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730 results for “biochemicals”
Dataset of Pedigree, genotypes, clinical and biochemical characteristics of families of Northeastern Mexico
<p>This dataset combines pedigree, genotypes, clinical and biochemical data of 37 families of Northeastern Mexico. Primary reference is the article:</p> <p>Gallardo‑Blanco, H.L., Villarreal‑Perez, J.Z., Cerda‑Flores, R.M., Figueroa, A., Sanchez‑Dominguez, C.N., Gutierrez‑Valverde, J.M. ... Martinez‑Garza, L.E. (2017). Genetic variants in KCNJ11, TCF7L2 and HNF4A are associated with type 2 diabetes, BMI and dyslipidemia in families of Northeastern Mexico: A pilot study. Experimental and Therapeutic Medicine, 13, 523-529. https://doi.org/10.3892/etm.2016.3990</p> <p><strong>If you use these data please cite the corresponding manuscript, which can be downloaded here:</strong></p> <p>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5348709/</p> <p>https://www.spandidos-publications.com/10.3892/etm.2016.3990</p> <p>This dataset contains genotypes for the following SNPs:</p> <p>rs2986742</p> <p>rs4846051</p> <p>rs1801131</p> <p>rs1801133</p> <p>rs6541030</p> <p>rs12130799</p> <p>rs11208654</p> <p>rs1137100</p> <p>rs12405556</p> <p>rs3118378</p> <p>rs3737576</p> <p>rs10923931</p> <p>rs7554936</p> <p>rs3737787</p> <p>rs2516839</p> <p>rs1040404</p> <p>rs4670767</p> <p>rs7578597</p> <p>rs13400937</p> <p>rs10496971</p> <p>rs2627037</p> <p>rs1801262</p> <p>rs1569175</p> <p>rs2975760</p> <p>rs3792267</p> <p>rs10510228</p> <p>rs1801282</p> <p>rs3856806</p> <p>rs4955316</p> <p>rs9809104</p> <p>rs4607103</p> <p>rs6548616</p> <p>rs734873</p> <p>rs5400</p> <p>rs2030763</p> <p>rs4402960</p> <p>rs1513181</p> <p>rs9291090</p> <p>rs10010131</p> <p>rs10007810</p> <p>rs385194</p> <p>rs1799883</p> <p>rs2504853</p> <p>rs7754840</p> <p>rs7745461</p> <p>rs1800750</p> <p>rs1800629</p> <p>rs361525</p> <p>rs12200998</p> <p>rs2397060</p> <p>rs192655</p> <p>rs1044498</p> <p>rs4463276</p> <p>rs731257</p> <p>rs864745</p> <p>rs32314</p> <p>rs2330442</p> <p>rs4717865</p> <p>rs3173798</p> <p>rs10954737</p> <p>rs854555</p> <p>rs3917542</p> <p>rs662</p> <p>rs705308</p> <p>rs3943253</p> <p>rs751141</p> <p>rs1471939</p> <p>rs12544346</p> <p>rs13266634</p> <p>rs7844723</p> <p>rs2242103</p> <p>rs1408801</p> <p>rs10811661</p> <p>rs10511828</p> <p>rs12779790</p> <p>rs3793791</p> <p>rs4746136</p> <p>rs1111875</p> <p>rs10885390</p> <p>rs11196175</p> <p>rs7903146</p> <p>rs10885406</p> <p>rs12255372</p> <p>rs290487</p> <p>rs4918842</p> <p>rs2237892</p> <p>rs10839880</p> <p>rs1837606</p> <p>rs5210</p> <p>rs5218</p> <p>rs5219</p> <p>rs2946788</p> <p>rs11227699</p> <p>rs7930460</p> <p>rs1800849</p> <p>rs1387153</p> <p>rs948028</p> <p>rs2270031</p> <p>rs2416791</p> <p>rs7961581</p> <p>rs2070586</p> <p>rs1503767</p> <p>rs2269793</p> <p>rs8050136</p> <p>rs818386</p> <p>rs2966849</p> <p>rs1879488</p> <p>rs757210</p> <p>rs2033111</p> <p>rs11652805</p> <p>rs10512572</p> <p>rs2125345</p> <p>rs12946618</p> <p>rs12946115</p> <p>rs12950541</p> <p>rs1885088</p> <p>rs3907047</p> <p>rs2071023</p> <p>rs2833479</p> <p>rs2833483</p> <p>rs2300386</p> <p>rs2835370</p> <p>rs1296819</p> <p>rs1892848</p> <p>rs4821004</p> <p> </p>
Physiological and biochemical data for an experiment examining transgenerational effects in response to MHWs in S. purpuratus from the Santa Barbara Channel
