dataesr/scientific-paragraphs-categorization huggingface dataset:
[76]. They targeted the CD45 gene in human HL-60 cells with three distinct gRNAs designed with CRISPRscan. Here, high mutagenesis percentages were obtained, touching almost 75 % of indels, which classifies CRISPRscan as a high-fidelity gRNA design tool. Based on the lack of a model that in different genome contexts widely agglutinates and demonstrates the efficiency provided by distinct sequence features, in 2015, Xu et al. [51]  launched the linear regression-based Spacer Scoring for CRISPR (SSC) tool. They aimed to develop an affordable model to design gRNAs for genome-wide functional screens, training it with as many gRNAs datasets as possible for that time. Despite the relatively low ROC-AUC related to its prediction power, Radzisheuskaya et al. [77]  utilized this tool to confirm that employing the correct gRNAs, explicitly designed for functional genome screens will highly improve the efficiency, although other factors impact the efficiency strongly.
CRISPOR's predicted gRNAs avoid using extremely GC-rich sequences, and the tool treats >4 mismatches much better than the MIT CRISPRtool. These patterns found by analyzing eight large datasets of off-target sites deliver an improved fidelity on CRISPOR prediction. Mutagenesis and gene knock-out research in the hexaploid Camelina sativa  [82]  employed the CRISPOR tool to design desired and exclude undesired gRNAs for targeting the microsomal oleate desaturase (FAD2) gene, whose knock-out leads to an accumulation of oleic acid in this plant. They selected two gRNAs, from which the second one harbors sequence features described by CRISPOR to improve the mutagenesis efficiency. Looking back on the sgRNA Scorer, Chari et al. [56]  structured this tool to analyze gRNA sets of high and low activity for two orthologs of the Cas9 protein.
For each ortholog, a separate SVM model was created. In 2017, the same team founded the sgRNA Scorer 2. 0  [84] , which inversely creates just one SVM model for both Cas9 orthologs by merging all gRNAs in high and low activity sets. With this, they aimed to design a model that predicts efficient gRNAs for distinct CRISPR systems, knowing that many orthologs exist for different CRISPR systems. Even though this tool was trained with a dataset of gRNAs targeting eukaryotic cell genes, Shen et al. [98]  used this tool to design 81 gRNAs targeting virulent Klebsiella phage genes. As expected, due to the cellular context, sgRNAScorer did not discriminate correctly between high-and-low-activity gRNAs. In 2016, the research done for CRISPOR's feature incorporation shall cause inconsistencies with the research by Abadi et al.
[52]. The latter team launched in 2017 a new predictor known as CRISTA (CRISPR Target Assessment), based on a regression model using the Random Forest algorithm. Their primary purpose was not to design a model for exclusively predicting gRNA on-target efficiency or potential off-target sites but to assess the cleavage efficacy of a particular genomic target by a specific gRNA. CRISTA included a treatment for DNA/RNA "bulges" in their algorithm, which can be understood as gaps in the gRNA/target hybridization. CRISPOR noticed these bulges, but their database analysis suggested no need for treating these gaps, disfavoring this tool for missing this important feature. CRISTA finally considered the necessity to deal with the formation of secondary structures inherent to RNA sequences by their learning model.
Furthermore, the DNA enthalpy, geometry, and the target location (chromosome number and distance from telomere and centromere) are some additional features inserted in the algorithm. In contrast to many other predicting tools, the CRISTA training dataset does not discard uncleaved sites (i. e. , targeted sequences with no gRNA activity), helping to avoid the design of identical zero-activity gRNAs. The CRISPR/Cas9 genome editing system left the scientific community with gigantic expectations. The promise of flawless gene knock-out, knock-in, or functional screens must be accomplished. In 2018, Chuai et al. [62]  finally included the use of deep neural network approaches for predicting and designing gRNAs into their novel tool, DeepCRISPR. Parallelly to CRISTA, DeepCRISPR seeks to predict both functional on-target gRNAs and avoid those with a propensity to rise off-target cuts.
