Miracle-dz/UniFAR-FTUs huggingface dataset:
Usual Dietary Intake Estimation Based on a Combination of Repeated 24-H Food Lists and a Food Frequency Questionnaire in the KORA FF4 Cross-Sectional Study

Background: Estimation of usual dietary intake poses a challenge in epidemiological studies. We applied a blended approach that combines the strengths provided by repeated 24-h food lists (24HFLs) and a food frequency questionnaire (FFQ). Methods: At least two web-based 24HFLs and one FFQ were completed by 821 participants in the KORA FF4 study. Consumption probabilities were estimated using logistic mixed models, adjusting for covariates and the FFQ data on consumption frequency. Intake amount of a consumed food item was predicted for each participant based on the results of the second Bavarian Food Consumption Survey (BVS II). By combining consumption probability and estimated consumption amount, the usual food intake for each participant was estimated. These results were compared to results obtained without considering FFQ information for consumption probability estimation, as well as to conventional FFQ data. Results: The results of the blended approach for food group intake were often higher than the FFQ-based results. Intraclass correlation coefficients between both methods ranged between 0.21 and 0.86. Comparison of both methods resulted in weighted kappa values based on quintiles ranging from fair (0.34) to excellent agreement (0.84). Omission of FFQ information in the consumption probability models distinctly affected the results at the group level, though individual intake data were slightly affected, for the most part. Conclusions: Usual dietary intake data based on the blended approach differs from the FFQ-based results both in absolute terms and in classification according to quintiles. The application of the blended approach has been demonstrated as a possible tool in nutritional epidemiology, as a comparison with published studies showed that the blended approach yields reasonable estimates. The inclusion of the FFQ information is valuable especially with regard to irregularly consumed foods. A validation study including biomarkers of dietary intake is warranted.
Prevalence and determinants of posttraumatic stress in adolescents following an earthquake

Context: The 2016 Aceh earthquake causes a significant mental health impact in adolescents. However, the prevalence and determinants have never been reported. Aims: The study aimed to estimate the prevalence rate and determinants of posttraumatic stress symptoms in adolescents following an earthquake. Settings and Design: This cross-sectional study was conducted in Pidie Jaya district approximately 6 months following the earthquake. Materials and Methods: The Trauma Screening Questionnaire (TSQ) was distributed to adolescents living around the earthquake-affected area. Statistical Analysis: The Chi-square test and binomial logistic regression were performed using GNU PSPP statistical software. Results: The prevalence of posttraumatic stress disorder (PTSD) was 47%, and the mean score of TSQ was 5.11 (standard deviation = 2.3). The presence of PTSD was associated with gender and age (P = 0.001). Logistic regression analysis confirmed that gender, being injured, witnessing someone injured, feeling stressed after the earthquake, and feeling scared of staying inside a building following the earthquake were independently and significantly associated with PTSD (P = 0.001). Conclusion: The prevalence of PTSD at 6 months following an earthquake is relatively high, and many factors are associated with the presence of PTSD.
A Cross-Sectional and Longitudinal Study of the Effects of a Mindfulness Meditation Mobile Application Platform on Reducing Stress and Anxiety

Introduction: The development of many lifestyle-related chronic disorders occurs as a result of stress and anxiety. In recent years, in order to overcome lifestyle-related problems, people are increasingly making use of mindfulness meditation mobile applications despite the fact that there is no substantial evidence that this practice has benefits for their health. Aim: Testing the effectiveness of this method in reducing stress and anxiety through the development of a mindfulness meditation mobile application was the aim of this project. Methodology: Two independent studies were conducted. For both the studies, IBM SPSS Statistics 23.0 software was used to perform the statistical analysis. The first study was conducted cross-sectionally between 111 meditators and 111 non-meditators. The use of Depression, Anxiety, Stress Scale (DASS) was employed in assessing the stress and anxiety. It was found out that stress (P < 0.000) and anxiety (P < 0.000) are significantly reduced for meditators in comparison with the non-meditators. A substantial reduction was observed using the cross-sectional study, and a longitudinal study was carried out to affirm the effectiveness of this method in reducing stress and anxiety. 67 users were shortlisted for the study and their stress and anxiety level was measured before and after practicing mindfulness meditation. Result: The result revealed that there was a substantial reduction in stress (P = 0.01) and anxiety (P = 0.02). Conclusion: Therefore, mindfulness meditation mobile application can serve as another medium of delivery in addressing the problems of stress and anxiety. However, future research is warranted to determine the biological effects of mindfulness meditation.
Differential susceptibility & replication potential of Vero E6, BHK-21, RD, A-549, C6/36 cells & Aedes aegypti mosquitoes to three strains of chikungunya virus

