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Abstract Transposable elements are ubiquitous mobile DNA sequences generating insertion polymorphisms, contributing to genomic diversity. We present GraffiTE, a flexible pipeline to analyze polymorphic mobile elements insertions. By integrating state-of-the-art structural variant detection algorithms and graph genomes, GraffiTE identifies polymorphic mobile elements from genomic assemblies or long-read sequencing data, and genotypes these variants using short or long read sets. Benchmarking on simulated and real datasets reports high precision and recall rates. GraffiTE is designed to allow non-expert users to perform comprehensive analyses, including in models with limited transposable element knowledge and is compatible with various sequencing technologies. Here, we demonstrate the versatility of GraffiTE by analyzing human, Drosophila melanogaster, maize, and Cannabis sativa pangenome data. These analyses reveal the landscapes of polymorphic mobile elements and their frequency variations across individuals, strains, and cultivars.

Abstract Zingiber striolatum Diels is a unique medicine food homology plant native to China. In recent years, due to severe habitat destruction, studying the impact of climate change on the distribution of wild resources is of great significance for the ecological conservation and artificial cultivation of Z. striolatum. This study collected 141 valid species distribution records, and 37 environmental variables, and projected two future climate scenarios (SSP126 and SSP585) for two periods (2050s and 2090s). By employing Pearson analysis, Maximum Entropy Model (MaxEnt), and Geographic Information System (ArcGIS), we predicted the potential suitable habitats for Z. striolatum under present and future climates, as well as identified the dominant environmental variables influencing its distribution. The results indicated that the MaxEnt model performed well (AUC > 0.9) with high accuracy and reliability. The dominant environmental factors included Precipitation of driest quarter (39.0 ~ 473.8 mm), Precipitation of wettest quarter (593.2 ~ 1269.4 mm), Temperature annual range (9.8 ~ 28.6℃), and Mean diurnal range (6.5 ~ 9.6℃). The highly suitable areas for Z. striolatum were mainly distributed in western and southern Yunnan, northern and western Guangxi, Guangdong, Fujian, and central Hainan. Under future climate change, the centroid of the total suitable area for Z. striolatum is projected to shift towards the southwest (Yungui Plateau) at higher elevations.

The prolonged mechanical performance degradation and failure of elastic components in mechanical systems can pose significant safety risks and disrupt related operations. While helical compression springs' mechanical behavior and failure mechanisms at high temperatures have been well-studied, their mechanical properties and microstructural deformation mechanisms at extreme cryogenic temperatures remain largely unexplored. This paper conducts experiments to investigate the detailed mechanical properties and microstructural evolution of Ti-6Al-4V helix springs under such conditions. Initially, we examined the mechanical properties at cryogenic temperatures and compared them with those at high temperatures. It was observed that the logarithmic constitutive model applicable at high temperatures does not suitably describe the spring's mechanical behavior at cryogenic temperatures. At extremely low temperatures, the mechanical properties of the spring were enhanced. Furthermore, we characterized the microstructural deformation mechanisms using electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM), including twinning, slip activation, and dislocation evolution. The findings suggest that the deformation mechanism in Ti-6Al-4V springs under cryogenic conditions is predominantly governed by twinning and basal and prismatic slip. Due to the significant increase in the critical resolved shear stress (CRSS) of the pyramidal slip systems at cryogenic temperatures, these slip systems may not be activated during the deformation process. Additionally, the volume fraction of twinning shows a significant positive correlation with both cryogenic temperature and mechanical stress.

Abstract In this work, we use the AdS/CFT correspondence to study the behavior of a triply heavy baryon within anisotropic backgrounds. Beginning with the total action of the three quarks, we derive the balance equation for the three-quark system and compute the separation distance and potential energy. Our results reveal a consistent decrease in both the separation distance and potential energy for the A configuration and the B configuration as the anisotropy coefficient a increases. This suggests that the presence of an anisotropic background promotes the dissolution of the three-quark system. Additionally, we compare the potential energies of the A and B configurations and observe that the A configuration has a slightly smaller potential energy, suggesting greater stability compared to the B configuration.

We demonstrate an all-optical method that online produces “microstructured” targets by manipulating a Laguerre-Gaussian (LG) prepulse. Besides the enhancement of the proton number, the cutoff energy is nearly doubled. Such transient “microstructured” targets are characterized using both offline and online approaches. Based on probe experiments and hydrodynamic simulations, we develop a simple model to describe the electron density distribution upon the arrival of the main pulse. Two-dimensional particle-in-cell simulations reveal that the annular plasma acts as an oscillating cavity for hot electrons and results in a cascaded acceleration effect. The combination of low-intensity LG prepulses and ultraintense Gaussian pulses opens another avenue for fine-tuning laser-plasma interactions spatiotemporally in interdisciplinary applications.

Background: The reference intervals (RIs) is defined as the central 95 % range of reference values from healthy individuals. The establishment of appropriate medical RIs for specific populations is crucial for accurate diagnosis and treatment of disease. However, the RIs for 25-hydroxyvitamin D (25(OH)D) in Chinese pediatric individuals are currently not available. This retrospective study aimed to establish pediatric RIs for serum 25-hydroxyvitamin D (25(OH)D) in the Nanjing area of China. Methods: After data filtering and deletion of outliers, 133,562 serum 25(OH)D records were finally included in this study. The effects of age, sex, and season on 25(OH)D levels were assessed, and the 2.5 % and 97.5 % percentile points were applied as the lower limit and upper limit of the RIs, respectively. Results: Age-distribution analysis of serum 25(OH)D levels revealed that children aged 4–12 months old hold the highest 25(OH)D levels, and levels subsequently decreased with age while remaining relatively stable in children aged 7–15 years old. An analysis of sex-specific differences demonstrated that serum 25(OH)D levels in girls were significantly higher than those of boys 7 years old (P

Alveolar bone defect repair remains a persistent clinical challenge for periodontitis treatment. The use of peripheral functional seed cells is a hot topic in periodontitis. Herein, we explored the cellular behaviors and osteogenic ability of adipose-derived mesenchymal stem cells (ADSCs) treated with black phosphorus quantum dots (BPQDs). Additionally, macrophage polarization, osteogenic effects and angiogenesis were investigated through the paracrine pathway regulated by BPQD-modified ADSCs. Our results demonstrated that BPQDs showed good biocompatibility with ADSCs and BPQD-modified ADSCs could improve the bone repair in vivo inflammatory microenvironment by regulating osteogenesis and osteoimmunomodulation. The BPQDs increased the osteogenic differentiation of ADSCs via the Wnt/β-catenin and BMP2/SMAD5/Runx2 signaling pathway. In addition, BPQD-modified ADSCs promoted the osteogenic effect of BMSCs and facilitated the polarization of macrophages from M1 towards M2 phenotype transformation through the paracrine pathway in the periodontitis microenvironment. This strategy provides a novel idea for treatment of alveolar bone defects for periodontitis in the foreseeable future.

