Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of In Vitro Antimicrobial Activity of Five Sri Lankan Medicinal Plants against Selected Human Pathogenic Bacteria

Introduction Antibiotic resistance is one of the greatest threats of the 21st century. Scientists search for potential antimicrobial sources that can cope with antibiotic resistance. Plants used in traditional medicine can be identified as potential candidates for the synthesis of novel drug compounds to act against antibiotic-resistant bacteria. Objective To determine the potential antimicrobial effects of ethanol, aqueous, and hexane extracts of five Sri Lankan medicinal plants against four human pathogens. Methods Asparagus falcatus (tubers), Asteracantha longifolia (whole plant), Vetiveria zizanioides (roots), Epaltes divaricata (whole plant), and Coriandrum sativum (seeds) were used in the study. Plant extracts were screened against four clinically important Gram-positive and Gram-negative bacterial strains, Staphylococcus aureus (ATCC 25923), Escherichia coli (ATCC 25922), Pseudomonas aeruginosa (ATCC 27853), and Klebsiella pneumoniae (ATCC 700603). Antibacterial activity of plant extracts were monitored using the agar disc diffusion method. Eight concentrations of each positive plant extract were used to determine the minimum inhibitory concentration (MIC) by 5-fold dilution of plant extracts yielding a serial dilution of the original extract. Results Ethanol, aqueous, and hexane extracts of E. divaricata gave the maximum zones of inhibition of 16.3 mm, 7.4 mm, and 13.7 mm and MIC values of 0.48 mg/ml, 1.2 mg/ml, and 1.6 mg/ml, respectively, against S. aureus. Ethanol and hexane extracts of V. zizanioides gave the maximum zones of inhibition of 12.1 mm and 11.4 mm and MIC values 2.4 mg/ml and 0.003 mg/ml, respectively, against S. aureus. None of the other plants were effective against any microorganism used for the study. Conclusions It can be concluded that E. divaricata and V. zizanioides crude ethanol, aqueous, and hexane extracts exhibited significant in vitro antibacterial activity against S. aureus, and the active compounds isolated from them can be potential sources for the synthesis of antibacterial drugs.
Propagation of Chaos for Stochastic Spatially Structured Neuronal Networks with Fully Path Dependent Delays and Monotone Coefficients driven by Jump Diffusion Noise

Spatially structured neural networks driven by jump diffusion noise with monotone coefficients, fully path dependent delay and with a disorder parameter are considered. Well-posedness for the associated McKean-Vlasov equation and a corresponding propagation of chaos result in the infinite population limit are proven. Our existence result for the McKean-Vlasov equation is based on the Euler approximation, that is applied to this type of equation for the first time.
Approximate Edge Analytics for the IoT Ecosystem

IoT-enabled devices continue to generate a massive amount of data. Transforming this continuously arriving raw data into timely insights is critical for many modern online services. For such settings, the traditional form of data analytics over the entire dataset would be prohibitively limiting and expensive for supporting real-time stream analytics. In this work, we make a case for approximate computing for data analytics in IoT settings. Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing - based on the chosen sample size - can make a systematic trade-off between the output accuracy and computation efficiency. This motivated the design of APPROXIOT - a data analytics system for approximate computing in IoT. To realize this idea, we designed an online hierarchical stratified reservoir sampling algorithm that uses edge computing resources to produce approximate output with rigorous error bounds. To showcase the effectiveness of our algorithm, we implemented APPROXIOT based on Apache Kafka and evaluated its effectiveness using a set of microbenchmarks and real-world case studies. Our results show that APPROXIOT achieves a speedup 1.3X-9.9X with varying sampling fraction of 80% to 10% compared to simple random sampling.
Integrative omics to detect bacteremia in patients with febrile neutropenia

