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The Brazilian Soil Spectral Library (VIS-NIR-SWIR-MIR) Database: Open Access
<p><strong>Abstract:</strong></p> <p>NEW VERSION V.002 (Some Lat Long Coordinates added).</p> <p>Soil spectroscopy has emerged as a solution to the limitations associated with traditional soil surveying and analysis methods, addressing the challenges of time and financial resources. Analyzing the soil's spectral reflectance enables to observe the soil composition and simultaneously evaluate several attributes because the matter, when exposed to electromagnetic energy, leaves a "spectral signature" that makes such evaluations possible. The Soil Spectral Library (SSL) consolidates soil spectral patterns from a specific location, facilitating accurate modeling and reducing time, cost, chemical products, and waste in surveying and mapping processes. Therefore, an open access SSL benefits society by providing a fine collection of free data for multiple applications for both research and commercial use.</p> <p><strong>BSSL Description and Usefulness</strong></p> <p>The Brazilian Soil Spectral Library (BSSL), available at <a href="https://bibliotecaespectral.wixsite.com/english">https://bibliotecaespectral.wixsite.com/english</a>, is a comprehensive repository of soil spectral data. Coordinated by JAM Demattê and managed by the GeoCiS research group, the BSSL was initiated in 1995 and published by Demattê and collaborators in 2019. This initiative stands out due to its coverage of diverse soil types, given Brazil's significance in the agricultural and environmental domains and its status as the fifth largest territory in the world (IBGE, 2023). In addition, a Middle Infrared (MIR) dataset has been published (Mendes et al., 2022), part of which is included in this repository. The database covers 16,084 sites and includes harmonized physicochemical and spectral (Vis-NIR-SWIR and MIR range) soil data from various sources at 0-20 cm depth. All soil samples have Vis-NIR-SWIR data, but not all have MIR data.</p> <p>The BSSL provides open and free access to curated data for the scientific community and interested individuals. Unrestricted access to the BSSL supports researchers in validating their results by comparing measured data with predicted values. This initiative also facilitates the development of new models and the improvement of existing ones. Moreover, users can employ the library to test new models and extract information about previously unknown soil properties. With its extensive coverage of tropical soil classes, the BSSL is considered one of the most significant soil spectral libraries worldwide, with 42 institutions and 61 researchers participating. However, 47 collaborators from 29 institutions have authorized the data opening. Other researchers can also provide their data upon request through the coordinator of this initiative.</p> <p>The data from the BSSL project can also help wet labs to improve their analytical capabilities, contributing to developing hybrid wet soil laboratory techniques and digital soil maps while informing decision-makers in formulating conservation and land use policies. The soil's capacity for different land uses promotes soil health and sustainability.</p> <p><strong>Coverage</strong></p> <p>The BSSL data covers all regions of Brazil, including 26 states and the Federal District. It is in a <em>.xlsx</em> format and has a total size of 305 Mb. The table is structured in sheets with rows for observations, and columns, representing various soil attributes in the surface layer, from 0 to 20 cm depth. The database includes environmental and physicochemical properties (22 columns and 16,084 rows), Vis-NIR-SWIR spectral bands (2151 columns and 16,084 rows), and MIR channels (681 columns and 1783 rows). An ID unique column can merge the sheet for each attribute or spectral range.</p> <p><strong>Accessing original data source</strong></p> <p>Using these data requires their reference in any situation under copyright infringement penalty. Three mechanisms are available for users to reach the original and complete data contributors:</p> <p>a) Refer to sheet two for name and code-based searches;</p> <p>b) Visit the website <a href="https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes">https://bibliotecaespectral.wixsite.com/english/lista-de-cedentes</a> or locate the contributors' list by Brazilian state;</p> <p>c) Visit the website of the Brazilian Soil Spectral Service – Braspecs <a href="http://www.besbbr.com.br/">http://www.besbbr.com.br/</a>, an online platform for soil analysis that uses part of the current SSL (Demattê et al., 2022) - It was developed and managed by GeoCiS. There, owners from all over the country can be found.