dataesr/scientific-paragraphs-categorization huggingface dataset:
Scheme 1 Journal of the American Chemical Society Article Rutherford Appleton Laboratory using the apparatus described previously. 29, 30 Excitation was at 457 nm with 100 fs pulses of 400 nJ at 1 kHz focused to a spot size of 200 μm FWHM. The experiment employed two synchronized ultrafast lasers, one operating at 1 kHz for excitation and one at 10 kHz (the probe), arranged such that for every pump pulse ten probe spectra are collected and pump-probe time delays between 200 fs and 1 ms are accessible. TA difference spectra were recorded using a white light continuum generated in CaF 2 , with alternate pump pulses blocked by a chopper. Samples were rastered in the beam to ensure a fresh spot was irradiated by each pump pulse. Further details of the data collection and full details of the data processing are provided in the Supporting Information.
Further consideration of the Heme IV ↔ Heme III electron transfer rate constants and their implications for the electron transfer mechanism is presented in the Discussion that concludes this paper. Atomistic Description of Ru 77 :STC Photochemistry. As noted above, a key feature of the kinetic model describing the Ru 77 :STC photocycle is the presence of three Ru-conformers with different rate constants for electron exchange between the Ru-dye and Heme IV. We assessed the validity of this model through molecular docking and MD simulation to consider the nature of the predicted conformers. A structural model of Ru 77 :STC was obtained by docking 2 (4-CH 2 -4′methylbipyridine)] 2+ to the crystal structure  16  of STC (pdb identifier: 1M1Q) with serine 77 modified to cysteine (see the Methods and Supporting Information sections for details).
For all 96 wells, a set of operations are performed to find optimal image acquisition settings (Fig. 1  and Methods). Using these optimal settings, a single image is initially taken for each well to provide an estimate of cell density per well. The cell density is then used to determine the number of images that need to be taken at each well to reach a satisfactory number of cells for data analysis purposes. The time required to perform this task for the 96 wells is ~12 min. At this point, subsequent rounds of imaging can be performed at desired time intervals simply by utilizing the settings saved for each well. In summary, the imaging methodology described here is readily applicable to any large collection of bacterial strains and enables fast time-resolved imaging for quantifying dynamic phenotypes.
2 ). Supervised classification was used to assess whether the cell had lysed due to antibiotic action and to determine the morphology of the cells that were intact (not lysed). First, intact cells were discriminated from lysed cells using Partial Least Squares Discriminant Analysis  23  (PLS-DA) (Fig. 3b, c, d ) and then the intact cells were classified into 4 morphological categories using Soft Independent Modelling of Class Analogy 24 (SIMCA) (Fig. 3c, e ). The morphological classes included cells showing normal, small, elongated and round morphology (Fig. 3e ). Cells that did not belong to any of these four classes were assigned to the category 'deformed', which was found to encompass morphologies like elongated cells with bulges in the middle, constricted cells and fat or curvy cells. The procedure was applied on all images of the screening dataset.
e. intact, normal, round, elongated, small and deformed for each of the three time-points (columns G-X in Supplementary Data 1). Once the phenotypic profiles were obtained, we compared them against the wild-type response to identify mutants displaying atypical morphological dynamics. The wild type lyses in 45 min in presence of cefsulodin (Fig. 4a , b and Supplementary Table  1 ). As cells start to lyse, the proportion of deformed and round cells rises in the intact cell population, while the proportion of cells with normal morphology decreases (Fig. 4c  and Supplementary Table  2 ). As a control, we determined phenotypic profiles for 276 wild-type replicates distributed over three microplates (or batches). Then, the phenotypic profile of each mutant strain and the wild type was assessed for dissimilarity based on two distinct statistics, Squared Prediction Error (SPE) and Hotelling's T 2 (Fig.