Background Cancer chemotherapy-associated febrile neutropenia (FN) is a common condition that is deadly when bacteremia is present. Detection of bacteremia depends on culture, which takes days, and no accurate predictive tools applicable to the initial evaluation are available. We utilized metabolomics and transcriptomics to develop multivariable predictors of bacteremia among FN patients. Methods We classified emergency department patients with FN and no apparent infection at presentation as bacteremic (cases) or not (controls), according to blood culture results. We assessed relative metabolite abundance in plasma, and relative expression of 2,560 immunology and cancer-related genes in whole blood. We used logistic regression to identify multivariable predictors of bacteremia, and report test characteristics of the derived predictors. Results For metabolomics, 14 bacteremic cases and 25 non-bacteremic controls were available for analysis; for transcriptomics we had 7 and 22 respectively. A 5-predictor metabolomic model had an area under the receiver operating characteristic curve of 0.991 (95%CI: 0.972,1.000), 100% sensitivity, and 96% specificity for identifying bacteremia. Pregnenolone steroids were more abundant in cases and carnitine metabolites were more abundant in controls. A 3-predictor gene expression model had corresponding results of 0.961 (95%CI: 0.896,1.000), 100%, and 86%. Genes involved in innate immunity were differentially expressed. Conclusions Classifiers derived from metabolomic and gene expression data hold promise as objective and accurate predictors of bacteremia among FN patients without apparent infection at presentation, and can provide insights into the underlying biology. Our findings should be considered illustrative, but may lay the groundwork for future biomarker development.
Low-Dose Aspirin as Primary Prophylaxis for Cardiovascular Events in Rheumatoid Arthritis: An Italian Multicentre Retrospective Study

Objective To investigate the role of acetylsalicylic acid (ASA) in reducing the incidence of cardiovascular (CV) events in an Italian multicentre rheumatoid arthritis (RA) inception cohort. Methods The clinical charts of RA patients consecutively admitted to 4 Italian centres for their 1st visit from November 1, 2000, to December 31, 2015, and followed up till December 2016 were retrospectively investigated for the incidence of CV events. Patients were subdivided into two groups, namely, ASA- and non-ASA-treated groups. The Kaplan–Meier curve and log-rank test were used to investigate differences in event-free survival. Cox regression analysis was carried out to identify factors associated with CV event occurrence. Results Seven hundred forty-six consecutive RA patients were enrolled and followed up for a median of 5.6 years (range 2.9–8.9 years). The incidence rate (IR) of CV events was 8/1000 person-years (p-ys) in the overall cohort. The IR of CV events was significantly lower in the ASA-treated group with respect to the non-ASA-treated group (IR 1.7 vs. 11.8/1000 p-ys; p=0.0002). The CV event-free rate was longer in ASA-treated patients than in non-ASA-treated patients (log-rank test 12.8; p=0.0003). At multivariable analysis, arterial hypertension (HR 9.3) and hypercholesterolemia (HR 2.8) resulted to be positive predictors and ASA (HR 0.09) and hydroxychloroquine (HCQ) (HR 0.22) to be negative predictors. Conclusion The IR of CV events in our Italian multicentre cohort was lower than that reported in other European and non-European cohorts. Low-dose ASA may have a role in the primary prophylaxis of CV events in RA patients.
Universal Dependencies for Amharic

In this paper, we describe the process of creating an Amharic Dependency Treebank, which is the first attempt to introduce Universal Dependencies (UD) into Amharic. Amharic is a morphologically-rich and less-resourced language within the Semitic language family. In Amharic, an orthographic word may be bundled with information other than morphology. There are some clitics attached to major lexical categories with grammatical functions. We first explain the segmentation of clitics, which is problematic to retrieve from the orthographic word due to morpheme co-occurrence restriction, assimilation and ambiguity of the clitics. Then, we describe the annotation processes for POS tagging, morphological information and dependency relations. Based on this, we have created a Treebank of 1,096 sentences.
Associations Between Religiosity, Perceived Social Support, and Stimulant Use in an Untreated Rural Sample in the U.S.A.

