dataesr/scientific-paragraphs-categorization huggingface dataset:
API for additional analyses, customization, and prototyping neurotic functions as a standalone application and also as a library with classes and functions that may be used in scripts or Jupyter notebooks. With neurotic's API, users can read configuration files, load data, and apply processing steps as in the GUI. The resulting data objects are represented using Neo's object model  (Garcia et al. , 2014)  and may then be used for additional analyses. For example, after identifying peaks, creating spike trains, and detecting bursts using a neurotic configuration stored in a YAML file or in memory as a Python dictionary, a user might statistically analyze burst durations, plot inter-spike interval histograms, create publication-quality figures continued the time corresponding to the displayed video frame.
Panels can be zoomed using the mouse, and time can be expanded or contracted using the mouse or the time width input. User-defined events are listed (bottom right panel), providing bookmarks to different parts of the experiment. A tool called the epoch encoder (bottom left panel) allows users to label time periods (controls are collapsed here; see D for details). Some panels can be double-clicked to open dialogue windows with additional options (e. g. , to hide plotted signals). B, Before displaying a user interface like that in A, neurotic provides a window for selecting datasets. Users load a dataset list from a manually created human-readable YAML configuration file that provides paths to data files and other parameters (Fig. 2 ). If data and video files are available on the internet, neurotic can be configured to locate and download files if they are not already available locally.
Icons to the left of each dataset name indicate whether files are already available locally or not. This window provides menus for loading options and color themes. The most important of these is the toggleable fast loading option, which is available for a subset of data file types and provides much faster loading with less memory consumption in exchange for skipping data processing steps like filtering and spike detection. C, Using optional parameters provided in the configuration file (Fig. 2G ), peaks can be detected on signals using amplitude-threshold window discriminators (see Fig. 3  for algorithm). Peaks identified by the same discriminator are grouped as a unit and plotted both as points on the signals and as raster plots in a separate panel (hidden in A but shown in this panel). D, The epoch encoder panel displays horizontal bars to represent user-created, labeled time periods.
Periods of interest, such as a particular behavior, the phase of a rhythmic behavior, or the activity of an identified neuron may be labeled using the epoch encoder. These data are saved to a plain text spreadsheet format (CSV) that may be used for further analysis. The epoch encoder panel allows precise control of the temporal parameters of epochs, merging of adjacent epochs, and filling gaps between epochs. Epochs can be placed using the range selection feature (magenta rectangle) with the mouse by dragging its edges. Keyboard shortcuts exist for rapid encoding. A table lists the existing epochs and allows them to be modified or removed. using a plotting package, or export data as part of a larger analysis. The neurotic package includes an example Jupyter notebook containing a tutorial which can serve as a starting point for new users, and documentation is provided for the API.
The incision was then sutured closed, and animals were allowed to recover for 1-3 d. Animals were presented with food in the form of seaweed strips (Deluxe Sushi-Nori, nagai roasted seaweed, Nagai Nori USA INC). While animals fed, signals from the hook electrodes were amplified and filtered (Model 1700 Differential AC Amplifiers, A-M. Systems), digitized (NI PCIe-6251 and NI BNC-2111, National Instruments), and recorded at 5000 Hz using AxoGraph X (AxoGraph Scientific) in continuous acquisition mode and saved in the AxoGraph file format, which is readable by Neo. In some trials, double-sided tape was placed between two seaweed strips to create an unbreakable strip, and this was attached to a force transducer (model FT03E, Grass Figure  2. An example configuration file. To visualize experimental data with neurotic, datasets must be listed and described within human-readable plain text YAML configuration files.
Multiple configurations for the same or different datasets may be listed within one file and are separated into blocks using blank lines and indentation. This example shows one configuration that is similar to that used to create Figures  1, 3. Each set of parameters shown is optional. A, Each configuration is given a unique name, e. g. , "feeding experiment. " Details associated with the configuration are indented beneath the name. A brief description of the dataset may be provided. The names and descriptions of datasets are displayed when a YAML file is loaded, giving the user the option to select the dataset to view (Fig. 1B ). B, Paths to related files found locally on the computer, e. g. , signals and video from a single experiment, may be located within a single directory specified by data_dir (such a file tree structure is convenient but not necessary as neurotic can accept relative or absolute paths).
A file containing signal data that is readable by the Python package Neo  (Garcia et al. , 2014)  is specified using data_file. Neo supports many file types (see list: https://neo. readthedocs. io/en/latest/io. html#module-neo. io). Signals are read from the data file, processed according to other, optional configuration settings, and plotted. For example, in this figure, sections D, E, G affect plotting. C, A video file continued may be associated with the signal data. Synchronization parameters can be given for controlling the video and signal data alignment. For example, in this case, video capture began 2. 51 s before signal data acquisition, so À2. 51 is provided for video_offset to shift the video start time. Parameters for correcting for frame rate inaccuracies or clipping the video are also available.
