dataesr/scientific-paragraphs-categorization huggingface dataset:
The database, signal processing, and machine learning techniques described later are used for the accurate detection of four different gestures. The user was trained to perform the following four gestures: Fist (close), spread fingers (open), wave-in, and wave-out. The detected gestures were displayed on the screen to provide the user with feedback during the training phase  [22]. The concept behind the creation of the sEMG database using Myo is to enable a more generic arm design for any amputee with a similar arm amputation. The combination of the 8 different sEMG electrodes of Myo enabled more sEMG signal data for a better gesture recognition system. Figure  3  shows the raw sEMG signals acquired by Myo while the user is performing hand gestures prior to signal processing. The sEMG sensors of the armband are numbered from 1 to 8 to be able to match the signals with the muscles.
Electronics and Control The bionic hand actuators are controlled by a Chestnut board placed inside the bionic hand [25], featuring the ARM Cortex M0+ Processor. The Chestnut board is designed to be embedded within robotic hands. It can control up to four motors simultaneously. The mass of the board is 15 g, and its dimensions are small at 57 × 45 × 9 mm, allowing it to fit inside the bionic hand. All of the Myo armband data were acquired wirelessly via Bluetooth at a fixed sampling rate of 200 Hz and transmitted serially to a PC. Each transmitted serial datum corresponds to a gesture. These signals are compared with the trained model of gestures. A graphical user interface (GUI) screen for interfacing with the user was developed to indicate the detected gesture, as shown in Figure  7. The detected gesture by the Myo EMG sensors was mapped to perform hand movements; for example, closing the hand, opening the hand, or closing one finger or two fingers.
Feature Extraction and Classification In this work, a Myo armband was used to collect the data of the selected four gestures from twenty-three participants (twelve males and eleven females with ages ranging from 18 to 45 years). First, the armband was connected wirelessly to the computer, and several numerical algorithms were used to transform the collected data from the official Myo software, called Myo-Connect, to a matrix data format. This procedure simplified the data collection process and allowed visualization of data while recording. Only data used to train and test the offline classifiers were collected using numerical tools, while the online implementation of this project was be performed using Python code. There are three distinct phases involved: Data collection, data processing and rectification, and feature extraction.
As part of the data collection procedure, participants were instructed to keep an angle of 90 • at the elbow joint during data collection. The dataset was collected in several sessions (within a period of two months), and every time the Myo armband was attached at the same location around the forearm of all participants. In the first phase, data were collected from participants in several sessions, where data associated with four hand gestures were recorded: Spread fingers, closed hand, wave-in, and wave-out. The participants were instructed to move their hand from the resting position to perform one of the proposed gestures and then move back to the resting position for around four seconds. The participants repeated this procedure more than 10 times for each single gesture. The same procedure was applied for all four gestures.
Classification In this section, the extracted features along with the corresponding known outputs are used as the input data to train a classifier or recognition algorithms. Based on a pre-selected optimization algorithm, the classifier is trained to learn and identify patterns in the data and to respond to the inputs according to the given outputs. After successful training, the reliability of the classifier is tested with a different dataset. Training and testing classifiers help in validating the results and obtaining an accurate classification model. In this section, three classifiers are investigated: The artificial neural network (ANN), support vector machine (SVM), and decision trees (TD) algorithms, in order to identify which classifier is better suited for building the bionic hand.
To obtain an accurate SVM classifier, one should select the right value for the regularization parameter C, which is, in this case, C = 80, and the kernel parameter γ = 0. 04. The parameter values for the three classifiers were selected after performing a cross-validation process for each classifier. Each classifier was used to train and test the same dataset for a different set of parameters. The best model for each version of the three classifiers was selected based on its performance. Next, a statistical study was used to compare the testing results in order to select the best classifier among the three classifiers (ANN, SVM, and DT classifiers). First, each classifier was run for thirty trials, and the testing accuracy for the classification was stored in a table. The SVM classifier provided the highest classification result with a mean value of the training data equal to 91.
