Common spatial pattern for classification of loving kindness meditation EEG for single and multiple sessions.

While a very few studies have been conducted on classifying loving kindness meditation (LKM) and non-meditation electroencephalography (EEG) data for a single session, there are no such studies conducted for multiple session EEG data. Thus, this study aims at classifying existing raw EEG meditation data on single and multiple sessions to come up with meaningful inferences which will be highly beneficial when developing algorithms that can support meditation practices. In this analysis, data have been collected on Pre-Resting (before-meditation), Post-Resting (after-meditation), LKM-Self and LKM-Others for 32 participants and hence allowing us to conduct six pairwise comparisons for the four mind tasks. Common Spatial Patterns (CSP) is a feature extraction method widely used in motor imaginary brain computer interface (BCI), but not in meditation EEG data. Therefore, using CSP in extracting features from meditation EEG data and classifying meditation/non-meditation instances, particularly for multiple sessions will create a new path in future meditation EEG research. The classification was done using Linear Discriminant Analysis (LDA) where both meditation techniques (LKM-Self and LKM-Others) were compared with Pre-Resting and Post-Resting instances. The results show that for a single session of 32 participants, around 99.5% accuracy was obtained for classifying meditation/Pre-Resting instances. For the 15 participants when using five sessions of EEG data, around 83.6% accuracy was obtained for classifying meditation/Pre-Resting instances. The results demonstrate the ability to classify meditation/Pre-Resting data. Most importantly, this classification is possible for multiple session data as well. In addition to this, when comparing the classification accuracies of the six mind task pairs; LKM-Self, LKM-Others and Post-Resting produced relatively lower accuracies among them than the accuracies obtained for classifying Pre-Resting with the other three. This indicates that Pre-Resting has some features giving a better classification indicating that it is different from the other three mind tasks.

Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review.

The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient's autonomy.

Radiomics-Based Assessment of OCT Angiography Images for Diabetic Retinopathy Diagnosis.

Purpose: To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis. Design: Cross-sectional analysis of a retinal image dataset from a previous prospective OCTA study (ClinicalTrials.govNCT03422965). Participants: Patients with type 1 DM and controls included in the progenitor study. Methods: Radiomic features were extracted from fundus retinographies, OCT, and OCTA images in each study eye. Logistic regression, linear discriminant analysis, support vector classifier (SVC)-linear, SVC-radial basis function, and random forest models were created to evaluate their diagnostic accuracy for DM, DR, and R-DR diagnosis in all image types. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC) mean and standard deviation for each ML model and each individual and combined image types. Results: A dataset of 726 eyes (439 individuals) were included. For DM diagnosis, the greatest AUC was observed for OCT (0.82, 0.03). For DR detection, the greatest AUC was observed for OCTA (0.77, 0.03), especially in the 3 × 3 mm superficial capillary plexus OCTA scan (0.76, 0.04). For R-DR diagnosis, the greatest AUC was observed for OCTA (0.87, 0.12) and the deep capillary plexus OCTA scan (0.86, 0.08). The addition of clinical variables (age, sex, etc.) improved most models AUC for DM, DR and R-DR diagnosis. The performance of the models was similar in unilateral and bilateral eyes image datasets. Conclusions: Radiomics extracted from OCT and OCTA images allow identification of patients with DM, DR, and R-DR using standard ML classifiers. OCT was the best test for DM diagnosis, OCTA for DR and R-DR diagnosis and the addition of clinical variables improved most models. This pioneer study demonstrates that radiomics-based ML techniques applied to OCT and OCTA images may be an option for DR screening in patients with type 1 DM. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Salivary microbiota may predict the presence of esophageal squamous cell carcinoma.

