E3 Ligase VHL Promotes Group 2 Innate Lymphoid Cell Maturation and Function via Glycolysis Inhibition and Induction of Interleukin-33 Receptor.

Group 2 innate lymphoid cells (ILC2s) are a specialized subset of lymphoid effector cells that are critically involved in allergic responses; however, the mechanisms of their regulation remain unclear. We report that conditional deletion of the E3 ubiquitin ligase VHL in innate lymphoid progenitors minimally affected early-stage bone marrow ILC2s but caused a selective and intrinsic decrease in mature ILC2 numbers in peripheral non-lymphoid tissues, resulting in reduced type 2 immune responses. VHL deficiency caused the accumulation of hypoxia-inducible factor 1α (HIF1α) and attenuated interleukin-33 (IL-33) receptor ST2 expression, which was rectified by HIF1α ablation or inhibition. HIF1α-driven expression of the glycolytic enzyme pyruvate kinase M2 downmodulated ST2 expression via epigenetic modification and inhibited IL-33-induced ILC2 development. Our study indicates that the VHL-HIF-glycolysis axis is essential for the late-stage maturation and function of ILC2s via targeting IL-33-ST2 pathway.

Neonatal Exposure to Polystyrene Nanoplastics Impairs Microglia-Mediated Synaptic Pruning and Causes Social Behavioral Defects in Adulthood.

The increasing prevalence and persistence of nanoplastics (NPs) have become critical environmental concerns. These particles have the potential to enter the food chain and accumulate in living organisms, which exerts their adverse effects on human health. The release of nanoparticles from feeding bottles raises concerns about potential health issues, especially for newborns exposed to NPs at the neonatal stage. In this study, we examined the impacts of neonatal exposure to polystyrene nanoplastics (PS-NPs) on neurodevelopment. Our study demonstrates that exposure to PS-NPs in newborn mice impairs microglial autophagic function and energy metabolism, leading to the disruption of microglia-mediated synaptic pruning during early neurodevelopment. These mice subsequently develop social behavioral defects in adulthood, suggesting the long-lasting effects of neonatal PS-NP exposure on brain development and behavior. Together, these data provide insights into the mechanism by which PS-NPs affect early neurodevelopment, thus emphasizing the crucial need to address plastic pollution globally.

Lightweight Detection Methods for Insulator Self-Explosion Defects.

The accurate and efficient detection of defective insulators is an essential prerequisite for ensuring the safety of the power grid in the new generation of intelligent electrical system inspections. Currently, traditional object detection algorithms for detecting defective insulators in images face issues such as excessive parameter size, low accuracy, and slow detection speed. To address the aforementioned issues, this article proposes an insulator defect detection model based on the lightweight Faster R-CNN (Faster Region-based Convolutional Network) model (Faster R-CNN-tiny). First, the Faster R-CNN model's backbone network is turned into a lightweight version of it by substituting EfficientNet for ResNet (Residual Network), greatly decreasing the model parameters while increasing its detection accuracy. The second step is to employ a feature pyramid to build feature maps with various resolutions for feature fusion, which enables the detection of objects at various scales. In addition, replacing ordinary convolutions in the network model with more efficient depth-wise separable convolutions increases detection speed while slightly reducing network detection accuracy. Transfer learning is introduced, and a training method involving freezing and unfreezing the model is employed to enhance the network's ability to detect small target defects. The proposed model is validated using the insulator self-exploding defect dataset. The experimental results show that Faster R-CNN-tiny significantly outperforms the Faster R-CNN (ResNet) model in terms of mean average precision (mAP), frames per second (FPS), and number of parameters.

Lightweight Meter Pointer Recognition Method Based on Improved YOLOv5.

In substation lightning rod meter reading data taking, the classical object detection model is not suitable for deployment in substation monitoring hardware devices due to its large size, large number of parameters, and slow detection speed, while is difficult to balance detection accuracy and real-time requirements with the existing lightweight object detection model. To address this problem, this paper constructs a lightweight object detection algorithm, YOLOv5-Meter Reading Lighting (YOLOv5-MRL), based on the improved YOLOv5 model's speed while maintaining accuracy. Then, the YOLOv5s are pruned based on the convolutional kernel channel soft pruning algorithm, which greatly reduces the number of parameters in the YOLOv5-MRL model while maintaining a certain accuracy loss. Finally, in order to facilitate the dial reading, the dial external circle fitting method is proposed to calculate the dial reading using the circular angle algorithm. The experimental results on the self-built dataset show that the YOLOv5-MRL object detection model achieves a mean average precision of 96.9%, a detection speed of 5 ms/frame, and a model weight size of 5.5 MB, making it better than other advanced dial reading models.

