Different responses of soil bacterial community to plant-plant interactions under organic-inorganic fertilizers affect seedling establishment during subalpine forest succession.

Introduction: Rhizosphere bacterial community as a valuable indicator of soil quality and function, has been widespread studied. However, little knowledge is about the response of bacterial communities to plant-plant interaction and different fertilizers during secondary forest succession. Methods: We conducted a field pot experiment applying organic and inorganic fertilizers to monocultures and mixed cultures of dominant plant species from mid- to late-successional stages (Salix oritrepha, Betula albosinensis, and Picea asperata), and investigated the responses of plant growth and rhizosphere bacterial communities. Results and discussion: Results indicated that growth rate of plant height varied among plant species, but no significant differences were observed in soil bacterial diversity and composition among plant species or inter-specific interactions under control. Compared to control, inorganic fertilizer resulted in increases in plant growth and the relative abundance of Proteobacteria, Patescibacteria, Bacteroidetes and Gemmatimonadetes, while simultaneously leading to decrease in the relative abundance of Acidobacteria, Actinobacteria, Chloroflexi, Rokubacteria and Planctomycetes. When grown with other species, the bacterial communities in the mixture resembled those of S. oritrepha in singular monoculture under inorganic fertilizer treatment, but plant growth was not affected by interspecific interaction. Unlike inorganic fertilizer, organic fertilizer significantly affected bacterial communities and increased bacterial diversity, but did not alter the effects of plant-plant interactions on bacterial communities. It was also observed that organic fertilizer facilitated later successional species' growth (P. asperata and B. albosinensis) by the mid-successional species (S. oritrepha), ultimately facilitating secondary forest succession. In addition, plants at different successional stages harbor specific bacterial communities to affect their growth, and the bacterial communities contributed more than soil properties to the variations in the plant growth of S. oritrepha and P. asperata though the bacterial communities were regulated by soil factors. This finding highlights the significance of the rhizosphere bacteria on plant growth and plant community succession. It also emphasize the importance of considering both plant-plant interactions and diverse fertilizer types in forest restoration efforts and provide valuable insights into optimizing agronomic practices for secondary forest succession.

An active damping control strategy for suppressing LCL resonant point migration for three-phase grid-tied inverter.

LCL filters are extensively utilized in Grid-connected inverters due to their exceptional capability in suppressing high-frequency harmonics. The active damping method is commonly employed to mitigate the resonance peak of the LCL filter. However, this control strategy induces a shift in the natural resonance point. To address this issue, a novel active damping control strategy based on the principle of equivalent transformation is proposed in this paper, which not only effectively suppresses the resonance peak but also avoids deviation from the natural resonance point. Finally, experiments are carried out on a three-phase LCL Grid-connected inverter, and the experimental results show that the control strategy has good steady-state performance, dynamic response, and robustness under both rigid and ultra-weak network conditions.

Axial Impact Response of Carbon Fiber-Reinforced Polymer Structures in High-Speed Trains Based on Filament Winding Process.

The continuous increase in the operating speed of rail vehicles demands higher requirements for passive safety protection and lightweight design. This paper focuses on an energy-absorbing component (circular tubes) at the end of a train. Thin-walled carbon fiber-reinforced polymer (CFRP) tubes were prepared using the filament winding process. Through a combination of sled impact tests and finite element simulations, the effects of a chamfered trigger (Tube I) and embedded trigger (Tube II) on the impact response and crashworthiness of the structure were investigated. The results showed that both triggering methods led to the progressive end failure of the tubes. Tube I exhibited a mean crush force (MCF) of 891.89 kN and specific energy absorption (SEA) of 38.69 kJ/kg. In comparison, the MCF and SEA of Tube II decreased by 21.2% and 21.9%, respectively. The reason for this reduction is that the presence of the embedded trigger in Tube II restricts the expansion of the inner plies (plies 4 to 6), thereby affecting the overall energy absorption mechanism. Based on the validated finite element model, a modeling strategy study was conducted, including the failure parameters (DFAILT/DFAILC), the friction coefficient, and the interfacial strength. It was found that the prediction results are significantly influenced by modeling methods. Specifically, as the interfacial strength decreases, the tube wall is more prone to circumferential cracking or overall buckling under axial impact.

Exploring Multiconnectivity and Subdivision Functions of Brain Network via Heterogeneous Graph Network for Cognitive Disorder Identification.

