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1,751 results for “transmission”
WaterGEMS models for flow starved water transmission network
<p><strong>This data contains WaterGEMS models of a Water Transmission Network (WTN) in India. It deploys a newly developed modeling approach, Pressure Driven Analysis for Flow Straved Condition(PDA-FSC). It is used for evaluating the performance of flow-starved WTN. </strong></p>
Supplementary Data: Full Results: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the full output data from each of the scenarios considered in the above publication. They also include the post-processed input data, which might be useful if you want to rerun the scenarios with only small changes to the input data.</p> <p>The scripts to build the model, input data and result summaries can be found in a <a href="https://zenodo.org/record/1146665">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p>For each scenario, there is a <a href="https://github.com/PyPSA/PyPSA">PyPSA</a> network file in <a href="https://en.wikipedia.org/wiki/Hierarchical_Data_Format">HDF5 format</a> and a CSV of shadow prices.</p> <p>To read in a network file do:</p> <pre><code class="language-python">import pypsa network = pypsa.Network("network_file_name.h5")</code></pre> <p>All data is released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0).</p>
Supplementary material from the study on survival of African swine fever virus in feed, bedding materials and mechanical vectors, and their potential role in virus transmission
<p><span>Supplementary material from the experimental studies developed to analyse the survival of African swine fever virus in feed, bedding materials and mechanical vectors, and their potential role in virus transmission. </span></p> <p> </p>
Fig. 5 in ONCHOCERCA VOLVULUS transmission in the Mbam valley of Cameroon following 16 years of annual community-directed treatment with ivermectin, and the description of a new cytotype of SIMULIUM SQUAMOSUM
Fig. 5 Mean daily biting rates ± 1 standard error (SE) at the four collection sites based on three collection days each month. Biting rates were highest at the two riverside sites (Bayomen and Nyamongo I) and decreased with increasing distance from the river
Fig. 1 in ONCHOCERCA VOLVULUS transmission in the Mbam valley of Cameroon following 16 years of annual community-directed treatment with ivermectin, and the description of a new cytotype of SIMULIUM SQUAMOSUM
Fig. 1 Map of the study area showing adult and larval blackfly collection sites and dissection stations in relation to the Mbam river. Grey arrows show the direction of river flow and the inset map shows the location of the study area (black star) in Cameroon
Fig. 2 in ONCHOCERCA VOLVULUS transmission in the Mbam valley of Cameroon following 16 years of annual community-directed treatment with ivermectin, and the description of a new cytotype of SIMULIUM SQUAMOSUM
Fig. 2 Historical rainfall and river discharge for Bafia and the lower Mbam river. Bars show mean monthly rainfall (mm) ± 1 standard error (SE) at Bafia for years 1930–1994 [26]; dashed line shows mean monthly river discharge (m3/s) ± 1 standard error (SE) for years 1952–80 for the Goura gauge located on the Mbam river ≈25 km SE of Bafia (4.56703°N, 11.36740°E) [27]
Fig. 4 in ONCHOCERCA VOLVULUS transmission in the Mbam valley of Cameroon following 16 years of annual community-directed treatment with ivermectin, and the description of a new cytotype of SIMULIUM SQUAMOSUM
Fig. 4 Polytene chromosomes of S. squamosum E2 and S. mengense showing a chromosome 1 of S. squamosum E2 with fixed inversions 1S-1 and 1L-3, and inversion 1L-57 which was fixed in the specimens examined, b part of chromosome 3 of S. squamosum E2 male showing sex-linked band dimorphism 3C-Sp and sex-linked heterozygous inversion 3L/82 present in 19/20 male specimens examined,'b'= blister, and c chromosome 1 of S. mengense showing expanded centromere 1CER
Fig. 7 in ONCHOCERCA VOLVULUS transmission in the Mbam valley of Cameroon following 16 years of annual community-directed treatment with ivermectin, and the description of a new cytotype of SIMULIUM SQUAMOSUM
Fig. 7 Results of pool screening showing maximum likelihood estimate of the percentage of S. damnosum s.l. possessing L3 larvae in their heads (± 95% CI) based on black flies collected over the 12-month sampling period. The number above the bar denotes the maximum likelihood point estimate for each site
Fig. 3 in ONCHOCERCA VOLVULUS transmission in the Mbam valley of Cameroon following 16 years of annual community-directed treatment with ivermectin, and the description of a new cytotype of SIMULIUM SQUAMOSUM
Fig. 3 Head and thorax of late-instar larvae showing a S. squamosum E2, and b S. mengense with arrow pointing to tuft of hair-like scales on the anterior dorsum of the thorax
Fig. 6 Seasonal parasite transmission along the lower Mbam river showing a in ONCHOCERCA VOLVULUS transmission in the Mbam valley of Cameroon following 16 years of annual community-directed treatment with ivermectin, and the description of a new cytotype of SIMULIUM SQUAMOSUM