</p> <p><strong>Proceeding to data analysis</strong></p> <p>We registered and organized the samples at the ESALQ/USP Soil Laboratory. Some samples arrived without preliminary data analyses, so we analyzed them for soil organic matter (SOM), granulometry, cation exchange capacity (CEC), pH in water, and the presence of Ca, Mg, and Na, following the recommendations of Donagemma et al. (2011).</p> <p>The GeoCiS research group performed spectral analyses following the procedures described by Bellinaso et al. (2010). Demattê et al. (2019) provide detailed methods for sampling, preparation, and soil analyses, including reflectance spectroscopy. Latitude and longitude data can be requested directly from the data owner. In summary, the following steps are involved in data acquisition.</p> <p>a) We subjected the soil samples to a preliminary treatment, which involved drying them in an oven at 45°C for 48 hours, grinding them, and sieving them through a 2mm mesh;</p> <p>b) We placed the samples in Petri dishes with a diameter of 9 cm and a height of 1.5 cm;</p> <p>c) We homogenized and flattened the surface of the samples to reduce the shading caused by larger particles or foreign bodies, making them ready for spectral readings;</p> <p>d) The spectral analyses took place in a darkened room to avoid interference from natural light. We used a computer to record the electromagnetic pulses through an optical fiber connected to the sensor, capturing the spectral response of the soil sample;</p> <p>e) We obtained reflectance data in the Visible-Near Infrared-Shortwave Infrared (Vis-NIR-SWIR) range using a FieldSpec 3 spectroradiometer (Analytical Spectral Devices, ASD, Boulder, CO), which operates in the spectral range from 350 to 2500 nm;</p> <p>f) The sensor had a spectral resolution of 3 nm from 350-700 nm and 10 nm from 700-2500 nm, automatically interpolated to 1 nm spectral resolution in the output data, resulting in 2151 channels (or bands); and</p> <p>g) We positioned the lamps at 90° from each other and 35 cm away from the sample, with a zenith angle of 30°.</p> <p>The sensor captured the light reflected through the fiber optic cable, which was positioned 8 cm from the sample's surface.</p> <p>We used two 50W halogen lamps as the power source for the artificial light. It's important to note that we took three readings for each sample at different positions by rotating the Petri dish by 90°.</p> <p>Each reading represents the average of 100 scans taken by the sensor. From these three readings, we calculated the final spectrum of the samples. Notably, the laboratory's equipment and procedures for soil sample spectral analyses followed the ASD's recommendations, particularly about sensor calibration using a white spectralon plate as a 100% reflectance standard.</p> <p>For the analysis in the Middle Infrared (MIR) spectral region, we followed the procedures outlined by Mendes et al. (2022). We milled the soil fraction smaller than 2 mm, sieved it to 0.149 mm, and scanned it using a Fourier Transform Infrared (FT-IR) alpha spectroradiometer (Bruker Optics Corporation, Billerica, MA 01821, USA) equipped with a DRIFT accessory.</p> <p>The spectroradiometer measured the diffuse reflectance using Fourier transformation in the spectral range from 4000 cm<sup>-1</sup> to 600 cm<sup>-1</sup>, with a resolution of 2 cm<sup>-1</sup>. We conducted these measurements in the Geotechnology Laboratory of the Department of Soil Science at Esalq-USP. We took the average of 32 successive readings to obtain a soil spectrum. Sensor calibration took place before each spectral acquisition of the sample set by standardizing it against the maximum reflectance of a gold plate.