dataesr/scientific-paragraphs-categorization huggingface dataset:
Conceptors-Driven Network The behavior of an NCVO varies with respect to the value of the direct current flowing through the device. Thus, there is a need for neural Long-Term Memory (LTM)  [40]  capable of saving and regenerating a large number of time series patterns. We suggest the use of conceptors-reservoir computing  [41] , which passes serially through two phases: the loading phase where patterns are installed in the network and the recall phase in which the patterns are regained according to the conceptor's value. To model this system, an RNN with N neurons is built by randomly creating an N × N internal connection matrix W * , an N × K input weights matrix W in where K denotes the number of input datasets, and a N random bias vector b. In the loading phase, the scalar pattern p j (n) always drives the network by the following state-update equation: x j (n + 1) = tanh(W * x j (n) + W in p j (n) + b) (4) Note that j denotes the index of the pattern.
Generated Data from the Proposed Models In this subsection, we present the dynamics generated by both the ESN and conceptors-driven RNN, presented in the two previous sections and try to depict the real dynamic magnetizations of NCVO (see Section 4. 1). The data we processed were the spatial averaging of the two in-plane components of the outputted magnetization (M x and M y ) obtained from micro-magnetic simulations and considered here as gold reference signals (see Section 4. 1). The third dimension of magnetization was not studied as it only denotes the vortex polarity. The injected current ranged from 12 mA to 20 mA with a step of 0. 1 mA. Therefore, by varying the injected current with a 0. 1 mA step in the [12 mA, 20 mA] range in the micro-magnetic simulation framework, we obtained 81 reference datasets, which were further used for the training of both the ESN and conceptor-driven approaches.
Each of the 81 input datasets had a data length of T = 10,000 samples with a time step t s = 10 -11. The hyperparameter tuning of the ESN approach was done as in  [45]. For the ESN, the number of neurons was set to 3000, and the spectral radius α was tuned for each input current separately. Seventy percent of the data was used for training, and the rest was left for testing. Some of the obtained results with this approach of different waveforms are shown in Figures  5  and 6  for both M x and M y , respectively. At I = 12 mA no modulation was captured; at I = 12. 3 mA, 13. 3 mA, and 16 mA, the system was said to be chaotic; and for the rest, it was non-chaotic. On the contrary, the 3000 neuron conceptors-driven RNN was filled with the 81 training examples p j (n) one after the other before the regeneration phase.
Methods Data Collection Patients were included from an International Registry of Idiopathic Ventricular Fibrillation (IVF) that consists of 496 IVF patients  5  from 45 tertiary cardiac centers worldwide (collected between March 2004 and December 2015). The data are available on request from the authors. Criteria for inclusion in the registry were (1) sudden cardiac arrest with documented VF as the cause of arrest, (2) an absence of an underlying cause following comprehensive analysis for ischemic, electrical, infectious, or structural heart disease, and (3) age ≤16 years. The diagnostic criteria for IVF were based on the latest consensus guidelines. 1  Patients were systematically assessed at the referring centers after the index VF episode (before being assigned a diagnosis of IVF). Data on past clinical symptoms, including previous syncopal episodes, were collected.
ECG Analysis All ECGs (during the index hospitalization and at follow-up) were reviewed by 2 independent electrophysiologists (K. T. , V. K. ). In case of disagreement, the ECG was reviewed by a third electrophysiologist (N. D. ). ECGs were analyzed at a paper speed of 25 mm/s and an amplitude of 10 mm/mV. Digital calipers were used to perform measurements (EP Calipers version 1. 8 [http://www. epstudiossoftware. com/], EP Studios, 2017). P-wave duration and dispersion were calculated from the standard ECG during sinus rhythm where dispersion was defined as the difference between the minimum and maximum P-wave duration. Early repolarization was defined according to the latest consensus guidelines. 6.
Genetic Analysis SCN5A [n=1]; KCNH2 [n=1]; MYH7 [n=1]; SLC22A5 [n=1] ). Two patients did not provide consent for disclosure of their genetic results. Based on the rapidly emerging data on genetic variation in normal populations, we reevaluated the prevalence of the reported genetic variants in the gnomAD database, which spans %123 000 exomes (http://gnomad. b roadinstitute. org/). All variants were confirmed to be unique (Table  4 ).
