dataesr/scientific-paragraphs-categorization huggingface dataset:
Example of a complete analysis of the blind prediction of the RNA-Puzzle Puzzle 19. The functionality implemented in rna-tools can be accessed via command-line, imported in Python scripts or in Jupyter Notebooks (Figure  3D ). One such notebook is released together with rnatools and illustrates the steps performed by the Bujnicki group to collect information about the RNA-Puzzles Puzzle 19, the Twister Sister ribozyme (62) (https://github. com/ mmagnus/rna-tools/blob/master/rp19. ipynb). The analysis started with the secondary structure prediction using multiple wrappers implemented in rna-tools followed by the Rfam search for an RNA family that the sequence belongs to. At the time of this analysis, no RNA family for the sequence of the puzzle was presented in the Rfam database. A useful piece of information was provided by a successful hit in the PDB database, to the structure in the PDB database, Xrn1-resistant RNA from the 3 untranslated region of a flavivirus (PDB: 4PQV) (63).
Unbiased assessment of a prediction is the key to understand its performance and usability. It is beneficial to have a standard dataset, which can be used to benchmark the performance of a new method against all other prediction approaches. The RNA-Puzzles toolkit directly provides such a benchmark and has been used to demonstrate the accuracy of a novel prediction  (46). Although it is possible to run RNA structure prediction programs on other public datasets, such as Rfam and non-redundant dataset  (17) , RNA-Puzzles prediction stands for the best state-of-the-art blind prediction performance and includes structural diversity. In addition, selecting the top-quality model from a set of models generated by different prediction methods is another important step for an accurate prediction. Our benchmark set has also proved its usability in developing such a scoring model  (20).
Our datasets can be used as a standard test allowing for methods development and comparison. Moreover, we provide a unified kit of tools used already by our groups in previous research projects. RNA format, RNA normalizer and RNA assessment were used before to support all calculations in the RNA-Puzzle experiment. The rna-tools package was used in various scientific projects, to calculate stability of various U6 RNAs of the spliceosome (67), to process input files for SimRNAweb (RNA 3D structure prediction method) (58) and NPDock (RNA/DNAprotein docking method) (68), and to analyze data for RNArchitecture database (a classification system of RNA families with a focus on structural information) (69) and EvoClustRNA (RNA 3D structure prediction using multiple sequence alignment information)  (61).
MCQ-based methods were used i. a. to evaluate models in the second  (7)  and third (6) round of RNA-Puzzles, to identify structural patterns in plant pre-miRNAs  (70) , to build a database of conformers within the RNAfitme system  (71, 72). For the first time, we describe these tools and show how they can be integrated into one robust pipeline giving the users a way to provide a broad perspective on an RNA structure. The installation of computational tools is non-trivial and can sometimes cost much time even for computational experts. A user-friendly implementation will greatly help the use of a computational tool. Considering that users may have diverse preferences, our resource tools provide both command-line executives and Jupyter Notebook (73) based tutorials, while all the tools are documented.
Furthermore, we installed all the tools on a Docker image that can be easily downloaded and launched by the user, in particular, a biologist without programming skills. The Docker image saves the complicated actions required for installing all the tools. Finally, we release all of our datasets and computational tools at GitHub, which can be continuously updated if any bugs are detected. The 'fork' function of Github also facilitates novel computational methods or datasets being developed based on our resource, i. e. RNA-ligand interaction prediction. The Jupyter Notebook  (74)  workflow in the resource provides a standard example for RNA structure prediction evaluation. Jupyter Notebook is an open-source web application that allows users to create and share documents that contain live code, equations, visualizations, and explanatory text.
The tools implemented in the toolkit can be imported to such notebooks to create reproducible analyses that can be uploaded online and shared with the RNA structural bioinformatics community. One example of such analysis was described in the Result section for rna-tools. This approach of describing RNA bioinformatic analyses should help scientists to share their pipelines, e. g. , protocols used for modeling in RNA-Puzzles, that can be later reproduced and/or improved by others. And since the Jupyter Notebook has support for over 40 programming languages, including those popular in Data Science such as Python, R, Julia and Scala, this is a great approach to incorporate the toolkit into pipelines written in other languages. In this way, all the RNA structure analysis work can be efficiently shared and reproduced.
In addition, RNAQUA provides all the RNA structure comparison tools as a web service, which can alleviate the burden of software installation for noncomputationally oriented users. RNA structure comparison metrics have been developed since a decade ago  (24). The availability of these metrics as computational tools is limited and not systematic, which highlights the importance of our toolkit. We also share every detail in a standard workflow accepted by the RNA-Puzzles community, i. e. , when multiple structures have been solved for the same sequence, it is fair to consider all of them as native structures and use the nearest one to the prediction as the reference. Secondary structure analysis and visualization are useful aspects in understanding RNA 3D structure: rna-tools implements the easy transformation from 3D structure visualization in PyMOL  (75)  to 2D structure contacts annotation, thus enabling the intuitive comprehension from the biophysics aspects.
