RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION.

Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time. In this study, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect on longitudinal binary traits. We model dynamic genetic effect using smoothing splines, estimate model parameters by maximizing a double penalized quasi-likelihood function, design a joint test using a Cauchy combination method, and evaluate statistical significance via a retrospective approach to achieve robustness to model misspecification. Through simulations we illustrated that the retrospective varying-coefficient test was robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis. Pathway analysis identified two important pathways related to G-protein signaling and DNA damage. Our results demonstrated that RVMMAT could detect biologically relevant loci and pathways in a genome scan and provided insight into the genetic architecture of hypertension.

Cross-Modal Supervised Human Body Pose Recognition Techniques for Through-Wall Radar.

Through-wall radar human body pose recognition technology has broad applications in both military and civilian sectors. Identifying the current pose of targets behind walls and predicting subsequent pose changes are significant challenges. Conventional methods typically utilize radar information along with machine learning algorithms such as SVM and random forests to aid in recognition. However, these approaches have limitations, particularly in complex scenarios. In response to this challenge, this paper proposes a cross-modal supervised through-wall radar human body pose recognition method. By integrating information from both cameras and radar, a cross-modal dataset was constructed, and a corresponding deep learning network architecture was designed. During training, the network effectively learned the pose features of targets obscured by walls, enabling accurate pose recognition (e.g., standing, crouching) in scenarios with unknown wall obstructions. The experimental results demonstrated the superiority of the proposed method over traditional approaches, offering an effective and innovative solution for practical through-wall radar applications. The contribution of this study lies in the integration of deep learning with cross-modal supervision, providing new perspectives for enhancing the robustness and accuracy of target pose recognition.

A New Network Structure for Speech Emotion Recognition Research.

Deep learning promotes the breakthrough of emotion recognition in many fields, especially speech emotion recognition (SER). As an important part of speech emotion recognition, the most relevant acoustic feature extraction has always attracted the attention of existing researchers. Aiming at the problem that the emotional information contained in the current speech signals is distributed dispersedly and cannot comprehensively integrate local and global information, this paper presents a network model based on a gated recurrent unit (GRU) and multi-head attention. We evaluate our proposed emotion model on the IEMOCAP and Emo-DB corpora. The experimental results show that the network model based on Bi-GRU and multi-head attention is significantly better than the traditional network model at detecting multiple evaluation indicators. At the same time, we also apply the model to a speech sentiment analysis task. On the CH-SIMS and MOSI datasets, the model shows excellent generalization performance.

Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis.

BACKGROUND: Long-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to diagnose arrhythmias, and this is a particular issue in more remote and underdeveloped areas. The development of digital ECG and AI methods could assist clinicians who need to diagnose arrhythmias outside of the hospital setting. METHODS: We constructed a large-scale Chinese ECG benchmark dataset using data from 272,753 patients collected from January 2017 to December 2021. The dataset contains ECG recordings from all common arrhythmias present in the Chinese population. Several experienced cardiologists from Shanghai First People's Hospital labeled the dataset. We then developed a deep learning-based multi-label interpretable diagnostic model from the ECG recordings. We utilized Accuracy, F1 score and AUC-ROC to compare the performance of our model with that of the cardiologists, as well as with six comparison models, using testing and hidden data sets. RESULTS: The results show that our approach achieves an F1 score of 83.51%, an average AUC ROC score of 0.977, and 93.74% mean accuracy for 6 common arrhythmias. Results from the hidden dataset demonstrate the performance of our approach exceeds that of cardiologists. Our approach also highlights the diagnostic process. CONCLUSIONS: Our diagnosis system has superior diagnostic performance over that of clinicians. It also has the potential to help clinicians rapidly identify abnormal regions on ECG recordings, thus improving efficiency and accuracy of clinical ECG diagnosis in China. This approach could therefore potentially improve the productivity of out-of-hospital ECG diagnosis and provides a promising prospect for telemedicine.

