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115 results for “Time integration”
In the right place, at the right time: the integration of bacteria into the Plankton Ecology Group model
<p><strong><span>Background</span></strong></p> <p><span>Planktonic microbial communities have critical impacts on the pelagic food web and water quality status in freshwater ecosystems, yet no general model of bacterial community assembly linked to higher trophic levels and hydrodynamics has been assessed. In this study, we utilized a two-year survey of planktonic communities from bacteria to zooplankton on three freshwater reservoirs to investigate their spatiotemporal dynamics.</span></p> <p><strong><span>Result</span></strong></p> <p><span>We observed the site-specific presence and microdiversification of bacteria in lacustrine and riverine environments, as well as in deep hypolimnia. Moreover, we determined recurrent bacterial seasonal patterns driven by both biotic and abiotic conditions, which could be integrated into the well-known Phytoplankton Ecology Group (PEG) model describing primarily the seasonalities of larger plankton groups. Importantly, bacteria with different ecological potentials showed finely coordinated successions affiliated with four seasonal phases, including the spring bloom dominated by fast-growing opportunists, the clear-water phase associated with oligotrophic ultramicrobacteria, the summer phase characterized by phytoplankton bloom-associated bacteria, and the fall/winter phase driven by decay-specialists. </span><span> </span></p> <p><strong><span>Conclusion</span></strong></p> <p><span>Our findings elucidate the principles driving the spatiotemporal microbial community distribution in freshwater ecosystems. We suggest an extension to the original PEG model by integrating recurrent bacterial seasonal trends.</span></p>
Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device"
<p><strong>Code and Data for "Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device".</strong></p> <pre>Code to analyze data produced by the Quantum-Si benchtop device and semiconductor chip is provided in a Python library <strong>qsi_algo</strong> under several submodules: - <strong>rs_caller.py</strong>: Algorithm for calling RS segments (also called ROI segments throughout code). - <strong>rs_caller_controller.py</strong>: Code framework for executing RS calling and property computation in a distributed manner - <strong>rs_properties</strong>: Code for computing properties of identified RS - <strong>rs_classifier</strong>: Algorithms for identifying peptide states (i.e. residue calls) associated with an RS - <strong>utils.py</strong>: shared helper code - <strong>pulse_reader</strong>: reader for binary pulse file - <strong>filters</strong>: ROI and pulse filtering utilities - <strong>plotting</strong>: functions for visualization of data relevant to the analyses presented Jupyter notebooks (<strong>.ipynb</strong>) files are named according to the manuscript figure they are associated with. Analysis code inside uses provided RS (recognition segment) data to demonstrate filtering and residue-calling techniques required to replicate analyses shown in manuscript figures. Please note: several methods rely on randomization for model initialization and/or data sampling which can cause small deviations from equivalent analyses in published figures. The raw data produced from the Quantum-Si benchtop device and semiconductor chip for the assays presented in the accompanying study is presented in a pulse-called binary file format. Pulses can be used as input for RS identification and peptide state identification. Pre-segmented (RS-identified) files are included for convenience. The data contained in the files include: <strong>{run_id}.bin</strong>: Binary format for storing pulse info. The reader provided in <strong>qsi_algo.pulse_reader</strong> produces the following columns: - <strong>aperture_index</strong>: unique aperture index on chip - <strong>start_f</strong>: index of first frame in pulse, counted from the beginning of the run - <strong>end_f</strong>: index of last frame in pulse, counted from the beginning of the run - <strong>dur_f</strong>: duration of pulse in frames - <strong>dur_s</strong>: duration of pulse in seconds - <strong>ipd_f</strong>: interpulse duration in frames (number of frames since end of preceding pulse) - <strong>ipd_s</strong>: interpulse duration in seconds (time