dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison to the Pyo CHEM synthesis (abiotic autoxidation, in red). The common precursor 2,5-DMPA could also be synthesized from 2,5-dimethoxyacetophenone by a Willgerodt-Kindler reaction type  25. Step 4 is the final HCl precipitation followed by washing and drying. Doubling the amount of HBr (step 1) led to incomplete demethylation and the extra formation of 2,5-dihydroxyphenylacetaldehyde (~ 6%) identified from the HPLC-DAD spectrum (λ max 292 nm) and the EI-MS profile (molecular ion [M + 2TMS] at m/z 294, characteristic fragments [M -CHO] at m/z 265, and [M -CH 2 CHO] at m/z 251), similarly to the NIST data bank and previous data  34. In step 2, the addition of sulfite (Na 2 SO 3 )  25  was unnecessary because the solution was immediately buffered to 6. 8 and the polymerization by the laccase followed (step 3).
Weighing precisely the final purified pigment remains the only valuable technique for the quantification of pyomelanin as well as other melanin. a Identified by  13  C solid-state NMR (Fig. 2  and Table  1 ). b To confirm the global structure of Mel SYNTH and Mel SEPIA , assays on their degradation by alkaline-H 2 O 2 were conducted similarly to those on human melanin  41. After centrifugation, 10 μL of the reaction volume was injected into an RP-HPLC(DAD)-QToF system in negative mode and confirmed the L-Dopa melanin structure by the presence of the two markers PTCA and PDCA. c Elemental analysis (C, H, N, S) of the pigments was performed by combustion on a Thermo Finnigan EA 1112 analyzer equipped with an autosampler, all managed by the Eager Xperience software (THERMO SCIENTIFIC, France).
Methods Chemicals and enzymes. Solvents of mass spectrometry grade were supplied by BIOSOLVE (Dieuze, France), media for human keratinocytes, and mouse fibroblasts cultures from DUTSCHER (Brumath, France), main chemicals including standard melanin from SIGMA. Natural melanin (Mel SEPIA, reference M2649) consists of purified eumelanin from the ink of Sepia officinalis. Synthetic melanin (Mel SYNTH , M8631) is an L-Dopa melanin obtained from L-Tyr in presence of H 2 O 2. HGA and HGA-lactone were used as standards for HPLC and the GC-mass data bank. The Aspergillus sp. laccase (SAE0050) consisted of a highly concentrated and brown miscible-water solution (density 1. 15 g/mL, stored at 4 °C) obtained by submerged fermentation of the recombinant Myceliophtora thermophile laccase expressed in Aspergillus oryzae.
Aliquots (25 and 500 mL) were dispensed into flasks and sterilized by autoclaving at 120 °C for 20 min. Ca-and Mg-chloride stock solutions were sterilized separately, and the accurate volumes were added to the medium. L-Tyr and 4-hydroxyphenylacetic acid (4-HPA) stock solutions (250 mM in milliQ-H 2 O, neutralized, heated moderately, sterilized by 0. 2 µm pore size filtration) were added before inoculation at 5 mM final concentration. The strain was pre-cultivated twice for 2 days each, in 25 mL of the same saline basal medium containing either L-Tyr or 4-HPA. The preculture served as inoculum at 10% (v/v) for the culture in 500 mL volume that was agitated at 150 rpm, until the A 400 nm no longer changed. To overproduce pyomelanin, 500 mL cultures supplemented by repeated addition of 4-HPA amounts were carried out using the experimental design AZURAD software (a company of Marseille).
Acknowledgements We thank  Morgane Miodini  for technical assistance in the optimization of the laccase process,  Jean-Valère Naubron  for FTIR spectra,  Magalie Claeys-Bruno  from the  Institut des Sciences Moléculaires  ( iSm2 ,  Marseille)  for her help in the Azurad software operation,  Gregory Excoffier  for the elemental analyses, and  Marion Rollet  from the  Institut de Chimie Radicalaire  ( ICR, Marseille ) for the determination of the molecular weights.
