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5,371 results for “Flow”
Faster‐haplodiploid evolution under divergence‐with‐gene‐flow: Simulations and empirical data from pine‐feeding hymenopterans
<p>Although haplodiploidy is widespread in nature, the evolutionary consequences of this mode of reproduction are not well characterized. Here, we examine how genome-wide hemizygosity and a lack of recombination in haploid males affects genomic differentiation in populations that diverge via natural selection while experiencing gene flow. First, we simulated diploid and haplodiploid "genomes" (500-kb loci) evolving under an isolation-with-migration model with mutation, drift, selection, migration, and recombination; and examined differentiation at neutral sites both tightly and loosely linked to a divergently selected site. So long as there is divergent selection and migration, sex-limited hemizygosity and recombination cause elevated differentiation (i.e., produce a "faster-haplodiploid effect") in haplodiploid populations relative to otherwise equivalent diploid populations, for both recessive and codominant mutations. Second, we used genome-wide SNP data to model divergence history and describe patterns of genomic differentiation between sympatric populations of <em>Neodiprion lecontei </em>and <em>N. pinetum</em>, a pair of pine sawfly species (order: Hymenoptera; family: Diprionidae) that are specialized on different pine hosts. These analyses support a history of continuous gene exchange throughout divergence and reveal a pattern of heterogeneous genomic differentiation that is consistent with divergent selection on many unlinked loci. Third, using simulations of haplodiploid and diploid populations evolving according to the estimated divergence history of <em>N. lecontei </em>and <em>N. pinetum</em>, we found that divergent selection would lead to higher differentiation in haplodiploids. Based on these results, we hypothesize that haplodiploids undergo divergence-with-gene-flow and sympatric speciation more readily than diploids.</p>
Visitor Flows during Dutch Design Week 2017
<p>Data were collected during four different days of the Dutch Design Week event in October 2017. Respondents were approached next to the ticket office, which was located near the central train station. In order to collect data on event visitors’ characteristics and their spatial behavior, a mixed approach of using GPS devices and questionnaires was applied. GPS data were converted into an origin-destination matrix with found nodes (AOIs) for conducting network analysis. Visitor characteristics were later matched with the found nodes for further analysis.</p> <p>This dataset consists of two files: (i) origin-destination matrix of visitors (ii) visitor characteristics. two files can be combined with visitor IDs.</p>
Flowing Night Sky
<p>Honourable mention in the 2022 IAU OAE Astrophotography Contest, category Time lapses of celestial patterns.</p> <p> </p> <p>This time-lapse was shot from Slovakia in August 2020. By fixing the relative movement of the sky to Earth's rotation in some of the frames, we can experience a different perspective as a viewer.</p> <p>The Milky Way, our home galaxy, is visible throughout the whole video. The bright objects near the Milky Way are Jupiter and Saturn, close together, Jupiter being the brighter one.</p> <p>This video also shows the interaction of amateur astronomers observing the Perseids meteor shower with their telescopes pointed towards the sky. An unfortunate aspect of the art of astronomical observing, clouds can suddenly cover the whole sky. The fog occurs mostly because of the higher humidity after the rain.</p> <p>Most of the light trails in the sky are made by satellites, but some of them, appearing just very briefly and not very noticeably, are meteors, as the video was shot around the peak of Perseids meteor shower.</p> <p>Credit: Robert Barsa/IAU OAE (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>)</p>
Flow cytometry YFP and CFP data and deep sequencing data of populations evolving in galactose
