dataesr/scientific-paragraphs-categorization huggingface dataset:
Experiments A study of Parkinson's speech samples is developed to assess the performance of each of the system models and their associated inference procedures presented in Section 6. 2. The reference benchmark comparison will be based on the features and models introduced in  [29]  for the detection of ataxic speech. We aim to identify such an ataxic dysarthria symptom as a discriminative speech degradation symptom of Parkinson's with the proposed system models for the EMD and further compare SM2 and SM3 to standard speech practices of directly applying an ASR system on the raw speech data. We begin with an overview of the selected Parkinson's speech dataset and its experimental setup. The first section explains the required pre-processing and the procedure for balancing the datasets since the study had an uneven number of labelled sick vs healthy patients.
This is highly precious for the constructed method to avoid overfitting often occurring in ASR-SD systems. The structuring of training and testing sets is then presented. We defer the interested reader to the specialised details relating to the practical pre-processing and Fisher kernel construction methods given in the provided, sections 4 and 5 in S1 File. The validation model phase is described, and the description of our guideline reference model, introduced in 6. 1 is provided. Finally, the results obtained through our proposed models are described. Table  1  shows the different features used, over which data and the corresponding classifier. The classification procedure will be conducted at a patient level, providing a text-dependent and a speaker-dependent environment. Note that the python code required for the implementation of the three system models is given within this Github page https://github.
Data description and experimental set up The speech dataset considered for the analysis was provided by  [38]. It contains speech recordings from two populations: healthy participants and patients affected at various stages of Parkinson's disease progression. The recording environment uses a typical examination room for UK medical practices with dimensions of ten square meters in area and a reverberation time of approximately 500ms to perform the voice recordings. The voice recordings are performed in the realistic situation of doing a phone call and have been performed within the reverberation radius; hence, they can be considered "clean". The sampling rate is standard for speech at 44. 1 kHz and a bit depth of 16 Bit (audio CD quality). The dataset is split between two sets of recordings: in the first one, the selected participants are asked to make a phone call and then read out two tests: "The North Wind and the Sun" and "Tech.
We note that this database of speech signals was specifically selected given the quality of the recordings and its recording procedure. The procedure used is most aligned with the standard medical practice of relevance to telemonitoring solutions for remote Parkinson's disease detection prior to requesting the patient to travel to a hospital for further in-person testing. This is useful for pre-screening those likely to need to travel for initial diagnosis as well as for analysis of the impact on speech for disease progression analysis for those living remotely from specialist care or those unable to easily travel from their house to the hospital on a regular basis. There are 37 participants in total, of which 21 are healthy and 16 are sick, affected by Parkinson's disease at different stage levels.
Amongst the 21 healthy participants, 19 are female, while 2 are male. Of the 16 sick participants, 4 are female, and 12 are male. The dataset is therefore significantly unbalanced within both classes, i. e. healthy versus sick and male versus female. Furthermore, the Parkinson's participants are labelled according to the following scores: the HYR score, the UPDRS II-5 score and the UPDRS III-18 score introduced in 6. 1. Considering the UPDRS II-5 score, the Parkinson's participants are classified in a range between 0 and 3 at maximum, particularly for the female patients, 2 are at a 0 stage level, and 2 are at a 1 stage level. In the case of the sick male patients, 5 male patients are at a 0 stage level, 4 patients at 1 stage level, 2 patients at 2 stage level and 1 patient at a 3 stage level.
Pre-processing, balancing the dataset and construction of training and testing segments sets This subsection outlines a brief description of the pre-processing performed to obtain a balanced selection of speech records for the testing and inference tasks undertaken. As noted, the recordings taken into account are the read text only for each participant. Within the recording procedure, each participant was asked to make a phone call and then read two different texts above mentioned. Each audio file corresponds to a continuous, unsegmented recording of the read text at the sampling rate was 44. 1kHz. Therefore, we will have one audio file for each patient denoted as s(t). Depending on the patient, the reading order might change, and the recording lengths (due to different reading paces) vary between 73s and 203s.
