dataesr/scientific-paragraphs-categorization huggingface dataset:
Neighbourhood liveability patches We use the gridded dataset provided by the LBM project as the starting point for our dataset of neighbourhood liveability patches. We use the liveability scores made available for the year 2016. We do so firstly because it is the closest year to which there is a nationallyavailable aerial image  (2016). In total we use 51,781 grid cells from the dataset over the 13 built-up areas within our dataset. The samples used from each built-up area are shown in Table  1. We use the five domain scores and the overall liveability scores (middle and right columns of Fig. 1  respectively) as the liveability labels of our patches. For the overhead aerial imagery we use images from the national composite aerial image from 2017, made available by the Dutch government  (PDOK, 2017).
Data splitting We use data from 9 built-up areas for training and validation. Within each area we create square blocks of patches for validation, and we assign the rest to the training set. Through the overlap with neighbouring patches, some of the validation set is seen during training. However, this was not found to result in issues with generalization during testing. We use the remaining 4 built-up area as an independent test set. The 4 cities were chosen for their geographic diversity and their size. Dordrecht is proximate to Rotterdam and it is part of the Randstad area, which is the largest conurbation of the Netherlands. As Amsterdam and Rotterdam are part of the training dataset, Dordrecht is therefore the most similar city in the test set. Eindhoven and Hengelo are both cities which follow a different development pattern compared to those in our training split.
Both cities began to develop significantly as a result of industrialization, which makes them developmentally distinct from the cities in our training split. This difference in developmental context allows us to study how well our model adapts to unseen developmental layouts. Lastly, Beesel is a small village along the German border, which tests the model's ability to transfer to smaller settlements (as Beesel is the only village in the training dataset), and to remote regions. We show an example of our training/validation set stratification for the municipality of Amsterdam in Fig. 2. We show the number of samples per split in Table  1.
Set-up Our feature extraction model is a ResNet-50  (He et al. , 2016)  pretrained on ImageNet  (Deng et al. , 2009 ) from which we remove the final fully-connected layer. Our model is trained on a single NVIDIA TitanX GPU with a batch size of 20. We optimize our models using the AdamW optimizer  (Loshchilov and Hutter, 2019)  with an initial learning rate of 5𝑒 -5 and weight decay rate of 1𝑒 -4. We train our model for 15 epochs. To prevent that the model learns a set of features unrelated to the domain scores in feature matrix 𝐶, we set the weighting term 𝜆 of Eq. (  3 ) to 100 such that the model favours the correct prediction of the intermediate task over the predictions of the final linear layer. We assess the quality of our results with three metrics for each of the five domain scores of the LBM dataset, as well as for the overall liveability score.
Firstly, we calculate the root mean squared error as a measure of error for all scores. Secondly, we calculate the coefficient of determination 𝑅 2 to determine the quality of the fit for each score. Lastly we compute Kendall's 𝜏  (Kendall, 1938)  which measures the ranking of neighbourhood patches. This is possible because the liveability scores in our dataset may also be interpreted as ordinal variables, in the sense that the quality of each neighbourhood can be compared to every other neighbourhood, which represents a ranking. Kendall's 𝜏 ranges between a perfectly inverse correlation given as -1 to a perfect correlation given as 1. In order to assess how well a bottleneck model performs compared to an unconstrained model (research question 2) we also train an unmodified ResNet-50 model.
We use a perplexity (balance between global and local patterns) of 100, a learning rate of 500, early exaggeration (tendency for clusters to become compact) of 150, and we run our model for 1,000 iterations. We consider all patches in the dataset, rather than just the test set patches in order to analyse data structures across the 13 built-up areas. We can then overlay the neighbourhood typologies of each patch for each point in the reprojected 2-dimensional space, allowing us to infer the visual homogeneity of neighbourhood typologies for that particular score.