Thus, the step between field experiments and forensic cases application is high: Moreau  [15]  reviewed these pitfalls, but also acknowledged the interest of ecological decomposition experiments. He suggest that "the authors should explicitly recognize that the (successional field) study is descriptive, thus not allowing for transposition of the results to other situations or use in court". On the contrary, development-time datasets, which are quite simple to perform and an indispensable prerequisite for aging larvae, have received little attention during the last years. In this Special Issue, Matuszewski  [17]  highlights the need for development datasets for potentially useful species that regularly breed on cadavers, but for which no reference developmental data are available. For those species with wide geographical distributions, local developmental studies are required as there might be differences in the developmental rates between populations.
In this context, Shin et al. [18]  present baseline developmental data for the blow fly species Lucilia sericata in South Korea, commonly used as a forensic indicator and for which development-time datasets from other geographical regions are available  [17]. Regrettably, many researchers may be discouraged to perform basic developmental studies as they could sometimes be seen as "not innovative enough" for being published in some high-profile academic journals. In addition, as in the case of sample collection at the forensic scene, an updated review providing detailed and standardized rearing and sampling protocols for developmental studies would be highly beneficial and enable the comparison of different datasets  [17]. Laboratory analyses have indeed demonstrated that numerous biotic and abiotic parameters such as food type, larval behavior or bacterial load, to name a few, could significantly affect larval development  [19].
However, many other researchers and practitioners can face significant challenges if they approach this discipline for the first time. Again, updated and comprehensive review articles can be a valuable tool for those who aim to start forensic entomology in a particular country or region. In this Special Issue, Wang et al. [20]  review the recent progress in forensic entomology research and application made in China, providing an inventory of the different types of baseline studies conducted, as well as highlighting the need for proper entomological training for forensic practitioners. Collaboration between forensic practitioners and researchers is indeed of mutual benefit as it enables access to large forensic cases databases, providing new insights into the biology, ecology and forensic relevance of the insect species associated with human cadavers.
Such meta-analyses of forensic cases can identify, for example, patterns in the seasonal activity, the colonization of cadavers or the co-occurrence with other species, thus improving the interpretation of further forensic entomological evidence and stimulating additional research on forensically relevant species (e. g. ,  [21, 22] ). They can also shed some light on the forensic implications of traditionally neglected aspects of the insect activity on cadavers (e. g. ,  [23] ). However, these types of a posteriori analyses of forensic databases are still scarce. More frequent is the publication of case reports addressing one or a few particular cases (e. g. ,  [24] ). The publication of those particular case reports can be useful if, for example, they describe the occurrence of a species which had not previously been considered as forensically relevant  [25] , highlight how an entomological analysis can provide new and significant insights into a case  [26]  or show other utilities and applications of the evidence  [27].
Nonetheless, meta-analyses of large forensic cases datasets are desirable as they provide strong reference data for the most usual and required type of entomological analyses in case reports. In this sense, the preparation of a solid case report is pivotal, as it must show the reliability and significance of the performed entomological analyses. In this Special Issue, Kotzé et al. [28]  propose an overview of sections to be considered when drafting a forensic entomology case report, which could certainly contribute to the standardization of these reports among both experienced and novel practitioners. As a very specific area of study, forensic entomology is more often requested in unusual cases that require additional and/or alternative approaches, rather than routinely applied in forensic casework.
Even if a case requiring an entomological analysis is not particularly "unusual", it will always be unique, differing spatially and temporally from a priori similar cases  [5]. Therefore, the conclusions drawn from that entomological analysis and stated in a forensic report will be opinions based on science rather than undisputable scientific facts. That those opinions should be based on science means that they should rely both on a solid experience in the area and on related and published peer-reviewed studies. This emphasizes the importance of forensic entomology research, either if it consists of baseline studies stimulated by general needs (e. g. , development-time datasets) or on ad hoc experiments simulating the conditions of a specific case  [5, 17]. On the other hand, this also highlights the need for self-criticism and awareness among researchers and practitioners of the limitations of the application of forensic entomology  [29, 30].