Background: Limb remote ischemic postconditioning (LRIP) and paeoniflorin (PF) both can ameliorate cerebral ischemia reperfusion (I/R) injury. At present, whether LRIP combined with PF can achieve better therapeutic effect is unknown. Purpose: This study explored the alleviating effect and mechanism of LRIP in combination with PF on cerebral I/R injury in rats. Methods: Middle cerebral artery occlusion (MCAO) surgery was performed on rats except Sham group. Then PF (2.5 mg/kg, 5 mg/kg, 10 mg/kg) was administrated by intraperitoneal injection 10 min before the start of reperfusion. LRIP was operated on the left femoral artery at 0 h of reperfusion. Behavioral testing was used to assess neurological impairment, while TTC staining was used to examine infarct volume. Protein expression of MyD88, TRAF6, p38-MAPK and phosphorylation of p47phox in neutrophils from rat peripheral blood were tested by Western blot. Rat bone marrow neutrophils were extracted and incubated for 24 h with serum from rats after LRIP combined with PF. p38 MAPK inhibitor group was administrated SB203580 while the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase inhibitor group was administrated Apocynin. Neutrophils were stimulated by fMLP (10 μM). Reactive oxygen species (ROS) production and protein expression of MyD88, TRAF6, p38 MAPK, and p47phox (ser 304 and ser 345) were detected. Results: LRIP combined with PF (5 mg/kg) reduced cerebral infarct volume, ameliorated neurological deficit score (NDS), decreased fMLP-stimulated ROS release and downregulated the protein expression of MyD88, TRAF6, p38-MAPK and phosphorylation of p47phox (ser 304 and ser 345) in neutrophils. Conclusion: The protective effect of LRIP combined with PF on cerebral I/R injury was better than either alone. Taken together, we provided solid evidence to demonstrate that the combination of LRIP and PF had potential to alleviate cerebral I/R injury, which was regulated by MyD88-TRAF6-p38 MAPK pathway and neutrophil NADPH oxidase pathway.

The newly built Compact Laser Plasma Accelerator–Therapy facility at Peking University will deliver 60 J/1 Hz laser pulses with 30 fs duration. Driven by this petawatt laser facility, proton beams with energy up to 200 MeV are expected to be generated for tumor therapy. During high-repetition operation, both prompt radiation and residual radiation may cause safety problems. Therefore, human radiological safety assessment before commissioning is essential. In this paper, we simulate both prompt and residual radiation using the Geant4 and FLUKA Monte Carlo codes with reasonable proton and as-produced electron beam parameters. We find that the prompt radiation can be shielded well by the concrete wall of the experimental hall, but the risk from residual radiation is nonnegligible and necessitates adequate radiation cooling. On the basis of the simulation results, we discuss the constraints imposed by radiation safety considerations on the annual working time, and we propose radiation cooling strategies for different shooting modes.

Hand gesture recognition (HGR) plays a vital role in human-computer interaction. The integration of high-density surface electromyography (HD-sEMG) and deep neural networks (DNNs) has significantly improved the robustness and accuracy of HGR systems. These methods are typically effective for a fixed set of trained gestures. However, the need for new gesture classes over time poses a challenge. Introducing new classes to DNNs can lead to a substantial decrease in accuracy for previously learned tasks, a phenomenon known as “catastrophic forgetting,” especially when the training data for earlier tasks is not retained and retrained. This issue is exacerbated in embedded devices with limited storage, which struggle to store the large-scale data of HD-sEMG. Class-incremental learning (CIL) is an effective method to reduce catastrophic forgetting. However, existing CIL methods for HGR rarely focus on reducing memory load. To address this, we propose a memory-friendly CIL method for HGR using HD-sEMG. Our approach includes a lightweight convolutional neural network, named SeparaNet, for feature representation learning, coupled with a nearest-mean-of-exemplars classifier for classification. We introduce a priority exemplar selection algorithm inspired by the herding effect to maintain a manageable set of exemplars during training. Furthermore, a task-equal-weight exemplar sampling strategy is proposed to effectively reduce memory load while preserving high recognition performance. Experimental results on two datasets demonstrate that our method significantly reduces the number of retained exemplars to only a quarter of that required by other CIL methods, accounting for less than 5 ​% of the total samples, while still achieving comparable average accuracy.

Remote sensing (RS) visual question answering (VQA) is a task that answers questions about a given RS image by utilizing both image and textual information. However, existing methods in RS VQA overlook the fact that the ground truths in RS VQA benchmark datasets, which are algorithmically generated rather than manually annotated, may not always represent the most reasonable answers to the questions. In this article, we propose a multimodal momentum distillation model (RSMoDM) for RS VQA tasks. Specifically, we maintain the momentum distillation model during the training stage that generates stable and reliable pseudolabels for additional supervision, effectively preventing the model from being penalized for producing other reasonable outputs that differ from ground truth. Additionally, to address domain shift in RS, we employ the Vision Transformer (ViT) trained on a large-scale RS dataset for enhanced image feature extraction. Moreover, we introduce the multimodal fusion module with cross-attention for improved cross-modal representation learning. Our extensive experiments across three different RS VQA datasets demonstrate that RSMoDM achieves state-of-the-art performance, particularly excelling in scenarios with limited training data. The strong interpretability of our method is further evidenced by visualized attention maps.

Steady-state visual evoked potential (SSVEP) is one of the most used brain-computer interface (BCI) paradigms. Conventional methods analyze SSVEPs at a fixed window length. Compared with these methods, dynamic window methods can achieve a higher information transfer rate (ITR) by selecting an appropriate window length. These methods dynamically evaluate the credibility of the result by linear discriminant analysis (LDA) or Bayesian estimation and extend the window length until credible results are obtained. However, the hypotheses introduced by LDA and Bayesian estimation may not align with the collected real-world SSVEPs, which leads to an inappropriate window length. To address the issue, we propose a novel dynamic window method based on reinforcement learning (RL). The proposed method optimizes the decision of whether to extend the window length based on the impact of decisions on the ITR, without additional hypotheses. The decision model can automatically learn a strategy that maximizes the ITR through trial and error. In addition, compared with traditional methods that manually extract features, the proposed method uses neural networks to automatically extract features for the dynamic selection of window length. Therefore, the proposed method can more accurately decide whether to extend the window length and select an appropriate window length. To verify the performance, we compared the novel method with other dynamic window methods on two public SSVEP datasets. The experimental results demonstrate that the novel method achieves the highest performance by using RL.

The DC micro-grid system, as a new generation of shipboard DC micro-grid system, has the advantages of integrating renewable energy and enhancing the stability and reliability of the power system. For the energy distribution problem of energy storage battery charging and discharging in shipboard DC micro-grid, P-V voltage droop control and SOC-I current droop control methods are proposed. The SOC-I control method incorporated in the battery charging and discharging process can effectively balance the SOC and output power of the energy storage battery. For the on/off-grid process of shipboard power converter and onshore power, the control method of voltage and current control mode switching is proposed, which realizes the fast response of output voltage in off-grid mode and the controllable transfer of power interacting with the grid in grid-connected mode. Secondly, a pre-synchronized control based on amplitude and frequency modulation achieves a flexible on-grid/off-grid connection of the power converter. A method based on the parallel connection of multiple reduced-order generalized integrator (ROGI) is introduced to achieve suppression of output voltage unbalance as well as $5th$ and $7th$ harmonics. Finally, the efficiency and correctness of the proposed control method has been successfully evaluated an level 750KVA shipboard micro-grid system.

