Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Anti-Inflammatory Effects of Liposomal Astaxanthin on a Phthalic Anhydride-Induced Atopic Dermatitis Model

Previously, we found that astaxanthin (AST) elicited an anti-inflammatory response in an experimental atopic dermatitis (AD) model. However, the use of AST was limited because of low bioavailability and solubility. We hypothesized that liposome formulation of AST could improve this. In this study, we compared the anti-inflammatory and anti-dermatotic effects of liposomal AST (L-AST) and free AST. We evaluated the effect of L-AST on a phthalic anhydride (PA)-induced animal model of AD by analyzing morphological and histopathological changes. We measured the mRNA levels of AD-related cytokines in skin tissue and immunoglobulin E concentrations in the serum. Oxidative stress and transcriptional activities of signal transducer and activator of transcription 3 (STAT3) and nuclear factor (NF)-κB were analyzed via western blotting and enzyme-linked immunosorbent assay. PA-induced dermatitis severity, epidermal thickening, and infiltration of mast cells in skin tissues were ameliorated by L-AST treatment. L-AST suppressed AD-related inflammatory mediators and the inflammation markers, inducible nitric oxide synthase (iNOS) and cyclooxygenase (COX)-2 in PA-induced skin conditions. Oxidative stress and expression of antioxidant proteins, glutathione peroxidase-1 (GPx-1) and heme oxygenase-1 (HO-1), were recovered by L-AST treatment in skin tissues from PA-induced mice. L-AST treatment reduced transcriptional activity of STAT3 and NF-κB in PA-induced skin tissues. Our results indicate that L-AST could be more effective than free AST for AD therapy.
Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive Domain

We release large-scale datasets of users’ comments in two languages, English and Korean, for aspect-level sentiment analysis in automotive domain. The datasets consist of 58,000+ commentaspect pairs, which are the largest compared to existing datasets. In addition, this work covers new language (i.e., Korean) along with English for aspect-level sentiment analysis. We build the datasets from automotive domain to enable users (e.g., marketers in automotive companies) to analyze the voice of customers on automobiles. We also provide baseline performances for future work by evaluating recent models on the released datasets.
Tiny Word Embeddings Using Globally Informed Reconstruction

We reduce the model size of pre-trained word embeddings by a factor of 200 while preserving its quality. Previous studies in this direction created a smaller word embedding model by reconstructing pre-trained word representations from those of subwords, which allows to store only a smaller number of subword embeddings in the memory. However, previous studies that train the reconstruction models using only target words cannot reduce the model size extremely while preserving its quality. Inspired by the observation of words with similar meanings having similar embeddings, our reconstruction training learns the global relationships among words, which can be employed in various models for word embedding reconstruction. Experimental results on word similarity benchmarks show that the proposed method improves the performance of the all subword-based reconstruction models.
Model predictive control for comfort optimization in assisted and driverless vehicles

This paper presents a method to design a Model Predictive Control to maximize the passengers’ comfort in assisted and self-driving vehicles by achieving lateral and longitudinal dynamic. The weighting parameters of the MPC are tuned off-line using a Genetic Algorithm to simultaneously maximize the control performance in the tracking of speed profile, lateral deviation and relative yaw angle and to optimize the comfort perceived by the passengers. To this end, two comfort evaluation indexes extracted by ISO 2631 are used to evaluate the amount of vibration transmitted to the passengers and the probability to experience motion sickness. The effectiveness of the method is demonstrated using simulated experiments conducted on a subcompact crossover vehicle. The control tracking performance produces errors lower than 0.1 m for lateral deviation, 0.5° for relative yaw angle and 1.5 km/h for the vehicle speed. The comfort maximization results in a low percentage of people who may experience nausea (below 5%) and in a low value of equivalent acceleration perceived by the passenger (below 0.315 m / s 2 “not uncomfortable” by ISO 2631). The robustness at variations of vehicle parameters, namely vehicle mass, front and rear cornering stiffness and mass distribution, is evaluated through a sensitivity analysis.
Development of energy control system for parallel hybrid power system using dynamic equations and fuzzy theory

A fuzzy control strategy is developed in this study to manage the parallel hybrid power system of internal combustion engine (ICE) and electric motor (EM) for hybrid vehicles. The rules established for the fuzzy logic are based on the conditions of vehicle pedal position, vehicle velocity, and the state of charge to control the throttle position of the ICE and the switch position of EM in low-, mid-, and high-power cruising. The optimization of the control strategy can make vehicles achieving ECE 40 driving pattern. In addition, the ICE can work in an optimal operation range, thus reducing carbon emission. The EM may provide power according to the demand, such that the torque output of the output shaft of the power split device is twice of the input of the two power sources separately.
Heuristic hyperparameter optimization of deep learning models for genomic prediction