Arrhythmia, also known as an irregular heartbeat, is a common cardiovascular disease. Sometimes the presence of an arrhythmia can increase the risk of more serious heart conditions. Long-term monitoring of the heartbeat enables arrhythmia to be more easily diagnosed. To accurately detect arrhythmia, we developed a computational model that was able to detect six common types of arrhythmias from readings of the heart rate obtained using a device connected to a mobile phone. We showed that our model could diagnose these arrhythmias in over 270,000 people living in China. Our diagnostic system could enable arrhythmias to be diagnosed more easily outside of hospitals and therefore improve access to healthcare, particularly for those in remote settings.

Multi-scale characterization and analysis of cellular viscoelastic mechanical phenotypes by atomic force microscopy.

The viscoelasticity of cells serves as a biomarker that reveals changes induced by malignant transformation, which aids the cytological examinations. However, differences in the measurement methods and parameters have prevented the consistent and effective characterization of the viscoelastic phenotype of cells. To address this issue, nanomechanical indentation experiments were conducted using an atomic force microscope (AFM). Multiple indentation methods were applied, and the indentation parameters were gradually varied to measure the viscoelasticity of normal liver cells and cancerous liver cells to create a database. This database was employed to train machine-learning algorithms in order to analyze the differences in the viscoelasticity of different types of cells and as well as to identify the optimal measurement methods and parameters. These findings indicated that the measurement speed significantly influenced viscoelasticity and that the classification difference between the two cell types was most evident at 5 µm/s. In addition, the precision and the area under the receiver operating characteristic curve were comparatively analyzed for various widely employed machine-learning algorithms. Unlike previous studies, this research validated the effectiveness of measurement parameters and methods with the assistance of machine-learning algorithms. Furthermore, the results confirmed that the viscoelasticity obtained from the multiparameter indentation measurement could be effectively used for cell classification. RESEARCH HIGHLIGHTS: This study aimed to analyze the viscoelasticity of liver cancer cells and liver cells. Different nano-indentation methods and parameters were used to measure the viscoelasticity of the two kinds of cells. The neural network algorithm was used to reverse analyze the dataset, and the methods and parameters for accurate classification and identification of cells are successfully found.

The Effects of Photobiomodulation on Knee Function, Pain, and Exercise Tolerance in Older Adults: A Meta-analysis of Randomized Controlled Trials.

OBJECTIVE: To assess whether photobiomodulation therapy (PBMT) enhances the benefits of exercise in older adults. DATA SOURCES: PubMed, Scopus, Medline, and Web of Science, dated to February 2023. STUDY SELECTION: All included studies were randomized controlled trials of PBMT combined with exercise co-intervention in persons 60 years and older. OUTCOME MEASURES: Western Ontario and McMaster University Osteoarthritis Index (WOMAC-total, pain, stiffness and function), perceived pain intensity, timed Up and Go (TUG) Test, 6-min walk test (6MWT), muscle strength, and knee range of motion were included. DATA EXTRACTION: Two researchers independently performed data extraction. Article data were extracted in Excel and summarized by a third researcher. DATA SYNTHESIS: The meta-analysis included 14 of the 1864 studies searched in the database. No statistical differences were found between the treatment and control groups in terms of WOMAC-stiffness (mean difference [MD]=-0.31, 95% confidence interval [CI] -0.64 to 0.03), TUG (MD=-0.17, 95% CI -0.71 to 0.38), 6MWT (MD=32.2, 95% CI -44.62 to 109.01), or muscle strength (standardized mean difference=0.24, 95% CI -0.02 to 0.50). However, statistically significant differences were found for WOMAC-total (MD=-6.83, 95% CI -12.3 to -1.37), WOMAC-pain (MD=-2.03, 95% CI -4.06 to -0.01), WOMAC-function (MD=-5.03, 95% CI -9.11 to -0.96), visual analog scale/numeric pain rating scale (MD=-1.24, 95% CI -2.43 to -0.06), and knee range of motion (MD=1.47, 95% CI 0.07 to 2.88). CONCLUSIONS: In older adults who exercise regularly, PBMT can potentially provide additional pain relief, improve knee joint function, and increase knee joint range of motion.

microRNA-mRNA Analysis Reveals Tissue-Specific Regulation of microRNA in Mangrove Clam (Geloina erosa).

