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40 results for “time lag”
Figure 4. (a) Therapy player software screen, where a) is the stimuli time, b) is the total therapy time, c) is the file path, d) displays the numeric values of each sequence of the therapy, e) shows the current value, and f) shows the current lag angle for zenith and azimuth values; (b) USB mechanism for conversion, where a) USB-UART converter, and b) USB-Zigbee converter.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>Where tt time expended by the servomotors to point the laser to a given position and execute<br> a laser beam sequence; tspin is the time that a servomotor needs to spin one degree; ttol is a given the<br> tolerance time; θservo is the addition of degrees that both servos in a laser driver need to spin point<br> the laser in a given position; tstimuli is the time expended in execute a laser beam, between 250 and<br> 605 ms (Weiskrantz et al., 1991); T is the total time of all repetitions in a therapy, suggested<br> between 20 and 60 minutes and N is the number of repetitions in a therapy.</p>
Reproduction package for the paper 'Evidence for a dynamic corona in the short-term time lags of black hole X-ray binary MAXI J1820+070'
<p>This is a basic reproduction package for the paper 'Evidence for a dynamic corona in the short-term time lags of black hole X-ray binary MAXI J1820+070', Bollemeijer et al., 2024, MNRAS, 528, 558-576.</p> <ul> <li>This reproduction package aims for open science, with the internal API designation of 'Gold'.</li> <li>Authors: Niek Bollemeijer, Phil Uttley, Arkadip Basak, Adam Ingram, Jakob van den Eijnden, Kevin Alabarta, Diego Altamirano, Zaven Arzoumanian, Douglas J.K. Buisson, Andrew C. Fabian, Elizabeth Ferrara, Keith Gendreau, Jeroen Homan, Erin Kara, Craig Markwardt, Ronald A. Remillard, Andrea Sanna, James F. Steiner, Francesco Tombesi, Jingyi Wang, Yanan Wang and Abderahmen Zoghbi</li> <li>Paper DOI: https://doi.org/10.1093/mnras/stad3912</li> <li>Arxiv DOI: https://doi.org/10.48550/arXiv.2312.09835</li> <li>Published in the Monthly Notices of the Royal Astronomical Society (date of acceptance: 2023/12/12)</li> </ul> <h2>Raw Data</h2> <ul> <li>Raw event files for the described NICER observations can be obtained from the HEASARC at https://heasarc.gsfc.nasa.gov/cgi-bin/W3Browse/w3browse.pl. Select NICER as the telescope and search for MAXI_J1820+070.</li> <li>We used HEASoft v6.28 with standard reprocessing settings to obtain event lists to make light curves. See paper for details.</li> </ul> <h2>Software</h2> <ul> <li>Linux Ubuntu 22.04.</li> <li>Jupyter Notebook (7.0.7)</li> <li>Programming languages used: Python (3.12.1)</li> <li>Python packages used: numpy (1.26.3), matplotlib (3.8.2), scipy (1.12.0), astropy (6.0.0)</li> </ul> <h2>Figures and Tables</h2> <ul> <li>Figures can be reproduced from the ./figures/ folder.</li> <li>All material and data used are available as intermediate data products.</li> <li>Jupyter notebooks (.ipynb files) can be used to make all figures. Running all cells at once does not work, but you can choose the figure you want to remake and executing the relevant cells will result in those figures.</li> </ul> <h2>Intermediate data products</h2> <ul> <li>The light curve arrays that are made in the first few cells of the main Jupyter Notebook can be found in 'datafiles.zip'. </li> <li>The parameters for the Lorentzian fits of the power spectra and the grouping of different observations can be found in 'qpofitsc.txt' and 'obsidsa.txt', respectively, in the same zipped folder.</li> </ul> <h2>End-to-End analysis scripts</h2> <ul> <li>The three Jupyter notebooks that have been added can be used to make the figures and reproduce the main results of the paper. Evidence_for_a_dynamic_corona_main.ipynb is about the main body of the paper, Evidence_for_a_dynamic_corona_energy_bands.ipynb is used for a part of the Discussion involving multiple narrow energy bands and Evidence_for_a_dynamic_corona_sim.ipynb is about the simulations described in Appendix A.</li> </ul>
Data and scripts belonging to "Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape"
<p>Data and scripts belonging to Kaandorp et al., 2021 "Time lags of nitrate, chloride, and tritium in streams assessed by dynamic groundwater flow tracking in a lowland landscape". Hydrology and Earth System Sciences. </p>
A time-lagged association between the gut microbiome, nestling weight and nestling survival in wild great tits
