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). Codes are available at https://github.com/liuqk3/PUT.

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.

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.

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.

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.

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.

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.

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.

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.

Technology and engineering test of filling goaf with coal gangue slurry.

Based on the greening and low interference disposal requirements of coal gangue in high-yield and high-efficiency mines in Inner Mongolia and Shaanxi, and by integrating the existing theories and technologies such as underground filling technology of coal gangue, mine yellow mud grouting technology, and the evolution law of mining overburden fractures, a technology of filling goaf with coal gangue slurry for green disposal of coal gangue is proposed. The principle and technical framework of the technology of filling goaf with coal gangue slurry are clarified. This paper expounds on the technological process of technology of filling goaf with coal gangue slurry, establishes three types of slurry filling systems, such as centralized ground layout, ground and underground coordinated layout, and centralized underground layout, and constructs three slurry filling methods, including low-level grouting, adjacent level grouting, and high-level grouting, forming seven kinds of technology models of filling goaf with coal gangue slurry, including ground centralized pulping + high-level grouting, ground centralized pulping + adjacent grouting, ground centralized pulping + low level grouting, ground and underground coordinated pulping + adjacent grouting, ground and underground coordinated pulping + low level grouting, underground centralized pulping + adjacent grouting, and underground centralized pulping + low level grouting, and gives the selection process of technology models of filling goaf with coal gangue slurry. Based on the different conditions and requirements of Haidaze Coal Mine and Huangling No. 2 Coal Mine, engineering tests were carried out on two different technology modes, namely, ground and underground coordinated pulping + low level grouting and ground and underground coordinated pulping + adjacent grouting, based on simulation tests of low level grouting and adjacent grouting. The tests prove the feasibility of filling goaf with coal gangue slurry and explore the way for the theoretical research of the technology of filling goaf with coal gangue slurry and greening and low interference disposal of coal gangue.

The J2 evolution model and control technology of the main roadway surrounding rock under superimposed influence of double-coal seam mining.

Under double-seam mining, the main roadway surrounding rock is affected by the superposition of the advanced stress of the two-seam coal working faces. The stress superposition mode and degree are of great significance to the width calculation of the protective coal pillar and the determination of the critical control direction of the surrounding rock. This paper uses theoretical analysis, numerical simulation, and site engineering practice to carry out targeted research. The conclusions are as follows: Under different lateral pressure coefficients, the superposition evolution law of maximum principal stress direction of two coal seams with different offsets; Two developmental trends and three types of evolution models of J2 peak zone (the critical area of the stress increase and deflection changes) under different superimposed loading modes are summarized. Based on the typical asymmetric evolution model of the J2 peak zone, an asymmetric truss-cable co-anchoring method is proposed aimed at the J2 critical zone. The field monitoring results show that the main roadway surrounding rock is stable after support when the upper coal seam protective coal pillar is left 80 m, and the lower one is 60 m wide. It is of great reference importance for similar engineering practices.

Complex nonmonotonic responses of biodiversity to habitat destruction.

It has typically been assumed that habitat destruction, characterized by habitat loss and fragmentation, has consistently negative effects on biodiversity. While numerous empirical studies have shown the detrimental effects of habitat loss, debate continues as to whether habitat fragmentation has universally negative effects. To explore the effects of habitat fragmentation, we developed a simple model for site-occupancy dynamics in fragmented landscapes. With the model, we demonstrate that a competition-colonization trade-off can result in nonlinear oscillatory responses in biodiversity to both habitat loss and fragmentation. However, the overall pattern of habitat loss reducing species richness is still established, in line with empirical observations. Interestingly, the existence of localized oscillations in biodiversity can explain the mixed responses of species richness to habitat fragmentation per se observed in nature, thereby reconciling the debate on the fragmentation-diversity relationship. Therefore, this study offers a parsimonious mechanistic explanation for empirically observed biodiversity patterns in response to habitat destruction.

Engineering the Ultrasensitive Visual Whole-Cell Biosensors by Evolved MerR and 5' UTR for Detection of Ultratrace Mercury.