Conclusions and Recommendations for Future Work A customized 3D-printed bionic arm was designed, fabricated, and tested for a right arm amputee. The 3D-printed bionic arm was designed to have a low cost, comfort, light weight, durability, and appearance. To facilitate a generic control of the bionic arm, EMG data were collected for a set of four gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and feature extraction was performed for the purpose of training a classifier. The support vector machine classifier was found to out-perform the neural network and decision tree classifiers, reaching an average accuracy of 89. 93%. Real-time testing of the bionic arm with the associated classifier software enabled the user to perform his daily activities.
Database and spike sorting results In this study, 93 microelectrode trajectories (48 in PD and 45 in Dystonia) were used, yielding a total of 933 and 718 microelectrode recording (MER) segments within the posterior putamen (i. e. , posterior to the anterior commissure, Figure  1  and Video 1) of patients suffering from PD and Dystonia, respectively. Microelectrode trajectories from patients suffering from non-genetic (N = 27) and genetic (N = 18) Dystonia were pooled, since the results did not differ statistically. Applying automatic spike sorting on these MER segments identified 5237 units (2917 in PD and 2320 in Dystonia). The isolation score  (Joshua et al. , 2007 ) -ranging from 0 (i. e. , highly noisy) to 1 (i. e. , perfect isolation) -was calculated for each of these units. Moreover, since injury potentials are often observed during extracellular microelectrode recordings of striatal activity  (DeLong, 1971) , the stationarity of the firing rate and spike amplitude of the well-isolated units (isolation score !0.
TANs are hardly distinguishable from SPNs in our human databases In order to differentiate between the SPNs and the striatal cholinergic interneurons, the tonically active neurons (TANs, presumably the cholinergic interneurons) were retrospectively identified by three experts (DV, HB and MD) from the well-isolated and stationary striatal units included in both the PD and Dystonia databases. This identification was based on offline visual inspection of their electrophysiological features. The 3D-classifications of the putative SPNs and TANs, using their firing rate, the CV of their ISI and the score of the first principal component (PC1) of their spike waveform did not clearly discriminate them in either PD or dystonic patients (Figure  3  The same 3D-classification, but using the score of PC2 or PC3 (instead of PC1) yielded the same qualitative results.
Overall, it was difficult to distinguish the TANs from the SPNs in our human databases. Moreover, our spectral analyses did not reveal any significant differences between these two subtypes of striatal neurons in either disease (Figure  3 -figure supplement 2C and D, and Figure  3 -figure supplement 3C and D, third and fourth columns). Therefore, we considered our sorted striatal spiking activity to reflect SPN spiking activity and neglected the small fraction of TANs (which was probably smaller than our estimation).
Outliers and cluster analysis of the striatal spiking features Since we did not have a control condition or baseline levels, we could not identify the significant changes in the spiking features of the striatal neuronal activity. Nevertheless, we searched for outliers (i. e. , data points that differ significantly from other observations) of each distribution. We examined the extreme values in the distributions and characterized the data points that could belong to a different population than the rest of the sample set. For each distribution, the values of the spiking feature (i. e. , firing rate, CV of ISI, b power of the MUA or b power of STA-LFP)!2 or À2SDs of its mean value were considered outliers (empirical 68-95-99. 7 rule). Remarkably, almost all of the outliers corresponded to feature values ! 2 SDs of the mean value, thus indicating (as already observed) that the distributions of the values of the striatal spiking features were skewed to the right in both diseases.
In fact, in most of the scenarios, the second cluster contained a few data points with negative silhouette values, and if not, there were not enough data points to consider it a real cluster that represented a distinct and balanced SPN subpopulation. Equally important, in contrast with the view that discharge rate elevation and bursting activity coexist in the striatum of PD patients  (Singh et al. , 2016) , the tendency to burst (CV of the ISIs > 1;  Kaneoke and Vitek, 1996)  decreased for units with a high discharge rate (Figure  9 ). Similar 2D or higherorder (3 or 4D) k-means cluster analyses (using the discharge rate, CV of the ISIs, MUA b power or STA-LFP b power as input parameters) also failed to reveal two well-separated clusters. Figure  9. Cluster analysis using discharge properties does not reveal well-separated subpopulations of striatal units in parkinsonian (PD) and dystonic (DYST) patients.