Background & objectives: Chikungunya virus (CHIKV), a mosquito-borne arthritogenic virus causes infections ranging from febrile illness to debilitating polyarthralgia in humans. Re-emergence of the virus has affected millions of people in Africa and Asia since 2004. During the outbreak, a new lineage of the virus has evolved as an adaptation for enhanced replication and transmission by Aedes albopictus mosquito. A study was designed to compare the susceptibility of four vertebrate cell lines, namely Vero E6 (African green monkey kidney), BHK-21 (Baby hamster kidney), RD (human rhabdomyosarcoma), A-549 (human alveolar basal epithelial cell) and C6/36 (Ae. albopictus) to Asian genotype and two lineages of East, Central and South African (E1:A226 and E1:A226V) of CHIKV. Methods: One-step growth kinetics of different CHIKV strains was carried out in the above five cell lines to determine the growth kinetics and virus yield. Virus titre was determined by 50 per cent tissue culture infectious dose assay and titres were calculated by the Reed and Muench formula. Growth and virus yield of the three strains in Ae. aegypti mosquitoes was studied by intrathoracic inoculation and virus titration in Vero E6 cell line. Results: Virus titration showed Vero E6, C6/36 and BHK-21 cell lines are high virus yielding with all the three lineages while RD and A-549 yielded low virus titres. C6/36 cell line was the most sensitive and yielded the maximum titre. Ae. aegypti mosquitoes, when inoculated with high titre virus, yielded an almost equal growth with the three strains while rapid growth of E1:A226V and Asian strain was observed with 1 log virus. Interpretation & conclusions: C6/36 cell line was found to be the most sensitive and high yielding for CHIKV irrespective of lineages while Vero E6 and BHK-21 cell lines yielded high titres and may find application for vaccine/diagnostic development. Infection of Ae. aegypti mosquitoes with the three CHIKV strains gave almost identical pattern of growth.
Self-adaptive PSO memetic algorithm for multi objective workflow scheduling in hybrid cloud

Cloud computing is a technology in distributed computing that facilitate pay per model to solve large scale problems. The main aim of cloud computing is to give optimal access among the distributed resources. Task scheduling in cloud is the allocation of best resource to the demand considering the different parameters like time, makespan, cost, throughput etc. All the workflow scheduling algorithms available cannot be applied in cloud since they fail to integrate the elasticity and heterogeneity in cloud. In this paper, the cloud workflow scheduling problem is modeled considering make span, cost, percentage of private cloud utilization and violation of deadline as four main objectives. Hybrid approach of Particle Swarm Optimization (PSO) and Memetic Algorithm (MA) called Self-Adaptive Particle Swarm Memetic Algorithm (SPMA) is proposed. SPMA can be used by cloud providers to maximize user quality of service and the profit of resource using an entropy optimization model. The heuristic is tested on several workflows. The results obtained shows that SPMA performs better than other state of art algorithms.
A novel and complete approach for storing RDF(S) in relational databases

Resource Description Framework RDF and RDF Schema (collectively called RDF(S)) are the normative language to describe the Web resource information. With the massive growth of RDF(S) information, how to effectively store them is becoming an important research issue. By analysing the characteristics of RDF(S) data and schema semantic information in depth, this paper proposes a multiple storage model of RDF(S) based on relational databases. An overall storage framework, some detailed storage rules, a storage algorithm and a storage example are proposed. Also, the correctness of the storage approach is discussed and proved. Based on the proposed storage approach, a prototype storage tool is implemented, and experiments show that the approach and the tool are feasible.
Turbo Decoder Design based on an LUT-Normalized Log-MAP Algorithm

