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2,885 results for “transfers”
Efficient Drug-Target Interactions Prediction Framework via Transferable Knowledge Fusing
<p><span>Drug-target integrations (DTI) prediction is a niche in drug discovery, streamlining the search for potential drugs. Computer-aided drug discovery (CADD) has gained traction for its precise predictions, efficiency, and adaptability across various situations. Yet, the computational demands of current top CADD models hinder their practical use due to heavy resource needs.</span></p> <p><span>In this research, we introduce TransFusE DTI, an effective framework for predicting DTIs that leverages pre-trained knowledge to construct models that optimize predictive accuracy while minimizing computational demands. The encoder uses a pre-extracted embedding vector from ProtBERT to reduce computational load and adapts a smaller ProtBERT model. It also includes target-related functional text to boost predictive accuracy. We evaluate the performance of TransFusE DTI using three widely-recognized benchmark datasets: BIOSNAP, DAVIS, and BindingDB, and compare its results to prior studies.</span></p> <p><span>Our results demonstrate that TransFusE DTI exhibits superior predictive performance on the BIOSNAP and BindingDB datasets. Notably, the model's parameter count is only 60% of that of the previous top-performing model by </span><span><a href="https://www.mdpi.com/1999-4923/14/8/1710"><span>Kang et al. (2022)</span></a></span><span>, and it operates efficiently with a learning rate of 26%. Furthermore, the model's video memory requirement is 11.2 GB, rendering it suitable for use on general-purpose Graphics Processing Units (GPUs). </span></p>
Long-distance electron transfer along redox gradients in the subsurface
<p>Electron transfer (ET) is the fundamental process of life and element cycling. In the subsurface, the distance of ET determines its influencing area, normally as short as nanometers or micrometers. Here we provide direct evidence from column experiments that ET can last over 10 cm along the redox gradient in sediment columns, through a long-distance ET chain constituted by a series of short-distance electron hopping reactions involving with microbes and redox-active species like iron ions (and/or minerals) and natural organic matters. Microbial processes contribute largely under biologically active conditions, while chemical processes are important under extremely acidic/alkaline and cold conditions. Microbial and chemical processes synergistically mediate the long-distance ET process, with an estimated electron flux of 6.73 μmol e<sup>-</sup>/cm<sup>2</sup> per day. This long-distance ET represents an overlooked but important background process influencing subsurface biogeochemical processes and remediation activities.</p>
The international transfer of emission allowances scenario
<p>The model output data for the international transfer of emission allowances assessment.</p>
First-principles simulations of exciton transfer between N-heterocyclic carbene iridium (III) complexes in blue organic light-emitting diodes
<p>N-heterocyclic carbene (NHC) iridium (III) complexes are promising for the use as blue emitters in organic light-emitting diodes. Exciton transfer between such organometallic complexes is investigated using time-dependent density functional theory calculations. Casida's equation is solved to study absorption and emission of the neutral and charged complexes using the ORCA package. The Sternheimer equation implemented in the Octopus code is extended to take into account spin-orbit coupling and is applied to investigate triplet excitations. Real-time propagation as implemented in the Octopus code is used to simulate exciton dynamics in an emitter dimer and to extract the exciton coupling via explicit integration of transition densities.</p>
Engineering the Compositional Architecture of Core-Shell Upconverting Lanthanide-Doped Nanoparticles for Optimal Luminescent Donor in Resonance Energy Transfer: The Effects of Energy Migration and Storage
<p>Förster Resonance Energy Transfer (FRET) between single molecule donor (D) and acceptor (A) is well understood from fundamental perspective and is widely applied in biology, biotechnology, medical diagnostics and bio-imaging. However, the reliability of molecular FRET measurements can be affected by numerous artefacts which eventually hamper quantitative and reliable analysis, mostly due to issues with the donor and acceptor molecules. Lanthanide doped upconverting nanoparticles (UCNPs) have demonstrated their suitability as alternative donor species. Nevertheless, while they solved most disadvantageous features of organic donor molecules, such as photo-bleaching, spectral cross-excitation and emission bleed-through, the fundamental understanding and practical realizations of bio-assays with UCNP donors remain challenging. Among others, the actual donor ions in individual donor UCNPs are the numerous activator ions randomly distributed in the nanoparticle at various distances to acceptors