22,133 research outputs found
Classifying continuous, real-time e-nose sensor data using a bio-inspired spiking network modelled on the insect olfactory system
In many application domains, conventional e-noses are frequently outperformed in both speed and accuracy by their biological counterparts. Exploring potential bio-inspired improvements, we note a number of neuronal network models have demonstrated some success in classifying static datasets by abstracting the insect olfactory system. However, these designs remain largely unproven in practical
settings, where sensor data is real-time, continuous, potentially noisy, lacks a precise onset signal and
accurate classification requires the inclusion of temporal aspects into the feature set. This investigation
therefore seeks to inform and develop the potential and suitability of biomimetic classifiers for use with typical real-world sensor data. Taking a generic classifier design inspired by the inhibition and
competition in the insect antennal lobe, we apply it to identifying 20 individual chemical odours from
the timeseries of responses of metal oxide sensors. We show that four out of twelve available sensors
and the first 30 s(10%) of the sensorsâ continuous response are sufficient to deliver 92% accurate
classification without access to an odour onset signal. In contrast to previous approaches, once
training is complete, sensor signals can be fed continuously into the classifier without requiring
discretization. We conclude that for continuous data there may be a conceptual advantage in using
spiking networks, in particular where time is an essential component of computation. Classification
was achieved in real time using a GPU-accelerated spiking neural network simulator developed in our
group
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Sugar Alcohols Have a Key Role in Pathogenesis of Chronic Liver Disease and Hepatocellular Carcinoma in Whole Blood and Liver Tissues.
The major risk factors for hepatocellular carcinoma (HCC) are hepatitis C and B viral infections that proceed to Chronic Liver Disease (CLD). Yet, the early diagnosis and treatment of HCC are challenging because the pathogenesis of HCC is not fully defined. To better understand the onset and development of HCC, untargeted GC-TOF MS metabolomics data were acquired from resected human HCC tissues and their paired non-tumor hepatic tissues (n = 46). Blood samples of the same HCC subjects (n = 23) were compared to CLD (n = 15) and healthy control (n = 15) blood samples. The participants were recruited from the National Liver Institute in Egypt. The GC-TOF MS data yielded 194 structurally annotated compounds. The most strikingly significant alteration was found for the class of sugar alcohols that were up-regulated in blood of HCC patients compared to CLD subjects (p < 2.4 Ă 10-12) and CLD compared to healthy controls (p = 4.1 Ă 10-7). In HCC tissues, sugar alcohols were the most significant (p < 1 Ă 10-6) class differentiating resected HCC tissues from non-malignant hepatic tissues for all HCC patients. Alteration of sugar alcohol levels in liver tissues also defined early-stage HCC from their paired non-malignant hepatic tissues (p = 2.7 Ă 10-6). In blood, sugar alcohols differentiated HCC from CLD subjects with an ROC-curve of 0.875 compared to 0.685 for the classic HCC biomarker alpha-fetoprotein. Blood sugar alcohol levels steadily increased from healthy controls to CLD to early stages of HCC and finally, to late-stage HCC patients. The increase in sugar alcohol levels indicates a role of aldo-keto reductases in the pathogenesis of HCC, possibly opening novel diagnostic and therapeutic options after in-depth validation
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Comparison of free tropospheric western Pacific air mass classification schemes for the PEM-West A experiment
During September/October 1991, NASA's Global Tropospheric Experiment (GTE) conducted an airborne field measurement program (PEM-West A) in the troposphere over the western Pacific Ocean. In this paper we describe and use the relative abundance of the combustion products C2H2 and CO to classify air masses encountered during PEM-West A based on the degree that these tracers were processed by the combined effects of photochemical reactions and dynamical mixing (termed the degree of atmospheric processing). A large number of trace compounds (e.g., C2H6, C3H8, C6H6, NOy, and O3) are found to be well correlated with the degree of atmospheric processing that is reflected by changes in the ratio of C2H2/CO over the range of values from âź0.3 to 2.0 (parts per trillion volume) C2H2/ (parts per billion volume) CO. This C2H2/CO-based classification scheme is compared to model simulations and to two independent classification schemes based on air mass back-trajectory analyses and lidar profiles of O3 and aerosols. In general, these schemes agree well, and in combination they suggest that the functional dependence that other observed species exhibit with respect to the C2H2/CO atmospheric processing scale can be used to study the origin, sources, and sinks of trace species and to derive several important findings. First, the degree of atmospheric processing is found to be dominated by dilution associated with atmospheric mixing, which is found to primarily occur through the vertical mixing of relatively recent