Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular identification and infection pathology of Mycobacterium spp. in captive wild animals in Pakistan.

INTRODUCTION Animal tuberculosis is an infectious, chronic, granulomatous, and debilitating disease affecting animals as well as humans. However, in recent decades, there have been many endemic geographic localities where animal tuberculosis has been identified in wildlife reservoirs, limiting the eradication program in cattle. This study aimed to identify animal tuberculosis in captive zoo animals in Pakistan. METHODOLOGY In total, 185 morbid zoo animals were brought for postmortem examination at a veterinary postmortem facility. During the macroscopic examination, these animals were thoroughly examined for the presence of suggestive gross lesions of animal tuberculosis (granulomas/tubercles), and the pattern and distribution of these lesions in different organs. The Ziehl-Neelsen (ZN) staining was performed on smears prepared from granulomatous lesions of lung tissue followed by molecular identification of M. bovis and M. tuberculosis DNA using polymerase chain reaction (PCR). RESULTS The postmortem examination revealed that 8.1% (15/185) of animals had gross tuberculosis lesions on the lungs and lymph nodes. The ZN staining of tissue smears showed 5.40% positivity while M. bovis and M. tuberculosis DNA was identified in 3.78 % and 1.1% of investigated animals, respectively. CONCLUSIONS The study showed that animal tuberculosis is prevalent among wildlife in Pakistan and it may pose serious public health concerns to the people visiting these zoos and wildlife parks.
An Empirical Model for Estimating the Zonal Mean Aerosol Extinction Profiles from SAGE II Measurements

Abstract This paper presents an empirical model for estimating the zonal mean aerosol extinction profiles in the stratosphere over 10°-wide latitude bands between 60°S and 60°N, on the basis of Stratospheric Aerosol and Gas Experiment (SAGE) II aerosol extinction measurements at 1.02, 0.525, and 0.452 µm during the volcanically quiescent period between 1998–2004. First, an empirical model is developed for calculating the stratospheric aerosol extinction profiles at 1.02 µm. Then, starting from the 1.02 µm extinction profile and an exponential spectral dependence, an empirical algorithm is developed that allows the aerosol extinction profiles at other wavelengths to be calculated. Comparisons of the model-calculated aerosol extinction profiles at the wavelengths of 1.02, 0.525, and 0.452 µm and the SAGE II measurements show that the model-calculated aerosol extinction coefficients conform well with the SAGE II values, with the relative differences generally being within 15% from 2 km above the tropopause to 40 km. The model-calculated stratospheric aerosol optical depths at the three wavelengths are also in good agreement with the corresponding optical depths derived from the SAGE II measurements, with the relative differences being within 0.9% for all latitude bands. This paper provides a useful tool in simulating zonal mean aerosol extinction profiles, which can be used as representative background stratospheric aerosols in view of atmospheric modeling and remote sensing retrievals.
The prevalence of HBV, HCV, and HIV among hemodialysis patients in a tertiary care hospital in Mashhad, Iran.

INTRODUCTION Hepatitis and acquired immunodeficiency are major health concerns in high-risk patients with renal failure undergoing hemodialysis. This might be due to the number of blood transfusions, age, and dialysis duration. We aimed to investigate the prevalence of HBV, HCV, and HIV in hemodialysis patients to determine the effectiveness of preventive measures already in place and the possible correlation between various risk factors and viral infection in the Hemodialysis Center in Mashhad, Iran. METHODOLOGY Sixty-five patients were included in a retrospective cross-sectional study. The demographic information was collected. Hepatitis-B surface antigens, anti-HCV, and anti-HIV antibodies were screened using ELISA. RESULTS Out of 65 patients, 34 (52.3%) were male, and 31 (47.7%) were female. Mean duration of dialysis was 30.68 ± 26.39 months, and the mean age was 64.95 ± 14.09 years. We found 9 (13.8%) patients that were HBV positive (HbsAg-positive), and 3 (4.6%) patients were HCV positive. Sex and the number of blood transfusions were found to be risk factors for HBV infection and had statistical significance (p = 0.02 and p = 0.01, respectively). No statistical significance was found between HBV- and HCV- positivity and the mean age of patients (p = 0.84 and p = 0.76, respectively). All patients were HIV-negative. CONCLUSIONS Prevalence of HBV was high and significant. More preventive measures need to be developed, and further studies should be conducted to examine the effectiveness of these measures. Moreover, evaluating the prevalence rates of HBV, HCV, and HIV in other hospitals and dialysis centers in Mashhad is recommended to minimize viral infections. Initial HBV vaccination for patients that require hemodialysis is crucial.
Comparison of clinical features between nonsmokers with COPD and smokers with COPD: a retrospective observational study