In order to achieve this purpose, Chuai et al. designed an architecture with three fundamental networks: the main one can be understood as the pre-training network (known as "parent network" by the authors) that will recognize various features of gRNAs, using as input ∼ 0. 68 billion gRNA sequences targeting coding and non-coding human genes. The following two CNN use the pre-training network corresponding output. These last networks are trained using well-known, experimentally validated gRNAs with on-target or off-target activity, extracting all the distinctive features characterizing these sequences for further integration in the predictive capacity of the tool. In 2020, accordingly to the pre-training DeepCRISPR dataset based on human exons and intron genes, the tool helped to predict the off-target activity of gRNAs designed by Mintz et al.
[99]  that initially targets the PARP1 gene, for its inhibition, in triple-negative breast cancer (TNBC) cells, highlighting the importance of using CRISPR/Cas9 systems in preclinical studies. In the same year of the DeepCRISPR launch, Lin et al. [48]  focused on developing a tool that exclusively predicts off-target sites with a deep neural network framework. They named their tool as CNN_std, in which they adapted the biological ribonucleotide sequence of the gRNA for the computational environment in a matrix with 4 x 23 size, representing the four nucleotides and the 20-nt recognition sequence plus the 3-nt PAM sequence. This matrix has the correct format for input in the convolutional neural network. Also, Lin et al. utilized the CRISPOR dataset for training, validating, testing, and comparing CNN_std against previous off-target prediction tools such as the CFD score or the MIT CRISPR design tool, overperforming all these and other machine learning-based tools getting a ROC-AUC of 0.
972. Undoubtedly, the CRISPR/Cas9 systems had an enormous refinement with the introduction of deep neural networks, specifically CNN. [91]  were uncomfortable using deep neural networks for gRNA design. Despite the visible characteristics and performance obtained with these models (CNN or RNN), it is tough to interpret their results. Even more, it has been proven that conventional, simpler algorithms can perform the same work done by deep neural networks  [101]. Regarding the latter point, Muhammad et al. launched the on-target CRISPRpred(SEQ) predictor tool, whose SVM-based architecture was trained with the same training dataset for DeepCRISPR while mixing biological gRNA sequence features. In most of the benchmarks, CRISPRpred(SEQ) outperformed DeepCRISPR. On the other hand, CRISPRpred(SEQ) challenged DeepHF using the dataset generated by the latter; unluckily, the machine learning-based tool did not surpass DeepHF due to needing more specific tuning against DeepHF.
Another scope to achieve the desired interpretability in deep neural networks is presented by Xiao et al. [69]. Firstly, they provide a categorization of the existing deep neuronal networks, founded on the treatment of the model's input: methods in the spatial domain, whose input is transformed in a two-dimensional image, which is ready to work with convolutional neural networks for sequence feature extraction  [48, 62] ; methods in the temporal domain, for which the input is treated as a word, and works perfectly with recurrent convolutional networks  [89]. Xiao et al. then proposed an ensemble learning model that uses both the spatial and temporal domains to extract the necessary sequence features, in addition to an "attention mechanism" to give interpretability. The on-target model, which is named AttCRISPR, was further enhanced with hand-crafted biological features, finally overperforming even the DeepHF tool with its training dataset.
In recent years, almost all gRNA design tools have turned their vision to implement only deep neural networks, or hybrid models. These models are increasingly perfecting the predictive activity, getting more and more computationally flawless. In 2022, Zhang et al. [38]  launched the off-target CRISPR-IP predictor tool, which includes four layers, each of which performs distinct procedures focused on characterizing novel sequence features; these are CNN, Bi-directional Long-Short Term Memory (BiLSTM, an RNN derivative), attention layer, and finally a dense layer. The model uses as training dataset experimental information based on sequencing (SITE-seq and CIRCLE-seq). Finally, epigenetic information and bulge treatment were adapted to the model, resulting in high predicting performance. Regarding the most recent on-target prediction tool, Li et al.
