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Extremely large-scale antenna array (ELAA) at millimeter wave (mmWave) and Terahertz (THz) band has been considered a key technology for combating high attenuation in high-frequency bands in future 6G communications. Uniform circular arrays (UCAs) have attracted much attention because of their ability to provide flat beamforming gain at all angles. To realize efficient beamforming, beam training is widely used to acquire channel state information. However, with a large antenna number, the beam training overhead in ELAA systems becomes overwhelming. Moreover, with a large bandwidth, the beam defocus effect severely degrades beam training accuracy. To address these issues, this paper proposes a frequency-dependent focusing (FDF)-based beam training scheme to realize effective beam training in near-field wideband ELAA systems with UCA. Specifically, we first analyze the FDF property of UCA, where signals at different subcarriers can simultaneously focus on different distances. Then, by exploiting the FDF property to search different distances using different subcarriers simultaneously, we design a hierarchical codebook and propose an FDF-based beam training scheme. To reveal the effectiveness of the proposed scheme, we compare its necessary beam training overhead with that of existing schemes. Finally, the simulation results demonstrate that the proposed scheme can achieve accurate beam training in near-field wideband UCA systems with a low beam training overhead. [ABSTRACT FROM AUTHOR]

Background: Tegoprazan is a potassium-competitive acid blocker that inhibits gastric acid and which may be used for eradicating Helicobacter pylori. This study focuses on the pharmacokinetic interaction and safety between tegoprazan and the combination of clarithromycin, amoxicillin and bismuth in healthy Chinese subjects. Methods: An open-label, three-period, single-center, multiple-dosage, single-sequence, phase I trial was conducted in 22 healthy subjects. In period 1, the subjects took tegoprazan 50 mg twice daily for 7 days, and in period 2 they were administered clarithromycin 500 mg, amoxicillin 1000 mg and bismuth potassium citrate 600 mg twice daily for 7 days (days 14–20). Tegoprazan, clarithromycin, amoxicillin and bismuth potassium citrate were then administered in combination for 7 days (days 21–27) in period 3. Blood samples were collected up to 12 h after the last dose of each period. Safety assessments were performed in each period. Results: The geometric mean ratios (GMRs) [90% confidence interval (CI)] of maximum plasma concentration at steady state (Cmax,ss) and area under the plasma concentration–time curve over the dosing interval (AUCτ) at steady state were 195.93% (175.52–218.71%) and 287.54% (263.28–314.04%) for tegoprazan and 423.23% (382.57–468.22%) and 385.61% (354.62–419.30%) for tegoprazan metabolite M1, respectively. The GMRs (90% CI) of Cmax,ss and AUCτ were 83.69% (77.44–90.45%) and 110.30% (102.74–118.41%) for clarithromycin, 126.25% (114.73–138.93%) and 146.94% (135.33–159.55%) for 14-hydroxyclarithromycin, 75.89% (69.73–82.60%) and 94.34% (87.94–101.20%) for amoxicillin, and 158.43% (125.43–200.11%) and 183.63% (156.42–215.58%) for bismuth, respectively. All reported adverse events were mild. The frequency of adverse events during the coadministration stage was not higher than that during the single- or triple-drug administration stages. Conclusion: The plasma exposure of tegoprazan, M1, 14-hydroxyclarithromycin and bismuth was increased after the coadministration of tegoprazan, clarithromycin, amoxicillin and bismuth. The coadministration exhibited favorable safety and tolerability. Clinical Trials Registration: CTR20230643. [ABSTRACT FROM AUTHOR]

Human aging is a natural and inevitable biological process that leads to an increased risk of aging-related diseases. Developing anti-aging therapies for aging-related diseases requires a comprehensive understanding of the mechanisms and effects of aging and longevity from a multi-modal and multi-faceted perspective. However, most of the relevant knowledge is scattered in the biomedical literature, the volume of which reached 36 million in PubMed. Here, we presented HALD, a text mining-based human aging and longevity dataset of the biomedical knowledge graph from all published literature related to human aging and longevity in PubMed. HALD integrated multiple state-of-the-art natural language processing (NLP) techniques to improve the accuracy and coverage of the knowledge graph for precision gerontology and geroscience analyses. Up to September 2023, HALD had contained 12,227 entities in 10 types (gene, RNA, protein, carbohydrate, lipid, peptide, pharmaceutical preparations, toxin, mutation, and disease), 115,522 relations, 1,855 aging biomarkers, and 525 longevity biomarkers from 339,918 biomedical articles in PubMed. HALD is available at https://bis.zju.edu.cn/hald. [ABSTRACT FROM AUTHOR]

