An acetone microsensor with a ring oscillator circuit fabricated using the commercial 0.18 μm CMOS process

This study investigates the fabrication and characterization of an acetone microsensor with a ring oscillator circuit using the commercial 0.18 μm complementary metal oxide semiconductor (CMOS) process. The acetone microsensor contains a sensitive material, interdigitated electrodes and a polysilicon heater. The sensitive material is α-Fe2O3 synthesized by the hydrothermal method. The sensor requires a post-process to remove the sacrificial oxide layer between the interdigitated electrodes and to coat the α-Fe2O3 on the electrodes. When the sensitive material adsorbs acetone vapor, the sensor produces a change in capacitance. The ring oscillator circuit converts the capacitance of the sensor into the oscillation frequency output. The experimental results show that the output frequency of the acetone sensor changes from 128 to 100 MHz as the acetone concentration increases 1 to 70 ppm

A Web-Services-Based P2P Computing-Power Sharing Architecture

As demands of data processing and computing power are increasing, existing information system architectures become insufficient. Some organizations try to figure out how to keep their systems work without purchasing new hardware and software. Therefore, a Webservices-based model which shares the resource over the network like a P2P network will be proposed to meet this requirement in this paper. In addition, this paper also discusses some problems about security, motivation, flexibility, compatibility and workflow management for the traditional P2P power sharing models. Our new computing architecture - Computing Power Services (CPS) - will aim to address these problems. For the shortcomings about flexibility, compatibility and workflow management, CPS utilizes Web Services and Business Process Execution Language (BPEL) to overcome them. Because CPS is assumed to run in a reliable network where peers trust each other, the concerns about security and motivation will be negated. In essence, CPS is a lightweight Web-Services-based P2P power sharing environment and suitable for executing computing works in batch in a reliable networ

Unusual dyspnea in a hemodialysis patient: A case report

The typical clinical symptoms of hemothorax include a rapid development of chest pain or dyspnea, which may be life-threatening without immediate management. As we know, spontaneous hemothorax, a collection of blood within the pleural cavity without previous history of trauma or other cause, which usually onsets suddenly. The early and accurate diagnosis of spontaneous hemothorax is imperative in clinical practice. We reported a middle-age male undergoing regular hemodialysis was referred to our emergency department due to unknown cause of dyspnea and acute respiratory failure. Chest radiography revealed bilateral patchy infiltration of lung. Pleural tap analysis showed exudative pleural effusion with numerous red blood cells. Video-assisted thoracic surgery (VATS) were performed and confirmed the final diagnosis of spontaneous hemothorax. He was then successfully treated with the surgery of VATS combined chest tube thoracostomy

The Processing and Electrical Properties of Isotactic Polypropylene/Copper Nanowire Composites

Funding Information: The authors would like to thank MOST for financially supporting this work under grant No. MOST 110-2224-E-038-001. Publisher Copyright: © 2022 by the authors.Polypropylene (PP), a promising engineering thermoplastic, possesses the advantages of light weight, chemical resistance, and flexible processability, yet preserving insulative properties. For the rising demand for cost-effective electronic devices and system hardware protections, these applications require the proper conductive properties of PP, which can be easily modified. This study investigates the thermal and electrical properties of isotactic polypropylene/copper nanowires (i-PP/CuNWs). The CuNWs were harvested by chemical reduction of CuCl 2 using a reducing agent of glucose, capping agent of hexadecylamine (HDA), and surfactant of PEG-7 glyceryl cocoate. Their morphology, light absorbance, and solution homogeneity were investigated by SEM, UV-visible spectrophotometry, and optical microscopy. The averaged diameters and the length of the CuNWs were 66.4 ± 16.1 nm and 32.4 ± 11.8 µm, respectively. The estimated aspect ratio (L/D, length-to-diameter) was 488 ± 215 which can be recognized as 1-D nanomaterials. Conductive i-PP/CuNWs composites were prepared by solution blending using p-xylene, then melt blending. The thermal analysis and morphology of CuNWs were characterized by DSC, polarized optical microscopy (POM), and SEM, respectively. The melting temperature decreased, but the crystallization temperature increasing of i-PP/CuNWs composites were observed when increasing the content of CuNWs by the melt blending process. The WAXD data reveal the coexistence of Cu 2O and Cu in melt-blended i-PP/CuNWs composites. The fit of the electrical volume resistivity (ρ) with the modified power law equation: ρ = ρ o (V - Vc) -t based on the percolation theory was used to find the percolation concentration. A low percolation threshold value of 0.237 vol% and high critical exponent t of 2.96 for i-PP/CuNWs composites were obtained. The volume resistivity for i-PP/CuNWs composite was 1.57 × 10 7 Ω-cm at 1 vol% of CuNWs as a potential candidate for future conductive materials.publishersversionPeer reviewe

