Miracle-dz/UniFAR-FTUs huggingface dataset:
Taxonogenomics reveal multiple novel genomospecies associated with clinical isolates of Stenotrophomonas maltophilia

Stenotrophomonas maltophilia has evolved as one of the leading multidrug-resistant pathogens responsible for a variety of nosocomial infections especially in highly debilitated patients. As information on the genomic and intraspecies diversity of this clinically important pathogen is limited, we sequenced the whole genome of 27 clinical isolates from hospitalized patients. Phylogenomic analysis along with the genomes of type strains suggested that the clinical isolates are distributed over the Stenotrophomonas maltophilia complex (Smc) within the genus Stenotrophomonas. Further genome-based taxonomy coupled with the genomes of type strains of the genus Stenotrophomonas allowed us to identify five cryptic genomospecies, which are associated with the clinical isolates of S. maltophilia and are potentially novel species. These isolates share a very small core genome that implies a high level of genetic diversity within the isolates. Recombination analysis of core genomes revealed that the impact of recombination is more than mutation in the diversification of clinical S. maltophilia isolates. Distribution analysis of well-characterized antibiotic-resistance and efflux pump genes of S. maltophilia across multiple novel genomospecies provided insights into its antibiotic-resistant ability. This study supports the existence of multiple cryptic species within the Smc besides S. maltophilia, which are associated with human infections, and highlights the importance of genome-based approaches to delineate bacterial species. This data will aid in improving clinical diagnosis and for understanding species-specific clinical manifestations of infection due to Stenotrophomonas species.
Drosophila CaV2 channels harboring human migraine mutations cause synapse hyperexcitability that can be suppressed by inhibition of a Ca2+ store release pathway

Gain-of-function mutations in the human CaV2.1 gene CACNA1A cause familial hemiplegic migraine type 1 (FHM1). To characterize cellular problems potentially triggered by CaV2.1 gains of function, we engineered mutations encoding FHM1 amino-acid substitutions S218L (SL) and R192Q (RQ) into transgenes of Drosophila melanogaster CaV2/cacophony. We expressed the transgenes pan-neuronally. Phenotypes were mild for RQ-expressing animals. By contrast, single mutant SL- and complex allele RQ,SL-expressing animals showed overt phenotypes, including sharply decreased viability. By electrophysiology, SL- and RQ,SL-expressing neuromuscular junctions (NMJs) exhibited enhanced evoked discharges, supernumerary discharges, and an increase in the amplitudes and frequencies of spontaneous events. Some spontaneous events were gigantic (10-40 mV), multi-quantal events. Gigantic spontaneous events were eliminated by application of TTX – or by lowered or chelated Ca2+ – suggesting that gigantic events were elicited by spontaneous nerve firing. A follow-up genetic approach revealed that some neuronal hyperexcitability phenotypes were reversed after knockdown or mutation of Drosophila homologs of phospholipase Cβ (PLCβ), IP3 receptor, or ryanodine receptor (RyR) – all factors known to mediate Ca2+ release from intracellular stores. Pharmacological inhibitors of intracellular Ca2+ store release produced similar effects. Interestingly, however, the decreased viability phenotype was not reversed by genetic impairment of intracellular Ca2+ release factors. On a cellular level, our data suggest inhibition of signaling that triggers intracellular Ca2+ release could counteract hyperexcitability induced by gains of CaV2.1 function. AUTHOR SUMMARY Prior research has demonstrated that gain-of-function mutations in a gene important for neurotransmission (CACNA1A) are known to cause migraine in humans. We attempted to mimic some of those gain-of-function mutations in a simple genetic model organism and to examine neurotransmission by electrophysiology. Our findings yield potential clues as to how particular migraine-causing mutations may impact neurophysiology on a cellular level. We used the fruit fly Drosophila melanogaster and its model synapse, the neuromuscular junction (NMJ) to perform our studies. We document three main advances: 1) characterization of fruit fly models harboring gain-of-function calcium channel alterations known to cause human familial hemiplegic migraine type 1 (FHM1); 2) characterization of hyperactive neurotransmission caused by one of these alterations; and 3) an ability to quell hyperactive neurotransmission by impairing intracellular Ca2+ store release, through both genetic and pharmacological means. Our work contributes to a broader understanding of how pathological mutations could impact cellular physiology. More generally, the utilization of genetic model organisms promises to uncover potential ways to reverse those impacts.
The deoxyribose phosphate lyase of DNA polymerase β suppresses a processive DNA synthesis to prevent trinucleotide repeat instability