Sortilin-related receptor 1 (SORL1) is a critical gene associated with late-onset Alzheimer’s disease. SORL1 contributes to the development and progression of this neurodegenerative condition by affecting the transport and metabolism of intracellular β-amyloid precursor protein. To better understand the underlying mechanisms of SORL1 in the pathogenesis of late-onset Alzheimer’s disease, in this study, we established a mouse model of Sorl1 gene knockout using clustered regularly interspaced short palindromic repeats-associated protein 9 technology. We found that Sorl1-knockout mice displayed deficits in learning and memory. Furthermore, the expression of brain-derived neurotrophic factor was significantly downregulated in the hippocampus and cortex, and amyloid β-protein deposits were observed in the brains of Sorl1-knockout mice. In vitro, hippocampal neuronal cell synapses from homozygous Sorl1-knockout mice were impaired. The expression of synaptic proteins, including Drebrin and NR2B, was significantly reduced, and also their colocalization. Additionally, by knocking out the Sorl1 gene in N2a cells, we found that expression of the N-methyl-D-aspartate receptor, NR2B, and cyclic adenosine monophosphate-response element binding protein was also inhibited. These findings suggest that SORL1 participates in the pathogenesis of late-onset Alzheimer’s disease by regulating the N-methyl-D-aspartate receptor NR2B/cyclic adenosine monophosphate-response element binding protein signaling axis.

Abstract In this paper, we employ gauge/gravity duality to investigate the string breaking and melting of doubly-heavy tetraquark that includes two heavy quarks and two light antiquarks in a holographic model at finite temperature. Firstly, four different configurations of $${{\textrm{QQ}}}\bar{{\textrm{q}}}\bar{{\textrm{q}}}$$ QQ q ¯ q ¯ are studied at different separation distances of the heavy quarks at finite temperatures. At high temperature, $${{\textrm{QQ}}}\bar{{\textrm{q}}}\bar{{\textrm{q}}}$$ QQ q ¯ q ¯ will melt at certain distances and the screening distance has been given for different temperatures. As the temperature continues to increase, some configurations of doubly-heavy tetraquark can not exist. Furthermore, we investigate three decay modes of $${{\textrm{QQ}}}\bar{{\textrm{q}}}\bar{{\textrm{q}}}$$ QQ q ¯ q ¯ and compare the potential energy of $${{\textrm{QQ}}}\bar{{\textrm{q}}}\bar{{\textrm{q}}}$$ QQ q ¯ q ¯ with that of $${\textrm{QQq}}$$ QQq at finite temperature.

Abstract According to gauge/gravity correspondence, we study the holographic Schwinger effect within an anisotropic background. Firstly, the separate length of the particle–antiparticle pairs is computed within the context of an anisotropic background which is parameterized by dynamical exponent $$\nu $$ ν . It is found that the maximum separate length x increases with the increase of dynamical exponent $$\nu $$ ν . By analyzing the potential energy, we find that the potential barrier increases with the dynamical exponent $$\nu $$ ν at a small separate distance. This observation implies that the Schwinger effect within an anisotropic background is comparatively weaker when contrasted with its manifestation in an isotropic background. Finally, we also find that the Schwinger effect in the transverse direction is weakened compared to the parallel direction in the anisotropic background, which is consistent with the top-down model.

Deep learning (DL)-based methods have been successfully employed as asynchronous classification algorithms in the steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) system. However, these methods often suffer from the limited amount of electroencephalography (EEG) data, leading to overfitting. This study proposes an effective data augmentation approach called EEG mask encoding (EEG-ME) to mitigate overfitting. EEG-ME forces models to learn more robust features by masking partial EEG data, leading to enhanced generalization capabilities of models. Three different network architectures, including an architecture integrating convolutional neural networks (CNN) with Transformer (CNN-Former), time domain-based CNN (tCNN), and a lightweight architecture (EEGNet) are utilized to validate the effectiveness of EEG-ME on publicly available benchmark and BETA datasets. The results demonstrate that EEG-ME significantly enhances the average classification accuracy of various DL-based methods with different data lengths of time windows on two public datasets. Specifically, CNN-Former, tCNN, and EEGNet achieve respective improvements of 3.18%, 1.42%, and 3.06% on the benchmark dataset as well as 11.09%, 3.12%, and 2.81% on the BETA dataset, with the 1-second time window as an example. The enhanced performance of SSVEP classification with EEG-ME promotes the implementation of the asynchronous SSVEP-BCI system, leading to improved robustness and flexibility in human-machine interaction.

Convolutional neural networks (CNNs) have been successfully applied to motor imagery (MI)-based brain–computer interface (BCI). Nevertheless, single-scale CNN fail to extract abundant information over a wide spectrum from EEG signals, while typical multi-scale CNNs cannot effectively fuse information from different scales with concatenation-based methods. To overcome these challenges, we propose a new scheme equipped with attention-based dual-scale fusion convolutional neural network (ADFCNN), which jointly extracts and fuses EEG spectral and spatial information at different scales. This scheme also provides novel insight through self-attention for effective information fusion from different scales. Specifically, temporal convolutions with two different kernel sizes identify EEG $\boldsymbol{\mu }$ and $\boldsymbol{\beta }$ rhythms, while spatial convolutions at two different scales generate global and detailed spatial information, respectively, and the self-attention mechanism performs feature fusion based on the internal similarity of the concatenated features extracted by the dual-scale CNN. The proposed scheme achieves the superior performance compared with state-of-the-art methods in subject-specific motor imagery recognition on BCI Competition IV dataset 2a, 2b and OpenBMI dataset, with the cross-session average classification accuracies of 79.39% and significant improvements of 9.14% on BCI-IV2a, 87.81% and 7.66% on BCI-IV2b, 65.26% and 7.2% on OpenBMI dataset, and the within-session average classification accuracies of 86.87% and significant improvements of 10.89% on BCI-IV2a, 87.26% and 8.07% on BCI-IV2b, 84.29% and 5.17% on OpenBMI dataset, respectively. What is more, ablation experiments are conducted to investigate the mechanism and demonstrate the effectiveness of the dual-scale joint temporal-spatial CNN and self-attention modules. Visualization is also used to reveal the learning process and feature distribution of the model.

To investigate the propagation behavior of thermal cracks on the wheel tread under the conditions of long downhill ramps, a three-dimensional finite element model of a 1/16 wheel, including an initial thermal crack, was developed using the finite element software ANSYS 17.0. The loading scenarios considered include mechanical wheel–rail loads, both with and without the superposition of thermal wheel–brake shoe friction loads. The virtual crack closure method (VCCM) is employed to analyze the variations in stress intensity factors (SIFs) for Modes I, II, and III (KI, KII, and KIII) at the 0°, mid, and 90° positions along the crack tip. The simulation results show that temperature is a critical factor for the propagation of thermal cracks. Among the SIFs, KII (Mode II) is larger than KI (Mode I) and KIII (Mode III). Specifically, the thermal load on the wheel tread during braking contributes up to 23.83% to KII when the wheel tread reaches the martensitic phase transition temperature due to brake failure. These results are consistent with the observed radial propagation of thermal cracks in wheel treads under operational conditions.