The existing mercury whole-cell biosensors (WCBs, parts per billion range) are not able to meet the real-world requirements due to their lack of sensitivity for the detection of ultratrace mercury in the environment. Ultratrace mercury is a potential threat to human health via the food chain. Here, we developed an ultrasensitive mercury WCB by directed evolution of the mercury-responsive transcriptional activator (MerR) sensing module to detect ultratrace mercury. Subsequently, the mutant WCB (m4-1) responding to mercury in the parts per trillion range after 1 h of induction was obtained. Its detection limit (LOD) was 0.313 ng/L, comparable to those of some analytical instruments. Surprisingly, the m4-1 WCB also responded to methylmercury (LOD = 98 ng/L), which is far more toxic than inorganic mercury. For more convenient detection, we have increased another green fluorescent protein reporter module with an optimized 5' untranslated region (5' UTR) sequence. This yields two visual WCBs with an enhanced fluorescence output. At a concentration of 2.5 ng/L, the fluorescence signals can be directly observed by the naked eye. With the combination of mobile phone imaging and image processing software, the 2GC WCB provided simple, rapid, and reliable quantitative and qualitative analysis of real samples (LOD = 0.307 ng/L). Taken together, these results indicate that the ultrasensitive visual whole-cell biosensors for ultratrace mercury detection are successfully designed using a combination of directed evolution and synthetic biotechnology.

A Fast Simulation Method for Evaluating the Single-Event Effect in Aerospace Integrated Circuits.

With the continuous progress in integrated circuit technology, single-event effect (SEE) has become a key factor affecting the reliability of aerospace integrated circuits. Simulating fault injection using the computer simulation technique effectively reflects the SEE in aerospace integrated circuits. Due to various masking effects, only a small number of faults will result in errors; the traditional method of injecting one fault in one workload execution is inefficient. The method of injecting multiple faults in one workload execution will make it impossible to judge which fault results in errors because the propagation characteristic of SEE and faults may affect each other. This paper proposes an improved multi-point fault injection method to improve simulation efficiency and solve the problems of the general multi-point fault injection method. If one workload execution does not result in errors, multiple faults can be verified by one workload execution. If one workload execution results in errors, a specific grouping method can be used to determine which faults result in errors. The experimental results show that the proposed method achieves a good acceleration effect and significantly improves the simulation efficiency.

Copper Site Motion Promotes Catalytic NOx Reduction under Zeolite Confinement.

Ammonia-mediated selective catalytic reduction (NH3-SCR) is currently the key approach to abate nitrogen oxides (NOx) emitted from heavy-duty lean-burn vehicles. The state-of-art NH3-SCR catalysts, namely, copper ion-exchanged chabazite (Cu-CHA) zeolites, perform rather poorly at low temperatures (below 200 °C) and are thus incapable of eliminating effectively NOx emissions under cold-start conditions. Here, we demonstrate a significant promotion of low-temperature NOx reduction by reinforcing the dynamic motion of zeolite-confined Cu sites during NH3-SCR. Combining complex impedance-based in situ spectroscopy (IS) and extended density-functional tight-binding molecular dynamics simulation, we revealed an environment- and temperature-dependent nature of the dynamic Cu motion within the zeolite lattice. Further coupling in situ IS with infrared spectroscopy allows us to unravel the critical role of monovalent Cu in the overall Cu mobility at a molecular level. Based on these mechanistic understandings, we elicit a boost of NOx reduction below 200 °C by reinforcing the dynamic Cu motion in various Cu-zeolites (Cu-CHA, Cu-ZSM-5, Cu-Beta, etc.) via facile postsynthesis treatments, either in a reductive mixture at low temperatures (below 250 °C) or in a nonoxidative atmosphere at high temperatures (above 450 °C).

Deep mutational scanning of influenza A virus neuraminidase facilitates the identification of drug resistance mutations in vivo.

IMPORTANCE: NA is a crucial surface antigen and drug target of influenza A virus. A comprehensive understanding of NA's mutational effect and drug resistance profiles in vivo is essential for comprehending the evolutionary constraints and making informed choices regarding drug selection to combat resistance in clinical settings. In the current study, we established an efficient deep mutational screening system in mouse lung tissues and systematically evaluated the fitness effect and drug resistance to three neuraminidase inhibitors of NA single-nucleotide mutations. The fitness of NA mutants is generally correlated with a natural mutation in the database. The fitness of NA mutants is influenced by biophysical factors such as protein stability, complex formation, and the immune response triggered by viral infection. In addition to confirming previously reported drug-resistant mutations, novel mutations were identified. Interestingly, we identified an allosteric drug-resistance mutation that is not located within the drug-binding pocket but potentially affects drug binding by interfering with NA tetramerization. The dual assessments performed in this study provide a more accurate assessment of the evolutionary potential of drug-resistant mutations and offer guidance for the rational selection of antiviral drugs.