Instrument Division, Astro-Med Inc) oriented over the animal to measure downward pulling force during swallowing. Force was also amplified, low-pass filtered, digitized by a strain gauge conditioner (model 3170, Daytronic), and recorded using AxoGraph X. Simultaneously, video of behavior was captured at 30 frames per second using a webcam (Logitech HD Pro Webcam C920) and Logitech Webcam Software. Video was saved as MP4 files with H. 264 video encoding. A digital counter (C342-0562 totalizing counter, Veeder-Root) was programmed to increment at 10 Hz when the recording software was started; the counter was visible in the video recording at the start and end of the experiment, and pulses to the counter were recorded in AxoGraph X. This allowed the difference in timing between the start of video acquisition and the subsequent start of signal acquisition to be determined after the experiment, ensuring that the video and signal data could be synchronized by neurotic.
Results Using neurotic for reviewing behavioral video and synchronizing it with neural signals greatly reduces analysis time. Previously, alignment of video and neural signals, which were recorded and reviewed in separate programs with independent time bases, required that times of interest for every comparison had to be located separately in each program. neurotic solves the data stream alignment problem for old and new datasets. Moving through the video and finding the corresponding signal is done automatically in a unified interface, allowing for continuous playback and skipping to different parts of the experiment, making review easier, faster, and less prone to error. To illustrate the program's utility, we show alignment of behavior video with neural signals and force records (Fig. 4 ).
1D ) based on prior work. Amplitude windows for spike classes were estimated manually and specified in the configuration file. C, At load time, using algorithms from the Python package elephant (http://pypi. org/project/elephant), local maxima or minima were located within the given amplitude range and contained within a period with an appropriate epoch label. These points are displayed on the signals and as raster plots (Fig. 1C ). D, Subsets of each spike train were grouped into bursts defined by firing frequency thresholds (boxes). Notice that the initial B38 spike and the final B3 spike were excluded from the bursts because they did not meet the firing frequency conditions. Burst times may then be used for further analysis. load on feeding in Aplysia was simplified. Swallow durations were greater in the presence of load, and motor pattern changes could be quickly surveyed during multiple responses.
neurotic has broad applications because it uses the Neo Python library for reading a wide variety of file formats. For example, Figure  5  shows human EMG and kinematic data, stored as a binary MATLAB MAT-file, as a person maintains her balance while walking across a narrow beam. Left arm muscle activation timing corresponds to the moments that the participant could be seen correcting for a momentary balance loss. Similarly, a decrease in right shoulder elevation is seen simultaneously in both the video and a digitized record of a body marker tracking shoulder position in 3-D space. Using neurotic to explore an adaptive response to load during feeding. A, As an animal swallowed a strip of unloaded (unanchored) seaweed, activity of identified feeding motor neurons B6/B9 and B3 was identified from the extracellular nerve recording of BN2.
Discussion Using neurotic, behavioral and neural responses to stimuli can be identified and annotated rapidly and subjected to initial signal processing. Changes in neural activity during swallowing on unloaded and loaded food stimuli in Aplysia could be readily observed 4), and relationships between human movement and EMG were deducible (Fig. 5 ). In addition, dynamical model data could be readily incorporated and compared with empirical physiological data. Open source projects like this thrive on contributions from volunteer developers. Because neurotic uses the Neo package for reading electrophysiology files, members of the NeuralEnsemble community (http://neuralensemble. org) can readily contribute to its further development. In general, open source projects depend on an enthusiastic community of developers, and if neurotic attracts such a community, it will continue to develop.
The source code is stored on GitHub, which provides valuable collaboration tools such as issue tracking, pull request management, conversations, and task delegation. The open source model allows anyone to suggest improvements, submit bug fixes, or contribute new features; the community of maintainers seeks consensus on whether to accept contributions or request alterations; finally, changes are accepted only after passing a review process and automated testing. The complete history of the code can be found on GitHub, and prior releases of the software will remain available on distribution platforms like PyPI and Anaconda Cloud, so older versions of the software should always remain accessible. There are trade-offs scientists should consider when choosing to use either free, open source software for their research or proprietary, closed source software.
