Miracle-dz/UniFAR-FTUs huggingface dataset:
BCG Vaccination Induces M. avium and M. abscessus Cross-Protective Immunity

Pulmonary non-tuberculous mycobacterial (NTM) infections particularly caused by Mycobacterium avium complex (MAC) and Mycobacterium abscessus (MAB) are becoming major health problems in the U.S. New therapies or vaccines which will help prevent the disease, shorten treatment duration and/or increase treatment success rates are urgently needed. This study was conducted with the objective of testing the hypothesis that Bacillus Calmette Guerin (BCG), a vaccine used for prevention of serious forms of tuberculosis (TB) in children and adolescents in tuberculosis hyperendemic countries, induces cross-protective T cell immunity against Mycobacterium avium (MAV) and MAB. Human TB and NTM cross-protective T cells were quantified using flow cytometric assays. The ability of BCG expanded T cells to inhibit the intracellular growth of MAV and MAB was assessed in co-cultures with infected autologous macrophages. In both BCG-vaccinated and M. tuberculosis (Mtb)-infected mice, NTM cross-reactive immunity was measured using IFN-γ ELISPOT assays. Our results demonstrate the following key findings: (i) peripheral blood mononuclear cells from TB skin test-positive individuals contain MAV and MAB cross-reactive T cells, (ii) both BCG vaccination and Mtb infection of mice induce MAV and MAB cross-reactive splenic cells, (iii) BCG-expanded T cells inhibit intracellular MAV and MAB, (iv) CD4, CD8, and γδ T cells play important roles in inhibition of intracellular MAV and MAB and (v) BCG vaccination of healthy volunteers induces TB and NTM cross-reactive T cells. In conclusion, BCG-vaccination induces NTM cross-reactive immunity, and has the potential for use as a vaccine or immunotherapy to prevent and/or treat pulmonary NTM disease.
The Effect of Plyometric Training in Volleyball Players: A Systematic Review

Volleyball is considered a very explosive and fast-paced sport in which plyometric training is widely used. Our purpose was to review the effects of plyometric training on volleyball players’ performance. A systematic search was conducted according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines using PubMed, SciELO, SPORTDiscus, Medline, Scopus, Academic Search Complete, CINAHL and Web Science for articles published no later than December 2018. Any criteria were imposed for the included sample. The search focus was on interventional studies in which athletes underwent a plyometric program. To the 1831 articles found, another five were added, identified through other sources. Duplicated files were removed, titles and abstracts were screened, which left 21 remaining studies for extensive analysis. Results showed that the vertical jump (15 studies) was the major ability studied in plyometric training interventions, followed by strength (four studies), horizontal jump (four studies), flexibility (four studies) and agility/speed (three studies). In addition, it was observed that young (under 18 years old) female athletes were the most studied. The included studies indicated that plyometric training seems to increase vertical jump performance, strength, horizontal jump performance, flexibility and agility/speed in volleyball players. However, more studies are needed to better understand the benefits of plyometric training in volleyball players’ performance.
Metal Coordination Is Crucial for Geranylgeranyl Diphosphate Synthase–Bisphosphonate Interactions: A Crystallographic and Computational Analysis

Geranylgeranyl diphosphate synthase (GGPPS) is a central metalloenzyme in the mevalonate pathway, crucial for the prenylation of small GTPases. As small GTPases are pivotal for cellular survival, GGPPS was highlighted as a potential target for treating human diseases, including solid and hematologic malignancies and parasitic infections. Most available GGPPS inhibitors are bisphosphonates, but the clinically available compounds demonstrate poor pharmacokinetic properties. Although the design of novel bisphosphonates with improved physicochemical properties is highly desirable, the structure of wild-type human GGPPS (hGGPPS) bound to a bisphosphonate has not been resolved. Moreover, various metal-bisphosphonate–binding stoichiometries were previously reported in structures of yeast GGPPS (yGGPPS), hampering computational drug design with metal-binding pharmacophores (MBP). In this study, we report the 2.2 Å crystal structure of hGGPPS in complex with ibandronate, clearly depicting the involvement of three Mg2+ ions in bisphosphonate–protein interactions. Using drug-binding assays and computational docking, we show that the assignment of three Mg2+ ions to the binding site of both hGGPPS and yGGPPS greatly improves the correlation between calculated binding energies and experimentally measured affinities. This work provides a structural basis for future rational design of additional MBP-harboring drugs targeting hGGPPS. SIGNIFICANCE STATEMENT Bisphosphonates are inhibitors of geranylgeranyl diphosphate synthase (GGPPS), a metalloenzyme crucial for cell survival. Bisphosphonate binding depends on coordination by Mg2+ ions, but various Mg2+-bisphosphonate–binding stoichiometries were previously reported. In this study, we show that three Mg2+ ions are vital for drug binding and provide a structural basis for future computational design of GGPPS inhibitors.
A Novel Swarm Intelligence—Harris Hawks Optimization for Spatial Assessment of Landslide Susceptibility