Kelp forests of the Santa Barbara Channel have experienced prolonged marine heatwave (MHW) events that overlap in time with the phenology of life history events (e.g., gametogenesis and spawning) of marine invertebrates. To study the effect of thermal stress from MHWs during gametogenesis in the purple sea urchin (Strongylocentrotus purpuratus), adult urchins were acclimated to two conditions in the laboratory – a MHW (18°C) and a non-MHW (13°C) temperature at a time when gametogenesis would occur in situ. Following a four-month long acclimation period (October– January), adults were spawned and offspring from each parental condition were reared at MHW and non-MHW temperatures, creating a total of four offspring treatments. To assess for transgenerational effects in gamete traits, we measured egg size and biochemical composition. In addition, evidence of transgenerational effects was assessed by measuring embryo size and thermal tolerance of the progeny. Results indicated that MHW temperatures did affect life history traits. MHW-acclimated females had eggs with higher protein concentrations. Additionally, maternal thermal history influenced embryo body size at multiple stages of development while offspring developmental temperatures influenced body size only at the prism stage. Lastly, embryos from MHW-acclimated females were more thermally tolerant with higher LT50 values as compared to progeny from non-MHW-acclimated females. Overall, results showed that the thermal history of female S. purpuratus and developmental temperature influenced offspring traits and performance indicating that prolonged thermal stress, when it occurs during critical life history events, could influence reproductive success in situ. Moreover, our results suggest that transgenerational acclimation may aid in the capacity to resist the thermal stress associated with MHWs during early development in S. purpuratus.
Data set for the physical, chemical and biochemical modelling of the primary sedimentation tanks at the WWTP of Eindhoven
<p>These files contain data about measurement campaigns on the primary sedimentation tanks of the WWTP of Eindhoven (The Netherlands) in 2013 and 2014 and the routinely collected data for 2011 till 2013.</p> <p>The data was processed in the PhD of Youri Amerlinck, entitled "Model refinements in view of wastewater treatment plant optimization: improving the balance in sub-model detail."</p> <p>http://www.biomath.ugent.be/biomath/publications/download/amerlinckyouri_phd.pdf</p> <p><br> WWTP of Eindhoven PST Routine Measurements 2011_2013.csv<br> January 5, 2011 - June 14, 2013: <br> Routine analysis for BOD5, COD, TKN, TP, PO4, TSS</p> <p>WWTP of Eindhoven PST Reduced Capacity 2013.csv<br> June 24, 2013 - July 23, 2013 - September 9, 2013<br> Evaluation of reducing the capacity of the PST (including dosing of chemicals) for CODT, CODS, TP, PO4 ,TSS </p> <p>WWTP of Eindhoven PST measurement campaign full ASM 20140506.csv<br> May 6, 2014: <br> Full ASM fractionation BOD5, CODT, CODS, TSS, VSS TP, PO4 ,TN, NH4, NO3, pH</p> <p>WWTP of Eindhoven PST measurement campaign full ASM and Cations 20140902.csv<br> September 2, 2014:<br> Full ASM fractionation (repetition) and cation analysis (BOD10, CODT, CODS, TSS, VSS, TP, PO4 ,TN, NH4, NO3, pH - Ca, Mg, Na, K, Fe)</p>
Trypanosoma Epitope Dataset: Valid Epitopes and Randomly Generated Peptides with Biochemical Metrics and AI-Generated Scores
<p>This dataset contains information about valid linear B-Cell epitopes from the Trypanosoma genus, as well as randomly generated peptides. It includes biochemical metrics generated by the EpiBuilder-1.0 tool and scores generated by the BepiPred-3.0 software. The data was originally collected from the IEDB and UniProtKB platforms and has been processed and enhanced with these informations for researchers interested in understanding the molecular interactions between Trypanosoma protozoans and the immune system.</p>
Common biochemical and topological properties of metabolic genes recurrently dysregulated in tumors