Background: The effects of hydroxychloroquine (HCQ) on maternal and fetal outcomes in pregnant patients with systemic lupus erythematosus (SLE) have not been fully elucidated in recent research. This study aimed to provide a more comprehensive evaluation of the efficacy and safety of HCQ in pregnant patients with SLE. Methods: A systematic search of relevant articles was conducted using Medline, Embase, Scopus, Web of Science, and the Cochrane Library from inception to October 4th, 2023. The risk of bias in the included studies was assessed using the Newcastle-Ottawa Scale or Cochrane's Quality Assessment Form. Data on maternal and fetal outcomes of the HCQ treatment groups (HCQ+) and control groups (HCQ–) were extracted, analyzed and evaluated. Pooled odds ratios (ORs), pooled relative risks (RRs), and weighted mean differences with 95% confidence intervals (95% CIs) were calculated for the meta-analysis using Stata/MP V.18. Results: This meta-analysis included 17 studies, involving a total of 2238 pregnant patients with SLE. Compared to the HCQ– groups, the HCQ+ groups exhibited a significant reduction in the incidence of preeclampsia (RR = 0.51, 95% CI: 0.37–0.71), high lupus activity (RR = 0.77, 95% CI: 0.64–0.92), gestational hypertension (OR = 0.41, 95% CI: 0.18–0.91), premature delivery (RR = 0.71, 95% CI: 0.60–0.84), and fetal growth restriction (OR = 0.61, 95% CI: 0.43–0.85). Additionally, the HCQ+ groups exhibited a significant increase in fetal birth weight of 0.21 kg (95% CI: 0.09–0.33). No significant differences were revealed in the other 15 pregnancy outcomes between the two groups. Conclusions: Despite the presence of a certain heterogeneity among the included studies, this study provides a more comprehensive evaluation of the efficacy and safety of HCQ in pregnant patients with SLE.

Abstract Multi‐needle electrospinning is a simple and general method for mass preparation of nanofiber membrane, which has great industrial potential. However, the bending instability produced in the electrospinning process makes that the deposition uniformity of the nanofiber is still a big concern, resulting in non‐uniform nanofiber membrane, which seriously affects the application of electrospun membrane in environmental filtration, new energy and medical fields. In order to improve the uniformity of nanofiber deposition in multi‐needle electrospinning, an auxiliary flow field system (AFF) is proposed, which can effectively improve the uniformity of nanofiber deposition. After image processing, the uniformity of nanofiber deposition is quantified with the index of grey distribution, and the effectiveness of this method is verified. Combined with the multi‐physical field analysis, the influence mechanism of cross‐wind field on the uniformity of fibre deposition was revealed. By optimizing the experimental parameters, the non‐uniformity of nanofiber deposition was reduced by 49.19%. Based on multi‐needle electrospinning technology, a reliable idea (AFF) and experimental basis are provided for the uniform preparation of nanofiber membrane.

Clinical guidelines for gastric cancer treatment recommend intraoperative peritoneal lavage cytology to detect free cancer cells. Patients with positive cytology require neoadjuvant chemotherapy instead of instant resection, and conversion to negative cytology results in improved survival. However, pathologists’ or artificial intelligence's accuracy of cytological diagnosis is disturbed by manually produced, unstandardized slides. In addition, the elaborate infrastructure makes cytology accessible to a limited number of medical institutes. This work develops CellGAN, a deep learning method that enables label‐free virtual peritoneal lavage cytology by producing virtual hematoxylin–eosin‐stained images with single‐color stimulated Raman scattering microscopy. A structural similarity loss is introduced to overcome the challenge of unsupervised virtual pathology techniques that cannot accurately present cellular structures. This method achieves a structural similarity of 0.820 ± 0.041 and a nucleus area consistency of 0.698 ± 0.102, indicating the staining fidelity outperforms the state‐of‐the‐art method. Diagnosis using virtually stained cells reaches 93.8% accuracy and substantial consistency with conventional staining. Single‐cell detection and classification on virtual slides achieve a mean average precision of 0.924 and an area under the receiver operating characteristic curve of 0.906, respectively. Collectively, this method achieves standardized and accurate virtual peritoneal lavage cytology and holds great potential for clinical translation.

Abstract Mechanical forces, including flow shear stress, govern fundamental cellular processes by modulating nucleocytoplasmic transport of transcription factors like Yes‐associated Protein (YAP). However, the underlying mechanical mechanism remains elusive. In this study, it is reported that unidirectional flow induces biphasic YAP transport with initial nuclear import, followed by nuclear export as actin cap formation and nuclear stiffening. Conversely, pathological oscillatory flow induces slight actin cap formation, nuclear softening, and sustained YAP nuclear localization. To elucidate the disparately YAP spatiotemporal distribution, a 3D mechanochemical model is developed, which integrates flow sensing, cytoskeleton organization, nucleus mechanotransduction, and YAP transport. The results unveiled that despite the significant localized nuclear stress imposed by the actin cap, its inherent stiffness counteracts the dispersed contractile stress exerted by conventional fibers on the nuclear membrane. Moreover, alterations in nuclear stiffness synergistically regulate nuclear deformation, thereby governing YAP transport. Furthermore, by expanding the single‐cell model to a collective vertex framework, it is revealed that the irregularities in actin cap formation within individual cells have the potential to induce topological defects and spatially heterogeneous YAP distribution in the cellular monolayer. This work unveils a unified mechanism of flow‐induced nucleocytoplasmic transport, providing a linkage between transcription factor localization and mechanical stimulation.

Encoded by PTPN11, the Src-homology 2 domain-containing phosphatase 2 (SHP2) integrates signals from various membrane-bound receptors such as receptor tyrosine kinases (RTKs), cytokine and integrin receptors and thereby promotes cell survival and proliferation. Activating mutations in the PTPN11 gene may trigger signaling pathways leading to the development of hematological malignancies, but are rarely found in solid tumors. Yet, aberrant SHP2 expression or activation has implications in the development, progression and metastasis of many solid tumor entities. SHP2 is involved in multiple signaling cascades, including the RAS-RAF-MEK-ERK-, PI3K-AKT-, JAK-STAT- and PD-L1/PD-1- pathways. Although not mutated, activation or functional requirement of SHP2 appears to play a relevant and context-dependent dichotomous role. This mostly tumor-promoting and infrequently tumor-suppressive role exists in many cancers such as gastrointestinal tumors, pancreatic, liver and lung cancer, gynecological entities, head and neck cancers, prostate cancer, glioblastoma and melanoma. Recent studies have identified SHP2 as a potential biomarker for the prognosis of some solid tumors. Based on promising preclinical work and the advent of orally available allosteric SHP2-inhibitors early clinical trials are currently investigating SHP2-directed approaches in various solid tumors, either as a single agent or in combination regimes. We here provide a brief overview of the molecular functions of SHP2 and collate current knowledge with regard to the significance of SHP2 expression and function in different solid tumor entities, including cells in their microenvironment, immune escape and therapy resistance. In the context of the present landscape of clinical trials with allosteric SHP2-inhibitors we discuss the multitude of opportunities but also limitations of a strategy targeting this non-receptor protein tyrosine phosphatase for treatment of solid tumors.

Tea varieties play a crucial role on the quality formation of matcha. This research aimed to examine the impact of four specific tea plant varieties (Okumidori, Longjing 43, Zhongcha108, and E’Cha 1) on various aspects of matcha, including sensory evaluation, major components, color quality, volatile and non-volatile metabolomic profiles. The findings revealed that the levels of tea polyphenols, ester catechins, nonester catechins, and amino acids varied among these four varieties. Notably, 177 significant different metabolites, such as phenolic acids, flavonoids, tannins, alkaloids were identified among 1383 non-volatile compounds. In addition, 97 key aroma-active compounds were identified based on their odor activity value exceeding 1. Aldehydes, heterocyclic compounds, and ketones were closely associated with the formation of volatile metabolites. Overall, this study enhances our understanding of how different tea plant varieties impact the quality of matcha, and can provide valuable guidance for improving matcha varieties in a favorable direction.