Turbo codes have been widely used in wireless communication systems due to their good error correction performance. Under time division long term evolution (TD-LTE) of the 3rd generation partnership project (3GPP) wireless communication standard, a Log maximum a posteriori (Log-MAP) decoding algorithm with high complexity is usually approximated as a lookup-table Log-MAP (LUT-Log-MAP) algorithm and Max-Log-MAP algorithm, but these two algorithms have high complexity and high bit error rate, respectively. In this paper, we propose a normalized Log-MAP (Nor-Log-MAP) decoding algorithm in which the function max* is approximated by using a fixed normalized factor multiplied by the max function. Combining a Nor-Log-MAP algorithm with a LUT-Log-MAP algorithm creates a new kind of LUT-Nor-Log-MAP algorithm. Compared with the LUT-Log-MAP algorithm, the decoding performance of the LUT-Nor-Log-MAP algorithm is close to that of the LUT-Log-MAP algorithm. Based on the decoding method of the Nor-Log-MAP algorithm, we also put forward a normalization functional unit (NFU) for a soft-input soft-output (SISO) decoder computing unit. The simulation results show that the LUT-Nor-Log-MAP algorithm can save about 2.1% of logic resources compared with the LUT-Log-MAP algorithm. Compared with the Max-Log-MAP algorithm, the LUT-Nor-Log-MAP algorithm shows a gain of 0.25~0.5 dB in decoding performance. Using the Cyclone IV platform, the designed Turbo decoder can achieve a throughput of 36 Mbit/s under a maximum clock frequency of 44 MHz.
A Verified Compositional Algorithm for AI Planning

We report on our HOL4 verification of an AI planning algorithm. The algorithm is compositional in the following sense: a planning problem is divided into multiple smaller abstractions, then each of the abstractions is solved, and finally the abstractions’ solutions are composed into a solution for the given problem. Formalising the algorithm, which was already quite well understood, revealed nuances in its operation which could lead to computing buggy plans. The formalisation also revealed that the algorithm can be presented more generally, and can be applied to systems with infinite states and actions, instead of only finite ones. Our formalisation extends an earlier model for slightly simpler transition systems, and demonstrates another step towards formal treatments of more and more of the algorithms and reasoning used in AI planning, as well as model checking. 2012 ACM Subject Classification Computing methodologies → Artificial intelligence; Computing methodologies → Planning for deterministic actions; Computing methodologies → Planning with abstraction and generalization; Software and its engineering → Software verification
Parameter tuning of neural network for financial time series forecasting

: One of the most challengeable problems in pattern recognition domain is financial time series forecasting which aims to exactly estimate the cost value variations of a particular object in future. One of the best well-known financial time series prediction methods is Neural Network (NN) but it suffers from parameter tuning such as number of neuron in hidden layer, learning rate and number of periods that should be forecasted. To solve the problem, this paper proposes a new meta-heuristic-based parameter tuning scheme which is based on Harmony Search (HS). To improve the exploration and exploitation rates of HS, the control parameters of HS are adapted during the generations. Evaluation of the proposed method on several financial times series datasets shows the efficiency of the improved HS on parameter setting of NN for time series prediction
Sentiment analysis with term weighting and word vectors

: It is the sentiment analysis with which it is tried to predict the sentiment being told in the texts in an area where Natural Language Processing (NLP) studies are being frequently used in recent years. In this study sentiment extraction has been made from Turkish texts and performances of methods that are used in text representation have been compared. In the study being conducted, besides Bag of Words (BoW) method which is traditionally used for the representation of texts, Word2Vec, which is word vector algorithm being developed in recent years and Doc2Vec, being document vector algorithm, have been used. For the study 5 different Machine Learning (ML) algorithms have been used to classify the texts being represented in 5 different ways on 3000 pieces of labeled tweets belonging to a telecom company. As a conclusion it was seen that Word2Vec, being among text representation methods and Random Forest, being among ML algorithms were most successful and most applicable ones. It is important as it is the first study with which BoW and word vectors have been compared for sentiment analysis in Turkish texts.
mSphere of Influence: Modifying an Old Method To Study RNA-Protein Interactions

