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In recent years, non-contact radar detection technology has been able to achieve long-term and long-range detection for the breathing and heartbeat signals. Compared with contact-based detection methods, it brings a more comfortable and a faster experience to the human body, and it has gradually received attention in the field of radar sensing. Therefore, this paper extends the application of millimeter-wave radar to the field of health care. The millimeter-wave radar first transmits the frequency-modulated continuous wave (FMCW) and collects the echo signals of the human body. Then, the phase information of the intermediate frequency (IF) signals including the breathing and heartbeat signals are extracted, and the Direct Current (DC) offset of the phase information is corrected using the circle center dynamic tracking algorithm. The extended differential and cross-multiply (DACM) is further applied for phase unwrapping. We propose two algorithms, namely the compressive sensing based on orthogonal matching pursuit (CS-OMP) algorithm and rigrsure adaptive soft threshold noise reduction based on discrete wavelet transform (RA-DWT) algorithm, to separate and reconstruct the breathing and heartbeat signals. Then, a frequency-domain fast Fourier transform and a time-domain autocorrelation estimation algorithm are proposed to calculate the respiratory and heartbeat rates. The proposed algorithms are compared with the contact-based detection ones. The results demonstrate that the proposed algorithms effectively suppress the noise and harmonic interference, and the accuracies of the proposed algorithms for both respiratory rate and heartbeat rate reach about 93%.

This study aims to examine the levels of anxiety symptoms and perceived social support among the men who have sex with men (MSM) population, to assess the level of both anxiety and low social support, and associated factors in this population. The study used an Internet service platform for MSM between March and June 2024. Sociodemographic information, laboratory test data, and scores from the Generalized Anxiety Disorder scale (GAD-7), and Perceived Social Support Scale (PSSS) were collected among men who have sex with men (MSM). Decision tree model and binary logistic regression were used to analyze the factors associated with anxiety with low perceived social support. A total of 1070 MSM respondents were recruited, of whom 19.6% had anxiety symptoms, and 12.90% had low social support. The prevalence of anxiety symptoms was significantly higher among individuals with low social support (38.41%) than among those with medium or high social support (16.95%) (P < 0.001). Specifically, 4.95% of all respondents had both anxiety and low social support. Logistic regression analysis showed that employment status (P = 0.028), self-esteem (P < 0.001) and psychological resilience (P < 0.001) were significant factors associated with both anxiety symptoms and low social support in the MSM population. Furthermore, the decision tree model identified self-esteem and psychological resilience as key predictors of both anxiety and low social support in the MSM population (all P < 0.05). Our study demonstrated that in the MSM population in China's eastern region, the prevalence of both anxiety and low social support was relatively low. Employment status, self-esteem, and psychological resilience were identified as significantly correlated factors for them. To effectively reduce anxiety in this population, interventions should focus on enhancing these factors.

Background Drug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is not reliable. This makes it difficult to generalize such SNV-based models to clinical tumor data or single-cell level studies in the future. Results We present a new method, DualGCN, a unified Dual Graph Convolutional Network model to predict cancer drug response. DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two embeddings are fed into a multilayer perceptron to predict drug response. DualGCN incorporates prior knowledge on cancer-related genes and protein–protein interactions, and outperforms most state-of-the-art methods while avoiding using large-scale SNV data. Conclusions The proposed method outperforms most state-of-the-art methods in predicting cancer drug response without the use of large-scale SNV data. These favorable results indicate its potential to be extended to clinical and single-cell tumor samples and advancements in precision medicine.

The success of training computer-vision models heavily relies on the support of large-scale, real-world images with annotations. Yet such an annotation-ready dataset is difficult to curate in pathology due to the privacy protection and excessive annotation burden. To aid in computational pathology, synthetic data generation, curation, and annotation present a cost-effective means to quickly enable data diversity that is required to boost model performance at different stages. In this study, we introduce a large-scale synthetic pathological image dataset paired with the annotation for nuclei semantic segmentation, termed as Synthetic Nuclei and annOtation Wizard (SNOW). The proposed SNOW is developed via a standardized workflow by applying the off-the-shelf image generator and nuclei annotator. The dataset contains overall 20k image tiles and 1,448,522 annotated nuclei with the CC-BY license. We show that SNOW can be used in both supervised and semi-supervised training scenarios. Extensive results suggest that synthetic-data-trained models are competitive under a variety of model training settings, expanding the scope of better using synthetic images for enhancing downstream data-driven clinical tasks.

