Ethylene (C2H4) functions both as a key phytohormone regulating plant growth and development and as an essential feedstock in organic chemical synthesis. Reliable detection of C2H4 is critical for monitoring emissions during crop cultivation and ensuring safety in industrial transportation. However, most existing C2H4 sensors rely on noble-metal catalysts and/or high-operating temperature, which significantly constrain their practical applications. Moreover, achieving high specificity in C2H4 recognition remains a major challenge. Inspired by the signal transduction mechanism of plant C2H4 receptor, this study propose a biomimetic sensing strategy based on a facilely synthesized cuprous-cystine complex (Cu2Cyt), which features a sulfur-bridged Cu+ coordination center that mimics the biological binding site for C2H4 recognition. The noble-metal-free wearable sensor was fabricated by depositing a Cu2Cyt/MXene composite onto a flexible interdigital electrode, enabling room-temperature detection of C2H4 emitted from fruits or leaked from transport pipelines of chemical industries. It exhibits a detection range (0.05-5 ppm), an ultra-low detection limit of 1.07 ppb, fast response/recovery (51/92 s), high sensitivity of 3.64%·ppm-1 in trace concentration range of 0-0.5 ppm, and good reversibility and reproducibility. Overall, this work offers a bioinspired design strategy for low-cost, high-performance, noble-metal-free, and wearable sensors, capable of trace-level C2H4 monitoring.

Piezo1 plays a key role in the immune response during sepsis. To date, our understanding of the role of Piezo1 in inflammatory diseases has mostly been limited to influencing vasomotor function and regulating inflammatory infiltration. Whether and how Piezo1 in macrophages is involved in developing septic cardiac dysfunction has never been explored. Here, we have successfully established a mouse model with myeloid cell-specific knockdown of Piezo1. The intraperitoneal injection of lipopolysaccharide (LPS) resulted in a significant increase in cardiac macrophage infiltration, as well as an increase in the expression of inflammatory factors and the inflammatory response. However, myeloid cell-specific knockdown of Piezo1 impaired this response, leading to an increase in macrophage polarization towards the M2 type and the decreased inflammatory response. As a result, myocardial injury caused by sepsis was attenuated. We have also demonstrated that the PI3K/AKT pathway is significantly activated after Piezo1 knockdown, resulting in reduced myocardial dysfunction. Our data indicate that myeloid cell-specific knockdown of Piezo1 can influence macrophage polarization and thus exert cardioprotective effects in a murine model of sepsis, providing potential ideas and targets for the treatment of infectious cardiac dysfunction.

Aim:As a key mechanosensitive ion channel, Piezo1 plays a critical role in various brain functions, including the regulation of cerebral blood flow and neuronal excitability, by converting mechanical stimuli into biochemical signals. This study conducted a quantitative and visual analysis of the global research landscape, evolving trends, and knowledge structure of Piezo1 in brain research from 2014 to 2025. Methods:A comprehensive bibliometric analysis was conducted. We conducted a comprehensive literature search in the Web of Science and Scopus databases for publications focusing on Piezo1 in the brain from January 1, 2014, to October 1, 2025. After rigorous screening and deduplication, 173 studies were finally included in the analysis. Scientometric indicators and visualization tools were employed to examine publication trends, core journals, productive authors and countries, and keyword co-occurrence networks. Results:Annual publication output in this field increased rapidly, with an average growth rate of 34.48%. Research publications are concentrated in a limited number of high-impact journals, reflecting a strong academic focus. Keyword analysis identified core research hotspots, including "mechanotransduction," "ion channels," and "neuroinflammation," highlighting the pivotal role of Piezo1 in cerebral hemodynamics and neuropathology. Intellectual structure analysis revealed that foundational mechanistic studies dominate the current literature. Discussion:Although basic research on Piezo1 in the brain has advanced significantly, studies directly targeting its clinical translation are limited. These findings highlight a clear knowledge gap between mechanistic understanding and therapeutic applications. Future research should prioritize bridging this gap by fostering interdisciplinary collaborations that translate fundamental insights into clinical validation, thereby accelerating the development of Piezo1 as a novel therapeutic target for neurological disorders.

