dataesr/scientific-paragraphs-categorization huggingface dataset:
2a ), which changes amino acid glycine (G) to serine (S). Whether the change is associated with the disease severity and pathological difference, and whether those differences are associated with different life-cycle patterns needs to be further elucidated. The phylogenetic analysis based on the nad2 gene fragments showed that all the sequences isolated from AE patients were close to those isolates collected from rodents. In fact, some, such as sequences of AE2, AE12, AE24 and AE30, were identical to Em-XJ and Em-NX nad2 gene sequences. Alignment of these sequences combined with the sequences (n = 20) in the GenBank database shows that the amplified nad2 gene fragments (474 bp) contained 20 (4. 2%) SNPs. Phylogenic analysis in a previous study showed that these sequences are divided into four clades, termed as Asian strain, European strain, North American strain and Alaska strain  [13].
The distribution of the genotypes in the world Including our sequences, a total of 125 sequences from the GenBank database were employed to map the distribution of these genotypes of E. multilocularis worldwide (Fig. 4 ). The parasite exhibits different genotype patterns in different continents. In Europe, nad2 and cob are conserved, showing only one clade. In North America, there are three clades, the North American genotype (64-72%), the European genotype (21-29%) and the Alaskan genotype (7-8%). In Asia, cob gene sequences allowed us to distinguish two major genotypes.
Discussion The variations in the mitochondrial genes cox1 and nad1 have been used to determine the genotypes of E. granulosus (sensu lato)  [12, 15, 16] , which was classified into nine strains, now five species mainly according to their intermediate hosts, indicating that the host selection may play an important role in driving the genetic evolution. However, there is no evidence so far that such a selection is associated with the genomic variation of Echinococcus. In the study, we obtained 68 mitochondrial sequences for E. multilocularis from AE patients, including 36 cob and 32 nad2 gene sequences, offering basic information for identification of the relationship of host specificity and genetic variation. To our knowledge, this is the largest sequence dataset obtained from humans AE cases.
Gut microbiota profiling by HITChip The HITChip is a validated phylogenetic array produced by Agilent Technologies (Palo Alto, CA) and developed at Wageningen University, The Netherlands  [22, 23]. It contains over 4800 oligonucleotides targeting the V1 and the V6 region of the 16S rRNA gene from 1132 microbial phylotypes present in the human gut  [22]. The fulllength 16S rRNA gene was amplified from fecal DNA, and PCR products were further processed and hybridized to the microarrays as described previously  [24]. Data analyses were performed using R (www. r-project. org), including the microbiome package (https://github. com/ microbiome). Bacterial associations in the different patient groups and healthy subjects were assessed using principal component analysis (PCA) as implemented in CANOCO 5.
In addition, to assess primer specificity, we performed melt curve analysis using the Fluidigm melting curve analysis software (http://fluidigm-meltingcurve-analysis. software. informer. com/). All PCRs were performed in triplicate, and sample-primer combinations were only included in the analysis when all triplicate reactions resulted in a CT value below the detection limit. After completion of the nanolitre-scale qPCR assays, the transferable colistin resistance gene mcr-1 was described  [32]. To detect and quantify mcr-1, we developed primers (qPCR-mcr1-F: 5′-TCGGACTCAAAAGGCGT GAT-3′ and qPCR-mcr1-R: 5′-GACATCGCGGCATT CGTTAT-3′) for use in a standard qPCR assay. The mcr-1 gene was synthesized based on the sequence described in  [32]  by Integrated DNA Technologies (Leuven, Belgium) and used as a positive control in our assays.
Acknowledgements We thank  ServiceXS B. V. (Leiden , The Netherlands) for their assistance in the Fluidigm real-time PCR assays. We are grateful to  Erwin Zoetendal  and  Willem M. de Vos  for providing the material and data from the  Cohort study of intestinal microbiota among irritable bowel syndrome patients and healthy individuals' (CO-MIC ) funded by the unrestricted  Spinoza Award  to  Willem M. de Vos  from The  Netherlands Organization for Scientific Research.