We observed that a cell density of more than 125 normal shaped E. coli cells in the field-of-view caused aggregation of cells and problems with contour extraction. Even for rapidly multiplying bacteria (generation time of 20 min) and a requirement of minimum 30 cells at every time-point, capturing 9 images per well allows imaging and subsequent analysis for around 2 h. We believe that the image and data analysis framework that we described here can be implemented to any bacterial image dataset by training the classifier on specific subsets of cellular features. Cells suspended in liquid media are motile. This does not affect the analysis, because we compute phenotypes from the frequency of morphologies in the cell population. But, it precludes microscopic measurement of changes in single-cell physiology over time (like in a time-lapse).
The internal architecture of each phenotypic cluster revealed information regarding phenotypes associated with enhanced cell survival. Our morphological classification approach revealed subtle phenotypic distinctions. For example, we could discriminate between the mutants enriched with smooth long filamented cells and the mutants enriched in constricted or bulging long cells even though both phenotypes caused cells to grow longer. Achieving this degree of distinction highlights the large amount of information that can be derived from highthroughput microscopy image datasets when appropriate analysis techniques are implemented. In conclusion, to the best of our knowledge we report the first image-based systematic study of dynamic morphological changes in bacteria for a large collection of strains.
We did not include this image in the data analysis, however the number of cells segmented in this image was used to decide the number of images required from each well. Nine images were taken from the well if there were less than 15 cells, otherwise four images were taken. Image processing. Segmentation of bacterial cells from the images was challenging due to the substantial local differences in intensity and also because the antibiotic action resulted in cells with different morphologies and intensity profiles. First, the contrast between cells and background was enhanced by adaptive histogram equalization and top-hat filtering. Then, an appropriate cutoff value was calculated to binarize the image. Since we wanted to segment both intact cells (with high contrast to background) and lysed cells (with low contrast to background), this step was not very stringent.
As a consequence, some non-cell features were also present in the binarized regions. Therefore, in a final step we pruned regions that did not pass pre-defined minimal cell shape criteria. For every cell, 54 features were computed that quantified their intensity, shape and microenvironment characteristics (see Supplementary Information). The cell descriptors served as input for the supervised classification of cells based on their morphology. Data analysis. Cells were first classified as lysed or intact using a well-established discriminant technique, namely PLS-DA  23. The PLS-DA model for intact vs lysed cell discrimination was calibrated on a training set containing the values of the 54 descriptors measured for 3745 cells, randomly selected from various images captured at different time points during treatment with cefsulodin.
Both the PLS-DA and SIMCA models were trained using wild-type cells. Residuals analysis highlighted that both models were reasonably extendable for the determination of the intact/lysed status and morphological category of mutant cells. This is understandable because cells with similar morphologies are supposed to yield similar values of the intensity and shape descriptors even when they have differences in their genome. The proportion of cells in every morphology class was determined based on the abundance in the whole population including both intact and lysed cells. Prior to data analysis, we corrected the data from all plates containing the wild type and the mutants for batch effects (Supplementary Fig. 6 ). The correction is performed by centering and scaling to unit standard deviation data from each plate separately.
The enrichment analysis was carried out by two-tailed Fisher's exact test and all COG terms that showed a significant enrichment (p-value < 0. 05) are shown in Fig. 6. Turbidimetric studies. Optical density measurements were collected using a Synergy Mx Multi-mode Microplate Reader (Biotek). Overnight grown cultures were diluted 1:1000 in LB and further grown in Greiner 96-well plates for 2 h. Then, cefsulodin was added to a final concentration of 100 µg/ml in the wells and the OD was monitored at regular time intervals. OD was measured at 595 nm. Statistics and reproducibility. Image data was first analyzed to find the number of cells imaged for every mutant. Mutants/wells with less than 50 cells in total or more than 150 cells per frame were repeated. Data from every plate was preprocessed to correct for batch effects.
Every well/mutant data was normalized using the data from the whole plate. Three components are used for the PLS-DA model. For SIMCA analysis, the number of components used are: one for elongated cells, one for normal cells, three for round cells and two for small cells. The number of components in all cases were assessed by seven-fold cross validation. We achieved a high specificity and sensitivity (more than 81% for every cell class). Data analysis was performed without prior knowledge of the arrangement and order of strains. Many extreme phenotypes were validated either by microscopy on agar pads or using the 96-well plate assay but with more time-points. The CFU counting experiments were performed three times. Results are expressed as a mean ± SD. COG enrichment was tested by two-tailed Fisher's exact test.