Numerical simulations The samples investigated in the previous section were assumed to be random mixtures from a statistical point of view, because of the absence (to the best of our knowledge) of combined accurate measurements of n eff and µ a (µ a 0) in the literature. Although this approximation proved very efficient for the two cases mentioned, the validity of Eq. (  6 ) is now investigated for non-random mixtures. To this end, the theory is tested against optical simulations performed with the ray tracing software Zemax OpticStudio (www. zemax. com) used in non-sequential Fig. 2. Effective index of porous ceramics as a function of porosity reported by  [15]. The model corresponds to Eq. (  6 ) with β = n 2. mode in order to make Monte-Carlo simulations on virtual porous materials made of unsticky spheres and randomly oriented cubes, as well as sticky spheres (Fig.
MATERIALS AND METHODS We compiled a cohort comprising 679 individuals with frontal facial photographs and clinical features documented in HPO terminology. 3 The diagnoses of all individuals have previously been confirmed molecularly and are suitable for analysis by exome sequencing. In total, the cohort covers 105 different monogenic syndromes linked to 181 different genes. Of the individuals in this cohort, 446 were published and 233 have not been previously reported (see PMID column in Supplementary Table  1 ). The study was approved by the ethics committees of the Charité-Universitätsmedizin Berlin and of the University Hospital Bonn. Written informed consent was given by the patients or their guardians, including permission to publish photographs. Easy to understand, transparent information with both text and illustrations about the pattern recognition in our algorithm that processes personal data in the form of 2D portrait photographs can be found at https://www.
pediastudy. org/documents. Through technical and organizational measures (privacy by design), we process the photos and the data obtained from them in the least identifiable manner necessary for achieving the purpose. This respects the data minimization principle of data being adequate, relevant, and limited. In addition to the PEDIA data set, we analyzed a subset of the DeepGestalt study. By removing disorders that are confirmed by tests other than exome sequencing, such as Down syndrome (Supplementary Table  2 ), we ended up with 260 of 329 cases from the DeepGestalt set. 5  The facial images were analyzed with DeepGestalt, a deep convolutional neural network trained on more than 17,000 patient images. 5 The results of this analysis are gestalt scores that quantify the similarity to 216 different rare phenotypes per individual.
These vectors can also be used to identify duplicates in the DeepGestalt training set and test set without the need to access the original photos. To avoid overfitting, we excluded all cases of the PEDIA cohort from a DeepGestalt model that we used for benchmarking. It is noteworthy that the version of DeepGestalt available at Face2Gene will not yield the same results when photos of the PEDIA cohort are reanalyzed because it is built as a framework that aims to learn from every solved case. In addition to the image analysis, we performed semantic similarity searches with the annotated HPO terms by three different tools: Feature Match (FDNA), Phenomizer, and Bayesian Ontology Querying for Accurate Comparisons (BOQA). 8, 9 HPO terms for all published cases as well as the clinical notes in the electronic health records were independently extracted by two data curators.
DISCUSSION The guidelines for variant classification in the laboratory follow a qualitative heuristic that combines distinct types of evidence (functional, population, phenotype, etc. ). Interestingly, it is also compatible with Bayesian statistics  7  and the advantage of such a framework is that continuous evidence types can be integrated into the classification system. While in silico predictions about a variant's pathogenicity have a relatively long history in bioinformatics and machine learning, the quantification of phenotypic raw data such as facial images with artificial intelligence systems has just begun: the PEDIA approach uses scores from DeepGestalt for gene prioritization in combination with quantitative scores from the molecular level in Mendelian disorders identifiable by exome sequencing.
g. , case IDs 246245 and 204233 in Supplementary Table  1 ). 18 t is noteworthy that these shortcomings are mainly due to the limited training data for these particular genes and that they will most likely be overcome by more molecularly confirmed cases. DeepGestalt and PEDIA are therefore built as frameworks that will be improved continuously with additional data. In general, the use of artificial intelligence in medical sciences raises new or exacerbates existing ethical and legal issues as repositories of combined genotype and phenotype data become crucial for the machine learning community. 19, 20 Sharing portrait photos of individuals with rare diseases can be accomplished within the scope of even the most elaborate data privacy laws, such as the European Union General Data Protection Regulation 2016/679 (GDPR).