Insect Development Most frequently, forensic entomologists estimate the age of immature insects collected on a death scene and use this information as the minimum PMI  [12]. The reference developmental data for the species that was collected on a death scene is necessary for such estimation. Because developmental data may vary between geographical populations of insects, it is recommended to use reference data from the closest population  [10, [13] [14] [15]. Although there is constant progress in this field, with new species and populations gaining developmental data, still much research needs to be done. A review of developmental datasets available for insect species colonizing cadavers in central Europe reveals that among the most extensively researched are cosmopolitan species that colonize cadavers shortly after death and that were frequently reported from indoor cases (Table  2 ).
Although several important species have many datasets (e. g. , Lucilia sericata or Calliphora vicina), there are still species that regularly breed in cadavers but for which no dataset has been published (e. g. , Lucilia caesar, Hydrotaea ignava or Necrobia violacea) or only single datasets are available (e. g. , Stearibia nigriceps, Necrodes littoralis, Omosita colon or Necrobia rufipes). These species should become the hot taxa for forensic entomology research in Europe. The understudied species ought to be identified also for other geographical regions. Another point that needs our attention is the lack of standards and guidelines for developmental studies in forensic entomology. Several elements of the protocol for such studies were found to affect the quality of the resultant developmental data  [16] [17] [18] [19].
PAI may also support estimates of maximum PMI when insect evidence is absent  [104, 105]. PAI may be estimated using the temperature models for PAI  [106]. However, such models are available only for some taxa, and for several important taxa (e. g. , blow flies) PAI may not be estimated using the temperature data  [107, 108]. In such cases, insect succession studies with animal cadavers (preferably large pigs  [102] ) yield the best PAI reference data (e. g. , average seasonal PAIs). As for the PI, it has a more complex causal background than PAI, its predictions are inherently related with larger inaccuracy (Figure  1 ) and currently it may be approximated only based on the reference data from succession studies. Although in some habitats and locations robust PAI or PI datasets are available for many taxa, usually there is shortage of such data (Table  3 ).
In particular, indoor habitats need more attention. Therefore, pig decomposition studies to yield PAI and PI data of forensically relevant insects should be one of the priority research areas in forensic entomology. Third, decomposition experiments using pig cadavers may be useful to validate the PMI estimation protocols  [109]. Such experiments are especially suitable as proof-ofconcept studies or initial validation studies  [102]. Although validation of new methods is a priority in forensic sciences  [110] , datasets on the performance of insect-based methods for PMI are very limited. Validation using pig cadavers (ultimately also human cadavers  [102] ) should be another primary research area in forensic entomology (Section 6 of this article). There are guidelines for decomposition studies in forensic entomology  [102, [111] [112] [113] [114] [115].
Datasets on the pre-appearance interval (PAI) and the presence interval (PI) of the species that breed in large vertebrate cadavers in central Europe (species list compiled based on  [20] [21] [22] [23] [24] [25] ). I reviewed datasets derived from experiments performed in Europe and on pig cadavers only. A-adult stage PAI or PI, O-oviposition PAI, E-egg PI, L1-first instar larvae PAI, L-larval PI, L3-third instar larvae PAI or PI, Lm-mature larvae PAI or PI, P-puparial/pupal PAI or PI. S-spring, Su-summer, Au-autumn, W-winter. I-indoor habitats, Ou/uoutdoor, urban habitats, Ou/r-outdoor, rural habitats, F-forests. AT-Austria, ES-Spain, IT-Italy, PL-Poland, and PT-Portugal.