The aim is to explore the predictive value of salivary bacteria for the presence of esophageal squamous cell carcinoma (ESCC). Saliva samples were obtained from 178 patients with ESCC and 101 healthy controls, and allocated to screening and verification cohorts, respectively. In the screening phase, after saliva DNA was extracted, 16S rRNA V4 regions of salivary bacteria were amplified by polymerase chain reaction (PCR) with high-throughput sequencing. Highly expressed target bacteria were screened by Operational Taxonomic Units clustering, species annotation and microbial diversity assessment. In the verification phase, the expression levels of target bacteria identified in the screening phase were verified by absolute quantitative PCR (Q-PCR). Receiver operating characteristic (ROC) curves were plotted to investigate the predictive value of target salivary bacteria. LEfSe analysis revealed higher proportions of Fusobacterium, Streptococcus and Porphyromonas, and Q-PCR assay showed significantly higher numbers of Streptococcus salivarius, Fusobacterium nucleatum and Porphyromonas gingivalis in patients with ESCC, when compared with healthy controls (all P < 0.05). The areas under the ROC curves for Streptococcus salivarius, Fusobacterium nucleatum, Porphyromonas gingivalis and the combination of the three bacteria for predicting patients with ESCC were 69%, 56.5%, 61.8% and 76.4%, respectively. The sensitivities corresponding to cutoff value were 69.3%, 22.7%, 35.2% and 86.4%, respectively, and the matched specificity were 78.4%, 96.1%, 90.2% and 58.8%, respectively. These highly expressed Streptococcus salivarius, Fusobacterium nucleatum and Porphyromonas gingivalis in the saliva, alone or in combination, indicate their predictive value for ESCC.

Semi-automatic tumor segmentation of rectal cancer based on functional magnetic resonance imaging.

Background and purpose: Tumor delineation is required both for radiotherapy planning and quantitative imaging biomarker purposes. It is a manual, time- and labor-intensive process prone to inter- and intraobserver variations. Semi or fully automatic segmentation could provide better efficiency and consistency. This study aimed to investigate the influence of including and combining functional with anatomical magnetic resonance imaging (MRI) sequences on the quality of automatic segmentations. Materials and methods: T2-weighted (T2w), diffusion weighted, multi-echo T2*-weighted, and contrast enhanced dynamic multi-echo (DME) MR images of eighty-one patients with rectal cancer were used in the analysis. Four classical machine learning algorithms; adaptive boosting (ADA), linear and quadratic discriminant analysis and support vector machines, were trained for automatic segmentation of tumor and normal tissue using different combinations of the MR images as input, followed by semi-automatic morphological post-processing. Manual delineations from two experts served as ground truth. The Sørensen-Dice similarity coefficient (DICE) and mean symmetric surface distance (MSD) were used as performance metric in leave-one-out cross validation. Results: Using T2w images alone, ADA outperformed the other algorithms, yielding a median per patient DICE of 0.67 and MSD of 3.6 mm. The performance improved when functional images were added and was highest for models based on either T2w and DME images (DICE: 0.72, MSD: 2.7 mm) or all four MRI sequences (DICE: 0.72, MSD: 2.5 mm). Conclusion: Machine learning models using functional MRI, in particular DME, have the potential to improve automatic segmentation of rectal cancer relative to models using T2w MRI alone.

Restoration of the gut microbiota is associated with a decreased risk of hepatic encephalopathy after TIPS.

Background & Aims: Hepatic encephalopathy (HE) is a major complication after transjugular intrahepatic portosystemic shunt (TIPS) and is primarily influenced by the gut microbiota. We aimed to evaluate alterations in the microbiota after TIPS and the association between such alterations and HE. Methods: We conducted a prospective longitudinal study of 106 patients with cirrhosis receiving TIPS. Faecal samples were collected before and after TIPS, and the gut microbiota was analysed by 16S ribosomal RNA sequencing. Results: Among all patients, 33 developed HE (HE+ group) within 6 months after TIPS and 73 did not (HE- group), and 18 died during follow-up. After TIPS, the autochthonous taxa increased, whereas the potential pathogenic taxa decreased in the HE- group, and the autochthonous taxon Lachnospiraceae decreased in the HE+ group. Furthermore, synergism among harmful bacteria was observed in all patients, which was weakened in the HE- group (p <0.001) but enhanced in the HE+ group (p <0.01) after TIPS. Variations of 5 autochthonous taxa, namely, Coprococcus, Ruminococcus, Blautia, Ruminococcaceae_uncultured, and Roseburia, were negatively correlated with the severity of HE. Notably, increased abundances of Coprococcus and Ruminococcus were protective factors against HE, and the incidences of HE in patients with improved, stable, and deteriorated microbiota after TIPS were 13.3, 25.9, and 68.2%, respectively. Higher total bilirubin level, Child-Pugh score, model for end-stage liver disease score, Granulicatella, and Alistipes and lower Subdoligranulum before TIPS were the independent risk factors for death. Conclusions: Alterations in gut dysbiosis were negatively related to the occurrence and severity of post-TIPS HE, and the pre-TIPS microbiota were associated with death, suggesting the gut microbiota could be a promising potential biological target for screening suitable patients receiving TIPS and prevention and treatment of post-TIPS HE. Lay summary: Alterations in the gut microbiota after transjugular intrahepatic portosystemic shunt (TIPS) and the relationship between such alterations and post-TIPS hepatic encephalopathy (HE) remain unclear. We therefore performed this study and found that after TIPS, restoration of the gut microbiota, mainly characterised by expansion of autochthonous taxa, depletion of harmful taxa, and weakening of synergism among harmful bacteria, was inversely related to the occurrence and severity of post-TIPS HE.

