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A randomized, double-blind, placebo-controlled phase 2 study of eltrekibart, a novel septa-specific monoclonal antibody to CXCR1/2 ligands, in adults with hidradenitis suppurativa

The AOT Characters Dataset consists of 4,041 manually collected images of 14 characters from Attack on Titan, captured across diverse scenes and expressions. The data was gathered via HD screenshots and carefully cleaned by removing blurry or obstructed frames. When tested with various deep learning models, results showed competitive accuracy with Deep Danbooru on the full dataset. This demonstrates the dataset’s value for anime-specific tasks like character classification and supports reproducible model evaluation in stylized visual media.

Foram utilizados 50 camundongos machos Swiss, distribuídos em três grupos: dieta padrão (CTRL), dieta hiperlipídica contínua (HFD) e dieta hiperlipídica seguida de transição para dieta padrão (HFDt). O estudo avaliou composição corporal, morfometria da vesícula seminal, marcadores de estresse oxidativo (NOX-1, MPO, nitrotirosina), inflamação (TNF-α, IL-6, IL-10) e atividade glandular por imuno-histoquímica (SVS2, SVS3 e AR), além dos níveis plasmáticos de testosterona por ELISA.

This is a dataset of turbulent flows over two-dimensional (2D) ridges and three-dimensional (3D) hills with smooth and rough surfaces measured in a boundary layer wind tunnel. The dataset is based on the study reported in the research article entitled “A wind tunnel study of turbulent flow over a three-dimensional steep hill” by Ishihara, Hibi and Oikawa (1999), which examined the mean flow and turbulence characteristics over a 3D steep hill and its near-wake dynamics. The three velocity components on multiple representative horizontal and vertical planes around the hilly terrains were measured under different surface roughness conditions. Mean and fluctuating velocity profiles in the wake region are presented along with details of the measurement and data acquisition procedures.

LA-ICP-MS and atom probe data of sphalerite from Wusihe, Qingshan, and Shanshulin deposits.

Monkeypox virus (MPXV) has caused the 2022-2023 global mpox outbreak and the concurrent outbreaks in Africa. Mpox disproportionately affects immunologically vulnerable populations such as people living with HIV (PLWH). Currently no approved treatment is available. In response to ongoing global and endemic mpox outbreaks and to prepare for future poxvirus epidemics, we developed a robust artificial intelligence (AI) pipeline for discovering broad-spectrum poxvirus inhibitors that target the viral DNA polymerases. Among the identified leading candidates, we found that the clinically used antiretroviral drugs bictegravir and etravirine potently inhibit MPXV clade Ia, Ib and IIb infections in human intestinal and skin organoids. The broad anti-poxvirus activities of bictegravir and etravirine were further demonstrated against infections of other Orthopoxviruses such as vaccinia virus and cowpox virus. These findings support the repurposing of bictegravir and etravirine for treating mpox, especially for patients co-infected with HIV, warranting follow-up clinical investigation. The established AI pipeline and our antiviral drug discovery strategies bear major implications for responding to the ongoing mpox emergency and preparing for future poxvirus epidemics.  

Datasets used to generate the visualization supporting the paper entitled "Liquid metal magnetohydrodynamic mixed convection flow in a rectangular channel with volumetric heating and internally aligned cooling pipes"

This repository contains the source data and code for the paper titled *“Experimental Study on the Generalised Evolution Characteristics of Bedload Clusters.”* It includes the improved particle-tracking algorithm and the data tables for all figures presented in the paper; detailed descriptions are provided within the files.

Meta-analysis of raw data

This dataset was compiled as part of a project to design a cold storage system to combat post-harvest food loss in developing regions by integrating IoT technology with predictive machine learning. The project, which was developed for use by smallholder farmers in Uganda, aims to monitor and proactively control the environmental conditions in cold storage units to extend the shelf life of perishable goods. This dataset is specifically structured for machine learning applications, serving as the training and validation data for machine learning models. It contains environmental data points collected in a controlled cold storage environment. The data is organized into a comma-separated value (CSV) file with a total of 10996 entries in the following six columns: Fruit: A categorical variable indicating the type of fruit being stored (e.g., Orange, Pineapple, Banana, Tomato). Temp: The temperature inside the cold storage unit, measured in degrees Celsius (°C). Humid: The relative humidity (RH) of the environment, measured as a percentage (%). Light: The intensity of light exposure, measured in Lux. CO2: The concentration of carbon dioxide (CO₂) in the air, measured in parts per million (ppm). Class: A binary classification label (Good or Bad) that serves as the target variable for the predictive model, indicating whether the environmental conditions are optimal or suboptimal for spoilage prevention. The data's primary purpose is to provide a basis for training predictive models to classify environmental conditions and assess spoilage risk. The dataset is a valuable resource for researchers and practitioners in fields such as smart agriculture, food science, embedded systems, and machine learning. It can be used to: Train, validate, and test new predictive models for food spoilage. Analyze the correlation between specific environmental factors (temperature, humidity, CO2, and light) and fruit spoilage outcomes. Support the development of low-cost, intelligent monitoring systems for cold chain logistics and food preservation. This dataset and the associated project are intended to contribute to achieving the United Nations Sustainable Development Goals (SDGs), particularly those related to food security and sustainable agriculture.