EvRepSL: Event-Stream Representation via Self-Supervised Learning for Event-Based Vision.

Event-stream representation is the first step for many computer vision tasks using event cameras. It converts the asynchronous event-streams into a formatted structure so that conventional machine learning models can be applied easily. However, most of the state-of-the-art event-stream representations are manually designed and the quality of these representations cannot be guaranteed due to the noisy nature of event-streams.

Correction: Development of a carbon quantum dots-based fluorescent Cu probe suitable for living cell imaging.

Correction for 'Development of a carbon quantum dots-based fluorescent Cu probe suitable for living cell imaging' by Qiang Qu , , 2012, , 5473-5475, https://doi.org/10.1039/C2CC31000G.

Understanding collective behavior in biological systems through potential field mechanisms.

Collective behavior in biological systems emerges from local interactions among individuals, enabling groups to adapt to dynamic environments. Traditional modeling approaches, such as bottom-up and top-down models, have limitations in accurately representing these complex interactions. We propose a novel potential field mechanism that integrates local interactions and environmental influences to explain collective behavior.

Associations of serum uric acid-to-high density lipoprotein cholesterol ratio with age-related diseases and mortality among older population in the United States.

Background: Older adults experience numerous physiological and socioeconomic changes that increase the risk of chronic diseases. The uric acid to high-density lipoprotein cholesterol ratio (UHR) has emerged as a potential biomarker for assessing various health conditions. This study aimed to investigate the relationship between UHR and the prevalence of age-related diseases and mortality in a nationally representative sample of older adults in the United States.

Prognostic value of mechanical dyssynchrony in patients with heart failure: a systematic review.

Background: Heart failure (HF) significantly impacts quality of life and healthcare systems worldwide. Assessing left ventricular mechanical dyssynchrony (LVMD) is crucial for understanding cardiac function and optimizing treatments like cardiac resynchronization therapy (CRT). Phase analysis using gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has shown promise in predicting outcomes, yet recent comprehensive reviews are lacking.

A Data Driven Strategy for Implementation of Singlicate Analysis in Ligand Binding Assays Used for the Determination of Anti-drug Antibodies to a Multidomain Biotherapeutic.

A stepwise, data-driven approach for Anti-Drug Antibodies (ADA) singlicate assays has been developed to evaluate the feasibility of singlicate ligand binding assay (LBA) method development, qualification and validation to support our clinical programs. With initial precision runs in method validation and subsequent statistical analysis to directly compare duplicate and singlicate formats, our results indicated no meaningful difference in assay precision between duplicate and singlicate. Consequently, the rest of the ADA method validation proceeded with singlicate analysis yielding acceptable validation results.

The Association between Leucocyte Telomere Length and Survival Outcomes in Patients with Cardiovascular Disease.

Background: We explore the association between leucocyte telomere length (LTL) and all-cause and cardiovascular disease (CVD)-specific death in CVD patients.

Methods: We acquired 1599 CVD patients from a nationally representative US population survey for this study. We applied Kaplan-Meier curves, adjusted weighted Cox regression models, and restricted cubic spline to investigate the association between LTL and all-cause death.

Potential field mechanisms and distributed learning for enhancing the navigation of micro/nanorobot in biomedical environments.

In complex systems, single micro/nanorobots encounter challenges related to limited loading capacity and navigation, hindering their effective utilization in targeted therapy and drug delivery. To solve these challenges, this paper explores potential field mechanisms as a means to simulate natural collective behavior. This approach aims to enhance the navigation and efficiency of micro/nanorobots in high-demand therapeutic areas.

A Data Driven Strategy and Case Study for Implementation of Singlicate Analysis in Ligand Binding Assays Used for PK Quantitation.

Duplicate analysis has been a conventional practice in the industry for ligand-binding assays (LBA), particularly for plate-based platforms like Enzyme-linked immunosorbent assay (ELISA) and Meso Scale Discovery (MSD) assays. Recent whitepapers and guidance have opened a door to exploring the implementation of single-well (singlicate) analysis approach for LBAs. Although the bioanalytical industry has actively investigated the suitability of singlicate analysis, applications in supporting regulated LBA bioanalysis are limited.

Ceftazidime/avibactam versus polymyxin B in carbapenem-resistant Klebsiella pneumoniae infections: a propensity score-matched multicenter real-world study.

Objectives: In this retrospective observational multicenter study, we aimed to assess efficacy and mortality between ceftazidime/avibactam (CAZ/AVI) or polymyxin B (PMB)-based regimens for the treatment of Carbapenem-resistant Klebsiella pneumoniae (CRKP) infections, as well as identify potential risk factors.

Methods: A total of 276 CRKP-infected patients were enrolled in our study. Binary logistic and Cox regression analysis with a propensity score-matched (PSM) model were performed to identify risk factors for efficacy and mortality.