Unfortunately, minimum times needed to reach the post-feeding phase in large aggregations of larvae are not available for any species. Accordingly, it may be tempting to use minimum development times from the laboratory development studies, as there are many such datasets (Table  2 ). However, in such studies minimum development times are recorded at high and constant temperatures that may be suboptimal for the insects. Experiments using the tracking of blow fly larvae within aggregations indicated that they have a strong preference for the hottest part of the aggregation  [154]. This finding prompted the authors to state that the maximum temperatures of the aggregation represent the actual temperatures experienced by the larvae  [154]. More recent data demonstrated that larvae continuously move between the periphery and the inside of the aggregation, with individual larvae spending from 16 to 68% (mean 43%) of their time at the aggregation periphery  [155].
I believe that such errors could also yield a truly informative evaluation of the uncertainty in PMI estimates in casework. Most of the validation studies in forensic entomology were proof-of-assumptions or proof-of-concept studies (Table  4 ). Experiments fully validating the estimation protocols were rare. Only a few such datasets have been published; most used pig cadavers and were replicated moderately, at most. Human cadavers in anthropology research facilities (i. e. , body farms) could be used more extensively for that purpose. The estimation of PMI for such cadavers using mock crime scenarios could provide robust validation data. This research design is surprisingly underutilised at body farms. Similarly, validations using casework data were infrequent (Table  4 ). In order to use the casework data for the validation, a true PMI needs to be specified based on a confession or a witness statement about when the victim was last seen alive, or other non-insect evidence.
5 and 5. 6 days respectively, Tables  5  and 6 ), the latter type of the estimation was usually used for cadavers with a much larger PMI (Table  6 ); therefore, it had lower errors, which are relative values. Differences between the true and estimated PMI increased with the increase in the true PMI, and this relationship was particularly apparent when plotted for the development-based estimates (Figure  2 ). Summarizing, the protocols for the estimation of PMI based on insect evidence usually lack errors and their validity has been rather poorly demonstrated in a true forensic context. Therefore, validation studies using pig or human cadavers and casework data should be prioritized in forensic entomology. I think this is our greatest challenge. Insects 2021, 12, 314 ą ń   5. N-a number of PMI estimations in a dataset.
1 PMI determined based on non-insect evidence (a confession, a witness statement about when the victim was last seen alive, etc. ). 2 An absolute difference between the true PMI and the PMI estimated based on insect development. When the estimated PMI was presented as an interval, I calculated absolute differences between the true PMI and the lower and upper limit of the estimated interval and then averaged them to get the difference between the true and estimated PMI. 3 Error I = (the difference between the true and estimated PMI/true PMI) × 100. 4 Error II = (the difference between the true and estimated PMI/estimated PMI) × 100. When the estimated PMI was presented as an interval, a midpoint of the interval was used in denominator. N-a number of PMI estimations in a dataset. 1 PMI determined based on non-insect evidence (a confession, a witness statement about when the victim was last seen alive, etc.
Datasets with a Relatively Large Proportion of Unexplained Variance Statistical tests can generally separate variance into two components: the explained and unexplained variance. The explained variance is the variance associated with the study variables whose influence is being studied such as the PMI and postmortem accumulation of degree-days in successional studies of insects. The unexplained variance is any residual variance associated with random variance that occurs because experimental units always exhibit some differences, even in homogeneous conditions, as well as variance associated with unknown variables, called systematic variance. Systematic variance is non-random variance due to factors not manipulated or measured during the study. In forensic entomology, systematic variance can overly dominate the explained variance because insect occurrence is influenced by meteorology, animal behavior, life histories, microhabitat, etc.
Data Affected by Temporal and Spatial Effects As a rule, all insect successional studies carried out in the field are inevitably affected by temporal and spatial effects. Temporal effects occur because the same experimental units (i. e. , carcasses/cadavers) are sampled over and over during most forensic studies. In addition to sometimes causing oversampling problems  [33] , this leads to an interrelation of the samples over time. Moreover, repeated samples cannot be considered random because a sample taken at time 1 is necessarily collected before a sample at time 2. Such a dataset is unlikely to be stationary in the sense that the mean, variance, autocorrelation and other statistical properties are all constant over time, contravening another assumption of several statistical tests. Spatial effects occur because field conditions are often heterogeneous and because the placement of sampling units can create interdependence between units, thereby causing systematic spatial variation that results in observable data clusters.