anchored on the nanoparticle surface. Further, the power dependent, complex energy transfer upconversion and energy migration between sensitizing and activating lanthanide ions within UCNPs complicate the decay based analysis of <strong><em>D</em></strong>-<strong><em>A</em></strong> interaction. In this work, the assessment of designed virtual core-shell nanoparticle (VNP) models led us to the new designs of UCNPs, such as …@Er, Yb@Er, Yb@YbEr, which were experimentally evaluated as donor nanoparticles and compared to the simulations. Moreover, the specific properties of lanthanide-based upconversion motivated us to analyze not only steady-state luminescence and luminescence decay responses of both the UNCP donor and the sensitized acceptor, but also the effects of their luminescence rise kinetics upon RET was discussed in newly proposed disparity measurements. The presented studies help to understand the role of energy-transfer and energy migration between lanthanide ion dopants (due to their concentration and spatial distribution) and how the architecture of core-shell UCNPs affects their performance as FRET donors to organic acceptor dyes.</p>
Additional Resources for End-user Comprehension of Transfer Risks in Smart Contracts
<p>Google Docs versions of most of the PDFs can be found on https://linktr.ee/tethersurvey</p> <p> </p> <p>Additionally, a survey data visualizer is found on https://tether-survey.onrender.com/</p> <p> </p>
Additional Resources for Understanding End-User Perception of Transfer Risks in Smart Contracts
<p>This contains further resources for the work titled "Understanding End-User Perception of Transfer Risks in Smart Contracts", which is set to appear in CHI 2025.</p> <p>This work details an investigation into user understanding of transfer risks in ERC-20 blockchain smart contracts. An example transfer risk is a user being unable to transfer due to their account being blacklisted by the owner of the contract.</p> <p>A large portion of this work focuses on the most popular Ethereum smart contract (USD Tether). This details responses to a 110-participant survey on smart contract users, establishing their knowledge of transfer risks and various other perceptions. Included also are the results of statistical tests on these responses, the follow-up message to the respondents and more.</p> <p>Another portion of this work investigates the presence of transfer risks in other top ERC-20 contracts. The results of this investigation is also found here.</p> <p>This also includes results of blockchain analytics to establish the top ERC-20 addresses, as well as code used for processing.</p>
Changes in phytoplankton size-structure alter trophic-transfer in a temperate, coastal planktonic food-web
<p>Datasets, Metadata, Figures and Matlab codes used to produce the figures used in "Changes in phytoplankton size-structure alter trophic-transfer in a temperate, coastal planktonic food-web"</p>
Choosing predictors and complexity for ecosystem distribution models: effects on performance and transferability
<p>There is an increasing need for ecosystem-level distribution models (EDMs) and a better understanding of which factors affect their quality. We investigated how the performance and transferability of EDMs are influenced by (1) the choice of predictors, and (2) model complexity. We modelled the distribution of 15 pre-classified ecosystem types in Norway using 252 predictors gridded to 100 m × 100 m resolution. The ecosystem types are major types in the "Nature in Norway" system mainly defined by rule-based criteria such as whether soil or specific functional groups (e.g., trees) are present. The predictors were categorised into four groups, of which three represented proxies for natural, anthropogenic, or terrain processes ('ecological predictors') and one represented spectral and structural characteristics of the surface observable from above ('surface predictors'). Models were generated for five levels of model complexity. Model performance and transferability were evaluated with data collected independently of the training data. We found that (1) models trained with surface predictors only, performed considerably better and were more transferable than models trained with ecological predictors, and (2) model performance increased with model complexity, levelling off from around 10 parameters and reaching a peak around 20 parameters, while model transferability decreased with model complexity. Our findings support that surface predictors enhance EDM performance and transferability, most likely because they represent discernible surface characteristics of the ecosystem types. A poor match between the rule-based criteria that define the ecosystem types and the ecological predictors, which represent ecological processes, is a plausible explanation for why surface predictors better predict the distribution of ecosystem types. Our results indicate that, in most cases, the same models are not well suited focontrasting purposes, such as predicting where ecosystems are and explaining why they are there.</p>
