Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Characterization of Antimicrobial Resistance and Virulence Genes of Bacterial Pathogens from Bovine and Caprine Mastitis in Northern Lebanon

Mastitis is an infectious disease encountered in dairy animals worldwide that is currently a growing concern in Lebanon. This study aimed at investigating the etiology of the main mastitis-causing pathogens in Northern Lebanon, determining their antimicrobial susceptibility profiles, and identifying their antimicrobial resistance (AMR) genes. A total of 101 quarter milk samples were collected from 77 cows and 11 goats presenting symptoms of mastitis on 45 dairy farms. Bacterial identification was carried out through matrix-assisted laser desorption/ionization-time of flight mass spectrometry. Antimicrobial susceptibility was tested by disc diffusion and broth microdilution methods. Molecular characterization included polymerase chain reaction (PCR) screening for genes encoding extended-spectrum beta-lactamases (ESBLs) and plasmid-mediated AmpC among Enterobacterales isolates, and virulence factors among Staphylococcus isolates. Escherichia coli isolates were subjected to phylogenetic typing by a quadruplex PCR method. The most frequently identified species were Streptococcus uberis (19.2%), Streptococcus agalactiae (15.1%), E. coli (12.3%), and Staphylococcus aureus (10.96%). Gram-positive bacteria were resistant to macrolides and tetracycline, whereas gram-negative bacteria displayed resistance to ampicillin and tetracycline. Two ESBL genes, blaTEM (83.3%) and blaOXA (16.7%), and one AmpC beta-lactamase gene, blaCMY-II (16.7%), were detected among six E. coli isolates, which mainly belonged to phylogenetic group B1. Among Staphylococcus spp. , the mecA gene was present in three isolates. Furthermore, four isolates contained at least one toxin gene, and all S. aureus isolates carried the ica operon. These findings revealed the alarming risk of AMR in the Lebanese dairy chain and the importance of monitoring antimicrobial usage.
Modelling of Electrowetting-Induced Droplet Detachment and Jumping over Topographically Micro-Structured Surfaces

Detachment and jumping of liquid droplets over solid surfaces under electrowetting actuation are of fundamental interest in many microfluidic and heat transfer applications. In this study we demonstrate the potential capabilities of our continuum-level, sharp-interface modelling approach, which overcomes some important limitations of convectional hydrodynamic models, when simulating droplet detachment and jumping dynamics over flat and micro-structured surfaces. Preliminary calculations reveal a considerable connection between substrate micro-topography and energy efficiency of the process. The latter results could be extended to the optimal design of micro-structured solid surfaces for electrowetting-induced droplet removal in ambient conditions.
Perceiving utilitarian gradients: Heart rate variability and self-regulatory effort in the moral dilemma task

ABSTRACT It is not yet clear which response behavior requires self-regulatory effort in the moral dilemma task. Previous research has proposed that utilitarian responses require cognitive control, but subsequent studies have found inconsistencies with the empirical predictions of that hypothesis. In this paper, we treat participants’ sensitivity to utilitarian gradients as a measure of performance. We confronted participants (N = 82) with a set of five dilemmas evoking a gradient of mean utilitarian responses in a 4-point scale and collected data on heart rate variability and utilitarian responses. We found positive correlations between tonic and phasic HRV and sensitivity to the utilitarian gradient in the high tonic group, but not in the low tonic group. Moreover, the low tonic group misplaced a scenario with a selfish incentive at the high end of the gradient. Results suggest that performance is represented by sensitivity correlated with HRV and accompanied with a reasonable placement of individual scenarios within the gradient.
Acute and Subchronic Toxicity Studies of Aristolochic Acid A in Tianfu Broilers

