half-life of this enzyme is higher than that of α-amylase from B. licheniformis: 120 min at 70°C [76]. The thermal stability was considerably improved by addition of 0. 1% (w/v) CaCl2. 2H2O. Goyal et al. [53] obtained a half-life value of 3. 5 h at 80°C with α-amylase from Bacillus sp. I-3 in the presence of 0. 1 % (w/v) calcium chloride, while under the same conditions; α-amylase from L. fermentum 04BBA19 displayed a half-life of 6. 1 h. Due to its high thermostability, α-amylase from L. fermentum 04BBA19 could be highly competitive in industrial bioconversion reactions, as compared to α-amylase from Bacillus. In addition, this competitiveness is enhanced by the fact that lactobacilli, due to their non-pathogen character, are easily used in food industry [6]. The L. fermentum 04BBA19 α-amylase is active and stable in pH range of 4. 0 – 7. 0 (Fig. 5-b), which is the pH range of many foods.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: In this case, the population response lies in the one-dimensional subspace spanned by 𝐚 and the covariance matrix has rank one: 𝐂=⟨𝐲(t)𝐲⊤(t)⟩t=𝐚𝐚⊤⟨z(t)2⟩t. Now consider the case in which the neurons are not recorded simultaneously but are pooled from different sessions. In behavioural experiments it is unavoidable that the onset of (self-timed) neural responses will vary by tenths or hundreds of milliseconds. Hence, the individual response yi(t) of neuron i will experience a small time-shift τi so that yi(t)=aiz(t+τi), see Figure 15 for an example with Gaussian tuning curves. If τi is small we can do a Taylor expansion around t, yi(t)=aiz(t)+aiτiz′(t)+𝒪(τi2). Figure 15 Download asset Open asset Fourier-like artifacts in PCA. (Left) In this toy example, single neuron responses are generated from the same underlying Gaussian but are randomly shifted in time. (Right) First three PCA components of the population data. While the leading component resembles the true signal, higher order components look like higher Fourier harmonics. They are artifacts of the jitter in time. where we neglect higher-order corrections for simplicity, but the extension is straight-forward. Let 𝝉 be the vector of time-shifts of all neurons and let 𝐛=𝐚∘𝝉 be the element-wise vector product of 𝐚 and 𝝉, i. e. [𝐚∘𝝉]i=aiτi. Then the population response can be written as y(t)≈az(t)+bz′(t). Hence, the covariance matrix becomes approximately C≈aa⊤⟨z2(t)⟩t+bb⊤⟨z′2(t)⟩t, where we assumed for simplicity that 𝐚⟂𝐛.

