dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions Among the various iDILI databases or websites, the implementation of the U. S. Liver-Tox database in 2013 was highly appreciated as a new data source of drugs causing iDILI. However, its use by clinicians can be limited by problems of data quality, including missing detailed narratives and evidence-based case features, a lack of standard definitions such as liver injury, and the failure in all cases to use a strong method for evaluating causality such as RUCAM, although use of this method was initially promised by LiverTox. As it presently stands, there are major gaps between the promised details and the provided facts, as shown in this analysis. These gaps were unexpected and considered as a paradox, calling now for improvements. In essence, LiverTox will gain appreciation if iDILI cases of better quality are included.
Data Availability Statement: Data supporting the results of the analysis can be retrived from the following sources: LiverTox. Clinical and Research Information on Drug-Induced Liver Injury. Bethesda (MD): National Institute of Diabetes and Digestive and Kidney Diseases; 2012-. LiverTox Database. Updated: 9 February 2021. Available at: https://www. ncbi. nlm. nih. gov/books/NBK547 852/ accessed on 19 May 2021; Hoofnagle JH. Relaunched LiverTox remains important resource to diagnosis DILI. Available at: https://www. aasldnews. org/relaunched-livertox-remains-importantresource-to-diagnosis-dili/ accessed on 19 May 2021.
Therefore, one can deduce, using Similar software, that the noncentrosymmetric I4 subgroup is more appropriate to the SMO structure compared to the other possibility which is the centrosymmetric I4/m (see atomic positions in table  S1 ). This noncentrosymmetric character of the SMO structure can be attributed to an ordering of Sr atoms within the structural tunnels that we call tunnels C (see figure  2 ). Indeed, a full occupation by the strontium in tunnels C is not possible because there are many Sr-Sr distances equal to half c-axis (≈ 2. 87 Å) which is too short and electrostatically unfavourable. Moreover, the asymmetric unit in the noncentrosymmetric group I4 (aT, bT, cT) contains 7 Sr, 12 Mn and 24 O independent atoms and only some Sr-atoms are in special positions (see Table  S1 ). Considering a full occupation, the composition would correspond to the formula Sr5Mn12O24.
AFM characterization The measurements were performed using a Keysight 5500LS while a special ultra-low leakage amplifier is used in trans-impedance configuration (TIA) with part number ADA4530-1. The TIA amplifier is populated with a 10 GOhm feedback resistor, which give us a current noise level of 1:59 fA/Hz 0. 5 while the leakage current in these conditions is maintained at less than 10 fAdata obtained from the amplifier datasheet of Analog Devices Inc. At the output of the current amplifier, a cascade voltage amplifier increases the overall gain of the system. The RC time constant of the amplifier is estimated at 6 ms, which equals a strain feedback capacitor of 0. 1 pC. The two cascade amplifiers are calibrated with a known test resistor and a known bias applied, to see the calibration curves.
Acknowledgements A. C-G, C. J, R. G-B and J. M. V-F. acknowledges the financial support from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (No. 803004 ) and the  French Agence Nationale de la Recherche (ANR) , project  Q-NOSS ANR   ANR-16-CE09-0006-01. This project has received funding from the  EU-H2020   research and innovation Program  under grant agreement No 654360 having benefitted from the access provided by  ICMAB-CSIC in Barcelona  within the framework of the  NFFA-Europe Transnational Access Activity. This project has received funding from the European's  Union Horizon 2020 research and innovation programme  under Grant No. 823717-ESTEEM3. , the  Spanish Ministry of Economy  and Competitivity through Project  MAT2017-82970-C2-2-R , and the  Aragon Regional Government  through Project No.
Chlorophyll a fluorescence Chlorophyll a fluorescence (OJIP test) in vivo was measured using a Handy Pea fluorometer (Hansatech Ltd. , UK) run by the HandyPea software, as described in  Aksmann and Tukaj (2008)  and  Aksmann et al. (2016). The following OJIP parameters were analyzed: RC M -fraction of active PSII reaction centers in the sample referring to time, when F M (maximal fluorescence value under saturating light intensity and fully reduced Q A pool) is reached; performance index (PI) -an overall expression indicating algal 'vitality'; the activity of the water-splitting complex on the donor side of PSII (F V /F 0 ); the quantum yield of energy dissipation (φD 0 ); the maximum yield of primary photochemistry (φP 0 ); the efficiency of moving the electron by the trapped exciton further than Q A (ψ 0 ); the maximum yield of the electron transport (φE 0 ); the quantum yield of the electron transport from Q A -to the PSI end electron acceptors (φR 0 )  (Chen and Cheng, 2009; Krüger et al.
