dataesr/scientific-paragraphs-categorization huggingface dataset:
The purpose of this modification is to simulate the increase in snow density for each snow layer resulting from the weight of overlying snow and the presence of liquid water. Our adaptation of the method takes into account the effects of liquid water on the snowpack, while keeping the factor that accounts for grain metamorphosis constant. This approach ensures that the simulation accurately reflects the effects of both overlying snow and liquid water on the snowpack density. • We have incorporated a fine-tuned version of the latest new snow density method used in the Crocus snowpack model  (Vionnet et al. , 2012)  into our FSM2oshd software. This method takes into account air temperature and wind speed to accurately simulate the density of newly fallen snow. We carefully calibrated this function specifically for FSM2oshd by conducting an assessment against measurements of fresh snow that had settled over 24 h, while also considering the elevation.
Vf is calculated using the methods described in  Essery et al. (2008)  and Td(t) is calculated by overlaying the image with the position of the complete annual solar track as in  Jonas et al. (2020)  and  Webster et al. (2020). The use of spatially averaged, time-varying transmissivities is efficient because computationally expensive transmissivity calculations at the evaluation points can be performed offline, without compromising operational model run times. The national forest datasets used to derive canopy structure metrics across the OSHD domain (Figure  6 ) are described in detail by  Waser et al. (2015) ,  Waser et al. (2017). A canopy height model (Figure  2C ) was calculated at 1 m resolution based on LiDAR data covering all Switzerland. The data consists of a nationwide acquisition from 2003 and an ongoing campaign (2017-2023), with supplementary data from individual cantonal acquisitions.
LiDAR point density ranges from 1 to 30 pt/m2. The canopy height model is continually updated as new lidar data is acquired. Partitioning of grid cells into open and forest-covered fractions is based on a forest mask (Figure  2A ), with open areas influenced by the presence of forest included in the forest fraction. Further datasets were used to include information on canopy properties across the domain, which affect leaf area index and tree crown densities. A nationwide forest mix rate dataset (Figure  2D ) calculated using machine learning and Sentinel 1 and 2 images served to discriminate between deciduous and evergreen forest types  (Waser et al. , 2017). Whether forest is needleleaf or broadleaf was determined based on a combination of the Swiss forest ecoregions dataset (Figure  2B ) (https://opendata.
Data assimilation procedure Within the OSHD model framework, different data assimilation methods for precipitation exist which are used depending on the model requirements of the physics-based and conceptual model chains (Figure  1 ). The challenge of data assimilation within the Frontiers in Earth Science frontiersin. org OSHD framework is to make optimal use of available point observation of snow depth and air temperature, gridded products of precipitation and air temperature, and analysis and forecast products of the regional weather model. The data assimilation of point scale snow depth data and gridded precipitation products requires four steps of input and output processing (Figure  1 ): 1) inferring daily snowfall estimates from snow depth observations at stations, 2) providing an optimal first-guess solid precipitation field from gridded air temperature and precipitation products, 3) an assimilation step combining the first-guess solid precipitation fields with the point estimates of solid precipitation, and 4) updating calculated mass and energy fluxes with snow observations.
For the climatological runs the number of HS stations is reduced to 349 stations and TA-stations to 255 providing a full time-series of quality-controlled data from WY 1998 until today. Snow depth and air temperature data are extracted at daily timesteps and are quality checked. We convert daily point measurements of snow depth to water equivalent using a model based on the methods presented by  Martinec and Rango (1991). The adapted model describes the accumulation and settling of the snowpack for each layer. The density model applied for each layer has been calibrated based on data from the over 10,000 snow profiles used in the study by  Jonas et al. (2009). We can compute snowfall and melt rates from the increments/decrements in snow water equivalent. For an example of the computed solid precipitation and melt time series, see Figure  2  in  Magnusson et al.
