Miracle-dz/UniFAR-FTUs huggingface dataset:
Mycobacterium tuberculosis Lineages Associated with Mutations and Drug Resistance in Isolates from India

Current knowledge on resistance-conferring determinants in Mycobacterium tuberculosis is biased toward globally dominant lineages 2 and 4. In contrast, lineages 1 and 3 are predominant in India. ABSTRACT Current knowledge on resistance-conferring determinants in Mycobacterium tuberculosis is biased toward globally dominant lineages 2 and 4. In contrast, lineages 1 and 3 are predominant in India. In this study, we performed whole-genome sequencing of 498 MDR M. tuberculosis isolates from India to determine the prevalence of drug resistance mutations and to understand the genomic diversity. A retrospective collection of 498 M. tuberculosis isolates submitted to the National Institute for Research in Tuberculosis for phenotypic susceptibility testing between 2014 to 2016 were sequenced. Genotypic resistance prediction was performed using known resistance-conferring determinants. Genotypic and phenotypic results for 12 antituberculosis drugs were compared, and sequence data were explored to characterize lineages and their association with drug resistance. Four lineages were identified although lineage 1 predominated (43%). The sensitivity of prediction for isoniazid and rifampicin was 92% and 98%, respectively. We observed lineage-specific variations in the proportion of isolates with resistance-conferring mutations, with drug resistance more common in lineages 2 and 3. Disputed mutations (codons 430, 435, 445, and 452) in the rpoB gene were more common in isolates other than lineage 2. Phylogenetic analysis and pairwise SNP difference revealed high genetic relatedness of lineage 2 isolates. WGS based resistance prediction has huge potential, but knowledge of regional and national diversity is essential to achieve high accuracy for resistance prediction. IMPORTANCE Current knowledge on resistance-conferring determinants in Mycobacterium tuberculosis is biased toward globally dominant lineages 2 and 4. In contrast, lineages 1 and 3 are predominant in India. We performed whole-genome sequencing of 498 MDR M. tuberculosis isolates from India to determine the prevalence of drug resistance mutations and to understand genomic diversity. Four lineages were identified although lineage 1 predominated (43%). The sensitivity of prediction for isoniazid and rifampicin was 92% and 98%, respectively. We observed lineage-specific variations in the proportion of isolates with resistance-conferring mutations, with drug resistance more common in lineages 2 and 3. Disputed mutations (codons 430, 435, 445, and 452) in the rpoB gene were more common in isolates other than lineage 2. Phylogenetic analysis and pairwise SNP difference revealed high genetic relatedness of lineage 2 isolates. WGS based resistance prediction has huge potential, but knowledge of regional and national diversity is essential to achieve high accuracy for resistance prediction.
Adaptive Neural Network Control of an Uncertain 2-DOF Helicopter With Unknown Backlash-Like Hysteresis and Output Constraints

An adaptive neural network (NN) control is proposed for an unknown two-degree of freedom (2-DOF) helicopter system with unknown backlash-like hysteresis and output constraint in this study. A radial basis function NN is adopted to estimate the unknown dynamics model of the helicopter, adaptive variables are employed to eliminate the effect of unknown backlash-like hysteresis present in the system, and a barrier Lyapunov function is designed to deal with the output constraint. Through the Lyapunov stability analysis, the closed-loop system is proven to be semiglobally and uniformly bounded, and the asymptotic attitude adjustment and tracking of the desired set point and trajectory are achieved. Finally, numerical simulation and experiments on a Quanser’s experimental platform verify that the control method is appropriate and effective.
Heterogeneous coagulant potential of emicizumab in neonatal factor VIII–deficient plasma

Emicizumab prophylaxis reduces bleeding in hemophilia A (HA) patients. However, there are few data on emicizumab treatment in neonates with HA (neonate‐HA), and the procoagulant effects of emicizumab in these patients are unknown.
Origin of iodine preferential attack at sulfur in phosphorothioate and subsequent P-O or P-S bond dissociation