Non-alcoholic fatty liver disease (NAFLD) is a metabolic disorder characterized by excess lipid accumulation in the liver without significant consumption of alcohol. The transmembrane 6 superfamily member 2 (TM6SF2) E167K missense variant strongly associates with NAFLD in humans. The E167K mutation destabilizes TM6SF2, resulting in hepatic lipid accumulation and low serum lipid levels. However, the molecular mechanism by which TM6SF2 regulates lipid metabolism remains unclear. By using tandem affinity purification in combination with mass spectrometry, we found that apolipoprotein B (APOB), ER lipid raft protein (ERLIN) 1 and 2 were TM6SF2-interacting proteins. ERLINs and TM6SF2 mutually bound and stabilized each other. TM6SF2 bound and stabilized APOB via two luminal loops. ERLINs did not interact with APOB directly but still increased APOB stability through stabilizing TM6SF2. This APOB stabilization was hampered by the E167K mutation that reduced the protein expression of TM6SF2. In mice, knockout of Tm6sf2 and knockdown of Tm6sf2 or Erlins decreased hepatic APOB protein level, causing lipid accumulation in the liver and lowering lipid levels in the serum. We conclude that defective APOB stabilization, as a result of ERLINs or TM6SF2 deficiency or E167K mutation, is a key factor contributing to NAFLD.

Solar-driven CO 2 reduction to yield high-value chemicals presents an appealing avenue for combating climate change, yet achieving selective production of specific products remains a significant challenge. We showcase two osmium complexes, przpOs, and trzpOs, as CO 2 reduction catalysts for selective CO 2 -to-methane conversion. Kinetically, the przpOs and trzpOs exhibit high CO 2 reduction catalytic rate constants of 0.544 and 6.41 s −1 , respectively. Under AM1.5 G irradiation, the optimal Si/TiO 2 /trzpOs have CH 4 as the main product and >90% Faradaic efficiency, reaching −14.11 mA cm −2 photocurrent density at 0.0 V RHE . Density functional theory calculations reveal that the N atoms on the bipyrazole and triazole ligands effectively stabilize the CO 2 -adduct intermediates, which tend to be further hydrogenated to produce CH 4 , leading to their ultrahigh CO 2 -to-CH 4 selectivity. These results are comparable to cutting-edge Si-based photocathodes for CO 2 reduction, revealing a vast research potential in employing molecular catalysts for the photoelectrochemical conversion of CO 2 to methane. Solar-driven CO 2 conversion to produce solar fuels is an attractive way to harness solar energy and reduce carbon emissions. Here, the authors report two osmium complexes as highly active and selective CO 2 reduction catalysts for selective CO 2 -to-methane conversion on Si-based photocathodes.

The authors report that in mouse auditory cortex, the sensory-evoked spike responses of layer 2/3 (L2/3) excitatory cells were scaled down with preserved sensory tuning when animals transitioned from quiescence to active behaviors, while L4 and thalamic responses were unchanged. This laminar-specific gain control could be attributed to an enhancement of L1-mediated inhibition. Cortical sensory processing is modulated by behavioral and cognitive states. How this modulation is achieved by changing synaptic circuits remains largely unknown. In awake mouse auditory cortex, we found that sensory-evoked spike responses of layer 2/3 (L2/3) excitatory cells were scaled down with preserved sensory tuning when mice transitioned from quiescence to active behaviors, including locomotion, whereas L4 and thalamic responses were unchanged. Whole-cell voltage-clamp recordings revealed that tone-evoked synaptic excitation and inhibition exhibited a robust functional balance. The change to active states caused scaling down of excitation and inhibition at approximately equal levels in L2/3 cells, but resulted in no synaptic changes in L4 cells. This lamina-specific gain control could be attributed to an enhancement of L1-mediated inhibitory tone, with L2/3 parvalbumin inhibitory neurons also being suppressed. Thus, L2/3 circuits can adjust the salience of output in accordance with momentary behavioral demands while maintaining the sensitivity and quality of sensory processing.

