Applied Science and Technology Research Institute

Purpose of the Review Mounting evidence indicates that individuals with chronic obstructive pulmonary disease (COPD) face a heightened risk of severe outcomes upon contracting coronavirus disease 2019 (COVID-19). Current medications for COVID-19 often carry side effects, necessitating alternative therapies with improved tolerance. This review explores the biological mechanisms rendering COPD patients more susceptible to severe COVID-19 and investigates the potential of omega-3 polyunsaturated fatty acids (n-3 PUFAs) in mitigating the severity of COVID-19 in COPD patients. Recent Findings Current evidence indicates that COPD patients are at an increased risk of severe COVID-19 due to factors including compromised pulmonary function, dysregulated inflammation, weakened immune response, increased oxidative stress, elevated expression of angiotensin-converting enzyme (ACE2) receptors in the lungs, and genetic predispositions. Remarkably, n-3 PUFAs exhibit the potential in ameliorating the clinical outcomes of COPD patients with COVID-19 by modulating inflammation, reinforcing the body's antioxidant defenses, reducing viral entry and replication, and enhancing immunity. Summary N-3 PUFAs hold potential for improving COVID-19 outcomes in patients with COPD. However, there has been limited investigation into the therapeutic effects of n-3 PUFAs in enhancing clinical outcomes for COPD patients. Rigorous clinical studies are essential to evaluate the impact of n-3 PUFAs on COPD patients with concurrent COVID-19 infection.

Background The cytochrome b (CYTB) gene, a crucial component of the mitochondrial genome, plays a multifaceted role in cellular metabolism, energy production and various biological processes. Main body It is well known that the CYTB gene encodes a subunit of complex III in the electron transport chain, which is vital for the oxidative phosphorylation process and ATP generation. Various studies report that the CYTB gene not only encodes a core protein in the mitochondrial respiratory chain but also produces a long non‐coding RNA called lncCYTB, which participates in a variety of physiological and pathological processes. Inspiringly, a study has recently revealed that the CYTB gene also encodes a novel 187 amino acid long polypeptide, CYTB‐187AA, a mitochondrial DNA‐encoded protein produced by cytosolic translation and important for early mammalian development. Conclusion This review will provide insight into the functional and expression properties of the CYTB gene, as well as its unique non‐coding RNA signature, and describe the diseases associated with the CYTB gene, ranging from mitochondrial dysfunction to more complex genetic disorders.

Pedestrian location tracking in emergency responses and environmental surveys of indoor scenarios tend to rely only on their own mobile devices, reducing the usage of external services. Low-cost and small-sized inertial measurement units (IMU) have been widely distributed in mobile devices. However, they suffer from high-level noises, leading to drift in position estimation over time. In this work, we present a graph-based indoor 3D pedestrian location tracking with inertial-only perception. The proposed method uses onboard inertial sensors in mobile devices alone for pedestrian state estimation in a simultaneous localization and mapping (SLAM) mode. It starts with a deep vertical odometry-aided 3D pedestrian dead reckoning (PDR) to predict the position in 3D space. Environment-induced behaviors, such as corner-turning and stair-taking, are regarded as landmarks. Multi-hypothesis loop closures are formed using statistical methods to handle ambiguous data association. A factor graph optimization fuses 3D PDR and behavior loop closures for state estimation. Experiments in different scenarios are performed using a smartphone to evaluate the performance of the proposed method, which can achieve better location tracking than current learning-based and filtering-based methods. Moreover, the proposed method is also discussed in different aspects, including the accuracy of offline optimization and proposed height regression, and the reliability of the multi-hypothesis behavior loop closures. The video (YouTube) or (BiliBili) is also shared to display our research.

Source-free domain adaptation has developed rapidly in recent years, where the well-trained source model is adapted to the target domain instead of the source data, offering the potential for privacy concerns and intellectual property protection. However, a number of feature alignment techniques in prior domain adaptation methods are not feasible in this challenging problem setting. Thereby, we resort to probing inherent domain-invariant feature learning and propose a curriculum-style self-training approach for source-free domain adaptive semantic segmentation. In particular, we introduce a curriculum-style entropy minimization method to explore the implicit knowledge from the source model, which fits the trained source model to the target data using certain information from easy-to-hard predictions. We then train the segmentation network by the proposed complementary curriculum-style self-training, which utilizes the negative and positive pseudo labels following the curriculum-learning manner. Although negative pseudo-labels with high uncertainty cannot be identified with the correct labels, they can definitely indicate absent classes. Moreover, we employ an information propagation scheme to further reduce the intra-domain discrepancy within the target domain, which could act as a standard post-processing method for the domain adaptation field. Furthermore, we extend the proposed method to a more challenging black-box source model scenario where only the source model's predictions are available. Extensive experiments validate that our method yields state-of-the-art performance on source-free semantic segmentation tasks for both synthetic-to-real and adverse conditions datasets. The code and corresponding trained models are released at https://github.com/yxiwang/ATP .