, 2015). The robutness and modularity of this architecture makes this network the perfect candidate for our purpose. As shown in Figure  1 , this paper allows understanding and exploitation of this new type of information in the context of deep learning. This paper proposes the following main contributions: • Introduction of the polarimetry in the field of feature learning to discuss the advantages and disadvantages of such data. In addition, a dataset has been created for the experimental needs. • Creation of novel techniques allowing polarimetric data to be augmented by preserving the physical properties from this modality. • Detection and segmentation of reflective areas through standard convolutional deep learning techniques. The various past works on which this paper is based are presented in Section 2.
The commonly used Stokes vectors can be normalized by s 0 : S =   s1 s2 0   = 1 s 0   s 1 s 2 0  . (2) AoP and DoP can be deduced according to: DoP = s1 2 + s2 2 , (3) AoP = 1 2 tan -1 ( s 1 s 2 ). (4) The last parameter I is the intensity which is the combination of all polarized states intensities: I = P 0 + P 45 + P 90 + P 135 2. ( 5 ) After this computation, three gray-scale description images of the raw polarimetric data are obtained. We have chosen to build an HSL (Hue Saturation Luminance) image mapping the three previous sources of information. This colorspace allows specific behavior per channel which fit with the data provided by AoP, DoP and I. The hue is commonly a 360°periodic value, the saturation is a value between zero and one as well as the value for the luminance.
While rotating the camera counter-clockwise, the angle of polarization is rotated clockwise. Let θ be the applied rotation angle to the camera, R θ the rotation matrix and H the hue channel of the image: H rotated = R θ (H prev -θ). (7) At the end of this computation, the image will keep its physical properties and be rotated. As shown in Table  1 , a set of transformations has been developed to give the ability to extend any polarimetric images dataset and it is remarkable that only the hue channel needs some modifications to stick to physical properties. The translation is only a shift in the images, which means that there is no modification in the view point of the camera. Since a polarimetric camera is dependent on the actual position and view point, the hue channel remains invariant to translation.
A New Dataset: PolaBot Acquisition was conducted to provide a new multimodal dataset PolaBot with polarimetric images. To the best of our knowledge, no such specific dataset has been released yet. Moreover, in order to make this dataset reliable for different fields (robotic, autonomous navigation, etc. ), the acquisitions were made with a multi-modal system of four calibrated cameras. Three synchronized modalities are represented, two RGB from different angles, one NIR (Near-Infrared) and one polarimetric camera. In addition, this collection of information will allow a strong and efficient benchmark, giving the opportunity to compare standard modality to the polarimetry for the exact same scenes and application. This dataset is available at: http://vibot. cnrs. fr/polabot. html.
EXPERIMENTS To confirm our hypothesis of the polarimetric data being more efficient than standard modality for our application, experiments have been conducted, allowing a comparison. All the experiments were performed on the same dedicated server composed of an Nvidia Titan Xp (12GB Memory) GPU, 128GB of RAM and two CPU accumulating a total of 24 physical cores (48 threads). For the SegNet Network, internal parameters of the training must be set. We had to set the loss function and the optimizer. We decided to use Adam  (Kingma and Ba, 2014)  as optimizer and as the loss function the cross entropy loss, defined as: CEL(p, q) = -∑ ∀x p(x)log(q(x)), (8) where x represents the class, p(x) is the prediction for the x class and q(x) the ground truth. Also, for all the training, the learning rate was initialized as 10 -4 and a maximum of 500 epochs.
Training Results Metrics for each epoch has been computed. This procedure allows seeing the fitness evolution of the model. As shown in Figure  3 , both curves are different according to the data provided to the network. First, it is possible to see that the two processes did not stop at the same time. While the network with polarimetric data reached 500 epochs, the network processing RGB data ended at 432 epochs. Indeed, we had put into place a stopping criterion to prevent the network from decaying. This result means that the SegNet RGB has experienced a decrease in its validation metrics for more than 10 epochs. However, our process allows the recovery of the optimal state in order to assess the so-called "optimal" results. In a second step, it is possible to notice the differences in metric values.
