Robust methods for accurate diagnosis using pan-microbiological oligonucleotide microarrays

Abstract Background To address the limitations of traditional virus and pathogen detection methodologies in clinical diagnosis, scientists have developed high-throughput oligonucleotide microarrays to rapidly identify infectious agents. However, objectively identifying pathogens from the complex hybridization patterns of these massively multiplexed arrays remains challenging. Methods In this study, we conceived an automated method based on the hypergeometric distribution for identifying pathogens in multiplexed arrays and compared it to five other methods. We evaluated these metrics: 1) accurate prediction, whether the top ranked prediction(s) match the real virus(es); 2) four accuracy scores. Results Though accurate prediction and high specificity and sensitivity can be achieved with several methods, the method based on hypergeometric distribution provides a significant advantage in term of positive predicting value with two to sixty folds the positive predicting values of other methods. Conclusion The proposed multi-specie array analysis based on the hypergeometric distribution addresses shortcomings of previous methods by enhancing signals of positively hybridized probes.http://deepblue.lib.umich.edu/bitstream/2027.42/112825/1/12859_2009_Article_3270.pd

Integral techno-economic comparison and greenhouse gas balances of different production routes of aromatics from biomass with CO₂ capture

The techno-economic performance and CO2 equivalent (CO2eq) reduction potential of bio-based aromatic production cases with and without CO2 capture and storage (CCS) have been evaluated and compared to those of fossil-based aromatic production. The bio-cases include tail gas reactive pyrolysis (TGRP), catalytic pyrolysis (CP), hydrothermal liquefaction (HTL), gasification-methanol-aromatics (GMA), and Diels-Alder of furan/furfural combined with catalytic pyrolysis of lignin (FFCA). The crude oil-based naphtha catalytic reforming (NACR) routes have GHG emissions of 43.4 and 43.9 t CO2eq/t aromatics with and without CCS (NACR-CCS), respectively. Except for HTL, all the biomass cases with CCS show negative emissions between −6.1 and −1.1 t CO2eq/t aromatics with avoidance costs ranging from 27.7 to 93.3 $/t CO2eq. Under favorable conditions, GMA with CCS (GMA-CCS) has the lowest emissions (−14.6 t CO2eq/t aromatics), while CP with CCS (CP-CCS) shows the lowest avoidance cost (12.3$/t CO2eq). All biomass based aromatics production techniques are currently at the laboratory or demonstration stages, except for CP, which has pilot plants. The results indicate that bio-based aromatics production, with their reasonable avoidance costs and low, or potentially negative, greenhouse gas (GHG) emissions, are an attractive option to compensate for the expected aromatic production shortages in the coming decades

PFGDF: Pruning Filter via Gaussian Distribution Feature for Deep Neural Networks Acceleration

The existence of a lot of redundant information in convolutional neural networks leads to the slow deployment of its equipment on the edge. To solve this issue, we proposed a novel deep learning model compression acceleration method based on data distribution characteristics, namely Pruning Filter via Gaussian Distribution Feature(PFGDF) which was to found the smaller interval of the convolution layer of a certain layer to describe the original on the grounds of distribution characteristics . Compared with revious advanced methods, PFGDF compressed the model by filters with insignificance in distribution regardless of the contribution and sensitivity information of the convolution filter. The pruning process of the model was automated, and always ensured that the compressed model could restore the performance of original model. Notably, on CIFAR-10, PFGDF compressed the convolution filter on VGG-16 by 66:62%, the parameter reducing more than 90%, and FLOPs achieved 70:27%. On ResNet-32, PFGDF reduced the convolution filter by 21:92%. The parameter was reduced to 54:64%, and the FLOPs exceeded 42

Plane Double-Layer Structure of AC@S Cathode Improves Electrochemical Performance for Lithium-Sulfur Battery

Due to the high theoretical specific capacity of lithium-sulfur batteries, it is considered the most promising electrochemical energy storage device for the next generation. However, the development of lithium-sulfur battery has been restricted by its low cycle efficiency and low capacity. We present a Plane double-layer structure of AC@S cathode to improve the electrochemical performance of lithium-sulfur batteries. The battery with this cathode showed good electrochemical performance. The initial discharge capacity of the battery with the structure of AC@S cathode could reach 1,166 mAhg−1 at 0.1 C. After 200 cycles, it still remains a reversible capacity of 793 mAh g−1 with a low fading rate of 0.16% per cycle. Furthermore, the batteries could hold a discharge capacity of 620 mAh g−1 after 200 cycles at a typical 0.5 C rate. The improvement of electrochemical performance is attributed to that the polysulfide produced during charge/discharge can be better concentrated in the cathode by the planar double-layer structure, thus reducing the loss of sulfur