Real-Time Optimal States Estimation with Inertial and Delayed Visual Measurements for Unmanned Aerial Vehicles.

Motion estimation is a major issue in applications of Unmanned Aerial Vehicles (UAVs). This paper proposes an entire solution to solve this issue using information from an Inertial Measurement Unit (IMU) and a monocular camera. The solution includes two steps: visual location and multisensory data fusion. In this paper, attitude information provided by the IMU is used as parameters in Kalman equations, which are different from pure visual location methods. Then, the location of the system is obtained, and it will be utilized as the observation in data fusion. Considering the multiple updating frequencies of sensors and the delay of visual observation, a multi-rate delay-compensated optimal estimator based on the Kalman filter is presented, which could fuse the information and obtain the estimation of 3D positions as well as translational speed. Additionally, the estimator was modified to minimize the computational burden, so that it could run onboard in real time. The performance of the overall solution was assessed using field experiments on a quadrotor system, compared with the estimation results of some other methods as well as the ground truth data. The results illustrate the effectiveness of the proposed method.

Automatic Recognition Reading Method of Pointer Meter Based on YOLOv5-MR Model.

Meter reading is an important part of intelligent inspection, and the current meter reading method based on target detection has problems of low accuracy and large error. In order to improve the accuracy of automatic meter reading, this paper proposes an automatic reading method for pointer-type meters based on the YOLOv5-Meter Reading (YOLOv5-MR) model. Firstly, in order to improve the detection performance of small targets in YOLOv5 framework, a multi-scale target detection layer is added to the YOLOv5 framework, and a set of Anchors is designed based on the lightning rod dial data set; secondly, the loss function and up-sampling method are improved to enhance the model training convergence speed and obtain the optimal up-sampling parameters; Finally, a new external circle fitting method of the dial is proposed, and the dial reading is calculated by the center angle algorithm. The experimental results on the self-built dataset show that the Mean Average Precision (mAP) of the YOLOv5-MR target detection model reaches 79%, which is 3% better than the YOLOv5 model, and outperforms other advanced pointer-type meter reading models.

Text Recognition Model Based on Multi-Scale Fusion CRNN.

Scene text recognition is a crucial area of research in computer vision. However, current mainstream scene text recognition models suffer from incomplete feature extraction due to the small downsampling scale used to extract features and obtain more features. This limitation hampers their ability to extract complete features of each character in the image, resulting in lower accuracy in the text recognition process. To address this issue, a novel text recognition model based on multi-scale fusion and the convolutional recurrent neural network (CRNN) has been proposed in this paper. The proposed model has a convolutional layer, a feature fusion layer, a recurrent layer, and a transcription layer. The convolutional layer uses two scales of feature extraction, which enables it to derive two distinct outputs for the input text image. The feature fusion layer fuses the different scales of features and forms a new feature. The recurrent layer learns contextual features from the input sequence of features. The transcription layer outputs the final result. The proposed model not only expands the recognition field but also learns more image features at different scales; thus, it extracts a more complete set of features and achieving better recognition of text. The results of experiments are then presented to demonstrate that the proposed model outperforms the CRNN model on text datasets, such as Street View Text, IIIT-5K, ICDAR2003, and ICDAR2013 scenes, in terms of text recognition accuracy.

The deubiquitinase CYLD controls protective immunity against helminth infection by regulation of Treg cell plasticity.