Discussion As shown in the Table  3 , very high accuracy can be observed in all segmented classes using polarimetric data. As the data set is not generic, the sky remains on the same tone (blue), which gives a significant advantage over the RGB mode. The other classes where the RGB model is better are: road, water and none. These differences are minimal and can be explained in several ways. One of our hypotheses concerns the difference in manual segmentation for ground truth. RGB and polarimetry were segmented independently, increasing uncertainties. The difficulty of segmentation of certain classes must be taken into account. Another way to look at these results is to consider the advantages and disadvantages of cameras in relation to the dataset. For example, the road can be polarized if there is a high temperature; therefore, polarimetry would have an advantage over the RGB model.
Since the dataset is acquired in only one type of weather condition, the RGB may have an advance over the other model, which may explain these results. However, polarimetry model gives very high accuracy in all the classes. More precisely, when segmenting areas such as windows, cars and building, the model obtain a big positive difference compared to the RGB. The window segmentation is almost twice more performant using polarimetry model than RGB model. Indeed, these results can be explained by the polarization state of such areas.
CONCLUSION AND FUTURE WORK In this paper, we proposed the introduction of polarimetry to pixel-wise road scenes segmentation field. Since to our knowledge there was no dataset with outdoor scenes captured via polarimetry, we created our own dataset. This dataset being made up of several modalities, the key idea was to have a comparison measure. As polarimetric data require meticulous exploitation, we have developed an augmentation method to preserve the physical properties of this modality. This approach defines the possible transformations and provides the necessary formulas for a rotation or flipping. We then used our augmented dataset as input to the SegNet Network to estimate the results. After comparing the SegNet Polarimetry and the SegNet RGB we can deduce that polarimetry offers a considerable advantage over RGB.
Patients and methods This is a retrospective study of patients with miliary TB hospitalized between August 2016 and July 2018 in the Department of Internal Medicine and Infectious Diseases of the Kiffa Regional Hospital Center. TB diagnosis was established based on the presence of clinical and radiological signs suggestive of miliary TB. Pulmonary nodules were classified as follows: micronodular (1 to 3 mm in diameter), macronodular (> 3 mm in diameter), and reticulonodular densities characterized by nonspecific interstitial patterns. For each patient, epidemiological data (age, sex, geographical origin, bacille Calmette Guerin (BCG) vaccination status, history of TB), clinical and laboratory examinations (chest x-ray, search for acid-alcohol-fast bacilli), and information on disease evolution were collected.
com) and consensus sequences were assembled with CromasPRO 1. 5 software (http://technelysium. com. au/wp/chromaspro/). Sequences were then aligned using the MUSCLE method implemented in MEGA7  (Kumar et al. , 2016)  and analyzed for strain genotypization and characterization by comparing them to a database including a set of commonly applied vaccine strains and  Valastro et al. (2016)  reference sequences. The detected strains were classified as vaccines when they were identical or closely related (p-distance <0. 01) to one of the administered vaccine  (Worthington et al. , 2008). The other strains were considered: a field strain, if the p-distance was higher than 0. 01 compared to known vaccines or a vaccine-derived one, if they were closely related (p-distance <0. 01) to known vaccines not matching the ones applied in the farm.
ACKNOWLEDGMENTS We thank Ceva Animal Health Polska Sp. Z. o. o. (Warsaw, Poland) and  Ceva Santé Animal (Libourne, France)  for supporting the diagnostic activity and data collection for this study. This research was founded by the grant ( BIRD187958/18 ) from the  Department of Animal Medicine, Production and Health, University of Padua. This research was partially funded by  Ceva Animal Health Polska Sp. Z. o. o. (Warsaw, Poland). Ceva.