Geloina erosa is an important benthic animal in the mangrove, serving as an indicator organism for coastal environmental pollution. This study aimed to investigate the tissue-specific expression of miRNAs and their regulatory roles in predicted targets in G. erosa. Through miRNA sequencing and co-expression network analysis, we extensively studied the miRNA expression in three tissues: gills, hepatopancreas, and muscle. The results revealed a total of 1412 miRNAs, comprising 1047 known miRNAs, and 365 newly predicted miRNAs. These miRNAs exhibited distinct tissue-specific expression patterns. In the miRNA target gene prediction, a total of 7404 potential predicted targets were identified, representing approximately 33% of all unique transcripts associated with miRNAs. Further co-expression network analysis revealed nine modules, each showing a positive correlation with specific tissues (gills, hepatopancreas, or muscle). The blue module showed a significant correlation with gills (r = 0.83, p-value = 0.006), the black module was significantly related to the hepatopancreas (r = 0.78, p-value = 0.01), and the purple module was significantly correlated with muscle (r = 0.83, p-value = 0.006). Within these modules, related miRNAs tended to cluster together, while their correlations with other modules were relatively weak. Functional enrichment analysis was performed on miRNAs and their predicted targets in each tissue. In the gills, miRNAs primarily regulate immune-related genes, substance transport, and cytoskeletal organization. In the hepatopancreas, miRNAs suppressed genes involved in shell formation and played a role in cellular motor activity and metabolism. In muscle, miRNAs participate in metabolism and photoreceptive processes, as well as immune regulation. In summary, this study provides valuable insights into the tissue-specific regulation of miRNAs in G. erosa, highlighting their potential roles in immune response, metabolism, and environmental adaptation. These findings offer important clues for understanding the molecular mechanisms and biological processes in G. erosa, laying the foundation for further validation and elucidation of these regulatory relationships.

Ultrasound-guided pericapsular nerve group (PENG) block for early analgesia in elderly patients with hip fractures: a single-center prospective randomized controlled study.

BACKGROUND: The aim of this study was to compare the efficacy of ultrasound-guided PENG (pericapsular nerve group) block and drug therapy with intravenous flurbiprofen for early analgesia in elderly patients with hip fractures after hospitalization. METHODS: This is a single-center, observer-blinded, prospective, randomized, controlled trial. A total of 41 elderly patients (aged 60 or older) with hip fractures were enrolled in the current study. Patients were randomly assigned to two groups: Group P (ultrasound-guided PENG block, 20 mL of 0.375% ropivacaine) and Group F (intravenous flurbiprofen 50 mg). The primary outcome measure was the dynamic (passive straight leg raising 15°) NRS (numerical rating scale 0 to 10) pain scores at different time points. The secondary outcomes were the static NRS scores at different time points, the number of rescue analgesia sessions, patient satisfaction, and the incidence of complications. RESULTS: Patients in the two groups had comparable baseline characteristics. The group P had lower dynamic and static NRS scores at 15 min, 30 min, 6 h, and 12 h after intervention (P<0.05) than the group F. The highest NRS pain scores in the group P were still lower than the NRS scores in the group F at 30 min-12 h (Group F: 5.57±1.54 vs. Group P: 3.00±1.12, P<0.001), and there was no significant difference between the two groups at 12-24 h (Group F: 6.35±1.79 vs. Group P: 5.90±1.83, P>0.05). The group P had higher satisfaction scores (Group P: 9 (9,9) vs. Group F: 8 (7,8), P<0.001). There was no statistically significant difference in the number of rescue analgesics at 0-12 h or 12-24 h or the incidence of complications between the groups. CONCLUSIONS: Compared with intravenous flurbiprofen, ultrasound-guided PENG block provides better early analgesic effects in elderly patients with hip fractures, and a PENG block is safe for elderly patients with hip fractures after hospitalization. Trial registration This study was registered in the Chinese Clinical Trial Testing Center (ID: ChiCTR2200062400).

