dataesr/scientific-paragraphs-categorization huggingface dataset:
The original software codes of WTSI Mouse Database were transferred from WTSI. Then, the modification and operation of the database is performed in the local network of the RIKEN BioResource Center. Currently, the modified version of the software is termed as ''RIKEN LIMS''. The transfer of the LIMS operation has started late 2014. We gradually replace MUSDB operations to RIKEN LIMS with turning over of animals in the JMC's animal facility. RIKEN and WTSI are now planning to share the modified software codes.
Lessons learned For the long-term operation, there appear serious problems on the sustainability of MUSDB: (1) client applications are developed to work only on the previous versions of Windows TM OS platform and (2) the database table structure, which has fixed columns for specific measurement parameters, cannot cope with changes of experimental SOPs, which are needed for continuous operation of JMC. Therefore, replacement of LIMS was one of the indispensable plans in JMC. Original features of WTSI Mouse Database, which allows customisation of a lot of data entry fields for centrespecific attributes (e. g. rooms of animal facility, phenotypic SOPs, options for measurement parameters and so on), help the transitions of LIMS operations from MUSDB to RIKEN LIMS in JMC. However, it turned out that several ''customs'' in the core procedures in the mouse husbandry were unchangeable.
For example, in the JMC, both of operations of animal caretakers and phenotypers are deeply dependent on the information, which is represented in names of animals (i. e. sex, generation in the colony and sequential number in a generation). We found that changes of the naming system of animals were inferred to affect seriously to the efficiency of the total operations in the JMC. Therefore, we modified some parts of functions for basic husbandry operations in the software. In addition, we added some ''useful'' functions for phenotyping (e. g. quick starting of phenotyping data capture and universal data browser). Example 3: Baylor College of Medicine (BCM), Houston, TX, USA In 2011, Baylor College of Medicine adopted the WTSI Mouse Database application (as described above) for tracking of production and phenotyping data in the Knockout Mouse Phenotyping Program (KOMP2) project.
Lessons learned A large investment of time will be required to introduce IT members to a project of this scale. Without the understanding of the business logic of the project, the individual will find it difficult communicating between biologists and deliver on biologist requests. At the initial point of data collection IT encountered inconsistent records. These records were captured in excel sheets and machine outputs that varied in format. An investment of time to learn biologist tools and machines was required to evaluate data collections, machine data extraction, formats, and importation of data to central database. In general, machine output did not come with documentation, so communication between biologist and IT was vital to meet objectives. In addition, decisions made would have to be implemented and followed by training of biologist staff.
Example 4: Institut Clinique de la Souris (ICS), France The ICS LIMS for the phenotyping platforms (''BIOX'') is a custom-developed web-based application. It is based on a modular design to capture administrative and scientific data and is fully compliant with the IMPC export schemes. It is working in interaction with other parts of the ICS information system through web services. Project management: Biox enables semi-automatic project tracking with workflow description and scheduling information. Projects managers and administrative staff rely on it for planning and reporting. Animal facility management: A dedicated application is used to manage lines, mice and genomic data. It integrates well-defined roles and permissions for the interactions between scientists and technicians. Data interactions with Biox are performed through web services.
• Comparison between groups is done using Student test or Chi-square test of independence. • Power of statistical test used and effect size. • Reference ranges of the corresponding wild type mice are also displayed and graphed. Automated process exists to automatically generate Mammalian Phenotype Ontology annotations, as well as graphs generation (histograms, scatterplots, time series, boxplots). Data export and other systems connections: Data can be exported not only in standard excel spreadsheets but also in CSV or XML formats for external databases. Biox is connected to several other internal systems through web services: animal facility system (mice information), LDAP (client accounts) and genetic engineering system (mutation information). It also works in interaction with external databases: MGI (gene data), IMITS and IMPC database (data exports).
Lessons learned The ICS LIMS has evolved towards a modular design, which is essential to separate concerns and improve the pace of evolutions of the different parts. The standardised configuration of new tests also avoids custom developments and shortens the time to put them in production. QC and security are also vital for the reliability of the scientific results and thus for the Institute's image. They have been taken into account from the start and continuously improved through the years. Example 5: Mouse Biology Program, University of California, Davis, USA The Mouse Biology Program LIMS (MBP-LIMS) was designed and implemented in 2011. The requirements for the MBP-LIMS dictated a custom-built UAMP (Ubuntu, Apache, MySQL, PHP) architecture-based application that followed the workflow, allowed for flexible project and resource management for internal and external projects and interconnected with multiple other legacy systems existing in place such as those for colony management and mutant mouse production.