Significance Iodine-induced cleavage is widely used for detecting bacterial DNA phosphorothioation in gel electrophoresis, deep sequencing, and single-molecule optical mapping. However, we lack quantitative understanding of the phosphorothioate DNA (PT-DNA) cleavage efficiency and the chemoselectivity of this method for determination of phosphorothioate vs. phosphate. Computational explorations now reveal why iodine selectively attacks at sulfur in phosphorothioate links but not at normal phosphates. The active role of Tris buffer in the PT-DNA cleavage, and the factors controlling cleavage efficiency, were also revealed. Cleavage efficiency is limited by competition between the desired DNA backbone cleavage and unwanted P-S/P-O conversion. These mechanistic studies will guide the development of new methods for iodine-induced specific PT-DNA cleavage.
Psycho‐behavioural factors influencing COVID‐19 vaccine hesitancy among Korean adults: The moderating role of age

Abstract Aims This study aimed to identify the psycho‐behavioural factors influencing coronavirus disease 2019 (COVID‐19) vaccine hesitancy among Korean adults. We examined the primary effects of eHealth literacy, social responsibility and self‐efficacy on COVID‐19 vaccine hesitancy using the information‐motivation‐behavioural skills (IMB) model. The moderating effect of age on the relationship between psycho‐behavioural factors and COVID‐19 vaccine hesitancy was also examined. Design The study used a cross‐sectional survey design. Methods An online survey was completed by 500 Korean adults (253 men and 247 women) aged 20–60 years. Data collection took place in May 2021. The constructed hypothetical model for eHealth literacy, social responsibility, self‐efficacy and age was evaluated using partial least squares structural equation modelling (PLS‐SEM). Results eHealth literacy, social responsibility and self‐efficacy had significant negative effects on COVID‐19 vaccine hesitancy. Age had a significant moderating effect on COVID‐19 vaccine hesitancy. The moderating effects of age on the relationship between eHealth literacy and COVID‐19 vaccine hesitancy and between social responsibility and COVID‐19 vaccine hesitancy enhanced the explanatory power by high effect size. The overall fitness of the hypothetical model was good. The standardized root mean square residual value was 0.007, which explained 18.5% of the COVID‐19 vaccine hesitancy. Conclusion To reduce COVID‐19 vaccine hesitancy among Korean adults, a strategic approach that considers age should be adopted to enhance eHealth literacy, social responsibility and self‐efficacy. Impact The findings of this study suggest that providing correct health information online related to the COVID‐19 vaccine is likely to be effective in decreasing COVID‐19 vaccine hesitancy in older individuals, whereas inspiring a sense of social responsibility is more likely to be effective in younger individuals. These strategic nursing interventions could contribute to improving COVID‐19 vaccination rates among Korean adults.
Assessing the effect of a series of mutations on the dynamic behavior of phosphite dehydrogenase using molecular docking, molecular dynamics and quantum mechanics/molecular mechanics simulations

Abstract Discovered in Pseudomonas stutzeri, phosphite dehydrogenase (PTDH) is an enzyme that catalyzes the oxidation of phosphite to phosphate while simultaneously reducing NAD+ to NADH. Despite several investigations into the mechanism of reaction and cofactor regeneration, only a few studies have focused on improving the activity and stability of PTDH. In this study, we combine molecular docking, molecular dynamics (MD) simulation, and Quantum Mechanics/Molecular Mechanics (QM/MM) to identify the impact of 30 mutations on the activity and stability of PTDH. Molecular docking results suggest that E266Q, K76A, K76M, K76R, K76C, and R237K can act on the NAD+ binding site through relatively weak bond development due to their high free binding energy. Moreover, Mulliken population analysis and potential energy barrier indicate that T101A, E175A, E175A/A176R, A176R, and E266Q act on phosphite oxidation. The mutants M53N, M53A, K76R, D79N, D79A, T101A, W134A, W134F Y139F, A146S, E175A, F198I, F198M, E266Q, H292K, S295A, R301K, and R301A were found to act on the structural dynamic of PTDH. The remaining mutants cause the loss of the nitrogen atom of R237 and H292, respectively, inactivating the enzyme. This study provides specific explanations of how mutations affect weak interactions of PTDH. The results should allow researchers to conduct experimental studies to improve PTDH activity and stability. Communicated by Ramaswamy H. Sarma
Metalearning-Based Alternating Minimization Algorithm for Nonconvex Optimization

