Optimizing athlete's performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding of the game and, consequently, providing the opportunity to improve the athletic performance. Even though there is a panoply of research in pattern recognition, there is a gap when it comes to non-controlled environments, as during sports training and competition. This research paper combines the use of physiological and positional data as sequential features of different artificial intelligence approaches for action recognition in a real match context, adopting futsal as its case study. The traditional artificial neural networks (ANN) is compared with a deep learning method, Long Short-Term Memory Network, and also with the Dynamic Bayesian Mixture Model, which is an ensemble classification method. The methods were used to process all data sequences, which allowed to determine, based on the balance between precision and recall, that Dynamic Bayesian Mixture Model presents a superior performance, with an F1 score of 80.54% against the 33.31% achieved by the Long Short-Term Memory Network and 14.74% achieved by ANN.

The matching of cognitive load and working memory is the key for effective learning, and cognitive effort in the learning process has nervous responses which can be quantified in various physiological parameters. Therefore, it is meaningful to explore automatic cognitive load pattern recognition by using physiological measures. Firstly, this work extracted 33 commonly used physiological features to quantify autonomic and central nervous activities. Secondly, we selected a critical feature subset for cognitive load recognition by sequential backward selection and particle swarm optimization algorithms. Finally, pattern recognition models of cognitive load conditions were constructed by a performance comparison of several classifiers. We grouped the samples in an open dataset to form two binary classification problems: (1) cognitive load state vs. baseline state; (2) cognitive load mismatching state vs. cognitive load matching state. The decision tree classifier obtained 96.3% accuracy for the cognitive load vs. baseline classification, and the support vector machine obtained 97.2% accuracy for the cognitive load mismatching vs. cognitive load matching classification. The cognitive load and baseline states are distinguishable in the level of active state of mind and three activity features of the autonomic nervous system. The cognitive load mismatching and matching states are distinguishable in the level of active state of mind and two activity features of the autonomic nervous system.

Welding, a process that generates an aerosol containing gases and metal-rich particulates, induces adverse physiological effects including inflammation, immunosuppression and cardiovascular dysfunction. This study utilized microarray technology and subsequent pathway analysis as an exploratory search for markers/mechanisms of in vivo systemic effects following inhalation. Mice were exposed by inhalation to gas metal arc - stainless steel (GMA-SS) welding fume at 40 mg/m3 for 3 hr/d for 10 d and sacrificed 4 hr, 14 d and 28 d post-exposure. Whole blood cells, aorta and lung were harvested for global gene expression analysis with subsequent Ingenuity Pathway Analysis and confirmatory qRT-PCR. Serum was collected for protein profiling.

This study aimed to develop an automatic diagnostic scheme for bruxism, a sleep-related disorder characterized by teeth grinding and clenching. The aim was to improve on existing methods, which have been proven to be inefficient and challenging. We utilized a novel hybrid machine learning classifier, facilitated by the Weka tool, to diagnose bruxism from biological signals. The study processed and examined these biological signals by calculating the power spectral density. Data were categorized into normal or bruxism categories based on the EEG channel (C4-A1), and the sleeping phases were classified into wake (w) and rapid eye movement (REM) stages using the ECG channel (ECG1-ECG2). The classification resulted in a maximum specificity of 93% and an accuracy of 95% for the EEG-based diagnosis. The ECG-based classification yielded a supreme specificity of 87% and an accuracy of 96%. Furthermore, combining these phases using the EMG channel (EMG1-EMG2) achieved the highest specificity of 95% and accuracy of 98%. The ensemble Weka tool combined all three physiological signals EMG, ECG, and EEG, to classify the sleep stages and subjects. This integration increased the specificity and accuracy to 97% and 99%, respectively. This indicates that a more precise bruxism diagnosis can be obtained by including all three biological signals. The proposed method significantly improves bruxism diagnosis accuracy, potentially enhancing automatic home monitoring systems for this disorder. Future studies may expand this work by applying it to patients for practical use.

Pathogen identification in clinical routine is based on the cultivation of microbes with subsequent morphological and physiological characterisation lasting at least 24 hours. However, early and accurate identification is a crucial requisite for fast and optimally targeted antimicrobial treatment. Molecular biology based techniques allow fast identification, however discrimination of very closely related species remains still difficult.