<ol> <li>Natal body mass is a key predictor of viability and fitness in many animals. While variation in body mass and therefore viability of juveniles may be explained by genetic and environmental factors, emerging evidence points to the gut microbiota as an important factor influencing host health. The gut microbiota is known to change during development, but it remains unclear whether the microbiome predicts fitness, and if it does, at which developmental stage it affects fitness traits.</li> <li>We collected data on two traits associated with fitness in wild nestling great tits (<i>Parus major</i>): weight and survival to fledging. We characterised the gut microbiome using 16S rRNA sequencing from nestling faeces and investigated temporal associations between the gut microbiome and fitness traits across development at day 8 (D8) and day 15 (D15) post-hatching. We also explored whether particular microbial taxa were 'indicator species' that reflected whether nestlings survived or not.</li> <li>There was no link between mass and microbial diversity on D8 or D15. However, we detected a time-lagged relationship whereby the microbial diversity at D8 was negatively associated with weight at D15, while controlling for weight at D8. Indicator species analysis revealed that while several taxa were unique to birds that either survived or did not survive, there were no universal taxa that were consistently found across all birds within either survival group. This suggests that the presence of particular bacterial taxa may be sufficient, but not necessary for determining future survival, perhaps owing to functional overlap in microbiota.</li> <li>We highlight that measuring microbiome-fitness relationships at just one time point may be misleading, especially early in life. Instead, microbial-host fitness effects should be investigated longitudinally as there may be critical development windows in which key microbiota are established and prime host traits associated with nestling weight. Pinpointing which features of the gut microbial community impact on host fitness, and when during development this occurs, will shed light on population level processes and has the potential to support conservation.</li> </ol>
Data from: Time-lagged effects of weather on plant demography: drought and Astragalus scaphoides
Temperature and precipitation determine the conditions where plant species can occur. Despite their significance, to date, surprisingly few demographic field studies have considered the effects of abiotic drivers. This is problematic because anticipating the effect of global climate change on plant population viability requires understanding how weather variables affect population dynamics. One possible reason for omitting the effect of weather variables in demographic studies is the difficulty in detecting tight associations between vital rates and environmental drivers. In this paper, we applied Functional Linear Models (FLMs) to long-term demographic data of the perennial wildflower, Astragalus scaphoides, and explored sensitivity of the results to reduced amounts of data. We compared models of the effect of average temperature, total precipitation, or an integrated measure of drought intensity (Standardized Precipitation Evapotranspiration Index, SPEI), on plant vital rates. We found that transitions to flowering and recruitment in year t were highest if winter/spring of year t was wet (positive effect of SPEI). Counterintuitively, if the preceding spring of year t-1 was wet, flowering probabilities were decreased (negative effect of SPEI). Survival of vegetative plants from t-1 to t was also negatively affected by wet weather in the spring of year t-1, and for large plants, even wet weather in the spring of t-2 had a negative effect. We assessed the integrated effect of all vital rates on life history performance by fitting FLMs to the asymptotic growth rate, log(λt). Log(λt) was highest if dry conditions in year t-1 were followed by wet conditions in the year t. Overall, the positive effects of wet years exceeded their negative effects, suggesting that increasing frequency of drought conditions would reduce population viability of A. scaphoides. The drought signal weakened when reducing the number of monitoring years. Substituting space for time did not recover the weather signal, probably because the weather variables varied little between sites. We detected the SPEI signal when the analysis included data from two sites monitored over 20 years (2x20 observations), but not when analyzing data from four sites monitored over 10 years (4x10 observations).
