Selective direct motor cortical influence during naturalistic climbing.

It remains poorly resolved when and how motor cortical output directly influences limb muscle activity through descending projections, which impedes mechanistic understanding of cortical movement control. Here we addressed this in mice performing an ethologically inspired all-limb climbing behavior. We quantified the direct influence of forelimb primary motor cortex (caudal forelimb area, CFA) on muscle activity comprehensively across the muscle activity states that occur during climbing. We found that CFA informs muscle activity pattern, mainly by selectively activating certain muscles while exerting much smaller, bidirectional effects on their antagonists. From Neuropixel recordings, we identified linear combinations (components) of motor cortical activity that covary with these effects, finding that these components differ from those that covary with muscle activity or kinematics. Collectively, our results reveal an instructive direct motor cortical influence on limb muscles that is selective within a motor behavior and reliant on a new type of neural activity subspace.

Predicting the temporal-dynamic trajectories of cortical neuronal responses in non-human primates based on deep spiking neural network.

Deep convolutional neural networks (CNNs) are commonly used as computational models for the primate ventral stream, while deep spiking neural networks (SNNs) incorporated with both the temporal and spatial spiking information still lack investigation. We compared performances of SNN and CNN in prediction of visual responses to the naturalistic stimuli in area V4, inferior temporal (IT), and orbitofrontal cortex (OFC). The accuracies based on SNN were significantly higher than that of CNN in prediction of temporal-dynamic trajectory and averaged firing rate of visual response in V4 and IT. The temporal dynamics were captured by SNN for neurons with diverse temporal profiles and category selectivities, and most sensitively captured around the time of peak responses for each brain region. Consistently, SNN activities showed significantly stronger correlations with IT, V4 and OFC responses. In SNN, correlations with neural activities were stronger for later time-step features than early time-step features. The temporal-dynamic prediction was also significantly improved by considering preceding neural activities during the prediction. Thus, our study demonstrated SNN as a powerful temporal-dynamic model for cortical responses to complex naturalistic stimuli.

Auto-Spikformer: Spikformer architecture search.

Introduction: The 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. Methods: To 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 discussion: Our 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.

Stabilization of Pb/Zn mine tailings by modified fly ash: characterization, performance, and mechanism.

The presence of heavy metals in mine tailings poses a serious threat to the surrounding environment. In this study, we aimed to stabilize Pb/Zn-containing mine tailings using modified fly ash (FA) with various alkali solutions. Notably, the modification of FA with Na2SiO3 (NaSi-FA) resulted in the most significant structure changes. To understand the adsorption mechanism of Pb and Zn by modified FA, batch adsorption experiments were conducted. Doubling the adsorption capacity for both Pb and Zn was observed in the modified FA samples compared to unmodified samples. These results could be attributed to the enhanced surface area and porous structure, providing more anchor sites for the heavy metal ions. Additionally, the adsorption of Pb and Zn was found to follow the Langmuir isotherm and pseudo-second order kinetic. Molecular dynamics simulations further supported the notion that Pb and Zn ions could effectively exchange with Na ions within the N-A-S-H gel network, ultimately solidifying them in its structure. Stabilizing Pb/Zn tailings with NaSi-FA resulted in a significant decrease in the leaching of Pb and Zn. Specifically, the leading amount decreased by 55.2% for Pb and 35.3% for Zn, showcasing the superior performance of this stabilization method. This reduction in leaching indicates effective compliance with environmental regulations regarding the containment of Pb and Zn.

Direct training high-performance deep spiking neural networks: a review of theories and methods.

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.

How does parental rearing patterns of children in upper primary school impact social withdrawal? A mediating effect of emotional regulation.

Introduction: The present study endeavors to elucidate the impact of emotional regulation and parental rearing patterns on the social development of children in the upper grades of primary school. A burgeoning body of literature suggests that these factors significantly influence children's social adaptation and emotional well-being, yet a comprehensive examination of these relationships is warranted. Methods: Employing a cross-sectional design, this investigation utilized the Egna Minnen Beträffande Uppfostran (EMBU), Emotional Regulation Questionnaire for Children (ERQC), and Child Behavioural Issues Scale (CBCL) to assess a sample of 276 pupils across grades 4-6. The selection of these instruments allowed for a multifaceted evaluation of the constructs of interest. Results: A pronounced disparity in parental rearing practices, emotional regulation capabilities, and levels of social withdrawal was observed among the different grades, with grade 5 exhibiting the most pronounced effects. Parental emotional warmth demonstrated a significant positive correlation with children's emotional regulation abilities, while punitive, rejecting, and preferential behaviors were inversely correlated. The study established that parental rearing practices indirectly influence social withdrawal through the mediating role of children's emotional regulation, underscoring the complexity of this relationship. Conclusion: The results underscore the salient role of parental rearing and emotional regulation in the social development of children. The study contributes to the existing literature by providing a nuanced understanding of the mechanisms through which parenting styles and emotional competencies interplay to affect social withdrawal. Implications for educational practices and future research directions are discussed.

