dataesr/scientific-paragraphs-categorization huggingface dataset:
The Matthews Correlation Coefficient (MCC) is a good choice to demonstrate any biases in the dataset and even in the presence of imbalance classes  [146]. MCC is equivalent to the Pearson Correlation Coefficient for binary variables  [147]  and has been selected as an evaluation metric for micro-array-based predictive models by the MicroArray Quality Control (MAQC) Consortium  [148]. From the gathered studies, only six used MCC as a performance metric  [77, 118, 121, 122, 124, 126]. Table  10  presents the studies that, in compliance with OECD principles, applied measures of robustness and predictivity validation, and estimated the applicability domain. It should be noted that choosing the right metric depends on data distribution and splitting, and a combination or aggregation of metrics should be preferred.
The Algorithms Within the reviewed studies, trees, neural network, and regression algorithms were abundant compared to rules, bayes, or meta algorithms. Trees algorithms were used in most of the cases with RF being the most favorable approach applied. Trees are simple to understand and interpret and can be used even with small datasets. They are unaffected by data shortcomings that result in small changes of the outcome and are associated with high dimensionality, correlated variables, and missing values  [66]. RF has been demonstrated to be ideal for rigorous meta-analysis of complex and heterogeneous data [64]. Helma et al. [36]  note in their study that, with the exclusion of p-chem/proteomics descriptors, the RF model performed better than PLS and weighted average models. They showed excellent predictivity with small or large datasets, which performed well even with missing values.
Furxhi et al. [72]  demonstrated that RF ranks first among individual classifiers and compete with meta-algorithms. RF is highly tolerant of overfitting, as it combines a number of simple models and has the ability to deal with special issues, such as descriptors counting higher than observations  [40]. RT has shown great results in the sense of parsimony, but are more susceptible to biases relative to RF. In addition, RF has the benefit of fully investigating parameter's values as opposed to RT, which usually includes a small subset of the data set. RF is also less prone to data vulnerabilities due to overrepresentations in datasets, which cause instances to appear influential. RF's randomized selection ensures analysis of all variables  [63]. DT easily handles feature interactions and they are non-parametric but some drawbacks is the non-support of ongoing learning.
On the other hand, although lacking a mechanistic interpretation, RF has often been used for combining robustness, resources efficiency, and simple parameterizations. A neural network structure is not easily readable. Their trained parameterizations are hard to comprehend and they can be very resource and memory intensive. Bayesian regularized networks create models that are reasonably insensitive to the number of hidden layer nodes, which makes architecture optimization effortless  [58]. A lot of research has been dedicated to ANN especially in pattern recognition, and the advances in the algorithms have been ported to nanotoxicity applications. Due to their potential high complexity, ANN can accommodate plenty of data and still achieve high accuracies with evident computational cost. However, while ANN can accommodate large datasets, small datasets, on the other hand, render ANN prone to overfitting.
Challenges and Perspectives When only small data sets are available, models that have few parameters (low complexity) and/or a prior strength should be used and, in this case, a 'prior' can be interpreted as any assumption on how the data behaves. In linear regression, for instance, the number of parameters can be easily adapted and the models assume only linear interactions. In simple terms, Bayesian models such as Naive Bayes deal with a few parameters and a direct way to adjust their prior. Neural networks were the only ML algorithm reviewed using more datasets exceeding 1000 cases than smaller ones (Figure  3 , right). Trees and regression models were used, as expected, to handle smaller datasets. It is worth noting that Bayesian networks, although not frequently preferred, have been used in all ranges of dataset sizes.