Accurate classification or prediction of the brain state across individual subject, i.e., healthy, or with brain disorders, is generally a more difficult task than merely finding group differences. The former must be approached with highly informative and sensitive biomarkers as well as effective pattern classification/feature selection approaches. In this paper, we propose a systematic methodology to discriminate attention deficit hyperactivity disorder (ADHD) patients from healthy controls on the individual level. Multiple neuroimaging markers that are proved to be sensitive features are identified, which include multiscale characteristics extracted from blood oxygenation level dependent (BOLD) signals, such as regional homogeneity (ReHo) and amplitude of low-frequency fluctuations. Functional connectivity derived from Pearson, partial, and spatial correlation is also utilized to reflect the abnormal patterns of functional integration, or, dysconnectivity syndromes in the brain. These neuroimaging markers are calculated on either voxel or regional level. Advanced feature selection approach is then designed, including a brain-wise association study (BWAS). Using identified features and proper feature integration, a support vector machine (SVM) classifier can achieve a cross-validated classification accuracy of 76.15% across individuals from a large dataset consisting of 141 healthy controls and 98 ADHD patients, with the sensitivity being 63.27% and the specificity being 85.11%. Our results show that the most discriminative features for classification are primarily associated with the frontal and cerebellar regions. The proposed methodology is expected to improve clinical diagnosis and evaluation of treatment for ADHD patient, and to have wider applications in diagnosis of general neuropsychiatric disorders.

In plant innate immunity, the surface-exposed leucine-rich repeat receptor kinases EFR and FLS2 mediate recognition of the bacterial pathogen-associated molecular patterns EF-Tu and flagellin, respectively. We identified the Arabidopsis stromal-derived factor-2 (SDF2) as being required for EFR function, and to a lesser extent FLS2 function. SDF2 resides in an endoplasmic reticulum (ER) protein complex with the Hsp40 ERdj3B and the Hsp70 BiP, which are components of the ER-quality control (ER-QC). Loss of SDF2 results in ER retention and degradation of EFR. The differential requirement for ER-QC components by EFR and FLS2 could be linked to N-glycosylation mediated by STT3a, a catalytic subunit of the oligosaccharyltransferase complex involved in co-translational N-glycosylation. Our results show that the plasma membrane EFR requires the ER complex SDF2-ERdj3B-BiP for its proper accumulation, and provide a demonstration of a physiological requirement for ER-QC in transmembrane receptor function in plants. They also provide an unexpected differential requirement for ER-QC and N-glycosylation components by two closely related receptors.

Pattern recognition receptors (PRRs) play a critical role in the detection of microorganisms and the induction of inflammatory and immune responses. Using PCR and Western-blot analysis, this study investigated the differential expression in the intestine of 14 PRRs and nine associated cytokines. Thirty-two pigs were used to determine the expression of these markers (1) along the proximal/distal axis of the small intestine (duodenum, jejunum, and ileum) and (2) between the intestinal segments and their respective lymphoid organs (Peyer's patches [PP] and mesenteric lymph nodes [MLN]). Six additional animals were used to quantify the expression of these genes along the crypt/villus axis of jejunum, using microdissected samples. Most genes showed increased expression (1) in the distal than in the proximal parts of the small intestine (TLR3, 5, RIG-I, IL-1β, IL-8, and IFN-γ); (2) in lymphoid organs (TLR1, 2, 6, 9, 10, IL-10, TNF-α), especially the MLN (TLR4, 7, 8, NOD1, NOD2, NALP3, IFN-α, IL-6, IL-12, and TGF-β), than in intestinal segments. The analysis along the crypt/villus identified: (1) genes with higher expression in lamina propria (TLR1, 2, 4, 9, NOD1, NOD2, IL-1β, IL-10, TGF-β, TNF-α) and (2) genes with higher expression in the villus (TLR3, 5, 6, RIG-I, IL-6). These results highlight the differential expression of PRRs and cytokines along the proximal/distal and the crypt/villus axis of the intestine, contributing to a fine analysis of the complex functional architecture of the small intestine and should be related to the gut microbiota.

