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Abstract Clear cell renal cell carcinoma (ccRCC) is characterized by Von Hippel Lindau (VHL) gene loss of function mutation, which leads to the accumulation of hypoxia-inducible factor 2α (HIF2α). HIF2α has been well-established as one of the major oncogenic drivers of ccRCC, however, its therapeutic targeting remains a challenge. Through an analysis of proteomic data from ccRCCs and adjacent non-tumor tissues, we herein revealed that Ubiquitin-Specific Peptidase 7 (USP7) was upregulated in tumor tissues, and its depletion by inhibitors or shRNAs caused significant suppression of tumor progression in vitro and in vivo. Mechanistically, USP7 expression is activated by the transcription factors FUBP1 and FUBP3, and it promotes tumor progression mainly by deubiquitinating and stabilizing HIF2α. Moreover, the combination of USP7 inhibitors and afatinib (an ERBB family inhibitor) coordinately induce cell death and tumor suppression. In mechanism, afatinib indirectly inhibits USP7 transcription and accelerates the degradation of HIF2α protein, and the combination of them caused a more profound suppression of HIF2α abundance. These findings reveal a FUBPs-USP7-HIF2α regulatory axis that underlies the progression of ccRCC and provides a rationale for therapeutic targeting of oncogenic HIF2α via combinational treatment of USP7 inhibitor and afatinib.

Digital image processing is a constantly evolving field encompassing a wide range of techniques and applications. Researchers worldwide are continually developing various algorithms across multiple fields to achieve accurate image classification. Advanced computer vision algorithms are crucial for architectural and artistic analysis. The digitalization of art has significantly enhanced the accessibility and conservation of fine-art paintings, yet the risk of art theft remains a significant challenge. Improving art security necessitates the precise identification of fine-art paintings. Although current recognition systems have shown potential, there is significant scope for enhancing their efficiency. We developed an improved recognition system for categorizing fine-art paintings using convolutional transformers, specified by an attention mechanism to enhance focused learning on the data. As part of the most advanced architectures in the deep learning family, transformers are empowered by a multi-head attention mechanism, thus improving learning efficiency. To assess the performance of our model, we compared it with those developed using four pre-trained networks: ResNet50, VGG16, AlexNet, and ViT. Each pre-trained network was integrated into a corresponding state-of-the-art model as the first processing blocks. These four state-of-the-art models were constructed under the transfer learning strategy, one of the most commonly used approaches in this field. The experimental results showed that our proposed system outperformed the other models. Our study also highlighted the effectiveness of using convolutional transformers for learning image features.

Undergraduate medical education in China has shifted from educator-centered learning to self-directed learning (SDL) over the past few decades. Careful design of public engagement activities can enable SDL and empower medical students to pioneer public health and patient safety education. In this study, we aimed to innovate nervous system education by implementing a public engagement model that empowers students to learn about the nervous system by teaching the public. Our goal was to generate greater interest in the nervous system at the undergraduate stage, inspire students’ enthusiasm to pursue a career in neurology, and ultimately, contribute to health promotion. During the nervous system module of the second year of the undergraduate curriculum, students were given the option to participate in the public engagement model. Participants were tasked with the creation of educational videos focusing on knowledge, attitudes, and behaviors associated with the prevention and management of neurological diseases and their complications. The videos were made accessible to the general public through the university’s official channel at the end of the semester. A total of 117 students (67.24% of all students) chose to participate in the public engagement model. Female students and those with higher Grade Point Averages in the present semester were more likely to participate. The model received strong positive feedback from participants, as students found the public engagement task helpful in learning about the nervous system module as well as in enhancing their public engagement skills. Despite the time and effort consumption, participating in the public engagement task did not affect students’ exam scores. The public engagement task is an innovative model in the nervous system curriculum and has the potential to be integrated into a broader range of undergraduate courses. It empowers medical students to pioneer public health and patient safety education.

Most Siamese-based trackers adopt correlation operation to perform similarity matching on feature fusion of template branch and search branch. However, the correlation operation directly uses the template feature to slide the window on the search area feature, and it is difficult to distinguish the target and background information when encountering similar target interference and background clutter, which can easily lead to tracking failure. In this paper, a mixed attention Siamese Network (MASNet) is proposed, which neatly integrates the modelling of global information and mutual information into the Siamese tracking framework. Specifically, through the learnable attention module, the local and global dependencies are adaptively integrated, and the rich context information is captured to improve the discrimination of the network for similar targets. After that, the correlation of the context information of the two branches is aggregated and adaptively encoded into the two branches, which makes up for the shortcomings of single correlation operation. In addition, the framework adopts a three-layer feature fusion method, which enables the tracker to better adapt to object deformation and similar object interference. Extensive experiments on OTB100, UAV123, GOT-10k and LaSOT show that the proposed tracking method achieves comparable performance to the advanced trackers.