Cerebral microinfarcts represent the most prevalent form of ischemic brain injury in the elderly, particularly among those suffering from dementia, Alzheimer's disease, and vascular risk factors. Despite their commonality, effective treatments have remained elusive. Herein, a novel treatment utilizing a polymer‐formulated nerve growth factor capable of crossing the blood‐brain barrier is reported, which effectively reduced oxidative stress and neuronal apoptosis, reshaped microglia polarization at infarct sites, and decreased the overall microinfarct burden, leading to notable improvements in behavioral and cognitive functions in a mouse model. This work provides a promising new avenue for the treatment of cerebral microinfarcts and other neurodegenerative diseases.

Domain-adaptive semantic segmentation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. However, existing methods primarily focus on directly learning categorically discriminative target features for segmenting target images, which is challenging in the absence of target labels. This work provides a new perspective. We ob serve that the features learned with source data manage to keep categorically discriminative during training, thereby enabling us to implicitly learn adequate target representations by simply pulling target features close to source features for each category. To this end, we propose T2S-DA, which encourages the model to learn similar cross-domain features. Also, considering the pixel categories are heavily imbalanced for segmentation datasets, we come up with a dynamic re-weighting strategy to help the model concentrate on those underperforming classes. Extensive experiments confirm that T2S-DA learns a more discriminative and generalizable representation, significantly surpassing the state-of-the-art. We further show that T2S-DA is quite qualified for the domain generalization task, verifying its domain-invariant property.

Multi-modal trajectory analysis and trajectory recovery are essential tasks in transportation research, especially for offline vehicles, which enable comprehensive understanding of complex transportation systems and address the issue of incomplete or missing trajectory data. In this paper, we propose a novel Deep Trajectory Recovery Framework, DTRF, which can effectively tackle both challenges by using a combination of a Cellular Automata (CA) model and a Multi-Kernel Graph Neural Network (MKGNN) model. The CA model plays a crucial role in normalizing and representing multi-modal traffic data with diverse structures, sampling frequencies, and physical meanings. By capturing the inherent relationships among different modalities, the CA model enables our proposed framework to make better use of these multi-modal data from networked vehicles and roadside detectors and then generate data for traditional vehicles. The MKGNN model, built on the foundation of spectral graph theory, employs various kernels to model different driving characteristics. The use of multiple kernels allows the GNN model to capture a wide range of driving patterns, enhancing its ability to reconstruct missing trajectories accurately. To validate the effectiveness of our proposed model, extensive experiments are conducted on two datasets. The results demonstrate that our framework outperforms state-of-the-art baselines in terms of trajectory recovery, showcasing its efficiency and robustness.

Multi-robot coverage planning has gained significant attention in recent years. In this paper, we introduce a novel approach called APF-CPP (Artificial Potential Field Based Multi-Robot Online Coverage Path Planning) to enhance the collaboration of multi-robot systems to accomplish coverage tasks in unknown dynamic environments. Our approach presents a unique coverage policy that leverages the concept of artificial potential field (APF). In contrast to the conventional APFbased path planning methods that directly generate paths based on the field gradient, we utilize the APF to derive coverage policies for individual robots within a multi-robot system to achieve efficient task allocation and maintain regular coverage patterns. We have developed a policy update mechanism that allows the system to adapt its task allocation policy based on real-time conditions while minimizing the impact caused by policy changes. To better handle dead-end conditions, we use the APF concept to allocate tasks better during the dead-end recovery process. We also show that our algorithm has a low computational complexity and guarantees complete coverage in a finite time. We conduct extensive comparisons with other stateof- the-art (SOTA) approaches and validate our method through simulations and real-world experiments. The experimental results demonstrate the advantages of our proposed method over existing approaches and confirm the effectiveness and robustness of realworld implementation.

A computational model of ternary ion exchange (IOX) for strengthening glass is proposed to predict the cation concentration and residual stress distributions in glass after ternary IOX. The comparison between theoretical predictions and experimental results indicated the validates the model. Additionally, it provides a method to determine ion diffusivity and volume expansion through ternary IOX experiments. Simulations of K–Na–Li ternary IOX were conducted using the parameters calibrated based on experimental results from a thick silicate glass. Then the process parameters were changed to clarify their influences. Key findings reveal that for thick glass (where lateral expansion is negligible), the optimum ratio of K⁺ and Na⁺ concentrations in a molten salt is 2:1. We further consolidate the effects of process parameters by training a neural network (NN) and demonstrate that the NN can be a surrogate model to replace the time‐consuming simulations, which could be more adaptable by the glass industry.

