dataesr/scientific-paragraphs-categorization huggingface dataset:
UvrC structure Crystal structure of UvrC-C. UvrC-C, consisting of residues 366-617, was crystallized in space group P2 1 , containing one monomer per asymmetric unit, and the structure was determined to 1. 80 Å resolution by molecular replacement using TmUvrC RNase H and (HhH) 2 domains (PDB: 2NRT) as search models (Table  1 ). The UvrC-C structure comprises the RNase H endonuclease domain, responsible for the 5 incision event (residues 367-546) and two consecutive HhH motifs forming the (HhH) 2 domain (residues 550-608) implicated in DNA binding (Figure  4A ). Due to poor electron density, the short linker between the two domains (residues 547-549) could not be built reliably. Similarly, the first (residue 366) and last nine residues (609-617) of this construct are not visible. While the individual domains are structurally homologous to those of TmUvrC-C, with respective root-mean-square deviation (rmsd) values of 1.
Although no metal ion was bound in the active site of DrUvrC RNase H domain, the residues are positioned in a very similar conformation as in the Mn-bound structure of TmUvrC (PDB 2NRZ;  (26) ). This differs from the active site of G. stearothermophilus UvrC RNase H domain (PDB: 3C65, unpublished), which is more open due to a shift in the position of the long ␣4 helix bearing the conserved histidine (Supplementary Figure  S11 ). In the absence of Mn, however, H535 of DrUvrC points away from the two highly conserved aspartate residues (Supplementary Figure  S11 ). A major difference between TmUvrC and DrUvrC is the presence of a 17-residue insertion in between ␤-strands ␤6 and ␤7 of DrUvrC that forms a solvent-exposed loop (Loop1 in Figure  4 ) pointing away from the active site. This appears to be a particularity of DrUvrC, although short insertions are also observed in this position in other UvrCs (Figure  4C ).
The high-sequence coverage and large number of available sequences and structures of related domains and proteins in the database allowed Al-phaFold2 to produce models of DrUvrC with most regions exhibiting high-confidence scores (Supplementary Figure  S1 ). Regions with confidence scores lower than 70 correspond to either inter-domain regions or to parts of the Cterminal domain. We thus combined our crystal structure of UvrC-C with the predicted structure of UvrC-N to build the first complete model of a UvrC (Figure  5A ). The Al-phaFold2 predicted model of the UvrC-NEndo endonuclease domain is identical to the homology model we obtained using SWISS-MODEL and TmUvrC-NEndo as a template (Supplementary Figure  S12 ). The predicted model, however, revealed that besides the NEndo domain, UvrC-N was additionally composed of a small ␤-sheet motif (residues 95-125), a four-helix bundle (residues 126-231), and a second RNase H domain (residues 243-366, named RNase H1, as opposed to the C-terminal RNase H domain hereafter named RNase H2; Figure  5A ).
By overlaying, on the one hand, the RNase H2 domain of DrUvrC onto Bacillus halodurans RNase H domain bound to an RNA/DNA hybrid (PDB: 1ZBI) and, on the other hand, the (HhH) 2 domain of DrU-vrC onto E. coli RuvA bound to dsDNA (PDB: 1C7Y), we were able to build a putative model of UvrC-C bound to duplexed DNA (Figure  6A ). To achieve this DNA-bound conformation, the (HhH) 2 domain needs to undergo a near 180 • anti-clockwise rotation relative to the long ␣4 helix of the RNase H2 domain (Supplementary Movie S1), which can easily be achieved simply through changes in the conformation of the flexible linker connecting ␣4 from the RNase H2 domain to ␣5 in the (HhH) 2 domain. This linker also provides sufficient flexibility for the (HhH) 2 domain to position itself correctly so as to contact the DNA minor groove notably via its second HhH motif.
