Miracle-dz/UniFAR-FTUs huggingface dataset:
Functional Role of YnfA, an Efflux Transporter in Resistance to Antimicrobial Agents in Shigella flexneri

Shigella flexneri has become a significant public health concern accounting for the majority of shigellosis cases worldwide. Even though a multitude of efforts is being made into the development of a vaccine to prevent infections, the absence of a licensed global vaccine compels us to enormously depend on antibiotics as the major treatment option. ABSTRACT Shigella flexneri has become a significant public health concern accounting for the majority of shigellosis cases worldwide. Even though a multitude of efforts is being made into the development of a vaccine to prevent infections, the absence of a licensed global vaccine compels us to enormously depend on antibiotics as the major treatment option. The extensive-unregulated use of antibiotics for treatment along with natural selection in bacteria has led to the rising of multidrug-resistance Shigella strains. Out of the various mechanisms employed by bacteria to gain resistance, efflux transporters are considered to be one of the principal contributors to antimicrobial resistance. The small multidrug-resistance family consists of unique small proteins that act as efflux pumps and are involved in extruding various antimicrobial compounds. The present study aims to demonstrate the role of an efflux transporter YnfA belonging to the SMR family and its functional involvement in promoting antimicrobial resistance in S. flexneri. Employing various genetic, computational, and biochemical techniques, we show how disrupting the YnfA transporter, renders the mutant Shigella strain more susceptible to some antimicrobial compounds tested in this study, and significantly affects the overall transport activity of the bacteria against ethidium bromide and acriflavine when compared with the wild-type Shigella strain. We also assessed how mutating some of the conserved amino acid residues of YnfA alters the resistance profile and efflux activity of the mutant YnfA transporter. This study provides a functional understanding of an uncharacterized SMR transporter YnfA of Shigella.
Variability of Primary Sjögren's Syndrome Is Driven by Interferon‐α and Interferon‐α Blood Levels Are Associated With the Class II HLA–DQ Locus

Primary Sjögren's syndrome (SS) is the second most frequent systemic autoimmune disease, affecting 0.1% of the general population. To characterize the molecular and clinical variabilities among patients with primary SS, we integrated transcriptomic, proteomic, cellular, and genetic data with clinical phenotypes in a cohort of 351 patients with primary SS.
Genetic-Algorithm-Assisted Self-Scheduled Multidelay PIR Control: Experiments in a Car-Like Vehicle System

The path-tracking control of an intelligent vehicle always suffers from the high-frequency measurement noises. To confront this issue, this work puts forward a novel delayed output-feedback implementation of proportional-integral-derivation (PID) control, which is called multidelay proportional-integral-retarded (PIR) control. The mathematical model of the vehicle system is represented in the form of a linear parameter-varying (LPV) system, which uses the car position as the scheduling variable for regulation. On this basis, the multidelay PIR controller is designed such that the tracking errors gradually converge to zero with the aid of the proportional and integral actions, and the harmful high-frequency measurement noises are attenuated by the retarded term consisting of a few delayed proportional actions. To tune the PIR controller parameters, linear matrix inequalities (LMIs), derived by applying Taylor’s expansion to the retarded term, are used to compute the convex subcontroller gains. Then, the self-scheduled tracking controller is formulated as the weighted sum of convex subcontrollers, and the weight functions scheduled by the current position are adaptive to the different operational conditions. Experiments in real time using a laboratory car-like vehicle are employed to assess the performance of the proposed controller.
Loss of Novel Diversity in Human Gut Microbiota Associated with Ongoing Urbanization in China

Previous studies have reported the differences in human gut microbiota across populations of different urbanization levels, but most of the studies focused on populations across different geographic regions. In this study, we analyzed the impact of ongoing urbanization in neighborhoods within the same geographic region. ABSTRACT Recent rapid and large-scale urbanization has had a profound impact on human lifestyles and is associated with an increased risk of many diseases. Recent studies have revealed large differences in the human gut microbiota across populations in countries at different stages of urbanization. However, few studies have analyzed the impact of ongoing urbanization within the same geographic region. In this study, we sampled 214 participants in communities of different urbanization levels within two provinces of China and reconstructed draft prokaryotic genomes with metagenome sequences. The genomes were clustered into 447 species-level operational taxonomic units (OTUs), among which 196 did not have genomes in public reference databases according to the GTDB-Tk pipeline. The novel OTUs comprised 19.1% abundance in rural participants and 16.0% in urban participants, increasing the proportion of classified reads from 47.6% to 65.3% across all samples. Among the unknown OTUs, 26 OTUs present in rural samples were absent in urban participants, while 70 unknown OTUs were more abundant in rural than urban participants, suggesting potential loss and growth suppression of novel human symbionts during urbanization. Moreover, there were a higher number of genes, especially transporters, identified in genomes assembled from urban samples. This change in gene functionality indicates that urbanization not only altered the community structure of the human gut microbiota but also impacted its functional capacity. Taken together, these data show a dramatic change in the microbiota with urbanization and suggest the importance of cataloging microbial diversity from rural populations while these communities still exist. IMPORTANCE Previous studies have reported the differences in human gut microbiota across populations of different urbanization levels, but most of the studies focused on populations across different geographic regions. In this study, we analyzed the impact of ongoing urbanization in neighborhoods within the same geographic region. By assembling shotgun metagenome sequences, we reconstructed prokaryotic genomes from human gut microbiota and found that the novel bacterial OTUs were less abundant and less prevalent in urban participants than in rural participants, indicating potential loss and suppression of novel human symbionts during urbanization. Genes, including transporters and antibiotic resistance genes, were enriched in genomes of urban origins, suggesting change in functional potential of the microbiota. These findings suggest the significant influence of urbanization on human gut microbiota and the necessity of exploring the microbial diversity of rural populations.
Can oracle-based imitation learning improve neural machine translation with data aggregation?