Combining available gridded precipitation products To carry out the assimilation outlined in Step 3 of Figure  1 , obtaining the best possible prior estimate for solid precipitation on the model grid is crucial (Step 2, Figure  1 ). To achieve this, we gather data from various sources, including gridded daily precipitation products (RhiresD) from MeteoSwiss and interpolated temperature fields based on large station datasets. RhiresD provides a spatial analysis of daily precipitation over the OSHD domain, utilizing data from heated rain gauges, typically 430 within and 220 outside of Switzerland daily. The daily temperature interpolation follows the methods presented by  Frei (2014)  and employs data from over 300 stations covering most of the elevation range given by the OSHD domain. We combine these daily products with hourly data from COSMO 1 E to improve, for instance, precipitation phase determination to obtain the best possible priors of daily snowfall on the OSHD grid, which are further used in the optimal interpolation step described below.
Optimal interpolation of solid precipitation estimates at stations The gauge network utilized in the gridded precipitation products of MeteoSwiss has a limited representation of elevations above 1,200 m. Additional snow depth data from the snow observation networks, particularly the IMIS network, are incorporated to enhance the accuracy of solid precipitation information at high altitudes. The first-guess or background fields of daily snowfall obtained in the previous step (refer to Section 3. 2) serve as the basis for assimilating point solid precipitation rates inferred from snow depth measurements (refer to Section 4. 1). An optimal interpolation scheme (OI) is employed for the assimilation, efficiently updating the background field utilizing observations (step3, Figure  1 ). The OI method considers apriori-defined covariances between errors in the gridded input fields and point observations, accounting for observation errors.
Wind For higher-resolution model applications (sub-kilometre scale), two wind downscaling methods are available within the OSHD model framework. The two statistical approaches  (Helbig et al. , 2017; Winstral et al. , 2017)  aim to downscale gridded coarse-scale wind data to finer grids using information from the local topography. However, these two methods differ in the required wind data and topographic parameters. In the current operational model framework, the approach suggested by  Winstral et al. (2017)  is used. The statistical approach,  Winstral et al. (2017)  developed uses local topographic information at a much higher resolution than present in the forecast wind data to delineate the spatial organization of biases in the forecast products. The local terrain is statistically linked with observed forecast biases at stations located at different terrain shading locations.
While the OSHD model framework makes optimal use of the exceptional data availability in Switzerland, the OSHD developments also accounted for the challenges involved with the large fraction of high-alpine terrain in Switzerland inducing a high complexity of snow-atmosphere interactions. We have thus demonstrated strategies for data assimilation, downscaling techniques and sub-grid parametrizations of relevant processes at the snow-atmosphere interface in order to capture relevant snow processes at the catchment and ridge scale. At the same time the model is designed to optimize computational efficiency to meet the requirements of an operational snow hydrological service in an alpine country. The FSMoshd model is a leading-edge snow hydrological model that accurately captures forest snow processes, as demonstrated in recent studies  (Mazzotti et al.
, 2022; Odry et al. , 2022) , by incorporating various measurement types, such as gauges, radars, satellites, and snow observations  (Lundquist et al. , 2019). This integration improves the accuracy of data assimilation procedures and enhances the representation of forest snow processes. Moreover, it allows for precise spatial validation of the model results, particularly crucial in mountainous terrains. Overall, the combination of remote sensing and data assimilation can steadily improve the model system. development of the OSHD model framework. TJ has played a pivotal role in establishing the OSHD and has consistently been at the forefront, leading the way in driving its model developments since its inception. All authors contributed to the article and approved the submitted version.
Data availability statement We are pleased to present two comprehensive datasets from the conceptual and physics-based models that cover the entirety of Switzerland at the envidat repository. The first dataset comprises the climatology on snow water equivalent, snow melt runoff and snow fraction spanning 1998-2022, with a spatial resolution of 1 km and daily temporal resolution (EnviDat. https://www. doi. org/10. 16904/envidat. 401). The second dataset includes snow depth (m), snow water equivalent (mm), runoff from snow melt (mm) and snow cover fraction. The data is spanning the water years 2016-2022 at a high spatial resolution of 250 m. Data are stored as daily results (EnviDat. https://www. doi. org/10. 16904/envidat. 404). The FSM2oshd code is available at https://github. com/oshd-slf/FSM2oshd.