MLb-LDLr: A Machine Learning Model for Predicting the Pathogenicity of LDLr Missense Variants.

Untreated familial hypercholesterolemia (FH) leads to atherosclerosis and early cardiovascular disease. Mutations in the low-density lipoprotein receptor (LDLr) gene constitute the major cause of FH, and the high number of mutations already described in the LDLr makes necessary cascade screening or in vitro functional characterization to provide a definitive diagnosis. Implementation of high-predicting capacity software constitutes a valuable approach for assessing pathogenicity of LDLr variants to help in the early diagnosis and management of FH disease. This work provides a reliable machine learning model to accurately predict the pathogenicity of LDLr missense variants with specificity of 92.5% and sensitivity of 91.6%.

"Q-markers targeted screening" strategy for comprehensive qualitative and quantitative analysis in fingerprints of Angelica dahurica with chemometric methods.

Angelica dahurica is a famous functional food and herb. To guarantee quality of A. dahurica, a strategy "Q-markers targeted screening" was successfully developed by sufficient extraction of compounds and the targeted screening of qualitative and quantitative markers calculated through chemometric methods based fingerprints. Accelerated solvent extraction was selected due to its prominent advantages exhibiting the maximum extraction yields and varieties of compounds and especially excellent reproducibility (RSD < 1). After extraction, the fingerprints of A. dahuricae samples were established. For the preliminary herb authenticity, the targeted screening of 23 quantitative markers were performed by similarity analysis and hierarchical cluster analysis based on the fingerprints, which were identified by liquid chromatography tandem mass spectrometry (LC-MS). Subsequently, for further quality control, the targeted screening of nine quantitative markers were done by similarity analysis & linear discriminant analysis, which were determined by LC. Lastly, the strategy was successfully applied to quality assessment of A. dahurica samples.

Novel compound FLZ alleviates rotenone-induced PD mouse model by suppressing TLR4/MyD88/NF-κB pathway through microbiota-gut-brain axis.

Parkinson's disease (PD) is the second most common neurodegenerative disease, but none of the current treatments for PD can halt the progress of the disease due to the limited understanding of the pathogenesis. In PD development, the communication between the brain and the gastrointestinal system influenced by gut microbiota is known as microbiota-gut-brain axis. However, the explicit mechanisms of microbiota dysbiosis in PD development have not been well elucidated yet. FLZ, a novel squamosamide derivative, has been proved to be effective in many PD models and is undergoing the phase I clinical trial to treat PD in China. Moreover, our previous pharmacokinetic study revealed that gut microbiota could regulate the absorption of FLZ in vivo. The aims of our study were to assess the protective effects of FLZ treatment on PD and to further explore the underlying microbiota-related mechanisms of PD by using FLZ as a tool. In the current study, chronic oral administration of rotenone was utilized to induce a mouse model to mimic the pathological process of PD. Here we revealed that FLZ treatment alleviated gastrointestinal dysfunctions, motor symptoms, and dopaminergic neuron death in rotenone-challenged mice. 16S rRNA sequencing found that PD-related microbiota alterations induced by rotenone were reversed by FLZ treatment. Remarkably, FLZ administration attenuated intestinal inflammation and gut barrier destruction, which subsequently inhibited systemic inflammation. Eventually, FLZ treatment restored blood-brain barrier structure and suppressed neuroinflammation by inhibiting the activation of astrocytes and microglia in the substantia nigra (SN). Further mechanistic research demonstrated that FLZ treatment suppressed the TLR4/MyD88/NF-κB pathway both in the SN and colon. Collectively, FLZ treatment ameliorates microbiota dysbiosis to protect the PD model via inhibiting TLR4 pathway, which contributes to one of the underlying mechanisms beneath its neuroprotective effects. Our research also supports the importance of microbiota-gut-brain axis in PD pathogenesis, suggesting its potential role as a novel therapeutic target for PD treatment.

Validity and reliability of Raman spectroscopy for carotenoid assessment in cattle skin.

