Miracle-dz/UniFAR-FTUs huggingface dataset:
Health Recommender Systems: Systematic Review

Background Health recommender systems (HRSs) offer the potential to motivate and engage users to change their behavior by sharing better choices and actionable knowledge based on observed user behavior. Objective We aim to review HRSs targeting nonmedical professionals (laypersons) to better understand the current state of the art and identify both the main trends and the gaps with respect to current implementations. Methods We conducted a systematic literature review according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and synthesized the results. A total of 73 published studies that reported both an implementation and evaluation of an HRS targeted to laypersons were included and analyzed in this review. Results Recommended items were classified into four major categories: lifestyle, nutrition, general health care information, and specific health conditions. The majority of HRSs use hybrid recommendation algorithms. Evaluations of HRSs vary greatly; half of the studies only evaluated the algorithm with various metrics, whereas others performed full-scale randomized controlled trials or conducted in-the-wild studies to evaluate the impact of HRSs, thereby showing that the field is slowly maturing. On the basis of our review, we derived five reporting guidelines that can serve as a reference frame for future HRS studies. HRS studies should clarify who the target user is and to whom the recommendations apply, what is recommended and how the recommendations are presented to the user, where the data set can be found, what algorithms were used to calculate the recommendations, and what evaluation protocol was used. Conclusions There is significant opportunity for an HRS to inform and guide health actions. Through this review, we promote the discussion of ways to augment HRS research by recommending a reference frame with five design guidelines.
Research progress on faba bean and faba forage in food and feed types, physiochemical, nutritional, and molecular structural characteristics with molecular spectroscopy

Abstract This article aims to review recent research progress and update on faba bean seeds and plant in food and feed type, physiochemical, nutritional, and molecular structural characteristics with molecular spectroscopy with chemometrics (both univariate and multivariate techniques). The review focused on chemical and nutritional characterization of faba bean and faba forage and feeding strategies to improve its utilization. The molecular spectroscopic techniques for faba research and the association between molecular structure and nutrient availability and utilization in ruminant system were reviewed. The future research direction in faba research was also provided. The study provides an insight and a potential approach using molecular spectroscopy to study molecular chemistry and molecular structure and molecular nutrition interaction in faba bean seeds and plant.
Hierarchical and Stable Multiagent Reinforcement Learning for Cooperative Navigation Control

We solve an important and challenging cooperative navigation control problem, Multiagent Navigation to Unassigned Multiple targets (MNUM) in unknown environments with minimal time and without collision. Conventional methods are based on multiagent path planning that requires building an environment map and expensive real-time path planning computations. In this article, we formulate MNUM as a stochastic game and devise a novel multiagent deep reinforcement learning (MADRL) algorithm to learn an end-to-end solution, which directly maps raw sensor data to control signals. Once learned, the policy can be deployed onto each agent, and thereby, the expensive online planning computations can be offloaded. However, to solve MNUM, traditional MADRL suffers from large policy solution space and nonstationary environment when agents make decisions independently and concurrently. Accordingly, we propose a hierarchical and stable MADRL algorithm. The hierarchical learning part introduces a two-layer policy model to reduce the solution space and uses an interlaced learning paradigm to learn two coupled policies. In the stable learning part, we propose to learn an extended action-value function that implicitly incorporates estimations of other agents’ actions, based on which the environment’s nonstationarity caused by other agents’ changing policies can be alleviated. Extensive experiments demonstrate that our method can converge in a fast way and generate more efficient cooperative navigation policies than comparable methods.
Multicomponent Gold-Linked Glycoconjugate Vaccine Elicits Antigen-Specific Humoral and Mixed TH1-TH17 Immunity, Correlated with Increased Protection against Burkholderia pseudomallei