Abstract Background Gastric cancer is a highly prevalent cancer type and the underlying molecular mechanisms are not fully understood. Ubiquitin-specific peptidase (USP) 29 has been suggested to regulate cell fate in several types of cancer, but its potential role in gastric carcinogenesis remains unclear. Methods The expression of USP29 in normal and gastric cancer tissues was analyzed by bioinformatics analysis, immunohistochemistry and immunoblot. Gene overexpression, CRISPR-Cas9 technology, RNAi, and Usp29 knockout mice were used to investigate the roles of USP29 in cell culture, xenograft, and benzo[a]pyrene (BaP)-induced gastric carcinogenesis models. We then delineated the underlying mechanisms using mass spectrometry, co-immunoprecipitation (Co-IP), immunoblot, ubiquitination assay, chromatin immunoprecipitation (ChIP), quantitative real-time PCR (qRT-PCR), and luciferase assays. Results In this study, we found that USP29 expression was significantly upregulated in gastric cancers and associated with poor patient survival. Ectopic expression of USP29 promoted, while depletion suppressed the tumor growth in vitro and in vivo mouse model. Mechanistically, transcription factor far upstream element binding protein 1 (FUBP1) directly activates USP29 gene transcription, which then interacts with and stabilizes aurora kinase B (AURKB) by suppressing K48-linked polyubiquitination, constituting a FUBP1-USP29-AURKB regulatory axis that medicates the oncogenic role of USP29. Importantly, systemic knockout of Usp29 in mice not only significantly decreased the BaP-induced carcinogenesis but also suppressed the Aurkb level in forestomach tissues. Conclusions These findings uncovered a novel FUBP1-USP29-AURKB regulatory axis that may play important roles in gastric carcinogenesis and tumor progression, and suggested that USP29 may become a promising drug target for cancer therapy.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a rapidly spreading outbreak of coronavirus disease 2019 (the COVID-19 pandemic). COVID-19 has severely affected healthcare systems worldwide, as well as the global economy, and has significantly increased morbidity and mortality rates. The majority of COVID-19-related deaths occurred in older individuals, primarily among those with concomitant diseases, including metabolic, respiratory, and cardiovascular diseases. Aging hallmarks, such as cellular senescence, chronic inflammation, and genomic instability, partially explain the increased disease severity at the molecular level with advancing age. Other multifactorial considerations, including healthcare facilities, socioeconomic status, and dissemination of epidemic information, may help control morbidity in the elderly population. While the World Health Organization declared an end to the emergency status of COVID-19 in May 2023, physical and emotional impairments may persist after recovery from the virus. Precautions should therefore be taken to prevent future pandemics, and suitable emphasis should be placed on addressing persistent COVID-19 and preventing future pandemics.

A vectorized analytical model for the cutting dynamics in the spiral bevel gear face hobbing process has been developed, which is based on machine tool kinematics and vibration vectorization. The structural modal parameters of the cutter head spindle system are obtained through experimental modal analysis with hammer impact testing. The analytical model is utilized to simulate the generation of simulated vibration acceleration signals during spiral bevel gear hobbing. A wavelet threshold denoising method is applied to process the simulated vibration signals of the spiral bevel gear face hobbing with added white noise. Signal processing methods, including short-time Fourier transform are employed for time-domain analysis, frequency-domain analysis, and time–frequency-domain analysis of measured signals and simulated signals, thereby extracting the corresponding statistical features. In addition to the results of the experimental modal analysis, the causes of chatter in spiral bevel gear hobbing are discussed in detail, revealing that the main factor is cutter head vibration in the Y direction of the Hunan ZDCY CNC EQUIPMENT YKA2260 machine tool used in this research. The error in the time-domain characteristic parameters between simulated signals and measured vibration acceleration signals is within 15%, with a difference of 3.5% in spectral peak values. The predicted tooth surface morphology from simulation matches the actual morphology on the workpiece, comprehensively validating the reliability of the cutting dynamics model for the spiral bevel gear face hobbing process. Another conclusion drawn from numerical simulation experiments is that the amount of tooth surface waviness of the spiral bevel gears is the ratio of tool chatter frequency to cutting fundamental frequency.

Triple-negative breast cancer (TNBC) cells are often resistant to FAS (CD95)-mediated apoptosis, but the underlying molecular mechanism(s) is not fully understood yet. Notably, the expression of the type II transmembrane protein, CD74, is correlated with chemotherapy-resistant and more invasive forms of cancers via unknown mechanisms. Here, we analyzed gene expression pattern of cancer patients and/or patient-derived xenograft (PDX) models and found that mRNA and protein levels of CD74 are highly expressed in TNBC and correlated with cancer stem cells (CSCs) and epithelial–mesenchymal transition (EMT) properties. Mechanistically, we found that AKT activation is likely critical for maintaining CD74 expression and protein stability to favor its oncogenic functions. Physiologically, epidermal growth factor (EGF) along with CD74 could activate AKT signaling, likely through binding of phosphorylated AKT (S473) to CD74, whereas inhibition of AKT could impair stability of CD74. We also revealed that CD74 binds to FAS and interferes with the intrinsic signaling of FAS-mediated apoptosis. As such, selective targeting of the CD74/FAS complex using the AKT inhibitor along with the CD74-derived peptide could synergistically restore and activate FAS-mediated apoptosis. Therefore, our approach of mobilizing apoptosis pathways likely provides a rationale for TNBC treatment by targeting the CD74/FAS and CD74-AKT axes.

