dataesr/scientific-paragraphs-categorization huggingface dataset:
These barangays are in adjacent proximity to one another but are not juxtaposed. The control and the intervention areas were matched as carefully as possible according to the data on local dengue incidence. Once a dengue index case was reported within site, ovitraps (mosquito density monitoring devices) were installed for two weeks. The installation was done to evaluate whether mosquito densities (>5% ovitraps positive for mosquito larvae) were sufficiently high for active DENV transmission and, therefore, selectable as a study site.
Conclusions We demonstrated the application of GIS and the use of spatial analysis to detect DENV infection transmission in areas exposed to AD-PPF as an intervention to decrease dengue incidence. Using saliva samples collected from the participants from both control and intervention sites in Parañaque City, we gathered seroprevalence data for 3 months in 2 years and assessed the efficacy of AD-PPF. The intervention site was identified as having clustering of DENV infection in Month 0 of Year 1 and shifted to random dispersion of dengue cases at the end of Month 3 in Year 2. The shift in the spatial distribution of DENV infection across the intervention site that was not seen in the control area implies that the AD-PPF implementation decreased dengue incidence over a specific period. Our collected seroprevalence data also included asymptomatic and symptomatic infections, which provided a clearer view of the high-transmission risk areas.
Geoscientific models are facing increasing challenges to exploit growing datasets coming from remote sensing. Universal Differential Equations (UDEs), aided by differentiable programming, provide a new scientific modelling paradigm enabling both complex functional inversions to potentially discover new physical laws and data assimilation from heterogeneous and sparse observations. We demonstrate an application of UDEs as a proof of concept to learn the creep component of ice flow, i. e. a nonlinear diffusivity differential equation, of a glacier evolution model. By combining a mechanistic model based on a 2D Shallow Ice Approximation Partial Differential Equation with an embedded neural network, i. e. a UDE, we can learn parts of an equation as nonlinear functions that then can be translated into mathematical expressions.
We implemented this modelling framework as ODINN. jl, a package in the Julia programming language, providing high performance, source-tosource automatic differentiation (AD) and seamless integration with tools and global datasets from the Open Global Glacier Model in Python. We demonstrate this concept for 17 different glaciers around the world, for which we successfully recover a prescribed artificial law describing ice creep variability by solving ∼500,000 Ordinary Differential Equations in parallel. Furthermore, we investigate which are the best tools in the scientific machine learning ecosystem in Julia to differentiate and optimize large nonlinear diffusivity UDEs. This study represents a proof of concept for a new modelling framework aiming at discovering empirical laws for large-scale glacier processes, such as the variability of ice creep and basal sliding for ice flow, and new hybrid surface mass balance models.
Introduction In the past decade, remote sensing observations have sparked a revolution in scientific computing and modeling within Earth sciences, with a particular impact on the field of glaciology  (Hugonnet et al. , 2020; Millan et al. , 2022). This revolution is assisted by modelling frameworks based on machine learning  (Rasp et al. , 2018; Jouvet et al. , 2021) , computational scientific infrastructure (e. g. Jupyter and Pangeo  (Thomas et al. , 2016; Arendt et al. , 2018) ) and modern programming languages like Julia  (Bezanson et al. , 2017; Strauss et al. , 2023). Machine learning methods have opened new avenues for extending traditional physical modeling approaches with rich and complex datasets, offering advances in both computational efficiency and predictive power. Nonetheless, the lack of interpretability of some of these methods, including artificial neural networks, has been a frequent subject of concern when modelling physical systems  (Zdeborová, 2020).
These include the need to transition towards non-constant melt and accumulation factors for temperature-index mass balance models  (Bolibar et al. , 2022) , or the need to find a robust relationship to calibrate ice creep and basal sliding for different regions, topographies and climates  (Hock et al. , 2023). In terms of data assimilation and model parameter calibration, many different approaches to obtain differentiable glacier models have been developed  (Morlighem et al. , 2013; Goldberg and Heimbach, 2013; Brinkerhoff et al. , 2016). These inverse modelling frameworks enable the minimization of a loss function by finding the optimal values of parameters via their gradients. Nonetheless, all efforts so far have been applied to the inversion of scalar parameters, i. e. parameters that are stationary for a single inversion given a dataset.
