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The effects of progressive land use changes on the distribution, abundance and behavior of vector mosquitoes in Sabah, Malaysia
<b>Description: </b><p>The objectives of this study were:1) To investigate the effects of progressive land use change from pre-development forest, through forest clearing and cultivation to plantation maintenance on occurrence of vector mosquitoes.<br>2) To determine the status of Anopheles donaldi as a vector of malaria in changing land uses.<br>3) To study the seasonality, abundance and behaviour of vector mosquitoes in study areas.<br><br>Methods<br>Study sites:<br>Study areas were located at The SAFE Project field site:<br>1. areas between Maliau Basin Conservation Area (old growth site),<br>2. logged forest sites in the Benta Wawasan area (area undergoing clearing),<br>3. oil palm plantation sites in Benta Wawasan's Silangan Batu Estate (oil palm site)<br><br>Mosquito collection<br>Mosquito samplings (adults and immature stages) were taken at all 3 study areas every alternate month from January 2017 until December 2018. Every sampling month, 2 collectors (n=2) spent 1 night at each study area where all-night human landing collection were carried out at 3 different sampling points for each collector. Collectors performed outdoor landing catches from 18:00 to 06:00. They collected mosquitoes that landed on naked legs with aspirators. Collectors were given prophylaxis prior to the sampling activities. Collected mosquitoes were then placed at hourly intervals inside glass vials. Mosquitoes were morphologically identified using available dichotomous keys the following morning. In every sampling period, meteorological data such as air temperature, relative humidity, atmospheric pressure and wind speed was recorded on hourly basis using a handheld weather station.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/180"><b>The effects of progressive land use changes on the distribution, abundance and behavior of vector mosquitoes in Sabah, Malaysia</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Universiti Malaysia Sabah (Studentship)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Centre (SaBC) (Research licence Local)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3475408">here</a></p><p><b>Files: </b>This consists of 1 file: Evyen_Mosquito_data.xlsx</p><p><b>Evyen_Mosquito_data.xlsx</b></p><p>This file contains dataset metadata and 2 data tables:</p><ol><li><p><b>Mosquito_count</b> (described in worksheet Mosquito_count)</p><p>Description: The taxonomic identification of mosquitos caught</p><p>Number of fields: 6</p><p>Number of data rows: 40</p><p>Fields: </p><ul><li><b>Month</b>: month the mosquitos (Field type: categorical)</li><li><b>Species</b>: species ID of mosquitos caught (Field type: taxa)</li><li><b>MB</b>: Number of species caught in the Maliau Basin (Field type: numeric)</li><li><b>LFE</b>: Number of species caught in the LFE safe plot (Field type: numeric)</li><li><b>B_862</b>: Number of species caught in the B fragment SAFE (Field type: numeric)</li><li><b>Total</b>: Total caught per month (Field type: numeric)</li></ul></li><li><p><b>Mosquito_weather</b> (described in worksheet Mosquito_weather)</p><p>Description: The weather conditions of the mosquito samplings days</p><p>Number of fields: 9</p><p>Number of data rows: 203</p><p>Fields: </p><ul><li><b>Date</b>: Date of sampling (Field type: date)</li><li><b>Time</b>: Time of sampling (Field type: time)</li><li><b>Location</b>: Location of sampling (Field type: location)</li><li><b>Temperature</b>: Air tempreture (Field type: numeric)</li><li><b>Humidity</b>: Air humidity (Field type: numeric)</li><li><b>Wind Speed</b>: Wind (Field type: numeric)</li><li><b>Pressure</b>: Atomspheric pressure (Field type: numeric)</li><li><b>No.mosquito collected</b>: Number of mosquitos caught (Field type: numeric)</li><li><b>Notes</b>: Species (Field type: comments)</li></ul></li></ol><p><b>Date range: </b>2017-07-16 to 2018-08-21</p><p><b>Latitudinal extent: </b>4.4300 to 5.0700</p><p><b>Longitudinal extent: </b>116.5800 to 117.8200</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Arthropoda <br> -  -  -  Insecta <br> -  -  -  -  Diptera <br> -  -  -  -  -  Culicidae <br> -  -  -  -  -  -  <i>Heizmannia</i> <br> -  -  -  -  -  -  <i>Anopheles</i> <br> -  -  -  -  -  -  -  <i>Anopheles balabacensis</i> <br> -  -  -  -  -  -  -  <i>Anopheles latens</i> <br> -  -  -  -  -  -  <i>Culex</i> <br> -  -  -  -  -  -  -  <i>Culex sitiens</i> <br> -  -  -  -  -  -  -  <i>Culex vishnui</i> <br> -  -  -  -  -  -  <i>Aedes</i> <br> -  -  -  -  -  -  -  <i>Aedes albopictus</i> <br> -  -  -  -  -  -  -  <i>Aedes ganapathi</i> <br></div><p></p>
Fig. 1 in Ectoparasites of hedgehogs: From flea mite phoresy to their role as vectors of pathogens
Fig. 1. Map of the study area where hedgehogs were captured. A. Italy; B. Iran.
