cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3778406
title: KCNQ1 Antibodies for Immunotherapy of Long QT Syndrome Type 2

description: <strong>BACKGROUND</strong> Patients with long QT syndrome (LQTS) are predisposed to life-threatening arrhythmias. A delay in cardiac repolarization is characteristic of the disease. Pharmacotherapy, implantable cardioverter-defibrillators, and left cardiac sympathetic denervation are part of the current treatment options, but no targeted therapy for LQTS exists to date. Previous studies indicate that induced autoimmunity against the voltage-gated KCNQ1 K. channels accelerates cardiac repolarization. <strong>OBJECTIVES </strong>However, a causative relationship between KCNQ1 antibodies and the observed electrophysiological effects has never been demonstrated, and thus presents the aim of this study. <strong>METHODS</strong> The authors purified KCNQ1 antibodies and performed whole-cell patch clamp experiments as well as singlechannel recordings on Chinese hamster ovary cells overexpressing IKs channels. The effect of purified KCNQ1 antibodies on human cardiomyocytes derived from induced pluripotent stem cells was then studied. <strong>RESULTS</strong> The study demonstrated that KCNQ1 antibodies underlie the previously observed increase in repolarizing IKs current. The antibodies shift the voltage dependence of activation and slow the deactivation of IKs. At the single-channel level, KCNQ1 antibodies increase the open time and probability of the channel. In models of LQTS type 2 (LQTS2) using human induced pluripotent stem cell-derived cardiomyocytes, KCNQ1 antibodies reverse the prolonged cardiac repolarization and abolish arrhythmic activities. <strong>CONCLUSIONS</strong> Here, the authors provide the first direct evidence that KCNQ1 antibodies act as agonists on IKs channels. Moreover, KCNQ1 antibodies were able to restore alterations in cardiac repolarization and most importantly to suppress arrhythmias in LQTS2. KCNQ1 antibody therapy may thus present a novel promising therapeutic approach for LQTS2.
description: Additional funding acknowledgment: Fondation Gustave et Simone Prévot
DOI: 10.21227/j0a4-9w23
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.21227/kd4h-8k10
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.7284/134513
title: Multibeam Echosounder data collected during research cruise KM1808 using a Kongsberg EM122 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.21227/837a-xm80
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.21227/pa31-yx90
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.17632/7cpv86fwch.3
title: Data for: The role of political patronage on risk-taking behavior of banks in Middle East and North Africa region

description: Our sample contains 32 commercial banks operating in 6 MENA countries: Tunisia, Egypt, Lebanon, Jordan, Yemen and Iran. MENA region is made of many countries and large number of banks. Although the sample does not include all countries of the region, it is still representative for developing economies and countries characterized by authoritarian regimes and sharing same features (high level of corruption, weak legal system and business restrictions...). In such countries, political patronage may play a vital role. Annual financial data and key ratios are obtained from the Bureau van Dijk ‘Bankscope’ database. For the period of 2003-2014, an unbalanced panel data of 359 bank-year observations is constructed. For each individual we collect information on their biographies, current and former position held and political background such as positions in parliament, ministries and government, relationships with royal families, ministers or heads of state from various websites such as Bloomberg (https://www.bloomberg.com/), Zonebourse (https://www.zonebourse.com/) and Wikipedia (https://fr.wikipedia.org).
DOI: 10.21227/efxh-b384
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.25919/5eaadae091d8f
title: HyLogger3 Olympic Dam

description: Hyperspectral (VNIR/SWIR/TIR) drill core data set collected using HyLogger3 at GSSA's NVCL node at the Tonsley Core Library in Adelaide
DOI: 10.5281/zenodo.3778057
title: Systematic shotgun of bacterial genomes [fastq.gz]

description: Simulated shotgun from complete bacterial genomes. Genome accession is reported in the read name. This is a subsample (188,783,954 x 2 reads) of the whole dataset (1,887,895,180 x 2 reads). FASTQ format.
DOI: 10.5281/zenodo.3778058
title: Systematic shotgun of bacterial genomes [fastq.gz]

description: Simulated shotgun from complete bacterial genomes. Genome accession is reported in the read name. This is a subsample (188,783,954 x 2 reads) of the whole dataset (1,887,895,180 x 2 reads). FASTQ format.
DOI: 10.21227/3acn-h594
title: Proprioceptive Sensor Dataset for Quadruped Robots

