dataesr/scientific-paragraphs-categorization huggingface dataset:
Yet, most of them require basic bioinformatic or command-line knowledge, which can limit the accessibility to such integrative toolkits. Furthermore, for flexibility reasons, these tools have adopted a step-by-step approach, which can prevent an easy automation of the workflow, and hence hamper the analysis reproducibility. Results: We introduce SLIM, an open-source web application that simplifies the creation and execution of metabarcoding data processing pipelines through an intuitive Graphic User Interface (GUI). The GUI interact with well-established software and their associated parameters, so that the processing steps are performed seamlessly from the raw sequencing data to an annotated OTU-to-sample matrix. Thanks to a module-centered organization, SLIM can be used for a wide range of metabarcoding cases, and can also be extended by developers for custom needs or for the integration of new software.
This step can also be seen as quality filtering, because nonoverlapping reads are being discarded. For single-end sequenced libraries, this step can be ignored. 3. Quality filtering: It regroups multiple type of filters, including base-calling quality filters, PCR and sequencing errors denoiser  [15]  or chimera filter  [16]. This step is crucial to remove as much technical noise as possible in the data. 4. OTU clustering: This step has received most of the attention and is still an active field of bioinformatic research. The sequences are grouped by similarity into clusters that represent proxies for molecular species (de novo OTU clustering strategy). Open or closed reference OTU clustering strategies sequences are mapped represent alternatives (sequences are first clustered against a reference sequence database), even though they have been shown to be outperformed by de novo approaches in some cases  [17].
This step is critical to yield a maximum of biologically relevant information and has a strong impact on diversity measures and downstream analysis. 5. Taxonomic assignment: Putatively ascribe a taxonomic name to each OTU. Curated sequence databases such as SILVA  [18]  or PR2  [19]  for nuclear ribosomal markers, BOLD  [20]  or MIDORI  [21]  for cytochrome oxidase I or UNITE for fungal Internal Transcribed Spacer  [22]  can be used as reference. Important efforts are made to improve methods and algorithms for more accurate taxonomic assignment, and various approaches have been explored  [23] [24] [25] [26]. Multiple algorithms and software have been developed to perform one or multiple processing steps. They can be called sequentially via command-line or bash scripts to form an analysis pipeline, provided that the input/output file format between each of these software is handled correctly.
Wrappers and toolkits such as MOTHUR  [27] , USEARCH  [28] , QIIME  [29] , OBITools  [30]  or VSEARCH  [31]  have been developed specifically for routine analysis of eDNA metabarcoding data. However, non-specialists or command-line reluctant users might still not feel comfortable. Moreover, users are often left to find by themselves a relevant traceability system for their analysis, which can hamper the analyses reproducibility. The software galaxy  [32]  was developed to allow users to create their own pipelines through a web Graphical User Interface (GUI). However, it has been designed to remain as broad as possible in term of application. This means going through a long configuration and installation step prior any data analysis. A command-line free tool specifically designed for metabarcoding studies, yet flexible and powerful, would allow every scientist working with such sequencing data to be autonomous for the carry-out of these critical processing steps.
Implementation Overview SLIM is an web-application with a Graphical User Interface (GUI) that help users to create and execute their own metabarcoding pipelines using state-of-art, open-source and well-established software. The core of SLIM is based on the Node JavaScript runtime, an open source server framework that have been designed for the building of scalable network applications, by handling asynchronous and parallel events. The installation is made as easy as possible for system administrators, through bash scripts that fetch the dependencies, and deploy the web application into a docker container (www. docker. com). This means that the application can be deployed on various platform, from a personal computer to a local or cloud-based computing server. The development of SLIM was guided by the four following principles: Making it user-friendly for non-specialists This involves creating a Graphical User Interface (GUI) to avoid the need of any command line.
Making the installation and administration as easy as possible To facilitate the installation and the deployment of SLIM by systems administrators while ensuring the security and stability of a computing server configuration, we embedded the application in a docker container (https:// www. docker. com). Thanks to this solution, SLIM can be deployed on Unix-like OS (macOS and Linux). We created two bash scripts, one to fetch the application dependencies and another one to deploy it. The application is versioned and frozen into stable releases hosted in GitHub. Once deployed, SLIM includes a logging system that is accessible through docker commands.
Facilitating its extensibility The integration of new software into modules has been made as easy as possible. It requires only some knowledge of web-based languages (JavaScript and HTML) and for input/output file format handling (usually done by python scripts). Once the set of module's associated files are in place within the application folders, the integration itself is done automatically by the application core functions. Developers are encouraged to pull request their new modules and new features to the SLIM repository (https://github. com/yoann-dufresne/SLIM). These new features will be merged to SLIM and made available on the demonstration server.
