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Figure 4. Result of Second Fraction-Data Conflict Resolution among Same Entities in Web of Data
<p>In the last stage the size of the data set is examined. The number of instances of specialized<br> entities and total entities are calculated. For example "London" is an instance of a specialized entity.<br> In table 2 one of the properties of "London" is displayed in form of a triple. As is shown, the object<br> of the triple is dbpedia-owl:Place that contains "place" as a member of the specialized entity. And 'A<br> Trip to the Moon' is an instance of non-specialized entity because neither the subject nor the object<br> is in the specialized entities list. After calculating the number of specialized instances and total<br> instances the result of the second fraction of equation 4 is shown in figure 4.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 2: All axial slices of sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients.
<p>Results from the sLORETA imaging indicates decreased neuronal activation within the Left<br> Fusiform Gyrus located in the Temporal lobe and Parrahippocampal gyrus, which is located in the<br> Limbic Lobe (Table 1). This correlates with other fMRI findings where patients with early onset<br> AN have exhibited reduced unilateral blood flow in the temporal lobe. The Parahippocampal and<br> Fusiform Gyri are centers that process emotions. Previous studies in which these regions have<br> shown activation involve women that have distorted perceptions of their bodies from a cognitive<br> perspective (Santel et al., 2006). It is apparent from our findings that the Fusiform Gyrus may play<br> a vital role in the processing of visual appearance of the human body. There is also a correlation<br> with the somatosensory limbic pathway in the limbic lobe, due to the similarity of function.</p>
BRAIN Journal-Electrophysiological Neuroimaging using sLORETA Comparing 12 Anorexia Nervosa Patients to 12 Controls-Figure 1: sLORETA imaging results of Resting State EEG Supra-Threshold Voxels in both the Parahippocampal (limbic) and Fusiform (temporal) Gyri illustrating decreased neuronal activity in the Anorexia Nervosa patients relative to Control participants
<p>The findings of the sLORETA analysis indicated that, the difference is statistically<br> significant (p=0.03) using a one-tailed t-test: Anorexia > Controls. The brains of the patients with<br> Anorexia Nervosa illustrated decreased neuronal activity in the Left Fusiform Gyrus and the Left<br> Parahippocampal Gyrus (p=0.03) in the resting-state brains when Anorexia Patients were sitting for<br> 3min, as compared to the Controls sitting for 3minutes.</p>
AQL queries and benchmark results from PhD thesis "ANNIS: A graph-based query system for deeply annotated text corpora"
<p>These are the queries, the benchmark results and the evaluation scripts of the thesis "ANNIS: A graph-based query system for deeply annotated text corpora" (Thomas Krause 2018, Humboldt-Universität zu Berlin)</p> <p><strong>diss_2018-01-12_v0.5.0.csv </strong><br> Results of all configurations of executed benchmarks for graphANNIS and also the baseline times of relANNIS.</p> <p><strong>queries.zip</strong><br> Contains folders for each corpus containing all queries used for the benchmark. Each file-name begins with the ID of the query. The extension denotes the type, which can be one of the following:</p> <ul> <li><em>".</em>aql" contains the original AQL (ANNIS query language) query which was collected</li> <li>".json" is JSON representation of the parsed AQL query</li> <li>".count" is the number of matches a query should have</li> <li>".time" is the average time in milliseconds that was needed to execute the query in relANNIS on the benchmark system</li> <li>".corpora" contains the name of the corpus the query belongs to (should be only one corpus and the same as the folder name in the selection of queries in this data set)</li> <li>".relplan" contains the PostgreSQL plan for the query</li> <li>".graphplan" contains the graphANNIS plan for the query</li> </ul> <p><strong>evaluation-scripts.py/evaluation-scripts.ipynb</strong><br> Python scripts to perform the evaluation and generate the images. This are both a Python-file and the original notebook file that can be used with the Jupyter Notebook application.</p> <p><strong>relannis_benchmark_scripts.zip </strong><br> The files in this zip-file can be used to execute the benchmarks in the relANNIS system by piping the into the "annis.sh" command line tool of relANNIS</p>
Figure 2. The mean results, representing the importance of online/blended course components, for all participants-How to Mix the Ingredients for a Blended Course Recipe
<p>Almost two thirds (62%) of the institutions provide access to Open Source LMSs, while only a<br> third use proprietary LMSs (such as AeL or Blackboard), and we can also note that almost half (43%)<br> of the participants are not even interested in proprietary LMSs. All these figures demonstrate once<br> again the openness of the Romanian educa-tional system towards online/blended learning and,<br> furthermore, towards Open Source or free solutions. Specific policies related to open/blended courses<br> and trainings for devel-oping such courses are offered only by a third of the institutions, thus the<br> education managers and policy makers should pay an increased attention to these issues.<br> The importance of course elements was assessed on a five-point Likert scale, where 1 means<br> “not at all important” and 5 means “very important”. Nineteen elements of an online course were<br> evaluated on this scale. The results synthesized in Figure 2 illustrate the importance of each element for<br> all participants in this study, regardless of their e-learning experience.</p>
Results of survey to potential users of repositories of European Poetry on the informational needs