In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors—elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall—is prepared to develop the ANN and HHO–ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO–ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO–ANN = 0.773) the landslide pattern.
High Systemic Immune-Inflammation Index is an Adverse Prognostic Factor for Patients With Gastroesophageal Adenocarcinoma

Supplemental Digital Content is available in the text Objective: The aim of this study was to determine the clinical role of the systemic immune-inflammation index in patients with resectable adenocarcinoma of the gastroesophageal junction treated with or without neoadjuvant therapy. Background: Adenocarcinoma of the gastroesophageal junction is an aggressive disease, with less than 20% of overall patients surviving more than 5 years after diagnosis, while currently available clinical staging for esophageal cancer is lacking necessary accuracy. The systemic immune-inflammation index (SII) based on peripheral neutrophil, lymphocyte, and platelet counts has shown a prognostic impact in various malignancies. Methods: Data of consecutive patients undergoing esophagectomy (n = 320, 1992 to 2016) were abstracted. The cut point for high and low SII before neoadjuvant treatment and before surgery was calculated for illustration of the Kaplan-Meier curves. SII was used for the correlation with patients’ clinicopathological characteristics as a continuous variable. Survival was analyzed with Cox proportional hazards models using clinical or pathological staging, adjusting for other known survival predictors. Results: In both neoadjuvantly treated and primarily resected patients, high SII was significantly associated with diminished overall [hazard ratio (HR) 1.3, 95% confidence interval (95% CI) 1.2–1.4; HR 1.2, 95% CI 1.2–1.3, respectively] and disease-free survival (HR 1.3, 95% CI 1.2–1.3; HR 1.2, 95% CI 1.2–1.3, respectively). In multivariable survival analysis, SII remained an independent prognostic factor for overall survival (HR 1.3, 95% CI 1.2–1.4; HR 1.2, 95% CI 1.2–1.3, respectively) and disease-free survival (HR 1.3, 95% CI 1.2–1.3; HR 1.2, 95% CI 1.2–1.3, respectively) in primarily resected and neoadjuvantly treated patients. Conclusion: Elevated SII is an independent adverse prognostic factor in patients with resectable gastroesophageal adenocarcinomas with and without neoadjuvant treatment.
Clinical implications of extracellular vesicles in neurodegenerative diseases

ABSTRACT Introduction: Extracellular vesicles (EVs) released by neural cells play a crucial role in intracellular communication in both physiological and pathological states. Recent studies have shown that the neuropathogenic manifestation of many progressive nervous system diseases including Parkinson’s disease (PD), Alzheimer’s diseases (AD), and amyotrophic lateral sclerosis (ALS). These diseases are frequently found to be associated with the accumulation of misfolded proteins, exploit EVs for the spread of aggregates to naive cells in a prion-like mechanism. Therefore, characterization of EVs and understanding their mechanism of action could open a window of opportunity to discover biomarkers and therapeutic targets in a disease-specific manner. Areas covered: In this review, we discuss the role of neural cells-derived EVs in normal and disease states. We also highlight their biomedical potential in modern medicine, including the use of circulating EVs as biomarkers for diagnosis with a special focus on newly-identified potential biomarkers in neurodegenerative disease, and novel methodologies in EVs isolation. Expert opinion: Systematic and comprehensive analysis of EVs in different biofluid sources is needed. Considering the potential for tremendous clinical benefits of EVs research in neurodegenerative disease, there is also an urgent need to standardize neural cells-derived EV enrichment protocols for consensus results.
A Trypsin Family Protein Gene Controls Tillering and Leaf Shape in Barley