There is a growing interest among quantitative geneticists and animal breeders in the use of deep learning (DL) for genomic prediction. However, the performance of DL is affected by hyperparameters that are typically manually set by users. These hyperparameters do not simply specify the architecture of the model, they are also critical for the efficacy of the optimization and model fitting process. To date, most DL approaches used for genomic prediction have concentrated on identifying suitable hyperparameters by exploring discrete options from a subset of the hyperparameter space. Enlarging the hyperparameter optimization search space with continuous hyperparameters is a daunting combinatorial problem. To deal with this problem, we propose using differential evolution (DE) to perform an efficient search of arbitrarily complex hyperparameter spaces in DL models and we apply this to the specific case of genomic prediction of livestock phenotypes. This approach was evaluated on two pig and cattle datasets with real genotypes and simulated phenotypes (N=7,539 animals and M=48,541 markers) and one real dataset (N=910 individuals and M=28,916 markers). Hyperparameters were evaluated using cross validation. We compared the predictive performance of DL models using hyperparameters optimized by DE against DL models with “best practice” hyperparameters selected from published studies and baseline DL models with randomly specified hyperparameters. Optimized models using DE showed clear improvement in predictive performance across all three datasets.DE optimized hyperparameters also resulted in DL models with less overfitting and less variation in predictive performance over repeated retraining compared to non-optimized DL models.
Seed Bank and Vegetation in a Closed Depression in Agricultural Landscape: Relation to Moisture Conditions and Soil Properties

ABSTRACT The aim of the study was to estimate the effect of habitat factors such as moisture conditions and some physicochemical soil characteristics on the structure of the vegetation and the soil seed bank, and to compare species composition of the vegetation cover and the seed bank along the soil moisture and fertility gradients in closed depression in agricultural landscape. Based on the averaged results of the extent and time of stagnation of the water, along the transect four habitat moisture zones were distinguished (dry, periodically moist, moist, and wet). In eighteen study plots we analysed flora, soil seed bank, and contents of Ca, Mg, K, Mn, Fe and pH of the soil. The analysed closed depression is characterised by high variability in moisture conditions and in the physical and chemical properties of the soil. The most important factors affecting the diversity of the vegetation cover and seed bank were the duration of water stagnation and the content of Fe and Mn in the soil. The lowest plant species richness and the smallest seed resources were determined for the wet zone of the depression. An increase in the habitat moisture level was accompanied by a decline in the floristic similarity between the vegetation cover and the seed bank.
MIPMLP – Microbiome Preprocessing Machine Learning Pipeline

16S sequencing results are often used for Machine Learning (ML) tasks. 16S gene sequences are represented as feature counts, which are associated with taxonomic representation. Raw feature counts may not be the optimal representation for ML. We checked multiple preprocessing steps and tested the optimal combination for 16S sequencing-based classification tasks. We computed the contribution of each step to the accuracy as measured by the Area Under Curve (AUC) of the classification. We show that the log of the feature counts is much more informative than the relative counts. We further show that merging features associated with the same taxonomy at a given level, through a dimension reduction step for each group of bacteria improves the AUC. Finally, we show that z-scoring has a very limited effect on the results. These preprocessing steps are integrated into the MIPMLP - Microbiome Preprocessing Machine Learning Pipeline, which is available as a stand alone version at https://github.com/louzounlab/microbiome/tree/master/Preprocess or as a service at http://mip-mlp.math.biu.ac. il/Home Importance Microbiome composition has been proposed as a biomarker (mic-marker) for multiple diseases. However, a clear analysis of the optimal way to represent the gene sequence counts is still lacking. We propose a simple and straight forward method that significantly improves the accuracy of mic-marker studies. This method can be of use to merge two of the most important advances in biology in the last decade: Microbiome analysis, and the introduction of machine learning methods to biological studies.
Germ Cell Development in Male Perinereis nuntia and Gamete Spawning Mechanisms in Males and Females