WGX50 mitigates doxorubicin-induced cardiotoxicity through inhibition of mitochondrial ROS and ferroptosis.

BACKGROUND: Doxorubicin (DOX)-induced cardiotoxicity (DIC) is a major impediment to its clinical application. It is indispensable to explore alternative treatment molecules or drugs for mitigating DIC. WGX50, an organic extract derived from Zanthoxylum bungeanum Maxim, has anti-inflammatory and antioxidant biological activity, however, its function and mechanism in DIC remain unclear. METHODS: We established DOX-induced cardiotoxicity models both in vitro and in vivo. Echocardiography and histological analyses were used to determine the severity of cardiac injury in mice. The myocardial damage markers cTnT, CK-MB, ANP, BNP, and ferroptosis associated indicators Fe2+, MDA, and GPX4 were measured using ELISA, RT-qPCR, and western blot assays. The morphology of mitochondria was investigated with a transmission electron microscope. The levels of mitochondrial membrane potential, mitochondrial ROS, and lipid ROS were detected using JC-1, MitoSOX™, and C11-BODIPY 581/591 probes. RESULTS: Our findings demonstrate that WGX50 protects DOX-induced cardiotoxicity via restraining mitochondrial ROS and ferroptosis. In vivo, WGX50 effectively relieves doxorubicin-induced cardiac dysfunction, cardiac injury, fibrosis, mitochondrial damage, and redox imbalance. In vitro, WGX50 preserves mitochondrial function by reducing the level of mitochondrial membrane potential and increasing mitochondrial ATP production. Furthermore, WGX50 reduces iron accumulation and mitochondrial ROS, increases GPX4 expression, and regulates lipid metabolism to inhibit DOX-induced ferroptosis. CONCLUSION: Taken together, WGX50 protects DOX-induced cardiotoxicity via mitochondrial ROS and the ferroptosis pathway, which provides novel insights for WGX50 as a promising drug candidate for cardioprotection.

Centrosome clustering control in osteoclasts through CCR5-mediated signaling.

Osteoclasts uniquely resorb calcified bone matrices. To exert their function, mature osteoclasts maintain the cellular polarity and directional vesicle trafficking to and from the resorbing bone surface. However, the regulatory mechanisms and pathophysiological relevance of these processes remain largely unexplored. Bone histomorphometric analyses in Ccr5-deficient mice showed abnormalities in the morphology and functional phenotype of their osteoclasts, compared to wild type mice. We observed disorganized clustering of nuclei, as well as centrosomes that organize the microtubule network, which was concomitant with impaired cathepsin K secretion in cultured Ccr5-deficient osteoclasts. Intriguingly, forced expression of constitutively active Rho or Rac restored these cytoskeletal phenotypes with recovery of cathepsin K secretion. Furthermore, a gene-disease enrichment analysis identified that PLEKHM1, a responsible gene for osteopetrosis, which regulates lysosomal trafficking in osteoclasts, was regulated by CCR5. These experimental results highlighted that CCR5-mediated signaling served as an intracellular organizer for centrosome clustering in osteoclasts, which was involved in the pathophysiology of bone metabolism.

Mirror-symmetric double-negative metamaterial resonator with polarization insensitivity and tunable sandwiched structure for multiband wireless communications.