In this DNA-bound state, the active site of the RNase H2 domain of UvrC-C is correctly positioned relative to the DNA to catalyze the 5 incision reaction (Figure  6A ). Similarly, by overlaying the NEndo domain of DrU-vrC onto the structure of E. coli GIY-YIG endonuclease R. Eco29kl bound to DNA (PDB: 3NIC), we could build a model of NEndo bound to DNA (Figure  6B ). Finally, knowing that the RNase H2 domain incises the DNA 7 nucleotides 5 to the lesion and that NEndo incises the DNA 4 nucleotides 3 to the lesion, we could propose a model for the binding of the two endonuclease domains of DrUvrC to duplexed DNA (Figure  6C ). To assemble such a model, we used the DNA extracted from the co-crystal structure of the B. caldotenax UvrB-DNA pre-incision complex (PDB: 6O8F) (  73 ), which we extended on either end with standard B-form duplexed DNA.
ACKNOWLEDGEMENTS IBS acknowledges integration into the  Interdisciplinary Research Institute of Grenoble (IRIG, CEA). This work used the platforms of the Grenoble Instruct-ERIC center (ISBG; UAR 3518  CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology (PSB ), supported by  FRISBI  ( ANR-10-INBS-0005-02 ) and  GRAL , financed within the  University Grenoble Alpes graduate school (Ecoles Universitaires de Recherche)   CBH-EUR-GS  ( ANR-17-EURE-0003 ). We acknowledge the  European Synchrotron Radiation Facility  for provision of synchrotron radiation facilities and we would like to thank the  ESRF  staff for assistance and support in using beamline ID14-EH4.
Statistical analysis First, to compare the proportions of correct and incorrect trials among experimental conditions, we included only trials in which individuals made a choice (hereafter, response) and excluded trials in which individuals refused to participate (hereafter, refused trials). Then, we compared the proportions of refused and response trials among experimental conditions. Finally, we compared the proportions of individuals that reached the learning criterion (LC) among experimental conditions as well as the number of trials taken to reach this LC. Each analysis is described in detail below. The age ranges for each condition are summarized in the Supplementary Material (Table  S1 ). We performed all analyses using R software  (version 4. 3. 1)  in the integrated development environment RStudio  (RStudio Team, 2023).
Success rate To highlight cognitive performance differences among experimental conditions, we tted a generalized linear mixed model (GLMM) with binomial error structure and logit link function (using the glmmTMB function from the R package glmmTMB). We chose a binomial structure because our response variable was a categorical variable with two levels: 1 for a correct trial and 0 for an incorrect trial. We included as random factors the identity of the individuals and the trial number (ranging from 1 to 60), which we standardized (z-transformed). We added as xed factors the experimental group, the sex and age of the individuals, and the behavioural variables (agitation score and emergence time). The description of the model can be found in the Supplementary Material (Table  S2 ). Prior to running the model, we veri ed the absence of heteroscedasticity and overdispersion with the package DHARMa.
To test the effect of each factor on the response variable, we conducted type II Wald chi-square tests. When a categorical xed factor had a signi cant effect on the response variable, we followed the analysis with pairwise comparisons between groups using estimated marginal means (EMMs) with the Sidak method for p value adjustment (emmeans function from the R package emmeans). When a continuous xed factor had a signi cant effect on the response variable, we followed the analysis with a visualization of the model predictions using the function ggpredict from the R package ggeffects. We used ggplot2 and graphics packages to generate the gures.
Refusal rate To highlight differences in the refusal rate among experimental conditions, we tted a GLMM with binomial error structure and logit link function (using the glmmTMB function from the R package glmmTMB). We chose a binomial structure because our response variable was a categorical variable with two levels: 1 for a refused trial and 0 for a response trial. We included as random factors the identity of the individuals and the trial number (ranging from 1 to 60), which we standardized (z-transformed). We added as xed factors the experimental group, the sex and age of the individuals, and the behavioural variables (agitation score and emergence time). The description of the model can be found in the Supplementary Material (Table  S2 ). Prior to running the model, we veri ed the absence of heteroscedasticity and overdispersion with the package DHARMa.