in seconds elapsed since end of preceding pulse) - <strong>snr</strong>: signal-to-noise ratio (bin1_intensity / bin1_bg_std) - <strong>intensity</strong>: intensity of pulse (counts above baseline in bin1) - <strong>bin0_intensity</strong>: counts above baseline in bin0 - <strong>intensity_display</strong>: bin1_intensity + bin1_bg_mean - <strong>binratio</strong>: bin0_intensity / bin1_intensity - <strong>bg_mean</strong>: bin1 background mean in region of pulse - <strong>bg_std</strong>: bin1 background standard deviation in region pulse - <strong>bin0_bg_mean</strong>: bin0 background mean in region of pulse - <strong>bin0_bg_std</strong>: bin0 background standard deviation in region pulse <strong>{run_id}.csv.gz</strong>: Compressed comma-separated value file containing RS/ROI properties computed from raw pulses.bin file by included RS caller (example in <strong>rs_caller.py</strong>). - <strong>ap</strong>: unique aperture index on chip - <strong>ROI</strong>: ordinal ROI number in the aperture, 0-indexed - <strong>start_p</strong>: index (.loc) of first pulse in the ROI (inclusive) in pulse dataframe - <strong>end_p</strong>: index (.loc) of last pulse in the ROI (inclusive) in pulse dataframe - <strong>start_f</strong>: first frame of the first pulse in the ROI (inclusive) - <strong>end_f</strong>: Last frame of the last pulse in the ROI (exclusive) - <strong>start_s</strong>: Time (in seconds elapsed from beginning of run) of the start of the ROI - <strong>end_s</strong>: Time (in seconds elapsed from beginning of run) of the end of the ROI - <strong>dur_f</strong>: Duration in frames of the ROI - <strong>dur_s</strong>: Duration in seconds of the ROI - <strong>num_pulses</strong>: Number of pulses in the ROI (that also passed filtering during ROI-calling) - <strong>pw_mean</strong>: Mean pulse duration (in seconds) of pulses in the ROI - <strong>ipd_mean</strong>: Mean inter-pulse duration (in seconds) of pulses in the ROI - <strong>snr_mean</strong>: Mean signal-to-noise ratio of pulses in the ROI - <strong>intensity_mean</strong>: Mean intensity above baseline of pulses in the ROI - <strong>binratio_norm</strong>: Estimated pulse bin ratio of pulses in the ROI, according to the following equation: sum(bin0_intensity*dur_f) / np.sum(bin1_intensity*dur_f) - <strong>ROI_score</strong>: ROI quality score (0-1 from least to most likely to contain recognizer-peptide recognition pulsing) - <strong>binratio_skew</strong>: bin ratio correction factor accounting for binning signal timing differences across the chip. This factor has already been applied to the binratio_norm column</pre>
Supplementary material for the paper: Application of Time-Averaged and Integral-Based Measure for Measurement Results Variability Reduction in GSM/DCS/UMTS Systems
<p>Supplementary material for the paper: Application of Time-Averaged and Integral-Based Measure for Measurement Results Variability Reduction in GSM/DCS/UMTS Systems</p>
Supporting information for: The Time Requirements for Primary Care Consultations: Initial Sick Child Visits in Low- and Middle-income Countries Using the Integrated Management of Childhood Illness (IMCI) Clinical Algorithm
<p>Few studies have examined the time required for primary care consultations; none have focused on sick child visits in low- and middle-income countries (LMICs). This project begins to fill that gap by providing evidence-based estimates of the time needed for initial visits with under-five infants and children at public or not-for-profit facilities in countries using the Integrated Management of Childhood Illness (IMCI) clinical algorithm.</p> <p>Estimates of the mean expected duration of IMCI consultations require (a) classification profiles, i.e., tabulations of the gold standard health issues presented by patients less than 5 years old; (b) lists of the tasks included in applicable versions of the IMCI algorithm and the conditions that elicit them, and (c) an estimate of the time needed to perform tasks with no pre-defined minimum duration. The latter requires, in addition to classification profiles, information on rates of task performance and the mean observed duration of consultations.