Secondly, as part of our feature vector analyses, we can assess how our model perceives the homogeneity of different typology styles, as well as the links between certain planning styles as defined by Dutch planners. It is expected that patches with the same neighbourhood topologies would group together, as they share similar visual characteristics. Our typology reference dataset is formally defined by  Kleerekoper (2016). Here, we use a subset of 8 neighbourhood typologies, 𝑇 (see Table 2). In our selection we consider a variety of different design styles, number of building layers, and construction periods. The typologies are digitized by the climate atlas of the Netherlands initiative  (Kleerekoper et al. , 2018). This dataset consists of district-level polygons, listing the relative presence (%) of each typology in each district.
In Tables  4, 5 , and 6 respectively we show the RMSE, 𝑅 2 and Kendall's 𝜏 metrics obtained by the RGB-only model for each built-up area in our test dataset. Across all regions, our model is able to infer the general trend of all scores, with some noticeable exceptions. Firstly, the achieved metrics can vary strongly per region and domain score, For instance, the model generalizes far less well to Beesel, which is far smaller than the other test sites. However, the decrease in performance is dependent on the domain scores, with some scores being more affected than others. In Table  7  we show the metrics for the validation set. We also show the difference with the test set to show the capacity of each domain score to generalize to unseen regions. Based on the decrease of metrics between the validation and the test set, our results suggest that direct domain scores (physical environment and buildings) are easier to generalize than indirect domain scores.
10 ), as both of these typologies only appear after the 1990s. This period saw a paradigm shift towards sub-urban construction, though this cluster does not fully encapsulate sub-urban trends, as for instance villas are still predominantly present outside of it. The top-most cluster in the t-SNE diagram (near example 1 of Fig. 10 ) shows the dense inner city patterns that are present predominantly in Amsterdam and Rotterdam, both historically and pre-war districts. The visual dissimilarity of these areas from any other building style is particularly striking, as it forms a small but visually distinct cluster while much of the feature space tends to clump together. It shows that these areas have exceptional properties when it comes to building quality. And indeed, when compared to the other cities in the dataset, Amsterdam and Rotterdam are the two most metropolitan areas within the dataset with certain unique features, such as the canal houses in Amsterdam.
Our results show that the use of an end-to-end trained bottleneck model generally improves model performance to the final task of predicting liveability. Our bottleneck model matches the 𝑅 2 metric of the unconstrained baseline model, and slightly surpasses it on Kendall's 𝜏. This shows that a linear mapping from the domain scores (which are a decomposition of the overall liveability score) is sufficient for reconstructing the overall liveability score. The reported metrics corroborate earlier findings that the intermediate prediction of a semantic layer can increase the model's performance of the final task  (Levering et al. , 2020). As evidenced by the results, models trained on aerial imagery can transfer fairly well to unseen regions, even across developmental context. The cities in our training dataset have a longer history than two of the cities in our test set, namely Hengelo and Eindhoven.
Surveying efforts are expensive, hard to perform on a large scale, and sensitive to a variety of biases such as response bias (the tendency for respondents to give inaccurate answers) and participation bias (the inability or unwillingness of certain groups of residents to respond). A well-performed study at scale is therefore a labour-intensive process. The privacy of respondents also needs to be respected, which further complicates the spatial scale at which information is typically reported. This makes the LBM a remarkable project, as it is on a fine spatial scale and is partially modelled on the subjective opinions of residents. To our knowledge, it provides the first largescale yet fine-grained dataset of liveability which incorporates resident opinions, thus opening possibilities for understanding liveability in the Dutch context, but with limitations that we discuss in the next Section.
Our results indicate that A. Levering et al. domains that can be observed directly through aerial imagery are easier to generalize than domains which need proxies in order to be predicted. This has practical implications for using remote sensing to fill gaps in data availability for the purposes of predicting liveability. For instance, where possible amenity data should be derived from sources other than remote sensing imagery, as open geodata registries provide coverage of the most important amenities for most countries. However, if building types are largely homogeneous between two areas but building quality data is only available in one area, then it may be worthwhile to gap-fill this data through remote sensing. While liveability monitoring from Earth observation has been proven to work for several different research cases and for each of the different types of reference datasets that are available, the subjectivity of the topic continues to hamper comparisons across studies.