2D k-means cluster analysis with k = 2, using the firing rate and the CV of the ISIs of each unit as input parameters. Analysis was performed when considering all (upper panels), only the well-isolated (middle panels) and only the well-isolated stationary (lower panels) units. Markers (x) represent cluster centroids. Inset: silhouette values were calculated for each clustering to assess how well-separated the two resulting clusters were. Silhouette values ranges from +1 (data points are very distant from other clusters), through 0 (data points are not distinctly in one cluster or another), to À1 (data points are assigned to the wrong cluster). The online version of this article includes the following source data for figure  9:  Source data 1. 2D k-means cluster analysis for all units in PD.
Source data 2. 2D k-means cluster analysis for the well-isolated units in PD. Source data 3. 2D k-means cluster analysis for the well-isolated and stationary units in PD. Source data 4. 2D k-means cluster analysis for all units in Dystonia. Source data 5. 2D k-means cluster analysis for the well-isolated units in Dystonia. Source data 6. 2D k-means cluster analysis for the well-isolated and stationary units in Dystonia.
Patients and surgery Patients with PD and Dystonia were recruited from the movement disorders clinics at the Hadassah Medical Center in Jerusalem. All patients were scheduled to undergo implantation of DBS electrodes into the GPi and underwent MR imaging, and evaluation for motor and non-motor impairments within 30 days prior to surgery. Data were collected from 16 PD and 13 dystonic (non-genetic and genetic Dystonia) patients. Patient demographic information appears in Table  1. Note that our DBS recordings in ET patients started 10 mm above the thalamic target and therefore did not include striatal recordings. All patients met the criteria for DBS and signed a written informed consent for surgery that involved microelectrode recordings. This study was authorized and approved by the Institutional Review Board of Hadassah Hospital in accordance with the Helsinki Declaration (reference code: 0168-10-HMO).
Surgery was performed using a CRW stereotactic frame (Radionics, Burlington, MA, USA). BG target coordinates were chosen as a composite of the indirect anterior commissure-posterior commissure (AC-PC) atlas-based location and direct (1. 5 or 3Tesla) T2 magnetic resonance imaging, using Framelink 4 or 5 software (Medtronic, Minneapolis, USA). All recordings used in this study were made while the patients were fully awake (no sedation or anesthesia) and the PD patients were off dopaminergic medication (overnight washout >12 hr).
Group-based microelectrode track trajectories The trajectories of GPi DBS patients were reconstructed using the open source LeadDBS program (www. lead-dbs. org) using LeadDBS v2. 2. 3  (Horn et al. , 2019; Horn and Ku ¨hn, 2015). Lead Group, implemented within the Lead-DBS environment  (Treu et al. , 2020) , was used to graphically illustrate the group-based microelectrode track trajectories within the posterior putamen. Pre-operative imaging was performed on a 1. 5T or 3T MRI and included a pre-operative T1 and T2 sequence. All patients received post-operative CT. The post-operative CT was co-registered to anchor modality with the pre-operative T1 MRI, using a two-stage linear registration (rigid followed by affine) as implemented in Advanced Normalization Tools  (Avants et al. , 2008). Linear co-registration between the MR T1 and T2 modalities was done using SPM12  (Penny et al.
, 2007). Pre-(and post-) operative acquisitions were spatially normalized into MNI_ICBM_2009b_NLIN_ASYM space  (Fonov et al. , 2011)  based on pre-operative acquisitions using the Symmetric Normalization (SyN) registration approach as implemented in Advanced Normalization Tools  (Avants et al. , 2008). Nonlinear deformation into template space was achieved in five stages. After two linear (rigid followed by affine) steps, a nonlinear (whole brain) SyN-registration stage was followed by two nonlinear SyN-registrations that consecutively focused on the GP as defined by subcortical masks in Scho ¨necker et al. , 2009. DBS electrode localizations were corrected for brain shift in post-operative acquisitions by applying a refined affine transform calculated between the pre-and post-operative acquisitions that were restricted to the GP as implemented in the brain shift-correction module of Lead-DBS software  (Horn and Ku ¨hn, 2015).