The requirements also stressed ease of interaction for data collection and simple resource re-allocation (e. g. after initiation of a project). To allow for modular construction and maintenance, the application was built using the CakePHP MVC (Model View Controller) framework. The use of this framework permitted rapid development and implementation of core functionality with a 2-person team over 3 months. One of the major benefits of using this framework has been the built in database access, caching, validation, authentication and access level control (ACL). Particularly useful has been the application of ACL to blinding technicians to mouse genotype and gender information as well as ease of creating functional roles such as technician, supervisor, administrator, investigator, observer and IT.
Lessons learned User interface usability can have a huge contribution to data quality Collecting high-throughput data can be a mind numbing experience at the technician level and can easily result in poor-quality data. Project management, staging, and organisation all compete for technician attention and distracts them from what should be their focus, collecting a clean dataset. Tools that streamline and assist in the organisation and collection of data are not only good for technician buy-in of an application and are also critical for ensuring good data quality. Systems that do not follow the physical collection process or are difficult to interact with risk technician opt-out. In this situation, the technician will collect data offline or will avoid using the system altogether. At best, there is the risk of transcription errors while inputting data back into the system.
Give data analysis a higher priority when developing the application Having limited resources available to develop the MBP-LIMS, we chose to focus on data collection and workflow management initially rather than data analysis. While data collection for a new assay is critical and needs to be put in place first, data analysis should follow closely in order to detect startup problems with assay processes and procedures. This lag in data analysis led to several procedural issues not being addressed as quickly as they should have been and have affected the early data quality of some assays. Example 6: Informatics Group, Mary Lyon Centre (MLC), MRC Harwell, UK The MRC Harwell LIMS (AnonyMus) has been developed in order to support the work being undertaken within the Mary Lyon Centre. It has been in production use since 2004, undergoing iterative development since its release.
Functionality currently includes support and tracking of all husbandry processes; phenotyping and imaging across a broad spectrum of assays; drop-box data file loading; data reporting, analysis and visualisation tools for real-time QC; mouse production, micro-injection, import, export, archiving and re-derivation; genotyping with automated robotic control and cassette based calling logic; necropsy, histology and histopathology; service request management and MTA's; competency training; welfare assessments; cage management; UK Home Office licensing and automated scheduling and emailing of data reports and data exports. The MLC's operations adhere to a quality management system, which has been ISO 9001:2008 accredited since July 2010. AnonyMus is a Java web application interfacing with a relational database back end, being developed under Agile software development methods and principles.
There are currently 2. 5 million mouse records on the system, with over 100 million data points held within the database. In recognising that certain areas share common principles and elements, generic designs have been introduced in order improve our implementation efficiencies through reuse. Configurable elements of user interfaces are stored at the database level, with an underpinning software architecture that dynamically builds rich, user friendly interfaces at run time. This enables new data entry forms or new interactive query reports to be instantaneously provided to users, without the need for any changes to the application code itself. Phenotyping assay data entry forms can be configured by specifying the underlying data fields and data types, along with what user interface widgets to build at run time.
This requires sophisticated informatics systems to support coordination of the involved complex logistics as well as to allow storage and analysis of the huge amount of data generated. MausDB, the web-based LIMS of the German Mouse Clinic, has originally been developed in 2006  (Maier et al. 2008 ) and has been in use in our facility since then. Until 2011, this ''basic'' MausDB was a pure LAMP (Linux, Apache, MySQL, Perl) system. It provides functions for mouse husbandry and tracking, phenotyping workflow scheduling, phenotyping data capture and storage as well as subsequent automated data analysis using customised scripts written in R (R Core Team 2013) for statistics and visualisation. The German Mouse Clinic has implemented a Quality Management System for its systemic phenotyping activities, which has been ISO 9001:2008 certified in 2014.
In preparation for this, a comprehensive business process model has been developed, covering all processes starting with external request management, mouse import and colony breeding, phenotyping workflow scheduling, data capture, data analysis and visualisation, to results reporting. At the GMC, such a well-defined business process model is essential for planning, coordination, controlling and reporting on more than 100 projects per year (highthroughput primary and secondary screening projects as well as faculty research projects) running in a multi-parallel fashion. In order to implement full LIMS support for this business process model, the GMC started a large MausDB improvement and integration project in 2011. Since then, MausDB has consequently been re-engineered and supplemented by new software modules, each implementing support for a distinct process.
Key features Resource management and controlling is a very important issue at the GMC in order to optimise use of existing capacities. Therefore, a key feature of MausDB is to enable permanent target-performance comparison throughout the whole project. In our LIMS, any kind of standard or custom project can be defined by assigning more than 100 project attributes in the project editor module. As soon as the project starts, progress can be tracked in the projecttracking module, where the comparison between pre-defined tasks and the current status is visualised. This allows easy detection of required next step actions and project blockers. Another example is the project scheduler module, where predicted phenotyping capacities for all tests are compared to actual assigned tests for every week.