First, there is no standard definition for liveability, which plays a role in determining a common ground for liveability studies  (Paul and Sen, 2020). Second, the way it is measured varies for each study, as do the variables and methods used to measure liveability. We therefore consider liveability prediction to only be valid within the cultural context in which it is measured, with very limited generalization beyond this context. In other words, the values that make a place liveable are culture-and location-specific. As such, we do not believe that liveability prediction models could be applied out of the box in a completely different cultural context. While our models retain sufficient performance in unseen cities for the extrapolation of liveability surveys, the entirety of our dataset falls within the same cultural context, which is the Netherlands as a country.
As such, our dataset has a largely homogeneous cultural and policy context. Attempting to extrapolate outside of the Netherlands, e. g. attempting to predict liveability in Belgium or Germany with our model, will most likely be less successful, due to a difference in cultural and policy context. The LBM project is an ongoing project which is still being updated. While the input variables and the domains are updated between versions, the reference data upon which the liveability scores are calculated remains unchanged. Meanwhile, the aerial image data will be updated yearly for the foreseeable future. As such, the data used in our study can theoretically be used to test whether the relation between the spatial configuration of settlements and their liveability is persistent over time. As the temporal extent of the datasets increase throughout the years, this option will become more salient as significant changes to the liveability of a neighbourhood such as gentrification and impoverishment will take years to manifest.
Mapping of the anti-SARS-CoV-2 humoral response in infected and uninfected individuals. Sera obtained within 2 weeks after patient hospitalization were tested by peptide microarrays designed to present 487 overlapping peptides (15-mers with 11 amino acid overlap) from the 4 structural SARS-CoV-2 proteins E, M, N, and S (listed in suppl. Table  1 ). Peptide-specific IgG and IgM levels were evaluated for each individual. After data cleaning (see Methods), the values obtained for each peptide were compared between the 42 uninfected and the 82 SARS-CoV-2 infected people. A stringent statistical test identified eight peptides significantly higher in IgG p value in infected individuals (p < 3 × 10 -4 ): 4 in the protein S (S139, S140, S204 and S287) and 4 in the protein N (N41, N56, N99 and N100) (Table  2 ).
The peptide microarrays were incubated with sera (applied dilution 1:200) for 1 h at 30 °C, followed by incubation with 0. 1 μg/mL fluorescently labelled anti-human-IgG (Jackson ImmunoResearch, 109-605-098) or anti-human-IgM (Thermo Fisher Scientific, A51012) detection antibody. Washing steps were performed prior to every incubation step with 0. 5% Tween-20 in 1 × TBS. After the final incubation step the microarrays were washed again and dried in a microcentrifuge. Each microarray was scanned using a GenePix Autoloader 4300 SL50 (Molecular Devices, Pixel size: 10 μm). Signal intensities were evaluated using GenePix Pro 7. 0 analysis software (Molecular Devices) and ranged between 0 and 60,000. All the epitope-mapping experiments were performed in the same Laboratory, using the same machines and the same buffers, by batches of 6 samples at a time.
Acknowledgements This study would not have been possible without the instrumental and passionate contribution of the staffs of the Senegalese and the  Malagasy Ministry of Health , the hospitals and the health centers implicated in the inclusion of the patients, and the staffs of the  Institut Pasteur de Dakar  and the  Institut Pasteur de Madagascar. We express our gratitude to the patients who participated in the study. This work was supported by  COVID-19 Task-force of the International Pasteur Institute Network , the  Conservatoire National des Arts et Métiers CNAM, Paris-France , the  Institut Pasteur de Dakar , the  Institut Pasteur de Madagascar  and the  Welcome Trust Grant   221012/Z/20/Z.
Data availability The data that support the findings of this study are available from the Institut Pasteur de Dakar or the Institut Pasteur de Madagascar but restrictions apply to the availability of these data, which are protected for ethical reasons, and so are not publicly available. De-identified data are however available through the following link http:// griv. org/ epimap/ (Accession number: EPiMAP-221). Additionally, the study protocol is available for request.
