Effects of S. cerevisiae strains on the sensory characteristics and flavor profile of kiwi wine based on E-tongue, GC-IMS and 1H-NMR

The fermentation of kiwifruit into kiwi wine (KW) can represent a strategy to reduce the economic losses linked to fruits imperfections, spoilage, over production and seasonality. In the study, Pujiang kiwifruit, a China National Geographical Indication Product, was used as raw material to produce KW fermented by four commercial S. cerevisiae strains, namely Drop Acid Yeast, DV10, SY and RW. The sensory characteristics and flavor profile of KW were assessed by means of sensory evaluation, E-tongue, GC-IMS and 1H-NMR. KW fermented by RW strain obtained the higher sensory evaluation score. E-tongue could clearly distinguish the taste differences of KW fermented by distinct S. cerevisiae strains. A total of 128 molecules were characterized by GC-IMS and 1H-NMR, indicating that the combinations of multiple technologies could provide a comprehensive flavor profile of KW. The main flavor compounds in KW pertained to the classes of esters and alcohols. Several pathways were found to be differently altered by the fermentation with the different yeast strains, namely butanoate metabolism, glycerolipid metabolism, alanine, aspartate and glutamate metabolism, arginine biosynthesis, arginine and proline metabolism. The present study will facilitate screening suitable S. cerevisiae strains for KW production and provide a theoretical basis for large-scale production of KW

PDTD: a web-accessible protein database for drug target identification

<p>Abstract</p> <p>Background</p> <p>Target identification is important for modern drug discovery. With the advances in the development of molecular docking, potential binding proteins may be discovered by docking a small molecule to a repository of proteins with three-dimensional (3D) structures. To complete this task, a reverse docking program and a drug target database with 3D structures are necessary. To this end, we have developed a web server tool, TarFisDock (<it>Tar</it>get <it>Fis</it>hing <it>Dock</it>ing) <url>http://www.dddc.ac.cn/tarfisdock</url>, which has been used widely by others. Recently, we have constructed a protein target database, <it>P</it>otential <it>D</it>rug <it>T</it>arget <it>D</it>atabase (PDTD), and have integrated PDTD with TarFisDock. This combination aims to assist target identification and validation.</p> <p>Description</p> <p>PDTD is a web-accessible protein database for <it>in silico </it>target identification. It currently contains >1100 protein entries with 3D structures presented in the Protein Data Bank. The data are extracted from the literatures and several online databases such as TTD, DrugBank and Thomson Pharma. The database covers diverse information of >830 known or potential drug targets, including protein and active sites structures in both PDB and mol2 formats, related diseases, biological functions as well as associated regulating (signaling) pathways. Each target is categorized by both nosology and biochemical function. PDTD supports keyword search function, such as PDB ID, target name, and disease name. Data set generated by PDTD can be viewed with the plug-in of molecular visualization tools and also can be downloaded freely. Remarkably, PDTD is specially designed for target identification. In conjunction with TarFisDock, PDTD can be used to identify binding proteins for small molecules. The results can be downloaded in the form of mol2 file with the binding pose of the probe compound and a list of potential binding targets according to their ranking scores.</p> <p>Conclusion</p> <p>PDTD serves as a comprehensive and unique repository of drug targets. Integrated with TarFisDock, PDTD is a useful resource to identify binding proteins for active compounds or existing drugs. Its potential applications include <it>in silico </it>drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e. new pharmacological usage) or an existing target (i.e. new pharmacological or toxic relevance), thus it may be a valuable platform for the pharmaceutical researchers. PDTD is available online at <url>http://www.dddc.ac.cn/pdtd/</url>.</p

TarFisDock: a web server for identifying drug targets with docking approach

TarFisDock is a web-based tool for automating the procedure of searching for small molecule–protein interactions over a large repertoire of protein structures. It offers PDTD (potential drug target database), a target database containing 698 protein structures covering 15 therapeutic areas and a reverse ligand–protein docking program. In contrast to conventional ligand–protein docking, reverse ligand–protein docking aims to seek potential protein targets by screening an appropriate protein database. The input file of this web server is the small molecule to be tested, in standard mol2 format; TarFisDock then searches for possible binding proteins for the given small molecule by use of a docking approach. The ligand–protein interaction energy terms of the program DOCK are adopted for ranking the proteins. To test the reliability of the TarFisDock server, we searched the PDTD for putative binding proteins for vitamin E and 4H-tamoxifen. The top 2 and 10% candidates of vitamin E binding proteins identified by TarFisDock respectively cover 30 and 50% of reported targets verified or implicated by experiments; and 30 and 50% of experimentally confirmed targets for 4H-tamoxifen appear amongst the top 2 and 5% of the TarFisDock predicted candidates, respectively. Therefore, TarFisDock may be a useful tool for target identification, mechanism study of old drugs and probes discovered from natural products. TarFisDock and PDTD are available at

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings

The evolutionary history of 2,658 cancers.