A module-centered application All the implemented software and tools are independently encapsulated in modules. Each module is defined with its input files, its parameters and its output files. This organization structure makes it possible to create single or parallel workflows to study the impact of a specific step on the biological conclusions, by connecting outputs of modules to inputs of others (Fig. 1 ). This chaining organization makes SLIM flexible and adapted for a wide range of use cases. Indeed, adding and chaining modules is an intuitive way to design workflows. The processing modules that are readily available in SLIM and the ones that are planned to be included in future development is listed in Table  1. These future modules include for instance a mistagging filter  [33] , the DADA2  [15]  workflow for Amplicon Sequence Variant (ASV) inference, the CREST  [24]  and IDTAXA  [34]  taxonomic assignment method, the Short Read Archive (SRA) toolkit for fetching raw data directly from the application, but also some post-processing tools.
Results and discussion SLIM is a user-friendly web application specifically designed for the processing of raw metabarcoding data to obtain annotated OTU tables. It simplifies the use of state-of-art bioinformatics tools, by providing an intuitive GUI that allows users to quickly design their own analysis pipeline. It also facilitates the reproducibility of a such analysis, by sending to the user an email containing a configuration file that includes all the pipeline settings. Hence, reproducing an analysis requires only the raw sequencing dataset, the version of SLIM that was used, and this configuration file. We think that including such configuration file as supplementary material in publications will contribute to improve the reproducibility of metabarcoding analysis. Thanks to the use of web technologies, SLIM is cross-platform and is meant to be deployed on computing server and accessed remotely over local network or over the internet.
However, users with limited internet connection and developers can also install the application on their own personal computer running Unix-like OS (Linux or macOS). The future development and integration of new modules has been made as easy as possible and will make SLIM even more flexible and useful to the metabarcoding users community. This aspect is of prime importance as sequencing technologies are constantly being improved and keep in challenging our computing tools to extract biologically relevant information from this ever-growing amount of data. Denoising / ASV inference DADA2 planned  [15]  Reads joining PANDAseq yes  [37]  CASPER yes  [38]  VSEARCH yes  [31]  Chimeraremoval VSEARCH yes  [31]  OTU clustering VSEARCH yes  [31]  SWARM yes  [39]  Taxonomic assignement VSEARCH yes  [31]  CREST planned  [24]  IDTAXA planned  [34]  Postprocessing LULU yes  [35]  Biotic Indices planned  [36].
Conclusion For demonstration purpose, a server is accessible from the project website hosted on GitHub (https://trtcrd. github. io/SLIM/) and has been restricted to process up to one single full illumina MiSeq platform run (approximately 15 million reads) or to execute quickly an analysis on a provided example dataset. Authors' contributions YD, FL, LAPG, JP and TC conceived the project. YD performed the core development and most of the module's integration. TC contributed with module's integration and some User Interface elements. YD, FL, LAPG and TC extensively tested the application. TC wrote the paper with input from all the authors. All authors read and approved the final version of the manuscript.
Introduction With the development of structure monitoring technology, the hull safety monitoring has been paid more and more attention in shipbuilding industry. The real-time strain on hull structure is observed and analysed by the arrangement of sensors in some critical locations. And the corresponding assessment based on measured data is taken to guide the crews to avoid the structural failure accidents in complex sea environment. The application of hull safety monitoring system into ship has been regard as an effective mean to improve the survivability of ship navigation in harsh sea environment. In fact, many scholars and research institutions had carried out a series of experiments and correlative researches on hull monitoring technology. Lindemann firstly applied the structure monitoring technology into ships in the late 70's in twentieth Century  [1].
By combining a variety of technologies, the trimaran structural monitoring system can monitor the hull longitudinal strength and local structural strength simultaneously, and the number of connected monitoring sensors can reach up to 60. In addition, in order to demonstrate the feasibility and effectiveness of this system, the trimaran model equipped with SMAS is tested with different wave azimuths. The real-time fluctuation of strain and sectional monitoring values is recoded in monitoring system. And structural strength assessments are worked in the system to evaluate the structural state in short term and long term. Through comparison and analysis of the assessment results in different wave azimuths, the dangerous positions and its structural damage cause are found. Finally, the measured data and assessment result will become an important foundation on trimaran detailed optimization design.
Overall design of structural monitoring and assessment system The overall design of structural monitoring and assessment system is a key part in the system design. It can not only affect the system computing efficiency, but also determines the scalability of its functions. In order to make the monitoring system run and extend more efficiently, the multi-level and modular design method is applied in the overall design of trimaran monitoring system, as seen in Figure  1. Base on the structural monitoring purposes, the whole monitoring system is divided into three modules, which are real-time structural monitoring module, structural strength assessment module and structural monitoring database module. Each module owns its sub-modules to implement their specific functions. In real-time structural monitoring module, the real-time data of structure response is focused on.