Description of the "Mobile Device Voice Recordings at King's College London (MDVR-KCL)". The number of speakers is 37, split between healthy and sick patients. Furthermore, the gender and the UPDRS II-5 score are introduced in the Table. It is possible to observe how unbalanced the dataset is, particularly regarding gender and the UPDRS II-5 score. For each speaker, the dataset provides two sets of recordings. In our experiments, we use the read text and set the scenario to a text-dependent one. Moreover, we conduct our analysis by patient, and therefore we will be in a speaker-dependent setting. number of sick and healthy patients. To balance the representation of each patient, we compute the minimum number of segments for each patient by gender and then randomly select that minimum number of minibatches (5000 samples each batch) from each patient by sampling with replacement.
MDVR-KCL Dataset Once we have obtained a balanced representation of each patient with respect to the number of segments, the following step consists of constructing training and testing sets of segments for our classification task, divided into model estimation and model validation. Consider the female case as an example and note that an equivalent procedure is applied to the male case. To construct the training set, we firstly left one patient out for the testing set. Then from the remaining number of patients segments, i. e. N f ×18 for the healthy case and N f ×3 for the sick case, we randomly extract 80% of N f corresponding to 354 segments. Hence, we will have 354 segments representing the class of healthy patients and 354 segments representing the class of sick patients, randomly extracted from 18 and 3 patients equally represented.
e. how many times a specific model for the mini-batches was selected as best over its segment. In such a way, a "weighted" rule will be defined for the definition of the Fisher score in the testing procedure. Note that we will end with N f = 354 best models for the female families (i. e. both sick and healthy) and N m = 298 for the male families (i. e. both sick and healthy). The testing procedure computes the Fisher score vectors by evaluating the obtained best models on the testing data (also split by mini-batches) of each patient. By considering the healthy female case, for example, 354 models are evaluated on each mini-batch of every testing segment. In practice, one has 354 sets of hyperparameters describing one mini-batch, while the desired scenario would be having one set of hyperparameters per mini-batch.
In contrast, 5-HT 1A plasma membrane expression levels were similar with and without coexpression of 5-HT 2B (not illustrated). To confirm these results, we performed biotinylation assay, and evaluated the ratio of the membrane/total protein expression level for each receptor (See STAR methods). This showed that coexpression of the two receptors significantly decreased, by 31%, the amount of 5-HT 2B reaching the membrane (Figures  2C  and 2D ), but did not impact 5-HT 1A membrane expression (Figures  2C-2E ). Together, these data indicate that the 5-HT 2B membrane expression is reduced by coexpression of 5-HT 1A. Neuronal co-distribution of 5-HT 1A and 5-HT 2B in somatodendritic compartment Membrane proteins, including GPCRs, are targeted to specialized membrane domains, or clusters, via interactions with scaffolding proteins that are not only important for the regulation of synaptic transmission Figure  1.
33 Moreover, this distribution can be modified upon receptor stimulation, which will subsequently alter their function. We thus studied by confocal microscopy the distribution of 5-HT 2B and 5-HT 1A expressed alone or together, at the plasma membrane of cultured hippocampal neurons, in basal condition and after stimulation of one receptor or the other. Selective agonists of each receptor were selected in order to limit possible cross-activation: the 5-HT 1A selective agonist 8-OHDPAT, which displays 1 nM affinity for 5-HT 1A and 5 mM affinity for 5-HT 2B and the preferential 5-HT 2B agonist BW723C86, which has 10 nM affinity for 5-HT 2B , over 1 mM for 5-HT 1A , and over 10-fold lower affinity for other 5-HT 2 receptors (see PDSP database https://pdsp. unc. edu/databases/pdsp. php). We first quantified the number and size of 5-HT 2B clusters and their integrated intensity, which reflects the density of receptors per cluster.