Spike-triggered average of the LFP To investigate the spike-LFP relationship in the temporal domain, we also calculated the spike-triggered average (STA) of the LFPs  (Deffains et al. , 2016; Goldberg et al. , 2004). To do so, the LFP was recorded in the vicinity of the sorted units (i. e. , spiking activity and LFP were recorded on the same electrode). Each Z-normalized LFP was offline band-pass filtered from 4 to 12 Hz or from 13 to 30 Hz (4-pole Butterworth filter, filtfilt Matlab function). For comparison, STAs-LFP were also calculated after randomly shifting the timestamp of each spike of the spike train [i. e. , random time (comprised between 0 and 1 s) was added to the timestamp of each spike of the spike train] in order to abolish any relationship between LFP and spiking activity (Shift predictor).
Software and statistics Anatomical analysis was done with the open source LeadDBS program (www. lead-dbs. org). All the physiological data and statistical analyses were carried out using MATLAB R2016a routines (Mathworks, Natick, MA, USA, RRID:SCR_001622). Mann-Whitney U-tests and Wilcoxon signed rank tests were used for statistical comparisons of two unpaired and paired sample means, respectively. Statistical comparisons of two sample proportions were performed using Chi-square tests. Analysis of the subset of !10 s-MER segments with striatal well-isolated stationary units yielded similar results (Figure 3-figure supplement 5 and Figure  6 -figure supplement 1) to those reported here, thus indicating that our results were not confounded by the shortest MER segments. The criterion for statistical significance was set at p<0.
Acknowledgements We thank  Andreas Horn  and  Simon Oxenford  for their assistance in illustrating the group-based microelectrode track trajectories using Lead-DBS software,  Atira Bick  for pre-and post-operative imaging,  Esther Singer  for editing the manuscript and the patients for agreement to participate in the study and authorizing the use of their electrophysiological recordings. This study was supported by the  European Research Council (ERC) ,  Rosetrees ,  Israel Science Foundation (ISF)  and  Israel Authority for Innovation  grants to HB, and the  French National Research Agency (ANR)  and the  French National Center for Scientific Research (CNRS)  to MD.
A value of p < 0. 05 was considered statistically significant. Statistical analysis was done using GraphPad Prism version 8 (GraphPad Software Inc. ). No difference was observed regarding vasodilatory function in a NG condition. However, after 1-h hyperglycemic stress, endothelial function in both ASB groups was impaired compared to control group, although statistical significance was reached only with HD-ASB (Fig. 1B ). When expressed as a difference between NG and HG conditions, we observed a two-fold higher decrease in maximal relaxation in HD-ASB than in water group (Fig. 1C ). Interestingly, we reported significant changes in adipose tissue deposition of both ASB groups, with increased subcutaneous and visceral perirenal fat mass (Fig. 1E ), while total body weight remained comparable between the three groups (Fig.
, 2019). A great number of clinical trials have been designed to prolong retinal cell survival and halt vision decline. Usher patients have been included in some trials designed to treat late-onset progressive retinitis pigmentosa. For example, several patients with earlystage retinitis pigmentosa, including USH2 and USH3, have received implant capsules of human NT-501 cells that provide subretinal delivery of a ciliary neurotrophic factor (CNTF). This factor maintains photoreceptor cell survival, especially cones, and halts gradual vision loss (Trials #NCT00447980; #NCT01530659). This trial is now completed but the outcome of this study has not yet been published. The first Usher-dedicated gene therapy study in humans was a phase I/II clinical trial carried out using lentiviral (EIAV)-based sub-retinal delivery of the USH1B gene, MYO7A.
, 2014; Trapani et al. , 2014). A clinical trial is underway in Europe to supplement MYO7A with dual AAV vectors to target the retina of USH1B-affected patients (UshTher, Horizon, 2020; Trial #NCT02065011). AAV-mediated gene expression has been reported to be quite stable, lasting for several years in human clinical trials and in dogs  (Wojno et al. , 2013). However, despite the promise of gene replacement therapies, this approach is not amenable for a very large genes (>10 kb), or which expresses multiple essential isoforms, such as CDH23, USH2A, or GPR98/ADGVR1. Another clinical trial, also in phase I/II (ProQR, Stellar; Trial #NCT03780257), evaluates the safety and tolerability for a mutation-specific therapeutic strategy, an intravitreal injection of an antisense oligonucleotide (ASO), QR-421a, designed to skip the mutated exon 13 of the USH2A gene.