Self-architectural knowledge distillation for spiking neural networks.

Spiking neural networks (SNNs) have attracted attention due to their biological plausibility and the potential for low-energy applications on neuromorphic hardware. Two mainstream approaches are commonly used to obtain SNNs, i.e., ANN-to-SNN conversion methods, and Directly-trained-SNN methods. However, the former achieve excellent performance at the cost of a large number of time steps (i.e., latency), while the latter exhibit lower latency but suffers from suboptimal performance. To tackle the performance-latency trade-off, we propose Self-Architectural Knowledge Distillation (SAKD), an intuitive and effective method for SNNs leveraging Knowledge Distillation (KD). We adopt a bilevel teacher-student training strategy in SAKD, i.e., level-1 involves directly transferring same-architectural pre-trained ANN weights to SNNs, and level-2 encourages the SNNs to mimic ANN's behavior, considering both final responses and intermediate features aspects. Learning with informative supervision signals fostered by labels and ANNs, our SAKD achieves new state-of-the-art (SOTA) performance with a few time steps on widely-used classification benchmark datasets. On ImageNet-1K, with only 4 time steps, our Spiking-ResNet34 model attains a Top-1 accuracy of 70.04%, outperforming the previous same-architectural SOTA methods. Notably, our SEW-ResNet152 model reaches a Top-1 accuracy of 77.30% on ImageNet-1K, setting a new SOTA benchmark for SNNs. Furthermore, we apply our SAKD to various dense prediction downstream tasks, such as object detection and semantic segmentation, demonstrating strong generalization ability and superior performance. In conclusion, our proposed SAKD framework presents a promising approach for achieving both high performance and low latency in SNNs, potentially paving the way for future advancements in the field.

trans-Interacting Plasma Membrane Proteins and Binding Partner Identification.

Plasma membrane proteins (PMPs) play critical roles in a myriad of physiological and disease conditions. A unique subset of PMPs functions through interacting with each other in trans at the interface between two contacting cells. These trans-interacting PMPs (tiPMPs) include adhesion molecules and ligands/receptors that facilitate cell-cell contact and direct communication between cells. Among the tiPMPs, a significant number have apparent extracellular binding domains but remain orphans with no known binding partners. Identification of their potential binding partners is therefore important for the understanding of processes such as organismal development and immune cell activation. While a number of methods have been developed for the identification of protein binding partners in general, very few are applicable to tiPMPs, which interact in a two-dimensional fashion with low intrinsic binding affinities. In this review, we present the significance of tiPMP interactions, the challenges of identifying binding partners for tiPMPs, and the landscape of method development. We describe current avidity-based screening approaches for identifying novel tiPMP binding partners and discuss their advantages and limitations. We conclude by highlighting the importance of developing novel methods of identifying new tiPMP interactions for deciphering the complex protein interactome and developing targeted therapeutics for diseases.

SGLFormer: Spiking Global-Local-Fusion Transformer with high performance.

Introduction: Spiking 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. Methods: Recognizing 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. Results: SGLFormer 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%. Discussion: With 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.

Collaborative FFE-assisted simplified soft-output MLSE with LDPC for high-speed IM-DD transmissions.

In this paper, we propose a feed-forward equalizer (FFE)-assisted simplified soft-output MLSE (sMLSE) by collaborating the maximum likelihood sequence estimation (MLSE) with soft-decision low-density-parity-check (LDPC) decoding. The simplified sMLSE results in undetermined log-likelihood ratio (LLR) magnitudes when the reserved level is less than or equal to the half of modulation order. This severely degrades the performance of soft-decision forward error correction (SD-FEC) decoding. In the FFE-assisted simplified sMLSE, we use the LLRs calculated from pre-set FFE to replace these undetermined LLRs of simplified sMLSE. Thus, the proposed method eliminates the SD-FEC decoding performance degradation resulted from simplification. We conduct experiments to transmit 184-Gb/s PAM-4 or 255-Gb/s PAM-8 signal in IM-DD system at C-band to evaluate the performance of the proposed sMLSE. The results show that the proposed sMLSE can effectively compensate for the degradation of LLR quality. For 255-Gb/s PAM-8 signal transmissions, the FFE-assisted simplified sMLSE achieves almost the same SD-FEC decoding performance as the conventional sMLSE but with 85% complexity reduction.

