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Datasets for 'Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip'

Here we publish the datasets associated with our publication ‘Label-free imaging of 3D pluripotent stem cell differentiation dynamics on chip’. 3D cultures of human induced pluripotent stem cells (hiPSCs) were imaged while undergoing definitive endoderm (DE) differentiation. Data were acquired either on the live 3D cultures at various time points during the 3 days DE differentiation, or after fixation and immunostaining, with the 3D cultures being fixed at regular 24 h intervals during differentiation, as to form a timeline. Images were recorded with a standard confocal microscope using a xy-resolution of 0.25 μm/px and a z-resolution of 1 μm/plane.

opencc-by-4.0Jul 2022View details →

Dataset for the challenge at the 2nd MODE workshop on differentiable programming 2022

Data is in HDF5 format (with LZF compression). For specifics and details, please see <a href="http://github.com/GilesStrong/mode_diffprog_22_challenge">https://github.com/GilesStrong/mode_diffprog_22_challenge</a> The training file contains two datasets: <ul> <li>`'x0'`: a set of voxelwise X0 predictions (float32)</li> <li>`'targs'`: a set of voxelwise classes (int):</li> <li>0 = soil</li> <li>1 = wall</li> </ul>   The format of the datasets is a rank-4 array, with dimensions corresponding to (samples, z position, x position, y position). All passive volumes are of the same size: 10x10x10 m, with cubic voxels of size 1x1x1 m, i.e. every passive volume contains 1000 voxels. The arrays are ordered such that zeroth z layer is the bottom layer of the passive volume, and the ninth layer is the top layer. It can be read using e.g. the code below:   with h5py.File('train.h5', 'r') as f:   inputs = h5['x0'][()]   targets = h5['targs'][()] The test file only contains the X0 inputs: with h5py.File('test.h5', 'r') as h5:   inputs = h5['x0'][()] The private testing sample also contains targets. The private and public splits can be recovered using: from sklearn.model_selection import train_test_split pub, pri = train_test_split(targets, test_size=25000, random_state=3452, shuffle=True)

opencc-by-4.0Sep 2022View details →

Data from: Differential gene expression during recall of behaviorally conditioned immune enhancement in rats: a pilot study

Background: Behaviorally conditioned immune functions are suggested to be regulated by bidirectional interactions between CNS and peripheral immune system via the hypothalamic-pituitary-adrenal (HPA) axis, sympathetic nervous system (SNS), and the parasympathetic nervous system (PNS). Since the current knowledge about biochemical pathways triggering conditioned immune enhancement is limited, the aim of this pilot study was gaining more insights into that. Methods: Rats were conditioned with camphor smell and poly I:C injection, mimicking a viral infection. Following stimulus re-exposure, animals were sacrificed at different time points, and neural tissues along the HPA axis was analyzed with a rat genome array together with plasma protein using Luminex analysis. Results: In the hypothalamus, we observed a strong upregulation of genes related to Wnt/β-catenin signaling (Otx2, Spp1, Fzd6, Zic1), monoaminergic transporter Slc18a2 and opioid-inhibitory G-protein Gpr88 as well as downregulation of dopaminergic receptors, vasoactive intestinal peptide Vip, and pro-melanin-concentrating hormone Pmch. In the pituitary, we recognized mostly upregulation of steroid synthesis in combination with GABAergic, cholinergic and opioid related neurotransmission, in adrenal glands, altered genes showed a pattern of activated metabolism plus upregulation of adrenoceptors Adrb3 and Adra1a. Data obtained from spleen showed a strong upregulation of immunomodulatory genes, chemo-/cytokines and glutamatergic/cholinergic neurotransmission related genes, as also confirmed by increased chemokine and ACTH levels in plasma. Conclusions: Our data indicate that in addition to the classic HPA axis, there could be additional pathways as e.g. the cholinergic anti-inflammatory pathway (CAIP), connecting brain and immune system, modulating and finetuning communication between brain and immune system.

dryad40/100

Data from: Local adaptation in shell shape traits of a brooding chiton with strong population genomic differentiation

