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30,947 results for “Profile”
The raw data of the second replicate 0d primary and lipid metabolite profiling.
<p>The raw data of the second replicate 0d primary and lipid metabolite profiling.</p>
The raw data of the second replicate 3d primary and lipid metabolite profiling.
<p>The raw data of the second replicate 3d primary and lipid metabolite profiling.</p>
Figure 2 from: Abdullah E, Dhiaa S, Saleh K, Merkhan M (2021) Effect of esomeprazole on lipid profile in patients with peptic ulcer. Pharmacia 68(3): 613-617. https://doi.org/10.3897/pharmacia.68.e70292
Figure 2 Esomeprazole impaired lipid metabolism in patients with peptic ulcer. Esomeprazole significantly decreased TG and HDL and significantly elevated TC. Data expressed as mean±SD, p < 0.001, TG = triglycerides, TC = total cholesterol, HDL = high density lipoprotein.
Figure 3 from: Abdullah E, Dhiaa S, Saleh K, Merkhan M (2021) Effect of esomeprazole on lipid profile in patients with peptic ulcer. Pharmacia 68(3): 613-617. https://doi.org/10.3897/pharmacia.68.e70292
Figure 3 Esomeprazole modulated calculated hyperlipidemic indicators in patients with peptic ulcers. Esomeprazole significantly elevated LDL and non-HDL and has non-significant effects on vLDL. Data expressed as mean±SD, p < 0.001, LDL = Low density lipoprotein, vLDL = very low-density lipoprotein, non-HDL = non-high density lipoprotein.
Figure 71. Steatoda bipunctata. A–F, male. A–C, palp. A, mesial. B, ventral. C, ectal. D–G, prosoma. D, profile. E in Morphological phylogeny of cobweb spiders and their relatives (Araneae, Araneoidea, Theridiidae)
Figure 71. Steatoda bipunctata. A–F, male. A–C, palp. A, mesial. B, ventral. C, ectal. D–G, prosoma. D, profile. E, details of setae; note raised bases (123-1). F, posterior tip with stridulatory ridges (128-1) in two clearly separate patches (129-0). G, female with inconspicuous stridulatory ridges. Scale bars: 100 Mm.
Dataset Used in "Phase-I analysis for monitoring nonlinear profile signals in manufacturing processes"
<p>This is the dataset used in Ding, Zeng, and Zhou, 2006, “Phase-I analysis for monitoring nonlinear profile signals in manufacturing processes.” <em>Journal of Quality Technology</em>, Vol. 38(3), pp. 199-216. This data file has 528 data records, two fewer than the 530 number mentioned in the paper.</p>
Activity-based protein profiling for target identification of JCP276 in Mycobacterium tuberculosis
<p><span><span><span><span><span><span><span><span><span><span><span>The increasing incidence of antibiotic-resistant <i>Mycobacterium tuberculosis </i>infections is a growing global health threat necessitating the development of new antibiotics. Serine hydrolases (SHs) are a promising class of targets because of their importance for the synthesis of the mycobacterial cell envelope. We screened a library of small molecules containing serine-reactive electrophiles and identified a series of narrow spectrum inhibitors of <i>M. tuberculous </i>growth. Using these lead molecules we performed competitive activity-based protein profiling and identified SH targets, including enzymes with uncharacterized functions. Lipidomic analyses of compound-treated cultures revealed an accumulation of free lipids and a substantial decrease in lipooligosaccharides, linking SH inhibition to defects in cell envelope biogenesis. Mutant analysis revealed a path to resistance via the synthesis of mycocerates, but not through mutations to target enzymes. We conclude that simultaneous inhibition of multiple SH enzymes is likely to be an effective therapeutic strategy.</span></span></span></span></span></span></span></span></span></span></span></p>
Figure 4 from: Vaníčková L, Břízová R, Pompeiano A, Ferreira LL, de Aquino NC, Tavares RF, Rodriguez LD, Mendonça AL, Canal NA, do Nascimento RR (2015) Characterisation of the chemical profiles of Brazilian and Andean morphotypes belonging to the Anastrepha fraterculus complex (Diptera, Tephritidae). In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 193-209. https://doi.org/10.3897/zookeys.540.9649
Figure 4 - Principal component analyses (PCA) of transformed GC×GC/MS data of 48 female CHs from seven populations of the Anastrepha fraterculus cryptic species complex. (Variables factor map) projection of variables on the plane defined by the first two principal components. (Hierarchical clustering) score plot describing the populations and their clustering. Key: AL – Alagoas, AL, Brazil; BEN – Bento Gonçalves, RS, Brazil; CAC – Cachipay, Colombia; DUI – Duitama, Colombia; PEL – Pelotas, RS, Brazil; SAO – São Joaquim, SC, Brazil; SIB – Sibundoy, Colombia. A1–19 – n-alkanes; B1–11 – methylbranched hydrocarbons; C1–11 – alkenes; D1–7 – alkadienes. Colored boxes indicate particular clusters.