This opens the door to a new way of learning parametrizations and empirical laws of physical processes from data. This case study is based on ODINN. jl v0. 2  (Bolibar and Sapienza, 2023) , a new open-source Julia package, available on GitHub at https://github. com/ODINN-SciML/ODINN. jl. With this study, we attempt to share and discuss what are the main advances and difficulties in applying UDEs to more complex physical problems, we assess the current state of differentiable physics in Julia, and we suggest and project the next steps in order to scale this modelling framework to work with large scale remote sensing datasets. In this section we introduce the Partial Differential Equation (PDE) describing the flow of ice through the SIA equation, and we present its equivalent Universal Differential Equation (UDE) with an embedded neural network.
, 2020 ) du dt = f (u, t, U θ (u, t)), (5) where f is a known function that describes the dynamics of the system; and U θ (u, t) represents the universal approximator, a function parametrized by a vector parameter θ that takes as an argument the solution u and time t, as well as other parameters of interest to the physical system. In this study, the function f to be simulated is the SIA (equation (  2 )). Training such a UDE requires that we optimize with respect to the solutions of the SIA equation, which need to be solved using sophisticated numerical methods. The approach to fit the value of θ is to minimize the squared error between the observed ice surface velocity profile (described in section 3. 1 together with all other datasets used) at some given time and the predicted surface velocities using the UDE, an approach known as trajectory matching  (Ramsay and Hooker, 2017).
Instead of considering that the diffusivity D θ is the output of a universal approximator, we are going to replace the creep parameter A in Equation (  3 ) with a neural network with input T : D θ (T ) = 2 A θ (T ) n + 2 (ρg) n H n+2 ∥∇S∥ n-1. ( 7 ) The objective of A θ (T ) will be to learn the spatial variability of A with respect to T for multiple glaciers in different climates. In our toy model, this variability is described by prescribing an artificial law for which a reference dataset is generated. In order to generate this artificial law, we have used the relationship between ice temperature and A from  Cuffey and Paterson (2010) , and replaced ice temperatures with a relationship between A and T. This relationship is based on the hypothesis that T is a proxy of A. However, it ignores many other important physical drivers influencing the value of A, such as the temperature of ice, the type of fabric and the water content  (Cuffey and Paterson, 2010).
Training dataset The following data are used for the initial conditions of simulated glaciers: a Digital Elevation Model (DEM) for the glacier surface elevation S based on the Shuttle Radar Topography Mission from the year 2005 (SRTM Farr et al. (  2007 )), and estimated glacier ice thickness H from the global dataset from  (Farinotti et al. , 2019)  based on the glacier outlines around the year 2003 of the Randolph Glacier Inventory  (Consortium, 2017). All these datasets, together with all glacier information are retrieved using the Open Global Glacier Model (OGGM), an open-source glacier evolution model in Python providing a vast set of tools to retrieve and process climate and topographical data related to glaciers  (Maussion et al. , 2019)   world. This enables a good sampling of different climate conditions from which to take T to compute A.
Differential equation solver In order to solve the SIA Equation (2), we perform a discretization in the spatial domain to solve the problem as a combination of Ordinary Differential Equations (ODEs). The solution of the ODEs is evaluated in a constant grid, determined by the preprocessed datasets by OGGM. The resolution of the spatial grid is automatically adjusted depending on the glacier size and domain size, typically ranging between 100x100 to 200x200 grid points, which leads to a system of coupled ODEs ranging from 10,000 to 40,000 equations per glacier. All gradients and divergences are computed in a staggered grid to improve stability In order to create conditions similar to those one would encounter when using remote sensing observations for the functional inversions, we add Gaussian noise with zero mean and standard deviation 6 • 10 -18 (around 30% of the average A value) to the output of the prescribed law (Figure  3 ).