Comparing divergence in quantitative traits and neutral molecular markers, such as QST–FST comparisons, provides a means to distinguish between natural selection and genetic drift as causes of population differentiation in complex polygenic traits. Onithochiton neglectus (Rochebrune, 1881) is a morphologically variable chiton endemic to New Zealand, with populations distributed over a broad latitudinal environmental gradient. In this species, the morphological variants cluster into two geographically separated shell shape groups, and the phenotypic variation in shell shape has been hypothesised to be adaptive. Here, we assessed this hypothesis by comparing neutral genomic differentiation between populations (FST) with an index of phenotypic differentiation (PST). We used 7,562 putatively neutral single nucleotide polymorphisms (SNPs) across 15 populations and three clades of O. neglectus throughout New Zealand to infer FST. PST was calculated from 18 shell shape traits and gave highly variable estimates across populations, clades and shape groups. By systematically comparing PST with FST, we identified evidence of local adaptation in a number of the O. neglectus shell shape traits. This supports the hypothesis that shell shape could be an adaptive trait, potentially correlated with the ability to live and raft in kelp holdfasts.

opencc-zeroOct 2022View details →

Fig. 2 in Ventral And Lateral Spot Patterns Differentiation Between Three Smooth Newt Species (Amphibia: Salamandridae: Lissotriton)

Fig. 2. Color patterns of smooth newt species. Lissotriton schmidtleri: males from Domurcalı (lateral view) and Dursunköy (ventral view), females from Yassıören (lateral view) and Ka- racabey (ventral view), Turkey; L. vulgaris: Gatchina, Russia; L. kosswigi: males from Mollafeneri (lateral view) and Hacılar (ventral view), females from Alibahadır Köyü, Turkey; L. lanzi: males from Ldzaa (lateral view) and Sukhum (ventral view), females from Ldzaa

Fig. 1 in Ventral And Lateral Spot Patterns Differentiation Between Three Smooth Newt Species (Amphibia: Salamandridae: Lissotriton)

Fig. 1. Distribution of Lissotriton vulgaris, L. schmidtleri and L. kosswigi in the Western Palearctic (A) and western Turkey (B). Numbers for localities are given in Table 1

Fig. 4 in Ventral And Lateral Spot Patterns Differentiation Between Three Smooth Newt Species (Amphibia: Salamandridae: Lissotriton)

Fig. 4. Plot of centroids for females (A) and males (B) of Lissotriton kosswigi, L. schmidtleri and L. vulgaris in the space of the first and second canonical discriminant axes

Fig. 3 in Ventral And Lateral Spot Patterns Differentiation Between Three Smooth Newt Species (Amphibia: Salamandridae: Lissotriton)

Fig. 3. Polygons selected on the ventral surface of newts (A) and types of dark spot arrangements located on the border between lateral and ventral surfaces (B), where r is random

Figure 5 in Multiplex-PCR differentiation of two Hyalomma and two Haemaphysalis species (Acari: Ixodidae)

Figure 5. One percent agarose gel electrophoresis stained with Cyber Safe® showing ITS2 fragments amplified using primer pairs Fanas/Ran for Hyalomma anatolicum (amplicon size 749 bp) and Fanas/Ras for Hy. asiaticum (amplicon size 408 bp) (A), COI fragments amplified using primer pairs Fsul/Rpun and Fsul/Rsul for Haemaphysalis punctata (amplicon size 524 bp) and Ha. sulcata (amplicon size 614 bp) (B). (100 bp DNA ladder).

Figure 2 in Multiplex-PCR differentiation of two Hyalomma and two Haemaphysalis species (Acari: Ixodidae)

Figure 2. The ventral morphological aspect of representative male specimens of Haemaphysalis punctata (A) collected from Mazandaran province and Ha. sulcata (B) collected from Lorestan province; white arrows showing position of character spur of coxa IV (size of specimens was not considered).

Figure 1 in Multiplex-PCR differentiation of two Hyalomma and two Haemaphysalis species (Acari: Ixodidae)

Figure 1. The dorsal morphological aspect of representative male specimens of Hyalomma anatolicum (A) and Hy. asiaticum (B) both collected from Lorestan province; white and red arrows showing position of characters cervical grooves and dorsal posterior margin of the basis capituli, respectively (size of specimens was not considered).

