Miracle-dz/UniFAR-FTUs huggingface dataset:
Movement behaviors and cardiometabolic risk in schoolchildren

Growing evidence has accumulated in recent years showing that movement behaviors have important implications for health in children, especially for cardiovascular health, whose risk factors could track from childhood to adulthood. However, these findings are mixed and inconsistent in children. The aim of this study was to examine the relationship between different movement behaviors (sedentary behavior, physical activity and sleep duration) and cardiometabolic risk in schoolchildren. The study shows cross-sectional results of baseline measurement from 146 Spanish schoolchildren, aged 8-to-11 years old, participating in the MOVI-2 study. Movement behaviors were determined using accelerometry combined with self-reported sleep time. Cardiometabolic risk was assessed using a validated metabolic syndrome index. Logistic regression analysis showed that higher levels of vigorous physical activity (OR = 0.110, p = 0.004) and sleeping more than 9 hours (OR = 0.269, p = 0.015) could be protective factors against metabolic syndrome risk in children. ANCOVA analysis showed associations between vigorous physical activity and waist circumference (p < 0.001), and sleep time with insulin resistance (p = 0.017) and lipid profile (p = 0.035). No association was observed between light and moderate physical activity, sedentary behavior and metabolic syndrome (index and components). No statistically significant differences were found for blood pressure and any of the movement behaviors. Our data suggest that both the amount of vigorous physical activity accumulated and sleep duration are independently associated with higher cardiometabolic risk in children.
Identification of key candidate genes and miRNA-mRNA target pairs in chronic lymphocytic leukemia by integrated bioinformatics analysis

Chronic lymphocytic leukemia (CLL) is a malignant clonal proliferative disorder of B cells. Inhibition of cell apoptosis and cell cycle arrest are the main pathological causes of this disease, but its molecular mechanism requires further investigation. The purpose of the present study was to identify biomarkers for the early diagnosis and treatment of CLL, and to explore the molecular mechanisms of CLL progression. A total of 488 differentially expressed genes (DEGs) and 32 differentially expressed microRNAs (miRNAs; DEMs) for CLL were identified by analyzing the gene chips GSE22529, GSE39411 and GSE62137. Functional and pathway enrichment analyses of DEGs demonstrated that DEGs were mainly involved in transcriptional dysregulation and multiple signaling pathways, such as the nuclear factor-κB and mitogen-activated protein kinase signaling pathways. In addition, Cytoscape software was used to visualize the protein-protein interactions of these DEGs in order to identify hub genes, which could be used as biomarkers for the early diagnosis and treatment of CLL. Cytoscape software was also used to analyze the association between the predicted target mRNAs of DEMs and DEGs and increase knowledge about the miRNA-mRNA regulatory network associated with the progression of CLL. Taken together, the present study provided a bioinformatics basis for advancing our understanding of the pathogenesis of CLL by identifying differentially expressed hub genes, miRNA-mRNA target pairs and molecular pathways. In addition, hub genes may be used as novel biomarkers for the diagnosis of CLL and to guide the selection of CLL drug combinations.
Context Based- Tourism Recommender System: Towards Tourists' Context-Sensitive Preference Conceptual Model

With the fact that the Internet facilitates access to information, it will lead to creating information overload, particularly for travelers. It is hard for travelers to find the appropriate destination, and service providers to recommend the suitable destination. The paper aims to propose a context-sensitive preference conceptual model from the tourist's perspectives. Data collection was conducted by examining the literature review. The results show that the proposed solution is to filter information by using a recommender system. The system may need to be conducted by considering the tourists rating, collaboration, and products or services description. Further, the system should consider additional contextual information: location, time, social, and weather. Such a multidimensional context-based recommender system faces problems with the complexity of contextual data with many attributes used for filtering. Therefore, the additional contextual information on a context-based recommender system could be based on the expected contextual preferences. This model is derived from a contextual pre/post-filtering approach or contextual modeling.
Low-Rank Linear Embedding for Image Recognition

