dataesr/scientific-paragraphs-categorization huggingface dataset:
Assets, limitations, and outlook Considerations in Sections 4. 1 and 4. 2 underline the assets of a process-based modelling approach in terms of its capabilities to resolve individual process dynamics. Additionally, modelling allowed us to obtain spatially and temporally continuous information, which is not feasible with today's observation technology. Most ALS-based snow datasets that cover large areas are available for only a few temporal snapshots  (Safa et al. , 2021; Currier and Lundquist, 2018; Harpold et al. , 2014; Broxton et al. , 2019) , while existing attempts to acquire snow distribution time series are very limited in spatial extent and mostly cover one winter season only  (Koutantou et al. , 2022; Broxton and van Leeuwen, 2020). Process-level data that is both spatially and temporally explicit is even more scarce and extremely challenging to obtain  (Moeser et al.
, 2015; Malle et al. , 2019 Malle et al. , , 2021;; Mazzotti et al. , 2019b). Model application, in contrast, is only limited by the availability of driving meteorological data and surface datasets, and thus potentially applicable to extensive spatial and temporal domains. modelling community should strive for continued improvement. On one hand, the representation of some processes, especially those involving snow in the canopy, could still be improved  (Lundquist et al. , 2021; Lumbrazo et al. , 2022)  and associated uncertainties should be evaluated systematically across the full range of canopy structure and topographic diversity of the application domains of interest. At hyper-resolutions, mass and energy exchange between neighbouring locations becomes relevant  (Schlögl et al. , 2018) , and lateral coupling would likely improve the representation of expanding snow-free areas in spring.
Plausibility checks as presented in Section 2. 3 are indispensable for the verification of model use cases, as well as for continued model enhancement and refinement, and there is potential for improvement here as well. For instance, the use of RGB satellite data to validate melt out patterns (Section 2. 3) is promising despite limited visibility of snow under the canopy. Automated algorithms to extract snow cover information from RGB imagery are not currently applicable to forested complex terrain  (Deschamps-Berger et al. , 2020; Gascoin et al. , 2019) , but would encourage the use of such datasets for this purpose. If respective workflows are continuously improved, enabling simulations and observations to be used in tandem and to benefit from each other (e. g. through data assimilation approaches), hyper-resolution model applications at large temporal and spatial scales in the contexts discussed in Section 4.
Conclusion This study represents the first multi-year application of a hyper-resolution forest snow model capable of resolving tree-scale processes within a sub-alpine valley, aimed at investigating how snow cover dynamics and the underlying processes are shaped by the interplay of (1) canopy structure, (2) terrain, and (3) meteorological conditions. The chosen approach yielded process-level insights that could not be obtained based on snow distribution datasets alone. Our findings evidenced that all these three factors must be considered when attempting to explain spatio-temporal snow cover dynamics. Canopy structure exerts the primary control on accumulation patterns, yet the resulting snow distribution can be disrupted by ablation patterns, which are primarily driven by the distribution of shortwave radiation.
Acknowledgements. This study was partly funded by the  WSL Institute for Snow and Avalanche Research SLF  and by the  Swiss Federal Office for the Environment (FOEN). Part of this work was carried out at the  Centre des Études de la Neige (MétéoFrance)  under  SNSF  Postdoc. Mobility grant  P500PN_202741. We would like to thank  Kalliopi Koutantou  for providing UAV-LiDAR data and for many informal chats during fieldwork, which sparked the idea for this research. We thank  Christian Ginzler  for giving us access to Planetscope data. We are grateful to  Rebecca Mott  and  Jan Magnusson  from the  Operational Snow Hydrological Service at SLF  for their support on matters related to the meteorological input and the model framework and to  Richard Essery  for his help with FSM2-related questions.
Data and code availability. The FSM2 model code is available on GitHub under https://github. com/GiuliaMazzotti/FSM2/tree/hyres_enhanced_canopy. Model input datasets are available on WSL's data repository EnviDat (doi:10. 16904/envidat. 338). Author contributions. GM and TJ designed the study. CW did the radiation modelling and provided canopy structure input datasets for FSM2. LQ and BC contributed to the development of the model framework and the preparation of meteorological input data. GM performed FSM2 simulations and analysed the results, with input from TJ. GM wrote the manuscript, with feedback from all authors.
