dataesr/scientific-paragraphs-categorization huggingface dataset:
This strategy ensures that reactions that are vital for either growth or target product production, or those that produce key metabolites, are not selected as potential knockouts. Conversely, reactions that (i) significantly divert carbon away from target production; and (ii) consume a metabolite known to promote flux loss from target production; are selected preferentially. Once the first knockout is predicted, the model is modified to block this reaction, and the same selection process is used to select the second reaction to delete. This method can be applied iteratively to predict a number of modifications that should enhance target production whilst maintaining growth. All code was developed in Matlab to maintain compatibility with the COBRA Toolbox  (Schellenberger et al. , 2011) , and is available in Supplementary Folder 1.
Results As a case study, RobOKoD was applied to design an E. coli strain with a reverse β-oxidation cycle for butanol production. These results can be recreated by unzipping the code in Supplementary File 1, and running the test script iNS142_butanol. m in Matlab [requires the COBRA Toolbox  (Schellenberger et al. , 2011) , and if RobustKnock is to be tested, the Tomlab solver (Tomlab Optimization Inc. , Västerås, Sweden)]. This test script runs RobOKoD over a maximum of five iterations of knockout scoring, implementing the highest scoring knockout, generating a results document and reaction FVA profile plots for each iteration in the directory iNS142_butanol_results, and outputting an updated SBML model in which the knockouts have been implemented. It subsequently runs over-expression ranking, again generating output in the iNS142_butanol_results directory.
, 2014)  provides additional levels of constraints (including enzyme inhibition and activation) and is expected to improve the prediction of effective interventions. A longer term goal is therefore the production of detailed, large-scale kinetic models of the whole metabolic system  (Stanford et al. , 2013). When running OptKnock and RobustKnock, it was clear that OptKnock was more user friendly, owing to it being made available in the COBRA Toolbox for Matlab and therefore applicable to a number of MILP (mixed integer linear programming) solvers. This was not the case for RobustKnock, which required a non-standardized model structure and the use of a specific solver, Tomlab, which has limited free access. An additional goal of designing RobOKoD was therefore to ensure its accessibility and robustness by reusing freely-accessible solvers, extensively validated COBRA Toolbox methods, and standardized model formats such as SBML.
Acknowledgments NJS is grateful for funding under grant code  BB/M013189/1 , and the  European Union  under the Preparatory Phase Projects in the framework of  FP7  (project reference  312455 ). PM received the support of the  INRA  (program CJS) and of the  European Union  in the framework of the  Marie-Curie   FP7 COFUND People Program , through the award of an  AgreenSkills fellowship  (under grant agreement n • 267196). NS is grateful to the  BBSRC  for funding under grant code  BB/K019783/1. All authors thank both  Ettore Murabito  and  Michael Howard  for useful discussions whilst developing this work.
R esearch work involves data collection which, if performed using paper-based forms or registers, needs to be entered in an electronic database for easy analysis. Errors commonly occur during data entry, and ensuring quality during data entry is one of the most neglected components of research. 1, 2 There are several ways of reducing errors in data entry, including working together, whereby one person reads out the data and another enters the data by repeating it aloud, or using inbuilt checks within the data entry software that prevent the entry of implausible values. ] [5] [6]  While there are many tools available for analysis, very few ensure quality-assured data entry. Some of the high-quality data entry tools available include Epi-Data (EpiData Association, Odense, Denmark), Epi Info™ (US Centers for Disease Control and Prevention, Atlanta, GA, USA) and CSPro (US Census Bureau, Suitland, MD, USA), all of which are open access.
Setting The short 2-day course was developed and conducted by The Union South-East Asia Office, with funding support from the Eli Lilly Foundation (Indianapolis, IN, USA). From the perspective of the participants, the Setting: Training courses on data entry are few and far between compared to analysis. To address this gap, we conducted a short 2-day course on quality-assured data capture using EpiData for public health professionals in Bangalore and Puducherry, India, in 2013. Objective: To determine the proportion of participants who used EpiData and taught the software to others during the 2-years after training and explore the reasons for doing/not doing so. Design: Cross-sectional study with self-administered, semi-structured questionnaire developed using webbased Google Forms. We performed a manual thematic analysis to identify the major reasons for using/not using EpiData.
