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Objective: We analyze the characteristics and related factors of human chorionic gonadotropin (hCG) of pituitary origin to establish the reference interval to resolve clinical confusion and avoid harmful therapy to Chinese postmenopausal women with “positive” hCG. Materials and methods: This retrospective cohort study identified individuals who underwent hCG measurements at an academic hospital. Three gonadotropins (hCG, follicle stimulating hormone (FSH), and luteinizing hormone (LH)) was drawn from medical records. The age-stratified analyses were performed first. Then the correlations of hCG and FSH, LH as well as age were analysed. Finally, characteristics and associations of hCG, LH, and FSH were evaluated to identify pituitary hCG in postmenopausal women in clinical settings. Results: In total, 9796 cases from 11172 records met inclusion criteria and contributed 9796 hCG, 7541 FSH, and 7536 LH values. The upper reference interval for our cohorts was 5.3 IU/L. HCG, FSH, and LH concentration had no significant correlations with age. HCG moderately correlated with FSH (r = 0.47) and LH level (r = 0.53). However, it was FSH but not LH that manifested good clinical applicability. Conclusion: The prevalence of hCG≥5.0 IU/L and 5.3 IU/L in women ≥55 years is 2.8% and 2.3% in the study population from China. The level of hCG 5.3 IU/L was suggested to be the positive threshold for postmenopausal women. FSH≥40IU/L helps to distinguish the pituitary source of hCG in postmenopausal women whose serum hCG concentrations were between 5.3 and 16 IU/L.

Abstract 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.

Introduction: The epididymis is important for sperm transport, maturation, and storage.Methods: The head and tail of the epididymis of 5-week-old and 10-week-old C57 BL/6J male mice were used for single-cell sequencing.Results: 10 cell types including main, basal, and narrow/clear cells are identified. Next, we performed cell subgroup analysis, functional enrichment analysis, and differentiation potential prediction on principal cells, clear cells, and basal cells. Our study indicates that the principal cells are significantly involved in sperm maturation, as well as in antiviral and anti-tumor immune responses. Clear cells are likely to play a crucial role in safeguarding sperm and maintaining epididymal pH levels. Basal cells are implicated in the regulation of inflammatory and stress responses. The composition and functions of the various cell types within the epididymis undergo significant changes before and after sexual maturity. Furthermore, pseudo-temporal analysis elucidates the protective and supportive roles of epididymal cells in sperm maturation during sexual maturation.Discussion: This study offers a theoretical framework and forecasts for the investigation of epididymal sperm maturation and epididymal immunity.

Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal dynamics, and event-driven computation. The direct training algorithms based on the surrogate gradient method provide sufficient flexibility to design novel SNN architectures and explore the spatial-temporal dynamics of SNNs. According to previous studies, the performance of models is highly dependent on their sizes. Recently, direct training deep SNNs have achieved great progress on both neuromorphic datasets and large-scale static datasets. Notably, transformer-based SNNs show comparable performance with their ANN counterparts. In this paper, we provide a new perspective to summarize the theories and methods for training deep SNNs with high performance in a systematic and comprehensive way, including theory fundamentals, spiking neuron models, advanced SNN models and residual architectures, software frameworks and neuromorphic hardware, applications, and future trends.

IntroductionThe integration of self-attention mechanisms into Spiking Neural Networks (SNNs) has garnered considerable interest in the realm of advanced deep learning, primarily due to their biological properties. Recent advancements in SNN architecture, such as Spikformer, have demonstrated promising outcomes. However, we observe that Spikformer may exhibit excessive energy consumption, potentially attributable to redundant channels and blocks.MethodsTo mitigate this issue, we propose a one-shot Spiking Transformer Architecture Search method, namely Auto-Spikformer. Auto-Spikformer extends the search space to include both transformer architecture and SNN inner parameters. We train and search the supernet based on weight entanglement, evolutionary search, and the proposed Discrete Spiking Parameters Search (DSPS) methods. Benefiting from these methods, the performance of subnets with weights inherited from the supernet without even retraining is comparable to the original Spikformer. Moreover, we propose a new fitness function aiming to find a Pareto optimal combination balancing energy consumption and accuracy.Results and discussionOur experimental results demonstrate the effectiveness of Auto-Spikformer, which outperforms the original Spikformer and most CNN or ViT models with even fewer parameters and lower energy consumption.

