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The impact of the Coronavirus Disease 2019 (COVID-19) pandemic on microplastic (MP) occurrence in aquatic environments deserves an in-depth study. In this study, the occurrence of MPs and environmental flux of plastics before (2019) and during (2020 and 2021) the pandemic were comparatively investigated in various aquatic compartments in the Taihu Lake Basin in China. The field-based investigations from 2019 to 2021 for Taihu Lake have shown that, at the onset of the outbreak, the MP abundance declined at a rate of 62.3 %, but gradually recovered to the pre-pandemic level. However, the amount of plastics being released into aquatic environments showed a declining trend in 2020 and 2021 compared to those in 2019, with decrease rates of 13.7 % and 15.8 %, respectively. Characterization analysis of MP particles and source apportionment framework implied that while the contributions of tire abrasion and domestic waste to MP occurrence were depleted owing to the reduction in human activity during the pandemic, weathering and fragmentation of retained plastics contributed to the recovery of stored MPs. This study provides insights into the anthropogenic influences on MP occurrence, and supports policymakers in managing and controlling plastic contamination in large freshwater systems in the "new normal" phase., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Flexible intracortical probes offer important opportunities for stable neural interfaces by reducing chronic immune responses, but their advances usually come with challenges of difficult implantation and limited recording span. Here, we reported a mechanically adaptive and deployable intracortical probe, which features a foldable fishbone-like structural design with branching electrodes on a temperature-responsive shape memory polymer (SMP) substrate. Leveraging the temperature-triggered soft-rigid phase transition and shape memory characteristic of SMP, this probe design enables direct insertion into brain tissue with minimal footprint in a folded configuration while automatically softening to reduce mechanical mismatches with brain tissue and deploying electrodes to a broader recording span under physiological conditions. Experimental and numerical studies on the material softening and structural folding-deploying behaviors provide insights into the design, fabrication, and operation of the intracortical probes. The chronically implanted neural probe in the rat cortex demonstrates that the proposed neural probe can reliably detect and track individual units for months with stable impedance and signal amplitude during long-term implantation. The work provides a tool for stable neural activity recording and creates engineering opportunities in basic neuroscience and clinical applications., Competing Interests: Competing interests statement:The authors declare no competing interest.

Objective . Accurately diagnosing patients with disorders of consciousness (DOC) is challenging and prone to errors. Recent studies have demonstrated that EEG (electroencephalography), a non-invasive technique of recording the spontaneous electrical activity of brains, offers valuable insights for DOC diagnosis. However, some challenges remain: (1) the EEG signals have not been fully used; and (2) the data scale in most existing studies is limited. In this study, our goal is to differentiate between minimally conscious state (MCS) and unresponsive wakefulness syndrome (UWS) using resting-state EEG signals, by proposing a new deep learning framework. Approach . We propose DOCTer, an end-to-end framework for DOC diagnosis based on EEG. It extracts multiple pertinent features from the raw EEG signals, including time-frequency features and microstates. Meanwhile, it takes clinical characteristics of patients into account, and then combines all the features together for the diagnosis. To evaluate its effectiveness, we collect a large-scale dataset containing 409 resting-state EEG recordings from 128 UWS and 187 MCS cases. Main results . Evaluated on our dataset, DOCTer achieves the state-of-the-art performance, compared to other methods. The temporal/spectral features contributes the most to the diagnosis task. The cerebral integrity is important for detecting the consciousness level. Meanwhile, we investigate the influence of different EEG collection duration and number of channels, in order to help make the appropriate choices for clinics. Significance . The DOCTer framework significantly improves the accuracy of DOC diagnosis, helpful for developing appropriate treatment programs. Findings derived from the large-scale dataset provide valuable insights for clinics., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Spiking Neural Networks (SNNs), renowned for their low power consumption, brain-inspired architecture, and spatio-temporal representation capabilities, have garnered considerable attention in recent years. Similar to Artificial Neural Networks (ANNs), high-quality benchmark datasets are of great importance to the advances of SNNs. However, our analysis indicates that many prevalent neuromorphic datasets lack strong temporal correlation, preventing SNNs from fully exploiting their spatio-temporal representation capabilities. Meanwhile, the integration of event and frame modalities offers more comprehensive visual spatio-temporal information. Yet, the SNN-based cross-modality fusion remains underexplored. In this work, we present a neuromorphic dataset called DVS-SLR that can better exploit the inherent spatio-temporal properties of SNNs. Compared to existing datasets, it offers advantages in terms of higher temporal correlation, larger scale, and more varied scenarios. In addition, our neuromorphic dataset contains corresponding frame data, which can be used for developing SNN-based fusion methods. By virtue of the dual-modal feature of the dataset, we propose a Cross-Modality Attention (CMA) based fusion method. The CMA model efficiently utilizes the unique advantages of each modality, allowing for SNNs to learn both temporal and spatial attention scores from the spatio-temporal features of event and frame modalities, subsequently allocating these scores across modalities to enhance their synergy. Experimental results demonstrate that our method not only improves recognition accuracy but also ensures robustness across diverse scenarios., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The potential therapeutic benefits of human dental pulp stem cells (HDPSCs) in dental regenerative medicine have been demonstrated. However, little is known about the molecular mechanisms regulating the biological characteristics of HDPSCs. The experiment aims to explore whether VEGF activates signaling pathways such as FAK, PI3K, Akt, and p38 in HDPSCs, and to investigate the molecular mechanisms by which VEGF influences proliferation and migration of HDPSCs. Normal and inflamed human dental pulp (HDP) samples were collected, and the levels of VEGF in HDP were assessed. HDPSCs were cultured and purified. HDPSCs were stimulated with lipopolysaccharide (LPS) at gradient concentrations, and real-time quantitative polymerase chain reaction (qPCR) was used to assess changes in VEGF mRNA. Gradient concentrations of VEGF were used to stimulate HDPSCs, and cell migration ability was evaluated through scratch assays and Transwell chamber experiments. Phosphorylation levels of FAK, AKT, and P38 were assessed using Western blotting. Inhibitors of VEGFR2, FAK, AKT, P38, and VEGF were separately applied to HDPSCs, and cell migration ability and phosphorylation levels of FAK, AKT, and P38 were determined. The results indicated significant differences in VEGF levels between normal and inflamed HDP tissues, with levels in the inflamed state reaching 435% of normal levels (normal: 87.91 ng/mL, inflamed: 382.76 ng/mL, P < 0.05). LPS stimulation of HDPSCs showed a significant increase in VEGF mRNA expression with increasing LPS concentrations (LPS concentrations of 0.01, 0.1, 1, and 10 μg/mL resulted in VEGF mRNA expressions of 181.2%, 274.2%, 345.8%, and 460.9%, respectively, P < 0.05). VEGF treatment significantly enhanced the migration ability of HDPSCs in Transwell chamber experiments, with migration rates increasing with VEGF concentrations (VEGF concentrations of 0, 1, 10, 20, 50, and 100 ng/mL resulted in migration rates of 8.41%, 9.34%, 21.33%, 28.41%, 42.87%, and 63.15%, respectively, P < 0.05). Inhibitors of VEGFR2, FAK, AKT, P38, and combined VEGF stimulation demonstrated significant migration inhibition, with migration rates decreasing to 8.31%, 12.64%, 13.43%, 18.32%, and 74.17%, respectively. The migration rate with combined VEGF stimulation showed a significant difference (P < 0.05). The analysis of phosphorylation levels revealed that VEGF stimulation significantly activated phosphorylation of FAK, AKT, and P38, with phosphorylation levels increasing with VEGF concentrations (P < 0.05). The VEGF/VEGFR2 signaling axis regulated the migration ability of HDPSCs through the FAK/PI3K/AKT and P38MAPK pathways. This finding highlighted not only the crucial role of VEGF in injury repair of HDPSCs but also provided important clues for a comprehensive understanding of the potential applications of this signaling axis in dental regenerative medicine., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Dental pulp stem cells (DPSCs) are a class of cells with the potential of self-replication and multi-directional differentiation, which are widely considered to have great application value. It was to investigate miR-586 in DPSCs differentiated into odontoblast-like cells. In this article, human dental pulp stem cells (hDPSCs) were used as samples, and hDPSCs were co-cultured with endothelial progenitor cells (EPCs). Furthermore, a lentiviral expression vector for the miR-586 inhibitor was established. The effect of miR-586 inhibitor expression vector on the activity of hDPSCs was detected by Cell Counting Kit-8 (CCK-8). The differentiation of hDPSCs was tested by mineralized nodule staining. The expression of miR-586 and a gene related to dental cell differentiation in the pulp was subjected to detection by real-time quantitative PCR (qRT-PCR). As against the normal hDPSCs and the empty vector, the miR-586 lentivirus expression inhibition vector could visibly raise the expression of dentin sialophosphoprotein (DSPP) in hDPSCs; and the cell proliferation activity was visibly enhanced; In addition, the mRNA expressions of dentin-matrix acidic phosphoprotein 1 (DMP-1) and alkaline phosphatase (ALP) were visibly raised in the miR-586 lentivirus expression inhibition vector (all P < 0.05). Additionally, ALP activity was significantly enhanced (P < 0.05). The number of mineralized nodules was significantly increased (P < 0.05). MiR-586 plays a key regulatory function in DPSCs differentiated into odontoblast-like cells and is associated with specific molecular mechanisms., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: To evaluate the association of coagulation disorder score with the risk of in-hospital mortality in acute respiratory distress syndrome (ARDS) patients., Methods: In this cohort study, 7,001 adult patients with ARDS were identified from the Medical Information Mart for Intensive Care Database-IV (MIMIC-IV). Univariate and multivariate Logistic stepwise regression models were used to explore the associations of coagulation-associated biomarkers with the risk of in-hospital mortality in patients with ADRS. Restricted cubic spline (RCS) was plotted to explore the association between coagulation disorder score and in-hospital mortality of ARDS patients., Results: The follow-up time for in-hospital death was 7.15 (4.62, 13.88) days. There were 1,187 patients died and 5,814 people survived in hospital. After adjusting for confounding factors, increased risk of in-hospital mortality in ARDS patients was observed in those with median coagulation disorder score [odds ratio (OR) = 1.22, 95% confidence interval (CI): 1.01-1.47) and high coagulation disorder score (OR = 1.38, 95% CI: 1.06-1.80). The results of RCS indicated that when the coagulation disorder score >2, the trend of in-hospital mortality rose gradually, and OR was >1., Conclusions: Poor coagulation function was associated with increased risk of in-hospital mortality in ARDS patients. The findings implied that clinicians should regularly detect the levels of coagulation-associated biomarkers for the management of ARDS patients., Competing Interests: Conflict of interest statement None., (Copyright © 2024 Southern Society for Clinical Investigation. Published by Elsevier Inc. All rights reserved.)

