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Personal devices such as smartphones and tablets are rapidly becoming personal communication, information, and control centers. Apart from multitouch screens, human gestures are considered as a new interactive human–smart device interface. In this work, we propose a noncontact solution to implement hand gesture recognitions for smart devices. It is based on a continuous wave, time-division-multiplexing (TDM), single-input multiple-output (SIMO) Doppler radar sensor that can be realized by slightly modifying existing RF front ends of smart devices, and a machine-learning algorithm to recognize predefined gestures by classifying deterministic Doppler signals. An experimental setup emulating a smartphone-based radar sensor was implemented, and the experimental results verified the robustness and the accuracy of the proposed approach.

To date, 3-D radar imaging of moving objects in a multipath coordinate space close to antennas remains a technical challenge. In this work, we demonstrate the implementation of such 3-D motion imaging based on a continuous-wave, multiple-input multiple-output (MIMO) Doppler radar sensor (DRS). Working with a time-division-multiplex (TDM) scheme, this portable, cost-effective DRS is a bistatic system with one transmitting and one receiving channels, and a radio frequency (RF) switch in each channel toggling between two antennas. Assisted by algorithms specifically derived for the TDM-MIMO architecture, highly sensitive 3-D imaging performed in a multipath indoor environment was experimentally implemented. Numerical simulations and experimental demonstrations verified the effectiveness and accuracy of the proposed approach.

Most medical instruments invented to measure human heart activities, such as electrocardiograms (ECGs), rely on contact electrodes. This causes discomfort and limits application scenarios. Remote acquisition of $\mu \text{V}$ -level bioelectrical cardiac signals through ECG measurement is theoretically challenging. Based on the analysis of magnetic resonance imaging to the volume change of human hearts, we found that a single radar sensor can be used to remotely detect a Doppler cardiogram (DCG) at a distance up to 1 m, by retrieving Doppler signals induced by combined atrial and ventricular motions conducted to the skin of the back chest. This DCG can provide all the timing information of the P-wave, QRS-waves, and T-wave carried in ECGs. The implemented miniature remote sensor could be an ideal portable instrument for patients, such as burn victims, extending application scenarios to personal healthcare, battlefield rescue, and others.

Metagenomic binning is the step in building metagenome-assembled genomes (MAGs) when sequences predicted to originate from the same genome are automatically grouped together. The most widely-used methods for binning are reference-independent, operating de novo and enable the recovery of genomes from previously unsampled clades. However, they do not leverage the knowledge in existing databases. Here, we introduce SemiBin, an open source tool that uses deep siamese neural networks to implement a semi-supervised approach, i.e. SemiBin exploits the information in reference genomes, while retaining the capability of reconstructing high-quality bins that are outside the reference dataset. Using simulated and real microbiome datasets from several different habitats from GMGCv1 (Global Microbial Gene Catalog), including the human gut, non-human guts, and environmental habitats (ocean and soil), we show that SemiBin outperforms existing state-of-the-art binning methods. In particular, compared to other methods, SemiBin returns more high-quality bins with larger taxonomic diversity, including more distinct genera and species.

Aims We aimed to examine the associations of four extracranial artery indicators with cerebral small vessel disease (CSVD) and its total burden. Methods A total of 904 individuals aged 55-65 years old were included from the Taizhou Imaging Study. CSVD markers, including lacunes (LAC), white matter hyperintensities (WMH), cerebral microbleeds (CMB), and perivascular spaces (PVS), were rated based on brain magnetic resonance imaging. We also measured extracranial artery indices, including the brachial-ankle pulse wave velocity (baPWV), the ankle-brachial index, the carotid intima-media thickness (IMT), and carotid plaque. Linear and binary logistic regressions were adopted to test the associations among these four artery indicators and each CSVD marker when appropriate. Additionally, ordinal and multinomial logistic regressions were performed to assess the relationships between artery indicators and total CSVD score (range from 0-4 points). Results A total of 443 (49.0%) participants were found to have at least one of the CSVD markers, including 172 (19.0%) with WMH, 184 (20.4%) with LAC, 147 (16.3%) with CMB, and 226 (25.0%) with PVS. Increased baPWV was significantly associated with each CSVD marker, increasing carotid IMT was associated with LAC and PVS, and the presence of carotid plaque was associated with WMH volume and PVS. Moreover, per SD increment of baPWV (odds ratio [OR]: 1.29, 95% confidence interval [CI]: 1.11-1.50) and the presence of carotid plaque (OR: 1.42, 95% CI: 1.05-1.92) were significantly associated with greater total CSVD scores. Conclusion Increased baPWV and the presence of carotid plaque appear to be associated with total CSVD burden in rural regions in China.