As the data set expands further, the algorithm will improve for currently underrepresented ethnicities. Assistance with diagnosis of rare genetic disorders is highly valuable to clinicians, and by extension to the patients themselves and their families. Especially in inconclusive cases with findings of unknown clinical significance, additional evidence from computer-assisted analysis of medical imaging data could be a decisive factor. 13 n conclusion, the PEDIA study documents that exome variant interpretation benefits from computer-assisted image analysis of facial photographs. By including similarity scores from DeepGestalt, we improved the top 10 accuracy rate significantly compared with state-of-the-art algorithms. Artificial intelligence-driven pattern recognition of frontal facial patient photographs is therefore an example of nextgeneration phenotyping technology that has proven its clinical value for the interpretation of next-generation sequencing data.
ACKNOWLEDGEMENTS This work was funded by the  Deutsche Forschungsgemeinschaft  ( KR 3985/7-3 ,  KR 3985/6-1 ). M. A. M. is participant in the  BIH Charité Junior Clinician Scientist Program  funded by the  Charité-Universitätsmedizin Berlin  and the  Berlin Institute of Health. DISCLOSURE P. M. K. and K. W. G. receive compensation as consultants for FDNA Inc. H. D. E. , Y. H. , G. N. ,  O. Bar , O. S. , Y. G. , N. F. are employees of FDNA; T. K. is an employee of  GeneTalk GmbH. The other authors declare no conflicts of interest.
The ALICE data manifest very good agreement with these predictions. Nevertheless, the data is also compatible with the existence of a bound state, given a binding energy of B ΛΛ = 3. 2 +1. 6 -2. 4 (stat) +1. 8 -1. 0 (syst) MeV. The Run 3 of the LHC is expected to further increase the statistical significance of the Λ-Λ correlation function and allow the scattering parameters to be constraint even more precisely in the future.
Acknowledgements The ALICE collaboration is grateful to the  HAL QCD collaboration  for providing lattice results regarding the Λ-Λ interaction. We are particularly thankful to  Prof. Tetsuo Hatsuda  and  Prof. Kenji Sasaki  for the and Scientific Research,  Institute of Atomic Physics and Ministry of Research and Innovation and Institute of Atomic Physics ,  Romania ;  Joint Institute for Nuclear Research (JINR) ,  Ministry of Education and Science of the Russian Federation, National Research Centre Kurchatov Institute, Russian Science Foundation and Russian Foundation for Basic Research, Russia; Ministry of Education, Science, Research and Sport of the Slovak Republic , Slovakia;  National Research Foundation of South Africa, South Africa; Swedish Research Council (VR)  and  Knut & Alice Wallenberg Foundation (KAW), Sweden ;  European Organization for Nuclear Research, Switzerland ;  National Science and Technology Development Agency (NSDTA) ,  Suranaree University of Technology (SUT)  and  Office of the Higher Education Commission  under  NRU  project of Thailand, Thailand;  Turkish Atomic Energy Agency (TAEK) ,  Turkey ;  National Academy of Sciences of Ukraine, Ukraine ;  Science and Technology Facilities Council (STFC) ,  United Kingdom ;  National Science Foundation  of the  United States of America (NSF)  and  United States Department of Energy, Office of Nuclear Physics (DOE NP) ,  United States of America.
8% (v/v) aqueous ethanol as extraction solvent. These conditions were adjusted to the material and highest RA content was therefore obtained following 45 minutes extraction in an ultrasound bath operating at 30 kHz and using pure ethanol as extraction solvent. Under these conditions, an RA content of 110. 8 mg/g DW was measured from lyophilized P. scutellarioides leaves. The relative RA purity was estimated according to the method described by Falé et al. [11]  for P. barbatus leaves decoction. Under our optimal extraction conditions, RA was estimated to represent around 70% of the total absorption of the wavelength using PDA detection. This was further confirmed by DEDL detection (data not shown). Our optimal conditions are quite similar to those described by Caleja et al. [34]  for the USAE of RA from R.
[11]  for P. barbatus leaves decoction. Under our optimal extraction conditions, RA was estimated to represent around 70% of the total absorption of the wavelength using PDA detection. This was further confirmed by DEDL detection (data not shown). Our optimal conditions are quite similar to those described by Caleja et al. [34]  for the USAE of RA from R. officinalis leaves, but comparatively our results showed a higher extraction yield with P. scutellarioides leaves as starting material. In the literature, RA extraction yields from Lamiaceae leaves using USAE greatly varied  [40, 41] , ranging from 0. 01 mg/g DW in H. cuspidatus  [31]  to 86. 3 mg/g DW in M. officinalis  [34] , whereas a higher yield of 12. 6 mg/g DW was obtained using Marantha depressa (Maranthaceae) leaves as starting material  [42].
