Miracle-dz/UniFAR-FTUs huggingface dataset:
In Silico Drug Discovery Strategies Identified ADMET Properties of Decoquinate RMB041 and Its Potential Drug Targets against Mycobacterium tuberculosis

ABSTRACT The highly adaptive cellular response of Mycobacterium tuberculosis to various antibiotics and the high costs for clinical trials, hampers the development of novel antimicrobial agents with improved efficacy and safety. Subsequently, in silico drug screening methods are more commonly being used for the discovery and development of drugs, and have been proven useful for predicting the pharmacokinetics, toxicities, and targets, of prospective new antimicrobial agents. In this investigation we used a reversed target fishing approach to determine potential hit targets and their possible interactions between M. tuberculosis and decoquinate RMB041, a propitious new antituberculosis compound. Two of the 13 identified targets, Cyp130 and BlaI, were strongly proposed as optimal drug-targets for dormant M. tuberculosis, of which the first showed the highest comparative binding affinity to decoquinate RMB041. The metabolic pathways associated with the selected target proteins were compared to previously published molecular mechanisms of decoquinate RMB041 against M. tuberculosis, whereby we confirmed disrupted metabolism of proteins, cell wall components, and DNA. We also described the steps within these pathways that are inhibited and elaborated on decoquinate RMB041’s activity against dormant M. tuberculosis. This compound has previously showed promising in vitro safety and good oral bioavailability, which were both supported by this in silico study. The pharmacokinetic properties and toxicity of this compound were predicted and investigated using the online tools pkCSM and SwissADME, and Discovery Studio software, which furthermore supports previous safety and bioavailability characteristics of decoquinate RMB041 for use as an antimycobacterial medication. IMPORTANCE This article elaborates on the mechanism of action of a novel antibiotic compound against both, active and dormant Mycobacterium tuberculosis and describes its pharmacokinetics (including oral bioavailability and toxicity). Information provided in this article serves useful during the search for drugs that shorten the treatment regimen for Tuberculosis and cause minimal adverse effects.
Relationship with the Father of the Baby and Pregnancy-Related Anxiety among Pregnant Black Women

Abstract Purpose: Pregnancy-related anxiety may increase the risk of preterm birth. Effective coping strategies and social support may help minimize pregnancy-related anxiety. Study Design: Secondary analysis of cross-sectional data. Methods: A sample of 408 pregnant non-Hispanic Black women completed questionnaires between 19 and 31 weeks gestation. Mediation analysis with structural equation modeling was used to explore effects of the relationship with the father of the baby on pregnancy-related anxiety. Results: Support from the father of the baby was negatively associated with avoidance coping (r = -.22, p < .001) and pregnancy-related anxiety (r = - .17, p < .001), whereas conflict with the father of the baby was positively associated with avoidance coping (r = .37, p < .001) and pregnancy-related anxiety (r = .29, p < .001). Avoidance coping was positively associated with pregnancy-related anxiety (r = .34, p < .001). After adjustment, avoidance coping partially mediated the effect of conflict with the father of the baby on pregnancy-related anxiety. Clinical Implications: Discussions with women about management of pregnancy-related anxiety should consider her current social support and coping mechanisms. Providers should offer support and resources on adaptive coping strategies. In this study of 408 pregnant Black women, their relationship with the father of the baby was evaluated for its potential effect on pregnancy-related anxiety. Support from the father of the baby and conflict with the father of the baby significantly affected pregnancy-related anxiety. During prenatal care, discussions with the pregnant woman about the relationship with the father of the baby may be helpful in identifying stress and offering guidance as needed to decrease anxiety such as listening to their concerns, antenatal yoga, group prenatal classes, extended childbirth education, cognitive behavioral therapy, or counseling.
Structural and psychological empowerment and organizational commitment among staff nurses in Jordan.