Limitations of the study In this work, intracellular interactions between 5-HT 1A and 5-HT 2B data are mainly based on overexpression studies either on transfected cells and hippocampal neurons or on in vivo infected 5-HT neurons. When reliable antibodies will be available, it will be necessary to further validate these findings on endogenous receptors in vivo. Although established on ex vivo endogenously expressed receptors, it remains to be determined in vivo if 5-HT 2B stimulation controls directly (e. g. , by PKC activation and thus by phosphorylation) SK channels, and/or 5-HT 1A or if this regulation is relying exclusively on 5-HT 1A /5-HT 2B dimeric interactions and/or competition to common effectors. Software and algorithms ImageJ (Schneider et al. 54  ) https://imagej. nih. gov/ij/ into AAV2.
5-HT 2B agonist BW-723C86 (BW) or antagonist RS-127445 (RS), 5-HT 1A agonist 8-OH-DPAT or antagonist NAN-190 (NAN) were from Tocris (UK). Competition was performed at concentration between 10 À11 to 10 À5 M, diluted in binding buffer in 96-well plates for 60 min at room temperature. Membranes were harvested by rapid filtration onto Whatman GF/B glass fiber filters (Brandell) pre-soaked with cold saline solution and washed three times. Filters were placed in 6-mL scintillation vials and counted. Data in disintegrations/min were converted to femtomoles and normalized to protein content (ranging from 0. 1 to 1 mg/well). At least three independent experiments were performed in duplicate. Non-permeabilized whole cell radioligand binding assay Washed cells were solubilized by the addition of 500 mL of SDS 1%.
Thresholded clusters were binarized, and binarized regions were outlined and iScience Article transferred onto raw data to determine the mean 5-HT 2B cluster number, area and fluorescence intensity. For quantification, clusters comprising at least 3 pixels were considered. The dendritic surface area of regions of interest was measured to determine the number of clusters per 10 mm 2. Due to the variability between cultures, the number of clusters in each culture was normalized to the respective control values, allowing for comparisons between cultures. As the 5-HT 1A presents an expression pattern in small clusters, the average fluorescent intensity per pixel was measured in a defined ROI. For each culture, we analyzed 7-12 dendrites per experimental condition. A total of about 10 neurons were analyzed per condition from three to five independent cultures.
The threshold to define the border was set to 1000 detections/mm 2. All pixels containing <2 detections were considered empty, and their intensity value was set to 0. The intensity of pixels with 2 detections was set to 1. The resulting binary image was analyzed with the function ''regionprops'' of MATLAB to extract the surface area of each cluster identified by this function. Density was calculated as the total number of detections in the pixels belonging to a given cluster, divided by the area of the cluster.
Data acquisition was performed using Patchmaster software (Heka Elektronik). The liquid junction potential (À5 mV) was left uncorrected. Signals were sampled at 20 kHz and filtered at 4 kHz. Data analysis was performed using Igor Pro (Wavemetrics). Statistical analyses were performed using Prism (GraphPad). The normality of data distribution was tested using Shapiro-Wilk's test. Unpaired two-tailed t-tests (for normally distributed datasets) or Mann-Whitney tests (for non-normally distributed datasets) were used for comparisons between two groups. For multiple comparisons, we used two-way ANOVA followed by Sidak's test. Values of p < 0. 05 were considered statistically significant.
QUANTIFICATION AND STATISTICAL ANALYSIS Experimental design and statistical analysis Statistical analyses were performed using GraphPad Prism 7 software. Starting from our experience in previous publications,  24, 29  we calculated the standard deviation of each group of animals for each test as well as the differences between the means of these groups. By setting the type I and II error risks at 5 and 20%, respectively, the size of the groups were determined by calculating the statistical power using the G*Power software version 3. 1. 9. 6. To ensure reproducibility, when relevant, experiments were performed at least three times independently. To avoid litter bias in the mouse experiments, experimental groups are composed of animals from different litters randomly distributed using Graphpad software.
All analyses were conducted with blinding to the experimental condition. Putative outliers were determined by the ROUT method. Data are presented as bars and the line at the mean G S. E. M. (standard error of the mean). Comparisons between two groups following a normal distribution were analyzed using two-tailed unpaired t-test with or without Welch's correction. Normality was assessed using the D'Agostino & Pearson omnibus normality test. All data normally distributed for more than two groups were examined by either ordinary two-way ANOVA followed by Tukey's multiple comparisons or repeated measure two-way ANOVA followed by Sidak's multiple comparisons test. Data not passing the normality test were analyzed using the Kruskal-Wallis test followed by Dunn's multiple comparison test. p < 0. 05 was predetermined as the threshold for statistical significance (See Table  S1  for full statistical analysis).
