Miracle-dz/UniFAR-FTUs huggingface dataset:
Contribution of bacterial and host factors to pathogen “blooming” in a gnotobiotic mouse model for Salmonella enterica serovar Typhimurium-induced enterocolitis

ABSTRACT Inflammation has a pronounced impact on the intestinal ecosystem by driving an expansion of facultative anaerobic bacteria at the cost of obligate anaerobic microbiota. This pathogen “blooming” is also a hallmark of enteric Salmonella enterica serovar Typhimurium (S. Tm) infection. Here, we analyzed the contribution of bacterial and host factors to S. Tm “blooming” in a gnotobiotic mouse model for S. Tm-induced enterocolitis. Mice colonized with the Oligo-Mouse-Microbiota (OMM12), a minimal bacterial community, develop fulminant colitis by day 4 after oral infection with wild-type S. Tm but not with an avirulent mutant. Inflammation leads to a pronounced reduction in overall intestinal bacterial loads, distinct microbial community shifts, and pathogen blooming (relative abundance >50%). S. Tm mutants attenuated in inducing gut inflammation generally elicit less pronounced microbiota shifts and reduction in total bacterial loads. In contrast, S. Tm mutants in nitrate respiration, salmochelin production, and ethanolamine utilization induced strong inflammation and S. Tm “blooming. ” Therefore, individual Salmonella-specific inflammation-fitness factors seem to be of minor importance for competition against this minimal microbiota in the inflamed gut. Finally, we show that antibody-mediated neutrophil depletion normalized gut microbiota loads but not intestinal inflammation or microbiota shifts. This suggests that neutrophils equally reduce pathogen and commensal bacterial loads in the inflamed gut.
PRO-LDM: Protein Sequence Generation with a Conditional Latent Diffusion Model

Deep learning-driven protein design holds enormous potential despite the complexities in sequences and structures. Recent developments in diffusion models yielded success in structure design, but awaits progress in sequence design and are computationally demanding. Here we present PRO-LDM: an efficient framework combining design fidelity and computational efficiency, utilizing the diffusion model in latent space to design proteins with property tuning. The model employs a joint autoencoder to capture latent variable distributions and generate meaningful embeddings from sequences. PRO-LDM (1) learns representations from biological features in natural proteins at both amino-acid and sequence level; (2) generates native-like new sequences with enhanced diversity; and (3) conditionally designs new proteins with tailored properties or functions. The out-of-distribution design enables sampling notably different sequences by adjusting classifier guidance strength. Our model presents a feasible pathway and an integratable tool to extract physicochemical and evolutionary information embedded within primary sequences, for protein design and optimization.
Histone mark age of human tissues and cells

Background Aging involves intricate epigenetic changes, with histone modifications playing a pivotal role in dynamically regulating gene expression. Our research comprehensively analyzes seven key histone modifications across various tissues to understand their behavior during human aging and formulate age prediction models. Results These histone-centric prediction models exhibit remarkable accuracy and resilience against experimental and artificial noise. They showcase comparable efficacy when compared with DNA methylation age predictors through simulation experiments. Intriguingly, our gene set enrichment analysis pinpoints vital developmental pathways crucial for age prediction. Unlike in DNA methylation age predictors, genes previously recognized in animal studies as integral to aging are amongst the most important features of our models. We also introduce a pan-histone-mark, pan-tissue age predictor that operates across multiple tissues and histone marks, reinforcing that age-related epigenetic markers are not restricted to particular histone modifications. Conclusion Our findings underscore the potential of histone marks in crafting robust age predictors and shed light on the intricate tapestry of epigenetic alterations in aging.
Structures of wild-type and a constitutively closed mutant of connexin26 shed light on channel regulation by CO2

