Research on the evolutionary patterns and control of surrounding rock superimposed stress field local area loading in double-layer Island face main roadway.

Double-layer island working face main roadway coal pillars are affected by complex mining stress superposition, when different coal pillar width combinations, the surrounding rock stress field will produce different degrees of regional loading increase effect; the study of the surrounding rock stress field regional superposition loading increase law is meaningful to explaining the failure mode of the roadway and determining the critical control area. This study combines numerical simulation with on-site monitoring and other methods and draws the following conclusions: The superimposed loading increase law ("decreasing" → "increasing") of the abutment pressure and deviatoric stress in the lower coal seam of the double-layer island working face during the mining; the type of the principal stress deflection in the advance working face region; and by obtaining the three types of development morphology of the deviatoric stress peak zone of the roadway and its corresponding nine evolution modes (one type of circular tube → four types of inverse hyperbolic body → four types of hyperbolic body) in the double-layered island working face mining. Indicated the critical reinforcement area corresponding to the main roadway when at different combinations of coal pillar widths; determined the main track roadway protective coal pillars width for 40 m and the shape of the roadway peak deviatoric stress zone is the inverse class hyperbolic body mode; according to the evolution mode of the peak deviatoric stress zone, determined the synergistic failure control program for the asymmetric critical zone of the roadway surrounding rock which is a targeted scientific support method; after the feedback of on-site monitoring and, the support program is reasonable and effective.

Comparing the adverse effects of ketamine and esketamine between genders using FAERS data.

Background: Ketamine was developed as an anesthetic. Esketamine is the isolated S-enantiomer of racemic ketamine. They provide new avenues for the treatment of depression, especially treatment-resistant depression. Considering differences in the pharmacokinetics and hormonal status of ketamine in patients of different genders, sex-based differences in esketamine adverse drug events (ADE) may also be observed. This study presents data mining and safety analysis of adverse events of ketamine and esketamine between genders, promoting the individualization of clinical practice. Methods: Adverse drug reactions to ketamine and esketamine reported between the first quarter of 2004 and the second quarter of 2023 in the U.S. Food and Drug Administration on Adverse Event Reporting System (FAERS) were extracted. Thereafter, the reporting odds ratio (ROR) with 95% confidence interval (CI) was calculated. Results: A total of 2907 female reports and 1634 male reports on esketamine were included in the analysis. ROR mining showed that completed suicide, decreased therapeutic product effects, urinary retention, and hypertension were common in men. Additionally, 552 female and 653 male ketamine reports were recorded. ROR mining revealed that toxicity to various agents, bradycardia, cystitis and agitation, were more likely to occur in men, whereas women were more likely to develop suicidal ideation, increased transaminase levels, sclerosing cholangitis, and sterile pyuria. Conclusion: The adverse events of esketamine and ketamine differ across genders, which should be considered in clinical practice to provide individualized treatment.

Experience sharing on perioperative clinical management of gastric cancer patients based on the "China Robotic Gastric Cancer Surgery Guidelines".

BACKGROUND: With the popularization of robotic surgical systems in the field of surgery, robotic gastric cancer surgery has also been fully applied and promoted in China. The Chinese Guidelines for Robotic Gastric Cancer Surgery was published in the Chinese Journal of General Surgery in August 2021. METHODS: We have made a detailed interpretation of the process of robotic gastric cancer surgery regarding the indications, contraindications, perioperative preparation, surgical steps, complication, and postoperative management based on the recommendations of China's Guidelines for Robotic Gastric Cancer Surgery and supplemented by other surgical guidelines, consensus, and single-center experience. RESULTS: Twenty experiences of perioperative clinical management of robotic gastric cancer surgery were described in detail. CONCLUSION: We hope to bring some clinical reference values to the front-line clinicians in treating robotic gastric cancer surgery. TRIAL REGISTRATION: The guidelines were registered on the International Practice Guideline Registration Platform ( http://www.guidelines-registry.cn ) (registration number: IPGRP-2020CN199).

Comparative Analysis of Therapeutic Efficacy and Adverse Reactions among Various Thrombolytic Agents.

