Miracle-dz/UniFAR-FTUs huggingface dataset:
High prevalence of encoding RhoA-targeting toxin in clinical isolates of Staphylococcus aureus

Staphylococcus aureus , a major causative agent of human infection produces a large 1 array of virulence factor including various toxins. Among them, the host RhoA GTPase ADP- 2 ribosylating EDIN toxins are considered as potential virulence factors. Using the polymerase 3 chain reaction, we analyzed the virulence profile of 256 S. aureus isolates from various 4 clinical sites of infections. We developed specific primers to detect the three isoforms of edin 5 encoding genes. We found a prevalence of 14% (36 bacteria) of edin encoding genes among 6 these clinical isolates. Strikingly, we found that 90% of all edin -bearing S. aureus isolates 7 carried the type-C allele. Both the spa types and the profile of virulence factors of these edin - 8 positive isolates are highly variable. Notably, we show for the first time that edin -C positive 9 isolates were more frequently recovered from deep-seated infections than other types of 10 infections. Our present work thus strongly suggests that presence of edin-C is a risk factor of 11 S. aureus dissemination in tissues and thus represents a predictive marker for a pejorative 12 evolution of staphylococcal infections. 13 14 15 16 Abstract Staphylococcus aureus , a major causative agent of human infection produces a large array of virulence factor including various toxins. Among them, the host RhoA GTPase targeting EDIN toxins are considered as potential virulence factors. Using the polymerase chain reaction, we analyzed the virulence profile of 256 S. aureus strains isolated from various clinical sites of infections. We developed specific primers to detect the three isoforms of edin encoding genes. We found a prevalence of 14% (36 strains) of edin encoding genes among these clinical strains. Strikingly, we found that 90% of all edin -bearing S. aureus strains carried the type-C allele. Both the spa types and the profile of virulence factors of these edin -positive strains are highly variable. Notably, we show for the first time that edin -C positive strains were more frequently recovered from deep-seated infections than other types of infections. Our present work thus strongly suggests that presence of edin-C is a risk factor of S. aureus dissemination in tissues and thus represents a predictive marker for a pejorative evolution of staphylococcal infections.
Reading Comprehension Achievement and Vocabulary Mastery through Social Constructivist Strategies

: This research aims at investigating whether: (1) teaching reading comprehension using K-W-L influences students’ reading comprehension achievement and vocabulary mastery, (2) teaching reading comprehension with Reciprocal Teaching (RT) influences students’ reading comprehension achievement and vocabulary mastery, (3) the students having high reading comprehension achievement and vocabulary mastery will achieve better results when taught with K-W-L, and (4) the students having low reading comprehension achievement and vocabulary mastery will achieve better results when taught with Reciprocal Teaching, and (5) the students’ social values grow among students. Factorial design 2x2 was used in this research. The subjects were the second-semester students of Business Administration study program consisting of four classes of high and low. The first group was treated with K-W-L and the second group was treated with RT. The data were analyzed by using t-test. Based on the analyses, K-W-L and Reciprocal Teaching significantly affect students’ achievements on reading comprehension and vocabulary, but students' achievements on reading comprehension and vocabulary mastery taught by using RT are higher than those of the students taught by using K-W-L. Social values such as cooperative, responsible, respectful, and critical among students also grow.
Supplementary Materials For XDO: A Double Oracle Algorithm for Extensive-Form Games

Proof. Consider an infostate si “ pai , oi , . , ai, ot`1 i q for player i as covered in the restricted game if any of player i’s population policies chooses action ai in infostate si “ pai , oi , . , at ́1 i , oiq. At each but the final iteration, at least one player i has vipBRipπ ́iq, πr ̊ ́iq ą vipπq ` . Since πr ̊ i is an -BR to πr ̊ ́i in the restricted game, the BR BRipπ ́iq must be choosing at least some action ai at some non-terminal infostate si that was not previously chosen by any population policy. Adding this action to the restricted game covers at least one previously uncovered infostate: all infostates si “ psi, ai, oiq, for any observation oi. All infostates will therefore be covered in at most ř
(cid:96) 0 -S PARSE C ANONICAL C ORRELATION A NALYSIS