AIM The purpose of this study was to examine the relationships between structural empowerment, psychological empowerment, and organizational commitment in Jordanian staff nurses. BACKGROUND Empowering nurses is one of the most effective strategies for increasing efficiency. Therefore, studying the contribution of structural and psychological empowerment to organizational commitment will provide a base for future interventions to improve nurses' working environment. MATERIALS AND METHODS This cross-sectional study used snowball sampling to recruit staff nurses working in Jordanian public and private hospitals. The sample consisted of 180 nurses. Data were collected using three valid and reliable self-reporting questionnaires that were distributed in electronic format. RESULTS Nurses reported a moderate level of both structural and psychological empowerment, and a high level of organizational commitment. Significant positive relationships were found between structural empowerment and psychological empowerment and organizational commitment (r = .62, p < .001) and (r = .57, p < .001) respectively. CONCLUSION Staff nurses reported moderate levels of both structural and psychological empowerment, and have a high level of organizational commitment. Positive relationships between structural empowerment, psychological empowerment, and organizational commitment were found among Jordanian staff nurses.
High-resolution Face Swapping via Latent Semantics Disentanglement

We present a novel high-resolution face swapping method using the inherent prior knowledge of a pre-trained GAN model. Although previous research can leverage generative priors to produce high-resolution results, their quality can suffer from the entangled semantics of the latent space. We explicitly disentangle the latent semantics by utilizing the progressive nature of the generator, deriving structure at-tributes from the shallow layers and appearance attributes from the deeper ones. Identity and pose information within the structure attributes are further separated by introducing a landmark-driven structure transfer latent direction. The disentangled latent code produces rich generative features that incorporate feature blending to produce a plausible swapping result. We further extend our method to video face swapping by enforcing two spatio-temporal constraints on the latent space and the image space. Extensive experiments demonstrate that the proposed method outperforms state-of-the-art image/video face swapping methods in terms of hallucination quality and consistency. Code can be found at: https://github.com/cnnlstm/FSLSD_HiRes.
Bayesian Image Super-Resolution With Deep Modeling of Image Statistics

Modeling statistics of image priors is useful for image super-resolution, but little attention has been paid from the massive works of deep learning-based methods. In this work, we propose a Bayesian image restoration framework, where natural image statistics are modeled with the combination of smoothness and sparsity priors. Concretely, first we consider an ideal image as the sum of a smoothness component and a sparsity residual, and model real image degradation including blurring, downscaling, and noise corruption. Then, we develop a variational Bayesian approach to infer their posteriors. Finally, we implement the variational approach for single image super-resolution (SISR) using deep neural networks, and propose an unsupervised training strategy. The experiments on three image restoration tasks, i.e., ideal SISR, realistic SISR, and real-world SISR, demonstrate that our method has superior model generalizability against varying noise levels and degradation kernels and is effective in unsupervised SISR. The code and resulting models are released via https://zmiclab.github. io/projects.html.
Testing the identiﬁcation of causal eﬀects in data

This study demonstrates the existence of a testable condition for the identiﬁcation of the causal eﬀect of a treatment on an outcome in observational data, which relies on two sets of variables, namely observed covariates to be controlled for and a suspected instrument. Under a causal structure commonly found in empirical applications, the testable conditional independence of the suspected instrument and the outcome given the treatment and the covariates has two implications. First, the instrument is valid, i.e. it does not directly aﬀect the outcome (other than through the treatment) and is unconfounded conditional on the covariates. Second, the treatment is unconfounded conditional on the covariates such that the treatment eﬀect is identiﬁed. We suggest tests of this conditional independence based on doubly robust estimators and investigate their ﬁnite sample performance in a simulation study. We also apply our testing approach to the evaluation of the impact of fertility on female labor supply when using the sibling sex ratio of the ﬁrst two children as supposed instrument, which by and large points to a violation of our testable implication, at least for the moderate set of socio-economic covariates considered.
Examining Variations Between Everyday Life Handedness and Lateral Preferences for Sport-Specific Skills in Children