The prevalence of age-related diseases has been progressively increasing worldwide. The pathogenesis of these disorders, including cancer, is closely associated with an age-dependent decline in cellular functions. There are multiple layers of crosstalk between aging processes and oncogenesis. Therefore, a better understanding of the interplay between aging and cancer will facilitate the development of novel therapeutic intervention strategies for malignancies at advanced ages. Recent review articles have concisely summarized and updated aging and cancer hallmark features and adequately discussed the interplays at both the cellular and molecular levels. Because aging and cancer share several key mechanisms that define the hallmarks, targeting shared features, such as cellular senescence, may be beneficial for cancer prevention and treatments. Notably, senolysis, an innovative therapeutic intervention for selectively removing senescent cells, holds great promise for developing new therapeutic approaches for cancers and other age-related diseases, such as viral infections and cardiovascular diseases. Herein we briefly summarize the recently updated knowledge on aging and cancer hallmarks, as well as the advances in senolysis for age-related conditions.

IntroductionThe Metagenomics next-generation sequencing (mNGS) and GeneXpert MTB/RIF assay (Xpert) exhibited a sensitivity for tuberculosis (TB) diagnostic performance. Research that directly compared the clinical performance of ddPCR analysis, mNGS, and Xpert in mycobacterium tuberculosis complex (MTB) infection has not been conducted.MethodsThe study aimed to evaluate the diagnostic performance of ddPCR compared to mNGS and Xpert for the detection of MTB in multiple types of clinical samples. The final clinical diagnosis was used as the reference standard.ResultsOut of 236 patients with suspected active TB infection, 217 underwent synchronous testing for tuberculosis using ddPCR, Xpert, and mNGS on direct clinical samples. During follow-up, 100 out of 217 participants were diagnosed with MTB infection. Compared to the clinical final diagnosis, ddPCR produced the highest sensitivity of 99% compared with mNGS (86%) and Xpert (64%) for all active MTB cases. DiscussionTwenty-two Xpert-negative samples were positive in mNGS tests, which confirmed the clinical diagnosis results from ddPCR and clinical manifestation, radiologic findings. Thirteen mNGS-negative samples were positive in ddPCR assays, which confirmed the clinical final diagnosis.ddPCR provides a higher sensitive compared to Xpert and mNGS for MTB diagnosis, as defined by the high concordance between ddPCR assay and clinical final diagnosis.

Background: Pancreatic adenocarcinoma (PAAD) is a lethal malignancy with high levels of heterogeneity. Pyroptosis is thought to influence the development of various tumors. Nevertheless, the role of pyroptosis-related genes (PRGs) in prognostic risk stratification and therapeutic guidance for PAAD remains ambiguously. Methods: Transcriptome profile and clinical information of PAAD patients were retrieved from The Cancer Genome Atlas (TCGA) as well as Gene Expression Omnibus (GEO) databases, followed by differential analysis. Patients were divided into distinct pyroptosis phenotype subtypes based on the characteristic of differently expressed PRGs (DEPRGs). Then a PRG signature was established through univariate analysis and LASSO algorithm in the training set to assess the prognostic risk, and its reliability was verified in the validation set using receiver operating characteristic(ROC) curve. The correlation of risk score with tumor microenvironment(TME), TMB and chemotherapeutic drug sensitivity were also analyzed. In addition, a nomogram was constructed to promote better clinical application. Results: A total of 28 DEPRGs were determined in the integrated TCGA-GEO datasets. Patients were divided into three pyroptosis phenotype subtypes, Kaplan-Meier curve suggested patients in cluster B had a worse prognosis than those in cluster A and C. Then a price signature comprised of 8 PRGs was generated. TME analysis suggested that the low-risk subgroup displayed potential stronger antitumor immune effect and might respond better to immune checkpoint inhibitors (ICIs) therapy. Furthermore, PRG signature exhibited favorable discriminatory ability for TMB status and the sensitivity of multiple conventional chemotherapeutic agents including paclitaxel. Ultimately, we constructed a promising nomogram according to the risk score and N stage with good predictive accuracy compared with the actual overall survival (OS) probabilities. Conclusion: We established an 8-gene signature that could be regarded as an independent prognostic risk factor for PAAD patients. The 8-gene signature could provide rationale for immunotherapy and chemotherapy, which might help clinicians make precise individualized treatment regimens.