Melioidosis is a complex human disease associated with a wide range of complications caused by the Gram-negative bacillus Burkholderia pseudomallei. The global burden of melioidosis is estimated to have 165,000 cases per year and 89,000 fatal outcomes. ABSTRACT Burkholderia pseudomallei is the causative agent of melioidosis, a fatal disease with a high mortality rate. The intrinsic resistance to commonly used antibiotics combined with the complex bacterial life cycle has hampered the development of preventive and therapeutic interventions and vaccines. Furthermore, the need of humoral and cell-mediated immunity in protection against B. pseudomallei has complicated the development of effective vaccines. Antigen delivery vaccine platforms that promote humoral and cellular responses while maintaining a safe profile are a roadblock to developing subunit vaccines against intracellular pathogens. Gold nanoparticles (AuNPs) were used for the delivery of multicomponent antigens with the goal of inducing vaccine-mediated immunity, promoting protection against melioidosis disease. Different nanoglycoconjugates using predicted immunogenic protein candidates, Hcp1, FlgL, OpcP, OpcP1, OmpW, and hemagglutinin, were covalently coupled to AuNPs, together with the lipopolysaccharide (LPS) from Burkholderia thailandensis, which acted as an additional antigen. Animals immunized with individually coupled (AuNP-protein-LPS) formulations containing OpcP or OpcP1, together with CpG as an adjuvant, showed a significant increase in protection, whereas a nanovaccine combination (AuNP-Combo2-LPS) showed significant and complete protection against a lethal intranasal B. pseudomallei challenge. Animals immunized with AuNP-Combo2-LPS showed robust humoral antigen-specific (IgG and IgA) responses with higher IgG2c titer, indicating a TH1-skewed response and promotion of macrophage uptake. In addition, immunization with the nanovaccine combination resulted in a mixed antigen-specific TH1-TH17 cytokine profile after immunization. This study provides the basis for an elegant and refined multicomponent glycoconjugate vaccine formulation capable of eliciting both humoral and cell-mediated responses against lethal B. pseudomallei challenge. IMPORTANCE Melioidosis is a complex human disease associated with a wide range of complications caused by the Gram-negative bacillus Burkholderia pseudomallei. The global burden of melioidosis is estimated to have 165,000 cases per year and 89,000 fatal outcomes. The endemicity of B. pseudomallei includes a wide range of tropical regions in Asia, Africa, Latin America, and Australia. Therefore, a viable alternative to prevent human infections is the development of an effective vaccine; however, no approved vaccine for human use is available. This study provides a vaccine strategy against B. pseudomallei and an immune-stimulatory platform to induce strong humoral and T-cell-mediated immunity.
Genetic Analysis of DinG Family Helicase YoaA and Its Interaction with Replication Clamp Loader Protein HolC in Escherichia coli

Maintaining genomic stability is crucial for all living organisms. Replication of DNA frequently encounters barriers that must be removed to complete genome duplication. ABSTRACT The XP-D/DinG family of DNA helicases contributes to genomic stability in all three domains of life. Here, we investigate the role of one of these proteins, YoaA, of Escherichia coli. In E. coli, YoaA aids in tolerance to the nucleoside azidothymidine (AZT), a DNA replication inhibitor, and physically interacts with a subunit of the DNA polymerase III holoenzyme, HolC. We map the residues of YoaA required for HolC interaction to its C terminus by yeast two-hybrid analysis. We propose that this interaction competes with HolC’s interaction with HolD and the rest of the replisome; YoaA indeed inhibits growth when overexpressed, dependent on this interaction region. By gene fusions, we show that YoaA is repressed by LexA and induced in response to DNA damage as part of the SOS response. Induction of YoaA by AZT is biphasic, with an immediate response after treatment and a slower response that peaks in the late log phase of growth. This growth-phase-dependent induction by AZT is not blocked by lexA3 (Ind−), which normally negates its self-cleavage, implying another means to induce the DNA damage response that responds to the nutritional state of the cell. We propose that YoaA helicase activity increases access to the 3′ nascent strand during replication; consistent with this, YoaA appears to aid in the removal of potential A-to-T transversion mutations in ndk mutants, which are prone to nucleotide misincorporation. We provide evidence that YoaA and its paralog DinG may also initiate template switching that leads to deletions between tandem repeats in DNA. IMPORTANCE Maintaining genomic stability is crucial for all living organisms. Replication of DNA frequently encounters barriers that must be removed to complete genome duplication. Balancing DNA synthesis with its repair is critical and not entirely understood at a mechanistic level. The YoaA protein, studied here, is required for certain types of DNA repair and interacts in an alternative manner with proteins that catalyze DNA replication. YoaA is part of the well-studied LexA-regulated response to DNA damage, the SOS response. We describe an unusual feature of its regulation that promotes induction after DNA damage as the culture begins to experience starvation. Replication fork repair integrates both DNA damage and nutritional signals. We also show that YoaA affects genomic stability.
Identification of a Preliminary Plasma Metabolome-based Biomarker for Circadian Phase in Humans