In recent years, the rapid development of Deep Neural Networks (DNNs) has posed significant challenges in terms of training duration and costs. High-frequency, low-power photonic computing has emerged as a highly promising solution. However, the substantial cost of data conversion and the limitations introduced by noise in photonic devices continue to hinder the realization of high-precision and energy-efficient DNN training. To address this challenge, we propose a novel photonic accelerator, ROCKET, based on the Residue Number System (RNS). RNS is based on modular arithmetic and enables support for high-precision computation through parallel multi-path low-precision operations. First, we leverage specialized lookup tables to enable high-throughput, low-latency conversions between high-precision and lowprecision numerical representations. Next, we design a lowpower photonic accelerator architecture utilizing intensity modulators, which minimizes the number of computational components while maximizing data reuse. Subsequently, we propose a hybrid photonic-electronic pipelined dataflow to maximize parallelism within the photonic-electronic computation path. Finally, we develop a high-frequency (4.096 GHz) hybrid photonic-electronic prototype using FPGA, Radio Frequency (RF), and photonic components to validate the feasibility of the ROCKET. Our large-scale simulations on seven mainstream DNN models show that, compared to the A100 GPU, TPU v4, and the state-of-the-art photonic accelerator Mirage, ROCKET achieves speedups of 33x, 243x, and 198x, respectively, while saving energy by factors of 64x, 204x, and 142x.

Introduction:The effects of acute sleep deprivation on cognitive function and inflammatory responses remain inadequately defined. This study aimed to evaluate changes in cognitive function and inflammatory responses among anesthesiologists and nurses in the operating room following 24-hour shift work-induced sleep deprivation. Material and Methods:Forty anesthesiologists and nurses were assigned to either the sleep deprivation group (n = 20, working from 8:00 AM to 8:00 AM the following day) or the rest control group (n = 20, working regular hours from 8:00 AM to 5:00 PM). All participants underwent assessments of cognitive functions and peripheral blood sample collections for brain-derived neurotrophic factor (BDNF), tumor necrosis factor-alpha (TNF-α), and interleukin-1 beta (IL-1β) at 8:00 AM, 4:00 PM, 0:00, and 8:00 AM the following day. Cognitive functions were assessed using the Trail Making Test and the Stroop Color-Word Test. Results:Cognitive assessments revealed no significant effect on reaction time following one night of sleep deprivation (P > 0.05 for all). However, the learning effect from repeated administrations of the Trail Making Test and the Stroop Color-Word Test did not result in decreased reaction times in the sleep deprivation group (P > 0.05 for all). Increased fluctuations in serum levels of TNF-α and BDNF were observed after 24-hour shift work at 8:00 AM (P < 0.05 for all). Conclusion:Sleep deprivation induced by 24-hour shifts did not impair cognitive performance but did affect learning ability in anesthesiologists and nurses. Additionally, sleep deprivation caused increased fluctuations in serum levels of TNF-α and BDNF at 8:00 AM.

As deep learning expands across emerging domains, computational demands are pushing traditional electronic accelerators to their limits. Silicon photonics has emerged as a promising technology for accelerating deep learning workloads, but precision remains a challenge due to noise and non-idealities. In this paper, we present BITLUME, a novel photonic computing unit that enables multiplications beyond 8-bit precision through a precision-flexible scheme. We further propose an optimized round-truncation algorithm and data mapping strategy for BITLUME to reduce optoelectronic conversions, enhance data reuse, and maintain computational accuracy. A hybrid optoelectronic architecture integrating BITLUME is developed and validated using a prototype built with FPGA, RF, and photonic components, achieving 3.7× lower end-to-end latency than the A100 GPU in dot product. Simulations of training seven DNN models at FP32 show that BITLUME achieves up to 3.35× and 10.78× speedup, and 1.53× and 4.12× energy savings, compared to the state-of-the-art photonic accelerator and A100 GPU, respectively.

