dataesr/scientific-paragraphs-categorization huggingface dataset:
Antimicrobial Activity Antimicrobial compounds that do not present toxic effects are particularly of interest for applications in the food industry in order to clean processing equipment or for food preservation  [72, 73]. Cyanobacteria produce 85 families of metabolites isolated from various strains, which display potent antimicrobial activity (representing a third of the 260 molecule families listed in the database)  [18]. Below, we summarize the different antimicrobial metabolites (organized by type of antimicrobial activity) that have been isolated from cyanobacteria so far and the corresponding relevant information (see Tables  1 2 3 4 5 ). We also detail some examples of specific molecules that exhibit interesting bioactivity profiles such as the selective profile for their activity, which present broad-spectrum action together with the absence of associated cytotoxicity.
Cytotoxic Activity The first type of activity test was performed to determine the potential of molecules as anticancer agents due to cytotoxicity. Different cell lines derived from tumor cells, like the HeLa cell line (derived from cervical cancer), KB (HeLa derivative), LoVo (human colon tumor), H-460 (human lung cancer), and MCF-7 (human breast cancer) have been used to assess this activity. Most of the time, the molecules investigated were tested against two or more cell lines to detect a potent specificity and to evaluate their potential for drug development. According to this review, 110 families of metabolites isolated from cyanobacteria showed cytotoxicity, which represents 43% of the molecule families listed in the database. The best example of potent anticancer molecules derived from cyanobacteria is the dolastatin family  [191].
As a result, BACE1 inhibitors could be promising targets for the development of new therapeutics against Alzheimer's disease  [351, 358]. Considering these activities, we assume that members of the grassystatins-tasiamides family constitute promising components for the development of antiproliferative agents, immune response modulatory compounds, and therapeutics for Alzheimer's disease treatment. During the process of database construction, we noticed that five metabolite families showed a remarkable ability to bind to cannabinoid receptors (CB1 and CB2). These metabolites were grenadamide  [359] , the semiplenamides  [360] , serinolamide A  [361] , mooreamide A  [362] , and the columbamides  [363]. CB1 and CB2 are cell membrane receptors that belong to the endocannabinoid system (ECS), which is an important part of the human physiological system.
Conclusions In this review, all available information concerning the beneficial activities of natural products of cyanobacteria was gathered. To write this review, a molecular database of the various families of metabolites isolated from cyanobacteria was constructed from the systematic analysis of 670 articles. The derived database represents 260 families of metabolites. It groups various types of information concerning the taxonomy of producing strains, the respective chemical classes, the origin strain habitats, and the tested/demonstrated activities for each member of the family, together with the related full references. According to this review, from the above 300 different genera of cyanobacteria (referenced by the taxonomy published by  Komarek et al. in 2014)   [27] , 90 have, so far, been reported to produce bioactive metabolites.
Anti-inflammatory metabolites extracted from the database. Stigonema sp. , Nostoc punctiforme, Anabaena variabilis, Anabaena ambigua, Aphanocapsa/Synechocystis sp. (assembly), Aulosira fertilissima, Calothrix sp. , Calothrix parietina, Calothrix crustacea, Chlorogloeopsis sp. , Chroococcidiopsis sp. , Chroococcus sp. ; Cylindrospermum sp. , Diplocolon sp. , Entophysalis granulos, Gloeocapsa sp. , Hapalosiphon sp. , Hapalosiphon fontinalis; Lyngbya sp. , Lyngbya aestuarii, Nostoc parmelioides, Nostoc commune, Nostoc microscopium, Nostoc pruniforme, Phormidium sp. , Pleurocapsa sp. , Rivularia atra, Rivularia sp. , Schizothrix sp. , Scytonema sp. , Tolyothrix sp. , Tolypothrix tenni, Westiellopsis prolifica, Scytonema hoffmani.
Antioxidant metabolites extracted from the database. Aphanothece halophytica, Gloeocapsa sp. , Euhalothece sp. , Microcystis aeruginosa, Arthrospira sp. CU2556, Lyngbya sp. CU2555, Leptolyngbya sp. , Phormidium sp. , Lyngbya cf. aestuarii, Microcoleus chthonoplastes, Microcoleus sp. , Oscillatoria spongelidae, Trichodesmium spp. , Anabaena sp. , Anabaena doliolum, Anabaena variabilis PCC 7937, Nostoc sp. , Nostoc commune var. Vaucher, Nostoc commune, Scytonema sp. , Nostoc punctiforme ATCC 29133, Nostoc sp. HKAR-2 and HKAR-6,.