Fig. 2 in Ectoparasites of hedgehogs: From flea mite phoresy to their role as vectors of pathogens
Fig. 2. Caparinia tripilis mites in phoretic association with Archaeopsylla erinacei flea.
Fig. 1 in Theromyzon maculosum (Rathke, 1862) as a vector of potentially pathogenic fungi in aquatic ecosystems
Fig. 1. Species diversity of yeast-like fungi in different environments.
DCP-MTL: Vectorization of Agricultural Cultivation Field Parcels via Boundary-Parcel Multi-Task Learning Network in Ultra-High-Resolution Remote Sensing Images
<p><span>This paper introduces the first UHR UAV dataset specifically for CFP, designed to evaluate the performance of the proposed model in identifying these parcels. </span><span>The dataset offers ultra-high spatial resolution, various field parcel types, and broad geographic coverage. </span><span>Figure 8 </span><span>shows </span><span>the spatial distribution of the study data. Jilin Province is the primary region for training and evaluating the model, while Hebei, Henan, Anhui, Zhejiang, and Hainan are auxiliary regions for testing the model's transferability. </span></p>
Table 1 in German CULex pipienS biotype MoLeStUS and CULex torrentiUM are vector-competent for Usutu virus
<p><b>Table 1</b> Infection, dissemination, and transmission rates of mosquitoes infected with the German USUV Africa 2 strain</p><table><tbody><tr><th><b>Blood meal virus titer (TCID</b> <b>50</b> <b>/ml)</b></th><th><b>Mosquito species</b></th><th><b>Dpi</b></th><th><b>Infection rate (%) (95% CI)</b></th><th><b>Mean viral load bodies (viral copies/µl of total RNA)</b></th><th><b>Dissemination rate (%) (95% CI)</b></th><th><b>Mean viral load legs plus wings (viral copies/µl of total RNA)</b></th><th><b>Transmission rate (%) (95% CI)</b></th></tr></tbody><tbody><tr><th>High titer 10 7.4</th><td><i>Culex pipiens</i> biotype <i>molestus</i> a</td><td>14</td><td>8/10 (80.0) (44.4–97.5)</td><td>6.9 × 10 5</td><td>3/8 (37.5) (8.5–75.5)</td><td>9.0 × 10 3</td><td>3/3 (100) (29.2–100)</td></tr><tr><th></th><td></td><td>21</td><td>4/6 (66.7) (22.3–95.7)</td><td>5.6 × 10 5</td><td>4/4 (100) (39.7–100)</td><td>1.5 × 10 4</td><td>3/4 (75.0) (19.4–99.4)</td></tr><tr><th></th><td><i>Cx.pipiens</i> biotype <i>molestus</i> b</td><td>16</td><td>13/16 (81.3) (54.4–96.0)</td><td>1.9 × 10 6</td><td>13/13 (100) (75.3–100)</td><td>7.8 × 10 4</td><td>2/13 (15.4) (1.9–45.4)</td></tr><tr><th></th><td></td><td>21</td><td>8/10 (80.0) (44.4–97.5)</td><td>8.1 × 10 5</td><td>8/8 (100) (63.1–100)</td><td>7.8 × 10 4</td><td>4/8 (50.0) (15.7–84.3)</td></tr><tr><th></th><td><i>Aedes aegypti</i> d</td><td>14</td><td>0/53 (0) (0–6.7)</td><td>NA</td><td>NA</td><td>NA</td><td>NA</td></tr><tr><th></th><td></td><td>21</td><td>4/22 (18.2) (5.2–40.3)</td><td>2.3 × 10 5</td><td>1/4 (25.0) (0.6–80.6)</td><td>5.5 × 10 3</td><td>0/1 (0) (0–97.5)</td></tr><tr><th>Low titer 10 5.1</th><td><i>Cx.pipiens</i> biotype <i>molestus</i> a</td><td>14</td><td>2/36 (5.6) (0.7–18.7)</td><td>1.2 × 10 2</td><td>0/2 (0) (0–84.2)</td><td>NA</td><td>NA</td></tr><tr><th></th><td></td><td>21</td><td>1/19 (5.3) (0.7–18.7)</td><td>5.4 × 10 1</td><td>0/1 (0) (0–84.2)</td><td>NA</td><td>NA</td></tr><tr><th></th><td><i>Cx.torrentium</i> c</td><td>14</td><td>1/8 (12.5) (0.3–52.7)</td><td>2.8 × 10 1</td><td>0/1 (0) (0–97.5)</td><td>NA</td><td>NA</td></tr><tr><th></th><td></td><td>21</td><td>1/8 (12.5) (0.3–52.7)</td><td>3.9 × 10 6</td><td>1/1 (100) (2.5–100)</td><td>4.7 × 10 4</td><td>1/1 (100) (2.5–100)</td></tr></tbody></table><p>Transmission rates include results from the saliva inoculation on Vero cells and from the RT-qPCRs of cell culture supernatants.All mosquitoes were incubated for 14/16 or 21 days.Absolute quantification of virus copies/µl of total RNA was performed via an RT-qPCR-based calibration curve</p><p><i>CI</i> confidence interval, <i>dpi</i> days post infection, <i>NA</i> not applicable</p><p><sup>a</sup> <i>Cx.pipiens</i> biotype <i>molestus</i> laboratory colony from“Wendland,” Lower Saxony,Germany</p><p><sup>b</sup> <i>Cx.pipiens</i> biotype <i>molestus</i> laboratory colony from Novi Sad,the Republic of Serbia</p><p><sup>c</sup> <i>Cx.torrentium</i> field-collected colony near Berlin and Bonn,North Rhine-Westphalia,Germany</p><p><sup>d</sup> <i>Ae. aegypti</i> laboratory colony from Malaysia (Bayer CropScience,Langenfeld,Germany)</p>