As  [97]  presented in their CNN-XG tool, hybrid frameworks for the gRNA design seem to be really feasible to surpass all past architectures, while providing the best features of learning-based algorithms. Consequently, the assembly of hybrid models including approaches from deep neural networks and machine learning must be investigated in depth. The challenges for future research in sgRNA design are enormous. Among others, computational models should focus on tuning the hyperparameters that appear in the architecture design to increase the model's user interpretability. It is of major relevance to include in the model biological features found in the laboratory and in silico. Thus, for the next steps in gRNA design, many more CRISPR/Cas9 activity datasets are required to address biological and epigenetic concerns: the more data from different human and plant cell lines, and unicellular organisms, the more biological functional features found.
[58] CRISPRater LR 2017 Spearman 0. 67 Includes data from new cell lines. [50] SgRNAScorer 2. 0 SVM 2017 Accuracy: 0. 737, Precision: 0. 728, Recall of 0. 758 [84] CRISPRpred SVM 2017 AUROC: 0. 85. AUPRC: 0. 56. MCC: 0. 4 K-mer encoding over Broad GPP. [85] DeepCRISPR CNN 2018 Spearman 0. 406 [62] DeepCpf1 CNN 2018 Spearman:0. 873 [86] DeepCas9 CNN 2018 Spearman 0. 351 [87] TUSCAN RF 2018 Spearman: 0. 55 [88] Cell lines HCT116, DeepHF RNN 2019 Spearman: 0. 867 HEK293T, HELA, [89] HL60. DeepSpCas9 1DCNN 2019 Spearman: 0. 91 [90] CRISPRpred(SEQ) SVM 2020 Spearman: 0. 829. AUROC: 0. 893 Haeussler and DeepHF datasets. [91] One hot encoding GNL-Scorer AdaBoost 2020 Spearman: 0. 502 over 10 public [92] datasets. C-RNN CRISPR RNN 2020 Spearman: 0. 877. AUROC: 0. 976 Includes data from new cell lines.
Preprints (www. preprints. org) | NOT PEER-REVIEWED | Posted: 17 April 2023 doi:10. 20944/preprints202304. 0398. v1 algorithms Unluckily, DeepCRISPR and CNN_std implemented and architectures that neglected the biological features underlying the gRNA feasible design, thus missing characteristics probed to be crucial for this objective. Also, the Chari et al. sgRNA Scorer, the first and second version [56, 84] , used an algorithm trained with datasets obtained from diverse Cas9 orthologs, then being capable of predicting under a more comprehensive array of RGNs (RNA-guided nucleases). In 2019, Wang et al. [89]  compared the predicting performance of an RNN and a conventional CNN. They found that RNN beats CNN and other machine learning algorithms. The dataset used for training, validation, and testing is based on their own experiments in human cells, emphasizing the use of three Cas9 orthologs: WT-SpCas9 (wild-type Streptococcus pyogenes Cas9), eSpCas9 (enhanced), and SpCas9-HF1 (High Fidelity).
GATE (Geant4 Application for Tomographic Emission ) is an open-source software based on Geant4 libraries, initially dedicated to medical physics, from imaging to radiotherapy and radiation protection  [36] [37] [38] [39] [40]. Geant4 (GEometry ANd Tracking) is a simulation toolkit for the passage of particles through matter  [41]. Covering a wide range of applications from radiation protection and medical physics to high energy physics, astrophysics and space science, it offers the ability of modelling and simulating from nanoscale up to macroscale  [42] [43] [44] [45] [46]. Geant4-DNA is dedicated to the simulation of the biological damage in the DNA scale. The set of physics processes used here are adapted to micro-and nano-dosimetry in liquid water allowing the tracking of particles down to eV energies  [47] [48] [49] [50].
Materials and methods The simulation study builds upon the experimental characterization of radionuclides present in water and sediments of five mineral springs in Auvergne, Massif Central, France (Tables  1  and 2 ). The springs are open for public use and no permit was required to access them and collect samples (Fig  1). In two of these springs-Mariol and Chateldon-the deformation rate of different diatom species was documented as a function of the water 222 Rn content  [35]. In the following subsections, we first describe how the aquatic environment and the diatoms have been characterized, to then, elaborate the multi-scale simulations using GATE and Geant4-DNA softwares.
