dataesr/scientific-paragraphs-categorization huggingface dataset:
Conversely, the absence of an intermediary stage indicates that both gains occurred within the same time window. Mutational signature analysis. The mutational signature analysis was performed for SBS following three steps as recently described 18 : (1) mutational signature de novo extraction; (2) assignment, and (3) fitting. In the first step, we ran both the hierarchical Dirichlet process (hdp: https://github. com/nicolaroberts/hdp) and SigProfiler 9 workflow to extract the involved known and potentially unknown SBS mutational signatures. hdp was run with four independent posterior sampling chains followed by 20,000 burn-in iterations, and the collection of 200 posterior samples off each chain with 200 iterations between each. SigProfiler was run with 1000 total iteration. To further validate the definition of which mtational signatures are involved in MM and the robustness of the extracted mutational process, we analyzed the five-nucleotide context using both approaches.
In this way, we reduced the bleeding of mutational signatures between samples and quantified the contribution of each mutational process using the most recent reference  9. Importantly, because each sample is analyzed individually, the results will be reproducible irrespective of other samples included in the cohort. CIs were generated by drawing 1000 mutational profiles from the multinomial distribution, each time repeating the mutational signature fitting procedure, and finally taking the 2. 5th and 97. 5th percentile for each mutational signature. Mutational signature transcriptional strand bias analysis was performed by applying the Poisson test function in R, incorporated into mmsig. The source code of mmsig is available on GitHub: https:// github. com/evenrus/mmsig. All three steps for mutational signature analysis (de novo extraction, assignment, and fitting) were applied to recently published data from human pluripotent stem cell lines exposed to melphalan and to 15 controls  22.
In lymphomas, the correlation between age and SBS5 was completely lost among patients with >10,000 mutations  8. To divide CoMMpass data in high and low APOBEC, we used the 4th quartile of APOBEC mutational burden among patients with evidence of APOBEC activity (49 mutations)  26. Estimating patient age when landmark events occurred. To demonstrate the existence of a clock-like mutational process in MM, we explored the association between SBS5/SBS1 and patient's age by a linear regression model (lm R function) across a large cohort of newly patients with available whole-exome sequencing data (CoMMpass; NCT01454297) and across WGS data from B cell lymphoproliferative disorders (i. e. , CLL, MM, and B cell non-Hodgkin lymphoma). Next, following a recently published statistical workflow  4, 5, 38, 39  , we used an LME model (lmer R function) to estimate the mutation rate per year using CoMMpass samples collected at different time points for 72 patients.
Acknowledgements This work is supported by the  Memorial Sloan Kettering Cancer Center NCI   Core Grant  ( P30 CA008748 ), the  Instituto de Salud Carlos III  (project  PMP15/00007 , to F. N. , E. C. ), the  "la Caixa" Foundation  Grant No. HR17-00221  ( Health Research 2017 Program , to F. N. , E. C. ), the  Ministerio de Economía y Competitividad (MINECO)   SAF2013-45836-R  (to E. C. ) from  Plan Nacional de I+D+I, Generalitat de Catalunya Suport Grups de Recerca   AGAUR 2017-SGR-1142  (to E. C. ), the  European Regional Development Fund "Una manera de hacer Europa ,"  Department of Veterans Affairs Merit Review Award   I01BX001584-01  (to N. M. ), and  NIH  grants  P01-155258  (to H. A. -L. , P. J. C. , K. C. A. , N. M. ) and  5P50CA100707-13  (to N. M. , H. A. -L. , K. C. A. ). F. M. is supported by the  International Myeloma Society (IMS) ,  American Society of Hematology , the  International Myeloma Foundation , and  The Society of Memorial Sloan Kettering Cancer Center.
N. B. is funded by the  University of Milan  (project  22597 -PSR2017_DIP_032 ) and by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 817997 ). X. S. P. is supported by the  Ministerio de Economía y Competitividad  Grant No. SAF2017-87811-R. F. N. is supported by a pre-doctoral fellowship of the  MINECO  ( BES-2016-076372 ). E. C. is supported by  ICREA  under the  ICREA Academia programme. The work of R. S. in the framework of the ICGC MMML-Seq and derived mutational signatures has been funded by  BMBF  ( FKZ 01KU1002A ,  01KU1505  and  031A428H ).
Data availability Sequence files are available at the European Genome-phenome archive under the accession codes: EGAD00001003309 WGS data from 30 multiple myeloma patients; phs000348. v2. p1 WGS data from 22 multiple myeloma patients; EGAS00001001692: WGS data from 89 chronic lymphocytic leukemia and 107 B cell lymphomas; phs000748. v1. p1: WXS and bulk RNA sequencing data from 933 and 792 multiple myeloma patients, respectively (CoMMpass trial); EGAS00001003709 WXS data from 40 patients with relapsed/refractory multiple myeloma; GSE117156: single-cell RNA sequencing data from 29 newly diagnosed patients and 11 control donors.