In order to navigate safely and effectively with humans in close proximity, robots must be capable of predicting the future motions of humans. This study first consolidates human studies in motion, intention, and preference into a discretized human model that can readily be used in robotics decision making algorithms. Cooperative Markov Decision Process (Co-MDP), a novel framework that improves upon Multiagent MDPs, is then proposed for enabling socially aware robot obstacle avoidance. Utilizing the consolidated and discretized human model, Co-MDP allows the system to (1) approximate rational human behavior and intention, (2) generate socially-aware robotic obstacle avoidance behavior, and (3) remain robust to the uncertainty of human intention and motion variance. Simulations of a human-robot co-populated environment verify Co-MDP as a feasible obstacle avoidance algorithm. In addition, the anthropomorphic behavior of Co-MDP was assessed and confirmed with a human-in-the-loop experiment. Results reveal that participants can not directly differentiate agents that were controlled by human operators from Co-MDP, and the reported confidences of their choices indicates that the predictions from participants were backed by behavioral evidence rather than random guesses. Thus the main contributions for this paper are: consolidating past human studies of rational human behavior and intention into a simple, discretized model; the development of Co-MDP: a robotic decision framework that can utilize this human model and maximize the joint utility between the human and robot; and an experimental design for evaluation of the human acceptance of obstacle avoidance algorithms. [ABSTRACT FROM AUTHOR]

As technology advances, Human-Robot Interaction (HRI) is boosting overall system efficiency and productivity. However, allowing robots to be present closely with humans will inevitably put higher demands on precise human motion tracking and prediction. Datasets that contain both humans and robots operating in the shared space are receiving growing attention as they may facilitate a variety of robotics and human-systems research. Datasets that track HRI with rich information other than video images during daily activities are rarely seen. In this paper, we introduce a novel dataset that focuses on social navigation between humans and robots in a future-oriented Wholesale and Retail Trade (WRT) environment (https://uf-retail-cobot-dataset.github.io/). Eight participants performed the tasks that are commonly undertaken by consumers and retail workers. More than 260 minutes of data were collected, including robot and human trajectories, human full-body motion capture, eye gaze directions, and other contextual information. Comprehensive descriptions of each category of data stream, as well as potential use cases are included. Furthermore, analysis with multiple data sources and future directions are discussed. Measurement(s) Robot and human trajectories, human full-body motion capture, and eye gaze directions. Technology Type(s) Mobile robot, inertial measurement unit-based motion capture system, and eye tracker. [ABSTRACT FROM AUTHOR]

Background: Taking ischemic and bleeding risks into consideration, insufficient data exist on dual antiplatelet therapy after percutaneous coronary intervention in elderly Chinese patients with coronary artery disease. Objective: We aimed to investigate the effectiveness and safety of ticagrelor in comparison with clopidogrel on a background of aspirin for elderly Chinese patients with coronary artery disease 12 months after percutaneous coronary intervention. Methods: A single-center retrospective cohort study was conducted. Selected from patients with coronary artery disease aged ≥ 75 years from January 2010 to July 2019, 908 eligible subjects receiving dual antiplatelet therapy after percutaneous coronary intervention for up to 12 months were consecutively enrolled in the study. The included patients received ticagrelor in combination with aspirin (n = 264) or clopidogrel in combination with aspirin (n = 644). Effectiveness endpoints were evaluated by the major adverse cardiovascular events, encompassing all-cause death, non-fatal myocardial infarction, and clinically driven revascularization. The safety endpoints were recorded as the incidence of Bleeding Academic Research Consortium bleeding. Results: The patients who were treated with ticagrelor were slightly younger than those who were treated with clopidogrel (79.1 ± 3.7 vs 80.7 ± 4.5 years, p < 0.01). The ticagrelor cohort contained a higher percentage of patients undergoing a prior percutaneous coronary intervention (37.9% vs 24.5%, p < 0.01), and a lower percentage of smokers (19.3% vs 27.2%, p < 0.05), compared with the clopidogrel cohort. The levels of glucose, total cholesterol, and low-density lipoprotein-cholesterol in the ticagrelor group were higher while the level of triglycerides and high-density lipoprotein-cholesterol were lower (p < 0.05) than those in the clopidogrel group. Left main percutaneous coronary intervention was performed more frequently among the ticagrelor-treated patients (23.5% vs 9.3%, p < 0.01), while patients in the clopidogrel group underwent more left circumflex percutaneous coronary intervention (34.3% vs 23.1%, p < 0.01). We found that ticagrelor was associated with a lower incidence of major adverse cardiovascular events than clopidogrel using the inverse probability of treatment weighting model (odds ratio, 0.493; 95% confidence interval 0.356–0.684). There was no difference in terms of the risk of Bleeding Academic Research Consortium bleeding between the two groups (p > 0.05). Conclusions: Ticagrelor was associated with a lower incidence of major adverse cardiovascular events than clopidogrel at 12 months in elderly Chinese patients with coronary artery disease, without a significant increase of Bleeding Academic Research Consortium bleeding events. [ABSTRACT FROM AUTHOR]

