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7,351 results for “stroke”
Predictive models for secondary Epilepsy in patients with acute Ischemic Stroke within one year
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Post-stroke upper limb kinematics of a set of daily living tasks
<p>This dataset contains upper limb kinematics of twenty chronic stroke-subjects and five healthy control subjects that were collected within a cross-sectional, observational study (Clinicaltrials.gov Identifier: NCT03135093). Participants were measured when performing a set of 30 daily living activities, including gesture movements, grasping actions and tool-mediated upper limb activities. Kinematic parameters were captured by use of inertial sensing and stored in software specific XML file format (.mvnx) allowing import to programs such as MATLAB and Microsoft Excel. Each mvnx-file represents one trial execution and is named according to the participant ID, task number, tested upper limb and repetition.</p> <p>This data collection is part of a large multimodal dataset collected and shared between collaborators of a European project under grant agreement No.688857.</p>
Mridangam Stroke Dataset
<p>The Mridangam Stroke dataset is a collection of 6977 audio examples of individual strokes of the Mridangam in various tonics. The dataset comprises of 10 different strokes played on Mridangams with 6 different tonic values.</p> <p>This is an updated version of the dataset, as the original version 1.0 presents some silent or wrong annotated tracks.</p> <p><strong>Audio content</strong></p> <p>The dataset provides audio examples for each of the strokes. There are six different tonics and ten different stroke labels.</p> <p>The audio examples were recorded from a professional Carnatic percussionist in a semi-anechoic studio conditions by Akshay Anantapadmanabhan using SM-58 microphones and an H4n ZOOM recorder. The audio was sampled at 44.1 kHz and stored as 16 bit wav files. The dataset can be used for training models for each Mridangam stroke. </p> <p><strong>Metadata</strong></p> <p>The whole dataset is organized by the tonic, into 6 packs. Each audio file is named as, </p> <pre><code><StrokeName>_<Tonic>_<InstanceNumber>.wav <Tonic> = {B, C, Csh, D, Dsh, E} <StrokeName> = {Bheem, Cha, Dheem, Dhin, Num, Ta, Tha, Tham, Thi, Thom}</code></pre> <p><strong>Using this dataset</strong></p> <p>A detailed description of the Mridangam and its strokes can be found in the following paper. A part of the dataset was used in the paper. Please cite it if you use the dataset in your work.</p> <blockquote> <p>Akshay Anantapadmanabhan, Ashwin Bellur, Hema A. Murthy, "Modal analysis and transcription of strokes of the mridangam using non-negative matrix factorization," in Proc. of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013), pp.181-185, May 2013</p> </blockquote> <p><a href="http://hdl.handle.net/10230/25756">http://hdl.handle.net/10230/25756</a></p> <p>We are interested in knowing if you find our datasets useful! If you use our dataset please email us at <a href="mailto:mtg-info@upf.edu">mtg-info@upf.edu</a> and tell us about your research.</p> <p><strong>Contact</strong></p> <p>If you have any questions or comments about the dataset, please feel free to write to us: </p> <p>Akshay Anantapadmanabhan (akshay.anantapadmanabhan@gmail.com)</p> <p> </p> <p><a href="http://compmusic.upf.edu/mridangam-stroke-dataset">http://compmusic.upf.edu/mridangam-stroke-dataset</a></p>
Quantification of stroke lesion volume using epidural EEG in a cerebral ischaemic rat model