Data from: "Cross-realm transferability of species distribution models – species characteristics and prevalence matter more than modelling methods applied"
<h2>Abstract</h2> <p>This data contains occurrence observations (presence-absence) of 11 aquatic macrophytes from Bothnian Sea and Lake Puruvesi, and environmental covariates used to build species distribution models (SDMs) in paper "Cross-realm transferability of species distribution models – species characteristics and prevalence matter more than modelling methods applied" in Ecological Modelling.</p> <p>In addition to data files, also R code for fitting the SDMs is supplied, as is the R code to replicate the analysis conducted in the paper. The data is stored in rdata format (point data), without coordinate information due to data policy restrictions. The species in the data are <em>Isoëtes lacustris, Isoëtes echinospora, Ranunculus reptans, Ranunculus schmalhausenii, Potamogeton berchtoldii, Potamogeton perfoliatus, Potamogeton gramineus, Myriophyllum alterniflorum, Equisetum fluviatile, Eleocharis acicularis and Elodea canadensis. </em>The environmental covariates are bottom water salinity, turbidity, sandy substrate occurrence, colored dissolved organic matter (CDOM), surface fetch, sampling depth, total nitrogen and total phosphorus, and distance to closest <em>Phragmites australis </em>reed bed. </p> <h2>Objective of the study </h2> <p>The modelling objective of the paper was species distribution model (SDM) transferability assesment. Transferability was assessed using models built in marine areas in projecting the distributions of the target species in Lake Puruvesi, Saimaa, Eastern Finland. Macrophyte mapping data from Lake Puruvesi was used as independent test data, against which transferability of the models was assessed.</p> <h2>Location</h2> <p>The species data was collected from two geographic areas: Bothnian Bay (Baltic Sea) and Lake Puruvesi (Eastern Finland). The marine observations from Bothnian Bay were split into three overlapping areas (areas 1-3), to test the effect of input data gradient length to SDM transferability. The largest marine sampling area (Area 3) ranged from 62.95, 65.91 latitude and 19.14, 27.99 longitude. Area 2 ranged from 63.95, 65.91 latitude and 21.53, 27.99 longitude. Area 1 ranged from 64.91, 65.91 latitude and 23.82, 27.99 longitude. The Hummonselkä subbasin of Lake Puruvesi, where the macrophyte test data was collected, is located at 61.89, 62.05 latitude and 29.58, 29.78 longitude.</p> <h2>Species data </h2> <p>The species observations were collected using diving transects placed in the floor of the sea or lake, and species observations were recorded in 2 m22 grid cells separated by 10 meters along the transect or 1 meters depth, depending which criteria was met first. The species data was collected in 2010 - 2020 from marine area, and 2017 from Lake Puruvesi. All macrophytes in 2 x 1 m frames were identified to species level by the diver, and the data contained information on species presence or absence in each grid cell. The diving transects were conducted using systematic survey protocol used in the underwater inventories of the Finnish Underwater Biodiversity Survey Program (VELMU) (Frosblom & Virtanen et al. 2024). The locations of the diving transects were not randomly distributed, but were placed using expert judgement. As all our study species are macroscopic and relatively easily identifiable in the field (with the exception of possibility of mixing <em>I. echinospora</em> and <em>I. lacustris</em>), we consider the absences in our observation data to indicate true absences. That said, as the observation area is rather small (2 m2), it is possible that a species may be found in the site of investigation (e.g. a small lagoon) but be located outside the vegetation sampling grid.</p> <h2>Environmental data</h2> <h3>Bottom water salinity</h3> <p>The seasonal mean bottom water salinity was modeled using a generalized additive model with mean salinity as response, with log-link and gamma distribution for the errors. This was necessary to keep the resulting predictions positive. Bottom depth, CDOM, river influence and spatial location were used as predictors. Data from 448 locations were used and each location had a minimum of three observations. The model was validated using 30 % of the data left outside of the model fitting. The explained deviance of the model was 0.94 and the correlation between raw data and predicted values was 0.95 with few outliers.</p> <h3>Turbidity</h3> <p>Maps of turbidity (in FNU, Formazin Nephelometric Unit) were generated from Sentinel-2 Multi-Spectral Imager (MSI) observations using the Case-2 Regional Coast Colour (C2RCC) bio-optical inversion model, containing separate atmospheric correction and water quality parts. Before computing C2RCC, the original 10-meter input data was downsampled to 60 meters. The output variable of the C2RCC processor correlative to turbidity is the backscattering of total suspended sediments at 443 nm, which was further calibrated into turbidity (FNU) values using SYKE's empirical equations for coastal waters and clear lakes (for a similar approach, see Attila et al. (2013) and Sagerman, Hansen, and Wikström (2020)). Monthly observations of turbidity were aggregated into median composites to reduce the effects of cloud cover and other disturbances. Due to low solar elevation and ice cover in winter, the turbidity distribution maps are generated only for the summer months (May to September). The current processing covers years 2017 to 2021. An average raster layer was created from monthly observations as input for SDM building.