Results: Of 46 participants, 38 (83%) responded. Of 31 participants involved in research, 17 (55%) had used Epi-Data, of whom 6 (35%) had performed double entry and validation. Of the 14 who did not use EpiData, 11 had used MS Excel or SPSS/Epi Info for data entry. Of the 38 respondents, 29 (76%) had taught EpiData to other colleagues and students. Reasons for using EpiData included its user-friendliness, its being open access and the ease in preventing data entry errors. Reasons for not performing double entry included lack of time and manpower.
Long-term impact of a short EpiData course 262 objectives of the course were 1) to understand the importance of quality-assured data capture; 2) to have a working knowledge of basic EpiData software; and 3) to use this knowledge to develop relevant data entry tools. In this 2-day course, teaching involved lectures and software demonstrations followed by exercises developed using a real operational research project. All participants who attended the course were nominated by the heads of the respective institutions. Participants were given an overview of EpiData entry (version 3. 1), and were taught how to make a data entry instrument with inbuilt checks and perform double data entry and validation through exercises. Trained mentors helped the participants during hands-on sessions during the 2-day workshop.
Data analysis Quantitative data were analysed using EpiData (version 2. 2. 2. 183), and summarised using frequencies and proportions. Thematic analysis was used to analyse the open-ended responses for questions related to using/not using EpiData and/or double entry and validation. The data obtained were compiled, and the principal investigator read the responses to become familiar with the data. To ensure that the results were a reflection of the data, the themes were related back to the original data. The analysis was reviewed by a second reviewer to reduce bias and enhance interpretive credibility. Given the small amount of data, we performed the analysis manually.
RESULTS Of 46 participants from 18 institutions who successfully completed the course, 38 (83%) (29 [76%] males; median [range] age 33 [25-56] years) responded to the questionnaire. Of these, 24 (65%) were medical college faculty and the rest included postgraduate medical students (n = 7), scientists at research institutes (n = 2), a medical records technician (n = 1), a research associate in a non-governmental organisation (n = 1) and a data analyst (n = 1). Of the 38 participants, 35 (92%) did not know EpiData before the course and were learning about it for the first time. During the 2 years after training, 31 (82%) participants participated in at least one research project (median 2 [range 1-6]), of whom 17 (55%) used EpiData. The key reasons for choosing EpiData can be categorised into the following themes: simplicity and ease of use; free availability; the ability to reduce the possibility of error and ensure data quality with inbuilt checks and the unique feature of double entry and validation; the ability to enable quicker and uniform data entry across multiple sites, especially in multicentre research studies; smaller file size, enabling easy data sharing by e-mail among colleagues; the stable and interactive nature of the software; minimal need for or reduced time for data cleaning before analysis; and ease of training others in data entry.
Of those who used EpiData, 6 (35%) performed double entry and validation. The main reasons for not performing double entry included lack of time, insufficient manpower and large sample size, although two participants mentioned that they were determined to perform double entry in their next project. Of 14 participants who did not use EpiData, 11 used Microsoft Excel (Mi cro soft Corp. , Redmond, WA, USA) and the rest used SPSS (IBM Corp. , Armonk, New York, USA) and/or Epi Info for data entry. One person mentioned that they had used Epi Info before the course but changed to EpiData after the course. The main reason for not using EpiData was increased familiarity and comfort level in using other tools, not only for themselves but also for their research team members and other departmental colleagues.