BACKGROUND: Type 2 immunity can be modulated by regulatory T (Treg) cell activity. It has been suggested that the deubiquitinase cylindromatosis (CYLD) plays a role in the development or function of Treg cells, implying that it could be important for normal protective immunity, where type 2 responses are prevalent. OBJECTIVE: We sought to investigate the role of CYLD in Treg cell function and TH2 cell immune responses under steady-state conditions and during helminth infection. METHODS: Foxp3-restricted CYLD conditional knockout (KO) mice were examined in mouse models of allergen-induced airway inflammation and Nippostrongylus brasiliensis infection. We performed multiplex magnetic bead assays, flow cytometry, and quantitative PCR to understand how a lack of CYLD affected cytokine production, homing, and suppression in Treg cells. Target genes regulated by CYLD were identified and validated by microarray analysis, coimmunoprecipitation, short hairpin RNA knockdown, and transfection assays. RESULTS: Treg cell-specific CYLD KO mice showed severe spontaneous pulmonary inflammation with increased migration of Treg cells into the lung. CYLD-deficient Treg cells furthermore produced high levels of IL-4 and failed to suppress allergen-induced lung inflammation. Supporting this, the conditional KO mice displayed enhanced protection against N brasiliensis infection by contributing to type 2 immunity. Treg cell conversion into IL-4-producing cells was due to augmented mitogen-activated protein kinase and nuclear factor κB signaling. Moreover, Scinderin, a member of the actin-binding gelsolin family, was highly upregulated in CYLD-deficient Treg cells, and controlled IL-4 production through forming complexes with mitogen-activated protein kinase kinase/extracellular receptor kinase. Correspondingly, both excessive IL-4 production in vivo and the protective role of CYLD-deficient Treg cells against N brasiliensis were reversed by Scinderin ablation. CONCLUSIONS: Our findings indicate that CYLD controls type 2 immune responses by regulating Treg cell conversion into TH2 cell-like effector cells, which potentiates parasite resistance.

Therapeutic Drug Monitoring of Voriconazole in Children from a Tertiary Care Center in China.

Voriconazole is a broad-spectrum triazole antifungal and the first-line treatment for invasive aspergillosis (IA). The aim of this research was to study the dose adjustments of voriconazole as well as the affecting factors influencing voriconazole trough concentrations in Asian children to optimize its daily administration. Clinical data were analyzed of inpatients 2 to 14 years old who were subjected to voriconazole trough concentration monitoring from 1 June 2015 to 1 December 2017. A total of 138 voriconazole trough concentrations from 42 pediatric patients were included. Voriconazole trough concentrations at steady state ranged from 0.02 to 9.35 mg/liter, with high inter- and intraindividual variability. Only 50.0% of children achieved the target range (1.0 to 5.5 mg/liter) at initial dosing, while 35.7% of children were subtherapeutic, and 14.3% of children were supratherapeutic at initial dosing. There was no correlation between initial trough concentrations and initial dosing. A total of 28.6% of children (12/42) received an adjusted dose according to trough concentrations. Children <6, 6 to 12, and >12 years old required a median oral maintenance dose to achieve the target range of 11.1, 7.2, and 5.3 mg/kg twice daily, respectively (P = 0.043). The average doses required to achieved the target range were 7.7 mg/kg and 5.6 mg/kg, respectively, and were lower than the recommended dosage (P = 0.033 and 0.003, respectively). Affecting factors such as administration routes and coadministration with proton pump inhibitors (PPIs) explained 55.3% of the variability in voriconazole exposure. Therapeutic drug monitoring (TDM) of voriconazole could help to individualize antifungal therapy for children and provide guidelines for TDM and dosing optimization in Asian children.

Progress in Research and Application of HIV-1 TAT-Derived Cell-Penetrating Peptide.

Human immunodeficiency virus type I (HIV-1) transactivator of transcription (TAT) is encoded by HIV-1. It is a peptide rich in basic amino acids and belongs to the protein transduction domain family. It has been found that HIV-1 TAT and its core peptide segment TAT47-57 play an important role in promoting the cellular uptake of coupled bioactive macromolecules, such as peptides, proteins, oligonucleotides, and drug molecules. HIV-1 TAT can also significantly increase the soluble expression of extrinsic proteins. However, the mechanism behind the cellular uptake of HIV-1 TAT-derived cell-penetrating peptide remains unclear. This review focuses on the research into HIV-1 TAT-derived cell-penetrating peptide over the last years. We briefly discuss TAT's structural features, functions and applications, the mechanism of its cellular internalization, current challenges, and their possible solutions. At the end of this review, we provide a summary and predict the future research directions and potential applications of HIV-1 TAT when it is used as a cell-penetrating peptide.

T follicular helper cells, T follicular regulatory cells and autoimmunity.