Abstract Trinucleotide repeat (TNR) instability is associated with over 42 neurodegenerative diseases and cancer, for which the molecular mechanisms remain to be elucidated. We have shown that the DNA base excision repair (BER) pathway and its central component, DNA polymerase β (pol β), in particular, its polymerase activity plays an active role in regulating somatic TNR instability. Herein, we revealed a unique role of the pol β dRP lyase in preventing somatic TNR instability. We found that deficiency of pol β deoxyribose phosphate (dRP) lyase activity locked the pol β dRP lyase domain to a dRP group, and this ‘tethered’ pol β to its template forcing the polymerase to perform a processive DNA synthesis. This subsequently promoted DNA strand slippage allowing pol β to skip over a template loop and causing TNR deletion. We showed that the effects were eliminated by complementation of the dRP lyase deficiency with wild-type pol β protein. The results indicate that pol β dRP lyase activity restrained the pol β-dRP interaction to suppress a pol β processive DNA synthesis, thereby preventing TNR deletion. This further implicates a potential of pol β dRP lyase inhibition as a novel treatment of TNR-expansion diseases.
Identifying and Exploiting Potential miRNA-Disease Associations With Neighborhood Regularized Logistic Matrix Factorization

With the rapid development of biological research, microRNAs (miRNA) have become an attractive topic because lots of experimental studies have revealed the significant associations between miRNAs and diseases. However, considering that experiments are expensive and time-consuming, computational methods for predicting associations between miRNAs and diseases have become increasingly crucial. In this study, we proposed a neighborhood regularized logistic matrix factorization method for miRNA-disease association prediction (NRLMFMDA) by integrating miRNA functional similarity, disease semantic similarity, Gaussian interaction profile kernel similarity, and experimentally validation of disease-miRNA association. We used Gaussian interaction profile kernel similarity to cover the shortage of the traditional similarity to make it more reasonable and complete. Furthermore, NRLMFMDA also considered the important influences of the neighborhood information and took full advantage of them to improve the accuracy of the miRNA-disease association prediction. We also improved the accuracy by giving higher weights to the known association data in the process of calculating the potential association probabilities. In the global and the local leave-one-out cross validation, NRLMFMDA got the AUCs of 0.9068 and 0.8239, respectively. Moreover, the average AUC of NRLMFMDA in 5-fold cross validation was 0.8976 ± 0.0034. All the three kinds of cross validations have shown significant advantages to a number of previous models. In the case studies of breast neoplasms, esophageal neoplasms and lymphoma according to known miRNA-disease associations in the recent version of HMDD database, there were 78, 80, and 74% of top 50 predicted related miRNAs verified to have associations with these three diseases, respectively. In the further case studies for new disease without any known related miRNAs and the previous version of HMDD database, there were also high proportions of the predicted miRNAs verified by experimental reports. All the validation experiment results have demonstrated the effectiveness and practicability of NRLFMDA to predict the potential miRNA-disease associations.
Factors Influencing Mobility Relative to Nutritional Status among Elderly Women with Diabetes Mellitus