In this article, we propose a novel solution for nonconvex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization problem into a set of subproblems corresponding to each variable and then iteratively optimizes each subproblem using a fixed updating rule. However, due to the intrinsic nonconvexity of the original optimization problem, the optimization can be trapped into a spurious local minimum even when each subproblem can be optimally solved at each iteration. Meanwhile, learning-based approaches, such as deep unfolding algorithms, have gained popularity for nonconvex optimization; however, they are highly limited by the availability of labeled data and insufficient explainability. To tackle these issues, we propose a meta-learning based alternating minimization (MLAM) method that aims to minimize a part of the global losses over iterations instead of carrying minimization on each subproblem, and it tends to learn an adaptive strategy to replace the handcrafted counterpart resulting in advance on superior performance. The proposed MLAM maintains the original algorithmic principle, providing certain interpretability. We evaluate the proposed method on two representative problems, namely, bilinear inverse problem: matrix completion and nonlinear problem: Gaussian mixture models. The experimental results validate the proposed approach outperforms AM-based methods.
Narsoplimab, a Mannan-Binding Lectin-Associated Serine Protease-2 Inhibitor, for the Treatment of Adult Hematopoietic Stem-Cell Transplantation–Associated Thrombotic Microangiopathy

PURPOSE Hematopoietic stem-cell transplantation–associated thrombotic microangiopathy (HSCT-TMA) is a serious complication with significant mortality and no approved therapy. HSCT-TMA results from endothelial injury, which activates the lectin pathway of complement. Narsoplimab (OMS721), an inhibitor of mannan-binding lectin-associated serine protease-2 (MASP-2), was evaluated for safety and efficacy in adults with HSCT-TMA. METHODS In this single-arm open-label pivotal trial (NCT02222545), patients received intravenous narsoplimab once weekly for 4-8 weeks. The primary end point (response rate) required clinical improvement in two categories: (1) laboratory TMA markers (both platelet count and lactate dehydrogenase) and (2) organ function or freedom from transfusion. Patients receiving at least one dose (full analysis set [FAS]; N = 28) were analyzed. RESULTS The response rate was 61% in the FAS population. Similar responses were observed across all patient subgroups defined by baseline features, HSCT characteristics, and HSCT complications. Improvement in organ function occurred in 74% of patients in the FAS population. One-hundred-day survival after HSCT-TMA diagnosis was 68% and 94% in FAS population and responders, respectively, whereas median overall survival was 274 days in the FAS population. Narsoplimab was well tolerated, and adverse events were typical of this population, with no apparent safety signal of concern. CONCLUSION In this study, narsoplimab treatment was safe, significantly improved laboratory TMA markers, and resulted in clinical response and favorable overall survival.
Altered basal ganglia infraslow oscillation and resting functional connectivity in complex regional pain syndrome

Complex regional pain syndrome (CRPS) is a painful condition commonly accompanied by movement disturbances and often affects the upper limbs. The basal ganglia motor loop is central to movement, however, non‐motor basal ganglia loops are involved in pain, sensory integration, visual processing, cognition, and emotion. Systematic evaluation of each basal ganglia functional loop and its relation to motor and non‐motor disturbances in CRPS has not been investigated. We recruited 15 upper limb CRPS and 45 matched healthy control subjects. Using functional magnetic resonance imaging, infraslow oscillations (ISO) and resting‐state functional connectivity in motor and non‐motor basal ganglia loops were investigated using putamen and caudate seeds. Compared to controls, CRPS subjects displayed increased ISO power in the putamen contralateral to the CRPS affected limb, specifically, in contralateral putamen areas representing the supplementary motor area hand, motor hand, and motor tongue. Furthermore, compared to controls, CRPS subjects displayed increased resting connectivity between these putaminal areas as well as from the caudate body to cortical areas such as the primary motor cortex, supplementary and cingulate motor areas, parietal association areas, and the orbitofrontal cortex. These findings demonstrate changes in basal ganglia loop function in CRPS subjects and may underpin motor disturbances of CRPS.
Risk Prediction of Major Adverse Cardiovascular Events Occurrence Within 6 Months After Coronary Revascularization: Machine Learning Study