In this paper, a distributed Q-Learning-based Model Predictive Control (MPC) is proposed, which integrates the alternative-direction-multiplier-method (ADMM), to improve the computational efficiency of the reinforcement learning based MPC algorithm. Specifically, this method transforms the MPC optimization problem into multiple sub-optimization problems where the dynamics of each subsystem are independent, which is suitable for using ADMM by adding intermediate variables and corresponding equality constraints. The algorithm iteratively solves the optimal inputs of each sub-optimization objective and updates the global value function. The convergence of the proposed distributed Q-learning algorithm is analyzed. The Actor–Critic network structure is used in the implementation of the proposed method. The simulation results show that the proposed distributed Q-Learning algorithm requires fewer steps to solve optimization problems and has lower calculation time.

To enhance the precision of inference, deep neural network (DNN) models have been progressively growing in scale and complexity, leading to increased latency and computational resource demands. This growth necessitates scalable architectures, such as chiplet-based accelerators, to accommodate the substantial volume of deep learning inference tasks. However, the efficiency, energy consumption, and scalability of existing accelerators are severely constrained by metallic interconnects. Photonic interconnects, on the contrary, offer a promising alternative, with their advantages of low latency, high bandwidth, high energy efficiency, and simplified communication processes. In this paper, we propose ChipAI, an accelerator designed on photonic interconnects for accelerating DNN inference tasks. ChipAI implements an efficient hybrid optical network that supports effective inter-chiplet and intra-chiplet data sharing, thereby enhancing parallel processing capabilities. Additionally, we propose a flexible dataflow leveraging the ChipAI architecture the characteristics of DNN models, facilitating efficient architectural mapping of DNN layers. Simulation various DNN models demonstrates that, compared to the state-of-the-art chiplet-based DNN accelerator photonic interconnects, ChipAI can reduce the DNN inference time and energy consumption by up to 82% 79%, respectively.

To investigate the functional and molecular mechanisms by which Piezo1regulates HT-22 hippocampal neuronal autophagy, and to explore whether Piezo1 regulates hippocampal neuronal autophagy via the Ca2+/Calpain, CaMKKβ, or Calcineurin pathways. The impacts of Piezo1 inhibition, activation and gene knockdown on the autophagy of HT22 neurons was investigated by Western blotting, PCR and immunofluorescence. The changes of intracellular calcium (Ca2+) concentration were also observed. To pinpoint the specific downstream Ca2+ signaling pathway by which Piezo1 modulates autophagy, the calcium chelator BAPTA-AM, the Calpain inhibitor PD151746, and the CaMKKβ inhibitor STO609 were employed either alone or in combination. Enhanced autophagy was observed when Piezo1 was activated using the agonist Yoda1, manifesting as increased release of autophagic vacuoles, enhanced LC3 II/LC3 I ratio, decreased p62 protein level, and elevated nuclear translocation and expression of the TFEB protein. ATG7 knockdown by ATG7 shRNA mitigated the effects of Yoda1 on LC3 II/LC3 I ratio and p62 protein levels. The Piezo1 inhibitor GsMTx4 partially reversed the autophagy caused by starvation in HT22 neurons while Yoda1 still activated autophagy in the presence of BDNF. Following Piezo1 knockdown, neuronal autophagy was decreased. Piezo1-induced autophagy was accompanied with an increased cytoplasmic concentration of Ca2+. The calcium chelator BAPTA-AM partly reversed Piezo1 activation-induced autophagy, which was also mitigated by blocking calcineurin/TFEB signaling or Calpain signaling. Piezo1 modulates the autophagy of HT-22 neurons by activating Ca2+/Calpain and Calcineurin/TFEB pathways.