Carotenoids are powerful antioxidants capable of helping to protect the skin from the damaging effects of exposure to sun by reducing the free radicals in skin produced by exposure to ultraviolet radiation, and they may also have a physical protective effect in human skin. Since carotenoids are lipophilic molecules which can be ingested with the diet, they can accumulate in significant quantities in the skin. Several studies on humans have been conducted to evaluate the protective function of carotenoids against various diseases, but there is very limited published information available to understand the mechanism of carotenoid bioavailability in animals. The current study was conducted to investigate the skin carotenoid level (SCL) in two cattle skin sets - weaners with an unknown feeding regime and New Generation Beef (NGB) cattle with monitored feed at three different ages. Rapid analytical and sensitive Raman spectroscopy has been shown to be of interest as a powerful technique for the detection of carotenoids in cattle skin due to the strong resonance enhancement with 532 nm laser excitation. The spectral difference of both types of skin were measured and quantified using univariate and linear discriminant analysis. SCL was higher in NGB cattle than weaners and there is a perfect classification accuracy between weaners and NGB cattle skin using carotenoid markers as a basis. Further work carried out on carotenoid rich NGB cattle skin of 8, 12 and 24 months of age identified an increasing trend in SCL with age. The present work validated the ability of Raman spectroscopy to determine the skin carotenoid level in cattle by comparing it with established HPLC methods. There is an excellent correlation of R2 = 0.96 between the two methods that could serve as a model for future application for larger population studies.

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review.

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

Prebiotics enhance the biotransformation and bioavailability of ginsenosides in rats by modulating gut microbiota.

BACKGROUND: Gut microbiota mainly function in the biotransformation of primary ginsenosides into bioactive metabolites. Herein, we investigated the effects of three prebiotic fibers by targeting gut microbiota on the metabolism of ginsenoside Rb1 in vivo. METHODS: Sprague Dawley rats were administered with ginsenoside Rb1 after a two-week prebiotic intervention of fructooligosaccharide, galactooligosaccharide, and fibersol-2, respectively. Pharmacokinetic analysis of ginsenoside Rb1 and its metabolites was performed, whilst the microbial composition and metabolic function of gut microbiota were examined by 16S rRNA gene amplicon and metagenomic shotgun sequencing. RESULTS: The results showed that peak plasma concentration and area under concentration time curve of ginsenoside Rb1 and its intermediate metabolites, ginsenoside Rd, F2, and compound K (CK), in the prebiotic intervention groups were increased at various degrees compared with those in the control group. Gut microbiota dramatically responded to the prebiotic treatment at both taxonomical and functional levels. The abundance of Prevotella, which possesses potential function to hydrolyze ginsenoside Rb1 into CK, was significantly elevated in the three prebiotic groups (P < 0.05). The gut metagenomic analysis also revealed the functional gene enrichment for terpenoid/polyketide metabolism, glycolysis, gluconeogenesis, propanoate metabolism, etc. CONCLUSION: These findings imply that prebiotics may selectively promote the proliferation of certain bacterial stains with glycoside hydrolysis capacity, thereby, subsequently improving the biotransformation and bioavailability of primary ginsenosides in vivo.

Polychlorinated biphenyls altered gut microbiome in CAR and PXR knockout mice exhibiting toxicant-associated steatohepatitis.

Polychlorinated biphenyls (PCBs) are persistent organic pollutants associated with non-alcoholic fatty liver disease (NAFLD). Previously, we demonstrated that the PCB mixture, Aroclor1260, exacerbated NAFLD, reflective of toxicant-associated steatohepatitis, in diet-induced obese mice, in part through pregnane-xenobiotic receptor (PXR) and constitutive androstane receptor (CAR) activation. Recent studies have also reported PCB-induced changes in the gut microbiome that consequently impact NAFLD. Therefore, the objective of this study is to examine PCB effects on the gut-liver axis and characterize the role of CAR and PXR in microbiome alterations. C57Bl/6 (wildtype, WT), CAR and PXR knockout mice were fed a high fat diet and exposed to Aroclor1260 (20 mg/kg, oral gavage, 12 weeks). Metagenomics analysis of cecal samples revealed that CAR and/or PXR ablation increased bacterial alpha diversity regardless of exposure status. CAR and PXR ablation also increased bacterial composition (beta diversity) versus WT; Aroclor1260 altered beta diversity only in WT and CAR knockouts. Distinct changes in bacterial abundance at different taxonomic levels were observed between WT and knockout groups; however Aroclor1260 had modest effects on bacterial abundance within each genotype. Notably, both knockout groups displayed increased Actinobacteria and Verrucomicrobia abundance. In spite of improved bacterial diversity, the knockout groups however failed to show protection from PCB-induced hepato- and intestinal- toxicity including decreased mRNA levels of ileal permeability markers (occludin, claudin3). In summary, CAR and PXR ablation significantly altered gut microbiome in diet-induced obesity while Aroclor1260 compromised intestinal integrity in knockout mice, implicating interactions between PCBs and CAR, PXR on the gut-liver axis.