Background: Smoking is a major risk factor for chronic obstructive pulmonary disease (COPD); however, the similarities and differences in clinical presentation between smokers and nonsmokers are not fully described in patients with COPD. This study was designed to address this issue in a general teaching hospital in the People’s Republic of China. Methods: The medical records of patients hospitalized with a lung mass for further evaluation at Zhongshan Hospital, Fudan University, from January 2006 to December 2010 were reviewed and the data of interest were collected. The definition of COPD was according to Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric criteria. Participants who had a previous exacerbation within 4 weeks of admission, airflow limitation due to abnormalities in the large airways, or with other pulmonary diseases were excluded. Included subjects were divided into nonsmokers with COPD and smokers with COPD by a cutoff of a 5 pack-year smoking history. Results: A total of 605 subjects were included in the final analysis. The average age was 64.8 ± 8.5 years and 62.0% (375/605) were smokers. Eighty percent of the patients had mild to moderate disease (GOLD grade 1–2). Age and years with COPD were comparable between the two groups. Compared with smokers with COPD, nonsmokers with COPD were more likely to be female, reported less chronic cough and sputum, have less emphysema on radiologic examination, and higher measures of forced expiratory volume in the first second percent predicted (FEV 1 ), forced expiratory volume in one second/forced vital capacity (FEV 1 /FVC%) percent predicted, maximal voluntary ventilation percent predicted, diffusing capacity of lung (DLCO) percent predicted, and DLCO/alveolar volume percent predicted, with lower levels of residual volume percent predicted and residual volume/total lung capacity percent predicted. There were no significant differences between the two groups with regard to distribution of disease severity, vital capacity percent predicted, total lung capacity percent predicted, PaO 2 , PaCO 2 , modified Medical Research Council dyspnea score, wheezing, airway reversibility, and comorbidities. Smoking amount (pack-years) was correlated negatively with FEV 1 percent predicted, FEV 1 /FVC% percent predicted, inspiratory capacity percent predicted, inspiratory capacity/total lung capacity percent predicted, and DLCO percent predicted, and correlated positively with GOLD grade and symptoms. Conclusion: Non-smokers with COPD had less impairment in airflow limitation and gas exchange, and a lower prevalence of emphysema, chronic cough, and sputum compared with their smoking counterparts. Tobacco cessation is warranted in smokers with COPD.
Gut check: Unveiling the influence of acute exercise on the gut microbiota

The human gastrointestinal microbiota and its unique metabolites regulate a diverse array of physiological processes with substantial implications for human health and performance. Chronic exercise training positively modulates the gut microbiota and its metabolic output. The benefits of chronic exercise for the gut microbiota may be influenced by acute changes in microbial community structure and function that follow a single exercise bout (i.e., acute exercise). Thus, an improved understanding of changes in the gut microbiota that occur with acute exercise could aid in the development of evidence‐based exercise training strategies to target the gut microbiota more effectively. In this review, we provide a comprehensive summary of the existing literature on the acute and very short‐term (<3 weeks) exercise responses of the gut microbiota and faecal metabolites in humans. We conclude by highlighting gaps in the literature and providing recommendations for future research in this area.
Clinical Management Guidelines for Obstetrician – Gynecologists interim update

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Unravelling Robust Brain-Behavior Links of Depressive Symptoms Through Granular Network Models: Understanding Heterogeneity and Clinical Implications

Importance: Major depressive disorder (MDD) is highly heterogeneous and prevalent globally, with diverse neurobiological underpinnings. Understanding the interplay between psychopathological symptoms and biological factors is critical for elucidating its etiology and persistence. Objective: We aimed to evaluate the utility of using symptom-brain networks to parse the heterogeneity of depressive symptomatology in a large adolescent sample. Design, Setting, Participants: We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1,317 adolescents (52.49% female, age: mean=18.5, SD=0.72). Main outcomes and measures: Two network models were estimated: one including an overall depression severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS symptom/item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging. Results: The network based on individual symptom scores revealed associations between cortical thickness measures and specific symptoms, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor.=-0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = -0.10), and mOFC was negatively associated with anhedonia (partial cor. = -0.05). Conclusions and Relevance: This study showcases the utility of network models at parsing heterogeneity in depression, linking individual symptoms with specific neural substrates. We discuss the clinical relevance and implications of this approach and outline the next steps to integrate neurobiological and cognitive markers to further unravel MDD's phenotypic heterogeneity.
RAIN: Your Language Models Can Align Themselves without Finetuning