Through globalization of the industry and the collaboration of nations world wide the importance of machine translated documents is rising. Many new ideas and concepts were proposed in the recent years to improve the overall quality of machine translation (MT). A lot of focus in machine learning is going towards the research of alternative learning techniques, as the basic existing paradigms such as supervised, unsupervised and reinforcement learning are not a perfect fit for every task. Imitation learning is a technique which combines the exploratory aspect of reinforcement with the efficiency of supervised learning. Through the usage of an interactive expert, the learning model, also called student, is able to obtain intermediate feedback for it’s predictions at any given point in time. Thereby the student is aware of it’s mistakes by considering the difference of it’s and the expert’s prediction from this point. Imitation learning can improve all major problems for training artificial neural networks given the right expert. These are: the time it takes to train a model, the acquisition of data and most importantly the overall performance given some specific metric. The most common quality evaluation metric used for MT is BLEU. It is based on the n-gram precision between the generated translation, given some input, and the reference translation. Therefore it is non-differentiable and can not be used as a loss to train a MT model directly. For the training usually the maximum likelihood estimation (MLE) is used, such that the likelihood of each token in the output, given the input sequence, is maximized. This creates a discrepancy between training (MLE) and validation objective (BLEU). This thesis tries to overcome this issue by directly learning on the differences of the expected BLEU from the student and the expert in an imitation learning scenario. The expert is represented by a traditional statistical machine translation (SMT) model that should help the student in solving the problems mentioned above. For this a novel data aggregation method Aggregate Data using approximated BLEU explorations (ADBLEU) based on imitation learning was implemented. After conducting several experiments, validating different approaches of data aggregation, it is shown that it is not possible to significantly improve the state-of-the-art student, due to limitations of the SMT expert.
Application of Data Mining Techniques for Classification of Traffic Affecting Environments

This research studied different data mining techniques use for classifying data on traffic volume and factors influencing traffic. The data that was analyzed consisted of 31, 147 records from the westbound traffic data volume of MN DOT ATR station 301, which is roughly midway between Minneapolis and St Paul, MN . The data that was retrieved from the UCI Machine Learning Repository were from 2014 to 2018. The studies showed that the Decision Tree (DT) is the technique that provides the highest accuracy for data classification at 79.79 percent, followed by the k Nearest Neighbor ( KNN), with accuracy at 72.79 percent with k = 1, and finally, the Support Vector Machine (SVM) with accuracy at 59.54 percent. Additionally, DT can identify that time data, which is an essential factor affecting the traffic volume.
Opposing Shh and Fgf signals initiate nasotemporal patterning of the retina

The earliest known determinants of retinal nasotemporal identity are the transcriptional regulators Foxg1, which is expressed in the prospective nasal optic vesicle, and Foxd1, which is expressed in the prospective temporal optic vesicle. Previous work has shown that, in zebrafish, Fgf signals from the dorsal forebrain and olfactory primordia are required to specify nasal identity in the dorsal, prospective nasal, optic vesicle. Here we show that Hh signaling from the ventral forebrain is required for specification of temporal identity in the ventral optic vesicle and is sufficient to induce temporal character when activated in the prospective nasal retina. Consequently, the evaginating optic vesicles become partitioned into prospective nasal and temporal domains by the opposing actions of Fgfs and Shh emanating from dorsal and ventral domains of the forebrain primordium. In absence of Fgf activity, foxd1 expression is established irrespective of levels of Hh signalling, indicating that the role of Shh in promoting foxd1 expression is only required in the presence of Fgf activity. Once the spatially complementary expression of foxd1 and foxg1 is established, the boundary between expression domains is maintained by mutual repression between Foxd1 and Foxg1. retinal regionalisation and, as a consequence, abnormal targeting of retinal axonal projections in the tectum. Together with previous data, our study shows that NT patterning of the prospective retina is initiated in the optic vesicles by the opposing actions of the Fgf and Shh pathways. Although loss of Shh signalling leads to compromised specification of temporal identity and loss of Fgf signalling to compromised nasal identity, optic vesicles in which both pathways are blocked show recovery of foxd1 expression, indicating that in the absence of Fgf activity the role of Shh in promoting temporal identity is dispensible. Overall, our results suggest that it is the appropriate balance between Shh and Fgf signals that ensures appropriate NT regionalisation in the forming eyes.
Temperature Accelerated Molecular Dynamics investigation of conformational changes in acetylcholine binding protein