The fluctuation of structural response like strain can be recorded and showed by curve graph and histogram. The preliminary analysis and measured data warning is also taken in this module. In preliminary analysis, the statistical characteristic value such as significant amplitude values, maximum and mean value is calculated and guided to help crew understand the basic parameters of structure response more clearly. When the structural responses are violent and beyond to designed alarms threshold, the warning message will appear and point out the warning location. And the out-of-range proportion on the warning threshold is record and store into structural monitoring database. In structural strength assessment module, all programs are worked around calculation and display about the structural strength state.
The structural strength assessments in this module include the longitudinal structural strength assessment, fatigue structural strength assessment and simplified yield strength assessment. The characteristics of trimaran structural strength state in different time-domain are recoded by these methods. The short-term structure state is evaluated by longitudinal structural strength assessment. And the instantaneous dangerous structure state is recoded by simplified yield strength assessment. The potential damage of structural strength state in long term is also monitored by the fatigue structural strength assessment. Structural monitoring database is an assemblage of measured structural response and strength assessment results. The ship basic information and system operation record is also added in this database.
Finally, this database forms a complete logbook about trimaran's structural state. Through the actual ship test and preliminary research, it is known that the simple singlethreaded calculation method often takes too much time and limits the number of monitoring sensors. In order to make the trimaran monitoring system work more efficiency, multithreading parallel computing method is adopted. Figure  2  shows the multi-threading scheme of the monitoring system. Five threads are designed for the trimaran monitoring system. They are data collection and display thread, data extraction and storage thread, longitudinal structure strength thread, simplified yield strength thread and fatigue structure strength thread. Each thread extracts and analyses the related data independently, then finishes its own function.
Finally, the data extraction and storage thread will extract corresponding location information and give warning toward crew. In structural strength assessment module, the three kinds of structural strength assessment use their own thread. There is no interference between these threads, so it improves the calculation speed of assessment system. The longitudinal strength thread is based on the strength reliability method to assess hull sectional structure state at different longitudinal positions. The strength thread counts time and calculates the structural failure probability every 30 minutes. The simplified yield strength thread is aimed to evaluate instantaneous structure state. The max strain value in preliminary analysis is transmitted by data collection and display thread to simplified yield strength thread.
And the instantaneous yield indictor is calculated and classified into corresponding grades. The fatigue structural strength thread is based on the Palmgren-Miner damage rule, and run itself every 5 minutes. The potential damage of structural fatigue failure is measured by the fatigue cumulative damage. The fatigue damage in 5 min is calculated and added with previous fatigue damage to become final fatigue cumulative damage in fatigue structural strength thread. In addition, all assessment results will be written into database by their own threads. In structural monitoring database, the duty of data extraction and storage thread is to write and update the data in structural monitoring database. All query and extraction of data from monitoring database is worked by this thread. The extraction and storage thread also record the crew system operation and abnormal monitoring data for subsequent accident analysis.
(5). And the computational method on the corresponding parameter of Rayleigh distribution k is also given in Eq. (  6 ). d x F k = - - (5) 22 2 d k  = (6) Where d  is the standard deviation of real-time structure response, and it is statistically calculated every 30 minutes. The cyclic number of real-time data n is also counted. According to the principle of sequence statistics, the distribution of the extreme value on structural response can be obtained as follows. 2 2 1 exp( ) n D x F k  = - -   (7) The ability of structural resistance is considered generally to obey Gaussian distribution. Therefore, the probability density function on the structural resistant ability () C fx is calculated by Eq. (  8 ). () 1 exp( ) 2 2 c c c c x f    - =- ( 8 ) c s i d  =- (9) Where c  is the standard deviation of the probability density function on the structural resistant ability; c  is mean value of the probability density function;  In fact, the trimaran ultimate strength at different cross-sections needs to consider many factors such as structural failure forms and material yield characteristics, so the accuracy of ultimate strength calculation is difficult to guarantee.
Establishment of structural monitoring database To store monitoring data more conveniently, based on the Structured Query Language (SQL) server, a kind of data extraction and storage method is developed to establish a trimaran structural monitoring database. In order to record the trimaran structural information as much detail as possible, seven tables are designed in this monitoring database, as seen in Figure  7. Each table consists of time data and corresponding monitoring data. The data extraction and storage thread plays a critical role in the data extraction and storage method for trimaran structural monitoring. It will be able to write the data into the corresponding table, and also extract the data from these tables when the monitoring data is inquired. In long-term structural monitoring, the huge quantity of monitoring data like realtime measured data is high possible to lower the system querying speed.