Large language models (LLMs) often demonstrate inconsistencies with human preferences. Previous research typically gathered human preference data and then aligned the pre-trained models using reinforcement learning or instruction tuning, a.k. a. the finetuning step. In contrast, aligning frozen LLMs without requiring alignment data is more appealing. This work explores the potential of the latter setting. We discover that by integrating self-evaluation and rewind mechanisms, unaligned LLMs can directly produce responses consistent with human preferences via self-boosting. We introduce a novel inference method, Rewindable Auto-regressive INference (RAIN), that allows pre-trained LLMs to evaluate their own generation and use the evaluation results to guide rewind and generation for AI safety. Notably, RAIN operates without the need of extra data for model alignment and abstains from any training, gradient computation, or parameter updates. Experimental results evaluated by GPT-4 and humans demonstrate the effectiveness of RAIN: on the HH dataset, RAIN improves the harmlessness rate of LLaMA 30B from 82% of vanilla inference to 97%, while maintaining the helpfulness rate. On the TruthfulQA dataset, RAIN improves the truthfulness of the already-well-aligned LLaMA-2-chat 13B model by 5%.
Developing a Novel Image Marker to Predict the Responses of Neoadjuvant Chemotherapy (NACT) for Ovarian Cancer Patients

OBJECTIVE Neoadjuvant chemotherapy (NACT) is one kind of treatment for advanced stage ovarian cancer patients. However, due to the nature of tumor heterogeneity, the patients' responses to NACT varies significantly among different subgroups. To address this clinical challenge, the purpose of this study is to develop a novel image marker to achieve high accuracy response prediction of the NACT at an early stage. METHODS For this purpose, we first computed a total of 1373 radiomics features to quantify the tumor characteristics, which can be grouped into three categories: geometric, intensity, and texture features. Second, all these features were optimized by principal component analysis algorithm to generate a compact and informative feature cluster. Using this cluster as the input, an SVM based classifier was developed and optimized to create a final marker, indicating the likelihood of the patient being responsive to the NACT treatment. To validate this scheme, a total of 42 ovarian cancer patients were retrospectively collected. A nested leave-one-out cross-validation was adopted for model performance assessment. RESULTS The results demonstrate that the new method yielded an AUC (area under the ROC [receiver characteristic operation] curve) of 0.745. Meanwhile, the model achieved overall accuracy of 76.2%, positive predictive value of 70%, and negative predictive value of 78.1%. CONCLUSION This study provides meaningful information for the development of radiomics based image markers in NACT response prediction.
Descent and Cyclotomic Redshift for Chromatically Localized Algebraic K-theory

We prove that $T(n+1)$-localized algebraic $K$-theory satisfies descent for $\pi$-finite $p$-group actions on stable $\infty$-categories of chromatic height up to $n$, extending a result of Clausen-Mathew-Naumann-Noel for $p$-groups. Using this, we show that it sends $T(n)$-local Galois extensions to $T(n+1)$-local Galois extensions. Furthermore, we show that it sends cyclotomic extensions of height $n$ to cyclotomic extensions of height $n+1$, extending a result of Bhatt-Clausen-Mathew for $n=0$. As a consequence, we deduce that $K(n+1)$-localized $K$-theory satisfies hyperdescent along the cyclotomic tower of any $T(n)$-local ring. Counterexamples to such cyclotomic hyperdescent for $T(n+1)$-localized $K$-theory were constructed by Burklund, Hahn, Levy and the third author, thereby disproving the telescope conjecture.
Ollivier curvature, Isoperimetry, concentration, and Log-Sobolev inequalitiy

We introduce a Laplacian separation principle for the the eikonal equation on Markov chains. As application, we prove an isoperimetric concentration inequality for Markov chains with non-negative Ollivier curvature. That is, every single point from the concentration profile yields an estimate for every point of the isoperimetric estimate. Applying to exponential and Gaussian concentration, we obtain affirmative answers to two open quesions by Erbar and Fathi. Moreover, we prove that the modified log-Sobolev constant is at least the minimal Ollivier Ricci curvature, assuming non-negative Ollivier sectional curvature, i.e., the Ollivier Ricci curvature when replacing the $\ell_1$ by the $\ell_\infty$ Wasserstein distance. This settles a recent open Problem by Pedrotti. We give a simple example showing that non-negative Ollivier sectional curvature is necessary to obtain a modified log-Sobolev inequality via positive Ollivier Ricci bound. This provides a counterexample to a conjecture by Peres and Tetali.
MTD: Multi-Timestep Detector for Delayed Streaming Perception