<p><span>Copy-number and point mutations form the basis for most evolutionary novelty through the process of gene duplication and divergence. While a plethora of genomic sequence data reveals the long-term fate of diverging coding sequences and their cis-regulatory elements, little is known about the early dynamics around the duplication event itself. In microorganisms, selection for increased gene expression often drives the expansion of gene copy-number mutations, which serves as a crude adaptation, prior to divergence through refining point mutations. Using a simple synthetic genetic system that allows us to distinguish copy-number and point mutations, we study their early and transient adaptive dynamics in real-time in <em>Escherichia</em> <em>coli</em>. We find two qualitatively different routes of adaptation depending on the level of functional improvement selected for: In conditions of high gene expression demand, the two types of mutations occur as a combination. Under</span><span> low gene expression demand, negative epistasis between the two types of mutations renders them mutually exclusive. Thus, owing to their higher frequency, adaptation is dominated by copy-number mutations. Ultimately, due to high rates of reversal and pleiotropic cost, copy-number mutations may not only serve as a crude and transient adaptation but also <a>constrain</a></span><span> sequence divergence over evolutionary time scales.</span></p>
Surge-induced Crevasse dynamics of Monacobreen Glacier, Svalbard, through SAR observations and subglacial flow estimations
<p>This repository contains the datasets of glacial surface velocity, subsurface velocities, and the basal shear stress published in the paper 'Surge-induced Crevasse dynamics of Monacobreen Glacier, Svalbard, through SAR observations and subglacial flow estimations' in AGU Earth and Space Sciences. The detailed methodological description along with the computational methods have been discussed in detail in the article.</p>
Optical trapping of micro-particles and bacterial cells in single channel and flow-focusing microfluidic devices
<p><strong>Video 1</strong> - The video shows the flow-focusing and trapping of 1.84 μm bacteria-sized particles flowing at a sample flow rate of 0.1 μL/min. The horizontal sheath flow rate 1 μL/min and the vertical sheath flow rate is 0.5 μL/min. Trapping is achieved using a maximum laser power of 250mW. </p> <p><strong>Video 2</strong> - The video shows the flow and fluorescence trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 0.013 μL/min. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p> <p><strong>Video 3</strong> - The video shows the flow and trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 1 μL/min. Increased flow rate results in continuous transient trapping of the cells is achieved at a trapping power of 250mW. The microchannel surface is not treated with pluronic F-127, therefore lot of particles stick to the channel surface. </p> <p><strong>Video 4</strong> - The video shows the flow and trapping of 1.84 μm bacteria-sized particles flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p> <p><strong>Video 5</strong> - The video shows the flow and trapping of <em>E. coli</em> MG1655 flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p> <p><strong>Video 6</strong> - The video shows the flow and trapping of <em>S. aureus</em> 6538 flowing at a flow rate of 0.013 μL/min. Trapping is achieved at a laser power of 250mW. The channel surface is treated with pluronic F-127 to prevent cell adhesion. </p>
Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography
<p>This repository contains raw data and analysis routines of the publication <strong>“<em>Sub-diffusion flow velocimetry with number fluctuation optical coherence tomography</em>”</strong> in Optics Express (doi.org/10.1364/OE.474279<em>). </em>The reader is free to use the scripts and data in this depository if the manuscript is correctly cited in their work. For further questions, feel free to contact the corresponding author. Python 3.7 was used for programming. Keep in mind that running files with larger time series length may take up to 10 minutes and 2D flow profile analysis may take up to one hour.</p> <p>For 1D depth-resolved measurements each dataset includes diffusion, focus (beam shape) calibration, and flow measurements for different discharge rates, <em>Q</em>. For all measurements time series length is 31000 points and the sampling rate is 5.5 kHz. Diffusion measurements are performed on a static sample with a stationary beam. Focus (waist) calibration measurements are performed by moving the OCT beam over the static sample with a known velocity. Flow measurements are performed on the flowing sample with the stationary beam. Each measurement is averaged 6 times. The analysis process is as follows: Firstly, the beam waist (focus) calibration is performed using the script ‘Beam Shape.py’. For improved accuracy it is preferable to perform several measurements and average beam waist values at every depth. Secondly, the Doppler angle is determined using a flow measurement with the largest discharge rate using the script ‘Doppler Angle.py’. Thirdly, the flow profiles are obtained with predetermined calibration parameters using the script ‘Flow Profile.py’. Finally, the particle number density is calculated using the script ‘Number Density.py’. This requires knowledge of particle size for calculating the theoretical number density values. The particle size can be determined using the script ‘Diffusion.py’. All file names are sufficiently descriptive, showing whether it is diffusion, focus (waist) calibration or flow measurement.