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Cooperative channel allocation and scheduling are key issues in wireless mesh networks with multiple interfaces and multiple channels. In this paper, we propose a load balance link layer protocol (LBLP) aiming to cooperatively manage the interfaces and channels to improve network throughput. In LBLP, an interface can work in a sending or receiving mode. For the receiving interfaces, the channel assignment is proposed considering the number, position and status of the interfaces, and a task allocation algorithm based on the Huffman tree is developed to minimize the mutual interference. A dynamic link scheduling algorithm is designed for the sending interfaces, making the tradeoff between the end-to-end delay and the interface utilization. A portion of the interfaces can adjust their modes for load balancing according to the link status and the interface load. Simulation results show that the proposed LBLP can work with the existing routing protocols to improve the network throughput substantially and balance the load even when the switching delay is large.

Background Axillary lymph node dissection (ALND) is a standard procedure for early-stage breast cancer (BC) patients with three or more positive sentinel lymph nodes (SLNs). However, ALND can lead to significant postoperative complications without always providing additional clinical benefits. This study aims to develop machine-learning (ML) models to predict non-sentinel lymph node (non-SLN) metastasis in Chinese BC patients with three or more positive SLNs, potentially allowing the omission of ALND. Methods Data from 2217 BC patients who underwent SLN biopsy at Shantou University Medical College were analyzed, with 634 having positive SLNs. Patients were categorized into those with ≤ 2 positive SLNs and those with ≥ 3 positive SLNs. We applied nine ML algorithms to predict non-SLN metastasis. Model performance was evaluated using ROC curves, precision-recall curves, and calibration curves. Decision Curve Analysis (DCA) assessed the clinical utility of the models. Results The RF model showed superior predictive performance, achieving an AUC of 0.987 in the training set and 0.828 in the validation set. Key predictive features included size of positive SLNs, tumor size, number of SLNs, and ER status. In external validation, the RF model achieved an AUC of 0.870, demonstrating robust predictive capabilities. Conclusion The developed RF model accurately predicts non-SLN metastasis in BC patients with ≥ 3 positive SLNs, suggesting that ALND might be avoided in selected patients by applying additional axillary radiotherapy. This approach could reduce the incidence of postoperative complications and improve patient quality of life. Further validation in prospective clinical trials is warranted.

Background Mammalian enabled (MENA) protein is a member of the enabled/vasodilator stimulated phosphoprotein (Ena/VASP) protein family, which regulates cytoplasmic actin network assembly. It plays a significant role in breast cancer invasion, migration, and resistance against targeted therapy and chemotherapy. However, its role in the efficacy of endocrine therapy for the hormone receptor-positive (HR + ) breast cancer patients is not known. This study investigated the role of MENA in the resistance against tamoxifen therapy in patients with HR + breast cancer and the underlying mechanisms. Methods MENA expression levels in the clinical HR + breast cancer samples (n = 119) were estimated using immunohistochemistry (IHC) to determine its association with the clinicopathological features, tamoxifen resistance, and survival outcomes. Western blotting (WB) and quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) analysis was performed to estimate the MENA protein and mRNA levels in the tamoxifen-sensitive and -resistant HR + breast cancer cell lines. Furthermore, CCK8, colony formation, and the transwell invasion and migration assays were used to analyze the effects of MENA knockdown on the biological behavior and tamoxifen sensitivity of the HR + breast cancer cell lines. Xenograft tumor experiments were performed in the nude mice to determine the tumor growth rates and tamoxifen sensitivity of the control and MENA knockdown HR + breast cancer cells in the presence and absence of tamoxifen treatment. Furthermore, we estimated the growth rates of organoids derived from the HR + breast cancer patients (n = 10) with high and low MENA expression levels when treated with tamoxifen. Results HR + breast cancer patients with low MENA expression demonstrated tamoxifen resistance and poorer prognosis compared to those with high MENA expression. Univariate and multivariate Cox regression analysis demonstrated that MENA expression was an independent predictor of tamoxifen resistance in patients with HR + breast cancer. MENA knockdown HR + breast cancer cells showed significantly reduced tamoxifen sensitivity in the in vitro experiments and the in vivo xenograft tumor mouse model compared with the corresponding controls. Furthermore, MENA knockdown increased the in vitro invasion and migration of the HR + breast cancer cells. HR + breast cancer organoids with low MENA expression demonstrated reduced tamoxifen sensitivity than those with higher MENA expression. Mechanistically, P-AKT levels were significantly upregulated in the MENA-knockdown HR + breast cancer cells treated with or without 4-OHT compared with the corresponding controls. Conclusions This study demonstrated that downregulation of MENA promoted tamoxifen resistance in the HR + breast cancer tissues and cells by enhancing the AKT signaling pathway. Therefore, MENA is a promising prediction biomarker for determining tamoxifen sensitivity in patients with HR + breast cancer.