Human Endogenous Retroviruses (HERVs) and Mammalian apparent LTRretrotransposons (MaLRs) are retroviral sequences that integrated into the germline cells millions year ago. Transcripts of these LTR-retrotransposons are present in several tissues, and their expression is modulated in pathological conditions, although their function remains often far from being understood. In this work, we focused on the HERVs/MaLRs expression and modulation in a scenario of immune system activation. We used a public dataset of Human Peripheral Blood Mononuclear Cells (PBMCs) RNA-seq from 15 healthy participants to a clinical trial before and after the exposure to Lipopolysaccharide (LPS), for which we established an RNA-seq workflow for the identification of expressed and modulated cellular genes and LTR-retrotransposon elements.
In any case, many intriguing questions about the effective role of HERV and MaLR expression in immunity are still unsolved, and, in this respect, identifying the individual LTRretrotransposon genomic localization and coding capacity could help to understand of their potential effects in this context  (32). In this context, with the aim of investigating possible interactions between LTR-retrotransposons and the immune host response, we performed a comprehensive description of HERV and MaLR transcriptome observed in the analysis of a public RNA-seq dataset of PBMCs samples from patients in vivo stimulated with LPS. This experimental design has allowed mimicking an inflammation gram-negative induced. We focused our attention on the identification of specific expressed loci, as well as of those that were modulated as a result of stimulation with LPS.
Description of the LTR-retrotransposon transcriptome in PBMCs With the aim to detect the HERV/MaLR transcriptome in PBMCs, we defined an RNA-seq pipeline to be used on a public RNA-seq (GEO:GSE87290) that included the PBMC transcriptome of 15 healthy volunteers before and after in vivo stimulation with 1ng/Kg LPS. Using this pipeline we could discriminate 424,515 loci from the hervgdb4 database, including those of 197,341 HERVs and those of 227,174 MaLRs  (33). Importantly, as the hervgdb4 database has been created as part of the design of Affymetrix HERV-V3 array probes  (33) , the HERV and MaLR loci are included in the database as 881,603 hervgdb4 fragments (Fig. S1 )  (33). Moreover, according to the annotation accuracy of the fragments and to their source, the loci were part of two different subsets, i) the highly informative HERV_prototypes and ii) the roughly annotated HERV_Dfam and MaLR_Dfam both collected from the Dfam database (Fig.
HML-10, HML-8 and HML-9 groups were also very active, with a proportion of 25. 8%, 26. 4% and 25% expressed loci, respectively. In general, the II beta-like groups were those showing overall the greatest percentage of activation. The proportions of expressed proviruses compared to solitary LTRs within the 3 classes were 55%, 61% and 64%, respectively (data not shown). Furthermore, in order to better define the HERV transcriptome in PBMCs, we decided to analyze the most intact proviruses that have been identified, classified and characterized in Vargiu et al. (4). These sequences have been collected by Vargiu et al. after the analysis of the software RetroTector (ReTe) on the human genome assembly GRCh 37/hg19, which searched for the presence of conserved motif hits and from these allowed to reconstruct the coordinates of 3173 HERVs satisfying distance constraints  (4, 35).
We hence analyzed the modulation of these human genes, observing that all of them were up-regulated as a consequence of LPS stimulation, as summarized in Fig. 6. The fragment with the lowest adjusted p-value (6. 99E-36) was 081524901ERVH5LU3I, at coordinates chr8:103002077-103002365. This fragment is the U3 region of a 5'LTR belonging to an HERVH provirus (chr8:103002064-103004587), and its expression levels were increased from an average TPM value of 2. 4 to an average TPM value of 124. 5 after LPS stimulation. It is worth to note that, even if this LTR sequence is not co-localized with coding genes, it is integrated in a promoter-flanking region that is affected by copy number variation according to ENSEMBL annotations (data not shown), possibly suggesting a potential transcriptional control role.
