H Dong / J He (@5.5) vs Z Kulambayeva / Y Ma (@1.12)
10-09-2019

Our Prediction:

Z Kulambayeva / Y Ma will win
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H Dong / J He – Z Kulambayeva / Y Ma Match Prediction | 10-09-2019 02:30

Interestingly, while most pericytes are believed to be of mesodermal origin, some studies have suggested that CNS pericytes derive from the neural crest [58,59,60,61], and thus may be functionally distinct from peripheral pericytes [8]. Additionally, the increased ratio of pericytesto ECs found in the brain (1:31:1, as compared to 1:100 in skeletal muscle) further support an important role for pericytes in BBB function, as increased pericyte coverage throughout the body has been correlated with increased vessel tightness [62]. An important question regarding BBB induction by pericytes is how this interaction is localized to the CNS, as pericytes are found throughout the body.

The same applies to some extend also to genome-wide RNA-seq transcriptomics and especially to the MS-based global proteomic profiling, which would benefit from standardized analytical approaches to extract more accurate and complete gene expression and protein activity profiles. With regard to other cancer or drug classes, which especially require multi-marker panels, the microarray-based gene expression and targeted protein abundance profiles appear currently as the most predictive source of signal (Costello et al. This is likely due to the fact that these profiling platforms have been around for some time already, and available tailored processing methods have been developed for these. 2018). Similarly, a recent transcript-level machine learning work demonstrates how the RNA-seq technology offers additional predictive signal, when compared to gene-level expression or mutation information (Safikhani et al. For the more recent NGS-based platforms, such as DNA copy number or point mutations, we are still lacking the knowledge of how to best utilize all the hidden nuggets of information available from the raw sequencing data for drug response prediction; instead, one needs to rely only on the most processed, limited gene-level data available (Table 1). 2014). Therefore, we argue that we will need improvements both in the computational methods and in the experimental assays in order to convincingly show the added value of big data for drug response prediction. 2012; Pemovska et al. 2017). For example, our pilot study showed that the MS-based proteomics can significantly improve the drug response predictions, but only after filtering out most of the protein measurements (Ali et al. Based on the lessons learned from the DREAM Challenges and other related benchmarking studies, the NGS-based big data is not yet among the most predictive genomic or molecular features for drug response prediction globally, except for the few known examples of cancer types that are driven by single somatic aberrations, such as BCR-ABL-positive chronic myeloid leukemia, non-small cell lung cancer or BRAF in melanoma, with clinically actionable small-molecule inhibitors available (Flaherty et al. 2015).

It does so through a pairwise comparison between any two decoys. Although this approach takes more computing time than ranking single scores directly, it is more sensitive to capture the differences among models and less prone to systematic errors of single scores on the decoys. Our method tries to capture the correlation between score differences and actual structural quality difference as well as the complementarity among these scores. Consensus GDT method depends on the decoy distribution and relies on geometric information of protein structures only, while single scoring functions produce a wide range of values for different decoys, which makes their scores unstable and noisy. Because of using single score information, PWCom is more correlated to the real GDT score with respect to the native structure than consensus methods. Our new approach combines the advantages of consensus GDT method and single scoring functions through pairwise comparison and a two-stage machine-learning scheme.

Interactions between these components contribute to the development and maintenance of the healthy BBB [6,7,8], although the relative contributions of each component and the specific mechanisms by which these processes occur is an area of active research, which will be discussed in more detail later. ECs in the CNS are supported structurally and functionally by pericytes, basement membrane, and astrocytes [5]. Paracellular transport is restricted by tight junctions (TJs) that stitch together adjacent ECs, while transcellular transport is regulated by a combination of specialized transporters and efflux pumps. The physical integrity of the barrier is derived from the endothelial cells (ECs) that line the brain microvasculature and tightly control paracellular and transcellular transport [2]. Transporters supply essential nutrients to the brain, while efflux pumps counter the passive entry of small molecules, including many toxins, but also many potential therapeutics.