<p>Although tumors exhibit numerous metabolic alterations, it’s unclear if common objectives and constraints underlie diverse metabolic changes. Here we interpret cancer gene expression, copy number variation, and survival data using a computational model, MetOncoFit. MetOncoFit evaluates142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topological attributes, and reaction flux. Meta-analysis of tumor databases using MetOncoFit revealed that metabolic enzymes with high catalytic activity were frequently up-regulated in many tumors and associated with poor survival. MetOncoFit also identified metabolites that were hot-spots of dysregulation. MetOncoFit illuminates how enzyme activity and metabolic network architecture influences tumorigenesis.</p>
Dataset for 'Organic Electrochemical Transistors Printed from Degradable Materials as Disposable Biochemical Sensors'
<p>This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the publication entitled “Organic Electrochemical Transistors Printed from Degradable Materials as Disposable Biochemical Sensors”.</p> <p>This work aims to study the fabrication of organic electrochemical transistors using more environmentally-friendly materials, in particular carbon contacts and polylactic acid (PLA) as substrate. Organic electrochemical transistors (or OECTs) offer applications in biosensing, for example for point-of-care devices. We use a combination of additive manufacturing methods (screen printing and inkjet printing) to manufacture these transistors and solve the issues with fabricating them on a low-temperature substrate such as PLA. We also assess these transistors as disposable sensors for the detection of various ion concentrations as well as glucose. The data that was collected in the frame of this work is present in this repository. More information about the contents of the dataset is present in the included README files.</p>
Biochemical Characterization of Mouse Retina of an Alzheimer's Disease Model by Raman Spectroscopy
<p>Raman raw data for the paper "Biochemical Characterization of Mouse Retina of an Alzheimer’s Disease Model by Raman Spectroscopy"</p> <ul> <li>two datasets of Raman images from cross-sectional and en face mouse retinas without processing</li> </ul>
Dataset - Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties
<p>Data needed to reproduce the results from the manuscript “Uncertainty Reduction in Biochemical Kinetic Models: Enforcing Desired Model Properties" by L. Miskovic, J. Beal, M. Moret, and V. Hatzimanikatis</p> <p>1. Data generated with the ORACLE workflow that was used in the iSCHRUNK training:</p> <ul> <li>Classification label vectors for the three analyzed metabolic concentration cases: <ul> <li>Reference case: class_vector_train_ref.mat</li> <li>Extreme1 case: class_vector_train_ex1.mat</li> <li>Extreme2 case: class_vector_train_ex2.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: training_set_ref.mat</li> <li>Extreme1 case: training_set_ex1.mat</li> <li>Extreme2 case: training_set_ex2.mat</li> </ul> </li> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ref.mat</li> <li>Extreme1 case: ccXTR_ex1.mat</li> <li>Extreme2 case: ccXTR_ex2.mat</li> </ul> </li> <li>Thermodynamics-based Flux Analysis (TFA) models for the three cases: <ul> <li>Reference case: tfa_ref.mat</li> <li>Extreme1 case: tfa_ex1.mat</li> <li>Extreme2 case: tfa_ex2.mat</li> </ul> </li> <li>Parameter names identical for the three cases <ul> <li>parameterNames.mat</li> </ul> </li> </ul> <p>2. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 4).