Miracle-dz/UniFAR-FTUs huggingface dataset:
The Impact of Human Immunodeficiency Virus Infection on Gut Microbiota α-Diversity: An Individual-level Meta-analysis

Correspondence: S. A. Tuddenham, Department of Medicine, Division of Infectious Disease, Johns Hopkins University, Bayview Campus, 5200 Eastern Ave, MFL Center Tower, Suite 381, Baltimore, MD 21224 (studden1@jhmi.edu). Clinical Infectious Diseases 2020;70(4):615–27 © The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. DOI: 10.1093/cid/ciz258 The Impact of Human Immunodeficiency Virus Infection on Gut Microbiota α-Diversity: An Individual-level Meta-analysis
Original Article Demographic and clinical profile of vernal keratoconjunctivitis at a tertiary eye care center in India

Access this article online Website: www.ijo. in DOI: 10.4103/0301-4738.119431 PMID: ***** Quick Response Code: Purpose: To study the demographic and clinical profile of patients with vernal keratoconjunctivitis (VKC) at a tertiary eye care center in India. Materials and Methods: Retrospective chart analysis of 468 patients of VKC seen from January 2006 to December 2006. Results: Mean age at presentation was 12 years. Majority of the patients had mixed pattern disease (72%). Chronic perennial disease was seen in 36% patients. Personal or family history of allergies was noted in 5% patients. Severe disease based on clinical grading was present in 37% patients. Moderate to severe vision loss was seen in 12% of total population. Persistent disease beyond 20 years of age was found in 12% patients. VKC-related complications such as corneal scarring (11%), shield ulcer (3%), keratoconus (6%), and limbal stem cell deficiency (1.2%) were seen. Treatment-related complications like corticosteroid-induced cataract and glaucoma were seen in 6% and 4% of patients, respectively. Conclusion: Clinical pattern of VKC seen in the tropical climate of India is essentially similar to that seen in other tropical countries. Few distinct features that we noted represent chronic perennial disease, low association with atopy, and higher propensity for disease and treatment-related complications.
Motivation matters: Examining vegetarianism, weight motivations, and eating disorder psychopathology among college students

Objective: Previous research has suggested a link between vegetarianism, broadly defined, and symptoms of eating disorders (ED). However, the literature supporting this link is mixed and limited by possible measurement artifacts. Using data from a national sample of college students, the present study examines ED symptomatology among three groups: (1) vegetarians whose meat avoidance is motivated by weight concerns; (2) non-weight motivated vegetarians; and (3) nonvegetarians. Method: Participants include 9,910 students from 12 colleges and universities across the United States who participated in the web-based Healthy Bodies Study. ED symptomatology was measured using the Short-Eating Disorder Examination-Questionnaire (S-EDE-Q). First, multigroup confirmatory factor analysis was conducted to test measurement invariance (MI) of the SEDE-Q across weight-motivated vegetarians, non-weight-motivated vegetarians, and nonvegetarians. Genderand BMI-adjusted ANCOVA was used to compare S-EDE-Q scores across groups. Results: 9.3% of participants were vegetarian. Cis-female and gender minority students were more likely to be vegetarian; those who became vegetarians after entering college were more likely to report weight-related motivations. Strict MI was supported for the S-EDE-Q global and subscale scores. Weight-motivated vegetarians reported higher levels of restraint, shape/weight overvaluation, body dissatisfaction, and global ED psychopathology relative to other participants. Discussion: To our knowledge, this is the first to explicitly link weight motivations for vegetarianism to ED psychopathology in a large, representative sample of young adults. Results Corresponding Author: Hana F. Zickgraf, Ph.D., 5948 S. Maryland Ave, MC 3077, Chicago, IL, 60637, zickgraf@uchicago.edu. Conflicts of interest statement The authors have no conflicts of interest to disclose. HHS Public Access Author manuscript Int J Eat Disord. Author manuscript; available in PMC 2021 September 01. Published in final edited form as: Int J Eat Disord. 2020 September ; 53(9): 1506–1514. doi:10.1002/eat.23335. A uhor M anscript
AJA1900021 59..67