description: These datasets are of the hydraulically actuated robot HyQ’s proprioceptive sensors. They include absolute and relative encoders, force and torque sensors, and MEMS-based and fibre optic-based inertial measurement units (IMUs). Additionally, a motion capture system recorded the ground truth data with millimetre accuracy. In the datasets HyQ was manually controlled to trot in place or move around the laboratory. The sequence includes: forward and backwards motion, side-to-side motion, zig-zags, yaw motion, and a mix of linear and yaw motion. All of the datasets are at least five minutes long. The aim of these datasets is to test state estimation using only proprioceptive sensors.
DOI: 10.21227/52m0-sr58
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.7488/defdf9eb-871d-4cad-af48-c9913f46e8cd
title: Mobility, Mood and Place - Co-created Environments

description: Three associated datasets generated by the Co-created Environments strand of the 3-year research project Mobility, Mood and Place (PI Professor Catharine Ward Thompson). 1. Environmental audits of older adults' walking routes - the FASTVIEW audit tool was used in combination with Google Street View to complete virtual street audits on the walking routes selected by 19 physically active older adults. Linked to the publication: Brookfield & Tilley 2016 Using virtual street audits to understand the walkability of older adults’ route choices. 2. Environmental preferences of persons with experience of stroke - Qualitative focus group data collected from 3 community stroke clubs capturing the views of 26 individuals with experience of stroke. The focus groups explored participants' 'enjoyable environments' (where they liked to spend time, what environments made them feel 'good' and so on). Linked to the publication: Brookfield & Mead 2016 Physical environments and community reintegration post-stroke: Qualitative insights from stroke clubs 3. Environmental preferences of diverse older adults (healthy volunteers, stroke survivors and people with dementia) - Interview and focus group data collected from 22 older adults including healthy volunteers (14), stroke survivors (5) and people with dementia (2) covering such subjects as: attitudes to the home, preferred environments, important aspects of the home and neighbourhood. Linked to the publications: Brookfield et aL. 2017 The uncommon impact of common environmental details on walking in older adults, and, Brookfield et al. 2015 The home as enabler of active lifestyles among older people.
description: See 'Description'.
DOI: 10.21227/rcb1-pa62
title: Error-Concealed Video Dataset (ECVD)

description: This is a dataset of 120 error-concealed video clips. The clips were generated from 6 CIF, 6 HD and 6 Full-HD test video sequences. Each of those sequences was error concealed with 4 Error Concealment (EC) techniques: Motion Copy, Motion Vector Extrapolation, Decoder Motion Vector Estimation (DMVE) + Boundary Matching Algorithm (BMA), and Adaptive Error Concealment Order Determination (AECOD). The dataset also includes the original (loss free) video clips, as well as the subjective ranking of the error-concealed videos. The original purpose for generating this dataset is to evaluate the performance of various Image/Video Quality Assessment (I/VQA) methods in how well they compare the quality of error-concealed videos. In other words, if the output of EC technique A is a better-quality video than EC technique B, which I/VQA metric predicts this correctly.For more information please refer to the following paper:M. Kazemi, M. Ghanbari, and S. Shirmohammadi, “The Performance of Quality Metrics in Assessing Error-Concealed Video Quality,” IEEE Transactions on Image Processing, vol. 29, pp. 5937-5952, 2020.
DOI: 10.21227/wch5-1353
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.21227/29c1-pw81
title: Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT

description: This paper applies AI (artificial intelligence) technology to analyze low-dose HRCT (High-resolution chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training. The model improves the existing techniques used for low-dose HRCT image inspection through an application of Stacked Autoencoders (SAEs) structures using the segmentation function for the area object detection model on Mask-RCNN. As a result, the proposed approach can quickly analyze X-ray images in detecting abnormalities in patients with lab-confirmed coronavirus even before clinical symptoms appear. In addition to detecting early abnormalities, area object detection model reveals a finding not seen in the latest cases of COVID-19. Most noteworthy, the study has shown that all COVID-19 patients exhibit an associated bilateral pleural effusion. The features are augmented to the model for the improvement of detection quality improvement and the shorten of the examination period.
DOI: 10.21227/724k-j073
title: Mask-RCNN detection of COVID-19 pneumonia symptoms by employing Stacked Autoencoders in deep unsupervised learning on Low-Dose High Resolution CT

description: This paper applies AI (artificial intelligence) technology to analyze low-dose HRCT (High-resolution chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training. The model improves the existing techniques used for low-dose HRCT image inspection through an application of Stacked Autoencoders (SAEs) structures using the segmentation function for the area object detection model on Mask-RCNN. As a result, the proposed approach can quickly analyze X-ray images in detecting abnormalities in patients with lab-confirmed coronavirus even before clinical symptoms appear. In addition to detecting early abnormalities, area object detection model reveals a finding not seen in the latest cases of COVID-19. Most noteworthy, the study has shown that all COVID-19 patients exhibit an associated bilateral pleural effusion. The features are augmented to the model for the improvement of detection quality improvement and the shorten of the examination period.
DOI: 10.21227/9nnq-x606
title: ECo-PPTD dataset