Thrombosis is a major health concern that contributes to the development of several cardiovascular diseases and a significant number of fatalities worldwide. While stent surgery is the current recommended treatment according to the guidelines, percutaneous coronary intervention (PCI) is the optimal approach for acute myocardial infarction (AMI). However, in remote areas with limited resources, PCI procedures may not be feasible, leading to a delay in treatment and irreversible outcomes. In such cases, preoperative thrombolysis becomes the primary choice for managing AMI in remote settings. The market for thrombolytic drugs is continuously evolving, and identifying a safe and effective thrombolytic agent for treating AMI is crucial. This study evaluated Urokinase, Alteplase, and Recombinant Human TNK Tissue-type Plasminogen Activator for Injection (rhTNK) as representatives of first-, second-, and third-generation thrombolytic drugs, respectively. The research included in vitro thrombolysis experiments, exposure of human cardiomyocytes, zebrafish tail vein injections, and vascular endothelial transgenic zebrafish models. The findings revealed that rhTNK is the most effective thrombolytic drug with the least adverse effects and lowest bleeding rate, highlighting its potential as the preferred treatment option for AMI. The order of thrombolytic effectiveness was Urokinase < Alteplase < rhTNK, with adverse effects on cardiomyocytes post-thrombolytic therapy ranking similarly as Urokinase < Alteplase < rhTNK, while the bleeding rate after thrombolysis followed the order of Urokinase > Alteplase > rhTNK.

High-Fidelity and Efficient Pluralistic Image Completion with Transformers.

Image completion has made tremendous progress with convolutional neural networks (CNNs), because of their powerful texture modeling capacity. However, due to some inherent properties (e.g., local inductive prior, spatial-invariant kernels), CNNs do not perform well in understanding global structures or naturally support pluralistic completion. Recently, transformers demonstrate their power in modeling the long-term relationship and generating diverse results, but their computation complexity is quadratic to input length, thus hampering the application in processing high-resolution images. This paper brings the best of both worlds to pluralistic image completion: appearance prior reconstruction with transformer and texture replenishment with CNN. The former transformer recovers pluralistic coherent structures together with some coarse textures, while the latter CNN enhances the local texture details of coarse priors guided by the high-resolution masked images. To decode diversified outputs from transformers, auto-regressive sampling is the most common method, but with extremely low efficiency. We further overcome this issue by proposing a new decoding strategy, temperature annealing probabilistic sampling (TAPS), which firstly achieves more than 70× speedup of inference at most, meanwhile maintaining the high quality and diversity of the sampled global structures. Moreover, we find the full CNN architecture will lead to suboptimal solutions for guided upsampling. To render more realistic and coherent contents, we design a novel module, named texture-aware guided attention, to concurrently consider the procedures of texture copy and generation, meanwhile raising several important modifications to solve the boundary artifacts. Through dense experiments, we found the proposed method vastly outperforms state-of-the-art methods in terms of four aspects: 1) large performance boost on image fidelity even compared to deterministic completion methods; 2) better diversity and higher fidelity for pluralistic completion; 3) exceptional generalization ability on large masks and generic dataset, like ImageNet. 4) Much higher decoding efficiency over previous auto-regressive based methods.

Deep learning-assisted multispectral imaging for early screening of skin diseases.

INTRODUCTION: Melanocytic nevi (MN), warts, seborrheic keratoses (SK), and psoriasis are four common types of skin surface lesions that typically require dermatoscopic examination for definitive diagnosis in clinical dermatology settings. This process is labor-intensive and resource-consuming. Traditional methods for diagnosing skin lesions rely heavily on the subjective judgment of dermatologists, leading to issues in diagnostic accuracy and prolonged detection times. OBJECTIVES: This study aims to introduce a multispectral imaging (MSI)-based method for the early screening and detection of skin surface lesions. By capturing image data at multiple wavelengths, MSI can detect subtle spectral variations in tissues, significantly enhancing the differentiation of various skin conditions. METHODS: The proposed method utilizes a pixel-level mosaic imaging spectrometer to capture multispectral images of lesions, followed by reflectance calibration and standardization. Regions of interest were manually extracted, and the spectral data were subsequently exported for analysis. An improved one-dimensional convolutional neural network is then employed to train and classify the data. RESULTS: The new method achieves an accuracy of 96.82 % on the test set, demonstrating its efficacy. CONCLUSION: This multispectral imaging approach provides a non-contact and non-invasive method for early screening, effectively addressing the subjective identification of lesions by dermatologists and the prolonged detection times associated with conventional methods. It offers enhanced diagnostic accuracy for a variety of skin lesions, suggesting new avenues for dermatological diagnostics.