Roadside diversity in relation to age and surrounding source habitat: evidence for long time lags in valuable green infrastructure
<p>1. The severe and ongoing decline in semi-natural grassland habitat during the past two centuries means that it is important to consider how other, marginal grassland habitat elements can contribute to landscape-level biodiversity, and under what circumstances.</p> <p>2. To examine how habitat age and the amount of core grassland habitat in the surrounding landscape affect diversity in green infrastructure, we carried out inventories of 36 rural road verges that were either historical (pre-1901) or modern (established post-1901 and before 1975), and were surrounded by relatively high (>15%) or low (<5%) levels of grassland habitat. We recorded the number of plant species, grassland specialists, grassland conservation species and the fraction of the landscape's species and specialists found in the road verge.</p> <p>3. Road verge communities were characterised by high levels of grassland specialist species (35% of the 161 species recorded), with road verge sites supporting 15-20% of the specialist species found in the surrounding 25 km2 landscape.</p> <p>4. Richness of species and specialists were more closely related to road age than to the amount of surrounding habitat. Higher diversity in historical roads, despite the majority of modern roads being at least 60 years old, suggests a long time lag in the establishment of grassland communities in marginal grassland habitats. We identified no effect of historical surrounding land use on present day diversity in road verges.</p> <p>5. Road verge richness was not affected by the amount of surrounding grassland. This could be due to the relatively low amounts of grassland remaining in all landscapes, together with dispersal limitation commonly found in grassland plant communities contributing to a potential time lag.</p> <p>6. We identified road verges as potentially very important habitats for grassland communities. Because of the high levels of grassland specialists present, these and other marginal grasslands and grassland green infrastructure should be explicitly considered in landscape-scale conservation management. Practitioners looking to identify the most species-rich road verges should aim to find the oldest possible, while long time lags in community assembly suggests that seed sowing may be appropriate to enhance roadside diversity, even in decades-old road verges.</p>
Metamorphosed mélange in eastern Himalayan syntaxis: An implication for a time lag of India-Asia collision
<p>This supporting information provides Text S1 that includes a detailed description of analytical methods, four supplemental figures, and three supplemental tables.</p>
Data from: Accumulating time lags across biodiversity levels following land-use change
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Data from: Vegetation growth responses to climate change: A cross-scale analysis of biological memory and time-lags using tree ring and satellite data
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A time-lagged association between the gut microbiome, nestling weight and nestling survival in wild great tits
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A non-equilibrium species distribution model reveals unprecedented depth of time lag responses to past environmental change trajectories
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Data from: Time-lagged effects of weather on plant demography: drought and Astragalus scaphoides
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Data from: Roadside diversity in relation to age and surrounding source habitat: evidence for long time lags in valuable green infrastructure
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A spatial genomic approach identifies time lags and historic barriers to gene flow in a rapidly fragmenting Appalachian landscape
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Fig. 3. Time lag between the 1 in How long does it take to discover a species?
Fig. 3. Time lag between the 1st specimen collected, the 15th specimen collected and the moment when at least 15 specimens are correctly determined for Aframomum. Each line represents a different species in the genus. Average time lag to accumulate 15 collections of Aframomum species is 60.5 years, whereas it takes, on average, another four decades to accumulate 15 correctly identified ones.
Data from: Large-scale genetic panmixia in the blue shark (Prionace glauca): a single worldwide population, or a genetic lag-time effect of the "grey zone" of differentiation?
The blue shark Prionace glauca, among the most common and widely studied pelagic sharks, is a top predator, exhibiting the widest distribution range. However, little is known about its population structure and spatial dynamics. With an estimated removal of 10 to 20 million individuals per year by fisheries, the species is classified as "Near Threatened" by International Union for Conservation of Nature. We lack the knowledge to forecast the long-term consequences of such a huge removal on this top predator itself and on its trophic network. The genetic analysis of more than 200 samples collected at broad scale (from Mediterranean Sea, North Atlantic and Pacific Oceans) using mtDNA and nine microsatellite markers allowed to detect signatures of genetic bottlenecks but a nearly complete genetic homogeneity across the entire studied range. This apparent panmixia could be explained by a genetic lag-time effect illustrated by simulations of demographic changes that were not detectable through standard genetic analysis before a long transitional phase here introduced as the "population grey zone". The results presented here can thus encompass distinct explanatory scenarios spanning from a single demographic population to several independent populations. This limitation prevents the genetic-based delineation of stocks and thus the ability to anticipate the consequences of severe depletions at all scales. More information is required for the conservation of population(s) and managements of stocks, which may be provided by large scale sampling not only of individuals worldwide, but also of loci genome-wide.