Measuring individual circadian phase is important to diagnose and treat circadian rhythm sleep-wake disorders and circadian misalignment, inform chronotherapy, and advance circadian science. Initial findings using blood transcriptomics to predict the circadian phase marker dim-light melatonin onset (DLMO) show promise. Alternatively, there are limited attempts using metabolomics to predict DLMO and no known omics-based biomarkers predict dim-light melatonin offset (DLMOff). We analyzed the human plasma metabolome during adequate and insufficient sleep to predict DLMO and DLMOff using one blood sample. Sixteen (8 male/8 female) healthy participants aged 22.4 ± 4.8 years (mean ± SD) completed an in-laboratory study with 3 baseline days (9 h sleep opportunity/night), followed by a randomized cross-over protocol with 9-h adequate sleep and 5-h insufficient sleep conditions, each lasting 5 days. Blood was collected hourly during the final 24 h of each condition to independently determine DLMO and DLMOff. Blood samples collected every 4 h were analyzed by untargeted metabolomics and were randomly split into training (68%) and test (32%) sets for biomarker analyses. DLMO and DLMOff biomarker models were developed using partial least squares regression in the training set followed by performance assessments using the test set. At baseline, the DLMOff model showed the highest performance (0.91 R2 and 1.1 ± 1.1 h median absolute error ± interquartile range [MdAE ± IQR]), with significantly (p < 0.01) lower prediction error versus the DLMO model. When all conditions (baseline, 9 h, and 5 h) were included in performance analyses, the DLMO (0.60 R2; 2.2 ± 2.8 h MdAE; 44% of the samples with an error under 2 h) and DLMOff (0.62 R2; 1.8 ± 2.6 h MdAE; 51% of the samples with an error under 2 h) models were not statistically different. These findings show promise for metabolomics-based biomarkers of circadian phase and highlight the need to test biomarkers that predict multiple circadian phase markers under different physiological conditions.
Chemical and biological basis for development of novel radioprotective drugs for cancer therapy

Abstract Ionizing radiation (IR) causes chemical changes in biological systems through direct interaction with the macromolecules or by causing radiolysis of water. This property of IR is harnessed in the clinic for radiotherapy in almost 50% of cancers patients. Despite the advent of stereotactic radiotherapy instruments and other advancements in shielding techniques, the inadvertent deposition of radiation dose in the surrounding normal tissue can cause late effects of radiation injury in normal tissues. Radioprotectors, which are chemical or biological agents, can reduce or mitigate these toxic side-effects of radiotherapy in cancer patients and also during radiation accidents. The desired characteristics of an ideal radioprotector include low chemical toxicity, high risk to benefit ratio and specific protection of normal cells against the harmful effects of radiation without compromising the cytotoxic effects of IR on cancer cells. Since reactive oxygen species (ROS) are the major contributors of IR mediated toxicity, plethora of studies have highlighted the potential role of antioxidants to protect against IR induced damage. However, owing to the lack of any clinically approved radioprotector against whole body radiation, researchers have shifted the focus toward finding alternate targets that could be exploited for the development of novel agents. The present review provides a comprehensive insight in to the different strategies, encompassing prime molecular targets, which have been employed to develop radiation protectors/countermeasures. It is anticipated that understanding such factors will lead to the development of novel strategies for increasing the outcome of radiotherapy by minimizing normal tissue toxicity.
The association between apolipoprotein A‐1 plasma level and premature coronary artery disease: A systematic review and meta‐analysis

Apolipoprotein A‐1 (Apo A‐1) is a constituent of high‐density lipoprotein (HDL) and emerging evidences put forward a potential association between Apo A‐1 plasma levels and premature coronary artery disease (pCAD). The aim of the present study is to gather relative literature and perform a systematic review and meta‐analysis regarding the association between serum ApoA‐1 levels and pCAD.
Deep Neural Network to Predict Diabetes: A Data Science Approach

Diabetes has become a famous and lethal disease among the low and medium-income countries. People could not overcome this deadly abnormal condition due to the current lifestyle, food habit and the genetic transmittance. Medical practitioners provide advice to prevent the diabetic condition and medications to control as this disease does not have a permanent cure. However, the detection of the disease is being a tidy process and deployment of machine learning predictive models to conduct smart diagnosis/detection is vital in the healthcare domain nowadays. Though several machine learning models were built in this regard, deploying a Deep Neural Network seems less focused. Therefore, a Deep Neural Network model was built with the support of complete preprocessing, class balancing, normalization, feature selection process and hyper-parameter tuning using the cross-validated searching technique. The model achieved 88% of accuracy and 0.88 ROC score and standing out as a promising predictive model in diagnosing/detecting diabetes.
Title : The complete , functional and dynamic cycle of the bacterial Initiation Factor 3 1 2

Title: The complete, functional and dynamic cycle of the bacterial Initiation Factor 3 1 2 Authors and Affiliations: 3 Jose A. Nakamoto, Roberto Spurio, Andrey L. Konevega, Attilio Fabbretti, and Pohl 4 Milón 5 6 1 Laboratory of Applied Biophysics and Biochemistry, Centre for Research and Innovation, 7 Health Sciences Faculty, Universidad Peruana de Ciencias Aplicadas (UPC), 15023 Lima, 8 Peru; 9 10 2 Laboratory of Genetics, School of Biosciences and Veterinary Medicine, University of 11 Camerino, 62032 Camerino, Italy; 12
PoPulation DemograPhy of Rhinoclemmys funeRea ( Black river turtle ) at a ProtecteD forest reserve in costa rica