easyExon – A Java-based GUI tool for processing and visualization of Affymetrix exon array data

<p>Abstract</p> <p>Background</p> <p>Alternative RNA splicing greatly increases proteome diversity and thereby contribute to species- or tissue-specific functions. The possibility to study alternative splicing (AS) events on a genomic scale using splicing-sensitive microarrays, including the Affymetrix GeneChip Exon 1.0 ST microarray (exon array), has appeared very recently. However, the application of this new technology is hindered by the lack of free and user-friendly software devoted to these novel platforms.</p> <p>Results</p> <p>In this study we present a Java-based freeware, easyExon <url>http://microarray.ym.edu.tw/easyexon</url>, to process, filtrate and visualize exon array data with an analysis pipeline. This tool implements the most commonly used probeset summarization methods as well as AS-orientated filtration algorithms, e.g. MIDAS and PAC, for the detection of alternative splicing events. We include a biological filtration function according to GO terms, and provide a module to visualize and interpret the selected exons and transcripts. Furthermore, easyExon can integrate with other related programs, such as Integrate Genome Browser (IGB) and Affymetrix Power Tools (APT), to make the whole analysis more comprehensive. We applied easyExon on a public accessible colon cancer dataset as an example to illustrate the analysis pipeline of this tool.</p> <p>Conclusion</p> <p>EasyExon can efficiently process and analyze the Affymetrix exon array data. The simplicity, flexibility and brevity of easyExon make it a valuable tool for AS event identification in genomic research.</p

A Deep Learning Approach for Predicting Antidepressant Response in Major Depression Using Clinical and Genetic Biomarkers

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1–3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy

A powerful and efficient multivariate approach for voxel-level connectome-wide association studies

We describe an approach to multivariate analysis, termed structured kernel principal component regression (sKPCR), to identify associations in voxel-level connectomes using resting-state functional magnetic resonance imaging (rsfMRI) data. This powerful and computationally efficient multivariate method can identify voxel-phenotype associations based on the whole-brain connectivity pattern of voxels, and it can detect linear and non-linear signals in both volume-based and surface-based rsfMRI data. For each voxel, sKPCR first extracts low-dimensional signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations by an adaptive regression model. The method's power is derived from appropriately modelling the spatial structure of the data when performing dimension reduction, and then adaptively choosing an optimal dimension for association testing using the adaptive regression strategy. Simulations based on real connectome data have shown that sKPCR can accurately control the false-positive rate and that it is more powerful than many state-of-the-art approaches, such as the connectivity-wise generalized linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). Moreover, since sKPCR can reduce the computational cost of non-parametric permutation tests, its computation speed is much faster. To demonstrate the utility of sKPCR for real data analysis, we have also compared sKPCR with the above methods based on the identification of voxel-wise differences between schizophrenic patients and healthy controls in four independent rsfMRI datasets. The results showed that sKPCR had better between-sites reproducibility and a larger proportion of overlap with existing schizophrenia meta-analysis findings. Code for our approach can be downloaded from https://github.com/weikanggong/sKPCR. [Abstract copyright: Copyright © 2018 Elsevier Inc. All rights reserved.