dataesr/scientific-paragraphs-categorization huggingface dataset:
Simulated and hybrid datasets In addition to the experimental datasets, we have built 20 simulated datasets with images 256 × 256 pixels of width and height, same as the patches of DS exp. Briefly, the background was generated by a combination of Poisson noise and Perlin noise with a random intensity between [80, 150]. Elastic transformations were added to this noise to approximate the variety of textured background noise in the experimental images. Spots were generated as circles, randomly placed in the image, and then convolved with a Gaussian function that approximates PSF. Their size randomly ranges from 4 to 9 pixels in diameter, including Gaussian smoothing. Two different types of simulated datasets were generated DS fixed and DS var , each containing 10 datasets defined according to the number of spots per image  ∈ { [10.
100] mod 10}, see Table  1. For example, in the DS  20  fixed dataset each image contains 20 spots and in the DS 70 var dataset each image contains 70 spots. Each image in the DS fixed dataset has the same fixed spot intensity for all spots randomly chosen in the interval [160, 220], while in the DS var dataset the spot intensity is randomly chosen from the same interval for each spot, resulting in images with variable spot intensity. In addition to the experimental and simulated datasets, we have built a hybrid dataset DS hybrid where the experimental data from DS exp is augmented by appending an additional 25% of simulated images generated with both variable spots' intensity and variable spots' number per image, within the [160, 220] and [10, 100] intervals, respectively. The intensity of the spots is calculated with respect to the intensity of the background noise, so that the generated images have a SNR between 10 and 40.
Ground truth Since the goal of our network is to learn to transform an image  into (), where intensity at spots' location is enhanced, the training step has to be provided with the enhanced counterpart of each image In this work we implement  as a kernel of 3×3 pixels at all locations where the spots were annotated in the experimental dataset DS exp or generated for DS fixed , DS var and DS hybrid (see Table  1 ). The kernel has the same pixel values for all the spots, in order to drive the network to learn to enhance all spots up to the same level of intensity, regardless of the initial intensity in the acquired data. The enhancement kernel of DeepSpot is smaller than the smallest spot size in our datasets, therefore it is not expected to augment the size spot. This is particularly important for spatially close spots.
Results We trained the DeepSpot network on our 20 simulated training datasets DS fixed and DS var as well as on the experimental and hybrid datasets DS exp and DS hybrid , resulting in 22 models M fixed , M var , M exp and M hybrid. Training parameters were optimized with HyperOpt algorithm  (39)  and the ASHA scheduler  (40). The best configuration obtained and used for further trainings had the learning rate of 0. 0001, the dropout rate of 0. 2 and the batch size of 32 and 128 filters per convolution. q 4 q 5 q 6 q 7 q 1 q 2 q 3 Figure 4. Spot matching by 1-neighbor -d tree between the detected mRNA spots { 1 ,. ,  9 } depicted in blue and annotated spots { 1 ,. ,  7 } depicted in red. The -d tree construction for { 1 ,. 9 } is shown on the left. Using the matching radius depicted by circles, the -d tree queries for  1 and  2 shown in red, lead to the same leaf  2 and correspond to an ambiguous match, while query for  3 leads to an unique match.
mRNA spots  1 ,  4 and  8 are the False Negatives. Each of the resulting 22 models was evaluated on the 21 test data sets from Table  1 , yielding 462 enhanced test datasets (8100 images total). To assess whether DeepSpot enhancement enables easy spot detection, we applied the ICY spot detector  (10)  to the images enhanced by different models. Moreover, we defined an unique set of ICY parameters that matches the shape and intensity of the enhancing kernel of the DeepSpot network: scale 3, sensitivity 20 and scale 7, sensitivity 100. We then evaluated whether the detected spots from the enhanced images matched well with the annotated ground truth of spots' coordinates.