Abstract Background Pancreatic cancer is a highly lethal disease with no effective treatments. Lactobacillus casei (L. casei) and Lactobacillus reuteri (L. reuteri) exhibited therapeutic effects on several cancers, but their roles in pancreatic cancer are unknown. This study aims to explore how L. casei & L. reuteri influence pancreatic cancer and the underlying mechanisms. Methods Pancreatic cancer cells were treated with L. casei & L. reuteri and co-cultured with macrophages in a transwell system in vitro. Pancreatic cancer xenograft model was established and L. casei & L. reuteri was used to treat mice in vivo. MTT, CCK-8 assay or immunohistochemical staining were used to determine the proliferation of pancreatic cancer cells or tumor tissues. Transwell assay was applied to test the migration and invasion of pancreatic cells. RT-qPCR was utilized to assess TLR4 and MyD88 expressions in pancreatic cells or tumor tissues. WB, immunofluorescence staining, or flow cytometry was used to evaluate the M1/M2 polarization of macrophages. Besides, the composition of gut microbiota of tumor-bearing mice was determined by 16 S rRNA sequencing, and ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) untargeted metabolomics was used to evaluate the metabolic profiles of feces. Results L. casei & L. reuteri inhibited the proliferation, migration, invasion of pancreatic cancer cells and pancreatic cancer cell-induced M2 polarization of macrophages by suppressing TLR4. Meanwhile, L. casei & L. reuteri repressed pancreatic cancer growth and promoted M1 macrophage polarization. Besides, L. casei & L. reuteri reduced fecal Alloprevotella and increased fecal azelate and glutamate in nude mice, while TLR4 inhibitor TAK-242 increased Clostridia UCG-014, azelate, uridine, methionine sulfoxide, oxypurinol, and decreased glyceryl monoester in the feces of pancreatic tumor-bearing mice. Fecal oxypurinol and glyceryl monoester levels were positively or negatively associated with gut Clostridia UCG-014 abundance, respectively. Conclusion L. casei & L. reuteri alleviate pancreatic cancer by inhibiting TLR4 to promote macrophage M1 polarization and regulate gut microbial homeostasis.

Sanghuangporus baumi is a kind of medicinal fungus with high activity. The aim of the present study was to investigate the immunoregulatory effects of Sanghuangporus baumi mycelia polysaccharides (SJ) on lipopolysaccharide (LPS)-induced RAW264.7 cells. In this study, Sanghuangporus baumi mycelia polysaccharides (SJ) were taken as the study subjects，and LPS-stimulated RAW264.7 macrophages were used as the cell model. The experiment was divided into three groups: Control group, LPS (model) and LPS+SJ groups. A CCK-8 kit was used to detect the viability of RAW264.7 cells. The effects of SJ on nitric oxide, and cytokines (IL-6, IL-1β, and TNF-α) production in LPS-stimulated RAW264.7 macrophages were measured by enzyme-linked immunoassay assay (ELISA). Furthermore, the effects of SJ on the protein levels of IL-6, TNF-α, IL-1β and inducible nitric oxide synthase (iNOS) were investigated by Western-Blot analysis. The results indicated that SJ increased 20.5%~50% cell viability at different concentrations compared to the control group. SJ at 200 μg/mL potently inhibited the release of nitric oxide and cytokine production in LPS-stimulated RAW264.7 (P

Abstract Radiotherapy is often used to treat various types of cancers, but radioresistance greatly limits the clinical efficiency. Recent studies have shown that radiotherapy can lead to ferroptotic cancer cell deaths. Ferroptosis is a new type of programmed cell death caused by excessive lipid peroxidation. The induction of ferroptosis provides a potential therapeutic strategy for radioresistance. As the most common post-transcriptional modification of mRNA, m6A methylation is widely involved in the regulation of various physiopathological processes by regulating RNA function. Dynamic m6A modification controlled by m6A regulatory factors also affects the susceptibility of cells to ferroptosis, thereby determining the radiosensitivity of tumor cells to radiotherapy. In this review, we summarize the mechanism and significance of radiotherapy induced ferroptosis, analyze the regulatory characteristics of m6A modification on ferroptosis, and discuss the possibility of radiosensitization by enhancing m6A-mediated ferroptosis. Clarifying the regulation of m6A modification on ferroptosis and its significance in the response of tumor cells to radiotherapy will help us identify novel targets to improve the efficacy of radiotherapy and reduce or overcome radioresistance.

Detecting small and densely packed objects in images remains a significant challenge in computer vision. Existing object detection methods often exhibit low accuracy and frequently miss detection when identifying dense small objects and require larger model parameters. This study introduces a novel detection framework designed to address these limitations by integrating advanced feature fusion and optimization techniques. Our approach focuses on enhancing both detection accuracy and parameter efficiency. The approach was evaluated on the open-source VisDrone2019 data set and compared with mainstream algorithms. Experimental results demonstrate a 70.2% reduction in network parameters and a 6.3% improvement in mAP@0.5 over the original YOLOv7 algorithm. These results demonstrate that the enhanced model surpasses existing algorithms in detecting small objects.

In the dynamic and uncertain stock market, precise forecasting and decision-making are crucial for profitability. Traditional deep neural networks (DNN) often struggle with capturing long-term dependencies and multi-scale features in complex financial time series data. To address these challenges, we introduce CMR-DQN, an innovative framework that integrates discrete wavelet transform (DWT) for multi-scale data analysis, temporal convolutional network (TCN) for extracting deep temporal features, and a GRU–LSTM–Attention mechanism to enhance the model’s focus and memory. Additionally, CMR-DQN employs the Rainbow DQN reinforcement learning strategy to learn optimal trading strategies in a simulated environment. CMR-DQN significantly improved the total return rate on six selected stocks, with increases ranging from 20.37% to 55.32%. It also demonstrated substantial improvements over the baseline model in terms of Sharpe ratio and maximum drawdown, indicating increased excess returns per unit of total risk and reduced investment risk. These results underscore the efficiency and effectiveness of CMR-DQN in handling multi-scale time series data and optimizing stock market decisions.

Transition-metal-catalyzed directed C–H functionalization with various carbene precursors has been widely employed for constructing a wide range of complex and diverse active molecules through metal carbene migratory insertion processes. Among various carbene precursors, iodonium ylides serve as a novel and emerging carbene precursor with features including easy accessibility, thermal stability and high activity, which have attracted great attention from organic chemists and have achieved tremendous success in organic transformation. In this review, recent progress on the application of iodonium ylides with multifunctional coupling characteristics in C–H bond activation reactions is summarized, and the potential of iodonium ylides is discussed.