Figure 2 from: Vaníčková L, Břízová R, Pompeiano A, Ferreira LL, de Aquino NC, Tavares RF, Rodriguez LD, Mendonça AL, Canal NA, do Nascimento RR (2015) Characterisation of the chemical profiles of Brazilian and Andean morphotypes belonging to the Anastrepha fraterculus complex (Diptera, Tephritidae). In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 193-209. https://doi.org/10.3897/zookeys.540.9649
Figure 2 - Principal component analyses (PCA) of transformed GC×GC/MS data of seven male-borne volatiles produced by groups of 20 sexually mature individuals from seven populations of the Anastrepha fraterculus cryptic species complex. Variables factor map represents projection of variables on the plane defined by the first two principal components. Hierarchical clustering is score plot describing the populations and their clustering. Key: AL – Alagoas, AL, Brazil; BEN – Bento Gonçalves, RS, Brazil; DUI – Duitama, Colombia; IBA – Ibague, Colombia; PEL – Pelotas, RS, Brazil; SAO – São Joaquim, SC, Brazil; SIB – Sibundoy, Colombia. Epianas. – Epianastrephin; Z.E.Far – (Z, E)-α-farnesene; E.E.Far – (E, E)-α-farnesene; Nonen.1.ol – (Z)-3-nonen-1-ol; Nonadien. – (E, Z)-3,6-nonadien-1-ol; a.Pinene – α-pinene. Colored boxes indicate particular clusters.
Figure 3 from: Vaníčková L, Břízová R, Pompeiano A, Ferreira LL, de Aquino NC, Tavares RF, Rodriguez LD, Mendonça AL, Canal NA, do Nascimento RR (2015) Characterisation of the chemical profiles of Brazilian and Andean morphotypes belonging to the Anastrepha fraterculus complex (Diptera, Tephritidae). In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 193-209. https://doi.org/10.3897/zookeys.540.9649
Figure 3 - Principal component analyses (PCA) of transformed GC×GC/MS data of 48 male CHs from seven populations of the Anastrepha fraterculus cryptic species complex. Variables factor map represents projection of variables on the plane defined by the first two principal components. Hierarchical clustering is score plot describing the populations and their clustering. Key: AL – Alagoas, AL, Brazil; BEN – Bento Gonçalves, RS, Brazil; CAC – Cachipay, Colombia; DUI – Duitama, Colombia; PEL – Pelotas, RS, Brazil; SAO – São Joaquim, SC, Brazil; SIB – Sibundoy, Colombia. A1–19 – n-alkanes; B1–11 – methylbranched hydrocarbons; C1–11 – alkenes; D1–7 – alkadienes. Colored boxes indicate particular clusters.
Figure 1 from: Vaníčková L, Břízová R, Pompeiano A, Ferreira LL, de Aquino NC, Tavares RF, Rodriguez LD, Mendonça AL, Canal NA, do Nascimento RR (2015) Characterisation of the chemical profiles of Brazilian and Andean morphotypes belonging to the Anastrepha fraterculus complex (Diptera, Tephritidae). In: De Meyer M, Clarke AR, Vera MT, Hendrichs J (Eds) Resolution of Cryptic Species Complexes of Tephritid Pests to Enhance SIT Application and Facilitate International Trade. ZooKeys 540: 193-209. https://doi.org/10.3897/zookeys.540.9649
Figure 1 - Heat map of seven male-borne volatiles (columns) identified by GC×GC/MS analyses in seven populations (rows) of the Anastrepha fraterculus cryptic species complex. The dendrograms were created using correlation-based distances and the Ward method of hierarchical clustering (P < 0.05). Key: AL – Alagoas, AL, Brazil; BEN – Bento Gonçalves, RS, Brazil; DUI – Duitama, Colombia; IBA – Ibague, Colombia; PEL – Pelotas, RS, Brazil; SAO – São Joaquim, SC, Brazil; SIB – Sibundoy, Colombia. Epianas – Epianastrephin; Z.E.Far – (Z, E)-α-farnesene; E.E.Far – (E, E)-α-farnesene; Z.Nonenol – (Z)-3-nonen-1-ol; E.Z.Nona – (E, Z)-3,6-nonadien-1-ol; a.Pinene – α-pinene.