A simple colour visualisation enables project managers to detect overbooked (yellow, red) or idle capacities (white) at a glance. A key aspect of MausDB's overall architecture is full integration of all functional modules in one common LIMS session. In this architecture, the legacy Perl-based ''basic'' MausDB described above is considered a module just as the new Java-based modules. Therefore, it can be transparently integrated in terms of session management and interfaces, although two different frontends are still in use. This concept allows running this module stand alone, providing a fully featured mouse colony management system for facilities that only need basic mouse husbandry and tracking functions. As a matter of fact, MausDB is the central LIMS of the Helmholtz Center Munich, with 16 independent MausDB installations managing animal colonies for different Helmholtz institutes on campus.
Different MausDBs host mice, rats, hamsters and other mammal species. Another 17 MausDB installations are currently running in different mouse facilities worldwide. The GMC emphasises independence from commercial solutions and the freedom to customise and adapt our LIMS to upcoming requirements at any time. Not least to this fact, MausDB is consequently built on non-commercial components. For instance, the reporting engine combines the free software packages R (R Core Team 2013) and LaTeX (http://latex-project. org/). At the push of a button, it is able to produce comprehensive printed PDF project reports for our customers with 100-200 pages, including statistics and embedded graphs. So far, MausDB is holding 330,000 mice and their associated demographic and phenotyping data, with an average of about 10,000 live mice at any time.
About 110 scientists, technicians, animal caretakers and project managers in the GMC are currently using MausDB. MausDB is compliant with current EU regulations concerning reporting of animals used for scientific purposes  (THE EUROPEAN COMMISSION 2012). MausDB collects all data needed for generation of the required reports at different time points (import, weaning, experimental assignment, culling). A separate reporting module collects and processes all relevant data to generate the report in the format specified by the EU directive. This module is even capable of collecting data from different, independent MausDB installations. At our institution, this allows running separate MausDB installations for different institutes, working groups or animal species without mutual access but still being able to report on the overall animal use.
Lessons learned Generic projects are more suitable to organise work and data than a biological concept like mouse line In the first years of GMC operation, it seemed natural to organise work and data by mouse lines and respective genes. When projects involved the same gene a second time, we needed to discriminate datasets, which turned out to be difficult with this concept. Therefore, we introduced generic projects as major concept, within which mouse line and gene are just attributes amongst others. Operational flexibility comes by well-defined processes and LIMS modules functionally supporting them As with many large operations, processes in the GMC changed over the years and in turn, our LIMS had to change as well. An important lesson learned at the GMC was that we are far more flexible and adaptable when using a modular LIMS architecture that supports well-defined processes.
Acknowledgments We thank  Manuela O ¨stereicher  and  Susan Marschall  for critical reading of the manuscript. This work was funded by the  German Federal Ministry of Education and Research  ( Infrafrontier grant   01KX1012 ). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creative commons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
In addition, the data generated by newly sequenced genomes are helpful for identifying antibiotic and vaccine targets by way of a comparative analysis  (7). Nowadays, only seven complete genomes and two drafts are available in the National Center for Biotechnology Information (NCBI) database (http://www. ncbi. nlm. nih. gov/genome/). This scenario shows that more genomic knowledge is required in order to better characterize the virulence mechanisms of this emergent pathogen. In the current study, we present the genome sequence of C. ulcerans strain FRC11, isolated from a 74-year-old human a with leg ulcerans infection in Toulouse, France. This strain was first identified as Corynebacterium pseudotuberculosis  (8) , but recent analysis shows that it belongs to C. ulcerans. The sequencing, assembly, and annotation of this strain were performed by the teams from the Laboratory of Cellular and Molecular Genetics (LGCM) and the National Reference Laboratory for Aquatic Animal Diseases (AQUACEN), both located at the Federal University of Minas Gerais, Belo Horizonte, Minas Gerais, Brazil, and the Laboratory of Polymorphic DNA (LPDNA) at the Federal University of Pará, Belém, Pará, Brazil.
The platform used for sequencing was the Ion Torrent Personal Genome Machine (PGM) system (Life Technologies), using a fragment library. The quality of the raw data was analyzed using the Web tool FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). The assembly was done using the Simple Manager for Bacterial Assemblies (SIMBA) interface (http://ufmg-simba. sourceforge. net). The reads with good quality were assembled using a de novo strategy with the software MIRA 4. 0  (9). The assembly produced a total of 30 contigs, with a coverage of 179. 14ϫ and an N 50 contig length of 236. 335. Additionally, a scaffold was created using the CONTIGuator 2 software (10), using the genome sequence of C. ulcerans strain 0102 (accession no. NC_018101. 1) (11) as a reference. The gap closure was performed automatically using SIMBA and manually using the CLC Genomics Workbench 7 software.
