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Colony-age-dependent variation in cuticular hydrocarbon profiles in subterranean termite colonies
<p>Cuticular hydrocarbons (CHCs) have, in insects, important physiological and ecological functions, such as protection against desiccation and as semiochemicals in eusocial taxa, including termites. CHCs are, in termites, known to vary qualitatively and/or quantitatively among species, populations, or seasons. Changes to hydrocarbon profile composition have been linked to varying degrees of aggression between termite colonies, although the variability of results among studies suggests that additional factors might have been involved. One source of variability may be colony age; however, this factor has never been investigated. We studied caste-specific patterns of CHC profiles in Coptotermes gestroi colonies of four different age classes (6, 18, 30, and 42 months). The CHC profiles were variable among castes in the youngest colonies, but progressively converged with increasing colony age. Young colonies had a less-defined CHC identity compared to older ones, which likely obscures the colony's ability to detect non-nestmates. Our data suggest that there is no selective pressure on an early-defined colony CHC profile, potentially allowing incipient colonies to merge non-agonistically with competing conspecifics as an indirect result. </p>
Figure 6 in A novel epidermal gland type in lizards (α-gland): structural organization, histochemistry, protein profile and phylogenetic origins
Figure 6. Transmission electron microscopy of the epithelium from the femoral area of Tropidurus catalanensis. A, general view of the glandular epithelium showing major skin layers and abundance of vesicles inside glandular cells. B–G, magnified view of glandular tissue showing: B, clusters of melanin granules and numerous vesicles; C, aggregations of four different types of vesicles (V1, V2, V3 and V4); D, E, Golgi apparatuses amid secretory vesicles; F, autophagocytic events (arrowheads indicate membrane projections); G, iridophores present in the apical portion of the dermis. Legend: Go, Golgi complex; Ir, iridophore; Me, melanin granule; Nu, nucleus; V1, vesicles type 1; V2, vesicles type 2; V3, vesicles type 3; V4, vesicles type 4.
Figure 3 in A novel epidermal gland type in lizards (α-gland): structural organization, histochemistry, protein profile and phylogenetic origins
Figure 3. Maderson & Chiu's (1970) model of epidermal gland evolution. Steps of the model are briefly described in items A–G.
Figure 2 in A novel epidermal gland type in lizards (α-gland): structural organization, histochemistry, protein profile and phylogenetic origins
Figure 2. Histological structure of unspecialized skin and epidermal glands. Diagrammatic representation of the unspecialized squamate skin and major epidermal gland types, illustrating their respective secretion mechanisms as hypothesized by Maderson (1972).
Figure 5. A in A novel epidermal gland type in lizards (α-gland): structural organization, histochemistry, protein profile and phylogenetic origins
Figure 5. A, unspecialized scales from the pre-cloacal flap of a male Stenocercus caducus (MZUSP-R 82815). B, unspecialized scales from the femoral area of a female Tropidurus chromatops (MZUSP-R 106266). C, scales with α-glands from the femoral area of male T. chromatops (MZUSP-R 106263). Note that β-keratin layers are not present in A and C because they were lost during sample preparation. D, unspecialized scale (stage I) from the humeral region of a female T. chromatops (MZUSP-R 106266). E, detail of a scale with α-gland (stage IV) from the femoral area of a male T. xanthochilus (MZUSP-R 106342). F, detail of a scale with α-gland (stage V) from the pre-cloacal flap of a male T. xanthochilus (MZUSP-R 106342). G, detail of a scale with α-gland (stage VI) from the femoral areal of a male T. xanthochilus (MZUSP-R 106336). H, part of the inner generation of an α-gland from the femoral area of a male Plica plica (MTR 18918) showing the glandular stratum with a large number of secretory vesicles (indicated with an asterisk). I, part of the inner generation of the α-gland from the femoral area of a male T. catalanensis (MZUSP-R 106470) with melanophores transferring melanin granules into glandular cells and numerous melanin granules accumulated in their cytoplasm. J, glandular tissue of the outer generation
Figure 9 in A novel epidermal gland type in lizards (α-gland): structural organization, histochemistry, protein profile and phylogenetic origins
Figure 9. Ancestral state reconstructions of α-gland/flash mark related characters of tropidurid lizards.