Commercial solutions that provide video synchronization capabilities similar to neurotic are few, and those proprietary alternatives may require expensive investments in software licenses and specific hardware. Maintaining proprietary systems depends on the continued financial viability of the company that makes them; old versions may stop working, and new hardware may be needed to upgrade software. For open source programs, maintainability again depends on the community. In some important ways, open source is superior for reproducibility and transparency, because the code is available for inspection, users suggest and often implement improvements to the code, and bugs are usually found and fixed quickly. Although proprietary systems often provide expensive "plugins" that can be added to the original software, open source projects excel at extensibility, because users can readily add functionality, or even specialize the program further for their own applications.
The major investment for an open source project is the time that needs to be devoted to the project, whereas the major investment associated with proprietary software and hardware solutions is the money that needs to be invested into the hardware and software. There are exciting possibilities for future development of neurotic. More signal processing options could be provided, such as customizable or even arbitrary multistage analysis procedures. Sound could be added to video playback. More advanced spike sorting techniques could be integrated [e. g. , using results from tridesclous (tridesclous. readthedocs. io) or NeuroChaT  (Islam et al. , 2019) ]. The pyqtgraph GUI library has many capabilities that expand neurotic's interactive and annotation features. Performance could be improved by incorporating GPU acceleration using the VisPy library (http://www.
This article presents two algorithms developed based on two different techniques, from clusterization theory, namely k-means clustering technique and Fuzzy C-means technique, respectively. In this context, the study offers a sustained comparison of the two algorithms in order to properly choose one of them, depending on the image to be analyzed and the solution that is desired. Algorithms are used in image processing, respectively as application of image processing techniques in brain computed tomography image analysis. There were also compared the results obtained by running the algorithms with a different number of centroids, as well as the execution times of each algorithm in part. Image processing and obtaining the results presented in this document was made possible by using the MATLAB R2018b environment.
The hemorrhagic one results from the perforation or rupture of a blood vessel in the brain. In order for the results to give confidence, the data set from the input of the presented system, in this case the tomography to be analyzed, must be made to a higher quality, since the filtering methods that can be applied to it can introduce values that will alter the final result. Image processing and obtaining the results presented in this document was made possible by using the MATLAB R2018b environment. This study contains five chapters namely the introduction, other three distinct chapters, followed by the conclusions of the developments produced in the fifth and last chapter. The second chapter describes the k-means algorithm and the possibilities of its application in medical imaging. Also, this chapter contains the set of results obtained from the analysis of a tomography.
• Centroid type. The technique randomly selects the centroid and allocates the rest of the points according to the distance from the centroid. The k-means algorithm stores information in the central memory, defining a number of k centroids and associating the points of their nearest centroid. A centroid can migrate together with the assignment of points. In order to obtain the results presented hereby in this article, it was necessary to use the kmeans function (division function) in the Statistics and Machine Learning toolbox contained in the MATLAB R2018b software. This function returns the number of each cluster to which a point has been assigned, after dividing the information into the k clusters. Each point in the information is processed as an object with a well-defined place in space.
• Resume the algorithm and select the solution with the smallest total sum of distances. Considering the advantages of the k-means algorithm, we find that we can easily use it in tomography analysis to determine if a patient has had a stroke or Alzheimer's  [5] -  [8]. Thus, we developed a program in the MATLAB R2018b programming environment that works as follows [3]: • The image containing the brain to be analyzed is loaded (Figure  1 ). • The image is translated into gray tones (using the rgb2gray function). • Clusters are created by partitioning information. • It is advisable to display a silhouette chart to verify the extent to which the points in each group are close to the neighboring groups. This value ranges from 1 (indicating points that are far removed from neighboring groups) to 0 (points that are not distinct in one cluster or another) to -1 (points that are probably allocated to the wrong cluster).
Of course, the hypothesis from which the program starts is essential in the process of determining the result. Therefore, using the 'Replicate' parameter, various solutions can be tested by repeating the clustering process starting from different values of the centroids and returning the result with the smallest total sum of distances between all the tests. The k-means algorithm can be applied, also in the MATLAB environment, using the imsegkmeans function that segments the image into k clusters set by the user. The function labels the clusters, and then, through another function, labeloverlay, we can display the clusters with one color, depending on the associated tag. This method helps to quickly identify the areas of interest in the image. Thus, in Figure  5 , it is easier to identify a disease such as Alzheimer's using the program described  [9] -  [11].
III. FCM ALGORITHM The Fuzzy C-means technique is one of the most popular image clustering methods  [5] ,  [12]. By having an easy   implementation, this process can provide desirable results if the hardware resources of the system running are generous. For 2D datasets, runtime, memory required, and output quality do not require very high values. On the other hand, for 3D sets, significant resources are recommended in order to achieve an efficient implementation. The computational efficiency is obtained using the histogram of the image intensities during the clustering process instead of the raw image data  [13] ,  [14]. The elements of the FCM algorithm are: • a set of n objects X = {x 1 , x 2. x n }, where x i is a point with d dimensions • a partitioning matrix W = w i,j ∈ [0, 1], i = 1.
