dataesr/scientific-paragraphs-categorization huggingface dataset:
Forensic entomologists in different countries have conducted comprehensive work on the faunal succession of insects. Succession patterns have been identified under different types of environments (e. g. , burying  [111] , vehicle environments  [112] , and dry environments  [113] ) and treatments (e. g. , burning  [114] , hanging  [115] , and clothing  [116] ). Although these studies provided an important database, the difference between the insect fauna of China and other countries, as well as differences in other factors (e. g. , climate and flora) justify the need for establishing patterns of insect succession for different regions of China  [102]. Currently, insect succession data are not available for most parts of China. Most existing studies only concentrated on investigating species composition while failing to meet the requirements for establishing a succession matrix, which is required for PMI estimations.
Material and Methods Larval specimens were identified first as the genus Sargus Fabricius, 1798 [=Geosargus Bezzi, 1907] based on available literature  [11] , then to the species level based on the keys provided by Rozkošny  [12]. Species-level identification and subsequent photographic documentation was performed using a Leica M205 FA stereo microscope (Leica Microsystems GmbH, Wetzlar, Germany) with imaging software provided by the manufacturer. FOCUS Projects 4 Professional software (Franzis Verlag GmbH, Haar, Germany) was used to perform photo stacking. To confirm species identifications, the barcode region of cytochrome oxidase unit I (COI) was amplified from DNA extracted from three specimens, and compared to data stored in online repositories. DNA extraction was performed in the Molecular Laboratory of the Department of Invertebrate Zoology and Hydrobiology at the University of Lodz, Poland.
82% similarity, with all sequences belonging to Sargus bipunctatus (Scopoli, 1763) (BIN URI: BOLD:ACI9008). Nineteen records were collected in Vancouver, Canada, and one from Frankfurt, Germany. The obtained COI sequence was then deposited in the BOLD v4 and GenBank online repositories under accession numbers: BOLD Process ID: DPTPL001-21 (Sample ID: DptPL_Lodz_LA_1); GenBank: MW661345 (dx. doi. org/10. 5883/DS-DIPTPL) to make it available for future studies  [15, 16].
2. Published developmental datasets based on laboratory studies accurately reflect developmental patterns in the nature of the insect evidence collected: The vast majority of published and accepted insect developmental datasets have been derived under laboratory conditions. These conditions usually applied a range of temperature profiles (constant or with daily variations), controlled humidity and specified light: dark cycles. When developing in a natural environment, none of the abovementioned factors are controlled, and can affect development accordingly  [10] [11] [12]. Temperature cycles fluctuate greatly, both daily and seasonally  [13] , humidity is dependent on a number of factors, including season and precipitation, and light: dark cycles are highly dependent on season and region (not to mention possible artificial lighting conditions).
Molecular Data and Identification We successfully amplified the full 658bp COI barcode sequence for two specimens of Fannia sp. 1, and two specimens of Fannia sp. 3. Due to poor quality material, it was only possible to amplify a short 311 bp fragment for Fannia sp. 2. All sequences can be accessed in GenBank through the following accession numbers: MW670438 (Fannia_sp_1_1), MW670439 (Fannia_sp_1_2), MW670440 (Fannia_sp_2), MW670441 (Fannia_sp_3_1) and MW670442 (Fannia_sp_3_2). The newly sequenced COI barcodes were then supplemented with additional 4421 COI barcode sequences, representing 67 Fannia species, retrieved from the online databases BOLD. Among those sequences 114 have not been assigned to species or recognized as uncertain identifications. Comparison of COI barcodes for Fannia sp.