</p> <h3>Probability of sandy substrate </h3> <p>Random forest model was used to classify sandy bottoms from Sentinel 2 MSI satellite images in shallow water areas. Identifying sandy substrate is based on the higher reflectance compared to other substrates. The model was trained and validated using diver recorded field observations in the Baltic area, and diver recorded and echo sounding observations in the freshwater area. For full coverage including areas beyond the shallow water, the satellite image classification was combined with boosted regression tree modelling result in the Baltic, and echo sounding based product in the freshwater region. The resulting layers were probabilities of sandy substrate with 10-meter cell resolution.</p> <h3>CDOM</h3> <p>We used different methods to estimate the CDOM levels in Bothnian Bay and Lake Puruvesi, based on biogeochemical model data and satellite images. For Lake Puruvesi, we applied the Finnish Environment Institute's (Syke) in-house CDOM algorithm to the Sentinel-2 MSI images processed by the C2RCC bio-optical processor (Brockmann et al. 2016). The observations in 10 m resolution were aggregated as monthly averages for each month of the summer season (May to October) from 2017 to 2021. For Bothnian Bay, we used Syke's in-house Sentinel-2 MSI CDOM layers (resolution: 60 m) aggregated as seasonal averages (1 Jul to 7 Sep). CDOM values are given as absorption coefficient of CDOM at 400 nm [m⁻¹].</p> <h3>Surface fetch</h3> <p>A surface fetch raster was produced to the Puruvesi and Bothnian Bay. The analysis required a feature layer of shorelines from Puruvesi and Baltic Sea. First, we created polyline from north to south spanning over the whole area of interest with a gap of 20 meters which is also the resolution of the output raster. These lines were then cut each time they hit the shoreline and the part of the line that was overlapping land was removed. The distance of the remaining lines was then calculated and a point with the distance value was created every 20 meters. Each time the line was cut when hitting an island for example and starting again from the other side of the island, the distance calculation started from 0. This created a point dataset with a distance value in each point. We repeated the procedure for 15 times for different compass directions with 22.5 degree intervals and calculated average fetch for each point location on 20 meters grid from these 15 point layers.</p> <h3>Depth</h3> <p>Depth was measured by a diver using a dive computer while surveying each vegetation grid cell, and measured depth was used when projecting model results to Puruvesi (transferability performance). In addition, a depth model for the freshwater region was created from Sentinel 2 MSI satellite image using the logarithmic band ratio model of blue and red band. The model was calibrated using diver recorded field observations and validated against echo sounding measurements. For more complete coverage and to include deep areas, echo soundings from multiple sources were combined with the satellite derived bathymetry. The cell resolution of the resulting depth layer was 10 meters.</p> <h3>Total nitrogen and phosphorus</h3> <p>Mean total nitrogen and phosphorus layers for marine area were produced using ArcGIS "splines with barriers" tool for the EEZ of Finland with 20 meters spatial resolution (Virtanen et al. 2018). Summer (July - September) nutrient measurements from 0 to 10 meters depth between 2010 and 2020, obtained from the VESLA database, were used as input data for the interpolation.</p> <p>Nitrogen and phosphorus measurements in Puruvesi between 2010 and 2020 was gathered from the VESLA database. Data from July to September was selected to represent the growing season. A mean value of NTOT and PTOT was then calculated for each location. Spline with Barriers (SwB) tool was used to interpolate the values (Arcmap 10.7.1). The tool uses a feature layer as barrier to create the raster representing only the area of interest. For the barrier and the extent of the interpolated raster we used a shapefile representing Lake Puruvesi shoreline. The resolution was set to 5x5 meters. SwB tool created an "extent box" around the area of interest which was removed with Extract by Mask tool using the shoreline feature layer. After the interpolation we noticed that either one of the locations was situated on land or the polygon used as barrier was "leaking". SwB doesn´t interpolate areas that doesn't have locations with values or aren´t connected