Background As a major health hazard, the incidence of coronary heart disease has been increasing year by year. Although coronary revascularization, mainly percutaneous coronary intervention, has played an important role in the treatment of coronary heart disease, major adverse cardiovascular events (MACE) such as recurrent or persistent angina pectoris after coronary revascularization remain a very difficult problem in clinical practice. Objective Given the high probability of MACE after coronary revascularization, the aim of this study was to develop and validate a predictive model for MACE occurrence within 6 months based on machine learning algorithms. Methods A retrospective study was performed including 1004 patients who had undergone coronary revascularization at The People’s Hospital of Liaoning Province and Affiliated Hospital of Liaoning University of Traditional Chinese Medicine from June 2019 to December 2020. According to the characteristics of available data, an oversampling strategy was adopted for initial preprocessing. We then employed six machine learning algorithms, including decision tree, random forest, logistic regression, naïve Bayes, support vector machine, and extreme gradient boosting (XGBoost), to develop prediction models for MACE depending on clinical information and 6-month follow-up information. Among all samples, 70% were randomly selected for training and the remaining 30% were used for model validation. Model performance was assessed based on accuracy, precision, recall, F1-score, confusion matrix, area under the receiver operating characteristic (ROC) curve (AUC), and visualization of the ROC curve. Results Univariate analysis showed that 21 patient characteristic variables were statistically significant (P<.05) between the groups without and with MACE. Coupled with these significant factors, among the six machine learning algorithms, XGBoost stood out with an accuracy of 0.7788, precision of 0.8058, recall of 0.7345, F1-score of 0.7685, and AUC of 0.8599. Further exploration of the models to identify factors affecting the occurrence of MACE revealed that use of anticoagulant drugs and course of the disease consistently ranked in the top two predictive factors in three developed models. Conclusions The machine learning risk models constructed in this study can achieve acceptable performance of MACE prediction, with XGBoost performing the best, providing a valuable reference for pointed intervention and clinical decision-making in MACE prevention.
Quantifying the Photochemical Damage Potential of Contrast‐Enhanced Fluorescence Imaging Products: Singlet Oxygen Production

The benefits of contrast‐enhancing imaging probes have become apparent over the past decade. However, there is a gap in the literature when it comes to the assessment of the phototoxic potential of imaging probes and systems emitting visible and/or near‐infrared radiation. The primary mechanism of fluorescent agent phototoxicity is thought to involve the production of reactive molecular species (RMS), yet little has been published on the best practices for safety evaluation of RMS production levels for clinical products. We have proposed methods involving a cell‐free assay to quantify singlet oxygen [(SO) a known RMS] generation of imaging probes, and performed testing of Indocyanine Green (ICG), Proflavine, Methylene Blue, IR700 and IR800 at clinically relevant concentrations and radiant exposures. Results indicated that SO production from IR800 and ICG were more than two orders of magnitude below that of the known SO generator Rose Bengal. Methylene Blue and IR700 produced much higher SO levels than ICG and IR800. These results were in good agreement with data from the literature. While agents that exhibit spectral overlap with the assay may be more prone to errors, our tests for one of these agents (Proflavine) appeared robust. Overall, our results indicate that this methodology shows promise for assessing the phototoxic potential of fluorophores due to SO production.
Development of QSAR models for in silico screening of antibody solubility

