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By mimicking the neurons and synapses of the human brain and employing spiking neural networks on neuromorphic chips, neuromorphic computing offers a promising energy-efficient machine intelligence. How to borrow high-level brain dynamic mechanisms to help neuromorphic computing achieve energy advantages is a fundamental issue. This work presents an application-oriented algorithm-software-hardware co-designed neuromorphic system for this issue. First, we design and fabricate an asynchronous chip called “Speck”, a sensing-computing neuromorphic system on chip. With the low processor resting power of 0.42mW, Speck can satisfy the hardware requirements of dynamic computing: no-input consumes no energy. Second, we uncover the “dynamic imbalance” in spiking neural networks and develop an attention-based framework for achieving the algorithmic requirements of dynamic computing: varied inputs consume energy with large variance. Together, we demonstrate a neuromorphic system with real-time power as low as 0.70mW. This work exhibits the promising potentials of neuromorphic computing with its asynchronous event-driven, sparse, and dynamic nature. Mimicking high-level abstraction of the brain to achieve energy advantages is a fundamental issue in neuromorphic computing. Here, the authors fabricate an asynchronous chip and demonstrate a high-accuracy neuromorphic system with power consumption of 0.7mW.

Spiking neural networks (SNNs) are biologically more plausible and computationally more powerful than artificial neural networks due to their intrinsic temporal dynamics. However, vanilla spiking neurons struggle to simultaneously encode spatiotemporal dynamics of inputs. Inspired by biological multisynaptic connections, we propose the Multi-Synaptic Firing (MSF) neuron, where an axon can establish multiple synapses with different thresholds on a postsynaptic neuron. MSF neurons jointly encode spatial intensity via firing rates and temporal dynamics via spike timing, and generalize Leaky Integrate-and-Fire (LIF) and ReLU neurons as special cases. We derive optimal threshold selection and parameter optimization criteria for surrogate gradients, enabling scalable deep MSF-based SNNs without performance degradation. Extensive experiments across various benchmarks show that MSF neurons significantly outperform LIF neurons in accuracy while preserving low power, low latency, and high execution efficiency, and surpass ReLU neurons in event-driven tasks. Overall, this work advances neuromorphic computing toward real-world spatiotemporal applications. Spiking neural networks struggle to encode spatiotemporal input features effectively. Here, the authors introduce a Multi-Synaptic Firing neuron, a neuron model enabling simultaneous encoding of spatial intensity and temporal dynamics through diverse synaptic thresholds.

Hepatocellular carcinoma (HCC) usually occurs on the basis of chronic liver inflammatory diseases and cirrhosis. The liver microenvironment plays a vital role in the tumor initiation and progression. Exosomes, which are nanometer-sized membrane vesicles are secreted by a number of cell types. Exosomes carry multiple proteins, DNAs and various forms of RNA, and are mediators of cell-cell communication and regulate the tumor microenvironment. In the recent decade, many studies have demonstrated that exosomes are involved in the communication between HCC cells and the stromal cells, including endothelial cells, macrophages, hepatic stellate cells and the immune cells, and serve as a regulator in the tumor proliferation and metastasis, immune evasion and immunotherapy. In addition, exosomes can also be used for the diagnosis and treatment HCC. They can potentially serve as specific biomarkers for early diagnosis and drug delivery vehicles of HCC. Chinese herbal medicine, which is widely used in the prevention and treatment of HCC in China, may regulate the release of exosomes and exosomes-mediated intercellular communication. In this review, we summarized the latest progresses on the role of the exosomes in the initiation, progression and treatment of HCC and the potential value of Traditional Chinese medicine in exosomes-mediated biological behaviors of HCC.

Multilocus variable-number tandem repeat analysis (MLVA) is widely used for genotyping of Bordetella pertussis , the causative bacteria for pertussis. However, MLVA genotyping is losing its discriminate power because prevalence of the epidemic MT27 strain (MLVA-27) is increasing worldwide. To address this, we developed a single nucleotide polymorphism (SNP) genotyping method for MT27 based on multiplexed single-base extension (SBE) assay. A total of 237 MT27 isolates collected in Japan during 1999–2018 were genotyped and classified into ten SNP genotypes (SG1 to SG10) with a Simpson’s diversity index (DI) of 0.79 (95% CI 0.76–0.82). Temporal trends showed a marked increase in the genotypic diversity in the 2010s: Simpson’s DI was zero in 1999–2004, 0.16 in 2005–2009, 0.83 in 2010–2014, and 0.76 in 2015–2018. This indicates that the SNP genotyping is applicable to the recently circulating MT27 strain. Additionally, almost all outbreak-associated MT27 isolates were classified into the same SNP genotypes for each outbreak. Multiplexed SBE assay allows for rapid and simple genotyping, indicating that the SNP genotyping can potentially be a useful tool for subtyping the B. pertussis MT27 strain in routine surveillance and outbreak investigations.

The accurate prediction of mold sticking breakout is an important prerequisite to ensure the stable and smooth production of the continuous casting process. When sticking breakout occurs, the sticking region expands vertically along the casting direction and horizontally along the strand width direction, forming a V-shaped area on the strand surface. This paper uses computer vision technology to visualize the temperature of mold copper plates, extract the geometric and movement characteristics of the sticking region from time and space perspectives, and construct feature vectors to characterize the V-shaped sticking breakout region. We train and test the auxiliary classifier WGAN-GP (ACWGAN-GP) model on true and false sticking feature vector samples, developing a breakout prediction method based on computer vision and a generative adversarial network. The test results show that the model can distinguish between true sticking breakout and false sticking breakout in terms of mold copper plate temperature, providing a new approach for monitoring abnormalities in the continuous casting process.