<p>We have uploaded the dataset of rat EEG in response to the specific sound stimuli. The dataset includes rat EEG of normal subjects (n = 10), mild (n = 7), moderate (n = 7), and severe (n = 7) right auditory cortical infarction subjects. </p> <p>The dataset can be analyzed using MATLAB software. For power spectrum density (PSD) analysis of the data, we used the zero-phase forward and reverse Infinite Impulse Response Butterworth filter of 4th order. Further, we averaged the last 300 ms of the signal before stimulus onset as a baseline correction. Next, we down-sampled the data from 1,200 to 600 Hz. Subsequently, we conducted the PSD analysis using signals obtained from the target stimulus onset to 1 s using Welch’s method, which is one of the most widely used periodogram methods for determining the power density of EEG frequency components. The parameter was set to divide the EEG signals into eight sections of equal length, each with a 50% overlap based on the Hamming window. We defined the frequency range for each band as follows: Delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz). We calculated the relative power for each frequency band by summing all of the absolute PSD values across the four bands to compute the total power followed by dividing the absolute value for each frequency band with the total power. Finally, we calculated the DAR (delta/alpha ratio) and the DTABR ((delta + theta) / (alpha + beta) ratio), which were computed by the relative power of the relevant frequency bands. Further, we analysed AEPs in response to the target sound stimuli by averaging all of the epochs between 300 ms before the stimuli onset to 500 ms after it. The AEP amplitude was defined as the highest recorded voltage following the sound stimulus. The latency of the components of the AEPs was defined as the duration from stimulus onset to the peak amplitude.</p>
Weighted haltere and imposed haltere stroke reduction tethered flying Drosophila kinematics
<p class="CxSpFirst">In the true flies (Diptera) the hind wings have evolved into specialized mechanosensory organs known as halteres, which are sensitive to gyroscopic and other inertial forces. Together with the fly's visual system, the halteres direct head and wing movements through a suite of equilibrium reflexes that are crucial to the fly's ability to maintain stable flight. As in other animals (including humans), this presents challenges to the nervous system as equilibrium reflexes driven by the inertial sensory system must be integrated with those driven by the visual system in order to control an overlapping pool of motor outputs shared between the two of them. Here, we introduce an experimental paradigm for reproducibly altering haltere stroke kinematics and use it to quantify multisensory integration of wing and gaze equilibrium reflexes. We show that multisensory wing-steering responses reflect a linear superposition of individual haltere-driven and visually-driven responses, but that multisensory gaze responses follow a non-linear integration logic.</p>
Data from: Profile of and risk factors for post-stroke cognitive impairment in diverse ethno-regional groups
OBJECTIVE: To address the variability in prevalence estimates and inconsistencies in potential risk factors for post-stroke cognitive impairment (PSCI) using a standardised approach and individual participant data (IPD) from international cohorts in the STROKOG consortium. METHODS: We harmonised data from thirteen studies based in eight countries. Neuropsychological test scores 2 to 6 months after stroke or TIA and appropriate normative data were used to calculate standardised cognitive domain scores. Domain-specific impairment was based on percentile cut-offs from normative groups, and associations between domain scores and risk factors were examined using one-stage IPD meta-analysis. RESULTS: In a combined sample of 3,146 participants admitted to hospital for stroke (97%) or TIA (3%), 44% were impaired in global cognition and 30–35% in individual domains 2 to 6 months after the index event. Diabetes and a history of past stroke were strongly associated with poorer cognitive function after covariate adjustments; hypertension, smoking and atrial fibrillation had weaker domain-specific associations. While there were no significant differences in domain impairment among ethno-racial groups, some inter-ethnic differences were found in the effects of risk factors on cognition. CONCLUSIONS: This paper confirms the high prevalence of PSCI in diverse populations, highlights common risk factors, in particular diabetes, and points to ethno-racial differences which warrant attention in the development of prevention strategies.
