dataesr/scientific-paragraphs-categorization huggingface dataset:
The infected patient was a 66-year-old male whose sample was collected on 12 March 2021 during the second wave of the epidemic. As the study was performed retrospectively, investigation of the patient's travels could not be conducted. Mutation assessment using Nextclade (v1. 14. 1) and CoVsurver mutation web applications on the GISAID website  [21]  showed that this P. 1. 14 isolate's genome was characterized by 33 substitutions compared with the SARS-CoV-2 Wuhan-Hu-1/2019 (Genbank: MN908947) reference genome (Supplementary Data S3). The most frequent of these substitutions was the replacement of C with U, which occurred 12 times. These single nucleotide variations resulted in 22 non-synonymous amino acid mutations and 3 deletions distributed as follows: 3 non-synonymous mutations (P80R, R203K, G204R) in the nucleocapsid protein; 3 non-synonymous mutations (S1188L, K1795Q, S2947N) and 3 deletions (S3675-, G3676-, F3677-) in ORF1a; 3 non-synonymous mutations (P314L, E1264D, A2513S) in ORF1b; 1 non-synonymous mutation (S253P) in ORF3a; 1 non-synonymous mutation (E92K) in ORF8; 1 non-synonymous mutation (Q77E) in ORF9b; and 10 non-synonymous mutations (L18F, T20N, P26S, D138Y, K417T, D614G, H655Y, T1027I, P1162S, V1176F) in the spike protein.
In addition to the spike protein mutations common to the P. 1 lineage, this P. 1. 14 isolate's genome also harbored the P1162S mutation in the spike protein. Of the 291 sequences analyzed, the P1162S mutation occurred only in the P. 1. 14 isolate. Compared with the source P. 1 lineage, this sublineage was marked by an absence of the combined E484K and N501Y mutations. We marked the mutation sites on the P. 1. 14 isolate using 3D structural visualization of the Spike protein (Figure  4B ). A search for the sequences to which the Senegalese P. 1. 14 isolate was most closely related was performed in the global EpiCov database on the GISAID platform. The results showed that this Senegalese isolate's genome was linked to 59 genomes classified as VOC Gamma originating from the United States, Canada, Mexico, Argentina and Brazil.
The maximum likelihood tree further showed that the Senegalese isolate was most closely related to an isolate from Toronto and available in GISAID under accession number EPI_ISL_5316162 (Figure  4A ). The Senegalese isolate is available on GISAID under accession number EPI_ISL_16942137. was most closely related was performed in the global EpiCov database on the GISAID platform. The results showed that this Senegalese isolate's genome was linked to 59 genomes classified as VOC Gamma originating from the United States, Canada, Mexico, Argentina and Brazil. The maximum likelihood tree further showed that the Senegalese isolate was most closely related to an isolate from Toronto and available in GISAID under accession number EPI_ISL_5316162 (Figure  4A ). The Senegalese isolate is available on GISAID under accession number EPI_ISL_16942137.
Discussion SARS-CoV-2 infections have been a global health priority since December 2019. Due to its high contagiousness, SARS-CoV-2 has spread rapidly and due to its relatively high mutation rate, variations are likely to appear during each replication cycle  [25]. Since its emergence, different sequencing protocols have been adopted to allow the study of SARS-CoV-2's genetic diversity and evolution  [26]. In Senegal, most SARS-CoV-2 genomes were sequenced using amplicon-based high-throughput sequencing following the ARTIC protocol  [27]. The analysis of sequence data from these samples was mostly oriented towards evaluation of the circulating SARS-CoV-2 lineages. In the present study, the COVIDSeq sequencing protocol was applied to COVID-19-positive samples collected during the deadliest wave of the epidemic in Senegal.