</p> <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>ccXTR_ValidNeg.mat</li> </ul> </li> <li>Parameter sets used in validation <ul> <li>validation_set_neg.mat</li> </ul> </li> </ul> <p>3. Validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Table 3).</p> <ul> <li>Negative control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ValidRef_neg_agg.mat</li> <li>Extreme1 case: ccXTR_ValidEx1_neg_agg.mat</li> <li>Extreme2 case: ccXTR_ValidEx2_neg_agg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_ref_neg_agg.mat</li> <li>Extreme1 case: validation_set_ref_neg_agg.mat</li> <li>Extreme2 case: tvalidation_set_ref_neg_agg.mat</li> </ul> </li> </ul> </li> </ul> <ul> <li>Positive control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes for the three cases. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_ValidRef_pos_agg.mat</li> <li>Extreme1 case: ccXTR_ValidEx1_pos_agg.mat</li> <li>Extreme2 case: ccXTR_ValidEx2_pos_agg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_ref_pos_agg.mat</li> <li>Extreme1 case: validation_set_ex1_pos_agg.mat</li> <li>Extreme2 case: validation_set_ex2_pos_agg.mat</li> </ul> </li> </ul> </li> </ul> <p>4. Reassignment study: validation data generated with the ORACLE workflow with the parameters constrained using the information obtained with the iSCHRUNK (Figure 6 and Table 4).</p> <ul> <li>Negative control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_Valid_reassignment_neg.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_neg_reassignment.mat</li> </ul> </li> </ul> </li> <li>Positive control: <ul> <li>Flux control coefficients of the xylose uptake rate (XTR) with respect to the network enzymes. For the statistics and the figures we have used the population with removed outliers. <ul> <li>Reference case: ccXTR_Valid_reassignment_pos.mat</li> </ul> </li> <li>Parameter sets used for training for the three analyzed metabolite concentration cases. As parameters, we used the degree of saturation of the enzyme active site, σ<sub>A</sub>, which is constrained between 0 and 1.<sub> </sub> <ul> <li>Reference case: validation_set_pos_reassignment.mat</li> </ul> </li> </ul> </li> </ul> <p> </p> <p> </p>
Screening of 2694 RdRP virtual screening hits in RdRP/Nsp7/Nsp8 biochemical assay and confirmation in cellular SARS-CoV-2 assay
<p>This report describes the most relevant results of virtually screening the Janssen Pharmaceutica compound collection for potential activity against SARS-CoV-2 RNA polymerase and confirmation of potential hits in a biochemical SARS-CoV RTC assay and A549-hACE2 cell-based anti-SARS-CoV-2 assay.</p>
Exploiting Pretrained Biochemical Language Models for Targeted Drug Design
<p>This repository contains materials for the paper,<em> Exploiting Pretrained Biochemical Language Models for Targeted Drug Design, </em>which<em> </em>has been accepted for publication in <em>Bioinformatics</em> Published by Oxford University Press.</p> <p><em>data.zip</em> contains vocabulary files for the pretrained models, additional information regarding proteins (PFAM family, protein similarity) and interactions filtered from <a href="https://www.bindingdb.org/bind/index.jsp">BindingDB</a> which are further split into train, validation and test sets and used to train target specific molecule generation models. </p> <p><em>models.zip </em>includes files for the models trained in this study. </p> <p><em>predictions.zip </em>comprises the compounds generated with the targeted models and the result of their evaluation with respect to benchmarking metrics. </p> <p><em>docking.zip </em>contains <em>targets/ </em>including PDB files of the test proteins selected for docking evaluation, <em>ligands/ </em>including SDF files for molecules generated with the targeted models and two decoding strategies (i.e. beam search and sampling) and <em>complex/ </em>including docking outputs. </p> <p> </p> <p> </p>