Figure 4 in A novel epidermal gland type in lizards (α-gland): structural organization, histochemistry, protein profile and phylogenetic origins
Figure 4. Ventral view of (A) a male Tropidurus chromatops Harvey & Gutberlet, 1998 (MHNC-R 3018) from ~30 km W Florida, Santa Cruz, Bolivia, (B) a male T. melanopleurus Boulenger, 1902 (IBIGEO-R 5331) from Aguas Blancas, Salta, Argentina, (C) a female T. xanthochilus Harvey & Gutberlet, 1998 (MZUSP-R 106321) from Santo Antônio do Leverger, Mato Grosso, Brazil, and (D) a male T. etheridgei Cei, 1982 (AMNH-R 176273) from Filadelfia, Boquerón, Paraguay, illustrating the location and coloration of flash marks observed (or not) on the ventral body of tropidurines. Black squares in (A) indicate body areas from which we collected skin samples for histological examination. In (D) the yellow coloration covering the background of the black flash-marks of T. etheridgei might either represent a transient ontogenetic state or an instance in which a yellow background persists throughout life.
Hydroxymethylation profile of cell free DNA is a biomarker for early colorectal cancer
<p>The files in this data release represent processed data from the FORESEE study conducted by Cambridge Epigenetix Ltd, and reported in the preprint manuscript: "Hydroxymethylation profile of cell free DNA is a biomarker for early colorectal cancer" (<a href="https://www.researchsquare.com/article/rs-667874/v1">Walker et al. 2021</a>). </p> <p> </p> <p>As described in the manuscript, classifiers were trained and validated on genomic features extracted from sequencing datasets across cases and controls. Several classes of genomic features were constructed for training and validation data sets which are described below:</p> <p> </p> <p><strong>CRC_enhancer_znorm_training_matrix_v1.csv<br> CRC_enhancer_znorm_validation_matrix_v1.csv</strong></p> <p>Columns contain sample names, rows contain genomic features. </p> <p><em>Description of feature generation process. </em></p> <p>To calculate 5hmC levels at gene enhancers, we first calculated read counts using Bam readcounts v0.01. RPKM were calculated over candidate gene-enhancers downloaded from GeneCards v4.4. 5hmC enrichment was computed as the log2 ratio between the hydroxymethylome library RPKM and the input library RPKM after the inclusion of pseudocounts. Feature scaled (z-score normalization) 5hmC levels of enhancers quantile-normalized over samples.</p> <p><br> <strong>CRC_cegxdelfi_znorm_training_matrix_v1.tsv<br> CRC_cegxdelfi_znorm_validation_matrix_v1.tsv</strong></p> <p>Columns contain sample names, rows contain genomic features. <br> </p> <p><em>Description of feature generation process. </em><br> We divided the genome into 100KB bins and quantified cfDNA fragment sizes per bin. We removed blacklisted regions, genomic gaps (UCSC table) and non-standard chromosomes a priori. We excluded outlier bins in fragment size, only retaining fragments between 100nt to 220nt length. Finally, we split the genome into 100KB bins (in total 26170 non-overlapping genomic regions) and calculated the following characteristics of fragment size distribution per genomic bin: number of short fragments (100-150nt), number of long fragments (151-220nt), ratio between short and long fragments and the total number of fragments. This approach generates 26170 features per metric and per sample. The last step is the averaging of the 100 KB bins into larger non-overlapping genomic regions of 5 MB (in total 512 bins).</p> <p><br> <strong>CRC_cegxnps_znorm_training_matrix_v1.tsv</strong></p> <p><strong>CRC_cegxnps_validation_matrix_v1.tsv</strong></p> <p> Columns contain sample names, rows contain genomic features. <br> <em>Description of feature generation process. </em><br> Further detail in the manuscript: <a href="http://www.researchsquare.com/article/rs-667874/v1">Walker et al. 2021</a></p> <p><br> <strong>FORESEE_sample_description.tsv</strong></p> <p>This file holds sample data for colorectal cancer and control samples described in <a href="http://www.researchsquare.com/article/rs-667874/v1">Walker et al. 2021</a></p> <ul> <li>The sample_name column links to the column names in the *_matrix.tsv files</li> <li>The columns denoted raw_file1 and raw_file2 link the sample metadata with the enhancer readcount files contained in the gh_readcount_training.tar and gh_readcount_validation.tar.