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"Shi, Ling"
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Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors
Some recent studies use a convolutional neural network (CNN) or long short-term memory (LSTM) to extract gait features, but the methods based on the CNN and LSTM have a high loss rate of time-series and spatial information, respectively. Since gait has obvious time-series characteristics, while CNN only collects waveform characteristics, and only uses CNN for gait recognition, this leads to a certain lack of time-series characteristics. LSTM can collect time-series characteristics, but LSTM results in performance degradation when processing long sequences. However, using CNN can compress the length of feature vectors. In this paper, a sequential convolution LSTM network for gait recognition using multimodal wearable inertial sensors is proposed, which is called SConvLSTM. Based on 1D-CNN and a bidirectional LSTM network, the method can automatically extract features from the raw acceleration and gyroscope signals without a manual feature design. 1D-CNN is first used to extract the high-dimensional features of the inertial sensor signals. While retaining the time-series features of the data, the dimension of the features is expanded, and the length of the feature vectors is compressed. Then, the bidirectional LSTM network is used to extract the time-series features of the data. The proposed method uses fixed-length data frames as the input and does not require gait cycle detection, which avoids the impact of cycle detection errors on the recognition accuracy. We performed experiments on three public benchmark datasets: UCI-HAR, HuGaDB, and WISDM. The results show that SConvLSTM performs better than most of those reporting the best performance methods, at present, on the three datasets.
Journal Article
Transcriptome profile of rat genes in injured spinal cord at different stages by RNA-sequencing
Background
Spinal cord injury (SCI) results in fatal damage and currently has no effective treatment. The pathological mechanisms of SCI remain unclear. In this study, genome-wide transcriptional profiling of spinal cord samples from injured rats at different time points after SCI was performed by RNA-Sequencing (RNA-Seq). The transcriptomes were systematically characterized to identify the critical genes and pathways that are involved in SCI pathology.
Results
RNA-Seq results were obtained from total RNA harvested from the spinal cords of sham control rats and rats in the acute, subacute, and chronic phases of SCI (1 day, 6 days and 28 days after injury, respectively;
n
= 3 in every group). Compared with the sham-control group, the number of differentially expressed genes was 1797 in the acute phase (1223 upregulated and 574 downregulated), 6590 in the subacute phase (3460 upregulated and 3130 downregulated), and 3499 in the chronic phase (1866 upregulated and 1633 downregulated), with an adjusted
P
-value <0.05 by DESeq. Gene ontology (GO) enrichment analysis showed that differentially expressed genes were most enriched in immune response, MHC protein complex, antigen processing and presentation, translation-related genes, structural constituent of ribosome, ion gated channel activity, small GTPase mediated signal transduction and cytokine and/or chemokine activity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the most enriched pathways included ribosome, antigen processing and presentation, retrograde endocannabinoid signaling, axon guidance, dopaminergic synapses, glutamatergic synapses, GABAergic synapses, TNF, HIF-1, Toll-like receptor, NF-kappa B, NOD-like receptor, cAMP, calcium, oxytocin, Rap1, B cell receptor and chemokine signaling pathway.
Conclusions
This study has not only characterized changes in global gene expression through various stages of SCI progression in rats, but has also systematically identified the critical genes and signaling pathways in SCI pathology. These results will expand our understanding of the complex molecular mechanisms involved in SCI and provide a foundation for future studies of spinal cord tissue damage and repair.
The sequence data from this study have been deposited into Sequence Read Archive (
http://www.ncbi.nlm.nih.gov/sra
; accession number PRJNA318311).