Abstract We developed a continuous learning system (CLS) based on deep learning and optimization and ensemble approach, and conducted a retrospective data simulated prospective study using ultrasound images of breast masses for precise diagnoses. We extracted 629 breast masses and 2235 images from 561 cases in the institution to train the model in six stages to diagnose benign and malignant tumors, pathological types, and diseases. We randomly selected 180 out of 3098 cases from two external institutions. The CLS was tested with seven independent datasets and compared with 21 physicians, and the system’s diagnostic ability exceeded 20 physicians by training stage six. The optimal integrated method we developed is expected accurately diagnose breast masses. This method can also be extended to the intelligent diagnosis of masses in other organs. Overall, our findings have potential value in further promoting the application of AI diagnosis in precision medicine.

Abstract At present, temporal and spatial contexts are widely used to improve the adaptability of a tracker. However, most existing methods usually focus on one aspect of the temporal or spatial context and rarely exploit them simultaneously. In this paper, a context‐aware Siamese Network (CSNet) is proposed, which skilfully integrates the modelling of temporal and spatial context into the Siamese tracking framework. Specifically, CSNet consists of a context‐based channel attention module and a context‐based cross‐attention module. The former aggregates spatial context information from different channels and dynamically emphasizes target features, which makes it easier for the tracker to distinguish the target from the background. The latter propagates the temporal context from the previous frames to the current frame to establish the part‐level relationship between the search region and the historical target state, which enables the tracker better adapt to the target deformation. In addition, to further mine context information, the CSNet is equipped with a state‐aware strategy to control the contribution of different context information in tracking. Extensive experiments on OTB2015, UAV123, GOT‐10k, LaSOT, and TrackingNet show that the proposed tracking method achieves comparable performance to the advanced trackers.

N6-methyladenosine (m6A), the most abundant mRNA modification in mammalian cells, is responsible for mRNA stability and alternative splicing. The METTL3-METTL14-WTAP complex is the only methyltransferase for the m6A modification. Thus, regulation of its enzymatic activity is critical for the homeostasis of mRNA m6A levels in cells. However, relatively little is known about the upstream regulation of the METTL3-METTL14-WTAP complex, especially at the post-translational modification level. The C-terminal RGG repeats of METTL14 are critical for RNA binding. Therefore, modifications on these residues may play a regulatory role in its function. Arginine methylation is a post-translational modification catalyzed by protein arginine methyltransferases (PRMTs), among which PRMT1 preferentially methylates protein substrates with an arginine/glycine-rich motif. In addition, PRMT1 functions as a key regulator of mRNA alternative splicing, which is associated with m6A modification. To this end, we report that PRMT1 promotes the asymmetric methylation of two major arginine residues at the C-terminus of METTL14, and the reader protein SPF30 recognizes this modification. Functionally, PRMT1-mediated arginine methylation on METTL14 is likely essential for its function in catalyzing the m6A modification. Moreover, arginine methylation of METTL14 promotes cell proliferation that is antagonized by the PRMT1 inhibitor MS023. These results indicate that PRMT1 likely regulates m6A modification and promotes tumorigenesis through arginine methylation at the C-terminus of METTL14.

Computing resource measurement and computing routing are essential technologies in the computing first network (CFN), serving as its foundational elements. This paper introduces a Software Defined Computing First Network (SD-CFN) architecture. Building upon this framework, a Dynamic-Static Integrated Computing Resource Measurement Mechanism (DCRMM) is proposed, incorporating methods such as the entropy weight method and K-Means clustering. The DCRMM algorithm outperforms the Maximum-closest Static Algorithm (MSA) and Maximum Closest Dynamic Algorithm (MDA) in terms of node stability, node utilization, and node matching accuracy. Additionally, a Reinforcement Learning and Software Defined Computing First Networking Routing (RSCR) algorithm is presented as a software-defined computing routing solution within the SD-CFN. RSCR introduces a knowledge plane responsible for computing routing calculations. It comprehensively considers factors such as link latency, available bandwidth, and packet loss rate. Simulation experiments conducted on the GÉANT topology demonstrate that RSCR outperforms the OSPF algorithm in terms of link latency, packet loss rate, and throughput. DCRMM and RSCR offer innovative solutions for computing resource measurement and computing routing in computing first networks.