1, sp. 2 and sp. 3 with COI data deposited in BOLD and GenBank, enabled identification of these unknown specimens. Sequences obtained for Fannia sp. 1 (Fannia_sp_1_1 and Fannia_sp_1_2) were identified as Fannia nigra Malloch, 1910, based on 100% similarity with sequences provided from a male (KY511202) and a female (KY511203) of F. nigra by Grzywacz et al. [10]. Sequences obtained for Fannia sp. 3 (Fannia_sp_3_1 and Fannia_sp_3_2) were identified as Fannia collini d'Assis-Fonseca, 1966, based on 99. 85% similarity with sequences obtained from male specimens (KY511166, KY511167, KY511168) of F. collini by Grzywacz et al. [10]. The sequence obtained for Fannia sp. 2 (Fannia_sp_2) was identified as Fannia pallitibia (Rondani, 1866) based on 100% similarity with an unpublished sequence assigned to F.
pallitibia and available in BOLD. To validate obtained identifications, the COI dataset retrieved from BOLD was supplemented with two additional sequences of F. pallitibia (voucher specimens jka09-04927 and KWi-1777 accessible through the Finnish Museum of Natural History (FMNH)) and a newly obtained sequence of Fannia pruinosa (Meigen, 1826), a species closely related with F. pallitibia. COI barcode sequences, referring to unidentified Fannia sp. 1, 2 and 3, were compared against the combined dataset using SpeciesIdentifier v1. 8  [41]. This approach confirmed identifications obtained from the BOLD Identification System. Fannia_sp_2 sequence was found 100% similar with F. pallitibia (jka09-04927). Additionally, phylogenetic analysis confirmed those identifications (Figure  1 ), and sequences Fannia_sp_1_1 and Fannia_sp_1_2 clustered with those referring to F.
Taxonomy Using DNA barcoding we were able to assign unknown Fannia morpho-types to species and reveal their potential forensic utility. However, accuracy of identification by means of DNA barcoding depends on many factors, such as availability of extensive reference library of gene sequences  [42]. Taxonomic representation of Fanniidae in reference databases is uneven, with sampling biased towards common species while many other species are underrepresented or represented only by single sequences  [10]. Even though undersampling may increase error rate of DNA barcoding  [43] , in our previous study we observed very high accuracy in species identification using COI barcode sequences in Fanniidae  [10]. A distinct barcoding gap was revealed even for species in which females are indistinguishable, e.
immutica in the online GenBank database (MF874564), and our molecular analysis showed interspecific distance between F. immutica and F. collini was higher than 12%. Based on literature data  [27, 28, 47]  and examination of specimens deposited in the collection of the Natural History Museum (London, UK), we propose the following combination of characters for the separation of these two species in the key of Rozkošný et al. [27] : 49. Two prealar setae, anterior prealar seta longer than half length of posterior notopleural seta and inserted nearer to suture than to supraalar seta. 49a 49a. Hind tibia with 8-10 short anterodorsal setae in addition with 1 strong seta; lower orbital setae in the middle of fronto-orbital plates. F. immutica Collin -Hind tibia with 1 strong anterodorsal seta, without additional setae above; lower orbital setae inserted nearer to outer margin of fronto-orbital plates.
Forensic Importance Imprecise identification of material collected during carrion succession experiments gives an incomplete image of necrophagous entomofauna. Comparison of COI barcode sequences against those in depository databases enabled unequivocal identification of fanniids and thus better understanding of the composition of carrion insect assemblages in Central Europe. For this reason, the results we obtained in this study, specifically identifications of Fannia sp. 1 as Fannia nigra, have already been used in practice and detailed analysis of F. nigra association with carrion has been published in Jarmusz et al. [16]. A significant correlation between the appearance time of F. nigra third instar larvae and the onset of active decay of carrion decomposition process was revealed by Jarmusz et al.
The amplification programme (initial heat activation step at 95 • C for 10 min, 35 cycles of 95 • C for 1 min, 49. 8 • C for 1 min, 72 • C for 1 min, and a final extension step at 72 • C for 10 min) was set up on a BioRad C1000 Thermal Cycler (Bio-Rad Laboratories, Inc. , Hercules, CA, USA). A standard gel electrophoresis, in 1. 5% agarose gel stained with Midori Green Advanced DNA Stain (Geneflow, Elmhurst, UK), was used to check PCR products. In case of positive results, 15 µL of PCR products were purified using QIAquick PCR Purification Kit ® (QIAGEN, Hilden, Germany) following the manufacturer's instructions. Purified amplicons were sequenced by Eurofins (Eurofins Operon MWG, Ebersberg, Germany) following the standard Sanger method. For species identification purposes, DNA sequences were searched on GenBank database through BLASTn ® tool (NCBI, Bethesda, MD, USA).