For further preparation and isolation of total RNA, the Total RNA Mini Kit (A&A Biotechnology, Poland) was used according to the manufacturer's instructions for cell cultures. The first strand of cDNA was synthesized from 200 ng of total RNA by oligohexamer (dT) primers, using the iScript™ cDNA Synthesis Kit 170-8891 (Bio-Rad Laboratories, Inc. ). As a template to perform real-time PCR (RT-PCR), the cDNA pool was used, and the log-linear phase was set at 45 cycles. The RT-PCRs were performed in the presence of the fluorescent DNA-binding dye SYBR Green I (iTaq™ Universal SYBR® Green Supermix, Bio-Rad Laboratories, Inc. ), following manufacturer's protocol. The gene encoding α tubulin 1 (TUA1) transcript was used as internal quantifying control, as its level was stable during the experiment (data not shown).
In the first stage, all samples were heated to 95 • C for 3 min, followed by 45 cycles of 95 • C for 15 s, 55 • C for 30 s, 72 • C for 30 s, and a final extension step at 75 • C for 2 s. The C T value (the fractional cycle number at which fluorescence passes a fixed threshold) was used to compare the target RNA values in the samples. The calculations were performed as described in the manufacturer's protocol  (Bio-Rad Laboratories, Inc, 2010). Transcript abundance of each analyzed gene was expressed as the relative transcript level referenced to the value obtained for the 0th hour, given as a value equal to 1. All PCR reactions were performed using the CFX96 Touch™ Real-Time PCR Detection System and the included CFX Maestro™ Software (Bio-Rad Laboratories, Inc. ) for experimental design and data analysis.
Statistical analysis Statistical analysis was performed using the software packages Microsoft Excel (Office 356), Statistica 9. 0 (StatSoft), and Origin (Origin Lab Corporation, USA). All data were expressed as a mean of at least three independent experiments ± SE. Significant differences between control and treatment groups were defined using a selection of statistical tests (e. g. , Mann-Whitney U test, Student's t-test); p-value < 0. 05 was considered significant.
Photosynthetic parameters In the synchronously grown cultures, the cell number remained constant during the light period of the cell cycle, while the cell size (volume) changed significantly. Thus, the photosynthetic pigment content in each experimental variant was recalculated per unit of cell volume (100 fL). At the beginning of the cell cycle, in control zoospores, the contents of photosynthetic pigments were as follows: 0. 34 ± 0. 02 pg × (100 fL) -1 for chlorophyll a, 0. 16 ± 0. 06 pg × (100 fL) -1 for chlorophyll b, and 0. 10 ± 0. 01 pg × (100 fL) -1 for carotenoids. After 24 h, the values in the control cultures reached 0. 38 ± 0. 05 pg × (100 fL) -1 for chlorophyll a, 0. 29 ± 0. 06 pg × (100 fL) -1 for chlorophyll b, and 0. 08 ± 0. 02 pg × (100 fL) -1 for carotenoids (data not shown).
2014 Nunes et al. , 2015;; Tricarico et al. 2018). In total, the Global Register of Introduced and Invasive Species (GRIIS,  Pagad et al. 2018 ; National Database on Alien Species, IS-PRA) lists in excess of 3,000 known alien species in Italy, at present, with 15% of those species considered invasive. These have occurred through various pathways, such as agriculture, angling or horticulture, to name only a few. As such, Italy is today referred to as a hotspot and gateway for several groups of invasive species  (Occhipinti-Ambrogi 2002; Occhipinti-Ambrogi et al. 2011; Castaldelli et al. 2013; Nunes et al. 2014 Nunes et al. , 2015)). In particular, aquatic ecosystems in Italy have been burdened with well-intentioned introduction efforts in support of recreational angling (Occhipinti-Ambrogi 2002;  Gherardi et al.
