Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying Mixed Mycobacterium tuberculosis Infection and Laboratory Cross-Contamination during Mycobacterial Sequencing Programs

The detection of laboratory cross-contamination and mixed tuberculosis infections is an important goal of clinical mycobacteriology laboratories. The objective of this study was to develop a method to detect mixtures of different Mycobacterium tuberculosis lineages in laboratories performing mycobacterial next-generation sequencing (NGS). ABSTRACT The detection of laboratory cross-contamination and mixed tuberculosis infections is an important goal of clinical mycobacteriology laboratories. The objective of this study was to develop a method to detect mixtures of different Mycobacterium tuberculosis lineages in laboratories performing mycobacterial next-generation sequencing (NGS). The setting was the Public Health England National Mycobacteriology Laboratory Birmingham, which performs Illumina sequencing on DNA extracted from positive mycobacterial growth indicator tubes. We analyzed 4,156 samples yielding M. tuberculosis from 663 MiSeq runs, which were obtained during development and production use of a diagnostic process using NGS. The counts of the most common (major) variant and all other variants (nonmajor variants) were determined from reads mapping to positions defining M. tuberculosis lineages. Expected variation was estimated during process development. For each sample, we determined the nonmajor variant proportions at 55 sets of lineage-defining positions. The nonmajor variant proportion in the two most mixed lineage-defining sets (F2 metric) was compared with that of the 47 least-mixed lineage-defining sets (F47 metric). The following three patterns were observed: (i) not mixed by either metric; (ii) high F47 metric, suggesting mixtures of multiple lineages; and (iii) samples compatible with mixtures of two lineages, detected by differential F2 metric elevations relative to F47. Pattern ii was observed in batches, with similar patterns in the M. tuberculosis H37Rv control present in each run, and is likely to reflect cross-contamination. During production, the proportions of samples in the patterns were 97%, 2.8%, and 0.001%, respectively. The F2 and F47 metrics described could be used for laboratory process control in laboratories sequencing M. tuberculosis genomes.
Shall We Focus on the Eosinophil to Guide Treatment with Systemic Corticosteroids during Acute Exacerbations of COPD?: PRO

In an era of precision medicine, it seems regressive that we do not use stratified approaches to direct treatment of oral corticosteroids during an exacerbation of chronic obstructive pulmonary disease (COPD). This is despite evidence suggesting that 40% of COPD patients have eosinophilic inflammation and this is an indicator of corticosteroid response. Treatments with oral corticosteroids are not always effective and not without harm, with significant and increased risk of hyperglycemia, sepsis, and fractures. Eosinophils are innate immune cells with an incompletely understood role in the pathology of airway disease. They are detected at increased levels in some patients and can be measured using non-invasive methods during states of exacerbation and stable periods. Despite the eosinophil having an unknown mechanism in COPD, it has been shown to be a marker of length of stay in severe hospitalized exacerbations, a predictor of risk of future exacerbation and exacerbation type. Although limited, promising data has come from one prospective clinical trial investigating the eosinophil as a biomarker to direct systemic corticosteroid treatment. This identified that there were statistically significant and clinically worsened symptoms in patients with low eosinophil levels who were prescribed prednisolone, demonstrating the potential utility of the eosinophil. In an era of precision medicine our patients’ needs are best served by accurate diagnosis, correct identification of maximal treatment response and the abolition of harm. The peripheral blood eosinophil count could be used towards reaching these aims.
Analysing Customer Engagement of Turkish Airlines Using Big Social Data

Companies started taking advantage of the unlocked potential of Big Social Data, however, research on airlines’ use of social media is limited. This research aims to investigate to what extent Turkish Airlines can utilize their Facebook page to improve performance metrics. This study will exploit the concepts of Big Social Data, customer satisfaction, sentiment analysis to answer the research questions by employing dataand text mining, machine learning. The results showed a weak relationship between the business data and Facebook data, however, the findings provided explanations to customer behavior and showed that most of the company’s Facebook users were likely to purchase a Turkish Airline ticket. Therefore, Turkish Airlines could utilize their Facebook page in the short-term to improve revenue-generating indicators such as customer satisfaction and likelihood of purchase.
Age at death in cohorts of middle-aged men followed-up until nearly extinction: the European areas of the Seven Countries Study