Anderson  [18]  documented the minimum and maximum time taken to reach each developmental stage as a way to estimate the time since death. Shortly afterward, Grassberger and Reiter  [19]  illustrated morphological length and stage changes using isomegalen-and isomorphen-diagrams. Nevertheless, Roe and Higley  [21]  emphasized that methodological inconsistencies in the previous studies made it difficult to apply error rates or confidence intervals to cases within a given region. These inconsistencies stimulated the launch of studies on blind validation  [22]  and field validation  [23]  of development datasets. The goal of the present study was to generate practical development data for L. sericata, the most common indoor insect species in South Korea  [13]. The rearing experiments were replicated five times at seven different temperatures, held constant throughout the investigation, to analyze the changes in ADH or ADD according to developmental stage and body length.
Data Fit and Statistical Analysis SigmaPlot (version 10. 0) and Microsoft Office Excel 2007 were used for plotting all graphs and for performing basic statistical analyses. Two-way ANOVA, without replication, was conducted using the SAS program (ProcMIXED, SAS9. 4)  [29]  to determine the differences among minimum mean hours spent in each development stage and temperature (p ≤ 0. 05)  [23]. In addition, the growth data from egg to adult were fitted with a four-parameter sigmoid model to determine the minimum growth rate (y 0 ) as well as the optimum development temperature (x 0 ): F(x) = y 0 + a/ 1 + exp -x -x 0 /b (3) where y 0 is the minimum developmental rate, x 0 is the inflection or the optimum development temperature in the sigmoid curve, "a" is the difference between the maximum and minimum developmental rates, and "1/b" is the steepness of the sigmoid curve  [27, 30].
minPMI was also evaluated using the larvae measurements applying the data available in the literature  [58]  when the samples were already fixed. In this case, the size of the larvae was compared with diagrams reporting the relations between size, temperature and time. Whereas some authors only report the size as a function of the temperature and time  [58] [59] [60] , others also provide the formulas and the interval of confidence of the larval length based on the ADDs  [61]. Larvae were counted and then measured using Leica M60 stereomicroscope equipped with a CCD camera (Leica, Wetzlar, Germany) and the automatic measurement tool. When the numbers of larvae were under fifty, measurements were performed on all the specimens. When the count exceeded fifty, only fifty larvae were considered.
Emergence of adult flies after nine days in the laboratory at 24 • C, therefore, accumulated to 126 ADD. The day of the discovery (day 0) of the body was added with 0 ADD due to 15 h in the refrigerator at 5 • C, which left 76-79 ADD prior to discovery. For each day before discovery, the respective average daily temperature was calculated from weather data (Table  1 ). The most likely day of egg deposition was determined to be nine days prior to the discovery of the body. Weather data showed no rainfall or cold temperature episode for this or the previous day. With the age of P. regina established to have been 9 days based on both results, we were confident that this was the most likely minimum time that had elapsed since the deposition of the body. During the TV show, however, the most probable PMI was still given as multiple weeks up to several months on grounds of the skeletonization of the head and the thorax.
Acknowledgments: The author is thankful to  Jeffery Tomberlin  as this manuscript was developed following his suggestion made in the context of the Digital Congress of Forensic Entomology in September 2020. The author also wishes to thank  D. Boudreau ,  N. Hammami ,  K. LeBlanc ,  L. Tousignant  and three anonymous reviewers for comments on a previous version of this manuscript.
Institutional Review Board Statement: Not applicable. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Funding: This research was funded by a grant ( ex60%-DiBEST Unical-2017 ) from the  Department of Biology, Ecology and Earth Sciences, University of Calabria, Arcavacata di Rende (Cosenza, Italy ) assigned to  Professor Teresa Bonacci. Institutional Review Board Statement: Not applicable. Funding: This work was supported by the  National Science Center of Poland  [grant number  2016/23/NZ8/02123  to P.