We hypothesise that these costs are substantial, although a considerable difference in costs amongst ecosystem types can be expected. Based on data from current literature, we synthesised and described, for the first time, the costs of invasions on the Italian economy. More particularly, we first aimed at depicting how these costs are distributed according to the a) invasive species or broader taxonomic groups (i. e. classes or orders), b) socioeconomic sectors, c) geographic regions and d) cost types, whilst examining the effects of habitat type within each of these descriptors. Second, we determined how the overall costs have changed since cost reporting began and whether these costs of invasions are depicting a particular trend over time.
Methods To investigate the invasion costs on the Italian economy, we used cost data collected in the InvaCost database  (2,419 entries; Diagne et al. 2020a, b)  concerning the global costs of invasive species, based on published literature, enabling comprehensive quantification of costs associated with invasive species at various spatial and temporal scales. This updatable database was constructed, based on both published and grey literature and enables the most comprehensive cost quantifications associated with invasive species. All the methodological procedures, from literature searches to data collation, have been detailed elsewhere  (Diagne et al. 2020b; Angulo et al. 2021a). All cost entries were standardised to a common and up-to-date currency (US dollars (US$) 2017). We complemented the data following two specific ways: on one hand, we have added cost data collected from non-English documents, including Italian (5,212 entries;  Angulo et al.
2021a. https://doi. org/10. 6084/m9. figshare. 12928136). Further, we added supplementary cost data from new references containing cost information (ca. 2,300 entries; https://doi. org/10. 6084/m9. figshare. 12928145. v1). The period of estimation across reported costs varied considerably, spanning periods of several months to several years. For the purpose of the analysis and to derive the total cumulative cost of invasions over time, we considered the duration time (i. e. number of years) over which each cost occurred. For this purpose, we defined the duration of each cost entry. We based this on the difference between the starting ("Probable starting year low margin" column) and ending ("Probable ending year low margin" column) years of the reported costs. When no period of impact was specified in one and/or the other column(s), we counted only a single year unless the authors were certain that the costs had been repeated up to a certain year.
The obtained figures corresponded to the total cumulative cost along a defined period for each entry. From the full database, we identified cost entries related to the Italian economy by filtering data using the 'Official country' column. In addition to the already available information present in the database, we added six further cost records, summarised in the work of one of the authors  (Tricarico et al. 2018 ) and sent them to invacost@updates. fr to be integrated into the InvaCost database. The final dataset can be found in Suppl. material 1. Finally, the invasion costs were specifically estimated from all entries according to method reliability, i. e. indicating the reliability of cost estimates, based on the type of publication and method of estimation. Estimates in peer-reviewed publications or official reports or with documented, repeatable and/or traceable methods were designated as High reliability; all other estimates were designated as Low reliability  (Diagne et al.
For costs that were estimated at a spatial scale below the Country level within Italy, we considered them in a finer-scale Regional analysis, using the 'Spatial scale' and 'Details' columns. We estimated global average annual costs of invasive species in Italy represented in the InvaCost database by quantifying the temporal trends in cost accumulations. We performed these estimates for the period from 1990 to 2020. To investigate invasion costs in Italy over time and, hence, to identify whether costs are saturating over time or continuously increasing, we used the summarizeCosts function of the 'invacost' R package  (Leroy et al. 2021 ). We thus determined decadal average costs since 1990, as well as the cumulative and average annual cost of that entire time period. Overall, this approach allowed for trends in raw data cost to be examined over time, corresponding to the impacted year when the cost was incurred.
Results There were 50 economic cost entries (40 of which from the original InvaCost database) associated with Italy. After expansion, the collective 207 expanded database entries totalled US$ 819. 76 million between 1990 and 2020 (US$ 26. 44 million per year). From these, 76% of costs were actually realised (i. e. assigned to Observed category in the 'Implementation' column) and 97% of the total costs were considered as of High reliability ('Method reliability' column) and, thus, derived from peer-reviewed or traceable sources.
