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The health and fitness of vertebrates are constantly challenged by environmental stresses, which include exposure to diseases that pose significant evolutionary pressures on immune genes. The bluethroat (Luscinia svecica), has been extensively studied for its haemosporidian parasite diversity across Europe showing a high diversity and prevalence rate. Given possible evolutionary pressure exerted by these pathogens, the present study used high-throughput amplicon sequencing to explore the genetic diversity and selection mechanisms of three immunity genes (MHC I exon 3, TLR3 and TLR4) in three European bluethroat populations. Specifically, we seek to (1) characterize the genetic diversity of these immunity genes, (2) detect selection signatures shaping their diversity, and (3) investigate the relationship between these immunity genes and haemosporidian parasites (Plasmodium and Leucocytozoon). Selection analysis was conducted using FUBAR and SLAC methods, whereas GLM regression was employed to explore the correlation between MHC genes and haemosporidian parasites. Despite geographical and ecological differences, nucleotide and haplotype diversities were similar across all populations. Most frequent haplotypes were found to be shared by both red-spotted (L. s. svecica) and white-spotted (L. s. cyanecula) bluethroat subspecies. The “insular” Krkonoše population, despite being biogeographically peripheral and experiencing minor inbreeding, did not show significantly reduced immunogenetic diversity. Selection analysis revealed a higher presence of purifying selection in TLR genes and a combination of purifying and diversifying selection in the MHC gene, reflecting their evolutionary constraints and functional importance, while haemosporidian parasite pressure was not a major driver of genetic diversity.

The detailed treatment response of patients with calcinosis

1. Research Hypotheses: This study was designed to test the central hypothesis that the mechanisms governing soil organic carbon (SOC) accumulation differ under long-term nitrogen gradient fertilization between cropland and bare land in the Fluvo-Aquic soil region of the North China Plain. 2. Data Description: This dataset comprises crop yield data collected annually from 2009 to 2025 across 21 experimental plots, alongside soil sample analysis data obtained in 2025 from 42 experimental plots. Key measured indicators include: Soil Carbon Fractions: Soil Organic Carbon (SOC), Dissolved Organic Carbon (DOC), particulate organic carbon (POC), mineral-associated organic carbon (MAOC), nonprotected organic carbon (Non-C), physically protected organic carbon (Phy-C), chemically protected organic carbon (Che-C), biochemically protected organic carbon (Bio-C). Other Soil Properties: Total Nitrogen (TN) and pH. 3. Notable Findings: Our analysis revealed distinct regulatory pathways for SOC accumulation. In cropland systems, Bio-C exerted a dominant influence on POC, which subsequently governed SOC sequestration. Conversely, in bare land systems, the limited presence of Che-C attenuated the role of POC, resulting in MAOC serving as the principal regulator of SOC dynamics. 4. Data Interpretation and Reuse Potential: The data are presented in a structured Excel format, readily accessible for statistical analysis. Users can leverage this dataset to compare the effects of nitrogen application rates and land use types on soil carbon pools and physicochemical properties. It is suitable for calibrating biogeochemical models, refining regional SOC stock estimates, and evaluating the efficacy of land management strategies aimed at enhancing soil carbon sequestration.

This dataset provides the numerical data used to generate all figures and tables in the manuscript by Park et al., titled “Mineral transformations driven by Acidimicrobium sp. A6 increase the bioavailability of crystalline Fe oxides in co-cultures with Geobacter sulfurreducens.”

Python Code to run simheuristics algorithm

it is about the electronical properties of Bi2Se3 thin films

This dataset was developed as part of the research described in: L.Santopietro, F. Pietrapertosa, A.Pilogallo, M. Salvia Current efforts in regional climate planning: a dataset from Italian NUTS2 regions (https://doi.org/10.1016/j.dib.2024.111223) M. Salvia, A.Pilogallo, L.Santopietro, F. Pietrapertosa A methodological framework for assessing regional climate efforts. Learning from its application in Italy (https://doi.org/10.1016/j.jclepro.2024.144299) This dataset focuses on the regional climate planning of the 20 Italian regions (NUTS2 level) as of January 2024. It provides key data and information on the regional climate planning (both mitigation and adaptation), the contents of additional dataset (EUROSTAT and Climate Indicators for Italian RegiOns) and regional climate legislation. It is organised into seven spreadsheets containing the following data respectively: 1. Metadata description: List of the sheets structuring the dataset, with a rationale per each colum and section 2. Regional Climate Policies: List of the regional plans (both adaptation and mitigation), Regional Sustainable Development Strategy (RSDS),  Transnational regional networks and initiatives engagement, Climate Emergency Declaration submitted at NUTS2 level, List of regions included in the UNFCCC platform with the commitments and actions of the Non-State Actor Zone for Climate Action (NAZCA) 3. Additional dataset: Italian regional data from EUROSTAT (as of 2023) and Climate Indicators for Italian RegiOns, an Italian database (as of 2021) 4. Adaptation content analysis: content analysis on the regional adaptation strategies/plans according to a predefined list of key indicators 5. Monitoring & Evaluation: List of monitoring and evaluation indicators, with a focus on those provided for the Italian National Adaptation Plan (NAP) at regional level 6. RVA & NAP Sectors: List of National Adaptation Plan (NAP) sectors evaluated in the Risk and Vulnerability Assessment (RVA) and included in the Regional Adaptation Plans 7. Regional climate laws: List of the current regional climate laws in force (as of January 2024)

This document constitutes supplementary materials for the study entitled "Combination secukinumab and surgical intervention for moderate-to-severe hidradenitis suppurativa: A retrospective cohort study"

The Mathematica notebook "Long-term growth curves of trees with unknown ages (Ricker Nov 2025).nb" carries out all calculations that are developed in the article "Modeling long-term tree growth curves indirectly with piecewise linear regression and explaining factors, when tree ages are unknown", by Ricker et al. (2025) in "Environmental and Ecological Statistics". The two Excel files are two different input files. Please see the instruction file for details of how to use the notebook. For just inspecting the code, without the Mathematica software, the notebook and output files are provided as PDF files.

This dataset offers multi-view 3D point clouds and standardized body measurement records of Jining Qing Goats across key growth stages, captured using Microsoft Kinect DK depth cameras, to support research in 3D reconstruction and automated livestock morphometric analysis.