Chromosome-level Assemblies of Three Candidatus Liberibacter solanacearum Vectors: Dyspersa apicalis (Förster, 1848), Dyspersa pallida (Burckhardt, 1986), and Trioza urticae (Linnaeus, 1758) (Hemiptera: Psylloidea)
<p>Genomic datasets generated from three species of psyllid insect (Hemiptera: Psylloidea). This repository includes chromosome-scale genomic assemblies, mitochondrial genomes, co-assembled bacterial genomes, coding sequence annotations, transposable element annotations, and called SNPs, as well as files related to comparative genomics analyses. </p> <p><strong>Dataset contains:</strong><br><strong>From Trioza urticae genome assembly:</strong><br> - Genome assembly (fasta)<br> - Suspected contaminant seqeunces removed from the genome assembly (fasta)<br> - T. urticae derived Candidatus Carsonella ruddii primary endosymbiont co-assembled genome (fasta)<br> - Transposable element annotations from EarlgreyTE:<br> - - Transpoable element library (fasta)<br> - - Predicted TEs (bed and gff)<br> - - Figures (pdf)<br> - Gene predictions from braker3+ :<br> - - Braker gene predictions (gft and aa) <br> - - Longest isoforms (faa)<br> - - - Interproscan annotation of gene predicitions (tsv)</p> <p><strong>From Dyspersa pallida (Trioza anthrisci) genome assembly:</strong><br> - Genome assembly (fasta)<br> - Suspected contaminant seqeunces removed from the genome assembly (fasta)<br> - D. pallida mitochondrial genome assembly (fasta)<br> - D. pallida derived Candidatus Carsonella ruddii primary endosymbiont co-assembled genome (fasta)<br> - Transposable element annotations from EarlgreyTE:<br> - - Transpoable element library (fasta)<br> - - Predicted TEs (bed and gff)<br> - - Figures (pdf)<br> - Gene predictions from braker3+ :<br> - - Braker gene predictions (gft and aa) <br> - - Longest isoforms (faa)<br> - - - Interproscan annotation of gene predicitions (tsv)</p> <p><strong>From Dyspersa apicalis (Trioza apicalis) genome assembly:</strong><br> - Genome assembly (fasta)<br> - Suspected contaminant seqeunces removed from the genome assembly (fasta)<br> - D. apicalis mitochondrial genome assembly (fasta)<br> - D. apicalis derived Candidatus Carsonella ruddii primary endosymbiont co-assembled genome (fasta)<br> - Transposable element annotations from EarlgreyTE:<br> - - Transpoable element library (fasta)<br> - - Predicted TEs (bed and gff)<br> - - Figures (pdf)<br> - Gene predictions from braker3+ :<br> - - Braker gene predictions (gft and aa) <br> - - Longest isoforms (faa)<br> - - - Interproscan annotation of gene predicitions (tsv)</p> <p><strong>From comparative genomics analysis:</strong><br> - Orthofinder analysis<br> - - Output of orthofinder analysis comparing protein predictions from de novo psyllid assemblies with other hemiptera proteomes (tsv and fasta)<br> - Cafe5 analysis<br> - - Output of cafe analysis comparing protein predictions from de novo psyllid assemblies with other hemiptera proteomes (excel, png, tab)<br> - - Enrichment analysis of GO and KO terms associated with expanded/contracted gene families at the Dyspersa taxonomic node (excel and tiff)<br> - - Enrichment analysis of GO and KO terms associated with expanded/contracted gene families at the D. pallida taxonomic node (excel and tiff)<br> - - Enrichment analysis of GO and KO terms associated with expanded/contracted gene families at the D. apicalis taxonomic node (excel and tiff)<br> - - - Plots showing expansion/contraction of different orthogroups across the hemiptera phylogeny (png)<br> - Time calibrated phylogenetic tree of hemiptera including psyllids produced by iqtree2 (txt)<br> - Time