Data Source From 12,885 surveyed women, a sub-sample of 3,249 women were included in the analysis. The inclusion criteria were: i) women had at least one child born within five years before the date of survey; ii) respond to the question on survival status (alive/death at the time of survey) of this child; iii) respond to the question about the date of child death if applicable; and iii) respond to the question on household cooking fuels use.
Strength and weakness The main strength of the study was based on the first-ever conducted a nationally representative survey with 98% response rate. Moreover, the analysis of large-scale data with appropriate statistical adjustments for confounders makes this study findings more reliable for policymaking. The temporal association between IAP exposure and child mortality outcomes cannot be established due to the cross-sectional nature of the study. Besides, the associations could be affected by unmeasured confounders as well as different health outcomes such as preterm birth, low birth weight, and other morbidity factors could be associated with underfive child mortality despite the exposure to IAP. Third, information related to birth and death of the children was reported by mothers that may introduce recall bias, but it is unlikely that the mother would incorrectly report the birth and death of their children.
Results Metabolomic data analysis and metabolite identification. Thirty-seven randomly chosen frozen tissue samples (21 clear cell, 9 papillary and 7 chromophobe) were used as the training set for initial RCC subtype classification model development (Tables  1  and 2 ). The metabolites from each sample were extracted in parallel and analysed by LC-MS/MS. Raw spectrum data were then integrated in MZmine (Version 2. 29) for chromatographic alignment and peak detection. 1591 and 898 peaks were isolated in positive and negative mode, respectively, and 852 (pos) and 469 (neg) could be identified in the human metabolome database. After elimination of the duplicates that were identified in both polarities, 1042 metabolites were selected for further analysis (Supplementary Dataset). For 8 of these RCC tumours (5 ccRCC and 3 chroRCC), we also collected adjacent normal tissue from the same nephrectomy surgical specimen and performed the same metabolomic profiling in order to determine the baseline of metabolic levels in healthy controls.
In addition to the classic PLS model, the orthogonal PLS model separates the dataset variation into two parts: predictive and orthogonal, and therefore allows improved interpretability  29, 30. The optimum OPLS-DA model for RCC histological subtype classification was established with 2 predictive components and 1 orthogonal component, a R 2 X (cum) of 44. 2% and a goodness-of-fit R 2 of 75. 0% (Fig. 1a ). The model shows discrimination between all three main subtypes of RCC. Moreover, all 37 samples were classified correctly according to their corresponding subtype. Cross-validation tests and permutation tests were performed to evaluate the predictive capability of this model. For cross-validation tests we withheld, by turns, 1/7 of the cohort as validation sets. The goodness-of-prediction Q 2 estimated from these validation sets was 59.
Metabolic pathway analysis. For a better understanding of metabolic dysregulation among RCC subtypes, we performed two types of pathway analysis. Metabolite Set Enrichment Analysis was performed using the Small Molecule Pathway Database (Fig. 2a ) and Metabolic Pathway Analysis was performed using the KEGG database, which also calculates the impact of each pathway using topology analysis in addition to the classic enrichment analysis  31, 32  (Fig. 2b ). Amino acid metabolism appeared to be the most frequently modified pathways   Heterogeneity within a tumour. For 6 ccRCC, several samples were collected from each surgical piece to evaluate the heterogeneity within the tumours using metabolomics. We performed a hierarchical cluster analysis (HCA) on each set of data. For 1 of the 6 tumours, within the same surgical specimen, we observed a necrosis zone, a sarcomatoid zone and a conventional ccRCC zone.
However, it is known that renal cell carcinoma is a very heterogeneous group of tumours that display different behaviours. The RCC subtypes differ in their histologic appearance, genetic profile and response to drug treatment. To develop new subtype-specific therapeutic approaches, it is essential to understand the differences and similarities between subtypes on a molecular level  11. In this study we have established an extensive overview of the metabolic profiles of the main RCC subtypes. Over 1000 metabolites were identified and semi-quantified for each RCC subtype. Our data suggest that metabolic reprogramming mechanisms are different between RCC subtypes. Moreover, pathway analyses revealed significant differences between the RCC subtypes, mainly concerning amino acid and fatty acid metabolism.