Discussion We used an RNA-seq approach and the hervgdb4 database  (33)  to assess an overview of the LTR-retrotransposons transcriptome in PBMCs. This analysis has allowed, for the first time, to investigate HERVs and MaLRs proviruses and solo LTRs expression in human patients by mimicking gram-negative bacterial infection, reveling a total of 35686 LTR-retrotransposon elements expressed in PBMCs. Previously, the same database has been used by  Mommert et al. (29)  to identify expressed and modulated loci in an ex vivo system of LPS stimulation and endotoxin tolerance, through microarray analyses  (29). Interestingly, the percentage of expressed elements in our in vivo model (6. 8% of hervgdb4 fragments and 8. 4% of hervgdb4 loci expressed) was slightly higher than the one measured in in vitro experiments (5.
Moreover, the use of the Vargiu et al. database (  4 ) has allowed us to analyze the expression of the most intact HERV proviruses for which we can hypothesize a higher probability of protein production. The pattern of counts distribution among classes of the most intact HERV proviruses is similar to the one obtained when considering the hervgdb4 loci, and it is mostly in agreement with the information on HERV-H, HML-2, HERV-E and HERV-W transcriptional activity in PBMCs already reported in previous studies  (37). Importantly, this study has given information on the overall expression of the groups, but not on the single loci. Moreover, the expression of HERV-E has been reported to be characteristic of only a small percentage of the subjects analyzed, while a large portion of both hervgdb4 loci (the 24% of the group) and most intact HERVs (the 28% of the group) is expressed in PBMCs, in the present study.
RNA-seq data and quality control We used a RNA-seq dataset public available (GEO:GSE87290), including the transcriptome of PBMCs from healthy humans (n=15) before and after 1ng/kg LPS exposure. We checked the quality of the RNA sequences by using FastQC Galaxy Version 0. 72 (http://www. bioinformatics. babraham. ac. uk/projects/fastqc). Low quality reads were trimmed with Trim Galore! V. 0. 4. 3. 1 (https://github. com/FelixKrueger/TrimGalore). All the mentioned analyses were done on Galaxy release_17. 09 locally installed (http://galaxyproject. org/).
Reads mapping and counting HISAT2 Galaxy Version 2. 1. 0 was used with default parameters to map reads to genome assembly hg38. We assessed the quality of the alignments by using the stats function of bamtools Galaxy Version 2. 0. 1  (50). We counted the reads mapping to each hervgdb4 fragment and human gene by using the "union" mode in htseq-count Galaxy Version 0. 6. 1galaxy3 (51) , and hervgdb4 database  (33)  and gencode. v27 (52)  for respectively LTRretrotransposon and gene annotations. All the mentioned analyses were done on Galaxy release_17. 09 locally installed (http://galaxyproject. org/). We calculated the expression values of the expressed hervgdb4 fragments and genes as Transcripts Per Million (TPM).
Raw data filtering and transcriptome analysis We selected all the genes and hervgdb4 fragments with at least 1 count in at least 10 samples, conventionally considering them as expressed in at least one experimental condition and we used the obtained data for the analysis of HERVs/MaLRs transcriptome in PBMCs. We used the GenomicRanges v. 1. 30. 3 (53) R (version 3. 4. 4) package to obtain the coordinates of the most intact HERV proviruses (4) from those of the expressed hervgdb4 fragments.
Differential gene and hervgdb4 fragment expression analysis We performed a differential expression analysis of the genes and hervgdb4 fragments by using the DESeq2. v. 1. 18. 1 R (version 3. 4. 4) package  (54)  and the filtered raw counts as input. During the analysis, false discovery rate/adjusted p-value were used for multiple test comparison according to the Benjamini-Hochberg procedure (55). We used a threshold (FDR <= 0. 01 and absolute values of log2 Fold Change >= 1) to identify the modulated elements. The differentially expressed hervgdb4 fragments were visualized in a volcano plot built by using ggplot 3. 0. 0 in R (version 3. 4. 4). We sorted the hervgdb4 fragments by adjusted p-values to recognize the mostly differentially expressed. We compared the TPM expression values with the filtered adjusted p-values (<= 0.