Abstract Metagenomic binning is an essential technique for genome-resolved characterization of uncultured microorganisms in various ecosystems but hampered by the low efficiency of binning tools in adequately recovering metagenome-assembled genomes (MAGs). Here, we introduce BASALT (Binning Across a Series of Assemblies Toolkit) for binning and refinement of short- and long-read sequencing data. BASALT employs multiple binners with multiple thresholds to produce initial bins, then utilizes neural networks to identify core sequences to remove redundant bins and refine non-redundant bins. Using the same assemblies generated from Critical Assessment of Metagenome Interpretation (CAMI) datasets, BASALT produces up to twice as many MAGs as VAMB, DASTool, or metaWRAP. Processing assemblies from a lake sediment dataset, BASALT produces ~30% more MAGs than metaWRAP, including 21 unique class-level prokaryotic lineages. Functional annotations reveal that BASALT can retrieve 47.6% more non-redundant opening-reading frames than metaWRAP. These results highlight the robust handling of metagenomic sequencing data of BASALT.

Abstract Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both the neuroscience and AI fields. The major bottleneck during simulating detailed compartment models is the ability of a simulator to solve large systems of linear equations. Here, we present a novel Dendritic Hierarchical Scheduling (DHS) method to markedly accelerate such a process. We theoretically prove that the DHS implementation is computationally optimal and accurate. This GPU-based method performs with 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience tasks. We investigate how spatial patterns of spine inputs affect neuronal excitability in a detailed human pyramidal neuron model with 25,000 spines. Furthermore, we provide a brief discussion on the potential of DeepDendrite for AI, specifically highlighting its ability to enable the efficient training of biophysically detailed models in typical image classification tasks.

IntroductionSpiking Neural Networks (SNNs), inspired by brain science, offer low energy consumption and high biological plausibility with their event-driven nature. However, the current SNNs are still suffering from insufficient performance.MethodsRecognizing the brain's adeptness at information processing for various scenarios with complex neuronal connections within and across regions, as well as specialized neuronal architectures for specific functions, we propose a Spiking Global-Local-Fusion Transformer (SGLFormer), that significantly improves the performance of SNNs. This novel architecture enables efficient information processing on both global and local scales, by integrating transformer and convolution structures in SNNs. In addition, we uncover the problem of inaccurate gradient backpropagation caused by Maxpooling in SNNs and address it by developing a new Maxpooling module. Furthermore, we adopt spatio-temporal block (STB) in the classification head instead of global average pooling, facilitating the aggregation of spatial and temporal features.ResultsSGLFormer demonstrates its superior performance on static datasets such as CIFAR10/CIFAR100, and ImageNet, as well as dynamic vision sensor (DVS) datasets including CIFAR10-DVS and DVS128-Gesture. Notably, on ImageNet, SGLFormer achieves a top-1 accuracy of 83.73% with 64 M parameters, outperforming the current SOTA directly trained SNNs by a margin of 6.66%.DiscussionWith its high performance, SGLFormer can support more computer vision tasks in the future. The codes for this study can be found in https://github.com/ZhangHanN1/SGLFormer.

