dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2016) , and services are available that will completely process samples from nucleic acid extraction to sequence analysis (e. g. , https://dnasense. com/index. php; https://microbe. med. umich. edu/microbiome-core/microbialcommunity-profiling; https://www. baseclear. com; https:// www. northumbria. ac. uk/business-services/engage-withus/research/nu-omics/). Data storage and interpretation are aided by the availability of publicly accessible sequence databases (e. g. , National Center for Biotechnology Information [NCBI], European Molecular Biology Laboratory  [EMBL] ). Taking samples for such analyses (Table  3 ) would therefore provide resources with which to conduct future research. Alternatively, FCM-based community fingerprinting has recently gained attention as a more accessible and less costly alternative to sequencing-based approaches  (Barriuso et al.
The latter, however, is only possible if combined with appropriate analytical techniques, that is, high-resolution mass spectrometry, which are still beyond the technical expertise and budget of many current CROs. Identifying catalysts of chemical substance transformations. There are 100 000 chemical substances  (Wang et al. , 2020)  and the microbial world is equally, if not more, diverse, making the identification or even prediction of enzymatic transformations challenging  (Wackett & Robinson, 2020). However, the number of different functional groups that might undergo enzymatic transformation is actually more limited, and for many of these functional groups, reaction rules have been developed to predict plausible biodegradation pathways for new substances (e. g. , Eawag-BBD (Biodegradation/Biocatalysis Database)  (Gao et al.
com/journal/ieam which the soils/sediments are sourced and how this affects potential changes in microbial communities, for example, soils/sediments typically exposed to colder conditions may be poorly adapted to temperatures of 20 °C and vice versa. Additional work should reevaluate the dependence of temperature from multimedia perspective, and at different scales (lab, local, regional; also see below). Improving validation, benchmarking, and linking tests to field and monitoring data While field and monitoring data provide real-world data, sometimes, it is difficult to link those outcomes to outcomes from laboratory tests. It has been proposed that reference chemical substances with known environmental degradation behavior could be used to validate or benchmark the accuracy of laboratory tests in identifying persistent substances and avoid some of the variations in tests mentioned in the foregoing sections (e.
Modeling Chemical regulations allow, to varying degrees, the use of models to predict fate properties, such as biotransformation. We therefore summarize recent approaches and progress in developing predictive models that support persistence assessment in the sense that they provide a prediction of microbial biotransformation half-lives and/or formation of products (e. g. , TPs, NER, etc. ) in different environmental compartments as relevant in a regulatory context (i. e. , agricultural soils, aquatic sediments, surface water, groundwater aquifer, etc. ; Table  3 ). Since such models need sound experimental data to be trained and validated, we also cover recent efforts to compile databases of microbial biotransformation of substances. Compilation of high-quality biotransformation data. Large and curated collections of biotransformation data (i.
Doing so requires database formats that allow storage of chemical reaction information (e. g. , SMILES [Simplified Molecular Input Line Entry Specification] and SMIRKS, representing molecules and reactions, respectively  [Daylight, 2020] ), and information on the type and certainty of the analytical evidence supporting the reported reaction (see, e. g. ,  Schymanski et al. , 2014 , for reporting of identification confidence based on LC-MS data). Several such data compilations, with more or less complete annotation of metadata, seem to reside with individuals or organizations (e. g. , US EPA [Environmental Protection Agency] [  Boethling et al. , 1994] , Nolte  [Nolte et al. , 2018] , or LMC [Laboratory of Mathematical Chemistry] Oasis  [Dimitrov et al. , 2011; Karabunarliev et al. , 2012] ).
However, so far, we are only aware of a couple of efforts to make these types of data compilations publicly available that is, Eawag-BBD/PPS, formerly UM-BBD/PPS (Eawag [formerly: University of Minnesota] Biodegradation/Biocatalysis Database and Pathway Prediction System)  (Gao et al. , 2009) , and envipath. org (Latino et al. , 2017; Wicker et al. , 2016). We believe that the lack of high-quality curated databases is related to the fact that, unlike in other related research areas, for example, in molecular biology, there is no requirement by journals in the field to deposit biotransformation data into public repositories. This leads to, first, a lack of commonly agreed formats for reporting biotransformation and related metadata, and second, difficulties in identifying funding bodies that support the maintenance and curation of biotransformation data.