ABSTRACT Although monoclonal antibodies (mAbs) have been shown to be extremely effective in treating a number of diseases, they often suffer from poor developability attributes, such as high viscosity and low solubility at elevated concentrations. Since experimental candidate screening is often materials and labor intensive, there is substantial interest in developing in silico tools for expediting mAb design. Here, we present a strategy using machine learning-based QSAR models for the a priori estimation of mAb solubility. The extrapolated protein solubilities of a set of 111 antibodies in a histidine buffer were determined using a high throughput PEG precipitation assay. 3D homology models of the antibodies were determined, and a large set of in house and commercially available molecular descriptors were then calculated. The resulting experimental and descriptor data were then used for the development of QSAR models of mAb solubilities. After feature selection and training with different machine learning algorithms, the models were evaluated with external test sets. The resulting regression models were able to estimate the solubility values of external test set data with R2 of 0.81 and 0.85 for the two regression models developed. In addition, three class and binary classification models were developed and shown to be good estimators of mAb solubility behavior, with overall test set accuracies of 0.70 and 0.95, respectively. The analysis of the selected molecular descriptors in these models was also found to be informative and suggested that several charge-based descriptors and isotype may play important roles in mAb solubility. The combination of high throughput relative solubility experimental techniques in concert with efficient machine learning QSAR models offers an opportunity to rapidly screen potential mAb candidates and to design therapeutics with improved solubility characteristics.
Short-term effects of air pollutant exposure on small airway dysfunction, spirometry, health-related quality of life, and inflammatory biomarkers in wildland firefighters: a pilot study

ABSTRACT We aim to assess small airway dysfunction, spirometry, health-related quality of life (HR-QoL), and inflammatory biomarkers between the wildland firefighters and healthy controls. Lung function including impulse oscillometry (IOS) and spirometry, HR-QoL measured by the 36-Item Short Form Survey (SF-36) and St. George’s Respiratory Questionnaire (SGRQ), high sensitivity C-Reactive Protein (hsCRP), and D-dimer were collected twice in the non-pollution period and pollution period. Gaussian regression was used for comparison all parameters between two groups. The results showed that the changes in area under reactance curve between 5 Hz and resonant frequency (AX) between the non-pollution and pollution periods in wildland firefighters was significantly higher compared to healthy controls (adjusted mean difference = 0.15 kPa/L, 95%CI;0.01, 0.28, p = 0.032). Our pilot study concluded that there was short-term effect on small airway function in wildland firefighter air pollutant exposure. However, there was no effect on spirometry, HRQoL, and inflammatory biomarkers in young wildland firefighters. Clinical Trials Registry: Study ID: TCTR20200828005
Novel Extended State Observer Design for Uncertain Nonlinear Systems via Refined Dynamic Event-Triggered Communication Protocol

In this article, an extended state observer (ESO) design problem is investigated for uncertain nonlinear systems subject to limited network bandwidth. First, for rational information exchange scheduling, a dynamic event-triggered (DET) communication protocol is proposed. Different from the traditional static event-triggered strategies with fixed thresholds, an internal dynamic variable is introduced to be adaptively adjusted by a dual-directional regulating mechanism. Thus, more desirable tradeoff between observation performance and communication resource efficiency is achieved. Second, inspired by our early work on Takagi–Sugeno fuzzy ESO (TSFESO), a novel paradigm of event-triggered TSFESO is initially proposed. Third, under the DET mechanism, the TSFESO design approach is derived to carry out exponential convergence for estimation error dynamics. Finally, the effectiveness of the proposed method is verified by numerical examples. The nonlinear estimating efficiency and linear numerical tractability are integrated in TSFESO. In addition, a generalized ESO formulation is developed to allow some nonadditive uncertainties incompatible with total disturbance, such as improved event-triggered strategy, and thus, the application sphere of ESO is further expanded.
Prediction of transmission counts by a radial basis function neural network

The microblog as a platform used to serve the communication of the internet, has been developing rapidly. The blog has become an important way to get information and to transmit information (1). The blog message can be transmitted, and we as consumer can transmit them by a few simple and convenient operation. As to the suddenly happened situation, the transmission can cause a very direct influence to the transmission and spread of the situation itself. Here we set an example of the topic, the share of the photography course, and we also refer rbf neural network to the research of the transmission of the relevant situations, and we will continue the optimization of width status in the kernel function combining Fruit fly Optimization Algorithm. First, this essay analyzes different factors effecting blog's rewet counts in many different aspects. On this base, we classify the blogs according to the rewet counts, and we predict the blogs' rewet counts by rbf basing on the foa, which has been optimized. Under different numbers' samples, the experimental result has certain reference value by contrasting and analyzing the predicted result of foa-rbf model.