</p> <p>For 2D depth and laterally resolved measurements each dataset includes diffusion, focus (beam shape) calibration, M-scan and B-scan flow measurements for different discharge rates, <em>Q</em>. Diffusion and focus calibration measurements are same as in 1D. M-scan flow measurements are performed on a flowing sample with a stationary beam. They are same as flow measurements in 1D and are only used for determining the Doppler angle. B-scan flow measurements are performed by moving the OCT beam over the flowing sample with a known velocity. 2D flow profiles can be determined using the script ‘2D Flow Profile.py’. The table below summarizes all datasets and Python scripts uploaded to this repository.</p> <table align="center"> <tbody> <tr> <td> <p><strong>Name</strong></p> </td> <td> <p><strong>Usability</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>Dataset, 15-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 0.34 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 16-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.74 deg and alignment angle of 2.3 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 22-03-2022.zip</p> </td> <td> <p>1D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.00 deg and alignment angle of 1.15 deg.</p> </td> </tr> <tr> <td> <p>Dataset, 08-07-2022.zip</p> </td> <td> <p>2D measurements</p> </td> <td> <p>Dataset for Doppler angle of 1.84 deg and alignment angle of 0 deg.</p> </td> </tr> <tr> <td> <p>Chirp.data</p> </td> <td> <p>All measurements</p> </td> <td> <p>File containing k-interpolation data</p> </td> </tr> <tr> <td> <p>ReadOCTFile.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>Written by Jos de Wit, this module reads and imports spectra from raw OCT files.</p> </td> </tr> <tr> <td> <p>Processing.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This module contains all analysis and processing routines.</p> </td> </tr> <tr> <td> <p>Diffusion.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines particle size from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Beam Shape.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines axial beam shape from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Doppler Angle.py</p> </td> <td> <p>All measurements</p> </td> <td> <p>This script determines Doppler angle from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Flow Profile.py</p> </td> <td> <p>1D measurements</p> </td> <td> <p>This script determines flow profiles from raw OCT spectra.</p> </td> </tr> <tr> <td> <p>Number Density.py</p> </td> <td> <p>1D measurements</p> </td> <td> <p>This script determines particle number density raw OCT spectra.</p> </td> </tr> <tr> <td> <p>2D Flow Profile.py</p> </td> <td> <p>2D measurements</p> </td> <td> <p>This script determines 2D flow profiles from raw OCT spectra.</p> </td> </tr> </tbody> </table> <p> </p>
Ki-67 and Bcl-2 data by flow cytometry in non-malignant bone marrow aspirates and aspirates from patients with myeloid malignancies.
<p>This Data in Brief article displays a flow cytometric assay that was used for the acquisition and analyses of proliferation and anti-apoptosis in hematopoietic cells. This dataset includes analysis of the Ki-67 positive fraction (Ki-67 proliferation index) and Bcl-2 positive fraction (Bcl-2 anti-apoptotic index) of the different myeloid bone marrow (BM) cell population in non-malignant BM, and the BM disorders myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML). The present dataset comprises 1) the percentage of the CD34 positive blast cells, erythroid cells, myeloid cells and monocytic cells, and 2) the determined Ki-67 positive fraction and Bcl-2 positive fraction of these cell populations in tabular form. This allows the comparison and reproduction of the data when these analyses are repeated in a different setting. As gating the Ki-67 positive and Bcl-2 positive cells is a critical step in this assay, different gating approaches were compared to determine the most sensitive and specific approach. BM cells from aspirates of 50 non-malignant, 25 MDS and 50 AML cases were stained with 7 different antibody panels and subjected to flow cytometry for determination of the Ki-67 positive cells