Thyroid cancer is a prevalent endocrine malignancy whose global incidence has risen over the past several decades. Ferroptosis, a regulated form of cell death distinguished by the excessive buildup of iron-dependent lipid peroxidates, stands out from other programmed cell death pathways in terms of morphological and molecular characteristics. Increasing evidence suggests a close association between thyroid cancer and ferroptosis, that is, inducing ferroptosis effectively suppresses the proliferation of thyroid cancer cells and impede tumor advancement. Therefore, ferroptosis represents a promising therapeutic target for the clinical management of thyroid cancer in clinical settings. Alterations in ferroptosis-related genes hold potential for prognostic prediction in thyroid cancer. This review summarizes current studies on the role of ferroptosis in thyroid cancer, elucidating its mechanisms, therapeutic targets, and predictive biomarkers. The findings underscore the significance of ferroptosis in thyroid cancer and offer valuable insights into the development of innovative treatment strategies and accurate predictors for the thyroid cancer., Competing Interests: Declaration of Competing Interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Masson SAS.. All rights reserved.)

Differentiated thyroid cancer (DTC) is the predominant type of thyroid cancer, with some patients experiencing relapse, distant metastases, or refractoriness, revealing limited treatment options. Chimeric antigen receptor (CAR)-modified Natural Killer (NK) cells are revolutionary therapeutic agents effective against various resistant cancers. Thyroid-stimulating hormone receptor (TSHR) expression in DTC provides a unique tumor-specific target for CAR therapy. Here, we developed an innovative strategy for treating DTC using modified NK-92 cells armed with a TSHR-targeted CAR. The modified cells showed enhanced cytotoxicity against TSHR-positive DTC cell lines and exhibited elevated degranulation and cytokine release. After undergoing irradiation, the cells effectively halted their proliferative capacity while maintaining potent targeted killing ability. Transfer of these irradiation-treated cells into NSG mice with DTC tumors resulted in profound tumor suppression. NK-92 cells modified with TSHR-CAR offer a promising, off-the-shelf option for advancing DTC immunotherapy., Competing Interests: Declaration of competing interest All authors disclosed no relevant relationships., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Thyroid cancer is a common endocrine malignancy that poses considerable therapeutic challenges in treating anaplastic carcinoma and advanced aggressive disease. Immunotherapy has become a prominent strategy for cancer treatment, and has shown remarkable advancements in recent years. In this study, we utilized visualization and bibliometric tools to analyze publications on thyroid cancer immunotherapy from the Web of Science Core Collection (WoSCC). A total of 409 articles were included, with an annual increase in both publications and citations since 2016. China leads research efforts in this area, while the University of Texas System and UTMD Anderson Cancer Center rank first in publication output. The journal Thyroid has garnered the highest citations. Notable authors contributing to this field include Antonelli Alessandro, Fallahi Poupak, and Wang Yu. Current research hotspots include immune checkpoint inhibitors, combination therapies involving immunotherapy with targeted therapy, CAR-T cell therapy, and modulation of the tumor microenvironment, all of which underscore the evolving landscape and potential for innovative treatments in thyroid cancer.

Silage is an excellent method of feed preservation; however, carbon dioxide, methane and nitrous oxide produced during fermentation are significant sources of agricultural greenhouse gases. Therefore, determining a specific production method is crucial for reducing global warming. The effects of four temperatures (10 °C, 20 °C, 30 °C, and 40 °C) on silage quality, greenhouse gas yield and microbial community composition of forage sorghum were investigated. At 20 °C and 30 °C, the silage has a lower pH value and a higher lactic acid content, resulting in higher silage quality and higher total gas production. In the first five days of ensiling, there was a significant increase in the production of carbon dioxide, methane, and nitrous oxide. After that, the output remained relatively stable, and their production at 20 °C and 30 °C was significantly higher than that at 10 °C and 40 °C. Firmicutes and Proteobacteria were the predominant silage microorganisms at the phylum level. Under the treatment of 20 °C, 30 °C, and 40 °C, Lactobacillus had already dominated on the second day of silage. However, low temperatures under 10 °C slowed down the microbial community succession, allowing, bad microorganisms such as Chryseobacterium, Pantoea and Pseudomonas dominate the fermentation, in the early stage of ensiling, which also resulted in the highest bacterial network complexity. According to random forest and structural equation model analysis, the production of carbon dioxide, methane and nitrous oxide is mainly affected by microorganisms such as Lactobacillus, Klebsiella and Enterobacter, and temperature influences the activity of these microorganisms to mediate gas production in silage. This study helps reveal the relationship between temperature, microbial community and greenhouse gas production during silage fermentation, providing a reference for clean silage fermentation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