Purpose: Longitudinal population-based cohort data were used to develop a standardized classification system for age-related hearing impairment using thresholds for frequencies (0.5–8 kHz) typically measured in cohort studies. Method: Audiometric testing data collected in the Epidemiology of Hearing Loss Study from participants (n = 1,369) with four visits (1993–1995, 1998–2000, 2003– 2005, and 2009–2010) were included (10,952 audiograms). Cluster analyses (Wald’s method) were used to identify audiometric patterns. Maximum allowable threshold values were defined for each cluster to create an ordered scale. Progression was defined as a two-step change. Results: An eight-step scale was developed to capture audiogram shape and severity of hearing impairment.
Effect of omega-3 polyunsaturated fatty acid (n-3 PUFA) supplementation to lactating sows on growth and indicators of stress in the post-weaned pig.

Dietary n-3 PUFA are precursors for lipid metabolites that reduce inflammation. Two experiments were conducted to test the hypothesis that enriching the sow diet in n-3 PUFA during late gestation and throughout lactation reduces stress and inflammation and promotes growth in weaned pigs. A protected fish oil product (PFO; GromegaTM) was used to enrich the diet in n-3 PUFA. In the initial experiment, time-bred gilts were fed a gestation and lactation diet supplemented with 0 (control; n = 5), 0.25 (n = 4), 0.5 (n = 4) or 1% (n = 5) PFO from 101 ± 2 d of gestation to d 16 of lactation. Adding 1% PFO to the diet increased the n-3:n-6 PUFA ratio in colostrum and milk compared to controls (P = 0.05). A subsequent experiment was performed to determine if supplementing the sow diet with 1% PFO improved growth and reduced circulating markers of acute inflammation and stress in the offspring. Plasma was harvested from piglets (16 /treatment group) on d 0 (d of weaning) and d 1 and 3 postweaning. Pigs from the 1% PFO treatment group weighed more (P = 0.03) on d 3 postweaning and had a greater (P ˂ 0.05) n-3:n-6 PUFA ratio in plasma on each day sampled compared to 0% PFO controls. There was an overall treatment effect on plasma total cortisol (P = 0.03) and haptoglobin (P = 0.04), with lesser concentrations in pigs on the 1% PFO diet. Plasma corticosteroid-binding globulin (CBG) concentrations were not different between treatment groups but were less (P ˂ 0.001) on d 1 and 3 when compared to d 0. The resultant free cortisol index [FCI (cortisol/CBG)] was less (P = 0.02) on d 1 and 3 for pigs from the 1% treatment group compared to the controls. An exvivo lipopolysaccharide (LPS) challenge of whole blood collected on d 0 and 1 was used to determine if 1% PFO attenuated release of inflammatory cytokines (IL-1β, IL-6, and TNF-α). Blood from pigs within the 1% PFO treatment group tended (P = 0.098) to have a lesser mean concentration of TNF-α in response to LPS compared to blood from controls. These results suggest that providing a PFO supplement as 1% of the diet to sows beginning in late gestation and during lactation can increase the n-3:n-6 PUFA ratio in their offspring, which may improve growth and reduce the acute physiological stress response in the pigs postweaning.
Small cell osteosarcoma of the mandible: Case

Small cell osteosarcoma is a rare histological subtype of osteosarcoma. It was first described in 1979 by Sim et al. as resembling Ewing’s tumor, being made up of small round cells. [1] Small cell osteosarcoma affecting the mandible is even rarer, with only three published cases available in the literature. [2-4] We present a case of small cell osteosarcoma of the mandible with a review of its clinical and radiographic features, its cytologic and histologic characteristics, as well as its immunohistochemistry and molecular genetics.
A meta-analysis of passive immunization studies shows an association of serum neutralizing antibody titer with protection against SHIV challenge

