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With the rapid development of smart devices and their applications, using mobile devices for human–computer interaction has become important. Recent work uses ultrasound to perceive gestures. However, it is difficult to represent the time and frequency information of gestures, and, they are often classified as combined actions in continuous multiple gestures. We propose the Doppler Object Detection Algorithm (DODA) to decouple the information from each gesture’s time and frequency domain in continuous gestures and output the gesture classifications. DODA thus maps the feature information of multiple gestures from Doppler frequency shift images to information about real gesture actions. We present time domain Intersection over Union (Tiou), which computes the Tiou between each adjacent gesture to obtain more accurate prediction fields. We use the static exception eliminate algorithm (SEEA) to eliminate the effects of frame activity anomalies and use the mapping relationship of the DODA algorithm for data enhancement. We design an UltraWX to deploy on any mobile device. Our experimental results show that UltraWX can effectively segment and recognize continuous gestures, and output the start and end time of each gesture, and UltraWX can achieve 93.6% recognition accuracy for continuous gestures in complex environments.

With the global spread of the novel coronavirus, avoiding human-to-human contact has become an effective way to cut off the spread of the virus. Therefore, contactless gesture recognition becomes an effective means to reduce the risk of contact infection in outbreak prevention and control. However, the recognition of everyday behavioral sign language of a certain population of deaf people presents a challenge to sensing technology. Ubiquitous acoustics offer new ideas on how to perceive everyday behavior. The advantages of a low sampling rate, slow propagation speed, and easy access to the equipment have led to the widespread use of acoustic signal-based gesture recognition sensing technology. Therefore, this paper proposed a contactless gesture and sign language behavior sensing method based on ultrasonic signals—UltrasonicGS. The method used Generative Adversarial Network (GAN)-based data augmentation techniques to expand the dataset without human intervention and improve the performance of the behavior recognition model. In addition, to solve the problem of inconsistent length and difficult alignment of input and output sequences of continuous gestures and sign language gestures, we added the Connectionist Temporal Classification (CTC) algorithm after the CRNN network. Additionally, the architecture can achieve better recognition of sign language behaviors of certain people, filling the gap of acoustic-based perception of Chinese sign language. We have conducted extensive experiments and evaluations of UltrasonicGS in a variety of real scenarios. The experimental results showed that UltrasonicGS achieved a combined recognition rate of 98.8% for 15 single gestures and an average correct recognition rate of 92.4% and 86.3% for six sets of continuous gestures and sign language gestures, respectively. As a result, our proposed method provided a low-cost and highly robust solution for avoiding human-to-human contact.

This letter presents a D‐band 5‐bit vector‐modulated phase shifter based on quadrature hybrids for D‐band phased arrays. The proposed vector‐modulated phase shifter employs two stages of quadrature hybrid followed with a single‐pole double‐throw switch for I/Q phase splitting and vector‐summing. To improve the isolation performance, traveling‐wave single‐pole double‐throw switches together with transistor parallel inductors are adopted. A marchand balun followed with two variable gain amplifiers is placed between the two quadrature hybrids for I/Q amplitude weighting. A proof‐of‐concept vector‐modulated phase shifter is designed and fabricated in 0.13 μm SiGe BiCMOS technology and occupies a core chip area of 0.45 mm2. Measurement results show that the RMS phase error is 1.6° at 149 GHz and the RMS gain error is below 2.8 dB over 146–156 GHz. The DC power consumption is 148 mW.

Functional dyspepsia (FD) is the most common clinical gastrointestinal disease, with complex and prolonged clinical symptoms. The prevalence of FD is increasing year by year, seriously affecting the quality of life of patients. The main causes of FD are related to abnormal gastrointestinal dynamics, increased visceral sensitivity, Helicobacter pylori (HP) infection, intestinal flora disturbance and psychological factors. A review of the relevant literature reveals that the mechanisms of traditional Chinese medicine (TCM) in the treatment of FD mainly involve the following pathways:5-HT signal pathway, AMPK signal pathway,C-kit signal pathway, CRF signal pathway, PERK signal pathway,NF-κB signal pathway. Based on a holistic concept, TCM promotes gastrointestinal motility, regulates visceral sensitivity and alleviates gastrointestinal inflammation through multiple signal pathways, reflecting the advantages of multi-level, multi-pathway and multi-targeted treatment of FD.