</li> </ul> <p>The columns in the table are briefly described below:</p> <p><em>sample_name</em>:<em> </em>Sample identifier<br> <em>Title</em>: Composed of the the disease name, gender and sample_name<br> <em>Source_name</em>: Tissue source<br> <em>Organism</em>: Contains the term: “Homo sapiens”<br> <em>Characteristics_indication</em>: Disease indication <br> <em>Characteristics_stage</em>: Cancer stage where appropriate. Indicated by roman numerals (I,II,III,IV)<br> <em>Characteristics_gender</em>: Described as “Female” or “Male”<br> <em>Characteristics_ethnicity</em>: Ethnicity description<br> <em>Characteristics_age_at_collection</em>: Age value in years<br> <em>Molecule</em>: Contains the value “cell free DNA”<br> <em>Description</em>: Contains value: “Training sample” or “Validation sample”</p> <p><em>Processed_data_file</em>: Contains the term: “CRC_enhancer_training_matrix” or “CRC_enhancer_validation_matrix”. <br> <em>raw_file1</em>: Refers to the readcount file from the 5hmC capture library<br> <em>raw_file2</em>: Refers to the readcount file from the Input control (shallow sequenced) library</p> <p> </p> <p><strong>gh_readcount_training.tar<br> gh_readcount_validation.tar</strong></p> <p>These tar files include the raw read counts computed across enhancer regions for case and control data and are referenced in the FORESEE_sample_description.tsv file.</p> <p> </p> <p><strong>Manuscript Abstract</strong></p> <p>Our classifier discriminated CRC samples from controls with an area under the receiver operating characteristic curve (AUC) of 90% (sensitivity was 55% at 95% specificity). Performance was similar for early stage 1 (AUC 89%) and late stage 4 CRC (AUC 94%). Performance was independent of the proportion of tumor-DNA in the cell free DNA. </p> <p>We expanded the classifier to include information about cell free DNA fragment size and abundance across the genome. Overall performance was similar (AUC 91%), with gains in sensitivity (63% at 95% specificity). </p> <p>The 5-hydroxymethylcytosine signal allows detection of CRC, even in cell free DNA samples with undetectable tumor DNA. Including 5-hydroxymethylcytosine in multi-analyte screening, will improve sensitivity for early-stage cancer. </p>
Figure 6 in Integrative insect taxonomy based on morphology, mitochondrial DNA, and hyperspectral reflectance profiling
Figure 6. Neighbour-joining (NJ) tree (Kimura two-parameter, K2P) for 37 sequences of combined cytochrome c oxidase subunit I (COI) and 16S. The node support: bootstrap NJ (K2P)/NJ (Tamura three-parameter, T3P)/minimum evolution (ME) (K2P). Bootstrap values of less than 50 are not displayed.
Figure 5 in Integrative insect taxonomy based on morphology, mitochondrial DNA, and hyperspectral reflectance profiling
Figure 5. Neighbour-joining tree (Kimura two-parameter, K2P) for 39 barcode cytochrome c oxidase subunit I (COI) sequences. The node support: bootstrap neighbour-joining (NJ) (K2P)/NJ (Tamura three-parameter, T3P)/minimum evolution (ME) (K2P). Bootstrap values of less than 50 are not displayed.
Figure 4 in Integrative insect taxonomy based on morphology, mitochondrial DNA, and hyperspectral reflectance profiling
Figure 4. Male: A–H, Bundera heichiana Li & Wang, 1991; I–P, Bundera emeiana Li & Wang, 1994. A, I, habitus, dorsal view; B, J, habitus, lateral view; C, K, head, dorsal view; D, L, face; E, M, pygofer, lateral view; F, N, aedeagal, lateral view; G, O, aedeagal, ventral view; H, P, connective and style, ventral view.
Figure 3 in Integrative insect taxonomy based on morphology, mitochondrial DNA, and hyperspectral reflectance profiling
Figure 3. Male: A–H, Bundera pellucida Li & Wang, 2001; I–P, Bundera sp. 4; Q–X, Bundera sp. 3. A, I, Q, habitus, dorsal view; B, J, R, habitus, lateral view; C, K, S, head, dorsal view; D, L, T, face; E, M, U, pygofer, lateral view; F, N, V, aedeagal, lateral view; G, O, W, aedeagal, ventral view; H, P, X, connective and style, ventral view.
Figure 1 in Integrative insect taxonomy based on morphology, mitochondrial DNA, and hyperspectral reflectance profiling
Figure 1. Average reflectance profiles and dorsal habitus of the seven species included in this study.
Figure 4. Similarity profile CqN for q in Diversity of orb-weaving spiders (Arachnida: Araneae) from tropical dry forest in Northern Colombia, with eleven new records for the country
Figure 4. Similarity profile CqN for q = 0, q = 1 and q = 2 among forests. The bars correspond to 95% confidence intervals.