The SNR depends on the methods chosen and the quality of the images (e. g. , resolution, georeferencing, orthorectification) used for cross-correlation  (Heid and Kääb, 2012; Millan et al. , 2019). The time interval between images that are correlated is an important parameter, as a larger time interval will increase the signal (i. e. , the displacement) relative to the noise of the measurement. Annual to multi-year time periods allow expected displacements to be large relative to the noise, so that automated procedures can effectively recover accurate velocity variations on global (e. g. , ice sheets, mountain ranges) and local (e. g. , a small mountain glacier) scales  (Quincey et al. , 2009; Rignot et al. , 2011; Scherler et al. , 2011b; Dehecq et al. , 2015; Mouginot et al. , 2017)  These observations, when coupled with appropriate modelling and complementary datasets (e.
Recent studies have proposed efficient and accurate use of the large database of available satellite imagery to retrieve accurate short-term changes in glacier velocity  (Altena et al. , 2019; Derkacheva et al. , 2020; Riel et al. , 2021; Hippert-Ferrer et al. , 2021). However, most of these studies have focused on fast outlet glaciers  (Derkacheva et al. , 2020; Riel et al. , 2021)  or other fast glaciers with large monthly-scale velocity changes  (Altena et al. , 2019; Millan et al. , 2019; Hippert-Ferrer et al. , 2021). It is not yet clear whether these approaches can be used to study the short-term dynamics of mountain glaciers in general, or only for some of the largest and fastest glaciers. Largescale assessments of velocity changes on short time scales and over complex areas such as mountain ranges are therefore still lacking.
Such an approach has been shown to significantly increase measurement precision  (Altena et al. , 2019; Derkacheva et al. , 2020). We have observed that Sentinel-2 imagery can help to resolve patterns that are only hinted at using Landsat-8 only. However, the increase in spatial and temporal resolution obtained with the Sentinel-2 imagery does not offer a significant advantage as shown by the similar resolution we obtain (Figure  2 ). We suggest that further improvement of our protocol could be made using the approaches presented in  Altena et al. (2019)  and  Derkacheva et al. (2020) , which would allow combining velocity obtained from different sensors and over different time spans with more advanced statistical approaches. The high precision we obtain shows the advantage of combining a statistical approach with a robust measurement of surface displacement to improve the level of comprehension on large satellite imagery datasets.
Acknowledgments U. N thanks  B. Altena ,  F. Brun ,  A. Gilbert ,  A. Kääb  and  L. Malatesta  for helpful comments and discussions. U. N greatly thanks  J. Mouginot  for helping with the processing and the discussions. The authors thank  A. Lambretch  for providing meteorological data on the Fedchenko Glacier (Figure S1). D. S acknowledges funding from the  European Research Council  under the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement  759639. This research was partially funded by the  Resnick Sustainability Institute at the California Intsitute of Technology.
Code and data availability The glacier surface velocity maps as well as the along flowline velocity profiles data used for in the study are available at https://doi. org/10. 5281/zenodo. 7142273. (Nanni, 2022b) under a Creative Commons Attribution 4. 0 International license. V. 1. 0 of the codes we developed to conduct our study (as detailed in Section 3) is preserved at https://doi. org/10. 5281/zenodo. 7142273 (Nanni, 2022b) under a Creative Commons Attribution 4. 0 International license. The codes are to be used in combination with ENVI's COSI-Corr add-on software, which is freely available at http://www. tectonics. caltech. edu/slip_history/spot_coseis/ (last access: September 9 th 2022; Leprince et al. , 2007). An python version of COSI-Corr is currently under development and can be found here: https://github.