The crux of label-efficient semantic segmentation is to produce high-quality pseudo-labels to leverage a large amount of unlabeled or weakly labeled data. A common practice is to select the highly confident predictions as the pseudo-ground-truths for each pixel, but it leads to a problem that most pixels may be left unused due to their unreliability. However, we argue that every pixel matters to the model training, even those unreliable and ambiguous pixels. Intuitively, an unreliable prediction may get confused among the top classes, however, it should be confident about the pixel not belonging to the remaining classes. Hence, such a pixel can be convincingly treated as a negative key to those most unlikely categories. Therefore, we develop an effective pipeline to make sufficient use of unlabeled data. Concretely, we separate reliable and unreliable pixels via the entropy of predictions, push each unreliable pixel to a category-wise queue that consists of negative keys, and manage to train the model with all candidate pixels. Considering the training evolution, we adaptively adjust the threshold for the reliable-unreliable partition. Experimental results on various benchmarks and training settings demonstrate the superiority of our approach over the state-of-the-art alternatives.

Major depressive disorder (MDD), affecting over 264 million individuals globally, is associated with immune system dysregulation and chronic neuroinflammation, potentially linked to neurodegenerative processes. This review examines blood-brain barrier (BBB) dysfunction in MDD, focusing on key regulators like matrix metalloproteinase 9 (MMP9), aquaporin-4 (AQP4), and ATP-binding cassette subfamily B member 1 (ABCB1). We explore potential mechanisms by which compromised BBB integrity in MDD may contribute to neuroinflammation and discuss the therapeutic potential of omega-3 polyunsaturated fatty acids (n-3 PUFAs). n-3 PUFAs have demonstrated anti-inflammatory and neuroprotective effects, and potential ability to modulate MMP9, AQP4, and ABCB1, thereby restoring BBB integrity in MDD. This review aims to elucidate these potential mechanisms and evaluate the evidence for n-3 PUFAs as a strategy to mitigate BBB dysfunction and neuroinflammation in MDD.

Fish can use hydrodynamic stimuli, decoded by lateral line systems, to explore the surroundings. Eyeless species of the genus Sinocyclocheilus have evolved conspicuous horns on their heads, whereas the specific function of which is still unknown. Meanwhile, the eyeless cavefish exhibits more sophisticated lateral line systems and enhanced behavioral capabilities (for instance rheotaxis), compared with their eyed counterparts. Here, the influence of head horn on the hydrodynamic perception capability is investigated through computational fluid dynamics, particle image velocimetry, and a bioinspired cavefish model integrated with an artificial lateral line system. The results show strong evidence that the head horn structure can enhance the hydrodynamic perception, from aspects of multiple hydrodynamic sensory indicators. It is uncovered as that the head horn renders eyeless cavefish with stronger hydrodynamic stimuli, induced by double‐stagnation points near the head, which are perceived by the strengthened lateral line systems. Furthermore, the eyeless cavefish model has ≈17% higher obstacle recognition accuracy and lower cost (time and sensor number) than eyed cavefish model is conceptually demonstrated, by incorporating with machine learning. This study provides novel insights into form‐function relationships in eyeless cavefish, in addition paves the way for optimizing sensor arrangement in fish robots and underwater vehicles.

Adult central nervous system (CNS) neurons down-regulate growth programs after injury, leading to persistent regeneration failure. Coordinated lipids metabolism is required to synthesize membrane components during axon regeneration. However, lipids also function as cell signaling molecules. Whether lipid signaling contributes to axon regeneration remains unclear. In this study, we showed that lipin1 orchestrates mechanistic target of rapamycin (mTOR) and STAT3 signaling pathways to determine axon regeneration. We established an mTOR-lipin1-phosphatidic acid/lysophosphatidic acid-mTOR loop that acts as a positive feedback inhibitory signaling, contributing to the persistent suppression of CNS axon regeneration following injury. In addition, lipin1 knockdown (KD) enhances corticospinal tract (CST) sprouting after unilateral pyramidotomy and promotes CST regeneration following complete spinal cord injury (SCI). Furthermore, lipin1 KD enhances sensory axon regeneration after SCI. Overall, our research reveals that lipin1 functions as a central regulator to coordinate mTOR and STAT3 signaling pathways in the CNS neurons and highlights the potential of lipin1 as a promising therapeutic target for promoting the regeneration of motor and sensory axons after SCI.