Abstract Objectives: To describe age at death (AD) in ten cohorts of middle-aged men followed-up until nearly extinction as a function of personal characteristics measured at baseline. Material and Methods: Cohorts of men aged 40–59 years in five European countries (Finland, the Netherlands, Italy, Serbia, Greece) were examined and then followed-up for mortality during 45 (3 cohorts) or 50 years (7 cohorts). Multiple linear regression (MLR) models were computed with AD as dependent variable and 35 personal characteristics as independent variables. Results: Out of 7047 men, 6798 (96.5%) died in 45–50 years. Average AD was 74.4 (median 75) years. Only five variables were significant in all the five countries when tested by MLR. By pooling together all countries and selecting variables by a stepwise technique, 20 were significant (p < .05): age at entry examination, being married, being a never smoker or an ex-smoker (versus current smokers), forced expiratory volume, high socio-economic status, arm circumference, subscapular skinfold, having moderate or vigorous physical activity (all with a positive relationship with AD); father early mortality, laterality/linearity index, high systolic blood pressure, high serum cholesterol, having a major cardiovascular disease, diabetes or chronic bronchitis (these with a negative relationship), while body mass index, subdivided into four classes, showed a parabolic relationship (versus a reference class). Each factor was associated with a difference of months or a few years to AD, while their combinations were associated with several years of greater or smaller AD. Conclusions: The availability of a relatively small number of risk factors measured in middle-aged men allows making rough estimates of AD as related to all-cause mortality during a follow-up of 50 years. AD is also a valuable metrics to describe past health in nearly extinct populations. Key messages We aimed at studying the age at death (AD) of middle-aged men of the European cohorts of the Seven Countries Study, followed-up 45–50 years until nearly extinction, as a function of personal characteristics measured at baseline. Multiple linear regression (MLR) models were computed with AD as dependent variable and 35 personal characteristics or risk factors as independent variables and only five were significant in all individual countries, whereas pooling together all countries, 20 variables were significant as selected by stepwise MLR. Each factor was associated with differences of months or a few years to AD, while their combinations were associated with several years of greater or smaller AD. AD is a valuable metrics to describe past health in nearly extinct populations: it might also be used to communicate years of life gained by preventive measures on modifiable variables.
Spotlight on fundus autofluorescence

Fundus autofluorescence (FAF) imaging is based on the fluorescence from ocular endogenous fluorophores located in the retinal pigment epithelium and choroid, mainly lipofuscin and melanin. It is a noninvasive technique that provides information about the spatial distribution of lipofuscin/melanin and retinal pigment epithelium health status. An overview about the fluorophores responsible for FAF imaging and the usefulness of FAF imaging in the noninvasive assessment and monitoring of retinal diseases, such as age-related macular degeneration, diabetic retinopathy, diabetic macular edema, central serous chorioretinopthy, retinitis pigmentosa and retinal dystrophies, are discussed in this review.
Derivative-Based Signal Detection for High Data Rate Molecular Communication System

The molecular communication via diffusion (MCvD) is one of the most promising techniques for nano-networks due to its advantages of bio-compatibility and energy efficiency. Due to the diffusion nature of the molecules, the peak time of the channel impulse response (CIR) is relatively large. To avoid severe inter-symbol-interference effect, the symbol interval is usually set larger than the peak time of the CIR. Therefore, the data rate becomes the main limitation of MCvD. In this letter, a derivative-based detection scheme is proposed based on the properties that the peak time of the CIR’s derivative is much shorter than that of CIR itself and the tail of CIR’s derivative vanishes much faster than that of the CIR. The simulation results demonstrate that the proposed scheme performs better than the state-of-the-art detection schemes in terms of bit error rate (BER) at high data rate where the symbol interval is shorter than the peak time of CIR. The impacts of the relative symbol interval, released molecule number, sampling rate, and receiver volume on the BER performance of the proposed scheme are investigated.
Accurate and adaptive imputation of summary statistics in mixed-ethnicity cohorts

Motivation Methods based on summary statistics obtained from genome‐wide association studies have gained considerable interest in genetics due to the computational cost and privacy advantages they present. Imputing missing summary statistics has therefore become a key procedure in many bioinformatics pipelines, but available solutions may rely on additional knowledge about the populations used in the original study and, as a result, may not always ensure feasibility or high accuracy of the imputation procedure. Results We present ARDISS, a method to impute missing summary statistics in mixed‐ethnicity cohorts through Gaussian Process Regression and automatic relevance determination. ARDISS is trained on an external reference panel and does not require information about allele frequencies of genotypes from the original study. Our method approximates the original GWAS population by a combination of samples from a reference panel relying exclusively on the summary statistics and without any external information. ARDISS successfully reconstructs the original composition of mixed‐ethnicity cohorts and outperforms alternative solutions in terms of speed and imputation accuracy both for heterogeneous and homogeneous datasets. Availability and implementation The proposed method is available at https://github.com/BorgwardtLab/ARDISS. Supplementary information Supplementary data are available at Bioinformatics online.
Lambda Containers: A Comprehensive Anti-Tamper Framework for Games by Simulating Client Behavior in a Cloud