<p>This excel file presents the results of a survey to potential final users of digital repertoires of the European Poetry in order to understand their information needs. This file also presents results of the analysis to the survey: answers to the specific needs of the results - in black if the domain model responds to the needs; green if a new element to the domain is added due to the result.</p> <p>This survey was done in the context of the definition of a domain model for the european poetry. A domain model (or data model) is a milestone in the process of development of a metadata application profile.</p> <p>Together with this survey, other efforts were taken in place:</p> <p>1) Analysis of the structure of databases that serve digital repertoires of European Poetry</p> <p>2) Analysis of the information needs of graphical user interphaces of the digital repertoires of European Poetry</p> <p>The map https://goo.gl/O0mqhI presents the repertoires used and the phases of the analysis.</p> <p>This work is framed in a Starting Grant research project Poetry Standardization and Linked Open Data: POSTDATA (ERC-2015-STG-679528), funded by European Research Council (ERC).</p>
The Brain and Propranolol Pharmacokinetics in the Elderly-Figure 1.(a)Results of the Monte-Carlo simulations to describe pharmacokinetics of young patients with validation from the Taegtmeyer 2014 publication(Taegtmeyer et al., 2014)
<p>Propranolol has been found to be therapeutically effective, to obtain a clinical response by<br> beta-adrenoceptor blockade, at plasma levels of greater than 20 ng/mL(Coltart et al., 1971;<br> Frishman, 1988; Johnsson and Regàrdh, 1976). Thus, to display the data, we used highlighted<br> plasma concentration where the pharmacokinetic curve falls below 20ng/mL threshold for<br> therapeutic efficacy in the patient’s plasma.</p>
Figure 2.(a)Results of the Monte-Carlo simulations describing a population of elderly patients after a single oral dose of propranolol.-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>In effort to identify the recommended Propranolol dosage for elderly patients, we identified<br> the patient package inserts from the Food and Drug Administration (FDA) Inderal label, who<br> manufacture propranolol. Based from FDA Wyeth Propranolol label, for dosing in the geriatric<br> population,the label states that there were not sufficient numbers of clinical study participants who<br> were 65-years and older to properly determine the difference in response young and elderly<br> patients.</p>
Figure 4. Simulation (Monte-Carlo, n=200) results elderly patients taking a 10mg oral dose resulting in similar Cmax, maximum plasma concentration, to the young patients taking a 40mg oral dose. The dotted lines illustrate the 10th and 90th percentiles of plasma levels of the elderly population with a 10mg oral administration of propranolol.-The Brain and Propranolol Pharmacokinetics in the Elderly
<p>Thus, the package insert (see 1) recommends clinicians start at the lower end of the dosing<br> range, without further details.<br> Similarly, Pfizer manufactures Inderal® LA (Propranolol HCI), which is the long-acting<br> form of propranolol and their package insert (see 2) states, “There is no information available for<br> elderly patients.” Though the kinetics for the long-acting formdiffers from the standard form,<br> manufactured by Wyeth, we would suspect a 10mg dose for the elderly would achieve a similar<br> maximum plasma concentration (Cmax) to that of the younger patient cohort.This 10mg, which is<br> 25% of the original 40mg, dosing schedule is based on our simulations at 10mg in the geriatric<br> population.</p>
Figure 8. Sample Emotion Classification Result-Classification of Human Emotion from Deap EEG Signal Using Hybrid Improved Neural Networks with Cuckoo Search
<p>For all the 32 participants the EEG signal has to be sampled and process their emotions. The<br> emotions are depends on the music and video clips. In this paper the video clips are changed from<br> one person to other person. Here hybrid feed forward neural networks with radial basis function;<br> probabilistic neural network classifier is used to classify the emotions from EEG. PNN is very fast<br> and insensitive neural network which provides the optimized classification result. Compare to the<br> multi layer perception neural network it provide accurate result. It classifies the emotion into two<br> different groups like arousal and valence. Figure 8 shows that the model implemented result of<br> emotion classification and person identification.</p>
Results of a research software programming and development survey at the University of Reading
<p>In 2017 an online survey of University of Reading staff active in or supporting research and registered PhD students was undertaken to assess the nature and extent of research programming and software development activities in the University, and to understand how the University might provide guidance, training and support. The survey was a administered by the Research Data Manager on behalf of the University's Research Data Management Steering Group. The survey ran from 1st November to 15th December 2017 and collected a total of 170 responses.</p> <p>The survey sought responses from anyone in the University who was involved in any of the following activities:</p> <ul> <li>writing code and using software for numerical and statistical analysis;</li> <li>creating and contributing to computational models or simulations;</li> <li>conducting Text and Data Mining (TDM) and content analysis;</li> <li>creating and contributing to software distributed as a product or implemented as a service;</li> <li>creating data visualisations;</li> <li>using markup languages to structure and render content.</li> </ul> <p>The survey was distributed using the Bristol Online Survey. A dataset of anonymised survey responses and a PDF of the survey questions are here included.</p>
Figure 3. Graph created directly from the results.-Browsing Semantic Data in Slovakia