calibrated phylogenetic tree of hemiptera including psyllids produced by astral (txt)<br> - C. Ca ruddii primary endosymbiont phylogenetic tree (txt)</p> <p><strong>From psyllid population resequencing:</strong><br> - Resequencing data<br> - - High confidence biallelic SNPs from D. pallida resequenced samples called against the de novo D. pallida genome assembly (vcf)<br> - - High confidence biallelic SNPs from D. apicalis resequenced samples called against the de novo D. apicalis genome assembly (vcf)<br> - - High confidence biallelic SNPs from resequenced samples called against the reference C. Ca ruddi endosymbiont genome assembly (vcf)<br> - - For suspected contanimant contigs removed from the D. pallida genome assembly; predicted identity, and coverage in each resequenced D. pallida sample (txt)<br> - - For suspected contanimant contigs removed from the D. apicalis genome assembly; predicted identity, and coverage in each resequenced D. apicalis sample (txt)<br> - - - Qualimap evaluation of resequencing data aligned to de novo psyllid genome for each resequenced sample (pdf)<br><br><br></p>
Training word vectors on text from The Physics Teacher using Word2Vec
<p>This notebook and dataset allows one to play around with word vectors trained on text from articles in the journal <a href="https://pubs.aip.org/aapt/pte">The Physics Teacher</a> published between 1963 (the start of publication) and 2020, around 15000 articles in total.</p> <p>The primary datafile, "TPT_word2vec_words_bigrams_V1.pkl", is a list of cleaned text from these articles. It contains a list, within which each paper is a sub-list. Each sentence in that paper is yet another sub-list which contains the words in that sentence in order. However, in the data cleaning process we have removed “stop words” (like if, and, but, etc.), punctuation, symbols, and numbers, as well as lowercased all words and combined words that frequently go together into one (like “high” and "school” to “high_school”). Here is an example of 3 sentences taken from a random paper: <br> <br>[['magnet', 'spin', 'tape', 'magnetize', 'strongly', 'time', 'pole', 'approach'],<br>['magnet', 'place', 'center', 'counterweight', 'period', 'magnetize', 'pulse', 'twice', 'long'],<br>['trial', 'tape', 'examine', 'sprinkle_iron', 'filing', 'length'], ... ]</p> <p>In the notebook, we create a set of word vectors from these sentences using the Word2Vec technique, first published by Mikolov et al. (2013):</p> <p>Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space (No. arXiv:1301.3781). arXiv. https://doi.org/10.48550/arXiv.1301.3781</p> <p>The notebook includes code for both loading in word vectors from a trained model (also included, "TPT_word2vec.model") and creating the same model from the TPT text dataset. Note that word vectors are randomly initialized, so we include a random seed to make this training replicable. Changing the seed will alter some of the results (although the changes seem fairly minor).</p> <p>With a trained model, we demonstrate some applications of word vectors: adding and subtracting meanings <br>(for example "experiment" - "uncertainty" = "demonstration") and visualizing low-dimensional representations of word vectors.</p> <p>In order to install the required packages, you can use the requirements.txt file. If using pip, run "pip install requirements.txt". Or, if using Anaconda (recommended), you can use "conda install --file requirements.txt". You will also need the software to run jupyter notebooks, which can be installed with Anaconda or pip.</p>
2.4 million GLOVE word/phrase vectors SQLite database trained on PubMed abstracts
<p>This is a 2.4 million GLOVE word/phrase vectors SQLite database trained on PubMed 2021 abstracts that can be used as word/phrase embeddings in machine learning applications.</p>
Genome-wide association analysis identifies naturally segregating genetic variation associated with the rapid evolution of diapause in Aedes albopictus, an invasive vector mosquito.