This paper presents a novel Double-negative (DNG) metamaterial (MM) resonator with a mirror-symmetric configuration, designed to exhibit multiband resonances in the S, C, and X bands. The resonator is fabricated using advanced processing techniques on a Rogers 5880 substrate and features electrodeposited copper. It consists of four equal regions, each containing interconnected split-ring resonators that are connected through a cross-shaped structure to ensure mirror symmetry. The simulation results demonstrate six resonance points at frequencies of 2.45 GHz, 4.27 GHz, 6.86 GHz, 8.98 GHz, 10.69 GHz, and 11.65 GHz. These resonances are characterized by near-zero/negative permeability, negative permittivity, refractive index, and impedance. Furthermore, the cross-polarization effect of incident waves is investigated. Additionally, the potential for tunability of resonance frequencies is explored through a sandwiched configuration of the MM resonator, achieved by modifying the cover of the resonating patch. Moreover, the equivalent circuit model of the proposed MM resonator is in good agreement with practical measurements, validating the simulation results. The new tuning method for MM resonators holds the promise for future sensing applications and wireless communications.

Preparation and adsorption properties of magnetic chitosan/sludge biochar composites for removal of Cu2+ ions.

The magnetic chitosan/sludge biochar composite adsorbent was prepared using chitosan, Fe3O4, and sludge biochar as raw materials. The composite adsorbent was able to achieve rapid solid-liquid separation under an applied magnetic field. The morphology and microstructure of the composite adsorbent were characterized by FTIR, XRD, SEM, VSM, and BET analysis. The adsorption performance of the composite adsorbent on Cu2+ was investigated through static adsorption experiments, and the effects of adsorbent dosage, initial concentration of Cu2+, initial pH of the solution, and adsorption temperature on the adsorption efficiency of Cu2+ were discussed. The results showed that chitosan and Fe3O4 were successfully loaded on sludge biochar. When the initial concentration of Cu2+ was 30 mg/L, the dosage of the magnetic chitosan/sludge biochar composite material was 0.05 g, the adsorption time was 180 min, pH was 5, and the temperature was room temperature, the maximum removal rate of Cu2+ reached 99.77%, and the maximum adsorption capacity was 55.16 mg/g. The adsorption kinetics and adsorption isotherm data fitted well with the pseudo-second-order kinetic model and Langmuir adsorption isotherm model, indicating that the adsorption process was chemisorption with monolayer coverage.

Brain Registration and Evaluation for Zebrafish (BREEZE)-mapping: A pipeline for whole-brain structural and activity analyses.

Here, we present Brain Registration and Evaluation for Zebrafish (BREEZE)-mapping, a user-friendly pipeline for the registration and analysis of whole-brain images in larval zebrafish. We describe steps for pre-processing, registration, quantification, and visualization of whole-brain phenotypes in zebrafish mutants of genes associated with neurodevelopmental and neuropsychiatric disorders. By utilizing BioImage Suite Web, an open-source software package originally developed for processing human brain imaging data, we provide a highly accessible whole-brain mapping protocol developed for users with general computational proficiency. For complete details on the use and execution of this protocol, please refer to Weinschutz Mendes et al. (2023).1.

Effects of Short-Term Exposure of Chloramine-T Solution on the Characteristics of Light-Cured and Chemical-Cured Adhesives.

This study evaluated the effect of a 0.5% chloramine T solution on a chemical-cured universal adhesive by comparing the light-cured, one-step, self-etch adhesive for the bonding performance, mechanical properties, and resin-dentin interfacial characteristics. Caries-free human molars were randomly assigned into eight groups based on the bonding systems employed (Bond Force II, BF and Bondmer Lightless, BL), the immersion solutions used before bonding (0.5% chloramine T solution and distilled water), and the immersion durations (5 and 60 min). Microtensile bond strength (µTBS), nanoleakage evaluation, and nanoindentation tests were performed, and the surface morphology of the resin-dentin interface was examined using a focus ion beam/scanning ion microscopy system. Immersion in chloramine-T for 5 min significantly decreased the µTBS of Bondmer Lightless (from 22.62 to 12.87 MPa) compared with that in distilled water. Moreover, there was also a decreasing trend after immersing in chloramine-T for 60 min (from 19.11 to 13.93 MPa). Chloramine T was found to have no effect on the hardness, elastic modulus, or morphological characteristics of the ion-beam milled resin-dentin interfacial surfaces in the tested adhesives, suggesting that chloramine T might reduce the bond strength by interfering with the interaction and the sealing between the adhesive resin and dentin in the chemical-cured universal adhesive, albeit without affecting the mechanical properties.