The knowledge of protein-nanoparticle interplay is of crucial importance to predict the fate of nanomaterials in biological environments. Indeed, protein corona on nanomaterials is responsible for the physiological response of the organism, influencing cell processes, from transport to accumulation and toxicity. Herein, a comparison using four different proteins reveals the existence of patterned regions of carboxylic groups acting as recognition sites for naked iron oxide nanoparticles. Readily interacting proteins display a distinctive surface distribution of carboxylic groups, recalling the geometric shape of an ellipse. This is morphologically complementary to nanoparticles curvature and compatible with the topography of exposed FeIII sites laying on the nanomaterial surface. The recognition site, absent in non-interacting proteins, promotes the nanoparticle harboring and allows the formation of functional protein coronas. The present work envisages the possibility of predicting the composition and the biological properties of protein corona on metal oxide nanoparticles.

The stimulant drug methylphenidate (MPH) and the non-stimulant drug atomoxetine (ATX) are both widely used for the treatment of attention deficit/hyperactivity disorder (ADHD), but their differential effects on human brain function are poorly understood. PET and blood oxygen level dependent (BOLD) fMRI have been used to study the effects of MPH and BOLD fMRI is beginning to be used to delineate the effects of MPH and ATX in the context of cognitive tasks. The BOLD signal is a proxy for neuronal activity and is dependent on three physiological parameters: regional cerebral blood flow (rCBF), cerebral metabolic rate of oxygen and cerebral blood volume. To identify areas sensitive to MPH and ATX and assist interpretation of BOLD studies in healthy volunteers and ADHD patients, it is therefore of interest to characterize the effects of these drugs on rCBF. In this study, we used arterial spin labeling (ASL) MRI to measure rCBF non-invasively in healthy volunteers after administration of MPH, ATX or placebo. We employed multi-class pattern recognition (PR) to discriminate the neuronal effects of the drugs, which accurately discriminated all drug conditions from one another and provided activity patterns that precisely localized discriminating brain regions. We showed common and differential effects in cortical and subcortical brain regions. The clearest differential effects were observed in four regions: (i) in the caudate body where MPH but not ATX increased rCBF, (ii) in the midbrain/substantia nigra and (iii) thalamus where MPH increased and ATX decreased rCBF plus (iv) a large region of cerebellar cortex where ATX increased rCBF relative to MPH. Our results demonstrate that combining ASL and PR yields a sensitive method for detecting the effects of these drugs and provides insights into the regional distribution of brain networks potentially modulated by these compounds.

The alginate oligosaccharides have been widely used in agriculture, medicine, and food industries due to their versatile physiological functions such as antioxidant, anticoagulant, and antineoplastic activities. The bifunctional alginate lyases can degrade the alginate polysaccharide more efficiently into alginate oligosaccharides. Therefore, it is crucial to discover new bifunctional alginate lyase for alginate oligosaccharide production.

The detection of single nucleotide polymorphisms (SNPs) and insertion/deletions (indels) with precision from high-throughput data remains a significant bioinformatics challenge. Accurate detection is necessary before next-generation sequencing can routinely be used in the clinic. In research, scientific advances are inhibited by gaps in data, exemplified by the underrepresented discovery of rare variants, variants in non-coding regions and indels. The continued presence of false positives and false negatives prevents full automation and requires additional manual verification steps. Our methodology presents applications of both pattern recognition and sensitivity analysis to eliminate false positives and aid in the detection of SNP/indel loci and genotypes from high-throughput data. We chose FK506-binding protein 51(FKBP5) (6p21.31) for our clinical target because of its role in modulating pharmacological responses to physiological and synthetic glucocorticoids and because of the complexity of the genomic region. We detected genetic variation across a 160 kb region encompassing FKBP5. 613 SNPs and 57 indels, including a 3.3 kb deletion were discovered. We validated our method using three independent data sets and, with Sanger sequencing and Affymetrix and Illumina microarrays, achieved 99% concordance. Furthermore we were able to detect 267 novel rare variants and assess linkage disequilibrium. Our results showed both a sensitivity and specificity of 98%, indicating near perfect classification between true and false variants. The process is scalable and amenable to automation, with the downstream filters taking only 1.5h to analyze 96 individuals simultaneously. We provide examples of how our level of precision uncovered the interactions of multiple loci, their predicted influences on mRNA stability, perturbations of the hsp90 binding site, and individual variation in FKBP5 expression. Finally we show how our discovery of rare variants may change current conceptions of evolution at this locus.