Funding This work was supported by The  Netherlands Organisation for Health Research and Development ZonMw (Priority Medicine Antimicrobial Resistance ; grant  205100015 ) and by the  European Union Seventh Framework Programme  ( FP7-HEALTH-2011 -single-stage) " Evolution and Transfer of Antibiotic Resistance" (EvoTAR) , under grant agreement number  282004. In addition, W. v. S is supported by a  NWO-VIDI  grant ( 917. 13. 357 ) and L. L. by  Academy of Finland  grants  295741  and  307127.
Schiff bases derived from hydrophobic S-alkyl/aryl groups and their complexes reveal promising potential applications  (Beshir et al. , 2008). In fact the reaction of dithiocarbazates with different aldehydes and ketones leads to ligands with modified donor properties, bringing about intriguing coordination geometries in metal complexes, a feature that affects also the bioactivity of these compounds such as antibacterial, antifungal, anticancer, antitumor, wound healing, cell motility as well as antioxidant activities  (Beshir et al. , 2008; Ali et al. , 2002; Tampouris et al. , 2007; da S. Maia et al. , 2010; Singh et al. , 2009). Surprisingly a bis-dithiocarbazate nickel complex has been recently tested also for the photocatalytic production of hydrogen  (Wise et al. , 2015). A search in the Cambridge Structural Database  (Groom et al.
The products were recrystallized from a mixture of chloroform and acetonitrile (5:1). Dark reddish brown and orange red single crystals of Ni II and Pd II complexes, respectively, suitable for X-ray diffraction analysis, were obtained by slow evaporation from a mixture of chloroform and acetonitrile (1:1) after a week. Brown single crystals of CuL 2 and yellow ones of ZnL 2 were obtained at room temperature by slow evaporation from a mixture of chloroform/ acetonitrile (3:1) and of dichloromethane/acetonitrile (4:1), respectively. The physical and spectroscopic data of all compounds 2-7 are given below:    162. 26(CH‚N), 6), 7),. UV-vis spectrum [CHCl 3 , k max nm (log e, L mol À1 cm À1 )]: 244(4. 17), 338(4. 90). HRMS (FAB) Calc. for C 30 H 43 N 4 S 4 Pb (M+1): 795. 2059; Found (M+1): 795.
2125. Molar conductivity (1. 0 Â 10 À5 M; CHCl 3 , X À1 cm 2 mol À1 ): 0. 00; l eff = Diamagnetic. 2. 3. 1. [NiL 2 ] (2) 2. 4. X-ray data collection and structure determinations X-ray diffraction patterns of metal complexes 3 and 4 were collected by using a Rigaku R-AXIS RAPID diffractometer equipped with CCD. The experiments were performed at 173 K with Mo Ka radiation (k = 0. 71075 A ˚). Cell refinement, indexing and scaling of the data sets were carried out using the Crystal Structure package  (Rigaku Inc. , 2010). All the structures were solved by direct methods  (Sheldrick, 2008)  and subsequent Fourier analyses and refined by the full-matrix leastsquares method based on F 2 with all observed reflections  (Sheldrick, 2008). An absorption correction was applied to the data  (Rigaku Inc.
, 1995). The contribution of H atoms, at geometrical calculated positions, was introduced in the final cycles of refinement. Ortep drawings were done with program Cameron  (Watkin et al. , 1993)  implemented in the WinGX package  (Farrugia, 1999). Table  1  summarizes the pertinent crystallographic data and refinement details of 3 and 4. Crystal data of ligand 1 and of complexes 2 and 6, discussed in this work, have been reported by us elsewhere  (Howlader et al. , 2015a,b; Begum et al. , 2015).
, 2010; Low et al. , 2014). In the ligand the signals at 1. 76, 1. 47, 1. 34 and 0. 91 ppm corresponding to C-11, C-12, C-13,14 and C-15, respectively, all show slight variation upon complexation. However, the paramagnetic electron configuration of complex 3 hampered the identification of the functional group through 1 H NMR spectrum  (Yazdanbaksh et al. , 2009). The 13 C NMR spectral data of the complexes in solution confirm the structure as observed in solid state by X-ray diffraction. The room temperature molar conductance values of the complexes (10 À5 M in CHCl 3 solution) are indicative of nonelectrolytic nature  (Crouse et al. , 2004; Chew et al. , 2004). The Lassaigne's test (halogen test) indicated the absence of chloride ions in the Pd II complex 6. The magnetic moment of Cu II complex 3 is 1.
