dataesr/scientific-paragraphs-categorization huggingface dataset:
In Russia, about 40 million cases of infectious diseases are registered annually among farm animals, while economic losses from diseases amount to 30%, and the amount of damage is more than $200 million. The following measures need to be adopted: 11. 1. Blockchain technologies for tracking the entire path of food production, processing and sale. 11. 2. Creation of phytosanitary and veterinary databases and digital platforms for analyzing and predicting the spread of animal and plant diseases. 11. 3. Development of systems for the monitoring, diagnosis and prevention of plant and animal diseases. 12 Technologies of the Logging Industry The technologies of the logging industry are extremely important for Russia, which has more than 20% of the world's forest reserve. The forest complex occupies an important place in the country's economy, due to the performance of important environmental protection and environmental-forming functions.
2021b). Plant-and insect-based meat alternatives and protein sources can substitute for ASF without compromising its nutritional benefits. Improvements in the scientific assessment's methods regarding the health-related, environmental and socioeconomic impacts of reduction in ASF are needed. A potentially very significant contribution to deepened insights around the health-related aspects of diets is the "Periodic Table of Food Initiative (PTFI)," a global effort to create a public database of the bio-chemical composition and function of the food that we eat using the latest mass spectrometry technologies and bioinformatics. 3 If then combined with the micro-biome science of human nutrition  (Kau et al. 2011) , the perspectives on healthy diets may further shift and related health and information actions can become more concrete, including for the prevention of obesity.
Acknowledgements Acknowledgements We gratefully acknowledge the peer review of a background paper on which this comment is based by these reviewers: Acknowledgements Helpful comments on an earlier draft by five external peer reviewers and by colleagues in the  Scientific Group  are gratefully acknowledged. Acknowledgements Helpful comments on an earlier draft by five external peer reviewers, and by colleagues in the  Scientific Group , are gratefully acknowledged. Acknowledgements TH acknowledges support by the  National Science Foundation  ( CBET 1855937  and  OISE-2020635 ) and FE acknowledges support by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under Germany's  Excellence Strategy   -EXC 2070 -390732324 ). This chapter was published in  Nature Food ,  2 832-834  (2021).
This work was funded by the New Venture Fund and the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS ), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details, please visit https://ccafs. cgiar. org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organisations. Acknowledgments The authors wish to thank the  United National Food Systems Summit Fruit and Vegetables Working Group  for their original input to the original UNFSS working paper. Funding for this research was provided by long-term strategic donors to the  World Vegetable Center: Taiwan ,  UK  aid from the  UK government ,  United States Agency for International Development (USAID) ,  Australian Centre for International Agricultural Research (ACIAR) ,  Germany ,  Thailand ,  Philippines ,  Korea , and  Japan.
Acknowledgements The author acknowledges the detailed and very useful comments from  Joachim von Braun ,  Johan Swinnen  and  Rob Vos. Those comments significantly improved the different versions of this document. However, they are not responsible for any of the remaining errors and omissions. This chapter is an abridged and updated version of  Díaz-Bonilla  (2021). Acknowledgements This chapter was drafted by  Claudia Canales  and  Robin Fears , in discussion with  Volker ter Meulen. We thank colleagues from  EASAC , the  Biosciences Steering Panel  and the  Working Group on FNSA  (EASAC 2017) for helpful discussions and, in particular,  Tim Benton ,  Thomas Elmqvist  and  Aifric O'Sullivan  for making important points. Acknowledgements We would like to thank  Ms. Jiajia Wang  and  Ms. Zhuanlin Wang  ( SAAS, PKU ) for their execellent research support.