Elemental and biochemical nutrient limitation of zooplankton: A meta-analysis
<p>Primary consumers in aquatic ecosystems are frequently limited by the quality of their food, often expressed as phytoplankton elemental and biochemical composition. However, effects of these food quality indicators vary across studies, and we lack an integrated understanding of how elemental (e.g., nitrogen, phosphorus) and biochemical (e.g., fatty acid, sterol) limitations interactively influence aquatic food webs. Here we present results of a meta-analysis using >100 experimental studies, confirming that limitation by N, P, fatty acids, and sterols all have significant negative effects on zooplankton performance. However, effects varied by grazer response (growth versus reproduction), specific manipulation, and across taxa. While P limitation had greater effects on zooplankton growth than fatty acids overall, P and fatty acid limitation had equal effects on reproduction. Furthermore, we show that: nutrient co-limitation in zooplankton is strong; effects of essential fatty acid limitation depend on P availability; indirect effects induced by P limitation exceed direct effects of mineral P limitation; and effects of nutrient amendments using laboratory phytoplankton isolates exceed those using natural field communities. Our meta-analysis reconciles contrasting views about the role of various food quality indicators, and their interactions, for zooplankton performance, and provides a mechanistic understanding of trophic transfer in aquatic environments.</p>
Dataset for "High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO2 diffusion and efficient light use"
<p>Dataset used in the paper</p> <p>Retta MA, Van Doorselaer L, Driever SM, Yin X, de Ruijter NCA, Verboven P, Nicolaï BM, Struik PC. High photosynthesis rates in Brassiceae species are mediated by leaf anatomy enabling high biochemical capacity, rapid CO<sub>2</sub> diffusion and efficient light use. New Phytol. 2024 Sep 18. doi: 10.1111/nph.20136. PMID: 39294895.</p> <p>Please cite the paper presenting this datase.</p> <h1><strong>Plant Species and Inbred Lines:</strong></h1> <ul> <li><em>Hirschfeldia incana L. (7th generation inbred line 190003 HIN-NIJ-07-B) </em></li> <li><em>Brassica nigra L. (3rd generation inbred line 210093 BNI-DG1-03-B)</em></li> <li><em>Brassica rapa L. (inbred line ‘R-o-18’)</em></li> <li><em>Arabidopsis thaliana (accession Columbia)</em></li> </ul> <h1><strong>Growth Conditions:</strong></h1> <ul> <li><em>Media:</em> Rock-wool blocks (Grodan Plantop, Roermond, Netherlands, 10×10×7.5 cm)</li> <li><em>Fertigation:</em> Nitrogen-rich nutrient solution via automated dripping system.</li> <li><em>Light Conditions:</em> 12 h day/12 h night, light intensity of 200 µmol m-2 s-1 and 1800 µmol m-2 s-1</li> <li><em>Temperature:</em> Day/Night temperatures of 23 °C and 20 °C, respectively.</li> <li><em>Relative Humidity:</em> 70%</li> </ul> <h1><strong>Codes</strong></h1> <p><strong>Species:</strong></p> <ul> <li><em>Hirschfeldia incana L. - H. incana</em></li> <li><em>Brassica nigra L. - B. nigra</em></li> <li><em>Brassica rapa L. - B. rapa</em></li> <li><em>Arabidopsis thaliana - A. thaliana</em></li> </ul> <p><strong>Light conditions:</strong></p> <ul> <li><em>High light - HL</em></li> <li><em>Low light - LL</em></li> </ul> <p><strong>Replicates:</strong></p> <ul> <li><em>Biological replicates were labeled with numbers, e.g. replicate one from high light grown Hirschfeldia incana is referred to as HiHL1</em></li> </ul> <h1><strong>Measurements</strong></h1> <h2><strong>Leaf Gas Exchange and Chlorophyll Fluorescence Measurements (GasExchangeData.zip):</strong></h2> <ul> <li>Four leaves per species per treatment.