dataesr/scientific-paragraphs-categorization huggingface dataset:
-RMSE : Root mean square deviation Fig. 16  and 17 shows the outline of the proposed model. Figure  16  shows the logic followed for the calibration of the Wake Oscillator model. Figure  17  presents the methodology followed to train and build our machine learning model (ENS). First, the historical data is pre-processed. Next, the proposed model selects the most relevant input features from our dataset. Finally, an optimal structure of our ENS model was obtained with the combination of two types of models : a series of Xgboost models combined with a DNN model thanks to Ridge regression to obtain a final model called ENS. 18  and 19. Fine tuning of the hyper-parameters makes it possible to achieve an optimal configuration as in Figure  20. This is an example of a single time series. Our database consists of 60 time series.
+ k m * ẑm where k i denotes the weights assigned to each base model (XGboost and DNN). These weights are optimized using the square of the sum of the difference between the actual y/d and predicted y/d, as follows: the literature suggests that to determine the generalization performance of models, our dataset is divided into three subsets: training, validation, and test set, having 80%, 10%, and 10% of total data, respectively. -Training set is used to train and build the sub models XGBoost and DNN. -Validation set is used to determine the performance of different sub-model architectures (number of regression trees in the XGBoost, number of hidden layers and units or neurons for the DNN architecture). -Test set is used to determine the error rate of the generalisation by the mean of the root mean square error (RMSE) and mean bias error (MBE).
IV. RESULTS In this section, we will present the different results of the prediction of the values of y/d according to different machine learning models as well as our ENS model. Figure  24  also details the values of some hyper parameters of the models taken into consideration in this study. This benchmark mainly concerns RMSE type errors and the R2 Score, because these two metrics are the most significant, often widely used in the state of the art, especially in the validation of machine learning models whether in regression or in classification. [110]  This section is divided into three sections: i) the visualization of the data collected using the two models: WO and ST and the various adjustments made in order to produce an approximation of the two models, ii) Build our training dataset, the modeling and presentations of the first results with several learning models and finally iii) the prediction results of the value of y/d using our ENS model.
Figures 19, 20 and 21  give an overview of the values of y/d of the two models (WO and ST) according to the values of the hyper-parameters. Adjusting these values makes it possible to improve this approximation, as we can see in Figures18 and 19. Fine tuning of the hyper-parameters makes it possible to achieve an optimal configuration as in Figure20. This is an example of a single time series. Our database consists of 60 time series. This represents a total recording of nearly 5620 seconds. We need a suitable metric to compare 2 time series. One of the widely used metrics is the RMSE metric. This method requires having 2 time series of the same length, ie with the same number of points. In order to obtain time series with the same number, we apply a transformation on the reference model, basing the definition of the timestep (dt) and the output step (dr) on the final time of the WO model and on the number of total points.
1 ) 1 Figures 19, 20 and 21  give an overview of the values of y/d of the two models (WO and ST) according to the values of the hyper-parameters. Adjusting these values makes it possible to improve this approximation, as we can see in Figures18 and 19. Fine tuning of the hyper-parameters makes it possible to achieve an optimal configuration as in Figure20. This is an example of a single time series. Our database consists of 60 time series. This represents a total recording of nearly 5620 seconds. We need a suitable metric to compare 2 time series. One of the widely used metrics is the RMSE metric. This method requires having 2 time series of the same length, ie with the same number of points. In order to obtain time series with the same number, we apply a transformation on the reference model, basing the definition of the timestep (dt) and the output step (dr) on the final time of the WO model and on the number of total points.
One of the costless solution would be to reconstruct the traffic flow from aggregated information (travel duration estimations), available on web services. Previous work validates that machine learning based approach is a promising way to reconstruct "sensor like traffic flow data" from aggregated information like the ones proposed by Google services  (Li et al. , 2019a,b). Applied such an approach would permit, for instance, to infer on a realistic traffic demand at each entrance of a city (i. e. the flow of incoming vehicles). Machine learning over aggregated information approach is based on accessible databases that can provide information regarding the transportation condition (travel duration) at a given location and at a given time. In 2007, the Google company has extended Google Maps by adding Google Live Traffic, the visualization of traffic information in real time  (van den Haak et al.