The structural and molecular binding characteristics of BcnA argue against the notion that antibiotic binding is the primary function of this lipocalin. In addition to BcnA and BcnB (PDB IDs 5IXH and 5IXG, respectively)  [27] , several YceI bacterial lipocalins have been crystallized from Escherichia coli (PDB ID 1Y0G), Pseudomonas syringae (PDB ID 3Q34), Campylobacter jejuni (PDB ID 2FGS), Helicobacter pylori (PDB ID 3HPE)  [30] , Pseudomonas aeruginosa (PDB ID 7BWL)  [31] , Thermus thermophilus (PDB ID 1WUB)  [32] , Saccharophagus degradans (PDB ID 2X32)  [33] , and Treponema pallidum (PDB ID 5JK2)  [34]. These molecules reveal remarkable structural conservation despite notable differences in their amino acid sequences. Further, an isoprenoid molecule, typically octaprenylphosphate, octaprenylphenol, or ubiquinone-8 (Ubi-8, coenzyme Q), has been detected in the lipocalin β-barrel tunnel of most of the structures.
yqhD orthologs are conserved in thousands of bacterial genomes but unlinked to bcnA and lcoA. Strain K56-2 contains an yqhD ortholog, K562_20604 (GenBank accession CP053301), which we renamed peroxidative stress-associated aldehyde reductase gene (psrA). To evaluate whether PsrA contributes to reduce peroxidation levels, we constructed a ΔpsrA mutant, which was examined for peroxidation with DPPP under tellurite challenge. In unchallenged ΔpsrA bacteria, the peroxidation levels were comparable to those of K56-2, but the peroxidation levels in the mutant increased significantly under tellurite stress (Fig 1A, S1 Data). These results suggest the accumulation of short aldehydes inducing additional membrane lipid peroxidative stress. Further examination of the PsrA polypeptide by de novo homology modeling revealed a predicted structure that overlaps to that of the E.
coli YqhD protein (PDB ID 1OJ7) despite both proteins only sharing 21% amino acid identity (Fig Next, we investigated whether mutants unable to produce BcnA, LcoA, and PsrA can overcome physical stress conditions known to cause peroxidation. One such condition is cold  stress, which increases lipid peroxidation in plants and involves lipocalins in thermotolerance  [43, 44]. Compared to the wild-type strain, ΔbcnA, ΔlcoA, and ΔbcnABΔlcoA mutants showed significant reduction in bacterial recovery (p � 0. 0001) when incubated at 8˚C for 24 hours (Fig  1B ). The bacterial recovery reached levels like the wild-type strain upon complementation of ΔbcnA and ΔlcoA mutants with the corresponding plasmids restoring BcnA and LcoA function, respectively. It should be noted that because these mutants are in-frame gene deletions, lcoA should be functional in ΔbcnA, and, conversely, bcnA should be functional in ΔlcoA.
Image analysis Most imaging experiments were performed independently by 2 individuals, and the quantitation was performed with multiple images to reduce selection bias. Quantification of bacterial peroxidation and CL by fluorogenic probes was performed using Fiji Is Just ImageJ (FIJI)  [86]  as previously described by Christine Labno (https://www. unige. ch/medecine/bioimaging/files/1914/ 1208/6000/Quantification. pdf). The analysis was based on determining the average gray values of each bacterial cell, where the sum of gray values is divided by the number of pixels. Briefly, micrographs were imported into FIJI and converted into 8-bit images and subsequently transformed into binary images. If required, background noise was removed with the FIJI "Despeckle" function. Using the measurement tool, mean gray values were measured to determine the fluorescence intensity, and the results were automatically exported into a cvs file and processed with GAU : Pleaseprovid raphPad Prism version 9, GraphPad Software, San Diego, CA, USA to prepare graphs and perform statistical analysis.
The fluorescence intensity across the long axis of the cell was normalized to the average intensity of the cell under no treatment. The average intensity was obtained using MicrobeJ. The results were saved to a cvs file. The fluorescence intensity distribution and the identification of intensity peaks were graphed using Python version 3. 8 (http://www. python. org) script, which can be accessed in GitHub (https://github. com/jdYEXzhM/Quantification/blob/main/ Quantification. py).
Bioinformatics The synteny analysis to evaluate the genetic neighborhood of bcnA homologs in different β-proteobacteria and other bacterial families was based on MultiGeneBlast  [39]. Genomes were downloaded from NCBI, https://www. ncbi. nlm. nih. gov, to produce a database, following the instruction for MultiGeneBlast. For the architecture search, the query consisted of YceI lipocalin family sequences downloaded from the pfam database, http://pfam. xfam. org. The database was searched for significant hits applying, 20% minimal identity of Blast hit for the query, and 20 kb as the maximum kilobase distance between genes. All the other parameters were set as default.