</li> <li>Conducted using a LI-6800 (LI-COR, Lincoln, NE, USA) on the mid-position of the youngest fully expanded leaf.</li> <li>The resoponse of photosynthesis to irradiance and external CO2 concentrations augumneted with multi-phase flash fluorescence were made</li> </ul> <h2><strong>Optical Properties Measurement </strong>(<strong>Absorbance & chlorophyll.zip):</strong></h2> <ul> <li><em>Leaves:</em> Four leaves per species per treatment.</li> <li>Leaf transmittance and reflectance measured using a dual channel spectrophotometer (absorptance_reflac_data_355_750.xlsx)</li> <li>Chlorophyll content measured using a spectrophotometer (Chlorophyll.xlsx).</li> </ul> <h2><strong>Stomatal Density and Size Analysis </strong>(<strong>Stomata.zip):</strong></h2> <p><strong>Sampling:</strong></p> <ul> <li><em>Leaves:</em> Four leaves per species per treatment.</li> <li><em>Plants:</em> Samples taken from three different plants.</li> <li><em>Leaf-side:</em> abaxial and adxial leaf side.</li> </ul> <p><strong>Microscopy Setup:</strong></p> <ul> <li>Stomatal imprints made using clear nail polish, imaged using a light microscope at 20x.</li> </ul> <p><strong>Data Output:</strong></p> <ul> <li><em>Imaging Results:</em> .jpg files organised under folders for species e.g. AtHL\R1 T+B.zip contains images ofimprints of top (T) and bottom (B) leaf sides from replicate plant 1 (R1) of A. thaliana grown under high light (AtHL). The images are named as for example, AT_HL_BOTTOM_R1_A_stacked_minimum.jpg, The leters A to E label various imges made from one imprint.</li> </ul> <h2><strong>Light and Electron Microscopy of Leaf Sections (</strong><strong>CellwallChloroplast.zip):</strong></h2> <p><strong>Sampling:</strong></p> <ul> <li><em>Leaves:</em> Four leaves per species per treatment.</li> <li><em>Plants:</em> Samples taken from four different plants.</li> </ul> <p><strong>Sample preparation</strong></p> <ul> <li>Leaf samples fixed, dehydrated, embedded in Araldite, and sectioned for imaging.</li> <li>1 µm think sections were made for light microscopy</li> <li>Sections of 70 nm were double stained for TEM</li> </ul> <p><strong>Microscopy Setup:</strong></p> <ul> <li>Mesophyll cells imaged at 400x and 700x to measure chloroplast coverage.</li> <li>Electron microscopy performed with Zeiss EM900 electron microscope.</li> </ul> <h2><strong>Mesophyll Chlorophyll (ConfocalData.zip):</strong></h2> <p><strong>Sampling:</strong></p> <ul> <li><em>Leaves:</em> Three leaves per species per treatment.</li> <li><em>Plants:</em> Samples taken from three different plants.</li> <li><em>Thickness:</em> 200 ± 10 µm sections prepared using a sliding microtome</li> </ul> <p><strong>Microscopy Setup:</strong></p> <ul> <li><em>Microscope:</em> Leica DM8 inverted scope equipped with a Stellaris 5 confocal microscope (Leica Microsystems, Wetzlar, Germany).</li> <li><em>Excitation:</em> 490 nm excitation laser line</li> <li><em>Fluorescence Recording:</em> Chlorophyll autofluorescence recorded in a spectral range of 660−700 nm.</li> <li><em>Objective:</em> Leica objective ×10/0.4 NA.</li> <li><em>Z-Stacks:</em> 85–112 µm depth, two random positions per sample</li> </ul> <p><strong>Data Output:</strong></p> <ul> <li><em>Imaging Results:</em> Z-stacks of chlorophyll autofluorescence in mesophyll cells.</li> <li><em>Spectral Information:</em> Chlorophyll autofluorescence recorded in the 660−700 nm range.</li> </ul> <p><strong>Analysis:</strong></p> <ul> <li><em>Software:</em> The confocal files are in .lif format and can be viewed using Leica application suite (LASx), ImageJ</li> </ul>