DeepSpot enhances the mRNA spot signal to the same intensity To avoid the manual selection of the detection threshold, it is imperative to have a homogeneous spot intensity for the whole dataset in order to use unique set of parameters for all images. As expected, intensities were closer to 255 when training and test datasets belong to the same category. For example, models trained on data with fixed intensities M fixed and applied to data with fixed intensities DS fixed produced enhanced spot intensities very close to 255. Similarly enhancement close to 255 could be observed when models M var were evaluated on DS var Of particular interest are the enhancement results from the M exp training, that are close to 250 for every dataset, experimental or simulated. Hybrid model enhances intensities to 241 on the experimental dataset.
In general, the enhanced spot intensities were between 241 and 255, representing a variation of only 5. 4% from the maximum intensity which is sufficient to fully separate smFISH spots from the background in the enhanced images. 3  including the mean F1score, precision, recall and ambiguous matches, with 95% confidence interval. For a given model  each metric was computed for all enhanced images (8100 total). The mean metric value  and 95% confidence interval  were then calculated separately for each model type. Due to the high prevalence of True Negatives, instead of the Accuracy measure, we calculated the F1-Score, which gives an indication of the model accuracy with a better balance between classes than the actual accuracy measure, by not including True Negatives. We compared the F1-scores for each of the 14 genes present in the test dataset, no major difference in DeepSpot performance can be observed for these genes (Supplementary Figure  S3 ).
This consistency in performance across different SNR values (Supplementary Figure  S2 ) shows that DeepSpot has the capacity to enhance spots in images with varying characteristics. The results in Table  3  indicate that the number of FP is very low for all models, given that both precision and recall are high. Importantly, M hybrid has shown best overall performance in terms of precision, recall and F1-score, thus indicating that the mRNA spot enhancement by M hybrid leads to the least FP and FN counts in the downstream mRNA spot detection. Figure  5. Heatmap of the F1-scores obtained by each of the 22 models when evaluated on the 21 test datasets described in Table  1. Figure  5  shows the mean F1-scores for each of the 22 models evaluated on the 21 datasets. This heatmap indicates that (i) models trained on the DS i fixed training sets perform better on the corresponding DS i fixed test sets rather than on variable intensity test sets, (ii) models trained on DS i var training sets show better performance on the corresponding test sets rather than on fixed intensity test sets.
It also shows that M var models are globally better than M fixed models. A plausible hypothesis is that training on variable intensities makes models better at generalizing on other data. Finally, M hybrid is the model that has the best overall performance, including the experimental dataset. Again, the diversity of training data drives this model to be more robust to newly encountered data. More generally, given that the F1-score is above 90% for all the models, we can conclude that the architecture of the DeepSpot neural network is particularly suited for the task of mRNA spot enhancement.
DeepSpot enables more accurate spot detection compared with deepBlink The main objective of our DeepSpot method is to circumvent the parameter fine-tuning and enable the downstream spot detection with an unique parameter set. As such, this objective fits well with the one that the authors of deepBlink  (7)  have pursued, despite the fact that the latter proposes a new spot detection method, while our goal is to fit a spot enhancement step into commonly used workflows. Consequently, deepBlink constitutes a relevant comparison target. We compared the accuracy of spot detection by the model M dB made available on deepBlink associated GitHub with that of DeepSpot when trained on hybrid data M hybrid , both on our datasets as well as on the dataset provided by the authors of deepBlink, DS dB. Table  4  shows the F1-scores for each dataset https://doi.
org/10. 1017/S2633903X22000034 Published online by Cambridge University Press category. It should be noted that deepBlink's precision is close to 99% while its recall is very low for certain datasets (Supplementary Figure  S4 ), meaning that the main drawback of deepBlink is in terms of false negatives (missing true spots). We illustrate this on Figure  6  where we provide a comparison of deepBlink and DeepSpot on both experimental and simulated data example. DeepSpot clearly outperformed deepBlink on our datasets (Table  4 ). However, we found that M dB performed better on experimental data than on simulated data, presumably because deepBlink model has only been trained on experimental data. M hybrid results were consistent on all of our datasets. We have also applied our M hybrid model to the smFISH test dataset DS dB provided by the authors of deep-Blink.