Output: contrast


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: Another approach is to integrate ontologies into data models, e. g. , FuGE [40] and MAGE-OM [41]. In both models, classes, representing domain objects, use associations to the class OntologyTerm, that represents ontology terms. For example, to specify the experimental equipment (e. g. , a sequencer) more precisely, the FuGE model uses the association "equipmentModel" between the Equipment and OntologyTerm classes to specify the equipment product name. The approach allows the flexible integration of ontological terms into the models. Both models use this mechanism to specify domain entity types or generic properties. At first glance, this is similar to the type attributes and generic properties presented here. However, the models do not constrain the set of allowed properties. Our approach for deriving the knowledge model from ontologies is a further development of the FuGE and MAGE-OM approach. Another approach to utilize ontologies in domain modeling is to derive a class model from an ontology [42]. The approach defines transformation rules, which, for example, map concepts to classes and relations to associations. These rules allow the automatic conversion of ontologies into object-oriented models. However, a static class model is generated. Such a model is too inflexible to serve as a MB domain model. Changes in the ontology would require new transformations and result in new domain models. In contrast, our approach creates a dynamic representation of ontology concepts; it is a similar approach but enhances the flexibility significantly. Outlook and current workIntegration of data processing componentsAnother aspect of our current investigation are extension points that allow the integration of extended data processing components. These components should be seen as algorithms, formal described by type attributes. The description includes the I/O ports and configuration parameters of the algorithms, see section "Consequences for the structure of software components. " This description provides enhanced capabilities in using data processing components. For example, the description of I/O ports allows an easy and semantically correct combination of processing components. Together with the formal description of exchanged data, one can ensure semantic compatibility. In this way, users will be able to join data processing components safely and create their own computing pipelines. ConclusionOur aim was the development of a domain model for linear biological macromolecules that can easily integrate new or modified domain concepts and nevertheless provide a stable basis for implementation models.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: 2H2O. The main ALAB that have been isolated for the past decade are summarized in Table 4. No study has dealt with the thermostability of their amylases, except the case reported by Aguilar et al. (2000) concerning the properties of the extracellular amylase produced by L. manihotivorans LMG 18010T. This strain produced an amylase with a moderate themostability exhibiting maximum activity at 55°C. The strain L. fermentum 04BBA19 appears as the first ALAB producing highly thermostable amylase. The potential industrial application of this strain could be the bioconversion of inexpensive raw material as starch into lactic acid in single step process. On the other hand this strain and its amylase are potential candidates for food industries (making of high density gruels, baking, brewing) and for the production of biodegradable plastic from starchy raw material. Figure 7. Effect of metal salts and EDTA on the activity of α-amylase from L. fermentum 04BBA19. The data shown are averages of triplicate assays with SD within 10% of mean value. BacteriaStrains4. The main amylolytic lactic bacteria strains isolated during the past two decade Advertisement 5. ConclusionL. fermentum 04BBA19 which is a soil isolate produced very high thermostable α-amylase. This is the first study dealing with high thermostable amylase from a lactic acid bacterium. According to its properties, this enzyme is a good candidate for starch hydrolysis at high temperature. An economical process could be attained through the use of this enzyme at the liquefaction stage at high temperatures. On the other hand the fact that thermostable amylase and lactic acid production can be combined in single fermentation step would not only provide a way to make gruels with high energy density, but also improve its safety, since lactic acid bacteria fermentation is an efficient way to inhibit food-borne pathogens. Owing to the importance of this finding, further studies will focus on the development of an accurate method for preparing high energy density complementary food using local starchy sources and the L. fermentum 04BBA19 strain.

Output: corroborate


Input: Claim: Memory can be perfectly recalled with the right stimulus. [SEP] Text: The cross correlation functions are very similar between high (red) and low (blue) variance epochs. (f) Firing rate lag with respect to the stimulus, estimated from the location of the peak cross-correlation, vs. standard deviation of the stimulus. No significant change in lag was observed (p=0. 133, t-test). Figure 8 with 2 supplements Download asset Open asset A modified two state receptor model reproduces Weber’s Law and adaptive slowdown in LFP responses. (a). Or-Orco complexes (C) can be bound or unbound and active or inactive. (b) We assume (un)binding rates are much faster than (in)activation rates. Activity of the complex feeds back onto the free energy difference between active and inactive conformations, which also decreases the activation and inactivation rates of the complex (Equations 1–4). A mono-lobed filter converts receptor activity into LFP signals (Equation 5). We fit the model to Gaussian (Figures 3 and 5) and naturalistic data (Figures 1–2). In these fits, α=12. 5 s−1, β=1. 26 s−1, εL=0. 86, Kon=0. 1 V and Koff=400 V. (c) Model gain vs. mean stimulus. Red line is the Weber-Fechner prediction (ΔR/ΔS∼1/S). (c) LFP gain vs. model gain. (e) Model response lag with respect to stimulus vs. mean stimulus. (f) LFP and model responses to naturalistic stimulus. (g) The model reproduces LFP responses to similar-sized whiffs that vary inversely with the size of preceding whiffs. (cf. Figure 2). (h) LFP responses vs. model responses for every whiff in the naturalistic stimulus. Figure 8—figure supplement 1 Download asset Open asset Steady state activity as a function of the stimulus background. At high stimulus background, the steady state activity of the receptor complex is a0 (here, 1/2). The model is unable to adapt perfectly to lower stimulus backgrounds, since ε is bounded by εL. This causes the steady state activity to decrease.