<p>The raw data for genotype calls, the output files from the genotype calls, the code to replicate the analysis, and the output of the analysis.</p>
Trypanosoma cruzi in Mexican Neotropical vectors and mammals: Wildlife, livestock, pets, and human population
<p><span>The aim of the present study has been to provide primary evidence of <em>Trypanosoma cruzi</em> landscape genetics in the Mexican Neotropics.</span><span> <em>T. cruzi</em> and DTU prevalence were analyzed in landscape communities of vectors, wildlife, livestock, pets, and sympatric human populations using endpoint PCR and sequencing of all relevant amplicons from mitochondrial (kDNA) and nuclear (ME, 18S, 24Sα) gene markers. Although 98% of the infected sample set (N=2963) contained single or mixed infections of DTUI (TcI, 96.2%) and TcVI (22.6%), TcIV and TcII were identified. The sensitivity of individual markers varied and was dependent on the host taxon; kDNA, ME, and 18S combined identified 95% of infections. ME genotyped 90% of vector infections, but 60% of mammals (36% wildlife), while neither 18S nor 24Sα typed more than 20% of mammal infections. Available gene fragments to identify or genotype <em>T. cruzi</em> are not universally sensitive for all landscape parasite populations, highlighting important <em>T. cruzi</em> heterogeneity among mammal reservoir taxa and triatomine species.</span></p>
Supplementary Data: Global fits of vector-mediated s-channel simplified models for scalar and fermionic dark matter with GAMBIT
<p>This record contains the YAML files, data files, and some of the plotting scripts for: "Global fits of vector-mediated s-channel simplified models for scalar and fermionic dark matter with GAMBIT". The paper can be found at https://arxiv.org/abs/2209.13266.</p> <p>Samples have been created using GAMBIT and figures can be reproduced with pippi.</p>
Identification of microbial taxa present in Ctenocephalides felis (cat flea) reveals widespread co-infection and associations with vector phylogeny
<p><strong>Background </strong></p> <p><em>Ctenocephalides</em> <em>felis</em>, the cat flea, is the most common ectoparasite of cats and dogs worldwide. As a cause of flea allergy dermatitis and a vector for two genera of zoonotic pathogens (<em>Bartonella</em> and <em>Rickettsia</em> spp.), the effect of the <em>C</em>. felis <em>microbiome</em> on pathogen transmission and vector survival is of substantial medical importance to both human and veterinary medicine. The aim of this study was to assay the pathogenic and commensal eubacterial microbial communities of individual <em>C</em>. <em>felis</em> from multiple geographic locations and analyze these findings by location, qPCR pathogen prevalence, and flea genetic diversity.</p> <p><strong>Methods </strong></p> <p>16S Next Generation Sequencing (NGS) was utilized to sequence the microbiome of fleas collected from free-roaming cats, and the <em>cox1</em> gene was used for flea phylogenetic analysis. NGS data were analyzed for 168 individual fleas from seven locations within the US and UK. Given inconsistency in the genera historically reported to constitute the <em>C</em>. <em>felis</em> microbiome, we utilized the decontam prevalence method followed by literature review to separate contaminants from true microbiome members.</p> <p><strong> Results </strong></p> <p>NGS identified a single dominant and cosmopolitan amplicon sequence variant (ASV) from <em>Rickettsia</em> and <em>Wolbachia</em> while identifying one dominant <em>Bartonella</em> <em>clarridgeiae</em> and one dominant <em>Bartonella henselae/Bartonella</em> <em>koehlerae</em> ASV. Multiple less common ASVs from these genera were detected within restricted geographical ranges. Co-detection of two or more genera (<em>Bartonella</em>, <em>Rickettsia</em>, and/or <em>Wolbachia</em>) or multiple ASVs from a single genus in a single flea was common. <em>Achromobacter</em>, <em>Peptoniphilus</em>, and <em>Rhodococcus</em> were identified as additional candidate members of the <em>C</em>. <em>felis</em> microbiome on the basis of decontam analysis and literature review. <em>Ctenocephalides</em> <em>felis</em> phylogenetic diversity as assessed by the <em>cox1</em> gene fell within currently characterized clades while identifying seven novel haplotypes. NGS sensitivity and specificity for <em>Bartonella</em> and <em>Rickettsia</em> spp. DNA detection was compared to targeted qPCR.