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Non-coding variantsin the human genome significantly influence human traits and complex diseases via their regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences. However, it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods. To solve this issue, we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets: (1) rare germline variants from clinical relevant sequence variants (ClinVar), (2) rare somatic variants from Catalogue Of Somatic Mutations In Cancer (COSMIC), (3) common regulatory variants from curated expression quantitative trait locus (eQTL) data, and (4) disease-associated common variants from curated genome-wide association studies (GWAS). All 24 tested methods performed differently under various conditions, indicating varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve (AUROC) of 0.4481–0.8033 and poor for rare somatic variants from COSMIC (AUROC = 0.4984–0.7131), common regulatory variants from curated eQTL data (AUROC = 0.4837–0.6472), and disease-associated common variants from curated GWAS (AUROC = 0.4766–0.5188). We also compared the prediction performance of 24 methods for non-coding de novomutations in autism spectrum disorder, and found that the combined annotation-dependent depletion (CADD) and context-dependent tolerance score (CDTS) methods showed better performance. Summarily, we assessed the performance of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.

Non-coding variantsin the human genome significantly influence human traits and complex diseases via their regulation and modification effects. Hence, an increasing number of computational methods are developed to predict the effects of variants in human non-coding sequences. However, it is difficult for inexperienced users to select appropriate computational methods from dozens of available methods. To solve this issue, we assessed 12 performance metrics of 24 methods on four independent non-coding variant benchmark datasets: (1) rare germline variants from clinical relevant sequence variants (ClinVar), (2) rare somatic variants from Catalogue Of Somatic Mutations In Cancer (COSMIC), (3) common regulatory variants from curated expression quantitative trait locus (eQTL) data, and (4) disease-associated common variants from curated genome-wide association studies (GWAS). All 24 tested methods performed differently under various conditions, indicating varying strengths and weaknesses under different scenarios. Importantly, the performance of existing methods was acceptable for rare germline variants from ClinVar with the area under the receiver operating characteristic curve (AUROC) of 0.4481–0.8033 and poor for rare somatic variants from COSMIC (AUROC = 0.4984–0.7131), common regulatory variants from curated eQTL data (AUROC = 0.4837–0.6472), and disease-associated common variants from curated GWAS (AUROC = 0.4766–0.5188). We also compared the prediction performance of 24 methods for non-coding de novomutations in autism spectrum disorder, and found that the combined annotation-dependent depletion (CADD) and context-dependent tolerance score (CDTS) methods showed better performance. Summarily, we assessed the performance of 24 computational methods under diverse scenarios, providing preliminary advice for proper tool selection and guiding the development of new techniques in interpreting non-coding variants.

High-fidelity in vitrotumor models are important for preclinical drug discovery processes. Currently, the most commonly used model for in vitrodrug testing remains the two-dimensional (2D) cell monolayer. However, the natural in vivotumor microenvironment (TME) consists of extracellular matrix (ECM), supporting stromal cells and vasculature. They not only participate in the progression of tumors but also hinder drug delivery and effectiveness on tumor cells. Here, we report an integrated engineering system to generate vessel-supported tumors for preclinical drug screening. First, gelatin-methacryloyl (GelMA) hydrogel was selected to mimic tumor extracellular matrix (ECM). HCT-116 tumor cells were encapsulated into individual micro-GelMA beads with microfluidic droplet technique to mimic tumor–ECM interactions in vitro. Then, normal human lung fibroblasts were mingled with tumor cells to imitate the tumor–stromal interaction. The tumor cells and fibroblasts reconstituted in the individual GelMA microbead and formed a biomimetic heterotypic tumor model with a core–shell structure. Next, the cell-laden beads were consociated into a functional on-chip vessel network platform to restore the tumor–tumor microenvironment (TME) interaction. Afterward, the anticancer drug paclitaxel was tested on the individual and vessel-supported tumor models. It was demonstrated that the blood vessel-associated TME conferred significant additional drug resistance in the drug screening experiment. The reported system is expected to enable the large-scale fabrication of vessel-supported heterotypic tumor models of various cellular compositions. It is believed to be promising for the large-scale fabrication of biomimetic in vitrotumor models and may be valuable for improving the efficiency of preclinical drug discovery processes.

High performance extended math library is used by many scientific engineering and artificial intelligence applications, which usually involves many common mathematical computations and the most time-consuming functions. In order to take full advantage of the high performance processors, these functions need to be parallelized and optimized intensively. It is common for processor vendors to supply highly optimized commercial math library. For example, Intel maintains oneMKL, and NVIDIA has cuBLAS, cuSolver, and cuFFT. In this paper, we release a new-generation high-performance extended math library, xMath 2.0, specifically designed for the SW26010-Pro many-core processor, which includes four major modules:BLAS, LAPACK, FFT, and SPARSE. Each module is optimized for the domestic SW26010-Pro processor, leveraging parallelization on the many-core CPE mesh and optimization techniques such as assembly instruction rearrangement and computation-communication overlapping. In xMath2.0, the BLAS module has an average performance increase of 146.02 times over the MPE version of GotoBLAS2, and the performance of BLAS level 3 functions has increased by 393.95 times. The LAPACK module (calling xMath BLAS) is 233.44 times better than LAPACK (calling GotoBLAS2). And the FFT module is 47.63 times faster than FFTW3.3.2. The library has been deployed on the domestic Sunway TaihuLight Pro supercomputer, which have been used by dozens of users.

