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In vivo study characterization and analysis, Figure 6 part 2

Autism is one of the most common neurodevelopmental conditions worldwide, impacting roughly 1 in 36 children, as per the latest estimates from the Centers for Disease Control and Prevention (CDC). Autism is associated with lifelong challenges, leading to significant social, educational, and psychological impacts for both the children and their families. Yet, in Iraq, there exists a critical lack of research on this condition to the extent that no official estimate of its prevalence has been reported. This gap severely hinders the ability to provide adequate healthcare and support systems for these children and their families. To address this gap, the International Branch of Tehran University for Medical Sciences and Al-Subtain Academy for Autism and Neurodevelopmental Disorders have conducted nine studies as a part of a research collaboration titled “Epidemiological, Clinical, and Psychosocial Aspects of Iraqi Children with Autism”. As a part of this collaboration, we showed that psychosocial quality of life is generally lower than physical quality of life for parents of autistic children, highlighting the nature of the majority of challenges associated with providing care for these children. Using multiple linear regression analysis, lower quality of life was demonstrated among mothers, younger parents, those with female children, and those whose children had other comorbid conditions. Our results can be used to improve the allocation of support, including interventions aimed at enhancing family functioning, to parents who truly need it.

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This dataset contains raw experimental data acquired from H₂ temperature-programmed reduction (H₂-TPR), CO₂ temperature-programmed desorption (CO₂-TPD), X-ray diffraction (XRD), and transmission electron microscopy (TEM) analyses. The dataset includes mass spectrometry data presented as mass-to-charge ratio (m/z) versus time and micro-gas chromatography (micro-GC) outputs given as peak area versus time or temperature. These data support the characterization and evaluation of catalytic materials.

The content of elements in 426 wines (Muscat, Riesling, Chardonnay, Merlot and Cabernet)

This dataset contains culture-based enumeration and phenotypic characterization of bacteria isolated from five municipal sewage treatment plants (STPs) in Gujarat, western India—two located in Vadodara District and three in Anand District. Water samples were collected from three major treatment stages at each STP: (i) Influent (raw incoming sewage), (ii) Aeration tank (biological treatment zone), and (iii) Final effluent (post-chlorination discharge point). The primary goal was to analyze variations in bacterial load and morphology throughout the treatment process, with a specific focus on the impact of chlorination. Sterile 50 mL centrifuge tubes were autoclaved and used for sampling. Samples were immediately transported to the laboratory in ice-packed containers to maintain bacterial viability. Serial dilutions up to 10⁻⁸ were prepared using 0.8% saline and 100 µL from each dilution was plated on nutrient agar using the spread plate method. After 24 hours of incubation at 37°C, colony-forming units per milliliter (CFU/mL) were calculated for each sample. Isolated colonies were further sub-cultured and studied for colony morphology (color, size, margin, elevation, and surface characteristics) and Gram reaction via standard Gram staining procedures. This dataset includes: CFU/mL data for each treatment stage across all five STPs; Morphological traits for each distinct bacterial colony type; Gram reaction results (positive/negative); Graphical representation of bacterial reduction across treatment stages; Comparative analysis between STPs of Vadodara and Anand Districts; and Correlation plots showing relationships between treatment efficiency and bacterial type distribution. This dataset provides important baseline information about microbial diversity, abundance, and disinfection response in domestic sewage environments. It is highly relevant for researchers working on environmental microbiology, wastewater treatment, water quality assessment, and antimicrobial resistance (AMR) tracking. These data may also assist in selecting target organisms for genomic or transcriptomic studies in future investigations focused on AMR development due to disinfection stress. The data are presented in structured spreadsheets with accompanying legends and metadata files. Files are organized by District and STP with all associated morphological and Gram-staining observations. Visuals such as bar plots and correlation heatmaps are included in PNG format. This resource can serve as a baseline for future comparative studies and aid in decision-making related to wastewater treatment upgrades or AMR mitigation efforts in STPs at global scale.

Data for: Lithium isotopes reveal silicate weathering-driven carbonate formation in marine sediments

This is a Maple program sheet, providing instruction for the numerical solution of the paper "A two-sector model of healthcare provision with directed search" by Karine Lamiraud et Radu Vranceanu (ESSEC Business School), April 2025.

LitchiLeaf4001 is a curated image dataset focused exclusively on lychee (Litchi chinensis) leaves. It was collected to support research and development in computer vision, machine learning, and deep learning for plant disease detection. This dataset is designed to empower the agricultural AI community, particularly in Bangladesh, where lychee is a commercially important fruit crop. The dataset consists of 4,001 images captured from lychee orchards in three agriculturally diverse districts: Dhaka, Manikganj, and Gaibandha, between December 2024 and April 2025. It includes both healthy and diseased leaves affected by common visual conditions found in lychee plants. Class Distribution: 1. Anthrax – 620 images 2. Curly Leaf – 368 images 3. Dried Leaf – 555 images 4. Healthy Leaf – 662 images 5. Insect Hole – 1,157 images 6. Yellow Mosaic Virus – 639 images. Location: 1. Dhaka: [Latitude (°N): 23.8103, Longitude (°E): 90.4125] 2. Manikganj: [Latitude (°N): 23.8617, Longitude (°E): 89.9333] 3. Gaibandha: [Latitude (°N): 25.3287, Longitude (°E): 89.5284] Potential Applications: 1. Computer Vision: - Disease region detection and segmentation - Leaf health visual feature extraction - Real-time visual monitoring of lychee trees 2. Machine Learning: - Multiclass classification of lychee leaf diseases - Development of explainable AI (XAI) models for plant health assessment - Decision support tools for agricultural advisors 3. Deep Learning: - CNN-based disease recognition (e.g., InceptionV3, ResNet, MobileNet) - Attention-based DL models for fine-grained disease spotting - Integration with GANs for synthetic data augmentation 4. Smart Agriculture / AgriTech: - Mobile-based plant disease diagnostic apps - IoT-integrated crop monitoring systems - Early warning systems for lychee disease outbreaks

Photobiological and biochemical analysis data of Lessonia spicata (Valparaíso Bay, 2022)