Locality preserving projections (LPP) has been widely studied and extended in recent years, because of its promising performance in feature extraction. In this paper, we propose a modified version of the LPP by constructing a novel regression model. To improve the performance of the model, we impose a low-rank constraint on the regression matrix to discover the latent relations between different neighbors. By using the <inline-formula><tex-math notation="LaTeX"> $L_{2,1}$</tex-math></inline-formula>-norm as a metric for the loss function, we can further minimize the reconstruction error and derive a robust model. Furthermore, the <inline-formula><tex-math notation="LaTeX">$L_{2,1}$ </tex-math></inline-formula>-norm regularization term is added to obtain a jointly sparse regression matrix for feature selection. An iterative algorithm with guaranteed convergence is designed to solve the optimization problem. To validate the recognition efficiency, we apply the algorithm to a series of benchmark datasets containing face and character images for feature extraction. The experimental results show that the proposed method is better than some existing methods. The code of this paper can be downloaded from <uri>http://www.scholat. com/laizhihui</uri>.
Free-Living Nematodes Together With Associated Microbes Play an Essential Role in Apple Replant Disease

Apple replant disease (ARD) is a severe problem in apple production worldwide. It is caused by a complex of soil biota, leading to small discolorated roots, as well as increased biosynthesis of phytoalexins, total phenolic compounds and antioxidants. We sampled soil from randomized field plots with either apple trees affected by ARD, which were five times replanted every second year, or with healthy trees growing in plots, which had a grass cover during this period. We investigated the contribution of nematodes to ARD by dissecting the soil biota from plots infested with ARD and non-infested control plots into a nematode and a microbe fraction. Nematode communities significantly differed between ARD and control soil as revealed by high-throughput sequencing of 18S rRNA genes. Plant-parasitic nematodes were too low in abundance to explain root damage, and did not significantly differ between ARD and control soil. Their separate and synergistic effect on ARD symptoms of susceptible M26 apple rootstocks was analyzed 4 and 8 weeks after inoculation in three greenhouse experiments. Inoculants were either nematodes from ARD plots (NARD), NARD plus microbes from ARD plots (MARD), NARD plus microbes from control plots (MCon), nematodes from control plots NCon plus MARD, NCon plus MCon, MARD, or MCon, or non-inoculated control. In all three experiments, the combination NARD plus MARD had the strongest adverse effect on the plants, with respect to growth parameters of shoots and roots, total phenolic compounds and phytoalexins in roots, and antioxidants in leaves. NARD also induced ARD but less than NARD plus MARD. NARD plus MCon had delayed effects on the plants compared to NARD plus MARD, suggesting that detrimental nematode-microbe interactions built up with time. Effects of MARD or NCon plus MARD were minor or not distinguishable from those of MCon or non-inoculated control. Overall, the source of the inoculated nematodes -ARD or control soil- and the interaction between ARD nematodes and microbes were highly significant factors determining ARD. In conclusion, exploring the associations of nematodes and microbes in ARD soils will give the chance to unravel the etiology of ARD.
Cluster analysis identifying patients with COPD at high risk of 2-year all-cause mortality

The objective of the article is to identify clusters of patients with COPD according to factors known to be associated with mortality and to verify whether clusters’ assignment is associated with 2-year mortality. Patients (n = 141) were evaluated by bioelectrical impedance, maximal inspiratory pressure (MIP), one-repetition maximum test of the quadriceps femoris (1RMQF) and BODE index (body mass index; airflow obstruction (spirometry); dyspnea (modified Medical Research Council scale); and exercise capacity (6-minute walk test (6MWT) distance). Vital status was retrospectively checked 2 years after the assessments, and time to death was quantified for those deceased in this period. K-means analysis identified two clusters. Patients in cluster one (CL I, n = 69) presented an impaired clinical status in comparison to cluster two (CL II, n = 72). Receiver operating characteristics curves identified the cutoffs discriminating patients composing CL I: forced expiratory volume in the first second <44%pred; 6MWT <479 m; 1RMQF <19 kg; and maximum inspiratory pressures <73 cmH2O (area under the curve range 0.750–0.857). During the follow-up, 19 (13%) patients deceased, 15 in CL I (22%) and 4 in CL II (0.06%) (p = 0.005). CL I was associated with a higher risk of 2-year mortality (hazard ratio (95% confidence interval): 4.3 (1.40–12.9), p = 0.01). A cluster of patients with COPD highly associated with 2-year mortality was statistically identified, and cutoffs to identify these subjects were provided.
Design and Implementation of Smart Campus Automatic Settlement PLC Control System for Internet of Things