Brain serves as a critical cornerstone of human intelligence, which involves a series of complex neuropsychological activities that lead to the coordination of various functions in the brain network. In recent years, brain network analysis methods based on graph neural networks (GNNs) have attracted increasing attention for the identification of brain disorders. However, these methods generally assume that the brain network is a homogeneous graph while ignoring its heterogeneity among human brain activities, which is reflected in both the complex connectivity of the brain network and distinctive brain functions. To overcome this problem, we propose a heterogeneous subdivision GNN (HSGNN), which captures the heterogeneous connections and functions of the brain network simultaneously. Specifically, we first employ two fundamental brain connectivity patterns to capture both statistical dependency and directional information flow among different brain regions and construct a heterogeneous brain connectivity network for each subject. Then, we develop a functional subdivision method that encodes brain networks into multiple latent feature subspaces corresponding to heterogeneous brain functions and extracts features of brain networks accordingly. Considering the intricate interactions of brain functions to facilitate cognitive activities within the brain network, we further employ the self-attention mechanism to obtain comprehensive representations of brain networks in a joint latent space. Finally, we propose a composite loss function to train the model for obtaining the heterogeneous brain network representation, which can be utilized for disease classification. The experimental results in the Alzheimer's Disease Neuroimaging Initiative (ADNI) and Autism Brain Imaging Data Exchange (ABIDE) datasets demonstrate that our method outperforms several state-of-the-art (SOTA) methods to identify different types of brain cognitive-related disorders.

Hyper-Interferon Sensitive influenza induces adaptive immune responses and overcomes resistance to anti-PD-1 in murine non-small cell lung cancer.

Despite recent advances in immunotherapy with immune checkpoint inhibitors (ICI), many patients with non-small cell lung cancer (NSCLC) fail to respond or develop resistance after an initial response. In situ vaccination (ISV) with engineered viruses has emerged as a promising antigen-agnostic strategy that can both condition the tumor microenvironment (TME) and augment anti-tumor T cell responses to overcome immune resistance. We engineered a live attenuated viral vaccine, Hyper-Interferon Sensitive virus (HIS), by conducting a genome-wide functional screening and introducing eight interferon (IFN)-sensitive mutations in the influenza genome. Compared to wild-type (WT) influenza, HIS replication was attenuated in immunocompetent hosts, enhancing its potential as a safe option for cancer therapy. HIS ISV elicited robust yet transient type I IFN responses in murine NSCLCs, leading to an enrichment of polyfunctional effector Th1 CD4 and cytotoxic CD8 T cells into the tumor. HIS ISV demonstrated enhanced anti-tumor efficacy compared to WT in multiple syngeneic murine models of NSCLC with distinct driver mutations and varying mutational burden. This efficacy was dependent on host type 1 IFN responses and T lymphocytes. HIS ISV overcame resistance to anti-PD-1 in LKB-1 deficient murine NSCLC, resulting in improved overall survival and enduring systemic tumor-specific immunity. These studies provide compelling evidence to support further clinical evaluation of HIS as a novel 'off-the-shelf' ISV strategy for patients with NSCLC refractory to ICI.

Design, Synthesis, and Antimicrobial Activity of Amide Derivatives Containing Cyclopropane.

As an important small organic molecule, cyclopropane is widely used in drug design. In this paper, fifty-three amide derivatives containing cyclopropane were designed and synthesized by introducing amide groups and aryl groups into cyclopropane through the active splicing method, and their antibacterial and antifungal activities were evaluated in vitro. Among them, thirty-five compounds were new compounds, and eighteen compounds were known compounds (F14, F15, F18, F20-F26, F36, and F38-F44). Bioassay results disclosed that four, three, and nine of the compounds showed moderate activity against Staphylococcus aureus, Escherichia coli, and Candida albicans, respectively. Three compounds were sensitive to Candida albicans, with excellent antifungal activity (MIC80 = 16 µg/mL). The molecular docking results show that compounds F8, F24, and F42 have good affinity with the potential antifungal drug target CYP51 protein.

Detection of Short-Chain Chlorinated Aliphatic Hydrocarbons through an Engineered Biosensor with Tailored Ligand Specificity.

Short-chain chlorinated aliphatic hydrocarbons (SCAHs), commonly used as industrial reagents and solvents, pose a significant threat to ecosystems and human health as they infiltrate aquatic environments due to extensive usage and accidental spills. Whole-cell biosensors have emerged as cost-effective, rapid, and real-time analytical tools for environmental monitoring and remediation. While the broad ligand specificity of transcriptional factors (TFs) often prohibits the application of such biosensors. Herein, we exploited a semirational transition ligand approach in conjunction with a positive/negative fluorescence-activated cell sorting (FACS) strategy to develop a biosensor based on the TF AlkS, which is highly specific for SCAHs. Furthermore, through promoter-directed evolution, the performance of the biosensor was further enhanced. Mutation in the -10 region of constitutive promoter PalkS resulted in reduced AlkS leakage expression, while mutation in the -10 region of inducible promoter PalkB increased its accessibility to the AlkS-SCAHs complex. This led to an 89% reduction in background fluorescence leakage of the optimized biosensor, M2-463, further enhancing its response to SCAHs. The optimized biosensor was highly sensitive and exhibited a broader dynamic response range with a 150-fold increase in fluorescence output after 1 h of induction. The detection limit (LOD) reached 0.03 ppm, and the average recovery rate of SCAHs in actual water samples ranged from 95.87 to 101.20%. The accuracy and precision of the proposed biosensor were validated using gas chromatography-mass spectrometry (GC-MS), demonstrating the promising application for SCAH detection in an actual environment sample.