Aim Prone positioning during general anaesthesia is one of the most difficult practices for the perioperative nurse. Patients in this position are vulnerable to many preventable complications. However, no studies have developed an evidence‐based tool to improve nursing practice during general anaesthesia and prone positioning. This study aimed to develop and test a general anaesthesia and prone position nursing checklist for use by the circulating nurse. Design A prospective pre‐post study was performed between October 2020 and March 2021. Methods The WHO checklist development model and evidence‐based methods guided the checklist development process. We prospectively observed circulating nurses that attended to prone general anaesthesia during posterior lumbar spine surgery for 3 months before and after the introduction of the general anaesthesia and prone position nursing risk checklist. The main outcomes were successful delivery of essential prone positional nursing practices during each surgery and the nurse's opinion of the checklist's efficacy and utility. Results A general anaesthesia and prone position nursing checklist comprised of 4 pause points and 22 necessary nursing practices was developed. Seventy‐two nurses participated in this study. Use of the checklist significantly increased the average performance of essential practices during each surgery from 72.72%–95.45%. Three measures had a compliance rate of 100%. The delivery rate of 14 measures was significantly improved, 91.7% of nurses considered the checklist easy to use, and 94.4% nurses would want the checklist to be used if they underwent a prone position and general anaesthesia operation.

Satellites image segmentation is useful in a variety of fields. Professionals in related industries can gain a lot of important information by segmenting satellite images, but processing them is difficult due to the complex background and various locations of interest. The simplest basic picture segmentation approach is multilevel threshold segmentation, but the results can be unsatisfactory due to its computational complexity and high computation time. This paper presents an improved bald eagle search algorithm based on the standard bald eagle search algorithm. This method is used to find the best threshold values for the Renyi entropy multilevel threshold methods. To expand the diversity of the bald eagle population and the bald eagle's prey space, the strategy uses Chaotic Tent and Levy Fight method. The method is utilized to do multilevel segmentation of color satellite images, and the results suggest that it is capable of doing so. At the same time, this method has fewer iterations and can achieve better threshold values than typical methods such as standard bald eagle search optimization algorithm.

This study aimed to explore the neurobiological mechanism underlying intellectual disability (ID) in patients with Waardenburg syndrome (WS) identified in a Chinese family. The proband was initially diagnosed with severe ID and then 10 of the 11 extended family members underwent further medical examinations (I-1 declined to be examined). Whole-exome sequencing (WES) revealed that 6 (II-1, II-3, III-1, III-4, III-5, and III-6) of the members share the pathogenic variant of c.230 C > G of SNAI2 (also known as SLUG ), a causing gene of WS. All of the mutation carriers in the third generation presented moderate to severe ID, along with severe anxiety, mild level of depression, and serious social dysfunction. But they did not show any signs of hearing loss and heterochromia iris, which are considered features of WS. Animal experiments with Snai2 −/− (also known as Slugh −/− ) mice were used to model the WS patients. All the Snai2 −/− mice exhibited cognitive impairment, depigmented hair, and lower neural activity in the brain. The bulk RNA-seq revealed transcriptional alterations related to energy metabolism, cell growth and differentiation. The snRNA-seq and spatial transcriptomics further showed a developmental arrest of astrocyte lineage cells in the Snai2 −/− mice. Moreover, Snai2 −/− mice presented higher expression of genes related to IFN-α, IFN-γ, and IL-6, reduced Fos + and GFAP + cells, as well as low expression of EAAT1 in the hippocampus and frontal cortex. These data demonstrate that Snai2 deletion leads to developmental arrest of astrocyte lineage cells thereby impairing neuron-astrocyte interactions, ultimately resulting in cognitive impairment as seen in the WS patients.

