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13,064 results for “predictions”
Streamflow Prediction in Human-Regulated Catchments Using Multiscale LSTM Modeling with Anthropogenic Similarities
<p>These codes are used in the paper entitled"Streamflow Prediction in Human-Regulated Catchments Using Multiscale LSTM Modeling with Anthropogenic Similarities" which is submitted to the Journal of Water Resources Research. The code for the differentiable parameter learning (DPL) model can be downloaded at https://doi.org/10.5281/zenodo.7091334. The code for LSTM to reproduce our analysis is available at <a href="https://github.com/neuralhydrology/neuralhydrology">https://github.com/neuralhydrology/neuralhydrology</a>. The SWORD database utilized in our study can be accessed at https://zenodo.org/records/10013982. The geometric dataset of the global river attribute information for every river reach, the values of all pressure indicators (DOF, DOR,SED, USE, RDD and URB) and the values for the CSI are available at https://doi.org/10.6084/m9.figshare.7688801.</p>
Smart Poqueira: Predicting Rural Parking Lot Feasibility with Sensor-Questionnaire Integration
<p>We introduce a dataset comprising 525 instances of visitor behavior data from Pampaneira, Bubión, and Capileira in the Sierra Nevada National Park, Granada, Spain. Collected in January, March, and July 2023, the questionnaires excluded locals and residents. Conducted in parking lots, the questionnaires gathered information like license plate numbers, residential postcodes, visit frequency, and overnight stays. Additionally, data from four Hikvision license plate recognition (LPR) cameras tracking vehicle movement in each village during the same period supplement the dataset, enhancing understanding of individual mobility patterns. To further enrich the dataset, contextual details such as holiday days, vehicle provenance, and socio-demographic information, aiding in the validation and enhancement of questionnaire-derived data.</p> <p>The dataset comprises 26 variables, including: total_distance, nights, visits_dif_weeks, visits_dif_months, fidelity, total_entries, avg_nights, std_nights, total_holiday, avg_holiday, std_holiday, total_workday, avg_workday, std_workday, total_high_season, avg_high_season, std_high_season, total_low_season, avg_low_season, std_low_season, entry_in_holiday, entry_in_high_season, population, avg_gross_income, km_to_area, and park_price_will_affect_behaviour.</p>
Can ingredients based forecasting be learned? Disentangling a random forest's severe weather predictions
<p>Machine learning (ML)-based models have been rapidly integrated into forecast practices across the weather forecasting community in recent years. While ML tools introduce additional data to forecasting operations, there is a need for explainability to be available alongside the model output, such that the guidance can be transparent and trustworthy for the forecaster. This work makes use of the algorithm tree interpreter (TI) to disaggregate the contributions of meteorological features used in the Colorado State University Machine Learning Probabilities (CSU-MLP) system, a random forest-based ML tool that produces real-time probabilistic forecasts for severe weather using inputs from the Global Ensemble Forecast System v12. TI feature contributions are analyzed in time and space for CSU-MLP day-2 and 3 individual hazard (tornado, wind, and hail) forecasts and day-4 aggregate severe forecasts over a 2-yr period. For individual forecast periods, this work demonstrates that feature contributions derived from TI can be interpreted in an ingredients-based sense, effectively making the CSU-MLP probabilities physically interpretable. When investigated in an aggregate sense, TI illustrates that the CSU-MLP system's predictions use meteorological inputs in ways that are consistent with the spatiotemporal patterns seen in meteorological fields that pertain to severe storms climatology. This work concludes with a discussion on how these insights could be beneficial for model development, real-time forecast operations, and retrospective event analysis.</p>
Clinical Dataset for Artificial Intelligence-Driven Predictive Modeling for Home Discharge in Neurological and Orthopedic Conditions