Risk and predictors of stroke recurrence after ischaemic stroke: a prospective cohort study in a mountainous province of central highlands in Vietnam
<p>Dataset associated with "Risk and predictors of stroke recurrence after ischaemic stroke: a prospective cohort study in a mountainous province of central highlands in Vietnam" manuscript</p>
GR-OXMSTR-N10N11 Oximetry/heartrate and brain strokes
<p> Dataset: GR-OXMSTR-N10N11</p> <p> Oximetry/heartrate and brain strokes<br /> Greece, Nov.2010 - Nov.2011<br /> <br /> Release Notes</p> <p> Copyright (c) 2016 by Harris V. Georgiou</p> <p>========================================================<br /> Release: Jul 30, 2016</p> <p> - Version: 1.1a<br /> - Format: .xls/.txt/.mat<br /> ========================================================</p> <p><br /> This file contains important information about the current version of the dataset package. Downloading and using this material hints that you accept the EULA/Terms-of-Use (please read carefully).</p> <p>We welcome your comments and suggestions.</p> <p>_______________________________________________<br /> WHAT'S IN THIS PACKAGE?</p> <p>- Overview<br /> - Available file formats<br /> - Files and Datasets<br /> - License Agreement</p> <p>_______________________________________________<br /> OVERVIEW</p> <p>Obstructive sleep apnea-hypopnea syndrome (OSAHS) is a common disorder, in which upper airway resistance is increased during sleep due to upper airway dilator muscle relaxation and airway narrowing. It is a common disorder and a recognized public health problem, affecting roughly 2-4% of adult male and 1-2% adult female population. It is still under-diagnosed and believed to be linked with severe cardiovascular diseases, including hypertension, chronic fatigue, metabolic disorders, daytime sleepiness, etc.</p> <p>Additionally, recent studies in neurophysiology link brain strokes with some abnormal variances and change of patterns in oxygene saturation (SpO2) in the blood, due to (possible) damage in the brain, especially in the stem.</p> <p>This package contains two sets of data including real heart rate and SpO2 measurements throughout multiple timezones (awake or asleep) for a total of 118+55 unique patients of normal, stroke, and stem stroke classes. The data are of complete-signal registration in full resolution, so that it can be used for thorough signal processing (e.g. spectral analysis), feature extraction and classification for computer-aided diagnosis (CAD).</p> <p>The sources of the datasets were created in collaboration with the people of Acute Stroke Unit, Dept of Neurology, Eginition, School of Medicine, National Kapodistrian University of Athens (NKUA/UoA), Greece.</p> <p>These datasets have already been used in various publications describing OSAHS characterization. Detailed description and related conclusions can be found at:</p> <p>* Harris V. Georgiou, "Adaptive detection and severity level characterization algorithm for Obstructive Sleep Apnea Hypopnea Syndrome (OSAHS) via oximetry signal analysis" (28-Aug-2013) -- https://arxiv.org/abs/1308.6203</p> <p>_______________________________________________<br /> AVAILABLE FILE FORMATS</p> <p>The datasets are available in the following formats (included):</p> <p>*.xls : MS-Excel/LibreOffice native spreadsheets<br /> *.asc : space-separated plaintext spreadsheets<br /> *.mat : Matlab/Octave native data file (workspace)</p> <p>These data formats are equivalent, i.e., they contain the exact same sets of data. Normally, at least one of them should be compatible with any major programming platform (e.g. Matlab, Octave, R) or any native programming language for arbitrary handling (e.g. C, Java).</p>
Stroke Based MNIST Data
<p>The following dataset contains the MNIST dataset in stroke/point form. The data in this repository was based on the data obtained from the following project: https://github.com/edwin-de-jong/mnist-digits-stroke-sequence-data</p>
Characteristics of the initial stage and return stroke currents of rocket-triggered lightning flashes in southern China