to the main body of water. To fix this, the raster was extended outwards based on the values of nearby cells and after that the raster was masked again to remove any cells on land. The phosphorus interpolation provided negative values in southern parts on Enanlahti in Kontiolahti and Muholanlahti. These negative values were caused by considerably larger phosphorus values in Enanlahti Lamminniemi (9m) Enanlahti Lamminniemi (4m) locations when compared with the nearby Puruvesi Enanlahti location. The interpolation apparently continued to decrease the values according to the trend set by the difference between these locations and caused it to reach negative values. The southern parts of the bay, about 750 meters, was removed and new values were calculated based on the surrounding cells with Focal Statistics tool. The interpolations were validated by removing 20 % of the locations and reproducing the interpolation. The removed locations and their values were then compared to the interpolated raster. R∗2∗2 value from phosphorus interpolation model was 0.91 after removing two outliers and R22 value from nitrogen interpolation model was 0.715 after removing one outlier.</p> <h3>Distance to closest reed </h3> <p>The aquatic vegetation (<em>Phragmites australis</em> reeds) presence/absence maps were also generated from Sentinel-2 MSI data. The processing included extracting one month of data (July 2019) from green and near-infra-red bands from Sentinel-2 Global Mosaic (S2GM) service and transforming those to normalized-difference vegetation indices (NDVIs). After that, Bayesian statistics were used to predict the posterior probability of vegetation occurrence when distance from shore and NDVI were used as predictor variables. The posterior variable was thresholded and the resulting vegetation presence areas were sieved so that both too small vegetation areas (fewer than 5 pixels) or areas that were not directly attached to shoreline were removed. The resulting map has 10 m pixel size and tentatively represents the locations of reed belts or other shoreline-attached vegetation. This EO-based layer could also be referred to as helophytes or helophytic macrophytes, as it denotes a specific zone of vegetation with emergent aquatic plants containing leaf-green, particularly those that grow densely and have horizontally oriented leaves. In some lakes, this layer can represent, for example, thick stands of <em>Equisetum fluviatile</em>, although in most cases, it is associated with common reed belts. The approach is described in more detail in Koponen et al. (2022).</p> <h2>Data partitioning </h2> <p>Data was partitioned with 70/30 splitting into training and test (interpolation accuracy) data. The splitting was repeated 100 times for each species by randomly selecting 70 % of observations which were used to build each of the SDMs (GLM, GAM, BRT and BART). The partitioning was repeated for each of the three input data areas and 11 species. The input data indexes for replicating the split are supplied in the data files. </p> <h2>R code </h2> <p>Code files contain scripts for fitting the SDM models described in the paper using the data. Also code for calculating calidation statistics (modelling results) and code for statistical analyses for making inferences in the paper, are supplied. </p> <h2>Additional info</h2> <p>More details on modelling protocol may be found in the original paper in Ecological Modelling, and in the Supplementary Information file of the original paper, which follows the model reporting template "ODMAP" by Zurell et al. (2020).</p> <h2>References </h2> <p><span>Attila, J., et al. 2013. MERIS Case II water processor comparison on coastal sites of the northern Baltic Sea. - Remote Sensing of Environment 128: 138-149.</span></p> <p><span>Brockmann, C., et al. 2016. Evolution of the C2RCC neural network for Sentinel 2 and 3 for the retrieval of ocean colour products in normal and extreme optically complex waters. - In: Living Planet Symposium. p. 54.</span></p> <p><span>Forsblom, L., et al. 2024. Finnish inventory data of underwater marine biodiversity<span> </span>-Scientic data</span></p> <p><span>Koponen, S., et al. 2022. Blue Carbon Habitats: – a comprehensive mapping of Nordic salt marshes for estimating Blue Carbon storage potential. - Nordisk Ministerråd.</span></p> <p><span>Sagerman, J., et al. 2020. Effects of boat traffic and mooring infrastructure on aquatic vegetation: A systematic review and meta-analysis. - Ambio 49: 517-530.</span></p> <p><span>Zurell, D., et al. 2020. A standard protocol for reporting species distribution models. - Ecography 43: 1261-1277.</span></p> <p></p> <p> </p> <p> </p> <p> </p> <p> </p> <p> </p>
Dataset: Redox-Activated Proton Transfer through a Redundant Network in the Qo Site of Cytochrome bc1
<p>This dataset contains initial molecular configurations and an example script used with the pDynamo3 library to obtain the results published in the paper "Redox-Activated Proton Transfer through a Redundant Network in the Qo Site of Cytochrome bc1" by Guilherme M. Arantes (USP, Brazil).</p>