Molecular Mechanisms in Pathophysiology of Mucopolysaccharidosis and Prospects for Innovative Therapy.

Mucopolysaccharidoses (MPSs) are a group of inborn errors of the metabolism caused by a deficiency in the lysosomal enzymes required to break down molecules called glycosaminoglycans (GAGs). These GAGs accumulate over time in various tissues and disrupt multiple biological systems, including catabolism of other substances, autophagy, and mitochondrial function. These pathological changes ultimately increase oxidative stress and activate innate immunity and inflammation. We have described the pathophysiology of MPS and activated inflammation in this paper, starting with accumulating the primary storage materials, GAGs. At the initial stage of GAG accumulation, affected tissues/cells are reversibly affected but progress irreversibly to: (1) disruption of substrate degradation with pathogenic changes in lysosomal function, (2) cellular dysfunction, secondary/tertiary accumulation (toxins such as GM2 or GM3 ganglioside, etc.), and inflammatory process, and (3) progressive tissue/organ damage and cell death (e.g., skeletal dysplasia, CNS impairment, etc.). For current and future treatment, several potential treatments for MPS that can penetrate the blood-brain barrier and bone have been proposed and/or are in clinical trials, including targeting peptides and molecular Trojan horses such as monoclonal antibodies attached to enzymes via receptor-mediated transport. Gene therapy trials with AAV, ex vivo LV, and Sleeping Beauty transposon system for MPS are proposed and/or underway as innovative therapeutic options. In addition, possible immunomodulatory reagents that can suppress MPS symptoms have been summarized in this review.

6,6'-Diheptyl-3,3'-bis-[(pyridin-3-yl)ethyn-yl]-5H,5'H-di-pyrrolo-[1,2-b:1',2'-g][2,6]naphthyridine-5,5'-dione.

The complete mol-ecule of the title compound, C42H42N4O2, is generated by a crystallographic centre of symmetry. The pendant heptyl chains adopt extended conformations and the dihedral angle between the pyrrole and pyridine rings is 8.18 (15)°. In the crystal, the mol-ecules are arranged in columnar stacks propagating in the [010] direction via slipped aromatic π-π stacking inter-actions.

CAR T Cells for Treating Severe Atopic Allergic Diseases.

The prevalence of allergic diseases is rising rapdly in the US and the world. While antibody drugs and corticosteroids can provide symptom relief, they cannot cure allergic diseases. Described herein is a novel approach to treating severe atopic allergic diseases - chimeric antigen receptor-engineered T cells - that target and eliminate the cells that produce the causative agent of all atopic allergic diseases, immunoglubulin E (IgE).

Modulation of Neuronal Activity and Saccades at Theta Rhythm During Visual Search in Non-human Primates.

Active exploratory behaviors have often been associated with theta oscillations in rodents, while theta oscillations during active exploration in non-human primates are still not well understood. We recorded neural activities in the frontal eye field (FEF) and V4 simultaneously when monkeys performed a free-gaze visual search task. Saccades were strongly phase-locked to theta oscillations of V4 and FEF local field potentials, and the phase-locking was dependent on saccade direction. The spiking probability of V4 and FEF units was significantly modulated by the theta phase in addition to the time-locked modulation associated with the evoked response. V4 and FEF units showed significantly stronger responses following saccades initiated at their preferred phases. Granger causality and ridge regression analysis showed modulatory effects of theta oscillations on saccade timing. Together, our study suggests phase-locking of saccades to the theta modulation of neural activity in visual and oculomotor cortical areas, in addition to the theta phase locking caused by saccade-triggered responses.

Cancer Cell-Derived Exosomes Promote HCC Tumorigenesis Through Hedgehog Pathway.

PURPOSE: Hepatocellular carcinoma (HCC) accounts for more than 80% of primary liver cancers and is one of the leading causes of cancer-related death in many countries. Cancer cell-derived exosomes are shown to mediate communications between cancer cells and the microenvironment, promoting tumorigenesis. Hedgehog signaling pathway plays important roles in cancer development of HCC. METHODS: Exosomes were isolated from culture medium of HCC cell lines PLC/PRF/5 and MHCC-97H and were found to promote cancer cell growth measured with cell proliferation and colony formation assay. HCC cells cultured with cancer cell-derived exosome had increased cancer stem cell (CSC) population demonstrated by increased cell sphere formation CSC marker expressions. Hedgehog protein Shh was found to be highly expressed in these two HCC cell lines and preferably carried by exosomes. When Shh was knocked down with shRNA, the resulting exosomes had a reduced effect on promoting cancer cell growth or CSC population increase compared to normal cell-derived exosomes. RESULTS: The ability of PLC/PRF/5 cells to form tumor in a xenograft model was increased by the addition of the exosomes from control cancer cells but not the exosomes from Shh knocked down cancer cells. Finally, the higher plasma Exo-Shh levels were associated with later tumor stages, higher histological grades, multiple tumors, and higher recurrence rates. CONCLUSION: This study demonstrated that HCC cells secreted Shh via exosome and promote tumorigenesis through the activated Hedgehog pathway.