ACKNOWLEDGMENTS We thank the  Cell and Tissue Imaging Facility of the Institut du Fer aMoulin  (namely  Theano Eirinopoulou ,  Mythili Savariradjane ), where all image acquisitions and analyses have been performed,  Aude Muzerelle  for help with stereotaxic viral injections, and the staff of the IFM animal facility (namely  Baptiste Lecomte ,  Gae ¨l Grannec, Francois Baudoin, Anna-Sophia Lourenco ,  Emma Courteau  and  Eloise Marsan ). This work has been supported by grants from the  Agence Nationale de la Recherche  ( ANR-17-CE16-0008 ,  ANR-11-IDEX-0004-02 ), the  Fondation pour la Recherche Medicale (Equipe FRM   DEQ2014039529  to L. M and  R22184DD  to S. L. ) and the  Federation pour la Recherche sur le Cerveau  ( FRC-2019-19F10  to L. M and  R20020DD  to S. L. ). This manuscript should be distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (https://creativecommons.
Landscape dynamics and the Phanerozoic diversification of the biosphere Tristan Salles 1,3 ✉ , Laurent Husson 2,3 ✉ , Manon Lorcery 1,2 & Beatriz Hadler Boggiani  1  The long-term diversification of the biosphere responds to changes in the physical environment. Yet, over the continents, the nearly monotonic expansion of life started later in the early part of the Phanerozoic eon  1  than the expansion in the marine realm, where instead the number of genera waxed and waned over time  2. A comprehensive evaluation of the changes in the geodynamic and climatic forcing fails to provide a unified theory for the long-term pattern of evolution of life on Earth. Here we couple climate and plate tectonics models to numerically reconstruct the evolution of the Earth's landscape over the entire Phanerozoic eon, which we then compare to palaeodiversity datasets from marine animal and land plant genera.
Reliefs are changing over time and mass transfers are crucial to the expansion of life: both on the continents and in the oceans, nutrient availability is determined by landscape dynamics. Understanding the impact of nutrient fluxes thus requires a comprehensive quantitative approach that we develop herein, leaving aside the role of truly biotic processes. Here we propose a new method to quantify the global-scale physiographic changes over the Phanerozoic eon, applying the landscape evolution model goSPL  12, 13  to a series of global-scale palaeo-elevation reconstructions, consistently tied to a plate tectonic model  14  and to a series of palaeoclimatic reconstructions  15  (Fig. 1 ). Our approach allows us to jointly quantify the tectonic uplift at long wavelengths and the high-resolution dissection of the landscape (Methods and Extended Data Figs.
1  and 2 ). Model outputs, including high-resolution topography, continental erosion and sedimentation rates, drainage networks and sediment and freshwater yields to the oceans (all datasets released online  16  ), allow estimation of the impacts of surface processes on the physiography of the Earth throughout the entire Phanerozoic (Fig. 1 ). Sensitivity tests using alternative climatic and tectonic models (Methods) point to spatial variations and differences in the magnitude of erosion rates although global temporal trends remain mostly insensitive (Extended Data Fig. 6a, c ).
Article trends in bulk sediment transfer (Fig. 2a ) that can be tied to continental elevations and surface runoff (that is, precipitation minus evapotranspiration; Extended Data Fig. 6a ). Two phases of sustained fast erosion separated by a quieter period mark the long-term Phanerozoic evolution. The Palaeozoic phase relates to an increase in continental runoff from the Silurian period to the Carboniferous period, and to higher reliefs until the assembly of Pangaea during Permian times. Lower continental elevations and more arid conditions prevailed until Pangaea breakup after the Triassic period. During this period, up to 30% of eroded materials were trapped in the terrestrial domain (Fig. 2a ). The Meso-Cenozoic phase of erosion, from the Jurassic period onwards, is marked by a more than twofold increase in erosion flux, fostered by higher runoff and by the rising reliefs of the Cenozoic mountain belts.