About this article

Platforms for configuring BBB cells are subject to many technical design considerations. In the context of recapitulating the complete BBB, an ideal platform would supply physiological levels of shear stress as well as facilitate the correct spatial organization of NVU components, allowing them to form realistic cell-cell junctions and basement membrane. While the transwell assay remains the most widely used platform, a number of models have sought to satisfy these other criteria. In vitro platforms have been classified and compared in Table2.

TEER is measured between electrodes located in each compartment. (b) A microfluidic version of the transwell model. TEER and permeability measurements for assessing barrier function. (a) The transwell model, with an EC monolayer on the apical side of the membrane, and supporting cell types in the contact and non-contact positions on the underside of the membrane and in the basolateral chamber. Permeability is measured by introducing a solute of interest into the apical chamber and measuring the time-dependent concentration in the basolateral chamber.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

2018). 2014; Ammad-ud-din et al. 2018; Turki et al. Supervised machine learning models offer the opportunity for multi-marker prediction of drug responses using multi-omics and multi-task learning approaches that leverage information across both patient samples as well as across drug similarities (Costello et al. 2014; Majumder et al. Rather than conducting a systematic review of all the works published on this broad topic (please see, e.g., Azuaje 2017), we describe below application cases of supervised machine learning models to drug response prediction in cancer cell lines and discuss to which extend these models could be also applied in a clinical setting to individualized treatment selection once large-enough patient cohorts become available. We also critically evaluate whether the current learning approaches benefit from the use of big-scale omics data, which still mainly originate from the NGS-based technologies, and provide our perspective on the future directions required for supporting clinical applicability, both in terms of improved modeling frameworks and most informative omics measurements used as input for these models. Therefore, most of the learning studies to date have been done using large panels of cancer cell lines (Garnett et al. 2016; Yao et al. 2016), although there are also a few recent examples aiming at clinical treatment predictions in patient samples (Sadanandam et al. 2018). 2012; Barretina et al. 2013; Geeleher et al. 2016; Noren et al. 2015; Ding et al. 2015; Iorio et al. However, the accuracy of such machine learning models depends critically on the availability of high-quality training data from large-enough sample sizes. 2016; Cichonska et al. 2012; Seashore-Ludlow et al.

The bar-headed goose isolates from this outbreak were found sharing PB2 genes common to HPAI H5N1 circulating in live bird markets in Tibet [39]. Based on the bird migration model, water birds such as the great-crested grebe (Podiceps cristatus), tufted duck (Aythya fuligula), whooper swan (Cygnus cygnus) and black-headed gull (Chroicocephalus ridibundus) appear to be key to the widespread dissemination of subclade 2.3.2 viruses, and the bar-headed goose and ruddy shelduck, two migratory hosts for HPAI H5N1 along the Central Asia Flyway, emerged as potential vectors for the movement of clade 2.2.1 and clade 2.2.2 viruses (Newman et al. unpubl.).

MATCH SUMMARY

Phylogenetic analysis of the genomic sequences of the human cases has been widely used to trace the origin and evolution of these HPAI pathotypes. Therefore, human infection with H5N1 virus is most likely to be associated with direct or indirect contact with infected birds or wildfowl [60, 68, 69], although the possibilities of inter-personal transmission of HPAI H5N1 and environment-to-human transmission still exist [57, 58, 67, 69, 70]. Phylogenetic analyses have also shown that all the eight segments of the human H5N1 strains from Thailand, Indonesia and other Asian countries from 2004 and 2005 were closely related to the avian isolates of genotype Z [56, 58, 66]. The majority of the genomic sequences of the human H5N1 strains were reported to be derived from avian strains [47, 51, 56-58, 64-67]. Although the human H5N1 isolates from Hong Kong SAR from 2002 were still of avian origin and they were closely related to the genotype Z and Z+ viruses, their internal proteins had a different origin with the H5N1 viruses that caused the first known case of human infection in Hong Kong in 1997 [65]. For example, the genomic sequences responsible for the first human infection with H5N1 were found to be all avian-like [47, 64].