</p> <p>The IMCI clinical algorithm and the research surrounding it provide unusually rich sources of such information. Developed in the mid 1990s by the World Health Organization and the United Nations Children’s Fund, the IMCI algorithm seeks to reduce child mortality in LMICs by improving the technical quality of primary care services. For infants less than 2 months old, the algorithm focuses on bacterial infections, feeding problems, low weight, and, in some versions, jaundice. For children 2-59 months old, the foci include acute respiratory infections, especially pneumonia; diarrhea; fevers, especially malaria and measles; malnutrition, and anemia. Immunization status is a concern for both age groups. The algorithm provides a scheme to classify the health issues with which infants and children present, an array of tasks providers may be expected perform, and criteria by which tasks are elicited. Research on the design and utility of the algorithm, its effects on provider performance, and related topics furnishes data on the prevalence of gold standard IMCI classifications in a variety of patient populations. In some cases, it also enables one to calculate the time required to perform tasks.</p> <p>I found such information by searching MEDLINE, the database of the International Network for Rational Use of Medicines, the websites of the WHO and its regional offices, GOOGLE, and GOOGLE SCHOLAR using search terms such as ‘Integrated Management of Childhood Illness’, ‘observational’, ‘prospective’, ‘classification’, ‘clinical signs’, ‘health facility survey’, and ‘validity’. I also reviewed studies that cited a qualified study and, conversely, material included in the bibliographies of qualified studies.</p> <p>The supplemental information files contain the following:</p> <p>WORKBOOK S1_STUDIES USED</p> <p>Lists features of, and sources for, the studies used to construct classification profiles and to estimate the time required to perform the average task with no predefined minimum duration. With 2 exceptions (see below, DATA S1 and DATA S2), all the studies have been published or are readily available on the internet. None of the data can be used to identify individuals.</p> <p>DATA S1_REPORT OF THE HEALTH FACILITY SURVEY IN BOTSWANA, 2007-08 and DATA S2_REPORT OF THE HEALTH FACILITY SURVEY IN TANZANIA, 2003</p> <p>PDF files of Health Facility Survey reports that were found on the internet but have since been taken down.</p> <p>DATA S3_BURKINA FASO CHART BOOKLET, 2015</p> <p>PDF provided <span>Drs. Sophie Sarrassat (London School of Hygiene and Tropical Medicine) and Serge M. A. Somda (Université Nazi BONI).</span></p> <p>WORKBOOK S2_CLASSIFICATION PROFILES: INFANTS; WORKBOOK S3_CLASSIFICATION PROFILES: CHILDREN IN UPPER MIDDLE-INCOME COUNTRIES; WORKBOOK S4_CLASSIFICATION PROFILES: CHILDREN IN LOWER MIDDLE-INCOME COUNTRIES (I); WORKBOOK S5_CLASSIFICATION PROFILES: CHILDREN IN LOWER MIDDLE-INCOME COUNTRIES (II), and WORKBOOK S6_CLASSIFICATION PROFILES: CHILDREN IN LOW INCOME COUNTRIES </p> <p>The design of the worksheets in these workbooks is described in TEXT S1_NOTES OF THE CONSTRUCTION OF CLASSIFICATION PROFILES (see below).</p> <p>WORKBOOK S7_IMCI CLINICAL TASKS</p> <p>Lists the clinical tasks provided by relevant IMCI algorithms for the care of infants and children. Consists of 6 worksheets covering mandatory tasks, conditional assessments, and treatment and counseling tasks for infants and children.</p> <p>WORKBOOK S8_MINUTES PER TASK WITH NO MINIMUM DURATION</p> <p>Provides estimate of the mean time required to perform a task with no minimum duration for each of 7 populations for which the required data are available, corrected, where necessary, for the effect of an observer on the rate and pace of task performance. Also provides a geometric mean for all 7 populations.</p> <p>TEXT S1_NOTES ON METHODOLOGY</p> <p>WORD document describing the steps involved in estimating the expected durations of consultations.</p> <p>TEXT S2_NOTES OF THE CONSTRUCTION OF CLASSIFICATION PROFILES</p> <p>WORD document describing the steps involved in constructing each profile, problems encountered, and how they were solved.</p> <p>TEXT S3_NOTES ON THE IDENTIFICATION OF IMCI CLINICAL TASKS</p> <p>WORD document describing the standards used in identifying clinical tasks in IMCI algorithms.</p> <p>TEXT S4_NOTES ON THE ESTIMATION OF MINUTES PER TASK WITH NO MINIMUM DURATION</p> <p>WORD document describing the steps involved in estimating the mean time required to perform a task with no predefined minimum duration, problems encountered, and how they were solved.</p>
Normalized NMR integration values from the metabolomic analysis of Drosophila larvae extracts from 2 genotypes at 3 time points.
