Miracle-dz/UniFAR-FTUs huggingface dataset:
Guidelines for the Provision and Assessment of Nutrition Support Therapy in the Pediatric Critically Ill Patient: Society of Critical Care Medicine and American Society for Parenteral and Enteral Nutrition

This document represents the first collaboration between 2 organizations—the American Society for Parenteral and Enteral Nutrition and the Society of Critical Care Medicine—to describe best practices in nutrition therapy in critically ill children. The target of these guidelines is intended to be the pediatric critically ill patient (>1 month and <18 years) expected to require a length of stay >2–3 days in a PICU admitting medical, surgical, and cardiac patients. In total, 2032 citations were scanned for relevance. The PubMed/MEDLINE search resulted in 960 citations for clinical trials and 925 citations for cohort studies. The EMBASE search for clinical trials culled 1661 citations. In total, the search for clinical trials yielded 1107 citations, whereas the cohort search yielded 925. After careful review, 16 randomized controlled trials and 37 cohort studies appeared to answer 1 of the 8 preidentified question groups for this guideline. We used the GRADE criteria (Grading of Recommendations, Assessment, Development, and Evaluation) to adjust the evidence grade based on assessment of the quality of study design and execution. These guidelines are not intended for neonates or adult patients. The guidelines reiterate the importance of nutrition assessment—particularly, the detection of malnourished patients who are most vulnerable and therefore may benefit from timely intervention. There is a need for renewed focus on accurate estimation of energy needs and attention to optimizing protein intake. Indirect calorimetry, where feasible, and cautious use of estimating equations and increased surveillance for unintended caloric underfeeding and overfeeding are recommended. Optimal protein intake and its correlation with clinical outcomes are areas of great interest. The optimal route and timing of nutrient delivery are areas of intense debate and investigations. Enteral nutrition remains the preferred route for nutrient delivery. Several strategies to optimize enteral nutrition during critical illness have emerged. The role of supplemental parenteral nutrition has been highlighted, and a delayed approach appears to be beneficial. Immunonutrition cannot be currently recommended. Overall, the pediatric critical care population is heterogeneous, and a nuanced approach to individualizing nutrition support with the aim of improving clinical outcomes is necessary.
Exploring Joint AB-LSTM With Embedded Lemmas for Adverse Drug Reaction Discovery

This work focuses on the detection of adverse drug reactions (ADRs) in electronic health records (EHRs) written in Spanish. The World Health Organization underlines the importance of reporting ADRs for patients’ safety. The fact is that ADRs tend to be under-reported in daily hospital praxis. In this context, automatic solutions based on text mining can help to alleviate the workload of experts. Nevertheless, these solutions pose two challenges: 1) EHRs show high lexical variability, the characterization of the events must be able to deal with unseen words or contexts and 2) ADRs are rare events, hence, the system should be robust against skewed class distribution. To tackle these challenges, deep neural networks seem appropriate because they allow a high-level representation. Specifically, we opted for a joint AB-LSTM network, a sub-class of the bidirectional long short-term memory network. Besides, in an attempt to reinforce lexical variability, we proposed the use of embeddings created using lemmas. We compared this approach with supervised event extraction approaches based on either symbolic or dense representations. Experimental results showed that the joint AB-LSTM approach outperformed previous approaches, achieving an f-measure of 73.3.
Single-molecule observation of nucleotide induced conformational changes in basal SecA-ATP hydrolysis

We visualize ATP-driven domain dynamics of individual SecA molecules in a near-native setting using atomic force microscopy. SecA is the critical adenosine triphosphatase that drives preprotein transport through the translocon, SecYEG, in Escherichia coli. This process is thought to be regulated by conformational changes of specific domains of SecA, but real-time, real-space measurement of these changes is lacking. We use single-molecule atomic force microscopy (AFM) to visualize nucleotide-dependent conformations and conformational dynamics of SecA. Distinct topographical populations were observed in the presence of specific nucleotides. AFM investigations during basal adenosine triphosphate (ATP) hydrolysis revealed rapid, reversible transitions between a compact and an extended state at the ~100-ms time scale. A SecA mutant lacking the precursor-binding domain (PBD) aided interpretation. Further, the biochemical activity of SecA prepared for AFM was confirmed by tracking inorganic phosphate release. We conclude that ATP-driven dynamics are largely due to PBD motion but that other segments of SecA contribute to this motion during the transition state of the ATP hydrolysis cycle.
What explains the lacklustre response to Zika in Brazil? Exploring institutional, economic and health system context