Autonomous driving systems require real-time environmental perception to ensure user safety and experience. Streaming perception is a task of reporting the current state of the world, which is used to evaluate the delay and accuracy of autonomous driving systems. In real-world applications, factors such as hardware limitations and high temperatures inevitably cause delays in autonomous driving systems, resulting in the offset between the model output and the world state. In order to solve this problem, this paper propose the Multi- Timestep Detector (MTD), an end-to-end detector which uses dynamic routing for multi-branch future prediction, giving model the ability to resist delay fluctuations. A Delay Analysis Module (DAM) is proposed to optimize the existing delay sensing method, continuously monitoring the model inference stack and calculating the delay trend. Moreover, a novel Timestep Branch Module (TBM) is constructed, which includes static flow and adaptive flow to adaptively predict specific timesteps according to the delay trend. The proposed method has been evaluated on the Argoverse-HD dataset, and the experimental results show that it has achieved state-of-the-art performance across various delay settings.
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss

Although deep learning (DL) has led to several breakthroughs in many disciplines as diverse as chemistry, computer science, electrical engineering, mathematics, medicine, neuroscience, and physics, a comprehensive understanding of why and how DL is empirically successful remains fundamentally elusive. To attack this fundamental problem and unravel the mysteries behind DL's empirical successes, significant innovations toward a unified theory of DL have been made. These innovations encompass nearly fundamental advances in optimization, generalization, and approximation. Despite these advances, however, no work to date has offered a way to quantify the testing performance of a DL-based algorithm employed to solve a pattern classification problem. To overcome this fundamental challenge in part, this paper exposes the fundamental testing performance limits of DL-based binary classifiers trained with hinge loss. For binary classifiers that are based on deep rectified linear unit (ReLU) feedforward neural networks (FNNs) and ones that are based on deep FNNs with ReLU and Tanh activation, we derive their respective novel asymptotic testing performance limits. The derived testing performance limits are validated by extensive computer experiments.
Weakened vortex stretching effect in three scale hierarchy for the 3D Euler equations

We consider the 3D incompressible Euler equations under the following three scale hierarchical situation: large-scale vortex stretching the middle-scale, and at the same time, the middle-scale stretching the small-scale. In this situation, we show that, the stretching effect of this middle-scale flow is weakened by the large-scale. In other words, the vortices being stretched could have the corresponding stress tensor being weakened.
Physical Properties of the Young Asteroid Pair 2010 UM26 and 2010 RN221

The main belt asteroids 458271 (2010 UM26) and 2010 RN221 share almost identical orbital elements, and currently appear as comoving objects ∼30″ apart in the plane of the sky. They are products of the breakup of a parent object, or the splitting of a binary, with a separation age measured in decades rather than thousands or millions of years as for most other asteroid pairs. The nature of the precursor body and the details of the breakup and separation of the components are unknown. We obtained deep, high-resolution imaging using the Hubble Space Telescope to characterize the pair and to search for material in addition to the main components that might have been released upon breakup. The primary and secondary have absolute magnitudes H = 17.98 and 19.69, respectively, and effective diameters 760 and 350 m (assuming geometric albedo 0.20). The secondary/primary mass ratio is 0.1, assuming equal densities. Time-series photometry shows that the primary rotates with period ∼5.9 hr and has a small photometric range (0.15 mag), while the period of the secondary is undetermined (but ≳20 hr) and its lightcurve range is at least 1 mag. The primary rotation period and component mass ratio are consistent with a simple model for the breakup of a rotationally unstable precursor. However, unlike other observationally supported instances of asteroid breakup, neither macroscopic fragments nor unresolved material are found remaining in the vicinity of this asteroid pair. We suggest that the pair is a recently dissociated binary, itself formed earlier by rotational instability of 2010 UM26.
Semi-Supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Selection

Identification of the family to which a malware specimen belongs is essential in understanding the behavior of the malware and developing mitigation strategies. Solutions proposed by prior work, however, are often not practicable due to the lack of realistic evaluation factors. These factors include learning under class imbalance, the ability to identify new malware, and the cost of production-quality labeled data. In practice, deployed models face prominent, rare, and new malware families. At the same time, obtaining a large quantity of up-to-date labeled malware for training a model can be expensive. In this article, we address these problems and propose a novel hierarchical semi-supervised algorithm, which we call the HNMFk Classifier, that can be used in the early stages of the malware family labeling process. Our method is based on non-negative matrix factorization with automatic model selection, that is, with an estimation of the number of clusters. With HNMFk Classifier, we exploit the hierarchical structure of the malware data together with a semi-supervised setup, which enables us to classify malware families under conditions of extreme class imbalance. Our solution can perform abstaining predictions, or rejection option, which yields promising results in the identification of novel malware families and helps with maintaining the performance of the model when a low quantity of labeled data is used. We perform bulk classification of nearly 2,900 both rare and prominent malware families, through static analysis, using nearly 388,000 samples from the EMBER-2018 corpus. In our experiments, we surpass both supervised and semi-supervised baseline models with an F1 score of 0.80.