Therefore, in order to avoid this influence, the partition function is used in each table. The storage space of table is divided into 48 regions. All region addresses are recorded by the partition function, so the system can find the corresponding storage location quickly. When all region storage is full, the thread will create some specified documents to output these data one region by one region. At same time, the method of cyclic covering region is also done to keep the number of regions unchanged. After the data output, the early stage region is regard as a new empty region to restart storage for the latest monitoring data. Certainly, the partition function still mark the outputted data addresses and can help the thread to find corresponding data in stored documents. Through system running test in long-term (six months), it is verified that the design of data storage improves the efficiency on data querying, and make the system speed of data extraction less than 15s averagely.
The realtime strain and sectional indicator is monitored and stored in the system database successfully. And the simplified yield strength assessment in the system can record instantaneous structural state effectively. The overall and local structural condition of trimaran is also evaluated autonomously in the system module of longitudinal and fatigue strength assessments. And all these monitoring and assessment data by this monitoring system will give the foundation of the actual trimaran structural monitoring. (2) The location at bow end of side hull is regarded as the most dangerous position for vertical bending deformation. Although the distribution of longitudinal structural resistance is considered in longitudinal structural strength assessment, the violent wave load response is still found as the main factor to determine this dangerous position of vertical bending deformation.
ACKNOWLEDGMENTS Special thanks to all members in ship structural mechanics laboratory of  Harbin Engineering University and Dalian Maritime University  for the preparation of this trimaran test. This research was supported by the  National Natural Science Foundations of China Design and Model Test of Structure Monitoring and Assessment System for Trimaran Haoyun Tang ,  Huilong Ren ,  Qi Zhong 133  (No. 51679049  and No. 51709030 ) and the  Fundamental Research Funds for the Central Universities  ( 3132019134 ). The authors express their gratitude to these foundations.
Motivation: Genomic data analyses such as Genome-Wide Association Studies (GWAS) or Hi-C studies are often faced with the problem of partitioning chromosomes into successive regions based on a similarity matrix of high-resolution, locus-level measurements. An intuitive way of doing this is to perform a modified Hierarchical Agglomerative Clustering (HAC), where only adjacent clusters (according to the ordering of positions within a chromosome) are allowed to be merged. But a major practical drawback of this method is its quadratic time and space complexity in the number of loci, which is typically of the order of 10 4 to 10 5 for each chromosome. Results: By assuming that the similarity between physically distant objects is negligible, we are able to propose an implementation of this adjacency-constrained HAC with quasilinear complexity.
This simple constraint is well suited to genomic applications, in which loci can be ordered along chromosomes provided that an assembled genome is available. The resulting method can be seen as a heuristic for segmentation; it provides not only a single partition of the original loci, but a sequence of nested partitions. This idea of incorporating such constraints was previously mentioned by  Lebart [1978]  and  Grimm [1987] , and an R package implementing this algorithm, rioja  [Juggins, 2018] , has been developed 1. However, the algorithm remains quadratic in both time and space. Its time complexity cannot be improved because all of the p 2 similarities have to be computed to perform the clustering. To circumvent this difficulty, we assume that the similarity between physically distant loci is zero, where two loci are deemed to be "physically distant" if they are separated by more than h other loci.
The main contribution of this paper is to propose an adjacency-constrained clustering algorithm with quasi-linear complexity (namely, O(ph) in space and O(p(h + log(p))) in time) under this assumption, and to demonstrate its relevance for genomic studies. The rest of the paper is organized as follows. In Section 2 we describe the algorithm, its time and space complexity and its implementation. The resulting segmentation method is then applied on GWAS datasets (Section 3) and on Hi-C datasets (Section 4), in order to illustrate that the above assumption makes sense in such studies, and that the proposed methods can be used to recover biologically relevant signals.
Band similarity The implementation provided in rioja takes as an input a p×p (dense) dissimilarity matrix, making its space complexity quadratic. Algorithm 1 can be made sub-quadratic in space in situations where the similarity matrix is sparse (see  Ah-Pine and Wang [2016]  for similar considerations in the unconstrained case) or when the similarities can be computed on the fly, that is, at the time they are required by the algorithm, as in  Dehman et al. [2015]. However, its time complexity is intrinsically quadratic in p because all of the p 2 similarities are used to compute the linkages (Algorithm 1, line 3). In applications where adjacency-constrained clustering is relevant, such as Hi-C and GWAS data analysis, this quadratic complexity is a major practical drawback because p is typically of the order of 10 4 to 10 5 for each chromosome.