Optimization Once the gradient has been computed by one of the previous methods, optimization of the total loss function without any extra regularization penalty to the weights in the loss function was performed using a Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimizer with parameter 0. 001. We also tested ADAM  (Mogensen and Riseth, 2018)  with a learning rate of 0. 01. BFGS converges in fewer epochs than ADAM, but it had a higher computational cost per epoch. Overall, BFGS performed better in different scenarios, resulting in a more reliable UDE training. For this toy model, a full epoch was trained in parallel using 17 workers, each one for a single glacier. Larger simulations will require batching either across glaciers, or across time in case a dataset with dense temporal series were used.
Scientific computing in the cloud In recent years, scientific workflows in Earth and environmental sciences have benefited from transitioning from local to cloud computing  (Gentemann et al. , 2021; Abernathey et al. , 2021; Mesnard and Barba, 2020). The benefits of cloud computing are two-fold. On one side, it facilitates the creation of shared computational environments, datasets and analysis pipelines, which leads to more reproducible scientific results by enabling standalone software containers (e. g. Binder) for other researchers to easily reproduce scientific results. On the other hand, working in cloud environments lowers the access bar for many scientists from unrepresented groups across multiple institutions and continents. As part of this new approach in terms of geoscientific computing, we are computing everything directly in the cloud using a JupyterHub through the 2i2c infrastructure.
Application to functional inversions of glacier physical processes This first implementation of a UDE on glacier ice flow modelling serves as a baseline to tackle more complex problems with large datasets. One main simplification of this current setup needs to be overcome in order to make the model useful at a global scale for exploring and discovering empirical laws. In this study, only ice deformation (creep) has been taken into account in the diffusivity. Basal sliding, at the ice-bedrock interface, will have to be included in the SIA equation to accommodate different configurations and behaviours of many glaciers around the world. Therefore, a logical next step would be to infer D in Equation (3), including the sliding coefficient C from Equation (2) using a UDE. Nonetheless, this is by nature an ill-posed problem, since the only available ice velocity observations are from the surface, encompassing both creep and basal sliding.
This results in degeneracy (i. e. equifinality), making it very challenging to disentangle the contributions of each physical processes to ice flow. This is particularly true for datasets with average annual surface velocities, since both physical processes contribute to glacier velocities, with no obvious way of separating them. In order to overcome such issues, using new remote sensing datasets with high temporal resolution, like  Nanni et al. (2023)  with observations every 10 days, can help better constrain the contribution of each physical process. This implies that we cannot only exploit the spatial dimension with multiple glaciers, but also rich time series of the fluctuations of glacier velocities along the seasons. Such dynamics can help disentangle the main contributions of creep during the winter season, and the onset of sliding during the summer season as the subglacial hydrological network activates due to melt.
Interestingly, depending on the used ice surface velocity observations, the need of a numerical solver and a UDE are not imperative for a functional inversion. For a single snapshot of ice surface velocities between two dates (e. for the inversions, the inversion is extremely sensitive to time discrepancies in the input datasets, making it potentially quite vulnerable to noisy or temporally misaligned datasets. Alternatively, the optimization of the NN for ice rheology inference based on ice surface velocities has proved to be robust to the noise added by the SMB component. This serves to validate an alternative glacier ice dynamics model calibration strategy to those of the majority of large-scale glacier models (e. g. OGGM and GloGEM;  Huss and Hock (2015) ). By being able to calibrate separately ice rheology and MB, one can avoid many equifinality problems that appear when attempting to calibrate both components at the same time.