</p> <p> </p> <p><strong>Dataset characterization</strong></p> <p>The database, named BSSL_DB_Key_Soils, has five sheets containing the key soil attributes, Vis-NIR-SWIR and MIR datasets, descriptions of the contributors and the proximal sensing methods used for spectral soil analysis. The sheets can be linked by "ID_Unique" columns, which bring the corresponding rows according to the data type. Some cells are empty because collaborators have already provided data in this way. However, we have decided to keep them in the database because they have other soil key attributes. Every Column in the data sheets is described as follows:</p> <p> </p> <p><strong>Sheet 1. BSSL_Soil_Attributes_Dataset</strong></p> <p>Column 1. <strong>ID_unique</strong>: Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data;</p> <p>Column 3. <strong>Vis_NIR_SWIR_availability</strong>: availability of spectral data in visible, near-infrared, and shortwave infrared ranges;</p> <p>Column 4. <strong>MIR_availability</strong>: availability of spectral data in the middle infrared range;</p> <p>Column 5. <strong>Sampling</strong>: type of soil sampling;</p> <p>Column 6. <strong>Depth_cm</strong>: soil surface layer depth in centimeters; </p> <p>Column 7. <strong>Lat</strong>: Latitude; </p> <p>Column 8. <strong>Lat</strong>: Longitude; </p> <p>Column 9. <strong>Region</strong>: Brazilian geographical region of samples' source;</p> <p>Column 10. <strong>Municipality</strong>: Brazilian municipality of samples' source;</p> <p>Column 11. <strong>State</strong>: Brazilian Federation Unit of samples' source;</p> <p>Column 12. <strong>Vegetation</strong>: type of vegetal covering;</p> <p>Column 13. <strong>Biome</strong>: groupings of ecosystems that share similar characteristics and span different regions;</p> <p>Column 14. <strong>Geology</strong>: type of rock matter from local soil sampling;</p> <p>Column 15. <strong>Sand_gkg</strong>: Content of the soil fraction with grain size between 2 and 0.053 mm, expressed in grams per kilogram;</p> <p>Column 16. <strong>Clay_gkg</strong>: Content of soil fraction with grain size smaller than 0.002 mm, expressed in grams per kilogram;</p> <p>Column 17. <strong>SOM_gkg</strong>: Soil organic matter content, expressed in grams per kilogram;</p> <p>Column 18. <strong>pH_H2O</strong>: Soil hydrogen ion potential measured in water;</p> <p>Column 19. <strong>Ca_mmolkg</strong>: Exchangeable calcium content in the soil, expressed in millimoles per kilogram;</p> <p>Column 20. <strong>Mg_mmolkg</strong>: Exchangeable magnesium content in the soil, expressed in millimoles per kilogram;</p> <p>Column 21. <strong>Na_mmolkg</strong>: Exchangeable sodium content in the soil, expressed in millimoles per kilogram; and</p> <p>Column 22. <strong>CEC_Ph7_mmolkg</strong>: Cation exchange capacity of the soil at neutral pH, expressed in millimoles per kilogram.</p> <p> </p> <p><strong>Sheet 2. BSSL_Vis_NIR_SWIR_Dataset</strong></p> <p>Column 1. <strong>ID_Unique</strong>: Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data; and</p> <p>Column 3 – 2153. <strong>350 – 2500</strong>: Reflectance in 2151 spectral bands in nanometers from visible and near-infrared to shortwave infrared range (350 – 2500 nm).</p> <p> </p> <p><strong>Sheet 3. BSSL_MIR_Dataset</strong></p> <p>Column 1. <strong>ID_Unique:</strong> Sequential code assigned to every record;</p> <p>Column 2. <strong>Owner_code:</strong> Acronym assigned to each contributor who allowed access to their proprietary data; and</p> <p>Column 3 – 683. <strong>4000 – 600:</strong> Reflectance in 681 spectral bands in centimeters in the middle infrared range (4000 – 600 cm<sup>-1</sup>).</p> <p> </p> <p><strong>Sheet 4. Contributors</strong></p> <p>Column 1. <strong>Owner_code</strong>: Acronym assigned to each contributor who allowed access to their proprietary data, which identifies and links it to datasets;</p> <p>Column 2. <strong>Owner</strong>: Name of the collaborator who agreed to the availability of the data;</p> <p>Column 3. <strong>E-mail</strong>: Contact the e-mail of the owner for more information or a data request;</p> <p>Column 4. <strong>Institution</strong>: Contributor's affiliation;</p> <p>Column 5. <strong>Samples NIR</strong>: Number of Vis-NIR-SWIR samples sent to the BSSL collection;</p> <p>Column 6. <strong>Samples MIR</strong>: Number of MIR samples sent to the BSSL collection;</p> <p> </p> <p><strong>Sheet 5. Metadata</strong></p> <p>Column 1. <strong>Material and Methods</strong>: Description of procedures performed for soil data analyses</p> <p> </p> <p><strong>Expectation and Social Relevance</strong></p> <p>These data can impact various disciplines such as soil surveying, soil attribute mapping, soil analysis, soil mineralogy, soil management zones, precision agriculture, development of new datasets and scientific groups, and others. We expect this contribution to be valuable and useful to the soil research community in promoting this non-renewable natural resource's conservation and sustainable use.</p>