In scenarios of indoor localization of mobile robots, Global Positioning System (GPS) signals are prone to loss due to interference from urban building environments and cannot meet the needs of robot localization. On the other hand, traditional indoor localization methods based on wireless signals such as Bluetooth and WiFi often require the deployment of multiple devices in advance, and these methods can only obtain distance information and are unable to obtain the attitude of the positioning target in space. This paper proposes a method for the indoor localization of mobile robots based on a depth camera. Firstly, we extracted ORB feature points from images captured by a depth camera and performed homogenization processing. Then, we performed feature matching between adjacent two frames of images, and the mismatched points are eliminated to improve the accuracy of feature matching. Finally, we used the Iterative Closest Point (ICP) algorithm to estimate the camera’s pose, thus achieving the localization of mobile robots in indoor environments. In addition, an experimental evaluation was conducted on the TUM dataset of the Technical University of Munich to validate the feasibility of the proposed depth-camera-based indoor localization system for mobile robots. The experimental results show that the average localization accuracy of our algorithm on three datasets is 0.027 m, which can meet the needs of indoor localization for mobile robots.

Sterol-regulatory element binding proteins (SREBPs) are the key transcriptional regulators of lipid metabolism. The activation of SREBP requires translocation of the SREBP precursor from the endoplasmic reticulum to the Golgi, where it is sequentially cleaved by site-1 protease (S1P) and site-2 protease and releases a nuclear form to modulate gene expression. To search for new genes regulating cholesterol metabolism, we perform a genome-wide CRISPR/Cas9 knockout screen and find that partner of site-1 protease (POST1), encoded by C12ORF49, is critically involved in the SREBP signaling. Ablation of POST1 decreases the generation of nuclear SREBP and reduces the expression of SREBP target genes. POST1 binds S1P, which is synthesized as an inactive protease (form A) and becomes fully mature via a two-step autocatalytic process involving forms B'/B and C'/C. POST1 promotes the generation of the functional S1P-C'/C from S1P-B'/B (canonical cleavage) and, notably, from S1P-A directly (non-canonical cleavage) as well. This POST1-mediated S1P activation is also essential for the cleavages of other S1P substrates including ATF6, CREB3 family members and the α/β-subunit precursor of N-acetylglucosamine-1-phosphotransferase. Together, we demonstrate that POST1 is a cofactor controlling S1P maturation and plays important roles in lipid homeostasis, unfolded protein response, lipoprotein metabolism and lysosome biogenesis.

With the rapid development of hydrogen fuel cell vehicles, the research on the throttling effect of high-pressure hydrogen is crucial to the safety of hydrogen circulation systems for fuel cells. This paper studies the Joule-Thomson coefficients (μJT) of ten gas state equations. The four equations, Van Der Waals (VDW), Redlich-Kwong (RK), Soave-Redlich-Kwong (SRK), and Beattie Bridgeman (BB), were selected for calculation. These were compared with the database of the National Institute of Standards and Technology (NIST), aiming to determine the optimal state equation under different temperature and pressure conditions. The empirical formula of the μJT pressure and temperature was compounded, and the temperature rise effect was further calculated using the empirical formula of compounding. The results show that the calculated value of μJT by using the VDW equation in the low-pressure range (0–2 MPa) is closer to the value in the NIST database with an error less than 0.056 K·MPa−1. The tendency of μJT described by the RK equation corresponds to the NIST database; meanwhile, the maximum error in the SRK equation is 0.143916 K·MPa−1. The BB equation is more applicable within the pressure range of 20 to 50 MPa with a maximum error of 0.042853 K·MPa−1. The fitting error of the empirical formula is within 9.52%, and the relative error of the calculated temperature rise is less than 4%. This research might provide several technical ideas for the study of the throttling effect of hydrogen refueling stations and the hydrogen circulation system of on-board hydrogen fuel cells.