<p><span>In recent years, the fusion of the medical and computer science domains has gained significant traction in the scientific research landscape. Progress in both fields has enabled the generation of a vast amount of data used for making predictions and identifying interesting clusters and pathways. The Machine Learning model's application in the medical domain is one of the most compelling and challenging topics to explore, bridging the gap between Artificial Intelligence (AI) and healthcare. The combination of AI and medical information offers the possibility to create tools that can benefit both healthcare providers and physicians. This enables the enhancement of rehabilitation therapy and patient care. In the rehabilitation context, this work provides an alternative perspective: prediction of patients’ home discharge upon completing the rehabilitation protocol. Demographic and clinical data were collected from electronic Medical Record. </span></p> <p><span>The original analysis dataset includes clinical and demographic data of adults admitted to the neurology and orthopedic departments of a rehabilitation hospital in Italy from January 2015 to August 2022. The completion of patients’ ADR-r form</span><span> resulted in the collection of data for 10520 individuals, whose information is distributed across 120 initial features. We anonymized dataset rows. Clinical data, including the primary reason for rehabilitation, any associated medical conditions, impairments, and admission/discharge mBi scores were collected. </span></p> <p><span>A legend file is enclosed to explain variable labels.</span></p>
Species occurrence locations and environmental parameters used for predicting the potential planting regions of Pterocarpus santalinus
<p>This study explores the habitat distribution of <em>Pterocarpus santalinus</em>, a valuable rosewood species, across China, focusing on its response to current and future climate changes. Utilizing the MaxEnt model, we assess its suitable habitat under present conditions and future climate scenarios (SSPs126, SSPs245, and SSPs585). Our findings reveal that the current suitable habitat, spanning approximately 409,600 km², is primarily located in the central and southern parts of Guangdong, Guangxi, Fujian, Yunnan, as well as in the Hainan provinces, along with the coastal regions of Taiwan, and the Sichuan-Chongqing border. The habitat's distribution is significantly influenced by climatic factors such as temperature seasonality (bio4), mean temperature of the wettest quarter (bio8), annual mean temperature (bio1), and annual precipitation (bio12), while terrain and soil factors play a lesser role. Under future climate scenarios, the suitable habitat for <em>P. santalinus</em> is projected to expand, with a northeastward shift in its distribution center. This research not only sheds light on the geoecological characteristics and geographical distribution of <em>P. santalinus</em> in China but also offers a scientific basis for planning its cultivation areas and enhancing cultivation efficiency under changing climate conditions.</p>
Dataset and scripts from: Predicting organismal response to marine heatwaves using dynamic thermal tolerance landscape models
<p>Marine heatwaves (MHWs) can cause thermal stress in marine organisms, experienced as extreme 'pulses' against the gradual trend of anthropogenic warming. When thermal stress exceeds organismal capacity to maintain homeostasis, organism survival becomes time-limited and can result in mass mortality events. Current methods of detecting and categorizing MHWs rely on statistical analysis of historic climatology, and do not consider biological effects as a basis of MHW severity. The reemergence of ectotherm thermal tolerance landscape models provides a physiological framework for assessing the lethal effects of MHWs by accounting for both the magnitude and duration of extreme heat events. Here, we used a simulation approach to understand the effects of a suite of MHW profiles on organism survival probability across 1) three thermal tolerance adaptive strategies, 2) interannual temperature variation, and 3) seasonal timing of MHWs. We identified survival isoclines across MHW magnitude and duration where acute (short duration-high magnitude) and chronic (long duration-low magnitude) events had equivalent lethal effects on marine organisms. While most research attention has focused on chronic MHW events, we show similar lethal effects can be experienced by more common but neglected acute marine heat spikes. Critically, a statistical definition of MHWs does not accurately categorize biological mortality. By letting organism responses define the extremeness of a MHW event, we can build a mechanistic understanding of MHW effects from a physiological basis. Organism responses can then be transferred across scales of ecological organization and better predict marine ecosystem shifts to MHWs. </p>