Connexins allow intercellular communication by forming gap junction channels (GJCs) between juxtaposed cells. Connexin26 (Cx26) can be regulated directly by CO2. This is proposed to be mediated through carbamylation of K125. We show that mutating K125 to glutamate, mimicking the negative charge of carbamylation, causes Cx26 GJCs to be constitutively closed. Through cryo-EM we observe that the K125E mutation pushes a conformational equilibrium towards the channel having a constricted pore entrance, similar to effects seen on raising the partial pressure of CO2. In previous structures of connexins, the cytoplasmic loop, important in regulation and where K125 is located, is disordered. Through further cryo-EM studies we trap distinct states of Cx26 and observe density for the cytoplasmic loop. The interplay between the position of this loop, the conformations of the transmembrane helices and the position of the N-terminal helix, which controls the aperture to the pore, provides a mechanism for regulation.
Cell therapy with IL-10-producing group 2 innate lymphoid cells suppresses Graft-versus-Host disease

IL-10 producing group 2 innate lymphoid cells (ILC210) have immunoregulatory functions, and limit harmful immune responses across various tissues. Despite their crucial roles in maintaining immune homeostasis, the cell therapy potential of human ILC210 has not been demonstrated, due to both limited numbers in human peripheral blood and lack of definitive markers for identification. Here, we isolate and expand circulating human ILC210, and assess their cell therapy potential in a humanized model of Graft-versus-Host Disease (GVHD). Cell therapy with human ILC210 decreased GVHD severity and prolonged survival of NOD-scid IL2Rγnull (NSG) mice. Adoptive transfer of ILC210 inhibited pathogenic T cell proliferation and intestinal infiltration, and suppressed CD4+ Th1 and CD8+ Tc1 cells in an IL-4 and IL-10 dependent manner. Critically, increased proportions of ILC2s did not correlate with higher rates of cancer relapse in HSCT recipients, and adoptive transfer of ILC210 did not compromise graft-versus-leukemic (GVL) effects in a humanized model. Finally, we identify CD49d and CD86 as novel markers that discriminate ILC210 from conventional ILC2s. Collectively, these findings demonstrate the potential of harnessing ILC210 in cell therapies for GVHD and other immune-driven pathologies.
The association between fibrinolysis markers and body composition in black adults in the North West Province of South Africa

ii OPSOMMING iv TABLE OF CONTENTS vi LIST OF TABLES x LIST OF FIGURES xi LIST OF ADDENDA xii LIST OF ABBREVIATIONS xiii CHAPTER 1: INTRODUCTION 1 1.1 BACKGROUND 1 1.2 AIM AND OBJECTIVES 4 1.3 RESEARCH TEAM 6 1.4 STRUCTURE OF THIS DISSERTATION 6 CHAPTER 2: LITERATURE REVIEW 8 2.
Modified mRNA Formulation and Stability for Cardiac and Skeletal Muscle Delivery

Directly injecting naked or lipid nanoparticle (LNP)-encapsulated modified mRNA (modRNA) allows rapid and efficient protein expression. This non-viral technology has been used successfully in modRNA vaccines against SARS-CoV-2. The main challenges in using modRNA vaccines were the initial requirement for an ultra-cold storage to preserve their integrity and concerns regarding unwanted side effects from this new technology. Here, we showed that naked modRNA maintains its integrity when stored up to 7 days at 4 °C, and LNP-encapsulated modRNA for up to 7 days at room temperature. Naked modRNA is predominantly expressed at the site of injection when delivered into cardiac or skeletal muscle. In comparison, LNP-encapsulated modRNA granted superior protein expression but also additional protein expression beyond the cardiac or skeletal muscle injection site. To overcome this challenge, we developed a skeletal-muscle-specific modRNA translation system (skeletal muscle SMRTs) for LNP-encapsulated modRNA. This system allows controlled protein translation predominantly at the site of injection to prevent potentially detrimental leakage and expression in major organs. Our study revealed the potential of the SMRTs platform for controlled expression of mRNA payload delivered intramuscularly. To conclude, our SMRTs platform for LNP-encapsulated modRNA can provide safe, stable, efficient and targeted gene expression at the site of injection.
Turn-It-Up: Rendering Resistance for Knobs in Virtual Reality through Undetectable Pseudo-Haptics