Acknowledgments The author MSB thanks the Dept. of  Chemistry, University of Rajshahi, Rajshahi and also Dept. of Chemistry, Shahjalal University of Science and Technology, Sylhet-3114, Sylhet  for laboratory facilities, and  MCS  thanks the Dept. of  Applied Chemistry, Faculty of Engineering, University of Toyama, Japan , for analytical facilities. The  World Academy of Sciences (TWAS)  is greatly acknowledged for a partial financial support (Ref:  14-050 RG/CHE/AS_G; UNESCO FR 34028605 ). MAH is thankful to  Prof. R. Poirier ,  Dept. of Chemistry, Memorial University, Canada , and the  Atlantic Computational Excellence Network (ACENET)  for allocating computational resource for some calculations. Authors are grateful to donors for the implementation of a computational platform in Bangladesh http://computchembiochem.
Acknowledgements This work was financially supported by  EC  through the  ERC  project  SUPRAFUNCTION  ( GA-257305 ) and the  Marie Sklodowska-Curie ITN projects iSwitch  (GA No. 642196 ) and  SYNCHRONICS  (GA No. 643238 ), the  ANR Labex  project  CSC  ( ANR-10-LABX-0026 CSC ) within the  Investissement d'Avenir program   ANR-10-IDEX-0002-02 , and the  International Center for Frontier Research in Chemistry (icFRC). Received: ((will be filled in by the editorial staff)) Revised: ((will be filled in by the editorial staff)) Published online: ((will be filled in by the editorial staff)).
DEDIPAC aimed to promote and facilitate knowledge exchange and dissemination and aimed to establish collaborations that would lead to new research and last beyond the initial three-year funding period. A main purpose of DEDIPAC was, thus, to bring together researchers from various relevant disciplines from different countries. This is why it was proposed and presented as a Knowledge Hub, rather than a project or program. Furthermore, because DEDIPAC was specifically aimed at making the best possible use of available evidence, data, and expertise, the three TAs adopted similar strategies and methodologies that encompassed: -The provision of methods and a harmonised set of reliable and validated measures to be used for future research, surveillance, and monitoring of the individual, social, and environmental determinants of dietary, physical activity, and sedentary behaviour; -The provision of state-of-the-art overviews of up-todate evidence regarding the determinants of these behaviours in different demographic groups by means of series of systematic literature reviews, and mapping/scoping reviews; -The creation of a level playing field by enabling expert meetings, capacity building, and career development for young researchers, as well as through the creation of integrative frameworks for research; -Improved use of relevant, existing data through secondary data analyses, including dataset pooling and variable harmonisation; -Improved dissemination and application of findings and results through the creation of an online platform that provides toolboxes for researchers, practitioners, and policy makers.
Overview of the state-of-the-art and identification of gaps For goals 1-3, 19 systematic literature reviews were performed to provide an overview of the state-of-the-art in terms of assessment methods and tools. It was shown that dietary behaviour is usually assessed using food frequency questionnaires (FFQs) in etiologic research and 24-h dietary recalls or food record methods in the context  Lakerveld et al. [43]  Identifying and sharing data for secondary data analysis of physical activity, sedentary behaviour and their determinants across the life course in Europe: general principles and an example from DEDIPAC of surveillance. These are self-reported, but standardised methods for which validity has been evaluated at the level of selected nutrients using advanced measurement-error models.
Pan-European standardisation of methods and instruments is lacking, which also results in data being largely incomparable. The state-of-the-art assessment methods for dietary, physical activity, and sedentary behaviour and determinants that were identified in the systematic literature reviews were summarised in a publicly available, online toolbox that includes information on the validity, reliability, and acceptability (perceived by participants) of the assessment methods. The toolbox can be found here: https:// www. dedipac. eu/toolbox/. Additionally, analyses of secondary data were conducted using existing datasets (e. g. the Attitude Behaviour Change study  [15] ; the Swedish Neighborhood and Physical Activity study  [16] ; the Health Survey for England  [17] ; a Norwegian physical activity prevalence study  [18] ; and a Portuguese physical activity prevalence study)  [19].
