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Figure 8 in Molecular evolutionary trends and biosynthesis pathways in the Oribatida revealed by the genome of Archegozetes longisetosus
Figure 8 Reconstruction of the biosynthetic pathway leading to monoterpenes in Archegozetes longisetosus. a – Representative gas chro- matogram of the mite′ gland content; in order of retention time: 2-hydroxy-6-methyl-benzaldehyde (2,6-HMBD), neral (( Z)-3,7-dimethylocta- 2,6-dienal) neryl formate (( Z)-3,7-dimethyl-2,6-octadienyl formate), tridecane, 3-hydroxybenzene-1,2-dicarbaldehyde (γ-acaridial). Further alkanes/alkenes (pentadec-7-ene, pentadecane, heptadeca-6,9-diene, heptadec-8-ene, heptadecane) are not shown. Monoterpenes are marked
Figure 5 in Molecular evolutionary trends and biosynthesis pathways in the Oribatida revealed by the genome of Archegozetes longisetosus
Figure 5 The genomic organization of the Hox genes and life-stage specific expression patters of developmental genes in Archegozetes longisetosus. a – Schematic of the genomic region enclosing the ArchegozetesHox cluster. The genomic organization of the Hox cluster is collinear,
Figure 4 in Molecular evolutionary trends and biosynthesis pathways in the Oribatida revealed by the genome of Archegozetes longisetosus
Figure 4 Comparison of repeat content estimations and transposable element (TE) landscape of Archegozetes longisetosus. a – Repetitive element categories of Archegozetes based on the results from RepeatModeler and MITETracker. LINE= long interspersed nuclear element, LTR= long terminal repeat. b – Comparison of total repetitive content among Archegozetes, other model chelicerates and the fly. All values are from the respective genome paper of the species, except for the fly. c – Repeat divergence plot showing TE activity through time for the major TE superfamilies of Archegozetes.Transposable elements with a low divergence from the consensus were recently active, while TEs diverging
Negative emissions technologies and pathways database
<p>This database has been developed to support mathematical analyses of different greenhouse gas removal technologies and their associated deployment implications. The technologies considered in this dataset includes bioenergy with CO2 capture and storage (BECCS), direct air capture and storage (DACCS), afforestation (AF), and enhanced weathering (EW) as they are a representative set of the most commonly reported negative emissions technologies. </p> <p> </p>
Proteomic data sets after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway analysis (IPA Qiagen)
<p>List of fold change proteomic data sets of dFCM- 39 vs. 12Day and105 vs. 12Day, cFCM- 40 vs. 12Day and115 vs. 12Day , mouse heart 90 vs. 1 day after selecting mitochondrial proteins from the scaffold software for Ingenuity Pathway Analysis (IPA Qiagen)</p>
Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
<p>Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high-salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the five known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesize glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of two major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen.</p>
CONCEPT-DIABETES DATA MODEL TO ANALYSE HEALTHCARE PATHWAYS OF TYPE 2 DIABETES
<p><strong>Technical notes and documentation on the common data model of the project CONCEPT-DM2. </strong></p> <p>This publication corresponds to the Common Data Model (CDM) specification of the CONCEPT-DM2 project for the implementation of a federated network analysis of the healthcare pathway of type 2 diabetes, version v0.2.0.</p> <p><strong>Aims of the CONCEPT-DM2 project: </strong></p> <p>General aim: To analyse chronic care effectiveness and efficiency of care pathways in diabetes, assuming the relevance of care pathways as independent factors of health outcomes using data from real life world (RWD) from five Spanish Regional Health Systems.</p> <p>Main specific aims:</p> <ul> <li>To characterize the care pathways in patients with diabetes through the whole care system in terms of process indicators and pharmacologic recommendations</li> <li>To compare these observed care pathways with the theoretical clinical pathways derived from the clinical practice guidelines</li> <li>To assess if the adherence to clinical guidelines influence on important health outcomes, such as cardiovascular hospitalizations.</li> <li>To compare the traditional analytical methods with process mining methods in terms of modeling quality, prediction performance and information provided.