Rendering haptic feedback for interactions with virtual objects is an essential part of effective virtual reality experiences. In this work, we explore providing haptic feedback for rotational manipulations, e.g., through knobs. We propose the use of a Pseudo-Haptic technique alongside a physical proxy knob to simulate various physical resistances. In a psychophysical experiment with 20 participants, we found that designers can introduce unnoticeable offsets between real and virtual rotations of the knob, and we report the corresponding detection thresholds. Based on these, we present the Pseudo-Haptic Resistance technique to convey physical resistance while applying only unnoticeable pseudo-haptic manipulation. Additionally, we provide a first model of how C/D gains correspond to physical resistance perceived during object rotation, and outline how our results can be translated to other rotational manipulations. Finally, we present two example use cases that demonstrate the versatility and power of our approach.
A Bayesian Networks Approach for Predicting Protein-Protein Interactions from Genomic Data

We have developed an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast. Our method naturally weights and combines into reliable predictions genomic features only weakly associated with interaction (e.g., messenger RNAcoexpression, coessentiality, and colocalization). In addition to de novo predictions, it can integrate often noisy, experimental interaction data sets. We observe that at given levels of sensitivity, our predictions are more accurate than the existing high-throughput experimental data sets. We validate our predictions with TAP (tandem affinity purification) tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint.
Information Retrieval on the Web

Introduction How do we find information on the Web? Although information on the Web is distributed and decentralized, the WWW can be viewed as a single, virtual document collection. In that regard, the fundamental questions and approaches of traditional information retrieval (IR) research (e.g., term weighting, query expansion) are likely to be relevant in Web document retrieval 1. Findings from traditional IR research, however, may not always be applicable in a Web setting. The Web document collection–massive in size and diverse in content, format, purpose, and quality–challenges the validity of previous research findings that are based on relatively small and homogeneous test collections. Moreover, some traditional IR approaches, while applicable in theory, may be impossible or impractical to implement in a Web setting. For instance, the size, distribution, and dynamic nature of Web information make it extremely difficult to construct a complete and up-to-date data representation of the kind required for a model IR system. To further complicate matters, information seeking on the Web is diverse in character and unpredictable in nature. Web searchers come from all walks of life and are motivated by all kinds of information need. The wide range of experience, knowledge, motivation and purpose means that searchers can express diverse types of information need in a wide variety of ways with differing criteria for satisfying their needs. Conventional evaluation measures, such as precision and recall, may no longer be appropriate for Web IR, where a test collection 1 For the remainder of this paper, IR will imply text-based retrieval unless otherwise stated. 2 representative of dynamic and diverse Web data is all but impossible to construct. Finding information on the Web creates many new challenges for, and exacerbates some old problems in, IR research. At the same time, the Web is rich in new types of information not present in most previous IR test collections. Hyperlinks, usage statistics, document markup tags, and bodies of topic hierarchy such as Yahoo! (http://www.yahoo.com) present an opportunity to leverage Web-specific document characteristics in novel ways that go beyond the term-based retrieval framework of traditional IR. Consequently, researchers in Web IR have reexamined the findings from traditional IR research to discover which conventional text retrieval approaches may be applicable in Web settings, while exploring new approaches that can accommodate Web-specific characteristics. Web data still consist mostly of text, which means that various text retrieval tools (e.g., term weighting, term similarity computation, …
New Oscillation Theorems for Second-Order Superlinear Neutral Differential Equations with Variable Damping Terms

In this paper, we investigate the oscillatory behavior of solutions of second-order differential equations with a superlinear neutral term and a damping term in noncanonical forms. We do not place any additional conditions on the damping term. Therefore, our results apply to new classes of equations that were not covered by the previous criteria. According to symmetry between the negative and positive solutions of the studied equation, we present some new criteria that ensure the oscillation of all solutions by using the Riccati transformation and comparison method. We provide an example to illustrate our main results.
Sharp increase of Saharan dust intrusions over the Western Mediterranean and Euro-Atlantic region in winters 2020-2022 and associated atmospheric circulation