Secondary data analyses A data pooling taskforce that spanned the WPs in TA1 and TA2 was established to develop a strategy for secondary data analysis. This group devised a five-step methodology covering 1) the identification of relevant datasets across Europe; 2) the development of a dataset compendium that included details on the design, study population, measures, and level of accessibility of data from each study; 3) the definition of key topics and approaches for secondary analyses; 4) the acquisition of access to datasets; and 5) the development of a data harmonisation platform, and pooling and harmonisation of the data  [43]. Based on this, a variety of approaches to secondary data analysis were identified, including re-analysis of a single, existing dataset, 'federated' meta-analyses of two or more datasets based on a common data-analytical syntax applied to locally stored data, and the pooling, harmonisation and re-analyses of multiple datasets.
To assist with these analytical approaches, a two-day statistical analysis workshop was organised in Amsterdam, the Netherlands, that specifically focused on the challenges associated with conducting secondary data analysis, handling pooling and harmonisation issues, and the provision of support in the area of advanced statistical techniques (e. g. , federated data analyses, Bayesian analyses, mediation/moderation analyses, and handling missing data). In addition, WP2. 2 and WP2. 3 held a combined, three-day writing retreat in Ghent, Belgium to further define approaches, make progress on specific questions, as well as identify unresolved issues regarding the pooling and harmonisation process. Critical aspects of the FAIR principles of data management and stewardship  [44]  were monitored throughout the phases of the secondary data analyses, from the identification of potentially relevant datasets to the actual reuse of data.
The FAIR principles suggest that each data resource, associated metadata, and complimentary files should be easy to find ('Findable'); provide relevant metadata from these datasets, on the types of variables, age groups under study, study design, measurement instruments used, time frame, etc. (' Accessible'); be 'Interoperable' and thus use a consistent data format and taxonomy for knowledge representation; and, finally, they should be 'Reusable' , i. e. , made available for further analyses. The topics and general outcomes of the secondary data analyses are briefly summarised below. We found that many different approaches to handling the multiple data sources were used, as were a wide variety of methods of statistical analysis. Regarding determinants of dietary behaviour, a total of 14 research questions have been or are currently being addressed using secondary and/or federated meta-analysis  [45] [46] [47] [48] [49] [50].
The published articles are listed in Table  2. Compiling the physical activity and sedentary behaviour data resulted in a detailed list of 150 datasets. A total of 14 of these datasets were eventually obtained and reused to address 10 exemplar research questions on determinants of physical activity and sedentary behaviour (Table  2  contains the published papers). So far, these manuscripts have relied on a variety of methods of analysing the data, such as Bayesian Network analysis of the determinants of physical activity and sedentary behaviour provided by the Eurobarometer dataset, meta-regression to examine more complex interactions with selected moderator/mediator variables of physical activity behaviour within harmonised datasets, and Chi-squared automatic interaction detection (CHAID) to examine the hierarchy of socio-demographic correlates of remaining sedentary for an extended period of time  [51].
The latter analyses included over 27,000 participants and showed that current occupation was primary discriminator. A deeper profiling revealed that highly educated adults with white-collar jobs, who had no difficulties paying bills, and used the internet frequently were most likely to sit too much. In WP2. 4, due to the limited availability of data on behaviour and determinants across ethnic minorities, specific case studies and secondary data analysis on single datasets were conducted, as opposed to federated meta-analysis or pooled analyses  [52]. In general, these efforts revealed a clear lack of indicators of ethnic minority status in the studies that were included in the compendium. Furthermore, quite often the numbers of ethnic minorities included in some of the pan-European studies were too small to enable detailed analysis of specific groups, thus they are often 'lumped' together to increase statistical power.