To test the effect of each factor on the response variable, we conducted type II Wald chi-square tests. When a categorical xed factor had a signi cant effect on the response variable, we followed the analysis with pairwise comparisons between groups using estimated marginal means (EMMs) with the Sidak method for p value adjustment (emmeans function from the R package emmeans). When a continuous xed factor had a signi cant effect on the response variable, we followed the analysis with a visualization of the model predictions using the functions ggpredict from the R package ggeffects. We used ggplot2 and graphics packages to generate the gures.
Learning criterion To highlight learning performance differences among groups, we ran two models: one to compare the number of individuals reaching the LC per group and one to compare the number of trials before the LC was reached. We tted a GLMM with a binomial error structure and logit link function (using the glmmTMB function from the R package glmmTMB). We chose a binomial structure because our response variable was a categorical variable with two levels: 1 when the individual reached the LC and 0 when the individual did not reach the LC. To compare the number of trials before the individual reached the LC, we only included individuals that eventually reached the LC. Then, we tted a GLMM with a Gaussian error structure and logit link function (using the glmmTMB function from the R package glmmTMB).
For both of these models, we included as the identity of the individuals as a random factor, and we added the experimental group as a xed factor. The description of the model can be found in the Supplementary Material (Table  S2 ). We veri ed the absence of heteroscedasticity and overdispersion with the package DHARMa. To test the effect of each factor on the response variable, we conducted type II Wald chi-square tests. When a categorical xed factor had a signi cant effect on the response variable, we followed the analysis with pairwise comparisons between groups using estimated marginal means (EMMs) with the Sidak method for p value adjustment (emmeans function from the R package emmeans). We used the ggplot2 package to generate gures.
The objective of this paper is to present and critically evaluate NatApp as an appropriate instrument to close the gap for a convenient digital tool to foster the uptake of small-scale, field-integrated measures via a transparent information provision and application process. To evaluate this, we explore how NatApp compares with other tools, considering their contribution to foster biodiversity in agricultural landscapes via AECM or other nature conservation measures. First, the concept and modules of NatApp are presented. Subsequently, NatApp is compared with other digital tools available on the market that also aim to reduce the negative impacts of agriculture on the environment or even encourage the implementation of nature conservation measures. Within this analysis, the strengths and weaknesses of NatApp are discussed.
Development of the NatApp Concept The development of NatApp went through several stages. The first idea of NatApp emerged in 1998, when concepts for improving the maintenance of plant and animal species typical for agricultural landscapes were planned and presented. Next, the developed ideas and concept were published in a manual for the arrangement and optimized cultivation of small-area habitats for biodiversity  [29]. Before every following project phase, the provision of digital tools on the market having the same aim as the NatApp was verified. A first transfer of the thus-far developed concept toward a digital support tool took place a few years later, when a smartphone-based documentation of nature conservation measures on fields and grassland was designed  [30]. The development of the first prototype took place in close collaboration with the AECM responsible agencies at the county and state levels in Germany, the German organization for nature conservation, and the midsize company for agricultural soft-and hardware, HELM Software.
Main Modules of the NatApp NatApp consists of four main modules: (1) the information desk (Infothek), which offers a catalog of possible nationwide and federal state-specific AECM from which farmers can choose; (2) the planning tool, which supports farmers with the application of AECM by drawing on standardised geometric data (ESRI shape files) of established, federal state specific application software programs (Integrated Administration and Control System-IACS); (3) the monitoring tool, which assists farmers with the implementation of AECM by providing guidance during the implementation; and (4) the documentation tool, which offers geotagged photos and recordings of the implementation process (Figure  1 ).
Planning Tool In the planning tool (2), users can import the subscribed measures and corresponding plots and manage the implementation of those measures. The planning tool is designed as a web application. With its help, farmers can directly apply for selected AECM via direct access to the IACS digital application software of each federal state  [14, 30]. All relevant farm and plot data deposited in the IACS system are imported as shape files into the planning tool and are synchronized with the measurement information of the selected AECM. Therefore, farmers do not need to re-enter their plot data in NatApp. The IACS software includes up-to-date references and information about each farm plot. By manually drawing in the Agrar Global Positioning System (GPS), farmers can add additional information, such as plot size or usage of the farm plots.