Acknowledgments: The authors are grateful to the  Institut de Recherche en Santé, de Surveillance Epidémiologique et de Formation (Diamniadio, Senegal)  and the  Institut Hospitalo-Universitaire Méditerranée Infection (Marseille, France) , which contributed to the success of this study. The authors also thank the team in charge of the extraction and amplification of SARS-CoV-2 genetic material at  IRESSEF (Diamniadio, Senegal)  and the team in charge of the genomics of SARS-CoV-2 at  IHU-Méditerranée Infection.
theia-land. fr/; accessed on 24 October 2022) developed by LEGOS (Laboratoire d'Etudes en Géophysique et Océanographie Spatiales) and CNES (Centre National d'Etudes Spatiales)  [14, 15]. In the present study, we carry out an analysis of CSL variations over the period of January 1993 to December 2021 using multi-satellite altimeter measurements. Two monthly CSL change series are included in the analysis. One is the specially calibrated CSL change series provided by the Hydroweb project and the other is estimated from the global SLA grids distributed by the Copernicus Marine Environment Monitoring Service (CMEMS, https://marine. copernicus. eu/; accessed on 27 July 2022). The main goals are to (1) study CSL variations at different time scales during the past 30 years; (2) compare CSL changes from the two data sources; and (3) investigate potential discrepancies between the two altimeter-based CSL estimates and identify the causes for the discrepancies.
Considering IB or not will give the same result, as the derived mean lake level change is not affected by the IB effect. This means that if the IB correction was implemented correctly in the CMEMS SLA grids, the atmospheric loading effect should not be responsible for the observed discrepancy between the Hydroweb and CMEMS CSL series. Upon the clarifications from the CMEMS Sea Level Thematic Center (Françoise Mertz, Mercator-Ocean, personal communications), during the CMEMS SLA data processing, an approximate IB correction was applied for the Caspian Sea, in which the reference mean pressure used was the same as for the ocean, i. e. , global mean sea level pressure over the ocean (P ocn ). The difference between the approximate reference mean pressure (P ocn ) and the actual reference mean pressure (P casp ) is expected to lead to a significant difference in the IB corrections and thus SLA estimates.
Conclusions and Discussion We analyze satellite altimeter observed CSL changes over the period of January 1993 to December 2021 using the specially calibrated lake level data from the Hydroweb project and the global SLA grids provided by CMEMS. The two CSL series agree very well at interannual and longer time scales, but show significantly large discrepancies at seasonal and shorter time scales. The discrepancies are found to be introduced by the approximate IB corrections applied to the CMEMS SLA grids over the Caspian Sea. After applying the correct IB corrections using the ECMWF ERA5 sea surface pressure fields, the two altimeter-based CSL series agree remarkably well. Over the 30-year period, satellite altimeter observed CSL series show a significant decreasing trend on top of strong seasonal variations.
Decadal variations are also evident in CSL change. The estimated linear trends are -5. 37±0. 11 and -5. 40±0. 11 cm/yr for the Hydroweb and CMEMS CSL series, respectively. The annual amplitudes and phases of the two estimates are 17. 03±1. 33 cm/266±4 degrees vs. 15. 79±1. 30 cm/267±5 degrees. The CSL series show notable acceleration in the decreasing trend. For the period since 2005, the trends have increased to -8. 86±0. 10 and -8. 81±0. 10 cm/yr, respectively for the two altimeter CSL series. The Caspian Sea is an enclosed water body separated from the ocean. The IB correction over the Caspian Sea or any enclosed lakes needs to be treated separately from the ocean by using the correct reference mean pressure. The actual IB effects over the Caspian Sea are significantly smaller than the approximate estimate in the CMEMS SLA grid data product.
Decadal variations are also evident in CSL change. The estimated linear trends are -5. 37 ± 0. 11 and -5. 40 ± 0. 11 cm/yr for the Hydroweb and CMEMS CSL series, respectively. The annual amplitudes and phases of the two estimates are 17. 03 ± 1. 33 cm/266 ± 4 degrees vs. 15. 79 ± 1. 30 cm/267 ± 5 degrees. The CSL series show notable acceleration in the decreasing trend. For the period since 2005, the trends have increased to -8. 86 ± 0. 10 and -8. 81 ± 0. 10 cm/yr, respectively for the two altimeter CSL series. The Caspian Sea is an enclosed water body separated from the ocean. The IB correction over the Caspian Sea or any enclosed lakes needs to be treated separately from the ocean by using the correct reference mean pressure. The actual IB effects over the Caspian Sea are significantly smaller than the approximate estimate in the CMEMS SLA grid data product.