</p> <p><strong>Conclusions </strong></p> <p>Our findings confirm the widespread coinfection of fleas with multiple bacterial genera and strains, proposing three additional microbiome members. The presence of minor <em>Bartonella</em>, <em>Rickettsia</em>, and <em>Wolbachia</em> ASVs was found to vary by location and flea haplotype. These findings have important implications for flea-borne pathogen transmission and control. </p>
The association of host and vector characteristics with Ctenocephalides felis pathogen and endosymbiont infection
<p>Surveillance of the flea species and flea-borne pathogens infecting cats is important for both human and animal health. Multiple zoonotic <em>Bartonella</em> and <em>Rickettsia</em> species are known to infect the most common flea-infesting cats and dogs worldwide: <em>Ctenocephalides</em> <em>felis</em>, the cat flea. The ability of other flea species to transmit pathogens is relatively unexplored. We aimed to determine cat host and flea factors independently associated with flea infection with <em>Bartonella</em> and <em>Rickettsia</em> species. We also compared the presence and prevalence of cat host and flea pathogen infection by geographic location. To accomplish these aims, we performed qPCR for the detection of <em>Bartonella</em>, hemotropic <em>Mycoplasma</em>, <em>Rickettsia</em>, and <em>Wolbachia</em> DNA using paired cat and flea samples obtained from free-roaming cats presenting for spay or neuter across multiple geographic locations in the United States. A logistic regression model was employed to identify the effect of cat (sex, body weight, geographic location, and <em>Bartonella</em>, hemotropic <em>Mycoplasma</em>, and <em>Rickettsia</em> spp. infection) and flea (clade, pathogen infection, and <em>Wolbachia</em> infection) factors on <em>C. felis Bartonella clarridgeiae</em> infection. From 189 free-roaming cats, we collected 84 fleas from four flea species: <em>Ctenocephalides</em> <em>felis</em> (78/84, 92%), <em>Cediopsylla</em> <em>simplex</em> (4/84, 5%), <em>Orchopeas</em> <em>howardi</em> (1/84), and <em>Nosopsyllus</em> <em>fasciatus</em> (1/84). <em>Ctenocephalides</em> <em>felis</em> were phylogenetically assigned to Clades 1, 4, and 6 by <em>cox1</em> gene amplification. <em>Rickettsia</em> <em>asembonensis</em> (52/84, 62%) and <em>B. clarridgeiae</em> (16/84, 19%) were the most common pathogenic bacteria detected in fleas. Our model identified host cat sex and body weight as independently associated with <em>B. clarridgeiae</em> infection in fleas. When controlling for cat sex, body weight, and number of fleas collected from each cat, flea infection with <em>B. clarridgeiae</em> was not associated with geographic location, flea infection with Rickettsia spp. or Wolbachia spp., or cat infection with <em>B. clarridgeiae</em>. <em>Rickettsia asembonensis</em>, <em>Rickettsia</em> <em>felis</em> (7/84, 8%), and <em>Bartonella</em> <em>henselae</em> (7/84, 8%) were only found in fleas from specific clades: <em>R. felis</em> was detected only in Clades 1 and 6, while <em>B. henselae</em> and <em>R. asembonensis</em> were detected only in Clade 4. <em>Wolbachia</em> spp. also displayed clade specificity with strains other than <em>Wolbachia</em> wCfeT only infecting fleas from Clade 6. There was poor flea and host agreement for <em>Bartonella</em> spp. infection; however, there was agreement in the <em>Bartonella</em> species detected in cats and fleas by geographic location. These findings reinforce the importance of considering reservoir host attributes and vector phylogenetic diversity in epidemiological studies of flea-borne pathogens. Furthermore, while flea pathogen infection was not indicative of infection in a specific host cat, it may provide insight into the pathogens present in specific geographic areas. Widespread sampling from across the United States is necessary to identify the geographic, host, and vector factors driving flea-borne pathogen presence and transmission.</p>
