Modeling the Performance of an Integrated Photoelectrolysis System with 10× Solar Concentrators

Two designs for an integrated photoelectrolysis system that uses a 10× concentrating solar collector have been investigated in detail. The system performance was evaluated using a multi-physics model that accounted for the properties of the tandem photoabsorbers, mass transport, and the electrocatalytic performance of the oxygen-evolution and hydrogen-evolution reactions (OER and HER, respectively). The solar-to-hydrogen (STH) conversion efficiencies and the ohmic losses associated with proton transport in the solution electrolyte and through the membrane of the photoelectrolysis system were evaluated systematically as a function of the cell dimensions, the operating temperatures, the bandgap combinations of the tandem cell, and the performance of both the photoabsorbers and electrocatalysts. Relative to designs of optimized systems that would operate without a solar concentrator, the optimized 10× solar concentrator designs possessed larger ohmic losses and exhibited less uniformity in the distribution of the current density along the width of the photoelectrode. To minimize resistive losses while maximizing the solar-to-hydrogen conversion efficiency, η_(STH), both of the designs, a two-dimensional “trough” design and a three-dimensional “bubble wrap” design, required that the electrode width or diameter, respectively, was no larger than a few millimeters. As the size of the electrodes increased beyond this limiting dimension, the η_(STH) became more sensitive to the performance of the photoabsorbers and catalysts. At a fixed electrode dimension, increases in the operating temperature reduced the efficiency of cells with smaller electrodes, due to degradation in the performance of the photoabsorber with increasing temperature. In contrast, cells with larger electrode dimensions showed increases in efficiency as the temperature increased, due to increases in the rates of electrocatalysis and due to enhanced mass transport. The simulations indicted that cells that contained 10% photoabsorber area, and minimal amounts of Nafion or other permselective membranes (i.e. areal coverages and volumetric fractions of only a few percent of the cell), with the remaining area comprised of a suitable, low-cost inert, non porous material (flexible polymers, inert inorganic materials, etc.) should be able to produce high values of η_(STH), with η_(STH) = 29.8% for an optimized design with a bandgap combination of 1.6 eV/0.9 eV in a tandem photoabsorber system at 350 K

A sensitivity analysis to assess the relative importance of improvements in electrocatalysts, light absorbers, and system geometry on the efficiency of solar-fuels generators

A sensitivity analysis has been performed for a variety of generic designs for solar-fuels generators. The analysis has revealed the relative importance of reductions in the overpotentials of electrocatalysts, of improvements in the materials properties of light absorbers, and of optimization in the system geometry for various different types of solar-fuels generators, while considering operation at a range of temperatures as well as under a variety of illumination intensities including up to 10-fold optical concentration

Topology-Aware Uncertainty for Image Segmentation

Segmentation of curvilinear structures such as vasculature and road networks is challenging due to relatively weak signals and complex geometry/topology. To facilitate and accelerate large scale annotation, one has to adopt semi-automatic approaches such as proofreading by experts. In this work, we focus on uncertainty estimation for such tasks, so that highly uncertain, and thus error-prone structures can be identified for human annotators to verify. Unlike most existing works, which provide pixel-wise uncertainty maps, we stipulate it is crucial to estimate uncertainty in the units of topological structures, e.g., small pieces of connections and branches. To achieve this, we leverage tools from topological data analysis, specifically discrete Morse theory (DMT), to first capture the structures, and then reason about their uncertainties. To model the uncertainty, we (1) propose a joint prediction model that estimates the uncertainty of a structure while taking the neighboring structures into consideration (inter-structural uncertainty); (2) propose a novel Probabilistic DMT to model the inherent uncertainty within each structure (intra-structural uncertainty) by sampling its representations via a perturb-and-walk scheme. On various 2D and 3D datasets, our method produces better structure-wise uncertainty maps compared to existing works.Comment: 19 pages, 13 figures, 5 table

A Bayesian Downscaler Model to Estimate Daily PM2.5 levels in the Continental US

There has been growing interest in extending the coverage of ground PM2.5 monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, satellite based monitoring network has a strong potential to complement the ground monitor system in terms of the spatial-temporal availability of the air quality data. However, most existing calibration models focused on a relatively small spatial domain and cannot be generalized to national-wise study. In this paper, we proposed a statistically reliable and interpretable national modeling framework based on Bayesian downscaling methods with the application to the calibration of the daily ground PM2.5 concentrations across the Continental U.S. using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors in 2011. Our approach flexibly models the PM2.5 versus AOD and the potential related geographical factors varying across the climate regions and yields spatial and temporal specific parameters to enhance the model interpretability. Moreover, our model accurately predicted the national PM2.5 with a R2 at 70% and generates reliable annual and seasonal PM2.5 concentration maps with its SD. Overall, this modeling framework can be applied to the national scale PM2.5 exposure assessments and also quantify the prediction errors.Comment: 14 pages, 6 figure