Given the present scarceness of nanotoxicity data, the use of effective modelling of small datasets is required  [29]. However, even the best algorithm trained with small datasets can be defeated by less sophisticated algorithms trained with more data  [151]. Datasets and/or databases integration can be a solution to data scarcity, which generates new hypotheses and knowledge  [152]. Karcher et al. [152]  highlighted the importance of data integration in nanotechnology and provided recommendations for advancing integration. Regarding the number of descriptors in the models reviewed (Figure  4  left), ~75% of the studies used less than 10 descriptors, which reflects computational limitations or a lack of data. In Furxhi I. et al. [18] , a thorough analysis of the data issue in computational nanotoxicology is provided, stretching from missing data to experimental protocols and concepts.
Targeting multiplicity and arbitrariness in model implementation, the EU funded Horizon 2020 project NanoSolveIT (https://nanosolveit. eu/ (Webpage accessed autumn 2019)) aims at delivering a validated, sustainable, multi-scale nano-informatics strategy, via OECD-style case studies for the assessment of potential adverse effects of NM on human health and the environment. The project includes the development of cost effective nano-informatics tools and models based on Artificial Intelligence for the prediction of crucial NMs functionalities and adverse effects from descriptors and physical characteristics of NMs. Nanoforms toxicity databases are available at a developmental stage and data obtained from research studies originate from different experimental procedures. Furthermore, the development of reliable data sets from a computational perspective requires that data be sufficient to allow splitting after assessing its accuracy and suitability specifically for computational use  [8].
Knowledge-based expert systems often refer to data-driven modeling. Those systems of expertise derive information from both literature and databases and are considered important tools for predicting toxicity. Considering the lacunas and variations in the accessible nanotoxicity data, knowledge-based expert systems can be a valuable approach for QSARs with a kind of "text data mining" capacity constantly capturing new knowledge that emerges in the literature and knowledge-transfer extracting knowledge from diverse fields  [14, 29].
Conclusions This review of the current state-of-the-art ML computational tools in nanotoxicology, addressing both human health and eco-toxicological endpoints, identified several models that provide prediction to numerous nanotoxicological outcomes. The main conclusions are: i a variety of ML algorithms have been used during the last decade with non-linear modelling gaining popularity; ii linear regression is still a popular method, enriched with nonlinear techniques; iii there is a clear shift from theoretical descriptors and traditional QSAR modelling to models incorporating nano-specific features, even though there is limited consensus on which features must be considered; iv there is great diversity in data pre-processing techniques depending on datasets and the ML algorithm chosen; v there is little technical convergence in pre-modelling stage methods compared to model implementation and validation; vi there is, in general, a lack of justification of model selection.
Method: We will perform a thorough search of peer-reviewed journal articles and grey literature sources documenting the occurrence of translocations in the context of protected areas. Two databases will be used: Web of science core collection and Scopus, with a supplementary search in Google Scholar. Multiple key specialized websites will also be used. All bibliographic data will be extracted, managed, and screened in Microsoft excel. Three screening stages will be undertaken (title, then abstract, then full texts) against predefined inclusion criteria. The retained relevant literature will be subjected to coding and meta-data extraction. No formal validity appraisal will be undertaken. The Map will particularly highlight translocation operations in terms of origin and destination (i. e. translocating from one protected area to another, within the same area, and from and to non-protected areas) by taxonomic group, among other important factors (e.
Search strings Firstly, a scoping exercise was conducted in the Web of Science Core Collection database to explore the efficiency of chosen words and the number of articles returned. In accordance with our main objective, we combined all search terms relating to protected areas and wildlife translocations. Concerning protected areas, the chosen key words represent synonyms of the different types of reserves and management categories that exist. Thus, the search string that produced the highest efficiency is presented below (see Additional file 2 for test list details and Additional file 3 for information of the building process of the search string). to primarily avoid extinction of populations of the focal species  [12]  This occurs if the persistence of a species in its indigenous range is threatened from current or future impacts than at alternative sites  [12]  (ii) Ecological replacement: This refers to the intentional manual transfer/movement and release of an organism outside its indigenous range/ historical distribution, to perform a specific ecological function  [12]  This is used to re-establish an ecological function lost through extinction; involving the most suitable existing sub-species, or a close relative of the extinct species  [12]  Reintroduction This refers to the intentional manual transfer/movement and release of an organism inside its indigenous range/historical distribution but from which it has disappeared or become extinct locally, regionally, or otherwise (No conspecifics are present in situ)  [12]  The conservation aim is to re-establish a viable population of the focal species within its historical range  [12]  Supplementation This refers to the intentional manual transfer/movement and release of an organism into the existing distribution of a population of conspecifics  [12]  The aim is to enhance and reinforce population viability e.