The accurate determination of the free nicotine content in cigarette smoke is crucial for assessing cigarette quality, studying harm and addiction, and reducing tar levels. Currently, the determination of free nicotine in tobacco products primarily relies on methods such as pH calculation, nuclear magnetic resonance (NMR) spectroscopy, headspace solid-phase microextraction (HS-SPME), and traditional solvent extraction. However, these methods have limitations that restrict their widespread application. In this study, the free nicotine in cigarette smoke was directly extracted by using cyclohexane according to the traditional solvent extraction method and detected via gas chromatography-mass spectrometry. Compared with the traditional two-phase solvent extraction, our experimental method is easy to execute and eliminates the influence of aqueous solutions on the original distribution of nicotine in cigarette smoke particulate matter. Furthermore, the presence of protonated nicotine in tobacco does not affect the determination. Compared with HS-SPME and NMR spectroscopy, our approach, which involves solvent extraction followed by chromatographic separation and instrumental detection, offers simplicity, improved precision, better detection limits, and reduced interference during the instrumental detection stage. The standard addition recoveries in the conducted experiment ranged from 96.2% to 102.5%. The limit of detection was 2.8 μg/cig, and the correlation coefficient R2 for the quadratic regression of the standard curve exceeded 0.999. The relative standard deviation for parallel samples was between 1.7% and 3.4% (n = 5), fully meeting the requirements for the determination of free nicotine in cigarette smoke. Analysis of cigarette samples from 38 commercially available brands revealed that the content of free nicotine ranged from 0.376 to 0.716 mg/cig, with an average of 0.540 mg/cig, and free nicotine accounted for 39.1%–88.8% of the total nicotine content.

In digital cameras, we find a major limitation: the image and video form inherited from a film camera obstructs it from capturing the rapidly changing photonic world. Here, we present vform, a bit sequence array where each bit represents whether the accumulation of photons has reached a threshold, to record and reconstruct the scene radiance at any moment. By employing only consumer-level complementary metal–oxide semiconductor (CMOS) sensors and integrated circuits, we have developed a spike camera that is 1000× faster than conventional cameras. By treating vform as spike trains in biological vision, we have further developed a spiking neural network (SNN)-based machine vision system that combines the speed of the machine and the mechanism of biological vision, achieving high-speed object detection and tracking 1000× faster than human vision. We demonstrate the utility of the spike camera and the super vision system in an assistant referee and target pointing system. Our study is expected to fundamentally revolutionize the image and video concepts and related industries, including photography, movies, and visual media, and to unseal a new SNN-enabled speed-free machine vision era.

Abstract It has been widely accepted that solar magnetic field manipulates all solar activities, especially violent solar bursts in solar corona. Thus, it is extremely important to reconstruct three-dimentional (3D) magnetic field of solar corona from really observed photospheric magnetogram. In this paper, a large-scale dataset of 3D solar magnetic fields of active regions is built by using the nonlinear force-free magnetic field (NLFFF) extrapolation from vector magnetograms of Helioseismic and Magnetic Imager (HMI) on Solar Dynamics Observatory (SDO). In this dataset, all space-weather HMI active region patches (SHARPs) with the corresponding serial numbers of national oceanic and atmospheric administration (NOAA) are included. They are downloaded from the SHARP 720 s series of JSOC every 96 minutes. In addition, each sample is labelled with a finer grained label for solar flare forecast. This paper is with the purpose of open availability of data resource and source code to the peers for refraining from repeated labor of data preparation. Meanwhile, with such a large-scale, high spatio-temporal resolution and high quality scientific data, we anticipate a wide attention and interest from artificial intelligence (AI) and computer vision communities, for exploring AI for astronomy over such a large-scale dataset.

In recent years, there has been an increasingly prevalent trend of ransomware attacks, with malicious organizations employing various techniques to gain system privileges and subsequently engaging in extortion through methods such as encrypting files or leaking information. Current research predominantly focuses on the analysis of ransomware using existing features, but there has been scarce exploration of the behavioral patterns associated with ransomware attacks. In light of this situation, we propose a ransomware attack similarity analysis method based on the ATT&CK matrix. To initiate this analysis, a substantial amount of network threat intelligence is sifted through to select reliable and comprehensive ransomware attack incidents. From these incidents, we extract attack tactics, techniques, and procedural information. Subsequently, we employ the TF-IDF algorithm to calculate the keyword weights within attack descriptions. Based on these weights, we utilize the cosine similarity algorithm to compare the similarity between attack events. This approach reveals critical technical and tactical information employed by the attacking organizations, enabling researchers to gain a deeper understanding of the behavioral patterns of the attackers. Finally, we propose countermeasures corresponding to the critical attack techniques employed by these malicious organizations. These countermeasures aim to enhance network security defenses and reduce the risks associated with ransomware attacks.