Perinereis nuntia is a fully segmented worm with complete intersegmental septa. A previous study of females revealed that germ cells of this animal originate in the tail end segment, called the pygidium. Germ cells were duplicated in the pygidium, transferred to a newly generated segment, and then settled in the parapodia. Within each segment, the settled germ cells proliferated in the parapodia and then migrated into a body cavity area to begin meiotic development. Currently, there is not much information about differences between male and female germ cell development. Therefore, we conducted monthly in situ hybridization analyses using the germ cell marker Pn-piwi and histological examinations. Germ cells detected by Pn-piwi initially settled in the distal areas of the parapodia on both sides of each segment, then formed a large germ cell cluster in each parapodium, and finally, small germ cell clusters were formed by the separation of the large clusters. The small clusters migrated to the deeper body cavity area during growth by segment addition. Until the female germ cells began vitellogenesis, the sex of germ cells could not be identified by morphological observation. Thus, male and female P. nuntia may have the same mechanism of germ cell provision to all segments. At the time of spawning, sperm were released from nephridiopores at the 2nd through 15th segments from the pygidium, while eggs were released through ruptures in the skin of 2–3 segments between the 10th and 30th segments from the tail.
SequenceBouncer: A method to remove outlier entries from a multiple sequence alignment

Phylogenetic analyses can take advantage of multiple sequence alignments as input. These alignments typically consist of homologous nucleic acid or protein sequences, and the inclusion of outlier or aberrant sequences can compromise downstream analyses. Here, I describe a program, SequenceBouncer, that uses the Shannon entropy values of alignment columns to identify outlier alignment sequences in a manner responsive to overall alignment context. I demonstrate the utility of this software using alignments of available mammalian mitochondrial genomes, bird cytochrome c oxidase-derived DNA barcodes, and COVID-19 sequences.
Can machine learning aid in identifying disease genes? The case of autism spectrum disorder

Discovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: Can machine learning aid in the discovery of disease genes? We collected thirteen published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.
Correcting MEG Artifacts Caused by Overt Speech

Recording brain activity during speech production using magnetoencephalography (MEG) can help us to understand the dynamics of speech production. However, these measurements are challenging due to the induced artifacts coming from several sources such as facial muscle activity, lower jaw and head movements. Here, we aimed to characterise speech-related artifacts and subsequently present an approach to remove these artifacts from MEG data. We recorded MEG from 11 healthy participants while they pronounced various syllables in different loudness. Head positions/orientations were extracted during speech production to investigate its role in MEG distortions. Finally, we present an artifact rejection approach using the combination of regression analysis and signal space projection (SSP) in order to correct the induced artifact from MEG data. Our results show that louder speech leads to stronger head movements and stronger MEG distortions. Our proposed artifact rejection approach could successfully remove the speech-related artifact and retrieve the underlying neurophysiological signals. As the presented artifact rejection approach was shown to remove artifacts induced by overt speech in the MEG, it will facilitate research addressing the neural basis of speech production with MEG.
Diet-induced oxalate nephropathy from excessive nut and seed consumption

Oxalate is a metabolite consumed in nuts, beans and leaves, and excreted in urine. Oxalosis can cause nephropathy. We describe a rare case of a high-oxalate diet intended for irritable bowel syndrome (IBS) treatment causing oxalate nephropathy. A 59-year-old woman with a history of controlled hypertension presented with creatinine 1.8 mg/dL, increased from baseline 1.3 mg/dL. She denied recent illness, urinary stones, medication adjustments, herbal supplements and non-steroidal anti-inflammatory drugs use. Diet included six tablespoons of chia seeds and five handfuls of almonds daily to manage IBS symptoms. Her electrolytes, urinalysis and renal ultrasound were unremarkable. Her 24-hour urine output revealed increased oxalate and low citrate. Renal biopsy showed glomerulosclerosis, fibrosis and calcium oxalate deposition. She switched to a low-oxalate diet, with improvement in laboratory markers. An earlier dietary history could have raised concern for oxalosis prior to renal biopsy. Providers should be trained to identify at-risk patients and provide appropriate dietary counselling.
Predicting Erroneous Financial Statements Using a Density-Based Clustering Approach

In this paper, we present a novel machine-learning approach to detect and predict restated financial statements. Our approach is based on DBSCAN, a cluster analysis algorithm. In contrast to prior methods, we assume that firms which perform different than their peers (outliers) are more likely to restate. By modifying DBSCAN to also incorporate temporal variation of these differences, we optimize the algorithm to fit financial data. We test our model for US data and benchmark against prior findings in accounting research. Our results show that the modified version of DBSCAN is more efficient than prior approaches. Best results are obtained if we cluster based on only two or three features. We outperform prior approaches regarding the precision to identify restatements. As with prior results, detection error increases for material restatements.
Genetic Variants Shaping Inter-individual Differences in Response to Dietary Intakes—A Narrative Review of the Case of Vitamins