Metagenomic binning is the step in building metagenome-assembled genomes (MAGs) when sequences predicted to originate from the same genome are automatically grouped together. The most widely-used methods for binning are reference-independent, operating de novo and allow the recovery of genomes from previously unsampled clades. However, they do not leverage the knowledge in existing databases. Here, we propose SemiBin, an open source tool that uses neural networks to implement a semi-supervised approach, i.e. SemiBin exploits the information in reference genomes, while retaining the capability of binning genomes that are outside the reference dataset. SemiBin outperforms existing state-of-the-art binning methods in simulated and real microbiome datasets across three different environments (human gut, dog gut, and marine microbiomes). SemiBin returns more high-quality bins with larger taxonomic diversity, including more distinct genera and species. SemiBin is available as open source software at https://github.com/BigDataBiology/SemiBin/.

Objectives An altered microbiota, which can be described quantitatively, has been identified as playing a pivotal role in host vascular physiology, and it may contribute to various diseases. The aim of this study was to better understand the role of the gut microbiota in vascular physiology in a subclinical elderly population, and to investigate how lifestyle affects the composition of host gut microbiota to further impact the pathogenesis of vascular diseases. Methods We performed a population-based faecal metagenomic study over 569 elderly asymptomatic subclinical individuals in rural China. An association network was built based on clinical measurements and detailed epidemiologic questionnaires, including blood chemistry, arterial stiffness, carotid ultrasonography, and metagenomic datasets. Results By analyzing the breadth, depth and impact of each node of the association network, we found carotid arterial atherosclerosis indices, including intima-media thickness (IMT), were essential in the network, and were significantly associated with living habits, socio-economic status, and diet. Using mediation analysis, we found that higher frequency of eating fresh fruits and vegetables, and more exercise significantly reduced carotid atherosclerosis in terms of IMT, peak systolic velocity and end-diastolic velocity values through the mediation of Alistepes, Oligella and Prevotella. Gut microbes explained 16.5% of the mediation effect of lifestyle on the pathogenesis of carotid atherosclerosis. After adjustment, Faecalicatena [odds ratio (OR) = 0.12 ∼0.65] was shown to be protective against the formation of carotid atherosclerosis, independently, while Libanicoccus (OR = 1.46 ∼4.20 ) was associated with increased carotid arterial IMT. KEGG/KO Kyoto Encyclopedia of Genes and Genomes/ KEGG Orthology (KEGG/KO) analyses revealed a loss of anti-inflammation function in IMT subjects. Conclusion Our study revealed a Chinese population–wide phenotype–metagenomic association network and a mediation effect of gut microbiota on carotid artery atherosclerosis, hinting at potential therapeutic and preventive uses for microbiota in vascular diseases.

In the past decades, continuous Doppler radar sensor-based bio-signal detections have attracted many research interests. A typical example is the Doppler heartbeat detection. While significant progresses have been achieved, reliable, time-domain accurate demodulation of bio-signals in the presence of unavoidable DC offsets remains a technical challenge. Aiming to overcome this difficulty, we propose in this paper a novel demodulation algorithm that does not need to trace and eliminate dynamic DC offsets based on approximating segmented arcs in a quadrature constellation of sampling data to directional chords. Assisted by the principal component analysis, such chords and their directions can be deterministically determined. Simulations and experimental validations showed fully recovery of micron-level pendulum movements and strongly noised human heartbeats, verifying the effectiveness and accuracy of the proposed approach., Comment: Accepted by IEEE Transactions on Instrumentation & Measurement