According to previous studies, marine heatwaves (MHWs) significantly suppress the phytoplankton chlorophyll-a concentration (Chl a) in tropical oceans. However, pre-MHW Chl a has rarely been considered as a reference value. In this study, the Chl a for the periods preceding and during MHWs events was used to explore the impact of MHWs on Chl a from 1998 to 2022 in the South China Sea (SCS). The Chl a response to MHWs in different regions was further discussed based on the Chl a variation characteristics. The results showed that the Chl a response to MHWs exhibited regional variability. Interestingly, there was a large proportion of positive Chl a anomalies (∼0.55) in the estuary and offshore regions during MHWs; however, Chl a anomalies were mostly negative in the upwelling regions. These different response patterns are related to background conditions, including nutrient concentrations, wind-driven dynamics, and light availability. In upwelling regions, negative Chl a anomalies were primarily due to the weakening of wind speeds, Ekman pumping velocities, and upwelling intensities. In estuarine regions, positive Chl a anomalies were caused by enhanced light availability, whereas in offshore regions, there were attributed to the increased atmospheric wet deposition. These results have improved our understanding of the impact of MHWs on marine ecosystems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Advances in artificial intelligence enable neural networks to learn a wide variety of tasks, yet our understanding of the learning dynamics of these networks remains limited. Here, we study the temporal dynamics during learning of Hebbian feedforward neural networks in tasks of continual familiarity detection. Drawing inspiration from network neuroscience, we examine the network's dynamic reconfiguration, focusing on how network modules evolve throughout learning. Through a comprehensive assessment involving metrics like network accuracy, modular flexibility, and distribution entropy across diverse learning modes, our approach reveals various previously unknown patterns of network reconfiguration. We find that the emergence of network modularity is a salient predictor of performance and that modularization strengthens with increasing flexibility throughout learning. These insights not only elucidate the nuanced interplay of network modularity, accuracy, and learning dynamics but also bridge our understanding of learning in artificial and biological agents.

Sleep staging is essential for sleep assessment and plays an important role in disease diagnosis, which refers to the classification of sleep epochs into different sleep stages. Polysomnography (PSG), consisting of many different physiological signals, e.g. electroencephalogram (EEG) and electrooculogram (EOG), is a gold standard for sleep staging. Although existing studies have achieved high performance on automatic sleep staging from PSG, there are still some limitations: 1) they focus on local features but ignore global features within each sleep epoch, and 2) they ignore cross-modality context relationship between EEG and EOG. In this paper, we propose CareSleepNet, a novel hybrid deep learning network for automatic sleep staging from PSG recordings. Specifically, we first design a multi-scale Convolutional-Transformer Epoch Encoder to encode both local salient wave features and global features within each sleep epoch. Then, we devise a Cross-Modality Context Encoder based on co-attention mechanism to model cross-modality context relationship between different modalities. Next, we use a Transformer-based Sequence Encoder to capture the sequential relationship among sleep epochs. Finally, the learned feature representations are fed into an epoch-level classifier to determine the sleep stages. We collected a private sleep dataset, SSND, and use two public datasets, Sleep-EDF-153 and ISRUC to evaluate the performance of CareSleepNet. The experiment results show that our CareSleepNet achieves the state-of-the-art performance on the three datasets. Moreover, we conduct ablation studies and attention visualizations to prove the effectiveness of each module and to analyze the influence of each modality.

Thanks to their event-driven nature, spiking neural networks (SNNs) are surmised to be great computation-efficient models. The spiking neurons encode beneficial temporal facts and possess excessive anti-noise properties. However, the high-quality encoding of spatio-temporal complexity and also its training optimization of SNNs are restricted by means of the contemporary problem, this article proposes a novel hierarchical event-driven visual device to explore how information transmits and signifies in the retina the usage of biologically manageable mechanisms. This cognitive model is an augmented spiking-based framework consisting of the function learning capacity of convolutional neural networks (CNNs) with the cognition capability of SNNs. Furthermore, this visual device is modeled in a biological realism way with unsupervised learning rules and advanced spike firing rate encoding methods. We train and test them on some image datasets (Modified National Institute of Standards and Technology (MNIST), Canadian Institute for Advanced Research (CIFAR)10, and its noisy versions) to show that our mannequin can process greater vital data than present cognitive models. This article also proposes a novel quantization approach to make the proposed spiking-based model more efficient for neuromorphic hardware implementation. The outcomes show this joint CNN-SNN model can reap excessive focus accuracy and get more effective generalization ability.

Low phytic acid (lpa) crop is considered as an effective strategy to improve crop nutritional quality, but a substantial decrease in phytic acid (PA) usually has negative effect on agronomic performance and its response to environment adversities. Myo-inositol-3-phosphate synthase (MIPS) is the rate-limiting enzyme in PA biosynthesis pathway, and regarded as the prime target for engineering lpa crop. In this paper, the rice MIPS gene (RINO2) knockout mutants and its wild type were performed to investigate the genotype-dependent alteration in the heat injury-induced spikelet fertility and its underlying mechanism for rice plants being imposed to heat stress at anthesis. Results indicated that RINO2 knockout significantly enhanced the susceptibility of rice spikelet fertility to heat injury, due to the severely exacerbated obstacles in pollen germination and pollen tube growth in pistil for RINO2 knockout under high temperature (HT) at anthesis. The loss of RINO2 function caused a marked reduction in inositol and phosphatidylinositol derivative concentrations in the HT-stressed pollen grains, which resulted in the strikingly lower content of phosphatidylinositol 4,5-diphosphate (PI (4,5) P 2 ) in germinating pollen grain and pollen tube. The insufficient supply of PI (4,5) P 2 in the HT-stressed pollen grains disrupted normal Ca 2+ gradient in the apical region of pollen tubes and actin filament cytoskeleton in growing pollen tubes. The severely repressed biosynthesis of PI (4,5) P 2 was among the regulatory switch steps leading to the impaired pollen germination and deformed pollen tube growth for the HT-stressed pollens of RINO2 knockout mutants., (© 2024 Society for Experimental Biology and John Wiley & Sons Ltd.)

Miniaturization of wireless neural-recording systems enables minimally-invasive surgery and alleviates the rejection reactions for implanted brain-computer interface (BCI) applications. Simultaneous massive-channel recording capability is essential to investigate the behaviors and inter-connections in billions of neurons. In recent years, battery-free techniques based on wireless power transfer (WPT) and backscatter communication have reduced the sizes of neural-recording implants by battery eliminating and antenna sharing. However, the existing battery-free chips realize the multi-channel merging in the signal-acquisition circuits, which leads to large chip area, signal attenuation, insufficient channel number or low bandwidth, etc. In this work, we demonstrate a 2mm×2mm battery-free neural dielet, which merges 128 channels in the wireless part. The neural dielet is fabricated with 65nm CMOS process, and measured results show that: 1) The proposed multi-carrier orthogonal backscatter technique achieves a high data rate of 20.16Mb/s and an energy efficiency of 0.8pJ/bit. 2) A self-calibrated direct digital converter (SC-DDC) is proposed to fit the 128 channels in the 2mm×2mm die, and then the all-digital implementation achieves 0.02mm 2 area and 9.87μW power per channel.

Sensory information recognition is primarily processed through the ventral and dorsal visual pathways in the primate brain visual system, which exhibits layered feature representations bearing a strong resemblance to convolutional neural networks (CNNs), encompassing reconstruction and classification. However, existing studies often treat these pathways as distinct entities, focusing individually on pattern reconstruction or classification tasks, overlooking a key feature of biological neurons, the fundamental units for neural computation of visual sensory information. Addressing these limitations, we introduce a unified framework for sensory information recognition with augmented spikes. By integrating pattern reconstruction and classification within a single framework, our approach not only accurately reconstructs multimodal sensory information but also provides precise classification through definitive labeling. Experimental evaluations conducted on various datasets including video scenes, static images, dynamic auditory scenes, and functional magnetic resonance imaging (fMRI) brain activities demonstrate that our framework delivers state-of-the-art pattern reconstruction quality and classification accuracy. The proposed framework enhances the biological realism of multimodal pattern recognition models, offering insights into how the primate brain visual system effectively accomplishes the reconstruction and classification tasks through the integration of ventral and dorsal pathways.