and Bcl-2 positive cells of the different myeloid cell populations. The Ki-67 or Bcl-2 positive cells were then divided by the total number of cells of the respective cell population to generate the Ki-67 positive fraction (Ki-67 proliferation index) or the Bcl-2 positive fraction (Bcl-2 anti-apoptotic index). The presented data may facilitate the establishment and standardization of flow cytometric analyses of the Ki-67 proliferation index and Bcl-2 anti-apoptotic index of the different myeloid cell populations in non-malignant BM as well as MDS and AML patients in other laboratories. Directions for proper gating of the Ki-67 positive and Bcl-2 positive fraction are crucial for achieving standardization among different laboratories. In addition, the data and the presented assay allows application of Ki-67 and Bcl-2 in a research and clinical setting and this approach can serve as the basis for optimization of the gating strategy and subsequent investigation of other cell biological processes besides proliferation and anti-apoptosis. These data can also promote future research about the role of these parameters in diagnosis of myeloid malignancies, prognosis of myeloid malignancies and therapeutic resistance against anti-cancer therapies in these malignancies. As specific populations based on cell biological characteristics were identified, these data can be useful for evaluating gating algorithms in flow cytometry in general by confirming the outcome (e.g. MDS or AML diagnosis) with the respective proliferation and anti-apoptotic profile of these malignancies. The Ki-67 proliferation index and Bcl-2 anti-apoptotic index may potentially be used for classification of MDS and AML based on supervised machine learning algorithms, while unsupervised machine learning can be deployed at the level of single cells to potentially distinguish non-malignant from malignant cells to identify minimal residual disease. Therefore, the present dataset may be of interest for internist-hematologists, immunologists with affinity for hemato-oncology, clinical chemists with sub-specialization of hematology and researchers in the field of hemato-oncology.</p>
An experimental and numerical study of turbulent oscillatory flow over an irregular rough wall
<p>Phase-averaged experimental data, phase- and spatial-averaged numerical data, and digitized roughness as described in: Dunbar, D., van der A, D.A., Scandura, T., O'Donoghue, T. (2023). An experimental and numerical study of turbulent oscillatory flow over an irregular rough wall. Journal of Fluid Mechanics, 955, A33, doi:10.1017/jfm.2022.1090.</p> <p>If interested in the time-series data and/or the spatially varying numerical data please contact the corresponding author of the paper. </p>
Data from: A study on the Influence of submergence ratio on the transportation of suspended sediment in a partially vegetated channel flow
<p><span>Riparian or aquatic vegetation thrives with seasons. The understanding of canopies' Submergence-Ratio SR (stems' height to water depth) influence on suspended sediments' transportation is still limited. Thus, Large Eddy Simulations (LES) coupled with the Discrete Phase Method (DPM) are used to investigate the particles' 3-dimensional distribution in a partially vegetated straight channel. The spanwise distribution of particles is quantified by the Probability Density Function (PDF), showing a non-uniformity of particles in time as quantified by the PDF variance. The findings and conclusions: (Ⅰ) With SR rising, the particles' depletion effects exerted by the vegetation-side mixing layer are improved along the interface between vegetated and vegetation-side bare channel region. However, the SR has little effect on the variance of the particles' PDF in the spanwise direction when the mixing layer is fully developed. (Ⅱ) During the developing stage of the over-canopy mixing layer, submerged vegetation with higher SR gain a stronger upwards (vertical) entrainment capability. </span><span>The case (SR=60%) has a higher sediment concentration than other cases in the fully developed vertical mixing layer region above canopy.</span><span> (III) </span><span>The vertical suspension of particles in the vegetation-side bare channel region is analysed. Particles migrating from the vegetated region are entrained into the vegetation-side bare channel region by turbulent structures. Nevertheless, the vertical concentration profile is more uniform in the vegetated region than in the vegetation-side bare channel at the same streamwise location. The cases SR=40% and 60% still have higher sediment concentrations than other cases in the vegetation-side bare channel's upper region.</span></p>
