dataesr/scientific-paragraphs-categorization huggingface dataset:
Neutron count rates can be strongly affected by the presence of snow cover, resulting in inaccurate soil moisture measurements. Unfortunately, in most cases no additional snow measurements were available at the CRNS sites. Therefore, we used the ECMWF climate reanalysis data product ERA5-Land  (Muñoz Sabater, 2019)  to indicate snow cover events. For this, we flagged neutron count data when the 24 h moving average of the ERA5 SWE (snow water equivalent) product exceeded 1 mm. To indicate unrealistically high values in the CRNSderived soil moisture time series, we flagged values for soil moisture that were greater than local soil porosity. Because local measurements of soil porosity were not available, we estimated porosity using available information on bulk density and soil organic carbon content.
However, NM stations are rare, representing only a few latitudes and often not providing continuous signals over long periods of time. The NM at Jungfraujoch (Switzerland) is one of the few stations that provides reliable long-term data that can be used for COSMOS stations in Europe due its central location. Hence, scaling of the Jungfraujoch signal is needed to match the widespread distribution of COSMOS stations in Europe. According to  Schrön et al. (2016) , the intensity correction factor can be calculated as follows: C inc = 1 + γ I I ref -1 -1 , (4) in which I is the count rate of incoming cosmic-ray neutrons of a neutron monitoring station, I ref is the incoming count rate at an arbitrary time, and is an amplitude scaling factor to adjust for the mentioned geomagnetic effects. It depends on the cutoff rigidity of the local site and the neutron monitor used (see, e.
The largest initiative for mapping the human proteome using IHC is the Human Protein Atlas (HPA) project  (3) (4) (5) (6) (7) , covering all major normal tissues and organs, as well as the most common forms of cancer. The open-access database visualizes the expression of >80% of all human proteins in >10 million highresolution images, constituting an excellent resource for comparison of cell type-specific expression patterns identified with large-scale transcriptomics approaches, which has Despite the IHC technology having been used for decades and is a standard method in clinical pathology, the main approach for evaluation of IHC staining patterns is still the rather subjective manual assessment. A manual observer has the advantage of identifying technical staining errors or artifacts, but it is both time-consuming and costly.
Additionally, manual annotation is error-prone and poorly reproducible, as it may lead to fatigue or mislabeling of images due to lack of experience in detecting the correct cell types or structures, or technological challenges related to staining intensity or identification of small objects. Manual annotation is commonly faced with two types of errors, i) false negatives where true positive staining is missed or neglected, and ii) false positives where lack of protein expression is falsely interpreted as positive. Histological samples consist of a mixture of different cell types that can be challenging to distinguish even by a trained eye, and setting a manual threshold of what is regarded as negative/positive is tedious and highly difficult. This leads to challenges in large-scale approaches aiming at aligning IHC datasets with data generated by other quantitative methods, such as scRNA-seq.
Another challenge is correctly addressing the accuracy of automated predictions, which is especially important when implementing algorithms in a clinical setting, but also in whole-proteome approaches such as the HPA project to be able to compare results between different proteins at a global, proteome-wide level. Addressing prediction accuracy requires a large dataset of manually annotated images, but also a method to score the confidence in the prediction. Few existing large-scale imaging datasets are labelled in detail at a cell type-specific level, and methods for addressing prediction accuracy are not currently considered by many state-of-the-art algorithms. Bayesian neural networks (BNNs) learn a distribution with a prior distribution on its weights and are currently considered state-of-the-art for estimating uncertainty in model prediction, thereby constituting an important element when building automated workflows for annotation of histological images, which was shown in a recent pilot study  (18).
Tissues and protein profiling Human tissue samples for IHC analysis in the HPA dataset were collected and handled in accordance with Swedish laws and regulations. The output of the last convolution layer is the CNN feature. We kept the main characteristics of the VGG Net-19 architecture, and connected handcrafted features to the end of the CNN feature as input to the fully connected layers. Handcrafted features were extracted separately from the CNN. These handcraft features mainly reflect color, shape and texture features of the image as complementary to the CNN features. Dropweights regularization allowed us to apply variational inference during test time to achieve improved performance. We applied dropweights followed by a sigmoid activated layer to the network in the fully connected layer as an approximation to the Gaussian Process (GP), to cast it as approximate Bayesian inference for the meaningful estimation of model uncertainty.
We have observed that the use of class weighting during model fitting degrades the performance. In this multi-label detection task there were many labels that could be present -therefore, we did not want to penalize other classes in favor of only one being present to address class imbalance. The cell type labels in multi-label datasets may be correlated and a prediction for a cell type is not mutually exclusive. Therefore, we utilized label correlation information during classification. For the cost function for multi-label classification, we selected the sigmoid function with the addition of binary cross-entropy. A grid search scheme was adopted based on Matthews Correlation Coefficients (MCC)to determine the optimal thresholds for each dimension on the model outcome, which improves the accuracy of the model.