Background: The prevalence of functional disability is very high among elderly people. Malnourished individuals are less likely to recover from limited mobility and have higher risk of deterioration of functional disability. This study investigated factors influencing mobility relative to nutritional status in elderly women with diabetes mellitus. Methods: We analyzed data of 464 elderly women with diabetes mellitus, obtained from the 2014 cross-sectional survey of the Korea Institute for Health and Social Affairs. Results: In the well-nourished group, body mass index, difficulties in daily activities due to visual acuity loss, muscle strength, and depressive symptoms were significant predictors of mobility; the explanatory power of this model was 24.0% (F=12.905; P<0.001). In the malnourished group, muscle strength, depressive symptoms, and nutritional status were significant predictors of mobility; the explanatory power of this model was 40.5% (F=16.589; P<0.001). Conclusion: The level of mobility relative to nutritional status varied widely among elderly women with diabetes mellitus. Therefore, to improve mobility in malnourished elderly people, public health nurses must provide comprehensive nutrition management and a support system in addition to physical and psychological interventions.
A Bayesian approach to Mendelian randomization with multiple pleiotropic variants

Summary We propose a Bayesian approach to Mendelian randomization (MR), where instruments are allowed to exert pleiotropic (i.e. not mediated by the exposure) effects on the outcome. By having these effects represented in the model by unknown parameters, and by imposing a shrinkage prior distribution that assumes an unspecified subset of the effects to be zero, we obtain a proper posterior distribution for the causal effect of interest. This posterior can be sampled via Markov chain Monte Carlo methods of inference to obtain point and interval estimates. The model priors require a minimal input from the user. We explore the performance of our method by means of a simulation experiment. Our results show that the method is reasonably robust to the presence of directional pleiotropy and moderate correlation between the instruments. One section of the article elaborates the model to deal with two exposures, and illustrates the possibility of using MR to estimate direct and indirect effects in this situation. A main objective of the article is to create a basis for developments in MR that exploit the potential offered by a Bayesian approach to the problem, in relation with the possibility of incorporating external information in the prior, handling multiple sources of uncertainty, and flexibly elaborating the basic model.
A Fine-grained Parallel Approach for one Logical Process on Multi-core Machines

Currently, the time management algorithms applied in various parallel discrete event simulation (PDES) engines take the logical process as the smallest parallel unit which corresponds to a physical process and represents a sequential simulation. Before the simulation system is running, all the entities are distributed to each logical process. The parallelism of the simulation system depends on the parallelism between logical processes. The performance of this parallel approach is greatly affected by the entity distribution scheme. For example, when the computing hotspots are assigned to one LP, the performance of the parallel simulation is almost the same as that of the serial simulation. And it is difficult to find a suitable entity distribution scheme when it comes to simulation with hotspots which migrate between LPs dynamically. Besides, we always use as many LPs as possible to improve parallelism, but this also brings greater communication and synchronization overhead. Focusing on these drawbacks of current simulation engines, we propose an approach which supports fine grain parallelism in one LP. Based on the traditional time management algorithms, our approach can process events of different entities in one LP in parallel with multi threads.
FBOSS: building switch software at scale

The conventional software running on network devices, such as switches and routers, is typically vendor-supplied, proprietary and closed-source; as a result, it tends to contain extraneous features that a single operator will not most likely fully utilize. Furthermore, cloud-scale data center networks often times have software and operational requirements that may not be well addressed by the switch vendors. In this paper, we present our ongoing experiences on overcoming the complexity and scaling issues that we face when designing, developing, deploying and operating an in-house software built to manage and support a set of features required for data center switches of a large scale Internet content provider. We present FBOSS, our own data center switch software, that is designed with the basis on our switch-as-a-server and deploy-early-and-iterate principles. We treat software running on data center switches as any other software services that run on a commodity server. We also build and deploy only a minimal number of features and iterate on it. These principles allow us to rapidly iterate, test, deploy and manage FBOSS at scale. Over the last five years, our experiences show that FBOSS's design principles allow us to quickly build a stable and scalable network. As evidence, we have successfully grown the number of FBOSS instances running in our data center by over 30x over a two year period.
Accurate indoor mapping using an autonomous unmanned aerial vehicle (UAV)