<p>This study investigates the initial stage (IS) and return stroke (RS) currents of 50 triggered lightning flashes (TLFs) that were conducted in southern China. The IS of the negative TLFs has a longer duration, and larger average current, charge transfer, and action integral than those reported elsewhere, with geometric means (GMs) of 347.9 ms, 132.5 A, 45.1 C, and 10.0 × 10<sup>3</sup> A<sup>2</sup> s, respectively. Two positive TLFs containing no RS have much greater average currents, charge transfers, and action integrals in the IS when compared with the negative TLFs. The RS has a greater peak current (17.2 kA; GM, same to below), charge transfer within 1 ms (1.3 C), and action integral within 1 ms (5.8 × 10<sup>3</sup> A<sup>2</sup> s), and shorter 10% to 90% rise time (0.4 μs) than elsewhere. The peak current is prominently correlated with the rate of rise, charge transfer within 1 ms, and action integral within 1 ms. Furthermore, when the total duration of the RS and any following continuing currents is longer than 40 ms, the peak current, charge transfer within 1 ms, and action integral within 1 ms of the RS are seldom greater than 25 kA, 2.6 C, and 15 × 10<sup>3</sup> A<sup>2</sup> s, respectively. It is indicated that TLFs containing RSs tend to have a longer duration but a smaller charge transfer during the IS than those without RS. The peak current of the RS is weakly correlated with its preceding silence period when there was no channel base current.</p>
Preliminary Breakdown Process of Winter Positive Cloud-to-Ground Lightning Flash and Its relation to the Following First Return Stroke
<p>The file <em>+CG statistics.xlsx</em> contains various statistical parameters for 60 +CG events.</p> <p>The files <em>3D_UPB.dat</em>, <em>3D_DPB.dat</em>, <em>3D_IRPB1.dat</em>, and <em>3D_IRPB2.dat</em> provide the 3D location results for the four example events discussed in the main text. Each file includes data organized in four lines, representing time (ms), x (m), y (m), and z (m), respectively.</p> <p><strong> </strong></p>
[Dataset] APACPH-23-136: RESEARCH TREND ON STROKE CAREGIVER BURDEN, A BIBLIOMETRIC ANALYSIS
<p>Supplementary Material for Poster Presentation</p><p>APACPH-23-136: RESEARCH TREND ON STROKE CAREGIVER BURDEN, A BIBLIOMETRIC ANALYSIS</p>
ALAMEDA Data: Bridging the Early Diagnosis and Treatment Gaps of Brain Diseases (Parkinson's Disease, Multiple Slerosis and Stroke)
<p><strong>ALAMEDA</strong> is an Horizon 2020 Research and Innovation project that aims to bridge the early diagnosis and treatment gap of brain diseases via smart, connected, proactive and evidence-based technological interventions. Its vision is to research and prototype new generation Artificial Intelligence (AI) systems to support brain disorders patients' healthcare, focusing on Parkinson's Disease (PD), Multiple Sclerosis (MS) and Stroke.</p> <p>To this end, three (one for each disease) small scale validation pilots were performed in real world settings. Throughout these pilots, various types of data, such as accelerometer, gyroscopic, heart rate, etc., were collected via smart wearable sensors from the patients enrolled. The smart devices that were employed include: a Fitbit smartwatch, a GENEActiv smart bracelet, Novel Loadsol insole sensors and a prototype smart belt with triaxial accelerometers and gyroscopes embedded. Moreover, the patients underwent several clinical assessments and filled in numerous both disease-specific and non-disease-specific questionnaires.</p> <p>In this record, both raw and processed sensory data are combined with both clinical and patient reported outcomes (PROs) to form different disease-specific datasets. More specifically:</p> <ul> <li>For <strong>Parkinson's disease</strong>: Three datasets are provided (one for tremor detection, one for dyskinesia detection, and one for Hoehn & Yahr score estimation) alongside the vertical ground reaction force recordings.</li> <li>For <strong>Multiple Sclerosis</strong>: Two datasets are provided (one for Expanded Disability Status Scale (EDSS) scores classification and one that accumulates clinical data and individual scores from various MS-related questionnaires) alongside the vertical ground reaction force and the smart belt recordings.</li> <li>For <strong>Stroke</strong>: Two datasets are provided (one for rehabilitation exercises' recognition and one for walking classification, both with and without manual annotations) alongside the smart belt recordings.</li> </ul> <p>More information about the datasets provided can be found in the respective READ ME files that are included in the current record.</p>
All Tasks- STROKE - ALAMEDA Bracelet Data with manual Annotations