The energy bands of charged defect predicted by the HamGNN-Q model
<p>The dataset contains graph representations of GaAs defects for testing in the study that were not present in the training set, including single-point vacancies, interstitial atom defects, defect clusters, substitution defects, and large-sized polarons with varying background charges. charged_defect_hamiltoian.ckpt is the network weights for the HamGNN-Q model. config_charge.yaml is the input file of the model.</p>
Dataset for the Global Prediction Of Total Organic Carbon In Marine Sediments Using Deep Neural Networks (nn-toc)
<p>The data folder contains the raw features and labels used for training machine learning models to predict total organic carbon in marine sediments. </p> <p>The data folder has three subfolders:</p> <ol> <li>raw : contains the labels, features and other data used to train the machine learnign models</li> <li>interim : transformed data, which has to be reproduced</li> <li>output : output from the models, used for analysis and visualisation</li> </ol> <p>The data folder has to be integrated in the Git repository nn-toc, to execute the code.</p> <p> </p> <p> </p>
Stereoisomers are not Machine Learning's Best Friends: Experimental results of the prediction of the association constant between a cyclodextrin and a guest with Stereo2vec
<p>This study addresses the challenge of accurately identifying stereoisomers in cheminformatics which originates from our objective to apply machine learning to predict association constant between a cyclodextrin and a guest. Identifying stereoisomers is indeed crucial for machine learning applications. Current tools offer various molecular descriptors, including their textual representation as Isomeric SMILES which can distinguish stereoisomers. But such representation is text-based and does not have a fixed size, so a conversion is needed to make it usable to machine learning approaches. Word embedding techniques can be used to solve this problem. Mol2vec, a word embedding approach for molecules, offers such a conversion. Unfortunately, it cannot distinguish between stereoisomers due to its inability to capture the spatial configuration of molecular structures. This study proposes several approaches that use word embedding techniques to handle molecular discrimination using stereochemical information of molecules or considering Isomeric SMILES notation as a text in Natural Language Processing. Our aim is to generate a distinct vector for each unique molecule, correctly identifying stereoisomer information in cheminformatics. The proposed approaches are then compared on our original machine learning task: predicting the association constant between a cyclodextrin and a guest molecule.</p>
Data from: Frequency-dependent tolerance to aircraft disturbance drastically alters predicted impact on shorebirds
<p>This data package includes data and R script belonging to the publication "Frequency-dependent tolerance to aircraft disturbance drastically alters predicted impact on shorebirds" by van der Kolk et al. (2024) in Ecology Letters.</p> <p>The script analysis_figures_vanderKolketal2024.R can be used to reproduce all analysis and figures in the manuscript. The Figures are stored in the Output folder.</p> <p>The data folder includes an excel file with metadata explaining all columns in the csv files.</p>
Artifact of the paper: Light-weight prediction for improving energy consumption in HPC platforms
<p>Please refer to the <a href="../records/11208389/files/artifact-overview.pdf?download=1&preview=1" target="_blank" rel="noopener">artifact-overview.pdf</a> file in this dataset for instructions to reproduce the experiments we have conducted for this article, or for more context about the article.</p>
Retrieve, Merge, Predict: Augmenting Tables with Data Lakes