Calvin Tiengwe works on posttranscriptional gene regulation and iron homeostasis in the parasitic protozoan Trypanosoma brucei. In this mSphere of Influence article, he reflects on how the paper “Comprehensive identification of RNA-protein interactions in any organism using orthogonal organic phase separation (OOPS)” by Queiroz et al. (Nat Biotechnol 37:169–178, 2019, https://doi.org/10.1038/s41587-018-0001-2) influenced his research by providing a tool to capture RNA-protein complexes on a global scale using acid guanidinium thiocyanate-phenol-chloroform (AGPC), an old method hitherto applied for RNA, DNA, or protein purification. ABSTRACT Calvin Tiengwe works on posttranscriptional gene regulation and iron homeostasis in the parasitic protozoan Trypanosoma brucei. In this mSphere of Influence article, he reflects on how the paper “Comprehensive identification of RNA-protein interactions in any organism using orthogonal organic phase separation (OOPS)” by Queiroz et al. (Nat Biotechnol 37:169–178, 2019, https://doi.org/10.1038/s41587-018-0001-2) influenced his research by providing a tool to capture RNA-protein complexes on a global scale using acid guanidinium thiocyanate-phenol-chloroform (AGPC), an old method hitherto applied for RNA, DNA, or protein purification.
Ketoacid Analogues Supplementation in Chronic Kidney Disease and Future Perspectives

Diet is a key component of care during chronic kidney disease (CKD). Nutritional interventions, and, specifically, a restricted protein diet has been under debate for decades. In order to reduce the risk of nutritional disorders in very-low protein diets (VLDP), supplementation by nitrogen-free ketoacid analogues (KAs) have been proposed. The aim of this review is to summarize the potential effects of this dietary therapy on renal function, uremic toxins levels, and nutritional and metabolic parameters and propose future directions. The purpose of this paper is also to select all experimental and randomized clinical studies (RCTs) that have compared VLDP + KA to normal diet or/and low protein diet (LPD). We reviewed the SCOPUS, WEB of SCIENCES, CENTRAL, and PUBMED databases from their inception to 1 January, 2019. Following duplicate removal and application of exclusion criteria, 23 RCTs and 12 experimental studies were included. LPD/VLPD + KAs appear nutritionally safe even if how muscle protein metabolism adapts to an LPD/VLPD + KAs is still largely unknown. VLPD + KAs seem to reduce uremic toxins production but the impact on intestinal microbiota remains unexplored. All studies observed a reduction of acidosis, phosphorus, and possibly sodium intake, while still providing adequate calcium intake. The impact of this diet on carbohydrate and bone parameters are only preliminary and need to be confirmed with RCTs. The Modification of Diet in Renal Disease study, the largest RCTs, failed to demonstrate a benefit in the primary outcome of the decline rate for the glomerular filtration rate. However, the design of this study was challenged and data were subsequently reanalyzed. However, when adherent patients were selected, with a rapid rate of progression and a long-term follow up, more recent meta-analysis and RCTs suggest that these diets can reduce the loss of the glomerular filtration rate in addition to the beneficial effects of renin-angiotensin-aldosterone system (RAAS) inhibitors. The current evidence suggests that KAs supplemented LPD diets should be included as part of the clinical recommendations for both the nutritional prevention and metabolic management of CKD. More research is needed to examine the effectiveness of KAs especially on uremic toxins. A reflection about the dose and composition of the KAs supplement, the cost-effective features, and their indication to reduce the frequency of dialysis needs to be completed.
A quantitative screen for metabolic enzyme structures reveals patterns of assembly across the yeast metabolic network