Author contribution The study conception was developed by the PMIP4 Working Group, consisting of RI, LM, TO, AA, NB, MK, UM, and PV. BS, LG, SS, and RI contributed to the study design, with LM, TO, and AA providing additional feedback and close communication with BS. The design of the experiments and running of them was performed by RI, LG, LM, TO, AA, NB, CH, FH, MK, UM, JM, and PV. Material preparation and data collection was performed by BS. The manuscript was prepared by BS with contributions from all co-authors, who read and approved the final manuscript.
If implemented in the early stages, this strategy could improve thrombo-inflammation in patients with severe COVID-19 by replacing inflammatory molecules and accumulated activated haemostatic factors with healthy plasma components, thereby delaying or preventing ICU admission  (21). The ongoing Therapeutic Plasma Exchange to Alleviate the Hyperinflammatory Condition During Severe Covid-19 Infections (CovidEP) trial (ClinicalTrials Identifier: NCT04751643) addresses this issue and assesses the requirement for intubation and invasive ventilation in patients undergoing 3 TPE sessions in 3 consecutive days compared to patients receiving standard treatments including assisted ventilation, oxygen supplementation, non-invasive ventilation, vasopressive support and corticosteroids, etc. In parallel to this clinical study, the results of which are not available yet, it has been proposed to evaluate in vitro the inflammatory potential of the plasma of patients undergoing TPE vs.
Method Patient samples and ethical considerations In accordance with the French Public Health Code (Article L. 1223-3) and the procedures of the French Blood Bank Ethics Board (Etablissement Français du Sang-EFS), volunteer blood donors signed the donation form indicating that they did not object to the use of their donation for non-therapeutic purposes. Thus, in accordance with the Declaration of Helsinki, informed and written consent has been obtained from all the healthy donors who participated in this study for blood sampling for scientific purposes. Plasma samples from hospitalised COVID-19 adults were obtained during the CovidEP clinical trial (NCT04751643) upon information and with the written consent of the patient or, if this was not possible, from a trusted individual. COVID-19 patients were randomized in two groups, a control group (n = 10) receiving standard treatments (assisted ventilation, oxygen supplementation, non-invasive ventilation, invasive ventilation, antibiotic, vasopressive support or corticosteroids) and a TPE group (n = 10) receiving standard treatments and, 24 h after inclusion, three consecutive TPE sessions, i.
Expression of platelet and endothelial activation markers Platelet membrane expression of P-selectin (CD62P) and CD63 activation markers following the co-culture of platelet and endothelial cells was assessed after immunostaining of platelets with APC-CD41a (for gating strategy), PE-CD62P and FITC-CD63 or corresponding isotypic controls. Platelets were fixed with ThromboFix prior to flow cytometry analysis. ICAM-1/CD54 membrane expression was assessed on ECs by immunostaining after trypsinization and cell washing. ECs were fixed in 2% paraformaldehyde which was replaced with PBS before the flow cytometry analyses. The analyses were performed either on a BD CANTO II flow cytometer and analysed with FlowJo V10. 8 (Ashland, USA) software or on a BD Guava ® easyCyte TM 8HT device and analysed with BD FACS Diva TM software (BD Biosciences).
Inflammatory factor quantification The amount of inflammatory factors in culture supernatants was measured using Luminex technology or specific ELISA. ELISA was used to quantify RANTES. Luminex technology was used to measure sCD40l, sCD62P and interleukin (IL)-6. Luminex and ELISA data were expressed in pg/ml. In order to assess molecule production during ECs and platelet co-culture on COVID-19 plasma stimulation, the quantity of each factor was assessed in COVID-19 D0 and D4 plasma and then subtracted from the amount assessed in culture supernatants.