By early-2016, the international community began to pressure Brazil for a stronger policy response to Zika. In contrast to what was seen in the past, however, these international pressures did not elicit such a response. In this article, we explore why this was the case, reviewing the government’s policy response and the broader political and economic context shaping this response. The authors used single case study analysis and qualitative sources, such as books, journal articles, and government policy reports to support their empirical claims. We found that despite increased international pressures from the WHO, domestic political factors and economic recession hampered the government’s ability to strengthen its health systems response to Zika. Consequently, those states most afflicted by Zika have seen policy initiatives that lack sufficient funding, administrative and human resource capacity. This study revealed that despite a government’s deep foreign policy history of positively responding to international pressures through a stronger policy response to health epidemics, a sudden change in government, rising political instability, and economic recession can motivate governments to abandon this foreign policy tradition and undermine its response to new public health threats.
Patient-Derived Induced Pluripotent Stem Cells and Organoids for Modeling Alpha Synuclein Propagation in Parkinson's Disease

Parkinson's disease (PD) is an age-associated, progressive neurodegenerative disorder characterized by motor impairment and in some cases cognitive decline. Central to the disease pathogenesis of PD is a small, presynaptic neuronal protein known as alpha synuclein (a-syn), which tends to accumulate and aggregate in PD brains as Lewy bodies or Lewy neurites. Numerous in vitro and in vivo studies confirm that a-syn aggregates can be propagated from diseased to healthy cells, and it has been suggested that preventing the spread of pathogenic a-syn species can slow PD progression. In this review, we summarize the works of recent literature elucidating mechanisms of a-syn propagation, and discussed the advantages in using patient-derived induced pluripotent stem cells (iPSCs) and/or induced neurons to study a-syn transmission.
An Intelligent Agriculture Application Based on Deep Learning

Ever since the Internet of Things was developed, countries around the world have been applying the technology to solve current issues in agriculture, such as combating climate change and reducing labor costs. In the moment, intelligent agriculture has focused mainly on greenhouse cultivation; however, it takes significant amount of material and labor resources to build greenhouses, and their products are mostly high-valued cash crops. This study employs an outdoor farm as the field of experimentation and uses self-made model vehicles that patrols regularly to obtain information on the farm's crops. The study first establishes deep learning in intelligent agriculture to understand the farmer's farming skills so that such information may in turn help in further establishing an Internet of Things system that realizes automated farming systems. This study utilizes small, self-made model vehicles to gather crop information every day. The vehicles' designed functions include the following: (1) automated patrol along preprogrammed routes, (2) automatic photographing of each area, (3) prevention of collision between the small, self-made model vehicles, and (4) data collection of each area's temperature and humidity conditions. In this study, deep learning is employed mainly towards training and prediction of conditions based on the crop's temperature and humidity data as well as weather information. Deep learning is established first, followed by the Internet of Things, in order to achieve intelligent agriculture. The study must first acquire the farmers' cultivation skills; since such knowledge is passed on through experience and without any recordings, it can only be retrieved by means of the small model vehicles. In addition, the study also conducted simulation through hands-on experiments. The proposed scheme first collects all environment-related factors and conducts training on related coefficients, then utilizes the automatic irrigation system to allow the same kind of crops to grow in the most ideal environment. The system focuses on outdoor farms; it establishes an intelligent agriculture using minimal costs, resolving issues such as number of sensors needed, farm network accessibility, and electrical wiring.
Spectral Simplicial Theory for Feature Selection and Applications to Genomics

The scale and complexity of modern data sets and the limitations associated with testing large numbers of hypotheses underline the need for feature selection methods. Spectral techniques rank features according to their degree of consistency with an underlying metric structure, but their current graph-based formulation restricts their applicability to point features. We extend spectral methods for feature selection to abstract simplicial complexes and present a general framework which can be applied to 2-point and higher-order features. Combinatorial Laplacian scores take into account the topology spanned by the data and reduce to the ordinary Laplacian score in the case of point features. We demonstrate the utility of spectral simplicial methods for feature selection with several examples of application to the analysis of gene expression and multi-modal genomic data. Our results provide a unifying perspective on topological data analysis and manifold learning approaches.
Fast Multichannel Nonnegative Matrix Factorization with Constraints on Active Source Candidates

This paper presents an extension of the model of multichannel nonnegative matrix factorization (MNMF) by introducing constraints on active source candidates in the time-frequency (TF) bins. An algorithm of the proposed MNMF considerably faster than the conventional one is then developed under the constraint that at most two sources are active in each TF bin. To reduce the number of matrix operations in MNMF, the proposed algorithm simultaneously diagonalizes each pair of two active source spatial covariance matrices. As a result, the computational time complexity of the fast MNMF is comparable to those of TF mask approaches without assuming the sparsity constraint of source spectra unlike them. The experimental results show that the fast MNMF is only about twice as slow as the corresponding TF mask approach while maintaining the separation performance of the original MNMF.
Meeting Review: 2018 International Workshop on Structure and Function of the Lentiviral gp41 Cytoplasmic Tail