Despite the large number of studies available on nicotinic acetylcholine receptors, a complete account of the mechanistic aspects of their gating transition in response to ligand binding still remains elusive. As a ﬁrst step toward dissecting the transition mechanism by accelerated sampling techniques, we study the ligand-induced conformational change of the acetylcholine binding protein (AChBP), a widely accepted model for the full receptor extracellular domain. Using unbiased Molecular Dynamics (MD) and Temperature Accelerated Molecular Dynamics (TAMD) simulations we investigate the AChBP transition between the apo and the agonist-bound state. In long standard MD simulations, both conformations of the native protein are stable, while the agonist-bound structure evolves toward the apo one if the orientation of few key sidechains in the orthosteric cavity is modiﬁed. Conversely, TAMD simulations initiated from the native conformations are able to produce the spontaneous transition. With respect to the modiﬁed conformations, TAMD accelerates the transition by at least a factor 10. The analysis of some speciﬁc residue-residue interactions points out that the transition mechanism is based on the
A novel deep learning approach for short-term wind power forecasting based on infinite feature selection and recurrent neural network

There are many features that have been taken into consideration for wind power forecasting. Since properly ranking these relevant features, often redundant, can be quite difficult, highly accurate short term wind power forcasting remains a big challenge. Another noted problem that adversely impacts the accuracy of wind forcasting stems from the weakness of the prevailing prediction models based on the feedforward neural network (FNN) in handling wind power time series. This paper thus attempts to address the aforementioned problems in short-term wind power forecasting with a novel approach that combines the infinite feature selection (Inf-FS) with the recurrent neural networks (RNN). In particular, all the possible features related to wind forecast are first clustered into multiple feature sets, after which the identified feature sets are mapped onto the paths of a graph built for Inf-FS. Traversing such a graph helps effectively determine/rank the significance of the features according to their stability and classification accuracy measured in the feature space. The proposed wind prediction model then feeds the ranked features into a deep learning prediction system enabled by RNN, whose neurons have self-feedback loops to help gather the past decisions, and thus be more effective than FNN for wind power prediction. The proposed wind power prediction approach is demonstrated through the experimental evaluations using a dataset from the National Renewable Energy Laboratory (NREL). The result shows that the accuracy of short-term wind power forecast is increased by 11%, 29%, 33%, and 19% in spring, summer, autumn and winter, respectively, over that achieved using the traditional approaches.
A note on dual giant gravitons in AdS 4 × CP 3

We study some of the properties of dual giant gravitons — D2-branes wrapped on an S2 ⊂ AdS4 — in type IIA string theory on AdS4 × CP. In particular we confirm that the spectrum of small fluctuations about the giant is both real and independent of the size of the graviton. We also extend previously developed techniques for attaching open strings to giants to this D2-brane giant and focus on two particular limits of the resulting string sigma model: In the pp-wave limit we quantize the string and compute the spectrum of bosonic excitations while in the semiclassical limit, we read off the fast string Polyakov action and comment on the comparison to the Landau-Lifshitz action for the dual open spin chain.
Pullout Behavior of Steel Mechanically Stabilized Earth Reinforcements

Instrumented pullout tests of unprecedented scope and scale explore the pullout behavior for three steel mechanically stabilized earth reinforcement types: ribbed strips, ladder-like strips, and three-wire bar mat grids. These data quantify the distribution of pullout resistance between longitudinal elements and illustrate the nature of certain reinforcement deformations. Consistent with characteristic inextensible pullout behavior and soil-reinforcement interaction, synthesized strain-gage data illustrate linear stress reduction along the embedment length during pullout for all three reinforcement styles. For ladder-like strips, the axial force divides evenly between the two longitudinal elements. For the three-wire bar mat grid, the center bar carries approximately 40% of the axial force, whereas each outside bar carries approximately 30% of the axial force. Observed pullout-induced deformation in the transverse elements of three-wire bar mat grids having widely spaced longitudinal bars is conceptually different from extensible behavior and suggests the need for refinement in current pullout resistance formulations.
A multi-species comparison of 13 C from whole wood , extractive-free wood and holocellulose