Histological analysis of skeletal muscle is of major interest for understanding its behavior in different pathophysiological conditions, such as the response to different environments or myopathies. In this context, many software programs have been developed to perform automated high-content analysis. We created MuscleJ, a macro that runs in ImageJ/Fiji on batches of images. MuscleJ is a multianalysis tool that initially allows the analysis of muscle fibers, capillaries, and satellite cells. Since its creation, it has been used in many studies, and we have further developed the software and added new features, which are presented in this article. We converted the macro into a Javalanguage plugin with an improved user interface. MuscleJ2 provides quantitative analysis of fibrosis, vascularization, and cell phenotype in whole muscle sections.
Therefore, several software programs (Sup. Table  1 ) have been developed to automate the estimation of these different parameters, including MuscleJ  [1] , an automated ImageJ macro that we created to quantify multiple types of histological data from muscle immunofluorescence slide images. Initially, MuscleJ was able to automatically extract the fiber CSA and Feret diameters, the number of centronucleated fibers, the number of centronuclei, the number of satellite cells and capillaries (initially called "vessels") per fiber, and the fiber typing  [1]. MuscleJ begins its analysis with fiber segmentation, which then defines four regions of interest (ROIs): the ROI fiber (ROI F ), the ROI centronucleated fibers (ROI CNF ), the ROI satellite cells (ROI SC ), and the ROI vessels (ROI V ). Specific staining is then quantified, and the results are automatically stored in results' files.
Sample data panel Under physiological conditions, CSA is homogeneous across fiber regions, making it possible to use this parameter to discriminate between what can and cannot be labeled as a fiber. This is not the case when skeletal muscle is damaged, as in myopathies or after injury, where fiber size can be very heterogeneous. We have taken this point into account and have introduced an option, named Pathophysiology, where the user can choose between Healthy and Damaged fiber populations (Fig. 1 ). In damaged muscles, the heterogeneity of fiber CSA is increased, and MuscleJ2 flexibly considers a wider range of CSA measurements. The contribution of the Damaged option (selected in the Pathophysiology tab) is illustrated in Fig. S1 , where the mouse tibialis anterior was partially injured, resulting in significant variability in fiber CSA between injured and uninjured parts.
In the Sample Data panel, the user can inform the plugin of the anatomical origin of the sections, i. e. , from limb or diaphragm muscle (Fig. 1 ). This option was added because of the large difference between classical hind limb muscles and the diaphragm, the latter usually being cut in a folded state (Fig. S2 ). When the Diaphragm option is selected, MuscleJ2 does not fill in holes to account for the actual surface of the tissue. As this type of skeletal muscle is studied with particular interest in pathological states  [4, 5] , this option now offers the possibility of analyzing it with MuscleJ2. It is now possible to analyze a section of skeletal muscle divided into several pieces in the image, whereas in the first version of MuscleJ, only the largest region was selected. This allows the analysis of different skeletal muscle subsections grouped on the same image, which is particularly useful for muscles with different chiefs, such as the quadriceps femoris or the gastrocnemius, which can be separated into several parts during the cryosection preparation.
Data acquisition panel We have developed an algorithm applicable to images obtained from a wider range of more recent equipment, which is why the selection of the Acquisition system (Apotome/Wide field/…) and the File format is no longer necessary. MuscleJ2 can easily work on different image formats (such as. czi,. lif,. tiff …) supplied by the majority of gold standard image acquisition systems (Fig. S3 ). Importantly, image quality is a prerequisite for good analysis, and the acquisition system must be carefully selected before the batch experiments are performed. In the Volume option, the user must inform MuscleJ2 if the images contain a single Z or a stack of Z. When the Z-stack option is selected, MuscleJ2 will automatically perform a maximum intensity projection prior to any analysis (Fig.