A mutant screen for high tiller number in barley isolates a gene controlling tiller development. Tillering or branching is an important agronomic trait in plants, especially cereal crops. Previously, in barley (Hordeum vulgare) ‘Vlamingh’, we identified the high number of tillers1 (hnt1) mutant from a γ-ray-treated segregating population. hnt1 exhibited more tillers per plant, narrower leaves, and reduced plant height compared with the wild-type parent. In this study, we show that the hnt1-increased tiller number per plant is caused by accelerated outgrowth of tiller buds and that hnt1 narrower leaves are caused by a reduction in vascular tissue and cell number. Genetic analysis revealed that a 2-bp deletion in the gene HORVU2Hr1G098820 (HvHNT1), encoding a trypsin family protein, was responsible for the hnt1 mutant phenotype. Gene function was further confirmed by transgenic complementation with HvHNT1 and RNA interference experiments. HvHNT1 was expressed in vascular tissue, leaf axils, and adventitious root primordia and shown to negatively regulate tiller development. Mutation of HvHNT1 led to the accumulation of a putative cyclophilin-type peptidyl-prolyl cis/trans-isomerase (HvPPIase), which physically interacts with the HvHNT1 protein in the nucleus of plant cells. Our data suggest that HvHNT1 controls tiller development and leaf width through HvPPIase, thus contributing to understanding of the molecular players that control tillering in barley.
Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block

Recently, massive architectures based on Convolutional Neural Network (CNN) and self-attention mechanisms have become necessary for audio classification. While these techniques are state-of-the-art, these works' effectiveness can only be guaranteed with huge computational costs and parameters, large amounts of data augmentation, transfer from large datasets and some other tricks. By utilizing the lightweight nature of audio, we propose an efficient network structure called Paired Inverse Pyramid Structure (PIP) and a network called Paired Inverse Pyramid Structure MLP Network (PIPMN). The PIPMN reaches 96\% of Environmental Sound Classification (ESC) accuracy on the UrbanSound8K dataset and 93.2\% of Music Genre Classification (MGC) on the GTAZN dataset, with only 1 million parameters. Both of the results are achieved without data augmentation or model transfer. Public code is available at: https://github.com/JNAIC/PIPMN
Channeling: a new class of dissolution in complex porous media

The current conceptual model of mineral dissolution in porous media is comprised of three dissolution patterns (wormhole, compact, and uniform) - or regimes - that develop depending on the relative dominance of flow, diffusion, and reaction rate. Here, we examine the evolution of pore structure during acid injection using numerical simulations on two porous media structures of increasing complexity. We examine the boundaries between regimes and characterise the existence of a fourth regime called channeling, where already existing fast flow pathways are preferentially widened by dissolution. Channeling occurs in cases where the distribution in pore throat size results in orders of magnitude differences in flow rate for different flow pathways. This focusing of dissolution along only dominant flow paths induces an immediate, large change in permeability with a comparatively small change in porosity, resulting in a porosity-permeability relationship unlike any that has been previously seen. This work demonstrates that our current conceptual model of dissolution regimes must be modified to include channeling for accurate predictions of dissolution in applications such as geologic carbon storage and geothermal energy production.
SizeGAN: Improving Size Representation in Clothing Catalogs

Online clothing catalogs lack diversity in body shape and garment size. Brands commonly display their garments on models of one or two sizes, rarely including plus-size models. To our knowledge, our paper presents the first method for generating images of garments and models in a new target size to tackle the size under-representation problem. Our primary technical contribution is a conditional generative adversarial network that learns deformation fields at multiple resolutions to realistically change the size of models and garments. Results from our two user studies show SizeGAN outperforms alternative methods along three dimensions -- realism, garment faithfulness, and size -- which are all important for real world use.
A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and #P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance. With the rapid development of artificial intelligence, technologies based on Machine Learning (ML) have achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out.
On Output Activation Functions for Adversarial Losses: A Theoretical Analysis via Variational Divergence Minimization and An Empirical Study on MNIST Classification