A classic problem of a joint calibration is the ambiguity in increasing/decreasing accumulation vs increasing/decreasing Glen's coefficient (A). ODINN. jl, with its fully differentiable codebase, provides a different strategy consisting in two main steps: (i) Calibrating the ice rheology from observed ice surface velocities  (Millan et al. , 2022) , observed ice thicknesses  (Consortium, 2019)  and DEMs; (ii) calibrating the MB component (e. g. melt and accumulation factors) with the previously calibrated ice rheology based on both point glaciological MB observations and multiannual geodetic MB observations  (Hugonnet et al. , 2020). This maximises the use of current glaciological datasets for model calibration, even for transient simulations. Such a differentiable modelling framework presents both the advantages of complex inversions and the possibility of performing data assimilation with heterogeneous and sparse observations.
Conclusions Despite the ever increasing amounts of new Earth observations coming from remote sensing, it is still extremely challenging to translate complex, sparse, noisy data into actual models and physical laws. Paraphrasing  Rackauckas et al. (2020) , "In the context of science, the well-known adage a picture is worth a thousand words might well be a model is worth a thousand datasets". Therefore, there is a clear need for new modelling frameworks capable of generating data-driven models with the interpretability and constraints of classic physical models. Universal Differential Equations (UDEs) embed a universal function approximator (e. g. a neural network) inside a differential equation. This enables additional flexibility typical from data-driven models into a reliable physical structure determined by a differential equation.
We presented ODINN. jl, a new modelling framework based on UDEs applied to glacier ice flow modelling. We illustrated how UDEs, supported by differentiable programming in the Julia programming language, can be used to retrieve empirical laws present in datasets, even in the presence of noise. We did so by using the Shallow Ice Approximation PDE, and learning a prescribed artificial law as a subpart of the equation. We used a neural network to infer Glen's coefficient A, determining the ice viscosity, with respect to a climatic proxy for 17 different glaciers across the world. The presented functional inversion framework is robust to noise present in input observations, particularly on the surface mass balance, as shown in an experiment. This study can serve as a baseline for other researchers interested in applying UDEs to similar nonlinear diffusivity problems.
Code availability. The source code of ODINN. jl v0. 2. 0  (Bolibar and Sapienza, 2023)  used in this study is available as an open-source Julia package: https://github. com/ODINN-SciML/ODINN. jl. The package includes Continuous Integration tests, installation guidelines on how to use the model and a Zenodo DOI: https://zenodo. org/record/8033313. OGGM v1. 6  (Maussion et al. , 2023)  is also available as an open-source Python package at: https://github. com/OGGM/oggm, with documentation and tutorials available at https://oggm. org.
16S rRNA Sequences Processing The software package Quantitative Into Microbial Ecology (QIIME) 2 pipeline (v. 2021. 4) [31]  was used for the analysis of sequencing data. First, 16S rRNA gene sequences were first demultiplexed and then quality trimmed based on the average quality per base of the forward and reverse reads using DADA2 software  [32]  implemented in QIIME2. Reads were then merged and chimeric variants were removed. The resulting representative sequences were taxonomically assigned using a pre-trained naïve Bayes taxonomic classifier  [33]  based on SILVA database version 132  [34]  and the 515F/806R primer set. The resulting taxonomic data tables were collapsed at genus level and low abundant taxa were removed by filtering taxa with less than 10 total reads and present in less than 30% of samples.
This pipeline mostly uses the random forests ensemble learning as a supervised regression method  [35]. Differential abundance of taxa and pathway analyses were performed using the R package 'DeSeq2'  [36] , which applies data normalization based on negative binomial distribution and links variance and mean by local regression, and compares the magnitude of the changes, expressing it as log ratio (log fold change). The comparisons were performed with the Wald test. The number of shared taxa or predicted pathways in the different experimental conditions were visualized using Venn diagrams implemented in the online tool (http://bioinformatics. psb. ugent. be/webtools/Venn/, accessed on 12 October 2022). Differences were considered significant when p < 0. 05. For differential network analysis Jaccards index (Jacc) was calculated with Network Construction and Comparison for Microbiome Data (NetComi) script  [37]  on R studio  [38].