Fig. 6 Seasonal parasite transmission along the lower Mbam river showing a combined parity and infection rates for flies dissected at Bayomen and Nyamongo I riverside sites (L1–L2 = percentage of flies infected with developing parasite stages only, L3H = percentage of flies containing L3 stages in the head), and b monthly transmission potentials at Bayomen, Nyamongo I, and Egona II estimated based on dissection data only. Ondouano not shown since no larvae were found in dissected flies
Рис. 7. ГнезΑо ΑаΛьневосточного аиста, распоΛоженное на гнезΑовой пΛатформе, установΛенной на опоре ΑЭП-500 в Серышевском районе Амурской обΛасти Fig. 7. The Oriental stork nest, built on the special platform installed on a 500 kV power transmission pole in the Seryshevsky district of the Amur region in Oriental stork (Ciconia boyciana Swinhoe) breeding population survey in the Amur region in 2018-2019
Рис. 7. ГнезΑо ΑаΛьневосточного аиста, распоΛоженное на гнезΑовой пΛатформе, установΛенной на опоре ΑЭП-500 в Серышевском районе Амурской обΛасти Fig. 7. The Oriental stork nest, built on the special platform installed on a 500 kV power transmission pole in the Seryshevsky district of the Amur region
High temperatures reduce growth, infection, and transmission of a naturally occurring fungal plant pathogen
<p>Climate change is rapidly altering the distribution of suitable habitats for many species as well as their pathogenic microbes. For many pathogens, including vector-borne diseases of humans and agricultural pathogens, climate change is expected to increase transmission and lead to pathogen range expansions. However, if pathogens have a lower heat tolerance than their host, increased warming could generate 'thermal refugia' for hosts. Predicting the outcomes of warming on disease transmission requires detailed knowledge of the thermal tolerances of both the host and the pathogen. Such thermal tolerance studies are generally lacking for fungal pathogens of wild plant populations, despite the fact that plants form the base of all terrestrial communities. Here, we quantified three aspects of the thermal tolerance (growth, infection, and propagule production) of the naturally occurring fungal pathogen <em>Microbotryum lychnidis-dioicae</em>, which causes a sterilizing anther-smut disease on the herbaceous plant <em>Silene latifolia</em>. We also quantified two aspects of host thermal tolerance: seedling survival and flowering rate. We found that temperatures >30 degreeC reduced the ability of anther-smut spores to germinate, grow, and conjugate in vitro. In addition, we found that high temperatures (30 degreeC) during, or shortly after the time of inoculation strongly reduced the likelihood of infection in seedlings. Finally, we found that high summer temperatures in the field temporarily cured infected plants, likely reducing transmission. Notably, high temperatures did not reduce survival or flowering of the host plants. Taken together, our results show that the fungus is considerably more sensitive to high temperatures than its host plant. A warming climate could therefore result in reduced disease spread or even local pathogen extirpation, leading to thermal refugia for the host.</p>
The Potential of Deep Learning Object Detection in Citizen-Driven Snail Host Monitoring to Map Putative Disease Transmission Sites
<p><a name="_Hlk158802145"></a><span>Schistosomiasis is a neglected tropical disease caused by parasitic flukes transmitted by freshwater snails. Despite increasing efforts of mass drug administration, schistosomiasis remains a public health concern and the World Health Organization recommends complementary snail control. To address the need of broad-scale and actual snail distribution data to guide snail control, we adopted a citizen science approach and recruited citizen scientists (CS) to perform weekly snail sampling in the endemic setting in Uganda. Snails were identified, sorted and counted according to genus, photographed and uploaded for expert-led validation and feedback. However, expert validation is time-consuming and introduces a delay in verified data output. Thus, artificial intelligence could provide a solution by means of automated detection and counting of multiple snails collected from the field. Trained on approximately 2500 citizen-collected images, the resulting model can simultaneously detect and count Biomphalaria and Radix snails with average precision of 98.1% and 98.8% respectively. The object detection model also agreed with the expert’s decision averagely for 98.8% of the test images and could be ran in real-time (24.6 images per second). We conclude that the automatic and instant detection can rapidly and reliably validate data submitted by CS in the field, ultimately minimizing the expert validation efforts and thereby facilitating the mapping of putative schistosomiasis transmission sites. An extension to a mobile application could equip citizen scientists in remote areas with instant learning opportunities and expert-like identification skills, overcoming the need for on-site training and extensive expert intervention. </span></p>
