dataesr/scientific-paragraphs-categorization huggingface dataset:
Notes 1. https://ourworldindata. org/coronavirus/country/guinea#what-is-the-cumulative-number-of-confirmed-cases consulted on June 21 2023. 2. National Agency for Health Security of Guinea (ANSS) weekly epidemiological update, November 2022 (Réunion hebdomadaire d'information épidémiologique, 17 novembre 2022, ANSS, Conakry). 3. In Guinea Malaria death rate was nearly 30 times higher than COVID-19 (83,6 and 2. 8 deaths per 100,000 individuals respectively using latest 2019 data for malaria and 2021 data for Covid which bore the greatest burden for the country during the pandemic to date. Source: https://ourworldindata. org/covid-deaths; https:// ourworldindata. org/malaria, consulted on June 21 2023 4. Consider the disparity in the number of clinical trials conducted in Guinea versus France, as seen on clinicaltrials.
Acknowledgements We wish to thank the healthcare workers and other participants who shared their anxieties, hopes, and experiences. We are also grateful to the Guinean team who took part in the data collection:  Fanny Attas ,  Amadou Tidiane Barry ,  Marie-Yvonne Curtis  ( PhD ),  Pr Moustapha Keïta-Diop ,  Laurent Gnouma Koniono , and  Gassim Sylla.
Homologous search The search of TsmK homologs was performed by BLASTp  [53]  based on the Uniref90 database  [52]  using as a query the TsmK sequence of A. baumannii ATCC 17978 strain. To infer the presence of a particular gene on a specific genome, we look for homologs of this gene on the given genome using BLASTp. When inferring TsmK presence, we used the already mentioned A. baumannii ATCC 17978 TsmK sequence as a query. In the case of TssM, Tssj, and TssB, we queried the TssM, TssJ, and TssB sequences from a selected list of pathogenic T6SSs (S2 Table ). Homologous sequences whose blast e-value was higher than 1-e20 or whose query coverage was below 60% were discarded and hence not used to determine the presence of a particular gene on a given genome. Uniref90 and the different genomes were converted to blast databases using the makeblastdb application  [53].
TaxID and genome assignment To assign a TaxID to a given sequence, we used UniProt (UniProt Consortium 2021) annotations and the NCBI taxonomy database (UniProt Consortium 2021). Sequences that could not be assigned a TaxID were discarded. A given TaxID might have multiple subtree TaxIDs. When this happens, a unique genome could not be assigned to it. In this case, we discarded the TaxID and any sequence associated with it. EDirect NCBI tools  [54]  were used to download the unique genome associated with a TaxID with no subtree with multiple TaxIDs. Acinetobacter genomes were obtained from a list of 6041 Acinetobacter TaxIDs collected from the NCBI taxonomy database. Apart from TaxIDs with multiple TaxIDs, the ones whose associated genome has less than 500 genes were discarded. As a result, we ended up with a set of 893 Acinetobacter genomes carrying a TssB gene (used as an indicator of a complete T6SS operon).
Protein conservation We used a previously developed closeness metric  [31]  to compute the conservation level of proteins of interest. As input, we used the TssM, TsmK, TssB, and TssK sequences obtained from the 893 Acinetobacter genomes aligned with Mafft  [56]. In the case of only A. baumannii, we filter the sequences using the Uniprot annotations and the NCBI taxonomy database. A. baumannii TsmK models. Two transmembrane helices in the N-terminal region of A. baumannii TsmK (from I5 to W24 and from F36 to I57) were predicted using the TMHMM server  [57]. The sequence of the transmembrane helices was not included in the structural modeling of A. baumannii TsmK (from Y58 to S470) performed by AlphaFold2  [58, 59] , TrRosetta  [60]  and RaptorX  [26]. To identify consistent regions between the three TsmK models, we conducted a structural alignment using TMalign  [61].
Bioinformatic data analysis, visualization, and storage To generate the multiple sequence alignment (MSA) figures (Figs 6B, S4H, S6A and S6B) and compute the residue conservation, we used JalView  [62]. The molecular visualizations of TsmK models (Figs 4A, 4C, S4E and S4F) were rendered with UCSF Chimera  [63]. MSA residue conservation was represented on a sequence logo plot using the WebLogo server (Fig  6B )  [64]. Secondary structure topological diagrams (Fig 4B  and 4D ) were assembled with Top-Draw  [65]. All Python and shell scripts developed explicitly for this work are available with the initial genomic data (TsmK, TssM, TssJ, and TssB sequences) and the resulting homologous sequences at https://github. com/IFilella/AbAsaBTssM.