Amarendra Pegu1, Bhavesh Borate2, Yunda Huang2,3, Matthias G. Pauthner4, Ann J. Hessell5, Boris Julg6, Nicole A. Doria-Rose1, Stephen D. Schmidt1, Lindsay N. Carpp2, Michelle D. Cully1, Xuejun Chen1, George M. Shaw7, Dan H. Barouch8,6, Nancy L. Haigwood5, Lawrence Corey2, Dennis R. Burton4,6, Mario Roederer1, Peter B. Gilbert2,9, John R. Mascola1,*, Ying Huang2,9,*,# 1Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland, 20892, USA.
Adenylation enzyme characterization using γ-18O4-ATP pyrophosphate exchange

We present here a rapid, highly sensitive nonradioactive assay for adenylation enzyme selectivity determination and characterization. This method measures the isotopic back exchange of unlabeled pyrophosphate into γ–O4-labeled ATP via MALDI-TOFMS, ESI-LC/MS or ESI-LC/MS/MS and is demonstrated for both nonribosomal (TycA, ValA) and ribosomal synthetases (TrpRS, LysRS) of known specificity. This low volume (6μL) method detects as little as 0.01% (600 fmol) exchange, comparable in sensitivity to previously reported radioactive assays and readily adaptable to kinetics measurements and high throughput analysis of a wide spectrum of synthetases. Finally, a previously uncharacterized A-T didomain from anthramycin biosynthesis in the thermophile S. refuinius was demonstrated to selectively activate 4-methyl-3-hydroxyanthranilic acid at 47 °C, providing biochemical evidence for a new aromatic β–amino acid activating adenylation domain and the first functional analysis of the anthramycin biosynthetic gene cluster.
Uncovering the molecular mechanism for dual effect of ATP on phase separation in FUS solution

Recent studies reported that adenosine triphosphate (ATP) could inhibit and enhance the phase separation in prion-like proteins. The molecular mechanism underlying such a puzzling phenomenon remains elusive. Here, taking the fused in sarcoma (FUS) solution as an example, we comprehensively reveal the underlying mechanism by which ATP regulates phase separation by combining the semiempirical quantum mechanical method, mean-field theory, and molecular simulation. At the microscopic level, ATP acts as a bivalent or trivalent binder; at the macroscopic level, the reentrant phase separation occurs in dilute FUS solutions, resulting from the ATP concentration–dependent binding ability under different conditions. The ATP concentration for dissolving the protein condensates is about 10 mM, agreeing with experimental results. Furthermore, from a dynamic point of view, the effect of ATP on phase separation is also nonmonotonic. This work provides a clear physical description of the microscopic interaction and macroscopic phase diagram of the ATP-modulated phase separation.
Conformally Schwarzschild cosmological black holes

We thoroughly investigate conformally Schwarzschild spacetimes in different coordinate systems to seek for physically reasonable models of a cosmological black hole. We assume that a conformal factor depends only on the time coordinate and that the spacetime is asymptotically flat Friedmann–Lemaître–Robertson–Walker Universe filled by a perfect fluid obeying a linear equation state p = wρ with w > −1/3. In this class of spacetimes, the McClure–Dyer spacetime, constructed in terms of the isotropic coordinates, and the Thakurta spacetime, constructed in terms of the standard Schwarzschild coordinates, are identical and do not describe a cosmological black hole. In contrast, the Sultana–Dyer and Culetu classes of spacetimes, constructed in terms of the Kerr–Schild and Painlevé–Gullstrand coordinates, respectively, describe a cosmological black hole. In the Sultana–Dyer case, the corresponding matter field in general relativity can be interpreted as a combination of a homogeneous perfect fluid and an inhomogeneous null fluid, which is valid everywhere in the spacetime unlike Sultana and Dyer’s interpretation. In the Culetu case, the matter field can be interpreted as a combination of a homogeneous perfect fluid and an inhomogeneous anisotropic fluid. However, in both cases, the total energy–momentum tensor violates all the standard energy conditions at a finite value of the radial coordinate in late times. As a consequence, the Sultana–Dyer and Culetu black holes for −1/3 < w ⩽ 1 cannot describe the evolution of a primordial black hole after its horizon entry.
An Embedded Feature Selection Framework for Control