Three-dimensional modeling of PsrA The amino acid sequence of the putative aldehyde reductase from B. cenocepacia, PsrA, was downloaded from https://burkholderia. com. The crystallized structure of the YqhD aldehyde reductase of E. coli was downloaded from RCSB PDB (https://www. rcsb. org) using the PDB ID: 1OJ7  [88]. The three-dimensional modeling of the PsrA structure was made using the I-TASSER online server (https://zhanglab. dcmb. med. umich. edu/I-TASSER) [89]  using the crystalized structure of E. coli as a template for modeling. The crystallized structure of E. coli was used as the template for generating the predicted model. The structural comparison analysis was made using the align function of PyMOL (TAU : Pleaseprovidethemanufacturernameandlocatio he PyMOL Molecular Graphics System, Version 2.
Up until now, measuring the retinal vascular network FD with SS-OCTA remains complex and there is no normative database available  [21]. We decided to investigate FD because it can potentially quantify the retinal microvascular network in a single scalar quantity. Retinal vascular network FD has been widely studied with fundus photography  [9, 10, 12]  and SD-OCTA  [22, 23] , but the literature on FD and SS-OCTA is sparse. Moreover, the merit of this non-invasive parameter has been proven in various multidisciplinary areas combining ophthalmology with cardiology  [24, 25] , neurology  [8, 26] , and endocrinology  [12, 27]. Nevertheless, before using this metric in clinical practice or clinical trials, it is essential to determine the repeatability and reproducibility of the measurement. Repeatability is defined as the agreement of measurements obtained from different scanning sessions with the same device, the same eye and the same operator.
Study Participants We conducted a cross-sectional descriptive study in the department of ophthalmology at Dijon University Hospital, France, between 1 November 2017, and 1 June 2018. The protocol was approved by the regional ethics committee and followed the tenets of the Declaration of Helsinki. All patients gave their written informed consent for the examinations before enrolment. We followed the GRRAS (Guidelines for Reporting Reliability and Agreement Studies)  [29]. We enrolled consecutive healthy volunteers between 18 and 55 years of age. The participants' demographic data were collected, including age, sex, and ocular history. Participants with a self-reported eye disease, refractive abnormalities (high myopia < -6 diopters, hyperopia > +6 diopters, astigmatism < -3 diopters), cardiovascular history or neurological history were excluded from the analysis.
All participants benefited from a comprehensive eye examination and SS-OCTA acquisition on the same day. We also measured blood pressure with an automated device, after 5 min of rest in a seated position. Weight and height were recorded to calculate each participant's body mass index (BMI). Globe axial length was measured using the IOL master 500 (Carl Zeiss Meditec, Jena, Germany). Measurements were taken on both eyes, but the data from one eye only were retained for analysis, according to the following procedure: (1) SS-OCTA of the right eye for participants born in even-numbered years and the left eye for those born in odd-numbered years; (2) if a scan was uninterpretable for one eye, the scan of the other eye was retained for analysis; and (3) if both eyes had inadequate signal strength (<7/10) or abnormal segmentation or significant artefacts on the perfusion map (lines or gap), the participant was excluded.
We investigated both superficial and deep retinal capillaries (Figure  1 ), which were automatically segmented by the SS-OCTA system software. Automatic projection artefact removal algorithm was not applied since it was not available on our SS-OCTA device at the time of study inclusion. The superficial retinal capillary layer was defined as the layer which starts from the internal limiting membrane (ILM) and extends to the inner plexiform layer (IPL). The deep retinal capillary layer was defined as the layer which starts from the IPL and extends to the outer plexiform layer (OPL).