Objective: The purpose of this study was to develop and validate a nomogram model for the prediction of survival outcome in rectal cancer patients who underwent surgical resection.Methods: A total of 9,919 consecutive patients were retrospectively identified using the Surveillance, Epidemiology, and End Results (SEER) database. Significant prognostic factors were determined by the univariate and multivariate Cox analysis. The nomogram model for the prediction of cancer-specific survival (CSS) in rectal cancer patients were developed based on these prognostic variables, and its predictive power was assessed by the concordance index (C-index). Calibration curves were plotted to evaluate the associations between predicted probabilities and actual observations. The internal and external cohort were used to further validate the predictive performance of the prognostic nomogram.Results: All patients from the SEER database were randomly split into a training cohort (n = 6,944) and an internal validation cohort (n = 2,975). The baseline characteristics of two cohorts was comparable. Independent prognostic factors were identified as age, pT stage, lymph node metastasis, serum CEA level, tumor size, differentiation type, perineural invasion, circumferential resection margin involvement and inadequate lymph node yield. In the training cohort, the C-index of the nomogram was 0.719 (95% CI: 0.696–0.742), which was significantly higher than that of the TNM staging system (C-index: 0.606, 95% CI: 0.583–0.629). The nomogram had a C-index of 0.726 (95% CI: 0.691–0.761) for the internal validation cohort, indicating a good predictive power. In addition, an independent cohort composed of 202 rectal cancer patients from our institution were enrolled as the external validation. Compared with the TNM staging system (C-index: 0.573, 95% CI: 0.492–0.654), the prognostic nomogram still showed a better predictive performance, with the C-index of 0.704 (95% CI: 0.626–0.782). Calibration plots showed a good consistency between predicted probability and the actual observation in the training and two validation cohorts.Conclusion: The nomogram showed an excellent predictive ability for survival outcome of rectal cancer patients, and it might provide an accurate prognostic stratification and help clinicians determine individualized treatment strategies.

Abstract At present, the target of interest in visual tracking is given in the form of a bounding box. Due to the randomness of the target shape, the bounding box may contain a lot of non‐target information. When encountering complex tracking scenarios, the performance of the tracker reduces severely. To address this problem, in this letter, the authors propose a novel tracking framework based on the joint of the visual template and natural language (VNTrack) to alleviate the impact of bounding box ambiguity. Specifically, the authors first use a pre‐trained language model to extract the features of the language description of the target. Then, a feature alignment module is designed to align and enhance the visual template feature and natural language feature. In addition, the authors design a multimodal query module to fuse the visual template, natural language, and search region information. Experimental results over tracking benchmarks with language annotations show that the proposed VNTrack is competitive among the state‐of‐the‐art trackers.

In software-defined networking (SDN), the traffic forwarding delay highly depends on the latency associated with updating the forwarding rules in flow tables. With the increase in fine-grained flow control requirements, due to the flexible control capabilities of SDN, more rules are being inserted and removed from flow tables. Moreover, the matching fields of these rules might overlap since multiple control domains might generate different rules for similar flows. This overlap implies dependency relationships among the rules, imposing various restrictions on forwarding entries during updates, e.g., by following update orders or storing entries at specified locations, especially in flow tables implemented using ternary content addressable memory (TCAM); otherwise, mismatching or packet dropping will occur. It usually takes a while to resolve and maintain dependencies during updates, which hinders high forwarding efficiency. To reduce the delay associated with updating dependent rules, in this paper, we propose an updating algorithm for TCAM-based flow tables. We formulate the TCAM maintenance process as an NP-hard problem and analyze the inefficiency of existing moving approaches. To solve the problem, we propose an optimal moving chain for single rule updates and provide theoretical proof for its minimum moving steps. For multiple rules arriving at a switch simultaneously, we designed a dynamic approach to update concurrent entries; it is able to update multiple rules heuristically within a restricted TCAM region. As the update efficiency concerns dependencies among rules, we evaluate our flow table by updating algorithms with different dependency complexities. The results show that our approach achieves about 6% fewer moving steps than existing approaches. The advantage is more pronounced when the flow table is heavily utilized and rules have longer dependency chains.

Steel large-megawatts wind turbines have the light-damping and long-period properties, resulting in the adverse vibrations under the wind loads. In this paper, a novel tuned mass damper refitted via inner platform (IP-TMD) is proposed to control the excessive vibration of steel wind turbine tower (WTT). Firstly, the dynamic equation of steel WTT controlled by the IP-TMD system is established according to the principle of virtual work, and its dynamic coefficient and frequency ratio at corresponding fixed points are deduced. Then, the optimal frequency ratio and optimal stiffness and damping coefficients are obtained by the system optimization. Furthermore, a numerical simulation research is employed to analyze the frequency-response curves and resonance mitigation effect of IP-TMD under harmonic excitation. Finally, the vibration control efficiency of IP-TMD is calculated using the Wilson-θ method under the wind loadings; the results indicated that IP-TMD is able to reduce the dynamic response of steel WTT over 45% compared with the uncontrolled WTT cases.

This study investigated the pathogenesis of age-related macular degeneration (AMD) using histological methods that are commonly used for atherosclerotic vascular disease (ASVD). 1 normal, 3 early dry AMD, and 1 late dry AMD eyes were obtained from the Lions Eye Bank of Oregon and systematically dissected. They were stained with hematoxylin and eosin, Oil red O, Masson, Elastica van Gieson, Alizarin red, and Prussian blue. Additionally, the normal and late dry AMD eyes were immunostained for a-smooth muscle actin, CD45, and CD68 with Nile red and DAPI. Correlations were found between severity of AMD and lipid accumulation in the deep sclera (+), numbers of drusen between the Bruch's membrane and retinal pigment epithelium (RPE) (+), amount of collagen in the deep sclera (+), and amount of elastin in the deep sclera (-) (P < 0.1). Geographic atrophy, RPE detachment, and abnormal capillary shape and distribution in the choriocapillaris were observed in the fovea of late AMD. There were no stenosis, plaque, hemorrhage, and calcification. Additionally, late AMD tended to have higher smooth muscle thicknesses of the choroidal vascular walls, lower numbers of T lymphocytes in the choroid, and higher numbers of macrophages near the RPE and in the choroid relative to normal (P < 0.1). Macrophages-derived foam cells were detected near the Bruch's membrane in late AMD. Therefore, the present study showed many histological characteristics of ASVD in AMD, which suggests an association between them; however, there were also some histological characteristics of ASVD that were not found in AMD, which indicates that there exist pathogenic differences between them. The results generally support the vascular model of AMD, but some details still need clarification.