Data from: Time-lag in responses of birds to Atlantic Forest fragmentation: restoration opportunity and urgency
There are few opportunities to evaluate the relative importance of landscape structure and dynamics upon biodiversity, especially in highly fragmented tropical landscapes. Conservation strategies and species risk evaluations often rely exclusively on current aspects of landscape structure, although such limited assumptions are known to be misleading when time-lag responses occur. By relating bird functional-group richness to forest patch size and isolation in ten-year intervals (1956, 1965, 1978, 1984, 1993 and 2003), we revealed that birds with different sensitivity to fragmentation display contrasting responses to landscape dynamics in the Brazilian Atlantic Forest. For non-sensitive groups, there was no time-lag in response: the recent degree of isolation best explains their variation in richness, which likely relates to these species' flexibility to adapt to changes in landscape structure. However, for sensitive bird groups, the 1978 patch area was the best explanatory variable, providing evidence for a 25-year time-lag in response to habitat reduction. Time-lag was more likely in landscapes that encompass large patches, which can support temporarily the presence of some sensitive species, even when habitat cover is relatively low. These landscapes potentially support the most threatened populations and should be priorities for restoration efforts to avoid further species loss. Although time-lags provide an opportunity to counteract the negative consequences of fragmentation, it also reinforces the urgency of restoration actions. Fragmented landscapes will be depleted of biodiversity if landscape structure is only maintained, and not improved. The urgency of restoration action may be even higher in landscapes where habitat loss and fragmentation history is older and where no large fragment remained to act temporarily as a refuge.
Evidence of time-lag in the provision of ecosystem services by tropical regenerating forests to coffee yields
<p>Abstract Restoration of native tropical forests is crucial for protecting biodiversity and ecosystem functions, such as carbon stock capacity. However, little is known about the contribution of early stages of forest regeneration to crop productivity through the enhancement of ecosystem services, such as crop pollination and pest control. Using data from 610 municipalities along the Brazilian Atlantic Forest (30 m spatial resolution), we evaluated if young regenerating forests (less than 20 years old) are positively associated with coffee yield and whether such a relationship depends on the amount of preserved forest in the surroundings of the coffee fields. We found that regenerating forest alone was not associated with variations in coffee yields. However, the presence of young regenerating forest (within a 500 m buffer) was positively related to higher coffee yields when the amount of preserved forest in a 2 km buffer is above a 20% threshold cover. These results further reinforce that regional coffee yields are influenced by changes in biodiversity-mediated ecosystem services, which are explained by the amount of mature forest in the surrounding of coffee fields. We argue that while regenerating fragments may contribute to increased connectivity between remnants of forest fragments and crop fields in landscapes with a minimum amount of forest (20%), older preserved forests (more than 20 years) are essential for sustaining pollinator and pest enemy's populations. These results highlight the potential time lag of at least 20 years of regenerating forests' in contributing to the provision of ecosystem services that affect coffee yields (e.g., pollination and pest control). We emphasize the need to implement public policies that promote ecosystem restoration and ensure the permanence of these new forests over time.</p>
Data from: Observed and dark diversity dynamics over millennial time scales: Fast-life history traits linked to expansion lags of plants in northern Europe
<p>Global change drivers (e.g. climate and land use) affect the species and functional traits observed in a local site but also its dark diversity—the set of species and traits locally suitable but absent. Dark diversity links regional and local scales and, over time, reveals taxa under expansion lags by depicting the potential biodiversity that remains suitable but is absent locally. Since global change effects on biodiversity are both spatially and temporally scale dependent, examining long-term temporal dynamics in observed and dark diversity would be relevant to assessing and foreseeing biodiversity change. Here, we used sedimentary pollen data to examine how both taxonomic and functional observed and dark diversity changed over the past 14500 years in northern Europe. We found that taxonomic and functional observed and dark diversity increased over time, especially after the Late Glacial and during the Late Holocene. However, dark diversity dynamics revealed expansion lags related to species' functional characteristics (dispersal limitation and stress intolerance) and an extensive functional redundancy when compared to taxa in observed diversity. We highlight that assessing observed and dark diversity dynamics is a promising tool to examine biodiversity change across spatial scales, its possible causes, and functional consequences.</p>
Data from: Respiration shapes response speed and accuracy with a systematic time lag
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Allen Brain Atlas
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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.
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