BACKGROUND: Cerebral microbleeds (CMBs) are more prevalent in Asian populations, and have been associated with increased risk of stroke, dementia and mortality. So far, risk factors for CMBs other than hypertension were merely known. Previous studies have shown that polymorphisms at aldehyde dehydrogenase 2 (ALDH2) gene were independently associated with the risk of stroke. Its role in CMBs, however, remains unclear. This study aimed to evaluate the associations of ALDH2 gene polymorphisms with CMBs in Chinese elderly. METHODS: Using bio-specimen and data collected at baseline survey of the population-based Taizhou Imaging Study (TIS) (phase I), we genotyped the single nucleotide polymorphisms (SNPs) at ALDH2 among 549 individuals aged 55–65 years, and rs671 was used as surrogate marker of ALDH2. CMBs were detected on brain magnetic resonance imaging (MRI), and further categorized as strictly lobar or as deep/mixed. Logistic regression models were used to evaluate the associations of the variants at ALDH2 and CMBs. RESULTS: CMBs were present in 103 individuals (18.8%). Forty-one point three percent participants were with ALDH2 *2 allele and 5.1% had ALDH2 *2/*2 genotype. Subjects with ALDH2 *1 allele were more likely to be drinker, have hypertension or CMBs than those with *2 allele (all P

BackgroundMicrobiomic research has grown in popularity in recent decades. The widespread use of next-generation sequencing technologies, including 16S rRNA gene-based and metagenomic shotgun-based methods, has produced a wealth of microbiome data. At present, most software and analysis workflows for analysis and processing of microbiomic data are command line-based, which requires considerable computing time and makes interaction difficult.ResultsTo provide a command-line free, integrated, user interface friendly and online/local deployable microbiome analysis tool, we developed Microbiome Automated Analysis Workflows (MAAWf). The MAAWf assesses taxonomy, protein-coding genes, metabolic pathways, carbohydrate-active enzymes (CAZy) and antibiotic resistance genes (ARGs) for WMS, and clusters operational taxonomic units, alpha-/beta-diversity and functional annotations for 16S. MAAWf generates similar results to currently popular tools, but the running time was much shorter. MAAWf is freely accessible and source code available at http://www.maawf.com.ConclusionsMAAWf is a visual, integrated, rapid analysis tool that enables remote and local computing of microbiome data.

Computer-based hand gesture recognition (HGR) remains a technical challenge due to complicated image processing algorithms and excessive occupation of computational resources. Recently, wireless sensing and detection based on continuous-wave (CW) Doppler radar sensors (DRSs) have been intensively investigated, based on which experimental HGRs implemented with cost-effective, miniaturized hardware and linearized, highly efficient algorithms have been experimentally demonstrated. This DRS-based solution is able to recognize definitive, meaningful signatures of human gestures retrieved from low-sampling-rate data, exhibiting promising potential for practical applications. This chapter aims to introduce this progress. The content mainly includes an introduction to the basis of Doppler radar sensing, the architecture, and algorithms suitable for HGRs, and experimental HGR demonstrations based on such architecture and algorithms.

Reliable and repeatable tunability gives functional diversity for reconfigurable plasmonics devices, while reversible and large mechanical deformation enabled by soft materials provides a new way for the global or partial regulation of metadevices. Here, we demonstrate a soft metasurface with an out-of-plane design for tuning the energy of surface plasmon polaritons (SPPs) bloch wave using theory, simulation, and experiments. Our metasurface is composed of two-layered gold nanoribbon arrays (2GNRs) on a soft substrate. The out-of-plane coupling mechanism is systematically analyzed in terms of separation height effect. Moreover, by harnessing mechanical deformation, continuously tunable plasmonic resonance has been achieved in the visible and near-infrared ranges. We further studied the angle-dependent reflection spectra of our metastructure. Compared with its planar counterpart, our soft and two-layered metastructure exhibits diverse tunability and significant field enhancement by out-of-plane interactions. Our approach in designing soft metasurface with out-of-plane structures can be extended to more-complex photonic devices and finds prominent applications such as biosensing, high-density plasmonic circuits, surface-enhanced luminescence, and surface-enhanced Raman scattering.

An algebraic concentration moment (ACM)-PDF turbulent combustion model is proposed. It is formulated by jointly utilizing the explicit algebraic expressions for the second-order-moment of concentration fluctuation and the presumed probability density function (PDF) of gas instantaneous temperature. A set of analytical expressions for the time-averaged temperature relevant quantity is obtained for the closure of the time-averaged reaction rate. The model is applied to the simulation of turbulent flow and combustion in a swirl combustor. The calculated gas velocity, temperature, species concentrations, and turbulent fluctuating velocity are in agreement with the measured data. They are much improved over those obtained by the EBU-Arrhenius model.