of tree rings is a powerful metric for reconstructing past physiological responses to climate variation. However, accurate measurement and interpretation are complicated by diagenesis and the translocation of compounds with distinct isotopic signatures. Isolation and analysis of cellulose minimizes these complications by eliminating variation due to biosynthetic pathways; however, isolation of cellulose is time-consuming and has no clear endpoint. A faster and better-defined analytical method is desirable. Our objectives were to determine if there is a direct relationship between the isotopic compositions of whole wood (WW), whole wood treated with solvents to remove mobile extractives (extractive-free wood; EF) and holocellulose (HC) isolated by extractive removal and subsequent bleaching. We also determined if total C concentration could explain the isotopic composition and variation among these three wood components of each sample. A set of wood samples of diverse phylogeny, anatomy and chemical composition, was examined. The mean offset or difference between HC and EF δC was 1.07 ± 0.09‰ and the offset between HC and WW was 1.32 ± 0.10‰. Equivalence tests (with α = 0.05)indicated that the relationship between EF δC and HC δC had a slope significantly similar to 1 ± 5.5%, whereas for the WW δC: HC δC relationship, the slope was significantly similar to 1 ± 10.08%. A regression model using EF δC to predict HC δC had a slope of 0.97, which was not significantly different from unity (P = 0.264), whereas the regression for WW had a slope of 0.92 which was significantly different from unity (P = 0.0098). Carbon concentration was correlated with HC:WW offset and cellulose:EF offset (P = 0.0501 and 0.007, respectively), but neither relationship explained much of the variation (r 2 = 0.12 and 0.14, respectively). We suggest that HC extraction is unnecessary for most analyses of tree-ring δC; a simple solvent extraction is a suitable alternative for many applications.
Fusarium ear blight of wheat: the use of quantitative PCR and visual disease assessment in studies of disease control

Quantitative PCR analysis and visual disease assessment (VDA) were used to study Fusarium culmorum and F. poae ear blight of wheat and its fungicidal control in three glasshouse trials (1994–5 and 1996–7). VDA indicated high levels of both diseases in the trials, while PCR analysis showed that the amounts of F. poae detected in infected plants were low relative to the amounts detected for F. culmorum. PCR and VDA analysis both indicated that the demethylase-inhibiting (DMI) fungicides prochloraz and tebuconazole significantly decreased F. culmorum and F. poae ear blight. The PCR results, however, revealed levels of disease control by fungicide treatments that were consistently higher than those suggested by VDA. Overall, both fungicides appeared equally effective in controlling the two pathogens. PCR and VDA analysis indicated that the anilino-pyrimidine fungicide pyrimethanil had no significant effect on F. culmorum ear blight. Correlations between VDA at cereal growth stage 80 and PCR analysis were similar for F. culmorum and F. poae. Yield analysis, as measured by 1000 grain weight, indicated that DNA content more accurately predicted yield loss than did VDA scores. Inoculation with F. culmorum significantly reduced yield and significant relationships were observed between F. culmorum disease (as assessed visually or by PCR) and yield, with yield decreasing as disease increased. In contrast, inoculation with F. poae had no significant effect on yield and no significant relationships were observed between F. poae disease and yield. These results have important implications for field studies of Fusarium ear blight of wheat because they highlight the importance of identification of the causal organisms to the species level.
A Framework for Supporting Natural Interaction with Printed Matter in Ambient Intelligence Environments

—Paper is a widely used material which - through appropriate technological augmentation - has the potential to become a widely accepted means of interaction. Ambient Intelligence bears the promise of smart, adaptive and user-friendly environments, anticipating user needs in an unobtrusive manner. So far, there is no systematic approach to paper augmentation in Ambient Intelligence. Addressing this need, this paper introduces an extensible context-aware interaction framework to enable the integration of printed matter into Ambient Intelligence environments
NovoGraph: Human genome graph construction from multiple long-read assemblies de novo [version 2; referees: 2 approved]

Genome graphs are emerging as an important novel approach to the analysis of high-throughput human sequencing data. By explicitly representing genetic variants and alternative haplotypes in a mappable data structure, they can enable the improved analysis of structurally variable and hyperpolymorphic regions of the genome. In most existing approaches, graphs are constructed from variant call sets derived from short-read sequencing. As long-read sequencing becomes more cost-effective and enables assembly for de novo increasing numbers of whole genomes, a method for the direct construction of a genome graph from sets of assembled human genomes would be desirable. Such assembly-based genome graphs would encompass the wide spectrum of genetic variation accessible to long-read-based assembly, including de novo large structural variants and divergent haplotypes. Here we present NovoGraph, a method for the construction of a human genome graph directly from a set of assemblies. NovoGraph de novo constructs a genome-wide multiple sequence alignment of all input contigs and creates a graph by merging the input sequences at positions that are both homologous and sequence-identical