, 2018; Jeske, 2013). Here, the notion of real time means the current state and is applied to qualify the service of FCD provided. In more detail, Google exploits users' position data of Android smart-phones, in order to get a significantly fast and accurate mapping of the traffic. This data is called Floating Car Data  (FCD) , which can also be collected by any localization system embedded in a car and sent to the service provider via a mobile connection. Generally, these raw Floating Car Data are aggregated to provide more intelligible and relevant information regarding traffic condition. For example, in Google Maps, FCD are used to give a real-time traffic information using colored road section 1  which is determined by the navigation system or, as in the case of Google Live Traffic, by the smart phone and is sent to the service provider via a mobile phone connection.
Therefore, such an expensive traffic flow measurement method makes the mentioned simulation process out of reach. Preliminary result was published on the 15th World Conference on Transport Research 2019  (Li et al. , 2019a)  and on the 6th International Conference on Control Decision and Information Technologies 2019  (Li et al. , 2019b)  where traffic flows is estimated according to Floating Car Data (FCD) from Google Maps only on the basis of regressors trained using machine learning techniques instead of using stationary physical equipment (such as loop detectors  (Cheung et al. , 2005)  or video cameras  (Coifman et al. , 1998) ). In this paper, we present an extension of the previous works with an increased experiment setup that permits to automatize the model definition. Firstly, we show experimentally that, among 19 types of regression methods (including Linear Regression Models, Regression Trees, Support Vector Machines and Ensemble of Trees), the Gaussian Process Regression (GPR) is the most suitable machine learning method to obtain the best fitting criterion with respect to our dataset.
Secondly, a selection of the adequate regressor is computed from a set of regressors (multi-model) to estimate traffic flows from FCD, based on the different types of travel duration profiles. This multi-model approach can greatly reduce the estimation error, by precisely clustering days presenting different types of travel duration profiles. Experiments are conducted by comparing estimated flows with real ones provided from induction loop sensors. The results we obtain seem promising enough to say that correct transportation flows models could be obtained with a very light use of real traffic sensors. This paper is organized as follows: the next section describes related works and the different usages of aggregated FCD in the context of transportation networks modeling. The third section focuses on the problem we choose to address that is building sensor like data flow measurements from aggregated data.
Problem description The problem is the estimation of traffic flow on a lane based on travel duration estimated by Google Maps thanks to FCD, as illustrated in Fig. 1. We consider a road section of a certain length (1. 2 km in the example of Fig. 1 ), we measure the traffic flow on this section thanks to a physical sensor (in the example of Fig. 1  data were collected using a Doppler radar (24. 165 GHz/100mw EIRP)) located in the middle of the section and in parallel we estimate the travel time on this section thanks to Google Maps. The objective is to find the relationship between this travel time and the vehicle flow measured on the ground. According to the Fundamental diagram of traffic flow theory, travels duration and traffic flows are linked with each other, and their nonlinear relationship can be traditionally formulated with some special statistical model-based methods based on many assumptions, with lots of parameters to be tuned  (Wu et al.
Proposed system structure The proposed system structure consists of three parts: data collection, machine learning model training and trained model testing, as illustrated in Fig. 2. For data collection, traffic flows are measured by a sensor over a specified and limited period of time , which is 26 weeks in this work. Over this same period of time, We retrieve directly travels durations, which are calculated by Google map servers with a complicated algorithm based on the collected floating car data. In other words, through the Google maps API, the  travel duration can be obtained by giving the GPS for origin and destination of the chosen road segment, and the time-stamp, as shown in Table  1. The GPS for origin and destination is got manually from the Google Map on the chosen road. The time-stamp is a serial of numbers to define a certain instantaneous time elapsed since 1970, January 1  , 0 h, 0 min, 0 s.
