Miracle-dz/UniFAR-FTUs huggingface dataset:
Anger and substance use in HIV-positive patients with chronic pain

ABSTRACT Chronic pain increases the risk of substance use in people living with HIV (PLWH). Depression and anxiety have also been identified as risk factors for substance use among PLWH. Relatedly, other negative mood states, such as anger, may influence chronic pain among PLWH. The current cross-sectional study examined whether the distinct negative mood state of anger is associated with substance use among 187 PLWH who report chronic pain. Using negative binomial regression analyses, we found higher levels of anger were positively associated with alcohol use. Higher levels of anger were inversely associated with benzodiazepine use. No association was found between anger and marijuana use, and there were no significant interactions between anger and pain severity on substance use. Our findings suggest that anger is an independent risk factor for substance use among PLWH and chronic pain. Addressing anger may be useful when adapting behavioral therapies in the treatment of pain among PLWH.
Using population selection and sequencing to characterize natural variation of starvation resistance in Caenorhabditis elegans

Starvation resistance is important to disease and fitness, but the genetic basis of its natural variation is unknown. Uncovering the genetic basis of complex, quantitative traits such as starvation resistance is technically challenging. We developed a synthetic-population (re)sequencing approach using molecular inversion probes (MIP-seq) to measure relative fitness during and after larval starvation in Caenorhabditis elegans. We applied this competitive assay to 100 genetically diverse, sequenced, wild strains, revealing natural variation in starvation resistance. We confirmed that the most starvation-resistant strains survive and recover from starvation better than the most starvation-sensitive strains using standard assays. We performed genome-wide association (GWA) with the MIP-seq trait data and identified three quantitative trait loci (QTL) for starvation resistance, and we created near isogenic lines (NILs) to validate the effect of these QTL on the trait. These QTL contain numerous candidate genes including several members of the Insulin/EGF Receptor-L Domain (irld) family. We used genome editing to show that four different irld genes have modest effects on starvation resistance. Natural variants of irld-39 and irld-52 affect starvation resistance, and increased resistance of the irld-39; irld-52 double mutant depends on daf-16/FoxO. DAF-16/FoxO is a widely conserved transcriptional effector of insulin/IGF signaling (IIS), and these results suggest that IRLD proteins modify IIS, although they may act through other mechanisms as well. This work demonstrates efficacy of using MIP-seq to dissect a complex trait and it suggests that irld genes are natural modifiers of starvation resistance in C. elegans.
Surgical declarative knowledge learning: concept and acceptability study

Abstract Improving surgical training by means of technology assistance is an important challenge that aims to directly impact surgical quality. Surgical training includes the acquisition of two categories of knowledge: declarative knowledge (i.e. ‘knowing what’) and procedural knowledge (i.e. ‘knowing how’). It is essential to acquire both before performing any particular surgery. There are currently many tools for acquiring procedural knowledge, such as simulators. However, few approaches or tools allow a trainer to formalize and record surgical declarative knowledge, and a trainee to have easy access to it. In this paper, we propose an approach for structuring surgical declarative knowledge according to procedural knowledge and based on surgical process modeling. A dedicated software application has been implemented. We evaluated the concept and the software usability on two procedures with different medical populations: endoscopic third ventriculostomy involving 6 neurosurgeons and preparation of a surgical table for craniotomy involving 4 scrub nurses. The results of both studies show that surgical process models could be a well-adapted approach for structuring and visualizing surgical declarative knowledge. The software application was perceived by neurosurgeons and scrub nurses as an innovative tool for managing and presenting surgical knowledge. The preliminary results show that the feasibility of the proposed approach and the acceptability and usability of the corresponding software. Future experiments will study impact of such an approach on knowledge acquisition.
A Simple and Practical Method for Fluence Determination in Bench‐Scale UV‐LED Setups