ABSTRACT Background: Religiosity and perceived social support (SS) may serve as protective factors for more severe substance use in adults. Objectives: This study sought to examine whether aspects of religiosity and SS are associated with longitudinal reductions in stimulant use over three years in an untreated sample of rural drug users. Methods: Respondent-driven sampling was used to recruit stimulant users (N = 710) from Arkansas, Kentucky, and Ohio. Follow-up interviews were conducted at 6-month intervals for 36 months. Results: Our bivariate findings indicate that higher religiosity was associated with lower odds and fewer days of methamphetamine and cocaine use. After controlling for covariates, higher religiosity was associated with fewer days of crack cocaine use, but more days of methamphetamine use among a small sample of users in the two final interviews. Higher SS from drug-users was also associated with higher odds and days of methamphetamine and powder cocaine use, while higher SS from nondrug users was associated with fewer days of methamphetamine use. Conclusions/Importance: Our bivariate findings suggest that higher levels of religiosity may be helpful for some rural individuals in reducing their drug use over time. However, our multivariate findings suggest a need for further exploration of the potential effects of religiosity on longer-term drug use, especially among those who continue to use methamphetamine and/or remain untreated. Our findings also highlight the potential deleterious effect of SS from drug users on the likelihood and frequency of methamphetamine and powder cocaine use over time among untreated rural drug users.
Activity-dependent aberrations in gene expression and alternative splicing in a mouse model of Rett syndrome

Significance Rett syndrome (RTT) is a severe neurological disease affecting girls in their early childhood. The underlying cause of most RTT cases is mutations in the gene methyl-CpG-binding protein 2 (MECP2). How the loss of MeCP2 function in the brain due to these mutations causes such severe neurological symptoms remains a mystery. Here, we show widespread aberrations in gene expression and anomalous patterns of alternative splicing, specifically when neurons of RTT mice are stimulated. Furthermore, these aberrations occur in conjunction with higher seizure susceptibility in response to neuronal stimulation in these RTT mice. Our findings suggest that MeCP2 is required for adjusting the robust changes in gene transcription and for proper regulation of alternative splicing during neuronal stimulation. Rett syndrome (RTT) is a severe neurodevelopmental disorder that affects about 1 in 10,000 female live births. The underlying cause of RTT is mutations in the X-linked gene, methyl-CpG-binding protein 2 (MECP2); however, the molecular mechanism by which these mutations mediate the RTT neuropathology remains enigmatic. Specifically, although MeCP2 is known to act as a transcriptional repressor, analyses of the RTT brain at steady-state conditions detected numerous differentially expressed genes, while the changes in transcript levels were mostly subtle. Here we reveal an aberrant global pattern of gene expression, characterized predominantly by higher levels of expression of activity-dependent genes, and anomalous alternative splicing events, specifically in response to neuronal activity in a mouse model for RTT. Notably, the specific splicing modalities of intron retention and exon skipping displayed a significant bias toward increased retained introns and skipped exons, respectively, in the RTT brain compared with the WT brain. Furthermore, these aberrations occur in conjunction with higher seizure susceptibility in response to neuronal activity in RTT mice. Our findings advance the concept that normal MeCP2 functioning is required for fine-tuning the robust and immediate changes in gene transcription and for proper regulation of alternative splicing induced in response to neuronal stimulation.
20 Particles Formation Using Supercritical Fluids