The emphasis is placed on quantifying the excess energy that can be injected into the university. This study aims to provide a clear understanding of the site's energy behavior by considering meteorological data and consumption fluctuations. In our study, we focus on linear programming approaches using Matlab. Below is Table  1 , which summarizes the different works that are closely related to our case study, whether it is their configuration, approach, or strategies used. This paper is structured into five sections, each addressing specific aspects of the study. The first section provides an overview and introduction to the research topic. The second section delves into the architecture and mathematical modeling of the studied system. In the third section, the problem formulation is presented, along with an analysis of the constraints and limitations of the system under study.
Discussion and results The energy flow prediction for the next day in a power plant is determined using the linear programming algorithm. The algorithm considers the actual weather data and load profile as input to forecast the power generated by the battery, excess power, and power drawn from the utility grid. The meteorological data recorded by a station installed at the University of Djibouti is used in this study. Fig. 3  illustrates the patterns of temperature, irradiation, and wind speed for three different scenarios: October 7-8, 2021; November 29-30, 2021; and July 4-5, 2022. To evaluate the performance of the two algorithms, three scenarios were formulated in this study. Scenario 1: In this case, the power plant's performance is evaluated under the worst weather conditions recorded in Djibouti.
The weather data from October 7, 2021, and October 8, 2021, were used for this case study. Due to forgetfulness or other reasons, certain buildings in Djibouti might continue to operate without being turned off, making it important to simulate this situation. Scenario 2: This scenario assumes that the devices in the power plant operate for a full day, resulting in a significantly higher charging power compared to the expected charging power. The weather data from November 29, 2021, and November 30, 2021, were used for this case study. Scenario 3: This scenario considers summer weather conditions with high load and weather fluctuations. The weather data from July 4, 2022, and July 5, 2022, were used for this case study. This scenario is inspired by real situations that occurred in Djibouti.
Some have also called for greater transparency and better management of commercial interests in researching the associations between food products and health outcomes  (Lesser et al. 2007). Emerging evidence suggests that, eventually, dietary recommendations may be personalized to optimize human health outcomes based on individual characteristics  (Fenech et al. 2011 ; Precision Nutrition 2020), but science is still far from achieving this goal. • Imperfect knowledge of the nutrient and "anti-nutrient" content of food: Our ability to fully characterize dietary patterns of populations and individuals (where data permit) is highly dependent on the quality of the food composition tables, i. e. , databases containing the amounts of nutrients in foods per specific portion sizes. Unfortunately, there are many issues with food composition tables, including a lack of data or out-of-date information for many countries and world regions, particularly for less common foods (e.
We will discuss this issue at greater length in the next section, but it is very important to be able to capture household heterogeneity, in terms beyond income-level structure, production opportunities, food consumption patterns and potential locations, in order to properly assess the socio-economic and health implications of agrifood system transformation. The model is based on a set of macro and sectoral accounts updated for 2017 (GTAP database of social accounting matrix), and where national data can be modified easily. In addition, it has been made compatible with the last release of the latest FAO food balance sheets and the State of Food Security and Nutrition in the World 2020s prevalence of undernourishment and cost of healthy diets numbers. It also benefits from a large dataset on farm and trade policies, in particular, from the Ag-Incentives project.
4  It is also compatible with various emissions (farm and non-farm databases) and satellite accounts. The household dataset is largely based on the POVANA database, not only comprising LSMS surveys, but also reconciled with other macroeconomic accounts and data sources. In terms of commodity and sector coverage, the GTAP database covers 67 sectors, of which 21 are food or agrifood products. The MIRAGRODEP dataset has been extended to cover additional products. In particular, some key staples like cassava and key inputs like fertilizers have been disaggregated. This chapter is organized as follows: after the introduction, Sect. 2 presents the main objectives of the agrifood systems summit that will be targeted through the modeling and how the different objectives will be used to develop the scenarios to be modeled.