Acknowledgments: The authors owe thanks to Giuseppe Antonio Biagio, Museum of Natural History of Calabria and Botanical Garden, University of Calabria, for technical assistance during the experiments. The authors also wish to thank the Centro Funzionale Multirischi-ARPACAL (Agenzia Regionale per la Protezione dell'Ambiente della Calabria) (Catanzaro, Italy) for kindly providing the air temperature data.
Data Availability Statement: The obtained COI sequence is available at: Barcode of Life Data System (BOLD) (http://boldsystems. org), Process ID: DPTPL001-21 (Sample ID: DptPL_Lodz_LA_1). GenBank: (https://www. ncbi. nlm. nih. gov/genbank/) Genbank Accession number: MW661345 (dx. doi. org/10. 5883/DS-DIPTPL).
Discussion For the entomological expertise in this case, publications analyzing US populations of P. regina were used. This could be seen as the wrong dataset for European populations. Marchenko  [12]  worked with Eastern European populations of the species but calculated the pupal stage of P. regina to being short. His data did not correspond to our results of 7-8 days for this development period. Other publications were more comparable  [11] [12] [13] [14]. Due to its very detailed data, Nabity et al. 's study  [11]  was exclusively used for our calculations of accumulated degree-days. A large number of variables affects decomposition and taphonomic processes. The interpretation of post-mortem interval determination can therefore be deceptive  [15]. Skeletonization is oftentimes expected to occur only after long exposure times.
Transcriptomic analysis by RT-qPCR The molecular response of C. jejuni was investigated using RT-qPCR instead of a global approach like RNA-seq because the final objective of this work will be to correlate the phenotypic response of C. jejuni (inactivation) with its transcriptomic response, using mathematical modeling to identify biomarkers of stress. The search for biomarkers represents a challenge, and this proof of concept was investigated using RT-qPCR as already performed by  Desriac et al. , 2012 Desriac et al. , , 2013 Desriac et al. , , 2015. To this end, quantitative data had to be provided using a quantitative method. This was why we chose RT-qPCR rather than RNA-seq, which only provides the relative abundance of reads. Moreover, RT-qPCR is appropriate to study well-known phenotypes and associated genes well described in the literature.
To do so, the gene classification according to metabolic pathways was retrieved from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, at least one gene from each metabolic pathway was picked up, but genes involved in several metabolic pathways and involved in different stress responses were preferentially selected. Out of 1600 initially selected genes, 40 were eventually kept for analysis (Table  1 ). Only the genes amplified by PCR and therefore present in the three strains were kept. Consequently, two genes -putA and rprBwere excluded from the analysis because they were not amplified in the C. jejuni C09MJLT205 strain and in the RM1221 and C94anses640 strains, respectively. The expression levels of the 38 remaining genes were then analyzed.
Selection of reference genes Reference genes are needed for normalized expression. They are usually housekeeping genes, and their expression should remain constant whatever the experimental conditions. Reference genes were selected among a pool of 44 genes composed of i) six genes previously described as potential reference genes for RT-qPCR in C. jejuni  (Ritz et al. , 2009) , and ii) the 38 genes selected as potentially involved in the stress response. Stability of gene expression was assessed as demonstrated by  Vandesompele et al. (2002). The gene expression stability measure (M) was calculated using the geNorm module in qBase + software (version 3. 2, Biogazelle). To determine the optimal number of reference genes, the pairwise variation Vn/n+1 was calculated between the two sequential normalization factors (NFn and NFn+1) for all samples.
Data analysis Gene expression quantification was performed after each step of the experimental setup (i. e. after heat stress and after successive heat and cold stresses). RT-qPCR results were expressed in relative quantity scaled to the expression of the same gene in the control sample (i. e. unstressed culture) using the delta Ct transformation with efficiency correction. Then, expression levels were normalized relatively to the expression levels of the reference genes. Reference genes were selected among the 44 genes. The normalization factor (NF) was calculated as the geometric mean of reference gene expression  (Vandesompele et al. , 2002). After normalization, relative gene expression was log 2 transformed for further ANOVA analysis  (Bengtsson et al. , 2005). For each strain, ANOVA analysis (α = 5%) using XLSTAT software (version 2018.