Background: The diagnosis and treatment of attention deficit hyperactivity disorder (ADHD) comorbid with epilepsy have been insufficiently addressed in China. We conducted a study in China to investigate the current status, diagnosis, and treatment of ADHD in children to further our understanding of ADHD comorbid with epilepsy, strengthen its management, and improve patients' quality of life., Methods: We carried out a multicenter cross-sectional survey of children with epilepsy across China between March 2022 and August 2022. We screened all patients for ADHD and compared various demographic and clinical factors between children with and without ADHD, including gender, age, age at epilepsy onset, duration of epilepsy, seizure types, seizure frequency, presence of epileptiform discharges, and treatment status. Our objective was to explore any possible associations between these characteristics and the prevalence of ADHD., Results: Overall, 395 epilepsy patients aged 6-18 years were enrolled. The age at seizure onset and duration of epilepsy ranged from 0.1-18 to 0.5-15 years, respectively. Focal onset seizures were observed in 212 (53.6%) patients, while 293 (76.3%) patients had epileptiform interictal electroencephalogram (EEG) abnormalities. Among the 370 patients treated with anti-seizure medications, 200 (54.1%) had monotherapy. Although 189 (47.8%) patients had ADHD, only 31 received treatment for it, with the inattentive subtype being the most common. ADHD was more common in children undergoing polytherapy compared to those on monotherapy. Additionally, poor seizure control and the presence of epileptiform interictal EEG abnormalities may be associated with a higher prevalence of ADHD., Conclusions: While the prevalence of ADHD was higher in children with epilepsy than in normal children, the treatment rate was notably low. This highlights the need to give more importance to the diagnosis and treatment of ADHD in children with epilepsy., (© 2024. Children's Hospital, Zhejiang University School of Medicine.)

Microplastics (MPs) have attracted increasing attention due to their ubiquitous occurrence in freshwater sediments and the detrimental effects on benthic invertebrates. However, a clear understanding of their downstream impacts on ecosystem services is still lacking. This study examines the effects of bio-based polylactic acid (PLA), fuel-based polyethylene terephthalate (PET), and biofilm-covered PET (BPET) MPs on the bioturbator chironomid larvae (Tanypus chinensis), and the influence on phosphorus (P) profiles in microcosms. The changes in biochemical responses and metabolic pathways indicated that MPs disrupted energy synthesis by causing intestinal blockage and oxidative stress in T. chinensis, leading to energy depletion and impaired bioturbation activity. The impairment further resulted in enhanced sedimentary P immobilization. For larval treatments, the internal-P loadings were respectively 11.4%, 8.6%, and 9.0% higher in the PLA, PET, and BPET groups compared to the non-MP control. Furthermore, the influence of bioturbation on P profiles was MP-type dependent. Both BPET and PLA treatments displayed more obvious impacts on P profiles compared to PET due to the changes in MP bioavailability or sediment microenvironment. This study connects individual physiological responses to broader ecosystem services, showing that MPs alter P biogeochemical processes by disrupting the bioturbation activities of chironomid larvae., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Spiking neural networks (SNNs) are brain-inspired models that utilize discrete and sparse spikes to transmit information, thus having the property of energy efficiency. Recent advances in learning algorithms have greatly improved SNN performance due to the automation of feature engineering. While the choice of neural architecture plays a significant role in deep learning, the current SNN architectures are mainly designed manually, which is a time-consuming and error-prone process. In this paper, we propose a spiking neural architecture search (NAS) method that can automatically find efficient SNNs. To tackle the challenge of long search time faced by SNNs when utilizing NAS, the proposed NAS encodes candidate architectures in a branchless spiking supernet which significantly reduces the computation requirements in the search process. Considering that real-world tasks prefer efficient networks with optimal accuracy under a limited computational budget, we propose a Synaptic Operation (SynOps)-aware optimization to automatically find the computationally efficient subspace of the supernet. Experimental results show that, in less search time, our proposed NAS can find SNNs with higher accuracy and lower computational cost than state-of-the-art SNNs. We also conduct experiments to validate the search process and the trade-off between accuracy and computational cost., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Spiking neural networks (SNNs) are considered an attractive option for edge-side applications due to their sparse, asynchronous and event-driven characteristics. However, the application of SNNs to object detection tasks faces challenges in achieving good detection accuracy and high detection speed. To overcome the aforementioned challenges, we propose an end-to-end Trainable Spiking-YOLO (Tr-Spiking-YOLO) for low-latency and high-performance object detection. We evaluate our model on not only frame-based PASCAL VOC dataset but also event-based GEN1 Automotive Detection dataset, and investigate the impacts of different decoding methods on detection performance. The experimental results show that our model achieves competitive/better performance in terms of accuracy, latency and energy consumption compared to similar artificial neural network (ANN) and conversion-based SNN object detection model. Furthermore, when deployed on an edge device, our model achieves a processing speed of approximately from 14 to 39 FPS while maintaining a desirable mean Average Precision (mAP), which is capable of real-time detection on resource-constrained platforms., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: Grb2 associated binding protein 1 (Gab1) is an adaptor protein that is important for intracellular signal transduction which involved in several pathological process. However, the role of Gab1 in pressure overload-induced ventricular arrhythmias (VAs) remain poorly understood. In the current study, we aimed to test the role of Gab1 in VA susceptibility induced by pressure overload., Methods: We overexpressed Gab1 in the hearts using an adeno-associated virus 9 (AAV9) system through tail vein injection. Aortic banding (AB) surgery was performed in C57BL6/J mice to induce heart failure (HF). Four weeks following AB, histology, echocardiography, and biochemical analysis were conducted to investigate cardiac structural remodeling and electrophysiological studies were performed to check the electrical remodeling. Western blot analysis was used to explore the underlying mechanisms., Results: The mRNA and protein expression were downregulated in AB hearts compared to sham hearts. Gab1 overexpression significantly reversed AB-induced cardiac structural remodeling including ameliorated AB-induced cardiac dysfunction, cardiac fibrosis, and inflammatory response. Moreover, Gab1 overexpression also markedly alleviated AB-induced electrical remodeling including ion channel alterations and VA susceptibility. Mechanistically, we found that TLR4/MyD88/NF-κB contributes to the cardio protective effect of Gab1 overexpression on AB-induced VAs., Conclusions: Our study manifested that Gab1 may serve as a promising anti-arrhythmic target via inhibiting TLR4/MyD88/NF-κB signaling pathway induced by AB., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Spiking neural networks (SNNs) are gaining increasing attention for their biological plausibility and potential for improved computational efficiency. To match the high spatial-temporal dynamics in SNNs, neuromorphic chips are highly desired to execute SNNs in hardware-based neuron and synapse circuits directly. This paper presents a large-scale neuromorphic chip named Darwin3 with a novel instruction set architecture, which comprises 10 primary instructions and a few extended instructions. It supports flexible neuron model programming and local learning rule designs. The Darwin3 chip architecture is designed in a mesh of computing nodes with an innovative routing algorithm. We used a compression mechanism to represent synaptic connections, significantly reducing memory usage. The Darwin3 chip supports up to 2.35 million neurons, making it the largest of its kind on the neuron scale. The experimental results showed that the code density was improved by up to 28.3× in Darwin3, and that the neuron core fan-in and fan-out were improved by up to 4096× and 3072× by connection compression compared to the physical memory depth. Our Darwin3 chip also provided memory saving between 6.8× and 200.8× when mapping convolutional spiking neural networks onto the chip, demonstrating state-of-the-art performance in accuracy and latency compared to other neuromorphic chips., (© The Author(s) 2024. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd.)