AIMS:This study investigated the roles of lateral basal forebrain glial cell line-derived neurotrophic factor (GDNF) signaling and cholinergic neuron activity, apoptosis, and autophagy dysfunction in sleep deprivation-induced increased risk of chronic postsurgical pain (CPSP) in mice. METHODS:Sleep deprivation (6 h per day from -1 to 3 days postoperatively) was administered to mice receiving skin/muscle incision and retraction (SMIR) to determine whether perioperative sleep deprivation induces mechanical and thermal pain hypersensitivity, increases the risk of chronic pain, and causes changes of basal forebrain neurons activity (c-Fos immunostaining), apoptosis (cleaved Caspase-3 expression), autophagy (LC3 and p62 expression) and GDNF expression. Adeno-associated virus (AAV)-GDNF was microinjected into the basal forebrain to see whether increased GDNF expression could reverse sleep deprivation-induced changes in pain duration and cholinergic neuron apoptosis and autophagy. Cholinergic neurons were further depleted by mu p75-SAP to examine whether the pain-prolonging effects of sleep deprivation still exist. RESULTS:Perioperative sleep deprivation enhanced pain sensation and prolonged pain duration in SMIR mice, which was accompanied by decreased cholinergic neuron activity and GDNF expression, increased apoptosis, and autophagy dysfunction in the substantia innominata (SI), magnocellular preoptic nucleus (MCPO), and horizontal diagonal band Broca (HDB) (hereafter lateral basal forebrain). Normalizing cholinergic neuron GDNF expression by AAV-GDNF in the lateral basal forebrain inhibited apoptosis and autophagy dysfunction and mitigated sleep deprivation-induced pain maintenance. Mice with selective lesion of lateral basal forebrain cholinergic neurons were resistant to the pain-enhancing and prolonging effects of sleep deprivation and the pain-alleviating effects of AAV-GDNF therapy. CONCLUSIONS:Perioperative sleep deprivation promotes chronicity of postsurgical pain possibly through decreasing basal forebrain GDNF signaling and causing cholinergic neuronal apoptosis and autophagy dysfunction.

Neurodegenerative diseases, marked by the gradual death of neurons, present a significant and growing public health challenge. Brain-derived neurotrophic factor (BDNF) is crucial for the survival, development, and synaptic plasticity of neurons. Studies have consistently demonstrated that perturbed BDNF communication pathways are associated with the development and progression of neurodegenerative conditions, underscoring their potential as therapeutic targets. This review aimed to summarize the existing findings regarding BDNF expression, metabolism, and signaling transduction. Furthermore, we reviewed the intricate roles of BDNF signaling pathways in neurodegenerative diseases, elucidating their contributions to disease onset and progression. The latest advancements in targeting BDNF for the treatment of neurodegenerative diseases, including the development of small molecules, nucleic acid-based therapeutics, and antibody-based approaches, were also summarized. Despite recent strides, challenges persist, including a lack of comprehensive understanding of BDNF modulation across diverse neurodegenerative contexts and the absence of clinically approved BDNF-targeted drugs.

The continuous increase in GPU performance benefits a wide range of high-performance computing (HPC) applications. Slower growth of transistor density and limited size of chip die are now posing significant challenges to scale GPUs. The chiplet technology provides a potential solution to surpass these limitations. However, the performance of these chiplet-based GPUs is often constrained by the metallic-based interconnects between the chiplets. Emerging technologies such as photonic interconnect can overcome the limitations of metallic interconnects, offering several superior properties, such as high bandwidth density and low energy consumption. In this paper, we propose SEECHIP: a Scalable and Energy-Efficient CHIPlet-based GPU architecture using photonic links. SEECHIP introduces a novel photonic inter-chiplet network that supports both unicast and broadcast communication, providing the same transmission bandwidth at both the sending and receiving ends. In addition, we propose a tailored hierarchical memory architecture, which is more suitable for the parallelization of general-purpose HPC applications. Simulation results using 14 benchmarks show that SEECHIP can achieve and reduction in execution time and energy consumption, respectively, as compared to other GPUs with metallic or photonic interconnects. Simulation results also show that SEECHIP has good scalability compared with the other GPUs.