High throughput profiling identified PA-L106R amino acid substitution in A(H1N1)pdm09 influenza virus that confers reduced susceptibility to baloxavir in vitro.

Baloxavir acid (BXA) is a pan-influenza antiviral that targets the cap-dependent endonuclease of the polymerase acidic (PA) protein required for viral mRNA synthesis. To gain a comprehensive understanding on the molecular changes associated with reduced susceptibility to BXA and their fitness profile, we performed a deep mutational scanning at the PA endonuclease domain of an A (H1N1)pdm09 virus. The recombinant virus libraries were serially passaged in vitro under increasing concentrations of BXA followed by next-generation sequencing to monitor PA amino acid substitutions with increased detection frequencies. Enriched PA amino acid changes were each introduced into a recombinant A (H1N1)pdm09 virus to validate their effect on BXA susceptibility and viral replication fitness in vitro. The I38 T/M substitutions known to confer reduced susceptibility to BXA were invariably detected from recombinant virus libraries within 5 serial passages. In addition, we identified a novel L106R substitution that emerged in the third passage and conferred greater than 10-fold reduced susceptibility to BXA. PA-L106 is highly conserved among seasonal influenza A and B viruses. Compared to the wild-type virus, the L106R substitution resulted in reduced polymerase activity and a minor reduction of the peak viral load, suggesting the amino acid change may result in moderate fitness loss. Our results support the use of deep mutational scanning as a practical tool to elucidate genotype-phenotype relationships, including mapping amino acid substitutions with reduced susceptibility to antivirals.

Early predictors of severe left main and/or three-vessel disease in patients with non-ST-segment elevation myocardial infarction: A dual-center retrospective study.

BACKGROUND: Early detection of patients concomitant with left main and/or three-vessel disease (LM/3VD) and high SYNTAX score (SS) is crucial for determining the most effective revascularization options regarding the use of antiplatelet medications and prognosis risk stratification. However, there is a lack of study for predictors of LM/3VD with SS in patients with non-ST-segment elevation myocardial infarction (NSTEMI). We aimed to identify potential factors that could predict LM/3VD with high SS (SS > 22) in patients with NSTEMI. METHODS: This dual-center retrospective study included a total of 481 patients diagnosed with NSTEMI who performed coronary angiography procedures. Clinical factors on admission were collected. The patients were divided into non-LM/3VD, Nonsevere LM/3VD (SS ≤ 22), and Severe LM/3VD (SS > 22) groups. To identify independent predictors, Univariate and logistic regression analyses were conducted on the clinical parameters. RESULTS: A total of 481 patients were included, with an average age of 60.9 years and 75.9% being male. Among these patients, 108 individuals had severe LM/3VD. Based on the findings of a multivariate logistic regression analysis, the extent of ST-segment elevation observed in lead aVR (OR: 7.431, 95% CI: 3.862-14.301, p < .001) and age (OR: 1.050, 95% CI: 1.029-1.071, p < .001) were identified as independent predictors of severe LM/3VD. CONCLUSION: This study indicated that the age of patients and the extent of ST-segment elevation observed in lead aVR on initial electrocardiogram were the independent predictive factors of LM/3VD with high SS in patients with NSTEMI.

Potential biomarkers for immune monitoring after renal transplantation.

Renal transplantation represents the foremost efficacious approach for ameliorating end-stage renal disease. Despite the current state of advanced renal transplantation techniques and the established postoperative immunosuppression strategy, a subset of patients continues to experience immune rejection during both the early and late postoperative phases, ultimately leading to graft loss. Consequently, the identification of immunobiomarkers capable of predicting the onset of immune rejection becomes imperative in order to facilitate early intervention strategies and enhance long-term prognoses. Upon reviewing the pertinent literature, we identified several indicators that could potentially serve as immune biomarkers to varying extents. These include the T1/T2 ratio, Treg/Th17 ratio, IL-10/TNF-α ratio, IL-33, IL-34, IL-6, IL-4, other cytokines, and NOX2/4.