The breeding of low phytic acid (LPA) crops is widely considered an effective strategy to improve crop nutrition, but the LPA crops usually have inferior seed germination performance. To clarify the reason for the suboptimal seed performance of LPA rice, this study investigated the impact of reduced seed phytic acid (InsP 6 ) content in rice ins(3)P synthase1 (EC 5.5.1.4, RINO1), one of the key targets for engineering LPA rice, knockouton cellular differentiation in seed embryos and its relation to myo-inositol metabolism and auxin signalling during embryogenesis. The results indicated that the homozygotes of RINO1 knockout could initiate differentiation at the early stage of embryogenesis but failed to form normal differentiation of plumule and radicle primordia. The loss of RINO1 function disrupted vesicle trafficking and auxin signalling due to the significantly lowered phosphatidylinositides (PIs) concentration in seed embryos, thereby leading to the defects of seed embryos without the recognizable differentiation of shoot apex meristem (SAM) and radicle apex meristem (RAM) for the homozygotes of RINO1 knockout. The abnormal embryo phenotype of RINO1 homozygotes was partially rescued by exogenous spraying of inositol and indole-3-acetic acid (IAA) in rice panicle. Thus, RINO1 is crucial for both seed InsP 6 biosynthesis and embryonic development. The lower phosphatidylinositol (4,5)-bisphosphate (PI (4,5) P 2 ) concentration and the disorder auxin distribution induced by insufficient inositol supply in seed embryos were among the regulatory switch steps leading to aberrant embryogenesis and failure of seed germination in RINO1 knockout., (© 2024 Scandinavian Plant Physiology Society.)

The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large-scale clinical resting-state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG-based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph-based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index-related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states., (© 2024 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Objectives: To explore the preoperative high-risk clinical factors for contralateral medium-volume central lymph node metastasis (conMVCLNM) in unilateral papillary thyroid carcinoma (uPTC) and the indications for dissection of contralateral central lymph nodes (conCLN)., Methods: Clinical and pathological data of 204 uPTC patients who underwent thyroid surgery at the Hangzhou First People's Hospital from September 2010 to October 2022 were collected. Univariate and multivariate logistic regression analyses were conducted to determine the independent risk factors for contralateral central lymph node metastasis (conCLNM) and conMVCLNM in uPTC patients based on the preoperative clinical data. Predictive models for conCLNM and conMVCLNM were constructed using logistic regression analyses and validated using receiver operating characteristic (ROC) curves, concordance index (C-index), calibration curves, and decision curve analysis (DCA)., Results: Univariate and multivariate logistic regression analyses showed that gender (P < 0.001), age (P < 0.001), tumor diameter (P < 0.001), and multifocality (P = 0.008) were independent risk factors for conCLNM in uPTC patients. Gender(P= 0.026), age (P = 0.010), platelet-to-lymphocyte ratio (PLR) (P =0.003), and tumor diameter (P = 0.036) were independent risk factors for conMVCLNM in uPTC patients. A predictive model was established to assess the risk of conCLNM and conMVCLNM, with ROC curve areas of 0.836 and 0.845, respectively. The C-index, the calibration curve, and DCA demonstrated that the model had good diagnostic value., Conclusion: Gender, age, tumor diameter, and multifocality are high-risk factors for conCLNM in uPTC patients. Gender, age, tumor diameter, and PLR are high-risk factors for conMVCLNM in uPTC patients, and preventive conCLN dissection should be performed., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wu, Huang, Huang, Pan, Li, Shi, Ding, Pan, Peng, Teng, Zhou, Luo and Zhang.)

Objective: To analyze etiologic factors of pediatric acute ataxia and to identify the severity of its underlying causes for urgent medical intervention., Methods: Clinical data of children diagnosed with acute ataxia between December 2015 and December 2021 from one national medical center were analyzed retrospectively., Results: A total of 99 children (59 boys, 40 girls), median age at disease onset 55 (range: 12-168) months, were enrolled. The median follow period was 46 (range 6-78) months. Eighty-six (86.9 %) children were diagnosed with immune-associated acute ataxia, among which acute post-infectious cerebellar ataxia (APCA) was the most common diagnosis (50.5 %), followed by demyelinating diseases of the central nervous system (18.2 %) and Guillain-Barré syndrome (9.1 %). On cerebrospinal fluid (CSF) examination, 35/73 (47.9 %) patients had pleocytosis (>5 cells/mm 3 ), and 18/73 (24.7 %) had elevated protein levels. Thirty-one patients (31.3 %) had an abnormal cerebral MRI. Children with other immune-associated acute cerebellar ataxia had more extracerebellar symptoms, intracranial MRI lesions, abnormal CSF results, longer hospital stay, higher recurrence rates and incidence of neurological sequelae than children with APCA., Conclusion: Immune-associated acute ataxia is the main cause of pediatric acute ataxia, among which APCA is the most common phenotype. However, some immune-associated diseases, especially autoantibody-mediated disease, which has a higher recurrence rate and neurological sequelae account for an increasing proportion of pediatric acute ataxia. When children present with extracerebellar symptoms, abnormal cranial MRI or CSF results, and without prodromal infection, prudent differential diagnosis is recommended., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Wireless implantable devices are widely used in medical treatment, which should meet clinical constraints such as longevity, miniaturization, and reliable communication. Wireless power transfer (WPT) can eliminate the battery to reduce system size and prolong device life, while it's challenging to generate a reliable clock without a crystal. In this work, we propose a self-adaptive dual-injection-locked-ring-oscillator (dual-ILRO) clock-recovery technique based on two-tone WPT and integrate it into a battery-free neural-recording SoC. The 2[Formula: see text]-order inter-modulation (IM2) component of the two WPT tones is extracted as a low-frequency reference for battery-free SoC, and the proposed self-adaptive dual-ILRO technique extends the lock range to ensure an anti-interference PVT-robust clock generation. The neural-recording SoC includes a low-noise signal acquisition unit, a power management unit, and a backscatter circuit to perform neural signal recording, wireless power harvesting, and neural data transmission. Benefiting from the 6.4 μW low power of the clock recovery circuit, the overall SoC power is cut down to 49.8 μW. In addition, the proposed clock-recovery technique enables both signal acquisition and uplink communication to perform as well as that synchronized by an ideal clock, i.e., an effective number of 9.6 bits and a bit error rate (BER) less than 4.8 × 10 -7 in chip measurement. The SoC takes a die area of 2.05 mm 2 , and an animal test is conducted in a Sprague-Dawley rat to validate the wireless neural-recording performance, compared to a crystal-synchronized commercial chip.

This paper proposes a novel pipeline to estimate a non-parametric environment map with high dynamic range from a single human face image. Lighting-independent and -dependent intrinsic images of the face are first estimated separately in a cascaded network. The influence of face geometry on the two lighting-dependent intrinsics, diffuse shading and specular reflection, are further eliminated by distributing the intrinsics pixel-wise onto spherical representations using the surface normal as indices. This results in two representations simulating images of a diffuse sphere and a glossy sphere under the input scene lighting. Taking into account the distinctive nature of light sources and ambient terms, we further introduce a two-stage lighting estimator to predict both accurate and realistic lighting from these two representations. Our model is trained supervisedly on a large-scale and high-quality synthetic face image dataset. We demonstrate that our method allows accurate and detailed lighting estimation and intrinsic decomposition, outperforming state-of-the-art methods both qualitatively and quantitatively on real face images.