This short article presents loss rates of honey bee colonies over winter 2017/18 from 36 countries, including 33 in Europe, from data collected using the standardized COLOSS questionnaire. The 25,363 beekeepers supplying data passing consistency checks in total wintered 544,879 colonies, and reported 26,379 (4. 8%, 95% CI 4. 7-5. 0%) colonies with unsolvable queen problems, 54,525 (10. 0%, 95% CI 9. 8-10. 2%) dead colonies after winter and another 8,220 colonies (1. 5%, 95% CI 1. 4-1. 6%) lost through natural disaster. This gave an overall loss rate of 16. 4% (95% CI 16. 1-16. 6%) of honey bee colonies during winter 2017/18, but this varied greatly from 2. 0 to 32. 8% between countries. The included map shows relative risks of winter loss at regional level. The analysis using the total data-set confirmed findings from earlier surveys that smaller beekeeping operations with at most 50 colonies suffer significantly higher losses than larger operations (p <.
Monitoring of losses of managed honey bee colonies is a core activity of the non-profit honey bee research association COLOSS. The COLOSS monitoring began in 2008, with a focus on losses over winter, the most important season for colony loss in Europe though not necessarily in all other countries. This ongoing research effort now involves many European and some additional countries, who undertake annual national surveys of beekeepers via a self-administered questionnaire involving standardized questions for comparability of responses  (van der Zee et al. , 2013). The data collection is organized at national level and takes place in different ways depending on the country, including internet, paper and email surveys and telephone calls. Here, we present summary results from the COLOSS survey of losses over winter 2017/18, conducted in spring 2018.
The Netherlands submitted data after being absent in the 2017 survey for the first time since the COLOSS monitoring began. More than 27,000 responses were submitted in total, of which 783 limited responses from the Netherlands unfortunately only allowed calculation of the mortality rate (percentage of wintered colonies which were reported as dead after winter), but a further 25,363 beekeepers provided valid and usable loss data according to the above checks. Of the 544,879 colonies wintered by these 25,363 beekeepers, 26,379 (4. 8%) colonies were reported lost due to unsolvable queen problems, 54,525 (10. 0%) colonies were reported dead after winter and 8,220 (1. 5%) colonies were reported as lost due to natural disaster. The numbers of participating beekeepers and colonies wintered are also the largest represented so far in our surveys.
For example, the UK had relatively high loss rates, as did most of the regions shown for Spain, while for Poland and Germany differences in risk levels can be seen between regions. Unfortunately, for some countries we still have data only from some regions, rather than national data. This situation has improved in Italy and Spain over the years of the COLOSS surveys, though relatively few regions of France, Bulgaria and Portugal are covered at present. Survey conditions in Algeria are difficult, and achieving national coverage is a challenge. The overall loss rate in winter 2017/18 was highest in Portugal (32. 8%), a new country to the survey. Other countries with high losses (above 25%) were Slovenia, Northern Ireland, England, Wales, Italy, and Spain, countries mostly in Western Europe. This pattern is similar to the results for winter 2015/16, but different from the last year.
For winter 2016/17 the highest winter loss rates were for Germany, Spain, Mexico, Malta, and Serbia. Bulgaria, another new country to this monitoring study, had the lowest loss rate, of just 2. 0%, though based on data from only 27 professional beekeepers. Other low loss rates were found for Belarus, Serbia, Israel, Algeria, and Slovakia (all 10% or lower). A year previously, loss rates were lowest in Norway, Northern Ireland and Algeria, and the year before that in Central Europe. Although most rates of loss from natural disaster were very low, the two highest rates this time were above 5% (Mexico) and 10% (Portugal). Winter losses related to queen problems varied between 1. 1% in Bulgaria to 20. 3% in Slovenia, whereas for winter 2016/ 17 the rate of this loss for Slovenia was the lowest among the participating countries.
For Sunflower, only for France did those responding "Yes" have a lower loss rate. Heather was significant for very few countries, and only for Scotland did those responding "Yes" have a lower loss rate. Autumn Forage Crops were also only significant for a small number of countries, but the beekeepers responding "No" had the lower loss rate. In considering each of the above forage sources in a single factor model for each country, many statistical tests have been carried out. Hence, at country level some model effects (Table  1 ) are likely to be significant by chance alone. Therefore in the text we focus on the relatively fewer results of the tests for the overall dataset, each of which is both highly significant and based on a very large sample size. The sizes of the significant forage effects for the overall data-set are relatively small; for example, concerning the question of whether Maize constituted an important forage source, the loss rates were 17.