</li> </ul> <p><strong>Study Design: </strong>It is a population-based retrospective observational study centered on all T2D patients diagnosed in five Regional Health Services within the Spanish National Health Service. We will include all the contacts of these patients with the health services using the electronic medical record systems including Primary Care data, Specialized Care data, Hospitalizations, Urgent Care data, Pharmacy Claims, and also other registers such as the mortality and the population register.</p> <p><strong>Cohort definition: </strong>All patients with code of Type 2 Diabetes in the clinical health records</p> <ul> <li>Inclusion criteria: patients that, at 2017-01-01 or during the follow-up from 2017-01-01 to 2022-12-31 had active health card (active TIS - tarjeta sanitaria activa) and code of type 2 diabetes (T2D, DM2 in spanish) in the clinical records of primary care (CIAP2 T90 in case of using CIAP code system)</li> <li>Exclusion criteria: <ul> <li>patients with no contact with the health system from 2017-01-01 to 2022-12-31</li> <li>patients that had a T1D (DM1) code opened after the T2D code during the follow-up.</li> </ul> </li> <li>Study period. From 2017-01-01 to 2022-12-31</li> </ul> <p><strong>Files included in this publication: </strong></p> <ul> <li>Datamodel_CONCEPT_DM2_diagram_v0.2.0.jpg</li> <li>Common data model specification (Datamodel_CONCEPT_DM2_v.0.2.0.xlsx)</li> <li>Synthetic datasets (Datamodel_CONCEPT_DM2_sample_data_v0.2.0) <ul> <li>sample_data1_dm_patient.csv</li> <li>sample_data2_dm_param.csv</li> <li>sample_data3_dm_patient.csv</li> <li>sample_data4_dm_param.csv</li> <li>sample_data5_dm_patient.csv</li> <li>sample_data6_dm_param.csv</li> <li>sample_data7_dm_param.csv</li> <li>sample_data8_dm_param.csv</li> </ul> </li> <li>Datamodel_CONCEPT_DM2_explanation_v0.2.0.pptx</li> </ul> <p><strong>CHANGE-LOG from version v0.1.0 to v0.2.0.</strong></p> <p>The main changes are the following:</p> <ul> <li>Missing data is now identified leaving the field empty</li> <li> <p>All ICD diagnosis given in the Datamodel refer to the root code, so that all codes and subcodes that start with the given codes need to be considered. For instance, if in the data model appears I21, then all codes I21.x should be included.</p> </li> <li> <p>All admissions registered in the CMBD will be included to facilitate the extraction procedure (annex 8 disappears).</p> </li> <li> <p>Three diagnosis codes and three procedure codes are now included in table dm_cmbd.</p> </li> </ul> <p><strong>CHANGE-LOG from version v0.2.0 to v0.3.0.</strong></p> <p>The main changes are the following:</p> <ul> <li>Variable 'copayment' (annex 2) change: cod 002.01 =>0; cod 002.02 =>1</li> <li>Variable 'visit_service' (annex 8) change: APR refers to Primary Care and APA refers to Pathological Anatomy</li> <li>Variable 'filglom' (annex 4) change: non numerical values compatible with '> 60' => 999</li> </ul>
Figure 4 in The marine live bait trade as a pathway for the introduction of non-indigenous species into California: patterns of importation and thermal tolerances of imported specimens
Figure 4. Average percent survival (± SE) of G. dibranchiata (A) and Perinereis sp. (B) after exposed to southern California thermal conditions for five days. Gray bars indicate significant difference. No difference in survival was observed among the three temperature treatments for G. dibranchiata (F2,8 = 2.67, p = 0.130). There was a significant difference in survival of Perinereis sp. among the treatments (F2,8 = 11.08, p = 0.005).
Figure 1 in The marine live bait trade as a pathway for the introduction of non-indigenous species into California: patterns of importation and thermal tolerances of imported specimens
Figure 1. Distribution by county of bait shops selling marine live bait in California according to survey responses. Live marine bait was sold in all counties shaded in grey. Numbers inside black circles indicate species sold in that county. Bait shops in shaded counties without black circles reported marine live bait sales but did not provide information on which species they sold.
Figure 3 in The marine live bait trade as a pathway for the introduction of non-indigenous species into California: patterns of importation and thermal tolerances of imported specimens
Figure 3. For each species of marine live bait, (A) the percentage of respondents who reported importing that species who answered that either seaweed (sometimes in combination with newspaper) or seawater were used as packing materials, and (B) the percentage of respondents who reported importing that species who observed hitchhikers in shipments of that species. Note that for (A), some respondents indicated that both seaweed and seawater were used as packing materials for a given species, so that the total percentage may add up to greater than 100 (e.g., for pileworms).