<p>Files composing the YADL data lake, for the paper "Retrieve, Merge, Predict: Augmenting Tables with Data Lakes (Experiment, Analysis & Benchmark Paper)"</p> <p>We present an in-depth analysis of data discovery for analytics in data lakes, focusing on table augmentation for given machine learning tasks. We analyze alternative methods used in the three key steps: retrieving joinable tables, merging information, and predicting with the resultant table. As data lakes, the paper uses YADL (Yet Another Data Lake) -- a novel dataset developed as a tool for benchmarking this data discovery task -- and Open Data US, a well-referenced real data lake. Through systematic exploration on both lakes, our study outlines the importance of accurately retrieving join candidates, and the efficiency of simple aggregation methods. We report new insights on the benefits of existing solutions and on the their limitations, aiming at guiding future research in this space.</p> <p>Archives provided here follow the notation used for the experiments, which is different from what is reported in the paper. The four YADL versions available here are:</p> <ul> <li>"binary_update" (YADL Binary)</li> <li>"wordnet_full" (YADL Base)</li> <li>"wordnet_vldb_10" (YADL 10k)</li> <li>"wordnet_vldb_50" (YADL 50k)</li> </ul>
Multimodel AI Prediction Network (MAPNet) applied to temperature forecasts
<p>In this repository are all the data/scripts necessary to reproduce the results of the article 'Multimodel AI Prediction Network (MAPNet) applied to temperature forecasts.'</p> <p>The files in NetCDF format and nomenclature <strong>TYPE</strong>_<strong>FORECAST</strong>_202201_202312.nc are the input data for the neural network, where <strong>TYPE</strong> (TMAX, TMIN) and <strong>FORECAST</strong> (F24, F48, F72). These files contain all the predictions from the atmospheric models and the 'observations.' This input data was divided into training, validation, and test periods.</p> <p>The files in NetCDF format and nomenclature <strong>CNN</strong>_<strong>TYPE</strong>_<strong>FORECAST</strong>.nc are the output data of the neural network, where <strong>TYPE</strong> (TMAX, TMIN) and <strong>FORECAST</strong> (F24, F48, F72). These files contain the TMAX and TMIN forecasts for the test period.</p> <p>The files in NetCDF format and nomenclature <strong>MODEL</strong>_<strong>TYPE</strong>_<strong>FORECAST</strong>.nc are the forecasts from the atmospheric models (BRAMS, ETA, WRF, SMEC) and the observations (SAMET) for the same test period. This data was used to compare the predictions of each model with the predictions of the neural network.</p> <p>The file CCN_Unet.py is a Python program containing the configurations of the convolutional neural network with a U-Net architecture used in the study.</p> <p>The files in ASCII format (.py, .gs) are scripts used for visualizing the results and generating the graphs. The other files are shapefiles and masks used for generating figures.</p>
Data from: Foraging predicts the evolution of warning coloration and mimicry in snakes
<p><span>Warning coloration and Batesian mimicry are classic examples of Darwinian evolution, but empirical evolutionary patterns are often paradoxical. We test whether a new factor, trade-offs with foraging, influences the evolution of striking coloration by integrating genetic and ecological data for aposematic and mimetic snakes (Elapidae and Dipsadidae). Our phylogenetic comparison of a total of 432 species demonstrated that dramatic changes in coloration were well-predicted by the foraging strategy. Multiple tests consistently indicated that warning coloration and conspicuous mimicry were more likely to evolve in species where foraging costs of conspicuous appearance were relaxed by the poor vision of their prey or concealed habitat. Reversion to crypsis was also well-predicted by ecology for elapids, but not for dipsadids. In contrast to a theoretical prediction and general trends, snakes' conspicuous coloration was correlated with secretive ecology, suggesting that a selection regime underlies evolutionary patterns. We also found evidence that mimicry of inconspicuous models (pitvipers) may have evolved in association with foraging demand for crypsis. These findings demonstrate that foraging is an important factor necessary to understand the evolution, persistence, and diversity of warning coloration and mimicry of snakes, highlighting the significance of additional selective factors in solving the warning coloration paradox.</span></p>