Reducing sensor requirements while keeping optimal control performance is crucial to many industrial control applications to achieve robust, low-cost, and computation-efficient controllers. However, existing feature selection solutions for the typical machine learning domain can hardly be applied in the domain of control with changing dynamics. In this paper, a novel framework, namely the Dual-world embedded Attentive Feature Selection (D-AFS), can efficiently select the most relevant sensors for the system under dynamic control. Rather than the one world used in most Deep Reinforcement Learning (DRL) algorithms, D-AFS has both the real world and its virtual peer with twisted features. By analyzing the DRL's response in two worlds, D-AFS can quantitatively identify respective features' importance towards control. A well-known active flow control problem, cylinder drag reduction, is used for evaluation. Results show that D-AFS successfully finds an optimized five-probes layout with 18.7% drag reduction than the state-of-the-art solution with 151 probes and 49.2% reduction than five-probes layout by human experts. We also apply this solution to four OpenAI classical control cases. In all cases, D-AFS achieves the same or better sensor configurations than originally provided solutions. Results highlight, we argued, a new way to achieve efficient and optimal sensor designs for experimental or industrial systems. Our source codes are made publicly available at https://github.com/G-AILab/DAFSFluid.
Cannibal dark matter decoupled from the standard model: Cosmological constraints

: An internally thermalized dark matter (DM) with only gravitational interaction with the standard model (SM) particles at low temperatures, may undergo number-changing self-scatterings in the early Universe, eventually freezing out to the observed DM abundance. If these reactions, such as a 3 → 2 process, take place when the DM is non-relativistic, DM cannibalizes itself to cool much slower than standard non-relativistic matter during the cannibal phase. As shown in earlier studies, if the cannibal phase takes place during the matter-dominated epoch, there are very strong constraints from structure formation. Considering scenarios in which the cannibal phase freezes out in the radiation-dominated epoch instead, we show that cannibal DM decoupled from the SM can be viable, consistent with all present cosmological constraints. To this end, we solve the coupled evolution equations of the DM temperature and density, and determine its abundance for diﬀerent DM self-couplings. We then evaluate the constraints on these parameters from the cosmic-microwave background power spectrum, the big-bang nucleosynthesis limits on the relativistic degrees of freedom, the Lyman- α limits on the DM free-streaming length and the theoretical upper bound on the 3 → 2 annihilation rate from S − matrix unitarity. We ﬁnd that depending upon the DM self-couplings, a scalar cannibal DM with mass in the range of around 90 eV to 600 TeV can make up the observed DM density and satisfy all the constraints, when the initial DM temperature ( T DM ) is lower than the SM one ( T SM ), with T SM / 8000 (cid:46) T DM (cid:46)
Explanation-based Counterfactual Retraining(XCR): A Calibration Method for Black-box Models

With the rapid development of eXplainable Artificial Intelligence (XAI), a long line of past work has shown concerns about the Out-of-Distribution (OOD) problem in perturbation-based post-hoc XAI models and explanations are socially misaligned. We explore the limitations of post-hoc explanation methods that use approximators to mimic the behavior of black-box models. Then we propose eXplanation-based Counterfactual Retraining (XCR), which extracts feature importance fastly. XCR applies the explanations generated by the XAI model as counterfactual input to retrain the black-box model to address OOD and social misalignment problems. Evaluation of popular image datasets shows that XCR can improve model performance when only retaining 12.5% of the most crucial features without changing the black-box model structure. Furthermore, the evaluation of the benchmark of corruption datasets shows that the XCR is very helpful for improving model robustness and positively impacts the calibration of OOD problems. Even though not calibrated in the validation set like some OOD calibration methods, the corrupted data metric outperforms existing methods. Our method also beats current OOD calibration methods on the OOD calibration metric if calibration on the validation set is applied.
Learning Debiased Classifier with Biased Committee