Canonical Correlation Analysis (CCA) models are powerful for studying the associations between two sets of variables. The canonically correlated representations, termed canonical variates are widely used in unsupervised learning to analyze unlabeled multi-modal registered datasets. Despite their success, CCA models may break (or overfit) if the number of variables in either of the modalities exceeds the number of samples. Moreover, often a significant fraction of the variables measures modality-specific information, and thus removing them is beneficial for identifying the canonically correlated variates. Here, we propose `0-CCA, a method for learning correlated representations based on sparse subsets of variables from two observed modalities. Sparsity is obtained by multiplying the input variables by stochastic gates, whose parameters are learned together with the CCA weights via an `0-regularized correlation loss. We further propose `0-Deep CCA for solving the problem of non-linear sparse CCA by modeling the correlated representations using deep nets. We demonstrate the efficacy of the method using several synthetic and real examples. Most notably, by gating nuisance input variables, our approach improves the extracted representations compared to other linear, non-linear and sparse CCA-based models.
Generalized bounding surface model for cohesive soils: a novel formulation for monotonic and cyclic loading

Constitutive models have been developed to simulate the soil response under different loading conditions. Within the wide spectrum of these conditions, earthquakes have been of interest to the research community worldwide. Traditionally, researchers have largely focused on simulating the response of cohesionless soils. Such soils, when subjected to earthquake loading, may experience the well-known phenomenon of liquefaction, which has led to much destruction in different parts of the world. By contrast, far less research has been done on simulating the response of cohesive soils subjected to cyclic loading. Consequently, certain issues associated with the simulation of such response are still being addressed. One of the biggest concerns during an earthquake is the amplification of seismic waves through a deposit of cohesive soil, which will result in significant ground movements, thus requiring higher levels of seismic resistance from the structures founded on such soils. A review of experimental results has identified several key characteristics of cyclically loaded cohesive soils that any rational mathematical simulation must account for. Accordingly, the scope of this dissertation is the development of an improved and generalized elastoplastic bounding surface model that is suitable for simulating the general response of cohesive soils, with emphasis on cyclic loading. In its most general form, the Generalized Bounding Surface Model (GBS-Model) for cohesive soils is a fully three-dimensional, time-dependent model that accounts for both inherent and stress induced anisotropy. The rotational hardening law and the shape hardening function were chosen after a thorough review of past modeling practices; in both cases, the selected functional form simplified earlier versions of the
Human Baseline for Zero-Shot Transfer Learning of Good Dogs

It is estimated that there are 900 million dogs in the world. That is a lot of dogs, and we assure you that each one is a good dog [2]. Unfortunately, we don’t have enough time to meet each dog de novo, and have inevitably needed to rely on word-of-mouth to learn about which dogs to meet and when. In this work, we demonstrate human-level performance for zero-shot dog recognition from features described by other humans. Human performance is robust (>85% accuracy), even when presented with challenging comparisons. This accuracy is in the same ballpark as Karpathy et al.’s work on a human baseline for ImageNet [6]. We believe that this work will help future researchers develop AI-based tools for super-human performance on word-of-mouth-based human-dog introductions. From a neuroscience perspective, this work also establishes the presence of a seeming information barrier between the visual cortex and the language system of the human brain.
NEW DESIGN FOR AN ELECTROTHERMALLY ACTUATED MICROGRIPPER AND FINITE ELEMENT SIMULATION RESULTS

New design for an electrothermally actuated SU-8 polymeric microgrippers is presented, simulated and analyzed. The electrothermally driven microgrippers were studied using computer simulations (the Finite Element Method using CoventorWare software tool). Numerical simulations - coupled electro-thermo-mechanical simulations - were performed. We investigated the influence of the temperatures achieved in the microgripers’ arms and the deflections of the tips, as functions of the applied voltages. The electro-thermo-mechanical behaviour of the microgrippers were investigated and an evaluation of the results obtained from simulations (using FEM) was performed. The simulations results show a good mechanical behaviour of the micromanipulator in order to operate in air. Key words: microgripper, electrothermal actuator, FEM
If you've trained one you've trained them all: inter-architecture similarity increases with robustness