Gene and protein sequence features augment HLA class I ligand predictions
<p>Dataset and analyses supporting the manuscript "Gene and protein sequence features augment HLA class I ligand predictions".</p> <p>The "peptides" files contain the mass-spec detected peptides obtained from HLA ligandomics performed on the indicated tumor lines. </p> <p>The "protein data" files contain the RNAseq data (TPM) and Ribosome profiling data (ribosome occupancy) per protein, for each tumor line. </p> <p>The "source data" zip archive contains the source data underlying the figures of the manuscript.</p> <p>The "HLA ligandome analyses" zip archive contains the R scripts used for all data analysis in the manuscript, including all data and output files. These analyses can also be found at https://github.com/kasbress/HLA_Ligandome_Analyses/</p> <p> </p> <p> </p>
A dataset for the investigation of upper limb torque prediction from EMG signals
<h1>Motivation</h1> <div>EMG-driven exoskeleton assistance requires the use of intention detection models to associate electromyographic signals with some feature of human movement, such as angular position, velocity, or joint torque. The goal of this dataset is to provide data that allows the benchmarking of such models for a variety of movements.</div> <h1>Short description</h1> <div>This dataset includes kinematic, dynamic and electromyographic data from 17 participants (11 males, age 28.2 ± 7 years, height 175.4 ± 7 cm, weight 70 ± 11 kg). These data were collected during the performance of a sagittal plane upper limb tracking task for single joint (elbow flexion/extension) and multiple joint (elbow and shoulder flexion/extension).</div> <h1>Methodology</h1> <div>A detailed description of the data collection methodology can be found here: https://www.biorxiv.org/content/10.1101/2024.01.11.575155v1</div> <h1>Data Description</h1> <div>The data set consists of 17 folders, one for each participant. Inside each folder you will find</div> <div>- A metadata file (<strong>SXX.json</strong>) containing information about the subject: age, sex, weight, height, and upper limb masses and lengths.</div> <div>- An OpenSim model file (<strong>scaledModel.osim</strong>) containing a scaled upper limb model of the given subject.</div> <div>- A <strong>MVC</strong> folder containing EMG data from maximal voluntary contraction trials</div> <div>- A <strong>SJ</strong> folder containing trial folders for the single joint condition (elbow flexion/extension).</div> <div>- A <strong>MJ</strong> folder containing test folders for the multi-joint condition (elbow and shoulder flexion/extension)</div> <h2>EMG Data</h2> <div>The files containing EMG data have the following header</div> <div><code>TIME,DELTAnt,DELTMed,DELTPost,PECT,LATI,TRILong,TRILat,TRIMed,BICLong,BICShort,BRA,BRD</code></div> <div>This corresponds to a time stamp and EMG signals from the anterior, median and posterior detloids, pectoralis major, latissimus dorsi, long, lateral and median triceps, long and short biceps, brachioradialis and brachialis.</div> <h2>MVC data</h2> <div>The MVC folder contains two files: <strong>emgMVCElbow.csv</strong> and <strong>emgMVCShoulder.csv</strong>. They were collected during the realisation of maximum voluntary contraction tasks and contain raw EMG data sampled at 2 kHz.</div> <h2>Trial data</h2> <h3>Single-joint condition</h3> <div>Single-joint trials contain 5 files:</div> <div>- <strong>emgFilt.csv</strong>: Filtered EMG signals, using a 20-450 Hz bandpass filter, a rectification, a 3Hz lowpass filter and normalized with MVC. Sampled at 100 Hz.</div> <div>- <strong>emgRaw.csv</strong>: Raw EMG signals. Sampled at 2 kHz.</div> <div>- <strong>humanPositions.csv</strong>: Angular position of the elbow in rad. Sampled at 100 Hz.</div> <div>- <strong>humanVelocities.csv</strong>: Angular velocity of the elbow in rad/s. Sampled at 100 Hz.</div> <div>- <strong>muscleTorque.csv</strong>: Joint torque of the elbow in N.m. Sampled at 100 Hz.</div> <h3>Multi-joint condition</h3> <div>Multi-joint trials contain 5 files:</div> <div>- <strong>emgFilt.csv</strong>: Filtered EMG signals, using a 20-450 Hz bandpass filter, a rectification, a 3Hz lowpass filter and normalized with MVC. Sampled at 100 Hz.</div> <div>- <strong>emgRaw.csv</strong>: Raw EMG signals. Sampled at 2 kHz.