假基因来源的lncRNA DUXAP8表观遗传沉默CDKN1A和KLF2促进胰腺癌细胞的生长

背景与目的最近研究表明假基因来源的长链非编码RNA(long non-coding RNAs,lncRNA)是癌症的关键调控因子。然而,在胰腺癌中却鲜有对lncRNA的鉴定和研究。我们旨在明确假基因来源的lncRNA DUXAP8与胰腺癌细胞生长的关系。方法我们通过比较分析3个来自GEO的独立数据集,筛选出与人胰腺癌相关的假基因来源的lncRNA。通过实时定量反转录PCR检测DUXAP8在胰腺癌组织和细胞中的相对表达水平。应用功能缺失的方法在体外和体内研究DUXAP8对胰腺癌细胞的增殖和凋亡的作用。在胰腺癌细胞中通过RNA免疫沉淀、染色质免疫沉淀和挽救实验分析DUXAP8与靶蛋白和基因的关系。结果 DUXAP8在胰腺癌组织中的表达水平显著高于配对的癌旁正常组织。DUXAP8的高表达与胰腺癌患者的肿瘤体积较大、病理分期较晚和总生存期较短相关。此外,在体外和体内通过siRNA或shRNA沉默DUXAP8表达可抑制胰腺癌细胞增殖并促进细胞凋亡。机制研究表明,DUXAP8部分地通过下调肿瘤抑制因子CDKN1A和KLF2表达调控胰腺癌细胞增殖。结论我们的结果表明,假基因来源的lncRNA DUXAP8的表达在胰腺癌进展中起到重要作用。DUXAP8可作为胰腺癌的候选生物标志物及新的治疗靶点

Increased Expression of Ganglioside GM1 in Peripheral CD4+ T Cells Correlates Soluble Form of CD30 in Systemic Lupus Erythematosus Patients

Gangliosides GM1 is a good marker of membrane microdomains (lipid rafts) with important function in cellular activation processes. In this study we found that GM1 expression on CD4+ T cells and memory T cells (CD45RO/CD4) were dramatic increased after stimulation with phytohaemagglutinin in vitro. Next, we examined the GM1 expression on peripheral blood CD4+ T cells and CD8+ T cells from 44 patients with SLE and 28 healthy controls by flow cytometry. GM1 expression was further analyzed with serum soluble CD30 (sCD30), IL-10, TNF-alpha and clinical parameters. The mean fluorescence intensity of GM1 on CD4+ T cells from patients with SLE was significantly higher than those from healthy controls, but not on CD8+ T cells. Increased expression of GM1 was more marked on CD4+/CD45RO+ memory T cells from active SLE patients. Patients with SLE showed significantly elevated serum sCD30 and IL-10, but not TNF-alpha levels. In addition, we found that enhanced GM1 expression on CD4+ T cells from patients with SLE positively correlated with high serum levels of sCD30 and IgG as well as disease activity (SLEDAI scores). Our data suggested the potential role of aberrant lipid raft/GM1 on CD4+ T cells and sCD30 in the pathogenesis of SLE

Tumor-Intrinsic Sirpa Promotes Sensitivity to Checkpoint Inhibition Immunotherapy in Melanoma

Checkpoint inhibition immunotherapy has revolutionized cancer treatment, but many patients show resistance. Here we perform integrative transcriptomic and proteomic analyses on emerging immuno-oncology targets across multiple clinical cohorts of melanoma under anti-PD-1 treatment, on both bulk and single-cell levels. We reveal a surprising role of tumor-intrinsic SIRPA in enhancing antitumor immunity, in contrast to its well-established role as a major inhibitory immune modulator in macrophages. The loss of SIRPA expression is a marker of melanoma dedifferentiation, a key phenotype linked to immunotherapy efficacy. Inhibition of SIRPA in melanoma cells abrogates tumor killing by activated CD

Investigating factors affecting road freight overloading through the integrated use of BLR and CART: a case study in China

Overloading of road freight vehicles accelerates road damage, creates unfair competition in the transport market, and increases safety risk. There is a dearth of research on the mining of data of highway Freight Weight (FW), and this paper therefore aims to discover factors affecting road freight overloading based on highway FW data, with a view of developing strategies to mitigate such occurrences. A comprehensive sampling survey of road freight transportation was conducted in Anhui Province (China). Vehicle Characteristics (VC), Operation Mode (OM), and transportation information from a total of 3248 trucks were collected. In order to take advantage of the strengths associated with both statistical modelling techniques and non-parametric methods, a Classification And Regression Tree (CART) technique was integrated with Binary Logistic Regression (BLR) to reveal the factors affecting road freight overloading. The classification efficacy test shows that the BLR–CART method outperformed the BLR method in term of accuracy. It is also revealed that the factors affecting overloading of freight vehicles are the Type of Transportation (ToT), Rated Load (RL), OM, FW during the investigation period, interaction between RL and FW, and interaction among RL, FW, and Average Haul Distance (AHD). Road transport authorities should pay greater attention to these factors in order to improve efficiency and effectiveness of overloading inspection

Evaluation of transmission reach and information rates in nonlinear optical fiber communication systems

Coherent optical fiber systems can achieve long-distance, large-capacity and high data-rate transmissions. The system performance of communication systems is generally evaluated with regard to the data capacity and the transmission reach. In this work, the performance of multi-channel (up to C-band) Nyquist-spaced coherent optical communication systems has been assessed in terms of achievable information rates, transmission distances and signal-to-noise ratios, considering different influencing factors, such as nonlinearity compensation, signal input power and modulation format. Numerical simulations and enhanced Gaussian noise (EGN) model have been carried out for different modulation formats including quadrature phase shift keying (QPSK), 16-ary quadrature amplitude modulation (16-QAM), 64-QAM and 256-QAM. It is found that in C-band (151-channel) Nyquist-spaced systems, the achievable information rates at the transmission distance of 6000 km are 19.3 Tbit/s for dual-polarization QPSK (DP-QPSK), 30.9 Tbit/s for DP-16QAM, 32.0 Tbit/s for DP64QAM and 32.2 Tbit/s for DP-256QAM, respectively, when electronic dispersion compensation is applied only. Such achievable information rates can be increased up to 38.3 Tbit/s for DP-16QAM, 47.2 Tbit/s for DP-64QAM and 47.8 Tbit/s for DP-256QAM, respectively, when the nonlinearity compensation is employed