Economic costs by taxonomic group A high proportion of the filtered database entries concerned invasive mammal species (number of expanded database entries n = 88; US$ 149. 81 million, Table  1 ). Invasive insects represented the second most reported class (n = 57; US$ 273. 42 million), followed by invasive Malacostraca (n = 33; US$ 24. 45 million). In turn, invasive plants (n = 5) and invasive nematodes (n = 13) had a combined overall cost of US$ 371. 72 million. Looking at specific orders, invasive Asterales contributed, with US$ 344. 80 million, the most to the cost burden, followed by invasive Coleoptera (US$ 153. 81 million), Rodentia (US$ 149. 43 million) and Diptera (US$ 116. 22 million). Taken together, all other orders accounted for less than US$ 100. 00 million (Table  1 ; Fig. 1 ).
Economic costs by geographical region For several publications within the database, specific information about the region where the cost actually occurred was provided. This indicated a difference in economic cost data between the north and the south of Italy. Regional information was present only for northern/central regions, which included Emilia-Romagna (US$ 99. 05 million; n = 27), Latium (US$ 24. 27 million; n = 27), Trentino (US$ 20. 27 million; n = 7), Lombardia (US$ 10. 45 million; n = 14), Piedmont (US$ 762. 63 thousand; n = 15), Tuscany (US$ 547. 21 thousand; n = 17), and Umbria (US$ 22. 33 thousand; n = 1). From southern regions, only Sicily had reported costs (US$ 6. 70 million; n = 4). Accordingly, there was a distinct lack of cost estimation concerning States in southern Italy.
Discussion The overall cost of invasive species in Italy has been estimated at US$ 819. 76 million  (€ 704. 78 million)  between 1990 and 2020. This can be seen as a conservative total cost  (Diagne et al. 2021)  and is most likely an underestimate of the actual costs, especially as costs have been occurring for several decades prior to the first Italian record and because very few known invasive taxa have reported costs. Indeed, many species or invasions have not been studied for their economic impact and many of those that have been studied are not publicly available (e. g. governmental reports, grey literature) and costs present therein were thus not recorded in the InvaCost database. Furthermore, it should be noted that some types of costs are simply difficult to quantify, especially regarding ecosystem services or damages and losses  (Spangenberg and Settele 2010).
The lack of such data is critical because it can give the false impression that costs due to invasive species are lower than they actually are. Consequently, due to a lack of synthesis of cost information from multiple sources, as well as disparate reporting amongst invasive taxa and invaded regions, decision-makers may not have the necessary information to evaluate the costs and benefits of invasive species management actions. As a result, this limits rationale to invest in preventative or control measures to reduce or manage invasions. However, while we acknowledge that the InvaCost database does not capture all available costs through the systematic searches employed, our data indicate that the economic costs of invasions and studies are growing exponentially over time and are unlikely to saturate soon.
Taxonomic, habitat and regional biases across current literature are prevalent, resulting in only a subset of invasions being evaluated. For example, to our knowledge, costs associated with phytosanitary inspections are not available. Moreover, several aquatic invasive species which are known to damage Italian freshwater ecosystems (e. g. freshwater crayfish) were only anecdotally recorded in the database without identifiable species, given a distinct lack of cost estimation. Additionally, impacts associated with invasive crayfish species, such as levee damaging burrowing behaviour  (Haubrock et al. 2019) , habitat engineering  (Barbaresi et al. 2004 ), in addition to direct impact on native biodiversity  (Gherardi and Acquistapace 2007) , were not present in the InvaCost database. This may be due to the cultural and financial benefits of introductions outweighing potential economic cost or the nature of ecological impact, leading to nonvalue costs, which are inherently harder to estimate (e.
g. native community changes, ecosystems changes) and, therefore, not reported. Despite the number of invasive species in Italy exceeding 3000  (Gherardi et al. 2008; Pagad et al. 2018 ; National Database on Alien Species, ISPRA), the recorded costs of invasive species in Italy concerned only very few species. For example, fish were totally missing, despite known invasions and costs. In addition, reported costs in Italy were lower than in many other EU countries  (Haubrock et al. 2021b ). Indeed, national-scale costs were an order of magnitude higher in the UK  (Cuthbert et al. 2021a) , Germany  (Haubrock et al. 2021c) , France  (Renault et al. 2021)  and Spain  (Angulo et al. 2021b ). This could reflect the insufficient investment by the Italian public services into the surveillance and management of these species (i.