description: Truth discovery techniques, which can obtain accurate aggregation results based on the weighted sensory data of users, are widely adopted in industrial sensing systems. However, there are some privacy matters that cannot be ignored in truth discovery process. While most of the existing privacy preserving truth discovery methods focus on the privacy of sensory data, they may neglect to protect the privacy of another equally important information, the tagged location information. In this paper, based on the three-layer architecture of edge computing, we introduce an efficient location privacy preserving truth discovery (ECo-LPPTD) scheme. By structuring data and applying homomorphic Paillier encryption, signcryption method, the privacy of location information and sensory data can be well protected in our scheme while the accuracy of aggregation can be guaranteed. Theoretical analysis and complete experiments performed on an industrial scenario demonstrate the efficiency of the proposed scheme.
DOI: 10.21227/b0ny-qq78
title: ECo-PPTD dataset

description: Truth discovery techniques, which can obtain accurate aggregation results based on the weighted sensory data of users, are widely adopted in industrial sensing systems. However, there are some privacy matters that cannot be ignored in truth discovery process. While most of the existing privacy preserving truth discovery methods focus on the privacy of sensory data, they may neglect to protect the privacy of another equally important information, the tagged location information. In this paper, based on the three-layer architecture of edge computing, we introduce an efficient location privacy preserving truth discovery (ECo-LPPTD) scheme. By structuring data and applying homomorphic Paillier encryption, signcryption method, the privacy of location information and sensory data can be well protected in our scheme while the accuracy of aggregation can be guaranteed. Theoretical analysis and complete experiments performed on an industrial scenario demonstrate the efficiency of the proposed scheme.
DOI: 10.21227/ppmp-mm26
title: Error-Concealed Video Dataset (ECVD)

description: This is a dataset of 120 error-concealed video clips. The clips were generated from 6 CIF, 6 HD and 6 Full-HD test video sequences. Each of those sequences was error concealed with 4 Error Concealment (EC) techniques: Motion Copy, Motion Vector Extrapolation, Decoder Motion Vector Estimation (DMVE) + Boundary Matching Algorithm (BMA), and Adaptive Error Concealment Order Determination (AECOD). The dataset also includes the original (loss free) video clips, as well as the subjective ranking of the error-concealed videos. The original purpose for generating this dataset is to evaluate the performance of various Image/Video Quality Assessment (I/VQA) methods in how well they compare the quality of error-concealed videos. In other words, if the output of EC technique A is a better-quality video than EC technique B, which I/VQA metric predicts this correctly.For more information please refer to the following paper:M. Kazemi, M. Ghanbari, and S. Shirmohammadi, “The Performance of Quality Metrics in Assessing Error-Concealed Video Quality,” IEEE Transactions on Image Processing, vol. 29, pp. 5937-5952, 2020.
DOI: 10.21227/h1bf-jz17
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.3886/e109942
title: Erdal Can Alkoclar

description: An Antiviral Formulation consisting of 3 1-Protopanaxatriol<br>Analouges with Simultenaous DNA Polymerase and Ribonucleotide Reductase<br>Suppressive Properties
DOI: 10.21227/j0m6-q628
title: EEG Data for Patients Receiving Intravenous Antibiotic Medication

description: In this dataset, we conduct a biological data collectionand analysis study for patients undergoing routine plannedtreatment. The acquired data (i.e., Electroencephalogram (EEG)and routine observational data such as temperature and bloodpressure) are processed using different machine learning anddeep learning models to learn the dynamic properties of brainelectrical activity from different patients. Thus, the primaryobjective of our study is the safe collection of EEG data frompatients receiving antibiotic therapy, in addition to analyzingthe acquired data for detecting the medication side effects, i.e.,that might indicate risk of seizure. Indeed, we propose twomachine learning models to analyze the acquired data fromdiverse patients: before, during, and after taking the medications.
DOI: 10.5281/zenodo.3778411
title: 2020 - Brazil fieldwork backup

description: 2020 - Brazil fieldwork backup. Fieldwork had to stop after 4 weeks due to the worldwide COVID-19 outbreak.
DOI: 10.5281/zenodo.3778410
title: 2020 - Brazil fieldwork backup

description: 2020 - Brazil fieldwork backup. Fieldwork had to stop after 4 weeks due to the worldwide COVID-19 outbreak.