Task-Oriented Multi-User Semantic Communications for VQA

Semantic communications focus on the transmission of semantic features. In this letter, we consider a task-oriented multi-user semantic communication system for multimodal data transmission. Particularly, partial users transmit images while the others transmit texts to inquiry the information about the images. To exploit the correlation among the multimodal data from multiple users, we propose a deep neural network enabled semantic communication system, named MU-DeepSC, to execute the visual question answering (VQA) task as an example. Specifically, the transceiver for MU-DeepSC is designed and optimized jointly to capture the features from the correlated multimodal data for task-oriented transmission. Simulation results demonstrate that the proposed MU-DeepSC is more robust to channel variations than the traditional communication systems, especially in the low signal-to-noise (SNR) regime

Deep Learning Enabled Semantic Communication Systems

Recently, deep learned enabled end-to-end (E2E) communication systems have been developed to merge all physical layer blocks in the traditional communication systems, which make joint transceiver optimization possible. Powered by deep learning, natural language processing (NLP) has achieved great success in analyzing and understanding large amounts of language texts. Inspired by research results in both areas, we aim to providing a new view on communication systems from the semantic level. Particularly, we propose a deep learning based semantic communication system, named DeepSC, for text transmission. Based on the Transformer, the DeepSC aims at maximizing the system capacity and minimizing the semantic errors by recovering the meaning of sentences, rather than bit- or symbol-errors in traditional communications. Moreover, transfer learning is used to ensure the DeepSC applicable to different communication environments and to accelerate the model training process. To justify the performance of semantic communications accurately, we also initialize a new metric, named sentence similarity. Compared with the traditional communication system without considering semantic information exchange, the proposed DeepSC is more robust to channel variation and is able to achieve better performance, especially in the low signal-to-noise (SNR) regime, as demonstrated by the extensive simulation results.Comment: 13 pages, Journal, accepted by IEEE TS

An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning

Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user's forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user's hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user's gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke

RDR2 Partially Antagonizes the Production of RDR6-Dependent siRNA in Sense Transgene-Mediated PTGS

Background: RNA-DEPENDENT RNA POLYMERASE6 (RDR6) and SUPPRESSOR of GENE SILENCING 3 (SGS3) are required for DNA methylation and post-transcriptional gene silencing (PTGS) mediated by 21-nt siRNAs produced by sense transgenes (S-PTGS). In contrast, RDR2, but not RDR6, is required for DNA methylation and TGS mediated by 24-nt siRNAs, and for cellto-cell spreading of IR-PTGS mediated by 21-nt siRNAs produced by inverted repeat transgenes under the control of a phloem-specific promoter. Principal Findings: In this study, we examined the role of RDR2 and RDR6 in S-PTGS. Unlike RDR6, RDR2 is not required for DNA methylation of transgenes subjected to S-PTGS. RDR6 is essential for the production of siRNAs by transgenes subjected to S-PTGS, but RDR2 also contributes to the production of transgene siRNAs when RDR6 is present because rdr2 mutations reduce transgene siRNA accumulation. However, the siRNAs produced via RDR2 likely are counteractive in wildtype plants because impairement of RDR2 increases S-PTGS efficiency at a transgenic locus that triggers limited silencing, and accelerates S-PTGS at a transgenic locus that triggers efficient silencing. Conclusions/Significance: These results suggest that RDR2 and RDR6 compete for RNA substrates produced by transgenes subjected to S-PTGS. RDR2 partially antagonizes RDR6 because RDR2 action likely results in the production of counteractiv

Decreased plasma nociceptin/orphanin FQ levels after acute coronary syndromes

Foregoing researches made on the N/OFQ system brought up a possible role for this system in cardiovascular regulation. In this study we examined how N/OFQ levels of the blood plasma changed in acute cardiovascular diseases. Three cardiac patient groups were created: enzyme positive acute coronary syndrome (EPACS, n = 10), enzyme negative ACS (ENACS, n = 7) and ischemic heart disease (IHD, n = 11). We compared the patients to healthy control subjects (n = 31). We found significantly lower N/OFQ levels in the EPACS [6.86 (6.21–7.38) pg/ml], ENACS [6.97 (6.87–7.01) pg/ml and IHD groups [7.58 (7.23–8.20) pg/ml] compared to the control group [8.86 (7.27–9.83) pg/ml]. A significant correlation was detected between N/OFQ and white blood cell count (WBC), platelet count (PLT), creatine kinase (CK), glutamate oxaloacetate transaminase (GOT) and cholesterol levels in the EPACS group.Decreased plasma N/OFQ is closely associated with the presence of acute cardiovascular disease, and the severity of symptoms has a significant negative correlation with the N/OFQ levels. We believe that the rate of N/OFQ depression is in association with the level of ischemic stress and the following inflammatory response. Further investigations are needed to clarify the relevance and elucidate the exact effects of the ischemic stress on the N/OFQ system