Dynamic multiple multicasts widely exist in several applications of optical network-on-chip. However, there is no good solution for routing and wavelength assignment for multiple multicasts in the mesh-based network. This paper proposes a new routing strategy based on a modified artificial fish swarm algorithm. The modified artificial fish model can support unicast and multicast in the mesh-based network. The routing and wavelength assignment for multiple multicasts can be solved based on this model. Then, we design a layer-based algorithm to assign wavelength for multiple multicasts, which can utilize wavelength and area resources more effectively. Simula-tion results show that our scheme works better than the other tree-based schemes regarding average commu-nication latency and power consumption. In general, our modified artificial fish swarm algorithm provides a universal platform to study different aspects of routing and wavelength assignment in mesh-based ONoC.

In computational biology, biological database search has been playing a very important role. Since the COVID19 outbreak, it has provided significant help in identifying common characteristics of viruses and developing vaccines and drugs. Sequence alignment, a method finding similarity, homology and other information between gene/protein sequences, is the usual tool in the database search. With the explosive growth of biological databases, the search process has become extremely time-consuming. However, existing parallel sequence alignment algorithms cannot deliver efficient database search due to low utilization of the resources such as cache memory and performance issues such as load imbalance and high communication overhead. In this paper, we propose an efficient sequence alignment algorithm on Sunway TaihuLight, called ESA, for biological database search. ESA adopts a novel hybrid alignment algorithm combining local and global alignments, which has higher accuracy than other sequence alignment algorithms. Further, ESA has several optimizations including cache-aware sequence alignment, capacity-aware load balancing and bandwidth-aware data transfer. They are implemented in a heterogeneous processor SW26010 adopted in the world's 6th fastest supercomputer, Sunway TaihuLight. The implementation of ESA is evaluated with the Swiss-Prot database on Sunway TaihuLight and other platforms. Our experimental results show that ESA has a speedup of 34.5 on a single core group (with 65 cores) of Sunway TaihuLight. The strong and weak scalabilities of ESA are tested with 1 to 1024 core groups of Sunway TaihuLight. The results show that ESA has linear weak scalability and very impressive strong scalability. For strong scalability, ESA achieves a speedup of 338.04 with 1024 core groups compared with a single core group. We also show that our proposed optimizations are also applicable to GPU, Intel multicore processors, and heterogeneous computing platforms.

Regulation of brain-derived neurotrophic factor (BDNF) in the basal forebrain ameliorates sleep deprivation-induced fear memory impairments in rodents. Antisense oligonucleotides (ASOs) targeting ATXN2 was a potential therapy for spinocerebellar ataxia, whose pathogenic mechanism associates with reduced BDNF expression. We tested the hypothesis that ASO7 targeting ATXN2 could affect BDNF levels in mouse basal forebrain and ameliorate sleep deprivation-induced fear memory impairments. Adult male C57BL/6 mice were used to evaluate the effects of ASO7 targeting ATXN2 microinjected into the bilateral basal forebrain (1 μg, 0.5 μL, each side) on spatial memory, fear memory and sleep deprivation-induced fear memory impairments. Spatial memory and fear memory were detected by the Morris water maze and step-down inhibitory avoidance test, respectively. Immunohistochemistry, RT-PCR, and Western blot were used to evaluate the changes of levels of BDNF, ATXN2, and postsynaptic density 95 (PSD95) protein as well as ATXN2 mRNA. The morphological changes in neurons in the hippocampal CA1 region were detected by HE staining and Nissl staining. ASO7 targeting ATXN2 microinjected into the basal forebrain could suppress ATXN2 mRNA and protein expression for more than 1 month and enhance spatial memory but not fear memory in mice. BDNF mRNA and protein expression in basal forebrain and hippocampus was increased by ASO7. Moreover, PSD95 expression and synapse formation were increased in the hippocampus. Furthermore, ASO7 microinjected into the basal forebrain increased BDNF and PSD95 protein expression in the basal forebrain of sleep-deprived mice and counteracted sleep deprivation-induced fear memory impairments. ASOs targeting ATXN2 may provide effective interventions for sleep deprivation-induced cognitive impairments.