Summary: Schlemm’s canal (SC) functions to maintain proper intraocular pressure (IOP) by draining aqueous humor and has emerged as a promising therapeutic target for glaucoma, the second-leading cause of irreversible blindness worldwide. However, our current understanding of the mechanisms governing SC development and functionality remains limited. Here, we show that vitronectin (VTN) produced by limbal macrophages promotes SC formation and prevents intraocular hypertension by activating integrin αvβ3 signaling. Genetic inactivation of this signaling system inhibited the phosphorylation of AKT and FOXO1 and reduced β-catenin activity and FOXC2 expression, thereby causing impaired Prox1 expression and deteriorated SC morphogenesis. This ultimately led to increased IOP and glaucomatous optic neuropathy. Intriguingly, we found that aged SC displayed downregulated integrin β3 in association with dampened Prox1 expression. Conversely, FOXO1 inhibition rejuvenated the aged SC by inducing Prox1 expression and SC regrowth, highlighting a possible strategy by targeting VTN/integrin αvβ3 signaling to improve SC functionality.

Summary: Low hierarchy and disruptive functionality of carbonaceous materials from biomass pyrolysis restrict their energy and environmental applications. Here, we report a cascade strategy to engineer bagasse into activated porous carbon with regulated morphology and functional groups. The initial pyrolysis of bagasse at 350°C –850°C facilitates curve sheet morphology and random meso-/micro-porosity. The subsequent alkali calcination leads to a micropore and comb-like structure with specific surface area and pore volume of 1,276.7 m2/g and 579.5 × 10−3 cm3/g, respectively. The last acid rinse removes the remaining –OH and –COO groups on the biochar surface. As a result, activated porous carbon processed at 450°C and activated with KOH/HCl exhibits the highest adsorption capacity of 103.4 mg/g at a rapid equilibrium rate fitted with Freundlich adsorption isotherm and facilitates steady mercury removal under pH 3–11. This study provides an effective approach to engineering agricultural waste into superior adsorbents for wastewater remediation. Science for society: Mercury contamination is a significant public health and environmental problem. Cost-effective and high-performance remediation methods have gained attention in both academic and industrial fields. Agricultural waste is an attractive resource as it provides rich carbon components with a modifiable structure to manufacture commercial adsorbents, namely, activated carbon. We present an approach that overcomes the existing issues in the complex structure and composition of activated carbon from waste heating. Highly orderly and regulated biochar fabricated from bagasse—sugarcane pulp—can be used for effective mercury adsorption under different conditions. This approach may facilitate the remediation of mercury-containing wastewater in a sustainable manner.

At present, the mechanism of fluorosis-induced damage to the hepatic system is unclear. Studies have shown that excess fluoride causes some degree of damage to the liver, including inflammation. The SDF-1/CXCR4 signaling axis has been reported to have an impact on the regulation of inflammation in human cells. In this study, we investigated the role of the SDF-1/CXCR4 signaling axis and related inflammatory factors in fluorosis through in vitro experiments on human hepatic astrocytes (LX-2) cultured with sodium fluoride. CCK-8 assays showed that the median lethal dose at 24 h was 2 mmol/l NaF, and these conditions were used for subsequent enzyme-linked immunosorbent assays (ELISAs) and quantitative real-time polymerase chain reaction (qPCR) analysis. The protein expression levels of SDF-1/CXCR4 and the related inflammatory factors nuclear factor-κB (NF-κB), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α) and interleukin 1β (IL-1β) were detected by ELISAs from the experimental and control groups. The mRNA expression levels of these inflammatory indicators were also determined by qPCR in both groups. Moreover, the expression levels of these factors were significantly higher in the experimental group than in the control group at both the protein and mRNA levels (P < 0.05). Excess fluorine may stimulate the SDF-1/CXCR4 signaling axis, activating the inflammatory NF-κB signaling pathway and increasing the expression levels of the related inflammatory factors IL-6, TNF-α and IL-1β. Identification of this mechanism is important for elucidating the pathogenesis of fluorosis-induced liver injury.

To meet the demands of laser-ion acceleration at a high repetition rate, we have developed a comprehensive diagnostic system for real-time and in situ monitoring of liquid sheet targets (LSTs). The spatially resolved rapid characterizations of an LST’s thickness, flatness, tilt angle and position are fulfilled by different subsystems with high accuracy. With the help of the diagnostic system, we reveal the dependence of thickness distribution on collision parameters and report the 238-nm liquid sheet generated by the collision of two liquid jets. Control methods for the flatness and tilt angle of LSTs have also been provided, which are essential for applications of laser-driven ion acceleration and others.

Magnetic graphene oxide nanocomposites (MGO NPs) have been widely studied in biomedical applications. However, their cytotoxicity and underlying mechanisms remain unclear. In this study, the biosafety of MGO NPs was investigated, and the mechanism involved in ferroptosis was further explored. MGO can produce cytotoxicity in ADSCs, which is dependent on their concentration. Ferroptosis was involved in MGO NP-induced ADSC survival inhibition by increasing total ROS and lipid ROS accumulation as well as regulating the expression levels of ferroptosis-related genes and proteins. GPX4 played a critical role in the MGO NP-induced ADSC ferroptosis process, and overexpressing GPX4 suppressed ferroptosis to increase cell survival. This study provides a theoretical basis for the biosafety management of MGO NPs used in the field of biomedical treatment.

Ultrasonic technology is widely applied in the engineering ceramic polishing processes without the limitation of material properties and ideally integrated into computer numerical control system. Ultrasonic-induced cavitation and mechanical vibration effect could accelerate the motion of solid abrasives. The individual behaviors of microjet/shockwave of ultrasonic cavitation in gases and liquids, and micro-abrasives with simple harmonic vibrations in solids and liquids has been extensively studied. To conduct a systematic and integrated study of abrasives behavior in the polishing contact region involving abrasive, surround-workpiece wall, ultrasonic physical vibration, and ultrasonic cavitation impact, a novel model integrating the free abrasive motion velocity and fixed abrasive indentation depth under multi-scale contact was proposed according to Hertzian contact theory, Greenwood-Williamson model, indentation deformation theory, the basic equations of cavitation bubble dynamics, cavitation impact control equations, and Newton's law of motion equation. The effects of ultrasonic amplitude, ultrasonic frequency, preloading force and particle size on the proposed model were investigated by theoretical analysis and numerical simulations. Ultrasonic physical vibration mainly influences the dynamic gap and further influence the number of different abrasives. Furthermore, the indentation depth of fixed abrasive depends mainly on the abrasive geometry. As the contact gap and abrasive size decrease, the indentation depth gradually decreases. Under the synergistic effect of cavitation-induced shock wave and microjet, the velocity of free abrasive in this paper is generally 0–150 m/s, and the kinetic energy of free abrasive increases roughly linearly with increasing frequency and approximately as a quadratic function with increasing particle size. Increasing the preloading force leads to a reduction in the abrasive kinetic energy. Besides, the kinetic energy induced by the shock wave has a cliff-like increment at an amplitude of 0.7–0.8 μm. It is revealed that the abrasive kinetic energy is suppressed by the cavitation bubble expansion and collapse at smaller ultrasonic pressure amplitude and surround-wall distance. This research provides a theoretical reference for the modeling of potential defects and material removal on the workpiece surface caused by abrasive motion during polishing, and reduces the trial cost for parameter optimization in actual polishing processing.