Fluorescence microscopy Sample preparation: Cells were spotted on thin pad supplemented with 3% agarose in PBS 1x, made inside a gene frame (Thermo Scientific-Gene Frame) taped to a microscope slide. An SR cover glasses (Marienfeld Superior-Precision cover glasses No. 1. 5H) was then used to seal the preparation. Imaging was performed on a DeltaVision OMX SR Imaging system (GEHealthcare) with an Olympus PlanApo N 60× 1. 42 NA oil immersion objective and liquid-cooled sCMOs cameras (IMM Microscopy Platform). Data analysis-Clusters density quantification and clusters per cell quantification: Images were acquired using excitation at 488 nm during 500 ms at 100%T for sf-GFP and excitation at 568nm during 500 ms at 100%T for mCherry. For each acquisition a BrightField image was generated. The masks of cells were obtained directly from a brightfield image by thresholding Images were filtered with FFT band pass (1 to 5 pixels) with FIJI  [66].
The fluorescent clusters were detected by local maxima algorithm in MicrobeJ plugin (  [67] ; http://www. microbej. com/ ) that yields the localization density maps and the cell histograms. The statistics of the number of clusters per cell were calculated with R (R Core Team (2023). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. https://www. R-project. org/). Data analysis-TIRF: Total Internal Reflection Fluorescence (TIRF) Imaging was realized using the RING-TIRF mode from DeltaVision OMX, with illumination angle around 80˚and an Olympus PlanApo N 60× 1. 49 NA oil immersion objective. Time-lapse recordings were analyzed with the following workflow: 1-projection of the mean of fluorescence channel. 2detection of the cell masks with cellpose  [68]  with the pre-trained model "cyto2" 3-filtering of the image by a bandpass FFT filter of FIJI/ImageJ, settings = minimum = 1 pixel, max = 5 pixels 4-cell tracking and contour intensity analysis and creation of kymographs with the MicrobeJ plugin on the FFT filtered fluorescence channel.
(Fig 5B ) The images were then analyzed using ImageJ (http://imagej. nih. gov/ij/) and the MicrobeJ plugin (http://www. microbej. com/). Data analysis-Kymographs: Dynamics of fluorescent clusters of the TssM protein. Timelapse was generated by acquiring 61 images every 10 seconds using excitation at 488 nm during 100 ms at 15%T. The masks of cells were obtained from the projection of the mean fluorescence channel, with Cellpose  [69]  with the pre-trained model "cyto2". In parallel fluorescence channel images were filtered with FFT band pass (1 to 5 pixels) with FIJI. Cell tracking, contour intensity analysis and creation of kymographs were then done with the MicrobeJ plugin on theses filtered images. Data analysis-3DSIM: Images were acquired using an excitation wavelength of 488 nm during 200 ms at 10% Transmission for the sfGFP reporter and an excitation of 568nm during 200 ms at 5%Transmission for the mCherry reporter.
For each acquisition, images of 512 × 512 pixels (pixel size of 80 nm) were acquired with a sample thickness of 1 μm (9 sections, Δz = 125 nm) centered at the middle of the bacteria. At each z position, SI images (five phases at three angles) were acquired for 3D-SIM imaging. Image analysis was performed using Fiji (version 1. 53c) to extract intensity profiles which were then processed with python (version 3. 9. 5). The best z position of each stack was first selected so that sfGFP-spot and the membrane appear clearly in the image. Then intensity profiles were wisely plotted through sfGFP-spot with an orientation perpendicular to the membrane and directed towards the.
PLOS PATHOGENS interior of the bacteria. The size of each profile has been set at 20 pixels (800 nm). Finally, fifty profiles were recovered for each mutant. The distance between the TssM and Omp28 was then measured with python (version 3. 9. 5) by fitting a gaussian curve to each profile and calculating the difference between the green maximum peak and the red maximum peak. For the heatmap, the images were acquired using excitation at 488 nm during 500 ms at 100% Transmission. The cell segmentation of the bright-field images was done using machine learning  [70]. The images were then analyzed using ImageJ (http://imagej. nih. gov/ij/) and the MicrobeJ plugin.