</div> <div>- <strong>humanPositions.csv</strong>: Angular positions of the upper limb in rad. Sampled at 100 Hz.</div> <div>- <strong>humanVelocities.csv</strong>: Angular velocities of the upper limb rad/s. Sampled at 100 Hz.</div> <div>- <strong>muscleTorque.csv</strong>: Joint torques of the upper limb in N.m. Sampled at 100 Hz.</div> <div>For this trial, kinematic and torque files use the following headers:</div> <div><code>TIME,elv_angle,shoulder_elv,shoulder_rot,elbow_flexion,pro_sup,deviation,flexion</code></div> <div>The columns correspond to the OpenSim model coordinates. For sagittal plane movement, columns of interest are <strong>shoulder_elv</strong> for shoulder flexion/extension and <strong>elbow_flexion</strong> for elbow flexion/extension.</div>
Statistical learning shapes pain perception and prediction independently of external cues
<h1>Dataset and code for the relevant analysis and results:</h1> <h3>"Statistical learning shapes pain perception and prediction independently of external cues"</h3> <p>Onysk, J., Whitefield, M., Gregory, N., Jain, M., Turner, G., Seymour, B., Mancini, F. (2024). eLife. <a href="https://doi.org/10.7554/eLife.90634.2">https://doi.org/10.7554/eLife.90634.2</a></p> <h2>1 - data_collection</h2> <p>Contains the code for the psychophysical experiment (PsychToolBox), including the sequence generations scripts.</p> <h2>2 - preprocessing</h2> <p>Contains code that preprocesses behavioural data from PsychToolBox. This includes linear transformation of inputs, exporting data to stan readable format and plotting Supplement figures.</p> <p>- The raw behavioural data can be found in <strong><em>preprocessing/data</em></strong>. Stan ready ready for each condition is found in <strong><em>preprocessing/stan_data</em></strong></p> <h2>3 - model_fit_analysis</h2> <p>Contains stan models used in the paper ('models/'), model fitting code ('fit_models_cs.R) (inlcuding HPC setup in 'hpc/'), initial analysis script for processing stan samples ('primary_analysis_cs.R'), as well as additional analyis scripts ('extra_analysis_cs.R', 'correlation_beh_model_cs.ipynb') that generate figures from the paper and supplement.</p> <p>- The posterior draws for parameters can be found in <em><strong>model_fit_analysis/output/cs_results</strong></em> </p> <h2>4 - model_recovery</h2> <p>Contains code that execute model and parameter recovery analysis ('mp_recovery.R'), including HPC setup ('hpc/'). The 'mp_rec_analyse.R' reproduces model and parameter recovery results from the supplement.</p> <h2>5 - Figures</h2> <p>Contains all the figures from the manuscript and the supplement.</p> <h2>6 - RDS_fits</h2> <p>Contains RStan fit objects for each condition for each model</p>
How can we biochemically validate protein function predictions with the Ras GTPase family? - Associated data
<p>This is the data that accompanies the pub "<a href="https://doi.org/10.57844/arcadia-74ad-345f">How can we biochemically validate ProteinCartography with the Ras GTPase family?</a>" It's part of a group of pubs focused on validating ProtienCartography that begins with "<a href="https://doi.org/10.57844/arcadia-cae9-96c4">A strategy to validate protein functions <em>in vitro</em></a><a href="https://doi.org/10.57844/arcadia-cae9-96c4">." </a></p> <p>For this repository, we ran ProteinCartography <a href="https://github.com/Arcadia-Science/ProteinCartography/releases/tag/v0.5.0">v0.5.0</a> using human HRas and KRas as our inputs for a single run (UniProt ID: <a href="https://www.uniprot.org/uniprotkb/P01112/entry">P01112</a> and <a href="https://www.uniprot.org/uniprotkb/P01116/entry">P01116</a>). We asked for 3,000 Foldseek hits and 7,000 BLAST hits for a total of 10,000 structures. The updated configuration file is in the zipped folder in this repository. Also included in the zipped folder are the inputs, structures of all hits, and all ProteinCartography results. </p> <p>Finally, we created a custom overlay for the protein map using this <a href="https://github.com/Arcadia-Science/2023-actin-embedding/blob/main/notebooks/3_plotting_overlays.ipynb">notebook</a> and the manually annotated TSV file in this repository, where we denoted which group of substrates a protein is predicted to act on based on its annotation from UniProt.</p>
How can we biochemically validate protein function predictions with the deoxycytidine kinase family? - Associated data