Maoji Jiu (MJ) is a kind of medicinal wine that has been widely used by Chinese people for many years to nourish and promote blood circulation. The purpose of this study was to investigate the hematopoietic effect of MJ on the metabolism of blood deficient rats and to explore the underlying hematopoietic regulation mechanisms. Blood deficiency model rats were induced by subcutaneous injection of N-acetylphenylhydrazine (APH) and intraperitoneal injection of cyclophosphamide (CTX). The plasma metabolic fingerprints of blood deficiency model rats with and without MJ treatment were obtained by using metabonomics based on ultra-high-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS). Orthogonal partial least squares-discriminant analysis (OPLS-DA) was used to evaluate the hematopoietic effect of MJ and identify potential biomarkers in the plasma of blood deficiency model rats. The levels of white blood cells (WBC), red blood cells (RBC) and hemoglobin (HGB) and the activity of antioxidant capacity showed a recovery trend to the control group after MJ treatment, while the dose of 10 mL/kg showed the best effect. In this study, thirteen potential biomarkers were identified, which were mainly related to seven metabolic pathways, including linoleic acid metabolism, d-glutamine and d-glutamate metabolism, alanine, aspartate and glutamate metabolism, tryptophan metabolism, pyrimidine metabolism, porphyrin and chlorophyll metabolism and arginine biosynthesis. Metabolomics was applied frequently to reflect the physiological and metabolic state of organisms comprehensively, indicating that the rapid plasma metabonomics may be a potentially powerful tool to reveal the efficacy and enriching blood mechanism of MJ.

Studies on model plants have shown that temporary soil flooding exposes roots to a significant hypoxic stress resulting in metabolic re-programming, accumulation of toxic metabolites and hormonal imbalance. To date, physiological and transcriptional responses to flooding in grapevine are poorly characterized. To fill this gap, we aimed to gain insights into the transcriptional and metabolic changes induced by flooding on grapevine roots (K5BB rootstocks), on which cv Sauvignon blanc (Vitis vinifera L.) plants were grafted. A preliminary experiment under hydroponic conditions enabled the identification of transiently and steadily regulated hypoxia-responsive marker genes and drafting a model for response to oxygen deprivation in grapevine roots. Afterward, over two consecutive vegetative seasons, flooding was imposed to potted vines during the late dormancy period, to mimick the most frequent waterlogging events occurring in the field. Untargeted transcriptomic and metabolic profiling approaches were applied to investigate early responses of grapevine roots during exposure to hypoxia and subsequent recovery after stress removal. The initial hypoxic response was marked by a significant increase of the hypoxia-inducible metabolites ethanol, GABA, succinic acid and alanine which remained high also 1 week after recovery from flooding with the exception of ethanol that leveled off. Transcriptomic data supported the metabolic changes by indicating a substantial rearrangement of primary metabolic pathways through enhancement of the glycolytic and fermentative enzymes and of a subset of enzymes involved in the TCA cycle. GO and KEGG pathway analyses of differentially expressed genes showed a general down-regulation of brassinosteroid, auxin and gibberellin biosynthesis in waterlogged plants, suggesting a general inhibition of root growth and lateral expansion. During recovery, transcriptional activation of gibberellin biosynthetic genes and down-regulation of the metabolic ones may support a role for gibberellins in signaling grapevine rootstocks waterlogging metabolic and hormonal changes to the above ground plant. The significant internode elongation measured upon budbreak during recovery in plants that had experienced flooding supported this hypothesis. Overall integration of these data enabled us to draft a first comprehensive view of the molecular and metabolic pathways involved in grapevine's root responses highlighting a deep metabolic and transcriptomic reprogramming during and after exposure to waterlogging.

To introduce the Hemorrhage Intensive Severity and Survivability (HISS) score, based on the fusion of multi-biomarker data; glucose, lactate, pH, potassium, and oxygen tension, to serve as a patient-specific attribute in hemorrhagic trauma.

Any profound comprehension of gene function requires detailed information about the subcellular localization, molecular interactions and spatio-temporal dynamics of gene products. We developed a multifunctional integrase (MIN) tag for rapid and versatile genome engineering that serves not only as a genetic entry site for the Bxb1 integrase but also as a novel epitope tag for standardized detection and precipitation. For the systematic study of epigenetic factors, including Dnmt1, Dnmt3a, Dnmt3b, Tet1, Tet2, Tet3 and Uhrf1, we generated MIN-tagged embryonic stem cell lines and created a toolbox of prefabricated modules that can be integrated via Bxb1-mediated recombination. We used these functional modules to study protein interactions and their spatio-temporal dynamics as well as gene expression and specific mutations during cellular differentiation and in response to external stimuli. Our genome engineering strategy provides a versatile open platform for efficient generation of multiple isogenic cell lines to study gene function under physiological conditions.