[New Design of Revivent TC System].

Left ventricular aneurysm (LVA) is a common complication of myocardial infarction. Traditional medical and surgical treatments are not effective or require high doctors' operational skills and patients' physical fitness. With the development of minimally invasive medical devices, it becomes possible for revivent TC system to treat LVA and reconstruct the left ventricle. This study introduces an existing product and its defect when used. From the perspective of clinical needs, we propose a new design of revivent TC system which realizes accurate force measurement and simplifies surgery.

Stability of motor cortex network states during learning-associated neural reorganizations.

A substantial reorganization of neural activity and neuron-to-movement relationship in motor cortical circuits accompanies the emergence of reproducible movement patterns during motor learning. Little is known about how this tempest of neural activity restructuring impacts the stability of network states in recurrent cortical circuits. To investigate this issue, we reanalyzed data in which we recorded for 14 days via population calcium imaging the activity of the same neural populations of pyramidal neurons in layer 2/3 and layer 5 of forelimb motor and premotor cortex in mice during the daily learning of a lever-press task. We found that motor cortex network states remained stable with respect to the critical network state during the extensive reorganization of both neural population activity and its relation to lever movement throughout learning. Specifically, layer 2/3 cortical circuits unceasingly displayed robust evidence for operating at the critical network state, a regime that maximizes information capacity and transmission and provides a balance between network robustness and flexibility. In contrast, layer 5 circuits operated away from the critical network state for all 14 days of recording and learning. In conclusion, this result indicates that the wide-ranging malleability of synapses, neurons, and neural connectivity during learning operates within the constraint of a stable and layer-specific network state regarding dynamic criticality, and suggests that different cortical layers operate under distinct constraints because of their specialized goals.NEW & NOTEWORTHY The neural activity reorganizes throughout motor learning, but how this reorganization impacts the stability of network states is unclear. We used two-photon calcium imaging to investigate how the network states in layer 2/3 and layer 5 of forelimb motor and premotor cortex are modulated by motor learning. We show that motor cortex network states are layer-specific and constant regarding criticality during neural activity reorganization, and suggests that layer-specific constraints could be motivated by different functions.

Cortical Circuit Dynamics Are Homeostatically Tuned to Criticality In Vivo.

Homeostatic mechanisms stabilize neuronal activity in vivo, but whether this process gives rise to balanced network dynamics is unknown. Here, we continuously monitored the statistics of network spiking in visual cortical circuits in freely behaving rats for 9 days. Under control conditions in light and dark, networks were robustly organized around criticality, a regime that maximizes information capacity and transmission. When input was perturbed by visual deprivation, network criticality was severely disrupted and subsequently restored to criticality over 48 h. Unexpectedly, the recovery of excitatory dynamics preceded homeostatic plasticity of firing rates by >30 h. We utilized model investigations to manipulate firing rate homeostasis in a cell-type-specific manner at the onset of visual deprivation. Our results suggest that criticality in excitatory networks is established by inhibitory plasticity and architecture. These data establish that criticality is consistent with a homeostatic set point for visual cortical dynamics and suggest a key role for homeostatic regulation of inhibition.

The Influence of Tool Rake Surface Geometry on the Hard Turning Process of AISI52100 Hardened Steel.

The hard turning process has been widely used in the field of hard material precision machining because of its high efficiency, low processing residual stress, and low environmental pollution. Due to its undesirably processing quality, it is still not a substitute for traditional grinding, so many studies have reported that the process has been optimized. However, there has been little research on the geometry optimization of hard cutting tools, which have a great influence on the traditional machining process. In this paper, two tools with different rake face shapes are designed. The finite element analysis method is used to compare their performance with a conventional plane tool while turning hardened steel. The results show that the cutting performance of the designed tool T1 and T2 (chip morphology, cutting force, and cutting temperature) and the quality of the machined surface are improved compared with the tool. The cutting force decreased by 12.72% and 14.74%, the cutting temperature decreased by 7.56% and 9.01%, respectively, and the surface residual stress decreased by 26.56% and 28.66%.