Cancer develops through a process of somatic evolution1,2. Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes3. Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)4, we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection

Inferring structural variant cancer cell fraction

Abstract: We present SVclone, a computational method for inferring the cancer cell fraction of structural variant (SV) breakpoints from whole-genome sequencing data. SVclone accurately determines the variant allele frequencies of both SV breakends, then simultaneously estimates the cancer cell fraction and SV copy number. We assess performance using in silico mixtures of real samples, at known proportions, created from two clonal metastases from the same patient. We find that SVclone’s performance is comparable to single-nucleotide variant-based methods, despite having an order of magnitude fewer data points. As part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) consortium, which aggregated whole-genome sequencing data from 2658 cancers across 38 tumour types, we use SVclone to reveal a subset of liver, ovarian and pancreatic cancers with subclonally enriched copy-number neutral rearrangements that show decreased overall survival. SVclone enables improved characterisation of SV intra-tumour heterogeneity

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Évaluation sur banc d'essai des algorithmes des machines ventilatoires

Sleep disordered breathing including sleep apnea is a major public health problem. It contributes to daytime sleepiness and is associated with chronic diseases. In recent years, a variety of ventilation devices have been developed with the objective of treating sleep disorders related to the upper airway obstruction (obstructive apnea) or the central command (central apnea). These devices operate with different algorithms, which are little known and protected by the device manufacturers. Since most devices are evaluated during patient treatment, it is difficult to compare them in the same conditions due to inter- and intra-patient variability. Bench test has been proposed to evaluate the device responses in standardized and reproducible conditions. This thesis was aimed to develop a respiratory bench model able to reproduce patients’ signals and also in concordance with human physiology. The bench model can take into account the pressure responses of tested devices and works in a “closed loop” setting.With this bench model, several commercially available auto-titrating continuous positive airway pressure devices were evaluated for their auto-titration algorithms as well as their pressure-relief modes. Also, three adaptive servo-ventilation devices were evaluated by subjecting various sleep disordered breathing events that were generated by another bench model of a similar principle. We demonstrated that eleven auto-titrating continuous positive airway pressure devices were not equivalent in terms of their treatment efficacy and the data accuracy in the device report. The pressure-relief modes may attenuate the efficacy if not adjusted at the time of their introduction. The responses of adaptive servo-ventilation devices were not sufficient to normalize the breathing flow and their efficacy depended on the initial settings.The current certification process of these ventilatory devices, which focus mainly on clinical aspects, may be completed by the results of our bench.Les troubles respiratoires du sommeil, notamment le syndrome d’apnée du sommeil, représentent un problème de santé publique. Ils contribuent aux symptômes diurnes comme la somnolence sévère et sont associés à des maladies chroniques.Depuis quelques années, une variété d’appareils de traitement ventilatoire a été développée pour traiter les troubles respiratoires du sommeil, principalement les maladies liées à l’obstruction de la voie aérienne supérieure (apnée obstructive) ou à la commande centrale (apnée centrale). Ces appareils fonctionnent suivant des principes différents, en raison de leurs propres algorithmes, qui sont souvent mal connus et protégés par les fabricants. Les évaluations des appareils de ventilation sont effectuées pendant les traitements cliniques chez des patients. Il est donc difficile de comparer ces différents appareils dans les mêmes conditions à cause des variabilités inter- et intra-patient. Un banc d’essai pourrait permettre de tester les réponses des appareils dans les conditions standardisées et reproductibles.Cette thèse a consisté à construire un banc d’essai qui permet de reproduire les signaux de patients et de respecter de la physiologie humaine. La réaction du banc d’essai prend aussi en compte la réaction de l’appareil à tester sur le système, i.e., ce modèle fonctionne en « boucle fermée ». Avec le banc d’essai construit, les différentes machines de pression positive continue (PPC) autopilotée disponibles sur le marché ont été évaluées pour leurs algorithmes ainsi pour leurs modes confort. De plus, trois machines de ventilation auto-asservie (ASV) ont été soumises aux différents événements respiratoires du sommeil créés par un autre modèle d’un principe similaire. Nous avons montré que les machines de PPC autopilotée ne sont pas équivalentes pour l’efficacité du traitement et la précision des données du rapport. Les modes confort pourraient éventuellement dégrader l’efficacité du traitement de PPC si la pression thérapeutique n’est pas ajustée lors de leur introduction au traitement. Pour les machines ASV, leurs réponses ne sont pas suffisantes pour normaliser la respiration et les réglages des machines peuvent influencer l’efficacité du traitement. Les résultats pourraient compléter les données cliniques et fournir une option complémentaire pour le processus futur de certification de ces dispositifs médicaux