emissions of surface layer trace species. Photochemical reactions play their major role by influencing the background concentrations of trace species that are entrained during the mixing (i.e., dilution) process. Second, a significant noncontinental source(s) of NO (and NOx) in the free troposphere is evident. In particular, the enhanced NO mixing ratios that were observed in convected air masses are attributed to either emissions from lightning or the rapid recycling of NOy compounds. Third, nonsoluble trace species emitted in the continental boundary layer, such as CO and hydrocarbons, are vertically transported to the upper troposphere as efficiently as they are to the midtroposphere. In addition, the mixing ratios of CO and hydrocarbons in the upper troposphere over the western Pacific may reflect a significant contribution from northern hemisphere land areas other than Asia. Finally, we believe that these results can be valuable for the quantitative evaluation of the vertical transport processes that are usually parameterized in models. Copyright 1996 by the American Geophysical Union
Microbial and metabolic succession on common building materials under high humidity conditions.
Despite considerable efforts to characterize the microbial ecology of the built environment, the metabolic mechanisms underpinning microbial colonization and successional dynamics remain unclear, particularly at high moisture conditions. Here, we applied bacterial/viral particle counting, qPCR, amplicon sequencing of the genes encoding 16S and ITS rRNA, and metabolomics to longitudinally characterize the ecological dynamics of four common building materials maintained at high humidity. We varied the natural inoculum provided to each material and wet half of the samples to simulate a potable water leak. Wetted materials had higher growth rates and lower alpha diversity compared to non-wetted materials, and wetting described the majority of the variance in bacterial, fungal, and metabolite structure. Inoculation location was weakly associated with bacterial and fungal beta diversity. Material type influenced bacterial and viral particle abundance and bacterial and metabolic (but not fungal) diversity. Metabolites indicative of microbial activity were identified, and they too differed by material
Set Aggregation Network as a Trainable Pooling Layer
Global pooling, such as max- or sum-pooling, is one of the key ingredients in
deep neural networks used for processing images, texts, graphs and other types
of structured data. Based on the recent DeepSets architecture proposed by
Zaheer et al. (NIPS 2017), we introduce a Set Aggregation Network (SAN) as an
alternative global pooling layer. In contrast to typical pooling operators, SAN
allows to embed a given set of features to a vector representation of arbitrary
size. We show that by adjusting the size of embedding, SAN is capable of
preserving the whole information from the input. In experiments, we demonstrate
that replacing global pooling layer by SAN leads to the improvement of
classification accuracy. Moreover, it is less prone to overfitting and can be
used as a regularizer.Comment: ICONIP 201
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
Identification of drug-target interactions (DTIs) plays a key role in drug
discovery. The high cost and labor-intensive nature of in vitro and in vivo
experiments have highlighted the importance of in silico-based DTI prediction
approaches. In several computational models, conventional protein descriptors
are shown to be not informative enough to predict accurate DTIs. Thus, in this
study, we employ a convolutional neural network (CNN) on raw protein sequences
to capture local residue patterns participating in DTIs. With CNN on protein
sequences, our model performs better than previous protein descriptor-based
models. In addition, our model performs better than the previous deep learning
model for massive prediction of DTIs. By examining the pooled convolution
results, we found that our model can detect binding sites of proteins for DTIs.
In conclusion, our prediction model for detecting local residue patterns of
target proteins successfully enriches the protein features of a raw protein
sequence, yielding better prediction results than previous approaches.Comment: 26 pages, 7 figure
Visual and computational analysis of structure-activity relationships in high-throughput screening data
Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets
Dynamic load balancing for the distributed mining of molecular structures
In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of
methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the
past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially
render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to
discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no
reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic
partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiverinitiated
load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer
Instituteâs HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed
approach also allows for dynamic resource aggregation in a non dedicated computational environment. These features make it suitable
for large-scale, multi-domain, heterogeneous environments, such as computational grids
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