In UV disinfection of water, the fluence of UV required to inactivate a target microorganism is determined based on the procedures developed for conventional mercury‐based UV lamps with collimation. In this regard, a simple and practical method with a mathematical model and radiometry is proposed for determining the fluence rate with UV light‐emitting diodes (UV‐LEDs). This method was applied to a bench‐scale UV‐LED setup and validated by comparing the calculations with the measurements using either a spectroradiometer or a chemical actinometer. The results showed high accordance with spectroradiometer outputs with a linear regression equation y = 0.997x (x: model calculation, y: spectroradiometer output, r2 = 0.999, P < 0.001 for n = 20) in an experiment varying the distance between the measurement points and the UV‐LEDs. Meanwhile, the proposed method and chemical actinometry exhibited 98% concordance. Furthermore, this method was applied to determine the fluence‐response profiles of Pseudomonas aeruginosa, and the results demonstrated that the proposed method was appropriate at two different distances between the UV‐LEDs and the solutions. To conclude, the proposed method can determine the fluence in a UV‐LED bench‐scale setup in a simple and practical way, which would potentially promote the research and development of water treatment using UV‐LEDs.
Haplogroup diversity in the Indian population using 23 Y-STRs

Abstract Background A Y-STR polymorphism study is a convenient tool in molecular anthropology and forensic DNA analysis. Aim Through standard ethical procedures, the proposed study explored the genetic scenario in male lineage in Madhya Pradesh, a central Indian state, by Y-STR genotyping and haplogroup studies. Subjects and methods Five hundred and eleven unrelated male blood samples were directly amplified, and fragment separation was done using capillary electrophoresis to generate a Y-STR profile for 23 forensic relevant markers through PowerPlex® Y 23 multiplex system. The different statistical methods were applied for studying the forensic and genetics parameters. Subsequently, population comparison was performed by AMOVA, PCoA, and MDS plot, and Haplogroups were predicted with Whit Athey's haplogroup predictor tool. Conclusion These data represented the potential value of the PowerPlex® Y-23 multiplex system for the forensic and human genetics application in the population of Madhya Pradesh, India. Simultaneously the Haplogroup analysis revealed information about the multi-geographic origin as well as multi-ethnic genetic affinities of the Madhya Pradesh population.
The sustainable health Agenda in the Americas: Pre-pandemic gaps and 2030 estimates of the SDGs indicators

The preliminary assessments of the impact of the COVID-19 pandemic have recently rekindled worries about the feasibility of the Sustainable Development Goals (SDGs). Notwithstanding the concern voiced by key academic and political actors, the actual evidence on the current gaps and distance from the goals is still very much unknown. This study estimates the global evolution curves for each health-related SDGs indicator in the World Health Organization’s SDGs platform. These curves synthesize the transnational trends at play in the evolution of each health-related topic, offering an average global counterfactual to compare with the actual information for each country. The empirical investigation focuses on the American continent, highlighting the health gaps before the COVID-19 outbreak in 33 countries of the region. The study also extrapolates these trends to predict the evolution of the health-related SDGs in each of these countries over the next decade using as the baseline scenario the International Monetary Fund’s economic forecasts. The results show a widening gap in the region, associated with the differential economic capacity of these countries. Some bottlenecks are shared by most countries in the continent, especially in the themes of violence and infectious diseases. The latter is likely to improve faster than other health themes in the next decade, whereas improvements in the theme non-communicable diseases can be more challenging. The findings provide much needed comparative evidence to guide the countries in the region to set priorities and concentrate efforts to accelerate progress in the health-related SDGs.
Remote ischemic preconditioning causes transient cell cycle arrest and renal protection by a NF-κB–dependent Sema5B pathway