Regarding technical features, we investigated: (i) Which approach do the tools use to get to their target output (e. g. , photos, satellite images, GPS system, etc. )?; (ii) How do they provide assistance (e. g. , are they guided by GPS or help with the documentation of the measures)?; (iii) Which data do they rely on for their operations (e. g. , field data or whole farm data)?; and (iv) Which additional valuable features do they offer to farmers (e. g. , calendars or additional map features)? With respect to their format, we explored: (i) In which format are they available: as a mobile app or web application?; (ii) Are they free of charge or fee-based?; (iii) On which software system are they running (i. e. , Android or iOS)?; and (iv) Which other user-friendly format or extras do they offer?.
The EGNSS4CAP, for example, is based on the Copernicus and Galileo programs. Conservis, CropSat, and Agricolus also rely on sentinel data for the implementation of their features. Geotagged photos, geolocation, and GPS and mapping tools are used for assistance regarding either the documentation or display of conservation measures (e. g. , geotagged photos of the EGNSS4CAP app or the geolocation and mapping tools of the COMET-Farm Tool, CropSat, and Agricolus) or the guidance to relevant field plots or sections (e. g. , phone-built in GPS in the AgroTutor app) (Table  2 ). -Monitoring production and vegetation status via satellite images -Status reports on implemented activities and applied inputs, non-automatic soil samples, and historical yield data -Necessary field data is centrally captured and stored in the system for purposes of documentation CropSat -Satellite images for calculation of vegetation indices and variable rate applications; map and location features -Display of subcrop-specific differences in five levels.
Agricolus -Sentinel-2 satellite images -Geolocated data for field optimization, crop planning, and task management All of the presented tools rely on the input of individual farm data for their calculations, although in a different range and scale. Some tools, such as the COMET-Farm tool, also rely on background data, such as climate or soil data, provided by state databases and accessible by the tools. Another additional valuable feature for the users is the calendar function, also described as a status report. Especially for tools providing management suggestions, a calendar or other type of reminder function and status report is a valuable function (Table  2 ). It supports appropriate handling of the tool and the prevention of undesired results due to incorrect operation. With regard to the format, the two typically occurring formats are an app application or a web-based platform (Figure  2 ).
Interactive maps the farmers/users can employ for precise and comparative analyses of their fields are often available. Except for the Conservis/Climate Field View, all tools are free of charge, and if developed as an app application, most of them run on iOS as well as on Android devices. The user-friendliness of the COMET-Farm Tool, for example, is characterized by the cost-free availability of the tool and the possibility even for non-registered users to calculate their emissions and create mitigation scenarios. Some tools (Conservis/Climate Field View) offer freely available upgrade software for testing for one year.
Unlike the EGNSS4CAP app, the target scope of NatApp is not only the fulfillment of obligatory documentation required by the paying agencies or the improvement of production patterns (CropSat, Agricolus) but also clearly a facilitation of access for farmers to edited information. In particular, the comprehensive, methodically structured Infothek with its up-to-date information is a unique and outstanding feature of NatApp compared with other digital tools. NatApp also aims to encourage farmers to participate actively and voluntarily in ecologically beneficial AECM, best fitting for their fields. Although some other tools rely partly on more advanced technologies/technical features, such as geotagged photos applied in NatApp (e. g. , Galileo and Sentinel data and images), gradual guidance through the entire application and documentation process of AECM in the documentation tool is a highly useful and advantageous feature for individual users.