As the rapid growth of smartphones, online games are starting to execute core logic on the client-side to enable rich user interactions. This trend makes it difficult to detect illegal modifications to games. This paper presents a comprehensive anti-tamper framework for games, which detects tampering by comparing the game state information submitted by the client and the genuine state information simulated by transparently executing the genuine app on a cloud. The key technology is a novel container pool that models the execution environment of apps as lambda functions by defining an app as a first-order function and a container as a higher-order function. This system, which is a meta-level system over the existing container system, provides a functional programming model designed to statically encapsulate heterogeneous environment of apps into containers and to dynamically simulate a specific game scene by injecting the game status into containers. This system optimally executes this process by translating the meta-level operations into the parameters to conventional containers. Experimental results using a real game product showed that 110 containers run in a scalable manner on a single server with 72 logical CPU cores installed on a public cloud, without sacrificing either the response or throughput performance.
Improving access to healthcare for women in Tanzania by addressing socioeconomic determinants and health insurance: a population-based cross-sectional survey

Objective This study was performed to explore the factors associated with accumulation of multiple problems in accessing healthcare among women in Tanzania as an example of a low-income country. Design Population-based cross-sectional survey. Setting Nationwide representative data for women of reproductive age obtained from the 2015–2016 Tanzania Demographic and Health Survey were analysed. Primary outcome measures A composite variable, ‘problems in accessing healthcare’, with five (1-5) categories was created based on the number of problems reported: obtaining permission to go to the doctor, obtaining money to pay for advice or treatment, distance to a health facility and not wanting to go alone. Respondents who reported fewer or more problems placed in lower and higher categories, respectively. Results A total of 13 266 women aged 15–49 years, with a median age (IQR) of 27 (20–36) years were interviewed and included in the analysis. About two-thirds (65.53%) of the respondents reported at least one of the four major problems in accessing healthcare. Furthermore, after controlling for other variables included in the final model, women without any type of health insurance, those belonging to the poorest class according to the wealth index, those who had not attended any type of formal education, those who were not employed for cash, each year of increasing age and those who were divorced, separated or widowed were associated with greater problems in accessing healthcare. Conclusion This study indicated the additive effects of barriers to healthcare in low-income countries such as Tanzania. Based on these results, improving uptake of health insurance and addressing social determinants of health are the first steps towards reducing women’s problems associated with accessing healthcare.
Indolyl-Pyridinyl-Propenone-Induced Methuosis through the Inhibition of PIKFYVE

Methuosis is a form of nonapoptotic cell death characterized by the accumulation of macropinosome-derived vacuoles. Herein, we identify PIKFYVE, a class III phosphoinositide (PI) kinase, as the protein target responsible for the methuosis-inducing activity of indolyl-pyridinyl-propenones (3-(5-methoxy-2-methyl-1H-indol-3-yl)-1-(4-pyridinyl)-2-propen-1-one). We further characterize the effects of chemical substitutions at the 2- and 5-indolyl positions on cytoplasmic vacuolization and PIKFYVE binding and inhibitory activity. Our study provides a better understanding of the mechanism of methuosis-inducing indolyl-pyridinyl-propenones.
Insights into the life strategy of the common marine diatom Chaetoceros peruvianus Brightwell

Chaetoceros peruvianus is a marine diatom species with circumglobal distribution. While frequently observed, it appears never to dominate the marine phytoplankton community hence it can be characterized as a rather opportunistic, generalistic species. Here we present ecological interpretations from a long-term data set on marine microphytoplankton in the northern Adriatic Sea, where the abundancies and relative contributions of C. peruvianus were observed along a set of steep ecological gradients. Limited supply of dissolved inorganic phosphate was identified as the driving ecological factor for this ecosystem. In parallel C. peruvianus was cultivated in monoclonal cultures and its morphological and physiological reaction to replete and phosphorus depleted medium was analysed. C. peruvianus reacted to phosphorus depletion by an increase in cell height and length as well as thickness and length of setae. This morphological reaction included an increase in cellular volume and calculated carbon content. Additionally, it represents the transition between two described morphological varieties, C. peruvianus and C. peruvianus var. robusta. C. peruvianus showed a significant induction of extracellular alkaline phosphatase activity if grown in phosphate depleted medium. Microscopical analysis demonstrated this activity to be located exclusively on the setae of the cells.
Learning Graph-Level Representations with Recurrent Neural Networks