Chronic inflammation, driven by dysregulated immune responses and oxidative stress, underlies the pathogenesis of numerous diseases, from neurodegeneration to cancer. Cycloastragenol (CAG), a bioactive triterpenoid derived from Astragalus membranaceus, has emerged as a multifaceted therapeutic candidate due to its unique ability to simultaneously modulate inflammatory signaling networks, while exhibiting a favorable safety profile in preclinical models. This study aims to systematically evaluate the molecular mechanisms of CAG, including its coordinated anti-inflammatory, immune-regulatory, and tissue-protective effects. By integrating evidence from pharmacology, metabolomics, and clinical studies, our aim is to elucidate the therapeutic potential of CAG and identify strategies to overcome its pharmacokinetic limitations for clinical translation. A comprehensive literature review was conducted using databases such as PubMed, Web of Science, and Science Direct, employing target keywords related to cycloastragenol, inflammation, and disease treatment. Our analysis reveals that CAG exerts multidimensional and networked anti-inflammatory effects by synergistically regulating key inflammatory nodes such as NF-κB, Nrf2, and the NLRP3 inflammasome, as well as by alleviating oxidative stress. It has demonstrated therapeutic potential in diseases such as cancer, neurological disorders, asthma, and visceral fibrosis. CAG exerts significant anti-inflammatory effects by targeting the axis associated with inflammation, oxidative stress, and immune dysregulation. However, future efforts need to focus on improving its bioavailability and verifying its safety in human trials to develop a new generation of anti-inflammatory therapies.

Multiple eruptive dermatofibroma (MEDF) is a rare presentation of dermatofibroma which is frequently associated with underlying diseases such as human immunodeficiency virus infection or systemic lupus erythematosus. It generally presents a characteristic histology with hyperplasia of the epidermis, prominent bundles of collagen and a diffuse proliferation of fibrocytes. We report a case of MEDF in a 30-year-old man who presented with a large number of dark brownish red maculopapules distributed over the trunk and extremities for more than 10 years. According to the pathology, the patient was diagnosed with MEDF. Infections and autoimmune diseases were ruled out. As he had no clinical symptoms, and presented with lesions widely distributed over the body, we gave no special treatment, but suggested a regular examination. Patients with MEDF usually have no pain and pruritus. If human immunodeficiency virus infection and systemic lupus erythematosus and other causes are ruled out, and lesions are widely distributed over the body, regular check-up is recommended without specific treatment.

In the United States, women have earned more bachelor’s degrees than men since the mid-1980s. We examine the historical continuities in this trend and its sources, as well as changes since 2000 in gender gaps in advanced credentials, fields of study, types of institutions attended, and financing for higher education. The gender gap in bachelor’s degrees has remained stable at a high level over this period and a female advantage in advanced degrees emerged, especially in professional degrees. The deepening gender divide in credentials coincided with rising shares of women attending for-profit institutions and an emerging gender divide in student indebtedness. Thus, women disproportionately carry the promise and bear the costs of educational expansion, with far reaching implications for the future.

Hepatocellular carcinoma (HCC) is a lethal malignancy with a lack of effective treatments particularly for the disease at an advanced stage. Even though immune checkpoint inhibitors (ICIs) have made great progress in the treatment of HCC, durable and ideal clinical benefits still cannot be achieved in plenty of patients with HCC. Therefore, novel and refined ICI-based combination therapies are still needed to enhance the therapeutic effect. The latest study has reported that the carbonic anhydrase XII inhibitor (CAXIIi), a novel type of anticancer drug, can modify the tumor immunosuppression microenvironment by affecting hypoxic/acidic metabolism and alter the functions of monocytes and macrophages by regulating the expression of C-C motif chemokine ligand 8 (CCL8). These observations shine a light on improving programmed cell death protein 1 (PD-1)/programmed cell death ligand-1 (PD-L1) immunotherapy in combination with CAXIIis. This mini-review aims to ignite enthusiasm to explore the potential application of CAXIIis in combination with immunotherapy for HCC.

Shaft sinking in soft, water-rich strata frequently suffers from low cutting efficiency, cycle-time mismatches between excavation and muck removal, and weak system-level coordination. To elucidate the synergistic mechanisms governing excavation–muck removal interactions and to realize end-to-end performance gains, we investigate the East Ventilation Shaft of the Xinjie Taigemiao mining district as a representative artificial ground freezing (AGF) project. First, drawing on the mechanics of frozen ground and field monitoring, we establish a relationship model linking advance rate, drum rotational speed, cutting depth, and muck production, thereby clarifying why lower rotational speeds, moderate cutting depths, and rational traction reduce energy consumption and mitigate disturbances to the frozen wall. Next, for muck handling, we build a full-process discrete element method (DEM) model, integrate design-of-experiments with response-surface optimization to identify key factors, calibrate contact models, and select collection geometries. The results show that a graded-angle collecting structure improves pile concentration and discharge compliance; combined with a tiered chain-bucket–vertical belt–twin-skip configuration, it delivers matched cycle times and stable “gather–convey–hoist” operation. Finally, two-stage full-scale tests jointly validate excavation and muck removal, demonstrating that the proposed synergy model and optimized parameters sustain continuous, efficient performance across operating conditions. The study provides a reusable mechanistic framework and parameterization blueprint for AGF shaft design and construction.