Previous work has shown that commonly-used metrics for comparing representations between neural networks overestimate similarity due to correlations between data points. We show that intra-example feature correlations also causes signiﬁcant overestimation of network similarity and propose an image inversion technique to analyze only the features used by a network. With this technique, we ﬁnd that similarity across architectures is signiﬁcantly lower than commonly understood, but we surprisingly ﬁnd that similarity between models with different architectures increases as the adversarial robustness of the models increase. Our ﬁndings indicate that robust networks tend toward a universal set of representations, regardless of architecture, and that the robust training criterion is a strong prior constraint on the functions that can be learned by diverse modern architectures. We also ﬁnd that the representations learned by a robust network of any architecture have an asymmetric overlap with non-robust networks of many architectures, indicating that the representations used by robust neural networks are highly entangled with the representations used by non-robust networks.
Zur and zinc increase expression of E. coli ribosomal protein L31 through RNA-mediated repression of the repressor L31p

Bacteria can adapt in response to numerous stress conditions. One such stress condition is zinc depletion. The zinc-sensing transcription factor Zur regulates the way enteric bacteria respond to severe changes in zinc availability. Under zinc sufficient conditions, Zn-loaded Zur (Zn2-Zur) is well-known to repress transcription of genes encoding zinc uptake transporters and paralogues of a few ribosomal subunits. Here, we report the discovery and mechanistic basis for the ability of Zur to up-regulate expression of the ribosomal protein L31 in response to zinc in E. coli. Through genetic mutations and reporter gene assays, we find that Zur achieves the up-regulation of L31 through a double repression cascade by which Zur first represses the transcription of L31p, a zinc-lacking paralogue of L31, which in turn represses the translation of L31. Mutational analyses show that translational repression by L31p requires an RNA hairpin structure within the l31 mRNA and involves the N-terminus of the L31p protein. This work uncovers a new genetic network that allows bacteria to respond to host-induced nutrient limiting conditions through a sophisticated ribosomal protein switching mechanism. Graphical Abstract
The Audience for Online Video-Sharing Sites Shoots Up

NOTES 1 John Horrigan, “Home Broadband Adoption 2009,” Pew Internet & American Life Project, June 17, 2009. Avai lable at: http://www.pewinternet. org/Reports/2009/10-HomeBroadband-Adoption-2009.aspx 2 The February 2007 question wording asks, "We ’d like to know if you ever use the internet to watch or download the following kinds of video.Do you ever watch or download movies or TV shows? " The April 2009 question wording asks, "Please tell me if you ever use the internet to do any of the following things.Do you ever use the internet to watch a television show or movie online? "
INDUSTRIALISATION OF AGRICULTURE AND THE ROLE OF SUPPLY CHAINS IN PROMOTING COMPETITIVENESS 1