One person mentioned conducting a research project a year after the course, by which time he had forgotten how to use EpiData and did not have time to refresh quickly. Another mentioned that the sample size was too small to warrant the effort required to set up an EpiData database, which was perceived to be tedious and time consuming. Eight participants said that they had used EpiData for purposes other than research, which included conducting injury surveillance in the emergency department of a hospital, population enumeration in areas served by their urban health centre, hospital census, setting up community health information systems, analysis of course feedback and as part of undergraduate and postgraduate teaching curricula on data management. A total of 29 (76%) participants said that they had taught Epi-Data to other colleagues and students in their settings, and 25 (86%) had used the course materials shared by The Union in the course.
DISCUSSION This survey showed that about half of the participants had used EpiData in their research projects and three quarters had taught the tool to others. This is encouraging, given the poor background of medical researchers in using software in general and the fact that most of the participants were not aware of EpiData before the course. By thinking strategically about the selection of participants, and with adequate mechanisms for providing follow-up support, this output can be improved further. Although the users appreciated the software for its simplicity and user friendliness, only a third performed double entry and validation, which is considered the gold standard in assuring quality, due to lack of time and dedicated manpower for data entry. This is an important lesson, underlining the importance of dedicated budgets for quality-assured data entry in research projects.
EpiData software also helps in calculating data entry time and estimating the human resource requirements accordingly. 8 Some participants mentioned the usefulness of EpiData in multicentre research studies, as has been shown previously. 9 ost of the participants who did not use EpiData reported using MS Excel or SPSS for data entry. These tools are primarily designed for data analysis, and are limited in their ability to ensure quality during data entry. One reason for the continued use of other tools was the comfort level and familiarity associated with them and the general resistance towards learning a new tool. This indirectly indicates the continuing neglect of the importance of data quality in research in general and in medical schools in particular. Alternatives other than familiarity or practice may act as a barrier to the comprehensive assurance of data quality.
dk/download. php) that has enhanced user-friendliness and reduces the initial learning curve. It was encouraging to note that EpiData was used for purposes other than research, including population enumeration and setting up disease surveillance and health information systems in the primary health centres. Changes such as these are likely to strengthen health systems in general and increase overall efficiency. Data obtained through such systems are likely to be quality-assured and this bodes well for conducting future operational research. 13 bout three quarters of the participants taught the tool to several colleagues and students in their network. This indicates the easy scalability of short courses, strengthened by standardised course content and teaching aids. Two participants mentioned having taught EpiData to hundreds of students in their institutions, possibly indicating that they had successfully integrated EpiData into the teaching curriculum.
To enhance this process, we plan to groom the interested participants as facilitators by providing them with opportunities for teaching and mentorship in the courses conducted by The Union over the next 5 years. As most of the participants had not heard about the EpiData tool before the course, there is a clear need for advocacy to increase the awareness of EpiData among medical researchers. Pre-conference workshops at national and regional level and meetings of professional associations may be utilised for this purpose. Self-learning videos posted online are also helpful in increasing awareness. The study has some strengths and some limitations. First, to the best of our knowledge, this is the first study to assess the impact of a training course on data entry. Second, the study assessed long-term impact, including not only knowledge transfer but also use of knowledge.
This indicates sustainable, long-term impact of the course beyond that indicated by the immediate post-course feedback. Third, the use of Google Forms for data collection reduced desirable responses to some extent due to the absence of face-to-face interactions; however, some social desirability bias cannot be ruled out given that the information was self-reported and could not be validated. Future assessments could incorporate some methods of validating the self-reported information by asking participants to share a copy of the Epi-Data triplet files used for data capture. Limitations are related to the non-response by 15% of the participants and the small sample size, which precluded us from performing a subgroup analysis. Finally, it was not possible to tease out whether the observed effect was due to the training course or due to the EpiData tool itself.