3D-Printed Pulsator to Enhance Mass Transfer in Electrochemical Reactors HardwareX submission
<p>This repository contains CAD drawing, data, Python codes, and Arduino sketch files for submission to HardwareX journal titled "3D-Printed Pulsator to Enhance Mass Transfer in Electrochemical Reactors". The CAD file can be open with FreeCAD 1.1.0dev or newer. Python codes were used to analyzed and generate figures from electrochemical data for the publication. Arduino sketch files were use for controlling the electrical motor used in the experimental setup for the publication.<br><br>The abstract of the publication is provided below.<br><br>This study presents a cost-effective diaphragm pulsator, constructed for approximately €500, designed to enhance mass transport in laboratory electrochemical reactors. The pulsator allows accurate control of pulsation frequency between 1 Hz and 6 Hz and displacement volume, with simple programmability using an Arduino microcontroller. The design features multiple chambers that effectively isolate corrosive liquids from the mechanical components, ensuring durability and extended operational life. The pulsator's 3D-printed components can be customized with different materials to suit various applications. Engineered to generate a pulsating flow profile that closely resembles a sinusoidal wave, video tracking analysis confirmed the sinusoidal nature of the flow, demonstrating consistent flow profile generation with adjustable frequency and amplitude. The maximum volume displacement achieved was 11.9 mL, which was reduced to 2.0 mL when the electrochemical cell was connected. Limiting current experiments with a ferri/ferrocyanide electrolyte showed that the mass transport coefficient of a typical cell increased from 2.3 × 10⁻³ cm/s under constant flow to 4.5 × 10⁻³ cm/s under pulsating conditions. These findings validate that the adjustable, Arduino-programmable sinusoidal pulsation generated by the diaphragm pulsator offers a practical and customizable method for enhancing mass transport in small-scale electrochemical reactors. </p>
Dimensions, stability and deformability of DOPC-cholesterol Giant Unilamellar Vesicles formed by droplet transfer – Extended Data
<p>This dataset contains the Underlying Data to the paper “ Dimensions, stability and deformability of DOPC-cholesterol Giant Unilamellar Vesicles formed by droplet transfer”.</p> <ul> <li> “deformation_size” folder containing scatter plots of σ with respect to GUVs rest radii <ul> <li>sd_deform_scatter_H1</li> <li>sd_deform_scatter_H2</li> <li>sd_deform_scatter_H3</li> </ul> </li> <li>“magnetic_device_support” folder containing the .stl files for 3D-printing the magnets-support of the magnetic device <ul> <li>magnetic_device_support_part1</li> <li>magnetic_device_support_part2</li> </ul> </li> <li>“size_distribution_magnetic” folder containing size distribution histograms comparing 100:0 DOPC:cholesterol and 60:40 DOPC:cholesterol samples, under the application of magnetic fields <ul> <li>sd_magnetic_size_dist_allfields</li> <li>sd_magnetic_size_dist_H1</li> <li>sd_magnetic_size_dist_H2</li> <li>sd_magnetic_size_dist_H3</li> </ul> </li> <li>“size_distribution_T0vsON” folder containing size distribution histograms comparing pristine samples (t<sub>0</sub>) and samples after overnight storage (ON), for different DOPC:cholesterol ratios <ul> <li>sd_size_dist_60_40</li> <li>sd_size_dist_71_29</li> <li>sd_size_dist_85_15</li> <li>sd_size_dist_100_0</li> </ul> </li> </ul>
Observation of Electroluminescence and Energy Transfer of Hyperbolic Polaritons
<p>Includes all codes and data related to the article titled "Electroluminescence and Energy Transfer Mediated by Hyperbolic Polaritons" for the main text, extended data and supplementary information. Version 3.</p>
Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage
<p>Processed data and code for "Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage," Naqvi et al 2025.</p> <p>Directory is organized into the following subfolders, each tar'ed and gzipped:</p> <p><strong>data_analysis.tar.gz - Processed data for modulation of TWIST1 levels and calculation of RE responsiveness to TWIST1 dosage</strong></p> <ul> <li>atac_design.txt - design matrix for ATAC-seq TWIST1 titration samples</li> <li>all.sub.150bpclust.greater2.500bp.merge.TWIST1.titr.ATAC.counts.txt - ATAC-seq counts from all samples over all reproducible ATAC-seq peak regions, as defined in Naqvi et al 2023</li> <li>atac_deseq_fitmodels_moded50.R - R code for calculating new version of ED50 and response to full depletion from TWIST1 titration data (note, uses drm.R function from <a href="https://doi.org/10.5281/zenodo.7689948">10.5281/zenodo.7689948</a>, install drc() with this version to avoid errors)</li> </ul> <p><strong>baseline_models.tar.gz - Code and data for training baseline models to predict RE responsiveness to SOX9/TWIST1 dosage</strong></p> <ul> <li>{sox9|twist1}.{0v100|ed50}.