Figure 3. Diversity profile for q between 0 and 2 in Diversity of orb-weaving spiders (Arachnida: Araneae) from tropical dry forest in Northern Colombia, with eleven new records for the country
Figure 3. Diversity profile for q between 0 and 2 of the forests studied. The shaded areas correspond to 95% confidence intervals.
Assessment of insight in hospitalized neurological patient: Cognitive profile and mood disorder
<p>Purpose:Many studies have evaluated the characteristics of insight, especially in psychiatric patient pop-ulations. However, this construct has been poorly examined within neurological disorders. We exploredthe relationship between altered insight, mood disorders and neurocognitive functioning in a sample ofpatients admitted to a neurological rehabilitation unit.Method:Each patient, at the time of hospitalization (T0), underwent an evaluation of the overall cogni-tive profile, of the frontal functions, and the compilation of scales aimed at investigating the 4 domainsunder examination of insight and anxiety-depressive symptomatology. After 3 months (T1), at the end ofthe rehabilitative and supportive treatment, the patients underwent the same evaluation performed atT0.Results:Our results showed significant differences between T0 and T1 in the variables examined relatedto insight. In particular, there was a correlation between the global cognitive profile, including executivefunctions, and all insight domains. This confirms how the degree of cognitive deficit, especially of exec-utive type, affects all levels of awareness of the individual.We have also found correlations between mood disorders and insight. In particular, our results showthat depression versus anxiety plays a fundamental role in a person’s awareness.Conclusions:The study of insight is fundamental not only for the relapses it could have on the patient, butalso on those to health care professionals. In fact, having an adequate insight could lead to a greater moti-vation of the patient to be more complimentary to pharmacological and rehabilitative therapies, alsofavoring social reintegration.</p>
Hypercalciuria in postmenopausal women with reduced bone mineral density is associated with different mineral metabolic profiles: effects of treatment with thiazides and anti-resorptives
<pre>Hypercalciuria in postmenopausal women with reduced bone mineral density is associated with different mineral metabolic profiles: effects of treatment with thiazides and anti-resorptives</pre>
Lipidomic profiling of Mycobacterium tuberculosis treated with JCP276, BMB034, or THL
<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>
Unfavorable triglyceride-rich particle profile in subclinical thyroid disease: A cross-sectional analysis of ELSA-Brasil
<p><span>Introduction: Subclinical thyroid disorders have been associated with atherosclerosis and increased cardiovascular risk. As triglyceride-rich lipoprotein particles (TRLP) have recently emerged as a casual factor for atherogenesis, the aim of this study was to evaluate the relationship between subclinical hypo and hyperthyroidism and TRLP subfractions.</span></p> <p><span>Methods: We selected 5,066 participants from the ELSA-Brasil cohort with available data of thyroid function and lipid profile measured by NMR. Individuals were divided into three groups by baseline thyroid function (subclinical hypothyroidism, euthyroidism, and subclinical hyperthyroidism). TRLP subfractions were analyzed through Nuclear Magnetic Resonance (NMR) spectroscopy. To examine the association between TRLP subfractions and thyroid function, we conducted univariate and multivariate linear regression models adjusted for demographic characteristics, BMI, diabetes, smoking status and alcohol use.</span></p> <p><span>Results: Of 3,304 individuals, 54% were women, with a mean age of 50.6±8,7 years, 51% white and 53% with at least college education. Of these individuals, 92% were euthyroid, whereas 6.8% had subclinical hypothyroidism and 1.2% had subclinical hyperthyroidism. The univariate linear regression showed that Very Small TRLP (p=0.026) and Very Large TRLP (p=0.008) were statistically increased in subclinical hypothyroidism when compared to euthyroidism. In subclinical hyperthyroidism, there was a reduction in total TRLP (p=0.003), seemingly driven by reduced Very Small-TRLP (p=0.067). The findings were confirmed when adjusted for demographic characteristics, as well as comorbidities.</span></p> <p>Conclusions: This study suggests that subclinical hypothyroidism is associated to very small and very large TRLP, which are related to an unfavorable atherogenic profile. Subclinical hyperthyroidism is associated to lower very small TRLP.</p>
An ocean turbulence data reduction scheme for autonomous, vertically profiling floats: datasets
<p>See <a href="https://doi.org/10.5281/zenodo.5719505">https://doi.org/10.5281/zenodo.5719505</a> instead</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.