Acknowledgements The authors express special gratitude to the experts who took part in the discussion of the most promising developmental areas for agricultural science and technology in Russia and in the world:  Alina Osmakova , Executive Director of the Technology Platform BioTech 2030,  Oleg Kobiakov , Director of the  FAO Liaison Office  with the  Russian Federation ,  Maxim Markovich , Director of the  Department of International Cooperation and Agricultural Export Development, Ministry of Agriculture of the Russian Federation ,  Irina Donnik , Vice-President of the  Russian Academy of Sciences ,  Irina Chernukha , Leading Researcher,  Federal Scientific Center for Food Systems  named after  V. M. Gorbatov ,  Mikhail Glubokovsky , Scientific adviser,  Russian Federal Research Institute of Fisheries and Oceanography, Viktor Tutelyan , Director,  Federal Research Centre of nutrition, biotechnology  and food safety,  Alla Kochetkova , Head of the  Laboratory of food biotechnologies  and specialized products,  Federal Research Centre of nutrition, biotechnology  and food safety,  Petr Tolmachev , Head of the  Department of World Economy of the Diplomatic Academy of the Ministry of Foreign Affairs of Russia.
Acknowledgements We would like to thank  Ritika Juneja , Consultant ( ICRIER ), for her excellent Food System in India. Challenges, Performance and Promise 827 research support in writing this paper. Acknowledgements This IAP Brief was drafted by  Robin Fears  and  Claudia Canales  in discussion with  Volker ter Meulen. We thank  Sheryl Hendriks  ( NASAC ),  Elizabeth Hodson  ( IANAS ) and  Paul Moughan  ( AASSA ) for their helpful advice. E. Hainzelin  (*) The  French Agricultural Research Centre for International Development (CIRAD ),  University of Montpellier, Montpellier, France  e-mail: etienne. hainzelin@cirad. fr  P. Caron High -Level Panel of Experts (HLPE ) of the  United Nations Committee on World Food Security and CIRAD , Vice president for  International Affairs, University of Montpellier, Montpellier, France F.
The strengthening of selection and genetic research in these industries is one of the areas of highest priority for their development. The main topics of research work in this field include: 4. 1. Creation of databases of genetic resources that form the basis of knowledge about hereditary variability and are a reference for the identification of new mutations and a source of valuable source material for breeding. 4. 2. Development and implementation of genomic assessment tools and creation ofRussian varieties and hybrids, as well as pure lines of highly productive animal breeds that correspond to the real economic and agro-climatic conditions of the agrifood sector. 4. 3. Methods of accelerated selection (marker-oriented, microclonal reproduction in vitro), which significantly reduce the time of breeding of new varieties and hybrids of plants.
To calculate the PME in EEE, it is first necessary to relate the MINCOTUR data with the UNU-Keys and EU-6 categories. This relationship is presented in Table  S1  of the Supplementary Materials. Once this relationship is established, the quantity of EEE POM can be estimated according to its UNU-Key. Next, knowing the composition of the 43 UNU-Keys to be studied (M i ) [kg element/UNU-key] (Supplementary Materials, Table  S2 ), the number of metals embedded in each equipment can be obtained and, by means of the price of the metals (P Mi ) [USD/kg element] (Supplementary Materials, Table  S3 ), obtain the PME in each UNU-Key (PME), using Equation (2). PME POM = POM•M i •P Mi (2) Most of the metal prices were obtained from the Federal Institute for Geosciences and Natural Resources of Germany  [26].
To ensure that all signals were in the linear range of the scanner's detector and to avoid any saturation of the signals various gains and PMT values were employed. Images were analyzed with Mapix software (version 8. 1. 0 Innopsys) and processed with our home-written Excel macro. The average fluorescence intensity and SD were measured for each compound after exclusion of the highest and lowest intensities from the spot replicates (n = 4).
Western blot MDCK-II cells were infected with indicated viruses at an MOI of 1 and cells were collected 24 hpi and subjected for standard western blot procedures. Virus purification from allantoic fluid of eggs inoculated by rg-AL or rg-HL-16 in three independent replicates, each 20 to 30 eggs, was conducted following the published protocol  [66]. NA was detected using rabbit anti-N1 antibodies or NA-universal HCA-2 antibody  [67]. GAPDH, β-actin, M1 and NP proteins were detected using commercial monoclonal antibodies and peroxidase-conjugated rabbit or mouse IgG (Sigma Aldrich). Antibody binding was detected by luminescence (Supersignal West Pico chemiluminescent substrate kit, Pierce, ThermoScientific, Rockford, IL, USA) in a BioRad Versa Doc System and Quantity One software. Relative quantification of different proteins to GAPDH or β-actin of four independent experiments was determined by image J software.