Polysomnography (PSG) recordings have been widely used for sleep staging in clinics, containing multiple modality signals (i.e., EEG and EOG). Recently, many studies have combined EEG and EOG modalities for sleep staging, since they are the most and the second most powerful modality for sleep staging among PSG recordings, respectively. However, EEG is complex to collect and sensitive to environment noise or other body activities, imbedding its use in clinical practice. Comparatively, EOG is much more easily to be obtained. In order to make full use of the powerful ability of EEG and the easy collection of EOG, we propose a novel framework to simplify multimodal sleep staging with a single EOG modality. It still performs well with only EOG modality in the absence of the EEG. Specifically, we first model the correlation between EEG and EOG, and then based on the correlation we generate multimodal features with time and frequency guided generators by adopting the idea of generative adversarial learning. We collected a real-world sleep dataset containing 67 recordings and used other four public datasets for evaluation. Compared with other existing sleep staging methods, our framework performs the best when solely using the EOG modality. Moreover, under our framework, EOG provides a comparable performance to EEG.

For Brain-Computer Interface (BCI) based on motor imagery (MI), the MI task is abstract and spontaneous, presenting challenges in measurement and control and resulting in a lower signal-to-noise ratio. The quality of the collected MI data significantly impacts the cross-subject calibration results. To address this challenge, we introduce a novel cross-subject calibration method based on passive tactile afferent stimulation, in which data induced by tactile stimulation is utilized to calibrate transfer learning models for cross-subject decoding. During the experiments, tactile stimulation was applied to either the left or right hand, with subjects only required to sense tactile stimulation. Data from these tactile tasks were used to train or fine-tune models and subsequently applied to decode pure MI data. We evaluated BCI performance using both the classical Common Spatial Pattern (CSP) combined with the Linear Discriminant Analysis (LDA) algorithm and a state-of-the-art deep transfer learning model. The results demonstrate that the proposed calibration method achieved decoding performance at an equivalent level to traditional MI calibration, with the added benefit of outperforming traditional MI calibration with fewer trials. The simplicity and effectiveness of the proposed cross-subject tactile calibration method make it valuable for practical applications of BCI, especially in clinical settings.

Major Depression Disorder (MDD) is a common yet destructive mental disorder that affects millions of people worldwide. Making early and accurate diagnosis of it is very meaningful. Recently, EEG, a non-invasive technique of recording spontaneous electrical activity of brains, has been widely used for MDD diagnosis. However, there are still some challenges in data quality and data size of EEG: (1) A large amount of noise is inevitable during EEG collection, making it difficult to extract discriminative features from raw EEG; (2) It is difficult to recruit a large number of subjects to collect sufficient and diverse data for model training. Both of the challenges cause the overfitting problem, especially for deep learning methods. In this paper, we propose DiffMDD, a diffusion-based deep learning framework for MDD diagnosis using EEG. Specifically, we extract more noise-irrelevant features to improve the model's robustness by designing the Forward Diffusion Noisy Training Module. Then we increase the size and diversity of data to help the model learn more generalized features by designing the Reverse Diffusion Data Augmentation Module. Finally, we re-train the classifier on the augmented dataset for MDD diagnosis. We conducted comprehensive experiments to test the overall performance and each module's effectiveness. The framework was validated on two public MDD diagnosis datasets, achieving the state-of-the-art performance.

Advanced oxidation processes (AOPs) are increasingly applied in water and wastewater treatment, but their energy consumption and chemical use may hinder their further implementation in a changing world. This study investigated the feasibility and mechanisms involved in a chemical-free nanobubble-based AOP for treating organic micropollutants in both synthetic and real water matrices. The removal efficiency of the model micropollutant Rhodamine B (RhB) by oxygen nanobubble AOP (98%) was significantly higher than for air (73%) and nitrogen nanobubbles (69%). The treatment performance was not significantly affected by pH (3-10) and the presence of ions (Ca 2+ , Mg 2+ , HCO 3 - , and Cl - ). Although a higher initial concentration of RhB (10 mg/L) led to a slower treatment process when compared to lower initial concentrations (0.1 and 1 mg/L), the final removal performance reached a similar level (∼98%) between 100 and 500 min. The coexistence of organic matter (humic acid, HA) resulted in a much lower reduction (70%) in the RhB removal rate. Both qualitative and quantitative analysis of reactive oxygen species (ROSs) using fluorescent probe, electron spin resonance, and quenching experiments demonstrated that the contributions of ROSs in RhB degradation followed the order: hydroxyl radical (•OH) > superoxide radical (•O 2 - ) > singlet oxygen ( 1 O 2 ). The cascade degradation reactions for RhB were identified which involve N-de-ethylation, hydroxylation, chromophore cleavage, opening-ring and final mineralisation processes. Moreover, the treatment of real water samples spiked with RhB, including natural lake water and secondary effluent from a sewage works, still showed considerable removals of the dye (75.3%-90.8%), supporting its practical feasibility. Overall, the results benefit future research and application of chemical free nanobubble-based AOP for water and wastewater treatment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background: Papillary thyroid cancer (PTC) is one of the most common endocrine malignancies with different risk levels. However, preoperative risk assessment of PTC is still a challenge in the worldwide. Here, the authors first report a Preoperative Risk Assessment Classifier for PTC (PRAC-PTC) by multidimensional features including clinical indicators, immune indices, genetic feature, and proteomics., Materials and Methods: The 558 patients collected from June 2013 to November 2020 were allocated to three groups: the discovery set [274 patients, 274 formalin-fixed paraffin-embedded (FFPE)], the retrospective test set (166 patients, 166 FFPE), and the prospective test set (118 patients, 118 fine-needle aspiration). Proteomic profiling was conducted by FFPE and fine-needle aspiration tissues from the patients. Preoperative clinical information and blood immunological indices were collected. The BRAFV600E mutation were detected by the amplification refractory mutation system., Results: The authors developed a machine learning model of 17 variables based on the multidimensional features of 274 PTC patients from a retrospective cohort. The PRAC-PTC achieved areas under the curve (AUC) of 0.925 in the discovery set and was validated externally by blinded analyses in a retrospective cohort of 166 PTC patients (0.787 AUC) and a prospective cohort of 118 PTC patients (0.799 AUC) from two independent clinical centres. Meanwhile, the preoperative predictive risk effectiveness of clinicians was improved with the assistance of PRAC-PTC, and the accuracies reached at 84.4% (95% CI: 82.9-84.4) and 83.5% (95% CI: 82.2-84.2) in the retrospective and prospective test sets, respectively., Conclusion: This study demonstrated that the PRAC-PTC that integrating clinical data, gene mutation information, immune indices, high-throughput proteomics and machine learning technology in multicentre retrospective and prospective clinical cohorts can effectively stratify the preoperative risk of PTC and may decrease unnecessary surgery or overtreatment., (Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.)

Ethanol is frequently used not only as priming but also as a solvent to dissolve hardly water-soluble phytohormones gibberellic acid (GA 3 ) and abscisic acid (ABA) in seed germination. However, the molecular and physiological mechanisms of ethanol's impact on seed germination remain elusive. In this report, we investigated how ethanol affected reactive oxygen species (ROS) during rice seed germination. Ethanol at a concentration of 3.5% (v/v) inhibited 90% seed germination, which was almost reversed by H 2 O 2 . H 2 O 2 contents in embryos were reduced by ethanol after 18 h imbibition. Antioxidant enzymes assays revealed that only superoxide dismutase (SOD) activities in seed embryos were lowered by ethanol, in line with the suppressed mRNA expression of SOD genes during imbibition. Additionally, compared to the mock condition, ethanol increased ABA contents but decreased GA (GA 1 and GA 3 ) in seed embryos, resulting in disharmonizing GA/ABA balance. Conceivably ethanol induced transcription of OsNCEDs, the key genes for ABA biosynthesis, and OsABA8ox3, a key gene for ABA catabolism. Furthermore, ethanol promoted ABA signaling by upregulating ABA receptor genes and ABA-responsive element (ABRE)-binding protein/ABRE-binding factors during imbibition. Overall, our results demonstrate that lowering of H 2 O 2 levels due to suppressed SOD activities in rice germinating seed embryos is the decisive factor for ethanol-induced inhibition of seed germination, and GA/ABA balance and ABA signaling also play important roles in ethanol's inhibitory impact on seed germination., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier GmbH. All rights reserved.)

