Miracle-dz/UniFAR-FTUs huggingface dataset:
Solvation properties of proteins in membranes

Transmembrane helices are generally believed to insert into membranes based on their hydrophobicity. Nevertheless, there are important exceptions where polar or titratable residues have great functional importance, for instance the S4 helix of voltage-gated ion channels. It has been shown experimentally that insertion can be accomplished by hydrophobic counterbalance, which enabled biological hydrophobicity scales that predict an arginine insertion cost of only 2.5 kcal/mol, compared to 14.9 kcal/mol in cyklohexane. Previous simulations of pure bilayers have produced values close to the pure hydrocarbon, which has lead to vivid discussion about the experimental conditions. Here, we have performed computer simulations of models better mimicking biological membranes by explicitly including protein helices at mass fractions from 15% to 55%. This has a striking effect on the solvation free energy of arginine, which drops more than a factor of two even for purely hydrophobic extra helices. With some polar residues present, the solvation cost comes close to experimental observation around 30% mass fraction, and negligible at 40%. The effect is mainly due to the extra helices making it easier for arginine to retain hydration water, with increasing amounts at higher protein mass fraction. These results offer a possible explanation to the previous discrepancy between the in vivo hydrophobicity scale and computer simulations, and highlight the importance of the relatively high protein contents in biological membranes. While many membrane proteins are stable in pure bilayers, the simplified models might not be sufficiently accurate descriptions of insertion for polar or charged residues in biological membranes.
Matrix moments in a real, doubly correlated algebraic generalization of the Wishart model

The Wishart model of random covariance or correlation matrices continues to find ever more applications as the wealth of data on complex systems of all types grows. The heavy tails often encountered prompt generalizations of the Wishart model, involving algebraic distributions instead of a Gaussian. The mathematical properties pose new challenges, particularly for the doubly correlated versions. Here we investigate such a doubly correlated algebraic model for real covariance or correlation matrices, which is known as matrix variate t-distribution in the statistics literature. We focus on the matrix moments and explicitly calculate the first and the second one, the computation of the latter is non-trivial. We solve the problem by relating it to the Aomoto integral and by extending the recursive technique to calculate Ingham–Siegel integrals. We compare our results with the Gaussian case.
RGBT Tracking via Multi-Adapter Network with Hierarchical Divergence Loss

RGBT tracking has attracted increasing attention since RGB and thermal infrared data have strong complementary advantages, which could make trackers all-day and all-weather work. Existing works usually focus on extracting modality-shared or modality-specific information, but the potentials of these two cues are not well explored and exploited in RGBT tracking. In this paper, we propose a novel multi-adapter network to jointly perform modality-shared, modality-specific and instance-aware target representation learning for RGBT tracking. To this end, we design three kinds of adapters within an end-to-end deep learning framework. In specific, we use the modified VGG-M as the generality adapter to extract the modality-shared target representations. To extract the modality-specific features while reducing the computational complexity, we design a modality adapter, which adds a small block to the generality adapter in each layer and each modality in a parallel manner. Such a design could learn multilevel modality-specific representations with a modest number of parameters as the vast majority of parameters are shared with the generality adapter. We also design instance adapter to capture the appearance properties and temporal variations of a certain target. Moreover, to enhance the shared and specific features, we employ the loss of multiple kernel maximum mean discrepancy to measure the distribution divergence of different modal features and integrate it into each layer for more robust representation learning. Extensive experiments on two RGBT tracking benchmark datasets demonstrate the outstanding performance of the proposed tracker against the state-of-the-art methods.
Proximate, Antinutritional, Microbial, and Sensory Acceptability of Bread Formulated from Wheat (Triticum aestivum) and Amaranth (Amaranthus caudatus)

Background Breads are made throughout the world. Bread can be prepared from cereal like wheat, maize, and rice. Nowadays, gluten intolerance, requirement of healthy, and nutritious products have increased and interests towards underutilized crops have also been increasing with the aim of improving global food security and to ease an adverse effect of climate changes. Amaranth is one of nutritionally balanced and naturally grown underutilized crops, but it is mainly considered weed in Africa including Ethiopia. Method The aim of the study is to develop bread from wheat and Amaranthus and to evaluate proximate composition, antinutritional, microbial, and sensory acceptability of bread. The experiment contained 100% wheat as control and four blending proportions (90% wheat and 10% amaranth, 80% wheat and 20% amaranth, 70% wheat and 30% amaranth, and 60% wheat and 40% amaranth). A complete randomized design is used for proximate composition, antinutritional, and microbial data analysis whereas a randomized complete block design with three replications was applied for sensory acceptability. SAS for windows version 9 was used for data analysis. Result The study revealed that moisture, protein, fat, fiber, and antinutritional content were increased as Amaranthus concentration is increased from 10% to 40%. However, carbohydrate, microbial load, and sensory acceptability were decreased. But the gross energy is constant. Conclusion From the study, it can be concluded that beside the good nutritional profile of Amaranthus, it has antinutritional content which needs to limit the concentration of Amaranthus in blending with other grains during product development.
Convergence Properties of Stochastic Hypergradients