Recent developments in defining the role of the lentiviral envelope glycoprotein (Env) cytoplasmic tail (CT) in Env trafficking and incorporation into virus particles have advanced our understanding of viral replication and transmission. To stimulate additional progress in this field, the two-day International Workshop on Structure and Function of the Lentiviral gp41 Cytoplasmic Tail, co-organized by Eric Freed and James Hoxie, was held at the National Cancer Institute in Frederick, MD (26–27 April 2018). The meeting served to bring together experts focused on the role of gp41 in HIV replication and to discuss the emerging mechanisms of CT-dependent trafficking, Env conformation and structure, host protein interaction, incorporation, and viral transmission. The conference was organized around the following three main hot topics in gp41 research: the role of host factors in CT-dependent Env incorporation, Env structure, and CT-mediated trafficking and transmission. This review highlights important topics and the advances in gp41 research that were discussed during the conference.
Rapid Weight Loss and Dietary Inadequacies among Martial Arts Practitioners from Poland

Healthy nutrition and maintaining a normal body weight are very important for reducing the risk of various diseases not only among the general population, but also among athletes, especially combat sport athletes. The aim of the study was to evaluate the prevalence of rapid weight loss (RWL) and to indicate eventual dietary inadequacies among professional martial arts practitioners. Sixty-two male athletes (aged: 23 ± 4) completed a questionnaire (i.e., frequency of food group consumption, questions about training and RWL) and single 24-h dietary recall. This study confirmed the high prevalence of RWL in athletes (58%) for two to three days before the competition, which allowed for reduction of 3.4 ± 1.0 kg (4.3 ± 1.5%) of their body weight. Many dietary inaccuracies were found such as: lower than recommended by nutrition experts, level of consumption of dairy products, grain products, fruit, and vegetables, and insufficient intake of energy, carbohydrates, minerals (especially iodine, potassium, calcium) and vitamins (especially D, folate, C, E) during the training day. Adequate nutrition is a natural support for the whole training process, and may allow for regulation of body weight in a longer period and in a safer manner; thus, there is a need for nutrition education dedicated to athletes and their trainers.
Is chronic low back pain a risk factor for diabetes? The Nord-Trøndelag Health Study

Objective The purpose of this study was to examine the risk of diabetes associated with the presence or absence of chronic low back pain, considering both cross-sectional and cohort data. Research design and methods Analyses were based on the Norwegian HUNT2 and HUNT3 surveys of Nord-Trøndelag County. The prevalence of diabetes was compared in groups with and without chronic low back pain among 45 157 participants aged 30–69 years. Associations between low back pain at baseline and risk of diabetes were examined in an 11-year follow-up of 30 380 individuals with no baseline diagnosis of diabetes. The comorbidity between diabetes and low back pain was assessed at the end of follow-up. All analyses were carried out considering generalized linear models incorporating adjustment for other relevant risk factors. Results Cross-sectional analyses did not reveal any association between low back pain and diabetes. With adjustment for age, body mass index, physical activity and smoking, the cohort study of women showed a significant association between low back pain at baseline and risk of diabetes (RR 1.30; 95% CI 1.09 to 1.54, p=0.003). The association differed between age groups (p=0.015), with a stronger association in relatively young women. In men, no association was found in the whole age range (RR 1.02; 95% CI 0.86 to 1.21, p=0.82). No association was observed between diabetes and chronic low back pain at the end of follow-up. Conclusion Among younger women, those with chronic low back pain may have an increased risk of diabetes.
Line Feature Based Extrinsic Calibration of LiDAR and Camera

Reliable extrinsic calibration is a crucial first step for multi-sensor data fusion, which is the key part of the autonomous vehicle to perceive the environment carefully and effectively. In this paper, we propose an effective extrinsic calibration pipeline to establish the transformation between camera and LiDAR and update the decalibration online on an autonomous driving platform. We obtain rotation extrinsic parameters using parallel lines features in road scene, and infer translation extrinsic parameters by an online search approach based on selective edge alignment of point cloud and image. In order to evaluate our calibration system, it is first validated on KITTI benchmark and compared with the baseline algorithm. After that, the proposed method is tested on our own data. The results show that our method has a better rotation accuracy and demonstrate the necessity of error correction online.
Abstract: Universal Measures of Support Are Needed: A Cross-Sectional Study of Health Literacy in Dupuytren’s Patients