A new panel with features related to the whole skeletal muscle section In this third panel, named Data Analysis by Section, we have introduced new functionalities that do not refer to individual fibers but to the total surface of the skeletal muscle (Fig. 1 ). All these analyses are performed on whole-slide image sections or on representative parts of the image manually cropped by users. For these analyses, the definition of ROI is not necessary, unlike other functionalities of the Data Analysis by Fiber panel, described below. Consequently, laminin staining is not mandatory, and artifact detection is not associated with these options. It is therefore the responsibility of the user to ensure that the muscle sections are correctly detected and do not contain holes or folds. However, staining for ECM or any fiber marker (except nuclear markers) is necessary for MuscleJ2 to delineate the section contours and estimate the total surface area.
As with all the other options, to allow viewing of the specific cells identified by MuscleJ2, their coordinates are saved in the ROI dedicated folder, and it is easy to return to any cell if needed. As an example, this new functionality was tested to detect F4/80-positive pan-macrophages (Fig. 2C ) in a series of cross sections of regenerating muscle. Any validated antibody giving rise to a distinct signal in any cell in skeletal muscle can be used, offering a large panel of data analysis. In a set of images, it is possible to quantify several cellular markers, albeit one at a time, by running the batch of images for each specific marker. Since cells positive for multiple labels will share the same located nucleus, they could be quantified by mixing (using open-access software such as R) all the files "SpecificDetails" for each muscle section by the column nucleus gravity center (x, y).
Discussion The development of MuscleJ2, running on ImageJ/Fiji, now significantly extends the functionality of the original macro to cover a wider range of possible quantification scenarios performed on fluorescence-labeled images of skeletal muscle sections. While numerous other software programs have been developed since the publication of the original MuscleJ, most of them are complementary and focus on specific parameters (Sup. Table  1 ). One of the major advantages of MuscleJ2 over other comparable software is its capacity to handle a combination of different types of analysis. All the described functions can be used together to provide new information. With the parallel development of new acquisition systems, which offer the possibility of working with more than four fluorescent markers, MuscleJ2 enables multioutput skeletal muscle analysis.
For example, it is possible to perform multiple labeling with the detection of laminin and nuclei associated with three types of myosin heavy chains, requiring 5 detection channels (Fig. S4 ) or more complex multicolor staining with codetection of laminin, vessels, and two types of myosin heavy chains using one of the protocols described by Bailly et al. [24]. The developed plugin is easy to use, and the troubleshooting annex of the User Guide will be completed based on feedback received from users to list the problems users encounter and their solutions. We encourage users to keep up to date with the changes that will be made in future versions of MuscleJ2, which will be updated and made available on GitHub. We will continue to implement new features in the plugin, which will be updated online and detailed on GitHub.
MuscleJ2 as a plugin in Fiji/ImageJ As Java-based public domain software implemented as a plugin for ImageJ (NIH, Bethesda, MD, USA, https:// imagej. nih. gov/ ij/) or Fiji  [25] , MuscleJ2 benefits from the facilities offered by Fiji/ImageJ for image input/output and preprocessing. The Java source codes were developed in the Eclipse IDE for Java Developers free environment (Version 4. 23. 0, www. eclip se. org) with ImageJ internal libraries. Thanks to the Java language, memory management has been optimized, and MuscleJ2 permits either a larger batch or image size than the previous macro MuscleJ. Moreover, several features have been optimized to increase the speed of Analysis by Fiber, e. g. , for morphological analysis, the time mean by section with MuscleJ2 is decreased by 2 and that for result cartographies by 10.
