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Topologically protected fermionic quasiparticles appear in metals, where band degeneracies occur at the Fermi level, dictated by the band structure topology. While in some metals these quasiparticles are direct analogues of elementary fermionic particles of the relativistic quantum field theory, other metals can have symmetries that give rise to quasiparticles, fundamentally different from those known in high-energy physics. Here, we report on a new type of topological quasiparticles—triple point fermions—realized in metals with symmorphic crystal structure, which host crossings of three bands in the vicinity of the Fermi level protected by point group symmetries. We find two topologically different types of triple point fermions, both distinct from any other topological quasiparticles reported to date. We provide examples of existing materials that host triple point fermions of both types and discuss a variety of physical phenomena associated with these quasiparticles, such as the occurrence of topological surface Fermi arcs, transport anomalies, and topological Lifshitz transitions.

Three-dimensional multimodality multi-object tracking has attracted great attention due to the use of complementary information. However, such a framework generally adopts a one-stage association approach, which fails to perform precise matching between detections and tracklets, and, thus, cannot robustly track objects in complex scenes. To address this matching problem caused by one-stage association, we propose a novel multi-stage association method, which consists of a hierarchical matching module and a customized track management module. Specifically, the hierarchical matching module defines the reliability of the objects by associating multimodal detections, and matches detections with trajectories based on the reliability in turn, which increases the utilization of true detections, and, thus, guides accurate association. Then, based on the reliability of the trajectories provided by the matching module, the customized track management module sets maximum missing frames with differences for tracks, which decreases the number of identity switches of the same object and, thus, further improves the association accuracy. By using the proposed multi-stage association method, we develop a tracker called MSA-MOT for the 3D multi-object tracking task, alleviating the inherent matching problem in one-stage association. Extensive experiments are conducted on the challenging KITTI benchmark, and the results show that our tracker outperforms the previous state-of-the-art methods in terms of both accuracy and speed. Moreover, the ablation and exploration analysis results demonstrate the effectiveness of the proposed multi-stage association method.

Multiple object tracking (MOT) in unmanned aerial vehicle (UAV) videos is a fundamental task and can be applied in many fields. MOT consists of two critical procedures, i.e., object detection and re-identification (ReID). One-shot MOT, which incorporates detection and ReID in a unified network, has gained attention due to its fast inference speed. It significantly reduces the computational overhead by making two subtasks share features. However, most existing one-shot trackers struggle to achieve robust tracking in UAV videos. We observe that the essential difference between detection and ReID leads to an optimization contradiction within one-shot networks. To alleviate this contradiction, we propose a novel feature decoupling network (FDN) to convert shared features into detection-specific and ReID-specific representations. The FDN searches for characteristics and commonalities between the two tasks to synergize detection and ReID. In addition, existing one-shot trackers struggle to locate small targets in UAV videos. Therefore, we design a pyramid transformer encoder (PTE) to enrich the semantic information of the resulting detection-specific representations. By learning scale-aware fine-grained features, the PTE empowers our tracker to locate targets in UAV videos accurately. Extensive experiments on VisDrone2021 and UAVDT benchmarks demonstrate that our tracker achieves state-of-the-art tracking performance.

Laser-induced graphene (LIG) has the advantages of one-step fabrication, prominent mechanical performance, as well as high conductivity; it acts as the ideal material to fabricate flexible strain sensors. In this study, a wearable flexible strain sensor consisting of three-dimensional (3D) wavy LIG and silicone rubber was reported. With a laser to scan on a polyimide film, 3D wavy LIG could be synthesized on the wavy surface of a mold. The wavy-LIG strain sensor was developed by transferring LIG to silicone rubber substrate and then packaging. For stress concentration, the ultimate strain primarily took place in the troughs of wavy LIG, resulting in higher sensitivity and less damage to LIG during stretching. As a result, the wavy-LIG strain sensor achieved high sensitivity (gauge factor was 37.8 in a range from 0% to 31.8%, better than the planar-LIG sensor), low hysteresis (1.39%) and wide working range (from 0% to 47.7%). The wavy-LIG strain sensor had a stable and rapid dynamic response; its reversibility and repeatability were demonstrated. After 5000 cycles, the signal peak varied by only 2.32%, demonstrating the long-term durability. Besides, its applications in detecting facial skin expansion, muscle movement, and joint movement, were discussed. It is considered a simple, efficient, and low-cost method to fabricate a flexible strain sensor with high sensitivity and structural robustness. Furthermore, the wavy-LIG strain senor can be developed into wearable sensing devices for virtual/augmented reality or electronic skin.