Systemic iron reduction by venesection alters the gut microbiome in patients with haemochromatosis.

BACKGROUND & AIMS: Iron reduction by venesection has been the cornerstone of treatment for haemochromatosis for decades, and its reported health benefits are many. Repeated phlebotomy can lead to a compensatory increase in intestinal iron absorption, reducing intestinal iron availability. Given that most gut bacteria are highly dependent on iron for survival, we postulated that, by reducing gut iron levels, venesection could alter the gut microbiota. METHODS: Clinical parameters, faecal bacterial composition and metabolomes were assessed before and during treatment in a group of patients with haemochromatosis undergoing iron reduction therapy. RESULTS: Systemic iron reduction was associated with an alteration of the gut microbiome, with changes evident in those who experienced reduced faecal iron availability with venesection. For example, levels of Faecalibacterium prausnitzii, a bacterium associated with improved colonic health, were increased in response to faecal iron reduction. Similarly, metabolomic changes were seen in association with reduced faecal iron levels. CONCLUSION: These findings highlight a significant shift in the gut microbiome of patients who experience reduced colonic iron during venesection. Targeted depletion of faecal iron could represent a novel therapy for metabolic and inflammatory diseases, meriting further investigation. LAY SUMMARY: Iron depletion by repeated venesection is the mainstay of treatment for haemochromatosis, an iron-overload disorder. Venesection has been associated with several health benefits, including improvements in liver function tests, reversal of liver scarring, and reduced risk of liver cancer. During iron depletion, iron absorption from the gastrointestinal (GI) tract increases to compensate for iron lost with treatment. Iron availability is limited in the GI tract and is crucial to the growth and function of many gut bacteria. In this study we show that reduced iron availability in the colon following venesection treatment leads to a change in the composition of the gut bacteria, a finding that, to date, has not been studied in patients with haemochromatosis.

Computational advances of tumor marker selection and sample classification in cancer proteomics.

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.

Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest.

Developing a computational method for recognizing preterm delivery is important for timely diagnosis and treatment of preterm delivery. The main aim of this study was to evaluate electrohysterogram (EHG) signals recorded at different gestational weeks for recognizing the preterm delivery using random forest (RF). EHG signals from 300 pregnant women were divided into two groups depending on when the signals were recorded: i) preterm and term delivery with EHG recorded before the 26th week of gestation (denoted by PE and TE group), and ii) preterm and term delivery with EHG recorded during or after the 26th week of gestation (denoted by PL and TL group). 31 linear features and nonlinear features were derived from each EHG signal, and then compared comprehensively within PE and TE group, and PL and TL group. After employing the adaptive synthetic sampling approach and six-fold cross-validation, the accuracy (ACC), sensitivity, specificity and area under the curve (AUC) were applied to evaluate RF classification. For PL and TL group, RF achieved the ACC of 0.93, sensitivity of 0.89, specificity of 0.97, and AUC of 0.80. Similarly, their corresponding values were 0.92, 0.88, 0.96 and 0.88 for PE and TE group, indicating that RF could be used to recognize preterm delivery effectively with EHG signals recorded before the 26th week of gestation.

Physico-elemental analysis of roasted organic coffee beans from Ethiopia, Colombia, Honduras, and Mexico using X-ray micro-computed tomography and external beam particle induced X-ray emission.

The physico-elemental profiles of commercially attained and roasted organic coffee beans from Ethiopia, Colombia, Honduras, and Mexico were compared using light microscopy, X-ray micro-computed tomography, and external beam particle induced X-ray emission. External beam PIXE analysis detected P, S, Cl, K, Ca, Ti, Mn, Fe, Cu, Zn, Br, Rb, and Sr in samples. Linear discriminant analysis showed that there was no strong association between elemental data and production region, whilst a heatmap combined with hierarchical clustering showed that soil-plant physico-chemical properties may influence regional elemental signatures. Physical trait data showed that Mexican coffee beans weighed significantly more than beans from other regions, whilst Honduras beans had the highest width. X-ray micro-computed tomography qualitative data showed heterogeneous microstructural features within and between beans representing different regions. In conclusion, such multi-dimensional analysis may present a promising tool in assessing the nutritional content and qualitative characteristics of food products such as coffee.