With the growing scale of the school and the growing student, traditional campus logistics services have been difficult to serve the college students with strong daily consumer demand. The huge consumption potential and demand have attracted more campus card construction, and the design of intelligent campus automatic settlement PLC control system for the Internet of Things has emerged. In the development process of campus logistics management, there are problems such as low operational efficiency and unreasonable management, plus the shopping consumption habits and time distribution characteristics of student groups. This design is the campus-based automatic settlement control system for the Internet of Things. Firstly, it discusses the principle of the Internet of Things technology, the principle, and the working method of the WinCC technology. According to the actual requirements of the school, the campus automatic settlement system should have based on the status quo and development. The construction of the campus automatic settlement system should fully consider the consumer management subsystem, the library monitoring subsystem, the student attendance monitoring subsystem, and the computer monitoring subsystem. The construction of the campus card system should have discussed in detail from the design of the system to the implementation of the system, organization of training, and so on. In addition, strict safety precautions have adopted to ensure efficient operation, safe management, and reliable monitoring of the entire system.
Large-Scale Simulation of the Phenotypical Variability Induced by Loss-of-Function Long QT Mutations in Human Induced Pluripotent Stem Cell Cardiomyocytes

Loss-of-function long QT (LQT) mutations inducing LQT1 and LQT2 syndromes have been successfully translated to human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) used as disease-specific models. However, their in vitro investigation mainly relies on experiments using small numbers of cells. This is especially critical when working with cells as heterogeneous as hiPSC-CMs. We aim (i) to investigate in silico the ionic mechanisms underlying LQT1 and LQT2 hiPSC-CM phenotypic variability, and (ii) to enable massive in silico drug tests on mutant hiPSC-CMs. We combined (i) data of control and mutant slow and rapid delayed rectifying K+ currents, IKr and IKs respectively, (ii) a recent in silico hiPSC-CM model, and (iii) the population of models paradigm to generate control and mutant populations for LQT1 and LQT2 cardiomyocytes. Our four populations contain from 1008 to 3584 models. In line with the experimental in vitro data, mutant in silico hiPSC-CMs showed prolonged action potential (AP) duration (LQT1: +14%, LQT2: +39%) and large electrophysiological variability. Finally, the mutant populations were split into normal-like hiPSC-CMs (with action potential duration similar to control) and at risk hiPSC-CMs (with clearly prolonged action potential duration). At risk mutant hiPSC-CMs carried higher expression of L-type Ca2+, lower expression of IKr and increased sensitivity to quinidine as compared to mutant normal-like hiPSC-CMs, resulting in AP abnormalities. In conclusion, we were able to reproduce the two most common LQT syndromes with large-scale simulations, which enable investigating biophysical mechanisms difficult to assess in vitro, e.g., how variations of ion current expressions in a physiological range can impact on AP properties of mutant hiPSC-CMs.
Effects of early, combined endurance and resistance training in mechanically ventilated, critically ill patients: A randomised controlled trial