Since distributed software systems are ubiquitous, their correct functioning is crucially important. Static verification is possible in principle, but requires high expertise and effort which is not feasible in many eco-systems. Runtime verification can serve as a lean alternative, where monitoring mechanisms are automatically generated from property specifications, to check compliance at runtime. This paper contributes a practical solution for powerful and flexible runtime verification of distributed, object-oriented applications, via a combination of the runtime verification tool LARVA and the active object framework PROACTIVE. Even if LARVA supports in itself only the generation of local, sequential monitors, we empower LARVA for distributed monitoring by connecting monitors with active objects, turning them into active, communicating monitors.
In this work we investigate the usage of LARVA to perform distributed monitoring of applications. LARVA generates a set of monitors for several entities of the observed system and we use PROACTIVE to coordinate the different monitors. PROACTIVE is an active object library that integrates well with LARVA because it has no specific syntax for parallelism and distribution: coordination between active objects is performed automatically by the middleware while the programmer only writes standard (sequential) Java code. This way standard LARVA monitors can be generated and PROACTIVE coordinates them in a natural manner. Further, while LARVA itself focuses on monitoring of control-oriented properties, we use the STARVOORS (STAtic and Runtime Verification of Object-ORiented Software) front-end for LARVA  [8] , to also support the monitoring of data-oriented (pre/post) properties.
Runtime verification with LARVA LARVA  [11]  is a runtime verification platform that allows to verify control-flow properties of Java programs, written in an automata-based specification language called DATE (Dynamic Automata with T imers and Events). LARVA generates runtime monitors as Java code out of the automata descriptions of the input properties, and links these monitors to the Java system by using ASPECTJ. We illustrate the use of LARVA with a small example, a banking system with access control, where users may login, logout, and withdraw money. Figure  1  sketches a Bank class (1a), and the DATE property (1b) that users must first login before being able to logout or withdraw, and logged-in users should not log in again. The DATE specification in Figure  1b  consists of three states: (i) the initial state out, describing the logged-out state of the program; (ii) the normal state in that models the logged-in program state; and (iii) the bad state bad that models the erroneous state as result of runtime violations.
Moreover, DATE transitions are labelled by a triple of the form e c a, where: e is an event that is connected to method invocations or executions in the program and triggers the transition; c is a condition that must hold for the transition to be taken; and a is an action, a code snippet that is executed when the transition is taken. In general, the condition-and action components of transitions may access or update global variables. In fact, since the LARVA compiler translates DATE specifications to Java, the c and a components may contain arbitrary Java snippets. The transitions in Figure  1b  use three different events, namely login, logout and withdraw, which correspond to invoking the corresponding methods in the implementation. The \result placeholder in the login transitions is bound to the return value of login, so the self-loop in out is taken if the login in the program appeared unsuccessful.
Any call to methods on b are actually translated to messages sent to this worker, but these details are neatly hidden by PROACTIVE. The API call at line 4 exposes the new active object b to the outside world, by assigning a network name to it, specified as the argument args[0] in the console. Note that, after executing line 4, the thread associated to the active object b keeps on running and is ready to process incoming method invocations. Another machine might run the Client program and connect to the active object hosted by the server (identified by the network name given as an input argument, args[0]). The client program will connect to the specified active object by the API-call in line 11 and line 12. Observe that the objects b returned in line 3 and line 11 by PROACTIVE are typed as Bank objects: one may use these as ordinary Java objects, even if the object b returned in line 11 is actually a stub-a proxy generated by PROACTIVE that translates all method invocations on b to network messages (actually to RMI communication) to the JVM that physically hosts the bank active object (here the JVM that runs the Server program).
In the remainder of this section, we discuss the different states and transitions used in Figure  4 , together with their underlying principles. Call-and execution triggers. The DATE specification format allows the distinction between method calls and method executions when specifying triggers. In Figure  4 , the superscript ↑ indicates that the transition should be taken upon calling the corresponding method in the program, whereas ↓ indicates an transition triggering upon starting the method execution. This distinction between call and execution events is supported by LARVA in the sequential setting, but becomes particularly useful when monitoring distributed objects. To give an example, when a client invokes a login method on a server active object, the client itself does not execute the method login.
Distributed monitor communication. Recall that LARVA monitors are translated to Java code (to be bound to the monitored implementation using ASPECTJ), and that the transition in the automata may contain arbitrary snippets of Java. We exploit this to realise monitor communication, as shown in Figure  5b , by instantiating active objects inside the LARVA monitors and have them connect to each other. Runtime monitors may call the public methods on the active objects of remote monitors and thereby influence their state. Moreover, since active objects are used as ordinary Java objects, the LARVA monitors may define triggers on the public methods of their active objects. By doing this, a transition can be taken when a remote monitor invokes a public method on a local active object, thus implementing monitor synchronisation.