Anomaly Detection of Permanent Magnet Synchronous Motor Based on Improved DWT-CNN Multi-Current Fusion.

The Permanent Magnet Synchronous Motor (PMSM) is the power source maintaining the stable and efficient operation of various pieces of equipment; hence, its reliability is crucial to the safety of public equipment. Convolutional Neural Network (CNN) models face challenges in extracting features from PMSM current data. A new Discrete Wavelet Transform Convolutional Neural Networks (DW-CNN) feature with fusion weight updating Long Short-Term Memory (LSTM) anomaly detection is proposed in this paper. This approach combines Discrete Wavelet Transform (DWT) with high and low-frequency separation processing and LSTM. The anomaly detection method adopts DWT and CNN by separating high and low-frequency processing. Moreover, this method combines the hybrid attention mechanism to extract the multi-current signal features and detects anomalies based on weight updating the LSTM network. Experiments on the motor bearing real fault dataset and the PMSM stator fault dataset prove the method's strong capability in fusing current features and detecting anomalies.

Transformer based Pluralistic Image Completion with Reduced Information Loss.

Transformer based methods have achieved great success in image inpainting recently. However, we find that these solutions regard each pixel as a token, thus suffering from an information loss issue from two aspects: 1) They downsample the input image into much lower resolutions for efficiency consideration. 2) They quantize 2563 RGB values to a small number (such as 512) of quantized color values. The indices of quantized pixels are used as tokens for the inputs and prediction targets of the transformer. To mitigate these issues, we propose a new transformer based framework called "PUT". Specifically, to avoid input downsampling while maintaining computation efficiency, we design a patch-based auto-encoder P-VQVAE. The encoder converts the masked image into non-overlapped patch tokens and the decoder recovers the masked regions from the inpainted tokens while keeping the unmasked regions unchanged. To eliminate the information loss caused by input quantization, an Un-quantized Transformer is applied. It directly takes features from the P-VQVAE encoder as input without any quantization and only regards the quantized tokens as prediction targets.Furthermore, to make the inpainting process more controllable, we introduce semantic and structural conditions as extra guidance. Extensive experiments show that our method greatly outperforms existing transformer based methods on image fidelity and achieves much higher diversity and better fidelity than state-of-the-art pluralistic inpainting methods on complex large-scale datasets (e.g., ImageNet). Codes are available at https://github.com/liuqk3/PUT.

Learning A Single Network for Robust Medical Image Segmentation with Noisy Labels.

Robust segmenting with noisy labels is an important problem in medical imaging due to the difficulty of acquiring high-quality annotations. Despite the enormous success of recent developments, these developments still require multiple networks to construct their frameworks and focus on limited application scenarios, which leads to inflexibility in practical applications. They also do not explicitly consider the coarse boundary label problem, which results in sub-optimal results. To overcome these challenges, we propose a novel Simultaneous Edge Alignment and Memory-Assisted Learning (SEAMAL) framework for noisy-label robust segmentation. It achieves single-network robust learning, which is applicable for both 2D and 3D segmentation, in both Set-HQ-knowable and Set-HQ-agnostic scenarios. Specifically, to achieve single-model noise robustness, we design a Memory-assisted Selection and Correction module (MSC) that utilizes predictive history consistency from the Prediction Memory Bank to distinguish between reliable and non-reliable labels pixel-wisely, and that updates the reliable ones at the superpixel level. To overcome the coarse boundary label problem, which is common in practice, and to better utilize shape-relevant information at the boundary, we propose an Edge Detection Branch (EDB) that explicitly learns the boundary via an edge detection layer with only slight additional computational cost, and we improve the sharpness and precision of the boundary with a thinning loss. Extensive experiments verify that SEAMAL outperforms previous works significantly.

Photocatalytic redox-neutral α-C(sp3)-H pyridination of glycine derivatives and N-arylamines with cyanopyridines.

A photo-induced α-C(sp3)-H decyanative pyridination of N-arylglycine derivatives with cyanopyridines was developed. This reaction was performed under organic photocatalytic and redox-neutral conditions via a radical-radical cross-coupling process. Besides, the protocol was also suitable for the C(sp3)-H pyridination of N-aryl tetrahydroisoquinolines as well as benzylamines.

Robust Model Watermarking for Image Processing Networks via Structure Consistency.