Cell-free expression (CFE) systems have been used extensively in systems and synthetic biology as a promising platform for manufacturing proteins and chemicals. Currently, the most widely used CFE system is in vitroprotein transcription and translation platform. As the rapidly increased applications and uses, it is crucial to have a standard biophysical model for quantitative studies of gene circuits, which will provide a fundamental understanding of basic working mechanisms of CFE systems. Current modeling approaches mainly focus on the characterization of E. coli-based CFE systems, a computational model that can be utilized for both bacterial- and mammalian-based CFE has not been investigated. Here, we developed a simple ODE (ordinary differential equation)-based biophysical model to simulate transcription and translation dynamics for both bacterial- and mammalian- based CFE systems. The key parameters were estimated and adjusted based on experimental results. We next tested four gene circuits to characterize kinetic dynamics of transcription and translation in E. coli- and HeLa-based CFE systems. The real-time transcription and translation were monitored using Broccoli aptamer, double stranded locked nucleic acid (dsLNA) probe and fluorescent protein. We demonstrated the difference of kinetic dynamics for transcription and translation in both systems, which will provide valuable information for quantitative genomic and proteomic studies. This simple biophysical model and the experimental data for both E. coli- and HeLa-based CFE will be useful for researchers that are interested in genetic engineering and CFE bio-manufacturing.

Carbon nanofibers (CNFs) have received extensive and in-depth studied as anodes for sodium-ion batteries (SIBs), and yet their initial Coulombic efficiency and rate capability remain enormous challenge at practical level. Herein, CNFs anchored with cobalt nanocluster (CNFs-Co) were prepared using chemical vapor deposition and thermal reduction methods. The as-prepared CNFs-Co shows a high initial Coulombic efficiency of 91% and a high specific discharge capacity of 246 mAh/g at 0.1 A/g after 200 cycles as anode for SIBs. Meanwhile, the CNFs-Co anode still delivers a high cycling stability with 108 mAh/g after 1000 cycles at 10 A/g. These excellent electrochemical properties could be attributed to the involved spin state Co, which endows CNFs with large interplanar spacing (0.39 nm) and abundant vacancy defects. Importantly, the spin state Co downshifts the p-band center of carbon and strengthens the Na+adsorption energy from −2.33 eV to −2.64 eV based on density functional theory calculation. This novel strategy of modulating the carbon electronic structure by the spin state of magnetic metals provides a reference for the development of high-performance carbon-based anode materials.

Construction of in vitrovascular models is of great significance to various biomedical research, such as pharmacokinetics and hemodynamics, and thus is an important direction in the tissue engineering field. In this work, a standing surface acoustic wave field was constructed to spatially arrange suspended endothelial cells into a designated acoustofluidic pattern. The cell patterning was maintained after the acoustic field was withdrawn within the solidified hydrogel. Then, interstitial flow was provided to activate vessel tube formation. In this way, a functional vessel network with specific vessel geometry was engineered on-chip. Vascular function, including perfusability and vascular barrier function, was characterized by microbead loading and dextran diffusion, respectively. A computational atomistic simulation model was proposed to illustrate how solutes cross the vascular membrane lipid bilayer. The reported acoustofluidic methodology is capable of facile and reproducible fabrication of the functional vessel network with specific geometry and high resolution. It is promising to facilitate the development of both fundamental research and regenerative therapy.

Background:Tetrahydrobiopterin (BH4) is a naturally occurring pteridine and cofactor for a variety of enzymes, including phenylalanine-4-hydroxylase, nitric oxide synthetase and glyceryl ether monooxygenase. BH4 is readily oxidized to dihydrobiopterin and biopterin (B), however only BH4 can provide proper cofactor functions. BH4 is the active ingredient in Kuvan™ for the treatment of phenylketonuria. In order to measure BH4 in plasma from nonclinical and clinical samples with good accuracy, precision, sensitivity and robustness, an LC–MS/MS method was validated. To overcome the oxidation of BH4 in postcollection plasma, the approach was to measure the concentration of BH4 indirectly by measuring B concentration and applying an oxidation conversion ratio. Different endogenous levels of BH4 are determined in human, monkey, dog, rabbit, rat and mouse plasma. Furthermore, the conversion ratio of BH4 to B for each species is different and determined empirically. Plasma is transferred into cryogenic vials containing 0.1% dithioerythritol to prevent oxidation of BH4. The samples are then extracted and oxidized under basic conditions. B is measured with LC–MS/MS using negative ion mode. Results:The method is accurate, and precise to within 15%. The lower limit of quantitation in matrix is 5, 50 or 100 ng/ml, depending on the species endogenous levels of BH4. The pharmacokinetics of a single oral dose at three concentrations of BH4 administered to C57BL/6 mice is presented. In this mouse study, the T1/2of BH4 in plasma was approximately 1.2 h. Conclusion:The validated LC–MS/MS method to determine plasma BH4 concentration described herein has been used to support many nonclinical and clinical toxicokinetic and pharmacokinetic studies. BH4 is sensitive to oxidation and has a complicated biology. The method successfully supported the approval of Kuvan for the treatment of phenylketonuria.

The status and progress of R&D activities of silicon-based thin-film solar cells in China are described briefly in this paper, including amorphous Si solar cells and microcrystalline Si film solar cells based on PECVD technology and polycrystalline film solar cells based on RTCVD technology. Especially, the microcrystalline thin-film solar cells and the tandem solar cells of amorphous Si with microcrystalline Si have made great progress. The polycrystalline film solar cells have made remarkable achievements as well. (© 2006 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

An improved pulsed rapid thermal annealing method has been used to crystallize amorphous silicon films prepared by PECVD. The solid-phase crystallization and dopant activation process can be completed with time–temperature budgets such as 10 cycles of 60-s 550°C thermal bias/1-s 850°C thermal pulse. A mean grain size more than 1000 Å and a Hall mobility of 24.9 cm2/V s are obtained in the crystallized films. The results indicate that this annealing method possesses the potential for fabricating large-area and good-quality polycrystalline silicon films on low-cost glass substrate.

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