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opencc-zeroJun 2024View details →

Dataset: AC Immune SA (ACIU) Stock Performance

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

opencc-zeroJun 2024View details →

Dataset: AC Immune SA (ACIU) Stock Performance

opencc-zeroJun 2024View details →

Dataset: Immuneering Corporation (IMRX) Stock Performance

opencc-by-4.0Jul 2024View details →

Normalized and batch-corrected concentration of 43 immune markers from 248 subjects with stress-related mental disorders and 36 healthy controls

<h1>Abstract</h1> In a subset of patients with mental disorders, such as depression, low-grade inflammation and altered immune marker concentrations are observed. However, these immune alterations are often assessed by only one data type and small markers panels. Here, we used a transdiagnostic approach and combined data from two cohorts to define subgroups of depression symptoms across the diagnostic spectrum through a large-scale multi-omics clustering approach in 237 individuals. The method incorporated age, body mass index (BMI), 43 plasma immune markers and RNA-seq data from peripheral mononuclear blood cells (PBMCs). Our initial clustering revealed four clusters, including two immune-related depression symptom clusters characterized by elevated BMI, higher depression severity and elevated levels of immune markers such as interleukin-1 receptor antagonist (IL-1RA), C-reactive protein (CRP) and C-C motif chemokine 2 (CCL2 or MCP-1). In contrast, the RNA-seq data mostly differentiated a cluster with low depression severity, enriched in brain related gene sets. This cluster was also distinguished by electrocardiography data, while structural imaging data revealed differences in ventricle volumes across the clusters. Incorporating predicted cell type proportions into the clustering resulted in three clusters, with one showing elevated immune marker concentrations. The cell type proportion and genes related to cell types were most pronounced in an intermediate depression symptoms cluster, suggesting that RNA-seq and immune markers measure different aspects of immune dysregulation. Lastly, we found a dysregulation of the SERPINF1/VEGF-A pathway that was specific to dendritic cells by integrating immune marker and RNA-seq data. This shows the advantages of combining different data modalities and highlights possible markers for further stratification research of depression symptoms. <h1>Methods</h1> The normalized and batch-corrected concentration of 43 immune markers from 237 subjects with stress-related mental disorders and 36 healthy controls was determined in plasma. This data was used in the initial analysis. Additionally, the same measurements are provided for 11 subjects with stress-related mental disorders used in a replication analysis. - blood was collected in the morning under fasted conditions and plasma stored at -80°C until further processing - samples were randomized into 96 well plates - immune marker concentration was measured with the Meso Scale Diagnostics V-PLEX Human Biomarker 54-Plex Kit and the MESO QuickPlex SQ 120 imager according to the manufacturer's instructions - additionally, high-sensitivity C-reactive protein (Tecan Group Ltd.), cortisol (Tecan Group Ltd.), interleukin (IL)-6 (Thermo Fisher Scientific), IL-6 soluble receptor (Thermo Fisher Scientific) and IL-13 (Thermo Fisher Scientific) was measured via ELISA according to the manufacturer's instructions - values below the detection limit in markers measured with ELISA were set to zero and values above the detection limit to the upper limit - the data was quantile-normalized (values were ranked and mapped to the quantiles of a standard normal distribution) - the normalized concentration was corrected for the biobank storage position (batch_variable) via a linear model and the residuals reported as the concentration The following markers were measured: fibroblast growth factor 2 (FGF2 or bFGF), cortisol, C-C motif chemokine 11 (CCL11 or eotaxin), CCL26 (eotaxin-3), vascular endothelial growth factor receptor 1 (VEGFR1 or Flt-1), hsCRP, intercellular adhesion molecule (ICAM)-1, interferon (IFN)-gamma, IL-1alpha, IL-1 receptor antagonist (IL-1RA), IL-10, IL-12/IL-23p40, IL-12p70, IL-13, IL-15, IL-16, IL-17A, IL-17B, IL-2, IL-27, IL-31, IL-5, IL-6 high sensitivity (IL-6HS), IL-7, IL-8HS, C-X-C motif chemokine 10 (CXCL10 or IP-10), CCL2  (MCP-1), CCL13 (MCP-4), CCL22 (MDC), CCL3 (MIP-1alpha), CCL4 (MIP-1beta), placental growth factor (PlGF), serum amyloid A (SAA), sIL-6R, CCL17 (TARC), angiopoietin-1 receptor (Tie-2), tumor necrosis factor (TNF or TNF-alpha), lymphotoxin-alpha (LT-alpha or TNF-beta), thymic stromal lymphopoietin (TSLP), vascular cell adhesion protein 1 (VCAM-1), vascular endothelial growth factor (VEGF)-A-HS, VEGF-C, VEGF-D The data is provided as a tab separated file.

opencc-by-4.0Mar 2016View details →

Fig. 6 in Trichoplusia ni (Lepidoptera: Noctuidae) survival, immune response, and gut bacteria changes afer exposure to Azadirachta indica (Sapindales: Meliaceae) volatiles