Publication databases to be searched All published material will be collected from the following databases (and managed in excel). • Web of Science (WOS) core collection. The entire database i. e. all citation indexes will be searched by Topic i. e. using the "TS" field tag, which searches for key words in the title, abstract and key-words of published documents (see Additional file 4 for Web of Science subscription details). • Scopus. We will equally search for all published documents. We will use the field tag "TITLE-ABS-KEY", which operates in the same way as the "TS" tag in WOS. These databases were chosen as they provide comprehensive citation data for numerous different academic disciplines. The English search string detailed above will be used for both literature sources. The search string will be adapted as necessary to account for the differences in the use of field tags and Boolean characters [an additional file provides details on number of search hits and dates of searches (see Additional file 5)].
Internet searches to be conducted A supplementary retrieval of publications will be undertaken using web-based search engines. • Google Scholar (https ://schol ar. googl e. com/). We used the same key words in the software programme Publish or perish (version 6) to retrieve all academic citations. The software's use of Boolean characters differs from WOS and Scopus. As a result, the search string was broken down into eight separate searches, in order to achieve a similar comprehensiveness, as only a single term can be included in the field "all of the words". Consequently, each sub-search was limited to the first 200 search hits, in line with recommendations  [40]. (Refer to Additional file 5. ) • A retrieval of theses will also be done using UK Theses and Dissertations (https ://ethos. bl.
Supplementary searches A call for literature will be made through the professional networks of Les Réserves Naturelles de France (RNF) and EuroParc. An advert will be published in the monthly newsletter of RNF. Euro-Parc, who act as a federation of protected areas at the European continental scale will also solicit their network. Since translocations programs are often carried out without being published in the form of scientific articles this will provide further opportunity to gather additional grey literature such as PhD and MSc theses, various technical reports, and other documentation. AirTable, which works like a database will be the specific software used to acquire the documents sent via the stakeholders' contacts.
Data coding strategy A thorough meta-data extraction for the Map will be performed by the same two members of the mapping team. Each selected article will be double coded. If, due to resource limitations, true double coding is not possible, a posteriori cross-check will be carried out and potential disagreements will be discussed until a consensus is reached. Concerning missing data, if data is not sufficiently detailed or simply unknown, it will be coded as such. The following meta-data will be extracted from all articles retained after completion of the screening process [an additional file is provided with full explanations (see Additional file 6)]:.
Study characteristics • Study country. • Capture and release site locality coded as two separate fields (name and geographic coordinates will be recorded if given). • Capture and release site climate types coded as two separate fields (under the Köppen-Geiger Climate Classification). • IUCN protected area management categories coded for each protected area. (This will be achieved by accessing the IUCN PAs database via http://www. prote ctedp lanet. net, and then matching each PA with the PAs in the database based on NAME). • Protected area (this will be coded in order to decipher if individuals are translocated from-to different protected areas, to, from, or within same protected area). i. From-to: transfer from one protected area (PA) to another. ii. To: transfer from a non-protected habitat i.
Study mapping and presentation A systematic map database will be provided, detailing all included articles from the full text screening stage. The systematic map will include all the metadata coded for each article. For the cases where more than one study is reported in the same article, each study will be recorded as a unique entry in the excel database with its corresponding geographical coordinates, if given, and a unique study ID. This database will be available as an open access excel spreadsheet and included as an appendix to the systematic map publication. The map database will be described in the map publication with summary figures and tables of the relevant study characteristics. A geographic map will present the location of each translocation event/study. Possible knowledge gaps (under-represented subtopics that warrant further primary research) and knowledge clusters (well-represented subtopics for full synthesis by a systematic review) will be identified by cross-tabulating key meta-data variables (e.