Spiking neural networks (SNNs) have shown advantages in computation and energy efficiency over traditional artificial neural networks (ANNs) thanks to their event-driven representations. SNNs also replace weight multiplications in ANNs with additions, which are more energy-efficient and less computationally intensive. However, it remains a challenge to train deep SNNs due to the discrete spiking function. A popular approach to circumvent this challenge is ANN-to-SNN conversion. However, due to the quantization error and accumulating error, it often requires lots of time steps (high inference latency) to achieve high performance, which negates SNN's advantages. To this end, this paper proposes Fast-SNN that achieves high performance with low latency. We demonstrate the equivalent mapping between temporal quantization in SNNs and spatial quantization in ANNs, based on which the minimization of the quantization error is transferred to quantized ANN training. With the minimization of the quantization error, we show that the sequential error is the primary cause of the accumulating error, which is addressed by introducing a signed IF neuron model and a layer-wise fine-tuning mechanism. Our method achieves state-of-the-art performance and low latency on various computer vision tasks, including image classification, object detection, and semantic segmentation. Codes are available at: https://github.com/yangfan-hu/Fast-SNN.

Objective: To evaluate the clinical effect of subcapsular saline injection (SCASI) after total thyroidectomy., Methods: A total of 77 patients who underwent total thyroidectomy in our hospital from January 2020 to December 2021 were selected and divided into the SCASI group (n = 43) and the non-SCASI group (n = 34). The general clinical data of the patients were collected, and serum parathyroid hormone (PTH) and serum calcium levels were determined preoperatively, on the 1st postoperative day, and at 1 and 6 months after the operation. These data were compared between groups., Results: There was no significant difference in postoperative complications between the two groups. The PTH and serum calcium levels in the SCASI group were significantly higher than those in the non-SCASI group on the 1st postoperative day (t = 2.340, 5.208, both P < 0.05), and the PTH levels in the SCASI group at 1 month after the operation were higher than those in the non-SCASI group (t = 2.141, P < 0.05). In addition, the proportion of transient and permanent hypoparathyroidism in the SCASI group was significantly decreased (χ 2  = 3.920, 3.948, P < 0.05)., Conclusion: Total thyroidectomy requires high surgical precision, and SCASI can reduce the incidence of temporary and permanent hypoparathyroidism., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Artificial aeration is a widely used approach in wastewater treatment to enhance the removal of pollutants, however, traditional aeration techniques have been challenging due to the low oxygen transfer rate (OTR). Nanobubble aeration has emerged as a promising technology that utilise nano-scale bubbles to achieve higher OTRs owing to their large surface area and unique properties such as longevity and reactive oxygen species generation. This study, for the first time, investigated the feasibility of coupling nanobubble technology with constructed wetlands (CWs) for treating livestock wastewater. The results demonstrated that nanobubble-aerated CWs achieved significantly higher removal efficiencies of total organic carbon (TOC) and ammonia (NH 4 + -N), at 49 % and 65 %, respectively, compared to traditional aeration treatment (36 % and 48 %) and the control group (27 % and 22 %). The enhanced performance of the nanobubble-aerated CWs can be attributed to the nearly three times higher amount of nanobubbles (Ø < 1 μm) generated from the nanobubble pump (3.68 × 10 8 particles/mL) compared to the normal aeration pump. Moreover, the microbial fuel cells (MFCs) embedded in the nanobubble-aerated CWs harvested 5.5 times higher electricity energy (29 mW/m 2 ) compared to the other groups. The results suggested that nanobubble technology has the potential to trigger the innovation of CWs by enhancing their capacity for water treatment and energy recovery. Further research needs are proposed to optimise the generation of nanobubbles, allowing them to be effectively coupled with different technologies for engineering implementation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Emerging memories have been developed as new physical infrastructures for hosting neural networks owing to their low-power analog computing characteristics. However, accurately and efficiently programming devices in an analog-valued array is still largely limited by the intrinsic physical non-idealities of the devices, thus hampering their applications in in-situ training of neural networks. Here, we demonstrate a passive electrochemical memory (ECRAM) array with many important characteristics necessary for accurate analog programming. Different image patterns can be open-loop and serially programmed into our ECRAM array, achieving high programming accuracies without any feedback adjustments. The excellent open-loop analog programmability has led us to in-situ train a bilayer neural network and reached software-like classification accuracy of 99.4% to detect poisonous mushrooms. The training capability is further studied in simulation for large-scale neural networks such as VGG-8. Our results present a new solution for implementing learning functions in an artificial intelligence hardware using emerging memories., (© 2023. Springer Nature Limited.)

Combining multimodal approaches with functional magnetic resonance imaging (fMRI) has catapulted the research on brain circuitries of non-human primates (NHPs) into a new era. However, many studies are constrained by a lack of appropriate RF coils. In this study, a single loop transmit and 16-channel receive array coil was constructed for brain imaging of macaques at 7 Tesla (7 T). The 16 receive channels were mounted on a 3D-printed helmet-shaped form closely fitting the macaque head, with fourteen openings arranged for multimodal devices around the cortical regions. Coil performance was evaluated by quantifying and comparing signal-to-noise ratio (SNR) maps, noise correlations, g-factor maps and flip-angle maps with a 28-channel commercial knee coil. The in vivo results suggested that the macaque coil has higher SNR in cortical regions and better acceleration ability in parallel imaging, which may benefit revealing mesoscale organizations in the brain., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Objective. Spike sorting, a critical step in neural data processing, aims to classify spiking events from single electrode recordings based on different waveforms. This study aims to develop a novel online spike sorter, NeuSort, using neuromorphic models, with the ability to adaptively adjust to changes in neural signals, including waveform deformations and the appearance of new neurons. Approach. NeuSort leverages a neuromorphic model to emulate template-matching processes. This model incorporates plasticity learning mechanisms inspired by biological neural systems, facilitating real-time adjustments to online parameters. Results. Experimental findings demonstrate NeuSort's ability to track neuron activities amidst waveform deformations and identify new neurons in real-time. NeuSort excels in handling non-stationary neural signals, significantly enhancing its applicability for long-term spike sorting tasks. Moreover, its implementation on neuromorphic chips guarantees ultra-low energy consumption during computation. Significance. NeuSort caters to the demand for real-time spike sorting in brain-machine interfaces through a neuromorphic approach. Its unsupervised, automated spike sorting process makes it a plug-and-play solution for online spike sorting., (© 2023 IOP Publishing Ltd.)

In this article, we propose a Thompson sampling algorithm with Gaussian prior for unimodal bandit under Gaussian reward setting, where the expected reward is unimodal over the partially ordered arms. To exploit the unimodal structure better, at each step, instead of exploration from the entire decision space, the proposed algorithm makes decisions according to posterior distribution only in the arm's neighborhood with the highest empirical mean estimate. We theoretically prove that the asymptotic regret of our algorithm reaches O(logT) , i.e., it shares the same regret order with asymptotic optimal algorithms, which is comparable to extensive existing state-of-the-art unimodal multiarm bandit (U-MAB) algorithms. Finally, we use extensive experiments to demonstrate the effectiveness of the proposed algorithm on both synthetic datasets and real-world applications.