<p>The tasks_all_total_with_manual_annotations.csv file contains accelerometer data and task labels of the pilot stroke patients that are given in folder datasets/bracelet/<strong>stroke</strong>. It includes 11 patients that are manually annotated and consists of 7 columns. Those columns are:</p> <ol> <li> <p>x, y, z, which represent the accelerometer values of the bracelets sensors used on either left or right wrist of the patients</p> </li> <li> <p>T and time, which represent the timestamp of the activity (time) and the period (T).</p> </li> <li> <p>Patient ID column, which is the number id of the patients.</p> </li> <li> <p>Task column, which represents the task performed by the patient.</p> </li> </ol> <p>This file contains the tasks that are described below. Inside of each parenthesis, is given the name of each task, based on the annotations that were provided on datasets/annotations/raw_medical_tracking/stroke/<strong> intense-monitoring_with_manual_annotations_v2.xlsx</strong> file and on the accelerometer data that were available on stroke pilot folder mentioned above. Those tasks are:</p> <ol> <ol> <li> <p>cane_above_head (Cane above the head)</p> </li> <li> <p>standing_on_forefeet (Standing on the forefeet)</p> </li> <li> <p>lateral_steps (Lateral steps)</p> </li> <li> <p>rotation_cane (Rotations using a cane)</p> </li> <li> <p>cane_to_chest (Cane-to-chest)</p> </li> <li> <p>lateral_movement (Lateral movements with cane)</p> </li> <li> <p>hands_on_cane (Hands on the cane)</p> </li> <li> <p>lifting_knees (Lifting the knees)</p> </li> <li> <p>normal_walk (Normal walking)</p> </li> <li> <p>tandem_walk (Tandem walking)</p> </li> <li> <p>bicycle_walk (Bycicle walking)</p> </li> <li> <p>walk_with_knees_raised (Walking with the knees raised)</p> </li> <li> <p>rowing_movement (Rowing movements)</p> </li> <li> <p>flexion_extension_knees (Flexion and extension of the knees)</p> </li> </ol> </ol> <p> </p> <p>The features used to recognize activities in stroke patients are x,y,z and Task.</p>
Stroke case - Smart Belt Dataset
<p><span><span>The dataset contains 22,056,714 samples collected from patients with stroke. The data was gathered using a smart belt with accelerometer and gyroscope sensors. Two belts were used to record the physical activities of the patients, where Belt 1 (B1) comprised 3,158,873 samples, and Belt 2 (B2) contained 18,897,841 samples. Each belt was attached at specific body locations around the waist, including the left, right, and middle hip. The variables generated from the sensors were recorded at a frequency of 100Hz and are described as follows:</span></span></p> <ol> <li> <p><span><span><strong>Packet ID</strong></span></span><span><span>: distinguishes data packets that change with time. </span></span></p> </li> <li> <p><span><span><strong>Timestamp:</strong></span></span><span><span> the time that a reading was taken on a sensor</span></span></p> </li> <li> <p><span><span><strong>Timer:</strong></span></span><span><span> Timer of the sensor</span></span></p> </li> <li> <p><span><span><strong>Acceleration X:</strong></span></span><span><span> Linear acceleration in the X-axis</span></span></p> </li> <li> <p><span><span><strong>Acceleration Y:</strong></span></span><span><span> Linear accelerations in the y-axis </span></span></p> </li> <li> <p><span><span><strong>Acceleration Z:</strong></span></span><span><span> Linear acceleration in the z-axis </span></span></p> </li> <li> <p><span><span><strong>Gyroscope X:</strong></span></span><span><span> Angular acceleration in the x-axis </span></span></p> </li> <li> <p><span><span><strong>Gyroscope Y:</strong></span></span><span><span> Angular acceleration in the y-axis </span></span></p> </li> <li> <p><span><span><strong>Gyroscope Z:</strong></span></span><span><span> Angular acceleration in the z-axis</span></span></p> </li> <li> <p><span><span><strong>Temperature:</strong></span></span><span><span> internal temperature for signal compensation</span></span></p> </li> </ol> <p><span><span>After acquiring raw sensor signals, a series of minor preprocessing tasks were performed on this dataset. These include reformatting the data with a suitable format, removing null values, and preparing the data for analysis.</span></span></p>
Diagnostic and prediction value of synthetic magnetic resonance imaging in acute ischemic stroke patients
<p>This is the supplementary tables for the above paper, mainly including original statistical analysis data.</p>
CPAISD: Core-Penumbra Acute Ischemic Stroke Dataset
