Miracle-dz/UniFAR-FTUs huggingface dataset:
Lipase Precursor-Like Protein Promotes Miltefosine Tolerance in Leishmania donovani by Enhancing Parasite Infectivity and Eliciting Anti-inflammatory Responses in Host Macrophages

The oral drug miltefosine (MIL) was introduced in the Indian subcontinent in the year 2002 for the treatment of visceral leishmaniasis (VL). However, recent reports on its declining efficacy and increasing relapse rates pose a serious concern. ABSTRACT The oral drug miltefosine (MIL) was introduced in the Indian subcontinent in the year 2002 for the treatment of visceral leishmaniasis (VL). However, recent reports on its declining efficacy and increasing relapse rates pose a serious concern. An understanding of the factors contributing to MIL tolerance in Leishmania parasites is critical. In the present study, we assessed the role of the lipase precursor-like protein (Lip) in conferring tolerance to miltefosine by episomally overexpressing Lip in Leishmania donovani (LdLip++). We observed a significant increase (∼3-fold) in the MIL 50% inhibitory concentration (IC50) at both the promastigote (3.90 ± 0.68 µM; P < 0.05) and intracellular amastigote (9.10 ± 0.60 µM; P < 0.05) stages compared to the wild-type counterpart (LdNeo) (MIL IC50s of 1.49 ± 0.20 µM at the promastigote stage and 3.95 ± 0.45 µM at the amastigote stage). LdLip++ parasites exhibited significantly (P < 0.05) increased infectivity to host macrophages and increased metacyclogenesis and tolerance to MIL-induced oxidative stress. The susceptibility of LdLip++ to other antileishmanial drugs (sodium antimony gluconate and amphotericin B) remained unchanged. In comparison to LdNeo, the LdLip++ parasites elicited high host interleukin-10 (IL-10) cytokine expression levels (1.6-fold; P < 0.05) with reduced expression of the cytokine tumor necrosis factor alpha (TNF-α) (1.5-fold; P < 0.05), leading to a significantly (P < 0.01) increased ratio of IL-10/TNF-α. The above-described findings suggest a role of lipase precursor-like protein in conferring tolerance to the oral antileishmanial drug MIL in L. donovani parasites.
Sleep problems and mental health among young Norwegian adolescents

Abstract Purpose: The present study investigated the associations between the aspects of sleep problems and mental health in Norwegian young adolescents. Materials and methods: Adolescents (12–13 years) were recruited from the primary schools in Telemark County, Norway. Sleep related problems were assessed by asking parents three questions related to their childrens’ sleep quality, sleep sufficiency, and daytime sleepiness. Parents reported data on mental health by the extended version of the Strengths and Difficulties Questionnaire (SDQ), and information on the child’s physical activity, screen time, eating pattern, gender, and background variables. Height and weight were objectively measured. Complete data were obtained for 690 adolescents. Results: Multiple logistic regressions were used. Bivariate analyses indicated important associations between the sleep aspects and mental health. When adjusting the sleep variables for each other, lifestyle factors and background variables, sleep quality problems were associated with any psychiatric problem (ORadj: 4.0, CI: 2.0-8.2), emotional problems (ORadj: 15.1, CI: 3.4-66.8), and hyperactivity-inattention problems (ORadj: 5.1, CI: 2.2-12.1). Daytime sleepiness problems were associated with any psychiatric problem (ORadj: 2.3, CI: 1.2-4.4), and hyperactivity-inattention problems (ORadj: 2.5, CI: 1.1-5.5). Bivariate associations between problems with sufficient sleep and mental health problems lost the significance when adjusted for other sleep variables. Conclusions: The results indicate that different aspects of sleep problems may be important underlying factors for mental health problems in adolescents, independently of lifestyle factors, BMI-category, gender, and background variables. This highlights the importance of examining specific sleep problems when investigating associations between sleep and mental health.
Deep Color Guided Coarse-to-Fine Convolutional Network Cascade for Depth Image Super-Resolution

Depth image super-resolution is a significant yet challenging task. In this paper, we introduce a novel deep color guided coarse-to-fine convolutional neural network (CNN) framework to address this problem. First, we present a data-driven filter method to approximate the ideal filter for depth image super-resolution instead of hand-designed filters. Based on large data samples, the filter learned is more accurate and stable for upsampling depth image. Second, we introduce a coarse-to-fine CNN to learn different sizes of filter kernels. In the coarse stage, larger filter kernels are learned by the CNN to achieve crude high-resolution depth image. As to the fine stage, the crude high-resolution depth image is used as the input so that smaller filter kernels are learned to gain more accurate results. Benefit from this network, we can progressively recover the high frequency details. Third, we construct a color guidance strategy that fuses color difference and spatial distance for depth image upsampling. We revise the interpolated high-resolution depth image according to the corresponding pixels in high-resolution color maps. Guided by color information, the depth of high-resolution image obtained can alleviate texture copying artifacts and preserve edge details effectively. Quantitative and qualitative experimental results demonstrate our state-of-the-art performance for depth map super-resolution.
Thumb-In-Motion: Evaluating Thumb-to-Ring Microgestures for Athletic Activity