Comparing long-term power sector pathways in Viet Nam: a simple cost-optimization approach with OSeMOSYS
<p>Six clicSAND files for OSeMOSYS Modelling in Viet Nam. The six files depict six different scenarios:</p> <p>1. Power Development Plan 7-based scenario</p> <p>2. Power Development Plan 8-based scenario</p> <p>3. Renewable Energy Development Strategy-based scenario</p> <p>4. Renewables-Led Pathway-based scenario</p> <p>5. Net Zero</p> <p>6. Clean Efficient Transition</p> <p>The Reference Energy System (RES) is also included to represent the relationship between estimated energy demands, energy conversion technologies, fuel mixes, and the resources required to satisfy the demands in the study.</p>
MOTU data. FHIR-standardized data collection on the clinical rehabilitation pathway of trans-femoral amputation patients.
<h3>Dataset presented in the article "MOTU on FHIR: A 10-year data collection on the clinical rehabilitation pathway of 1006 trans-femoral amputees".</h3> <p>Data has been anonymised prior the publication. The data are in Comma-separated-values (CSV) format. </p> <p>This work has been conducted within the framework of the MOTU++ project (PR19-PAI-P2).</p> <p><span>This research was co-funded by the Complementary National Plan PNC-I.1 "Research initiatives for innovative technologies and pathways in the health and welfare sector” D.D. 931 of 06/06/2022, DARE - DigitAl lifelong pRevEntion initiative, code PNC0000002, CUP: (B53C22006450001) and by the Italian National Institute for Insurance against Accidents at Work (INAIL) within the MOTU++ project (PR19-PAI-P2). </span></p> <p><span>Authors express their gratitude to all the AlmaHealthDB Team.</span></p>
Dataset: COMPASS Pathways plc (CMPS) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Supplementary material for "Genes divided according to the relative position of the longest intron show increased representation in different KEGG pathways"
<p>This is a 4th version of the Supplementary Information related to the article "Genes divided according to the relative position of the longest intron show increased representation in different KEGG pathways". It contains upgraded primary and control data used for gene set enrichment analyzes, results of these analyzes as well as our code for calculation of intron lenghts. Detailed information can be found in the above-mentioned article, which deals with the connection between the position of the longest introns in genes and biological functions of genes.</p>
Identification and characterization of ATOH7-regulated target genes and pathways in human neuroretinal development
<p>The files presented here represent the original data collected during the study presented in the Cells (MDPI) publication "Identification and characterization of ATOH7-regulated target genes and pathways in human neuroretinal development" (<a href="https://doi.org/10.3390/cells13131142">https://doi.org/10.3390/cells13131142</a>). The data include:</p> <ul> <li>scRNA sequencing data (matrix, features and barcodes)</li> <li>RNA sequencing data (fastq)</li> <li>CUT&RUN sequencing data (raw data: fastq; coverage: bigWig)</li> <li>RNA sequencing alignments, with custom built reference (RNA_alignments)</li> </ul>
FIGURE 4 in Seeking for gaps in taxonomic descriptions of endemic fishes: a pathway to challenge the Linnean shortfall in a Neotropical basin
FIGURE 4 | Histograms of the year of endemic fish species descriptions according to the fluvial hierarchy of watercourses. Higher negative skewness (-3,18) in low order streams (first to third order, left histogram), and lower negative skewness (-2,57) in high order streams (seventh to ninth order, right histogram).
FIGURE 1 in Seeking for gaps in taxonomic descriptions of endemic fishes: a pathway to challenge the Linnean shortfall in a Neotropical basin
FIGURE 1 | Spatial distribution of localities where the holotype of endemic fish species was found in the Tocantins-Araguaia River basin (grey circles). The red circles delimit the transition zone between upper, middle, and lower sections of the basin.