Passive Operating System Fingerprinting Revisited - Network Flows Dataset
<p>For the evaluation of OS fingerprinting methods, we need a dataset with the following requirements:</p> <ul> <li>First, the dataset needs to be big enough to capture the variability of the data. In this case, we need many connections from different operating systems.</li> <li>Second, the dataset needs to be annotated, which means that the corresponding operating system needs to be known for each network connection captured in the dataset. Therefore, we cannot just capture any network traffic for our dataset; we need to be able to determine the OS reliably.</li> </ul> <p>To overcome these issues, we have decided to create the dataset from the traffic of several web servers at our university. This allows us to address the first issue by collecting traces from thousands of devices ranging from user computers and mobile phones to web crawlers and other servers. The ground truth values are obtained from the HTTP User-Agent, which resolves the second of the presented issues. Even though most traffic is encrypted, the User-Agent can be recovered from the web server logs that record every connection’s details. By correlating the IP address and timestamp of each log record to the captured traffic, we can add the ground truth to the dataset.</p> <p>For this dataset, we have selected a cluster of five web servers that host 475 unique university domains for public websites. The monitoring point recording the traffic was placed at the backbone network connecting the university to the Internet.</p> <p>The dataset used in this paper was collected from approximately 8 hours of university web traffic throughout a single workday. The logs were collected from Microsoft IIS web servers and converted from W3C extended logging format to JSON. The logs are referred to as <em>web logs </em>and are used to annotate the records generated from packet capture obtained by using a network probe tapped into the link to the Internet.</p> <p>The entire dataset creation process consists of seven steps:</p> <ol> <li>The packet capture was processed by the Flowmon flow exporter (<a href="https://www.flowmon.com">https://www.flowmon.com</a>) to obtain primary flow data containing information from TLS and HTTP protocols.</li> <li>Additional statistical features were extracted using GoFlows flow exporter (<a href="https://github.com/CN-TU/go-flows">https://github.com/CN-TU/go-flows</a>).</li> <li>The primary flows were filtered to remove incomplete records and network scans.</li> <li>The flows from both exporters were merged together into records containing fields from both sources.</li> <li><em>Web logs </em>were filtered to cover the same time frame as the flow records.</li> <li><em>Web logs</em> were paired with the flow records based on shared properties (IP address, port, time).</li> <li>The last step was to convert the User-Agent values into the operating system using a Python version of the open-source tool <em>ua-parser</em> (<a href="https://github.com/ua-parser/uap-python">https://github.com/ua-parser/uap-python</a>). We replaced the unstructured User-Agent string in the records with the resulting OS.</li> </ol> <p>The collected and enriched flows contain 111 data fields that can be used as features for OS fingerprinting or any other data analyses. The fields grouped by their area are listed below:</p> <ul> <li>basic flow properties - flow_ID;start;end;L3 PROTO;L4 PROTO;BYTES A;PACKETS A;SRC IP;DST IP;TCP flags A;SRC port;DST port;packetTotalCountforward;packetTotalCountbackward;flowDirection;flowEndReason;</li> <li>IP parameters - IP ToS;maximumTTLforward;maximumTTLbackward;IPv4DontFragmentforward;IPv4DontFragmentbackward;</li> <li>TCP parameters - TCP SYN Size;TCP Win Size;TCP SYN TTL;tcpTimestampFirstPacketbackward;tcpOptionWindowScaleforward;tcpOptionWindowScalebackward;tcpOptionSelectiveAckPermittedforward;tcpOptionSelectiveAckPermittedbackward;tcpOptionMaximumSegmentSizeforward;tcpOptionMaximumSegmentSizebackward;tcpOptionNoOperationforward;tcpOptionNoOperationbackward;synAckFlag;tcpTimestampFirstPacketforward;</li> <li>HTTP - HTTP Request Host;URL;</li> <li>User-agent - UA OS family;UA OS major;UA OS minor;UA OS patch;UA OS patch minor;</li> <li>TLS - TLS_CONTENT_TYPE;TLS_HANDSHAKE_TYPE;TLS_SETUP_TIME;TLS_SERVER_VERSION;TLS_SERVER_RANDOM;TLS_SERVER_SESSION_ID;TLS_CIPHER_SUITE;TLS_ALPN;TLS_SNI;TLS_SNI_LENGTH;TLS_CLIENT_VERSION;TLS_CIPHER_SUITES;TLS_CLIENT_RANDOM;TLS_CLIENT_SESSION_ID;TLS_EXTENSION_TYPES;TLS_EXTENSION_LENGTHS;TLS_ELLIPTIC_CURVES;TLS_EC_POINT_FORMATS;TLS_CLIENT_KEY_LENGTH;TLS_ISSUER_CN;TLS_SUBJECT_CN;TLS_SUBJECT_ON;TLS_VALIDITY_NOT_BEFORE;TLS_VALIDITY_NOT_AFTER;TLS_SIGNATURE_ALG;TLS_PUBLIC_KEY_ALG;TLS_PUBLIC_KEY_LENGTH;TLS_JA3_FINGERPRINT;</li> <li>Packet