The melanocortin-4 receptor (MC4R) is highly expressed in the hypothalamus, and mutations in this gene are closely associated with the development of hereditary obesity and early-onset severe obesity in humans. Mc4r has been shown to be involved in the development of dilated cardiomyopathy. However, the current system for the early diagnosis and treatment of heart disease is not well established. In this study, we analyzed the effects of Mc4r knockout on cardiac function, cardiomyocyte morphology, fibrosis, and apoptosis in mice. Moreover, we explored the possible early molecular mechanisms by which Mc4r affects cardiac dysfunction via transcriptome sequencing of cardiac cells combined with bioinformatics analysis. Although the overall heart does not show organic changes, our study suggested that cardiomyocytes already show early abnormal changes at the molecular level. The sequencing results revealed that the genes that were differentially expressed between the two groups of mice were enriched mainly in the p53 signaling pathway and the hypoxia-inducible factor 1 (HIF-1) signaling pathway. We screened 10 key target genes via a protein–protein interaction (PPI) network and module analysis. Drugs targeting key genes were subsequently screened, and angiotensinogen ( Agt ) and Kit were identified as potential drug targets. We analyze relevant data through bioinformatics to screen for signaling pathways and key hub genes that are enriched in differentially expressed genes (DEGs), as well as molecules targeting the hub genes, in order to provide ideas for early prevention of heart disease caused by Mc4r gene defects or related obesity.

Lane and road marker segmentation is crucial in autonomous driving, and many related methods have been proposed in this field. However, most of them are based on single-frame prediction, which causes unstable results between frames. Some semantic multi-frame segmentation methods produce error accumulation and are not fast enough. Therefore, we propose a deep learning algorithm that takes into account the continuity information of adjacent image frames, including image sequence processing and an end-to-end trainable multi-input single-output network to jointly process the segmentation of lanes and road markers. In order to emphasize the location of the target with high probability in the adjacent frames and to refine the segmentation result of the current frame, we explicitly consider the time consistency between frames, expand the segmentation region of the previous frame, and use the optical flow of the adjacent frames to reverse the past prediction, then use it as an additional input of the network in training and reasoning, thereby improving the network’s attention to the target area of the past frame. We segmented lanes and road markers on the Baidu Apolloscape lanemark segmentation dataset and CULane dataset, and present benchmarks for different networks. The experimental results show that this method accelerates the segmentation speed of video lanes and road markers by 2.5 times, increases accuracy by 1.4%, and reduces temporal consistency by only 2.2% at most.

4D millimeter wave (mmWave) imaging radar is a new type of vehicle sensor technology that is critical to autonomous driving systems due to its lower cost and robustness in complex weather. However, the sparseness and noise of point clouds are still the main problems restricting the practical application of 4D imaging radar. In this paper, we introduce SMIFormer, a multi-view feature fusion network framework based on 4D radar single-modal input. SMIFormer decouples the 3D point cloud scene into 3 independent but interrelated perspectives, including bird’s-eye view (BEV), front view (FV), and side view (SV), thereby better modeling the entire 3D scene and overcoming the shortcomings of insufficient feature representation capabilities under single-view built from extremely sparse point clouds. For multi-view features, we proposed multi-view feature interaction (MVI) to exploit the inner relationship between different views by integrating features from intra-view interaction and cross-view interaction. We evaluated the proposed SMIFormer on the View-of-Delft (VoD) dataset. The mAP of our method reached 48.77 and 71.13 in the fully annotated area and the driving corridor area, respectively. This shows that 4D radar has great development potential in the field of 3D object detection.