Drought is one of the significant threats to the agricultural sector. However, there is limited knowledge on plant response to drought stress and post-drought recovery. Pandanus amaryllifolius, a moderate drought-tolerant plant, is well-known for its ability to survive in low-level soil moisture conditions. Understanding the molecular regulation of drought stress signaling in this plant could help guide the rational design of crop plants to counter this environmental challenge. This study aimed to determine the morpho-physiological, biochemical, and protein changes of P. amaryllifolius in response to drought stress and during recovery. Drought significantly reduced the leaf relative water content and chlorophyll content of P. amaryllifolius. In contrast, relative electrolyte leakage, proline and malondialdehyde contents, and the activities of antioxidant enzymes in the drought-treated and recovered samples were relatively higher than the well-watered sample. The protein changes between drought-stressed, well-watered, and recovered plants were evaluated using tandem mass tags (TMT)-based quantitative proteomics. Of the 1415 differentially abundant proteins, 74 were significantly altered. The majority of proteins differing between them were related to carbon metabolism, photosynthesis, stress response, and antioxidant activity. This is the first study that reports the protein changes in response to drought stress in Pandanus. The data generated provide an insight into the drought-responsive mechanisms in P. amaryllifolius.

To study whole-brain microstructural alterations in patients with end-stage renal disease (ESRD) and examine the relationship between brain microstructure and physiological indictors in the disease.

MicroRNAs (miRNAs) have emerged as fundamental regulators that silence gene expression at the post-transcriptional and translational levels. The identification of their targets is a major challenge to elucidate the regulated biological processes. The overall effect of miRNA is reflected on target mRNA expression, suggesting the design of new investigative methods based on high-throughput experimental data such as miRNA and transcriptome profiles. We propose a novel statistical measure of non-linear dependence between miRNA and mRNA expression, in order to infer miRNA-target interactions. This approach, which we name antagonism pattern detection, is based on the statistical recognition of a triangular-shaped pattern in miRNA-target expression profiles. This pattern is observed in miRNA-target expression measurements since their simultaneously elevated expression is statistically under-represented in the case of miRNA silencing effect. The proposed method enables miRNA target prediction to strongly rely on cellular context and physiological conditions reflected by expression data. The procedure has been assessed on synthetic datasets and tested on a set of real positive controls. Then it has been applied to analyze expression data from Ewing's sarcoma patients. The antagonism relationship is evaluated as a good indicator of real miRNA-target biological interaction. The predicted targets are consistently enriched for miRNA binding site motifs in their 3'UTR. Moreover, we reveal sets of predicted targets for each miRNA sharing important biological function. The procedure allows us to infer crucial miRNA regulators and their potential targets in Ewing's sarcoma disease. It can be considered as a valid statistical approach to discover new insights in the miRNA regulatory mechanisms.

An important link exists between intact metabolic processes and normal cognitive functioning; however, the underlying mechanisms remain unknown. There is accumulating evidence that the gut hormone ghrelin, an orexigenic peptide that is elevated during calorie restriction (CR) and known primarily for stimulating growth hormone release, has important extra-hypothalamic functions, such as enhancing synaptic plasticity and hippocampal neurogenesis. The present study was designed to evaluate the long-term effects of elevating acyl-ghrelin levels, albeit within the physiological range, on the number of new adult born neurons in the dentate gyrus (DG) and performance on the Spontaneous Location Recognition (SLR) task, previously shown to be DG-dependent and sensitive to manipulations of plasticity mechanisms and cell proliferation. The results revealed that peripheral treatment of rats with acyl-ghrelin enhanced both adult hippocampal neurogenesis and performance on SLR when measured 8-10 days after the end of acyl-ghrelin treatment. Our data show that systemic administration of physiological levels of acyl-ghrelin can produce long-lasting improvements in spatial memory that persist following the end of treatment. As ghrelin is potentially involved in regulating the relationship between metabolic and cognitive dysfunction in ageing and neurodegenerative disease, elucidating the underlying mechanisms holds promise for identifying novel therapeutic targets and modifiable lifestyle factors that may have beneficial effects on the brain.