wGRS is not higher in individuals with diseases known to be associated with CTS suggests that extraneous factors (such as oedema in hypothyroidism and synovitis in rheumatoid arthritis), rather than shared genetic factors, are likely to predispose to CTS in these groups. The negative correlation between wGRS and height explains the observation that CTS patients are on average >2cm shorter than non-CTS controls in the UK Biobank cohort. We are currently extending the wGRS to our own cohort of deeply phenotyped CTS patients, to correlate wGRS with symptoms and electrophysiological test severity, as well as surgical outcomes. We believe it is important for plastic surgeons to be mindful of the increasing role that genetics will play in our practice, and this study illustrates the proof of principle that a wGRS can potentially serve as useful tool in the future for the prognostication of CTS.
Green Tea Catechin Dramatically Promotes RNAi Mediated by Low-Molecular-Weight Polymers

Cytosolic delivery is the major challenge that limits the clinical translation of siRNA-based therapeutics. Although thousands of polymers have been developed for siRNA delivery, the efficiency–toxicity correlation is unsatisfactory. Here, we report a facile strategy to fabricate core–shell-structured nanoparticles with robust siRNA delivery efficiency. The nanoparticle is prepared by entropy-driven complexation of siRNA with a green tea catechin to yield a negatively charged core, followed by coating low-molecular-weight polymers to form the shell. This supramolecular strategy facilitates the polymers condensing siRNA into uniform nanoparticles. The nanoparticle specifically down-regulates target genes in vitro and in vivo, and efficiently attenuates chronic intestinal inflammation in an inflammatory bowel disease model. Notably, the highly efficient nanoparticles are applicable for various polymers with different topologies and chemical compositions, providing a versatile technique to break down the efficiency–toxicity correlation of cationic polymers. The proposed strategy in this study permits the development of a promising platform for polymer-mediated siRNA delivery.
A survey on adolescent health information seeking behavior related to high-risk behaviors in a selected educational district in Isfahan

Backgrounds The characteristics and conditions of growth and development have made adolescence one of the most vital and influential ages for prevention and health promotion, especially in the area of high-risk behaviors. Accordingly, the aim of this study was to determine adolescent health information seeking behavior related to high-risk behaviors in a selected educational district in Isfahan (Iran). Methodology The present study was of an applied type, which was conducted using the survey research method. The statistical population consisted of adolescent students at public schools in Isfahan (6519 subjects), and the sample size was determined to be 364 based on Cochran's formula. The sampling method was of a cluster sampling type, and the data collection tool was a researcher-made questionnaire. The validity of the questionnaire was approved by medical librarians, and using the Cronbach's alpha method, the reliability was obtained to be 0.85. SPSS 16 software was used for data analysis at two statistical levels: descriptive and inferential (independent t-test, one-sample t-test, chi-square, Pearson correlation coefficient and Mann-Whitney). Findings "Lack of mobility" was the most important health information need related to adolescent high-risk behaviors. The most important sources to obtain health information related to high-risk behaviors were "the Internet" with a mean score of 3.69 and "virtual social media" with a mean score of 3.49 out of 5. Adolescents had a positive attitude towards health information. The most important barriers to seeking health information were mentioned as follows: "difficulty in determining the quality of information found", "absence of appropriate information", and "concerns about the disclosure of their problems or illness to others". From the perspective of adolescents, the most important criterion for the evaluation of information quality was "the trueness and correctness of the information" and the need for health information related to high-risk behaviors was higher in girls than in boys. Conclusions/Significance Considering adolescents’ positive attitude towards use of health information, it is necessary to put valid information at their disposal through different information resources, taking into account their level of information literacy. Accordingly, medical librarians’ abilities are suggested to be used for the production, evaluation, and introduction of health-related reading materials in the field of high-risk behaviors in easy language and suitable for adolescents.
A SVM-Based Malware Detection Mechanism for Android Devices

Currently, Android phones accounted for over 85 % of all smartphone sales as of 2017. Because the system allows users to install the unofficial apps, it will be targeted by malware easily. Using general anti-virus software to scan apps usually detected a known virus species only. As for new type of unknown variant, is not detectable normally. In this paper, we present a SVM-based mechanism to detect the malware and normal apps. The proposed idea scanning and recording features for both required and used permissions of the list. We adopt the LibSVM to classify the unknown apps. The experimental results indicate the accurate rate of 99% for the correct identification of both benign and malware even for the unknown applications. We propose not only a simple but also feasible approach to detect mobile apps.