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

The majority of the surface area and thus transport within the BBB occurs in capillaries, which exhibit an average diameter of around 8m in humans [3]. These capillaries are perfusable to fluorescent beads and maintain barrier function when perfused with fluorescent dextran [121]. Yet the smallest microvessels fabricated through any of these techniques is approximately 2050m, due to the difficulties in achieving sufficiently high EC seed density in small channels without clogging [118, 122]. Non-brain capillary formation has recently been observed between adjacent microvessels in vitro. A difficult challenge facing in vitro BBB platforms is the fabrication of perfusable, capillary-dimension vascular networks. The prevalent approaches to overcome this issue are to stimulate capillary angiogenesis from larger microvessels, or to stimulate vasculogenesis of ECs embedded in a matrix.

(2010) built an ensemble regression model using random forest (RF) for drug sensitivity prediction in NCI-60 cell line panel. The model was also used to create drug-specific gene expression signatures and identify core cell lines associated with each drugs response. Other applications of RF include, e.g., Menden et al. (2017). Riddick et al. (2016), and Rahman et al. (2013), Nguyen et al.

2014), but we also extend to newer machine learning models introduced after the Challenge, especially those that also implement feature selection techniques to identify such combinations of genomic and other features from the multi-omics profiles that are most predictive of the drug response phenotypes. The primary focus here is on the NCI-DREAM7 Drug Sensitivity Prediction Challenge (Costello et al. Such predictive panels of biomarkers are critical for clinical translation. The selected machine learning models and omics measurements are described and compared here in the context of the DREAM Challenges, which provide systematic and objective means to assess the predictive power of the models and measurements by means of large-enough validation datasets that are hidden to the Challenge participants, and therefore can be used as independent test data. The Challenges organized by the Dialogue for Reverse Engineering Assessment and Methods (DREAM, http://dreamchallenges.org/) implement a community-based crowdsourcing solution for complex questions in biology and medicine, through collaborative competitions and open-data sharing, hosted by the Sage Bionetworks (http://sagebase.org/).

It is reported that the susceptibility of domestic pigs to HPAI H5N1 is low [87] and the HPAI H5N1 viruses are not transmitted among pigs under experimental conditions [88]. Therefore, they are regarded as a potential mixing vessel for avian and human influenza and the main intermediate host for AI viruses to make the appropriate genetic changes in order to infect humans [83-85]. Pigs have receptors that correspond to the AI-specific -2,3-NeuAcGal sialic acid linkage and human influenza-specific -2,6-NeuAcGal sialic acid linkage [81,82]. However, there was no evidence that pigs had transmitted wholly AI viruses of H5N1 and other subtypes to humans [86]. A field study showed that no sera positive for H5 was detected in samples collected from Fujian Province, China in 2004 and 2007 [89]. In addition, the swine H5N1 isolates were less virulent to mice than avian isolates [90].

4.INTER-SUBTYPE REASSORTMENT OF HPAI H5N1

hiPSC-derived BMECs have been obtained through a co-differentiation of ECs/neural cells, followed by a purification based on selective adhesion [20, 101,102,103]. hiPSC-derived BMECs also exhibit physiological values of TEER [20, 101,102,103]. In some cases, especially with low intrinsic TEER values, co-culture with pericytes and neural progenitor cell-derived astrocytes and neurons may increase TEER [23]. hiPSC-derived BMECs possess localized AJs and TJs, express BBB nutrient transporters and demonstrate polarized efflux of rhodamine 123 [20, 101,102,103].

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.

Astrocyte processes are terminated in end-feet that completely ensheath microvessels and capillaries in the brain [74]. This position as an intermediary allows astrocytes to coordinate key aspects of neurovascular coupling, including the regulation of blood flow to match local neuronal activity [29]. Astrocytes mediate signaling between neurons and BMECs. A single astrocyte contacts on average five different blood vessels and four different neuronal somata, supporting the function of roughly 2 million synapses [75, 76].

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.

In capillaries, these membranes are fused, while in post-capillary venules, they are separated by a perivascular gap, known as the Virchow-Robin space, a key location for leukocyte trafficking and immune cell regulation [4, 19, 64]. The basement membrane (BM) is a thin layer of extracellular matrix (ECM) surrounding the microvasculature. There are two layers of BM, with distinct composition, referred to as the vascular (or endothelial) BM and the parenchymal BM, located abluminal to the ECs and PCs, respectively [63]. The BM interacts with cells through physical and biomolecular pathways to mediate cell attachment and differentiation.

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An integrated BEMKL model based on the multi-omics profiling improved drug sensitivity prediction as compared to the prediction performance solely based on gene expression profile. Interestingly, this filtering reduced the MS data from 8113 proteins to 42 proteins, yet leading to statistically significant improvements. 2013). We further tested a number of general imputation methods as well as ones tailored for MS data (Webb-Robertson et al. 2018), we investigated the relative contribution of various omics profiles, focusing especially on the proteomics profiling for the drug sensitivity prediction in the NCI-60 pan-cancer cell line data (Shoemaker 2006). After considering only the completely measured proteins in MS-based profile, the predictive performance was increased significantly for molecularly-targeted drugs as compared to using MS data alone with all the proteins. 2018). The NCI-60 cell line panel comprises of 60 cell lines spanning over 9 cancer types, which are tested against ~15,000 anti-cancer therapeutics (Table 1). 2015), but these did not improve the prediction results as much as the data filtering (Ali et al. Multiple omics profiles are publicly available for these cell lines, including global mass spectrometry (MS)-based proteomic profiling (Gholami et al. Notably, although the global MS proteomic data includes a total of 8113 proteins, the NCI-60 cell lines contains, on average, 55% missing proteomic data, which greatly complicates the predictive modeling. In our recent work (Ali et al. However, considering only the completely measured census cancer genes from COSMIC (http://cancer.sanger.ac.uk/cosmic) for the MS and other omics profiles surprisingly improved drug response predictions for 75% of the NCI-60 drugs for both sets of selected 47 cytotoxic and 24 targeted drugs, separately.

Each target (protein) had hundreds of decoys. The best decoy in each target had a GDT score greater than 0.4, which ensured that the pool contained reasonably good decoys. We applied this method to three benchmark datasets from different model prediction methods. The third dataset contained 50 CASP 9 targets with decoys generated by our in-house template-based model generation tool MUFOLD. The second dataset consisted of 35 CASP 8 [20] targets predicted by Rosetta or Robetta. Figures 2, ,3,3, and and44 show the GDT distribution information, i.e., maximum, average and minimum GDT of each dataset respectively. The first dataset contained 56 targets with decoys generated by I-TASSER ab initio modeling method (http://zhanglab.ccmb.med.umich.edu/decoys/) [7].