Melt profiling Unless otherwise specified, the melting analysis was carried out in 96-well plate with 1. 2 μl of second-round PCR reaction product, 2 μl of IQ SYBR Green supermix or Precision Melt Supermix containing EvaGreen Dye (Bio-Rad) and 0. 16 μl of ROX Passive Reference Dye (Bio-Rad) in a 8 μl reaction mixture using a 7500 real Time PCR System instrument (Applied Biosystems). The following melt curve run method was used: 95°C for 15 s, 60°C for 1 min followed by an increase to 95°C with a ramp rate of 1% and continuous data acquisition (40 min long). When specified, melt profiling was performed using either CFX96 Touch or CFX384 Touch Real time PCR detection system instruments (Bio-Rad) with the same melting reaction mixture as given above except that no ROX Passive Reference Dye was added.
9-kb HIV amplicon using the melting profile of HIV SGA reactions performed with the 384-well instrument (  Fig 7). The onboard software present on the CFX384 Touch real time PCR system instrument allowed locating the positive reactions in a few seconds using the "Graph-1click" method. The time for analysis will be similar for a full 384 plate analysis, making the "Graph-1click" method extremely efficient for 384-well plate analysis. However, as mentioned previously, this method is not compatible with every onboard software. While the "Graph-2steps" method is compatible with any software and any dye, the time for analysis will increase from 3-4 min for a 96w to 12-16 min for a 384w. As a result, the "Excel sheet/values" method will be interesting when using the 384-well format since, as mentioned previously, its time for analysis will remain the same, between 5 and 8 minutes for a full 384-plate analysis.
We present three different methods, namely the "Graph-2steps", the "Excel sheet/values" and the "Graph-1click" methods, to analyze the melting profiles and locate the wells positive for full env amplicon. The 2 steps method will be compatible with both SIV and HIV studies and with any dye on any QPCR instrument. However, given that it takes about 4 minutes to analyze the melting profile of a full 96w plate, and about 16 minutes for a 384w plate, this method will not be the most appropriate for large scale screenings. With an analyzing time of less than one minute for both 96 and 384 well formats, the 1 click method will be preferred for large scale SIV and HIV studies. However, this method will require the ability to select the melting curves by clicking on the graphical interface, a function that is present on some of the common QPCR analysis softwares but not all.
Last, we present a third method no more based on graphical interface but on raw fluorescent intensity and Tm value filtering using excel software. This method can be very useful when access to QPCR software is restricted. Given that the most time consuming step is extraction of the raw data and elaboration of excel worksheet, this method will be more time-efficient for large scale studies. It is compatible with both HIV and SIV studies and with any QPCR software with an access to raw data. However, the use of Eva-Green will be necessary to have a higher potential for discrimination between melting curves and melting peaks. Our report was focused on full env amplicon since this is the longest, the most informative and most used amplicon for phylogenetic analysis. However, other areas in SIV and HIV genomes have been used, like gag or pol genes.
Auctions, and combinatorial auctions (CAs) , have been successfully employed to solve coordination problems in a wide range of application domains. However, the scale of CAs that can be optimally solved is small because of the complexity of the winner determination problem (WDP), namely of finding the bids that maximise the auctioneer's revenue. A way of approximating the solution of a WDP is to solve its linear programming relaxation. The recently proposed Alternate Direction Dual Decomposition algorithm (AD 3 ) has been shown to efficiently solve large-scale LP relaxations. Hence, in this paper we show how to encode the WDP so that it can be approximated by means of AD 3. Moreover, we present PAR-AD 3 , the first parallel implementation of AD 3. PAR-AD 3 shows to be up to 12. 4 times faster than CPLEX in a single-thread execution, and up to 23 times faster than parallel CPLEX in an 8-core architecture.
g. transportation  [31] , emergency resource coordination in disaster management  [26] , or agent coordination in agent-driven robot navigation  [32] ). However, although such application domains claim to be large-scale, namely involving thousands and even millions of bids, current results indicate that the scale of the CAs that can be optimally solved is small  [19, 25]. For instance, CPLEX (a state-of-the-art commercial solver) requires a median of around 3 hours to solve the integer linear program encoding the Winner Determination Problem (WDP) of a hard instance of a CA with only 1000 bids and 256 goods. This fact seriously hinders the practical applicability of current solvers to large-scale CAs. Linear Programming (LP) relaxations are a standard method for approximating combinatorial optimisation problems in computer science  [5].