Importance of Including Household-Level Modeling As discussed, a significant amount of the changes in agrifood systems will have very heterogeneous impacts across households, in terms of income opportunities and consumption space. Therefore, having a proper representation of the household heterogeneity is essential. The MIRAGRODEP CGE framework proposes two approaches to including household-level analysis (for details, see  Laborde et al. 2021b ). The choice of the precise method depends on the scale of the exercise (global assessment, or regionalor country-level requirements), data availability and the need to integrate feedback effects. Both approaches rely on our harmonized treatment of existing household surveys, which describe both the expenditures and the revenue account of each household, including the farm production module, when available (POVANA database).
Depending on the use of the modeling framework for the Agrifood Systems Summit (global or regional assessment, or country-level profiles), we propose using the two approaches alternatively. The existing coverage of countries for which we have detailed household data is available online 9 and is frequently updated. For countries that are not covered, we generate representative household distribution at the continental level based on available countries' data and reshape the distribution weights to target demographic and income macroeconomic indicators (e. g. , Poverty and GINI for each country). The existing dataset and country coverage could be expanded easily, since the POVANA database is based on systematic templates and protocols that could be used to add new countries or update data for those already covered.
In addition, considering how various consumers/producers at the household level may face differentiated mark-ups is not a trivial issue. For these reasons, we propose to flag this issue as one to be discussed as a major research question. In the meantime, existing results should be interpreted with caution. Most importantly, since our competition assumption changes the way that markets operate, this issue should not be fixed "outside" the model, with a complementary analysis upstream or downstream; rather, it should be tackled within the model. (b) Biophysical Balances and Soil Health: While the issue of soil health constitutes a key topic for the sustainability of agrifood systems, systematic datasets and actual causal linkages between production systems and evolution of soil health remain scarce.
Similarly, the substitution between manure and mineral fertilizer is not currently integrated, while manure on cultivated soil is still monitored for emissions purposes. This could be addressed, assuming the availability of technical expertise. (c) Household Data in Developed Countries: Due to the history of the modeling framework and the acuteness of food insecurity in developing economies, our household database does not include developed countries. Since the social and health implications in the developed economies should not be neglected, we should aim at addressing this data gap with the right set of partners.
Conclusions While identifying the conceptual linkages at play within the agrifood systems and the different trade-offs involved is essential, providing a quantification of these mechanisms is required to illustrate concepts, support informed decisions and trigger proper actions. Considering these interactions is a necessity during the whole process: at the diagnostic stage (i. e. , quantifying trade-offs), on the way to achieving core SDG targets (i. e. , the roadmap to 2030) and, finally, when designing policy responses that may also lead to various indirect effects. This chapter tries to develop a proper quantification approach based on a modeling platform that combines state-of-the-art and up-to-date databases covering all the metrics of interest (hunger, poverty, nutrition, and environmental indicators) and dynamic simulation models that explore future pathways and optimal policy responses.
Methodology This chapter uses a scoping review  (Harris et al. 2021; Liverpool-Tasie et al. 2020)  to assess the current evidence on gender issues in food systems. Given the broad range of key topics related to gender in food systems, topically relevant and published systematic reviews were purposively sampled to provide a baseline state of the evidence. After purposively sampling and identifying 16 systematic and scoping reviews to inform the baseline, additional articles were collected. Three databases (Google Scholar, ScienceDirect, and IFPRI's Ebrary) were used to gather and collect additional articles using key word searches aligned with 42 unique terms cross-referenced with the terms "gender" and "women. " A total of 198 articles were selected from these databases for review after meeting the following inclusion criteria: the articles had to be empirical and peer-reviewed, published in English, and have a geographic focus in low-or middle-income countries (LMICs).
The article also had to make an explicit reference to gender or women's empowerment and the key thematic term. For articles meeting these initial criteria, additional criteria were used to exclude some from the review, including if the methodology was inadequate to account for biases, or if the article was not relevant to agriculture or food systems. Duplicate articles from across the searches were eliminated from the database. Finally, additional articles were identified for inclusion from the citations in the articles collected above. All collected articles were managed in Zotero reference manager software. 2 This section presents the main findings of evidence relevant to the components of the gendered food system conceptual framework (Fig. 1 ): drivers and cross-cutting levers, shocks and stressors, food and value chains, food environment, consumer behavior, and outcomes.