ESA SEOM-IAS – Measurement and line parameter database O3 MIR region
<p>The database contains measurements and line parameters generated within the framework of the ESA project SEOM-IAS (Scientific Exploitation of Operational Missions - Improved Atmospheric Spectroscopy Databases), ESA/AO/1-7566/13/I-BG. Details on the project can be found at http://www.wdc.dlr.de/seom-ias/.</p> <p>The measurements were recorded and analysed at the German Aerospace Center (DLR) and the University of Reims (URCA) to provide a new line position and intensity database for ozone fundamentals in the mid infrared region. The data are compiled in five zip files, three for the measurements (O3_MIR_region_DLR_measurement_database_20112018.zip, O3_MIR_region_DLR_measurement_N2O2broad_database_07012021.zip, O3_MIR_region_URCA_measurement_database_20112018.zip), two for the line parameter databases (O3_MIR_region_DLR_parameter_database_08012021.zip, O3_MIR_region_URCA_parameter_database_20112018.zip) and a readme file (ESA_SEOM_IAS_O3MIRRegion_readme_V3.docx).</p> <p>Note: The DLR parameter database is replaced by a newer version.</p>
Dataset related to article "SMA-miRs (miR-181a-5p, -324-5p, and -451a) are overexpressed in spinal muscular atrophy skeletal muscle and serum samples"
<p>mice survival after treatment with anti-miR-181a-5p; mice weight after treatment with anti-miR-324-5p</p>
Data File for Manuscript "Comprehensive Molecular Simulation on Triple Negative Breast Cancer Transcriptomics Features of mir-145 and 3' UTR of ARF6 mRNA"
<p>This is a data file for the manuscript "Comprehensive Molecular Simulation on Triple Negative Breast Cancer Transcriptomics Features of mir-145 and 3’ UTR of ARF6 mRNA". It comprises of molecular docking (AUTODOCK VINA 4) and dynamics data (NAMD and VMD).</p>
Figure 3 in mir-331 negatively regulates thyroglobulin secretion via ERp29
Figure 3. Regulation of Tg expression and secretion by small interfering RNAs. (A) Regulation of ERp29 and ThrB expression using siERp29 and miRNA-331, respectively. (B) Regulation of Tg gene expression by siERp29 and miRNA-331. (C) Regulation of Tg secretion by siERp29 and miRNA-331. All experimental conditions in Figure 1 are described in the text. Data represent means ± standard deviation (SD) of at least 3 independent experiments. Statistical significance between multiple groups: one-way analysis of variance (ANOVA) test. GraphPad Prism 6 software (GraphPad Software Inc., San Diego, CA, USA). *P <0.05, **P <0.005, ***P <0.001, ****P <0.0001.
Figure 1 in mir-331 negatively regulates thyroglobulin secretion via ERp29
Figure 1. Gene expression of endoplasmic reticulum (ER) chaperones and Tg secretion by ERp29 overexpression. (A) In PCCL3 and ERp29over PCCL3 cells, gene expression was estimated for both ER chaperones and ER stress sensors using reverse-transcription polymerase chain reaction (RT-PCR). (B) ER chaperone protein expression was measured using western blotting. (C) Tg secretion by ER29 was measured using western blotting. (D) Tg secretion was measured using tunicamycin treatment followed by western blotting. All experimental conditions are described in detail in the text. Data represent means of at least 3 independent experiments.
Figure 2 in mir-331 negatively regulates thyroglobulin secretion via ERp29
Figure 2. Regulation of ER stress sensors by ERp29 overexpression. (A) Results of western blotting in PCCL3 and ERp29over PCCL3 cells, showing full-length ATF6 (*), partial-length ATF6 (**), IRE1 (←), and phosphorylated eIF2 alpha (←). (B) RT-PCR analysis showed the expression of 2 isoforms: spliced (XBP1S) and unspliced (XBP1U) xbp1 transcripts. All experimental conditions are described in detail in the text. Data represent means of at least 3 independent experiments.
Double shell domes in Tehran province- Iran (Emamzadeh Jafar Tomb, Emamzadeh Hasan Tomb, Emamzadeh Ghasem Tomb, Emamzadeh Mir MotaharTomb).