Data were acquired using the PASEF mode with parameters suggested by the manufacturer of the instrument. For the chromatography, a gradient of solvent B (0. 1% FA in acetonitrile) was applied from 2% to 20% (46 min) and 32% (60 min). Solvent A was 0. 1% FA in MS-grade water. Protein identification and quantitation were carried out with Maxquant software (version 2. 0. 3. 0)  [71]. The sequence database was compiled from the Gallus gallus proteome (UP000000539), downloaded from Uniprot  [72]  in August 2022, and the virus-specific sequences. Carbamidomethylation was set as a fixed modification of C residues, acetylation was allowed for protein N-termini as well as the oxidation of M residues and the phosphorylation of S, T, and Y residues. Intensity-based absolute quantification (iBAQ)  [73]  values from Maxquant were normalized to the iBAQ of the NP as a virusspecific internal standard  [74].
Genetic analysis and structural modelling. Amplification of all gene segments and Sanger sequencing of the rg-HL-16 and recombinant viruses was conducted as previously done  [3, 78]. All available NA genes of Egyptian A/H5N1 from 16-02-2006 to 31-12-2016 and Asian viruses from 1997 to 2016 were retrieved from the GenBank and GISAID. Sequences were aligned and edited using Geneious. Mutations in the NA compared to the parent 2. 2. 1 virus (A/chicken/Egypt/NLQP-06207/2006) were analyzed. 3D structures were generated by SWISS MODEL (http://swissmodel. expasy. org/). The 2D structure of N-acetylneuraminic acid [Nue5Ac], the most abundant natural ligand for binding to NA was compiled using Chem-Draw, 3D structure of Nue5Ac ligand was constructed using Chem 3D ultra 15. 0 software Molecular Modeling and Analysis; Cambridge Soft Corporation, USA [2014], then they were energetically minimized by using MOPAC [semi-empirical quantum mechanics], and saved as MDL MolFile [ �.
Acknowledgments The authors are grateful to  Dajana Helke ,  Nadine Bock  and  Barbara Bettin  for laboratory technical assistance, to  Christine Fast ,  Charlotte Schro ¨der ,  Ba ¨rbel Hammerschmidt ,  Frank Klipp ,  Doreen Fiedler ,  Matthias Jahn , and  Harald Manthei  for their help during the animal experiment. Ju ¨rgen Stech ,  Timm C. Harder ,  Ilaria Capua ,  Beate Crossley  and  Jessica Bogs  are thanked for providing the viruses and  Magdalena Murr  for her support in virus purification. Walter Fuchs  is thanked for providing the N1 antibodies and  Balal Sadeghi  for his support in the statistical analysis of the data. The authors, originating and submitting laboratories of the sequences from GISAID's Epiflu database are acknowledged for depositing sequences analyzed in this study.
This project was partially funded by grants from the  Deutsche Forschungsgemeinschaft  ( VE780/1-1 ,  AB 567 ) to EMA and the  DELTA-FLU Project  (ID:  727922 ) funded by the  European Union  to EMA and TCM as well as the  German Centre for Infection Research (DZIF) , partner site Giessen, Germany (TTU 01. 806 Nucleoside-booster to Co-PI SP),  DFG  funded  SFB 1021  (,  TP C01  to Co -PI SP  and  TP BO2  to Co-PI MM), and  National Research Centre  ( TT110801  and  12010126  to AM). LMZ and SF were supported by the  Federal Excellence Initiative of Mecklenburg Western Pomerania & the European Social Fund (ESF)   Grant   KoInfekt  ( ESF/ 14-BM-A55-0002/16 ). The glycan microarray experiments are supported by the  Netherlands Organization for Scientific Research  ( NWO TOPPUNT 718. 015. 003 ) to G.