Fig. 2. A in Morphometric And Biochemical Variation And The Distribution Of The Genus Apodemus (Mammalia: Rodentia) In Turkey
Fig. 2. A scatterplot of six Apodemus species based on CVA analysis on the pooled variance covariance matrix
Fig. 4. A in Morphometric And Biochemical Variation And The Distribution Of The Genus Apodemus (Mammalia: Rodentia) In Turkey
Fig. 4. A scatterplot of Sylvaemus species based on CVA analysis on the pooled variance covariance matrix
Fig. 1 in Morphometric And Biochemical Variation And The Distribution Of The Genus Apodemus (Mammalia: Rodentia) In Turkey
Fig. 1. Distribution map of the analyzed populations of the genus Apodemus in Turkey. 1 = Edirne, 2 = Velikaköprüsü (Kirklareli), 3 = Pınarhisar (Kirklareli), 4 = Büyükkarıştıran (Tekirdağ), 5 = Istanbul, 6 = Kemalpaşa (İzmir), 7 = Buharkent (Aydin), 8 = Balıkesir, 9 = Uludağ (Bursa), 10 = Çığlıkara (Antalya), 11 = Burdur, 12 = Beyşehir (Konya), 13 = Kütahya, 14 = Kocaeli, 15 = Akçakoca (Bolu), 16 = Bolu, 17 = Çaycuma (Zonguldak), 18 = Ankara, 19 = Konya, 20 = Sebil (Mersin), 21 = Niğde, 22 = Kayseri, 23 = Kırşehir, 24 = Yozgat, 25 = Samsun, 26 = Akkuş (Ordu), 27 = Sıvas, 28 = Göksun (K = Maraş), 29 = Kırıkhan (Hatay), 30 = Kilis-Gaziantep, 31 = Malatya, 32 = Nusasbin (Mardin), 33 = Efirli (Ordu), 34 = Bulancak (Giresun), 35 = Sümela (Trabzon), 36 = İkizdere (Rize), 37 = Ayder (Rize), 38 = Hopa (Artvin), 39 = Kutul (Artvin), 40 = Posof (Ardahan), 41 = Ardahan, 42 = Iğdır, 43 = Erzurum, 44 = Muş, 45 = Van
Fig. 5 in Seasonal analysis of condition, biochemical and bioenergetic indices of females of Brazilian flathead, Percophis brasiliensis
Fig. 5. Seasonal variation of inorganic matter content (ash) in muscle, gonad and liver of Percophis brasiliensis.
Fig. 1 in Seasonal analysis of condition, biochemical and bioenergetic indices of females of Brazilian flathead, Percophis brasiliensis
Fig. 1. Seasonal variation of mean and standard deviation of hepatosomatic index (HSI), gonadosomatic index (GSI) and condition index (K) estimated for Percophis brasiliensis.
Fig. 4 in Seasonal analysis of condition, biochemical and bioenergetic indices of females of Brazilian flathead, Percophis brasiliensis
Fig. 4. Seasonal variation of water content (moisture) in muscle, gonad and liver of Percophis brasiliensis.
Fig. 1 in The Dynamics Of Genetic Structure Of Round G O B Y N E O G O B I U S M E L A N O S T O M U S (Pa L L A S) Groupings In The Odessa Bay Of The Black Sea Utilizing Biochemical Marker Loci
Fig. 1. Frequencies of S-alleles by polymorphic locus Es2 in round goby groupings from different parts of the Odessa Bay. * – significant deviation of allele frequencies in round goby groupings from the south and the north part of the Odessa Bay (Р = 0,05); # – significant deviation of allele frequencies in round goby groupings in the south part of the Odessa Bay in 2015-2016 in comparison to 2013-2014 (Р = 0,05).
Fig. 2 in The Dynamics Of Genetic Structure Of Round G O B Y N E O G O B I U S M E L A N O S T O M U S (Pa L L A S) Groupings In The Odessa Bay Of The Black Sea Utilizing Biochemical Marker Loci
Fig. 2. Frequencies of S-alleles by polymorphic locus of myogene 3 in round goby groupings from different parts of the Odessa Bay * – significant deviation of allele frequencies in round goby groupings from the south and the north part of the Odessa Bay in 2013 and 2014 (Р = 0,05); # – significant deviation of allele frequencies in round goby groupings from the south part of the Odessa Bay in 2013-2014 in comparison to 2015 (Р = 0,05).
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.