<p>These photos illustrate the construction details in the space between the double-shell domes including the inner dome, the outer dome, Khashkhashi (Stiffeners), wooden structural elements, and interventions. Emamzadeh Jafar Tomb (J1-J20), Emamzadeh Hasan Tomb (H1-H34), Emamzadeh Ghasem Tomb (G1-G4), Emamzadeh Mir Motahar(M1-M8).</p>
EV based miR-6772-5p secreted by breast cancer cells in response to radiation exposure is involved in development of radioresistance
<p>This dataset serves as a supplementary material for the research article <em>EV-based miR-6772-5p secreted by breast cancer cells in response to radiation exposure is involved in development of radioresistance</em> by Tynjälä <em>et al</em>. For further details, please refer to our publication for a detailed description of the methods used to generate this dataset.</p> <p>In summary, we isolated and sequenced miRNAs from extracellular vesicles (EVs) isolated from irradiated and non-irradiated MCF7 cells. Sequencing libraries were prepared with QIAseq miRNA Library Kit (Qiagen, Germany) and sequenced with Illumina NextSeq 500 platform (Illumina). In-house bioinformatics workflow was used to process the sequencing data. Unique molecular identifier (UMI) sequences were first extracted and added to FASTQ header with UMI-tools while discarding the 3’ adapter and primer sequences. Reads shorter than 16 bp were discarded with cutadapt. Provided sequencing data is in FASTQ format and trimmed from adapter and UMI sequences. We have also provided miRNA counts and results of following differential gene expression analysis in tab-delimited format.</p> <p> </p> <p> </p>
miR-126 promotes M1 to M2 macrophage phenotype switching via VEGFA and KLF4
<p><strong>Background. </strong>Macrophage polarization and microRNA play crucial roles in the development of atherosclerosis (AS). The M1 macrophage phenotype contributes to the formation of plaques, while the M2 macrophage phenotype resolves inflammation and promotes tissue repair. MiR-126 has been found to play a role in regulating macrophage polarization in the context of AS. However, the exact mechanism of miR-126 requires further research.</p> <p><strong>Methods.</strong> The foam cell model was established by stimulating THP-1 with oxidized low-density lipoprotein (ox-LDL). We transfected foam cells with miR-126 mimic and its negative control. The transfection of miR-126 was implemented by riboFECT CP transfection kit. The levels of miR-126 and M1/M2 associated genes in foam cells were quantified using reverse transcription-quantitative PCR (RT-qPCR). Additionally, the expressions of CD86<sup>+</sup> and CD206<sup>+</sup> cells in foam cells were determined by flow cytometry. Western blotting and RT-qPCR were used to determine the protein and mRNA levels of the vascular endothelial growth factor A (VEGFA) and the transcriptional regulator Krüppel-like factor 4 (KLF4), respectively. Additionally, we detected endothelial cell migration after co-culturing endothelial cells and macrophages. MG-132 was used to indirectly activate the expression of VEGFA, and the expression of KLF4 was also evaluated.</p> <p><strong>Results. </strong>The activation of apoptosis and production of foam cells were boosted by the addition of ox-LDL. We transfected foam cells with miR-126 mimic and its negative control and observed that miR-126 greatly suppressed foam cell development and inhibited phagocytosis. Moreover, it caused pro-inflammatory M1 macrophages to switch to the anti-inflammatory M2 phenotype. This was reflected by the increase in anti-inflammatory gene expression and the decrease in pro-inflammatory gene expression. Additionally, miR-126 dramatically decreased the expressions of VEGFA and KLF4. The protein-protein interaction network analysis showed a significantly high correlation between miR-126, VEGFA, and KLF4. MiR-126 may also promote EC migration by activating macrophage PPAR γ expression and effectively suppressing macrophage inflammation. MG-132 indirectly activated the expression of VEGFA, and the expression of KLF4 also significantly increased, which indicates a direct or indirect relationship between VEGFA and KLF4.</p> <p><strong>Conclusion.</strong> Our study shows that miR-126 can reverse ox-LDL-mediated phagocytosis and apoptosis in macrophages. Consequently, the potential role of miR-126 was manifested in regulating macrophage function and promoting vascular endothelial migration.</p>
Dataset - PONE-D-20-09507 - Usefulness of circulating miR-146a and miR-16-5p microRNAs as prognostic biomarkers in community-acquired pneumonia
<p>This dataset shows a prospective observational study performed in a cohort of 153 patients admitted to hospital with CAP.</p> <p>Clinical and analytical variables were collected, and the main outcome variable was 30-day mortality.</p> <p>Small RNA was purified from patients´ plasma samples by column-based protocol, and retrotranscribed to cDNA (Exiqon's miRCURY ™ series kits), adding synthetic RNA controls (spike-in). The quality of the process was evaluated (QC control) and only 117 samples passed the test.</p> <p>FIRST STEP: Eight samples paired by age and gender were selected (4 patients who had suffered a cardiovascular event or death during follow-up and 4 who had not) and a panel of 752 human miRNAs was tested (miRCURY LNA ™ Universal - Ready-to-Use Human Panel , Exiqon), in order to determine a preliminary pattern of differential miRNA expression between patients with different CAP evolution.