Changing consumer demands, new technologies and increasing competition have caused major structural changes in the agri-food sector. These developments have led to increased interdependence among participants in food supply chains, from input supplier to producer to processor and retailer. Institutional arrangements have developed to improve co-ordination among firms in supply chains, reduce transaction costs and accelerate transmission of information. A major challenge for institutions in South Africa is to promote income growth and competitiveness of small-scale farmers, and their participation in value-adding supply chains. Agricultural economists in South Africa can make an increasing contribution to agribusiness development by researching relevant issues. The ongoing industrialisation of agriculture is also attracting more undergraduate students into agribusiness studies, which have become an important area for teaching by agricultural economists. INDUSTRIALISERING VAN DIE LANDBOU EN DIE ROL VAN LEWERINGSKETTINGS IN DIE BEVORDERING VAN MEDEDINGENDHEID Veranderende verbruikersvraag, nuwe tegnologiee en toenemende kompetisie het belangrike strukturele veranderinge in die agri-voedselsektor veroorsaak. Hierdie ontwikkelinge het gelei tot verhoogde interafhanklikheid onder deelnemers aan voedselleweringskettings, van insetleweransier tot produsent tot verwerker en kleinhandelaar. Institusionele reelings het ontwikkel om koordinering onder firmas in leweringskettings te verbeter, transaksiekoste te verlaag en inligtingsoordrag te versnel. ’n Hoofuitdaging vir instellings in Suid-Afrika is om inkomstegroei en mededingendheid van kleinskaalboere, en hulle deelname aan waardetoevoegende leweringskettings te bevorder. Landbou-ekonome in Suid-Afrika kan toenemend bydra tot agribesigheidontwikkeling deur toepaslike sake na te vors. Die voortdurende industrialisasie van die landbou lok ook meer voorgraadse studente na agribesigheidstudie wat ’n belangrike onderriggebied vir landbou-ekonome geword het.
Potential contributions of nitrifiers and denitrifiers to nitrous oxide sources and sinks in China's estuarine and coastal areas

Abstract. Nitrous oxide (N2O) is an important ozone-depleting greenhouse gas produced and consumed by microbially mediated nitrification and denitrification pathways. Estuaries are intensive N2O emission regions in marine ecosystems. However, the potential contributions of nitrifiers and denitrifiers to N2O sources and sinks in China's estuarine and coastal areas are poorly understood. The abundance and transcription of six key microbial functional genes involved in nitrification and denitrification, as well as the clade II-type nosZ gene-bearing community composition of N2O reducers, were investigated in four estuaries spanning the Chinese coastline. The results showed that the ammonia-oxidizing archaeal amoA genes and transcripts were more dominant in the northern Bohai Sea (BS) and Yangtze River estuaries, which had low nitrogen concentrations, while the denitrifier nirS genes and transcripts were more dominant in the southern Jiulong River (JRE) and Pearl River estuaries, which had high levels of terrestrial nitrogen input. Notably, the nosZ clade II gene was more abundant than the clade I-type throughout the estuaries except for in the JRE and a few sites of the BS, while the opposite transcript distribution pattern was observed in these two estuaries. The gene and transcript distributions were significantly constrained by nitrogen and oxygen concentrations as well as by salinity, temperature, and pH. The nosZ clade II gene-bearing community composition along China's coastline had a high level of diversity and was distinctly different from that in the soil and in marine oxygen-minimum-zone waters. By comparing the gene distribution patterns across the estuaries with the distribution patterns of the N2O concentration and flux, we found that denitrification may principally control the N2O emissions pattern.
Human-in-the-loop solution for scoring economic development from geospatial data

Reliable and timely measurement of economic activities is fundamental for understanding economic development and for delivering humanitarian aid and disaster relief where needed. However, many developing countries still lack reliable data. This paper introduces a novel approach for measuring economic development from high-resolution satellite images in the absence of ground truth statistics. Our method’s novelty is at breaking down a computationally challenging problem into sub-tasks, which involves a human-in-the-loop solution. With the combination of unsupervised learning and the partial orders of dozens of urban versus rural clusters, our method can estimate the economic development scores of over 10,000 satellite grids with less human labor than other baseline approaches (Spearman correlation of 0.851). We demonstrate how to apply our method to both developed and developing economies.
Basal forebrain cholinergic circuits orchestrate diverse cell types in the adult dentate gyrus to support neural stem cell function and spatial memory