Bilevel optimization problems are receiving increasing attention in machine learning as they provide a natural framework for hyperparameter optimization and meta-learning. A key step to tackle these problems in the design of optimization algorithms for bilevel optimization is the efficient computation of the gradient of the upper-level objective (hypergradient). In this work, we study stochastic approximation schemes for the hypergradient, which are important when the lower-level problem is empirical risk minimization on a large dataset. We provide iteration complexity bounds for the mean square error of the hypergradient approximation, under the assumption that the lower-level problem is accessible only through a stochastic mapping which is a contraction in expectation. Preliminary numerical experiments support our theoretical analysis.
A non local approximation of the Gaussian perimeter: Gamma convergence and Isoperimetric properties

We study a non local approximation of the Gaussian perimeter, proving the Gamma convergence to the local one. Surprisingly, in contrast with the local setting, the halfspace turns out to be a volume constrained stationary point if and only if the boundary hyperplane passes through the origin. In particular, this implies that Ehrhard symmetrization can in general increase the considered non local Gaussian perimeter.
Multiclass Yeast Segmentation in Microstructured Environments with Deep Learning

Cell segmentation is a major bottleneck in extracting quantitative single-cell information from microscopy data. The challenge is exasperated in the setting of microstructured environments. While deep learning approaches have proven useful for general cell segmentation tasks, existing segmentation tools for the yeast-microstructure setting rely on traditional machine learning approaches. Here we present convolutional neural networks trained for multiclass segmenting of individual yeast cells and discerning these from cell-similar microstructures. We give an overview of the datasets recorded for training, validating and testing the networks, as well as a typical use-case. We showcase the method’s contribution to segmenting yeast in microstructured environments with a typical synthetic biology application in mind. The models achieve robust segmentation results, outperforming the previous state-of-the-art in both accuracy and speed. The combination of fast and accurate segmentation is not only beneficial for a posteriori data processing, it also makes online monitoring of thousands of trapped cells or closed-loop optimal experimental design feasible from an image processing perspective.
Blood Eosinophil and Risk of Exacerbation in Chronic Obstructive Pulmonary Disease Patients: A Retrospective Cohort Analysis

Purpose Blood eosinophil is a readily available biomarker to reflect the eosinophilic inflammation in chronic obstructive pulmonary disease (COPD) patients, yet its association with exacerbation is inconclusive. It is uncertain which measurement, eosinophil percentage or absolute eosinophil count, should be used and what is the optimal cutoff for exacerbation prediction. Patients and Methods A total of 247 COPD patients were included in this retrospective cohort study. Blood eosinophil during stable disease state, baseline demographics, and clinical characteristics in 12 months after the index complete blood count (CBC) were recorded. Exacerbation frequencies were compared between patients with high and low blood eosinophil percentage using 2% as cut-off. Logistic regression and receiver operating characteristics (ROC) curve analyses were conducted. Results Patients with blood eosinophil ≥2% were associated with more frequent exacerbations than patients with eosinophil <2% in the 12 months after the index CBC (mean exacerbation 1.07 vs 0.34, p < 0.001). Higher blood eosinophil percentage conferred a higher risk of exacerbation. Adjusted odds ratio for exacerbation in 12 months after the index CBC for blood eosinophil ≥2% was 2.98 (95% confidence interval = 1.42–6.25). The area under the ROC curve of eosinophil percentage was significantly higher than that of absolute eosinophil count (0.678 vs 0.640, p = 0.010). The optimal cutoff of blood eosinophil percentage for exacerbation prediction was 2.8%. Conclusion Blood eosinophilia was associated with higher exacerbation risk in COPD patients. Further studies are required to elucidate the mechanism of eosinophilic inflammation in COPD and determine the optimal treatment strategy to reduce exacerbations.
Virus-induced p38 MAPK activation facilitates viral infection