Hardware and software requisites The plugin has been tested on different operating systems (OS) such as Windows7, 8, and 10/MacOS Monterey up to 12. 4/Ubuntu 20. 04 with the following minimum computer requirements: • RAM: 8 GB minimum, 16 GB highly suggested • System type: 64-bit operating system The Fiji/ImageJ environment is required with a maximum memory setting fixed to 75% of the computer's total memory, and the Bio-Formats plugin (https:// docs. openm icros copy. org/) must be present in the Plugins menu. The plugin has been tested on the following software versions: • Fiji/ImageJ version: from 1. 51e to 1. 53t • Java version (64 bits): From Java 1. 8. 0-66 to Java 1. 8. 0-172 • Used plugins: Bio-Formats plugins (up to release 6. 6) For more information about MuscleJ2 plugin installation and preliminary requests before starting MuscleJ2, please refer to the User's Guide Chap.
Directed evolution (DE) is a versatile protein-engineering strategy, successfully applied to a range of proteins, including enzymes, antibodies, and viral vectors. However, DE can be time-consuming and costly, as it typically requires many rounds of selection to identify desired mutants. Nextgeneration sequencing allows monitoring of millions of variants during DE and can be leveraged to reduce the number of selection rounds. Unfortunately the noisy nature of the sequencing data impedes the estimation of the performance of individual variants. Here, we propose ACIDES that combines statistical inference and in-silico simulations to improve performance estimation in DE by providing accurate statistical scores. We tested ACIDES first on a novel random-peptide-insertion experiment and then on several public datasets from DE of viral vectors and phage-display.
ACIDES framework. (A) We consider directed evolution (DE) experiments, where protein variants are screened over multiple rounds, and massive NGS datasets are collected. (B) From the obtained count data, we estimate a score (selectivity) for each variant. The higher the score, the better the variant for the task. Each score is estimated together with its 95%-confidence interval (CI). (C) Sorting the scores of all variants in descending order, we obtain a variant rank (naive rank). Due to statistical errors in the scores, the obtained rank is biased in general. To correct for this, using in-silico simulations based on the CIs of the scores, we reestimate the rank with 95%-CI (corrected rank). (D) From the obtained corrected rank, we compute Rank Robustness (RR). RR represents the percentage of the top 50 variants identified in the naive rank that also appear in the top 50 of the corrected rank.
(E,F) Examples of rank graphs for two synthetic datasets with different depths of NGS (per round) and numbers of unique variants (respectively, E: 10 7 , 5 × 10 4 ; F: 10 6 and 10 6 ). The true rank is shown as red crosses. In both cases, most red crosses are within the 95%-CI of the corrected rank. (G) RR for the two synthetic datasets. Note that RR multiplied by 50 (E:∼ 45. 3; F: ∼ 24. 6) roughly provides the number of the correct top-50 sequences, which are 46 and 23, respectively. (See Fig. s  S3 and S4 for more systematic comparison). [23, 30, 37] , yeast two-hybrid  [23]  and small nucleolar 73 RNA  [29]    sampling noise  [32] [33] [34]. For this scope, our approach 97 benefits from a negative binomial distribution [39-42].
Analysis of directed evolution and deep mutational scanning experiments In order to showcase ACIDES, we apply it to several screening datasets, where various proteins (and one RNA molecule) are screened using different experimental techniques (Table  I ). Specifically, we consider three  List and properties of experiments considered in this study. First column introduces dataset label and corresponds to the panels of Fig. 2. Reads/round corresponds to the average NGS counts per time points. # of variants is the number of unique variants that is detected in the NGS at least once during whole experiments. In Replicates, x × y means that there are x replicates that do not share the same initial library, each of which has y technical replicates (that shares the same initial library). form the task (fitness) or just there due to noise.
264 Overall these results show how our approach can be we take advantage of replicate datasets. We first investigate if the scores in each method are consistent over replicates. For this, we plot the scores obtained from one replicate against the scores obtained from the other (Fig. 4A, B ). The correlation between replicates is estimated using the coefficient of determination (R 2 ). The correlation quantifies the quality of the method, as higher ants with smaller/larger predicted statistical errors. ) For each set, we measure the correlation between two replicates as in (Fig. 4A, B ), and plot it as a function of the set size (Fig. 4C ). We observe ACIDES's correlation becomes more dominant as the set size decreases, suggesting the better quality of both the estimated scores and statistical errors.