CD4(+)T follicular helper (Tfh) cells are recognized as a distinct T-cell subset, which provides help for germinal center (GC) formation, B-cell development and affinity maturation, and immunoglobulin class switching, as an indispensable part of adaptive immunity. Tfh cell differentiation depends on various factors including cell-surface molecule interactions, extracellular cytokines and multiple transcription factors, with B-cell lymphoma 6 (Bcl-6) being the master regulator. T follicular regulatory (Tfr) cells are also located in the GC and share phenotypic characteristics with Tfh cells and regulatory T cells, but function as negative regulators of GC responses. Dysregulation of either Tfh or Tfr cells is linked to the pathogenesis of autoimmune diseases such as systemic lupus erythematosus. This review covers the basic Tfh and Tfr biology including their differentiation and function, and their close relationship with autoimmune diseases.

Cellular pharmacokinetic of methotrexate and its modulation by folylpolyglutamate synthetase and γ-glutamyl hydrolase in tumor cells.

BACKGROUND AND PURPOSE: Clinical studies showed that prolonged infusion of methotrexate (MTX) leads to more severe adverse reactions than short infusion of MTX at the same dose. We hypothesized that it is the saturation of folate polyglutamate synthetase (FPGS) at high MTX concentration that limits the intracellular synthesis rate of methotrexate polyglutamate (MTX-PG). Due to a similar accumulation rate, a longer infusion duration may increase the concentration of MTX-PG and, result in more serious adverse reactions. In this study, we validated this hypothesis. EXPERIMENTAL APPROACH: A549, BEL-7402 and MHCC97H cell lines were treated with MTX at gradient concentrations. Liquid chromatograph-mass spectrometer (UPLC-MS/MS) was used to quantify the intracellular concentration of MTX-PG and the abundance of FPGS and γ-glutamyl hydrolase (GGH). High quality data were used to fit the cell pharmacokinetic model. KEY RESULTS: Both cell growth inhibition rate and intracellular MTX-PG concentration showed a nonlinear relationship with MTX concentration. The parameter Vmax in the model, which represents the synthesis rate of MTX-PG, showed a strong correlation with the abundance of intracellular FPGS. CONCLUSION AND IMPLICATIONS: According to the model fitting results, it was confirmed that the abundance of FPGS is a decisive factor limiting the synthesis rate of MTX-PG. The proposed hypothesis was verified in this study. In addition, based on the intracellular metabolism, a reasonable explanation was provided for the correlation between the severity of adverse reactions of MTX and infusion time. This study provides a new strategy for the individualized treatment and prediction of efficacy/side effects of MTX.

Predictors of Acute Cerebellar Bulge Prior to Posterior Fossa Lesion Resection.

OBJECTIVE: A cerebellar bulge prior to posterior fossa resection is an emergency condition during surgery. Intraoperative cerebellar bulging not only increases the difficulty of lesion resection but also brings additional postoperative complications. Currently, there are few systematic reports on this topic. The predictors of cerebellar bulge and how to effectively prevent intraoperative cerebellar bulge are discussed in this article. METHODS: The clinical and imaging data of 527 patients with posterior fossa lesions who underwent resection at our hospital were retrospectively collected and analyzed. Perioperative clinical and imaging data were assessed. Variables were analyzed using univariate and multivariate regression analyses. RESULTS: Overall, 10.4% (55/527) of patients had intraoperative acute bulges. Multivariate analysis revealed that age <60 years, body mass index ≥24, lesion size ≥30 (mm), cerebellar tonsillar herniation and/or hydrocephalus, and perilesional edema (moderate-severe) were predictors of cerebellar bulging. Relief of the cerebellar bulge can be accomplished by excising the lesion, releasing cerebrospinal fluid, and removing the cerebellum (the outer one-third). Obvious cerebellar-related complications occurred in 4 patients postoperatively, and the symptoms disappeared after 6 months of follow-up. CONCLUSIONS: Cerebellar bulging during intraoperative posterior fossa resection deserves attention. Through the analysis of multiple factors related to cerebellar bulge, comprehensive evaluation and early intervention during the perioperative period are necessary. The incidence of cerebellar bulges can be reduced, and surgical complications related to cerebellar bulges can be avoided.

The Suitable Population for Opportunistic Low Bone Mineral Density Screening Using Computed Tomography.