The particle precipitation into micro and nanoparticles has been an active research field for decades (Chattopadhyay & Gupta, 2001; Kalogiannis et al., 2005; Rehman et al., 2001; Reverchon, 1999; Velaga et al., 2002; Yeo&Lee, 2004). The greatest requirement in the application of nanomaterials is its size and morphology control which determine the potential application of the nanoparticles, as their properties vary significantly with size. Micro and nanoparticles can be obtained by different techniques. Conventional techniques (spray drying, solute recrystallization, coacervation, freeze-drying, interfacial polymerization) present drawbacks such as excessive use of solvent, thermal and chemical solute degradation, structural changes, high residual solvent concentration, and mainly, difficulty of controlling the particle size (PS) and particle size distribution (PSD) during processing (He et al., 2004), so these techniques for particle formation may not be advisable. However, the application of supercritical fluids (SCFs) is an attractive alternative for this particle formation because remove these drawbacks. These supercritical fluids have larger diffusivities than those of typical liquids, resulting in higher mass-transfer rates. Moreover its solvent power and selectivity can be tuned altering the experimental conditions. There are two main ways of precipitating micro and nanoparticles using supercritical fluid as solvent, the RESS technique (Rapid Expansion of Supercritical Solutions); or using it as antisolvent, the SAS technique (Supercritical AntiSolvent); the choice between one or another depends on the active substance high or low solubility in the supercritical fluid. The RESS process consists of solubilising the active ingredient of interest in the supercritical fluid and then rapidly depressurising this solution through a nozzle, thus causing the precipitation, extremely fast, of this compound. In other words, the process is based on the transition of active compound from soluble to insoluble state when the carbon dioxide passes from the supercritical to the gaseous phase. This technique has been applied on the particle precipitation and co-precipitation of many active ingredients/polymers (Kongsombut et al., 2009; Sane & Limtrakul, 2009; Turk et al., 2006; Vemavarapu et al., 2009; Wen et al., 2010). The SAS technique, in all its variants, generally consists of spraying a solution of the solute to be precipitated into the supercritical fluid. The mass transfer behavior of the droplets is thought to be a key factor affecting particle morphology (Werling & Debenedetti, 1999). The volumetric expansion of the solvent reduces the solvation capacity of the solvent, causing the supersaturation of the liquid phase and the consequent generation of the particles. The SAS process has been carried out for many particles precipitation and polymeric encapsulation of particles of active ingredients (Ai-Zheng et al., 2009; Chong et al., 2009a;
Highly deficient alcohol health warning labels in a high‐income country with a voluntary system

INTRODUCTION AND AIMS To examine the prevalence and design elements of the voluntary health warning labels and related industry initiatives on a purposive sample of alcoholic beverage containers sold in New Zealand (NZ), a country with no mandatory health warning labels. DESIGN AND METHODS We selected a purposive (e.g. low-cost) sample of 59 local and imported beers, wines and ready-to-drink alcoholic beverage containers available in NZ in 2016-2017. We documented the occurrence, content, size, appearance and position of messages concerning drinking during pregnancy, drink-driving, other health effects and industry-led initiatives that could relate to warnings; and collected data about alcohol content, standard drinks, ingredients and energy information. RESULTS A majority (80%) of the alcoholic beverage containers had a pregnancy-related warning, 73% had industry-led initiatives (e.g. advising 'responsible' consumption) and 19% had drink-driving/heavy machinery warnings. Warning labels were small, with the average area of pregnancy-related and drink-driving/heavy machinery pictograms being 45 and 36 mm2 , respectively (i.e. pea-size). The average heights of pregnancy-related and drink-driving text were 1.6 and 2.2 mm, respectively. Pregnancy-related pictograms occupied between an average of 0.13% (wine) and 0.21% (ready-to-drink) of the available surface area of the alcoholic beverage container (i.e. less than 1/400th of the available space). Drink-driving pictograms occupied an average of 0.12% (imported beer), and 0.13% (NZ beer) of the available surface area. DISCUSSION AND CONCLUSIONS Voluntary recommendations in NZ appear to have been inadequate for producing health warnings on alcoholic beverage containers that are consistent with evidence-informed recommendations for effective labels. This finding suggests that mandatory standardised labelling outlining alcohol-related risks may be required to ensure adequate consumer information.
Two algorithms for non-rigid image registration and their evaluation

This paper presents two non-rigid image registration algorithms: Thirion's Demons method and its spline-based extension, and compares their performance on the task of inter-subject registration of MRI brain images. The methods are designed to be fast and derive their speed from the uncoupling of the correspondence calculation and deformation interpolation procedures, each of which are then amenable to efficient implementation. The evaluation results indicate that this uncoupling does not significantly limit the registration accuracy that can be achieved.
Distributing Deep Neural Networks for Maximising Computing Capabilities and Power Efficiency in Swarm