ABSTRACT Purpose: This study aimed to analyze both sport-specific lateral preferences and handedness for everyday life tasks among school-aged children. Method: A total of 533 children (254 males and 279 females) aged 6 to 15 years old were assessed. Children’s handedness was determined according to the laterality score from Edinburgh Handedness Inventory (EHI), while lateral preferences were assessed for 16 different sport-specific tasks. Results: The percentage of children with a left hand preference was lower for unilateral (10.5–14.3%) than for bilateral (19.5–31.7%) tasks. An increased prevalence of left-sided preference was also obtained for foot tasks (13.3–26.8%) and rotation along the vertical axis (28.5%). Similarly, hand preference for unilateral sport-specific tasks and EHI scores were largely correlated (r = 0.551–0.630), while these correlations were lower for bilateral hand tasks (r = 0.148–0.418), foot tasks (r = 0.201–0.386) and rotation preference (r = 0.129). Moreover, left-handed children evidenced less lateralized behavior for sport-specific tasks than right-handed children. Conclusions: The current study has shown that sport-specific lateral preferences and their correlations with handedness vary considerably depending on the task and individual characteristics in developmental ages. These findings emphasize the relevance of task-specific assessments of lateral preferences when looking at sports skills during childhood.
Analytical Scaling Solutions for the Evolution of Cosmic Domain Walls in a Parameter-Free Velocity-Dependent One-Scale Model

We derive an analytical approximation for the linear scaling evolution of the characteristic length L and the root-mean-squared velocity σv of standard frictionless domain wall networks in Friedmann–Lemaître–Robertson–Walker universes with a power law evolution of the scale factor a with the cosmic time t (a∝tλ). This approximation, obtained using a recently proposed parameter-free velocity-dependent one-scale model for domain walls, reproduces well the model predictions for λ close to unity, becoming exact in the λ→1− limit. We use this approximation, in combination with the exact results found for λ=0, to obtain a fit to the model predictions valid for λ∈[0,1] with a maximum error of the order of 1%. This fit is also in good agreement with the results of field theory numerical simulations, especially for λ∈[0.9,1]. Finally, we explicitly show that the phenomenological energy-loss parameter of the original velocity-dependent one-scale model for domain walls vanishes in the λ→1− limit and discuss the implications of this result.
Weakly supervised causal representation learning

Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone. We prove under mild assumptions that this representation is however identifiable in a weakly supervised setting. This involves a dataset with paired samples before and after random, unknown interventions, but no further labels. We then introduce implicit latent causal models, variational autoencoders that represent causal variables and causal structure without having to optimize an explicit discrete graph structure. On simple image data, including a novel dataset of simulated robotic manipulation, we demonstrate that such models can reliably identify the causal structure and disentangle causal variables.
The Nonuniformity of Poleward Flux Transport on the Solar Surface: A Statistical Method Applied to Solar Cycles 21–24

The poleward migration of the active regions’ magnetic flux on the solar surface plays an important role in the development of the large-scale field development, especially the polar-field reversal, which is a key process in the Babcock–Leighton-type solar dynamos. The poleward flux transport is nonuniform, centered around poleward surges as suggested by previous observations. The strong, long-lasting surges are related to activity complexes, and often result in violent polar-field reversal. However, the nonuniformity of poleward flux transport has not been evaluated quantitatively. We propose a statistical method to analyze the poleward flux transport during solar cycles 21–24 by considering the frequency distributions of the magnetic field at latitudes of poleward surges occurring during solar cycles. The nonuniformity is quantified as the kurtosis statistics representing the tailedness of the distributions. We test the method on results of surface flux transport simulations, and apply it to WSO, National Solar Observatory, MWO, and HMI data. We confirm that the poleward surges are of significance during solar cycles 21–24 in general. The kurtosis within a solar cycle is affected by different latitudes of the magnetic field and different data sources. The southern hemisphere of cycle 24 exhibits the largest kurtosis, agreeing with the super-surge concept from previous work. The significant nonuniformity of poleward flux transport originates from the nonrandomness of active regions, which favors the activity complexes as the origin of poleward surges.
Age-based disparities in telehealth use in an urban, underserved population in cancer and pulmonary clinics: A need for policy change