Data Report: Educational Pathway on Food and Nutrition in Amyotrophic Lateral Sclerosis on the AVASUS platform
<p><strong>Dataset name:</strong><em> nutri_als_dataset.csv </em></p> <p><strong>Version: </strong>1.0 </p> <p><strong>Dataset period:</strong> 06/01/2021 - 06/05/2024</p> <p><strong>Dataset Characteristics: </strong>Multivalued </p> <p><strong>Number of Instances: </strong>20967</p> <p><strong>Number of Attributes: </strong>9</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education<strong> </strong></p> <p><strong>Sources: </strong></p> <ul> <li>Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2024a);</li> <li>Brazilian Occupational Classification (CBO) (Brasil, 2024b);</li> <li>National Registry of Health Establishments (CNES) (Brasil, 2024c); </li> <li>Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2024d). </li> </ul> <p><strong>Description</strong>:<strong> </strong>The “nutri_als_dataset.csv” dataset (see Table 1) originates from participants of the educational pathway on Food and Nutrition in Amyotrophic Lateral Sclerosis. The educational pathway is available on the AVASUS (Brasil, 2024a). This dataset provides elementary data to analyze the scope of the educational pathway courses and the profile of their participants.</p> <p><br><strong>Note</strong>: The content of the dataset is provided in Brazilian Portuguese (pt-br), as it originates from native speakers.</p> <p><strong>Table 1: </strong>Description of AVASUS dataset features. </p> <div> <table> <tbody> <tr> <td> <p><strong>Attributes </strong></p> </td> <td> <p><strong>Description </strong></p> </td> <td> <p><strong>Datatype </strong></p> </td> <td> <p><strong>Value</strong></p> </td> </tr> <tr> <td> <p><strong>user_id</strong></p> </td> <td> <p>Unique identifier for a person (anonymous).</p> </td> <td> <p>Categorical</p> </td> <td> <p>Person unique identifier.</p> </td> </tr> <tr> <td> <p><strong>course_enrollment</strong></p> </td> <td> <p>Course enrollment period.</p> </td> <td> <p>Datetime </p> </td> <td> <p>year-month-day.</p> </td> </tr> <tr> <td> <p><strong>course_name</strong></p> </td> <td> <p>Name in Portuguese referring to the course.</p> </td> <td> <p>Categorical</p> </td> <td> <ul> <li> <p>Alimentação por sonda na ELA;</p> </li> <li> <p>Alimentação e Nutrição na ELA;</p> </li> <li> <p>Orientações nutricionais específicas na ELA; or</p> </li> <li> <p>Modificações Dietéticas na ELA.</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>certificate</strong></p> </td> <td> <p>The period in which the course participant obtained the right to a certificate.</p> </td> <td> <p>Datetime</p> </td> <td> <p>year-month-day hours, minutes, and seconds.</p> </td> </tr> <tr> <td> <p><strong>gender </strong></p> </td> <td> <p>Gender of the course participant. </p> </td> <td> <p>Categorical</p> </td> <td> <ul> <li> <p>Female;</p> </li> <li> <p>Male; or</p> </li> <li> <p>Not informed.</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>region</strong></p> </td> <td> <p>Brazilian region in which the participant resides.</p> </td> <td> <p>Categorical</p> </td> <td> <ul> <li> <p>North;</p> </li> <li> <p>Northeast;</p> </li> <li> <p>Central-West;</p> </li> <li> <p>Southeast;</p> </li> <li> <p>South;</p> </li> <li> <p>Abroad; or</p> </li> <li> <p>Not reported.</p> </li> </ul> </td> </tr> <tr> <td> <p><strong>course_evaluation</strong></p> </td> <td> <p>A score given to the course by the participant. </p> </td> <td> <p>Numerical</p> </td> <td> <p>0, 1, 2, 3, 4, 5, or NaN.</p> </td> </tr> <tr> <td> <p><strong>evaluation_commentary</strong></p> </td> <td> <p>Comment made by the participant about the course.</p> </td> <td> <p>Categorical</p> </td> <td> <p>Free text or NaN.</p> </td> </tr> <tr> <td> <p><strong>CBO</strong></p> </td> <td> <p>Participant occupation.</p> </td> <td> <p>Categorical</p> </td> <td> <p>Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem filiação formal.” (In English, “Individual without formal affiliation.”)