Rationale: Many viral infections are known to activate the p38 mitogen-activated protein kinase (MAPK) signaling pathway. However, the role of p38 activation in viral infection and the underlying mechanism remain unclear. The role of virus-hijacked p38 MAPK activation in viral infection was investigated in this study. Methods: The correlation of hepatitis C virus (HCV) infection and p38 activation was studied in patient tissues and primary human hepatocytes (PHHs) by immunohistochemistry and western blotting. Coimmunoprecipitation, GST pulldown and confocal microscopy were used to investigate the interaction of p38α and the HCV core protein. In vitro kinase assays and mass spectrometry were used to analyze the phosphorylation of the HCV core protein. Plaque assays, quantitative real time PCR (qRT-PCR), western blotting, siRNA and CRISPR/Cas9 were used to determine the effect of p38 activation on viral replication. Results: HCV infection was associated with p38 activation in clinical samples. HCV infection increased p38 phosphorylation by triggering the interaction of p38α and TGF-β activated kinase 1 (MAP3K7) binding protein 1 (TAB1). TAB1-mediated p38α activation facilitated HCV replication, and pharmaceutical inhibition of p38α activation by SB203580 suppressed HCV infection at the viral assembly step. Activated p38α interacted with the N-terminal region of the HCV core protein and subsequently phosphorylated the HCV core protein, which promoted HCV core protein oligomerization, an essential step for viral assembly. As expected, SB203580 or the HCV core protein N-terminal peptide (CN-peptide) disrupted the p38α-HCV core protein interaction, efficiently impaired HCV assembly and impeded normal HCV replication in both cultured cells and primary human hepatocytes. Similarly, severe fever with thrombocytopenia syndrome virus (SFTSV), herpes simplex virus type 1 (HSV-1) or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection also activated p38 MAPK. Most importantly, pharmacological blockage of p38 activation by SB203580 effectively inhibited SFTSV, HSV-1 and SARS-CoV-2. Conclusion: Our study shows that virus-hijacked p38 activation is a key event for viral replication and that pharmacological blockage of p38 activation is an antiviral strategy.
Reflection identities of harmonic sums and pole decomposition of BFKL eigenvalue

We analyze known results of next-to-next-to-leading(NNLO) singlet BFKL eigenvalue in $N=4$ SYM written in terms of harmonic sums. The nested harmonic sums building known NNLO BFKL eigenvalue for specific values of the conformal spin have poles at negative integers. We sort the harmonic sums according to the complexity with respect to their weight and depth and use their pole decomposition in terms of the reflection identities to find the most complicated terms of NNLO BFKL eigenvalue for an arbitrary value of the conformal spin. The obtained result is compatible with the Bethe-Salpeter approach to the BFKL evolution.
On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing

The vulnerability of face recognition systems to presentation attacks has limited their application in security-critical scenarios. Automatic methods of detecting such malicious attempts are essential for the safe use of facial recognition technology. Although various methods have been suggested for detecting such attacks, most of them over-fit the training set and fail in generalizing to unseen attacks and environments. In this work, we use transfer learning from the vision transformer model for the zero-shot anti-spoofing task. The effectiveness of the proposed approach is demonstrated through experiments in publicly available datasets. The proposed approach outperforms the state-of-the-art methods in the zero-shot protocols in the HQ-WMCA and SiW-M datasets by a large margin. Besides, the model achieves a significant boost in cross-database performance as well.
Periodontal Disease Status and Associated Risk Factors in Patients Attending a Tertiary Hospital in Northwest Ethiopia

Purpose Information on periodontal disease and its predictors in sub-Saharan Africa is sparse. This study aimed to determine the prevalence of periodontal disease and assess the relationship with associated factors in patients who attended the University of Gondar comprehensive hospital. Patients and Methods A hospital-based cross-sectional study was conducted from October 1, 2019, to March 10, 2020, at the University of Gondar comprehensive Hospital. Participants were recruited with a systematic random sampling technique, and interviewed for sociodemographic and medical information through a structured questionnaire. Two examiners evaluated the periodontal status of the study participants using the community periodontal index (CPI). Results Four hundred twenty participants were involved in the study. The mean age of the study participants was 29.87 (±7. 76). The majority of the study participants had a habit of tooth brushing (72.1%) and almost half of them did not have a fixed time to brush their teeth. Periodontal change (CPI>0) was noted in 42.4% of the participants and among that 21.7% had shallow pocket (pocket depth of 4–5 mm) and 5.95% had a deep periodontal pocket (≥6 mm). The prevalence was higher in 20–34 years (57.3%), females (58.98%), monthly income of <2500 ETB (82.02%), and frequent carbohydrate intakes (65.17%). Age (AOR=9.61, 95% CI: 6.42, 13.04), gender (AOR=2.00, 95% CI: 136, 2.97), educational status (AOR=3.25), residency (AOR= 1.66), monthly income (AOR=2.13), oral hygiene habit (AOR=4.85) and systemic disease (AOR=1.96) were significantly associated with periodontitis. Conclusion In the present study, 42.4% of the study participants encountered periodontal disease. The study confirmed that various sociodemographic risk factors/indicators are associated with an increased risk of periodontitis.
Long-term X-ray variability of the symbiotic system RT Cru based on Chandra spectroscopy