Funding: This study was supported by the  PolyU SHS  (Project ID:  P0042322 ),  LSGI Internal Research Funds  (Project ID:  P0041486 ), the  Natural Science Foundation of China  (Grant Agreement Nos. 12003057  and  11873075 ), and the  Natural Science Foundation of Shanghai  under Grant Agreement No. 20ZR1467400. This study was also partly supported by the  Opening Project  of the  Shanghai Key Laboratory of Space Navigation and Positioning Techniques , and made use of the  High-Performance Computing Resource  in the  Core Facility for Advanced Research Computing at Shanghai Astronomical Observatory, Chinese Academy of Sciences.
Data Availability Statement: Satellite altimeter observations of CSL change time series s are provided by the Hydroweb project (https://hydroweb. theia-land. fr/; accessed on 24 October 2022) and derived from the CMEMS sea level SLA grids (https://data. marine. copernicus. eu/; accessed on 27 July 2022). The ECMWF ERA5 reanalysis mean sea level pressure fields are provided by the Copernicus Climate Data Store (https://cds. climate. copernicus. eu/; accessed on 24 November 2022).
INTRODUCTION Due to their massive heat storage capacity, the oceans play a crucial role in climate regulation. Understanding their dynamics is essential in many applications such as oceanography, meteorology, navigation, and others. This has motivated the establishment of numerous satellite-based ocean-monitoring missions. Among them, satellite altimetry is used to retrieve the Sea Surface Height (SSH), a variable conditioning ocean circulation. The recovery of the global SSH from satellite imaging constitutes a challenging Spatio-Temporal interpolation image inverse problem. SSH is currently measured by various nadir-pointing altimeters, meaning that they can only take measurements vertically, along their very sparse ground tracks. The gridded SSH image is reconstructed through the Data Unification and Altimeter Combination System (DUACS)  (Taburet et al.
1 to 4. 4 km) than the SSH from the AVHRR instruments  (Emery et al. , 1989). These two variables are physically linked  (Leuliette and Wahr, 1999; Ciani et al. , 2020)  and this link can be learned in a machine learning framework. In several studies, using SST leads to major improvements in SSH inverse problems  (Archambault et al. , 2022; Fablet et al. , 2022). However, learning physical dynamics in a supervised framework requires high-resolution datasets that are not available in a real-world scenario. Therefore a solution is to use an Observing System Simulation Experiment (OSSE), a twin experiment that simulates the satellite interpolation inverse problem  (Gaultier et al. , 2016). The dataset produced with this method enables us to train a deep neural network in a supervised way, and then perform a transfer learning to real-world data.
Nevertheless, we have no guarantee that the simulation is truly realistic. To overcome the issue raised by the lack of ground truth data, we present in this work a method denoted Multimodal Unsupervised Spatio-Temporal Interpolation (MUSTI) able to train a deep neural network in an operational scenario, i. e. with only the SST images and the SSH along-track measurements. The main idea of this method is to estimate SSH images from SST fully gridded images while supervising the multimodal information transfer only at the location where we have access to measurements. From the cross-modal learning point of view, it can be seen as a Weakly-Supervised task, but from the inpainting point of view, it is an unsupervised problem as we have access to no fully gridded image. We test our method with two different neural architectures and on three datasets (two of them are simulated and one remote-sensed).
Observing System Simulation Experiment (OSSE) To test the reconstruction quality of the different methods we use an Observing System Simulation Experiment. To do so, a high-resolution simulation NATL60  (Ajayi et al. , 2019)  is considered as ground truth, upon which we simulate satellite orbits and measurements with realistic instrumental noise. It includes SWOT wide swaths, and nadir pointing observations as shown in Figure  1. Both tracks and measurement errors are performed by the swot-simulator software  (Gaultier et al. , 2016). As shown in Figure  2 , the daily data coverage of the observations is about 10% on average with strong periodic variations due to the SWOT satellite's path. Even when the data coverage reaches 20%, important Spatio-Temporal gaps remain (see the second row of Figure  1 ), hence the difficulty of the interpolation task.