An autonomous indoor aerial vehicle requires reliable simul- taneous localization and mapping (SLAM), accurate flight control, and robust path planning for navigation. This paper presents a system level combination of these existing technologies for 2D navigation. An Unmanned aerial vehicle (UAV) called URSA (Unmanned Recon and Safety Aircraft) that can autonomously flight and mapping indoors environments with an accuracy of 2 cm was developed. Performance in indoor environments was assessed in terms of mapping and navigation precision.
Memory awareness disruptions in amnestic mild cognitive impairment: comparison of multiple awareness types for verbal and visuospatial material

ABSTRACT Successful memory is normally accompanied by explicit awareness of retrieval and confidence in the accuracy of the retrieval product. Prior findings suggest that these features of metamemory can be dissociated from retrieval accuracy in Amnestic Mild Cognitive Impairment (aMCI). However, the literature on this question contains variable and conflicting results, likely because of differences in experimental conditions. We sought to systematically evaluate memory awareness disruptions in aMCI using multiple measures and stimulus formats within the same individuals. Memory awareness was tested with global predictions and postdictions, judgments of learning, confidence level ratings, and modified feeling-of-knowing ratings in tasks of visuospatial and verbal memory. These tests were administered to 14 individuals with aMCI and 15 healthy, age-matched controls. Memory awareness accuracy was calculated as the correspondence between subjective judgments and memory performance.Individuals with aMCI demonstrated impaired global and trial-level retrospective task awareness for visuospatial and verbal stimuli. Additionally, modified feeling-of-knowing awareness was impaired selectively for verbal stimuli. Statistical effect sizes for global awareness impairments were comparable to impairments in several objective neuropsychological memory assessments.Memory awareness (metamemory) disruptions in aMCI were most evident for a subset of subjective judgment types and task input modalities. These findings advance understanding of the nature of memory impairments in aMCI and support the utility of incorporating memory awareness testing to better characterize memory integrity in older adults.
Algorithm-assisted decision-making in the public sector: framing the issues using administrative law rules governing discretionary power

This article considers some of the risks and challenges raised by the use of algorithm-assisted decision-making and predictive tools by the public sector. Alongside, it reviews a number of long-standing English administrative law rules designed to regulate the discretionary power of the state. The principles of administrative law are concerned with human decisions involved in the exercise of state power and discretion, thus offering a promising avenue for the regulation of the growing number of algorithm-assisted decisions within the public sector. This article attempts to re-frame key rules for the new algorithmic environment and argues that ‘old’ law—interpreted for a new context—can help guide lawyers, scientists and public sector practitioners alike when considering the development and deployment of new algorithmic tools. This article is part of a discussion meeting issue ‘The growing ubiquity of algorithms in society: implications, impacts and innovations'.
Contrast-Enhanced Ultrasound (CEUS) Identifies Perfusion Differences Between Tibial Fracture Unions and Non-Unions

Abstract Purpose To assess the value of CEUS in the evaluation of tibial fracture perfusion and its ability to differentiate between physiologic and abnormal fracture healing. Materials and Methods From 2014 to 2017, 107 patients with tibial fractures or tibial non-unions underwent CEUS examination. CEUS was performed at the regular follow-up examination 26 weeks after osteosynthesis or before non-union surgery. Time-intensity curves (TICs) of the contrast enhancement in the fracture gap were generated, and volume parameters such as wash-in rate (WiR), peak enhancement (PE) and wash-in perfusion index (WiPI) were quantified. Results A total of 34 patients met the inclusion criteria of this study, including 14 consolidated fractures, 12 aseptic non-unions and 8 infected non-unions. WiR, PE and WiPI showed significantly lower values in aseptic non-unions compared to unions (p = 0.009, 0.009, 0.012, resp.). In contrast, infected non-unions showed higher values of WiR, PE and WiPI when compared to unions (p = 0.034, 0.056, 0.029, resp.). Conclusion CEUS represents a feasible method in the assessment of tibial fracture perfusion. Perfusion differences between aseptic and infected tibial non-unions as well as healing tibial fractures could be detected. The deviation of physiologic fracture perfusion seems to be associated with disturbed osseous regeneration leading to non-union.
Melanin-Based Skin Coloration Predicts Antioxidant Capacity in the Brown Trout (Salmo trutta)