The request can be done either on a Web browser or by Python programming. The travel duration data is captured each ten minutes from Google-map. In total, 26208 data are retrieved. Then the obtained traffic flows data and their corresponding travels durations are split into two sets: a training dataset with 50 percent for the machine learning model training, and a validation dataset with another 50 percent for the trained model testing. For the training of machine learning model, the estimation of traffic flows from travels durations is treated using regression models. Three types of models are proposed: single model, manual multi-model, and K-means multi-model (refers to Section 3. 3). The machine learning algorithm applied is the training of a Gaussian Processes Regressors (GPR) (refers to Section 4.
Acknowledgments This work was supported by the project  ORIO  (Observing the peRformances of urban Infrastructures and mobility/preventing collisions with vulnerable people using Opportunistic radar). The project ORIO is done within the framework of  ELSAT2020 , which is co-financed by the  European Union  with the  European Regional Development Fund , the  French state and the Hauts de France Region Council. The real traffic flow data from induction loops on the road ''86  Boulevard de la République, Douai, France '' was provided by the town hall of  Douai (Mairie de Douai ).
Making use of an already published dataset of a-syn treated microglia  (Sarkar et al. , 2020) , we identified 170 a-syn signature genes in our dataset. Expression values of the top 15 induced or suppressed genes from the signature were plotted as a heatmap (Figure  1D ). To further elaborate biological functions that are altered by a-syn, we performed pathway and gene ontology (GO) term analysis. We found that inflammation-related features were enriched, e. g. pathways for NFkB and TNFa, ferroptosis, apoptosis, and proteasome (Figure  1E ). These results were further supported by the finding that 66%-71% of the differentially expressed (DE) genes belong to the interferome  (Rusinova et al. , 2013). Using the Biological Networks Gene Ontology tool (BiNGO) to create a network of induced and suppressed biological processes (Figure  1F ), we confirmed that GO terms associated with inflammation and programmed cell death were highly enriched.
0 mm). The proportion of acceptors capable of establishing direct contact with donors that became a-syn-positive was on average 8% after 5 h co-culture (Figure  3A ). In contrast, we could not detect any a-syn-positive acceptors when cells were cocultured without direct cellular contact (data not shown). Importantly, dose-response curve analysis indicated that only at higher concentrations (>0. 25 mM) a-syn redistribution is a required step to reduce the individual cellular burden (Figure  S2A ). We adjusted the ratio of donor-to-acceptor cell numbers and analyzed changes in reactive oxygen species (ROS) levels. At a ratio of 1:3 (acceptors:donors), acceptors were not able to rescue donors anymore (Figure  S2B ). To prove that the observed a-syn within acceptors truly is a result of transfer, not of phagocytosis, we labeled donors with a CellTracer.
Interestingly, we found that ROCK1-knockout did not affect a-syn transfer, whereas ROCK2-knockout significantly increased the a-syn exchange rate (Figure  3L ). Together, these data indicate that activation of ROCK might inhibit the transfer of a-syn between microglia by downstream modulation of the actomyosin complex. Cell-to-cell transfer of fibrillar a-syn downregulates the inflammatory profile in microglia To study the impact of the transfer of a-syn, we examined transcriptomic changes in donors and acceptors over time. Cells were co-cultured and sorted by FACS, and their RNA was collected and sequenced. A Pearson Coefficient r was calculated for each group and time point as quantitative measure for the similarity of the transcriptomes over the time of co-culture and visualized as heatmap (Figure  4A ).
The schematic was created using BioRender. com and Adobe Illustrator. (legend continued on next page) to-cell connections between donors and acceptors (Figure  7E ). Exposure of patient-derived MDMi's to a-syn significantly elevated the ROS production (Figures  S8E  and S8F ) compared to baseline (untreated) levels, thus indicating that ROS might influence the transfer of a-syn between cells. Together, our data present evidence that microglia have the ability to actively connect to neighboring cells to share the amount of cytotoxic protein accumulations e. g. , as a strategy to provide aid and support of protein degradation and to attenuate inflammatory reactions. This mechanism has been proven by us to be conserved over different in vitro, ex vivo, and in vivo model systems.