Spectra were processed by the Proteome Discoverer software 3. 0 SP1 (version 3. 0. 1. 27 from ThermoFisher) using the Sequest HT algorithm and the Chimerys identification node with the database extracted from NCBI, Acinetobacter baumannii strain ATCC 17978 (Taxon ID 400667; 5376 entries, last modified 02/13/2023). In this study, proteins were identified at "high level" protein FDR confidence.
This work was funded by the  Centre National de la Recherche Scientifique , the  Aix-Marseille Universite , and grants from the  Agence Nationale de la Recherche  ( ANR-18-CE11-0023-01 ) and  European Society of Clinical Microbiology  to build the T6SS channel. features we report in this study will have profound implication in our understanding of the assembly and evolutionary diversity of different T6SSs, that warrants future investigation. and  Infectious Diseases (ESCMID)  to ED. ED is supported by the  Institut National de la Santeet de la Recherche Medicale (INSERM). YC is funded by a  Doctoral school PhD fellowship  from the FRM ( ECO20160736014  &  FDT201904008052 ). OK is funded by a  Doctoral school PhD fellowship  from  DGA  and  Aix-Marseille University  and by the  FRM  ( 01D19024292-A AID  &  FDT202204014851 ).
Unlike many related works that use machine learning for tracking and predicting residents' behaviors inside their homes, this paper focuses on predicting resident behavior outside their home (i. e. , arrival time as a context) to reduce the energy consumption of smart homes. One main objective of this paper is to find the most appropriate machine learning and neural network-based (MLNN) algorithm that can be integrated into the street traffic prediction system. To evaluate the performance of several MLNN algorithms, we utilize an Uber's dataset for the city of San Francisco and complete the missing values by applying an imputation algorithm. The prediction system can also be used as a route recommender to offer the quickest route for drivers.
g. , to save time and automotive fuel), but can also be used as useful information for various use cases and applications. Putting all pieces together, we propose a smart home use case to automatically adjust home temperature and hot water temperature. Here, the street traffic prediction system predicts the arrival time of a home resident (i. e. , who is driving) and, just a few minutes before the arrival, the cooling or heating devices will be turned on to reduce the energy consumption of the devices. The system predicts the street traffic situation using a machine learning technique (model). In general, machine learning and neural network-based (MLNN) algorithms play an important role on the performance of machine learning-based systems. However, the performance of an MLNN algorithm may depend on the application and/or the utilized dataset.
While an MLNN algorithm can show a great performance for one application, it may not necessarily provide this level of performance for another application or even another dataset. For this reason, we evaluate the performance of several famous MLNN algorithms to find which one is more appropriate for our application (i. e. , traffic prediction). We evaluate the performance of the following MLNN algorithms: K-nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machines (SVMs), multilayer perceptron (MLP), long short-term memory (LSTM), single layer perceptron (SLP) and categorical naive Bayes (CNB). In addition to MLNN algorithms, the utilized datasets play an important role in the street traffic prediction process and results. Even if the systems are efficient, certain limitations can occur because of the uncertainty in traffic-related data  [2].
While some datasets are available for the traffic situation of several highways/freeways or some special streets of different cities, finding comprehensive and complete datasets covering all small and large-size streets of a city is still a big issue. As a solution, in this paper, we utilize an Uber dataset (which covers a large number of small streets) for the city of San Francisco and then complete the missing values by applying an imputation algorithm. We use Scikit-Learn [3] and Pytorch  [4]  to implement the algorithms and consider the following evaluation metrics: Precision, F1-Score, and Accuracy. We summarize our contributions as follows: • Propose a use case for smart home to adjust the home temperature and hot water temperature by controlling the heating and cooling devices with the main goal of reducing the energy consumption of the devices; • Propose a dual-objective machine learning-based streets traffic prediction system.