Dentate gyrus (DG) is a critical structure involved in spatial memory and adult neurogenesis, two distinct processes dynamically regulated by local circuits comprising diverse populations of DG cells. It remains unknown how these DG cells are orchestrated to regulate these distinct hippocampal functions. Here we report activation of a cholinergic circuit from the Diagonal Band of Broca to DG promotes quiescent radial neural stem cell (rNSC) activation and spatial memory. Furthermore, single-nucleus RNA-sequencing reveals broad transcriptomic changes across DG mature and adult-born cells in response to cholinergic-circuit activation. Notably, neuronal populations exhibit cholinergic-activity-induced molecular changes related to synaptic functions crucial for spatial memory; while rNSCs exhibit changes related to structural remodeling and neurogenic proliferation crucial for hippocampal neurogenesis. Electrophysiology and NicheNet analyses reveal granule cells, endothelial cells, and astrocytes as potential intermediaries for cholinergic regulation of rNSCs. Our findings reveal cell-type-specific signaling mechanisms underlying cholinergic regulation of distinct DG functions.
PolyLU: The Over-Powered Activation Function

We present PolyLU, the polynomial linear unit, as an activation function with optimal performance characteristics, outperforming ReLU when applied to deep models. The celebrated ReLU activation function eliminates the vanishing gradient; our work takes this a step further, culminating in the ﬁeld’s ﬁrst trainable activation function that is also resistant to adversarial attacks. Experiments demonstrate the potential of PolyLU and support a mathematical analysis of PolyLU’s optimality.
How Robust are Neural Code Completion Models to Source Code Transformation?

Neural language models hold great promise as tools for computer-aided programming, but questions remain over their reliability and the conse-quences of overreliance. In the domain of natural language, prior work has revealed these models can be sensitive to naturally-occurring variance and malfunction in unpredictable ways. A more methodical examination is nec-essary to understand their behavior on programming-related tasks. In this work, we develop a methodology for systematically evaluating neural code completion models using common source code transformations. We measure the distributional shift induced by applying those transformations to a dataset of handwritten code fragments on four pretrained models, which exhibit varying degrees of robustness under transformation. Preliminary results from those experiments and observations from a qualitative analysis suggest that while these models are promising, they should not be relied upon uncritically. Our analysis provides insights into the strengths and weaknesses of different models, and serves as a foundation for future work towards improving the accuracy and robustness of neural code completion.
Potent Tolerogenic Responses Adaptor in Myeloid Dendritic Cells Induces Cutting Edge: DNA Sensing via the STING

Cytosolic DNA sensing via the stimulator of IFN genes (STING) adaptor incites autoimmunity by inducing type I IFN (IFN- ab ). In this study, we show that DNA is also sensed via STING to suppress immunity by inducing IDO. STING gene ablation abolished IFN-ab and IDO induction by dendritic cells (DCs) after DNA nanoparticle (DNP) treatment. Marginal zone macrophages, some DCs, and myeloid cells ingested DNPs, but CD11b + DCs were the only cells to express IFN- b , whereas CD11b + non-DCs were major IL-1 b producers. STING ablation also abolished DNP-in-duced regulatory responses by DCs and regulatory T cells, and hallmark regulatory responses to apoptotic cells were also abrogated. Moreover, systemic cyclic diguanylate monophosphate treatment to activate STING induced selective IFN- b expression by CD11b + DCs and suppressed Th1 responses to immunization. Thus, pre-viously unrecognized functional diversity among physiologic innate immune cells regarding DNA sensing via STING is pivotal in driving immune responses to DNA. 2013,
Convolutional neural network-based tissue-type segmentation of brain histology images

Tissue processing techniques such as histology and immunohistochemistry make it possible to characterize and analyze microstructural features of brain tissue. Most often, it is necessary to perform such analysis separately for gray matter (GM) and white matter (WM). In addition, precise registration of tissue images to MRI can be faciliated by delineating the GM and WM. Therefore, it is important to develop methods for precise, automatic segmentation of the GM in images of brain tissue. We manually labeled the cerebral cortex in 128 Nissl-stained histology slices. We used these labels to train learning algorithms to segment the cerebral cortex and compare the performance of deep learning (DL) with a U-Net to those obtained by machine learning (ML) methods. The DL algorithm segmented the GM from unseen brain histology slices with Jaccard index of 0.984 ± 0.008, surpassing human-level precision and the ML-based classifiers. The DL algorithm was capable of matching the manual segmentation in smoothly varying regions to a greater extent than in regions with increased curvature of the boundary between different tissue types.