Spatial User Interfaces, such as wearable fitness trackers are widely used to monitor and improve athletic performance. However, most fitness tracker interfaces require bimanual interactions, which significantly impacts the user's gait and pace. This paper evaluated a one-handed thumb-to-ring gesture interface to quickly access information without interfering with physical activity, such as running. By a pilot study, the most minimal gesture set was selected, particularly those that could be executed reflexively to minimize distraction and cognitive load. The evaluation revealed that among the selected gestures, the tap, swipe-down, and swipe-left were the most 'easy to use'. Interestingly, motion does not have a significant effect on the ease of use or on the execution time. However, interacting in motion was subjectively rated as more demanding. Finally, the gesture set was evaluated in real-world applications, while the user performed a running exercise and simultaneously controlled a lap timer, a distance counter, and a music player.
Prediction of electrocardiography features points using seismocardiography data: a machine learning approach

Seismocardiography (SCG) is a cardiac diagnostic method which evaluates vibrations of the chest via accelerometers. Current prototypical wearable SCG sensor patches are combined with electrocardiography (ECG) sensors which enables the estimation of cardiac timing intervals such as pre-ejection period (PEP). We envision a system with only SCG sensors to increase the battery run time and wearing comfort. Therefore, we present a method to predict the timing of ECG R-peak based on SCG data. The developed machine learning (ML) algorithms were evaluated with respect to algorithm complexity, body postures, and personalization schemes, on a dataset consisting of 10 subjects. Our findings show that R-peaks can be predicted with a mean error of 4:16 ms for supine, 9:43 ms for sitting, and 14:3 ms for standing posture. While personalization results only in minor improvements, using more complex ML classifier is beneficial for standing posture, which is more affected by motion artifacts.
Differences between stance and foot preference evident in Osprey (Pandion haliaetus) fish holding during movement

Skateboarders, snowboarders, and surfers all show stance preferences for which foot is forward while moving. We are unaware of other animals than humans with a stance preference, perhaps excepting Osprey, who fly their caught fish beneath them in a foot‐forward stance. We hypothesize there should be no difference between left foot forward, right foot back (conventional) versus right foot forward left foot back (goofy) stances or for fish holding with unilateral left or right foot. Online, publicly available, convenience images of Osprey catching fish were accessed and assessed by five independent reviewers using different Internet search engines or online photo series. Stance preference and footedness were tested using chi‐square analysis.
Noise-synchronizability of opinion dynamics

With the analysis of noise-induced synchronization of opinion dynamics with bounded confidence (BC), a natural and fundamental question is what opinion structures can be synchronized by noise. In the traditional Hegselmann-Krause (HK) model, each agent examines the opinion values of all the other ones and then choose neighbors to update its own opinion according to the BC scheme. In reality, people are more likely to interchange opinions with only some individuals, resulting in a predetermined local discourse relationship as introduced by the DeGroot model. In this paper, we consider an opinion dynamics that combines the schemes of BC and local discourse topology and investigate its synchronization induced by noise. The new model endows the heterogeneous HK model with a time-varying discourse topology. With the proposed definition of noise-synchronizability, it is shown that the compound noisy model is almost surely noise-synchronizable if and only if the time-varying discourse graph is uniformly jointly connected, taking the noise-induced synchronization of the classical heterogeneous HK model as a special case. As a natural implication, the result for the first time builds the equivalence between the connectivity of discourse graph and the beneficial effect of noise for opinion consensus.
The Impact of Leader Moral Humility on Follower Moral Self-Efficacy and Behavior

This study utilizes social–cognitive theory, humble leadership theory, and the behavioral ethics literature to theoretically develop the concept of leader moral humility and its effects on followers. Specifically, we propose a theoretical model wherein leader moral humility and follower implicit theories about morality interact to predict follower moral efficacy, which in turn increases follower prosocial behavior and decreases follower unethical behavior. We furthermore suggest that these effects are strongest when followers hold an incremental implicit theory of morality (i.e., believing that one’s morality is malleable). We test and find support for our theoretical model using two multiwave studies with Eastern (Study 1) and Western (Study 2) samples. Furthermore, we demonstrate that leader moral humility predicts follower moral efficacy and moral behaviors above and beyond the effects of ethical leadership and leader general humility.
A Randomized Double-Blind Trial of Ginger Root for Reducing Muscle Soreness and Improving Physical Performance Recovery Among Experienced Recreational Distance Runners