, 2017; Schäffer et al. , 2018; Trapp et al. , 2018). When a 14 C-labeled substrate is mineralized, the label will either remain in the biomass (yield Y) or in CO 2 (fraction 1 -Y). Theoretical yields can be calculated from thermodynamics (Gibbs energy) and structural data. Together with the measured CO 2 evolution in degradation tests and data on the microbial biomass, biogenic NER formation can be estimated. Further work with the model is required that would demonstrate and improve understanding of the influence of intrinsic properties of the substance and the environmental matrix on NER formation. A validation exercise, demonstrating the suitability of the model and improving confidence in the data generated, is ongoing. Advances and novel approaches in QSBR development. Generally, the field of quantitative-structure-biodegradation relationship (QSBR) development has matured from mostly using multivariate, linear modeling approaches to using more sophisticated machine-learning approaches  (Di Guardo et al.
, 2018; Mamy et al. , 2015). However, any development of QSBRs suffers from small and nonhomogeneous databases and widely varying rate constants. Therefore, recent efforts in the QSBR development have sought smart strategies to overcome these data limitations. Three major directions have been attempted: (i) joining of data sets across different study conditions and even environmental compartments, although this requires normalization of data to account for differences in physico-chemical conditions and biomass concentration and composition, which is a major challenge as discussed below; (ii) inclusion of prior knowledge to group substances into more homogeneous groups to obtain significant relationships within such groups; and (iii) grouping of substances according to their structural properties for prediction of physico-chemical properties and environmental fate endpoints  (Acharya et al.
, 2019; Rein et al. , 2016; Toräng et al. , 2003). Finally,  Nolte et al. (2020)  proposed a way to correct biotransformation rate constants for concentration-dependent adaptation. However, we believe that this approach should be validated further and become more mechanistically underpinned before being applied. Since quantitatively correcting for all influencing factors when joining data sets for improved model development will remain challenging, benchmarking, that is, the consideration of relative rather than absolute biotransformation behavior, has been suggested as an alternative approach to join biotransformation data from different experimental assays, without the need for explicit data normalization (see Improving validation, benchmarking and linking tests to field and monitoring data).
ACKNOWLEDGMENT This work was conducted as part of the  European Centre for Ecotoxicology and Toxicology of Chemicals (ECETOC) task force "Moving Persistence (P) Assessments  into the 21st Century" (2019)(2020)(2021). The authors would like to thank the  ECETOC scientific committee  and other task force members  Jens Bietz ,  John W. Davis ,  Delina Lyon ,  Jens C. Otte , and  Frédéric Palais  for their valuable comments during the preparation of this article. We are also grateful for suggestions on how to improve the manuscript from  Bennard Van Ravenzwaay ,  Björn Hidding ,  David Saunders ,  Charlie Meyer ,  Philip Botham ,  Christopher Hughes ,  Erik Van Miert ,  Gordon Sanders ,  Joop Hermens ,  Philipp Mayer ,  Andreas Häner , and  Anna Böhnhardt. The authors' time to contribute to this review was supported by their organizations.
Researching Pubmed and Scopus databases (October 2021), we eventually selected 48 papers, presenting overall 49 different models with numerical application to RVF. We categorized models as theoretical, applied, or grey, depending on whether they represented a specific geographical context or not, and whether they relied on an extensive use of data. We discussed their contributions to the understanding of RVF epidemiology, and highlighted that theoretical and applied models are used differently yet meet common objectives. Through the examination of model features, we identified research questions left unexplored across scales, such as the role of animal mobility, as well as the relative contributions of host and vector species to transmission. Importantly, we noted a substantial lack of justification when choosing a functional form for the force of infection.
Study selection A total of 372 records were identified from the two databases. After removal of duplicates, 248 records were screened at the title level, 146 at the abstract level, and 69 reports were fully read. Twenty-one reports were excluded during full-text reading: three were excluded due to incomplete model description  [41] [42] [43] , three modeled mosquito population only  [44] [45] [46] , ten were not mechanistic models  [47] [48] [49] [50] [51] [52] [53] [54] [55] [56] , three were review papers  [57] [58] [59]  and two were theoretical without application to RVF  [60, 61]. Eventually, 49 studies were selected for the present review (Fig  1 ). Among those, 26 were not present in the review by  Danzetta et al. (2016)   [37].