To complete the operational framework we use the SST from NATL60 simulation without noise. This data is thus not a realistic image as clouds and other noise sources should be added to be closer to real temperature observations. However, as we will test our method on real-world data as well, in this first experience we choose not to add any noise to the SST image. Since this high-resolution simulation is very computationally intensive, we only have access to one year of simulation, from the 1 st of October 2012 to the 30 up the methods that need it, then 42 days of simulation are used as a test set. To avoid data leakage between the test and the training data, we set aside 31 intermediary days of simulation and take the rest as a training set. We focus on a part of the North Atlantic Ocean: the Gulf Stream area, from latitude 33°to 43°and from longitude -65°to -55°.
Real-World data The OSSE data provide an idealistic scenario, with an optimal combination of altimeters and a noiseless sea surface temperature. Thus the surface temperature and height are overly correlated compared to a realistic scenario and the multimodal link between data should be easy to learn by a neural network. This motivates us to test the same method on real-world data. We use measures from different nadir altimeters acquired between the 1 st of December 2017 to the 31 st of January 2018. Once again to evaluate the method we leave aside the altimeter data from Cryosat-2 as a test set and some observations of another satellite (Jason 2) as a validation set. We underline the fact that as the SWOT mission is not launched yet, no data with wide swaths is available today. We use temperature data from the Multi-scale Ultra-high Resolution (MUR) SST  (Chin et al.
From supervised to unsupervised inversion Data assimilation and machine learning methods leverage different ways to constrain the inversion with different drawbacks. For instance, the models needed by data assimilation methods can be computationally intensive and suffer from various sources of error due to discretization or unresolved physics among others  (Janjić et al. , 2018). Also, some of the assimilation methods require the adjoint model which is not always available. On the other hand, the supervised machine learning frameworks need ground truth and thus use output data of complex physical models. If 4DVarNet has proven its capacity to interpolate the simulated data, transposing this training to realworld scenarios is still challenging and leads to domain adaptation issues. The performance of this approach does not only rely on the ability of a neural network to learn the physics embedded in the model but also on the trust that we have in the NATL60 simulation itself.
Experiment The MUSTI training procedure can be used with a wide range of convolutional neural architectures as long they bring an inductive bias toward out of tracks generalization. We test two deep neural net-Table  1 : Results of the different methods on the three data scenarios. We present hereafter the score of the different interpolation methods to which we compare ourselves. We give the score of the MUSTI method for an ensemble of 10 neural networks with different weights initialization and the mean performance of each member of the ensemble. For each scenario and neural architecture we tune the window size T , and the stopping epoch on the validation dataset, as described in Appendix C. FLAG In the real-world scenario, there is no ground truth to serve as a validation dataset, therefore we leave aside the observations from a satellite (Jason-2g) to tune the model's hyper-parameters on.
The DYMOST, MIOST, and 4DVarNet methods are in a nutshell, while DUACS and BFN are outperformed. Surprisingly, the two networks trained with MUSTI method do not have the same order of performance as on the OSSE data. The STAE ensemble outperforms other methods, while the U-net does not reach DYMOST, MIOST and 4DVarNet. It seems that the encoding-decoding process is useful to denoise the SST images while a skip connection is not able to do so. These results demonstrate the potential of unsupervised neural networks to deal with partially observed fields. The MUSTI method that was designed for an operational framework achieves better results in the real-world scenario, directly learning from realworld fields than a method supervised on an independent dataset. Furthermore, we show that it is possible to transfer multimodal information from the SST to the SSH fields without providing any covariance matrix, physical model information, or supervision with a full-field dataset.
PERSPECTIVES In the following, we discuss different ways to continue this work. Using MUSTI method in a transfer learning scheme Our method allows us to train a neural network with incomplete data as an operational framework requires. However, this method could also be used in a transfer learning scheme. We are interested in training a deep learning architecture on OSSE data in a traditional supervised way, and then using MUSTI method to fine-tune the model on real-world data. Multimodal fusion There are different ways to perform multimodal data fusion  (Ngiam et al. , 2011).
CONCLUSION We presented a method to include multimodal information in Spatio-Temporal image inverse problems in an unsupervised way. Relying on the hidden physical link between Sea Surface Height and Sea Surface Temperature, we train a neural network to fit the SSH along tracks observations starting from a fully gridded SST image. We show that the multimodal trans-fer performed by the network on the along-tracks data generalizes well where it has not been supervised. We tested two different neural architectures, a U-net and a Spatio-Temporal auto-encoder, on 3 datasets (2 simulations and a real-world scenario). On real-world data, we report a relative improvement of 13% compared to the operational product (DUACS) in terms of RMSE. We also show that our method is able to outperform supervised stateof-the-art interpolation architectures as they suffer from overfitting of the simulation upon which they are trained.
