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This is the primary data used in our publication by Kori et al., 2020
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Data from Western Blot. Protocols for immunoblotting.
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This folder contains BatClassify results files used to create Temporal Pass Plots in the accompanying article Fig. 2 (Richmond_Myotis_BatClassify_Results.csv), Fig. 3a (Richmond_Ppyg_Site_A_BatClassify_Results.csv) and Fig. 3b (Richmond_Ppyg_Site_B_BatClassify_Results.csv). All other files are used in the TPP vignette, which is provided in the supplementary material.
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The included tests were performed at McMaster University in Hamilton, Ontario, Canada by Dr. Phillip Kollmeyer (phillip.kollmeyer@gmail.com). If this data is utilized for any purpose, it should be appropriately referenced. -A brand new 3Ah LG HG2 cell was tested in an 8 cu.ft. thermal chamber with a 75amp, 5 volt Digatron Firing Circuits Universal Battery Tester channel with a voltage and current accuracy of 0.1% of full scale. these data are used in the design process of an SOC estimator using a deep feedforward neural network (FNN) approach. The data also includes a description of data acquisition, data preparation, development of an FNN example script. -Instructions for Downloading and Running the Script: 1-Select download all files from the Mendeley Data page (https://data.mendeley.com/datasets/cp3473x7xv/2). 2-The files will be downloaded as a zip file. Unzip the file to a folder, do not modify the folder structure. 3-Navigate to the folder with "FNN_xEV_Li_ion_SOC_EstimatorScript_March_2020.mlx" 4-Open and run "FNN_xEV_Li_ion_SOC_EstimatorScript_March_2020.mlx" 5-The matlab script should run without any modification, if there is an issue it's likely due to the testing and training data not being in the expected place. 6-The script is set by default to train for 50 epochs and to repeat the training 3 times. This should take 5-10 minutes to execute. 7-To recreate the results in the paper, set number of epochs to 5500 and number of repetitions to 10. -The test data, or similar data, has been used for some publications, including: [1] C. Vidal, P. Kollmeyer, M. Naguib, P. Malysz, O. Gross, and A. Emadi, “Robust xEV Battery State-of-Charge Estimator Design using Deep Neural Networks,” in Proc WCX SAE World Congress Experience, Detroit, MI, Apr 2020 [2] C. Vidal, P. Kollmeyer, E. Chemali and A. Emadi, "Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning," 2019 IEEE Transportation Electrification Conference and Expo (ITEC), Detroit, MI, USA, 2019, pp. 1-6.
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Temporal activity patterns can potentially reveal useful information about behaviour, phenological changes and emergence times for bat species; however, detailed assessments of temporal activity are infrequently performed or published for bats. Passive electronic devices, such as autonomous recording units and camera traps, are increasingly being used as a means of monitoring various species, communities and habitats. Data recorded by these devices inherently contain file metadata detailing the dates and times when data capture took place. We have utilised this metadata to create the Temporal Pass Plot (TPP), which provides intuitive, yet highly detailed, visualisations of temporal bat activity over prolonged periods of time. Furthermore, TPPs are produced using a common scale based upon activity within predetermined time-blocks, enabling direct comparisons between different sites and species to be performed. TPPs reveal inter- and intra-specific differences, and seasonal changes, in temporal activity. As a relatively untapped area of research, further study is required to evaluate associations between activity patterns and different behaviours (e.g. roosting, commuting and swarming). However, if this can be achieved, the scope of assessments that could be performed with passive monitoring technologies could be significantly expanded, enabling more detailed evaluations of habitat use to be performed with minimal disturbance to the target species. Although the TPP was principally designed for the purpose of studying bat activity, it can easily be adapted for other species that can are monitored using autonomous recording devices. Article data: This folder contains the data files used to create all three Temporal Pass Plots shown in the main article. Vignette data: This folder contains all files described in the TPP vignette, which is provided in the supplementary materials of the main article.
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Here we define the structural and biological basis by which a class of small molecules (lead compound denoted DT-061), selectively binds and stabilizes a single B-subunit containing Protein Phosphatase 2A (PP2A) heterotrimer to drive the targeted dephosphorylation of select PP2A substrates that include c-MYC. This work provides structural and molecular insights into PP2A holoenzyme regulation, identifies a new therapeutic strategy for protein complex targeting and activation, and presents a basis for phosphatase activating therapeutics.
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Origin graph files of the manuscript LiFePO4_S cathode proof. Figure 1 Cyclic Voltammetry of LiFePO4-S composite with LiPF6 electrolyte. Figure 2 Cyclic Voltammetry of LiFePO4-S composite with LiTFSI electrolyte. Figure 3 dQ/dV curves calculated from charge/discharge cycling data of the LiFePO4-S composite cathode with LiTFSI electrolyte: Lithiation in cycle 3 (a) and cycle 5 (b), and delithiation in cycle 3 (c) and cycle 5 (d). Figure 5 XRD pattern of hydrothermal carbon - LiFePO4 composite prepared in acetic acid. The pattern of LiFePO4 without carbon coating is shown as a reference. * Peak corresponding to graphitic carbon. Figure 6 Raman spectrum of hydrothermal carbon-LiFePO4 composite after thermal treatment at 650 °C. The Raman spectrum of LiFePO4 reagent without carbon coating is shown as a reference. Figure 7 dQ/dV curves calculated from charge/discharge cycling data of composite cathodes of carbon-coated LiFePO4 (prepared in acetic acid) and sulfur infiltrated in porous carbon: Lithiation in cycle 3 (a) and cycle 5 (b), and delithiation in cycle 3 (c) and cycle 5 (d).
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The files uploaded include working tables related to the bycatch mitigation and outcomes (Table A.1); seal exclusion device (SED) trials with problems encountered and how they were solved (Table A.2); square mesh net barrier trials (Table A.3); underwater footage analysed (Table A.4) and the current license conditions regarding SED requirements (Fig.A.1).
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In computer security, network botnets still represent a major cyber threat. Concealing techniques such as the dynamic addressing and the Domain Name Generation Algorithms (DGAs) require an improved and more effective detection process. To this extent, this data descriptor presents a collection of over 30 million manually-labelled algorithmically generated domain names decorated with a feature set ready-to-use for Machine Learning analysis. This proposed data set enables researchers to move forward the data collection, organization and pre-processing phases, eventually enabling them to focus on the analysis and the production of Machine-Learning powered solutions for network intrusion detection. To be as exhaustive as possible, 50 among the most important malware variants have been selected. Each family is available both as list of domains and as collection of features. To be more precise, the former is generated by executing the malware DGAs in a controlled environment with fixed parameters, while the latter is generated by extracting a combination of statistical and Natural Language Processing (NLP) metrics.
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Smoke Test on 17Jul2019 natscilivecustomer (Dataset-1) Smoke Test on 17Jul2019 natscilivecustomer (Dataset-2)
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