STAR+METHODS Detailed methods are provided in the online version of this paper and include the following: The authors would like to thank Prof. Dr. Manuela Neumann and the Tu ¨bingen Brain Bank as well as Prof. Dr. Annett Halle and the DZNE Brain Bank Bonn for kindly providing human brain samples. We thank Dr. Pawel Tacik, Dr. Dr. Sergio Castro-Gomez, and Dr. Sarah Bernsen for providing us with patients' blood samples. Many thanks to the patients and their spouses who have volunteered to donate blood. The authors would like to thank Maike Kreutzenbeck and Ulrike Stube for their support regarding RNA isolation and preparation for sequencing. We thank the DZNE light microscope facility (LMF) for providing microscopes and advice and the DZNE Image and Data Analysis Facility (IDAF) for providing analysis computers and software.
Following labeling, cells were collected, resuspended in 200 ml ice cold FACS solution, and measured by flow cytometry using the FACS CANTO II and the FACS DIVA software (Becton Dickinson, Heidelberg, Germany). Cell-to-cell transfer of a-syn aggregates was then analyzed and quantified using FlowJo, LLC (v3. 05470, Ashland, OR, USA). Flow cytometric analysis of monocytes-derived microglia (MDMi) After collection of MDMi on day 14, cells were washed in PBS + 2% BSA. Cells were incubated in PBS + 2% BSA for 30 min, then with fluorochrome-conjugated antibodies (5-20ml/10 6 cells) for 30 min in the dark on ice: CD11b-BV605 (BD Biosciences Cat#562721), CD11c-APC (BioLegend Cat#337208), CD14-PE-Cy7 (BD PharMingen Cat#557742), CD16-BV421 (BD Biosciences Cat#562874), CD36-APC (BD PharMingen Cat#550956), CD45-APC-Cy7 (BD Biosciences Cat#557833), CCR2-BV605 (BioLegend Cat#357214), CX3CR1-FITC (BioLegend Cat#341606), HLA-DP, DQ, DR-FITC (BD PharMingen Cat#555558), P2RY12-PE (BioLegend Cat#392104).
Reads were aligned with STAR (v2. 5. 3a) against the murine reference genome mm10. Transcripts were quantified with the Partek E/M algorithm and further processed for normalization in R with the DEseq2 algorithm. Batch effects derived by independent experiments were removed in the Partek Genomics Suite (v7. 18. 0402). The dataset was further optimized by flooring transcripts with minimal gene counts at least to £1 and the exclusion of transcripts with a mean expression £10 in every test condition. Differentially expressed genes were determined for a-syn versus untreated control microglia by a two-way-ANOVA including the experiment as batch effect (fold-change |1. 5|, FDR-adjusted p value £0. 05). Data visualization and biological interpretation were performed with the Partek Genomics Suite and R (v3.
Microscopy and image analysis All experiments were examined with a Nikon Eclipse Ti fluorescence microscope (Nikon, Tokyo, Japan), a Zeiss laser scan microscope 800 (Carl Zeiss, Oberkochen, Germany) or a Ti-Sapphire 2-photon laser scanning microscope (Nikon). Acquired images were processed using NIS-elements 4 (Nikon) and Fiji ImageJ (Wayne Rusband, National Institute of Health, USA). Three-dimensional reconstructions were processed using Imaris -Microscope Image Analysis Software (Bitplane, Oxford Instruments plc, Abingdon, UK).
Likewise the relatively high volume of the germination medium is limiting for screening chemical library of natural extracts for most of them available in small quantities. In this paper, we present a novel seed germination tool named Scree nSeed. The procedure (Fig. 1a ) uses 96-well microplates to sow seed samples in 96 splitted blocks and compact robots (described in Supplementary Fig. S1 online ) providing automated image acquisition every hour and exporting the data to a dedicated database. A time series of images of a well can be seen in Supplementary data S2 online. These data are then used to score individual germination events, thereby delivering germination kinetic parameters of the analyzed seed samples. The ScreenSeed procedure benefits from a fast sowing method delivering droplets of Arabidopsis seed suspensions.