Bacteria-based tumor therapy has recently attracted wide attentions due to its unique capability in targeting tumors and preferentially colonizing the core area of the tumor. Various therapeutic genes are also harbored into these engineering bacteria to enhance their anti-tumor efficacy. However, it is difficult to spatiotemporally control the expression of these inserted genes in the tumor site. Here, we engineer an ultrasound-responsive bacterium (URB) which can induce the expression of exogenous genes in an ultrasound-controllable manner. Owing to the advantage of ultrasound in tissue penetration, an acoustic remote control of bacterial gene expression can be realized by designing a temperature-actuated genetic switch. Cytokine interferon-γ (IFN-γ), an important immune regulatory molecule that plays a significant role in tumor immunotherapy, is used to test the system. Our results show that brief hyperthermia induced by focused ultrasound promotes the expression of IFN-γ gene, improving anti-tumor efficacy of URB in vitro and in vivo. Our study provides an alternative strategy for bacteria-mediated tumor immunotherapy. Several approaches have been recently proposed to engineer bacteria for cancer immunotherapy. Here the authors design an ultrasound-responsive bacterium for the controlled release of IFNy at the tumor site, promoting anti-tumor immune responses in preclinical models. [ABSTRACT FROM AUTHOR]

Introduction: Sierra Leone has one of the highest burdens of febrile illnesses in the world. As the incidence of malaria diminishes, a better understanding of the spectrum of etiological agents was important for accurate diagnosis and empirical treatment of febrile illness. Methods: Blood, nasopharyngeal, and fecal specimens were collected from febrile patients for serological, molecular detection, and microbiologic culture to identify potential pathogens. Results: For this prospective study, 142 febrile patients were enrolled. The prevalence of malaria was higher in children aged 5–15 years old (P = 0.185) and adults (P = 0.018). Acute respiratory infection (ARI) presented more commonly in the under 5 years old group (P = 0.009). For diarrhea, all children groups (P = 0.024) were predominant. A total of 22.5% of the febrile patients had malaria infection, 19.7% had typhoid infection, and 2.8% were coinfected with malaria and typhoid. ARI was the most common causes of fever, accounting for 31.7% of patients, influenza A virus, Mycoplasma pneumoniae, and five other respiratory pathogens were found. Diarrhea accounted for 16.2%, and seven kinds of diarrhea bacteria were isolated. Hepatitis B accounted for 8.5%, including five cases of spontaneous bacterial peritonitis, and ascites smear staining were both Gram-negative bacteria. Tuberculous encephalitis, parasitic diseases (ascaris and filariasis), and skin infection caused by Staphylococcus aureus accounted for 0.7%, 2.1%, and 0.7%, respectively. Conclusions: Evidence of a wide spectrum of febrile etiological agents other than malaria was identified. The spread of malaria rapid diagnostic tests (RDTs) out of hospital and establishment of a national standard for Widal test will reduce the misdiagnosis of febrile diseases. Antibiotics against Gram-negative bacteria are helpful for empirical treatment. Plain Language Summary: Sierra Leone has one of the highest burdens of febrile illnesses in the world. Evidence of a wide spectrum of febrile pathogens other than malaria has been proven in this study. We considered that the etiology of febrile patients was closely related to local geography, heredity, immune features, economic industry, living habits, air pollution, medical and health conditions, and this was fully analyzed and discussed. The screening process used in this study can further simplify and identify the etiological agents of fever in more than 70% of the study population. This laid the foundation for the establishment of a more simplified and efficient diagnosis and treatment process in the local area. We also found the characteristics of age distribution of different febrile diseases. Children were an important susceptible population to fever. This study indicated the importance of reliable diagnostic tests for febrile pathogens and provided the necessary information for RDT requirements. The spread of malaria RDTs out of hospital and establishment of a national standard for Widal test will reduce the misdiagnosis of febrile diseases. For empirical treatment, antimalarial treatment was still targeted at falciparum malaria in Sierra Leone. Antibiotics against Gram-negative bacteria contributed to the empirical treatment of febrile diseases. For patients with acute respiratory tract infection, Gram-positive coccal antibiotics could be candidates for treatment. In addition, systematic and professional treatment of liver diseases should be promoted to reduce infection complications. [ABSTRACT FROM AUTHOR]