High-Performance Liquid Chromatography (HPLC) Analysis Separation was performed using Varian (Les Ulis, France) high-performance liquid chromatography system equipped with Varian Prostar 230 pump Metachem Degasit, Varian Prostar 410 autosampler and Varian Prostar 335 Photodiode Array Detector (PAD) and driven by Galaxie version 1. 9. 3. 2 software. Hypersil PEP 300 C18 (Thermo Fisher Scientific, Illkirch, France), 250 × 4. 6 mm, 5 µm particle size equipped with a guard column Alltech (Thermo Fisher Scientific, Illkirch, France), 10 × 4. 1 mm was utilized at 35 • C was used for the separation. The mobile phase was composed of HPLC grade solvents: A is a mixture of HCOOH/H 2 O at pH 2. 1 and B is CH 3 OH. Throughout one-hour run, mobile phase composition varied with a nonlinear gradient 8% B (0 min), 12% B (11 min), 30% B (17 min), 33% B (28 min), 100% B (30-35 min), 8% B (36 min) at a flow rate of 1 ml/min.
Experimental Design Factorial experiment design and response surface plots were used to identify the optimal RA extraction conditions using XLSTAT2018 software (Addinsoft, Paris, France). Variables were coded at three levels -1, 0, and +1. The three independent variables and their values were selected following preliminary experiments: X 1 ethanol concentration (50, 75, and 100% v/v), X 2 ultrasound frequency (15, 30, and 45 kHz) and X 3 extraction duration (30, 45, and 60 min). Twenty-seven observations of different combinations were prepared by taking values of selected variables at different levels as shown in Table  2. Note that all independent observations were carried out in triplicate. The equation calculation as well as statistical analysis were performed as described previously  [24, 46].
RA contents in the 27 US extract samples. Author Contributions: Conceptualization, C. H. and D. T. ; Methodology, D. T. , L. G. , S. D. , and S. R. ; Software, S. D. ; Validation, C. H. , E. L. , and D. T. ; Formal analysis, C. H. and D. T. ; Investigation, L. G. , D. T. , S. D. , and S. R. ; Resources, C. H. and D. T. ; Data curation, C. H. and D. T. ; Writing-original draft preparation, C. H. ; Writing-review and editing, C. H. , D. T. , and E. L. ; Visualization, C. H. and D. T. ; Supervision, C. H. and D. T. ; Project administration, C. H. ; Funding acquisition, C. H. Funding: This research was supported by Cosmetosciences, a global training and research program dedicated to the cosmetic industry. Located in the heart of the Cosmetic Valley, this program led by University of Orléans is funded by the Région Centre-Val de Loire.
Recordings. The depth electrodes (AdTech, Behnke-Fried type) consisted of eight macrocontacts (platinum; diameter, 1. 3 mm) embedded on the surface of a polyurethane tube with a hollow lumen. Eight 40-m-diameter platinum microwires, including one used as a reference, protruded 3-6 mm into the cerebral tissue beyond the tip of the deepest macrocontact. The ECG was recorded from two cutaneous electrodes on the upper chest, but the exact positioning was not standardized. All signals were collected using a 160-channel recording system (Neuralynx, Atlas) with a 32 kHz sampling rate and a 0. 1-8000 Hz bandpass filter for microwire data and a 4 kHz sampling rate and 0. 1-1000 Hz bandpass filter for the ECG. Experimental design. During data collection, seated subjects fixated a central point on a gray background displayed on a laptop monitor.
Spike detection and spike time series. An adaptive filtering procedure  (Keshtkaran and Yang, 2014)  was applied to the raw data to limit powerline interference as well as harmonics. Spike detection and waveform sorting were performed using the semiautomatic procedure implemented in the software wave_clus  (Quiroga et al. , 2004). Data were first bandpass filtered between 300 and 3000 Hz (elliptic bandpass filter, fourth order) and spikes were detected using the automatic amplitude threshold algorithm. Thresholds for spike detection ranged between 3. 5 and 5. 5 SDs. Waveforms were then clustered according to the superparamagnetic clustering algorithm. Typically, one to three clusters were isolated from each microelectrode where spikes had been identified. A number of additional steps were applied for selecting valid units after cluster isolation.
