Device-Free Wireless Sensing for Gesture Recognition Based on Complementary CSI Amplitude and Phase.

By integrating sensing capability into wireless communication, wireless sensing technology has become a promising contactless and non-line-of-sight sensing paradigm to explore the dynamic characteristics of channel state information (CSI) for recognizing human behaviors. In this paper, we develop an effective device-free human gesture recognition (HGR) system based on WiFi wireless sensing technology in which the complementary CSI amplitude and phase of communication link are jointly exploited. To improve the quality of collected CSI, a linear transform-based data processing method is first used to eliminate the phase offset and noise and to reduce the impact of multi-path effects. Then, six different time and frequency domain features are chosen for both amplitude and phase, including the mean, variance, root mean square, interquartile range, energy entropy and power spectral entropy, and a feature selection algorithm to remove irrelevant and redundant features is proposed based on filtering and principal component analysis methods, resulting in the construction of a feature subspace to distinguish different gestures. On this basis, a support vector machine-based stacking algorithm is proposed for gesture classification based on the selected and complementary amplitude and phase features. Lastly, we conduct experiments under a practical scenario with one transmitter and receiver. The results demonstrate that the average accuracy of the proposed HGR system is 98.3% and that the F1-score is over 97%.

Photoinduced Synthesis of Sulfonyl-Containing Phosphorothioates via a Three-Component Reaction.

Both sulfonyl and phosphorothioate are important privileged structural motifs which are widely presented in pharmaceuticals and agrochemicals. Herein, we describe an efficient approach to synthesizing sulfonyl-containing phosphorothioates by merging photoredox and copper catalysis at room temperature. This protocol is compatible with a wide range of substrates and can be applied to the late-stage modification of complex molecules. Control experiments are conducted to demonstrate the generation of the sulfonyl radical in the transformation.

Synthesis of Sulfilimines via Aryne and Cyclohexyne Intermediates.

An efficient and metal-free approach for the synthesis of sulfilimines from sulfenamides with aryne and cyclohexyne precursors has been developed. The reaction proceeds through unusual S-C bond formation, which offers a novel and practical entry to access a wide range of sulfilimines in moderate to good yields with excellent chemoselectivity. Moreover, this protocol is amenable to gram-scale synthesis and is applicable to the transformation of the products into useful sulfoximines.

Metal-Free Chemoselective S-Arylation of Sulfenamides To Access Sulfilimines.

A novel and efficient S-arylation of sulfenamides with diaryliodonium salts for the synthesis of sulfilimines is developed. The reaction proceeds smoothly under transition-metal-free and air conditions, giving rapid access to sulfilimines in good to excellent yields via selective S-C bond formation. This protocol is scalable and exhibits a broad substrate scope, good functional group tolerance, and excellent chemoselectivity.

Deep Learning-Based Channel Estimation for mmWave Massive MIMO Systems in Mixed-ADC Architecture.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems can significantly reduce the number of radio frequency (RF) chains by using lens antenna arrays, because it is usually the case that the number of RF chains is often much smaller than the number of antennas, so channel estimation becomes very challenging in practical wireless communication. In this paper, we investigated channel estimation for mmWave massive MIMO system with lens antenna array, in which we use a mixed (low/high) resolution analog-to-digital converter (ADC) architecture to trade-off the power consumption and performance of the system. Specifically, most antennas are equipped with low-resolution ADC and the rest of the antennas use high-resolution ADC. By utilizing the sparsity of the mmWave channel, the beamspace channel estimation can be expressed as a sparse signal recovery problem, and the channel can be recovered by the algorithm based on compressed sensing. We compare the traditional channel estimation scheme with the deep learning channel-estimation scheme, which has a better advantage, such as that the estimation scheme based on deep neural network is significantly better than the traditional channel-estimation algorithm.

Machine learning-aided detection of heart failure (LVEF ≤ 49%) by using ballistocardiography and respiratory effort signals.