</p> <p>SECOND STEP: According to the preliminary data obtained, 25 candidate miRNAs were selected: 5 intended to be used as normalizers, 5 selected by statistical criteria (univariate association with mortality) and 15 selected from an exhaustive bibliographic search on miRNAs, sepsis, inflammation and / or cardiovascular disease, prioritizing those that appeared in a greater number of publications and those related to respiratory diseases. RT-PCR was carried out by hybridization with double-stranded flurochrome (ExiLENT SYBR® Green Master Mix) using the C1000 Touch CFX384 thermocycler (Bio-Rad).</p> <p>The relative amount of each miRNA was calculated with ∆Ct = CtmiRNA - CtUniSp2, and it was later normalized using the GeNorm algorithm. The final data was calculated with the formula 2<sup>-∆Ct</sup> and the values were expressed as the fold change (FC) of each miRNA with respect to UniSP2<em>.</em></p>
miR-145-5p mimic inhibits bone metastasis of prostate cancer via the regulation of epithelial mesenchymal transition
<p><strong>Background.</strong> The bone is the most common site of distant metastasis in prostate cancer. However, treatments for the bone metastasis of prostate cancer remain unsatisfactory. MicroRNAs (miRNAs) are small noncoding RNAs that play a variety of critical roles in tumor development and progression. Studies have confirmed that miRNA mimics could regulate the response to therapy in many cancers. <strong>Methods.</strong> In this study, a set of forty-four miRNAs were reduced in prostate cancer patients with bone metastases by high-throughput sequencing analysis. Wound healing and transwell assays and western blotting analysis were used to explore the role of miRNA mimic in prostate cancer bone metastasis.<strong> Results.</strong> These mimics of down-regulated miRNAs may be able to cure prostate cancer bone metastasis, including hsa-miR-221-3p, hsa-miR-222-3p, hsa-miR-133a-3p, hsa-miR-222-5p, hsa-miR-204-3p, hsa-miR-145-5p, hsa-miR-3681-5p, hsa-miR-184, hsa-miR-144-3p, hsa-miR-204-5p, and hsa-miR-221-5p. To further investigate the role of these miRNA mimics on prostate cancer bone metastasis, miR-145-5p was randomly selected for validation. Bioinformatics analysis showed that miR-145-5p target genes significantly affected TGF-beta signaling pathway. Wound healing and transwell assays and western blotting analysis revealed that miR-145-5p mimic inhibited proliferation, migration and invasion. Importantly, miR-145-5p mimic increased the expression of E-cadherin and reduced the expression of matrix metalloproteinase 2 and 9. These results revealed that miR-145-5p mimic mediated epithelial mesenchymal transition. Meanwhile, miR-145-5p mimic enhanced the level of caspase 9, which is an important promoter of apoptosis. These results indicate that miR-145-5p mimic could inhibit the progress of prostate cancer bone metastasis via regulation of epithelial mesenchymal transition. In addition, miR-145-5p mimic could induce the apoptosis of prostate cancer cells with bone metastases. In summary, the miR-145-5p mimic is expected to become a novel strategy for the treatment of tumor metastasis.</p>
Compositional Characterization of Glassy Volcanic Material From VNIR and MIR Spectra Using Partial Least Squares Regression Models
<p>This is supporting data for the paper titled "Compositional Characterization of Glassy Volcanic Material From VNIR and MIR Spectra Using Partial Least Squares Regression Models" by Leight et al. (submitted to JGR-P 11/23). Table S1 lists each spectrum used to train PLS models, its source, and which training datasets the spectrum was included in. Zip files contain the MIR and VNIR PLS model files. Model files are .asc, and can be run using the code at Ytsma, (2022), https://doi.org/10.5281/zenodo.7347345. </p>
Defining the role of the miR-145 – KLF4 – αSMA axis in mitral valvular interstitial cell activation in myxomatous mitral valve prolapse using the canine model
<p>Mitral valve prolapse (MVP) is a common valvular disease, affecting 2-3% of the adult human population and is a degenerative condition. 5-10% of the afflicted will develop severe mitral regurgitation, cardiac dysfunction, congestive heart failure, and sudden cardiac death. Naturally occurring myxomatous MVP in dogs closely resembles MVP in humans structurally, and functional consequences are similar. In both species, valvular interstitial cells (VICs) in affected valves exhibit phenotype consistent with activated myofibroblasts with increased αSMA expression. Using VICs collected from normal and MVP-affected valves of dogs, we analyzed the miRNA expression profile of the cells and their associated small extracellular vesicles (sEV) using RNA sequencing to understand the role of non-coding RNAs and sEV in MVP pathogenesis. miR-145 was shown to be upregulated in both the affected VICs and sEV, and overexpression of miR-145 by mimic transfection in quiescent VIC recapitulates the activated myofibroblastic phenotype. Concurrently, KLF4 expression was suppressed by miR-145, and KLF4 overexpression resulted in a decrease in αSMA expression, and its suppression resulted in an increase in αSMA expression, thus confirming the miR-145 – KLF4 – αSMA axis. Targeting this axis may serve as potential therapy in controlling pathologic abnormalities found in MVP valves.</p>
Supplementary material Next-generation sequencing reveals that miR-16-5p, miR-19a-3p, miR-451a and miR-25-3p cargo in plasma extracellular vesicles differentiates sedentary young males from athletes.