Statistical analysis Statistical analysis was performed using GraphPad Prism 8 software (San Diego, USA). Statistical significance was defined as 2-sided P value of less than. 05 and unadjusted for multiple comparisons. Data were expressed as the point estimate of mean ± the margin of error around the mean defining the 95% confidence interval. The non-parametric Wilcoxon matched-pairs signed rank test was used to assess the significance of parameter changes over time. p < 0. 05 was considered significant.
Acknowledgments: The authors are grateful to the  Federal University of Tocantins (UFT)  for the support received. This publication received financial support from  EDITAL Nº17/2021 , of the  Postgraduate Program in Environmental Science (PPGCiamb)  by the  UFT , of the  Postgraduate Program   in Environmental Science (PPGCiamb)  by the  UFT  and from  EDITAL Nº 40/2021  of the  Pro-Rectory for Research (PROPESQ)  by the  UFT  and the  Foundation for Research Support of the State of Tocantins (FAPT)  through the public notice number  01/2019.
The last family includes devices that can be personalized but have a reduced lifespan, this family is the Nearly Broken "NB" family. These three populations of samples were created after quality studies on functional, defective and semi-defective samples. The problematic samples were analyzed by X-ray or acoustic microscopy. The work presented in this article shows the advantages and disadvantages of these two measurement methods in characterizing the reliability of contactless devices in a manufacturing and customization context. Following the description of the measurement methods, solutions allowing automatic rejections of damaged devices are considered. Several studies (  [13] ,  [14] ,  [15] ,  [16] ) aim to detect out-of-distribution data by using machine learning techniques. In the following work are proposed two machine learning methodologies to be used for fast and reliable decision making between devices classified as "OK" and the others categories.
Then, the dataset has been split evenly (hence forming train and validation subsets), by taking care of having an even distribution of all three classes between the two parts. Additionally, the NB and KO classes were merged into a single not-OK class in each subset, therefore reducing the problem to a binary classification. Training on the first subset was then done for 150 epochs with the Adam optimizer. From these 150 epochs, the model with lowest validation mean squared error (MSE) loss has been kept. A pitfall of this approach however was that the end result quality was highly dependent on the initial dataset split. To mitigate this 100 trainings were run, each with a different data split. The median model in term of MSE loss computed on both the training and validation data has been selected.
The obtained results are the following: -The median model achieves 100% accuracy on both the train and validation datasets. -In particular, the OK devices are clearly identified by the model. Below are displayed two graphics showing characteristics of the trained neural network: -Figure  12:  A heatmap illustrating the classification capabilities of the model: dots in the blue area are assigned as OK, while dots in the yellow one are labelled as not-OK (keeping the color convention that red, blue, and black dots are OK, KO and NB respectively). -Figure  13 : The model train (blue) and validation (orange) MSE loss curves during training. The average inference time for a single sample is around 60 ms on a mid-range CPU, allowing this method to be done in real time during the passport personalization process.
However, some work has been done to lower further said inference time by saving the heatmap image underlying array, in order to directly read a precomputed approximation  of the activation value of a given sample from the heatmap. The image has been sampled in a 100x100 resolution, and its values quantized on 8-bit to reduce its size. This induces a small loss in fidelity from the neural network outputs (less than 1% of total loss relative difference) while retaining 100% accuracy on the whole dataset, in exchange for quicker response times: 20 µs per sample on average. The source code for the artificial neural network method is available at  [23].
D. Robustness Study Another validation step has been done in order to confirm the effectiveness and robustness of the two previous methods, by testing them on a new dataset containing OK, KO, NB et ROB samples. ROB points are measurements of the same OK device which have been made deliberately noisy, see Table  2  for more details. From an operational point of view, this involves studying the impact of device positioning errors (placement, angle of the device, presence of metal plates, etc. ). Additionally, the normalization parameters used in the neural network approach have been computed on the previous dataset, and are not to be recomputed. The new validation step will serve to show that the normalization generalizes well to new data points (from new measured devices). The new validation dataset without the ROB points yields once again 100% accuracy with the neural network method, as well as with the precomputed heatmap approach.