Theoretical models have helped to broadly explore the pathosystem behavior when dealing with a lot of uncertainty, but such papers often lack clarity. A difficulty for theoretical papers is to convey how mathematical analysis can be helpful to field practitioners down the line  [110]. Regarding applied and grey models, their specificity often relied on the geographical application and the dataset they used, rather than on a focused research question. Main outputs. The main output of a model, holding the key message of the studies, could pertain to one of four main categories (Table  1 ): i) parameter estimation (n = 8), ii) risk maps (n = 7), iii) comparison of scenarios, defined as a small set of simulations with specific parameters varied (or processes turned off) across a small set of values (n = 25), and iv) sensitivity analysis, where a large subset (if not all) of parameters are varied across a large set of values (e.
e. , the uniqueness of parameter values able to reproduce a given model trajectory) rather than the epidemiological impact of such a hypothesis. A single model  [81]  has looked at the possible effect of climate change on RVF risk, in Eastern Africa. This likely does not reflect a lack of interest for this issue, but could rather indicate that mechanistic modeling is not the preferred method to study such trends, compared to phenomenological (i. e. , statistical) models  [120] [121] [122] [123]. In their review, Metras et al. (2011)  [36]  had highlighted the widespread use of phenomenological models to assess RVF risk across spatio-temporal scales. Phenomenological models can play a key role in selecting relevant processes to include or characterize suitable habitats, by highlighting significant correlations in complex datasets  [124] [125] [126].
In addition, RVF being a transboundary animal disease, larger scale models are needed, able to gauge the role of animal movements in the transmission dynamics. Currently, international applied models do not incorporate connections between their spatial entities (Table B in S1 Text), probably due to a lack of data. A coordinated data collection effort is required across affected countries, focusing on both commercial and pastoral mobility, and making these data easily accessible to epidemiological research teams. Data. Data were used in 25 out of 49 (51%) models. Here, we define data as any raw information, as opposed to a readily available parameter value extracted from another study. Several types of data were used ( We identified a total of 102 datasets (Table C in S1 Text), corresponding to four datasets per model on average (102/25), ranging from two to ten.
Only 44% of all datasets used by models incorporated a spatial dimension (measures in at least two distinct locations), and 45% a time dimension (measures for at least two different time points) (Table C in S1 Text). Regarding epidemiological datasets (n = 24), 25% were spatialized, and 58% were time-series (Table  C  in S1 Text). This is lower than environmental datasets (n = 28), which were 57% spatialized and 86% time-series (Table C in S1 Text). This supports conclusions made in recent reviews  [24, 25]  which highlighted important gaps in RVF epidemiological data. Specifically, such gaps included the lack of fine-scale geographical metadata, preventing the study of within-country variation; the need for long-term studies in both endemic and non-endemic countries, to evaluate a possible increase in RVFV activity and exposure; and studies considering wildlife, livestock, and human concurrently, using standardized reporting and uniform case definitions  [24, 25].
Potential corrective measures would depend on whether such missing data are not collected or not made accessible. Most models with data managed to use at least one spatialized dataset (15/25, 60%) or time-series  (23/25, 92%, Table C in S1 Text). This indicates that mechanistic models can resort to all types of data to try and compensate for the lack of precision in epidemiological reporting. Five studies used epidemiological data not published elsewhere  [67, 71, 77, 78, 91] , showing that modeling studies can also be seen as a way to valorize new datasets. We categorized data use into three categories: calibration, input, and model assessment. Calibration was defined as the parametrization of one process or initial condition of the model, transforming the data in some way. This was done in 17 cases (Table C in S1 Text).
Input was the fact of using the raw data directly as a parameter or initial condition of the model. This was done in 20 cases (Table C in S1 Text). Model assessment referred either to parameter inference or qualitative estimation looking to maximize similarity between epidemiological model outputs and data. This was done in 12 cases (Table C in S1 Text). Ultimately, building accurate models helpful for policy makers requires the support of data. However, for RVF as well as for other infectious diseases, no single data source can be expected to inform each relevant parameter. Hence, the integration of information from many heterogeneous sources of data has become the norm  [134]. This is a challenging task, as different datasets will be of different quality, potentially dependent, or in conflict  [134].