</p> </td> </tr> </tbody> </table> </div> <p> </p> <p> </p> <p><strong>REFERENCES</strong></p> <p>Brasil (2024a). AVASUS - Virtual Learning Environment of the Brazilian Health System. Available from: <a href="https://avasus.ufrn.br/local/avasplugin/dashboard/transparencia.php">https://avasus.ufrn.br/local/avasplugin/dashboard/transparencia.php</a>. Accessed Jul 21, 2024.</p> <p>Brasil (2024b). CBO - classificação brasileira de ocupações. Available from: <a href="https://cbo.mte.gov.br/cbosite/pages/home.jsf">https://cbo.mte.gov.br/cbosite/pages/home.jsf</a>. Accessed Jul 21, 2024.</p> <p>Brasil (2024c). CNES - cadastro nacional de estabelecimentos de saúde. Available from: <a href="https://cnes.datasus.gov.br/">https://cnes.datasus.gov.br/</a>. Accessed Jul 21, 2024.</p> <p>Brasil (2024d). IBGE - Instituto Brasileiro de Geografia e Estatística. Estimativas da População. Available from: <a href="https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/39525-censo-2022-informacoes-de-populacao-e-domicilios-por-setores-censitarios-auxiliam-gestao-publica">https://agenciadenoticias.ibge.gov.br/agencia-noticias/2012-agencia-de-noticias/noticias/39525-censo-2022-informacoes-de-populacao-e-domicilios-por-setores-censitarios-auxiliam-gestao-publica</a>. Accessed Jul 21, 2024.</p> <p> </p> <p><strong>ARTICLE:</strong></p> <p>Data Report: Educational Pathway on Food and Nutrition in Amyotrophic Lateral Sclerosis on the AVASUS platform <br> </p> <p><strong>AUTHORS:</strong></p> <p>Karla M. D. Coutinho<sup>1,2</sup>, Felipe Fernandes<sup>2</sup>, Kelson C. Medeiros<sup>2,6</sup>, Karilany D. Coutinho<sup>2,4,5</sup>, Aline de Pinho Dias<sup>2,4</sup>, Ricardo A. M. Valentim<sup>2,4,5</sup>, Lúcia Leite-Lais<sup>3</sup>, Kenio Costa Lima1</p> <p> </p> <p><sup>1</sup>Postgraduate Program in Health Sciences, Federal University of Rio Grande do Norte, Natal, Brazil</p> <p><sup>2</sup>Laboratory of Technological Innovation in Health (LAIS), Federal University of Rio Grande do Norte (UFRN), Natal, Rio Grande do Norte, Brazil </p> <p><sup>3</sup>Department of Nutrition, Federal University of Rio Grande do Norte, Natal, Brazil</p> <p><sup>4</sup>Postgraduate Program in Management and Innovation in Health, Federal University of Rio Grande do Norte, Natal, Brazil</p> <p><sup>5</sup>Department of Biomedical Engineering, Federal University of Rio Grande do Norte, Natal, Brazil</p> <p><sup>6</sup>Federal Institute of Education, Science and Technology of Rio Grande do Norte, Natal, Brazil</p> <p> </p> <p> </p>
Fig. 5 in Heartworm and seal louse: Trends in prevalence, characterisation of impact and transmission pathways in a unique parasite assembly on seals in the North and Baltic Sea
Fig. 5. Scanning electron microscopic images of E. horridus attached to seal skin and fur. A: E. horridus utilizing a hair follicle of harbour seal skin. B: E. horridus attached to seal hair with the head pointing towards seal skin. C: E. horridus with six claws attached to seal fur. D: Close up of an unattached claw of E. horridus. Asterisks positioned on nits of E. horridus. Sale bars: A 200 μm, B 400 μm, C 400 μm, D 100 μm.
Fig. 4 in Heartworm and seal louse: Trends in prevalence, characterisation of impact and transmission pathways in a unique parasite assembly on seals in the North and Baltic Sea
Fig. 4. Histological sections and staining of E. horridus revealing filarial stages in E. horridus. A: Filarial stages (arrowheads) in the pharynx. bar = 20 μm. B: Filarial stage (arrow) in the mouth region. bar = 40 μm. C: Filarial stage (arrowhead) in the intestine (in) surrounded by erythrocytes (e). bar = 15 μm. D: Filarial stage in the haemocoel (hc) of the abdomen of E. horridus (square). E: Close up of filarial stage. mp = mouthparts, mo = mouth, cu = cuticula. A–C: Haematoxylin - Eosin stain, D: Giemsa stain.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.