The convolution operation is the most critical component in recent surge of deep learning research. Conventional 2D convolution needs O(C2K2) parameters to represent, where C is the channel size and K is the kernel size. The amount of parameters has become really costly considering that these parameters increased tremendously recently to meet the needs of demanding applications. Among various implementations of the convolution, separable convolution has been proven to be more efficient in reducing the model size. For example, depth separable convolution reduces the complexity to O(C⋅(C+K2)) while spatial separable convolution reduces the complexity to O(C2K). However, these are considered ad hoc designs which cannot ensure that they can in general achieve optimal separation. In this research, we propose a novel and principled operator called optimized separable convolution by optimal design for the internal number of groups and kernel sizes for general separable convolutions can achieve the complexity of O(C32K). When the restriction in the number of separated convolutions can be lifted, an even lower complexity at O(C⋅log(CK2)) can be achieved. Experimental results demonstrate that the proposed optimized separable convolution is able to achieve an improved performance in terms of accuracy-#Params trade-offs over both conventional, depth-wise, and depth/spatial separable convolutions.

Recurrent pregnancy loss (RPL) is both mental and physical health problem affecting about 1–5% of women of childbearing age. The etiology of RPL is complex, involving chromosomal abnormalities, autoimmune diseases, metabolic disorders, and endometrial dysfunction. The causes of abortion are still unknown in more than 50% of these cases. With the development of science and technology, an increasing number of scholars focus on this field and find that genetic factors may play an essential role in unexplained RPL, such as embolism-related genes, immune factor-related genes, and chromosomal numeric, and structural variation. This review summarizes the genetic factors associated with RPL, including genetic mutations and genetic polymorphisms, chromosomal variants, and chromosomal polymorphisms. Many related genetic factors have been found to be demographically and geographically relevant, some of which can be used for risk prediction or screening for the etiology of RPL. However, it is difficult to predict and prevent RPL due to uncertain pathogenesis and highly variable clinical presentation. Therefore, the genetic factors of RPL still need plentiful research to obtain a more accurate understanding of its pathogenesis and to provide more detection means for the screening and prevention of RPL.

A neuroprosthesis is a type of precision medical device that is intended to manipulate the neuronal signals of the brain in a closed-loop fashion, while simultaneously receiving stimuli from the environment and controlling some part of a human brain or body. Incoming visual information can be processed by the brain in millisecond intervals. The retina computes visual scenes and sends its output to the cortex in the form of neuronal spikes for further computation. Thus, the neuronal signal of interest for a retinal neuroprosthesis is the neuronal spike. Closed-loop computation in a neuroprosthesis includes two stages: encoding a stimulus as a neuronal signal, and decoding it back into a stimulus. In this paper, we review some of the recent progress that has been achieved in visual computation models that use spikes to analyze natural scenes that include static images and dynamic videos. We hypothesize that in order to obtain a better understanding of the computational principles in the retina, a hypercircuit view of the retina is necessary, in which the different functional network motifs that have been revealed in the cortex neuronal network are taken into consideration when interacting with the retina. The different building blocks of the retina, which include a diversity of cell types and synaptic connections—both chemical synapses and electrical synapses (gap junctions)—make the retina an ideal neuronal network for adapting the computational techniques that have been developed in artificial intelligence to model the encoding and decoding of visual scenes. An overall systems approach to visual computation with neuronal spikes is necessary in order to advance the next generation of retinal neuroprosthesis as an artificial visual system.