Bacterial Co-Occurrence Networks and Attack Tolerance Test Co-occurrence microbial networks were built for each condition using the taxonomic profiles at genera level. Microbial networks were used for the graphical representation and visualization of the microbial community assemblies and the quantification of the importance of bacterial taxa in the network community. In the networks, bacterial taxa are represented by nodes and the significant positive (weight > 0. 75) or negative (weight < -0. 75) co-occurrence interaction between the nodes are represented by edges. The network analysis and construction were performed using Sparse Correlations for Compositional data (SparCC) method  [40] , implemented in R studio environment  [38]. The software Gephi 0. 9. 5  [41] , was used to visualize the microbial networks and to measure the topological features of networks (i.
e. , number of nodes and edges, network diameter, average degree, weighted degree, average path length, modularity and number of modules). The progression of the 'microbiome structural state' was presented using the alpha diversity metric of observed feature  [42] , measured with QIIME2 pipeline  [31] , and the number of edges and nodes collected from Gephi  [41]. To test the network tolerance to attacks, the Network Strengths and Weaknesses Analysis (NetSwan) script  [43]  was conducted in R studio  [38]. This script simulates a network attack by removing first the nodes with the highest betweenness centrality value, and measures decreases on network connectivity. Connectivity values ranged between 0 (maximum of connectivity between nodes) and 1 (total disconnection between nodes).
Prediction of Functional Traits in the Bird Microbiome For the metabolic profiling of each sample, PICRUSt2 software  [44]  was used for the prediction of functional gene abundances based on 16S rRNA gene amplicon sequences. Briefly, the ASVs were aligned and placed into a reference tree (NSTI cut-off value of 2), which was then used to infer gene family copy numbers of each ASVs and finally determine gene family abundance per sample. Kyoto Encyclopedia of Genes and Genomes (KEGG) orthologs (KO)  [45] , Enzyme Classification numbers (EC) and Cluster of Orthologous Genes (COGs)  [46]  were used as gene family catalogs for the predictions. Pathway profiles were inferred from structured pathway mapping based on MetaCyc database  [47].
Vaisala PTU Sondes Vertical temperature and humidity profiles have been measured on a daily basis at Dome C since 2005, employing RS92 Vaisala radiosondes. The radiosonde data were taken using the standard Vaisala evaluation routines without any correction of sensor heating or time lag effect. In Antarctica, the sondes are known to have a cold bias of 1. 2 K from the ground to about 4 km altitude  [20, 21]  and a dry bias of 4% on integrated water vapor (IWV)  [22] , mainly between 630 and 470 hPa, with a correction factor for humidity varying within 1. 10-1. 15 for daytime  [23]. All year long, the sondes are attached to a meteorological balloon and launches are performed once per day at 12:00 UTC. The same Vaisala PTU sondes have been used and installed inside the drone (Figure  2 ). As when using the sonde attached to the balloons, the sonde has been (1) calibrated inside the station with the Digicora ground station, (2) installed inside the drone within a heated tent at ~500 m away from the station, and (3) the drone was then transported to the take-off area approximately 2000 m away northward from the station using a skidoo (Figure  3 ).
HAMSTRAD HAMSTRAD is a microwave radiometer that profiles atmospheric water vapour, liquid water and temperature above Dome C. Measuring at both 60 GHz (oxygen molecule line (O 2 ) to deduce the temperature) and 183 GHz (H 2 O line), the radiometer developed in 2007 by the RPG-Radiometer Physics GmbH German company under the model name LHATPRO was installed on-site for the first time in January 2009  [24]. The retrieval algorithm is based on a neural network method with a large variety of radiosonde observations all over the year. The same coefficients relying on the brightness temperature to the air temperature have been used since the beginning of the project in 2009. The biases between HAMSTRAD and the sonde are regularly estimated year after year. All the HAMSTRAD data are freely available at the following address: http: //www.