Repetitive Transcranial Magnetic Stimulation Alleviates MPTP-Induced Parkinson's Disease Symptoms by Regulating CaMKII-CREB-BMAL1 Pathway in Mice Model.

Background: Repetitive transcranial magnetic stimulation (rTMS) is a noninvasive neuromodulation technique that shows promise for the treatment of Parkinson's disease (PD). However, there is still limited understanding of the optimal stimulation frequencies and whether rTMS can alleviate PD symptoms by regulating the CaMKII-CREB-BMAL1 pathway. Methods: A PD mouse model was induced intraperitoneally with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) and treated with 1 Hz, 5 Hz, and 10 Hz rTMS. The neurological function, survival of dopaminergic neurons, and protein levels of Tyrosine hydroxylase (TH), α-synuclein(α-syn), and brain-derived neurotrophic factor (BDNF) in the striatum were measured to determine the optimal stimulation frequencies of rTMS treatment in PD mice. The levels of melatonin, cortisol, and the circadian rhythm of Brain and muscle ARNT-like 1 (BMAL1) in PD model mice were detected after optimal frequency rTMS treatment. Additionally, KN-93 and Bmal1siRNA interventions were used to verify that rTMS could alleviate PD symptoms by regulating the CaMKII-CREB-BMAL1 pathway. Results: Administration of 10 Hz rTMS significantly improved neurological function, increased the protein levels of TH and BDNF, and inhibited abnormal aggregation of a-syn. Furthermore, administration of 10 Hz rTMS regulated the secretion profile of cortisol and melatonin and reversed the circadian arrhythmia of BMAL1 expression. After the KN-93 intervention, the MPTP+rTMS+KN-93 group exhibited decreased levels of P- Ca2+/calmodulin-dependent protein kinase II (CaMKII)/CaMKII, P-cAMP-response-element-binding protein (CREB)/CREB, BMALI, and TH. After Bmal1siRNA intervention, the protein levels of BMAL1 and TH were significantly reduced in the MPTP+10 Hz+ Bmal1siRNA group. At the same time, there were no significant changes in the proportions of P-CaMKIIα/CaMKIIα and P-CREB/CREB expression levels. Finally, immunohistochemical analysis showed that the number of TH-positive neurons was high in the MPTP+10 Hz group, but decreased significantly after KN-93 and Bmal1siRNA interventions. Conclusion: Treatment with 10 Hz rTMS alleviated MPTP-induced PD symptoms by regulating the CaMKII-CREB-BMAL1 pathway. This study provides a comprehensive perspective of the therapeutic mechanisms of rTMS in PD.

An analysis of the safety of Sevoflurane drugs: A disproportionality analysis based on Food and Drug Administration Adverse Event Reporting System.

Sevoflurane is a volatile anesthetic that can tolerate inhalation induction and is widely used for inducing anesthesia due to its pleasant odor. As a drug that has been on the market for nearly 30 years, the vast majority of adverse reactions have been documented. This study aims to improve the adverse reactions related to Sevoflurane through the mining, organizing and analysis of Food and Drug Administration Adverse Event Reporting System database data. We collected, organized, and analyzed reports from the first quarter of 2004 to the fourth quarter of 2022. We performed disproportionality analysis algorithms, including reporting odds ratio, the proportional reporting ratio values, to quantify the signal values of different adverse events (AEs). A total of 1126 AEs and 27 system organ classes were identified by performing statistics analysis system software. By combining algorithm calculations, we create a forest map of the top 30 AEs of the reporting odds ratio signal. Based on the reviewing relevant literature, we found that the vast majority of AEs have been reported in relevant studies. However, there is currently no study revealing the correlation between atrial fibrillation and Sevoflurane, which means that atrial fibrillation may be an unreported AE of Sevoflurane. In the present study, we found that atrial fibrillation may be a new adverse reaction of Sevoflurane through the Food and Drug Administration Adverse Event Reporting System database, which can function as a novel guideline to guide us in the more standardized use of Sevoflurane in clinical practice.

CTFusion: CNN-transformer-based self-supervised learning for infrared and visible image fusion.

Infrared and visible image fusion (IVIF) is devoted to extracting and integrating useful complementary information from muti-modal source images. Current fusion methods usually require a large number of paired images to train the models in supervised or unsupervised way. In this paper, we propose CTFusion, a convolutional neural network (CNN)-Transformer-based IVIF framework that uses self-supervised learning. The whole framework is based on an encoder-decoder network, where encoders are endowed with strong local and global dependency modeling ability via the CNN-Transformer-based feature extraction (CTFE) module design. Thanks to the development of self-supervised learning, the model training does not require ground truth fusion images with simple pretext task. We designed a mask reconstruction task according to the characteristics of IVIF, through which the network can learn the characteristics of both infrared and visible images and extract more generalized features. We evaluated our method and compared it to five competitive traditional and deep learning-based methods on three IVIF benchmark datasets. Extensive experimental results demonstrate that our CTFusion can achieve the best performance compared to the state-of-the-art methods in both subjective and objective evaluations.

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.

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.

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.

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.

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.

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).

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.

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).

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.