Objective: To explore the suitable population of CT value for predicting low bone mineral density (low-BMD). Methods: A total of 1268 patients who underwent chest CT examination and DXA within one-month period retrospectively analyzed. The CT attenuation values of trabecular bone were measured in mid-sagittal plane from thoracic vertebra 7 (T7). Receiver operating characteristic (ROC) curves were used to evaluate the ability to diagnose low-BMD. Results: The AUC for diagnosing low BMD was larger in women than in men (0.894 vs 0.744, p < 0.05). The AUC increased gradually with the increase of age but decreased gradually with the increase in height and weight (p < 0.05). In females, when specificity was adjusted to approximately 90%, a threshold of 140.25 HU has a sensitivity of 69.3%, which is higher than the sensitivity of 36.5% in males for distinguishing low-BMD from normal. At the age of 70 or more, when specificity was adjusted to approximately 90%, a threshold of 126.31 HU has a sensitivity of 76.1%, which was higher than that of other age groups. Conclusion: For patients who had completed chest CTs, the CT values were more effective in predicting low-BMD in female, elderly, lower height, and lower weight patients.

Deep-agriNet: a lightweight attention-based encoder-decoder framework for crop identification using multispectral images.

The field of computer vision has shown great potential for the identification of crops at large scales based on multispectral images. However, the challenge in designing crop identification networks lies in striking a balance between accuracy and a lightweight framework. Furthermore, there is a lack of accurate recognition methods for non-large-scale crops. In this paper, we propose an improved encoder-decoder framework based on DeepLab v3+ to accurately identify crops with different planting patterns. The network employs ShuffleNet v2 as the backbone to extract features at multiple levels. The decoder module integrates a convolutional block attention mechanism that combines both channel and spatial attention mechanisms to fuse attention features across the channel and spatial dimensions. We establish two datasets, DS1 and DS2, where DS1 is obtained from areas with large-scale crop planting, and DS2 is obtained from areas with scattered crop planting. On DS1, the improved network achieves a mean intersection over union (mIoU) of 0.972, overall accuracy (OA) of 0.981, and recall of 0.980, indicating a significant improvement of 7.0%, 5.0%, and 5.7%, respectively, compared to the original DeepLab v3+. On DS2, the improved network improves the mIoU, OA, and recall by 5.4%, 3.9%, and 4.4%, respectively. Notably, the number of parameters and giga floating-point operations (GFLOPs) required by the proposed Deep-agriNet is significantly smaller than that of DeepLab v3+ and other classic networks. Our findings demonstrate that Deep-agriNet performs better in identifying crops with different planting scales, and can serve as an effective tool for crop identification in various regions and countries.

Salvation of Temporalis Muscle in Cranioplasty: A Technique Note.

OBJECTIVE: The preservation of the temporalis muscle during cranioplasty is very important for postoperative masticatory function and aesthetics. The key technique for temporalis muscle preservation is to find the surgical plane during the operation. METHODS: In the past, the so-called antiadhesion method could not be used to find the surgical plane very well. Here, we describe a novel method for separating the temporalis muscle and dura through an illustrative case, utilizing the natural space of the temporalis muscle at the bony border attachment point. RESULTS: The temporalis muscle is stretched by a wire saw to find the surgical plane quickly and accurately, maximizing the preservation of the temporalis muscle without damaging the dura. CONCLUSIONS: The technical effect is ideal, the operation is simple, and the technique is suitable for promotion.

HIV patients' bone loss before and after antiretroviral treatment and its possible mechanisms.

HIV infection has been reported to cause bone loss and a higher risk of fracture. Under normal conditions, bone metabolism is regulated by mesenchymal cells, osteoclasts differentiated from mononuclear macrophages, osteoblasts, and their expression of regulatory factors, such as receptor activator of nuclear factor-kappa B ligand (RANKL), M-SCF, and transforming growth factor-beta. The balance between bone resorption and osteogenesis depends on the balance between osteoclasts and osteoblasts. In addition, some immune cells, such as B-cells, T-cells, and other non-immune cells expressing RANKL, can contribute to osteoporosis under inflammatory conditions. HIV proteins consist of three types: regulatory proteins, accessory proteins, and structural proteins, which contribute to HIV-mediated bone loss partly by upregulating NF-κB expression, tumor necrosis factor alpha content, and release of inflammatory cytokines. Even worse, although antiretroviral therapy has reduced HIV infection mortality and successfully transformed acquired immunodeficiency syndrome into a chronic disease, its impact on bone loss should not be overlooked, especially when the drug contains tenofovir. This review analyzes some reports focusing on the overall osteolytic situation due to imbalances in osteogenesis and bone resorption due to HIV infection and antiviral therapy. The intrinsic mechanism of bone loss provides a reference for researchers to analyze the risk factors for HIV patients complicated with bone loss and helps clinicians to provide ideas for the intervention and prevention of bone loss during clinical treatment and chronic disease management of HIV patients.