Despite the proliferation of proteins that can form filaments or phase-separated condensates, it remains unclear how this behavior is distributed over biological networks. We have found that 60 of the 440 yeast metabolic enzymes robustly form structures, including 10 that assemble within mitochondria. Additionally, the ability to assemble is enriched at branch points on several metabolic pathways. The assembly of enzymes at the first branch point in de novo purine biosynthesis is coordinated, hierarchical, and based on their position within the pathway, while the enzymes at the second branch point are recruited to RNA stress granules. Consistent with distinct classes of structures being deployed at different control points in a pathway, we find that the first enzyme in the pathway, PRPP synthetase, forms evolutionarily conserved filaments that are sequestered in the nucleus in higher eukaryotes. These findings provide a roadmap for identifying additional conserved features of metabolic regulation by condensates/filaments.
Combined Single-Cell Manipulation and Chemomechanical Modeling to Probe Cell Migration Mechanism During Cell-to-Cell Interaction

Spatial presentations of chemical and mechanical information are key parameters for cell migration. However, previous theoretical and experimental studies focus on probing the mechanisms caused by a single type of stimulus, while ignoring the synergetic effects, especially for single cell migration during cell-to-cell interaction. Here we develop a chemomechanical model to assess the biochemical and biophysical modulators of single cell migration during cell-to-cell interaction. This model considers the stimulation of chemoattractant concentration gradient, influence of dynamic adhesion strength and relative motion between cells. The model is validated with single cell manipulation of leukemia cancer cell on stromal cell layer using optical tweezers. Both the modeling and experimental results demonstrate that cell migration velocity caused by chemotaxis can be biased by dynamic adhesion force, which is related to the retrograde flow of stromal cell layer. Besides, the biophysical modulators can influence the effect of drug treatment for specific signaling pathway. Our work provides a quantitative description of single cell migration in a complex environment that is close to realistic in vivo situation and is useful for further exploration of cell signaling pathway during cell-to-cell interactions for investigation of potential therapeutic strategy.
Sex-specific strategies of phosphorus acquisition in Populus cathayana as affected by soil P availability and distribution.

• Soil phosphorus (P) availability and its distribution influence plant growth and productivity, but how they affect the growth dynamics and sex-specific P acquisition strategies of dioecious plant species is poorly understood. • In this study, the impact of soil P availability and its distribution on dioecious Populus cathayana Rehd. was characterized. P. cathayana males and females were grown under three levels of P supply, and with homogeneous or heterogeneous P distribution. • Females had a greater total root length, specific root length (SRL), biomass and foliar P concentration under high P supply. Under P deficiency, males had a smaller root system than females but a greater exudation of soil acid phosphatase, and a higher colonization rate and arbuscular mycorrhizal hyphal biomass, suggesting a better capacity to mine P and a stronger association with arbuscular mycorrhizal fungi to forage P. • Heterogeneous P distribution enhanced growth and root length density in females. Female root proliferation in P-rich patches was related to increase foliar P assimilation. Localized P application for increasing P availability did not enhance the biomass accumulation and the morphological plasticity of roots in males, but it raised hyphal biomass. • Our findings indicate that sex-specific strategies in P acquisition relate to root morphology, root exudation and mycorrhizal symbioses, and they may contribute to sex-specific resource utilization patterns and niche segregation. This article is protected by copyright. All rights reserved.
The Role of the Molecular Clock in Promoting Skeletal Muscle Growth and Protecting against Sarcopenia

The circadian clock has a critical role in many physiological functions of skeletal muscle and is essential to fully understand the precise underlying mechanisms involved in these complex interactions. The importance of circadian expression for structure, function and metabolism of skeletal muscle is clear when observing the muscle phenotype in models of molecular clock disruption. Presently, the maintenance of circadian rhythms is emerging as an important new factor in human health, with disruptions linked to ageing, as well as to the development of many chronic diseases, including sarcopenia. Therefore, the aim of this review is to present the latest findings demonstrating how circadian rhythms in skeletal muscle are important for maintenance of the cellular physiology, metabolism and function of skeletal muscle. Moreover, we will present the current knowledge about the tissue-specific functions of the molecular clock in skeletal muscle.