ABM: agent-based model. All ABM models used individual animals as agents, except for Python Ndekou Tandong et al. (2020) [89] ( �� : agent-based modeling for animal mobility, with cities and trucks as agents exchanging animals, compartment model for transmission within cities. ) https://doi. org/10. 1371/journal. pntd. 0010339. t001 Metras et al. (2017) [78] and ran the parameter estimation algorithm on a new outbreak dataset. One of the two models described in EFSA AHAW Panel et al. (2020)[79] then made a stochastic model based on Metras et al. (2017, 2020) [77,78]. Tennant et al. (2021) [80] transformed the single-patch model of Metras et al. (2017) in Mayotte into a metapopulation model for the Comoros archipelago. Taylor et al. (2016) [81] used the model by Leedale et al. (2016) [82] set in Kenya and Tanzania to explore a new research question, i.
Data and preliminary analysis In addition to the BRIC composite index, we employ the daily stock market indices at close for its constituents (Brazil, Russia, India, and China), and the G7 (Canada, France, Germany, Italy, Japan, the UK, and the US) markets in our analysis. The specific market indices used were: NASDAQ-BRIC, Italy, Japan, and the UK; Brazil-IBOVESPA; Russia-Moscow Exchange; India-NIFTY 500; China-Shanghai Stock Exchange; Canada-Toronto S&P/TSX; France-CAC 40; Germany-DAX. The data set spanned between 11/12/2012 and 28/05/2021, yielding 1,652 common observations. The daily stock indices were supplied by EquityRT and are expressed in USD. The log-returns of the daily stock indices were computed as r t = lnP t -lnP t-1 , where r t defines the continuously compounded returns, P t represents the price of an asset in period t, and P t-1 represents the price of an asset in the previous period t-1.
The Barunı ´k and Křehlı ´k  [14]  spillover index was used with daily data from 11/12/2012, to 28/05/2021. Overall and net-pairwise spillover connectedness and contagion were assessed together for the BRIC index and its constituents, and a further examination with the G7 equities under a 100-day rolling window and forecast horizon. Our findings divulged that, together, the BRIC composite index transmits the greatest spillovers to developed markets. Focusing on the individual markets, we found France, Germany, and the UK to be the major providers of shocks in the short term. In the intermediate-term, we explicate that Canada and the US will join the group of countries that contribute the most spillovers to BRIC markets, while the UK will leave the group. France, Canada, Germany, and the United States are the greatest shock broadcasters in the long term.
Unsurprisingly, different data will be collected across several clinical trials, not all these data are published, and how published data are reported will vary considerably. For example, there was wide variability in the presenting of Phase I and II lung cancer safety data across 209 American Society of Clinical Oncology (ASCO) presentations between 2017 and 2019. 20 TRAEs were included in publications twice as often as TEAEs, AEs were reported across dose levels or by dose level, and few presentations defined key safety terms such as dose-limiting toxicity or serious AEs. Of note, dose-reduction strategies and denominators for laboratory tests were not defined in any of the 209 presentations reviewed. 20 Whilst considering these challenges, this review focuses on the published safety data for the four MET TKIs in use for the treatment of patients with MET ex14 NSCLC.
MET TKI Safety Data in MET ex14 Skipping NSCLC We have focused our review on the TRAE safety data from prospective clinical trials of MET TKIs in MET ex14 skipping NSCLC. While the trials reviewed below used different cut-offs for presenting safety data (range, ≥ 5% to > 15%), we have for consistency opted to describe AEs (using MedDRA preferred terms) occurring in ≥ 10% of patients, where feasible. Data from case studies and other indications have been included where necessary to provide further background. In several instances, more safety data are described for crizotinib as this has been available as an NSCLC treatment option since 2012. Neutralizing MET or HGF antibodies, as well as type II MET TKIs, are not discussed in this review. Here, safety data are presented for tepotinib, capmatinib, savolitinib, and crizotinib from 442 patients with MET ex14 skipping across four Phase I or II clinical trials.