Simple Summary Aristolochic acid (AA) is a chemical compound present in traditional Chinese medicine, which is widely used for anti-infection, anti-viral and anti-bacterial treatment, as antibiotics were banned in the poultry industry. However, long-term use of this drug in high doses can cause harmful damage to the kidneys of animals. Therefore, in this study, the acute toxicity test and subchronic toxicity test of AA were carried out in order to explore the nephrotoxicity mechanism of AA on Tianfu broilers. In this study, the LD50 of AA to male Tianfu broilers was 14.52 mg/kg. Subchronic exposure to high doses of AA in broilers can cause nephrotoxicity by breaking the redox balance to form oxidative stress, along with promoting apoptosis of renal cells. In conclusion, AA has been found to damage broilers’ kidneys in high doses. This study provides suggestions for the clinical application of traditional Chinese medicine containing AA in the poultry industry. Abstract Aristolochic acid (AA) is one of the components of some traditional Chinese medicines, which has high toxic potential in animals, leading to huge economic losses in the breeding industry. The purpose of this study is to evaluate the toxicology of AA on Tianfu broilers through acute and subchronic toxicity tests. The results showed that the median lethal dose of AA to Tianfu broilers was 14.52 mg/kg. After continuous intraperitoneal injection of AA solution (1.452 mg/kg) for 28 days, the swollen and necrotic renal tubular epithelial cells were histologically observed; in addition, blood urea nitrogen (BUN) and creatinine (Cre) were significantly increased, indicating AA could induce serious kidney lesions in broilers. Moreover, the ROS, the apoptosis rate and the depolarization rate of the mitochondrial membrane potential of broilers’ renal cells increased. The results of QRT-PCR showed that AA reduced the mRNA expressions of HO-1, NQO1, Raf-1 and Bcl-2, while the expressions of Bax and Caspase-3 increased, which show that AA aroused oxidative stress and promoted the apoptosis of renal cells. In conclusion, AA has been found to damage broilers’ kidneys by breaking the redox balance to form oxidative stress, along with promoting apoptosis of renal cells.
Age-appropriate vaccination practice and associated factors among mothers of children aged less than one year in the pastoral community of Afar region, Ethiopia

ABSTRACT Achieving and maintaining high-level immunization coverage is the priority of the health-care delivery system. However, any delay in receiving the vaccine leaves youngsters inadequately protected. Timely vaccination has scarcely been reported and given little attention in developing nations like Ethiopia, which hinders effective interventions. Therefore, this study aimed to assess age-appropriate vaccination practice and associated factors among mothers of children aged less than one year in the pastoral community. A community-based cross-sectional study has conducted among 340 mothers/caregivers of children aged less than one year in Samara-logia city administration. A systematic random sampling technique was employed to identify and enroll mothers-child paired. The logistic regression analysis had done to identify the factors associated with age-appropriate vaccination practice. The statistical association had measured, and a p-value < 0.05 was considered statistically significant. In this study, a total of 331 mothers/caregivers–child pairs participated with a response rate of 97.3%. The age-appropriate vaccination practice was 43.7% (95% CI, 38%, 49.5%). Mothers who had higher educational level (Adjusted odds ratio (AOR)) = AOR = 2.89, 95% CI (1.14, 7.3), antenatal care follow-up (AOR = 2.1, 95% CI (1.04, 4.1)), and had good knowledge on vaccination (AOR = 3.1, 95% CI (1.4, 6.78)) were associated with increased odds of age-appropriate vaccination practice.
Delegated Learning and Contract Commonality in Asset Management

We examine the pecuniary externalities that arise when active fund manager compensation contracts have common components. This commonality in the compensation structure and loadings on each component across funds reduces asset price informativeness, amplifies the distortions from active managers’ benchmark-hedging demand, and lowers the price of risk in financial markets. This is because contract commonality distorts investors’ capital allocation to active management, and active managers’ information acquisition and trading decisions. From a normative perspective, contract commonality increases the rigidity of the active industry size and performance-based fee. As a result, they do not vary enough with financial market conditions compared to a Planner’s economy. Quantitatively, an increase in asset payoff uncertainty increases the size and performance-based fee twice as much in the Planner economy compared to the decentralized economy. From a positive perspective, contract commonality contributes to the inconsistency of several widely adopted measures of active manager skill.
The adsorption capability of coal under integrated influence of temperature and pressure and predicted of content quantity of coal bed gas

It is made isothermal adsorption test of difference temperature for coal sample of representative. The equilibrium moisture processing of coal sample must be conducted before analysis, and the equilibrium moisture after processed is equivalent to the moistureholding capacity of coal and close to the moisture content in insitu coal. Research is discovered that the coal adsorption quantity are decreased with increasing temperatures under isotonic condition, same temperature, different pressure, the decrease quantity of different coal sample adsorption quantity is different. Affection of pressure is greater than temperature for coal adsorption under the integrated influence of temperatures and pressures in lowness temperature and pressure area, the adsorption quantity of coal are increased with temperature and pressure. Affection of temperature is greater than pressure for coal adsorption in higher temperature and pressure area, the adsorption quantity of coal are decreased. If coal metamorphose is different, turning point of adsorption quantity increase to decrease is different. The author sets up predicted method of coal bed gas content that is close to insitu storage condition of coal bed gas (include temperature, pressure, moisture, ash, coal metamorphose) in integrated influence, this method is used in forecast of resources quantity of coal bed gas.
The Performance of Crowdfunding Investors: Evidence from Random Assignment of Loans to Crowds and Institutions