Introduction Neuromuscular weakness resulting in severe functional impairment is common in critical care survivors. This study aimed to evaluate effects of an early progressive rehabilitation intervention in mechanically ventilated adults at risk. Methods This was a parallel, two-arm, assessor-blinded, randomised controlled trial with 6-months follow-up that was conducted in a mixed ICU of an academic centre in Switzerland. Previously independent, mechanically ventilated, critically ill adults with expected critical care stay ≥72 hours (n = 115) were randomised to a control group receiving standard physiotherapy including early mobilisation or to an experimental group with early endurance and resistance training combined with mobilisation. Primary endpoints were functional capacity (6-Minute Walk Distance) and functional independence (Functional Independence Measure) at hospital discharge. Secondary endpoints including muscle strength were assessed at critical care discharge. Safety was monitored closely by standard monitoring and predefined adverse events. Results Physiotherapy started within 48 hours of critical care admission while 97% of participants were still ventilated and 68% on inotropes. Compared to the control group (n = 57), the experimental group (n = 58) received significantly more physiotherapy (sessions: 407 vs 377, p<0.001; time/session: 25min vs 18min, p<0.001) and had less days with sedation (p<0.001). Adverse events were rare (0.6%) and without consequences. There were no significant between-group differences in 6-Minute Walk Distance (experimental 123m (IQR 25–280) vs control 100m (IQR 0–300); p = 0.542) or functional independence (98 (IQR 66–119) vs 98 (IQR 18–115); p = 0.308). Likewise, no differences were found for the secondary outcomes, except a trend towards improved mental health in the experimental group after 6 months (84 (IQR 68–88) vs 70 (IQR 64–76); p = 0.023). Conclusions Early endurance and resistance training in mechanically ventilated, intensive care patients does not improve functional capacity or independence at hospital discharge compared to early standard physiotherapy but may improve mental health 6-months after critical care discharge. Trial registration German Clinical Trials Register (DRKS): DRKS00004347, registered on 10 September 2012.
Reinforcement Learning Compensation based PD Control for Inverted Pendulum

In this paper, we present a Control Algorithm based on Reinforcement Learning for an inverted pendulum. By implementing the Q-Learning techniques in the PD control scheme, the pendulum is enabled to improve its online performance and adapt to changes in the parameters of the pendulum model. In a first step, Q-Learning is used so that the control can balance the pendulum towards its inverted vertical position; In a second step, we combine hybrid techniques of Q-Learning and PD control. With this combination, we can bring the pendulum to its inverted vertical position, regardless of the applied disturbance. Finally, the results of the simulation show the effectiveness of the proposed controller.
SAR Studies Leading to the Identification of a Novel Series of Metallo-β-lactamase Inhibitors for the Treatment of Carbapenem-Resistant Enterobacteriaceae Infections That Display Efficacy in an Animal Infection Model

The clinical effectiveness of carbapenem antibiotics such as meropenem is becoming increasingly compromised by the spread of both metallo-β-lactamase (MBL) and serine-β-lactamase (SBL) enzymes on mobile genetic elements, stimulating research to find new β-lactamase inhibitors to be used in conjunction with carbapenems and other β-lactam antibiotics. Herein, we describe our initial exploration of a novel chemical series of metallo-β-lactamase inhibitors, from concept to efficacy, in a survival model using an advanced tool compound (ANT431) in conjunction with meropenem.
An Augmented Lagrangian Neural Network for the Fixed-Time Solution of Linear Programming

In this paper, a recurrent neural network is proposed using the augmented Lagrangian method for solving linear programming problems. The design of this neural network is based on the Karush-Kuhn-Tucker (KKT) optimality conditions and on a function that guarantees fixed-time convergence. With this aim, the use of slack variables allows transforming the initial linear programming problem into an equivalent one which only contains equality constraints. Posteriorly, the activation functions of the neural network are designed as fixed time controllers to meet KKT optimality conditions. Simulations results in an academic example and an application example show the effectiveness of the neural network.
Book Recommendations Beyond the Usual Suspects Embedding Book Plots Together with Place and Time Information