Abstract Male infertility has become a global concern, accounting for 20–70% of infertility. Dysfunctional spermatogenesis is the most common cause of male infertility; thus, treating abnormal spermatogenesis may improve male infertility and has attracted the attention of the medical community. Mitochondria are essential organelles that maintain cell homeostasis and normal physiological functions in various ways, such as mitochondrial oxidative phosphorylation (OXPHOS). Mitochondrial OXPHOS transmits electrons through the respiratory chain, synthesizes adenosine triphosphate (ATP), and produces reactive oxygen species (ROS). These mechanisms are vital for spermatogenesis, especially to maintain the normal function of testicular Sertoli cells and germ cells. The disruption of mitochondrial OXPHOS caused by external factors can result in inadequate cellular energy supply, oxidative stress, apoptosis, or ferroptosis, all inhibiting spermatogenesis and damaging the male reproductive system, leading to male infertility. This article summarizes the latest pathological mechanism of mitochondrial OXPHOS disorder in testicular Sertoli cells and germ cells, which disrupts spermatogenesis and results in male infertility. In addition, we also briefly outline the current treatment of spermatogenic malfunction caused by mitochondrial OXPHOS disorders. However, relevant treatments have not been fully elucidated. Therefore, targeting mitochondrial OXPHOS disorders in Sertoli cells and germ cells is a research direction worthy of attention. We believe this review will provide new and more accurate ideas for treating male infertility.

Abstract Chronic obstructive pulmonary disease (COPD) and pulmonary hypertension (PH) are both chronic progressive respiratory diseases that cannot be completely cured. COPD is characterized by irreversible airflow limitation, chronic airway inflammation, and gradual decline in lung function, whereas PH is characterized by pulmonary vasoconstriction, remodeling, and infiltration of inflammatory cells. These diseases have similar pathological features, such as vascular hyperplasia, arteriolar contraction, and inflammatory infiltration. Despite these well-documented observations, the exact mechanisms underlying the occurrence and development of COPD and PH remain unclear. Evidence that mitochondrial dynamics imbalance is one major factor in the development of COPD and PH. Mitochondrial dynamics is precisely regulated by mitochondrial fusion proteins and fission proteins. When mitochondrial dynamics equilibrium is disrupted, it causes mitochondrial and even cell morphological dysfunction. Mitochondrial dynamics participates in various pathological processes for heart and lung disease. Mitochondrial dynamics may be different in the early and late stages of COPD and PH. In the early stages of the disease, mitochondrial fusion increases, inhibiting fission, and thereby compensatorily increasing adenosine triphosphate (ATP) production. With the development of the disease, mitochondria decompensation causes excessive fission. Mitochondrial dynamics is involved in the development of COPD and PH in a spatiotemporal manner. Based on this understanding, treatment strategies for mitochondrial dynamics abnormalities may be different at different stages of COPD and PH disease. This article will provide new ideas for the potential treatment of related diseases.

Dirac cone, one of the main characters of topological materials, provides us an approach to explore topological phase transitions and topological states. Single-element 2D-Xenes are prominent candidates for hosting Dirac cones. Till now, the multiple Dirac cones, Dirac-like cones, and semi-metal Dirac point have been discovered in them. However, it is still difficult to realize the tunable Dirac cones due to the lack of appropriate materials. Using first-principles calculations, this paper proposes that monolayer selenium with square lattice could achieve tunable Dirac cones and a topological phase transition. Double structural phases of the monolayer selenium can be distinguished according to strain applied, i.e., buckled square and buckled rectangular phases, which have rich Dirac physics. There exist four anisotropic Dirac cones in the buckled square phase, owing to fourfold symmetry. The buckled rectangular phase hosts a topological phase transition from a 2D topological insulator with double Dirac cones to a simple insulator, with a Dirac semi-metal having single Dirac point as the phase transition point. Moreover, the topological insulator has a global band gap of 0.16 eV, suggesting its potential utilizations in room-temperature devices. These studies will greatly promote the development of the Dirac physics and widen the application ranges of 2D-Xenes.