RT Cru belongs to the rare class of hard X-ray emitting symbiotics, whose origin is not yet fully understood. In this work, we have conducted a detailed spectroscopic analysis of X-ray emission from RT Cru based on observations taken by the Chandra Observatory using the Low Energy Transmission Grating (LETG) on the High-Resolution Camera Spectrometer (HRC-S) in 2015 and the High Energy Transmission Grating (HETG) on the Advanced CCD Imaging Spectrometer S-array (ACIS-S) in 2005. Our thermal plasma modeling of the time-averaged HRC-S/LETG spectrum suggests a mean temperature of $kT \sim 1.3$ keV, whereas $kT \sim 9.6$ keV according to the time-averaged ACIS-S/HETG. The soft thermal plasma emission component ($\sim1.3$ keV) found in the HRC-S is heavily obscured by dense materials ($> 5 \times 10^{23}$ cm$^{-2}$). The aperiodic variability seen in its light curves could be due to changes in either absorbing material covering the hard X-ray source or intrinsic emission mechanism in the inner layers of the accretion disk. To understand the variability, we extracted the spectra in the "low/hard" and "high/soft" spectral states, which indicated higher plasma temperatures in the low/hard states of both the ACIS-S and HRC-S. The source also has a fluorescent iron emission line at 6.4 keV, likely emitted from reflection off an accretion disk or dense absorber, which was twice as bright in the HRC-S epoch compared to the ACIS-S. The soft thermal component identified in the HRC-S might be an indication of a jet that deserves further evaluations using high-resolution imaging observations.
Safety of Hydroxychloroquine Among Outpatient Clinical Trial Participants for COVID-19

Abstract Background Use of hydroxychloroquine in hospitalized patients with coronavirus disease 2019 (COVID-19), especially in combination with azithromycin, has raised safety concerns. Here, we report safety data from 3 outpatient randomized clinical trials. Methods We conducted 3 randomized, double-blind, placebo-controlled trials investigating hydroxychloroquine as pre-exposure prophylaxis, postexposure prophylaxis, and early treatment for COVID-19 using an internet-based design. We excluded individuals with contraindications to hydroxychloroquine. We collected side effects and serious adverse events. We report descriptive analyses of our findings. Results We enrolled 2795 participants. The median age of research participants (interquartile range) was 40 (34–49) years, and 59% (1633/2767) reported no chronic medical conditions. Overall 2544 (91%) participants reported side effect data, and 748 (29%) reported at least 1 medication side effect. Side effects were reported in 40% with once-daily, 36% with twice-weekly, 31% with once-weekly hydroxychloroquine, compared with 19% with placebo. The most common side effects were upset stomach or nausea (25% with once-daily, 19% with twice-weekly, and 18% with once-weekly hydroxychloroquine, vs 11% for placebo), followed by diarrhea, vomiting, or abdominal pain (23% for once-daily, 17% twice-weekly, and 13% once-weekly hydroxychloroquine, vs 7% for placebo). Two individuals were hospitalized for atrial arrhythmias, 1 on placebo and 1 on twice-weekly hydroxychloroquine. No sudden deaths occurred. Conclusions Data from 3 outpatient COVID-19 trials demonstrated that gastrointestinal side effects were common but mild with the use of hydroxychloroquine, while serious side effects were rare. No deaths occurred related to hydroxychloroquine. Randomized clinical trials, in cohorts of healthy outpatients, can safely investigate whether hydroxychloroquine is efficacious for COVID-19. ClinicalTrials.gov Identifier NCT04308668 for postexposure prophylaxis and early treatment trials; NCT04328467 for pre-exposure prophylaxis trial.
Differential Privacy Meets Maximum-weight Matching

When it comes to large-scale multi-agent systems with a diverse set of agents, traditional differential privacy (DP) mechanisms are ill-matched because they consider a very broad class of adversaries, and they protect all users, independent of their characteristics, by the same guarantee. Achieving a meaningful privacy leads to pronounced reduction in solution quality. Such assumptions are unnecessary in many real-world applications for three key reasons: (i) users might be willing to disclose less sensitive information (e.g., city of residence, but not exact location), (ii) the attacker might posses auxiliary information (e.g., city of residence in a mobility-on-demand system, or reviewer expertise in a paper assignment problem), and (iii) domain characteristics might exclude a subset of solutions (an expert on auctions would not be assigned to review a robotics paper, thus there is no need for indistinguishably between reviewers on different fields). We introduce Piecewise Local Differential Privacy (PLDP), a privacy model designed to protect the utility function in applications where the attacker possesses additional information on the characteristics of the utility space. PLDP enables a high degree of privacy, while being applicable to real-world, unboundedly large settings. Moreover, we propose PALMA, a privacy-preserving heuristic for maximum-weight matching. We evaluate PALMA in a vehicle-passenger matching scenario using real data and demonstrate that it provides strong privacy, $\varepsilon \leq 3$ and a median of $\varepsilon = 0.44$, and high quality matchings ($10.8\%$ worse than the non-private optimal).