Output: pass


Input: Claim: Bioinformatics is crucial for analyzing genomic data. [SEP] Text: We achieved this through the strict separation of two model parts, the operational model and the knowledge model. These parts are connected by the meta model of the knowledge model to the whole system model. We validated our model against the EMBL standard and by implementing an MB framework (MCK) whose data structures are based on the model. We demonstrated that our model is able to integrate new domain concepts without changes to the operational model and therefore provides a stable interface for software programs. General use of the design pattern "Model" and its related patterns is the key to creating changeable, high-quality software solutions for MB. In the future, an integration of domain-specific ontologies will enrich the model and the modeling approach presented here even further; for example, in the form of enhanced expressiveness and automatic generation of knowledge models. The modeling approach allows the development of information systems for MB that can accommodate changes in the domain in a fast and flexible manner. This approach also provides the expressiveness and flexibility that is needed to develop a widely used domain model of MB, one which allows a shift to a global view of the domain and supports interoperability on a new level. Availability and requirementsOur models and the MCK are available at MCK requires Development Kit, version 1. 6 or higher, and Eclipse 3. 4 128. 714. 1306. 68. 120. 5. 4. 4. 12. 7. htm]Beale T: Archetypes: Constraint-based domain models for future-proof information systems. ; 4–8 November 2002; by: Baclawski K, Kilov H. 2002, 16–32. 235. 2005. 1999. 1992. 2006. 2004. 5. Edited by: Manolescu D, Völter M. Noble J: Addison-Wesley; 2005:3–24. 60. 3. Addison-Publishing Co. , Inc; 1997:47–65. 1997. 1997. 2. 0[ B: Components. Computer 1999, 32: 139–140. 2002. 2003. 384. 216. 290. 29. 274. 2005. , 6: 658. 90. 520. 2. 3[ : to Information Sources. Bioinformatics 2000, 16: 184–186. 1133. 0046. 0041-research0046. 0049. 2002. 2.

Output: pass


Input: Claim: Lactic acid bacteria are important for fermentation processes. [SEP] Text: 2008Enzyme activities of lactic acid bacteria from a pearl millet fermented gruel (ben-saalga) of functional interest in nutrition. Int. J. of Food Microbiol. 1283954009. GiraudE. LelongB. RaimbaultM. 1991Influence of pH and initial lactate concentration on the growth of Lactobacillus plantarum. Biotechnol 3696910. Morlon-GuyotJ. GuyotJ. P. PotB. Jacobe deHaut. I. RaimbaultM. 1998Lactobacillus manihotivorans sp. Nov. , a new starch-hydrolysing lactic acid bacterium isolated during cassava sour starch fermentation. Int. J. Syst. Bacteriol. 481101110911. AgatiV. J. P. GuyotJ. Morlon-GuyotP. TalamondHounhouigan. D. J. 1998Isolation and characterization of new amylolytic strains of Lactobacillus fermentum from fermented maize doughs (mawe and ogi) from Benin. J 855122012. SanniA. Morlon-GuyotJ. GuyotJ. P. New efficient amylase-producing strains of Lactobacillus plantarum and L. fermentum isolated from different Nigerian traditional fermented foods. 200272536213. GuyotJ. P. CalderonM. Morlon-GuyotJ. P. 2000Effect of pH control on lactic acid fermentation of starch by Lactobacillus manihotivorans LMG 180010T. J. Appl. Microbiol. 8817618214. Ji, GE. , 1992Isolation of amylolytic Bifidobacterium sp. Int57. J. Microbiol. Biotechnol. 2859115. 1997Purification of amylase secreted from Bifidobacterium adolescentis. J. Appl. Microbiol. 8326727216. . Parka KH, In K,. Jib GE1999Fermentation of rice using amylolytic Bifidobacterium. 5015516117.

Output: corroborate