Given the various possible explanations for the forage effects in the current data-set, the impact of agricultural versus natural forage should remain an important factor for future evaluation, especially in view of the current ban on various uses of widely applied neonicotinoid insecticides by the EU member states  (OJEU, 2018). In conclusion, even though our current loss data are based on over 25,000 beekeepers from 36 countries, we aim in the loss monitoring surveys to obtain higher response numbers at national level, and high-quality data. Obtaining a high response rate is important to avoid bias and to achieve higher precision in loss estimation, but also to enable better dissection of the risk factors. In practice national co-ordinators in some countries have reported difficulty in achieving sufficient cooperation to achieve a large sample size, including in Malta, Mexico, Israel and Serbia.
Acknowledgements The colony loss monitoring group which carried out this study is a core project of the COLOSS research association (prevention of honey bee colony losses), which supports regular workshops facilitating research discussions and collaboration between group members. The authors thank very much all the beekeepers who gave their time to complete the COLOSS questionnaire providing the data for this work, and the additional COLOSS partners who contributed to survey organisation, data collection and/or data processing. The authors are also grateful to various national funding sources for their support of some of the monitoring surveys [including, in the  Republic of Serbia, MPNTR-RS , through Grant No. (III46002),  Slovenian Research Program   P1-0164 , and " Zukunft Biene " (grant number:  100972 ) in Austria], and acknowledge the financial support by the  University of Graz  for open access.
Use of non-animal methods As already noted, non-animal alternatives to the LLNA have been validated, adopted by the OECD and which therefore appear in detailed guidance documents explaining how they should be used to make hazard identification decisions  (ECHA, 2017). Consequently, it was interesting to hear the report from ECHA concerning their experience, not least since regulations now require that new testing must start with in vitro/in chemico assessment. Some 315 substances (from the database of 17400) had been assessed solely using non-animal tests; of these 235 (75%) were not classified, whereas 80 were classified as skin sensitising. This proportion of classified/not classified is broadly similar to the ratio found with the LLNA based testing e. g. Angers-Loustau et al. (2011). Interestingly, only 11/80 positives were sub-categorised using the non-animal data, a challenge already anticipated for discussion by one of the workshop breakout groupssee below.
However, PS-4 is positive in the DPRA, but negative in the KE2 method. If one followed the advice published recently, that just two assays are best  (Roberts and Patlewicz, 2018) , the outcome would be positive, i. e. correct, since the positive dominates the negative. Using the preferred "regulatory" approach of undertaking a third assay, the h-CLAT indicates PS-4 as negative, in practice an incorrect outcome. Note however, that two further (so far unvalidated) in vitro assays were in fact positive. Perhaps the key point here is that no predictive system will be perfect, nor can perfection be achieved simply by generating more and more data. A second set of examples is presented in Table  2 , which involves an evaluation of a number of fragrance and cosmetic ingredients. The substances involved were proprietary, so only the general experience is presented, but a couple of key points are well made by this dataset.
• Related to the above, the Workshop recognised the need for a reli- able and substantial database of substances, categorised according to potency, using all the available evidence (mouse, human, guinea pig). The workshop was well aware that guinea pig data offers relatively little in terms of potency assessment, but also LLNA potency assessment, whilst valuable, has imperfections (e. g. Basketter et al. , 2009; Api et al. , 2017). Similarly, the recent collations of > 230 substances categorised according to their relative potency in humans (wholly on the basis of human data) contains its own, distinct, limitations, not least that many "no effect levels" are by no means the highest value  (Api et al. , 2017). Nevertheless, it was deemed to be of great value to amass an optimal dataset, supported by a general consensus concerning its accuracy, for example, perhaps an extended version of that already collated by Cosmetics Europe  (Hoffmann et al.
Conclusions/recommendations As an outcome of the EPAA cross sector workshop on skin sensitisation the following conclusions/recommendations were made: • As the experience in using methods and defined approaches has increased, there is still a need to build confidence, both in use and in regulatory acceptance, for example by discussing experience, learnings and difficulties within a users' forum. • In order to progress the evaluation of different approaches for the characterisation of potency and risk assessment, the participants highlighted the benefit of sharing case studies between the users and regulators. • Further, in order to build confidence in potency assessment the workshop recognised the need for a reliable and substantial database of substances/products, categorised according to potency, using all the available data (mouse, human, guinea pig).