An important service is the automatic synchronization of relevant farm and plot data deposited in the IACS system with the measurement information of the selected AECM. NatApp provides several setups for safe utilization and avoiding mistakes. One of them is the automatic verification of the correct location (right machine on the right plot) by the app prior to the implementation of an AECM. Hence, even external contractors or machinery staff, who do not know the farm and its associated territory well, can conduct an AECM, selected by the farm manager. The live recording of the implementation is always cached and, thus, allows the user to review the record and, if needed, correct it. Likewise, to lower the risk of management mistakes and possible sanctions, an implemented calendar and automated push notifications remind farmers of important management measures.
Finally, to ensure legally compliant documentation, NatApp precisely advises the user where and how to take geotagged pictures before and after the implementation of an AECM. With regard to the format, there is no significant difference between NatApp and the other digital tools. NatApp is also free of charge and runs on iOS as well as on Android devices. Of course, there is a difference in the spatial usability of NatApp, as it works with only German nature conservation databases. -Measures and requirements within the CAP are a dynamic and ever-changing realms → political and structural changes need to be included in the NatApp. -Digital tools can only support the users but never replace the advice by trained staff. -Link to established technologies in agriculture (e. g. , digital field map, access to the IACS), load of already entered data.
NatApp cannot replace personal nature conservation consulting by trained staff. Individual cases need competent and on-site advice. What role will NatApp play against this background? NatApp supplies farmers with a good overview of available AECM. When information limits are reached, NatApp provides references for further information and assistance. However, NatApp is an easily accessible and free-of-charge tool that runs on both common iOS and Android software (Table  3 ). Sensitive farm data are safely acquired and stored. NatApp has a role to play in contributing to the achievement of the Sustainable Development Goals (SDGs). SDG 2 ("Zero Hunger") aims to create a world with food security, improved nutrition, and sustainable agriculture  [38]. With a broader implementation of AECM, farmers can actively collectively contribute to this SDG.
Unique features, such as the Infothek, which offer a valuable structured source of information for farmers, have not yet been established in other digital tools available. As a mobile smartphone or tablet app, NatApp is an easy-to-use tool even in remote rural areas. Thereby, NatApp can stimulate the spread of information and communication technologies (ICT) in agriculture into outlying, lower structured areas and, due to its concept and target, enhance the agricultural and rural development there. However, it should be pointed out once again at this point, that our search was limited to the English-written literature and resources. Any tools published in the literature or developed in countries of a foreign language are not considered here. Nevertheless, this study defines the necessity, as well as the unique features, of Nat-App among other digital tools available.
A randomised trial is currently ongoing in France, named Targeted Laser in Diabetic Macular oEdema  (TaLa DME, NCT03751501) , aiming to investigate the potential of targeted photocoagulation of TelCaps in DMO to reduce the burden of intravitreal injections  (Castro Farías et al. , 2020). 1. 3. The journey of phase 2/3 randomised clinical trials of intravitreal treatments and management for DMO 1. 3. 1. Vascular endothelial growth factor inhibitors for DMO 1. 3. 1. 1. Pegaptanib. Pegaptanib (Macugen, Eyetech Pharmaceuticals, Inc. , New York, USA) is a pegylated aptamer, a chemically synthesised short strand of a ribonucleic acid molecule, that selectively inhibits the VEGF isoform A -165. A short-term study that evaluated intravitreal pegaptanib for DMO reported more eyes in the pegaptanib group gained VA of ≥10 letters at six months than those of the control group (34% versus 10%, P = 0.
The Fight Retinal Blindness! Registry The Fight Retinal Blindness! registry is an online, web-based platform for tracking real-world outcomes of retinal diseases and is the flagship module of the Save Sight Registries (SSR: http://savesightregist ries. org/)  (Gillies et al. , 2014b). Initially established in 2009 to monitor outcomes of neovascular age-related macular degeneration (nAMD) in Australia, it has since expanded its scope to include outcomes on DMO and retinal vein occlusion and has clinicians from over 20 countries across Europe, Asia, Africa and Pacific. The FRB! registry distinguishes itself from other sources of realworld data with standardised fields that capture a minimum dataset with in-built data validation, ensuring that the data are of higher quality than has previously been the case from an most electronic medical records.