During the winters of the 2020-2022 period, several intense North African dust intrusions affected Europe. Some of them displayed a duration never recorded before. They were referred to as exceptional by several international operational and 25 research institutions considering that wintertime is the season with minimum dust activity in the Mediterranean and Europe. These anomalous winter events with origin in North Africa largely affected western Mediterranean. T he
Integrating Behavioral Health into Primary Care

Abstract Depression is one of the more common diagnoses encountered in primary care, and primary care in turn provides the majority of care for patients with depression. Many approaches have been tried in efforts to improve the outcomes of depression management. This article outlines the partnership between the University of Washington (UW) Neighborhood Clinics and the UW Department of Psychiatry in implementing a collaborative care approach to integrating the management of anxiety and depression in the ambulatory primary care setting. This program was built on the chronic care model, which utilizes a team approach to caring for the patient. In addition to the patient and the primary care provider (PCP), the team included a medical social worker (MSW) as care manager and a psychiatrist as team consultant. The MSW would manage a registry of patients with depression at a clinic with several PCPs, contacting the patients on a regular basis to assess their status, and consulting with the psychiatrist on a weekly basis to discuss patients who were not achieving the goals of care. Any recommendation (eg, a change in medication dose or class) made by the psychiatrist was communicated to the PCP, who in turn would work with the patient on the new recommendation. This collaborative care approach resulted in a significant improvement in the number of patients who achieved care plan goals. The authors believe this is an effective method for health systems to integrate mental health services into primary care. (Population Health Management 2016;19:81–87)
Triaxial and Plane Strain Behavior of Natural Clay

Specimens of a saturated, undisturbed marine clay were K 0 consolidated and subsequently sheared under triaxial and plane strain conditions using a variety of stress paths and drainage conditions. It was found that both the undrained strength and the angle of shearing resistance were slightly larger in plane strain than under the corresponding triaxial conditions. Methods to estimate strains in undrained plane strain shear from the results of triaxial tests under identical stress paths have been developed. An extended form of Rendulic's hypothesis was used successfully to predict volumetric strains under plane strain conditions using undrained triaxial results.
Determination of the Thermal-tactile Simultaneity Window for Multisensory Cutaneous Displays*

Multisensory cutaneous displays have been developed to enhance the realism of objects touched in virtual environments. However, when stimuli are presented concurrently, tactile stimuli can mask thermal perception and so both these modalities may not be available to convey information to the user. In this study, we aim to determine the simultaneity window using the Simultaneity Judgment Task. A device was created that could present both tactile and thermal stimuli to the thenar eminence of the participant ’s left hand with various stimulus onset asynchronies (SOA). The experimental results indicated that the simultaneity window width was 639 ms ranging from −561 ms to 78 ms. The point of subjective simultaneity (PSS) was at −242 ms, indicating that participants perceived simultaneity best when the thermal stimulus preceded the tactile stimulus by 242 ms. These findings have implications for the design of stimulus presentation in multisensory cutaneous displays.
Detection and Classification of Diabetic Retinopathy Using Pretrained Deep Neural Networks

The retina is harmed by diabetic retinopathy (DR), a consequence of diabetes. Up to, 80% of people who have had diabetes for ten or more years are affected by this type of medical issue. Where the need for diabetic retinopathy identification is greatest, there is frequently a shortage of the necessary knowledge and tools. The majority of research in the area of diabetic retinopathy has relied on manual feature extraction or disease identification. So, the goal of this research is to make a deep learning neural network that can identify the disease in all of its forms. The suggested system enables a DR classification that accounts for normal eyes, mild DR, moderate DR, severe DR, and proliferative DR, which may aid ophthalmologists in making a preliminary decision. Our accuracy rates for estimating the degree of diabetic retinopathy from an image were 90% and 92% respectively, using the pre-trained Convolutional Neural Network (CNN) VGG-16 and MobileNet-V2.
PTTE:Power Tower Tilt Estimation Algorithm based on LiDAR Point Cloud