Vector species richness predicts local mortality rates by Chagas disease
<p>Vector species richness may drive the prevalence of vector-borne diseases by influencing pathogen transmission rates. The dilution effect hypothesis predicts that higher biodiversity reduces disease prevalence, but with inconclusive evidence. In contrast, the amplification effect hypothesis suggests that higher vector diversity may result in greater disease transmission by increasing and diversifying the transmission pathways. The relationship between vector diversity and pathogen transmission remains unclear and requires further study. Chagas disease is a vector-borne disease most prevalent in Brazil and transmitted by multiple species of Triatominae insect vectors, yet the drivers of spatial variation in its impact on human populations remain unresolved. We tested whether triatomine species richness, latitude, bioclimatic variables, human host population density, and socioeconomic variables predict Chagas disease mortality rates across over 5000 spatial grid cells covering all of Brazil. Results show that species richness of triatomine vectors is a good predictor of mortality rates caused by Chagas disease, which supports the amplification effect hypothesis. Vector richness and the impact of Chagas disease may also be driven by latitudinal components of climate and human socioeconomic factors. We provide evidence that vector diversity is a strong predictor of disease prevalence and give support to the amplification effect hypothesis.</p>
Analysis of the Scalar and Vector Random Coupling Models For a Four Coupled-Core Fiber
<p>The files with simulation results for ECOC 20223 submission "Analysis of the Scalar and Vector Random Coupling Models For a Four Coupled-Core Fiber".</p><p><strong>"4CCF_eigenvectorsPol"</strong> file is the Mathematica code which enables to calculate supermodes (eigenvectors of M(w)) and their propagation constants of 4-coupled-core fiber (4CCF). These results are uploaded to the python notebook <strong>"4CCF_modelingECOC" </strong>in order to plot them to get Fig. 2 in the paper. <strong>"TransferMatrix"</strong> is the python file with functions used for modeling, simulation and plotting. It is also uploaded in the python notebook <strong>"4CCF_modelingECOC"</strong>, where all the calculations for figures in the paper are presented<strong>.</strong></p><p> </p><p><strong>! </strong><i>UPD 25.09.2023: There is an error in the formula of birefringence calculation. It is in the function "CouplingCoefficients" in "TransferMatrix" file. There the variable "birefringence" has to be calculated according to the formula (19) [</i>A. Ankiewicz, A. Snyder, and X.-H. Zheng, "Coupling between parallel optical fiber cores–critical examination", Journal of Lightwave Technology, vol. 4, no. 9,pp. 1317–1323, 1986<i>]:</i></p><p>(4*U**2*W*spec.k0(W)*spec.kn(2, W_)/(spec.k1(W)*V**4))*((spec.iv(1, W)/spec.k1(W))-(spec.iv(2, W)/spec.k0(W)))</p><p>The correct formula gives almost the same result (the difference is 10^-5), but one has to use a correct formula anyway.</p><p><strong>! </strong><i>UPD 9.12.2023: I have noticed that in the published version of the code I forgot to change the wavelength range for impulse response calculation. So instead of seeing the nice shape as in the paper you will see resolution limited shape. To solve that just change the range of wavelengths, you can add "wl = [1545e-9, 1548e-9]" in the first cell after "Total power impulse response".</i></p><p><strong>P.s. </strong>In case of any questions or suggestions you are welcome to write me an email ekader@chalmers.se</p>