Many research laboratories need to manage, process, and analyze the increasingly large volumes and complexity of data being produced by state-of-the-art bioimaging platforms. OMERO is a popular open-source client-server application that provides a unified interface for managing and working with bioimages and their associated measurements and metadata. Integrating OMERO into analysis pipelines, such as those developed around the scientific Python ecosystem, will thus be a common pattern across the field of bioimaging. While OMERO has a powerful Python API, it provides minimal abstraction from the underlying OMERO object model and associated methods, which represent more complexity than most users are interested in for the context of an analysis script. We introduce ezomero, which was designed to provide a convenience layer on top of existing OMERO APIs and return data types that are either Python primitive or commonly used in scientific Python.
g. , OMERO. figure  (The Open Microscopy Environment, 2023a)  and  OMERO. iviewer (The Open Microscopy Environment, 2023b). OMERO is also able to serve as the foundation for image data repositories such as the Image Data Resource (https: //idr. openmicroscopy. org,  Williams et al. , 2017) , a utility that will become more important as research funding agencies require all primary research data to be either deposited into public repositories or otherwise broadly shared with the research community. One key advantage of OMERO is its well documented application programming interface (API) services, which enable developers and power users to both write clients that extend OMERO and script routine functions and workflows. Moreoever, although OMERO is a Java application, its API services can be invoked remotely from several other programming languages using bindings that are dynamically generated by Ice, an open-source remote procedure call framework developed by ZeroC.
The OME group has also written a more user friendly Python module called BlitzGateway, which simplifies many common OMERO operations and wraps OMERO objects with a more "Pythonic" interface. Python developers are able to access OMERO's Python bindings, BlitzGateway, and a command-line interface written in Python by installing the omero-py package (The Open Microscopy Environment and Glencoe Software, Inc. , 2023), maintained by OME and available on the Python Package Index (PyPI). While omero-py is a powerful library, one of its core tenets is maintaining crosslanguage support with other OMERO APIs-while this simplifies maintenance and documentation, for example, it means omero-py cannot be tailored to Python's strengths by definition. The Research Information Technology group at The Jackson Laboratory manages multiple OMERO deployments, for which we have written a collection of scripts for executing common administrative tasks.
We have also given a number of power-user/developer-focused workshops in which we teach how to interact with OMERO in the context of a Python script. Our experience with these development and teaching activities led us to two observations: 1) Some common OMERO tasks can be quite verbose when written in Python, even when using BlitzGateway, and 2) users that are proficient in using Python for bioimage analysis but who are not formally trained in computer science often struggle to work with the OMERO objects returned by omeropy. In addressing both of these issues, we developed over time a set of convenience functions that we felt provided a more comfortable Python interface. We ultimately bundled these functions into a Python package we call ezomero (The Jackson Laboratory, 2023). Although ezomero initially grew organically with need, we made two early choices that allowed us to later formulate a coherent API.
First, we decided to keep the use of OMERO-or ezomero-specific objects to a minimum, focusing instead on creating functions for which inputs and outputs are limited to either Python primitive types  (e. g. , strings, integers, etc)  or objects familiar to users of NumPy or other scientific Python libraries (e. g. , numpy. array). Second, we used function names that clearly explain the function's purpose through the use of HTTP vocabulary (e. g. , get, post, put) where applicable, and through consistent vocabulary (e. g. , link to, print) where no obvious HTTP verb made sense. Here we provide an overview of the current state of ezomero, further develop our rationale for its use, and demonstrate how it can be used to accelerate the development of OMERO-based image analysis.
Methods Source Code and Library Organization. Ezomero source code is available on Github (https://github. com/TheJacksonLaboratory/ezomero). We follow a simple Python package organization in which most functions are imported directly from the package root. Two modules, ezomero. json_api and ezomero. rois, contain functions for working with OMERO's json API and regions-of-interest (ROIs), respectively. Despite the relatively flat package structure, the code has been split over multiple files (e. g. , _gets. py, _posts. py, _importer. py) to improve readability and maintenance. In addition to the Python package directory, the code repository also contains separate directories for documentation (docs) and testing (tests). Ezomero source code is covered by the GPL-2. 0 license, following the licensing of omero-py.