—Rhinoclemmys funerea (Black River Turtle) is a widely distributed and abundant freshwater aquatic turtle in the Caribbean lowlands of Central America, but little research has examined population ecology of the species. Here I used mark-recapture techniques to describe population demographics, morphological variation, and natural history of R. funerea from a stream in La Selva Biological Station, a forest reserve in northeastern Costa Rica. I built Jolly-Seber population models with the POPAN function to estimate sex-specific variation in apparent survival, capture, and entry probabilities and population size. Population structure was characterized by more adults than juveniles and a male-biased sex ratio. Mark-recapture population models estimated males to have much higher local apparent survival (0.75) than females (0.15), suggesting that females may be transient in local stream populations. Model-estimated population size also described males (N = 35; 95% Confidence Interval [CI] = 29–43) as more abundant than females (N = 22; 95% CI = 16–31) in the study area, but derived population estimates suggested that the female population varied substantially during the study. Adults were sexually dimorphic in mass, plastron length, and features of tail morphology. The results provide the first empirical estimates of survival, population size, and population structure for R. funerea. Because the study population occurred in a relatively undisturbed private reserve, the population parameters described at La Selva may serve as reference demographic conditions for R. funerea and may be used to understand how riparian land-use and habitat change influences turtles in Costa Rica.
Does Guarantee Network Reduce Firms’ Earnings Management?

ABSTRACT Using data from China, this paper investigates the effect of a guarantee network on firms’ earnings management. The results suggest that guarantee networks significantly reduce earnings management and this effect is more pronounced under tight monetary policy. Further analyses suggest that guarantee networks reduce earnings management through supervision mechanism rather than financing mechanism and earnings management reduction effect depends on the property ownership and related-party guarantee of the network. Our study provides new insights on the positive effect of guarantee networks in China and contributes to the governance role of guarantee networks.
Interpersonal sensitivity and social avoidance and distress in female college students: Mediating role of self-inconsistency and moderating role of self-consciousness

This study aimed to explore the relationship between interpersonal sensitivity and college students’ social avoidance and distress. Our sample consisted of 544 Chinese female undergraduate college students selected through cluster sampling (mean age = 19.3 years, SD = 1.43 years). The students completed the Symptom Check-List-90 (SCL-90), Social Avoidance and Distress Scale (SADS), Self-Consistency and Congruence Scale (SCCS), and Self-Consciousness Scale (SCS). The results showed that higher interpersonal sensitivity predicted higher social avoidance and distress. Self-inconsistency scores played a mediating role in the relationship between interpersonal sensitivity and social avoidance and distress. Specifically, when self-inconstancy scores were high, interpersonal sensitivity was high, and social avoidance and distress was high. Moreover, self-consciousness moderated the relationship between interpersonal sensitivity and social avoidance and distress. Specifically, when self-consciousness scores were high, self-inconsistency was low, and social avoidance and distress was low. Consistent with self-consciousness theory, we conclude from these findings that interpersonal sensitivity effects on social avoidance and distress behaviours of college students would depend on their levels of self-inconsistency and self-consciousness.
Challenges and progress in the understanding and modelling of the wheel–rail creep forces

This paper surveys advances in the understanding and modelling of wheel–rail creep forces. The main focus is placed on tribological aspects, for which significant progress was made in the past two decades. We emphasise the role played by the surface conditions, i.e. the presence of liquid and solid layers, surface roughness, and near-surface plastic deformation. As these surface conditions may change over time, transient changes may be obtained in the creep force characteristic. This brings feedback into the picture, with consequences and opportunities for optimised adhesion recovery, traction control, and braking systems.
Decoding of Electroencephalogram Signals Shows No Evidence of a Neural Signature for Subitizing in Sequential Numerosity

Abstract Numerosity perception is largely governed by two mechanisms. The first so-called subitizing system allows one to enumerate a small number of items (up to three or four) without error. The second system allows only an approximate estimation of larger numerosities. Here, we investigate the neural bases of the two systems using sequentially presented numerosity. Sequential numerosity (i.e., the number of events presented over time) starts as a subitizable set but may eventually transition into a larger numerosity in the approximate estimation range, thus offering a unique opportunity to investigate the neural signature of that transition point, or subitizing boundary. If sequential numerosity is encoded by two distinct perceptual mechanisms (i.e., for subitizing and approximate estimation), neural representations of the sequentially presented items crossing the subitizing boundary should be sharply distinguishable. In contrast, if sequential numerosity is encoded by a single perceptual mechanism for all numerosities and subitizing is achieved through an external postperceptual mechanism, no such differences in the neural representations should indicate the subitizing boundary. Using the high temporal resolution of the EEG technique incorporating a multivariate decoding analysis, we found results consistent with the latter hypothesis: No sharp representational distinctions were observed between items across the subitizing boundary, which is in contrast with the behavioral pattern of subitizing. The results support a single perceptual mechanism encoding sequential numerosities, whereas subitizing may be supported by a postperceptual attentional mechanism operating at a later processing stage.