Lids were sealed with a plastic film and incubated directly in the ScreenSeed automate inside a thermo-regulated incubator (Memmert ICP 750) regulated at 20 °C ( ± 1 • C ). It can be noticed that the used plastic film is a low-density polyetylene clingfilm characterized for its high oxygen permeability coefficient and is not limiting for oxygen supply  36. To limit temperature variation in the plate between different experiments, the automate was always positioned exactly at the same place in the middle of the incubator. Abscisic acid (ABA; Sigma-Aldrich) and diniconazole (Sigma-Aldrich) solutions were prepared in purified water using a Millipore Milli-Q gradient water purification system. The ScreenSeed robot placed inside the incubator allowed monitoring seed germination with a given frequency (every hour in the context of this study) through an automated image acquisition system (Raspberry Pi Camera Module V2.
1) that was externally controlled through an Internet connection using in-house software and hardware technology (Fig. 1a ). This enabled the germination behavior of seed samples to be monitored every hour. For comparative purposes, germination tests in Petri dishes (Greiner Bio-one, diameter 9 cm) were carried out under the same incubation conditions (temperature, light and hygrometry) as for the analyses carried out in microplates (see Supplementary Data S3 online). The seeds (100 seeds per condition) were sown either on a water-soaked Whatman Grade filter paper ( 80 µL cm -2 ) or on 0. 8% (w/v) agarose (Dutscher). Four replicates were run in each condition analyzed (Supplementary Data S4 online). Seed germination was scored using a binocular magnifier (Olympus SZX10).
Computational data analyses. Images of seeds were acquired every hour and were saved in an in-house database for computational analyses. To score that a seed has completed its germination, the software had been trained to consider the event of radicle protrusion through the endosperm layer of the seed (see Fig. 1b ). The reliability of the annotation by this algorithm was estimated to be 99% for the determination of the number of seeds per well and 98% for the characterization of the germination event with time resolution of 1 h. All samples were also evaluated by visual inspection, which guaranteed a standardized annotation methodology uncorrelated to image recognition algorithms performance. Data visualization was provided by an in-house developed dashboard. This dashboard is designed for easy visual comparisons of metrics related to seed quality (Scree nSeed Lab.
) and enables the download of tabular files for further analyses from the raw data. The software automatically determines the main metrics of seed germination kinetics of a seed sample (Fig. 1c ), namely the maximal percentage of germination among total seeds ( G max ), the time to reach 50% of germination ( t 50 ), and the 80/20 time spread of germination ( U 80-20 , often called uniformity of germination), which is the time interval between the seeds reaching 20% to 80% of germination. All figures and tables reporting statistical results from the present study were acquired from this dashboard. Similar metrics can be computed from the R package germinationmetrics  37. In this paper, the metrics relative to the time of germination are relative, in the sense that they measure the time to reach a given percentage of germination among the seeds that have germinated.
For a comprehensive large-scale phenotyping process to be actionable, the described technology should be combined to additional software modules including a fast (or automated) annotation of seed germination (Fig. 1a ) and a web application to analyze and share the observation and statistical results. The statistical analyses and figures of this paper have been drawn from such a ScreenSeed web application. Comparison of the germination behavior of the seeds of the Ler and Col-0 accessions incubated in a range of ABA concentrations (Fig. 5 ) illustrated the value of this tool to characterize with high resolution genotype sensitivity to this inhibitor. In all tested ABA concentrations, Ler exhibited a lower G max than Col-0 (Table  3 ), which is in excellent agreement with the previously identified phenotypes of more pronounced dormancy and of higher ABA sensitivity in the Ler accession compared to Col-0  21, 47, 48.
mce: micropylar endosperm; rad: radicle. The scale bar indicates 100 µm. (c) Metrics of germination kinetics provided by the ScreenSeed dashboard. The kinetic curve is represented by cumulative germination percentage scored every hour during seed hydration. The metrics G max (maximal percentage of germination), U 80-20 (80/20 time spread it the time interval between 20% and 80% germination), t 50 (time required to reach 50% of the G max ) are automatically extracted from the database and can be compared between samples (Table3).