Approximate Bayesian Neural Network with dropweights variational inference for estimating model uncertainty Bayesian Neural Networks (BNN) provide a natural framework for modeling uncertainty. BNN methods are however intractable in computing the posterior of a network's where  ranges over all class labels. In general, the range of the obtained uncertainty values is dependent on e. g. the dataset, network architectures and the number of MC samples. Therefore, we normalized the estimated uncertainty to report our results and facilitate comparison across various sets and configurations. Estimation of entropy from the finite set of data suffers from a severe downward bias when the data is undersampled. Even small biases can result in significant inaccuracies when estimating entropy. We leveraged the plug-in estimate of entropy and the Jackknife resampling method to calculate bias-reduced entropy  (18, (35) (36) (37).
Cell type-specific expression based on manual annotation To get an overview of the protein expression pattern across the entire dataset, and determine the relationship between the 8 different cell types, pairwise Kendall correlation was used to create a heatmap of the protein expression correlations and the associated clusters (Figure  2a ). The analysis was based on the manual annotation of staining intensity across the entire dataset of 7,848 images. As expected, based on functional characteristics  (23) , there were three main clusters: i) somatic cells (Sertoli cells, Leydig cells and peritubular cells), ii) premeiotic cells (spermatogonia and preleptotene spermatocytes) and iii) meiotic/postmeiotic cells (pachytene spermatocytes, round/early spermatids and elongated/late spermatids). Of the 7,848 images analyzed, only 815 (10%) showed immunoreactivity in one cell type only, while most of the images were positive in two to five cell types (Figure  2b ).
Model performance based on subcellular localization and staining intensity The manual annotation of the cell type-specific protein expression did not only take into consideration which cell types were positive, but also in which subcellular organelle the staining was observed. In Table  3 , the DHC-thresholded model performance in the test dataset is presented on a subcellular level. Similarly, as in the whole dataset, (Figure  2d ), it was clear that some organelles were more common in certain testicular cell types, which may affect the overall accuracy, but it should also be noted that the patterns of different subcellular localizations appear differently in the various cell types based on the cell shape. In total, the best accuracy was found for staining patterns where all subcellular localizations (cytoplasmic, membranous and nuclear) were present.
Validation in an independent dataset of clinical samples We also explored the use of the same models that were trained on the HPA dataset for classifying images corresponding to clinical samples from a different laboratory. Table  5  documents the results of this independent dataset of 1,218 images corresponding to 58 individual samples for the DNN and our proposed HBNet with and without using the DHC Score. The full graphs for tradeoff between accuracy and retained images are presented in Supplementary Figure  2 , and Supplementary Table  2  shows the predictions per cell type for each of the 1,218 images in the independent dataset, along with DHC Score and manual annotation. As expected due to the small sample size and significant differences between the laboratories in both tissue pretreatment, staining protocol, equipment and digitization of images, the overall performance was lower with most cell types registering an accuracy of around 60%.
DISCUSSION In the evolving era of "big data", integration of datasets from different omics technologies such as genomics, transcriptomics and proteomics have shown increasing importance, paving the way for further understanding of the molecular processes involved in health and disease  (1). IHC constitutes the standard approach for spatial localization of proteins at a cell type-specific level. The technology originates from the early 1940s  (40)  and has emerged as a quick, simple and cost-effective method applicable to both diagnostic routine as well as basic and clinical research. The output of the IHC staining is typically a tissue section manually evaluated under a microscope, but with advances in digital pathology, large-scale digitization of stained sections is becoming more common.
The HPA database based on antibody-based proteomics constitutes the largest and most comprehensive knowledge resource for spatial localization of proteins in organs, tissues, cells and organelles. The HPA project has characterized >15,000 different proteins across >40 different normal tissues and organs, and 20 types of cancer  (3, 4) , with the publicly available database www. proteinatlas. org containing >10 million high-resolution images, thereby constituting a major resource for machine learning algorithms. In the present investigation, we focused on generating a novel in-depth annotation dataset based on images of normal testis generated as part of the HPA project, due to the complex architecture of this organ built up by several different cell types, and the unique nature of this tissue harbouring a large number of proteins not expressed anywhere else in the human body  (21, 22 10, 54).
The point predictions were combined with a confidence score (DHC), generated by a Monte-Carlo DropWeights method in conjunction with an approximate BNN with hybrid image features. The proposed HBNet architecture showed outstanding performance in both simple images with clear cell type-specific staining, and more complex images where several cell types showed positivity of varying intensity and staining patterns. The novel DHC Score adds another level of insight, particularly important for challenging cases where uncertain predictions can be highlighted. The model was tested on an independent dataset of IHC images corresponding to clinical samples from another laboratory, which showed lower overall accuracy. Independent datasets that are generated by different laboratories can be considered the most challenging approach for assessing if a model is fully generalizable, and despite acquiring all images that were digitally available by the other laboratory, it is a limitation that this independent dataset still only corresponded to 58 different samples.