Transcriptome profiling of derived-hepatocyte progenitors from human iPSCs with nanoCAGE - part1 - genomic alignments (hg19 + hg38)
<p>This repository contains genomic alignments (BED files) of paired-end nanoCAGE sequencing data (CAGEscan data) collected from Illumina MiSeq run IDs "170630_M00528_0292_000000000-B9JY8" (aka "NC_LIMMS") and "180221_M00528_0334_000000000-B6PJM" (aka "NC_LIMMS2"). FASTQ files were processed with the MOIRAI pipeline OP-WORKFLOW-CAGEscan-short-reads-v2.1 (Hasegawa et al. BMC Bioinformatics 2014 May 16;15:144. doi: 10.1186/1471-2105-15-144.). Filtered pairs of reads were aligned on the human genome assemblies hg19 and hg38. See tables below for a detailed description of the samples contained in each nanoCAGE library, including barcodes and index sequences used for the demultiplexing of sequencing reads. Corresponding raw sequencing data files (FASTQ files) were deposited at Zenodo under the following Digital Object Identifier: 10.5281/zenodo.1014009.</p> <p> </p> <p><em><strong>"170630_M00528_0292_000000000-B9JY8" ("NC_LIMMS") :</strong></em></p> <p><strong>ID Sample_name Barcode_number Barcode_sequence Index_sequence</strong></p> <p>1 iPSC_control_rep1 4 ACAGAT NNNNNNNN</p> <p>2 iPSC_control_rep2 24 ATCGTG NNNNNNNN</p> <p>3 iPSC_control_rep3 31 CACGAT NNNNNNNN</p> <p>4 S3P1_OK_rep1 36 CACTGA NNNNNNNN</p> <p>5 S3P1_OK_rep2 46 CTGACG NNNNNNNN</p> <p>6 S3P1_OK_rep3 63 GAGTGA NNNNNNNN</p> <p>7 S4P1_OK_rep1 79 GTATAC NNNNNNNN</p> <p>8 S4P1_OK_rep2 92 TCGAGC NNNNNNNN</p> <p>9 S4P1_OK_rep3 9 ACATGA NNNNNNNN</p> <p>10 S4P2_OK_rep1 21 ATCATA NNNNNNNN</p> <p>11 S4P2_OK_rep2 33 CACGTG NNNNNNNN</p> <p>12 S4P2_OK_rep3 45 CGATGA NNNNNNNN</p> <p>13 S1P1_rep1 57 GAGATA NNNNNNNN</p> <p>14 S1P1_rep2 69 GCTCTC NNNNNNNN</p> <p>15 S1P1_rep3 81 GTATGA NNNNNNNN</p> <p>16 S3P1_FAILED_rep1 93 TCGATA NNNNNNNN</p> <p>17 S3P1_FAILED_rep2 11 AGTAGC NNNNNNNN</p> <p>18 S3P1_FAILED_rep3 23 ATCGCA NNNNNNNN</p> <p>19 S4P1_FAILED_rep1 35 CACTCT NNNNNNNN</p> <p>20 S4P1_FAILED_rep2 47 CTGAGC NNNNNNNN</p> <p>21 S4P1_FAILED_rep3 59 GAGCGT NNNNNNNN</p> <p>22 S4P2_FAILED_rep1 71 GCTGCA NNNNNNNN</p> <p>23 S4P2_FAILED_rep2 83 TATAGC NNNNNNNN</p> <p>24 S4P2_FAILED_rep3 95 TCGCGT NNNNNNNN</p> <p> </p> <p><em><strong>"180221_M00528_0334_000000000-B6PJM" ("NC_LIMMS2"):</strong></em></p> <p><strong>ID Sample_name Barcode_number Barcode_sequence Index_sequence</strong></p> <p>25 PETRI_rep1 04 ACAGAT NNNNNNNN</p> <p>26 PETRI_rep2 24 ATCGTG NNNNNNNN</p> <p>27 PETRI_rep3 31 CACGAT NNNNNNNN</p> <p>28 BIOCHIP_E_rep1 6 CACTGA NNNNNNNN</p> <p>29 BIOCHIP_M_rep1 46 CTGACG NNNNNNNN</p> <p>30 BIOCHIP_S_rep1 63 GAGTGA NNNNNNNN</p> <p>31 BIOCHIP_E_rep2 79 GTATAC NNNNNNNN</p> <p>32 BIOCHIP_M_rep2 92 TCGAGC NNNNNNNN</p> <p>33 BIOCHIP_S_rep2 09 ACATGA NNNNNNNN</p> <p>34 BIOCHIP_E_rep3 21 ATCATA NNNNNNNN</p> <p>35 BIOCHIP_M_rep3 33 CACGTG NNNNNNNN</p> <p>36 BIOCHIP_S_rep3 45 CGATGA NNNNNNNN</p> <p>37 HEPATOCYTES_rep1 57 GAGATA NNNNNNNN</p> <p>38 HEPATOCYTES_rep2 69 GCTCTC NNNNNNNN</p> <p>39 iPSC_control_rep1-2 81 GTATGA NNNNNNNN</p> <p>40 BIOCHIP_E_rep2-2 93 TCGATA NNNNNNNN</p> <p>41 BIOCHIP_M_rep1-2 11 AGTAGC NNNNNNNN</p> <p>42 BIOCHIP_S_rep2-2 23 ATCGCA NNNNNNNN</p> <p> </p>
Fig 4 from: Riede K (2018) Acoustic profiling of Orthoptera: present state and future needs. Journal of Orthoptera Research 27(2): 203-215. https://doi.org/10.3897/jor.27.23700