Antral follicle Count (AFC) is a non-invasive biomarker used to assess ovarian reserves through transvaginal ultrasound (TVUS) imaging. Antral follicles’ diameter is usually in the range of 2–10 mm. The primary aim of ovarian reserve monitoring is to measure the size of ovarian follicles and the number of antral follicles. Manual follicle measurement is inhibited by operator time, expertise and the subjectivity of delineating the two axes of the follicles. This necessitates an automated framework capable of quantifying follicle size and count in a clinical setting. This paper proposes a novel Harmonic Attention-based U-Net network, HaTU-Net, to precisely segment the ovary and follicles in ultrasound images. We replace the standard convolution operation with a harmonic block that convolves the features with a window-based discrete cosine transform (DCT). Additionally, we proposed a harmonic attention mechanism that helps to promote the extraction of rich features. The suggested technique allows for capturing the most relevant features, such as boundaries, shape, and textural patterns, in the presence of various noise sources (i.e., shadows, poor contrast between tissues, and speckle noise). We evaluated the proposed model on our in-house private dataset of 197 patients undergoing TransVaginal UltraSound (TVUS) exam. The experimental results on an independent test set confirm that HaTU-Net achieved a Dice coefficient score of 90% for ovaries and 81% for antral follicles, an improvement of 2% and 10%, respectively, when compared to a standard U-Net. Further, we accurately measure the follicle size, yielding the recall, and precision rates of 91.01% and 76.49%, respectively.

Abstract Background Total knee arthroplasty (TKA) is an optimal option for patients with middle-to-end-stage knee osteoarthritis. However, the management of postoperative acute pain remains inefficient. Transcutaneous electrical acupoint stimulation (TEAS) is a nonpharmacological method to manage postoperative acute pain. Different frequencies of TEAS have been tested using varying parameters, but the optimal analgesic frequency remains controversial. The aim of this study was to explore the optimal analgesic frequency of TEAS for treating acute pain after the primary unilateral TKA. Methods/design This is a double-blind, randomized controlled trial. A total of 156 patients are randomly assigned to: G1, 5 Hz TEAS; G2, 100 Hz TEAS; G3, mixed TEAS (alternative use of daily 5 Hz and 100 Hz TEAS) and G4, placebo TEAS. In the G1, G2 and G3 groups, TEAS is conducted at acupoints SP9 and GB34 of the leg that was operated on (at a wave of continuous, balanced and asymmetrical biphasic square, with a pulse width of 200 μs, and a strong but comfortable current) for 30 min prior to a 30-min rehabilitation session per day for 2 weeks. In G4 group, TEAS is delivered at a strong but comfortable current for 30 s, then the current is gradually decreased to none over the next 15 s. The primary outcomes are measured before surgery, at baseline (POD 3, before TEAS intervention), week 1 and 2 after TEAS intervention with the Numeric Pain Rating Scale and The American Knee Society Score. The secondary outcomes include: (1) Active range of motion of the knee that was operated on; (2) Surface electromyography of both quadriceps; (3) Modified 30-s sit to stand test; (4) Additional usage of analgesia; and (5) SF-36. The additional outcomes include: (1) Patients’ satisfaction rate; (2) Patient’s expectation rate; and (3) Incidence of analgesia-related side effects. To test the blinding of participants and assessors, they are asked to guess whether the subjects received active or placebo TEAS within 5 min after the latest intervention. The safety and financial cost of TEAS are assessed. Discussion Mixed TEAS has more favorable effect on acute pain control than the placebo or 5 Hz or 100 Hz TEAS. Trial registration ChiCTR1800016347. Date of registration was 26 May 2018. Retrospectively registered.