Objective: Chondrocyte viability (CV) can be measured with the label-free method using second harmonic generation (SHG) and two-photon excitation autofluorescence (TPAF) imaging. To automate the image processing for the label-free CV measurement, we previously demonstrated a two-step deep-learning method: Step 1 used a U-Net to segment the lacuna area on SHG images; Step 2 used dual CNN networks to count live cells and the total number of cells in extracted cell clusters from TPAF images. This study aims to develop one-step deep learning methods to improve the efficiency of CV measurement. Method: TPAF/SHG images were acquired simultaneously on cartilage samples from rats and pigs using two-photon microscopes and were merged to form RGB color images with red, green, and blue channels assigned to emission bands of oxidized flavoproteins, reduced forms of nicotinamide adenine dinucleotide, and SHG signals, respectively. Based on the Mask R-CNN, we designed a deep learning network and its denoising version using Wiener deconvolution for CV measurement. Results: Using training and test datasets from rat and porcine cartilage, we have demonstrated that Mask R-CNN-based networks can segment and classify individual cells with a single-step processing flow. The absolute error (difference between the measured and the ground-truth CV) of the CV measurement using the Mask R-CNN with or without Wiener deconvolution denoising reaches 0.01 or 0.08, respectively; the error of the previous CV networks is 0.18, significantly larger than that of the Mask R-CNN methods. Conclusions: Mask R-CNN-based deep-learning networks improve efficiency and accuracy of the label-free CV measurement.

Glutathione peroxidase 8 (GPX8) is a key regulator of redox homoeostasis. Whether its antioxidant activity participates in the regulation of m6A modification is a crucial issue, which has important application value in cancer treatment. In this study, MeRIP-seq was used to explore the characteristics of transcriptome-wide m6A modification in GPX8-deficient oral cancer cells. Oxidative stress caused by the lack of GPX8 resulted in 1,279 hyper- and 2,287 hypo-methylated m6A peaks and 2,036 differentially expressed genes in GPX8-KO cells. Twenty-eight differentially expressed genes were related to the cell response to oxidative stress, and half of them changed their m6A modification. In GPX8-KO cells, m6A regulators IGF2BP2 and IGF2BP3 were upregulated, while FTO, RBM15, VIRMA, ZC3H13, and YTHDC2 were downregulated. After H2O2 treatment, the expression changes of RBM15, IGF2BP2, and IGF2BP3 were further enhanced. These data indicated that GPX8-mediated redox homoeostasis regulated m6A modification, thereby affecting the expression and function of downstream genes. This study highlights the possible significance of GPX8 and the corresponding m6A regulatory or regulated genes as novel targets for antioxidant intervention in cancer therapy.

Abstract Background Implantable collamer lens (ICL) has been widely accepted for its excellent visual outcomes for myopia correction. It is a new challenge in phakic IOL power calculation, especially for those with low and moderate myopia. This study aimed to establish a novel stacking machine learning (ML) model for predicting postoperative refraction errors and calculating EVO-ICL lens power. Methods We enrolled 2767 eyes of 1678 patients (age: 27.5 ± 6.33 years, 18–54 years) who underwent non-toric (NT)-ICL or toric-ICL (TICL) implantation during 2014 to 2021. The postoperative spherical equivalent (SE) and sphere were predicted using stacking ML models [support vector regression (SVR), LASSO, random forest, and XGBoost] and training based on ocular dimensional parameters from NT-ICL and TICL cases, respectively. The accuracy of the stacking ML models was compared with that of the modified vergence formula (MVF) based on the mean absolute error (MAE), median absolute error (MedAE), and percentages of eyes within ± 0.25, ± 0.50, and ± 0.75 diopters (D) and Bland-Altman analyses. In addition, the recommended spheric lens power was calculated with 0.25 D intervals and targeting emmetropia. Results After NT-ICL implantation, the random forest model demonstrated the lowest MAE (0.339 D) for predicting SE. Contrarily, the SVR model showed the lowest MAE (0.386 D) for predicting the sphere. After TICL implantation, the XGBoost model showed the lowest MAE for predicting both SE (0.325 D) and sphere (0.308 D). Compared with MVF, ML models had numerically lower values of standard deviation, MAE, and MedAE and comparable percentages of eyes within ± 0.25 D, ± 0.50 D, and ± 0.75 D prediction errors. The difference between MVF and ML models was larger in eyes with low-to-moderate myopia (preoperative SE > − 6.00 D). Our final optimal stacking ML models showed strong agreement between the predictive values of MVF by Bland-Altman plots. Conclusion With various ocular dimensional parameters, ML models demonstrate comparable accuracy than existing MVF models and potential advantages in low-to-moderate myopia, and thus provide a novel nomogram for postoperative refractive error prediction and lens power calculation.

Neonatal thrombocytopenia is one of the common hematological abnormalities in neonates, especially premature infants. Platelet transfusion is an effective method of treating neonatal thrombocytopenia, but its threshold selection and timing are controversial. Recent findings support the use of a lower platelet transfusion threshold (25×109/L) in neonates and highlight the potential harm that may arise from liberal blood transfusion strategy. In current clinical practice, the volume of neonatal platelet transfusion greatly exceeds that routinely used in adults, but there are significant functional differences between neonatal and adult platelets, and platelets may mediate several immune and inflammatory responses in multiple ways. In this paper, based on the recent developments of evidence-based medical evidence of neonatal platelet transfusion, we summarized the pathophysiological basis of neonatal restrictive blood transfusion strategy, platelet transfusion with different ABO blood types, and the pathogenesis and potential mechanism of adverse events related to platelet transfusion.

Gray blight disease stands as one of the most destructive ailments affecting tea plants, causing significant damage and productivity losses. However, the dynamic roles of defense genes during the infection of gray blight disease remain largely unclear, particularly concerning their distinct responses in resistant and susceptible cultivars. In the pursuit of understanding the molecular interactions associated with gray blight disease in tea plants, a transcriptome analysis unveiled that 10,524, 17,863, and 15,178 genes exhibited differential expression in the resistant tea cultivar (Yingshuang), while 14,891, 14,733, and 12,184 genes showed differential expression in the susceptible tea cultivar (Longjing 43) at 8, 24, and 72 h post-inoculation (hpi), respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses highlighted that the most up-regulated genes were mainly involved in secondary metabolism, photosynthesis, oxidative phosphorylation, and ribosome pathways. Furthermore, plant hormone signal transduction and flavonoid biosynthesis were specifically expressed in resistant and susceptible tea cultivars, respectively. These findings provide a more comprehensive understanding of the molecular mechanisms underlying tea plant immunity against gray blight disease.

Through gravity/gauge duality, the string breaking and running coupling constant of heavy quark-antiquark pair are investigated in the rotating background. For the meson decay mode QQ¯→Qq¯+Q¯q, we discuss the string breaking and running coupling in the parallel and transverse case. It is found that the parallel case has a more significant on string breaking and running coupling constant than the transverse case in the confined phase. The string-breaking distance and the maximum value of running coupling will decrease with the increase of angular velocity in the parallel case. Besides, we also investigate the running coupling and screening distance at finite angular velocity in the deconfined phase. It is found that the maximum values of the running coupling and screening distance are decreasing functions of angular velocity. The parallel case has a more significant influence on the running coupling and screening distance than the transverse case in the deconfined phase.