The DEDIPAC data warehouse proved to be useful for pooling datasets, but in general, the available data -or rather the lack thereof-often restricted harmonisation to just a few core (crude) outcome variables and some individual-level, socio-demographic correlates of these behaviours. The stepwise approach to secondary data analysis used was described in a 'methods' paper  [43] , as well as in a position paper that draws on the possibilities and impossibilities of secondary data analyses of pooled and harmonised data on determinants of sedentary behaviour. The main gaps identified were lack of datasets that specifically emphasise determinants of behaviour -especially at the more macro level and with a systems approach; too few longitudinal studies examining determinants; and inadequate coverage of European nations and age groups.
Objectives, methods, and results of TA3 TA3's overall objective was to contribute to better evaluation and benchmarking of public health interventions and policies related to dietary, physical activity, and sedentary behaviour across the life-course. The specific goals of TA3 were: 1) To improve the quality of public policies and intervention to promote healthy diet, physical activity, and to reduce sedentary behaviour by creating a database with examples of good practice; 2) To improve implementation (from research to practice/policy) and transferability (from practice/ policy to practice/policy) of public policies and intervention; 3) To develop and pilot test an online toolbox for developing, monitoring, and evaluating policies and multi-component interventions across Europe. The work within TA3 was organised into three WPs; the first aimed to realise goals 1 and 2, and the two other WPs focused on the development and pilot testing of the toolbox (goal 3) -one focused on policies, and the other on multi-component interventions.
The other characteristics (n = 17) were related to implementation and concerned issues such as participation processes, implementation partnerships, training of practitioners, the use of existing resources, maintenance, adaptation processes, and transferability. Next, a so-called 'quick scan' was conducted of relevant interventions and policies to identify potential good practices in ten countries involved in DEDIPAC. The 'founders' (N = 79) of the 'good practices' were subsequently approached and asked to complete an online questionnaire to retrieve information related to good-practice characteristics and to provide additional information regarding the main characteristics of the policy or intervention, monitoring and evaluation, and implementation. Finally, based on the 'quick scan' inventory of good practices, and the responses to the online questionnaire, an online database of examples of good practice in terms of public policies and multicomponent interventions (N = 52) was developed.
The purpose of creating this database was to increase the use and knowledge of good practices in designing and implementing public policies and multicomponent interventions. A total of 44 examples of good practices of policies and interventions from eight European countries represented in DEDIPAC were included in the database. The database contains information on intervention characteristics (including aim, target population, and behaviour, among others), monitoring and evaluation efforts and accomplishments and implementation, sustainability, and transferability conditions. Most of the interventions in the database focus on children in school settings and address both diet and physical activity. This database is publicly available online (https://www. dedipac. eu/toolbox/) and is supported by a factsheet with background information to support the use and dissemination of the database among policy makers and health-promotion professionals.
Improving implementation and transferability of policies and interventions To meet the second objective of TA3, an umbrella review was conducted to identify critical implementation and transferability conditions  [57]. This review focused on documents aimed at generating empirical evidence and evidence-based recommendations regarding implementation conditions for policies and interventions targeting healthy diet, physical activity, or sedentary behaviour. For the purpose of this umbrella review, so-called 'stakeholder documents' were also considered eligible for inclusion and, as such, the data banks of publications from eight major stakeholders, like the World Health Organization, were searched for relevant documents. Eighty-three conditions that were relevant to successful implementation were identified and these were further grouped according to the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework  [58].
It also enabled the systematic summary of procedures and measures related to economic evaluations, including the application of counterfactual methods to determine ex post effectiveness on the basis of quasi-experimental data  [60] , and process evaluation measures. All the information and content derived from the reviews, interviews, and inventories were then combined into a preliminary toolbox designed to aid in the development, evaluation, and implementation of public policies and multicomponent interventions. The toolbox was implemented as an online, wiki-based platform and was pilot tested in two rounds by DEDIPAC partners and external stakeholders, including policy makers and practitioners, respectively. Face-to-face interviews, telephone interviews, or written comments using a standardised feedback form were used to obtain feedback on issues such as visual appearance, technical features, functionality, content of the toolbox, as well as questions about omissions, remarks, and suggestions for improvement.