Fig. 6. Transcription of the 23S gene of Enterobacteria (428 bp) and ribosomal protein S5 gene (782 bp) from rRNA samples of Trichoplusia ni NL strain larval midguts, afer exposure to 10 g of neem leaves, determined by reverse transcriptase polymerase chain reaction (RT-PCR). PCR product of RNA not subject- ed to RT-PCR was taken as a negative control. Lane 1, DNA ladder 100 bp; lane 2, PCR product of plasmid DNA with the Enterobacteria insert as positive control; lane 3, PCR products of the 23S gene of Enterobacteria and the ribosomal protein S5 gene of T. ni from unexposed larvae; lanes 4, 6, and 8, PCR of control RNA; lane 5, RT-PCR products in gut from VOC-exposed T. ni larva, showing both 23S and ribosomal protein S5 gene amplification (1st replication); lane 7, RT-PCR products in gut from VOC-exposed T. ni larva, showing both 23S and ribosomal protein S5 gene amplification (2nd replication).

opencc-by-4.0Mar 2016View details →

Fig. 3 in Trichoplusia ni (Lepidoptera: Noctuidae) survival, immune response, and gut bacteria changes afer exposure to Azadirachta indica (Sapindales: Meliaceae) volatiles

Fig. 3. Mortality for NL and Gto strains of Trichoplusia ni exposed as neonate larvae for 7 d in sealed containers to VOCs from 1 or 10 g of dried neem stems compared with the unexposed controls. Data represent the mean ± standard deviation of 3 replicate experiments per treatment (90 larvae per replicate were tested).

opencc-by-4.0Mar 2016View details →

Fig. 1 in Trichoplusia ni (Lepidoptera: Noctuidae) survival, immune response, and gut bacteria changes afer exposure to Azadirachta indica (Sapindales: Meliaceae) volatiles

Fig. 1. Setup of the bioassay container for neem VOC exposure of Trichoplusia ni neonates. A) View of tray with 30 cups placed inside the 11 L plastic container with airtight lid for VOC exposure; B) view of tray with 30 cups with artificial diet infested with 3 neonates each and cardboard lid to allow VOC exchange; C) view of 1 L container with artificial diet (bottom) and 1 oz (29.6 mL) cups (top) with 1 g milled dried neem stems or leaves.

Рис. 5. ΔенΑрограмма меры разброса значений ΑΛя показатеΛей вΛияния опушенности и тоΛщины Λистовой пΛастинки картофеΛя на прожорΛивость Λичинок картофеΛьной коровки Fig. 5. Dendrogram representing the value scatter for the influence of pubescence and thickness of the potato leaf blade on the voracity of potato ladybug larvae in Role of potato immune factors in the trophic responses of Henosepilachna vigintioctomaculata Motschulsky, 1858

Рис. 5. ΔенΑрограмма меры разброса значений ΑΛя показатеΛей вΛияния опушенности и тоΛщины Λистовой пΛастинки картофеΛя на прожорΛивость Λичинок картофеΛьной коровки Fig. 5. Dendrogram representing the value scatter for the influence of pubescence and thickness of the potato leaf blade on the voracity of potato ladybug larvae

Figure2. Generation of negative feedbacks gets tuned once a TCR completes stimulation beyond the threshold l. A TCell generates activation signal to BCell once it gets stimulation of its k-TCRs.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns

A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as represented in Figure2.

Figure1. Static stimulation of a single TCR- The kinetic proofreading by the receptor on input x Є X forwards the receptor position p toward l. The receptor will generate negative feedback if p > β. The receptor will generate success signal when p== l.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns

FIGURES 2 a-g -AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns

The program was implemented in Matlab and tested with several patterns. The first, dataset1 consisted of 2 patterns each comprised of 100 points falling on two concentric circles of radii 10 and 20 respectively. The second, dataset2 consisted of 3 patterns each of 100 points falling on three concentric circles of radii 10, 15, and 20 respectively. The model was further tested for its capability to find clusters in patterns of open spatial form using dataset3 and dataset4 consisting of 200 and 300 points falling on 2 and 3 concentric semi circles respectively. As shown in the Figure2.a and Figure2.b, the algorithm is capable of determining spatial association of a data point with other data points belonging to its appropriate circle only. The results successfully demonstrated the capability of our model to automatically detect clean clusters of arbitrary shapes in the input data represented in closed spatial form. The model was found even capable of determining clusters of open spatial forms also, as shown in Figure2.c and Figure2.e. However, the output of the algorithm was found affected by the values of the algorithm parameters k and a. In the present experiment, k =8 and a =10 was sufficient for performing correct cluster associations. On the other hand, correct clustering for the dataset2, could be obtained with 10NN estimation i.e. k =10, with a.=15. Moreover setting k =15, with a.=15 was required for dataset4, as clustering error was observed with k =10, with a.=15, as in Figure2.d. Figure2.f and Figure2.g show the correct clustering even in presence of combination of open and closed form of input patterns. In each figure, the first sub-plot shows the original data and the second sub-plot shows the clusters identified by our program.