Figure 4 in Multiplex-PCR differentiation of two Hyalomma and two Haemaphysalis species (Acari: Ixodidae)

Figure 4. Fourth female coxal spur of Haemaphysalis sulcata (A) and Ha. punctata (B), both collected from Mazandaran province, Iran.

Figure 3 in Multiplex-PCR differentiation of two Hyalomma and two Haemaphysalis species (Acari: Ixodidae)

Figure 3. General schema of representative female dorsal scutum of Hyalomma anatolicum (A) and Hy. asiaticum (B), both collected from Lorestan province (size of specimens was not considered).

opencc-by-4.0Nov 2017View details →

Data and code for "Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR"

This data set provides data files and R code to accompany the article Differential methylation analysis of reduced representation bisulfite sequencing experiments using edgeR published by F1000Research. The data consists of Reduced Representation BS-seq methylation profiles of epithelial populations from the mouse mammary gland, with n=2 biological replicates for each of three cell populations. RNA-seq expression profiles of luminal and basal mammary epithelial populations are also provided. The R code undertakes an differential methylation analysis of the BS-seq profiles and demonstrates a strong negative correlation between the differential methylation and differential expression results.

FIGURE 7 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone

FIGURE 7. Location and geologic position of the Woodbine Group. A. General stratigraphic sequence and timescale for the Cretaceous of central and north central Texas showing the position of the Woodbine Group. Position of the AAS within the Woodbine is marked with an arrowhead. Terrestrial deposits represented by stippled intervals. Time scale based on Denne et al. (2016). Modified from Adams et al. (2011). B. Generalized map of geological units present as surface exposures in the Fort Worth basin with location of AAS shown. Modified after Strganac (2015) and Barnes et al. (1972).

FIGURE 4 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone

FIGURE 4. Modern Trachemys scripta plastron elements (UTK 2317) with shell disease. Photograph (A) and orthographic model based on µCT data (B) shown in ventral view. Frames on the photograph and model highlight specific areas of shell disease, shown on the right as both direct µCT data (C, E, G) and heatmapped slices illustrating bone density changes (D, F, H). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Patches of shell disease are indicated with purple arrows. Scale bars in A and B equal 5 cm. Scale bars in C, E, and G equal 5 mm.

FIGURE 2 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone

FIGURE 2. Fossil turtle shell fragments (DMNH 2013-07-1319) with putative bite marks. Photographs (A, G) and orthographic models based on µCT data (B, H) shown in external view. Frames on the photograph and model highlight specific areas with bite marks as both direct µCT data (C, E, I) and heatmapped slices illustrating bone density changes (D, F, J). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Specific bite marks are indicated with purple arrows. Scale bars in A, B, G, and H equal 2 cm. Scale bars in C, E, and I equal 5 mm.

FIGURE 1 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone

FIGURE 1. Modern Trachemys scripta shell (SAAF) with bite marks attributed to Mecistops cataphractus. Orthographic models of the shell, based on µCT data shown in dorsal (A) and ventral (B) views. Frames on the models highlight specific bite marks, shown on the right as both direct µCT data (C, E, G) and heatmapped slices illustrating bone density changes (D, F, H). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Specific bite marks are indicated with purple arrows. Scale bars in A and B equal 5 cm. Scale bars in C, E, and G equal 5 mm.

FIGURE 6 in Differentiating convergent pathologies in turtle shells using computed tomographic scanning of modern and fossil bone

FIGURE 6. Fossil turtle shell fragment (DMNH 2013-07-0563) with putative shell disease.. Photograph (A) shown in external view. Frames on the photograph and highlight specific areas with shell disease as both direct µCT data (B, D) and heatmapped slices illustrating bone density changes (C, E). In the heatmapped cross sections, colors range from purple (lowest density), to orange (medium density), to white (highest density). Specific patches of shell disease are indicated with purple arrows. Scale bar in A equals 2 cm. Scale bars in B and D equal 5 mm.