<p>Resulting graph is rather complex. There are 175 vertices and 201 edges, which were created directly, containing 158 persons and 17 companies. As we see on Figure 3, some filtering methods are required in order to create suitable overview of relations. Figure 4 thus shows the visualization of the same graph, but with tens of vertices merged. Now it contains 22 persons and 17 companies. A merging was performed for clarification and is built inside the visualization module. A simple condition says that a merging is performed if persons are unique, thus if a person is connected only to 1 firm. More formally, person vertices are merged, if their vertex degree equals 1 (each).</p>
Figure 2. Results for firm name "Váhostav" are in table. Each row defines a firm with its name, identification number and address. Then a connection is specified (whether it be a person or another firm).-Browsing Semantic Data in Slovakia
<p>We have searched for firm “Váhostav”, which is a rather big firm in Slovakia, with many press articles published about31. On Figure 2 there is a browsing window, for SBR data results, displaying tabular structure, which was refined from SBR dataset by continuous querying.</p>
Figure 7. Results obtained for the mean, the mean standard deviations and the mean standard errors of the independent speech variable in dyslalic subjects (both the control and the experimental group)
<p>Following the logopedic assessment of the investigated subjects, we moved on to the statistic processing of the gathered data, and we analysed the results obtained from the tests administered to the two groups after one year of speech therapy. The conducted analysis was directed both at the overall effectiveness of the speech therapy, and at the importance of strategies for the language development and stimulation, through the use of the computer-assisted Terapers system. It was noticed that the results obtained for the dyslalic subjects (children with pronunciation disorders) from the experimental group improved significantly due to the computer-based therapeutic program, compared to the subjects in the control group (who underwent classical therapy). The mean, the standard deviations and the standard error of the mean obtained for the independent speech variable in dyslalic children (control group and experimental group) are presented in Figure 7.</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 4. The EMD decomposition results for subject 2 when he imagines left hand movement
<p>Fig. 4 shows the EMD decomposition result of one-trial (left hand movement imagination) for subject 2 in the channels C3 and C4 respectively (the pre-filtered EEG signal used for this illustration is not corrupted by blinking artifact.). Each channel is decomposed into ten IMFs and one residue</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 4b. The EMD decomposition results for subject 2 when he imagines left hand movement
<p>d et al., 2011). Fig. 4 shows the EMD decomposition result of one-trial (left hand movement imagination) for subject 2 in the channels C3 and C4 respectively (the pre-filtered EEG signal used for this illustration is not corrupted by blinking artifact.). Each channel is decomposed into ten IMFs and one residue.</p>
BRAIN Journal-Micro Expression Recognition Using the Eulerian Video Magnification Method-Figure 2. Result of the implementation of EVM method.
<p>In our research, we focus on the parameter values in the second case. Then we convert the obtained video to the images sequence. The number of images is decreased after the implementation of the EVM method. The reason of this problem is the integration of multiple pixels in the images. The resulting output is a generic extract of all motions created in the face.</p> <p>In this study, two experiments are performed as follows: Phase1: investigating the emotional/unemotional detection on face in micro expressions. Phase2: investigating facial micro expressions recognition </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 3. (3.a) – The flowchart of Graph cuts method; (3.b)- the result of Graph cuts image segmentation.
<p>Figure 3 describes the steps implemented Graph cuts algorithm for the segmentation of human body parts. The results obtained are 5 main sections that include the hands, the legs, the center of the body (chest, waist, hips), and the head. The result of the display image is taken from the human image database, which was collected by us (Нгуен, 2016). </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 6. The result of building a 3D model based on RF and SVM classification with "Important features".
<p>From the chart of figure 6, we found that "Important Features" gave the best 3D model, which fits with the object in the image. The pattern is close to 90% compared with the true size. Apply classification algorithm RF increases the accuracy of the results and reduces computing time for the program. There are many methods for data classifying. One of them is the method of the support vector machine (SVM). The SVM method is represented by Vladimir N. Vapnik (1995) in Support Vector Machines (SVM) - a set of learning algorithms similar with the supervisor has two main tasks: the classification and the regression analysis. In this article we use the method of the SVM classification problem for the size of the human body with 5 classes to compare the performance between SVM methods and Random Forest algorithm. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 4. Flowchart and results of ICP algorithm
<p>The key concept of the standard ICP algorithm can be summarized in two steps: - Compute correspondences between the two scans. - Compute a transformation which minimizes the distance between corresponding points. It is forced to add a maximum matching threshold dmax. In most implementations of ICP, the choice of dmax represents a tradeoff between convergence and accuracy. A low-value result in bad convergence, a large value causes incorrect correspondences to pull the final alignment away from the correct value. Figure 4 describes the steps of the algorithm which determines the point features closest to object boundary. The result of the algorithm is described by images cut from the program (Нгуен, 2016)</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.