This is well exemplified by the sharp increase in average drainage basin areas about 240 million years ago (Ma) related to the development of the low-relief landmasses of Pangaea, which closely matches a major decrease in sediment and water flux. Following Pangaea breakup after 200 Ma, water and sediment flux resume owing to wetter climates and an overall increase in mean elevation range under renewed plate tectonic activity. We also predict that about 25% of the surface of present-day landmasses is covered by endorheic catchments (Fig. 2b ), in agreement with earlier estimates  23. Additionally, we corroborate the model-predicted sediment flux using the strontium isotopic ratio of seawater for the Phanerozoic, and with a sensitivity analysis based on different palaeo-elevation and palaeoclimate reconstructions (Methods).
For example, downstream sediment storage in endorheic basins or reduced precipitation due to orographic shadowing curbs the sediment yield to the oceans, and conversely, small exorheic basins might enhance transport in mid-elevation regions  29. The reconstructed net sediment flux to the ocean and the total number of marine families are strongly correlated (Pearson coefficient 0. 88) and sediment flux variation markedly matches the three main phases that span the Phanerozoic eon (Fig. 3  and Extended Data Fig. 10a ). This suggests that nutrient availability is a prime control on marine diversity, providing an explanation for the observed Palaeozoic plateau-as opposed to a continuous increase-and for the Mesozoic marine revolution  10  that sparked biodiversification until the present day, but also for the low-diversity period of Pangaea, when endorheic basins suddenly sequestered a vast amount of sediments over the continents, depriving oceans of about 30% of the nutrient source (Figs.
2a ), we suggest that the diversity of terrestrial plants was further hampered by the limited expanse of favourable edaphic conditions. Over the Permian and Triassic, pteridophytes were superseded by gymnosperms that further radiated (Fig. 4a ). At that time, the Pangaea supercontinent gathered more emerged lands than at any other time in the Phanerozoic (Fig. 2b ). Sediment-covered surface areas also rapidly increased owing to the development of large endorheic basins (up to 20% of the continental surface; Fig. 2b ), fed by a sustained flux of Article sediments from the high relief of the widespread circum-Pangaean orogenies  49  (Fig. 4a ). The massive development of these reliefs is also associated with an increase of physiographic variety (Fig. 4b ). The emergence of these conditions, which would favour the diversification of deep-rooting plants across a diverse range of physiographic environments, coincided with the development of gymnosperms (Fig.
4 ) that dominated terrestrial floras by the end of the Triassic 4. Gymnosperm diversity continued to rise during the early phases of Pangaea breakup before levelling off during the Jurassic and Cretaceous, along with a decrease in both continental deposition flux (Fig. 2a ) and physiographic variety (Fig. 4b ), as well as a relatively steady sediment coverage (Fig. 4a ), which all restrained the favourable rejuvenation of continental surfaces. Gymnosperms were superseded by angiosperms that diversified at unprecedented rates at least from the Cretaceous onwards (Fig. 4a  and Extended Data Fig. 10b ), although the timing remains largely controversial  50, 51. Common explanations invoke their efficient cross-pollination strategies and high growth rates  52, 53. Yet the period was also marked by extensive orogenic phases in North and South America and Eurasia (Fig.
Palaeo-elevation and precipitation forcing. To reconstruct the past physiography, goSPL relies on time-evolving boundary conditionsclimatic and palaeogeographic-that are used to compute the interplay between the solid Earth and the climate. To reconstruct high-resolution palaeo-elevations throughout the Phanerozoic, we use a series of 108 maps from the PALEOMAP palaeogeographic atlas  14  ranging from the Holocene epoch to the Cambrian-Precambrian boundary (541 Ma). These palaeo-maps are defined at approximately 5-Myr intervals, and each of them is represented as a regular grid with a resolution of 0. 1° × 0. 1° (approximately 10-km cell width at the Equator). These palaeogeographic maps were initially based on information related to lithofacies and palaeoenvironmental datasets  62  and supplemented and refined for more than 40 years with regional palaeogeographic atlases  14, 63.