In many vertebrate species, individuals exhibit large variation in the degree of melanin-based coloration on their body. Dark and pale individuals differ in diverse physiological and behavioral traits, suggesting that melanic coloration may reveal individual quality. However, research into the relationships between physiological and skin traits, in terms of melanin-based skin coloration, in wild fish is scant. Our correlative study aimed at investigating the relationships between physiology and melanin-based coloration of the skin of free-living brown trout (Salmo trutta Linnaeus, 1758). We scrutinized the relationships between body condition (body mass and Fulton’s K condition factor), oxidative status (plasma total antioxidant capacity and amount of pro-oxidant molecules), and the degree of melanin-based skin coloration assessed by digital photography and image analysis. We found heavier fish to be, on average, darker colored than paler conspecifics. Moreover, a significant covariation between plasma total antioxidant capacity and melanic coloration was noted. Our findings suggest that the melanin-based coloration of brown trout serves as a signal to communicate a better antioxidant defense to conspecifics.
Dietary Diversity in Cambodian Garment Workers: The Role of Free Lunch Provision

The objective of this paper is to compare food consumption by Cambodian garment workers with and without access to a free model lunch provision through a factory-based canteen. Data from an exploratory randomised controlled trial were analysed. In total, 223 female Cambodian garment workers were allocated to an intervention arm (six-month lunch provision) or a control arm. Dietary intake on workdays was assessed by qualitative 24-h recalls at baseline and twice at follow-ups during the period of lunch provision using the Food and Agricultural Organization (FAO) guideline on assessing women’s dietary diversity. In total, 158 participants provided complete data on the dietary intake over workdays at all interviews. Lunch provision resulted in a more frequent consumption of dark green leafy vegetables (DGLV), vitamin A-rich fruits, other fruits, and oils and fats during lunch breaks. In contrast, flesh meats, legumes, nuts and seeds, as well as sweets, were eaten at a lower frequency. Except for a higher consumption rate of vitamin A-rich fruits and a lower intake frequency of sweets, lunch provision had a less clear impact on total 24-h intake from different food groups and was not associated with a higher women’s dietary diversity score (WDDS). A more gap-oriented design of the lunch sets taking into account underutilised foods and the nutritional status of the workers is recommended.
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems

In recent years there has been a sharp rise in networking applications, in which significant events need to be classified but only a few training instances are available. These are known as cases of one-shot learning. Examples include analyzing network traffic under zero-day attacks, and computer vision tasks by sensor networks deployed in the field. To handle this challenging task, organizations often use human analysts to classify events under high uncertainty. Existing algorithms use a threshold-based mechanism to decide whether to classify an object automatically or send it to an analyst for deeper inspection. However, this approach leads to a significant waste of resources since it does not take the practical temporal constraints of system resources into account. Our contribution is threefold. First, we develop a novel Deep Reinforcement One-shot Learning (DeROL) framework to address this challenge. The basic idea of the DeROL algorithm is to train a deep-Q network to obtain a policy which is oblivious to the unseen classes in the testing data. Then, in real-time, DeROL maps the current state of the one-shot learning process to operational actions based on the trained deep-Q network, to maximize the objective function. Second, we develop the first open-source software for practical artificially intelligent one-shot classification systems with limited resources for the benefit of researchers in related fields. Third, we present an extensive experimental study using the OMNIGLOT dataset for computer vision tasks and the UNSW-NB15 dataset for intrusion detection tasks that demonstrates the versatility and efficiency of the DeROL framework.