Our resource brings various tools and datasets into one unified resource that can be easily downloaded and used by biologists interested in RNA 3D structure prediction and analysis. We think that the toolkit with its open code should be considered as a library of functions and tools rather than a complete package with a fixed set of functionalities. The toolkit is a framework of various functions. The users are invited to extend it with their scripts on the top of the existing tools. In this way, it is possible to adapt our tools for future cases. For example, to have a particular wrapper or variant of tools that can be used for a very specific application saving time and brainpower of the user to write the code from scratch. We believe that the RNA-Puzzle Toolkit will prompt new advances in the applications of the RNA 3D structure prediction and in method development.
Figure1. Scheme of the RNA-Puzzles toolkit. The toolkit is composed of three parts: tools for validation and standardization of PDB structure files, tools for analyzing the models, and the dataset of standardized submissions to the RNA-Puzzle. The user can start an analysis with single or multiple models. The first step is to standardize the formatting of analyzed structural models. Then, the user can run an analysis for a single model, such as Clash Score evaluation or base pair identification using various methods; or, for multiple models, various comparison methods are implemented. The tools can be accessed via command-line or Jupyter. The toolkit can be also executed as a Docker image that can be easily used.
ACKNOWLEDGEMENTS Authors contributions: M. M. developed rna-tools, combined the workflow, created the standardized dataset and wrote parts of the manuscript. M. A. developed and tested RNAQUA, and wrote parts of the manuscript, P. L. supervised the development of RNAQUA, T. Z. implemented MCQ algorithm, J. W. and T. Z. implemented LCS-TA. Y. C. provided technical support to web development. J. M. B. supervised the development of rna-tools. M. S. developed a concept for MCQ and LCS-TA algorithms, supervised their implementation and wrote parts of the manuscript. E. W. supervised the entire project and drafted the manuscript. Z. M. conceived the project, cleaned up the datasets, implemented the RNA structure format tool, format check tool and evaluation metrics (INF, DP), designed the website and wrote parts of the manuscript.
A simple model of heat and mass transfer between a bed of wood chips and building inside air has been set up and implemented into a validated building simulation code named ISOLAB. Numerical simulations were set up for the four climate zones of the island regulations and a focus has been made on the low altitude one (with high, solar irradiation, temperature and relative humidity). Simulation results give the thermal behavior of the building particularly the temperature and relative humidity of inside air temperature, and temperature and moisture content of wood chips. The obtained results lead to determine if the wood chips bed is suitable for the reference building and to verify its technical feasibility (wood species, size of the bed, integration to the building, etc. ). The results show that the use of a WCB help to decrease the building inside air temperature and water content up to 10°C less and 11.
Introduction Other works have shown that wood chips can help to reduce AC loads, for example in office building in Asia as an insulation of the roof  [1] [2] [3] [4] [5]. The work presented in this paper is also conducted as a proof of concept study. The main objective is to verify the possibility of using a wood chips bed (WCB) to regulate relative humidity inside the building, as it can be done with desiccant wheels  [6] [7] [8] [9] [10]. Indeed, such systems can help reducing the building energy consumption by reducing the building latent loads. To do so, the desiccant systems (desiccant wheels or others) modify the air injected in the building through the air conditioning system. The air relative humidity is modified when the air passes through the desiccant system. This proof of concept study was done by using a validated building simulation code (named ISOLAB  [11] ) in which a simple model of heat and mass transfers between wood chips and the building has been implemented.
Model Description The building simulation code ISOLAB can simulate the thermal behavior of a building according to its environmental conditions and its use. It considers the dimensions of the building, the thermal properties of the wall, its occupation and the air-conditioning system when it occurs, etc. Numerical simulation results are the building temperature field, the relative humidity and water content ones, the one of heat flux transfer through walls and the ones of particular systems (photovoltaic panels, heat production system, etc. ). It has been continually improved since its creation in 2002 with works on thermal modeling of different types of complex walls: photovoltaic panels  [14] , phase change materials  [15]  or vegetalized walls  [16]. This time, the building simulation code has been improved by implementing a simple model of heat and mass transfer in WCB and coupling it to the one of the building.
Conclusions This paper has presented a numerical study of a building coupled with a wood chips bed as the building ventilation system. The building and WCB has been presented and their modelling have been proposed. Numerical simulations have been run with the building simulation code ISOLAB and for 4 different climates of La Réunion Island and a focus have been made on the zone with the highest temperature and humidity (corresponding to low altitudes in La Réunion island). Results have shown that the WCB is an interesting way to reduce inside air temperature and water content depending on climate zoning, respectively from 7 to 10°C and from 11. 6 g. kg -1 to 10. 6 g. kg -. Numerical results of WCB building have also shown an impact on inside air relative humidity compared to a one without WCB (from 23 to 45%).