<p>A sedentary lifestyle is a leading risk factor for global mortality. No objective molecular biomarker of sedentarism is available. Extracellular vesicles miRNAs have been described to respond to exercise. Our aim was to identify the extracellular vesicle miRNA profile of chronically trained young male athletes, endurance and resistance, compared to their sedentary counterparts. A descriptive case-control design with 16 sedentary young men, 16 Olympic male endurance athletes and 16 Olympic male resistance athletes. Next Generation Sequencing and RT-qPCR, external and internal validation, were performed in order to analysed extracellular vesicle miRNA profiles. miR-16-5p, miR-19a-3p and miR-451a were significantly upregulated in SED compared to END and RES. Besides, miR-25-3p was specifically down-regulated in END compared to SED. Extracellular vesicle miR-16-5p, miR-19a-3p, miR-451a provide an objective signature of sedentarism irrespective of the type of exercise and miR-25-3p as a specific responder to endurance training. Therefore, this study provides for the first time an objective measure to categorise individuals as sedentary or trained in young male population and moreover, it highlights a common epigenetic modulation between models of training.</p>
Integrin restriction by miR-34 protects germline progenitors from cell death during aging
<p>During aging, regenerative tissues must dynamically balance the two opposing processes of proliferation and cell death. While many microRNAs are differentially expressed during aging, their roles as dynamic regulators of tissue regeneration have yet to be described. We show that in the highly regenerative <em>Drosophila</em> testis, <em>miR-34</em> levels are significantly elevated during aging. <em>miR-34</em> modulates germ cell death and protects the progenitor germ cells from accelerated aging. However, <em>miR-34</em> is not expressed in the progenitors themselves but rather in neighboring cyst cells that kill the progenitors. Transcriptomics followed by functional analysis revealed that during aging, <em>miR-34</em> modifies integrin signaling by limiting the levels of the heterodimeric integrin receptor αPS2 and βPS subunits. In addition, we found that in cyst cells, this heterodimer is essential for inducing phagoptosis and degradation of the progenitor germ cells. Together, these data suggest that the <em>miR-34</em> – integrin signaling axis acts as a sensor of progenitor germ cell death to extend progenitor functionality during aging.</p>
The yin and yang of hsa-miR-1244 expression levels during activation of the UPR control cell fate - Raw data for label-free holographic microscopy
<p>Regulation of endoplasmic reticulum (ER) homeostasis plays a critical role in maintaining cell survival. When ER stress occurs, a network of three pathways called the unfolded protein response (UPR) is activated to reestablish homeostasis. While it is known that there is cross-talk between these pathways, how this complex network is regulated is not entirely clear. Using human cancer and non-cancer cell lines, two different genome-wide approaches, and two different ER stress models, we searched for miRNAs that were decreased during the UPR and surprisingly found only one, miR-1244, that was found in all these conditions. The activation of both UPR adaptive and apoptotic signaling pathway was confirmed by parallel genome-wide mRNA expression arrays for Calu 3 cells and next generation sequencing for 16HBE14o- cells. We also verified that ER-stress related downregulation of miR-1244 expression occurred with 5 different ER stressors and was confirmed in another human cell line (HeLa S3). These analyses demonstrated that the outcome of this reduction during ER stress supported both IRE1 signaling and elevated BIP expression. Further analysis also revealed that this novel miRNA impacted all three pathways of the UPR using inhibitors specific for IRE1, ATF6, and PERK. This is the first example of a complex mechanism by which this miRNA serves as a regulatory check point for all 3 pathways that is switched off after UPR activation. In summary, the results indicate that ER stress reduction of <em>miR-1244 </em>expression contributes to the pro-survival arm of UPR.</p> <p>For real-time monitoring of cell viability, we applied real-time and label-free holographic microscopy-based monitoring of cell death and viability using HoloMonitor M4<strong>®</strong> time-lapse cytometer (Phase Holographic Imaging PHI AB, Lund, Sweden). Holographic microscopy was used to follow the optical thickness and irregularity of cells exposed for up to 24 h to Tm or Tg in the presence or absence of <em>miR-1244 </em>mimic or antagomiR. The images from up to 8 independent optical fields were collected and analyzed according to manufacture instructions with HoloMonitor® App Suite software. Healthy cells are irregular in shape and thin, whereas dying cells are round and thick . For all analysis, the same cells parameters qualification was applied.