Abstract Previous studies indicate that ginger root has pain-relieving properties, but these effects have not been thoroughly examined in the context of distance running. The purpose of this randomized, double-blind, placebo-controlled trial was to determine whether ginger root supplementation reduces muscle soreness and dysfunction from downhill running. Thirty-two runners (10 women, 22 men) were pair-matched and randomized to 1.425 g/day of ginger root or placebo for 5 days. A 40-minute downhill (−7.5%) run was completed on day 3. Before supplementation and at various points throughout a 5-day protocol, muscle soreness, pain-pressure threshold, vertical jump, and 1.5-mile run performance were evaluated. Differences between treatments were assessed using magnitude-based inferential statistics. Soreness while jogging likely increased to a lesser degree with ginger root from baseline to immediately postrun (Cohen’s d = −0.69; 90% confidence limit [CL] [−1.50, 0.12]) and from baseline to day 5 (d = −0.72; 90% CL [−1.61, 0.16]). Ginger root had a possible small benefit on vertical jump from baseline to day 5 (d = 0.24; 90% CL [0.05, 0.44]) but likely no meaningful impact on 1.5-mile run performance. Trivial or unclear effects were found for most other outcomes. No significant differences in adverse events were observed between treatments. Ginger root possibly has moderate beneficial effects on running-induced soreness but likely irrelevant effects on recovery of physical performance. Ginger represents one option for runners looking to temporarily reduce pain, although more studies are warranted to clarify its safety profile and its effects on long-term training adaptation.
Zero-Resource Multilingual Model Transfer: Learning What to Share

Modern natural language processing and understanding applications have enjoyed a great boost utilizing neural networks models. However, this is not the case for most languages especially low-resource ones with insufficient annotated training data. Cross-lingual transfer learning methods improve the performance on a low-resource target language by leveraging labeled data from other (source) languages, typically with the help of cross-lingual resources such as parallel corpora. In this work, we propose a zero-resource multilingual transfer learning model that can utilize training data in multiple source languages, while not requiring target language training data nor cross-lingual supervision. Unlike existing methods that only rely on language-invariant features for cross-lingual transfer, our approach utilizes both language-invariant and language-specific features in a coherent way. Our model leverages adversarial networks to learn language-invariant features and mixture-of-experts models to dynamically exploit the relation between the target language and each individual source language. This enables our model to learn effectively what to share between various languages in the multilingual setup. It results in significant performance gains over prior art, as shown in an extensive set of experiments over multiple text classification and sequence tagging tasks including a large-scale real-world industry dataset.
Fault-Tolerant Consensus with an Abstract MAC Layer

In this paper, we study fault-tolerant distributed consensus in wireless systems. In more detail, we produce two new randomized algorithms that solve this problem in the abstract MAC layer model, which captures the basic interface and communication guarantees provided by most wireless MAC layers. Our algorithms work for any number of failures, require no advance knowledge of the network participants or network size, and guarantee termination with high probability after a number of broadcasts that are polynomial in the network size. Our first algorithm satisfies the standard agreement property, while our second trades a faster termination guarantee in exchange for a looser agreement property in which most nodes agree on the same value. These are the first known fault-tolerant consensus algorithms for this model. In addition to our main upper bound results, we explore the gap between the abstract MAC layer and the standard asynchronous message passing model by proving fault-tolerant consensus is impossible in the latter in the absence of information regarding the network participants, even if we assume no faults, allow randomized solutions, and provide the algorithm a constant-factor approximation of the network size.
The most common causes and effects of technical debt: first results from a global family of industrial surveys

Background: The presence of technical debt (TD) brings risks to a software project and makes it difficult to manage. Several TD management strategies have been proposed, but considering actions that could explicitly prevent the insertion of TD in the first place and monitor its effects is not yet a common practice. Thus, while TD management is an important topic, it is also worthwhile to understand the causes that could lead development teams to incur different types of debt as well as the effects of their presence on software projects. Aims: The objective of this work is twofold. First, we investigate the state of practice in the TD area including the status quo, the causes that lead to TD occurrence, and the effects of existing TD. Second, we present the design of InsighTD, a globally distributed family of industrial surveys on causes and effects of TD, and the results of its first execution. Method: We designed the InsighTD in joint collaboration with several TD researchers. It is designed to run as an incremental large scale study based on continuous and independent replications of the questionnaire in different countries. Results: This paper presents the first results of the first execution of the survey. In total, 107 practitioners from the Brazilian software industry answered the questionnaire. Results indicate that there is a broad familiarity with the concept of TD. Deadlines, inappropriate planning, lack of knowledge, and lack of a well-defined process are among the top 10 cited and most likely causes that lead to the occurrence of TD. On the other side, low quality, delivery delay, low maintainability, rework and financial loss are among the top 10 most commonly cited and impactful effects of TD. Conclusion: With InsighTD, we intend to reduce the problem of isolated investigations in TD that are not yet representative and, thus, build a continuous and generalizable empirical basis for understanding practical problems and challenges of TD.
Forty years on from Alma Ata: present and future of Primary Health Care research