The genome was automatically annotated using Rapid Annotations using Subsystems Technology (RAST)  (12). The manual curation of the annotation was performed using the Artemis software  (13)  and the UniProt database (http://www. uniprot. org). The CLC Genomics Workbench 7 software was used to correct indel errors in the regions of homopolymers. The genome includes one circular chromosome of 2,442,826 bp (53. 35% GϩC content), and 2,210 genes were predicted, 2,146 of which are putative protein-coding genes, with 12 rRNAs and 51 tRNAs; 1 pseudogene was also identified. Nucleotide sequence accession number. This genome has been deposited in GenBank under the accession no. CP009622.
ACKNOWLEDGMENTS This work was supported by the  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ,  Ministério da Pesca e Aquicultura , and  Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG). We also acknowledge support from the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)  and  Rede Paraense de Genômica e Proteômica.
Prevalence of endoparasite infections in cats at the enrolment A field trial study was conducted in three geographical sites of Italy, (i. e. , Messina, Sassari and Bari), where the occurrence of feline lungworm infections had been previously reported  (Brianti et al. , 2012; Annoscia et al. , 2014; Giannelli et al. , 2014a; Tamponi et al. , 2014). From May to September 2014, client owned cats, which had regular access to the outdoor environment, were clinically examined. Following the receipt of the owners' informed consent, anamnestic data (i. e. , age, gender, clinical presentation and weight) were collected and faecal samples were examined for gastrointestinal parasites and lungworm nematodes. In particular, faeces (at least 10 g) were subjected to flotation with ZnSO 4 (specific gravity = 1.
Data analysis A. abstrusus and T. brevior Lpg were transformed into the natural logarithm (count +1) for calculation of geometric means for each species. The efficacy of the treatment was calculated as percentage, using the formula: efficacy (%) = 100[(C -T)/C], where C was the geometric mean of larvae before the treatment and T was the geometric mean following the administration of the product. The change in counts was analysed by modelling the logarithm of the counts +1 by a repeated measures analysis of variance, where the measurement time (pre-treatment versus post-treatment) was the repeated measurement within the individual animal. The mixed procedure in SAS ® Version 9. 1. 3 was used for the analysis with measurement time defined, as the fixed effect as well as repeated measure, and animal defined as the subject.
Statistical analyses Results are expressed as median [quartile 1-quartile 3] and presented as curves (median and interquartile range IQR) or boxplots (minimum to maximum). All data are showed in S1 to S8 Datasets. Statistical analyses were carried out using R software (www. r-project. org/, version 2. 15. 2). In mares, repeated measures were divided in three periods (pregnancy before diet, pregnancy during dietary treatment and lactation) and were analyzed in each period by non-parametric ANOVA using the F1-LD-F1 model of the nparLD function  [21] , taking into account the diet and time. This analysis compares one factor (= whole-plot factor, in this case, group category) and one repeated factor (= sub-plot factor; in this case, diet category), applied only during a given time period, with repeated measurements for each subject (continuous parameter), as for a two factor ANOVA (one inter and one intra.
Effects of diets in mares Pregnancy and parturition outcomes. Twenty-five pregnancies were obtained that were allocated to group F (n = 13) and group B (n = 12). All mares delivered spontaneously within the normal pregnancy length (range 331 to 356 days). One B foal with abnormal presentation caused dystocia in the mare and died. Pregnancy data relative to this mare were not discarded from further analysis. Twenty-four healthy foals were obtained (n = 13 in group F; n = 11 in group B): 7 fillies/6 colts in group F and 5 fillies/6 colts in group B. One F mare died and her colt was weaned at 150 days of age. Another F colt died around 150 days of age from septicemia. All other foals were weaned at 180 days of age. Bodyweight and BCS in mares. From gestational days 0 to 195, bodyweights were similar between both groups, but varied significantly with time (p<0.
001), with a significant diet:time interaction (p = 0. 007). F mares had significantly lower BCS on gestational days 225, 255, 285 and 315. From suckling days 30 to 180, a significant dietary effect (p<0. 001), time effect (p<0. 001) and diet:time interaction (p<0. 001) were observed. F mares had significantly lower BCS on suckling days 30, 60 and 90. Glucose homeostasis in mares. The effect of maternal diet and meal time on daily glucose and insulin concentrations was studied. Data are shown in Fig 3. Plasma glucose concentrations varied significantly with diet (p<0. 001) and time of the day (p<0. 001) with a significant diet:time interaction (p<0. 001). B mares had significantly higher plasma glucose concentrations at 8:30 AM, 10:30 AM, 2:30 PM, 3:30 PM, 4:30 PM, 5:30 PM and 6:30 PM than F mares.