Retinal Vascular Network Fractal Dimension Analysis Fractal dimension (FD) was estimated using the binary box-counting dimension from the SS-OCTA images  [30]. We used the box-counting method in FracLab for FD analysis but implemented our own pre-and post-processing routines in MATLAB. FracLab is a free software developed by the Anja team at Inria Rennes/Laboratoire Jean Leray-Université de Nantes. It is an open-source software mainly based in Matlab. Detailed information about the FD algorithm is available on the developers' website (https://project. inria. fr/ fraclab/ (accessed on 1 January 2019) and the code is freely available to the public. For this method, images were first binarised. This is a process where the greyscale intensity of all pixels in the image were converted to either 1 or 0.
Retinal Vascular Network Fractal Dimension Analysis Fractal dimension (FD) was estimated using the binary box-counting dimension from the SS-OCTA images  [30]. We used the box-counting method in FracLab for FD analysis but implemented our own pre-and post-processing routines in MATLAB. FracLab is a free software developed by the Anja team at Inria Rennes/Laboratoire Jean Leray-Université de Nantes. It is an open-source software mainly based in Matlab. Detailed information about the FD algorithm is available on the developers' website (https://project. inria. fr/fraclab/ (accessed on 1 January 2019) and the code is freely available to the public. To obtain the box-counting FD, the skeletonised images were superimposed on a grid of squares and the total number of boxes (squares) containing at least one pixel of the structure were counted.
To obtain the box-counting FD, the skeletonised images were superimposed on a grid of squares and the total number of boxes (squares) containing at least one pixel of the structure were counted. This process was repeated continuously by reducing the mesh size in each step (increasing the total number of meshes covering the entire image). All data points (total number of meshes counted in each step vs. mesh size) were plotted on a log-log plot and the slope of the line with the best fit to the data was taken as the measurement of box-counting FD. This measure was bounded on the interval [0, 1]. Lower FD values correspond to low complexity of vessel branching, while higher values correspond to greater complexity  [10]. The framework of the image processing steps is shown in Figure  2. Examples of swept-source optical coherence tomography angiography: original and processed OCTA images from a single patient as shown in Figure  3 , which enables better visualisation and comparison of two processed images, and a skeleton image with modified colour to red and with superimposed original grayscale (Supplementary Materials).
40 and 0. 59, the level of clinical significance is fair; when it is between 0. 60 and 0. 74, the level of clinical significance is good; and when it is between 0. 75 and 1. 00, the level of clinical significance is excellent  [31]. Repeatability and reproducibility were also evaluated with the coefficient of variation (CV) calculated as the standard deviation divided by the average of the measurements multiplied by 100. A CV of less than 5% indicates good reproducibility. In addition, intra-observer repeatability and inter-observer reproducibility were evaluated by means of Bland-Altman analysis  [32]. A p-value of less than 0. 05 was considered statistically significant. All statistical analyses were performed using the statistical software SAS (version 9. 4; SAS Institute, Inc. , Cary, NC, USA).
Mutations in the superoxide dismutase 1 (SOD1) gene are the second most common known cause of ALS. SOD1 variants express high phenotypic variability and over 200 have been reported in people with ALS. Investigating how different SOD1 variants affect the protein dynamics might help in understanding their pathogenic mechanism and explaining their heterogeneous clinical presentation. It was previously proposed that variants can be broadly classified in two groups, 'wild-type like' (WTL) and 'metal binding region' (MBR) variants, based on their structural location and biophysical properties. MBR variants are associated with a loss of SOD1 enzymatic activity. In this study we used molecular dynamics and large clinical datasets to characterise the differences in the structural and dynamic behaviour of WTL and MBR variants with respect to the wild-type SOD1, and how such differences influence the ALS clinical phenotype.
RESULTS To characterize the impact of one mutation at a time, we focussed only on SOD1 structures with one single amino acid variant. We could identify 13 WTL and MBR mutations in the Protein Data Bank (PDB) for which high quality structures were available. We divided them in the two categories based on whether they were previously reported to belong to one or whether they were in proximity of another mutation of known category. For example, we classified T2D as WTL as it is in proximity of A4V which was reported to exhibited normal SOD activity in vitro  [27]. The WTL group included T2D, A4V, G37R, L38V, T54R, G93R, I113T and the MBR variants were H46R, C57S, H80R, G85R, D124V and D125H. The structural location of the variants is shown in the Figure  1. The detailed structural information, clinical interpretation, and resolution of the PDB structures used in the study are shown in Table  1.