Results reported in Table  4  for the detection of spots by M dB from the images of the DS dB dataset are those obtained by the authors and originally reported in  (7). Not surprisingly, deepBlink model performance is better on their own smFISH images, however DeepSpot managed to have an F1-score of nearly 88%, a noticeable achievement since the model has not been trained on the DS dB data. Together, the results of Table  4  indicated that DeepSpot is a robust methodology that offers a generalist model for mRNA spot enhancement and ensures high quality downstream spot detection without parameter tuning.
Discussion In this work we introduced DeepSpot, a novel CNN architecture specifically designed to enhance RNA spots in FISH images, thus enabling the downstream use of well known spot detection algorithms, such as the ICY spot detector, without parameter tuning. In particular, the architecture of our network introduces the Context Aggregation for Small Object module that relies on sparse convolution to provide more context for enhancement of small objects corresponding to mRNA spots. DeepSpot network has been trained and tested on 21 simulated datasets, all with different signal and noise characteristics, as well as on a previously published experimental dataset that was annotated for spot locations. We have shown (i) that our approach achieves better performance when the training is performed on data with highly variable intensity and (ii) that performing training on a combination of experimental and simulated data is a viable approach in real-life setting.
Furthermore, we compared the performance of combining DeepSpot and ICY to that of the stateof-the-art deep learning-based method deepBlink and have shown that on average, DeepSpot enables a substantially better detection of mRNA spots than deepBlink. We found that DeepSpot / ICY workflow provided excellent quality spot detection on the test datasets corresponding to the datasets on which it has been trained, with the average F1-score above above 97%, but also achieved high precision results on fully unknown datasets with the F1-score of 88% for the datasets provided with the deepBlink publication. Taken together, the good results on both known and unknown data indicate that DeepSpot is a more generalist model than deepBlink and that it achieves a good balance between overfitting and underfitting.
List of datasets used for training (22 training datasets) and evaluation (21 test datasets) of the DeepSpot network. Images in the DS i var datasets have spot intensities between 160 and 220 for each image. For the fixed intensity datasets DS i fixed the spot intensity is set to one value within [160. 220] for a given image, but varies from image to image. 10 variable intensity DS i var and 10 fixed intensity DS i fixed datasets are named according to the number of spots in the images,. The dataset DS hybrid combines DS exp with 25% simulated images.
Competing Interests. None Data Availability Statement. DeepSpot  network along with the code for training and for mRNA spot enhancement is fully open source and available on GitHub at https://github. com/cbib/DeepSpot. Our pretrained models used in this study are also available on our GitHub page, as well as the Napari plugin. Data for simulated images for DS var , DS fixed and DS hybrid are available on Xenodo at https://doi. org/10. 5281/zenodo. 5724466 Hardware and framework. All the analysis presented in this paper are performed on 1 GPU, Tesla T4 with 16GB of memory, on a dedicated machine with two 2 CPU Intel Xeon Silver 4114 and 128Go RAM. Training curves and comparison with a vanilla U-Net architecture are available in the Supplementary Figure S5 and Supplementary Table 1. This work is implemented in Python 3.
Among the meteorological variables, to avoid correlated variables, we included in our model only the maximum of amplitude of pressure and temperature and the maximum of temperature and pressure or the average of pressure and temperature. The The logarithm of the number of people for each zone (called here "population") is also included in the model. The number of habitants by INSEE code is found on website "notreterritoire. maregionsud. fr" and we calculated the population for our defined 357 zones. Since data were available only for the years up to 2016, we assumed the population to be constant for 2017 and 2018. The association between environmental variables and CD events is studied on a period of 14 days (lags). The model applied is a generalized linear model with quasi-Poisson distribution.
Multivariate meta-analysis We employed a multivariate meta-analysis in order to synthesize results in multizones analysis with DLNM. It defines an average exposure-response association across the zones selected. The method of estimation for random effect multivariate meta-analysis used is restricted maximum likelihood (reml). The R package used is mixmeta. This model was applied at three level on our data. Before running the model, we removed 97 zones having less than 18 CD events (minimal empirical value of employability of the model). Global model on the entire region. We performed a multivariate meta-analysis with the DLNM (21) on the previously defined zones in order to identify global trends on the risks of CD by pollutants at the region level (figure  1B , insert VI). Global model on the zones grouped by pollution trends.