Streets Traffic Prediction Several approaches have been proposed to predict the traffic situations of streets and highways. Street traffic prediction approaches can be divided into two main categories: parametric (e. g. , exponential smoothing, the Kalman filtering  [10]  and ARIMA  [11] ) and non-parametric (e. g. , machine learning-based approaches). The parametric approaches estimate the traffic situation based on the strong theoretical assumption. The studies show that non-parametric approaches outperform parametric solutions because of their ability to deal with a large number of parameters and big data  [2, 12]. Jose  Braz et al. [13]  developed and compared three deep learning models for forecasting the traffic flow in the Barra and Costa Nova regions. They divided their dataset into training, validation, and testing sets (i.
e. , the holding out method). Kim et al. [14]  presented a structural RNN architecture that combines the road network map with the traffic speed data to predict the future traffic speed. They used a traffic speed dataset from the case studies of the SETA EU project  [15]. In contrast to the mentioned works, we utilize the walk forward validation. For the time series data analysis, the walk forward validation can offer a better result than holdout and k-fold cross-validation  [16]. In addition, one common challenge for the existing related work is that the traffic road datasets under their study only cover several inter-city highways or the main avenues of a city or a smaller number of streets which are most of the time sparsely located. When we want to predict the traffic situation of every requested route (i.
Dataset Description We utilized an Uber traffic dataset for San Francisco  [27]. We selected a zone covering a total number of 439 segments in San Francisco. Traffic observations were recorded in approximately 1-hour intervals for each street segment during 18 h per day (i. e. , from 6 AM to 24) for 5 weeks. The real dataset contains around 197,496 records. It then becomes equal to 276,570 records after calculating and filling its missing values (i. e. , by applying the imputation algorithm. Details in Section 5. 1. 1). Every row in the dataset refers to the situation of a segment at a specific timestamp. The columns correspond to the following content:.
Data Cleaning We found that the obtained dataset contains several missing lines (data) where the observations are not regularly and continuously recorded every 1 h for each segment during the considered 5 weeks. In order to solve this problem, we defined a two-step solution: step (1) dataset size reduction (to remove unnecessary lines/records); step (2) dataset completion (to add necessary/utilized missing data). • step 1-dataset size reduction: we found that a majority of the missing values were from the night. It is normal. Because Uber does not have a lot of services during the night (i. e. , between 00:00 and 6:00). On the other hand, we know that we usually do not have heavy traffic between 00:00 and 6:00 and, therefore, it is not necessary to use the traffic recommendation system during these hours.
As a result, we reduced the dataset size. Instead of considering observations for 24 h per day, we consider a dataset file keeping observations for 18 h per day (i. e. , recorded observations between 6:00 and 24:00). This could remove a large number of missing values from our dataset (approximately 7. 44 percent); • step 2-dataset completion: in order to find appropriate values for the missing parts and complete the dataset, we use the Bayesian temporal matrix factorization (BTMF) model recently proposed in  [28]. As an example, Figure  3  shows a part of the initial dataset for segment 0 (on 5 January 2020, from 6:00 to 24:00) with two missing values. Figure  4  shows that two missing values were calculated in step 2 (i. e. , using BTFM).
Hyper-Parameters Hyper-parameters play an important role in performance of the created models. We use the Scikit-learn's RandomSearch cross-validation  [29]  to find appropriate values for hyper-parameters. Recall that CNB does not have hyper-parameters to tune. One week (i. e. , 20%) of the dataset is used for tuning the hyper-parameters. The obtained hyper-parameter values are shown in Tables  1  and 2.
Validation Technique We know that our data (dataset) constitutes a time series. As some usual techniques such as the k-fold cross-validation technique do not work perfectly for time series datasets  [16] , we utilize the walk-forward validation technique (i. e. , a technique preserving the order of data). We separate our dataset into five equal units or partitions (i. e. , a unit is equal to one week). The units are chronologically ordered. We considered the last four consecutive weeks of the dataset (i. e. , 80% of the dataset) for training and testing our models. In each execution, we consider all the available data before the last unit as the training set and the last unit itself as our testing set. As shown in Figure  5 , for the first execution (i. e. , Execution 1), we consider the W1 unit for training and W2 for testing.