timings - NPM_CLIENT_NETWORK_TIME;NPM_SERVER_NETWORK_TIME;NPM_SERVER_RESPONSE_TIME;NPM_ROUND_TRIP_TIME;NPM_RESPONSE_TIMEOUTS_A;NPM_RESPONSE_TIMEOUTS_B;NPM_TCP_RETRANSMISSION_A;NPM_TCP_RETRANSMISSION_B;NPM_TCP_OUT_OF_ORDER_A;NPM_TCP_OUT_OF_ORDER_B;NPM_JITTER_DEV_A;NPM_JITTER_AVG_A;NPM_JITTER_MIN_A;NPM_JITTER_MAX_A;NPM_DELAY_DEV_A;NPM_DELAY_AVG_A;NPM_DELAY_MIN_A;NPM_DELAY_MAX_A;NPM_DELAY_HISTOGRAM_1_A;NPM_DELAY_HISTOGRAM_2_A;NPM_DELAY_HISTOGRAM_3_A;NPM_DELAY_HISTOGRAM_4_A;NPM_DELAY_HISTOGRAM_5_A;NPM_DELAY_HISTOGRAM_6_A;NPM_DELAY_HISTOGRAM_7_A;NPM_JITTER_DEV_B;NPM_JITTER_AVG_B;NPM_JITTER_MIN_B;NPM_JITTER_MAX_B;NPM_DELAY_DEV_B;NPM_DELAY_AVG_B;NPM_DELAY_MIN_B;NPM_DELAY_MAX_B;NPM_DELAY_HISTOGRAM_1_B;NPM_DELAY_HISTOGRAM_2_B;NPM_DELAY_HISTOGRAM_3_B;NPM_DELAY_HISTOGRAM_4_B;NPM_DELAY_HISTOGRAM_5_B;NPM_DELAY_HISTOGRAM_6_B;NPM_DELAY_HISTOGRAM_7_B;</li> <li>ICMP - ICMP TYPE;</li> </ul> <p>The details of OS distribution grouped by the OS family are summarized in the table below. The <em>Other </em>OS family contains records generated by web crawling bots that do not include OS information in the User-Agent.</p> <table> <thead> <tr> <th scope="col">OS Family</th> <th scope="col">Number of flows</th> </tr> </thead> <tbody> <tr> <td>Other</td> <td>42474</td> </tr> <tr> <td>Windows</td> <td>40349</td> </tr> <tr> <td>Android</td> <td>10290</td> </tr> <tr> <td>iOS</td> <td>8840</td> </tr> <tr> <td>Mac OS X</td> <td>5324</td> </tr> <tr> <td>Linux</td> <td>1589</td> </tr> <tr> <td>Ubuntu</td> <td>653</td> </tr> <tr> <td>Fedora</td> <td>88</td> </tr> <tr> <td>Chrome OS</td> <td>53</td> </tr> <tr> <td>Symbian OS</td> <td>1</td> </tr> <tr> <td>Slackware</td> <td>1</td> </tr> <tr> <td>Linux Mint</td> <td>1</td> </tr> </tbody> </table> <p> </p>
Demonstration of Simulation Tools for Electricity Markets considering Power Flow Analysis
<p>Two novel publicly available web services for electricity market (EM) simulation and study and power flow evaluation and validation are proposed, namely the Electricity Market Service (EMS) and Power Flow Service (PFS). EMS enables the simulation of two auction-based algorithms and the execution of three wholesale EMs. PFS provides the creation and evaluation of electrical grids from the transmission to distribution grids. Combining both services one can simulate EMs from wholesale to local markets and test if the results are compatible with a specific electrical grid. This dataset publishes the input and output data of a case study simulation scenario.</p>
Evaluation of synovial calprotectin by using a lateral flow stool test for the diagnosis of prosthetic joint infections
<p>The analysis of synovial fluid is a crucial step in the diagnostic process of prosthetic joint infections (PJI) and several studies illustrated the efficacy of synovial calprotectin in supporting the diagnosis of PJI. In this study, synovial calprotectin was analyzed by a commercial stool test to explore whether the detected levels might predict PJIs. The synovial fluids of 55 were analyzed. Calprotectin levels were compared to other synovial biomarkers of PJI. Of the 55 synovial fluids, 13 patients were diagnosed with PJI and 42 with an aseptic failure of the implant. The specificity, sensitivity, and AUC of calprotectin resulted 0.90, 0.85, and 0.86 (95%CI: 0.72-0.99), respectively with a set threshold of 226.5 µg/g. The values of calprotectin had a moderate and statistically relevant correlation with the synovial leucocyte counts (rs = 0.54, p = 0.0003) and percentage of synovial neutrophils (rs = 0.68, p = 0.0000). From this analysis, it can be concluded that synovial calprotectin is a valuable biomarker that correlates with other established indicator of local infection, and the use of a commercial lateral flow stool test could be a cost-effective strategy delivering rapid and reliable results and supporting the diagnostic process of PJI.</p>
Introgressive hybridization in the west Pacific pen shells (genus Atrina): Restricted interspecies gene flow within the genome