<p>We measured the metabolites related to energy production using 1H nuclear magnetic resonance spectroscopy (NMR). No alterations in the levels of carbohydrate stores or free amino acids were found between control and Sema1ai animals, corroborating the notion that the main metabolic changes are in the lipid metabolism. The exception is the glycolytic amino acid alanine (elevated in Sema1ai animals), confirming alterations in glycolysis. The levels of the ß-alanine amino acid are markedly reduced in 256 h AEL or 10.5-day-old Sema1ai animals, probably indicating muscle degeneration in the severely obese larvae that is consistent with the deteriorated state and reduced movement of the 10-day-old (256 hours) mutant larvae. Gluconeogenesis is stimulated by high lactate, and the concentration of lactate is higher in Sema1ai larvae than controls, though the difference is not statistically significant. Glycolysis is stimulated by glucose and inhibited by citrate, an early intermediate of the citric acid cycle. The increased citrate levels in the 10.5-day-old Sema1ai larvae suggest that glycolysis is lower at this age, consistent with the increased level of glucose in the severely obese larvae. The fact that both gluconeogenesis and glycolysis pathways are simultaneously enhanced in Sema1ai larvae support the hypothesis that the animals defecting in adiposity signaling are in a state of perceived energy insufficiency despite having sufficient energy stored.</p>
Machine Learning Integrated High Quantum Yield Blue Light Carbon Dots for Real-time and On-site Detection of Cr(VI) in Groundwater and Drinking Water
<p>RGB和Kmeans提取后含有Cr(VI)水样的图像数据</p>
In the right place, at the right time: the integration of bacteria into the Plankton Ecology Group model
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Data from: Exceptional fossils from Peru and an integrative phylogeny reconcile the evolutionary timing and mode of Gavialis and its kin
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Integrating floral trait and flowering time distribution patterns help reveal a more dynamic nature of co-flowering community assembly processes
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Morphological integration, canalization, and plasticity in response to emergence time in Abutilon theophrasti
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Time-integrated δ²H in n-alkanes and carbohydrates from boreal needles reveal intra-annual physiological and environmental signals
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[Model outputs] Identifying major hydrologic change drivers in a highly managed transboundary endorheic basin: integrating hydro‐ecological models and time‐series data mining techniques
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Analysis data for ""Integration of time-series meta-omics data reveals how microbial ecosystems respond to disturbance""
<p>Analysis data for the manuscript: "Integration of meta-omics data reveals how microbial ecosystems respond to disturbance"</p> <p>Files used with the repository: https://git-r3lab.uni.lu/malte.herold/laots_niche_ecology_analysis/</p> <p>The archive was split into multiple parts for uploading to zenodo which need to be joined in order to extract the files:</p> <pre><code class="language-bash">cat resultsdir_laots.tar.gz.part_* > resultsdir_laots.tar.gz tar xvfz resultsdir_laots.tar.gz</code></pre> <p>Version 2 contains additional files generated in the revision.</p> <p> </p>
Data from: Postglacial recolonization of North America by spadefoot toads: integrating niche and corridor modeling to study species' range dynamics over geologic time
<p>Understanding the factors that shape species' distributions is a key topic in biogeography. As climates change, species can either cope with these changes through evolution, plasticity or by shifting their ranges to track the optimal climatic conditions. Ecological niche modeling (ENM) is a widespread technique in biogeography that estimates the niche of the organism by using occurrences and environmental data to estimate species' potential distributions. ENMs are often criticized for failing to take species' dispersal abilities into consideration. Here, we attempt to fill this gap by combining ENMs with dispersal and corridor modeling to study the range dynamics of North American spadefoot toads (Scaphiopodidae) over the Holocene. We first estimated the current and past distributions of spadefoot toads and then estimated their past distributions from the Last Glacial Maximum (LGM) to the present day. Then, we estimated how each taxon recolonized North American by using dispersal and corridor modeling. By combining these two modeling approaches we were able to 1) estimate the LGM refugia used by the North American spadefoot toads, 2) further refine these projections by estimating which of the putative LGM refugia contributed to the recolonization of North America via dispersal, and 3) estimate the relative influence of each LGM refugium to the current species' distributions. The models were tested using previously published phylogeographic data, revealing a high degree of congruence between our models and the genetic data. These results suggest that combining ENMs and dispersal modeling over time is a promising approach to investigate both historical and future species' range dynamics.</p>
Data from: Integration of Random Forest with population-based outlier analyses provides insight on the genomic basis and evolution of run timing in Chinook salmon (Oncorhynchus tshawytscha)
Anadromous Chinook salmon populations vary in the period of river entry at the initiation of adult freshwater migration, facilitating optimal arrival at natal spawning. Run timing is a polygenic trait that shows evidence of rapid parallel evolution in some lineages, signifying a key role for this phenotype in the ecological divergence between populations. Studying the genetic basis of local adaptation in quantitative traits is often impractical in wild populations. Therefore, we used a novel approach, Random Forest, to detect markers linked to run timing across 14 populations from contrasting environments in the Columbia River and Puget Sound, USA. The approach permits detection of loci of small effect on the phenotype. Divergence between populations at these loci was then examined using both principle component analysis and FST outlier analyses, to determine whether shared genetic changes resulted in similar phenotypes across different lineages. Sequencing of 9107 RAD markers in 414 individuals identified 33 predictor loci explaining 79.2% of trait variance. Discriminant analysis of principal components of the predictors revealed both shared and unique evolutionary pathways in the trait across different lineages, characterized by minor allele frequency changes. However, genome mapping of predictor loci also identified positional overlap with two genomic outlier regions, consistent with selection on loci of large effect. Therefore, the results suggest selective sweeps on few loci and minor changes in loci that were detected by this study. Use of a polygenic framework has provided initial insight into how divergence in a trait has occurred in the wild.