Deep-circling thermal water upwelling and trickling to high geo-temperature roadway obviously alter the microclimate in mines, which brings difficulty to the prediction of airflow temperature and humidity. This is the basis of air-conditioning cooling load calculation. The heat and mass transfer between trickling water and airflow is rather complicated. Moreover, humid air exhibits the accumulation effect of heat and humidity in the long-distance flow process. In this paper, an apparatus was designed and developed to explore the influence of thermal water trickling on the airflow thermal parameters of a section of roadway (1L–39L, in which 1L–9L is the trickling section). The results show the following (1) With the rise of trickling water temperature, the total enthalpy difference of dry air in the roadway increases within a small range and that of humid air goes up nonlinearly. Besides, the increase of trickling water flow rate has an insignificant effect on the sensible heat of the airflow, while it plays a notable role in increasing the latent heat of the airflow. (2) High trickling water temperature results in a higher growth rate of humidity ratio at 19L than those at 29L and 39L in the early stage of thermal water trickling. Meanwhile, sensible heat exchange, which becomes strong after thermal water trickles for over 30 min, complicates the enthalpy difference variation rates of wet air at the three measuring points. (3) The three measuring points in the 19L–39L section all display a process of enthalpy growth with time. In the case of point 39L, the enthalpy difference of humid air surges sharply when the trickling water temperature is 80°C or the flow rate is 200 ml/min. The research results boast some reference value for thermal water management and microclimate change forecasting after the airflow passes through a trickling roadway.

T-cell acute lymphoblastic leukemias (T-ALLs) are aggressive and heterogeneous hematologic tumors resulting from the malignant transformation of T-cell progenitors. The major challenges in the treatments of T-ALL are dose-limiting toxicities of chemotherapeutics and drug resistance. Despite important progress in deciphering the genomic landscape of T-ALL, translation of these findings into effective targeted therapies remains largely unsuccessful. New targeted agents with significant antileukemic efficacy and less toxicity are in urgent need. We herein report that the expression of WEE1, a nuclear tyrosine kinase involved in cell cycle G2-M checkpoint signaling, is significantly elevated in T-ALL. Mechanistically, oncogenic MYC directly binds to the WEE1 promoter and activates its transcription. T-ALL cells particularly rely on the elevated WEE1 for cell viability. Pharmacological inhibition of WEE1 elicits global metabolic reprogramming which results in a marked suppression of aerobic glycolysis in T-ALL cells, leading to an increased dependency on glutaminolysis for cell survival. As such, dual targeting of WEE1 and glutaminase (GLS1) induces synergistic lethality in multiple T-ALL cell lines and shows great efficacy in T-ALL patient-derived xenografts. These findings provide mechanistic insights in the regulation of WEE1 kinase in T-ALL and suggest an additional vulnerability during WEE1 inhibitor treatments. In aggregate, we highlight a promising combination strategy of dual inhibition of cell cycle kinase and metabolic enzymes for T-ALL therapeutics.

Abstract. T-cell acute lymphoblastic leukemia (T-ALL) is a heterogeneous group of hematological tumors composed of distinct subtypes that vary in their genetic abnormalities. In the past decade, large-scale genomic analysis has shed new light on providing potentially important oncogenic or tumor suppressive candidates involved in the disease progression. Following in silico analysis, functional studies are usually performed to vigorously investigate the biological roles of candidate genes. For this purpose, animal models faithfully recapitulating the human disease are widely applied to decipher the mechanism underlying T-cell transformation. Conversely, an increased understanding of T-ALL biology, including identification of oncogene NOTCH1, TAL1 and MYC as well as tumor suppressor phosphatase and tensin homolog (PTEN), has significantly improved the development of T-ALL animal models. These progresses have opened opportunities for development of new therapeutic strategy to benefit T-ALL patients. In this review, we particularly summarize the mouse and zebrafish models used in T-ALL research and also the most recent advances from these in vivo studies.

This study proposes and validates an automated method for counting neurons in spinal cord injury (SCI) and then uses it to examine and compare the surviving cells in common types of SCI mechanisms. Moderate contusion, dislocation, and distraction SCIs were surgically induced in Sprague Dawley male rats (n = 6 for each type of injury). Their spinal cords were harvested 8 weeks post injury with 5 normal weight-matched rats. The spinal cords were cut, stained with anti-NeuN antibody and fluorescent Nissl, and imaged in the dorsal and ventral horns at various distances to the epicenter. Neurons in the images were automatically counted using an algorithm that was designed to filter non-soma-like objects based on morphological characteristics (size, solidity, circular pattern) and check the remaining objects for the double-stained nucleus/cell body features (brightness variation, brightness distribution, color). To validate the automated method, some of the images were randomly selected for manual counting. The number of surviving cells that were automatically measured by the algorithm was found to be correlated with the values that were manually measured by 2 observers ( P .05). Neurons in the dorsal and ventral horns were reduced after the SCIs ( P