Neural networks are prone to be biased towards spurious correlations between classes and latent attributes exhibited in a major portion of training data, which ruins their generalization capability. We propose a new method for training debiased classifiers with no spurious attribute label. The key idea is to employ a committee of classifiers as an auxiliary module that identifies bias-conflicting data, i.e., data without spurious correlation, and assigns large weights to them when training the main classifier. The committee is learned as a bootstrapped ensemble so that a majority of its classifiers are biased as well as being diverse, and intentionally fail to predict classes of bias-conflicting data accordingly. The consensus within the committee on prediction difficulty thus provides a reliable cue for identifying and weighting bias-conflicting data. Moreover, the committee is also trained with knowledge transferred from the main classifier so that it gradually becomes debiased along with the main classifier and emphasizes more difficult data as training progresses. On five real-world datasets, our method outperforms prior arts using no spurious attribute label like ours and even surpasses those relying on bias labels occasionally.
Fighting Fire with Fire: Avoiding DNN Shortcuts through Priming

Across applications spanning supervised classification and sequential control, deep learning has been reported to find"shortcut"solutions that fail catastrophically under minor changes in the data distribution. In this paper, we show empirically that DNNs can be coaxed to avoid poor shortcuts by providing an additional"priming"feature computed from key input features, usually a coarse output estimate. Priming relies on approximate domain knowledge of these task-relevant key input features, which is often easy to obtain in practical settings. For example, one might prioritize recent frames over past frames in a video input for visual imitation learning, or salient foreground over background pixels for image classification. On NICO image classification, MuJoCo continuous control, and CARLA autonomous driving, our priming strategy works significantly better than several popular state-of-the-art approaches for feature selection and data augmentation. We connect these empirical findings to recent theoretical results on DNN optimization, and argue theoretically that priming distracts the optimizer away from poor shortcuts by creating better, simpler shortcuts.
Active Learning with Safety Constraints

Active learning methods have shown great promise in reducing the number of samples necessary for learning. As automated learning systems are adopted into real-time, real-world decision-making pipelines, it is increasingly important that such algorithms are designed with safety in mind. In this work we investigate the complexity of learning the best safe decision in interactive environments. We reduce this problem to a constrained linear bandits problem, where our goal is to find the best arm satisfying certain (unknown) safety constraints. We propose an adaptive experimental design-based algorithm, which we show efficiently trades off between the difficulty of showing an arm is unsafe vs suboptimal. To our knowledge, our results are the first on best-arm identification in linear bandits with safety constraints. In practice, we demonstrate that this approach performs well on synthetic and real world datasets.
Towards Robust Blind Face Restoration with Codebook Lookup Transformer

Blind face restoration is a highly ill-posed problem that often requires auxiliary guidance to 1) improve the mapping from degraded inputs to desired outputs, or 2) complement high-quality details lost in the inputs. In this paper, we demonstrate that a learned discrete codebook prior in a small proxy space largely reduces the uncertainty and ambiguity of restoration mapping by casting blind face restoration as a code prediction task, while providing rich visual atoms for generating high-quality faces. Under this paradigm, we propose a Transformer-based prediction network, named CodeFormer, to model the global composition and context of the low-quality faces for code prediction, enabling the discovery of natural faces that closely approximate the target faces even when the inputs are severely degraded. To enhance the adaptiveness for different degradation, we also propose a controllable feature transformation module that allows a flexible trade-off between fidelity and quality. Thanks to the expressive codebook prior and global modeling, CodeFormer outperforms the state of the arts in both quality and fidelity, showing superior robustness to degradation. Extensive experimental results on synthetic and real-world datasets verify the effectiveness of our method.
Neural Networks as Paths through the Space of Representations

Deep neural networks implement a sequence of layer-by-layer operations that are each relatively easy to understand, but the resulting overall computation is generally difficult to understand. We consider a simple hypothesis for interpreting the layer-by-layer construction of useful representations: perhaps the role of each layer is to reformat information to reduce the"distance"to the desired outputs. With this framework, the layer-wise computation implemented by a deep neural network can be viewed as a path through a high-dimensional representation space. We formalize this intuitive idea of a"path"by leveraging recent advances in *metric* representational similarity. We extend existing representational distance methods by computing geodesics, angles, and projections of representations, going beyond mere layer distances. We then demonstrate these tools by visualizing and comparing the paths taken by ResNet and VGG architectures on CIFAR-10. We conclude by sketching additional ways that this kind of representational geometry can be used to understand and interpret network training, and to describe novel kinds of similarities between different models.