Documentation. Ezomero is autodocumented by Sphinx (https://www. sphinx-doc. org), which builds html documentation from Python docstrings. We chose to follow NumPy docstring conventions, allowing us to autogenerate documentation using Sphinx's napoleon extension (sphinx. ext. napoleon). We have published html documentation at https: //thejacksonlaboratory. github. io/ezomero. All files necessary for Sphinx generation of documentation is located in the docs directory repository.
Development Environment. As noted above, git/Github are used to manage ezomero version control. Users are invited to submit bug reports and feature requests using Github issues, and external contributions are welcomed. We have included a contribution guide as part of our repository, with guidelines for how to start contributing to ezomero, design principles that encapsulate the general direction we expect the project to pursue, and stylistic guidelines for consistency with existing code. A Code of Conduct is also provided, to detail the environment we would like to foster for collaboration and clearly delineate how interactions with users and contributors to our public Github repository should happen. We use pytest for testing. Unit tests, pytest configuration, and some test data are found in the tests direc-tory of the repository.
When developing locally, we use docker-compose to deploy an ephemeral OMERO instance, which includes OMERO. server, OMERO. web, and a PostgreSQL database. However, we also run tests on pull requests and associated commits using Github actions, so contributors are not required to set up their own testing environment. We have configured some basic OMERO objects and test data as fixtures, but for the most part, objects are created as needed for a particular unit test on the ephemeral OMERO server and torn down when the test is complete. Installation and Deployment. The main dependency for ezomero is omero-py, the Python library for interacting with an OMERO server provided by OME. Ezomero also requires numpy for interacting with numerical arrays, and has pandas as an optional dependency for interacting with tables.
Ezomero is available from PyPI, meaning a simple pip install ezomero is all that is required for installation, provided dependencies are available. Trying to automatically install ezomero from PyPI without the requisite dependencies can be problematic; in particular, omero-py depends on ZeroC-Ice's python library, which is not readily available from PyPI as a prebuilt package; Ice is built statically at install time. This process is time-consuming and a source of errors. As an alternative, prebuilt ZeroC-Ice packages can be installed via conda (from the conda-forge channel), and ezomero installation can be completed with pip. Full installation instructions are available on the ezomero GitHub repository.
Results Design. As noted in the introduction, ezomero was designed to minimize user interaction with OMERO model objects and to make routine administrative tasks simple to code. In this way, ezomero is meant to complement rather than replace the APIs available through omeropy. If a skilled developer intends to write highly efficient code that makes sophisticated use of the OMERO model objects, ezomero may not be a good solutionomero-py offers, for example, direct database querying options that can provide more complex functionality that ezomero does not aim to replicate. Our typical user is, for example, a graduate student that wants to analyze their image data stored in OMERO with scikit-image (  Van der Walt et al. , 2014)  or torchvision  (Marcel and Rodriguez, 2010 ) using a Jupyter Notebook.
Another potential user might be a core facility director that needs to write a script to perform a nightly administrative task. These users may want to write code quickly, without relying too heavily on OMERO documentation. Use of ezomero begins with the creation of a session for connecting to OMERO. server. A BlitzGateway object (provided by omero-py) is used for the connection; we typically name this object conn, which is consistent with OME's own examples. While users can create the BlitzGateway object directly, we have provided a convenience function, ezomero. connect, that can simplify the creation of this object by reading from stored connection parameters or environment variables, or prompting the user to input parameters, including a password, that were not passed to the function. Nearly all other ezomero functions take the conn object as the first parameter.
We have used HTTP vocabulary in our function names in an effort to make code more readable, with obvious purpose. We use "get" whenever data needs to be retrieved from the server and "post" whenever a function creates a new resource on the server. We use "put" in one case, ezomero. put_map_annotation, which will replace the key-value pairs in an OMERO map annotation. Other common verbs are "print" for displaying various kinds of information to Stdout, "filter" for narrowing lists of OMERO Image IDs, and "link" for creating Project, Dataset, and Screen links that are used to organize Images. Example use cases. This section aims to show the main advantages ezomero presents over the Blitz API for novice programmers and image analysts: a simplified, less verbose interface with an OMERO server, and usage of standard scientific Python data structures as inputs and outputs.