The purpose of the present work is to propose such a specification mechanism for high-level properties, which we call meta-properties, and to allow their automatic verification on C code in Frama-C thanks to a new plugin called MetAcsl. Motivation This work was motivated by several previous projects. During the verification of a hypervisor, we observed the need for a mechanism of specification and automatic verification of high-level properties, in particular, for global properties related to isolation and memory separation. Isolation properties are known as key properties in many verification projects, in particular, for hypervisors and micro-kernels  [3]. A similar need for specific high-level properties recently arose from a case study on a confidentiality-oriented page management system submitted by an industrial partner.
1 ontributions The contributions of this paper 2  include: a new form of high-level properties, which we call meta-properties, and an extension of the ACSL language able to express them (Sect. 2), a set of code transformations to translate meta-properties into native ACSL annotations that can be proved via the usual methods (Sect. 3), a Frama-C plugin MetAcsl able to parse C code annotated with metaproperties and to perform the aforementioned code transformations (Sect. 4), a case study: a confidentiality-oriented page system, where important security guarantees were expressed using meta-properties and automatically verified thanks to the code transformation with MetAcsl (Sect. 4).
Let F denote the set of functions defined in the current program, and P the set of native ACSL properties. Formally, we can define a meta-property as a triple (c, F, P ), where c is a context (see Sect. 2. 2), F ⊆ F and P ∈ P. Intuitively, we can interpret this triple as "∀f ∈ F , P holds for f in the context c". For the meta-property to be well-formed, P must be a property over a subset of G ∪ M(c), where G is the set of variables available in the global scope of the program and M(c) is the set of meta-variables provided by the context c. The actual MetAcsl syntax for defining a meta-property (c, F, P ) is meta [specification of F] c, P; An example is given by property M 1 (cf. lines 10-12 in Figure  1 ), where F = F, c = strong_invariant and P is the predicate stating that the status of any page should be either FREE or ALLOCATED.
Indeed, there exists a page (the one being modified) that has a status set to ALLOCATED because of the previous instruction (line 7) and for which the \separated clause is obviously false. Hence, the assertion fails, meaning that the whole meta-property M 2 cannot be proved. The fix consists in swapping lines 6 and 7 in Figure  2. After that, all assertions generated from M 2 are proved. A similar transformation for M 3 on page_read shows that the proof fails since the implementation allows an agent to read from any page without any check. Adding proper guards allows the meta-property to be proved. Conversely, if a meta-property is broken by an erroneous code update, a proof failure after automatically re-running MetAcsl helps to easily detect it. This verification technique is related to  [4]  as we generate annotations whose verification implies the validity of a high-level property.
Initial experiments confirm the interest of the proposed solution. Future Work We plan to establish a formal soundness proof for our transformation technique, thereby allowing MetAcsl to be reliably used for critical code verification. Moreover, we wish to refine the transformation technique in order to allow the proof of the generated specification to scale better when the number of meta-properties or the size of the code increases. Other future work directions include further experiments to evaluate the proposed approach on real-life software and for more complex properties.
To address the problem of resistance to paclitaxel treatment, we have investigated to which extent is possible to predict Breast Cancer (BC) patient response to this drug. We carried out a large-scale tumor-based prediction analysis using data from the US National Cancer Institute's Genomic Data Commons. These data sets comprise the responses of BC patients to paclitaxel along with six molecular profiles of their tumors. We assessed 10 Machine Learning (ML) algorithms on each of these profiles and evaluated the resulting 60 classifiers on the same BC patients. DNA methylation and miRNA profiles were the most informative overall. In combination with these two profiles, ML algorithms selecting the smallest subset of molecular features generated the most predictive classifiers: a complexity-optimized XGBoost classifier based on CpG island methylation extracted a subset of molecular factors relevant to predict paclitaxel response (AUC = 0.
, 2017). Machine Learning (ML) can be used to build in silico models able to predict tumor response to a given drug by combining multiple tumor features in an optimal manner  (Libbrecht and Noble, 2015; Ali and Aittokallio, 2018). The scarcity of suitable clinical data to build such predictors has been a major roadblock, which has made predictors based on cancer cell line data thrive  (Costello et al. , 2014; Ding et al. , 2016). Fortunately, response data from paclitaxel-treated BC patients along with comprehensive molecular profiles of their tumors are increasingly available. Such datasets represent an opportunity to improve our ability to anticipate which BC patients will respond to paclitaxel. We obtained them from the recently created Genomic Data Commons (GDC) of the US National Cancer Institute (NCI)  (Jensen et al.