</p> <p> </p> <p> </p> <p><strong>Figure 4. Overexpression of </strong><strong>miR-1244 promotes cell death. </strong>The results of real-time monitoring of cell viability are shown with the real time and label free holographic microscopy using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later monitored up to 24 h. Images were collected every 15 min (from 8 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p><strong>Figure 5. </strong><strong>miR-1244 influences the fate of cells challenged with Tm induced ER stress. </strong>The results of real-time monitoring of cell viability with the real time and label free holographic microscopy are shown using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later treated with Tm (2.5 µg/ml) up to 24 h. Images were collected every 15 min (from 5 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. <a name="OLE_LINK3"></a>Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p><strong>Figure 6. Exogenous </strong><strong>miR-1244 influences the fate of cells challenged with Tg induced ER stress. </strong>The results of real-time monitoring of cell viability with the real time and label free holographic microscopy are shown using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later treated with Tg (500 nM) up to 24 h. Images were collected every 15 min (from 5 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p> </p> <p> </p>
Raw Data for the article: Potential Anti-Metastatic Role of the Novel miR-CT3 in Tumor Angiogenesis and Osteosarcoma Invasion
<p>Osteosarcoma (OS) is the most common primary bone tumor mainly occurring in young adults and derived from primitive bone-forming mesenchyme. OS develops in an intricate tumor microenvironment (TME) where cellular function regulated by microRNAs (miRNAs) may affect communication between OS cells and the surrounding TME. Therefore, miRNAs are considered potential therapeutic targets in cancer and one of the goals of research is to accurately define a specific signature of a miRNAs, which could reflect the phenotype of a particular tumor, such as OS. Through NGS approach, we previously found a specific molecular profile of miRNAs in OS and discovered 8 novel miRNAs. Among these, we deepen our knowledge on the fifth candidate renamed now miR-CT3. MiR-CT3 expression was low in OS cells when compared with human primary osteoblasts and healthy bone. Through TargetScan, VEGF-A was predicted as a potential biological target of miR-CT3 and luciferase assay confirmed it. We showed that enforced expression of miR-CT3 in two OS cell lines, SAOS-2 and MG-63, reduced expression of VEGF-A mRNA and protein, inhibiting tumor angiogenesis. Enforced expression of miR-CT3 also reduced OS cell migration and invasion as confirmed by soft agar colony formation assay. Interestingly, we found that miR-CT3 behaves inducing the activation of p38 MAP kinase pathway and modulating the epithelial-mesenchymal transition (EMT) proteins, in particular reducing Vimentin expression. Overall, our study highlights the novel role of miR-CT3 in regulating tumor angiogenesis and progression in OS cells, linking also to the modulation of EMT proteins.</p>
miR-29a-3p, a new myokine orchestrating resistance exercise via coordinated metabolic responses
<p>NGS extracelular vesicles miRNA total reads of three pooled plasma samples from sedentary(C), endurance (E) and resitance (R) 4-week trained mice.</p>
AL589863.1 inhibits the progression of pancreatic cancer through regulating miR-671-5p/THBS1 axis
<p><span>We obtained <a name="OLE_LINK1"></a>mRNA and lncRNA expression profiles from 178 PC tissues and 4 normal pancreatic tissues in the TCGA database. We further downloaded the high throughput database of 167 normal tissues of the pancreas in the GTEx database. Then, the TCGA and GTEx datasets were integrated for further analysis. Three microarray datasets, including mRNA (GSE15471 and GSE62165) and miRNA (<a name="OLE_LINK2"></a>GSE32678), were downloaded from the GEO database. GSE15471 contained 39 pairs of <a name="OLE_LINK41"></a>pancreatic ductal adenocarcinoma tumors (PDAC) and adjacent normal tissues. GSE62165 included 118 PDAC and 13 adjacent normal samples. GSE32678 covered 25 PC samples and 7 adjacent normal samples. </span></p> <p><span>The Limma package in R was carried out to identify DEGs, <a name="_Hlk146015254"></a>DEmiRNAs, and DEl<a name="OLE_LINK4"></a><span>ncRNAs</span>. We identified the DEGs with threshold values of </span><span>|log 2 FC|> 1.5 and adjusted. <a name="OLE_LINK3"></a><em>p</em>-value< 0.05. </span><span>DEmiRNAs and DElncRNAs were determined with </span><a name="_Hlk146015501"></a><span>|log 2 FC|> 1.5/2.0 and <em>p</em>-value< 0.05</span><span>. Additionally, volcano plots were drawn to better visualize these DElncRNAs, DEmiRNAs, and DEGs using R software.</span></p>
ScienceDex guides
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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.