<p>This is the data that accompanies the pub "<a href="https://doi.org/10.57844/arcadia-1e5d-e272">How can we biochemically validate ProteinCartography with the deoxycytydine kinase family?</a>" It's part of a group of pubs focused on validating ProtienCartography that begins with "<a href="https://doi.org/10.57844/arcadia-cae9-96c4">A strategy to validate protein functions <em>in vitro</em></a><a href="https://doi.org/10.57844/arcadia-cae9-96c4">." </a></p> <p>For this repository, we ran ProteinCartography <a href="https://github.com/Arcadia-Science/ProteinCartography/releases/tag/v0.5.0">v0.5.0</a> on the deoxycytidine kinase (dCK) using human dCK as our input (UniProt ID: <a href="https://www.uniprot.org/uniprotkb/P27707/entry">P27707</a>). We asked for 3,000 Foldseek hits and 7,000 BLAST hits for a total of 10,000 structures. The updated configuration file is in the zipped folder in this repository. Also included in the zipped folder are the inputs, structures of all hits, and all ProteinCartography results. </p> <p>Finally, we created a custom overlay for the protein map using this <a href="https://github.com/Arcadia-Science/2023-actin-embedding/blob/main/notebooks/3_plotting_overlays.ipynb">notebook</a> and the manually annotated TSV file in this repository, where we denoted which group of substrates a protein is predicted to act on based on its annotation from UniProt.</p>
KIR2DL2/DL3+NKs and Helios+Tregs in peripheral blood predict nivolumab response in patients with metastatic renal cell cancer
<p><strong><span>Purpose</span></strong><span>: T<span>o identify predictive factors of nivolumab sensitivity, peripheral blood NKs and Tregs were evaluated </span>in patients with metastatic renal cancer (mRCC) enrolled in the REVOLUTION trial.</span></p> <p><strong><span>Experimental design:</span></strong><span> 57 mRCCs being treated with nivolumab, as at least second-line of therapy (REV), and 62 healthy donors (HDs) were longitudinally evaluated (</span><span>0-1-3-6-12 months</span><span>) for</span><span> peripheral NKs and Tregs, phenotype and function. </span><span>Multivariable logistic regression were conducted to identify the independent predictors. The </span><span>.632+ internal cross-validation was used to avoid overfitting. </span><span>The best cut-off value </span><span>based on three-months clinical-response</span><span> was applied to </span><span>progression-free survival (PFS)</span><span> and </span><span>overall survival (OS)</span><span>. Kaplan-Meier curves for PFS and OS were produced.</span></p> <p><strong><span>Results:</span></strong><span> </span><span>At pre-treatment, mRCCs displayed high frequency of <sup>NKp46+</sup>NKs, <sup>NKp30+</sup>NKs, <sup>KIR2DL1+</sup>NKs, <sup>KIR2DL2/DL3+</sup>NKs, and<sup> PD-1+</sup>NKs with reduced NK degranulation; as well as high frequency of Tregs, </span><sup><span>PD-1+</span></sup><span>Tregs,</span><sup><span> Helios+</span></sup><span>Tregs and </span><sup><span>ENTPD-1+</span></sup><span>Tregs</span><span>. Responder patients (R), identified as a clinical response after three-months of treatment,</span><span> presented at pre-treatment significantly low CD3<sup>+</sup>, high<sup> KIR2DL2/DL3+</sup></span><span>NKs</span><span>, high <sup>PD-1+</sup>Tregs and high <sup>Helios+</sup>Tregs. Upon multivariate analysis, only <sup>KIR2DL2/DL3</sup>NKs and <sup>Helios+</sup>Tregs held as independent predictors of nivolumab responsiveness. The <sup>KIR2DL2/DL3+</sup>NKs >35.3% identified patients with longer OS while the <sup>Helios+</sup>Tregs >34.3% displayed significantly longer PFS. After 1-month of nivolumab, R patients showed low CD3<sup>+</sup>, high NKs, <sup>KIR2DL2/DL3+</sup>NKs and <sup>ICOS+</sup>Tregs. Among these subpopulations, CD3<sup>+</sup> and <sup>KIR2DL2/DL3+</sup>NKs held as independent predictors of nivolumab efficacy. Low CD3<sup>+ </sup>(≤71%) significantly associated with longer PFS while high <sup>KIR2DL2/DL3+</sup>NKs (>23.3%) associated with both PFS and OS. </span></p> <p><strong><span>Conclusions:</span></strong><span> Pre-treatment evaluation of <sup>Helios+</sup>Tregs/<sup>KIR2DL2/DL3+</sup>NKs and one-month post-treatment CD3<sup>+</sup>/<sup> KIR2DL2/DL3+</sup>NKs will predict nivolumab response in mRCCs<span>.</span></span></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.