Monocular catadioptric panoramic depth estimation via improved end-to-end neural network model.

In this paper, we propose a monocular catadioptric panoramic depth estimation algorithm based on an improved end-to-end neural network model. First, we use an enhanced concentric circle approximation unfolding algorithm to unfold the panoramic images captured by the catadioptric panoramic camera and then extract the effective regions. In addition, the integration of the Non-local attention mechanism is exploited to improve image understanding. Finally, a depth smoothness loss strategy is implemented to further enhance the reliability and precision of the estimated depths. Experimental results confirm that this refined algorithm is capable of providing highly accurate estimates of object depth.

A hybrid machine learning and regression method for cell type deconvolution of spatial barcoding-based transcriptomic data.

Spatial barcoding-based transcriptomic (ST) data require cell type deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method, to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER uses a machine learning approach to remove the systematic difference between ST and scRNA-seq data (platform effects) explicitly and efficiently to ensure the linear relationship between ST data and cell type-specific expression profile. It also considers sparsity of cell types per capture spot and across-spots spatial correlation in cell type compositions. Based on the estimated cell type proportions, SDePER imputes cell type compositions and gene expression at unmeasured locations in a tissue map with enhanced resolution. Applications to coarse-grained simulated data and four real datasets showed that SDePER achieved more accurate and robust results than existing methods, suggesting the importance of considering platform effects, sparsity and spatial correlation in cell type deconvolution.

Improved Strong Tracking Cubature Kalman Filter for UWB Positioning.

For the problems of Non-Line-of-Sight (NLOS) observation errors and inaccurate predictive dynamics model in wireless ultra-wideband (UWB) positioning systems, an improved strong tracking cubature Kalman filter (ISTCKF) positioning algorithm is proposed in this paper. The main idea of the algorithm is as follows. First, the observations are reconstructed based on the weighted positioning results obtained from the predictive dynamics model and the least squares algorithm. Second, the difference in statistical properties between the observation noise and the NLOS errors is utilized to identify the NLOS observations by the corresponding judgment statistics obtained from the operation between the original observations and the reconstructed observations. The main positioning error of the UWB positioning system at the current moment is then judged by the NLOS identification results, and the corresponding fading factors are calculated according to the judgment results. Finally, the corresponding ISTCKF is constructed based on the fading factors to mitigate the main positioning error and obtain accurate positioning result in the UWB positioning system. In this paper, the reconstructed observations mitigate the observation noise in the original observation, and then the ISTCKF mitigates the main errors in the UWB positioning system. The experimental results show that the ISTCKF algorithm reduces the positioning error by 55.2%, 32.3% and 28.9% compared with STCKF, ACKF and RSTCKF, respectively. The proposed ISTCKF algorithm significantly improves the positioning accuracy and stability of the UWB system.

Destriping of Remote Sensing Images by an Optimized Variational Model.

Satellite sensors often capture remote sensing images that contain various types of stripe noise. The presence of these stripes significantly reduces the quality of the remote images and severely affects their subsequent applications in other fields. Despite the existence of many stripe noise removal methods in the research, they often result in the loss of fine details during the destriping process, and some methods even generate artifacts. In this paper, we proposed a new unidirectional variational model to remove horizontal stripe noise. The proposed model fully considered the directional characteristics and structural sparsity of the stripe noise, as well as the prior features of the underlying image, to design different sparse constraints, and the ℓp quasinorm was introduced in these constraints to better describe these sparse characteristics, thus achieving a more excellent destriping effect. Moreover, we employed the fast alternating direction method of multipliers (ADMM) to solve the proposed non-convex model. This significantly improved the efficiency and robustness of the proposed method. The qualitative and quantitative results from simulated and real data experiments confirm that our method outperforms existing destriping approaches in terms of stripe noise removal and preservation of image details.