Acute kidney injury increases morbidity and mortality, and previous studies have shown that remote ischemic preconditioning (RIPC) reduces the risk of acute kidney injury after cardiac surgery. RIPC increases urinary high mobility group box protein-1 (HMGB1) levels in patients, and this correlates with kidney protection. Here, we show that RIPC reduces renal ischemia-reperfusion injury and improves kidney function in mice. Mechanistically, RIPC increases HMGB1 levels in the plasma and urine, and HMGB1 binds to TLR4 on renal tubular epithelial cells, inducing transcriptomic modulation of renal tubular epithelial cells and providing renal protection, whereas TLR4 activation on nonrenal cells was shown to contribute to renal injury. This protection is mediated by activation of induction of AMPKα and NF-κB; this induction contributes to the upregulation of Sema5b, which triggers a transient, protective G1 cell cycle arrest. In cardiac surgery patients at high risk for postoperative acute kidney injury, increased HMGB1 and Sema5b levels after RIPC were associated with renal protection after surgery. The results may help to develop future clinical treatment options for acute kidney injury.
Outperforming RBM Feature-Extraction Capabilities by “Dreaming” Mechanism

Inspired by a formal equivalence between the Hopfield model and restricted Boltzmann machines (RBMs), we design a Boltzmann machine, referred to as the dreaming Boltzmann machine (DBM), which achieves better performances than the standard one. The novelty in our model lies in a precise prescription for intralayer connections among hidden neurons whose strengths depend on features correlations. We analyze learning and retrieving capabilities in DBMs, both theoretically and numerically, and compare them to the RBM reference. We find that, in a supervised scenario, the former significantly outperforms the latter. Furthermore, in the unsupervised case, the DBM achieves better performances both in features extraction and representation learning, especially when the network is properly pretrained. Finally, we compare both models in simple classification tasks and find that the DBM again outperforms the RBM reference.
Life History of Chrysodeixis includens (Lepidoptera: Noctuidae) on Positively Charged Zein Nanoparticles

Abstract Research indicates that nanoparticles can be an effective agricultural pest management tool, though unintended effects on the insect must be evaluated before their use in agroecosystems. Chrysodeixis includens (Walker) was used as a model to evaluate chronic parental and generational exposure to empty, positively charged zein nanoparticles ((+)ZNP) and methoxyfenozide-loaded zein nanoparticles (+)ZNP(MFZ) at low-lethal concentrations. To determine concentration limits, an acute toxic response test on meridic diet evaluated (+)ZNP(MFZ) and technical grade methoxyfenozide using two diet assay techniques. No differences in acute toxicity were observed between the two treatments within their respective bioassays. With these results, population dynamics following chronic exposure to low-lethal concentrations were evaluated. Parental lifetables evaluated cohorts of C. includens reared on diet treated with LC5 equivalents of (+)ZNP, (+)ZNP(MFZ), or technical grade methoxyfenozide. Compared to technical grade methoxyfenozide, (+)ZNP(MFZ) lowered both the net reproductive rate and intrinsic rate of increase, and was more deleterious to C. includens throughout its lifespan. This was contrasted to (+)ZNP, which showed no differences in population dynamics when compared with the control. To evaluate chronic exposure to (+)ZNP, generational lifetables reared cohorts of C. includens on LC5 equivalent values of (+)ZNP and then took the resulting offspring to be reared on either (+)ZNP or untreated diet. No differences in lifetable statistics were observed between the two treatments, suggesting that (+)ZNP at low ppm do not induce toxic generational effects. This study provides evidence into the effects of nanodelivered methoxyfenozide and the generational impact of (+)ZNP.
How does social context influence appraisal and help‐seeking for potential cancer symptoms in adults aged 50 and over? A qualitative interview study