Deploying neural networks models over embedded devices have an increased interest and many works is ongoing on that topic. Energy consumption, model sizes and inference time are critical issues as explained in the literature. In the context of IoT and edge computing, tradeoff have been studied in order to get a low cost but rapid answer, robust to connection issue exploiting early exiting or distributing deep neural networks. Those approaches exploits the cloud as an endpoint, balancing the load with respect to different computing capabilities. In this paper, we propose to extend those approaches to networks of embedded devices such as a swarm of drones, where every device has the same computing capabilities (in terms of energy and speed). Computing load may be balanced among the whole swarm in order to maximise either the lifetime of specific devices or lifetime of the whole swarm. We develop criteria to best cut and distribute those networks, validate them through power measurement and express the different tradeoffs we have to address.
SOD1 in cerebral spinal fluid as a pharmacodynamic marker for antisense oligonucleotide therapy.

BACKGROUND Therapies designed to decrease the level of SOD1 are currently in a clinical trial for patients with superoxide dismutase (SOD1)-linked familial amyotrophic lateral sclerosis (ALS). OBJECTIVE To determine whether the SOD1 protein in cerebral spinal fluid (CSF) may be a pharmacodynamic marker for antisense oligonucleotide therapy and a disease marker for ALS. DESIGN Antisense oligonucleotides targeting human SOD1 were administered to rats expressing SOD1G93A. The human SOD1 protein levels were measured in the rats' brain and CSF samples. In human CSF samples, the following proteins were measured: SOD1, tau, phosphorylated tau, VILIP-1, and YKL-40. PARTICIPANTS Ninety-three participants with ALS, 88 healthy controls, and 89 controls with a neurological disease (55 with dementia of the Alzheimer type, 19 with multiple sclerosis, and 15 with peripheral neuropathy). RESULTS Antisense oligonucleotide-treated SOD1G93A rats had decreased human SOD1 messenger RNA levels (mean [SD] decrease of 69% [4%]) and decreased protein levels (mean [SD] decrease of 48% [14%]) in the brain. The rats' CSF samples showed a similar decrease in hSOD1 levels (mean [SD] decrease of 42% [14%]). In human CSF samples, the SOD1 levels varied a mean (SD) 7.1% (5.7%) after additional measurements, separated by months, were performed. The CSF SOD1 levels were higher in the participants with ALS (mean [SE] level, 172 [8] ng/mL; P<.05) and the controls with a neurological disease (mean [SE] level, 172 [6] ng/mL; P<.05) than in the healthy controls (mean [SE] level, 134 [4] ng/mL). Elevated CSF SOD1 levels did not correlate with disease characteristics in participants with ALS or controls with dementia of the Alzheimer type, but they did correlate with tau, phosphorylated tau, VILIP-1 and YKL-40 levels in controls with dementia of the Alzheimer type. CONCLUSIONS SOD1 in CSF may be an excellent pharmacodynamic marker for SOD1-lowering therapies because antisense oligonucleotide therapy lowers protein levels in the rat brain and rat CSF samples and because SOD1 levels in CSF samples from humans are stable over time.
Laying the Groundwork for Knowledge Base Population: Nine Years of Linguistic Resources for TAC KBP

Knowledge Base Population (KBP) is an evaluation series within the Text Analysis Conference (TAC) evaluation campaign conducted by the National Institute of Standards and Technology (NIST). Over the past nine years TAC KBP evaluations have targeted information extraction technologies for the population of knowledge bases comprised of entities, relations, and events. Linguistic Data Consortium (LDC) has supported TAC KBP since 2009, developing, maintaining, and distributing linguistic resources in three languages for seven distinct evaluation tracks. This paper describes LDC's resource creation efforts for the various KBP tracks, and highlights changes made over the years to support evolving evaluation requirements.