Supplemental data for "Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission using a Hydrology-based Model"
<p>Supplemental data for "Investigating the Impact of Irrigation on Malaria Vector Larval Habitats and Transmission using a Hydrology-based Model"</p>
Notebooks and calculation files for: Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Models
<p>The files with simulation results for JLT submission "Modeling of the 3-Coupled-Core Fiber: Comparison Between Scalar and Vector Random Coupling Modelsr".</p> <p><strong>"3CCF_supermodes"</strong> file is the Mathematica code which enables to calculate supermodes (eigenvectors of M(w)) and their propagation constants of 3-coupled-core fiber (4CCF). These results are uploaded to the python notebook <strong>"3CCF_modelingJLTPaper" </strong>in order to plot them to get Fig. 3 in the paper. <strong>"TransferMatrix"</strong> is the python file with functions used for modeling, simulation and plotting. It is also uploaded in the python notebook <strong>"3CCF_modelingJLTPaper"</strong>, where all the calculations for figures in the paper are presented<strong>.</strong></p> <p> </p> <p> </p> <p><strong>! </strong><em>UPD 25.09.2023: There is an error in the formula of birefringence calculation. It is in the function "CouplingCoefficients" in "TransferMatrix" file. There the variable "birefringence" has to be calculated according to the formula (19) [</em>A. Ankiewicz, A. Snyder, and X.-H. Zheng, “Coupling between parallel optical fiber cores–critical examination”, Journal of Lightwave Technology, vol. 4, no. 9,pp. 1317–1323, 1986<em>]:</em></p> <p>(4*U**2*W*spec.k0(W)*spec.kn(2, W_)/(spec.k1(W)*V**4))*((spec.iv(1, W)/spec.k1(W))-(spec.iv(2, W)/spec.k0(W)))</p> <p>The correct formula gives almost the same result (the difference is 10^-5), but one has to use a correct formula anyway.</p> <p> </p> <p><strong>P.s. </strong>In case of any questions or suggestions or if you need more explanations, you are welcome to write me an email ekader@chalmers.se. If it seems like the code does not work or mistakes in simulations are found, I also appreciate letting me know.</p>
Exploring the activity of Chrysoperla carnea (Neuroptera: Chrysopidae) and Beauveria bassiana (Ascomycota: Hypocreales) on Neophilaenus campestris (Hemiptera: Aphrophoridae), vector of Xylella fastidiosa
<p>Raw data and R codes from the study "Exploring the activity of Chrysoperla carnea (Neuroptera: Chrysopidae) and Beauveria bassiana (Ascomycota: Hypocreales) on Neophilaenus campestris (Hemiptera: Aphrophoridae), vector of Xylella fastidiosa"</p>
Data from: Significant differences in capsid properties and potency between AAV vectors produced in Sf9 and HEK293 cells
<p>For successful vector-based gene therapy manufacturing, the selected adeno-associated virus (AAV) vector production system must produce vector at sufficient scale. However, concerns have arisen regarding the quality of vector produced using different systems. In this study, we compared AAV serotypes 1, 8, and 9 produced by two different systems (Sf9/baculovirus and HEK293/transfection) and purified by two separate processes. We evaluated capsid properties including protein composition, post-translational modification, particle content profiles, and in vitro and in vivo vector potency. Vectors produced in the Sf9/baculovirus system displayed reduced incorporation of viral protein 1 and 2 into the capsid, increased capsid protein deamidation, increased empty and partially packaged particles in vector preparations, and an overall reduced potency. The differences observed were largely independent of the harvest method and purification process. These findings illustrate the need for careful consideration when choosing an AAV vector production system for clinical production.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.