The intellectual property of deep networks can be easily "stolen" by surrogate model attack. There has been significant progress in protecting the model IP in classification tasks. However, little attention has been devoted to the protection of image processing models. By utilizing consistent invisible spatial watermarks, the work [1] first considered model watermarking for deep image processing networks and demonstrated its efficacy in many downstream tasks. Its success depends on the hypothesis that if a consistent watermark exists in all prediction outputs, that watermark will be learned into the attacker's surrogate model. However, when the attacker uses common data augmentation attacks (e.g., rotate, crop, and resize) during surrogate model training, it will fail because the underlying watermark consistency is destroyed. To mitigate this issue, we propose a new watermarking methodology, "structure consistency", based on which a new deep structure-aligned model watermarking algorithm is designed. Specifically, the embedded watermarks are designed to be aligned with physically consistent image structures, such as edges or semantic regions. Experiments demonstrate that our method is more robust than the baseline in resisting data augmentation attacks. Besides that, we test the generalization ability and robustness of our method to a broader range of adaptive attacks.

PointCAT: Contrastive Adversarial Training for Robust Point Cloud Recognition.

Notwithstanding the prominent performance shown in various applications, point cloud recognition models have often suffered from natural corruptions and adversarial perturbations. In this paper, we delve into boosting the general robustness of point cloud recognition, proposing Point-Cloud Contrastive Adversarial Training (PointCAT). The main intuition of PointCAT is encouraging the target recognition model to narrow the decision gap between clean point clouds and corrupted point clouds by devising feature-level constraints rather than logit-level constraints. Specifically, we leverage a supervised contrastive loss to facilitate the alignment and the uniformity of hypersphere representations, and design a pair of centralizing losses with dynamic prototype guidance to prevent features from deviating outside their belonging category clusters. To generate more challenging corrupted point clouds, we adversarially train a noise generator concurrently with the recognition model from the scratch. This differs from previous adversarial training methods that utilized gradient-based attacks as the inner loop. Comprehensive experiments show that the proposed PointCAT outperforms the baseline methods, significantly enhancing the robustness of diverse point cloud recognition models under various corruptions, including isotropic point noises, the LiDAR simulated noises, random point dropping, and adversarial perturbations. Our code is available at: https://github.com/shikiw/PointCAT.

Randomizing Human Brain Function Representation for Brain Disease Diagnosis.

Resting-state fMRI (rs-fMRI) is an effective tool for quantifying functional connectivity (FC), which plays a crucial role in exploring various brain diseases. Due to the high dimensionality of fMRI data, FC is typically computed based on the region of interest (ROI), whose parcellation relies on a pre-defined atlas. However, utilizing the brain atlas poses several challenges including 1) subjective selection bias in choosing from various brain atlases, 2) parcellation of each subject's brain with the same atlas yet disregarding individual specificity; 3) lack of interaction between brain region parcellation and downstream ROI-based FC analysis. To address these limitations, we propose a novel randomizing strategy for generating brain function representation to facilitate neural disease diagnosis. Specifically, we randomly sample brain patches, thus avoiding ROI parcellations of the brain atlas. Then, we introduce a new brain function representation framework for the sampled patches. Each patch has its function description by referring to anchor patches, as well as the position description. Furthermore, we design an adaptive-selection-assisted Transformer network to optimize and integrate the function representations of all sampled patches within each brain for neural disease diagnosis. To validate our framework, we conduct extensive evaluations on three datasets, and the experimental results establish the effectiveness and generality of our proposed method, offering a promising avenue for advancing neural disease diagnosis beyond the confines of traditional atlas-based methods. Our code is available at https://github.com/mjliu2020/RandomFR.

An Exploratory, Randomized, Double-Blind Clinical Trial of Dipraglurant for Blepharospasm.

BACKGROUND: Blepharospasm is treated with botulinum toxin, but obtaining satisfactory results is sometimes challenging. OBJECTIVE: The aim is to conduct an exploratory trial of oral dipraglurant for blepharospasm. METHODS: This study was an exploratory, phase 2a, randomized, double-blind, placebo-controlled trial of 15 participants who were assigned to receive a placebo or dipraglurant (50 or 100 mg) and assessed over 2 days, 1 and 2 hours following dosing. Outcome measures included multiple scales rated by clinicians or participants, digital video, and a wearable sensor. RESULTS: Dipraglurant was well tolerated, with no obvious impact on any of the measurement outcomes. Power analyses suggested fewer subjects would be required for studies using a within-subject versus independent group design, especially for certain measures. Some outcome measures appeared more suitable than others. CONCLUSION: Although dipraglurant appeared well tolerated, it did not produce a trend for clinical benefit. The results provide valuable information for planning further trials in blepharospasm. © 2024 International Parkinson and Movement Disorder Society.