As the core of deep learning methodologies, convolutional neural network (CNN) has received wide attention in the area of image recognition. In particular, it requires very precise, accurate and fine recognition power for medical imaging processing. Numerous promising prospects of CNN applications with medical prognosis and diagnosis have been reported in the related works, and the common goal among the literature is mainly to analyze the insights from the finest details of medical images and build a more suitable model with maximum accuracy and minimum error. Thus, a novel CNN model is proposed with the characteristics of multi-view feature preprocessing and swarm-based parameter optimization. Additional information of extra features from multi-view is discovered potentially for training, and simultaneously, the most optimal set of CNN parameters are provided by our proposed leader and long-tail-based particle swarm optimization. The purpose of such a hybrid method is to achieve the highest possibility of target recognition in medical images. Preliminary experiments over cardiovascular and mammogram datasets related to heart disease prediction and breast cancer classification, respectively, are designed and conducted, and the results indicate encouraging performance compared to other existing CNN model optimization methods. [ABSTRACT FROM AUTHOR]

Nowadays, with the development of wireless communication, people are relying on mobile devices heavily due to various application deployment and plentiful service experience of customers in wireless metropolitan area network (WMAN). The computing resources of the mobile devices are limited as they are restricted with physical size, battery capacity, etc. In order to release the resource limitation, cloudlet, an effective paradigm, is introduced to host the computing tasks offloaded from the mobile devices. Compared with the cloud computing, the cloudlets are deployed closer to provide customers with fewer task offloading delay. Currently, the offloaded task scale is increasing, which generates large quantities of energy exposure for task implementation among cloudlets. Taking advantage of live virtual machine (VM) migration technology, the energy consumption of cloudlets could be definitely reduced. But such migration across cloudlets also decreases the implementation performance of the tasks. Therefore, it is still a challenge to jointly optimize the execution performance and the energy consumption for cloudlet management in WMAN. In this paper, a novel virtual machine (VM) scheduling method is proposed to balance the implantation time and the energy consumption to cope with the above challenge. Specifically, a collection of available migration polices are obtained through heuristically searching of destination cloudlets for the running computing tasks. Then, simple additive weighting (SAW) and multiple criteria decision making (MCDM) techniques are leveraged to select the optimal VM scheduling strategy across cloudlets in WMAN. Finally, experimental results and evaluations validate our proposed method is both effective and feasible. [ABSTRACT FROM AUTHOR]

Lane detection is an essential part of safety assurance in intelligent vehicle and advanced driver assistance systems. Despite many methods having been proposed, there still remain challenges such as complex road surface and large curvature. In this paper, we present a robust lane detection method under structured roads to solve these issues. The method contains two parts: straight line detection in near field and curve matching in far field. Instead of generating top-view image by inverse perspective mapping (IPM), we propose a new form of IPM application to reduce noise that we only take advantage of sub-pixel-level spatial relations and project line segments obtained by line segments detector to top-view image. Then, we apply density-based spatial clustering of applications with noise to clustering segments and design a fusion method to extract the optimal lines combination from clusters. Finally, a weighted hyperbolic model is proposed to finish curve fitting. The results of experiment indicate that the method has robust performance in complex environment. [ABSTRACT FROM AUTHOR]

In this paper, we have considered a methodical approach to forecast the consequences of the influence of pulsed electromagnetic fields on electronic devices based on three defined conditions of the disruption of device functioning. The approach is based on the use of key parameters of pulse disturbances in critical circuits of electronic devices. Depending on the problem being solved and features of the object being tested, the key parameters can be: the amplitude of a voltage pulse in a critical circuit of an object; the Joule integral; the energy; the frequency of repetition of influencing pulses; the probability of a bit error; the number of errors in a transferred data packet; the data rate, etc. [ABSTRACT FROM AUTHOR]

Cross-modality visible-infrared person re-identification (VI-ReID) aims to recognize images with the same identity between visible modality and infrared modality, which is a very challenging task because it not only includes the troubles of variations between cross-cameras in traditional person ReID, but also suffers from the huge differences between two modalities. Some existing VI-ReID methods extract the modality-shared information from global features through single-stream or double-stream networks, while ignoring the complementarity between fine-grained and coarse-grained information. To solve this problem, our paper designs a multi-granularity feature utilization network (MFUN) to make up for the lack of shared features between different modalities by promoting the complementarity between coarse-grained features and fine-grained features. Firstly, in order to learn fine-grained shared features better, we design a local feature constraint module, which uses both hard-mining triplet loss and heterogeneous center loss to constrain local features in the common subspace, so as to better promote intra-class compactness and inter-class differences at the level of sample and class center. Then, our method uses a multi-modality feature aggregation module for global features to fuse the information of two modalities to narrow the modality gap. Through the combination of these two modules, visible and infrared image features can be better fused, thus alleviating the problem of modality discrepancy and supplementing the lack of modality-shared information. Extensive experimental results on RegDB and SYSU-MM01 datasets fully prove that our proposed MFUN outperforms the state-of-the-art solutions. Our code is available at https://github.com/ZhangYinyinzzz/MFUN. [ABSTRACT FROM AUTHOR]