Plasmonic metal nanostructures have been widely used to enhance the upconversion efficiency of the near‐infrared (NIR) photons into the visible region via the localized surface plasmon resonance (LSPR) effect. However, the direct utilization of low‐cost nonmetallic semiconductors to both concentrate and transfer the NIR‐plasmonic energy in the upconversion system remains a significant challenge. Here, a fascinating process of NIR‐plasmonic energy upconversion in Yb3+/Er3+‐doped NaYF4 nanoparticles (NaYF4:Yb‐Er NPs)/W18O49 nanowires (NWs) heterostructures, which can selectively enhance the upconversion luminescence by two orders of magnitude, is demonstrated. Combined with theoretical calculations, it is proposed that the NIR‐excited LSPR of W18O49 NWs is the primary reason for the enhanced upconversion luminescence of NaYF4:Yb‐Er NPs. Meanwhile, this plasmon‐enhanced upconversion luminescence can be partly absorbed by the W18O49 NWs to re‐excite its higher energy LSPR, thus leading to the selective enhancement of upconversion luminescence for the NaYF4:Yb‐Er/W18O49 heterostructures. More importantly, based on this process of plasmonic energy transfer, an NIR‐driven catalyst of NaYF4:Yb‐Er NPs@W18O49 NWs quasi‐core/shell heterostructure, which exhibits a ≈35‐fold increase in the catalytic H2 evolution from ammonia borane (BH3NH3) is designed and synthesized. This work provides insight on the development of nonmetallic plasmon‐sensitized optical materials that can potentially be applied in photocatalysis, optoelectronic, and photovoltaic devices. Nonmetallic plasmon‐induced selective enhancement of upconversion luminescence is observed in a layer‐structured NaYF4:Yb‐Er/W18O49 film due to the near‐infrared‐plasmonic energy upconversion. Based on this photonics process, an infrared‐driven plasmonic catalyst of NaYF4:Yb‐Er@W18O49 heterostructures is designed and synthesized, which exhibits a ≈35‐fold increase in catalytic H2 evolution upon IR excitation.

Noninvasive preimplantation genetic testing for aneuploidy based on embryonic cell‐free DNA (cfDNA) released in spent embryo culture media (SECM) has brought hope in selecting embryos that are most likely to implant and grow into healthy babies during assisted reproduction. However, maternal DNA contamination in SECM significantly hampers the reliability of embryonic chromosome ploidy profiles, leading to false negative results, particularly at high contamination levels. Here, we present DECENT (deep copy number variation (CNV) reconstruction), a deep learning method to reconstruct embryonic CNVs and mitigate maternal contamination in SECM from single‐cell methylation sequencing of cfDNA. DECENT integrates sequence features and methylation patterns by combining convolution modules, long‐short memory, and attention mechanisms to infer the origin of cfDNA reads. The benchmarking study demonstrated DECENT's ability to estimate contamination proportions and restore embryonic chromosome aneuploidies in samples with varying contamination levels. In contaminated SECM clinical samples, including one with more than 80% maternal reads, DECENT achieved consistent CNV recovery with invasive tests. Overall, DECENT contributes to enhancing the diagnostic accuracy and effectiveness of cfDNA‐based noninvasive preimplantation genetic testing, establishing a robust groundwork for its extensive clinical utilization in the field of reproductive medicine. DECENT is a deep learning method that enhances noninvasive preimplantation genetic testing by accurately reconstructing embryonic copy number variations (CNVs) from cell‐free DNA in spent embryo culture media. By mitigating maternal contamination, DECENT improves diagnostic accuracy, even with high contamination levels, offering a reliable, noninvasive alternative for selecting healthy embryos in assisted reproduction.

Understanding the dynamic nature of biological systems is fundamental to deciphering cellular behavior, developmental processes, and disease progression. Single-cell RNA sequencing (scRNA-seq) has provided static snapshots of gene expression, offering valuable insights into cellular states at a single time point. Recent advancements in temporally resolved scRNA-seq, spatial transcriptomics (ST), and time-series spatial transcriptomics (temporal-ST) have further revolutionized our ability to study the spatiotemporal dynamics of individual cells. These technologies, when combined with computational frameworks such as Markov chains, stochastic differential equations (SDEs), and generative models like optimal transport and Schrödinger bridges, enable the reconstruction of dynamic cellular trajectories and cell fate decisions. This review discusses how these dynamical system approaches offer new opportunities to model and infer cellular dynamics from a systematic perspective.