Recently a variety of methods have been developed to encode graphs into low-dimensional vectors that can be easily exploited by machine learning algorithms. The majority of these methods start by embedding the graph nodes into a low-dimensional vector space, followed by using some scheme to aggregate the node embeddings. In this work, we develop a new approach to learn graph-level representations, which includes a combination of unsupervised and supervised learning components. We start by learning a set of node representations in an unsupervised fashion. Graph nodes are mapped into node sequences sampled from random walk approaches approximated by the Gumbel-Softmax distribution. Recurrent neural network (RNN) units are modified to accommodate both the node representations as well as their neighborhood information. Experiments on standard graph classification benchmarks demonstrate that our proposed approach achieves superior or comparable performance relative to the state-of-the-art algorithms in terms of convergence speed and classification accuracy. We further illustrate the effectiveness of the different components used by our approach.
A Fast and Incremental Development Life Cycle for Data Analytics as a Service

Big Data does not only refer to a huge amount of diverse and heterogeneous data. It also points to the management of procedures, technologies, and competencies associated with the analysis of such data, with the aim of supporting high-quality decision making. There are, however, several obstacles to the effective management of a Big Data computation, such as data velocity, variety, and veracity, and technological complexity, which represent the main barriers towards the full adoption of the Big Data paradigm. The goal of this work is to define a new software Development Life Cycle for the design and implementation of a Big Data computation. Our proposal integrates two model-driven methods: a first method based on pre-configured services that reduces the cost of deployment and a second method based on custom component development that provides an incremental process of refinement and customization. The proposal is experimentally evaluated by clustering a data set of the distribution of the population in the United States based on contextual criteria.
On the Linearity of BST Thin Film Bulk Acoustic Resonators

Preliminary measurement results on the linearity of intrinsically switchable BST thin film bulk acoustic resonators (FBARs) are presented. Ferroelectric BST possesses electricfield-piezoelectricity, or electrostriction, which is employed to design intrinsically switchable BST FBARs. The linearity of these switchable resonators is an important factor for designing intrinsically switchable filters. The linearity of a single resonator is compared with two cascaded doubled size BST resonators. Third-order intercept point (IP3) measurement is performed, demonstrating the input IP3 improvement from 39 dBm to 43 dBm.
Active Learning on Service Providing Model: Adjustment of Robot Behaviors Through Human Feedback

As robots are put into humans’ daily life, the assigned tasks to robots are varied, and the different needs of people interacting with robots are immense. As a result, when facing different users, it is important for robots to personalize the interactions and provide user-desired services. This paper, therefore, proposes a learning strategy on the service-providing model. Through human feedback, the strategy enables the robot to learn the users’ needs, as well as preferences, and adjust its behaviors. Here, we assume that users’ needs and preferences may vary with time; hence the goal of this paper is to let the adjustment of robot behaviors be able to adapt to those variations. In turn, the service-providing model of the robot could adjust online as well. That is, it can select a new action from those favorable actions that have already been selected or an action that is not an unfavorable action but has annoyed humans recently. To implement our system, the service robot under discussion is applied to the home environment. For performance evaluation, we have performed extensive experiments that satisfactorily demonstrate that our robot can provide services to different users and adapt to their preference change.
Age-Related Differences in Complexity During Handgrip Control Using Multiscale Entropy

Changes in motor behavior during aging might be induced by complexity fluctuations in the neuromuscular system. Most previous studies have been performed based on single-scale entropies. In this paper, multiscale fuzzy entropy (MSFuzzyEn) was applied to characterize the changes in the complexity of simulated electromyogram (EMG) signals with the increasing motor unit number and signal-to-noise ratio. Age-related differences in multiscale complexity during handgrip control were also investigated. Ten young and 10 older adults were instructed to produce constant forces at 25%, 50%, and 75% of their maximal grip force with their dominant hands. The grip force and EMG signals of four forearm muscles were recorded simultaneously and analyzed using MSFuzzyEn. The simulation tests revealed that, as the time scale increased, the interference of noise in the EMG signals decreased. At time scale 1, the complexities of the force and EMG signals exhibited opposite changes with aging. When the time scale increased, we observed a loss in complexity with aging in both the force and EMG signals. These results confirmed the merits of MSFuzzyEn in noise abatement, and implied that entropy at relatively larger time scales might better characterize EMG signals. Further studies should extend the application of multiscale entropy in pathologies.