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

Future in depth studies of the influenza reservoir, along with large-scale data mining of genomic resources and the integration of epidemiological, genomic, and antigenic data, should enhance our understanding of antigenic drift and improve the detection and control of the emerging novel strains [12]. Recent large-scale genome sequencing of HPAI H5N1 viruses, antigenic typing and database information mining have significantly improved the study of HPAI virus origin, diversity, transmission, reassortment and evolution.

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].

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.).

2.A CASE STUDY: HPAI H5N1 VIRUSES FROM THE QINGHAI LAKE

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.

2017). 2015; Horvath et al. 2013; Iorio et al. 2018). Even though this is somewhat disappointing from the precision oncology perspective, this is not too surprising given that these clinical data have been used for decades by the medical doctors for both diagnostic and treatment selection purposes. 2015; Xu et al. 2016; Guinney et al. 2018). In case the sequencing data proves not to be alone sufficient for drug response prediction, then other, even more high-dimensional data sources, such as biomedical imaging or immune-profiling, might provide the necessary level of resolution required for the next leap for treatment selection (Friedman et al. It has been shown with other related applications, including bioimage analysis (Janowczyk and Madabhushi 2016; Wang et al. The next challenge for the computational community is therefore to show how to improve the prediction accuracies beyond that based on clinical information only, through using all the modern high-throughput biotechnologies such as genomics, proteomics, and metabolomics (Peddinti et al. For instance, how to deal with the redundancy between the predictive profiles in case of anti-correlations between CNV and somatic point mutations, which are widely observed both in tumor samples and in cancer cell lines (Ciriello et al. 2018; Ding et al. 2017) that when the feature spaces are large enough, deep-learning machine learning models can learn the most predictive signal from such big data, without the need of any processing of filtering steps, hence providing opportunity for significant improvements in precision oncology in terms of both treatment response prediction accuracy as well as resources and time required for data processing (Camacho et al. Future developments in the machine learning models should therefore be directed toward better use of the integrated and full information from the multiple omics datasets. 2016). 2018; Chang et al. 2016). 2017) and compound-target interaction prediction (Ma et al. 2017), clinical data from the cancer patients, including their standard laboratory tests and other patient characteristics, seems often to provide most predictive signatures for treatment responses (Ding et al. Based on multiple lines of evidence and benchmarking (Saez-Rodriguez et al.

The second category of QA methods take a set of structures as input and use the information from the decoy set to assign a score to each structure member. This works well when good models are among a major cluster, especially in CASP (Critical Assessment of Protein Structure Prediction) [20], where all participating groups try to submit their best models. Then in the second step, the weighted scores and other sequence-related features were directly mapped to the GDT score of each decoy by an SVM. In this paper, we proposed a new method to combine consensus GDT and knowledge-based scoring functions to obtain better QA performance. Thus, each decoy was represented by a sequence of structure codes (states). In this category, physical-based energies [10], [11] calculate atomic level energies of a decoy according to physical principles. After the comparison between all pairs of decoys, we calculated a score for each decoy in the pool based on the number of winning times. We also modified this method to attend the QA category of CASP 10 in 2012 and the server (MUFOLD-Server) was ranked the second place in terms of Pearson and Spearman correlation performance. Here, the structural state of each residue in a decoy was represented by the bond angles of four consecutive residues. For example, OPUS-Ca [12] uses the distance distributions of residue pairs and DDFire [13] constructs residue-specific all-atom potential of mean force from a database of native structures. In this method, the optimal weights for each of the component scores were obtained in the first optimization step to increase their discerning power. First, a consensus score called Position Specific Probability Sum (PSPS) was developed as one of the features. The consensus method utilizes geometric information exclusively from the decoy set without taking advantage of the biophysical or statistical properties within and between primary sequences and 3D structures. Several methods based on sequence-structure relationship train a scoring function or machine-learning model to estimate the quality of the predicted models [14], [15], [16], [17], [18]. Second, a two-stage method was developed to perform QA. Almost all the combination methods mentioned above use machine learning modules to capture the complex and remote relationship between feature scores and the decoy structural quality, such as GDT score. Knowledge-based scoring functions rely on statistical distributions of atoms in native structures. Consensus and knowledge-based scoring functions reveal different but complementary aspects of structural models. And the performance still has large room to improve. MUFOLD-WQA [21] is a variation of pure consensus approach which introduces a weight for each pair of decoys. Several works have been done to combine the two approaches using machine-learning methods such as neural network (NN) or support vector machine (SVM) [18], [22], [23], [24], [25]. The most widely used method is the consensus approach, such as nave Consensus Global Distance Test (CGDT) [19] which assigns a score to each decoy as its average structural similarity (GDT score) to all other members in the set. However, the energy value is sensitive to minor changes in structure, and hence it is hard to apply it to QA. We applied this method to three benchmark data sets from different protein structure prediction methods and demonstrated significant improvements over CGDT and state-of-art single scoring functions in terms of best model selection performance and Spearman correlation to actual GDT score. A probability score was calculated for each decoy of a set based on consensus. This method achieved some improvement over CGDT and single scoring functions in selection performance. The first one is a single model assessment approach, which takes one single structure as input and assigns a score to indicate its structural similarity or distance to the native. Specifically, for every two decoys, the first neural-network model decided whether they were structurally close or not in terms of their GDT scores to the native, and subsequently, the second model determined which one was closer to the native. For example, QMean [18] combines sequence-structure information such as atom-atom contact, secondary structure prediction and solvent accessibility into a scoring function to predict the quality of single models. Noticing the fact that knowledge-based scoring functions are relatively noisy and have low correlations to the actual decoy quality, in [26] we developed a protein-dependent scoring method to combine consensus and single scoring functions for decoy selection. Although this method alone did not have outstanding performance in decoy selection, it was quite different from all other methods, and outperformed CGDT when combined with other methods such as OPUS-Ca [12], DDFire [13] and RW [27]. QA methods roughly fall into two categories. We trained two neural-network models to sequentially capture the underlying correlation among different features (scoring functions).