Monitoring data-oriented aspects Distributed systems typically consist of nodes that perform local computations and distribute the computed results over the network, via protocols. Runtime verification of distributed systems should therefore cover both the computation and the distribution of data. Even though LARVA allows DATE specifications to use data to a limited extend, it is mostly focused on control-oriented properties. In order to extend our approach to the monitoring of data-oriented properties, we define ppDATE , an extension of DATE with Hoare triples (pp stands for pre/post-condition), as supported by the STAR-VOORS  [2, 8]  tool (STAtic and Runtime Verification of Object-ORiented Software). Hoare triples are of the standard form { P } m { Q }, with P and Q assertions expressed in first-order logic and m the header of a Java method.
distance(k) that calculates the Euclidean distance between the center of z and the specified key k. The variable localzone refers to the zone of the callee peer. This Hoare triple is checked every time the getZoneClosestTo method is called in the implementation. This method returns an object of type Zone-the zone of the neighbour closest to the destination peer. Availability. The source code of the ActiveCAN case study, together with verification instructions, are available online  3. Our experiments did not reveal runtime violation of properties (  1 ) and (2).
Conclusion This article shows the importance of two key challenges in monitoring of distributed applications: distributed monitoring by independent active monitors, and verification of properties mixing data-oriented and control-oriented aspects. From a technical point of view, this article provides two contributions addressing these two challenges. First it presents an effective distributed monitoring mechanism. This is realised thanks to active monitors that are generated by our framework. Our runtime verification environment combines monitor generation of the LARVA framework with an active object middleware, PROACTIVE. This way, the standard Java code generated by LARVA generates distributed active monitors at runtime to detect the violation of safety properties in a distributed monitoring fashion.
Acknowledgements. The authors would like to thank  Gordon Pace  and  Gerardo Schneider  for fruitful discussions in the course of this work, and  Mauricio Chimento  for implementing some adaptions in the STARVOORS tool. This work is partially supported by the  NWO   TOP 612. 001. 403project VerDi, and by the  COST Action IC1402 Runtime Verification beyond Monitoring.
To our knowledge, the only application of the LANCA ® model was proposed by Saad et al. ,  [16]   [17] , based on the LANCA ® 2010 version  [18]  integrating only three of the five impact categories. In these studies, the model was applied on seven very contrasted land uses to raise the importance of implementing regionalized characterization factors (CFs). The new updated LANCA ® version  [11]  integrates regionalized CFs, and proposes two approaches to apply LANCA ® : either the user has access to information of a studied system at a local scale and can apply the five different models within the foreground approach; or in the absence of available data, the user cannot run the models and uses the background characterization factors provided in the LANCA ® database defined at the country level within the background approach.
Case studies In order to study a contrasted range of agricultural land managements and pedo-climatic conditions, two case studies were selected in different tropical regions, one in Thailand and one in Brazil. The pedo-climatic context of each of the case study is presented in Table  1. The input data were directly extracted from the case studies without investigating any depth factor, as no requirement on the soil studied depth was specified in LANCA ®. In Thailand, we selected different land uses and in Brazil different management practices under the same land use, i. e. soybean cultivation. Those land managements were representative of the main cropping systems of the studied regions. In Thailand, three land uses were studied: an intensive cash crop (cassava -Manihot esculenta), a mature rubber tree plantation (Hevea brasiliensis), and a degraded secondary forest, the latter being taken as the reference system.
The evapotranspiration for the two sites were extracted from two regional studies. For the Brazilian case study, we took the regional mean annual evapotranspiration of Mato Grosso from Dias et al. [27]. As the study provided detailed evapotranspiration data for soybean and for cerrado, we used in this case differentiated values for the two land uses. For the case study in Thailand, we took the data from Chachoengsao region from Vudhivanich  [28]  for all the land uses, as no differentiated data were available. The C factor of the model was extracted from the table provided by McCuen  [29]  to fit with the rational method. This factor was compiled for the Precipitation-Evapotranspiration difference. In the 2016 LANCA ® version  [11] , the sealing factor was sometimes not clearly integrated in the calculation procedure.
Consistency and necessity of the five indicators The method is based on numerous empirical equations for the five impact categories. This multiparameter assessment of soil quality meets the expected demand to integrate soil complexity in the LCA framework with the use of accessible parameters rather than process-based modelling. It makes it possible to have a better understanding of the impacts on soil functioning. This consistent analysis of the soil system is conceptually relevant. However, the add-on of each category on the final assessment should be statistically tested. Indeed, some correlation analysis between CFs factors  [11]  proved to show a very strong correlation on the CFs provided by LANCA ® background data table at a global scale, using the 75 land use classes (Table  4 ). Globally, the background values for the infiltration reduction potential, the physicochemical filtration reduction potential and the groundwater regeneration reduction potential CFs values were strongly correlated, with a rpearson> 0.