Data from: Time-integrated habitat availability is a resource attribute that informs patterns of use in intertidal areas
In dynamic environments, resource availability may change by several orders of magnitude, over hours to months, but the duration of resource availability is not often included as a characteristic attribute of resources even though temporal resource dynamics might limit patterns of use. In our study of wading birds foraging in intertidal areas, tides cause large changes in the areal extent of shallow-water foraging habitat (i.e., the resource), but tides also constrain the duration of availability, which is often overlooked. We hypothesized that temporal constraints on habitat availability from tides would be reflected in patterns of habitat use by foraging birds. We estimated the time-integrated habitat availability and compared it to traditional habitat attributes (seagrass cover, substrate type, instantaneous water depth, and proximity to mangrove islands or deep water) that have strong evidential support for influencing patterns of use. To evaluate our hypotheses, we quantified habitat attributes at intertidal areas in the Florida Keys, U.S.A, where wading birds were observed foraging (Little Blue Heron, Egretta caerulea: N=183; Great White Heron, Ardea herodias occidentalis: N=162). We tested for non-random use by sampling habitat attributes at two spatial scales around the observed feeding locations and we analyzed the data using a conditional logistic regression model. There was no evidence that seagrass cover or substrate explained patterns of use. The proximity of foraging locations relative to mangroves and to deep water were important at both spatial scales but had lower effect sizes (odds-ratios) than time-integrated habitat availability and water depth, and the latter may only serve as a physical constraint on access. We found support that time-integrated habitat availability was a distinct resource attribute, had the greatest effect size (4-8-fold change in relative probability of use), and best explained patterns of habitat use at the largest spatial scale. In studies of resource use where changes in resource availability are non-linear or when strong constraints on access are imposed by behavior, incorporating time-integrated estimates of resource availability into analyses can improve insights into spatiotemporal patterns of resource use.
Plasma ion-line data from HAARP with varying integration times
<p>This data set contains movies of plasma ion-line signals from HAARP acquired using a UHF diagnostic radar.<br>The movies use differnt integration times for the same raw data set.</p>
Data from: Trapped within the city: Integrating demography, time since isolation and population-specific traits to assess the genetic effects of urbanization
Urbanization is a severe form of habitat fragmentation that can cause many species to be locally extirpated and many others to become trapped and isolated within an urban matrix. The role of drift in reducing genetic diversity and increasing genetic differentiation is well recognized in urban populations. However, explicit incorporation and analysis of the demographic and temporal factors promoting drift in urban environments are poorly studied. Here, we genotyped 15 microsatellites in 320 fire salamanders from the historical city of Oviedo (Est. 8th century) to assess the effects of time since isolation, demographic history (historical effective population size; Ne) and patch size on genetic diversity, population structure and contemporary Ne. Our results indicate that urban populations of fire salamanders are highly differentiated, most likely due to the recent Ne declines, as calculated in coalescence analyses, concomitant with the urban development of Oviedo. However, urbanization only caused a small loss of genetic diversity. Regression modelling showed that patch size was positively associated with contemporary Ne, while we found only moderate support for the effects of demographic history when excluding populations with unresolved history. This highlights the interplay between different factors in determining current genetic diversity and structure. Overall, the results of our study on urban populations of fire salamanders provide some of the very first insights into the mechanisms affecting changes in genetic diversity and population differentiation via drift in urban environments, a crucial subject in a world where increasing urbanization is forecasted.
An Integrated Framework for Real-time Intelligent Traffic Management of Smart Highways
<p>Raw data for the paper "An Integrated Framework for Real-time Intelligent Traffic Management of Smart Highways".</p>
Assessment of Agreement Between Two Measurement Methods of Left Ventricular Outflow Tract (LVOT) Velocity Time Integral (VTI)
ClinicalTrials.gov study NCT04360304. IPD Sharing: NO. Countries: 1. Publications: 1.
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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)
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.