Bench evaluation of the algorithms of ventilation treatment devices

Les troubles respiratoires du sommeil, notamment le syndrome d’apnée du sommeil, représentent un problème de santé publique. Ils contribuent aux symptômes diurnes comme la somnolence sévère et sont associés à des maladies chroniques.Depuis quelques années, une variété d’appareils de traitement ventilatoire a été développée pour traiter les troubles respiratoires du sommeil, principalement les maladies liées à l’obstruction de la voie aérienne supérieure (apnée obstructive) ou à la commande centrale (apnée centrale). Ces appareils fonctionnent suivant des principes différents, en raison de leurs propres algorithmes, qui sont souvent mal connus et protégés par les fabricants. Les évaluations des appareils de ventilation sont effectuées pendant les traitements cliniques chez des patients. Il est donc difficile de comparer ces différents appareils dans les mêmes conditions à cause des variabilités inter- et intra-patient. Un banc d’essai pourrait permettre de tester les réponses des appareils dans les conditions standardisées et reproductibles.Cette thèse a consisté à construire un banc d’essai qui permet de reproduire les signaux de patients et de respecter de la physiologie humaine. La réaction du banc d’essai prend aussi en compte la réaction de l’appareil à tester sur le système, i.e., ce modèle fonctionne en « boucle fermée ». Avec le banc d’essai construit, les différentes machines de pression positive continue (PPC) autopilotée disponibles sur le marché ont été évaluées pour leurs algorithmes ainsi pour leurs modes confort. De plus, trois machines de ventilation auto-asservie (ASV) ont été soumises aux différents événements respiratoires du sommeil créés par un autre modèle d’un principe similaire. Nous avons montré que les machines de PPC autopilotée ne sont pas équivalentes pour l’efficacité du traitement et la précision des données du rapport. Les modes confort pourraient éventuellement dégrader l’efficacité du traitement de PPC si la pression thérapeutique n’est pas ajustée lors de leur introduction au traitement. Pour les machines ASV, leurs réponses ne sont pas suffisantes pour normaliser la respiration et les réglages des machines peuvent influencer l’efficacité du traitement. Les résultats pourraient compléter les données cliniques et fournir une option complémentaire pour le processus futur de certification de ces dispositifs médicaux.Sleep disordered breathing including sleep apnea is a major public health problem. It contributes to daytime sleepiness and is associated with chronic diseases. In recent years, a variety of ventilation devices have been developed with the objective of treating sleep disorders related to the upper airway obstruction (obstructive apnea) or the central command (central apnea). These devices operate with different algorithms, which are little known and protected by the device manufacturers. Since most devices are evaluated during patient treatment, it is difficult to compare them in the same conditions due to inter- and intra-patient variability. Bench test has been proposed to evaluate the device responses in standardized and reproducible conditions. This thesis was aimed to develop a respiratory bench model able to reproduce patients’ signals and also in concordance with human physiology. The bench model can take into account the pressure responses of tested devices and works in a “closed loop” setting.With this bench model, several commercially available auto-titrating continuous positive airway pressure devices were evaluated for their auto-titration algorithms as well as their pressure-relief modes. Also, three adaptive servo-ventilation devices were evaluated by subjecting various sleep disordered breathing events that were generated by another bench model of a similar principle. We demonstrated that eleven auto-titrating continuous positive airway pressure devices were not equivalent in terms of their treatment efficacy and the data accuracy in the device report. The pressure-relief modes may attenuate the efficacy if not adjusted at the time of their introduction. The responses of adaptive servo-ventilation devices were not sufficient to normalize the breathing flow and their efficacy depended on the initial settings.The current certification process of these ventilatory devices, which focus mainly on clinical aspects, may be completed by the results of our bench

Ecological risk of human health in sediments in a karstic river basin with high longevity population

Health and longevity are common human goals, and environmental factors can have significant impacts on human health. This study aims to investigate the historical changes and sources of trace elements in the sediments of a typical karstic river basin with high longevity population in Hechi City, Guangxi, China and to evaluate the ecological risks of trace elements in sediments. The results showed that over the past 100 years, the contents of trace elements in the sediments were lower in the upper reaches than in the middle and lower reaches of the river. The sediments had high trace element contents in 1950–1959 and 1989–1998, while low contents appeared after 1998. These periods correspond to China's industrial growth in the early 1950s, the Great Leap Forward movement in the late 1950s, the reform and opening-up policy implemented in the 1980s–1990s and the environmental protection policies to strengthen pollution control that have been implemented since 2000. Limestone soil and carbonate rock are the main sources of sediment in the basin. Although the geological background values of Cd and other trace elements in the basin were relatively high, the high calcium content and alkalinity of the water and sediment in the basin reduced the bioavailability of Cd and other heavy metals. The mainstream of Panyang River had a low environmental risk, but the tributary Bama River where there is dense population poses a moderate risk