To achieve such a standard is undoubtedly a difficult task, as even stainings generated by the same equipment and protocols may differ between laboratories due to the exact batch or brand of the reagents  (55). Additionally, there are several steps in the workflow that can never be controlled for, such as preprocessing and fixation of already existing archived tissue material, making standardization almost impossible. Another possibility for future projects utilizing the proposed workflow is to include images generated by multiple laboratories in the initial training of the model, which would likely improve the overall generalizability. The unique framework for image annotation allows for dividing the dataset into images that are reliably classified by the model, and images that need to be examined by the manual observer, thereby reducing the manual burden.
Based on the encouraging performance of our proposed model for what constitutes a particularly challenging tissue, we believe that the approach is applicable also on other simpler organs with larger structures and less variability in protein expression at the cell typespecific level. There currently does not exist any other cell type-specific dataset as part of the HPA project or any other initiatives with the detailed resolution generated here, but generating more such in-depth characterizations is one of the objectives for future versions of the HPA, as an effort to directly align the protein-based data with single cell level information generated by scRNA-seq. This implies that the suggested workflow can be developed further for other organs in the future, but already now, the method can be used to cover the entire dataset of testis images corresponding to in total >15,000 proteins that have been stained with IHC as part of the HPA project.
We also thank Marine Seffals and Alain Fautrel at the H2P2 core facility (University of Rennes 1, US18, UMS3480 Biosit, Rennes, France) for data retrieval from past IHC experiments. The distribution of subcellular location (and lack of subcellular location due to no antibody staining) for each cell type in all 7,848 images showed that Leydig cells more often showed cytoplasmic staining, while Sertoli cells and peritubular cells had the highest proportion of images that were negative/lacked protein expression in these cell types. The area for which the HBNet model focused on for prediction of LRRC39 staining only contained unhealthy seminiferous ducts without the correct cell types. Similarly, RHOBTB2 had damaged seminiferous ducts where the cells had been separated from each other and several cell types were missing.
combined the three handcrafted features (HU, Haralick, HOG) with the CNN features and trained only the fully connected layers and the sigmoid layer. A training dataset (5,411 images) and a validation dataset (1,063 images) were used for model evaluation. We monitored the validation accuracy after every epoch and saved the model with the best accuracy on the validation dataset. All non-linearities were ReLU except for the sigmoid output layer. The models were trained and evaluated using Keras with a Tensorflow backend. During test time (1,374 images), dropweights were active and Monte Carlo (MC) sampling was performed by feeding the input image with 1000 MC samples through the HBNet.
parameters. The most common approach to estimate uncertainty in deep learning places distributions over each of the network's weight parameters. There are many methods proposed for quantifying uncertainty or confidence estimates approximated by MC dropout, including Laplace approximation, Markov chain MC (MCMC) methods, stochastic gradient MCMC variants such as Langevin Dynamics, Hamiltonian methods including Multiplicative Normalising Flows, Stochastic Batch Normalization, Maximum Softmax Probability, Heteroscedastic Classifier, and Learned Confidence Estimates including Deep Ensembles (33). Given a dataset  = { 1 ,  2 …. } and the corresponding labels  = { 1 ,  2 …   } where  ∈   be a d-dimensioned input vector and  ∈ {1 … …. } with   ∈ {1 … … }, C class label, a set of independent and identically distributed (i.
(65)workflow can also be used in other large-scale projects focusing on distinguishing between healthy and diseased tissues, widely applicable to e. g. cancer research but also routine diagnostics, if retrained specifically on datasets from other laboratories. The daily pathology workflow largely depends on manual microscopic evaluation of tissue sections, which may not only lead to a delayed disease diagnosis with potential worsened patient prognosis but also to a false diagnosis(65). Further advances in automated annotation of histological sections are therefore clearly warranted. Many pathology laboratories are now in the transition of starting to become fully digital, and recently the large European initiative BIGPICTURE was formed. This large-scale consortium with 70 million Euros of funding will until the year 2027 create a digital repository of 3 million slides corresponding to a wide range of disease areas.