Accordingly, the pre-processing procedure is necessary, whereas k-means clustering does not require pre-processing since it is unsupervised method and number of iterations is less in this case. Likewise, maximum lossless compression is achieved by k-means clustering. It also provides accurate results with minimal amount of data. Consequently, k-means clustering is more efficient than Fuzzy C Means method and is less error-sensitive, at the same time. The number of groups in which points can be assigned is set by the user before running the programs, depending on the component parts of the image (physically speaking, congruent with the different types of tissue). With the help of the programming environment MATLAB R2018b, two programs were implemented that processed (a series of filters were applied to eliminate the noise and adjust the contrast of the images) and analyzed the cranial tomography to detect the above-mentioned diseases.
How did this then new concept find its place among the other diagnostic concepts that had been used until then in this institution? Methods: In an attempt to answer this question, we implemented a methodology that combined a quantitative and a qualitative approach. The first is a retrospective descriptive statistical study whose objective is to establish the evolution of the proportion of the different diagnoses made at the Psychiatric Clinic of Strasbourg during the period 1920-1930. This study includes all hospitalized patients and uses admission records for data collection. This quantitative approach was complemented by a qualitative approach that consists in reconstructing the diagnostic trajectory of some patients with a diagnosis of schizophrenia after the period of introduction of this concept.
26 Consequently, radiprodil could be a candidate drug with the potential for controlling spasms. Radiprodil was initially developed as a treatment for neuropathic pain. The clinical experience with radiprodil was obtained in 460 adult individuals, including healthy volunteers in the phase 1 program and 332 patients with neuropathic pain. Development in neuropathic pain was terminated due to lack of efficacy, however these studies established a well-tolerated exposure range in adults which supported the dose selection for the ISS study. To enable pediatric development, a juvenile toxicology study in rat pups was conducted, and the pediatric oral formulation was developed. Bioequivalence of the pediatric formulation was demonstrated (NCT02647697)  27  and a microsampling technique for facilitating PK sampling in infant was validated.
Radiprodil: Preclinical Data The anticonvulsant effects of radiprodil were assessed in two standard models of generalized and focal seizures in adult mice. In the audiogenic seizure model, radiprodil displayed very potent, dose-dependent protective activity against generalized clonic convulsions, which are the key efficacy endpoint in this model. The obtained 50% effective dose (ED50) value was equal to 2. 1 mg/kg (Fig. 1A ). In contrast, radiprodil (up to 30 mg/kg) did not display any significant activity against focal seizures induced by electrical stimulation with 6 Hz current at 44 mA (data not shown). The anticonvulsant activity of radiprodil was also tested in male Wistar rats at different PN ages, that is, PN7, PN12, and PN70, using pentylenetetrazole (PTZ) as a convulsant which typically induces generalized tonic-clonic seizures in all age groups.
Radiprodil: Clinical Results of Phase 1b Testing in ISS Patients The clinical study was a phase 1b, open label, ascending dose adaptively designed study (NCT02829827). The first two participants were enrolled as sentinel subjects before the inclusion of any additional infants. The aim was to include 10 participants in the first part of the study until an interim analysis, but the study was stopped earlier because of the challenge in recruiting patients meeting all inclusion and exclusion criteria. 2 The exclusion criteria were as follows: -a diagnosis of ISS more than 6 months before screening -a structural brain abnormality that likely required surgery during the study -a clinically significant abnormality on electrocardiogram -any previous or ongoing medical condition that could have significantly interfered with the safety, tolerability and/or absorption, metabolism, and excretion of the study drug.
Rationale for dose selection The selection of the first lower dose and the upper dose limit was determined using available data from previous clinical experience with radiprodil and based on a number of considerations on the known NR2B pharmacology. First, safety considerations were based on the observation that 30 mg single or multiple dose were considered safe and well tolerated in adults also after long-term (14-week) treatment. The drug exposure associated with 30 mg was expected to achieve a maximum of 80% NR2B occupancy, that is below the uneventful drug concentration achieved in the juvenile toxicology dose (projected to achieve ~95% NR2B occupancy). Second, the lower drug concentration associated with anti-seizure efficacy in the preclinical rat pup PTZ model was observed with 60% NR2B occupancy, and the expected NR2B receptor occupancy associated with a dose of felbamate, that has affinity for NR2B, and is effective in ISS is around 30%.