To find the most suitable MLNN algorithm for traffic prediction, we evaluated the performance of a set of well-known MLNN algorithms (i. e. , KNN, DT, RF, SVM, MLP, LSTM and SLP) based on (a modified version of) a real Uber parking dataset in San Francisco. Although the RF algorithm shows the best performance, its training is very time-and energy-consuming. On the other hand, we can see that the performance of some simple algorithms such as CNB and DT is very close to RF. In this situation, it can be a good idea to use a simple algorithm with a lower execution time. For traffic prediction using machine learning, there are still many open research problems and challenges that have not been fully investigated. Finding appropriate solutions to consider abnormal conditions (e. g. , extreme weather or temporary traffic control) is one of the current challenges.
Out of 372 surveys identified via Google and Twitter, the first selection excluded 330 articles because they did not meet the objective of the study. Indeed, one of the keywords "survey" led to numerous noises that did not correspond to our search for questionnaires. Moreover, they were false positives, which were frequently related to journalistic investigations. Out of the remaining 42 surveys, the second selection excluded 12 surveys, because they were conducted outside the countries selected for the study (ie, France and Belgium). The screening process resulted in a final selection of 30 surveys. Afterward, 15 other studies were added via scouting of the collaborators of the selected studies (ie, newsletters and emails). In total, 45 surveys were included for final analysis. The selected studies were analyzed according to a reading grid based on the criteria proposed by the WHO for monitoring populations  1  and supplemented by data from the descriptive analysis of the studies reviewed.
, 2021)  is an ion-camera using electrostatic mirrors with 15% energy resolution. It has a field of view 1. 5π at a resolution of 20 • ×60 •. In addition it has the capability to measure the TOF of ions using an electrostatic shutter. In this study we only use omni-directional energy spectra at 32s time resolution from the imaging mode (IMG) of the sensor. In addition we use magnetic field data obtained by the MMO-MGF instrument  (Baumjohann et al. , 2020)  down-sampled to 4s time resolution and temperature and voltage data recorded by several platform house keeping sensors mounted on MMO, MPO, MTM and the solar arrays -usually down-sampled to 1min resolution. We should note that all instruments have been designed for operation after separation of the BepiColombo stack and not for operation in cruise phase.
That means that the field is more parallel to the spacecraft Z-axis. This could lead to an increased current inside of the MOSIF shield. It was realized after analyzing these first Mercury flyby observations that the spacecraft encountered an unusual plasma physical phenomenon. For this reason a special campaign was planned for March 2022 where all spectrometers were switched on for 3 consecutive days in low energy mode. Also the magnetometers were switched on for this campaign. Fig. 7  shows respective data obtained between March 11 and 15, 2022, when Bepi-Colombo was in pure solar wind at solar distances between 0. 43 and 0. 46 AU. The top panel shows energetic ion spectra observed by MIA. One can see that there was a solar energetic particle event on March 12 00:00-12:00UTC. This event also caused an increase in low energy ionized outgassing shown in the 3 panels below.
, 2021). From these studies we may conclude that the ion density caused by the outgassing around the Rosetta spacecraft never exceeded this threshold. Otherwise positively charged water ions would have been observed by the ion spectrometer onboard Rosetta in cruise phase. This is also consistent with the observed outgassing density of less than 10 7 /cm 3 (except for the first 100 days of the mission) reported by  Schläppi et al. (2010). In this paper we report on observations of spacecraft outgassing by the BepiColombo spacecraft in cruise phase  (Benkhoff et al. , 2021). BepiColombo was launched in Octo-    1. Due to the limited telemetry allocation for the MMO spacecraft a large amount of data gaps are encountered specifically for the larger data records. Acronyms are listed in Table ?? at the end of the paper.
In addition it has the capability to measure the TOF of ions using an electrostatic shutter. In this study we only use omni-directional energy spectra at 32s time resolution from the imaging mode (IMG) of the sensor. In addition we use magnetic field data obtained by the MMO-MGF instrument  (Baumjohann et al. , 2020)     the closest approach. A rotation of the spacecraft only happened before and after this period shown by the orientation of the spacecraft Z-axis in Fig. 2. We note first that the low energy signal was observed for a long time before and after the flyby at distances from Mercury of more than 100 Mercury radii (R M , Fig. 2 ). This means that it is not caused by the proximity of Mercury. The highest intensity was observed about 20min after the closest approach at a time when also the temperature of the MMO side panel and of the MOSIF shield reached their maxima (Fig.