Content-based recommendation of books and other media is usually based on semantic similarity measures. While metadata can be compared easily, measuring the semantic similarity of narrative literature is challenging. Keyword-based approaches are biased to retrieve books of the same series or do not retrieve any results at all in sparser libraries. We propose to represent plots with dense vectors to foster semantic search for similar plots even if they do not have any words in common. Further, we propose to embed plots, places, and times in the same embedding space. Thereby, we allow arithmetics on these aspects. For example, a book with a similar plot but set in a diﬀerent, user-speciﬁed place can be retrieved. We evaluate our ﬁndings on a set of 16,000 book synopses that spans literature from 500 years and 200 genres and compare our approach to a keyword-based baseline.
Estimating AP Location Using Crowdsourced Wi-Fi Fingerprints with Inaccurate Location Labels

The fundamental property of Wi-Fi signals for Wi-Fi positioning is that the signal strength of radio signals decreases as radio signals propagate. Since access points (APs) are sources of each Wi-Fi signal, knowing AP locations can help to exploit Wi-Fi fingerprints. However, investigating AP locations is a demanding task. Measuring absolute coordinates of AP locations is necessary for the extensive investigation, but manual measurement of absolute coordinates costs excessive human labor. In this paper, we propose an AP location estimating method using Wi-Fi fingerprints labeled with coordinates obtained from commercial location providers on smartphones. Both Wi-Fi fingerprints and location labels are implicitly collected from smartphone users. We apply nonlinear regression with log-distance path loss (LDPL) model on collected location-labeled Wi-Fi fingerprints to estimate AP locations. Estimated AP locations can accelerate radio map matching, simplify radio map construction, and assist AP location-based Wi-Fi positioning system.
Efficient Query Suggestion System using Users Search History

with the growing information burst on the World Wide Web, internet has placed high demands on search engines. Existing search engines provide most of the features for user query. But users of internet are not satisfied with them as they return thousands of documents in response to user query. So to develop user search intent application is challenging, satisfying increased expectations and diverse needs of user. Recorded user sessions/search logs are analyzed and used to form clusters. This clustering of user query is imperative for filtering the relevant results from web. Thus, by automating the optimization process we minimize user efforts; maximize user satisfaction for getting desired search.
Ultrasensitive and Fast Diagnostics of Viable Listeria Cells by CBD Magnetic Separation Combined with A511::luxAB Detection

The genus Listeria includes foodborne pathogens that cause life-threatening infections in those at risk, and sensitive and specific methods for detection of these bacteria are needed. Based on their unrivaled host specificity and ability to discriminate viable cells, bacteriophages represent an ideal toolbox for the development of such methods. Here, the authors describe an ultrasensitive diagnostic protocol for Listeria by combining two phage-based strategies: (1) specific capture and concentration of target cells by magnetic separation, harnessing cell wall-binding domains from Listeria phage endolysins (CBD-MS); and (2) highly sensitive detection using an adaptation of the A511::luxAB bioluminescent reporter phage assay in a microwell plate format. The combined assay enabled direct detection of approximately 100 bacteria per ml of pure culture with genus-level specificity in less than 6 h. For contaminated foods, the procedure included a 16 h selective enrichment step, followed by CBD-MS separation and A511::luxAB detection. It was able to consistently detect extremely low numbers (0.1 to 1.0 cfu/g) of viable Listeria cells, in a total assay time of less than 22 h. These results demonstrate the superiority of this phage-based assay to standard culture-based diagnostic protocols for the detection of viable bacteria, with respect to both sensitivity and speed.
Rotation-Invariant Magnetic Features for Inertial Indoor-Localization

Indoor-Localization using mobile end-user devices such as smartphones remains a highly discussed research topic. Applications range from guidance in large structures, assistance of visually impaired or even localization and guidance in rescue operations. Current approaches are often based on Radio-Frequency strength localization, which show insufficient precision and robustness. In this paper we investigate a magneto-based approach using rotation-invariant features compared with a pre-recorded grid-based map. Furthermore, we show, how this approach can be combined with relative localization using step recognition from inertial measurements. We test and evaluate both systems in simulation as well as real-world tests. We show, that a sub-meter precision localization accuracy can be reached using our magneto-intertial approach.