Spectral analysis of HRV and spike density. We analyzed both HRV and spike-density time series in the frequency domain in the four patients in whom we recorded 5 min of continuous data. Spectral analysis was performed using the FieldTrip toolbox for Matlab  (Oostenveld et al. , 2011) , resulting in a multitaper frequency transformation in sliding 60 s time windows with 6 s overlap. Coherence between the HRV and spikedensity spectral estimates was computed using the FieldTrip function ft_connectivityanalysis. m. Cross-correlation and coupling between inter spike interval (ISI) and CCD time series. To assess whether the fluctuations of neuronal spiking and heartbeat activity are correlated, we computed cross-correlograms between a smoothed neuronal ISI and CCD time series. Created in a manner similar to that used to produce a CCD time series, a neuronal ISI time series was generated by assigning to each ISI center time point the ISI duration.
Then, interpolation was applied using cubic splines. We estimated the coupling kernel, ␣(t), which expresses the systematic interaction between the two smoothed time series over the whole recording duration: CCD(t) ϭ l␣͑l ͒ ⅐ ISI͑t -l ͒. Specifically, we calculated this kernel using (inverse) Fourier transforms, ␣(t) ϭ ifft{fft(CCD(t))/fft(ISI(t))}. Computations were performed for each continuous recording block. Results were averaged across the available blocks. The contribution of each block was weighted by its duration. Detection of transient changes in instantaneous firing rate in response to heartbeats. Data were epoched from one R peak of the ECG to the next. Data analysis was performed by analyzing the data locked to the onset of the epoch, by analyzing the data locked to the end of the epoch, or by normalizing epoch duration from the current heartbeat to the next, latencies being then expressed in percentage of CCD.
Results were similar in all three cases and the results reported here correspond to the normalized epoch duration. The existence of transient increases or decreases in firing rate in the spike-density time series was then assessed by a clusterbased permutation procedure  (Maris and Oostenveld, 2007 ) that intrinsically corrects for multiple comparisons. For each unit, we created 10,000 surrogate datasets where R-peak latencies were shuffled, but the CCD distribution was preserved. We compared at each time point the observed spike-density value with the distribution of the 10,000 surrogate spike-density value to derive a Monte-Carlo p value. Adjacent time points with a Monte-Carlo p value Ͻ0. 01 defined candidate clusters. Monte-Carlo p values were then converted into z scores. In a second step, we summed over time the z scores of each candidate cluster.
We repeated this procedure on the 10,000 surrogate datasets, retaining for each surrogate dataset the largest z sum, to obtain the maximum (z sum) distribution that could be obtained under the null hypothesis. The comparison between the observed z sum and distribution of surrogate z sum identifies clusters of significantly increased or decreased firing in the original data, with a Monte-Carlo p Ͻ 0. 05, two-tailed. The resulting Monte-Carlo p values were corrected for multiple comparisons across recording sites using the FDR procedure  (Hochberg and Benjamini, 1990). Phase-response analysis. To quantify the effect of occurrence and timing of spikes on CCD, we calculated phase-response curves (PRCs) using a method adopted from  Blot et al. (2016;  where these curves are referred to as delayed spike curves due to the specific context).
05. Bayes factors were computed using the online tool (http://pcl. missouri. edu/bayesfactor), which is based on  Liang et al. (2008). Here, a Bayes factor Ͼ10 indicates strong evidence for the null hypothesis, between 3. 2 and 10 indicates substantial evidence for the null hypothesis, and Ͻ3. 2 indicates inconclusive evidence for or against the null hypothesis  (Kass and Raftery, 1995). Code accessibility. Matlab/Python code is freely available at https:// github. com/neuromethods/neural-firing-and-cardiac-cycle-duration.
The correlation between spontaneous firing rate and CCD is most prominent in the anterior part of the MTL and cingulate cortex The MTL was well represented in our dataset. In the MTL, 22. 7% of the units (20 of 88) showed a significant correlation with CCD. The most anterior MTL regions, corresponding to the anterior parahippocampal cortex, a region that encompasses the entorhinal and perirhinal cortices, anterior hippocampus, and amygdala, seemed to be particularly involved (Fig. 2 ). To quantify the difference between posterior and anterior MTL regions, we labeled MTL regions anterior if y Ն Ϫ7 and posterior if y Յ Ϫ10. The anterior MTL regions contained the highest proportion of units (36%, 18 of 50 units) with a significant CCD effect (Fig. 2B ; Table  1 ). This percentage fell to 5. 3% (two units of 38) for posterior ( y Յ Ϫ10) MTL recording sites.