<p>Abstract</p> <p>A compelling interest in marine biology is to elucidate how species boundaries between sympatric free‐spawning marine invertebrates such as bivalve molluscs are maintained in the face of potential hybridization. Hybrid zones provide the natural resources for us to study the underlying genetic mechanisms of reproductive isolation between hybridizing species. Against this backdrop, we examined the occurrence of introgressive hybridization (introgression) between two bivalves distributed in the western Pacific margin, Atrina japonica and Atrina lischkeana, based on single‐nucleotide polymorphisms (SNPs) derived from restriction site‐associated DNA sequencing. Using 1066 ancestry‐informative SNP sites, we also investigated the extent of introgression within the genome to search for SNP sites with reduced interspecies gene flow. A series of our individual‐level clustering analyses including the principal component analysis, Bayesian model‐based clustering, and triangle plotting based on ancestry–heterozygosity relationships for an admixed population sample from the Seto Inland Sea (Japan) consistently suggested the presence of specimens with varying degrees of genomic admixture, thereby implying that the two species are not completely isolated. The Bayesian genomic cline analysis identified 10 SNP sites with reduced introgression, each of which was located within a genic region or an intergenic region physically close to a functional gene. No, or very few, heterozygotes were observed at these sites in the hybrid zone, suggesting that selection acts against heterozygotes. Accordingly, we raised the possibility that the SNP sites are within genomic regions that are incompatible between the two species. Our finding of restricted interspecies gene flow at certain genomic regions gives new insight into the maintenance of species boundaries in hybridizing broadcast‐spawning molluscs.</p>
Sap flow in Juglans mandshurica trees during the growing season 2015 (I)
<p>The primary data on the sap flow of Manchurian walnut (Juglans mandshurica) trees under the conditions of introduction are given. The walnut population is located on the territory of the Volga-Kama Reserve. Coordinates N55.883288, E48.730038. The measurements were carried out from May 31 to June 4 year 2015. Using EMS51A instruments (EMS, Brno, Czech Republic). The measurement interval is 5 minutes. The meteorological<br> observations.</p>
Data for: Gene flow accelerates adaptation to a parasite
<p>Gene flow into populations can increase additive genetic variation and introduce novel beneficial alleles, thus facilitating adaptation. However, gene flow may also impede adaptation by disrupting beneficial genotypes, introducing deleterious alleles, or creating novel dominant negative interactions. While theory and fieldwork have provided insight as to the effects of gene flow, direct experimental tests are rare. Here, we evaluated the effects of gene flow on adaptation in the nematode <em>Caenorhabditis</em> <em>elegans</em> during exposure to the bacterial parasite <em>Serratia</em> <em>marcescens</em>. We evolved hosts against non-evolving parasites for ten passages while controlling host gene flow and source population. We used source nematode populations with three different genetic backgrounds (one similar to the sink population and two different) and two evolutionary histories (previously adapted to <em>S. marcescens</em> or naïve). We found that populations with gene flow exhibited greater increases in parasite resistance than those without gene flow. Additionally, gene flow from adapted populations resulted in greater increases in resistance than gene flow from naïve populations, particularly with gene flow from novel genetic backgrounds. Overall, this work demonstrates that gene flow can facilitate adaptation and suggests that the genetic architecture and evolutionary history of source populations can alter the sink population's response to selection.</p>
T-cell activity - Imaging flow cytometry experiment data
<p>Raw image data from the imaging flow cytometry experiments for correlating receptor localization and cytokine production.</p>
Supporting information for "Kinetics of CN(v=1) reactions with butadiene isomers at low temperature by cw-Cavity Ringdown in a pulsed Laval flow with theoretical modelling of rates and entrance channel branching
<p>This file contains the master equation inputs for all the reactions studied, as well as all the details on stationary points and VRC-TST fluxes necessary to reproduce the simulations. </p>
Kolmogorov flow datasets
<p>Quasiperiodic (Re=30) and chaotic (Re=34) datasets used in Racca et al. (https://arxiv.org/abs/2211.11379) and on the GitHub repository (https://github.com/MagriLab/CAE-ESN-Kolmogorov).</p>
dataset for "Corner-bridge flow as pore-scale flow mechanism controlling capillary pressure curve at low saturations"
<p>dataset for "Corner-bridge flow as pore-scale flow mechanism controlling capillary pressure curve at low saturations", including raw images from drainage experiments</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.