Funding small businesses used to be the exclusive domain of angel investors, venture capitalists, and banks. Crowds have only recently been recognized as an alternative source of financing. Whereas some have attributed great potential to the funding provided by crowds (crowdfunding), others have clearly been more skeptical. We join this debate by examining crowds’ performance to screen the creditworthiness of small and medium sized enterprises (SMEs) compared with the performance of institutions in the context of online peer-to-business lending markets. By exploiting the randomized assignment of originated loans to institutions and crowds in the online peer-to-business platform of Funding Circle, we report that crowds underperform institutions in screening SMEs and thus, relative to institutions, crowds miss the opportunity to lend at higher interest rates as adjusted by the likelihood of loan defaults. The interest rates set by crowds predict default 22% less accurately than those set by institutions. Moreover, the underperformance gap of crowds compared with institutions widens with risky loans, implying that crowds show limited expertise in their risk assessment. Overall, our findings highlight when crowds face limitations in evaluating SMEs.
GMM Weighting Matrices In Cross-Sectional Asset Pricing Tests

Cross-sectional asset pricing tests with GMM can generate spuriously high explanatory power for factor models when the moment conditions are specified such that they allow the estimated factor means to substantially deviate from the observed sample averages. In fact, by shifting the weights on the moment conditions, any level of cross-sectional fit can be attained. This property is a feature of the GMM estimation design and applies to strong as well as weak factors, and to all sample sizes and test assets. We reveal the origins of this bias theoretically, gauge its size using simulations, and document its relevance empirically.
The Working Capital Credit Multiplier

We propose and analyze a new mechanism through which financial frictions can affect firms’ operations and economic activity: the working capital credit multiplier. To isolate this multiplier, we develop an identification strategy that builds on cash flow seasonality and allows us to identify funding frictions in working capital without employing pure financial shocks. Our analysis provides evidence that some firms have limited capacity to fund working capital investments, which constraints their production capacity. This mechanism furthers our understanding of how funding frictions amplify and propagate the effects of economic shocks.
Rethinking conditional GAN training: An approach using geometrically structured latent manifolds

Conditional GANs (cGAN), in their rudimentary form, suffer from critical drawbacks such as the lack of diversity in generated outputs and distortion between the latent and output manifolds. Although efforts have been made to improve results, they can suffer from unpleasant side-effects such as the topology mismatch between latent and output spaces. In contrast, we tackle this problem from a geometrical perspective and propose a novel training mechanism that increases both the diversity and the visual quality of a vanilla cGAN, by systematically encouraging a bi-lipschitz mapping between the latent and the output manifolds. We validate the efficacy of our solution on a baseline cGAN (i.e., Pix2Pix) which lacks diversity, and show that by only modifying its training mechanism (i.e., with our proposed Pix2Pix-Geo), one can achieve more diverse and realistic outputs on a broad set of image-to-image translation tasks. Codes are available at https://github.com/samgregoost/Rethinking-CGANs.
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters

Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no attention has been paid to standardizing and analyzing the design of few-shot experiments. In this work, we highlight a fundamental risk posed by this shortcoming, illustrating that the model exhibits a high degree of sensitivity to the selection of few shots. We conduct a large-scale experimental study on 40 sets of sampled few shots for six diverse NLP tasks across up to 40 languages. We provide an analysis of success and failure cases of few-shot transfer, which highlights the role of lexical features. Additionally, we show that a straightforward full model finetuning approach is quite effective for few-shot transfer, outperforming several state-of-the-art few-shot approaches. As a step towards standardizing few-shot crosslingual experimental designs, we make our sampled few shots publicly available.
Using public clinical trial reports to evaluate observational study methods

Observational studies are valuable for estimating the effects of various medical interventions, but are notoriously difficult to evaluate because the methods used in observational studies require many untestable assumptions. This lack of verifiability makes it difficult both to compare different observational study methods and to trust the results of any particular observational study. In this work, we propose TrialVerify, a new approach for evaluating observational study methods based on ground truth sourced from clinical trial reports. We process trial reports into a denoised collection of known causal relationships that can then be used to estimate the precision and recall of various observational study methods. We then use TrialVerify to evaluate multiple observational study methods in terms of their ability to identify the known causal relationships from a large national insurance claims dataset. We found that inverse propensity score weighting is an effective approach for accurately reproducing known causal relationships and outperforms other observational study methods. TrialVerify is made freely available for others to evaluate observational study methods.
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision

The effectiveness of Neural Information Retrieval (Neu-IR) often depends on a large scale of in-domain relevance training signals, which are not always available in real-world ranking scenarios. To democratize the benefits of Neu-IR, this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains. Drawing on source-domain massive relevance supervision, MetaAdaptRank contrastively synthesizes a large number of weak supervision signals for target domains and meta-learns to reweight these synthetic “weak” data based on their benefits to the target-domain ranking accuracy of Neu-IR models. Experiments on three TREC benchmarks in the web, news, and biomedical domains show that MetaAdaptRank significantly improves the few-shot ranking accuracy of Neu-IR models. Further analyses indicate that MetaAdaptRank thrives from both its contrastive weak data synthesis and meta-reweighted data selection. The code and data of this paper can be obtained from https://github.com/thunlp/MetaAdaptRank.
Evaluating Mixed-Initiative Procedural Level Design Tools Using a Triple-Blind Mixed-Method User Study

Results from a triple-blind mixed-method user study into the effectiveness of mixed-initiative tools for the procedural generation of game levels are presented. A tool which generates levels using interactive evolutionary optimization was designed for this study which 1) is focused on supporting the designer to explore the design space and 2) only requires the designer to interact with it by designing levels. The tool identifies level design patterns in an initial hand-designed map and uses that information to drive an interactive optimization algorithm. A rigorous user study was designed which compared the experiences of designers using the mixed-initiative tool to designers who were given a tool which provided completely random level suggestions. The designers using the mixed-initiative tool showed an increased engagement in the level design task, reporting that it was effective in inspiring new ideas and design directions. This provides significant evidence that procedural content generation can be used as a powerful tool to support the human design process. support for the research of this article.
A Fast and Accurate Similarity-Constrained Subspace Clustering Algorithm for Hyperspectral Image

Accurate unsupervised classification of hyperspectral images (HSIs) is challenging and has drawn widespread attention in remote sensing due to its inherent complexity. Although significant efforts have been made to develop a variety of methods, most of them rely on supervised strategies. Subspace clustering methods, such as sparse subspace clustering (SSC), have become a popular tool for unsupervised learning due to their high performance. However, the computational complexity of SSC methods prevents their use on full HSIs. Furthermore, since SSC ignores the spatial information in the HSIs, its discrimination capability is limited, hampering the clustering results’ spatial homogeneity. To address these two relevant issues, in this article, we propose a fast algorithm that obtains a sparse representation coefficient matrix by first selecting a small set of pixels that best represent their neighborhood. Then, it performs spatial filtering to enforce the connectivity of neighboring pixels and uses fast spectral clustering to get the final clustering map. Extensive simulations with our proposed method demonstrate its effectiveness in unsupervised HSI classification, obtaining remarkable high clustering performance compared with state-of-the-art SSC-based algorithms and even novel unsupervised-deep-learning-based methods. Besides, the proposed method is up to three orders of magnitude faster than SSC when clustering more than $\text{2} \times \text{10}^4$ spectral pixels.
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation

Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages. However, much of this work only relies on the shared vocabulary and bilingual contexts to encourage the correlation across languages, which is loose and implicit for aligning the contextual representations between languages. In this paper, we plug a cross-attention module into the Transformer encoder to explicitly build the interdependence between languages. It can effectively avoid the degeneration of predicting masked words only conditioned on the context in its own language. More importantly, when fine-tuning on downstream tasks, the cross-attention module can be plugged in or out on-demand, thus naturally benefiting a wider range of cross-lingual tasks, from language understanding to generation. As a result, the proposed cross-lingual model delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark, covering text classification, sequence labeling, question answering, and sentence retrieval. For cross-lingual generation tasks, it also outperforms all existing cross-lingual models and state-of-the-art Transformer variants on WMT14 English-to-German and English-to-French translation datasets, with gains of up to 1 2 BLEU.
Green manure/cover crops and crop rotation in conservation agriculture on small farms.

The objective of this publication is to offer a reference material for extensionists, professors, agronomy students, technicians in general, and for farmers themselves. Through information that is up-to-date and richly illustrated, it strives to facilitate the adoption and diffusion of No-Tillage, the use of green manures, and the practice of crop rotation on small farms. The publication describes the principal species of green manures and, at the same time, informs in detail how to insert green manures into small farm production systems according to soil fertility and major crops. It also deals with the residual effect of green manures on main crops and analyzes the economic implications of these practices. j ti f t is li ti is t ff r r f r t ri l f r t si ists, r f ss rs, r st ts, t i i s i r l, f r f r rs t s l s. r i f r ti t t is -t t ri l ill str t , it stri s t f ilit t t ti iff si f ill , t s f r r s, t r ti f r r t ti s ll f r s. li ti s ri s t ri i l s i s f r r s , t t s ti , i f r s i t il t i s rt r r s i t s ll f r r ti s st s r i t s il f rtilit j r r s. It ls ls it t r si l ff t f r r s i r s l s t i i li ti s f t s r ti s.