RESULTS The C-Terminal Tail of uS19 Stabilizes the P-and A-Site tRNAs and Decoding Interactions in the Classical-PRE State Ribosome We isolated translating ribosomes from HEK293 cells in the presence of cycloheximide for single-particle cryo-EM analysis. The 2D and 3D classifications of the cryo-EM datasets collected on this sample revealed the presence of ribosomes predominantly in three conformations: the classical-PRE state, the hybrid-PRE state, and the POST state (Figures  1A-1D , S1, and S2; Table  S1 ). The two structures of human ribosomes in the hybrid-PRE and the POST states found in this study are similar to the conformational states of the previously reported porcine and human ribosomes  (Behrmann et al. , 2015; Khatter et al. , 2015; Natchiar et al. , 2017; Voorhees et al. , 2014).
Image acquisition and data processing Cryo-EM data were acquired on a C s -corrected (CEOS GmbH, Heidelberg, Germany) FEI Titan Krios microscope (Thermo Fischer Scientific) operated at 300 kV equipped with a Gatan-K2 Summit direct detector (operated in counting mode) (Gatan Inc. , USA) and Quantum-LS Gatan Image Filter (GIF, slit width of 20 eV). A total of 8942 micrographs recorded as movies (40 frames/micrograph) were acquired in three different datasets at a calibrated magnification of 58140x (corresponding to 0. 86 A ˚/pixel) using the previously published atomic model of the human ribosome (PDB: 6QZP; Natchiar et al. , 2017) with a defocus range of 0. 5 to 2. 5 mm. For the first dataset, 2536 micrographs were recorded with a dose of 35. 7 e -/A ˚2. For the second dataset, 941 micrographs were recorded with a dose of 40.
4 e -/A ˚2. For the third dataset, 5465 images were recorded with a dose of 32. 4 e -/A ˚2. CryoFLARE (www. cryoflare. org, developed in house) was used for automation of initial micrograph processing. CryoFLARE automates initial processing by combining MotionCor2, Gctf and Gautomatch for drift-correction and dose-weighting, CTF determination and particle picking (550 3 550 box size), respectively  (Zhang, 2016; Zheng et al. , 2017). All further processing was carried out in RELION-2. 1  (Scheres, 2012). Initial 2D classification was performed for each dataset individually. Subsequently, the good particles were merged and used for initial 3D refinement. After the 3D initial refinement, multiple rounds of 3D classification were performed including an initial unsupervised classification followed by masked 3D classifications and refinement (von Loeffelholz et al.
, 2017) (Figure  S1 ). For the masked classifications, the map segmentation was done in UCSF Chimera for obtaining the density for the region of choice followed by generation of the binary mask in  RELION-2. 1 (von Loeffelholz et al. , 2017; Pettersen et al. , 2004; Scheres, 2012). Subsequently, particles belonging specific conformation of ribosomes were refined. The resolution estimates of the maps are based on the Fourier Shell Correlation (FSC) curve at the 0. 143 criterion, calculated using two half datasets. The final maps were corrected for the modulation transfer function (MFT) of the K2 camera and sharpened by a B-factor automatically determined in RELION-2. 1  (Scheres, 2012). The map was subsequently low-pass filtered with the resolution cut-off corresponding to the FSC value of 0.