DATA AVAILABILITY JPEG files of all 7,848 images of the HPA dataset used in the present investigation, as well as the manually annotated protein expression in eight different cell types are available on v20. proteinatlas. org. Manual errors identified as part of this study have been corrected, which means that some of the presented protein expression data on the HPA will differ from the input data used for model training. All images from the independent dataset from another laboratory have been uploaded to the BioStudies repository (https://www. ebi. ac. uk/biostudies) under the accession S-BSST554.
obtained from all donors; or from HIV-1-negative cadavers at autopsy at Rennes The entire dataset was divided into three sets: A training set of 5,411 images, a validation University Hospital. IHC stainings corresponded to 31 different proteins using HPA set of 1,063 images, and a test set of 1,374 images. The three sets represent how the antibodies. All stained sections were digitized with a Hamamatsu slide scanner using a entire dataset was divided into work batches as part of the manual annotation workflow, 20x objective. Each of the digital images was cropped into multiple images of 3000x3000 where the validation set corresponding to 1,063 images was the original dataset pixels, to equal the size of the TMA cores in the HPA dataset, and thereby comprising published previously (23). This dataset was manually annotated by one observer and 1,218 images used as an independent dataset.
then quality controller by two observers including an expert in testis histology, thereby most likely representing a dataset with little risk of manual errors. The training set, which constituted the largest dataset of 5,411 images, was manually annotated by one observer, We used a Bayesian Neural Network-based approach to detect cell type-specific protein but not yet quality controlled. Finally, the test set of 1,374 images was manually annotated expression from multi-label IHC images. High-resolution digital images of IHC stained by one observer and quality controlled by one more junior independent observer, but this testis TMA cores corresponding to 512 testis elevated proteins (23), publicly available on may not be sufficient to identify all manual errors. the HPA version 18 (v18. proteinatlas.
org), were downloaded along with images from 2,282 proteins published in version 19 (v19. proteinatlas. org) that previously had been The independent dataset of 1,218 images acquired from another laboratory was manually manually annotated as showing IHC staining of moderate intensity in at least a subset of annotated by one observer based on staining intensity and subcellular localization, in the cells in testis. All proteins were analyzed with at least one antibody that was approved same manner as the HPA dataset. according to HPA criteria for antibody validation. For most of the proteins, three different images were available, and the total dataset comprised 7,848 images corresponding to 2,794 unique human proteins. Each antibody staining was manually re-annotated in eight different testicular cell types, including five germ cell types (spermatogonia, preleptotene (24)-577, 2005-388, 2007-159).
These parameters are derived during the almucantar measurement procedures, which are typically carried out early morning and late afternoon under clear-sky conditions. As a result, it is usually not possible to have these aerosol microphysical properties when an Earth observation satellite passes over an AERONET site. To address this problem, we propose a method to retrieve microphysical properties using a parametric model with two variables: the optical thickness at 440 nm, τ 440 , and the Ångström coefficient between 440 and 870 nm, α 440-870. We selected these two parameters because they are widely accessible (e. g. , from the AERONET network, which provides several measurements per clear-sky hour; from the satellite itself; or from climatology). We used 851 AERONET sites, for which the data were in a sufficient quantity and representative.
, 2002 Vermote et al. , , 2014)) , for the NASA Harmonized Landsat 8 Sentinel-2 project  (Vermote et al. , 2016; Claverie et al. , 2018) , or for the CEOS ACIX working group for atmospheric correction intercomparison  (Doxani et al. , 2018). In the former, we compare a surface reflectance retrieved from satellite data to a surface reflectance reference determined from the top-of-atmosphere (TOA) reflectance corrected using the accurate radiative transfer 6SV code  (Vermote et al. , 1997; Kotchenova et al. , 2006 Kotchenova et al. , , 2008;; Kotchenova and Vermote, 2007)  and detailed measurements of the atmosphere. An intermediate step consists of validating the aerosol optical thickness product derived from various sensors such as MODIS, MISR (Multi-angle Imaging Spectroradiometer), OMI (Ozone Monitoring Instrument), POLDER (Polarization and Directionality of the Earth's Reflectances), and Landsat, which is further used as an input to the atmospheric correction process.
Description of the dataset Aerosol microphysical property data were extracted from the AERONET measurements  (Holben et al. , 1998; Dubovik and King, 2000; Dubovik et al. , 2000; Sinyuk et al. , 2007; Gilles et al. , 2019). We used Level 2. 0 (quality-assured) of the "Version 3 direct sun" and of the "Version 3. 0 inversions", except for the percentage of sphericity %S ph , for which we used Level 1. 5 (in July 2021, Level 2. 0 was not yet available for this parameter). From these datasets, we selected all records corresponding to (1) aerosol optical thicknesses at four wavelengths (440, 675, 870, and 1020 nm); (2) aerosol Ångström coefficients between 440 and 870 nm, which allows us to determine the aerosol optical thickness at 550 nm; and (3) microphysical properties C vf , C vc , %C vf , %C vc , r vf , r vc , σ r , σ c , nr 440 , nr 650 , nr 850 , nr 1020 , ni 440 , ni 650 , ni 850 , ni 1020 , and %S ph.