This study examines and compares the walking function in contusion and distraction spinal cord injury (SCI) mechanisms. Moderate contusion and distraction SCIs were surgically induced between C5 and C6 in Sprague-Dawley male rats. The CatWalk system was used to perform gait analysis of walkway walking. The ladder rung walking test was used to quantify skilled locomotor movements of ladder rung walking. It was found that the inter-paw coordination, paw support, front paw kinematics, hind paw kinematics, and skilled movements were significantly different before and after contusion and distraction. Step sequence duration, diagonal support, forelimb intensity, forelimb duty cycle, forelimb paw angle, and forelimb swing speed were more greatly affected in distraction than in contusion at 2 weeks post-injury, whereas hindlimb stand was more greatly affected in contusion than in distraction at 8 weeks post-injury. After 8 weeks post-injury, diagonal coupling—variation, girdle coupling—variation, ipsilateral coupling—mean, forelimb maximum contact at, forelimb intensity, forelimb paw angle, and number of forelimb misplacements recovered to normal in contusion but not in distraction, whereas step sequence duration, ipsilateral coupling—variation, forelimb stand, forelimb duty cycle, hindlimb swing duration, hindlimb swing speed, and number of forelimb slips recovered to normal in distraction but not in contusion. Some of the behavioral outcomes, but not the others, were linearly correlated with the histological outcomes. In conclusion, walking deficits and recovery can be affected by the type of common traumatic SCI.

In this article, an improved malicious code intrusion detection method is presented based on the target tree for space information network, so as to adapt to the characteristics of space information network and the security requirements. The satellite system could realize the quick determination of the malicious code attacks formed by joint cooperation behaviors step by step, with the help of attack roadmap analysis at ground and malicious code behavior analyzing process at satellite. The simulation results show that the proposed method has the advantages of high detection rate, low satellite resource consumption, low latency, and in-depth analysis to those malicious code behaviors. It is demonstrated that the proposed detection method of malicious code is suitable for the space information network.

A typical application scenario of remote wireless sensor networks (WSNs) is identified as an emergency scenario. One of the greatest design challenges for communications in emergency scenarios is energy-efficient transmission, due to scarce electrical energy in large-scale natural and man-made disasters. Integrated high altitude platform (HAP)/satellite networks are expected to optimally meet emergency communication requirements. In this paper, a novel integrated HAP/satellite (IHS) architecture is proposed, and three segments of the architecture are investigated in detail. The concept of link-state advertisement (LSA) is designed in a slow flat Rician fading channel. The LSA is received and processed by the terminal to estimate the link state information, which can significantly reduce the energy consumption at the terminal end. Furthermore, the transmission power requirements of the HAPs and terminals are derived using the gradient descent and differential equation methods. The energy consumption is modeled at both the source and system level. An innovative and adaptive algorithm is given for the energy-efficient path selection. The simulation results validate the effectiveness of the proposed adaptive algorithm. It is shown that the proposed adaptive algorithm can significantly improve energy efficiency when combined with the LSA and the energy consumption estimation.

The engine cooling system must be able to match up with the stable operating conditions so as to guarantee the engine performance. On the working cycle level, however, the dynamic thermo-state of engines has not been considered in the cooling strategy. Besides, the frequent over-cooling boiling inside the gallery changes the cooling capacity constantly. It is necessary to study the coupling effect caused by the interaction of cooling flow and in-cylinder combustion so as to provide details of the dynamic control of cooling systems. To this end, this study develops a coupled modeling scheme of the cooling process considering the interaction of combustion and coolant flow. The global reaction mechanism is used for the combustion process and the multiphase flow method is employed to simulate the coolant flow considering the wall boiling and the interphase forces. The two sub-models exchange information of in-cylinder temperature, heat transfer coefficient, and wall temperature to achieve the coupled computation. The proposed modeling process is verified through the measured diesel engine power, in-cylinder pressure, and fire surface temperature of cylinder head. Then the effects of different cooling conditions on the cyclic engine performances are analyzed and discussed.

This paper investigates the performance of integrated wireless sensor and multibeam satellite networks (IWSMSNs) under terrestrial interference. The IWSMSNs constitute sensor nodes (SNs), satellite sinks (SSs), multibeam satellite and remote monitoring hosts (RMHs). The multibeam satellite covers multiple beams and multiple SSs in each beam. The SSs can be directly used as SNs to transmit sensing data to RMHs via the satellite, and they can also be used to collect the sensing data from other SNs to transmit to the RMHs. We propose the hybrid one-dimensional (1D) and 2D beam models including the equivalent intra-beam interference factor β from terrestrial communication networks (TCNs) and the equivalent inter-beam interference factor α from adjacent beams. The terrestrial interference is possibly due to the signals from the TCNs or the signals of sinks being transmitted to other satellite networks. The closed-form approximations of capacity per beam are derived for the return link of IWSMSNs under terrestrial interference by using the Haar approximations where the IWSMSNs experience the Rician fading channel. The optimal joint decoding capacity can be considered as the upper bound where all of the SSs’ signals can be jointly decoded by a super-receiver on board the multibeam satellite or a gateway station that knows all of the code books. While the linear minimum mean square error (MMSE) capacity is where all of the signals of SSs are decoded singularly by a multibeam satellite or a gateway station. The simulations show that the optimal capacities are obviously higher than the MMSE capacities under the same conditions, while the capacities are lowered by Rician fading and converge as the Rician factor increases. α and β jointly affect the performance of hybrid 1D and 2D beam models, and the number of SSs also contributes different effects on the optimal capacity and MMSE capacity of the IWSMSNs.