新文科建设赋予了一流本科专业建设新的内涵.在新文科背景下,专业定位是否精确关系到一流本科专业建设的成败.从专业定位的本质与内涵出发,基于一流本科专业建设的实践经验提炼出专业逐级定位法,最后从需求定位、边界定位、主体定位和特色定位四方面提出具体的实施策略,助推一流本科专业精准定位与高质量发展.

股权质押的控股股东可能利用股份回购所传递的积极信号提振股价,以降低控制权转移风险.运用2011-2019年沪深A股上市公司的数据,本文实证考察了控股股东股权质押与上市公司股份回购之间的关系,并运用2015年的万科股权争夺事件和2018年《公司法》股份回购条款的修订这两个外生冲击构建准自然实验进一步识别了二者之间的因果关系,发现控股股东股权质押与上市公司股份回购之间为显著的正相关关系,股权质押的控股股东将上市公司股份回购作为市值管理、控制权保护的策略性手段.异质性分析表明,当控制权转移风险更大时,控股股东股权质押与股份回购之间的正相关关系更为显著,并且质押风险越大,上市公司的股份回购行为越具市值管理的特征.经济后果分析表明,由于动机变异,股份回购之后上市公司的长期绩效表现和股票市场表现不会明显好转,并且股权质押下的股份回购公告的正面市场反应更弱.

Study Objectives This study verified that sleep deprivation before and after skin/muscle incision and retraction (SMIR) surgery increased the risk of chronic pain and investigated the underlying roles of microglial voltage-dependent anion channel 1 (VDAC1) signaling. Methods Adult mice received 6 hours of total sleep deprivation from 1 day prior to SMIR until the third day after surgery. Mechanical and heat-evoked pain was assessed before and within 21 days after surgery. Microglial activation and changes in VDAC1 expression and oligomerization were measured. Minocycline was injected to observe the effects of inhibiting microglial activation on pain maintenance. The VDAC1 inhibitor 4,4'-diisothiocyanostilbene-2,2'-disulfonic acid (DIDS) and oligomerization inhibitor VBIT-4 were used to determine the roles of VDAC1 signaling on microglial adenosine 5' triphosphate (ATP) release, inflammation (IL-1β and CCL2), and chronicity of pain. Results Sleep deprivation significantly increased the pain duration after SMIR surgery, activated microglia, and enhanced VDAC1 signaling in the spinal cord. Minocycline inhibited microglial activation and alleviated sleep deprivation-induced pain maintenance. Lipopolysaccharide (LPS)-induced microglial activation was accompanied by increased VDAC1 expression and oligomerization, and more VDAC1 was observed on the cell membrane surface compared with control. DIDS and VBIT-4 rescued LPS-induced microglial ATP release and IL-1β and CCL2 expression. DIDS and VBIT-4 reversed sleep loss-induced microglial activation and pain chronicity in mice, similar to the effects of minocycline. No synergistic effects were found for minocycline plus VBIT-4 or DIDS. Conclusions Perioperative sleep deprivation activated spinal microglia and increases the risk of chronic postsurgical pain in mice. VDAC1 signaling regulates microglial activation-related ATP release, inflammation, and chronicity of pain.

Under the inspiration of visualized nanobubbles associated with rugged interface, molecular simulations are performed to study thermally induced phase transition in NiTi shape-memory alloys. The perfect reversibility between the cubic austenite B2 and monoclinic martensitic B190 phases showed two types of morphological structures in the nanoscale. The nanostructures were established as the wall of the nanochannel, which was used to predict the nanobubble flow behavior in the ternary system. Results indicated that the interface with low-temperature B190 exhibited attractiveness to trap the nanobubble, resulting in low drag coefficient. Conversely, the interface with high-temperature B2 repulsed the nanobubbles as the bulk one, resulting in high drag coefficient. This study provides foresight to invest in shape memories for nanostructure temperature response, which helps realize nanobubble drag reduction.(c) 2023 Elsevier B.V. All rights reserved.

以"社会保障学"课程为例,阐明了混合式教学模式下"社会保障学"课程思政的内涵、实施路径、教学效果评价与反思,以期对现阶段线上线下混合式教学模式进行课程思政的育人路径进行初步探索.