{train|valid|test}.txt - Training/testing/validation data (ED50 or full TF depletion effect for SOX9 or TWIST1), split into train/test/validation folds</li> <li>HOCOMOCOv11_core_HUMAN_mono_jaspar_format.all.sub.150bpclust.greater2.500bp.merge.minus300bp.p01.maxscore.mat.cpg.gc.basemean.txt.gz - matrix of predictors for all REs. Quantitative encoding of PWM match for all HOCOMOCO motifs + CpG + GC content, plus unperturbed ATAC-seq signal</li> <li>train_baseline.R - R code to train baseline (LASSO regression or random forest) models using predictor matrix and the provided training data. <ul> <li>Note: training the random forest to predict full TF depletion is computationally intensive because it is across all REs, if doing this run on CPU for ~6 hrs. </li> </ul> </li> </ul> <p><strong>chrombpnet_models.tar.gz - Remainder of code, data, and models for fine-tuning and interpreting ChromBPNet mdoels to </strong><strong>predict RE responsiveness to SOX9/TWIST1 dosage</strong></p> <ul> <li>Fine-tuning code, data, models <ul> <li>{all|sox9.direct|twist1.bound.down}.{train|valid|test}.{ed50|0v100.log2fc}.txt - Training/testing/validation data (ED50 or full TF depletion effect for SOX9 or TWIST1), split into train/test/validation folds</li> <li>pretrained.unperturbed.chrombpnet.h5 - Pretrained model of unperturbed ATAC-seq signal in CNCCs, obtained by running ChromBPNet (https://github.com/kundajelab/chrombpnet) on DMSO-treated SOX9/TWIST1-tagged ATAC-seq data</li> <li>finetune_chrombpnet.py - code for fine-tuning the pretrained model for any of the relevant prediction tasks (ED50/ effect of full TF depletion for SOX9/TWIST1)</li> <li>best.model.chrombpnet.{0v100|ed50}.{sox9|twist1}.h5 - output of finetune_chrombpnet.py, best model after 10 training epochs for the indicated task</li> <li>chrombpnet.{0v100|ed50}.{sox9|twist1}.contrib.{h5|bw} - contribution scores for the indicated predictive model, obtained by running chrombpnet contribs_bw on the corresponding model h5 file.</li> <li>chrombpnet.{0v100|ed50}.{sox9|twist1}.contrib.modisco.{h5|bw} - TF-MoDIsCo output from the corresponding contribution score file</li> </ul> </li> <li>Interpretation code, data, models <ul> <li>contrib_h5_to_projshap_npy.py - code to convert contrib .h5 files into .npy files containing projected SHAP scores (required because the CWM matching code takes this format of contribution scores)</li> <li>sox9.direct.10col.bed, twist1.bound.down.10col.uniq.bed - regions over which CWMs will be matched (likely direct targets of each TF)</li> <li>match_cwms.py - Python code to match individual CWM instances. Takes as input: modisco .h5 file, SHAP .npy file, bed file of regions to be matched. Output is a bed file of all CWM matches (not pruned, contains many redundant matches).</li> <li>chrombpnet.ed50.{sox9|twist1}.contrib.perc05.matchperc10.allmatch.bed - output of match_cwms.py </li> <li>take_max_overlap.py - code to merge output of match_cwms.py into clusters, and then take the maximum (length-normalized) match score in each cluster as the representative CWM match of that cluster. Requires upstream bedtools commands to be piped in, see example usage in file. </li> <li>chrombpnet.ed50.{sox9|twist1}.contrib.perc05.matchperc10.allmatch.maxoverlap.bed - output of take_max_overlap.py. These CWM instances are the ones used throughout the paper.</li> </ul> </li> </ul> <p><strong>modisco_reports.zip -</strong><strong> TF-MoDIsCo reports from running on the fine-tuned ChromBPNet models</strong></p> <ul> <li>modisco_report_{sox9|twist1}_{0v100|ed50}: folders containing images of discovered CWMs and HTMLs/PDFs of summarized reports from running TF-MoDisCo on the indicated fine-tuned ChromBPNet model</li> </ul> <p><strong>chrombpnet_models_supp.tar.gz - Alternative ChromBPNet mdoels to </strong><strong>predict SOX9/TWIST1 ED50 using varying definitons of direct targets</strong></p> <ul> <li> <p>best.model.chrombpnet.ed50.twist1.3hdn.h5 - TWIST1 direct targets defined using response to full 3h depletion (as was done for SOX9 throughout the rest of the paper)</p> </li> <li> <p>best.model.chrombpnet.ed50.sox9.v5chip.h5 - SOX9 direct targets defined using V5 ChIP-seq from SOX9-tagged lines (as was done for TWIST1 throughout the paper)</p> </li> </ul> <p><strong>mirny_model.tar.gz - Code and data for analyzing and fitting Mirny model of TF-nucleosome competition to observed RE dosage response curves</strong></p> <ul> <li>twist1.strong.multi.only.ed50.cutoff.true.hill.txt - ED50 and signed hill coefficients for all TWIST1-dependent REs with only buffering Coordinators (mostly one or two) and no other TFs' binding sites. "ed50_new" is the ED50 calculation used in this paper. </li> <li>twist1.strong.weak{1|2|3}.ed50.cutoff.true.hill.txt - ED50 and signed hill coefficients for all TWIST1-dependent REs with only buffering Coordinators (mostly one or two) and the indicated number of sensitizing (weak) Coordinators and no other TFs' binding sites. "ed50_new" is the ED50 calculation used in this paper. </li> <li>MirnyModelAnalysis.py - Python code for analysis of Mirny model of TF-nucleosome competition. Contains implementations of analytic solutions, as well as code to fit model to observed ED50 and hill coefficients in the provided data files.</li> </ul> <p><strong>nucleoatac.tar.gz - Output files from running NucleoATAC on merged ATAC-seq from each of 5 TWIST1 dosages</strong></p> <ul> <li>TWIST1_{dosage}_merge.nucmap_combined.bed.gz - see NucleoATAC docs for output format</li> </ul>