Satellite networks, which have wide coverage and high throughput, are more and more important for the Internet. Low earth orbit (LEO) satellites have been more widely used than other orbiting satellites because they have lower unidirectional links (UDL) transmission delay. However, due to the variability and instability of satellite links, leading to relatively higher UDL transmission delay and more link failures for satellite networks. Thus, fast reroute (FRR) schemes, which can bypass the failure links and improve network performance, are badly needed in satellite networks. However, FRR need relatively large computing resources while satellites have limited resources. Thus, traditional routing technologies cannot be directly applied to satellite network due to frequent topology changes and poor computing performance. To meet the challenges, we propose a hybrid FRR schemes for satellite networks that combines the centralized computing with distributed segment routing (SR). With the scheme, satellites with relatively more computing resources can pre-compute the reroute paths, and distribute the routing rules to satellites that have more forwarding resources. We formulate the problem, propose a classification algorithm that can distinguish satellites according to their computing and forwarding resources. Additionally, we also propose a real-time backup path maintenance algorithm in the hybrid rerouting scheme. Finally, we conduct comprehensive simulations to evaluate the performance of the proposed algorithms, and the results show that under extreme conditions, the proposed LFR algorithm is 30.9% better than traditional algorithms in storage resource utilization; and the proposed LFA+ algorithm is 74.3% better than traditional algorithms in update time comparison.

Hand gesture recognition (HGR) based on surface electromyogram (sEMG) and Accelerometer (ACC) signals is increasingly attractive where fusion strategies are crucial for performance and remain challenging. Currently, neural network-based fusion methods have gained superior performance. Nevertheless, these methods typically fuse sEMG and ACC either in the early or late stages, overlooking the integration of entire cross-modal hierarchical information within each individual hidden layer, thus inducing inefficient inter-modal fusion. To this end, we propose a novel Alignment-Enhanced Interactive Fusion (AiFusion) model, which achieves effective fusion via a progressive hierarchical fusion strategy. Notably, AiFusion can flexibly perform both complete and incomplete multimodal HGR. Specifically, AiFusion contains two unimodal branches and a cascaded transformer-based multimodal fusion branch. The fusion branch is first designed to adequately characterize modality-interactive knowledge by adaptively capturing inter-modal similarity and fusing hierarchical features from all branches layer by layer. Then, the modality-interactive knowledge is aligned with that of unimodality using cross-modal supervised contrastive learning and online distillation from embedding and probability spaces respectively. These alignments further promote fusion quality and refine modality-specific representations. Finally, the recognition outcomes are set to be determined by available modalities, thus contributing to handling the incomplete multimodal HGR problem, which is frequently encountered in real-world scenarios. Experimental results on five public datasets demonstrate that AiFusion outperforms most state-of-the-art benchmarks in complete multimodal HGR. Impressively, it also surpasses the unimodal baselines in the challenging incomplete multimodal HGR. The proposed AiFusion provides a promising solution to realize effective and robust multimodal HGR-based interfaces.

Generalized zero-shot learning (GZSL) has significantly reduced the training requirements for steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs). Traditional methods require complete class data sets for training, but GZSL allows for only partial class data sets, dividing them into ‘seen’ (those with training data) and ‘unseen’ classes (those without training data). However, inefficient utilization of SSVEP data limits the accuracy and information transfer rate (ITR) of existing GZSL methods. To this end, we proposed a framework for more effective utilization of SSVEP data at three systematically combined levels: data acquisition, feature extraction, and decision-making. First, prevalent SSVEP-based BCIs overlook the inter-subject variance in visual latency and employ fixed sampling starting time (SST). We introduced a dynamic sampling starting time (DSST) strategy at the data acquisition level. This strategy uses the classification results on the validation set to find the optimal sampling starting time (OSST) for each subject. In addition, we developed a Transformer structure to capture the global information of input data for compensating the small receptive field of existing networks. The global receptive fields of the Transformer can adequately process the information from longer input sequences. For the decision-making level, we designed a classifier selection strategy that can automatically select the optimal classifier for the seen and unseen classes, respectively. We also proposed a training procedure to make the above solutions in conjunction with each other. Our method was validated on three public datasets and outperformed the state-of-the-art (SOTA) methods. Crucially, we also outperformed the representative methods that require training data for all classes.

Brain connectivity networks based on functional magnetic resonance imaging (fMRI) have expanded our understanding of brain functions in both healthy and diseased states. However, most current studies construct connectivity networks using averaged regional time courses with the strong assumption that the activities of voxels contained in each brain region are similar, ignoring their possible variations. Additionally, pairwise correlation analysis is often adopted with more attention to positive relationships, while joint interactions at the network level as well as anti-correlations are less investigated. In this paper, to provide a new strategy for regional activity representation and brain connectivity modeling, a novel homogeneous multiset canonical correlation analysis (HMCCA) model is proposed, which enforces sign constraints on the weights of voxels to guarantee homogeneity within each brain region. It is capable of obtaining regional representative signals and constructing covariation and contravariance networks simultaneously, at both group and subject levels. Validations on two sessions of fMRI data verified its reproducibility and reliability when dealing with brain connectivity networks. Further experiments on subjects with and without Parkinson’s disease (PD) revealed significant alterations in brain connectivity patterns, which were further associated with clinical scores and demonstrated superior prediction ability, indicating its potential in clinical practice.

The epileptic seizure prediction (ESP) method aims to timely forecast the occurrence of seizures, which is crucial to improving patients’ quality of life. Many deep learning-based methods have been developed to tackle this issue and achieve significant progress in recent years. However, the “black-box” nature of deep learning models makes the clinician mistrust the prediction results, severely limiting its clinical application. For this purpose, in this study, we propose a self-interpretable deep learning model for patient-specific epileptic seizure prediction: Multi-Scale Prototypical Part Network (MSPPNet). This model attempts to measure the similarity between the inputs and prototypes (learned during training) as evidence to make final predictions, which could provide a transparent reasoning process and decision basis (e.g., significant prototypes for inputs and corresponding similarity score). Furthermore, we assign different sizes to the prototypes in latent space to capture the multi-scale features of EEG signals. To the best of our knowledge, this is the first study that develops a self-interpretable deep learning model for seizure prediction, other than the existing post hoc interpretation studies. Our proposed model is evaluated on two public epileptic EEG datasets (CHB-MIT: 16 patients with a total of 85 seizures, Kaggle: 5 dogs with a total of 42 seizures), with a sensitivity of 93.8% and a false prediction rate of 0.054/h in the CHB-MIT dataset and a sensitivity of 88.6% and a false prediction rate of 0.146/h in the Kaggle dataset, achieving the current state-of-the-art performance with self-interpretable evidence.

Finer-grained decoding at a phoneme or syllable level is a key technology for continuous recognition of silent speech based on surface electromyogram (sEMG). This paper aims at developing a novel syllable-level decoding method for continuous silent speech recognition (SSR) using spatio-temporal end-to-end neural network. In the proposed method, the high-density sEMG (HD-sEMG) was first converted into a series of feature images, and then a spatio-temporal end-to-end neural network was applied to extract discriminative feature representations and to achieve syllable-level decoding. The effectiveness of the proposed method was verified with HD-sEMG data recorded by four pieces of 64-channel electrode arrays placed over facial and laryngeal muscles of fifteen subjects subvocalizing 33 Chinese phrases consisting of 82 syllables. The proposed method outperformed the benchmark methods by achieving the highest phrase classification accuracy (97.17 ± 1.53%, ${p} < 0.05$ ), and lower character error rate (3.11 ± 1.46%, ${p} < 0.05$ ). This study provides a promising way of decoding sEMG towards SSR, which has great potential applications in instant communication and remote control.