ACKNOWLEDGEMENTS This study was financially supported by the  French Center of Agricultural Research for Development (CIRAD)  and the  French National Research Agency (ANR)  within the framework of the  Heve adapt project  ( ANR-14-CE03-0012 ) and the  French Environment & Energy Management Agency (ADEME)  within the scope of the  SOCLE  project  1360C0097. The authors warmly thank the three anonymous reviewers for their contributions to the paper improvement.
Background RNAseq is nowadays the method of choice for transcriptome analysis. In the last decades, a high number of statistical methods, and associated bioinformatics tools, for RNAseq analysis were developed. More recently, statistical studies realized neutral comparison studies using benchmark datasets, shedding light on the most appropriate approaches for RNAseq data analysis. Nevertheless, performing an RNAseq analysis remains a challenge for the biologists. Results DiCoExpress is a workspace implemented in R that includes methods chosen based on their performance in neutral comparisons studies. DiCoExpress uses the pre-existing R packages as well as FactoMineR, edgeR and coseq, to perform quality control, differential, and co-expression analysis of RNAseq data. Users can perform the full analysis, providing a mapped read expression data le and a le containing the information on the experimental design.
Following the quality control step, the user can move on to the differential expression analysis performed using generalized linear models with no effort thanks to the automated contrast writing function. DiCoExpress proposes a list of comparisons based on the experimental design, and the user needs only to choose the one(s) of interest for his research question. A co-expression analysis is implemented using the coseq package. Identi ed co-expression clusters are automatically analyzed for enrichment of annotations provided by the user, and several result outputs proposed. We used DiCoExpress to analyze a publicly available Bra ssica napus L. RNAseq dataset on the transcriptional response to silicon treatment in plant roots and mature leaves. This dataset, including two biological factors and three replicates for each condition, allowed us to demonstrate in a tutorial all the features of DiCoExpress.
Several methods are available, and among the most used, we can nd RPKM (Reads Per Kilobase per Million mapped reads)  [6] , Upper quartile normalization  [7] , RLE (Relative Log Expression)  [8]  and TMM (Trimmed Mean of the M-values)  [9]. Multiple methods, based on different statistical modeling of data, are available to perform differential expression analysis. Negative binomial-based models with robust mean-variance modeling, have been used extensively at the beginning, and they are available in the R-packages edgeR  [10]  and DESeq  [8]. More recently, the linear models and their generalized extensions for negative binomial distributions (GLM) have been proposed to account for the versatility of multifactorial experiments. They are available in the R-package limma  [11]  for the linear models and in the R-packages edgeR  [12]  and DESeq2  [13]  for the generalized linear models.
Even with all these tools, a biologist is often in front of a real dilemma on how to analyze his dataset correctly. Indeed a characteristic shared by the majority of GUI tools developed up to date is to offer the user the possibility to choose among multiple statistical methods for each step of the analysis with no speci c propositions. However, the RNAseq data speci cities, such as heterogeneity of counts or overdispersion among biological replicates, represent a methodological challenge that has to be addressed by proper statistical modeling of the gene expression. It is worth noting that in the case of multifactorial experiments, if interaction terms are included in the modeling, the writing of the contrasts might become tricky, requiring a good understanding of some statistical concepts, not always mastered by a biologist.
As a result, the large-scale data analysis of RNAseq data is not straightforward for a biologist. DiCoExpress aims to offer a tool usable without advanced statistical knowledge and/or programming skills to analyze RNAseq projects with complete and balanced designs with at most two biological factors and one technical factor. To offer a validated set of tools, we based our choice on three neutral comparison studies  [18, 31, 32]. The idea of such studies is to design and implement a framework to generate realistic benchmark datasets with known truth to make an objective and reproducible performance assessment. Comparing normalization methods, Dillies et al. [31]  showed that the RLE method implemented in the package DESeq2  [14]  and the TMM method implemented in the package edgeR  [11]  demonstrate satisfactory behavior in the presence of highly expressed genes.
Linear models on transformed data or generalized linear models are consequently the most adapted for the differential analysis. Among these models, as also observed in Schurch et al. [35] , when the proportion of differentially expressed genes is low, the results obtained with the method implemented in the edgeR package are more satisfying. We thus chose the statistical model implemented in the edgeR package as a method of choice for differential expression data analysis. Moreover, we propose automatic writing of a large number of contrasts in order to facilitate the comparisons between the biological conditions considered in the experimental design. This automatic writing is a real advantage because, in the available R-packages, most contrasts in GLM with interactions between two factors must be handwritten and require thus an excellent understanding of the statistical modeling.