Power towers are the infrastructure for power transmission in the power grid, and to ensure the safety of power transmission, the state of power towers needs to be inspected regularly. To improve inspection efficiency, this paper proposes a method for detecting the tilt of towers based on the power inspection method of the UAV LiDAR point cloud. The method uses SCF-Net network to semantically segment the power targets in the scene after the UAV has collected the power corridor data, and then uses K-means clustering to obtain individual towers. Then the extracted individual pylons are used to propose a pylon tilt estimation algorithm based on cross-stretcher point cloud plane fitting and normal estimation. Experiments are conducted with the actual collected power corridor data, and the experimental results show that the IOU index of the classification effect of the SCF-Net network on power poles reaches 97.75, and the untilted poles and manually placed tilted poles can be accurately judged. Compared with other methods, the proposed algorithm has a high degree of automation, does not require too much human intervention, and has engineering application value.
DISSECTING THE EFFECT OF CREDIT SUPPLY ON TRADE: EVIDENCE FROM MATCHED CREDIT-EXPORT DATA

We to Mitchell Paul Manuel Molinie, Vega, and David Weinstein for helpful advice and discussions. We Diego Sergio Correia, Mogrovejo, Olcese, Javier Poggi, Adriana Valenzuela, and Lucciano Villacorta for outstanding help with the data. Juanita Gonzalez provided excellent research assistance. We thank participants at CEMFI, Columbia University GSB, Federal Reserve Bank of Peru, European Econometric Society, Federal Reserve Bank of Philadelphia, Fordham University, Harvard University, INSEAD, Instituto de Empresa, IPA Conference on Entrepreneurship and SME, Lon- don School of Economics, MIT Sloan, NBER International Trade and Investment, NBER International Finance and Monetary, NBER Corporate Finance, Ohio State University, Rothschild Caesarea Center, Royal Economic Society, Royal Holloway University London, Society for Economic Dynamics, 2012 Econo- metric Society Meeting, Tel Aviv University, Universidad Catolica de Chile, University of Michigan Ross School of Business, University of Minnesota Carlson School of Management, and World Bank seminars, workshops, and conferences for helpful comments. Paravisini, Rappoport, and Wolfenzon thank Jerome A. Chazen Institute of International Business for financial support. All errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research. NBER working papers circulated ABSTRACT We estimate the elasticity of exports to credit using matched customs and firm-level bank credit data from Peru. To account for non-credit determinants of exports, we compare changes in exports of the same product and to the same destination by firms borrowing from banks differentially affected by capital flow reversals during the 2008 financial crisis. We obtain elasticity estimates for the intensive and extensive margins of exports, size and frequency of shipments, and the method of freight and payment. Our results suggest that the credit shortage reduces exports through raising the cost of working capital for general production, rather than the cost of financing export-specific cash cycles or sunk entry investments.
Defect Screening on Nuclear Power Plant Concrete Structures: A Two-staged Method Based on Contrastive Representation Learning

Intelligent defect detection methods are important for the surface of the containment of nuclear power plants and face many challenges in the field of computer vision. Due to the irregular shapes and large variation of defects, as well as the similarity of the features between some defects and the background. Most existing deep learning-based defect detection networks suffer from insufficient feature extraction capabilities, making it difficult to detect defects from the background. Inspired by discrete representation learning methods, we propose a two-stage defect detection model called ConVQVAE, which uses a semi-supervised method for representation learning to achieve binary classification for defect detection. In addition, contrastive learning is performed in two stages to enhance the model's diversity representation ability and inter-class disentanglement representation ability. Finally, we experimentally verify that the proposed method can obtain good classification results.