Covariate shift estimation based adaptive ensemble learning for handling non-stationarity in motor imagery related EEG-based brain-computer interface.

The non-stationary nature of electroencephalography (EEG) signals makes an EEG-based brain-computer interface (BCI) a dynamic system, thus improving its performance is a challenging task. In addition, it is well-known that due to non-stationarity based covariate shifts, the input data distributions of EEG-based BCI systems change during inter- and intra-session transitions, which poses great difficulty for developments of online adaptive data-driven systems. Ensemble learning approaches have been used previously to tackle this challenge. However, passive scheme based implementation leads to poor efficiency while increasing high computational cost. This paper presents a novel integration of covariate shift estimation and unsupervised adaptive ensemble learning (CSE-UAEL) to tackle non-stationarity in motor-imagery (MI) related EEG classification. The proposed method first employs an exponentially weighted moving average model to detect the covariate shifts in the common spatial pattern features extracted from MI related brain responses. Then, a classifier ensemble was created and updated over time to account for changes in streaming input data distribution wherein new classifiers are added to the ensemble in accordance with estimated shifts. Furthermore, using two publicly available BCI-related EEG datasets, the proposed method was extensively compared with the state-of-the-art single-classifier based passive scheme, single-classifier based active scheme and ensemble based passive schemes. The experimental results show that the proposed active scheme based ensemble learning algorithm significantly enhances the BCI performance in MI classifications.

Identifying natural compounds as multi-target-directed ligands against Alzheimer's disease: an in silico approach.

Alzheimer's disease (AD) is a multi-factorial disease, which can be simply outlined as an irreversible and progressive neurodegenerative disorder with an unclear root cause. It is a major cause of dementia in old aged people. In the present study, utilizing the structural and biological activity information of ligands for five important and mostly studied vital targets (i.e. cyclin-dependant kinase 5, ß-secretase, monoamine oxidase B, glycogen synthase kinase 3ß, acetylcholinesterase) that are believed to be effective against AD, we have developed five classification models using linear discriminant analysis (LDA) technique. Considering the importance of data curation, we have given more attention towards the chemical and biological data curation, which is a difficult task especially in case of big data-sets. Thus, to ease the curation process we have designed Konstanz Information Miner (KNIME) workflows, which are made available at http://teqip.jdvu.ac.in/QSAR_Tools/ . The developed models were appropriately validated based on the predictions for experiment derived data from test sets, as well as true external set compounds including known multi-target compounds. The domain of applicability for each classification model was checked based on a confidence estimation approach. Further, these validated models were employed for screening of natural compounds collected from the InterBioScreen natural database ( https://www.ibscreen.com/natural-compounds ). Further, the natural compounds that were categorized as 'actives' in at least two classification models out of five developed models were considered as multi-target leads, and these compounds were further screened using the drug-like filter, molecular docking technique and then thoroughly analyzed using molecular dynamics studies. Finally, the most potential multi-target natural compounds against AD are suggested.

Polymannuronic acid ameliorated obesity and inflammation associated with a high-fat and high-sucrose diet by modulating the gut microbiome in a murine model.

Polymannuronic acid (PM), one of numerous alginates isolated from brown seaweeds, is known to possess antioxidant activities. In this study, we examined its potential role in reducing body weight gain and attenuating inflammation induced by a high-fat and high-sucrose diet (HFD) as well as its effect on modulating the gut microbiome in mice. A 30-d PM treatment significantly reduced the diet-induced body weight gain and blood TAG levels (P2·0). PM also had a profound impact on the microbial composition in the gut microbiome and resulted in a distinct microbiome structure. For example, PM significantly increased the abundance of a probiotic bacterium, Lactobacillus reuteri (log10 LDA score>2·0). Together, our results suggest that PM may exert its immunoregulatory effects by enhancing proliferation of several species with probiotic activities while repressing the abundance of the microbial taxa that harbor potential pathogens. Our findings should facilitate mechanistic studies on PM as a potential bioactive compound to alleviate obesity and the metabolic syndrome.