Polydopamine-Modified Titanium Dioxide Nanotube Arrays Doped with Calcium as a Sustained Drug Delivery System.

Titanium nanotube (TNT) arrays manufactured via electrochemical anodization have been widely used as local drug carriers due to their excellent biocompatibility and customizable nanotubular structures. However, the uncontrollable and abrupt drug release at the early stage decreases the drug release duration, leading to excessive drug concentration at the implantation site. In this study, a continuous drug delivery system based on TNTs was created. Initially, a basic ultrasound-assisted approach was utilized to deposit a polydopamine (PDA) coating onto TNTs to obtain PDA-modified TNTs. Next, TNTs-PDA were submerged in a calcium chloride solution to include Ca2+ through Ca2+ coordination between the PDA layer's catechol groups. Sodium alendronate (NaAL) was used as a model drug and loaded onto TNTs-PDA-Ca2+ by immersing them in an NaAL solution. In the final step, NaAL was covalently attached to TNTs-PDA-Ca2+ through coordination bonds with Ca2+. The samples underwent characterization through the use of various techniques, including field emission scanning electron microscopy, Fourier-transform infrared spectroscopy, X-ray diffraction patterning, X-ray photoelectron spectroscopy, and inductively coupled plasma emission spectrometry. The results indicated that the bioactivity of TNTs improved, and there was an enhancement in drug loading capacity and release performance due to modification with PDA and Ca2+. Furthermore, acidic conditions can cause significant drug release due to the cleavage of coordination bonds between the drug and Ca2+ ions. Thus, the aforementioned drug delivery system represents a potentially promising approach for achieving sustained and controllable drug release.

Fluorescence spectroscopic profiling of urine samples for predicting kidney transplant rejection.

Rejection is the primary factor affecting the functionality of a kidney post-transplant, where its prompt prediction of risk significantly influences therapeutic strategies and clinical outcomes. Current graft health assessment methods, including serum creatinine measurements and transplant kidney puncture biopsies, possess considerable limitations. In contrast, urine serves as a direct indicator of the graft's degenerative stage and provides a more accurate measure than peripheral blood analysis, given its non-invasive collection of kidney-specific metabolite. This research entailed collecting fluorescent fingerprint data from 120 urine samples of post-renal transplant patients using hyperspectral imaging, followed by the development of a learning model to detect various forms of immunological rejection. The model successfully identified multiple rejection types with an average diagnostic accuracy of 95.56 %.Beyond proposing an innovative approach for predicting the risk of complications post-kidney transplantation, this study heralds the potential introduction of a non-invasive, rapid, and accurate supplementary method for risk assessment in clinical practice.

Deep Image Matting With Sparse User Interactions.

Image matting is a fundamental and challenging problem in computer vision and graphics. Most existing matting methods leverage a user-supplied trimap as an auxiliary input to produce good alpha matte. However, obtaining high-quality trimap itself is arduous. Recently, some hint-free methods have emerged, however, the matting quality is still far behind the trimap-based methods. The main reason is that, some hints for removing semantic ambiguity and improving matting quality are essential. Apparently, there is a trade-off between interaction cost and matting quality. To balance performance and user-friendliness, we propose an improved deep image matting framework which is trimap-free and only needs sparse user click or scribble interaction to minimize the needed auxiliary constraints while still allowing interactivity. Moreover, we introduce uncertainty estimation that predicts which parts need polishing and conduct uncertainty-guided refinement. To trade off runtime against refinement quality, users can also choose different refinement modes. Experimental results show that our method performs better than existing trimap-free methods and comparably to state-of-the-art trimap-based methods with minimal user effort. Finally, we demonstrate the extensibility of our framework to video human matting without any structure modification, by adding optical flow-based sparse hint propagation and temporal consistency regularization imposed on the single frame.