Glacial Lake Image Dataset for "Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset"
<p>Glacial lake dataset for the paper <strong>"Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset" (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jhydrol.2025.133072" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.jhydrol.2025.133072</a>)</strong></p> <p>The GLID dataset contains a total of 18,367 samples, and the size of each sample is 512*512. Each sample consists of an image and a corresponding label. Four glacial lake types including supraglacial lake, proglacial lake, ice-marginal lake, and unconnected glacial lake are involved. The pixel value of the glacier lake in annotation map is labeled 255 and background is labeled 0.</p> <p>GLID.rar contains the training dataset (16,000 samples), and val.zip contains the validation dataset (2,367 samples).</p> <p>GLID_annotation.zip contains the annotated shapefile of GLID with a CRS of WGS 84.</p> <p>Optical_images_source.xlsx contains the optical images ID/names and acquisition time of each platform (e.g., WV2, LC08, S2B, and GF02) used in GLID.</p> <p>Transferability validation.zip contains the images, labels, and predictions for transferability validation, which is independent of GLID (not used for model training or validation). The file structure is shown below:</p> <p>Transferability validation.zip</p> <ul> <li>images <ul> <li>AS.tif</li> <li>GL.tif</li> <li>NA.tif</li> <li>SA.tif</li> </ul> </li> <li>labels <ul> <li>AS_gt.tif</li> <li>GL_gt.tif</li> <li>NA_gt.tif</li> <li>SA_gt.tif</li> </ul> </li> <li>predictions <ul> <li>AS_pred.tif</li> <li>GL_pred.tif</li> <li>NA_pred.tif</li> <li>SA_pred.tif</li> </ul> </li> </ul> <p>AS, GL, NA, and SA represent Asia, Greenland, North America, and South America, respectively. Four high-quality Landsat-8/9 images (each cloud cover less than 6%) were used for testing, and we manually annotated the glacial lakes in each image as labels. The pixel value of the glacier lake in annotation map is labeled 255 and background is labeled 0. Files in transferability validation.zip have a same CRS of WGS 84.</p> <p>If you find this dataset is helpful in your research, please consider <strong><em>cite </em></strong>this paper:</p> <blockquote> <p><em>Ma D, Li J, Jiang L. 2025. Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset. Journal of Hydrology 657: 133072.</em></p> </blockquote>
Multiple Speaker Separation from Noisy Sources in Reverberant Rooms using Relative Transfer Matrix
<p>This folder is the supplementary audio files for the paper "Multiple Speaker Separation from Noisy Sources in<br>Reverberant Rooms using Relative Transfer Matrix" submitted to the European Signal Processing Conference (EUSIPCO).</p>
Data related to publication "Coherent phase transfer for real-world twin-field quantum key distribution"
<p>These datasets have been used to produce Figure 3, 4 and 5 of manuscript: "Coherent phase transfer for real-world twin-field quantum key distribution". The files contain two arrays: time in seconds and normalised intensity.</p> <p>Explanation for "Data_fig3_XXX.txt": the files contains the raw data used to produce Fig. 3. The following timespans have been used:</p> <p>Data_fig3_stabilised.txt: t_start= 0.29 s, t_stop=0.292 s</p> <p>Data_fig3_unstabilised.txt: t_start=0.00225 s, t_stop=0.00424 s</p> <p>Explanation for "Data_fig4_XXX.txt" files: the procedure to obtain the phase deviation from the normalised interference is detailed in the text (Methods section). Datasets with different sampling rate have been combined to obtain the phase deviation on the long and short term.</p> <p>Explanation for "Data_fig5.txt": the files contains the raw data used to produce Fig. 5.</p>
Fig. 2 in Transfer of the Malagasy genera Humbertianthus and Macrostelia to Hibiscus (Malvaceae) with description of four new species
Fig. 2. – Known distribution of the seven species of Hibiscus L. highlighted in this paper.
Neural Policy Style Transfer with Twin-Delayed DDPG (NPST3)
<p>Neural Policy Style Transfer with Twin-Delayed DDPG (NPST3) dataset.</p> <p>The research leading to these results has received funding from: RoboCity2030-DIH-CM, Madrid Robotics Digital Innovation Hub, S2018/NMT-4331, funded by “Programas de Actividades I+D en la Comunidad de Madrid” and cofunded by Structural Funds of the EU; ROBOASSET, ”Sistemas robóticos inteligentes de diagnóstico y rehabilitación de ter apias de miembro superior”, PID2020-113508RB-I00 funded by AGENCIA ESTATAL DE INVESTIGACION (AEI); and “Programa propio de investigación convocatoria de movilidad 2020” from Universidad Carlos III de Madrid. The original data used in this project was obtained from mocap.cs.cmu.edu. The original data was created with funding from NSF EIA-0196217.</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.