<p>The dataset contains 112 non-contrast cranial CT scans of patients with hyperacute stroke, featuring delineated zones of penumbra and core of the stroke on each slice where present. The data in the dataset are anonymized using the Kitware DicomAnonymizer, with standard anonymization settings, except for preserving the values of the following fields:</p> <ul> <li>(0x0010, 0x0040) – Patient's Sex</li> <li>(0x0010, 0x1010) – Patient's Age</li> <li>(0x0008, 0x0070) – Manufacturer</li> <li>(0x0008, 0x1090) – Manufacturer’s Model Name</li> </ul> <p>The patient's sex and age are retained for demographic analysis of the samples, and the equipment manufacturer and model are kept for dataset statistics and the potential for domain shift analysis.</p> <p>The dataset is split into three folds:</p> <ul> <li>Training fold (92 studies, 8,376 slices).</li> <li>Validation fold (10 studies, 980 slices).</li> <li>Testing fold (10 studies, 809 slices).</li> </ul> <p>The dataset has the following structure:</p> <ul> <li>metadata.json – dataset metadata</li> <li>summary.csv – metadata of each study in a CSV format table</li> <li>Part of the dataset (train, val, and test) <ul> <li>Study <ul> <li>Slice <ul> <li>raw.dcm – original slice file</li> <li>image.npz – slice in Numpy array format</li> <li>mask.npz – segmentation mask in Numpy array format</li> <li>metadata.json – slice metadata in JSON format</li> </ul> </li> <li>metadata.json – study metadata in JSON format</li> </ul> </li> </ul> </li> </ul> <p>The metadata.json at the root of the dataset has the following format:</p> <ul> <li>generation_params – dataset generation parameters: <ul> <li>test_size – proportion of the test part</li> <li>val_size – proportion of the validation part</li> </ul> </li> <li>stats – statistical data: <ul> <li>common – general statistical data: <ul> <li>train_size_in_studies – number of studies in the training part of the dataset.</li> <li>train_size_in_images – number of slices in the training part of the dataset.</li> <li>val_size_in_studies – number of studies in the validation part of the dataset.</li> <li>val_size_in_images – number of slices in the validation part of the dataset.</li> <li>test_size_in_studies – number of studies in the test part of the dataset.</li> <li>test_size_in_images – number of slices in the test part of the dataset.</li> </ul> </li> <li>train – statistical data for the training part of the dataset: <ul> <li>min – minimum pixel value.</li> <li>max – maximum pixel value.</li> <li>mean – average pixel value.</li> <li>std – standard deviation for all pixel values.</li> </ul> </li> </ul> </li> </ul> <p>The metadata.json at the root of the study has the following format, if a field value is unknown, it is given as 'unknown':</p> <ul> <li>manufacturer – manufacturer of the tomograph.</li> <li>model – model of the tomograph.</li> <li>device – full name of the tomograph (manufacturer + model).</li> <li>age – patient's age in years.</li> <li>sex – patient's sex. M – male, F – female.</li> <li>dsa – whether cerebral angiography was performed. true if yes, false if no.</li> <li>nihss – NIHSS score.</li> <li>time – time in hours from the onset of the stroke to the conduct of the study. Can be either a number or a range.</li> <li>lethality – whether the person died as a result of this stroke. true if yes, false if no.</li> </ul> <p>The summary.csv contains the same fields as the `metadata.json` from the root of the study, plus two additional fields:</p> <ul> <li>name – name of the study.</li> <li>part – part of the dataset in which the study is located.</li> </ul>
Evaluation of comparative Efficacy of two routes of administration of Carathmus Tinctorius (Medicated Enema and Nasal Administration) as an Adjuvant Therapy with Standard Care With Physiotherapy in the Rehabilitation of Thrombolytic Stroke (Pakshaghat): A Randomized Controlled Trial Protocol
Open the record for dataset details and reuse information.
Abnormal Upward Attempted Leaders Before the First Return Stroke of a Rocket-triggered Lightning Flash
<p><span>The electric field and optical data</span></p>
Acute_ischemic_stroke_subregions_MCA
<p>This dataset contains information of 302 people with acute ischemic stroke within the territory of the middle cerebral artery. This information regards to age, sex, race, and predominance of the infarct in the frontal or temporoparietal areas.</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.