The approach, while expanding gene replacement therapy with AAVs to genes over 5 kB is still only applicable to genes under <~9 kB while allowing space for split genes, promoter, poly-adenylyation signals and, depending on the approach, splice acceptors and donors. Two Usher genes MYO7A (USH1B) and PCDH15 (USH1F) can be targeted for dual gene therapy. A clinical trial is underway in Europe to supplement MYO7A with dual AAV vectors to target the retina and vision loss of USH1B patients (UshStat, Horizon, 2020; NCT02065011), but the efficiency of this approach for Usher genes in the inner ear remains to be established.
Acknowledgements We thank  Jeffrey R. Holt  for critical review of the manuscript. The work in the authors' labs is supported by  NIH  ( R01-DC008853 ;  R56-DC016947 ), the  Usher Syndrome Society, Foundation Forschung contra Blindheit, Boston Children's Hospital Kirby Innovation Grant and Barber Fund  to GG and  French National Research Agency (ANR)  as part of the second " Investissements d'Avenir" programme (light4deaf ,  ANR-15-RHUS-0001 ),  ANR-HearInNoise -( ANR-17-CE16-0017 ),  LHW-Stiftung ,  Fondation Maladies Rares , and  Retina-France  to AE.
We computed the event-related potentials (ERP) to deviant tone minus standard tone, before and after tDCS, and compared R+ with R-using the same interaction contrast as for the resting-state. Five EEG recordings were discarded after automatic assessment of data quality. The analysis performed on the 55 remaining datasets (11 R+ and 44 R-) revealed a significant positive and left-lateralized anterior cluster spanning from 28 ms to 376 ms after the onset of the 5 th sound (p = 0. 008, g = 1. 40 [0. 84-1. 93], Fig. 4A ). Pre/post comparisons localized the origin of this effect to two significant clusters in R+ patient (a first posterior cluster from 52-312 ms, p = 0. 03, g = -0. 96 [-1. 52-0. 38] and a second left-lateralized anterior cluster from 68-392 ms, p = 0. 02; g = 1. 26 [0. 78-1. 84]).
Multivariate prediction of conscious state increases in responders to tDCS. Beyond univariate measures, we also assessed if a behavioral response to tDCS was associated with improvement of a multivariate EEG-based prediction of conscious state. To that end, we used the support vector machine classifier previously reported to distinguish VS/UWS from MCS using 68 resting-state EEG features (averages and fluctuations over time and space of the univariate markers described above). This algorithm was trained on a previously published database of 142 EEG recordings obtained from 98 patients (75 VS/UWS and 67 MCS)  13. For each patient we computed the prediction of being classified MCS, before and after tDCS, and used a non-parametric analysis of repeated measures  factorial design with MCS prediction as the dependent variable, the behavioral response as between-subjects factor (R+ vs.
Ethical statement. As patients were non-communicating patients, informed consents were obtained from the patients' relatives. The protocol conformed to the Declaration of Helsinki, to the French regulations, and was approved by the local ethic committee (Comité de Protection des Personnes Ile-de-France 1 (Paris, France), CPP n° 2013-A01385-40), under the code 'Recherche en soins courants'. This study was registered retrospectively on July 29 2019 on ClinicalTrial. gov: NCT04035655.
Importantly, the EEG cap was left in place throughout the tDCS session (stimulation electrodes were slithered underneath the EEG net), and before each recording, impedances were set below 100 kΩ. Preprocessing. EEG data were processed using an automatized and hierarchical pipeline for artefact removal and extraction of EEG measures previously described  13, 15, 47. Written in Python, C, and bash shell scripts and based on open-source technologies, including the software MNE  81  , the preprocessing workflow proceeded as follows: EEG recordings were band-pass filtered (using a Butterworth 6th order high-pass filter at 0. 5 Hz and a Butterworth 8th order low-pass filter at 45 Hz) with 50 Hz and 100 Hz notch filters. EEG during task were cut into epochs according to the onset of the fifth sound (800 ms before and 740 ms after) and resting-state EEG into 800 ms epochs with a 550 to 850 ms random jitter in-between (since this step is random, the preprocessing was repeated 100 times and subsequent measures were averaged over the 100 iterations).