Datasets Including Many Independent and Dependent Variables Often, in forensic studies involving cadavers/carcasses, a large amount of data is collected from each experimental unit (i. e. , cadavers/carcasses). For example, several studies record daily the presence of different taxa, the occurrence of egg masses, the size of maggots, the temperature of the cadaver, the stage of decomposition of the cadaver, the postmortem accumulation of degree days, etc. However, the scientific literature recommends that the number of variables remain low compared to the number of observations, because the chance of finding significant but biologically irrelevant relationships between the dependent and independent variables increases with the number of variables  [36]. Ignoring this recommendation is likely to result in autocorrelation between processes as well as autocorrelation between response variables.
With such a model, a small change in the data can result in a large change or even a change of sign in parameter estimates. Consequently, little confidence can be placed in a model affected by multicollinearity. Alpha inflation is also known as familywise error, experimentwise error or cumulative Type I error. Alpha (or α) refers to the significance level. In most experimental sciences, α = 0. 05, which implies that you have a 5% chance of incorrectly rejecting the true null hypothesis when you perform a statistical test (i. e. , you have a 5% chance of detecting an effect when there is none). As more tests are conducted on the same dataset, the likelihood of obtaining at least one erroneous significant result increases. For example, if the response to a given treatment (e. g. , shading) of 20 blowfly species sampled in an experiment is analyzed individually with α = 0.
How to Solve Problems Related to Having Data Interrelated in Time or Space If the objective of the study is to analyze only the temporal trends of a dataset, time series analysis such as used by Andow and Kiritani  [54]  in a study of saproxylic beetles is appropriate. If variables other than time need to be included in the model, then the GLM, GLMM, GAM and GAMM approaches suggested above are adequate (for an example, see  [5, 20] ). On the other hand, potential spatial relationships require the use of spatial statistics that specifically describe and model localized or geo-referenced data. To date, forensic entomologists have not ventured into spatial statistics, but this is bound to change as studies encompassing several locations are taking place. In contrast, spatial analyses are frequently used in studies on other decomposing substrates.
They are generally baited with a small amount, being 50-200 g, of various animal organs such as pork muscle, pork liver, beef liver, chicken liver, or squid  [1, 12, 15, 16]. Synthetic compounds such as sodium sulfide, which act as constant chemical attractants to necrophagous Diptera, may also be used as bait, often with pork liver  [8, 11, 13, 17, 18]. These small bait traps are emerging in forensic entomology as a method to record the local biodiversity of early-colonizing necrophagous Diptera species  [1, 15, 19] , which is known to vary according to the environmental conditions and the geographical region (e. g. ,  [1, 5, 9] ). The literature herein indeed shows that small bait traps are increasingly used to develop databases by forensic institutes and forensic medicine departments in various countries such as Australia, Austria, Brazil, Canada, Croatia, Germany, Portugal, Romania, Spain, Turkey, the United Kingdom, and the United States, to name a few.
Discussion Small bait traps are useful in forensic entomology to maintain a database of local and seasonal forensically important necrophagous insects  [20, [32] [33] [34] , which may be crucial for analysis of entomological evidence in a given area  [20]. That being said, the correlation between assemblages of Diptera captured by small bait traps and found on cadavers/carcasses has never been studied simultaneously, although some authors have used pre-existing data or literature to compare data  [1, 20, 21]. However, these methods may be problematic because geography, climate, species-specific habitat associations, and seasons are known to have an important impact on Diptera communities  [11, 19, [35] [36] [37] [38]. Additionally, some authors have demonstrated the importance of biases associated with small bait traps, such as those caused by weather factors, height of traps, and position, as well as biased sex ratios and age-classes of captured Diptera  [11, 39, 40].