Case report: An illusive cortical venous infarction mimicking glioma hemorrhage.

Cortical vein thrombosis (CVT) is a rare subtype of cerebral venous thrombosis. Because CVT is rare and its clinical and imaging findings are atypical, the misdiagnosis of CVT may be extremely high. We report a case of cortical venous infarction (CVI) secondary to CVT. Due to the atypical symptoms, we were perplexed about confirming the diagnosis between CVI and glioma hemorrhage. Eventually, CVT was confirmed by pathology combined with imaging.

Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning.

DNA-binding proteins (DBPs) play pivotal roles in many biological functions such as alternative splicing, RNA editing, and methylation. Many traditional machine learning (ML) methods and deep learning (DL) methods have been proposed to predict DBPs. However, these methods either rely on manual feature extraction or fail to capture long-term dependencies in the DNA sequence. In this paper, we propose a method, called PDBP-Fusion, to identify DBPs based on the fusion of local features and long-term dependencies only from primary sequences. We utilize convolutional neural network (CNN) to learn local features and use bi-directional long-short term memory network (Bi-LSTM) to capture critical long-term dependencies in context. Besides, we perform feature extraction, model training, and model prediction simultaneously. The PDBP-Fusion approach can predict DBPs with 86.45% sensitivity, 79.13% specificity, 82.81% accuracy, and 0.661 MCC on the PDB14189 benchmark dataset. The MCC of our proposed methods has been increased by at least 9.1% compared to other advanced prediction models. Moreover, the PDBP-Fusion also gets superior performance and model robustness on the PDB2272 independent dataset. It demonstrates that the PDBP-Fusion can be used to predict DBPs from sequences accurately and effectively; the online server is at http://119.45.144.26:8080/PDBP-Fusion/.

A novel analytical method to assess the effect of imipenem/cilastatin on liver function laboratory indexes in Chinese underage inpatients: Probability distribution curve.

OBJECTIVES: The primary objective of this study was to establish a novel method to assess the effect of imipenem/cilastatin (IMP) on liver function laboratory indexes in Chinese underage inpatients (inpatients aged <18 year-old). METHODS: A retrospective study was conducted in 188 underage inpatients who received IMP in Xiangya Hospital from January 2016 to April 2018. Demographic data and clinical information of these inpatients were collected. As there was no reference interval of minors, the occurrence of abnormal liver function was estimated by that of adults, temporarily. A new concept (mean-variance induced by drug, MVID) was introduced to analyze the characteristics of total bilirubin (TBil), direct bilirubin (DBil), alanine aminotransferase (ALT) and aspartate aminotransferase (AST). Effect of MVID of TBil, DBil, ALT and AST in different patients (aged<1 year old and aged ≥ 1 year old) were compared by Mann-Whitney U test. RESULTS: Estimating by reference intervals of adults, 57.4% underage inpatients (108/188) had abnormal liver function. According to the probability distribution curve of MVID, IMP can cause the increase of AST in 24% (0.62-0.38) Chinese underage inpatients, and the increase of ALT in 20% (0.60-0.40) Chinese underage inpatients. And liver protecting drugs can decrease MVID of ALT and AST. There were not statistically significant differences in MVID of TBil, DBil, ALT and AST in different patients (aged<1 year old and aged ≥ 1 year old); P value was 0.711, 0.734, 0.067 and 0.086, respectively. CONCLUSION: IMP can affect the liver function of 20-24% Chinese underage inpatients mainly by increasing the AST and ALT. IMP may induce hepatocellular injury, but not cholestasis. And liver protecting drugs can reverse the side effects caused by IMP. Age may not affect the effect of IMP on liver function.