Abstract Objective To investigate how social context and social network activation influence appraisal and help‐seeking for symptoms potentially indicative of cancer. Methods Semi‐structured telephone interview study. Community dwelling adults who had experienced at least one symptom potentially indicative of cancer within the last month were sampled from a national symptom survey. Results Thirty‐four interviews were conducted. Participants looked to peers and wider society to judge whether symptoms might be normal for their age. Involvement of others in symptom appraisal promoted an active management strategy, such as contacting a healthcare professional or trying a medication. There were practical, emotional, attitudinal, normative and moral barriers to involving others. Cancer narratives from significant others, public health campaigns and the media influenced symptom appraisal. Participants held mental representations of types of people who get cancer, for example, smokers and unfit people. This had two consequences. First, participants did not identify themselves as a candidate for cancer; impeding help‐seeking. Second, social judgements about lifestyle introduced stigma. Conclusion Involving friends/family in symptom appraisal facilitates help‐seeking but barriers exist to involving others. Campaigns to promote earlier cancer diagnosis should incorporate age‐appropriate narratives, address misconceptions about ‘types’ of people who get cancer and tackle stigma about lifestyle factors.
Field test of a coaxial dual rotor vertical axis turbine

Tidal current technology offers one of the most promising sources of marine power. Vertical axis turbines have some unique advantages over other types of machine, but there are a number of problems that hinder their development, notably their poor self-starting performance and high cost. In order to address these issues, a coaxial dual rotor vertical axis turbine with 60° shift angle has been developed and field tests have been carried out in Dalian China to evaluate the performance of the prototype. According to the test results, the range of the prototype’s tip speed ratio was from 1.5 to 3.4 with an optimum of 1.8. The capacity factors during the tidal cycle (about 6.5 h in the test site) of the flood tide and ebb tide were 23.9% and 17%. The system efficiency can reach about 28% when the current velocity is about 1.5 m/s. The side wall effect and velocimeter deviation are also analyzed. The main range of self-starting current velocity of the prototype is from 1.1 to 1.3 m/s. The probability that the turbine started spontaneously at current velocity below 1.4 m/s is 96%. The results show that the coaxial dual rotor turbine performs well on energy efficiency and self-starting performance without significant cost implications.
Oxidation of active cysteines mediates protein aggregation of S10R, the cataract‐associated mutant of mouse GammaB‐crystallin

The Ser10 to Arg mutation in mouse γB‐crystallin (MGB) has been associated with protein aggregation, dense nuclear opacity, and the degeneration of fiber cells in the lens core. Overexpression of the gap junction protein, connexin 46 (Cx46), was found to suppress the nuclear opacity and restore normal cell–cell contact. However, the molecular basis for the protein aggregation and related downstream effects were not evident from these studies. Here, we provide a comparison of the structures and solution properties of wild type MGB and the S10R mutant in vitro and show that, even though the mutation does not directly involve cysteine residues, some cysteines in the mutant protein are activated, leading to the enhanced formation of intermolecular disulfide‐crosslinked protein aggregates relative to the wild‐type. This occurs even as the protein structure is essentially unaltered. Thus, the primary event is enhanced protein aggregation due to the disulfide crosslinking of the mutant protein. We suggest that these aggregates eventually get deposited on fiber cell membranes. Since the gap junction protein, Cx46 is involved in the transport of reduced glutathione, we posit that these deposits interfere in Cx46‐mediated glutathione transport and facilitate the oxidative stress‐mediated downstream changes. Overexpression of Cx46 suppresses such oxidative aggregation. These studies provide a plausible explanation for the protein aggregation and other changes that accompany this mutation. If indeed cysteine oxidation is the primary event for protein aggregation also in vivo, then the S10R mutant mouse, which is currently available, could serve as a viable animal model for human age‐onset cataract.
Role of the CrcB transporter of Pseudomonas putida in the multi‐level stress response elicited by mineral fluoride