Refinement and model building The previously published structure of the human ribosome (PDB: 5LKS)  (Myasnikov et al. , 2016)  and (PDB: 6QZP)  (Natchiar et al. , 2017)  was chosen as the initial atomic model for the refinement. Since we isolated translating ribosomes, it is likely that the observed densities for the mRNA and tRNAs are averaged over multiple different species. For modeling purposes, we used the tRNA and mRNA models from the PDB 5AJ0  (Behrmann et al. , 2015)  and 3J7R  (Voorhees et al. , 2014). This initial model was docked into the map using UCSF Chimera and was subjected to initial round of rigid body refinement, and real-space refinement with global minimization, local grid search, simulated annealing and ADP (B-factor) refinement using real-space refine in PHENIX suite  (Adams et al.
ACKNOWLEDGMENTS This work was supported by the  Novartis Research Foundation  (J. A. C. ), the  Swiss National Science Foundation  grant  31003A_182314  (J. A. C. ), the  SNF-NCCR RNA & Disease  (J. A. C. ), the  Synapsis Foundation   2017 CDA 01  (V. B. ),  CNRS ,  Association pour la Recherche sur le Cancer (ARC) ,  Institut National du Cancer (INCa) ,  Ligue Nationale Contre le Cancer (Ligue) ,  Agence National pour la Recherche (ANR) , the  Fondation pour la Recherche Medicale (FRM) , the  French Infrastructure for Integrated Structural Biology (FRISBI ;  ANR-10-INSB-05-01 ), and Instruct-ERIC. We thank  C. Genoud  for EM support,  N. Thoma  ¨and his group for helpful discussions,  Dr. Lopez-Otin  and  Dr. Victor Quesada  for providing uS19 mutant cell lines, and  S. Chakrabarti  and  J.
She also sent a written "instruction for experimenters"to all collaborators to make sure that they closely followed the original procedure. The instruction document and the eprime code can be found here: https://osf. io/yqqfz/files/. All labs used masked experimenters (with one short-term exception; see below) and sent the raw data for analyses to the main author. All incoming data have been subsequently transferred into a sequential Bayesian analysis. The final BF score will be reported as evidence for or against the alternative hypothesis. In addition, post-hoc analyses of the variations in the evidence for the effect across labs and of the overall effect across time will be provided for exploratory reasons.
Software and computer The study was conducted using different computers and screens in different labs, all of which were equipped with Windows-run computers. For trial randomization, a quantum-based random number generator (QRNG) from Id Quantique was attached to the computer (see www. idquantique. com). This hardware device has passed both DIEHARD and NIST tests of randomness and is one of the most powerful means of generating true random numbers based on quantum superpositions  [33]  (see also various certificates from national agencies on their homepage). E-Prime 2. 0 or jsPsych 5. 0. 3 software was used for response registration and picture presentation. Some labs used keyboards and other response boxes (Black Box ToolKit USB response pad, Cedrus RB-740) for response registration. When keyboards were used, the left and right cursor keys served as response registration devices.
Thus, in any given trial either a left or a right key-press was registered, even though participants subjectively performed twokey simultaneous responses. After each response registration, a randomization procedure took place. The QRNG that was connected to each computer randomly created a bit (0/1) during each trial immediately after response registration. Since this QRNG does not operate with a buffer, it was ensured that this actual bit was always created after the key-press. Prior to data collection, an additional randomization was prepared, in which each participant number was linked to a list of 60 bits-one for each trial-using a QRNG. The combination of the prestored bit for each trial and the bit actually created after the response registration then defined whether a negative or neutral masked picture appeared afterwards.
Main analysis Since a sequential Bayesian testing approach was applied in this study, the final sample size was not predefined. Instead, an accumulative data collection and analysis strategy using Bayesian inference techniques for hypotheses testing was used, as suggested by  [8]. All 60 trials per participant were handled following the exact same protocol as the original study. That is, a mean score for each participant was computed and subsequentely subjected to the Bayesian analysis. This approach allows for data accumulation (i. e. , additional respondents can be tested and results added into the dataset) until a specified Bayes factor (BF) for H 1 (or H 0 ) has been reached. It also provides the option of ceasing data collection at a predetermined BF. We defined a BF of 10 as the stopping point for evidence for both H 0 and H 1.