Multimodal brain network analysis enables a comprehensive understanding of neurological disorders by integrating information from multiple neuroimaging modalities. However, existing methods often struggle to effectively model the complex structures of multimodal brain networks. In this paper, we propose a novel tensor-based graph convolutional network (TGNet) framework that combines tensor decomposition with multi-layer GCNs to capture both the homogeneity and intricate graph structures of multimodal brain networks. We evaluate TGNet on four datasets—HIV, Bipolar Disorder (BP), and Parkinson’s Disease (PPMI), Alzheimer’s Disease (ADNI)—demonstrating that it significantly outperforms existing methods for disease classification tasks, particularly in scenarios with limited sample sizes. The robustness and effectiveness of TGNet highlight its potential for advancing multimodal brain network analysis. The code is available at  https://github.com/rongzhou7/TGNet .

Carbon dots are zero‐dimensional carbon nanomaterials with quantum confinement effects and edge effects, which have aroused great interests in many disciplines such as energy, chemistry, materials, and environmental applications. They can be prepared by chemical oxidation, electrochemical synthesis, hydrothermal preparation, arc discharge, microwave synthesis, template method, and many other methods. However, the raw materials' high cost, the complexity and environmental‐unfriendly fabrication process limit their large‐scale production and commercialization. Herein, we review the latest developments of coal‐based carbon dots about selecting coal‐derived energy resources (bituminous coal, anthracite, lignite, coal tar, coke, etc.) the developments of synthesis processes, surface modification, and doping of carbon dots. The coal‐based carbon dots exhibit the advantages of unique fluorescence, efficient catalysis, excellent water solubility, low toxicity, inexpensive, good biocompatibility, and other advantages, which hold the potentiality for a wide range of applications such as environmental pollutants sensing, catalyst preparation, chemical analysis, energy storage, and medical imaging technology. This review aims to provide a guidance of finding abundant and cost‐effective precursors, green, simple and sustainable production processes to prepare coal‐based carbon dots, and make further efforts to exploit the application of carbon dots in broader fields. In this review, the latest research progress in the selection of carbon dots precursors (raw coal and its derivatives), preparation processes, physicochemical properties and applications (energy, catalytic, sensing, bioimaging, environmental pollutants monitoring etc.) are summarized, which provides constructive ideas for the further development of low‐cost and commercialized multi‐functional coal based carbon dots.

Glutamine addiction represents a metabolic vulnerability of cancer cells; however, effective therapeutic targeting of the pathways involved remains to be realized. Here, we disclose the critical role of interferon-related developmental regulator 1 (IFRD1) in the adaptive survival of hepatocellular carcinoma (HCC) cells during glutamine starvation. IFRD1 is induced under glutamine starvation to inhibit autophagy by promoting the proteasomal degradation of the key autophagy regulator ATG14 in a TRIM21-dependent manner. Conversely, targeting IFRD1 in the glutamine-deprived state increases autophagy flux, triggering cancer cell exhaustive death. This effect largely results from the nucleophilic degradation of histone H1.0 and the ensuing unchecked increases in ribosome and protein biosynthesis associated with globally enhanced chromatin accessibility. Intriguingly, IFRD1 depletion in preclinical HCC models synergizes with the treatment of the glutaminase-1 selective inhibitor CB-839 to potentiate the effect of limiting glutamine. Together, our findings reveal how IFRD1 supports the adaptive survival of cancer cells under glutamine starvation, further highlighting the potential of IFRD1 as a therapeutic target in anti-cancer applications.

Abnormal grain growth (AGG) in polycrystalline microstructures, characterized by the rapid and disproportionate enlargement of a few “abnormal” grains relative to their surroundings, can lead to dramatic, often deleterious changes in the mechanical properties of materials, such as strength and toughness. Thus, the prediction and control of AGG is key to realizing robust mesoscale materials design. Unfortunately, it is challenging to predict these rare events far in advance of their onset because, at early stages, there is little to distinguish incipient abnormal grains from “normal” grains. To overcome this difficulty, we propose two machine learning approaches for predicting whether a grain will become abnormal in the future. These methods analyze grain properties derived from the spatio-temporal evolution of grain characteristics, grain-grain interactions, and a network-based analysis of these relationships. The first, PAL ( P redicting A bnormality with L STM), analyzes grain features using a long short-term memory (LSTM) network, and the second, PAGL ( P redicting A bnormality with G CRN and L STM), supplements the LSTM with a graph-based convolutional recurrent network (GCRN). We validated these methods on three distinct material scenarios with differing grain properties, observing that PAL and PAGL achieve high sensitivity and precision and, critically, that they are able to predict future abnormality long before it occurs. Finally, we consider the application of the deep learning models developed here to the prediction of rare events in different contexts.