Structural and Functional Characterization of Mature Forms of Metalloprotease E495 from Arctic Sea-Ice Bacterium Pseudoalteromonas sp. SM495

E495 is the most abundant protease secreted by the Arctic sea-ice bacterium Pseudoalteromonas sp. SM495. As a thermolysin family metalloprotease, E495 was found to have multiple active forms in the culture of strain SM495. E495-M (containing only the catalytic domain) and E495-M-C1 (containing the catalytic domain and one PPC domain) were two stable mature forms, and E495-M-C1-C2 (containing the catalytic domain and two PPC domains) might be an intermediate. Compared to E495-M, E495-M-C1 had similar affinity and catalytic efficiency to oligopeptides, but higher affinity and catalytic efficiency to proteins. The PPC domains from E495 were expressed as GST-fused proteins. Both of the recombinant PPC domains were shown to have binding ability to proteins C-phycocyanin and casein, and domain PPC1 had higher affinity to C-phycocyanin than domain PPC2. These results indicated that the domain PPC1 in E495-M-C1 could be helpful in binding protein substrate, and therefore, improving the catalytic efficiency. Site-directed mutagenesis on the PPC domains showed that the conserved polar and aromatic residues, D26, D28, Y30, Y/W65, in the PPC domains played key roles in protein binding. Our study may shed light on the mechanism of organic nitrogen degradation in the Arctic sea ice

Up-regulation of METCAM/MUC18 promotes motility, invasion, and tumorigenesis of human breast cancer cells

<p>Abstract</p> <p>Background</p> <p>Conflicting research has identified METCAM/MUC18, an integral membrane cell adhesion molecule (CAM) in the Ig-like gene super-family, as both a tumor promoter and a tumor suppressor in the development of breast cancer. To resolve this, we have re-investigated the role of this CAM in the progression of human breast cancer cells.</p> <p>Methods</p> <p>Three breast cancer cell lines were used for the tests: one luminal-like breast cancer cell line, MCF7, which did not express any METCAM/MUC18, and two basal-like breast cancer cell lines, MDA-MB-231 and MDA-MB-468, which expressed moderate levels of the protein.</p> <p>MCF7 cells were transfected with the human METCAM/MUC18 cDNA to obtain G418-resistant clones which expressed the protein and were used for testing effects of human METCAM/MUC18 expression on <it>in vitro </it>motility and invasiveness, and <it>in vitro </it>and <it>in vivo </it>tumorigenesis. Both MDA-MB-231 and MDA-MB-468 cells already expressed METCAM/MUC18. They were directly used for <it>in vitro </it>tests in the presence and absence of an anti-METCAM/MUC18 antibody.</p> <p>Results</p> <p>In MCF7 cells, enforced METCAM/MUC18 expression increased <it>in vitro </it>motility, invasiveness, anchorage-independent colony formation (<it>in vitro </it>tumorigenesis), and <it>in vivo </it>tumorigenesis. In both MDA-MB-231 and MDA-MB-468 cells, the anti-METCAM/MUC18 antibody inhibited both motility and invasiveness. Though both MDA-MB-231 and MDA-MB-468 cells established a disorganized growth in 3D basement membrane culture assay, the introduction of the anti-METCAM/MUC18 antibody completely destroyed their growth in the 3D culture.</p> <p>Conclusion</p> <p>These findings support the notion that human METCAM/MUC18 expression promotes the progression of human breast cancer cells by increasing their motility, invasiveness and tumorigenesis.</p

Evolutionary Sequence Modeling for Discovery of Peptide Hormones

There are currently a large number of “orphan” G-protein-coupled receptors (GPCRs) whose endogenous ligands (peptide hormones) are unknown. Identification of these peptide hormones is a difficult and important problem. We describe a computational framework that models spatial structure along the genomic sequence simultaneously with the temporal evolutionary path structure across species and show how such models can be used to discover new functional molecules, in particular peptide hormones, via cross-genomic sequence comparisons. The computational framework incorporates a priori high-level knowledge of structural and evolutionary constraints into a hierarchical grammar of evolutionary probabilistic models. This computational method was used for identifying novel prohormones and the processed peptide sites by producing sequence alignments across many species at the functional-element level. Experimental results with an initial implementation of the algorithm were used to identify potential prohormones by comparing the human and non-human proteins in the Swiss-Prot database of known annotated proteins. In this proof of concept, we identified 45 out of 54 prohormones with only 44 false positives. The comparison of known and hypothetical human and mouse proteins resulted in the identification of a novel putative prohormone with at least four potential neuropeptides. Finally, in order to validate the computational methodology, we present the basic molecular biological characterization of the novel putative peptide hormone, including its identification and regional localization in the brain. This species comparison, HMM-based computational approach succeeded in identifying a previously undiscovered neuropeptide from whole genome protein sequences. This novel putative peptide hormone is found in discreet brain regions as well as other organs. The success of this approach will have a great impact on our understanding of GPCRs and associated pathways and help to identify new targets for drug development