, 2017). The GDC provides a unified data repository enabling data sharing across cancer genomic studies in support of precision medicine. The GDC feeds from several cancer genome programs at the NCI Center for Cancer Genomics, notably The Cancer Genome Atlas (TCGA)  (Weinstein et al. , 2013) , and offers a range of information-rich genomic, transcriptomic and epigenomic profiles, as well as clinical drug response data. These datasets, however, pose the challenge of being highdimensional. Each profile typically contains between hundreds and many thousands of features, but only tens of profiled tumors of the same cancer type and treated with the same drug. For example, a community challenge intended to predict drug response employed 53 BC cell lines  (Costello et al. , 2014) , while thousands of features from DNA copy-number variation, transcript expression, mutations, DNA methylation, and protein abundance profiles were considered.
MATERIAl AND METhODs gDC Data GDC molecular profiling and clinical data from the TCGA Breast Invasive Carcinoma or BRCA (https://portal. gdc. cancer. gov/projects/TCGA-BRCA) provide the basis for this study. Molecular profiles and clinical data come from release version 4. 0, except for miRNA and miRNA isoform (isomiR) expressions coming from release version 8. 0 (Release Notes -GDC Docs). TCGA-BRCA project gathers data from 1,098 patients, resulting in almost 13,000 files (around 130 GB). These datasets were retrieved and downloaded using the GDC Application Programming Interface (API). Table  S1  reports information about files collected from the GDC that have been used to generate datasets. very close treatment start and end time. These entries may form part of a drug combination. However, available drug response annotations do not allow to check this information.
Processing Molecular Profiles for Modelling The GDC works on harmonization of raw genomic data developing specific workflows to provide consistent and up-todate molecular profiles (GDC Reference Files | NCI Genomic Data Commons, Genomic Data Harmonization | NCI Genomic Data Commons). Available profiles comprise copy-number variation (CNV), DNA methylation, mRNA, miRNA and isomiR  (Ameres and Zamore, 2013)  expressions. In order to produce suitable inputs for ML algorithms and/or extract some specific information, we processed some of them. More details are available in the homonym section of Supplementary Methods. All the datasets produced from these molecular profiles are made of real-valued features.
Predicting Drug Response Using Ml Algorithms With Embedded Feature selection Most classifiers were built with ML algorithms capable of embedded feature selection to mitigate the impact of highdimensional data on their generalization on unseen data. Implementations of Classification And Regression Tree (CART)  (Breiman et al. , 1984)  and Random Forest (RF)  (Breiman, 2001)  were taken from the python library Scikit-learn version 0. 19. 1, while the ones of XGBoost (XGB)  (Chen and Guestrin, 2016)  version 0. 6 and LightGBM (LGBM)  (Ke et al. , 2017)  version 2. 0. 10 were respectively downloaded from https://github. com/ dmlc/xgboost and https://github. com/Microsoft/LightGBM. We also applied a Deep Neural Network (DNN) algorithm  (Bengio, 2009)  implemented with the python library Keras version 2.
2. 4 using the TensorFlow backend. In addition to these nonlinear models, linear models were generated with Logistic Regression (LR)  (Ranstam et al. , 2016) , which is also implemented in Scikit-learn. The visualization of Decision Trees (DTs) was done with the python package dtreeviz version 0. 2. 2. The homonym section in Supplementary Methods provides more details.
This leads to some OMC models that perform better than those considering all features from dataset (Table  S12  in Supplementary Results). This is especially the case for some methylation-based models that have been improved using OMC (Figure  1 ), unlike for models based on other profiles (Figure  S5 ). The improvement of OMC over the all-features approach is ML algorithm-dependent. CGI methylation-based OMC models have obtained improved predictive performance, using either RF or XGB. For instance, XGB-OMC models obtain a median MCC of 0. 25, which is significantly better than both permutation and all-features models (p-values equal 9. 30 ×•10 -4 and 2. 16 ×•10 -2 , respectively). In order to extract a robust subset of molecular factors potentially involved in paclitaxel response, the most informative features selected by these models were investigated (Table  S13  and S14   S7  and S8  (more details in Supplementary Methods), we found that these coordinates are related to the following 16 genes: CYP2D6, NDUFA6-AS1, RP4-669P10.
Horizontal axes show the employed ML algorithms to process CpG site (left) and CGI (right) methylation datasets, while vertical axes display MCCs achieved by classifiers. Light-pink, medium-pink, and indigo boxplots summarize the distributions of MCCs obtained by all-features, OMC and permutations models, respectively. Circles indicate ML algorithms releasing models with predictive performance improved using OMC. This shows that predictive accuracy depends on both the molecular profile and the ML algorithm. Here, the best models found are CpG site methylation-based RF-OMC, CGI methylation-based RF-OMC, and CGI methylation-based XGB-OMC. (C) Predictive performance of CART models. These models (light-pink boxes) were built considering all features in the profile with no hyperparameter tuning.