Development and validation of an automatic machine learning model to predict abnormal increase of transaminase in valproic acid-treated epilepsy.

Valproic acid (VPA) is a primary medication for epilepsy, yet its hepatotoxicity consistently raises concerns among individuals. This study aims to establish an automated machine learning (autoML) model for forecasting the risk of abnormal increase of transaminase levels while undergoing VPA therapy for 1995 epilepsy patients. The study employed the two-tailed T test, Chi-square test, and binary logistic regression analysis, selecting six clinical parameters, including age, stature, leukocyte count, Total Bilirubin, oral dosage of VPA, and VPA concentration.

Trends in cardiovascular risk factor prevalence, treatment, and control among US adolescents aged 12 to 19 years, 2001 to March 2020.

Background: Early-life cardiovascular risk factors (CVRFs) are known to be associated with target organ damage during adolescence and premature cardiovascular morbidity and mortality during adulthood. However, contemporary data describing whether the prevalence of CVRFs and treatment and control rates have changed are limited. This study aimed to examine the temporal trends in the prevalence, treatment, and control of CVRFs among US adolescents over the past 2 decades.

Efficacy of omadacycline in the treatment of : a case report.

, one of the main pathogens that causes community-acquired pneumonia, can lead to , a condition characterized predominantly by severe pneumonia. This disease, caused by the bacterium , can quickly progress to critical pneumonia and is often associated with damage to multiple organs. As a result, it requires close attention in terms of clinical diagnosis and treatment.

An Effective DNA-Based File Storage System for Practical Archiving and Retrieval of Medical MRI Data.

DNA-based data storage is a new technology in computational and synthetic biology, that offers a solution for long-term, high-density data archiving. Given the critical importance of medical data in advancing human health, there is a growing interest in developing an effective medical data storage system based on DNA. Data integrity, accuracy, reliability, and efficient retrieval are all significant concerns.

Generating Multi-Depth 3D Holograms Using a Fully Convolutional Neural Network.

Efficiently generating 3D holograms is one of the most challenging research topics in the field of holography. This work introduces a method for generating multi-depth phase-only holograms using a fully convolutional neural network (FCN). The method primarily involves a forward-backward-diffraction framework to compute multi-depth diffraction fields, along with a layer-by-layer replacement method (LRM) to handle occlusion relationships.

Optimizing hydropower scheduling through accurate power load prediction: A practical case study.

Hydropower stations that are part of the grid system frequently encounter challenges related to the uneven distribution of power generation and associated benefits, primarily stemming from delays in obtaining timely load data. This research addresses this issue by developing a scheduling model that combines power load prediction and dual-objective optimization. The practical application of this model is demonstrated in a real-case scenario, focusing on the Shatuo Hydropower Station in China.

Few-Shot Relation Extraction With Automatically Generated Prompts.

Relation extraction (RE) tends to struggle when the supervised training data is few and difficult to be collected. In this article, we elicit relational and factual knowledge from large pretrained language models (PLMs) for few-shot RE (FSRE) with prompting techniques. Concretely, we automatically generate a diverse set of natural language templates and modulate PLM's behavior through these prompts for FSRE.

The Burgeoning Significance of Liquid-Liquid Phase Separation in the Pathogenesis and Therapeutics of Cancers.

Liquid-liquid phase separation (LLPS) is a physiological phenomenon that parallels the mixing of oil and water, giving rise to compartments with diverse physical properties. Biomolecular condensates, arising from LLPS, serve as critical regulators of gene expression and control, with a particular significance in the context of malignant tumors. Recent investigations have unveiled the intimate connection between LLPS and cancer, a nexus that profoundly impacts various facets of cancer progression, including DNA repair, transcriptional regulation, oncogene expression, and the formation of critical membraneless organelles within the cancer microenvironment.

NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods.

Neural View Synthesis (NVS) has demonstrated efficacy in generating high-fidelity dense viewpoint videos using a image set with sparse views. However, existing quality assessment methods like PSNR, SSIM, and LPIPS are not tailored for the scenes with dense viewpoints synthesized by NVS and NeRF variants, thus, they often fall short in capturing the perceptual quality, including spatial and angular aspects of NVS-synthesized scenes. Furthermore, the lack of dense ground truth views makes the full reference quality assessment on NVS-synthesized scenes challenging.

Effects of Fe-N co-modified biochar on methanogenesis performance, microbial community, and metabolic pathway during anaerobic co-digestion of alternanthera philoxeroides and cow manure.

The performance of anaerobic digestion (AD) is susceptible to disturbances in feedstock degradation, intermediates accumulation, and methanogenic archaea activity. To improve the methanogenesis performance of the AD system, Fe-N co-modified biochar was prepared under different pyrolysis temperatures (300,500, and 700 °C). Meanwhile, pristine and Fe-modified biochar were also derived from alternanthera philoxeroides (AP).