Supplemental Digital Content is Available in the Text. ABSTRACT Background: During the COVID-19 pandemic, telehealth rapidly emerged as an essential health care service and became particularly important for patients with cancer and chronic conditions. However, the benefits of telehealth have not been fully realized for some of the most vulnerable populations due to inequitable access to telehealth capable technology. Purpose: This study aimed to assess accessibility and satisfaction with telehealth technology by vulnerable patients with cancer and pulmonary disease. Methodology: A paper survey and internet-based survey were developed and administered to adult (≥18 years) cancer and pulmonary clinic patients (July 1, 2020 to October 30, 2020). Results: Descriptive statistics and Fisher exact test were performed. Two hundred eleven patients completed the survey. Adults ≥50 years old (older) had reduced access to smartphone video capability and internet connection compared with adults less than 50 years old (59% vs. 90%, p < .01). Older adults reported more challenges with telehealth visits compared with younger adults (50.3%, 28.6%; p < .01). No difference in access to technology and preferences for telehealth versus in-person care was found by race, gender, or education level. Conclusions: Nearly all patients (95%) who had a previous experience with a telehealth visit felt confident in the quality of care they received via telehealth. Younger adults preferred video visits compared with older adults (75% vs. 50.6%, p < .01). Older adults were less likely to have access to smartphones with internet access, have more challenges with telehealth visits, and were less likely to prefer audio–video telehealth visits compared with younger adults. Implications: Ensuring equitable access to all health care delivery modalities by telehealth, including audio-only visits for patients across the age continuum, is paramount.
Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysis

Respiratory sound classification is an important tool for remote screening of respiratory-related diseases such as pneumonia, asthma, and COVID-19. To facilitate the interpretability of classification results, especially ones based on deep learning, many explanation methods have been proposed using prototypes. However, existing explanation techniques often assume that the data is non-biased and the prediction results can be explained by a set of prototypical examples. In this work, we develop a unified example-based explanation method for selecting both representative data (prototypes) and outliers (criticisms). In particular, we propose a novel application of adversarial attacks to generate an explanation spectrum of data instances via an iterative fast gradient sign method. Such unified explanation can avoid over-generalisation and bias by allowing human experts to assess the model mistakes case by case. We performed a wide range of quantitative and qualitative evaluations to show that our approach generates effective and understandable explanation and is robust with many deep learning models. Copyright © 2022 ISCA.
On the Road to Online Adaptation for Semantic Image Segmentation

We propose a new problem formulation and a corresponding evaluation framework to advance research on unsupervised domain adaptation for semantic image segmentation. The overall goal is fostering the development of adaptive learning systems that will continuously learn, without supervision, in ever-changing environments. Typical protocols that study adaptation algorithms for segmentation models are limited to few domains, adaptation happens offline, and human intervention is generally required, at least to annotate data for hyperparameter tuning. We argue that such constraints are incompatible with algorithms that can continuously adapt to different real-world situations. To address this, we propose a protocol where models need to learn online, from sequences of temporally correlated images, requiring continuous, frame-by-frame adaptation. We accompany this new protocol with a variety of baselines to tackle the proposed formulation, as well as an extensive analysis of their behaviors, which can serve as a starting point for future research.
Parameter-efficient Fine-tuning for Vision Transformers