Accurate tracking is the basis of behavioral analysis, an important research method in neuroscience and many other fields. However, the currently available tracking methods have limitations. Traditional computer vision methods have problems in complex environments, and deep learning methods are hard to be applied universally due to the requirement of laborious annotations. To address the trade-off between accuracy and universality, we developed an easy-to-use tracking tool, Siamese Network-based All-Purpose Tracker (SNAP-Tracker), a model-free tracking software built on the Siamese network. The pretrained Siamese network offers SNAP-Tracker a remarkable feature extraction ability to keep tracking accuracy, and the model-free design makes it usable directly before laborious annotations and network refinement. SNAP-Tracker provides a “tracking with detection” mode to track longer videos with an additional detection module. We demonstrate the stability of SNAP-Tracker through different experimental conditions and different tracking tasks. In short, SNAP-Tracker provides a general solution to behavioral tracking without compromising accuracy. For the user’s convenience, we have integrated the tool into a tidy graphic user interface and opened the source code for downloading and using (https://github.com/slh0302/SNAP).

Perfluoroalkyl substances (PFASs) exposure is suggested to interfere with fetal growth. However, limited investigations considered the roles of parity and delivery on PFASs distributions and the joint effects of PFASs mixture on birth outcomes. In this study, 506 birth cohorts were investigated in Hangzhou, China with 14 PFASs measured in maternal serum. Mothers with higher maternal ages who underwent cesarean section were associated with elevated PFASs burden, while parity showed a significant but diverse influence. A logarithmic unit increment in perfluorooctanoic acid (PFOA), perfluorooctane sulfonate (PFOS), and perfluorononane sulfonate (PFNS) was significantly associated with a reduced birth weight of 0.153 kg (95% confidence interval (CI): −0.274, −0.031, p = 0.014), 0.217 kg (95% CI: −0.385, −0.049, p = 0.012), and 0.137 kg (95% CI: −0.270, −0.003, p = 0.044), respectively. Higher perfluoroheptanoic acid (PFHpA) and perfluoroheptane sulphonate (PFHpS) were associated with increased Apgar-1 scores. PFOA (Odds ratio (OR): 2.17, 95% CI: 1.27, 3.71, p = 0.004) and PFNS (OR:1.59, 95% CI: 1.01, 2.50, p = 0.043) were also risk factors to preterm birth. In addition, the quantile-based g-computation showed that PFASs mixture exposure was significantly associated with Apgar-1 (OR: 0.324, 95%CI: 0.068, 0.579, p = 0.013) and preterm birth (OR: 0.356, 95% CI: 0.149, 0.845, p = 0.019). In conclusion, PFASs were widely distributed in the maternal serum, which was influenced by maternal characteristics and significantly associated with several birth outcomes. Further investigation should focus on the placenta transfer and toxicities of PFASs.

Skeleton-based 3D object retrieval is a very efficient method to query the sketch databases in numerous applications. However, few skeleton images are found so far in existing sketch benchmarks. In this paper, we provide an initial benchmark dataset consisting of skeleton sketches, including hand-drawn skeletons and skeletons extracted from 3D objects, and both of them are used to form a generic object class. Then we present a method for skeleton-based 3D object retrieval using a retina-like feature descriptor (S3DOR-RFD) based on the structural property of the human retina for processing complex visual information in a very efficient way. As part of the S3DOR-RFD algorithm, we combine artificial bee colony (ABC) in support vector machine (SVM) so as to improve the performance with automatic parameter selection, where one can make full use of the advantages of ABC and SVM to further improve the accuracy rate of 3D object retrieval. Experimental results indicate that skeleton sketches can be automatically distinguished from perspective sketches, and that the proposed S3DOR-RFD method works efficiently for selected object classes.