Tepotinib Based on data from the Phase II VISION study  11 , 21  ( Table  1  ; NCT02864992), an oral, ATP-competitive, highly selective type Ib MET inhibitor, tepotinib,  9 , 22  was first approved in 2020 in Japan for treatment of MET ex14 skipping NSCLC. 23 The ongoing VISION study is a large prospective study evaluating tepotinib in MET ex14 skipping NSCLC detected by liquid and/or tissue biopsy. 21 Patients received 500 mg tepotinib (450 mg active moiety) once daily in 21-day cycles; dose reductions to 250 mg once daily were permitted. At the primary analysis, median duration of treatment was 6. 9 months (range: < 0. 1-36. 7), and outcomes in 99 patients from the efficacy population were reported. In these patients, median age was 74 years (range: 41-94), 55% were male, 46% had a history of smoking, 78% had an Eastern Cooperative Oncology Group performance status (ECOG PS) score of 1, 90% had adenocarcinoma and 11% had brain metastases at baseline.
Other TRAEs with an incidence ≥ 10% were nausea, increased blood creatinine, vomiting, decreased appetite, fatigue, and increased ALT ( Table  2  ). Serious TRAEs occurred in 15% of patients (grade ≥ 3 in 13%), 12% of patients discontinued treatment due to TRAEs (grade ≥ 3 in 8%), and one patient died of pneumonitis, which was suspected to be treatment-related. TRAEs leading to dose reductions were not reported for MET ex14 skipping patients. Capmatinib is being evaluated further in pretreated patients with MET ex14 skipping NSCLC in the ongoing, randomized, openlabel, Phase III GeoMETry-III trial (NCT04427072) of capmatinib vs. docetaxel. 30 , 31 Primary completion is due July 2023, with final completion expected October 2024.
Savolitinib Savolitinib is an oral, ATP-competitive, selective type Ib inhibitor of MET, approved in China in 2021 for the treatment of MET ex14 skipping NSCLC. 9 , 14 Interim data from a Phase II study in 70 patients with MET ex14 skipping NSCLC have been reported ( Table  1  ; NCT02897479). 32 Patients received 600 mg or 400 mg savolitinib once daily in 21-day cycles; median duration of treatment was 6. 9 months (interquartile range, 2. 2-13. 8) or 6. 5 months (2. 7-10. 5), respectively; for 600 mg, 3 dose reductions were allowed (400 mg, then 300 mg, then 200 mg, all once daily) and for 400 mg, 2 dose reductions were allowed (300 mg then 200 mg, both once daily). For the 70 patients in the full analysis set, median age was 69 years (range: 65-75), 59% were male, 60% had never smoked, 83% had an ECOG PS of ≥ 1, 57% had adenocarcinoma, whilst 36% had pulmonary sarcomatoid carcinoma, and 21% had brain metastases at baseline.
Crizotinib Crizotinib is an oral, ATP-competitive, type Ia inhibitor of MET, ALK and c-ros oncogene 1 ( ROS1 ) approved for the treatment of ALK-positive NSCLC in 2011 and ROS1 -positive NSCLC in 2016. 9 , 33 , 34 The Phase I PROFILE 1001 trial has reported data from 69 patients (of 596 enrolled in total) with MET ex14 skipping NSCLC  35 , 36  ( Table  1  ; NCT00585195). Patients were treated with 250 mg crizotinib twice daily in 28-day cycles. A maximum of 2 dose reductions, to 200 mg twice daily and 250 mg once daily, was permitted. Median duration of treatment over which AE data were reported was 7. 4 months. Patients in the safety population, who had received at least one dose of crizotinib, had a median age of 72 years (range: 34-91), 58% were female, 62% had a history of smoking, 72% had an ECOG PS of ≥ 1, and 84% had adenocarcinoma.
Alexis Cortot et al vomiting, fatigue, and constipation ( Table  2  ). TRAEs leading to dose reduction occurred in 38% of patients, and discontinuations in 7%. One patient died of ILD, which was considered to be related to crizotinib. Patients with MET ex14 skipping were also included in the MET alteration arm of the Phase II METROS study (9/26 patients; NCT02499614). 37 For all patients with MET alterations, the most frequent TRAEs were respiratory symptoms (including dyspnea and cough), peripheral edema, nausea, fatigue, transaminase elevation, visual disorders, and pain. The safety profile for crizotinib in PROFILE 1001 was similar to Phase III data reported in patients with ALK-positive NSCLC. 38.