Recent advances in the field of nutrigenetics have provided evidence on how genetic variations can impact the individuals' response to dietary intakes. An objective and reliable assessment of dietary exposures should rely on combinations of methodologies including frequency questionnaires, short-term recalls or records, together with biological samples to evaluate markers of intake or status and to identify genetic susceptibilities. In an attempt to present current knowledge on how genetic fingerprints contribute to an individual's nutritional status, we present a review of current literature describing associations between genetic variants and levels of well-established biomarkers of vitamin status in free-living and generally healthy individuals. Based on the outcomes of candidate gene, genome-wide-association studies and meta-analyses thereof, we have identified several single nucleotide polymorphisms (SNPs) involved in the vitamins' metabolic pathways. Polymorphisms in genes encoding proteins involved in vitamin metabolism and transport are reported to have an impact on vitamin D status; while genetic variants of vitamin D receptor were most frequently associated with health outcomes. Genetic variations that can influence vitamin E status include SNPs involved in its uptake and transport, such as in SCAR-B1 gene, and in lipoprotein metabolism. Variants of the genes encoding the sodium-dependent vitamin C transport proteins are greatly associated with the body's status on vitamin C. Regarding the vitamins of the B-complex, special reference is made to the widely studied variant in the MTHFR gene. Methodological attributes of genetic studies that may limit the comparability and interpretability of the findings are also discussed. Our understanding of how genes affect our responses to nutritional triggers will enhance our capacity to evaluate dietary exposure and design personalized nutrition programs to sustain health and prevent disease.
Heterogeneous effects of e-commerce over technical efficiency in developing countries: Stochastic Frontier Analysis Approach

In developing countries, the business structure usually presents a non-homogeneous environment, between a few large companies and a long tail of unproductive small firms. In Peru, more than 90% of firms are microenterprises, but contribute with less than a third of the private national value added. Moreover, more than 50% of Peruvian enterprises are concentrated in the manufacturing and commerce sector. In this context, despite the positive effects of e-commerce on enterprise efficiency, widely studied in the industrial organization and management literature, only a reduced number of studies delves into the heterogeneity of these effects, particularly according to the firm's size, which is relevant in a polarized business environment. Therefore, the purpose of this study is to evaluate the effect of e-commerce (e-purchases and e-sales) over Peruvian enterprise technical efficiency for the manufacturing industry and wholesale and retail trade sector, considering effects over business size by using a ML Random-Effects Time-Varying Inefficiency Stochastic Frontier model with firm-level survey data collected by the Peruvian National Enterprise Survey (ENE). Our main findings indicate a positive relationship between the fact of carry out e-commerce and enterprise efficiency, but differentiated by business size and economic sector. Whereas e-purchases and e-sales affect positively the efficiency of the entire manufacturing industry, only e-sales reveal a significant effect over wholesale and retail trade sector. Moreover, the effect of e-purchases in the manufacturing industry gets positive and significantly intensify depending on the business size, as well as of e-sales in the wholesale and retail trade sector, revealing a heterogeneous effect of e-commerce over efficiency depending on business size.
Pengaruh suplementasi β-carotene terhadap kadar glukosa darah dan kadar malondialdehida pada tikus sprague dawley yang diinduksi Streptozotocin

Background : Hypergl y cemia on diabetes mellitus can cause increas ing of free radicals production . Free radicals caused lipid peroxidation reaction by forming malondialdehyde (MDA). β-carotene has antioxidant activity may inhibit the formation of ROS. Objective : To prove the effect of multilevel doses β-carotene 1 mg/kg BW, 20 mg/kg BW and 20 mg/kg BW on alternate days within 30 days orally supplementation on blood glucose level and MDA level on Sprague Dawley male rats induced by streptozotocin (STZ). Methods : Thirty rats were randomly divided into 5 groups: X 1 =Negative control/diabetic, X 2 (STZ 40 mg/kg BW + BC 1 mg/kg BW), X 3 (STZ 40 mg/kg BW + BC 10mg/kg BW), X 4 (STZ 40 mg/kg BW + BC 20 mg/kg BW), X 5 (technic control/non diabetic). β-Carotene supplementation was given by nasogastric tube on altern a te days within thirty days. Blood glucose level was measured by GOD-PAP and MDA level by ELISA with TBARS methods. Data was analized using paired t-test, w ilcoxon , one way anova and post hoc bonferroni. Results : there was a significant difference of blood glucose level (p = 0,0001) and MDA level (p = 0,0001) after suplementation β-carotene on alternate days within 30 days orally . β-carotene 10 mg/kg BW was the most effective and efficient dose to lowering blood glucose, while 20 mg/kg BW to lowering MDA level. Conclusion : The multilevel doses β-carotene (1 mg/kg BW, 10 mg/kg BW and 20 mg/kg BW) on alternate days within 30 days orally supplementation can decrease blood glucose and MDA level. β-carotene 10 mg/kg BW is the most effecetive and efficient to decrease blood glucose and β-carotene 20 mg/kg BW to decrease MDA level.