High temperature (HT) stress at reproductive stage is one of most important environment negatively affecting spikelet fertility and rice yield. In this study, the effect of HT exposure on the sugar composition and carbohydrate metabolism in developing anthers and its relation to floret fertility and pollen viability were investigated by different temperature regimes under well-controlled climatic condition. Result showed that HT exposure during microspore development significantly reduced the starch deposition in developing anther and evidently disrupted the spatial distribution of sugar and starch concentrations in different compartments of rice anther, with the higher ratio of sucrose to hexose concentrations in HT-stressed anthers relative to the control ones. Under HT exposure, the amount of starch deposition in the fraction of sporophytic tissues dropped evidently, while the concentrations of sucrose and starch in anther wall tissue enhanced significantly, suggesting that HT exposure impaired the translocation of sucrose from the anther wall tissue to the sporophytic tissues inside rice anther. Furthermore, we presented possible contribution of various genes and key enzymes involving in sugar conversion and carbohydrate metabolism in developing anther to the formation of HT-induced pollen abortion by disrupting the sugar utilization in HT-stressed anther. HT exposure suppressed the activities of cell wall and vacuolar invertase, hexokinase, and ADP-glucose pyrophosphorylase in developing anther, while it was opposite for the effect of HT exposure on sucrose synthase and fructokinase. HT-induced suppression of OsCWIN3 in the anther walls might be strongly responsible for the HT-induced impairments of sugar utilization in HT-stressed anthers., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Masson SAS. All rights reserved.)

Brain-computer interfaces (BCIs) provide a direct pathway from the brain to external devices and have demonstrated great potential for assistive and rehabilitation technologies. Endogenous BCIs based on electroencephalogram (EEG) signals, such as motor imagery (MI) BCIs, can provide some level of control. However, mastering spontaneous BCI control requires the users to generate discriminative and stable brain signal patterns by imagery, which is challenging and is usually achieved over a very long training time (weeks/months). Here, we propose a human-machine joint learning framework to boost the learning process in endogenous BCIs, by guiding the user to generate brain signals toward an optimal distribution estimated by the decoder, given the historical brain signals of the user. To this end, we first model the human-machine joint learning process in a uniform formulation. Then a human-machine joint learning framework is proposed: 1) for the human side, we model the learning process in a sequential trial-and-error scenario and propose a novel "copy/new" feedback paradigm to help shape the signal generation of the subject toward the optimal distribution and 2) for the machine side, we propose a novel adaptive learning algorithm to learn an optimal signal distribution along with the subject's learning process. Specifically, the decoder reweighs the brain signals generated by the subject to focus more on "good" samples to cope with the learning process of the subject. Online and psuedo-online BCI experiments with 18 healthy subjects demonstrated the advantages of the proposed joint learning process over coadaptive approaches in both learning efficiency and effectiveness.

Spiking neural networks (SNNs) have received significant attention for their biological plausibility. SNNs theoretically have at least the same computational power as traditional artificial neural networks (ANNs). They possess the potential of achieving energy-efficient machine intelligence while keeping comparable performance to ANNs. However, it is still a big challenge to train a very deep SNN. In this brief, we propose an efficient approach to build deep SNNs. Residual network (ResNet) is considered a state-of-the-art and fundamental model among convolutional neural networks (CNNs). We employ the idea of converting a trained ResNet to a network of spiking neurons named spiking ResNet (S-ResNet). We propose a residual conversion model that appropriately scales continuous-valued activations in ANNs to match the firing rates in SNNs and a compensation mechanism to reduce the error caused by discretization. Experimental results demonstrate that our proposed method achieves state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet 2012 with low latency. This work is the first time to build an asynchronous SNN deeper than 100 layers, with comparable performance to its original ANN.

Biometric features, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these biometric features are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on electroencephalogram (EEG), which is usually demonstrated unstable performance due to the low signal-to-noise ratio (SNR). For the first time, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of the high-performance brain biometrics. Specifically, we put forward a novel brain-based key generation approach called multidimensional Gaussian fitted bit allocation (MGFBA). The proposed MGFBA method extracts keys from the local field potential of ten rats with high reliability and high entropy. We found that with the proposed MGFBA, the average effective key length of the brain biometrics was 938 bits, while achieving high authentication accuracy of 88.1% at a false acceptance rate of 1.9%, which is significantly improved compared to conventional EEG-based approaches. In addition, the proposed MGFBA-based keys can be conveniently revoked using different motor behaviors with high entropy. Experimental results demonstrate the potential of using intracortical brain signals for reliable authentication and other security applications.

The triple-negative breast cancer (TNBC) subtype is the most aggressive type of breast cancer with a low survival prognosis and high recurrence rate. There is currently no effective treatment to improve it. In this work, we explored the effect of a synthetic compound named WXJ-103 on several aspects of TNBC biology. The human breast cancer cell lines MDA-MB-231 and MCF-7 were used in the experiments, and the cell viability was detected by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide method, and the cell migration and invasion abilities were detected by wound healing assay and Transwell invasion assay. Cell cycle and apoptosis experiments were analyzed by flow cytometry, and protein levels related to cyclin-dependent kinase (CDK) 4/6-cyclin D-Rb-E2F pathway were analyzed by western blotting. Then, in-vivo experiments were performed to determine the clinical significance and functional role of WXJ-103. The results show that WXJ-103 can inhibit the adhesion, proliferation, migration, and invasion of TNBC cells, and can arrest the cell cycle in G1 phase. The levels of CDK4/6-cyclin D-Rb-E2F pathway-related proteins such as CDK6 and pRb decreased in a dose-dependent manner. Therefore, the antitumor activity of WXJ-103 may depend on the inhibition of CDK4/6-cyclin D1-Rb-E2F pathway. This research shows that WXJ-103 may be a new promising antitumor drug, which can play an antitumor effect on TNBC and provide new ideas for the treatment of TNBC., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: Blood-oxygen-level-dependent functional MRI allows to investigte neural activities and connectivity. While the non-human primate plays an essential role in neuroscience research, multimodal methods combining functional MRI with other neuroimaging and neuromodulation enable us to understand the brain network at multiple scales., Approach: In this study, a tight-fitting helmet-shape receive array with a single transmit loop for anesthetized macaque brain MRI at 7T was fabricated with four openings constructed in the coil housing to accommodate multimodal devices, and the coil performance was quantitatively evaluated and compared to a commercial knee coil. In addition, experiments over three macaques with infrared neural stimulation (INS), focused ultrasound stimulation (FUS), and transcranial direct current stimulation (tDCS) were conducted., Main Results: The RF coil showed higher transmit efficiency, comparable homogeneity, improved SNR and enlarged signal coverage over the macaque brain. Infrared neural stimulation was applied to the amygdala in deep brain region, and activations in stimulation sites and connected sites were detected, with the connectivity consistent with anatomical information. Focused ultrasound stimulation was applied to the left visual cortex, and activations were acquired along the ultrasound traveling path, with all time course curves consistent with pre-designed paradigms. The existence of transcranial direct current stimulation electrodes brought no interference to the RF system, as evidenced through high-resolution MPRAGE structure images., Significance: This pilot study reveals the feasibility for brain investigation at multiple spatiotemporal scales, which may advance our understanding in dynamic brain networks., Competing Interests: Declaration of Competing Interest The Authors declare no Competing Financial or Non-Financial Interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Biometrics, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these traditional biometrics are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on an electroencephalogram (EEG), which typically demonstrates unstable performance due to the low signal-to-noise ratio (SNR). Thus, in this paper, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of a high-performance brain biometric. Significantly, this is the first study to investigate the features of intracortical brain signals for identification. Specifically, several features based on local field potential are computed for identification, and their performance is compared with different machine learning algorithms. The results show that frequency domain features and time-frequency domain features are excellent for intra-day and inter-day identification. Furthermore, the energy features perform best among all features with 98% intra-day and 93% inter-day identification accuracy, which demonstrates the great potential of intracraial brain signals to be biometrics. This paper may serve as a guidance for future intracranial brain researches and the development of more reliable and high-performance brain biometrics.