Purpose: Under the influence of COVID-19 and the in-hospital cost, the in-home detection of cardiovascular disease with smart sensing devices is becoming more popular recently. In the presence of the qualified signals, ballistocardiography (BCG) can not only reflect the cardiac mechanical movements, but also detect the HF in a non-contact manner. However, for the potential HF patients, the additional quality assessment with ECG-aided requires more procedures and brings the inconvenience to their in-home HF diagnosis. To enable the HF detection in many real applications, we proposed a machine learning-aided scheme for the HF detection in this paper, where the BCG signals recorded from the force sensor were employed without the heartbeat location, and the respiratory effort signals separated from force sensors provided more HF features due to the connection between the heart and the lung systems. Finally, the effectiveness of the proposed HF detection scheme was verified in comparative experiments. Methods: First, a piezoelectric sensor was used to record a signal sequences of the two-dimensional vital sign, which includes the BCG and the respiratory effort. Then, the linear and the non-linear features w.r.t. BCG and respiratory effort signals were extracted to serve the HF detection. Finally, the improved HF detection performance was verified through the LOO and the LOSO cross-validation settings with different machine learning classifiers. Results: The proposed machine learning-aided scheme achieved the robust performance in the HF detection by using 4 different classifiers, and yielded an accuracy of 94.97% and 87.00% in the LOO and the LOSO experiments, respectively. In addition, experimental results demonstrated that the designed respiratory and cardiopulmonary features are beneficial to the HF detection (LVEF ≤ 49 % ). Conclusion: This study proposed a machine learning-aided HF diagnostic scheme. Experimental results demonstrated that the proposed scheme can fully exploit the relationship between the heart and the lung systems to potentially improve the in-home HF detection performance by using both the BCG, the respiratory and the cardiopulmonary-related features.

Association of increased microvessel density with skeletal extramedullary disease relapse in multiple myeloma patients who have skeletal extramedullary disease at diagnosis.

The aim of the study was to investigate whether microvessel density (MVD) could be associated with skeletal extramedullary disease relapse (skeletal-EMDR) in patients with multiple myeloma (MM) who have skeletal-EMD at diagnosis. Seventy-nine newly diagnosed MM patients who have skeletal-EMD were retrospectively enrolled in this study. The 4-year cumulative incidence of skeletal-EMDR was 35.0%±8.3%. The 4-year probability of overall survival (OS) was 54.0%±7.6%. Multivariate analysis showed that skeletal-EMDR (HR = 4.144; 95% CI: 1.608-10.685; P = 0.003) was independently associated with inferior OS for the MM patients who have skeletal-EMD at diagnosis. The factors associated with skeletal-EMDR were MVD (HR = 3.990, 95%CI:1.136-14.018; P = 0.031), white blood cell (WBC) (HR = 0.262, 95% CI:0.090-0.769; P = 0.015), and the EMD sites involved at onset (HR = 0.263, 95% CI: 0.074-0.937; P = 0.039). The MVD in patients with thoracic and lumbar vertebrae as the involved sites at diagnosis was significantly lower than those with other sites involved (41.59 ± 14.39 vs. 60.82 ± 35.14, P=0.001). Our data suggest that increased MVD could be used to predict skeletal-EMDR, which is associated with inferior survival in patients with MM who have skeletal-EMD at diagnosis.

An integrated bioinformatical analysis of miR-19a target genes in multiple myeloma.

MicroRNA (miR)-19a, as an oncomiR, has been studied in several types of cancer; however, its role in the development and progression of multiple myeloma (MM) remains unclear. The present study used a bioinformatics approach to investigate the involvement of miR-19a in MM. miR-19a targets were predicted using target prediction programs, followed by screening for differentially expressed genes in MM. The function of these genes was then annotated using gene ontology term enrichment, signaling pathway enrichment and protein-protein interaction (PPI) analysis. In addition, natural language processing (NLP) was performed to identify genes associated with MM. A total of 715 putative targets of miR-19a were identified in the present study, of which 40 were experimentally validated. A total of 121 genes were identified to be differentially expressed in MM, including 80 upregulated genes and 41 downregulated genes. Among the differentially expressed genes, ras homolog family member B, clathrin heavy chain, prosaposin and protein phosphatase 6 regulatory subunit 2 were predicted target genes of miR-19a. The results of NLP revealed that 2 of the differentially expressed genes, Y-box binding protein 1 and TP53 regulated inhibitor of apoptosis 1, were reported to be associated with MM. In addition, 41 target genes of miR-19a were identified to be associated with the development and progression of MM. These results may aid in understanding the molecular mechanisms of miR-19a in the development and progression of MM. In addition, the results of the present study indicate that targets genes of miR-19a are potential candidate biomarkers for MM.