The impact of aging on reproductive outcomes has received considerable critical attention; however, there is much less information available on the effects of paternal age compared to the effects of maternal age. In this study, methylation levels of sperm rDNA promoter regions and Long Interspersed Nucleotide Element 1 (LINE-1) were measured using pyrosequencing and fertilization, day 3 good-quality embryo, pregnancies, and implantation results were assessed. We observed significantly increasing levels of DNA methylation in the sperm rDNA promoter regions with age based on stratifying the samples by age alone (P = 0.0001) and performing linear regression analysis (P < 0.0001). Meanwhile, no statistically significant correlations were observed between global LINE-1 methylation with age. No statistically significant correlations were observed between sperm rDNA promoter methylation levels and either the day 3 good-quality embryo rate or clinical pregnancy rate. In contrast, the correlation between sperm rDNA promoter methylation levels and fertilization (2 pronuclei) rate was nearly significant (P = 0.0707), especially the methylation levels of some individual CpG units (CpG_10, P = 0.0176; CpG_11, P = 0.0438; CpG_14, P = 0.0232) and rDNA promoter methylation levels measured using primerS2 (P = 0.0513). No significant correlation was found between sperm rDNA promoter methylation levels and fertilization rates (2 pronuclei, 1 pronuclei, and 1 polypronuclei). Our results demonstrate that sperm are susceptible to age-associated alterations in methylation levels of rDNA promoter regions, suggesting that sperm rDNA promoter methylation levels can be applied to DNA methylation-based age prediction, and that the aberrant methylation of rDNA promoters may be partially responsible for enhanced disease susceptibility of offspring sired by older fathers. Methylation levels of sperm rDNA promoter regions may correlate with polypronuclei rates of IVF programs.

Human gait has been shown to be an effective biometric measure for person identification at a distance. On the other hand, changes in the view angle pose a major challenge for gait recognition as human gait silhouettes are usually different from different view angles. Traditionally, such a multi-view gait recognition problem can be tackled by View Transformation Model (VTM) which transforms gait features from multiple gallery views to the probe view so as to evaluate the gait similarity. In the real-world environment, however, gait sequences may be captured from an uncontrolled scene and the view angle is often unknown, dynamically changing, or does not belong to any predefined views (thus VTM becomes inapplicable). To address this free-view gait recognition problem, we propose an innovative view-adaptive mapping (VAM) approach. The VAM employs a novel walking trajectory fitting (WTF) to estimate the view angles of a gait sequence, and a joint gait manifold (JGM) to find the optimal manifold between the probe data and relevant gallery data for gait similarity evaluation. Additionally, a RankSVM-based algorithm is developed to supplement the gallery data for subjects whose gallery features are only available in predefined views. Extensive experiments on both indoor and outdoor datasets demonstrate that the VAM outperforms several reference methods remarkably in free-view gait recognition.

The purinergic receptor P2X ligand-gated ion channel 3 (P2X3) is crucially involved in peripheral nociceptive processes of somatic and visceral pain. Endometriosis pain is considered as a kind of inflammatory and neuropathic pain. However, whether P2X3 is involved in endometriosis pain has not been reported up to date. Here, we aimed to determine whether P2X3 expression in endometriotic lesions is involved in endometriosis pain, which is regulated by inflammatory mediators through extracellular regulated protein kinases (ERK) signalling pathway. We found that P2X3 expressions in endometriosis endometrium and endometriotic lesions were both significantly higher as compared with control endometrium (P

Learning visual concepts from images is an important yet challenging problem in computer vision and multimedia research areas. Multiple kernel learning (MKL) methods have shown great advantages in visual concept learning. As a visual concept often exhibits great appearance variance, a canonical MKL approach may not generate satisfactory results when a uniform kernel combination is applied over the input space. In this paper, we propose a per-sample multiple kernel learning (PS-MKL) approach to take into account intraclass diversity for improving discrimination. PS-MKL determines sample-wise kernel weights according to kernel functions and training samples. Kernel weights as well as kernel-based classifiers are jointly learned. For efficient learning, PS-MKL employs a sample selection strategy. Extensive experiments are carried out over three benchmarking datasets of different characteristics including Caltech101, WikipediaMM, and Pascal VOC'07. PS-MKL has achieved encouraging performance, comparable to the state of the art, which has outperformed a canonical MKL.