Recent years have seen adversarial losses been applied to many fields. Their applications extend beyond the originally proposed generative modeling to conditional generative and discriminative settings. While prior work has proposed various output activation functions and regularization approaches, some open questions still remain unanswered. In this paper, we aim to study the following two research questions: 1) What types of output activation functions form a well-behaved adversarial loss? 2) How different combinations of output activation functions and regularization approaches perform empirically against one another? To answer the first question, we adopt the perspective of variational divergence minimization and consider an adversarial loss well-behaved if it behaves as a divergence-like measure between the data and model distributions. Using a generalized formulation for adversarial losses, we derive the necessary and sufficient conditions of a well-behaved adversarial loss. Our analysis reveals a large class of theoretically valid adversarial losses. For the second question, we propose a simple comparative framework for adversarial losses using discriminative adversarial networks. The proposed framework allows us to efficiently evaluate adversarial losses using a standard evaluation metric such as the classification accuracy. With the proposed framework, we evaluate a comprehensive set of 168 combinations of twelve output activation functions and fourteen regularization approaches on the handwritten digit classification problem to decouple their effects. Our empirical findings suggest that there is no single winning combination of output activation functions and regularization approaches across all settings. Our theoretical and empirical results may together serve as a reference for choosing or designing adversarial losses in future research.
The Metabolic Benefits of Menopausal Hormone Therapy Are Not Mediated by Improved Nutritional Habits. The OsteoLaus Cohort

Menopause alters body composition by increasing fat mass. Menopausal hormone therapy (MHT) is associated with decreased total and visceral adiposity. It is unclear whether MHT favorably affects energy intake. We aimed to assess in the OsteoLaus cohort whether total energy intake (TEI) and/or diet quality (macro- and micronutrients, dietary patterns, dietary scores, dietary recommendations)—evaluated by a validated food frequency questionnaire—differ in 839 postmenopausal women classified as current, past or never MHT users. There was no difference between groups regarding TEI or consumption of macronutrients. After multivariable adjustment, MHT users were less likely to adhere to the unhealthy pattern ‘fat and sugar: Current vs. never users [OR (95% CI): 0.48 (0.28–0.82)]; past vs. never users [OR (95% CI): 0.47 (0.27–0.78)]. Past users exhibited a better performance in the revised score for Mediterranean diet than never users (5.00 ± 0.12 vs. 4.63 ± 0.08, p < 0.04). Differences regarding compliance with dietary recommendations were no longer significant after adjustment for covariates. Overall, these results argue against a major role of TEI and diet quality as possible mediators of the MHT metabolic benefits. Future research on this relationship should focus on other potential targets of MHT, such as resting energy expenditure and physical activity.
Common fixed point, best approximation and generalized f-weak contraction multivalued mapping in p-normed spaces

Abstract We establish coincidence point and common fixed point results for multi-valued f-weak contraction mappings which assume closed values only. As an application, related common fixed point and invariant approximation are obtained in the setup of certain metrizable topological vector spaces. Our results provide extensions as well as substantial improvements of several well known results in the literature.
Organismal Natures

As other contributions to this volume show, biological substances are at the heart of Aristotle’s ontology. Within this ontology the natures of organisms play the central explanatory role (cf. Physics II and, esp., Parts of Animals). In several places throughout the corpus Aristotle draws our attention to the fact organismal natures are complex things. The nature of a living thing is divided into its material nature and its formal nature. The formal nature is identifi ed with the substantial being (ousia) of a thing, which Aristotle divides into nature understood as mover and nature understood as end. This complex structure (as well as its explanatory import) is set out explicitly in a key passage from Parts of Animals I 1:
Hyperbolic $P(\Phi)_2$-model on the plane

We study the hyperbolic Φ 2 -model on the plane. By establishing coming down from infinity for the associated stochastic nonlinear heat equation (SNLH) on the plane, we first construct a Φ 2 -measure on the plane as a limit of the Φ k+1 2 -measures on large tori. We then study the canonical stochastic quantization of the Φ 2 -measure on the plane thus constructed, namely, we study the defocusing stochastic damped nonlinear wave equation forced by an additive space-time white noise (= the hyperbolic Φ 2 -model) on the plane. In particular, by taking a limit of the invariant Gibbs dynamics on large tori constructed by the first two authors with Gubinelli and Koch (2021), we construct invariant Gibbs dynamics for the hyperbolic Φ 2 -model on the plane. Our main strategy is to develop further the ideas from a recent work on the hyperbolic Φ3-model on the three-dimensional torus by the first two authors and Okamoto (2021), and to study convergence of the socalled extended Gibbs measures, for which coming down from infinity for the associated SNLH with positive regularity plays a crucial role. By combining wave and heat analysis together with ideas from optimal transport theory, we then conclude global well-posedness of the hyperbolic Φ 2 -model on the plane and invariance of the associated Gibbs measure. As a byproduct of our argument, we also obtain invariance of the limiting Φ 2 -measure on the plane under the dynamics of the parabolic Φ 2 -model.