Overall characteristics of emergency room visits data and environmental database The emergency room visits data is composed by 43,400 CD events collected by the 47 emergency centers of Région Sud in the period from 1 st January 2013 to 31 st December 2018 reporting an event of CD with symptoms related to either heart failure (I50 codes, 98% of the events) or atrial fibrillation (I48 codes, 2% of the events) (table 1). A summary of emergency room visits data characteristic is reported in table 2. We normalized the number of events by the total population by year and by zone and we reported the incidence of CD events in Région Sud in figure  2A. Indeed, only a small percentage of the population is concerned, slightly increasing over the period of the study. As expected, we observed a higher incidence of CD events in wintertime compared with summer (figure  2B ) and no specific prevalence due to gender (figure  2C ).
By contrast, some strengths should be discussed. First, the data on CD were collected from all the 47 emergency centers of the region allowing complete coverage and the sample size is quite big to achieve good statistical performances. Secondly, the availability of the exact date of CD events and daily measurements of pollutants allowed the temporal alignment between air pollution exposure and CD incidence. Thirdly, we set up a statistical model that can be applied at different scales, from the entire region up to single cities/zones. In order to collect and to easily access to the results of this study, we developed a web application called HEART: Health -Environment PACA Region Tool. HEART can be used by local authorities in order to set up specific policies to lower down pollution or for public alerts when pollution raise above secure levels for the citizens that adapt to the different zones.
F) Lag-specific effect on CD events for increases of PM10 over the thresholds indicated in the legend in the plot for zones in cluster 4 (big cities on the coast). Maps were created with the R package maptools v1. 1-1. Tables  Table 1. Inclusion codes for patient population. Figure Legends Emergency code Explication Number of events I50=I50. 0, I50. 1, I50. 9, I500, I501, I509 Heart failure 42,769 I48 = I48. 0, I48. 1, I48. 2, I48. 3, I48. 4, I48. 9 associated with codes: J18. 9, T17. 9, R06. 0, T75. 1 Atrial fibrillation and flutter 631.
Our study is based on two different databases. The emergency room visits data, provided by the agency ORUPACA (https://ies-sud. fr/) that consists of 43,400 recorded events for individuals aged between 20 and 115 years old, who attended one of the 47 emergency centers of the Région Sud with symptoms related to CD daily maximum observed of NO 2 and O 3 and the daily average value of PM10, and 4 meteorological factors, namely: the daily maximal observed temperature (T) and pressure (P) and the difference between the maximal and.
Data availability  Project name: HEART  Project home page: https://github. com/UCA-MSI/HEART  Operating system(s): Platform independent  Programming language: R  Other requirements: R 3. 6 or higher.  License: e. g. GNU GPL 3. 0.  Any restrictions to use by non-academics: none. The authors declare that the data supporting the findings of this study are available within the article and its supplementary information files.
Our study is based on two different databases. The emergency room visits data, provided by the agency ORUPACA (https://ies-sud. fr/) that consists of 43,400 recorded events for individuals aged between 20 and 115 years old, who attended one of the 47 emergency centers of the Région Sud with symptoms related to CD daily maximum observed of NO 2 and O 3 and the daily average value of PM10, and 4 meteorological factors, namely: the daily maximal observed temperature (T) and pressure (P) and the difference between the maximal and value for NO 2 , O 3 , T, P, daily temperature amplitude ( ) and daily pressure amplitude ( ) and the average value for PM10. Finally, we filtered out 8 zones for which no CD events were registered during the period under study, for a total of 349 minimal value ( and zones (figure 1B, insert V).
Once the bases were reconciled, each emergency room ). between 2013 and 2018. Inclusion criteria include only patients with International Pre-processing and merging visit has been associated to the environmental data of its residential zone. The Classification database reconciliation procedure is fully explained in Supplementary Information. of Disease, version 10, (ICD-10, https://icd. who. int/browse10/2019/en#/) emergency codes: "I50" for heart failure and Missing values and outliers of environmental data "I48" for atrial fibrillation and flutter (table 1). We decided to include atrial fibrillation The environmental database contained missing data, specifically there were 43 days missing (2%) for the pollution data and 22 (1%) for the meteorological data over 2,191 days between 2013 and 2018.