Notably, in benchmark 2, the top GDT performance of PWCom is much higher than that of CGDT, with the improvement of 0.45290.4255=0.0274. As for Spearman correlation, PWCom is consistently better than CGDT in all three benchmarks. From Tables 1, ,22 and and3,3, we can see that PWCom is significantly and consistently better than CGDT in three benchmarks. In the other two benchmarks, PWCom score still improves in average top-1 GDT performance over CGDT, and even more over single scoring functions.

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The transwell assay is the most widely used in vitro assay for BBB research, with applications in drug screening and in mechanistic studies of BBB regulation [27, 85,86,87]. The addition of astrocytes, pericytes, and/or neurons, or media conditioned by these cells, in the basolateral chamber is often used to upregulate barrier function [48, 85]. In this assay, a confluent monolayer of ECs is formed on a porous membrane that separates apical and basolateral chambers (Fig.2a). [27]. The transport of solutes or cells from the apical to basolateral chamber can be used to determine permeability, mechanisms of transport, and the role of inflammatory cytokines, pathogens, etc.

This might be because CGDT is already very good as shown in the big performance gap between CGDT and single scoring functions, and there is no room to further improve it in this particular case. Although PWCom has better performance than single scoring functions, it does not improve over GDT in this case. From Figure 7, we can see that CGDT is almost linearly correlated with the actual GDT scores and Table 6 shows CGDT selects nearly the best one in the decoy set. In this case, CGDT is the best performer in terms of all three measures. Figure 7 and Table 6 show target T0396 from benchmark 2.

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Most dynamic models of the BBB extend the two-dimensional membrane-based approach by incorporating a 10m thick transwell membrane into a microfluidic device. In a variation of the membrane-based microfluidic models, an extracellular matrix can be incorporated into the channel underneath the porous membrane, allowing co-culture of other cell types in a 3D matrix [115] (Fig.2b). These devices are designed to be improvements over the transwell assay, while remaining relatively inexpensive and high-throughput, in order to be suitable for drug permeability studies. However, recapitulating the phenotype of brain pericytes and quiescent astrocytes remains a significant challenge. Although still featuring planar geometry and a porous membrane interfering with complete cell-cell contact, these models are closer to the microenvironment of the BBB, enabling more advanced in vitro studies of drug permeability which could also examine the effect on neurons. Permeability measurements can be made by adding small molecules to the culture media, and TEER can be measured through the use of integrated electrodes [112,113,114].

Introduction

The goal of this review is to define the challenges associated with recapitulating the human BBB in in vitro models and to provide perspective on future model development. First, the BBBs salient features will be outlined and its cellular components reviewed. Then, design criteria for developing a dynamic, multicellular, human BBB model will be established and recent progress towards these goals will be reviewed.