Abstract The presence of mineral fluoride (F−) in the environment has both a geogenic and anthropogenic origin, and the halide has been described to be toxic in virtually all living organisms. While the evidence gathered in different microbial species supports this notion, a systematic exploration of the effects of F− salts on the metabolism and physiology of environmental bacteria remained underexplored thus far. In this work, we studied and characterized tolerance mechanisms deployed by the model soil bacterium Pseudomonas putida KT2440 against NaF. By adopting systems‐level omic approaches, including functional genomics and metabolomics, we gauged the impact of this anion at different regulatory levels under conditions that impair bacterial growth. Several genes involved in halide tolerance were isolated in a genome‐wide Tn‐Seq screening—among which crcB, encoding an F−‐specific exporter, was shown to play the predominant role in detoxification. High‐resolution metabolomics, combined with the assessment of intracellular and extracellular pH values and quantitative physiology experiments, underscored the key nodes in central carbon metabolism affected by the presence of F−. Taken together, our results indicate that P. putida undergoes a general, multi‐level stress response when challenged with NaF that significantly differs from that caused by other saline stressors. While microbial stress responses to saline and oxidative challenges have been extensively studied and described in the literature, very little is known about the impact of fluoride (F−) on bacterial physiology and metabolism. This state of affairs contrasts with the fact that F− is more abundant than other halides in the Earth crust (e.g. in some soils, the F− concentration can reach up to 1 mg gsoil −1). Understanding the global effects of NaF treatment on bacterial physiology is not only relevant to unveil distinct mechanisms of detoxification but it could also guide microbial engineering approaches for the target incorporation of fluorine into value‐added organofluorine molecules. In this regard, the soil bacterium P. putida constitutes an ideal model to explore such scenarios, since this species is particularly known for its high level of stress resistance against a variety of physicochemical perturbations.
KGNMDA: A Knowledge Graph Neural Network Method for Predicting Microbe-Disease Associations

Accumulated studies discovered that various microbes in human bodies were closely related to complex human diseases and could provide new insight into drug development. Multiple computational methods were constructed to predict microbes that were potentially associated with diseases. However, most previous methods were based on single characteristics of microbes or diseases, that lacked important biological information related to microorganisms or diseases. Therefore, we constructed a knowledge graph centered on microorganisms and diseases from several existed databases to provide knowledgeable information for microbes and diseases. Then, we adopted a graph neural network method to learn representations of microbes and diseases from the constructed knowledge graph. After that, we introduced the Gaussian kernel similarity features of microbes and diseases to generate final representations of microbes and diseases. At last, we proposed a score function on final representations of microbes and diseases to predict scores of microbe-disease associations. Comprehensive experiments on the Human Microbe-Disease Association Database (HMDAD) dataset had demonstrated that our approach outperformed baseline methods. Furthermore, we implemented case studies on two important diseases (asthma and inflammatory bowel disease), the result demonstrated that our proposed model was effective in revealing the relationship between diseases and microbes. The source code of our model and the data were available on https://github.com/ChangzhiJiang/KGNMDA_master.
PCfun: a hybrid computational framework for systematic characterization of protein complex function

Abstract In molecular biology, it is a general assumption that the ensemble of expressed molecules, their activities and interactions determine biological function, cellular states and phenotypes. Stable protein complexes—or macromolecular machines—are, in turn, the key functional entities mediating and modulating most biological processes. Although identifying protein complexes and their subunit composition can now be done inexpensively and at scale, determining their function remains challenging and labor intensive. This study describes Protein Complex Function predictor (PCfun), the first computational framework for the systematic annotation of protein complex functions using Gene Ontology (GO) terms. PCfun is built upon a word embedding using natural language processing techniques based on 1 million open access PubMed Central articles. Specifically, PCfun leverages two approaches for accurately identifying protein complex function, including: (i) an unsupervised approach that obtains the nearest neighbor (NN) GO term word vectors for a protein complex query vector and (ii) a supervised approach using Random Forest (RF) models trained specifically for recovering the GO terms of protein complex queries described in the CORUM protein complex database. PCfun consolidates both approaches by performing a hypergeometric statistical test to enrich the top NN GO terms within the child terms of the GO terms predicted by the RF models. The documentation and implementation of the PCfun package are available at https://github.com/sharmavaruns/PCfun. We anticipate that PCfun will serve as a useful tool and novel paradigm for the large-scale characterization of protein complex function.