In computer vision, it has achieved great success in adapting large-scale pretrained vision models (e.g., Vision Transformer) to downstream tasks via ﬁne-tuning. Common approaches for ﬁne-tuning either update all model parameters or leverage linear probes. In this paper, we aim to study parameter-efﬁcient ﬁne-tuning strategies for Vision Transformers on vision tasks. We formulate efﬁcient ﬁne-tuning as a subspace training problem and perform a comprehensive benchmarking over different efﬁcient ﬁne-tuning methods. We conduct an empirical study on each efﬁcient ﬁne-tuning method focusing on its performance alongside parameter cost. Furthermore, we also propose a parameter-efﬁcient ﬁne-tuning framework, which ﬁrst selects submodules by measuring local intrinsic dimensions and then projects them into subspace for further decomposition via a novel Kronecker Adaptation method. We analyze and compare our method with a diverse set of baseline ﬁne-tuning methods (including state-of-the-art methods for pretrained language models). Our method performs the best in terms of the tradeoff between accuracy and parameter efﬁciency across three commonly used image classiﬁcation datasets.
Explainable Artificial Intelligence in Process Mining: Assessing the Explainability-Performance Trade-Off in Outcome-Oriented Predictive Process Monitoring

Recently, a shift has been made in the ﬁeld of Outcome-Oriented Predictive Process Monitoring (OOPPM) to use models from the eXplainable Artiﬁcial Intelligence paradigm, however the evaluation still occurs mainly through performance-based metrics not accounting for the implications and lack of actionability of the explanations. In this paper, we deﬁne explainability by the interpretability of the explanations (through the widely-used XAI properties parsimony and functional complexity) and the faithfulness of the explainability model (through monotonicity and level of disagreement). The introduced properties are analysed along the event, case, and control ﬂow perspective that are typical of a process-based analysis. This allows to quantitatively compare, inter alia, inherently created explanations (e.g., logistic regression coeﬃcients) with post-hoc explanations (e.g., Shapley values). Moreover, this paper contributes a guideline named X-MOP to practitioners to select the appropriate model based on the event log speciﬁcations and the task at hand, by providing insight into how the varying preprocessing, model complexity and post-hoc explainability techniques typical in OOPPM inﬂuence the explainability of the model. To this end, we benchmark seven classiﬁers on thirteen real-life events logs.
Tampered VAE for Improved Satellite Image Time Series Classification

The unprecedented availability of spatial and temporal high-resolution satellite image time series (SITS) for crop type mapping is believed to necessitate deep learning architectures to accommodate challenges arising from both dimensions. Recent state-of-the-art deep learning models have shown promising results by stacking spatial and temporal encoders. However, we present a Pyramid Time-Series Transformer (PTST) that operates solely on the temporal dimension, i.e., neglecting the spatial dimension, can produce superior results with a drastic reduction in GPU memory consumption and easy extensibility. Furthermore, we augment it to perform semi-supervised learning by proposing a classification-friendly VAE framework that introduces clustering mechanisms into latent space and can promote linear separability therein. Consequently, a few principal axes of the latent space can explain the majority of variance in raw data. Meanwhile, the VAE framework with proposed tweaks can maintain competitive classification performance as its purely discriminative counterpart when only $40\%$ of labelled data is used. We hope the proposed framework can serve as a baseline for crop classification with SITS for its modularity and simplicity.
TubeDETR: Spatio-Temporal Video Grounding with Transformers

We consider the problem of localizing a spatio-temporal tube in a video corresponding to a given text query. This is a challenging task that requires the joint and efficient modeling of temporal, spatial and multi-modal interactions. To address this task, we propose TubeDETR, a transformer-based architecture inspired by the recent success of such models for text-conditioned object detection. Our model notably includes: (i) an efficient video and text encoder that models spatial multi-modal interactions over sparsely sampled frames and (ii) a space-time decoder that jointly performs spatio-temporal localization. We demonstrate the advantage of our proposed components through an extensive ablation study. We also evaluate our full approach on the spatio-temporal video grounding task and demonstrate improvements over the state of the art on the challenging VidSTG and HC-STVG benchmarks.