-We show that while AD 3 is up to 12. 4 times faster than CPLEX in a single-thread execution, PAR-AD 3 is up to 23 times faster than parallel CPLEX in an 8-core architecture. Therefore PAR-AD 3 becomes the algorithm of choice to solve largescale WDP LP relaxations. To summarise, our results indicate that PAR-AD 3 obtains significant speed-ups on multi-core environments, hence increasing AD 3 's scalability and showing its potential for application to large-scale combinatorial optimisation problems in particular and for large-scale coordination problems that can be cast as combinatorial optimisation problems. The rest of the paper is organised as follows. First, we introduce some background on AD 3. Next, we detail how to encode the WDP for CAs by means of AD 3. Thereafter, we thoroughly describe PAR-AD 3 and afterwards we present empirical results.
Background Graphical models are widely used in computer vision, natural language processing and computational biology, where a fundamental problem is to find the maximum a posteriori probability (MAP) given a factor graph. Since finding the exact MAP is frequently an intractable problem, significant research has been carried out to develop algorithms that approximate the MAP. Linear Programming (LP) relaxations have been extensively applied to approximate the MAP for graphical models since  [30]. Typically, such application domains lead to sparse problems with a large number of variables and constraints (i. e beyond 10 4 ). As shown in  [36] , message passing algorithms have been proved to outperform state-ofthe-art commercial LP solvers (such as e. g. CPLEX) when approximating the MAP for large-scale problems.
Solving combinatorial auctions with AD 3 A Combinatorial Auction (CA) is an auction in which bidders can place bids for a combination of items instead of individual ones. In this scenario, one of the fundamental problems is the Winner Determination Problem (WDP), which consists in finding the set of bids that maximise the auctioneer's benefit. Notice that the WDP is an NP-complete problem. Although special-purpose algorithms have addressed the WDP (e. g. [11, 29] ), the state-of-the-art method for solving a WDP is to encode it as an integer linear program (ILP) and solve it using an off-the-shelf commercial solver (such as CPLEX  [1]  or Gurobi  [15] ). Nonetheless, this approach fails to scale to large CA instances. Indeed, as noticed in  [31] , real problems may involve up to millions of bids.
In contrast, the PAR-AD 3 SOA memory layout (figure  2b ) stores the properties of variables and edges sequentially, thus resulting in a different array for each edge or variable property. Now, iterating on all edges of the graph requires consecutive accesses to array elements. The AOS memory representation of AD 3 benefits from memory access patterns where all the variable properties are used together, meanwhile the SOA memory representation of PAR-AD 3 benefits from the access of any property traversing all variables or edges. To summarise, the PAR-AD 3 data representation transforms many scattered memory accesses into sequential, improving the memory access throughput. A derived advantage of the simplified edge access pattern is to foster better parallel scaling and vectorisation, but we need additional algorithmic transformations that are described in the next section.
Empirical evaluation In this section, we assess PAR-AD 3 performance against the state-of-the-art optimisation software CPLEX with the aim of determining the scenarios for which PAR-AD 3 is the algorithm of choice. We also quantify its current gains, both in sequential and parallel executions. To this end, we first find the data distributions and range of problems that are best suited for PAR-AD 3. Thereafter, we briefly analyse two algorithmic key features: convergence and solution quality. Afterwards, we quantify the speedups of PAR-AD 3 with respect to CPLEX in sequential and parallel executions. From this analysis we conclude that PAR-AD 3 does obtain larger benefits from parallelisation than CPLEX. Indeed, PAR-AD 3 achieves a peak speedup of 23X above CPLEX barrier, the state-of-the-art solver for sparse problems.