A Implementation details All networks are trained through an ADAM optimizer with the following parameters: β 1 = 0. 9, β 2 = 0. 999. We use an exponential learning rate scheduler with a starting learning rate of 10 -3 for the U-net and of 5 × 10 - 4  for the STAE and α = 0. 96. We determine the optimal hyper-parameters (window temporal size T and stopping epoch) on the validation dataset, other hyper-parameters are untuned.
Occurrence Data Collection Occurrence data on the studied species referred to the entire territory of Europe in order to increase the accuracy of the built models, despite the fact that the main focus of our investigation was to evaluate the potential future overlap of the considered species in Eastern Europe. The occurrence data for T. cristatus, L. vulgaris, and P. glenii was collected from the literature resources  [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [37] [38] [39] [40]  and online Global Biodiversity Information Facility databases  [41] [42] [43]. Data were supplemented by original datasets  [11, 28, 31, 37, 40]  and collection materials (I. I. Schmalhausen Institute of Zoology, National Academy of Sciences of Ukraine, Kyiv; Department of Ecology, Institute of Life Sciences and Technologies, Daugavpils University, Daugavpils, Latvia; all non-duplicate data), thus covering the most eastern range where P.
glenii was first reported. To account for potential sampling bias, we used the nearest neighbor distance ('ntbox' package in R  [44] ) method to thin the data: to avoid spatial autocorrelation, occurrence points ≤ 0. 1 units (meaning approximately the spatial resolution of the climate factors' database (. tiff map file, 2,5 spatial resolution) used for the research) away from each other were removed. As a result, the numbers of points used for modelling were 2096 for L. vulgaris, 2277 for T. cristatus, and 944 for P. glenii. Meanwhile, monitoring studies were carried out in the south of Latvia (near the town of Silene in 2018 and 2022) and in areas of north-western Ukraine (Volyn, Rivne, Zhytomyr, and Kyiv regions in 2017-2021). In order to find the evidence of co-habitation of the newts and fish, they were captured by a hoop-net alongside the water edge in spring months (April-May).
Environmental Data We used 35 bioclimatic variables from the CliMond dataset (  [45] [46] [47] , Table  S1 ), following A1B climate change prediction scenario of MIROC H global climate model. Out of 35 bioclimatic variables, highly correlated (>0. 7) predictors were removed using the 'virtualspecies' package in R, resulting in a selection of 16 variables: annual mean temperature ( • C) (Bio01), mean diurnal temperature range (mean (period max-min)) ( • C) (Bio02), isothermality (Bio02/Bio07) (Bio03), temperature seasonality (Bio04), min temperature of coldest week ( • C) (Bio06), temperature annual range (Bio05-Bio06) ( • C) (Bio07), mean temperature of wettest quarter ( • C) (Bio08), mean temperature of warmest quarter ( • C) (Bio10), mean temperature of coldest quarter ( • C) (Bio11), annual precipitation (mm) (Bio12), precipitation of driest week (mm) (Bio14), precipitation seasonality (Bio15), precipitation of driest quarter (mm) (Bio17), radiation of driest quarter (W m-2) (Bio25), lowest weekly moisture index (Bio30), and mean moisture index of warmest quarter (Bio34).
Models were run with climate data for 1970-2000 and for 2041-2060 (hereafter referred as 1975 and 2050 periods). These intervals were provided by CliMond for modelling the current distribution (most of the records of the studied species dated from 1975 to around 2000) and for building probabilistic models for 2050  [9]. We also used 19 long-term climatic variables from the Near-global environmental information for freshwater ecosystems (river ecosystems, long-term hydroclimatic variables following the "bioclim" framework, EarthEnv NGEI, HydroSHEDS, https://www. earthenv. org/streams (6 February 2022),  [48] , Table  S1 ). The dataset consists of near-global, spatially continuous, and freshwater-specific environmental variables in a standardized 1 km grid. This new set of variables provides a basis for spatial ecological and biodiversity analyses in freshwater ecosystems at near global extent.