2011; Kilian et al. 2012) , as it increases the attraction of fisheries, for example, for recreational angling. Therefore, in future studies, it would be important to address the costs of invasions for the economy, whilst considering potential values, particularly in terms of angling. In addition, we identified an exponential increase in costs through time since 1990, with annual average cost exceeding US$ 50 million in recent years. Given that the US$ 819. 76 million total is attributed to only 15 out of the more than 3,000 known alien species in Italy  (Pagad et al. 2018 ; National Database on Alien Species, ISPRA) and probably far from exhaustively for these 15 species (see above), it can be easily appreciated how significantly underestimated this overall cost is, regardless of how they are representative of these few species.
For instance, only one database entry referred to the grey squirrel (Sciurus carolinensis), which has been studied and managed thoroughly in Italy  (Genovesi and Bertolino 2001; Martinoli et al. 2010)  and no entries regarded the well-investigated costs from the impacts of invasive crayfish were found  (Gherardi et al. 2014). Further, the costs of management activities in Italy spent solely on IAS within the Union Concern list of EU Regulation 1143/2014, between 2016 and 2018 (in total 48 listed species for this period, 31 of which are present in Italy and 20 that were managed between 2016 and 2018), were estimated at EU€1. 85 million (~ US$2. 17 million) for those two years  (Alonzi et al. 2020). Annually, this figure suggests that the monetary investments into management efforts were between 24-times (considering the entire period) and 46-times (considering only the two recent years) lower than the total costs inferred by IAS at the national level.
However, it should be noted that (i) today, 42 of the now 66 IAS listed are present in Italy and, (ii) this cost does not consider the allocated funds for IAS at regional levels. As a result, the actual, unreported or unevaluated economic costs of all IAS in Italy must be staggering. In conclusion, the presented economic costs of biological invasions in Italy will contribute to informed decision-making at the national level and, thus, providing economic incentives for mitigating the arrival, spread and damage of invasive species. The relatively-high costs reported for Italy, despite the low number of entries in the database contrasting with the high number of invasive species, underlines the need for prevention and surveillance programmes, as costs spent on these are generally considered several magnitudes lower than active management  (Leung et al.
Acknowledgement The authors acknowledge the  French National Research Agency  ( ANR-14-CE02-0021 ) and the  BNP-Paribas Foundation Climate Initiative  for funding the  InvaCost  project that allowed the construction of the InvaCost database. The present work was conducted following a workshop funded by the  AXA Research Fund Chair of Invasion Biology  and is part of the  AlienScenario  project funded by  BiodivERsA and Belmont-Forum  call 2018 on biodiversity scenarios. RNC is funded through a research fellowship from the  Alexander von Humboldt Foundation. CD is funded by the  BiodivERsA-Belmont Forum  Project " Alien Scenarios " ( BMBF/PT DLR 01LC1807C ).
Detection was performed by using WesternBright ECL (K-12045-D50, Advansta). Densitometric analysis was performed using ImageJ software (version 1. 50i). For DM2 patient biopsies, samples were lysed in SDS urea (50mM Tris HCl ph 7. 8; 2% SDS, 10% Glycerol, 1mM EDTA, 6M UREA, 50mM NaF, 5mM Na2P2O7) sonicated for 10 seconds, quantified by using a nanodrop and loaded on polyacrylamide gel. Muscle biopsies used for this study were performed primarily for diagnostic (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. purposes, after receiving an informed consent from all patients also regarding their future use for research purposes and in agreement with the guidelines of the Ethical Committee of Our Institution.
RNA interference in S2 cell lines S2 cells (DGRC, RRID: CVCL_Z232; tested negative for mycoplasma) were cultured at 25°C in Schneider's insect medium (Sigma) supplemented with 10% heat-inactivated fetal bovine serum (FBS, Gibco). RNAi treatments were carried out according to  (Somma et al, 2008). dsRNA-treated cells were grown for 4-5 days at 25°C, and then processed for biochemical analyses. PCR products and dsRNAs were synthesized as described in  (Somma et al, 2008). The primers used in the PCR reactions were 35 nt long and all contained a 5' T7 RNA polymerase binding site (5'-TAATACGACTCACTATAGGGAGG-3') joined to a gene-specific sequence.