Journal Article
Coupled anaerobic methane oxidation and reductive arsenic mobilization in wetland soils
Anaerobic methane oxidation is coupled to the reduction of electron acceptors, such as sulfate, and contributes to their biogeochemical cycling in the environment. However, whether arsenate acts as an alternative electron acceptor of anaerobic methane oxidation and how this influences global arsenic transformations remains elusive. Here, we present incubations of arsenate-contaminated wetland soils from seven provinces in China. Using isotopically labelled methane, we find that anaerobic methane oxidation was linked to arsenate reduction at a rate approaching the theoretical arsenic/methane stoichiometric ratio of 4. In microcosm incubations with natural wetland soils, we find that the coupled pathway of anaerobic methane oxidation and arsenate reduction contributed 26 to 49% of total arsenic release from soils, with arsenic in the more soluble and toxic form arsenite. Comparative gene quantification and metagenomic sequencing suggest that the coupled pathway was facilitated by anaerobic methanotrophs, either independently or synergistically with arsenate-reducing bacteria through reverse methanogenesis and respiratory arsenate reduction. Further bioinformatic analyses show that genes coding for reverse methanogenesis and respiratory arsenate reduction are universally co-distributed in nature. This suggests that coupling of anaerobic methane oxidation and arsenate reduction is a potentially global but previously overlooked process, with implications for arsenic mobilization and environmental contamination.The coupling of anaerobic oxidation of methane and arsenate reduction is an important pathway of releasing arsenic from soils, according to incubation experiments of arsenate-contaminated wetland soils.
Journal Article
Assessing “Dangerous Climate Change”: Required Reduction of Carbon Emissions to Protect Young People, Future Generations and Nature
We assess climate impacts of global warming using ongoing observations and paleoclimate data. We use Earth's measured energy imbalance, paleoclimate data, and simple representations of the global carbon cycle and temperature to define emission reductions needed to stabilize climate and avoid potentially disastrous impacts on today's young people, future generations, and nature. A cumulative industrial-era limit of ∼500 GtC fossil fuel emissions and 100 GtC storage in the biosphere and soil would keep climate close to the Holocene range to which humanity and other species are adapted. Cumulative emissions of ∼1000 GtC, sometimes associated with 2°C global warming, would spur \"slow\" feedbacks and eventual warming of 3-4°C with disastrous consequences. Rapid emissions reduction is required to restore Earth's energy balance and avoid ocean heat uptake that would practically guarantee irreversible effects. Continuation of high fossil fuel emissions, given current knowledge of the consequences, would be an act of extraordinary witting intergenerational injustice. Responsible policymaking requires a rising price on carbon emissions that would preclude emissions from most remaining coal and unconventional fossil fuels and phase down emissions from conventional fossil fuels.
Journal Article
Phylogenetically and catabolically diverse diazotrophs reside in deep-sea cold seep sediments
Microbially mediated nitrogen cycling in carbon-dominated cold seep environments remains poorly understood. So far anaerobic methanotrophic archaea (ANME-2) and their sulfate-reducing bacterial partners (SEEP-SRB1 clade) have been identified as diazotrophs in deep sea cold seep sediments. However, it is unclear whether other microbial groups can perform nitrogen fixation in such ecosystems. To fill this gap, we analyzed 61 metagenomes, 1428 metagenome-assembled genomes, and six metatranscriptomes derived from 11 globally distributed cold seeps. These sediments contain phylogenetically diverse nitrogenase genes corresponding to an expanded diversity of diazotrophic lineages. Diverse catabolic pathways were predicted to provide ATP for nitrogen fixation, suggesting diazotrophy in cold seeps is not necessarily associated with sulfate-dependent anaerobic oxidation of methane. Nitrogen fixation genes among various diazotrophic groups in cold seeps were inferred to be genetically mobile and subject to purifying selection. Our findings extend the capacity for diazotrophy to five candidate phyla (Altarchaeia, Omnitrophota, FCPU426, Caldatribacteriota and UBA6262), and suggest that cold seep diazotrophs might contribute substantially to the global nitrogen balance.
Microbial nitrogen fixation could be important in the deep sea. Here the authors investigate metagenomes and metatranscriptomes of diazotrophs from deep sea cold seep sediments, reveal greater phylogenetic and functional diversity than hitherto known.
Journal Article