Figure3. APCs A1-A4 connected within range of cohesion-factor-threshold form members of one ARB-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns

Figure3 depicts this process. The model with the above specification then effectively detects self or non-self pathogens. In terms of its application to the task of clustering, this interpretation means making the affinities high within clusters and low across clusters. A pathogen corresponding to an outlier would not stimulate a TCR sufficiently and may not form part of any ARB.

opencc-by-4.0Feb 2018View details →

The effects of environmental history and thermal stress on coral physiology and immunity

This dataset has all data for the manuscript (Wall CB,  CA Ricci, GE Foulds, LD Mydlarz, RD Gates, HM Putnam (2018) The effects of environmental history and thermal stress on coral physiology and immunity. Marine Biology). Data included a zipped archive shape file for creating Kāne'ohe Bay map, physical data (light and temperature) from Kāne'ohe Bay and laboratory experiments, pCO2 data for Kāne'ohe Bay reef sites dowloaded from NOAA PMEL, and biological responses (PAM fluorometry, physiology, immune activity and oxidative profile).

opencc-by-4.0Mar 2019View details →

The skin commensal yeast Malassezia triggers a Th17-response that coordinates anti-fungal immunity and exacerbates skin inflammation

Data accompanying the publication: Sparber et al. The skin commensal yeast Malassezia triggers a Th17-response that coordinates anti-fungal immunity and exacerbates skin inflammation. 2019. Cell Host & Microbe. https://doi.org/10.1016/j.chom.2019.02.002

opencc-by-4.0Aug 2019View details →

sirselim/immunecell_methylation_paper_data: First release of data for immune cell epigenetics (methylation) manuscript

This is the first release of the data to be made public and accessible with the manuscript.

Fig. 2 in Experimental evaluation of pathogenicity and acquired immunity of Eimeria species, E. uekii and E. raichoi, infecting Japanese rock ptarmigans in a subspecies of the birds

Fig. 2. The number of oocysts per gram of feces (OPG) after inoculation with E. uekii and E. raichoi oocysts. Panels A and B show E. uekii and E. raichoi OPG, respectively. Chicks Nos. 1–3 were inoculated depending on the manner of oocysts E. uekii and E. raichoi as described at the bottom of Figure, and mouse no. 4 was inoculated with oocysts of E. raichoi.

Fig. 1 in Experimental evaluation of pathogenicity and acquired immunity of Eimeria species, E. uekii and E. raichoi, infecting Japanese rock ptarmigans in a subspecies of the birds

Fig. 1. Weight gain of Svalbard rock ptarmigans after inoculation with E. uekii and E. raichoi oocysts. The inoculation doses of E. uekii and E. raichoi were 4 104 and × 17 103 or 4 103 (A), 2 104 and 8 103 (B), 4 103 and 17 or 4 102 (C), and 4 102 and 17 or 4 101 (D), respectively. Group E was administered PBS as a × × × × × × × × control, and group F was inoculated with 4 104 oocysts of E. raichoi that had been passed in turkeys. "n" indicates the numbers of examined chicks.

Fig. 5 in Experimental evaluation of pathogenicity and acquired immunity of Eimeria species, E. uekii and E. raichoi, infecting Japanese rock ptarmigans in a subspecies of the birds

Fig. 5. Histopathological photomicrographs of sections of the intestines of challenged chicks. Panels A and B show the ileum and duodenum of the primary inoculated chick at 7 days PI, respectively. Panels C and D show the ileum and colon of a non-inoculated chick at 7 days PI, respectively. Arrows indicate developmental zoites (sexual stages in panels C and D). Scale bars are 20 μm in panels A and C and 50 μm in panels B and D.

Fig. 4 in Experimental evaluation of pathogenicity and acquired immunity of Eimeria species, E. uekii and E. raichoi, infecting Japanese rock ptarmigans in a subspecies of the birds

Fig. 4. Weight gain of Svalbard rock ptarmigans after challenge inoculation. Panels A and B show the weight of control (PBS) and primary inoculated chicks, respectively. Two-direction arrows indicate the period of challenge inoculation, 4 104 E. uekii and 2 103 E. raichoi for 5 days.

Fig. 3 in Experimental evaluation of pathogenicity and acquired immunity of Eimeria species, E. uekii and E. raichoi, infecting Japanese rock ptarmigans in a subspecies of the birds

Fig. 3. Histopathological photomicrographs of sections from an experimentally inoculated chick (Svalbard rock ptarmigan). Panel A shows the ileum of the chick at 4 days PI after inoculation with 4 × 104 E. uekii and 2 × 103 E. raichoi oocysts. Panel B is a higher magnification of the section. Arrows indicate developmental zoites or mature schizonts (in panel B). Scale bars in panels A and B are 20 μm and 50 μm, respectively.