Fig. 3 in Urogenital schistosomiasis transmission on Unguja Island, Zanzibar: characterisation of persistent hot-spots
Fig. 3 Number of human-water contact sites in persistent hot-spot and low-prevalence shehias in Unguja
Fig. 2 in Urogenital schistosomiasis transmission on Unguja Island, Zanzibar: characterisation of persistent hot-spots
Fig. 2 Map of Unguja Island, Zanzibar, showing the location of selected persistent hot-spot and low-prevalence shehias
Fig. 1 in Urogenital schistosomiasis transmission on Unguja Island, Zanzibar: characterisation of persistent hot-spots
Fig. 1 Flowchart showing the inclusion procedure for persistent hot-spot and low-prevalence shehias in Unguja
Fig. 3 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
Fig. 3 Seasonal variation of sibling species (An. coluzzii and An. gambiae) in the study area. Abbreviations: DS, dry season; RS, rainy season
Fig. 2 in Efficacy of Actellic 300 CS-based indoor residual spraying on key entomological indicators of malaria transmission in Alibori and Donga, two regions of northern Benin
Fig. 2 Mortality rate of Anopheles gambiae Kisumu (laboratory susceptible strain) after 30 min exposure to cement and mud walls treated with pirimiphos-methyl in 2017 (a) and 2018 (b). The red line indicates the WHO efficacy threshold (mortality of 80%) of an insecticide
Dataset of the "Unwrapping Non-Locality in the Image Transmission Through Turbid Media"
<p><strong>This post provides the dataset associated with the study titled "Unwrapping Non-Locality in Image Transmission Through Turbid Media."</strong></p> <p>The "X_random.npy" file contains speckle image data with dimensions (150,000, 128, 128). The first axis spans the 150,000 images used in this study. The first 120,000 images are speckles corresponding to randomly generated images. The next 10,000 images (from 120,000 to 130,000) correspond to the first 10,000 images of the CelebA dataset. The remaining 20,000 (from 130,000 to 150,000) images consist of the first 10,000 images of the CIFAR and MNIST datasets, respectively.</p> <p>The ground truth images (SLM commanded images) are provided in "gt_random.npy", "gt_cifar.npy", and "gt_mnist.npy" for randomly generated images, CIFAR, and MNIST datasets, respectively. Each image in these datasets is scaled to a resolution of 128 by 128 pixels. The faces dataset are the 128 by 128 resized images of the CelebA dataset.</p> <p>A code example of the presented neural network model and its architecture implementation is given in "<strong>GAM on ImageNet data.py</strong>". This code reads the training dataset (speckle images) from the Ref [1] study ("x_train.npy") and rescales them to 128 by 128 pixels. The rescaled data, along with their corresponding resized SLM commanded images ("y_train.npy"), are used for training the GAM model (the model presented in the manuscript). This code then saves three files: "Trained GAM.h5," "samp_final_16_0.npy," and "samp_final_16_1.npy," which contain the trained model weights and biases and the sampling index of the model nodes, as described in the manuscript and commented on in the "GAM on ImageNet data.py" code.</p> <p>Code for testing this model is also provided in "test GAM.py." This file reads the testing dataset from the [1] study, specifically "x_test_cat.npy," "x_test_horse.npy," "x_test_punch.npy," and "x_test_parrot.npy" speckle images. It applies the trained model ("Trained GAM.h5," "samp_final_16_0.npy," and "samp_final_16_1.npy") to them. The code then calculates the structural similarity index (SSIM) to the ground truth files, namely "y_test_cat.npy," "y_test_horse.npy," "y_test_punch.npy," and "y_test_parrot.npy" for the "x_test_cat.npy," "x_test_horse.npy," "x_test_punch.npy," and "x_test_parrot.npy" data, respectively.</p> <h3>Reference</h3> <p>[1] Caramazza, P., Moran, O., Murray-Smith, R., & Faccio, D. (2019). Transmission of natural scene images through a multimode fibre. Nature Communications, 10(1), 2029.</p>
Fig. 2 in ON THE VECTOR PROPERTIES OF HENOSEPILACHNA VIGINTIOCTOMACULATA MOTSCHULSKY, 1858 (COLEOPTERA: COCCINELLIDAE) IN THE TRANSMISSION OF POTATO VIRUSES
Fig. 2. Chemo-orientation of healthy Henosepilachna vigintioctomaculata. 1 – % of healthy insects on leaves of healthy plants; 2 – % of healthy insects on potato leaves infected with plant viruses (PVY, PVM, PVX, PVS, PLRV, PSTVd); healthy insects – H. vigintioctomaculata with no potato viruses detected in their bodies; healthy plants – the potato leaves that were not infected with any plant virus; infected plants – the potato leaves that were infected with one or several plant viruses (PVY, PVM, PVX, PVS, PLRV, and PSTVd).
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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)
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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.