Convolutional neural network (CNN)-based object detectors perform excellently but lack global feature extraction and cannot establish global dependencies between object pixels. Although the Transformer is able to compensate for this, it does not incorporate the advantages of convolution, which results in insufficient information being obtained about the details of local features, as well as slow speed and large computational parameters. In addition, Feature Pyramid Network (FPN) lacks information interaction across layers, which can reduce the acquisition of feature context information. To solve the above problems, this paper proposes a CNN-based anchor-free object detector that combines transformer global and local feature extraction (GLFT) to enhance the extraction of semantic information from images. First, the segmented channel extraction feature attention (SCEFA) module was designed to improve the extraction of local multiscale channel features from the model and enhance the discrimination of pixels in the object region. Second, the aggregated feature hybrid transformer (AFHTrans) module combined with convolution is designed to enhance the extraction of global and local feature information from the model and to establish the dependency of the pixels of distant objects. This approach compensates for the shortcomings of the FPN by means of multilayer information aggregation transmission. Compared with a transformer, these methods have obvious advantages. Finally, the feature extraction head (FE-Head) was designed to extract full-text information based on the features of different tasks. An accuracy of 47.0% and 82.76% was achieved on the COCO2017 and PASCAL VOC2007 + 2012 datasets, respectively, and the experimental results validate the effectiveness of our method.

Rheumatoid arthritis (RA) is a chronic autoimmune disease that affects the body. was a ethnic drug in China that was used to treat arthritis for hundreds of years. But, the therapeutic mechanism is so far unknown. Therefore, the chemical component and effect of on arthritis in rats were studied using HPLC-QTOF MS, micro-CT, and other experiments in this paper. Male Sprague-Dawley rats were used to assess the activity. HPLC QTOF-MS was used to analyze the chemical profile of the (PF). Bovine type II collagen and Complete Freund's Adjuvant were used to stimulate and construct the collagen-induced arthritis (CIA) model. Three dosages of PF (100 mg/kg, 200 mg/kg, 400 mg/kg) were used to evaluate activity. Methotrexate was used as the positive drug. H/E staining and micro-CT methods were used to monitor the pathological changes of CIA rats. ELISA method was used to assess the serum level of immune- and inflammation-related cytokines. Immunohistochemical experiments were used to test the gene expression in JAK and Nf-κB pathways. 42 compounds were identified from PF. PF administration lowered the increased spleen index compared with that of control and MTX groups, and partially restored body weight, reduced paw swelling, and arthritis score compared with the model group. Macroscopic assessment indicated inflamed paw with significant swelling in the model group, while the extent of inflammation and swelling was attenuated by both MTX and PF. H/E staining experiments demonstrated that pathological changes of synovial cells and infiltration of inflammatory cells were observed in the model group. In contrast, the MTX and PF treatment partially reversed these pathological changes. Micro-CT examination showed severe injuries and scars caused by inflammation for the model group, and in the high-dosage group (400 mg/kg) the inflammation-caused injuries and scars were dramatically ameliorated. Mechanism study showed that PF restored Nf-κB phosphorylation and JAK2 expression compared with the model group. possesses a potent effect on CIA rats. Nf-κB and JAK2 pathways are involved in its protective effect on CIA.

The asymmetric delay has a considerable impact on Global Navigation Satellite Systems (GNSS) Positioning, Navigation and Timing (PNT) applications. In GNSS analyses, the impacts of the asymmetric delay are commonly compensated by using the classical methods with considering the north-south and east-west horizontal gradients. In this paper, we have initiatively proposed an extended method where the north-south and east-west horizontal gradients as well as the second-order horizontal gradients are included to better fit the asymmetric delay. The modeling accuracy of the extended method was evaluated at globally distributed 905 GNSS stations during 40 days in 2020. Significant performance of the extended method respect to the classical method was found, where the hydrostatic and wet modeling accuracy at 4° elevation angle was improved from 5.3 and 10.6 mm to 1.6 and 4.9 mm by 70% and 54%, respectively. The GNSS Precise Point Positioning (PPP) performance using the extended method was also validated at 107 Multi-GNSS Experiment (MGEX) stations. The superior performance on the coordinate repeatability and significant effectiveness on the coordinate and Zenith Total Delay (ZTD) estimations were also found, and the maximal vertical (U) coordinate and ZTD difference biases reached 8.6 and −4.5 mm. The extended method is therefore recommended to substitute the classical methods in the GNSS analyses, especially under severe atmospheric conditions.