Director, Division of Health Systems and Public Health, Regional Office for Europe, World Health Organization, Co-Chair, Alma-Ata 40 Anniversary Technical Coordination Team, Director, Department of Health Systems and Safety, World Health Organization, Senior Advisor, Division of Health Systems and Public Health, European Centre for Primary Health Care, Regional Office for Europe, World Health Organization, Member, Alma-Ata 40th Anniversary Technical Coordination Team, Technical Officer, Department of Health Systems and Safety, World Health Organization, Professor of Health Services & Policy Research Group, Exeter Collaboration for Academic Primary Care (APEx) and NIHR PenCLAHRC, University of Exeter and World Health Organization Consultant
M-PACT: An Open Source Platform for Repeatable Activity Classification Research

There are many hurdles that prevent the replication of existing work which hinders the development of new activity classification models. These hurdles include switching between multiple deep learning libraries and the development of boilerplate experimental pipelines. We present M-PACT to overcome existing issues by removing the need to develop boilerplate code which allows users to quickly prototype action classification models while leveraging existing state-of-the-art (SOTA) models available in the platform. M-PACT is the first to offer four SOTA activity classification models, I3D, C3D, ResNet50+LSTM, and TSN, under a single platform with reproducible competitive results. This platform allows for the generation of models and results over activity recognition datasets through the use of modular code, various preprocessing and neural network layers, and seamless data flow. In this paper, we present the system architecture, detail the functions of various modules, and describe the basic tools to develop a new model in M-PACT.
Unsupervised Neural Word Segmentation for Chinese via Segmental Language Modeling

Previous traditional approaches to unsupervised Chinese word segmentation (CWS) can be roughly classified into discriminative and generative models. The former uses the carefully designed goodness measures for candidate segmentation, while the latter focuses on finding the optimal segmentation of the highest generative probability. However, while there exists a trivial way to extend the discriminative models into neural version by using neural language models, those of generative ones are non-trivial. In this paper, we propose the segmental language models (SLMs) for CWS. Our approach explicitly focuses on the segmental nature of Chinese, as well as preserves several properties of language models. In SLMs, a context encoder encodes the previous context and a segment decoder generates each segment incrementally. As far as we know, we are the first to propose a neural model for unsupervised CWS and achieve competitive performance to the state-of-the-art statistical models on four different datasets from SIGHAN 2005 bakeoff.
Half Thresholding Pursuit Algorithm for Fluorescence Molecular Tomography

<italic>Objective:</italic> Fluorescence Molecular Tomography (FMT) is a promising optical tool for small animal imaging. The <inline-formula><tex-math notation="LaTeX">$\ell _{1/2}$</tex-math></inline-formula>-norm regularization has attracted attention in the field of FMT due to its ability in enhancing sparsity of solution and coping with the high ill-posedness of the inverse problem. However, efficient algorithm for solving the nonconvex regularized model deserve to explore. <italic>Method:</italic> A Half Thresholding Pursuit Algorithm (HTPA) combined with parameter optimization is proposed in this paper to efficiently solve the nonconvex optimization model. Specifically, the half thresholding iteration method is utilized to solve <inline-formula><tex-math notation="LaTeX">$\ell _{1/2}$</tex-math></inline-formula>-norm model, pursuit strategy is used to accelerate the process of iteration, and the parameter optimization scheme is designed to obtain robust parameter. <italic>Results:</italic> Analysis and assessment on simulated and experimental data demonstrate that the proposed HTPA performs better in location accuracy and reconstructed fluorescent yield in less time cost, compared with the state-of-the-art reconstruction algorithms. <italic>Conclusion:</italic> The proposed HTPA combined with the parameter optimization scheme is an efficient and robust reconstruction approach to FMT.
Objective Evaluation or Subjective Evaluation in Digital Social Media: A further understanding of online user's rating of movies

Recommender systems exploit users' ratings to infer their preferences in order to propose recommendations of items that match up with users' interests. Recently, online movie review sites have combined RS and social media. However, in these sites, ratings can have different meaning for users: users may choose one criterion or another. Thus, users' ratings might not be reliable enough to infer their preference in this context. In this study, we find that users may rate movie either by referring to perceived movie quality and/or by referring to their own preferences. Furthermore, users chose different criteria in different contexts. We discuss how users' ratings can be interpreted more precisely in order to improve the performance of recommender systems.