Outliers were also detected and were processed as missing data. An outlier is an abnormal value, namely a value that can impossibly be feasible for the associated variable. Two methods were used: interpolation and random sampling. The interpolation method was used for variables with a trend or seasonality (O 3 , PM10, P, T) and the random sampling method for variables with a random distribution (NO 2 ), see Supplementary Information for details. Merging emergency room visits and environmental data The three databases had different resolutions: non-overlapping meshes of 4 km squares for pollution (figure 1B, insert I) and meteorological variables (figure 1B, insert II), and postal codes for emergency room visits data (figure 1B, insert III). In order to associate pollutant and meteorological measurements to each emergency room visit, we first needed to change the data resolution of the three databases.
is Fourrier, commonly used function for time series of periodic data, the type of initialization is funFEM. According to the Integrated Completed Likelihook (ICL) criterion, 6 clusters are obtained. The cluster 1 corresponds to countryside (69 zones), cluster 2 to coast-side/medium size cities (23 zones), cluster 3 to mountains (31 zones), cluster 4 to coast-side/ big cities (17 zones), cluster 5 to highway (58 zones) and cluster 6 to west side (53 zones). Finally, we applied the same model at the pollution clusters level. (22)ake into account local fine structures of pollution distribution, we employed the co-clustering algorithm "multiFunLBM"(22)on the environmental dataset in order to identify groups of zones with homogeneous trends of pollutions (figure1B, insert VII). To define groups of zones with similar trends of pollution, we used a specific coclustering method developed for time series with R package funLBM.
Flow Cytometric Analysis and Cytokine Quantification in Culture Supernatants DCs were collected after the indicated time of infection and preincubated for 30 min at 4 • C in PBS containing 2% goat serum plus 0. 2% sodium azide and washed twice with 1% bovine serum albumin (BSA). Then, cells were incubated for 30 min at 4 • C with anti-human CD14-FITC (clone REA599 1:50), anti CD1a-APC (clone REA736 1:50), anti CD80-PE (clone REA661 1:50), anti-CD-83-VioFITC (clone REA714 1:100), and the specific REA control (S) antibodies human IgG1 (all purchased from Miltenyi Biotec, Bergisch Gladbach, Germany). Flow cytometry analysis was performed using FACSCalibur (BD Biosciences, NJ, USA) and FlowLogic software (Miltenyi Biotec). The culture supernatants of DC were assayed to evaluate the pro-inflammatory cytokines TNF-α (DY210, assay range: 15.
Default parameters were used for all software tools unless otherwise noted. TrimGalore v. 0. 6. 2  [32]  was used for reads trimming, and read quality was checked with FastQC v. 0. 11. 9  [33]. Raw paired-end reads were assembled de novo with Unicycler v. 0. 4. 7  [34] , and assembly metrics were calculated using QUAST v. 5. 0. 0  [35]. Structural gene prediction and functional annotation were carried out using the Prokka pipeline v. 1. 14. 0  [36].
Potential ligand interactions (NE, 5HT, phentolamine, and propranolol) were investigated in silico by the molecular docking technique using the predicted structure of the putative adrenergic sensor of E. faecium NCIMB10415. Essential hydrogen atoms, Gasteiger charges, and solvation parameters were added with the aid of AutoDock tools. Affinity (grid) maps of 80-by 80-by 80-Å grid points and 0. 375-Å spacing were generated using the Autogrid program  [42]. AutoDock parameter set-and distance-dependent dielectric functions were used to calculate the van der Waals and electrostatic terms, respectively. Docking simulations were made using the Genetic Algorithm (GA). The structure of NE (CID: 439260), 5HT (CID: 160436), phentolamine (CDI: 5775), and propranolol (CID: 4946) were downloaded from PubChem (http://www.