Fig 4 A data warehouse for sound management. The scheme illustrates elements and workflow for acoustic profiling of Orthoptera. Songs are sampled either by recording individual songsters (Targeted Recordings), or entire acoustic scenes, each of which could contain several Orthoptera songs. Targeted recordings are treated like specimens, with time and locality stamps and, preferably, a voucher specimen. All databases listed in Table 1 are designed to store individual recordings. These distributed databases could be federated via ABCD- or Darwin-protocol. Soundscapes require distinct data management of large multimedia files. Orthoptera songs could be extracted manually or semi-automatically as sound snippets, and eventually be identified (ID) manually, or using automatic sound recognition algorithms (ASR). Many snippets can be extracted from each scene, resulting in a one-to-many relationship between scenes and snippets.
Fig 2 from: Riede K (2018) Acoustic profiling of Orthoptera: present state and future needs. Journal of Orthoptera Research 27(2): 203-215. https://doi.org/10.3897/jor.27.23700
Fig 2 The SYSTAX database. Screenshot of the new SYSTAX user interface, to be released under www.systax.org. A search for the Neotropical tettigoniid genus Anaulacomera recovers several sound recordings from a voucher specimen of a hitherto undescribed species, documented by photographs. Faceting allows searching by images or sounds exclusively.
Fig 1 from: Riede K (2018) Acoustic profiling of Orthoptera: present state and future needs. Journal of Orthoptera Research 27(2): 203-215. https://doi.org/10.3897/jor.27.23700
Fig 1 Web-based sound analysis tool for the Macaulay Sound Library, Cornell Lab (https://www.macaulaylibrary.org). Macaulay Library provides more than 400,000 playable audio files (http://macaulaylibrary.org/index.do), and even permits spectrographic online visualization using RavenViewer as a free browser plugin (http://www.birds.cornell.edu/brp/software/sound-analysis-tools). The example shows a recording of a Virtuoso katydid by T. Walker, who provided most of the Orthoptera sound recordings for this sound library. For further details, see text.
Fig 3 from: Riede K (2018) Acoustic profiling of Orthoptera: present state and future needs. Journal of Orthoptera Research 27(2): 203-215. https://doi.org/10.3897/jor.27.23700
Fig 3 Embedding metadata within sound files. Metadata were embedded within wav and mp3 fields directly from the SYSTAX database using Soundminer software (http://store.soundminer.com/). Metadata are visible within most mp3-players, displaying the species name as "TrackTitle" and the recordist as "Artist" (Courtesy: S. Ingrisch).
Pain profile during orthodontic levelling and alignment with fixed appliances reported in randomized trials: a systematic review with meta-analyses
<p>Dataset for all analyses</p>
MALDI-TOF mass spectrometry profiling of bovine skim milk for subclinical mastitis detection
<p>MALDI-TOF spectra acquired from bovine milk collected from dairy cows in Piedmont (Italy)</p>
Spatial profile of tumor microenvironment in PD-L1-negative and PD-L1-positive triple negative breast cancer
<p><strong>Supplementary Materials</strong></p>
IODP Expedition 350 Laser height profile (section half)
<p>Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.</p>
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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.