Since the identification of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the etiological agent of the current COVID-19 pandemic, a rapid and massive effort has been made to obtain the genomic sequences of this virus to monitor (in near real time) the phylodynamic and diversity of this new pathogen. However, less attention has been given to the assessment of intra-host diversity. RNA viruses such as SARS-CoV-2 inhabit the host as a population of variants called quasispecies. We studied the quasispecies diversity in four of the main SARS-CoV-2 genes (ORF1a, ORF1b, S and N genes), using a dataset consisting of 210 next-generation sequencing (NGS) samples collected between January and early April of 2020 in the State of Victoria, Australia. We found evidence of quasispecies diversity in 68% of the samples, 76% of which was nonsynonymous variants with a higher density in the spike (S) glycoprotein and ORF1a genes.
Samples In total, 217 samples of PRJNA613958 BioProject and related metadata were recovered from the NCBI website using the SRA-Toolkit (http://ncbi. github. io/sra-tools/). This BioProject involves more than 1000 Australian NGS samples from the State of Victoria. Our dataset represents a subsample of this BioProject obtained between 31 January 2020 and 8 April 2020. Our selection criteria involved sequences obtained using NextSeq 550 technology only.
Identification of iSNVs iSNVs were identified in the ORF1a, ORF1b, S and N genes of the SARS-CoV-2 genome in each of the samples. We considered iSNVs to be those with a median alternative allele frequency (AAF) between 5% and 50%. The bioinformatics pipeline involved the following steps: low-quality read trimming with Trimmomatic  [34] ; alignment of the reads using Bowtie2  [35]  with the SARS-CoV-2 reference sequence  [3] ; conversion of the same file alignments to bam files using samtool  [36] ; sorting the bam files and removing duplicate sequences with MarkDuplicate (http://broadinstitute. github. io/picard/). ViVarSeq  [37]  scripts derived a consensus sequence from the alignments obtained in the last step. The trimmed reads were realigned to the consensus sequence using Bowtie2. VirVarSeq identified the variants and their frequencies.
Identification of the iSNV Haplotypes To determine if nonsynonymous iSNVs cosegregate in the same sequences (haplotypes), all reads spanning the specific region of the SARS-CoV-2 genome containing these variants were identified using the pysam (https://pysam. readthedocs. io/en/latest/faq. html) module of Python from the alignment of reads to the reference sequence of this virus. At each position of the region of interest, the reads and the respective nucleotides were identified. With this information, we determined the proportion of the reads that carried the combination of variants of interest.
To explore the presence of a temporal signal, a phylogenetic reconstruction was applied using iqtree  [47, 48]  software with the -m option grouped to TEST, allowing the identification of the best model for partitions representing the four genes and a bootstrap analysis of 1000 replicates. The outliers were identified in this phylogenetic tree with the TempEst  [49]  software, using the regression analysis of the phylogenetic distance of the tips to the root and the collection time. This pruned tree was scaled with treedater  [50]  software using a strict molecular clock. The phylogenetic clusters were annotated using Pangolin software (https://github. com/cov-lineages/pangolin).
Origin of iSNVs in the Late Group To investigate the possible causes of the new diversity in iSNVs observed in the Late group, we evaluated whether the samples with iSNVs in this group were associated with patients who imported the virus into Australia or with patients infected locally. In the dataset, 150 samples came from imported cases, 47 were contaminated locally, and no information was available for the remaining 13 samples. In total, 48. 7% (73/150) and 40. 4% (19/47) of the patients from imported and local cases, respectively, carried iSNVs of the Early and/or Late group. Of the patients who acquired the virus outside Australia, all (73/73) harbored Early-group iSNVs and 6. 84% (5/73) harbored at least one Late-group variant. Among the patients infected locally, 89. 5% (17/19) carried Early-group variants and 42.
As mentioned above, the C > T and G > T substitutions were predominant in the dataset. Because at least C > T may be the product of the action of the host's enzymatic defense system, most nonsynonymous mutations in SARS-CoV-2 likely do not involve a selective advantage. Although a significant proportion of SARS-CoV-2 quasispecies diversity may not represent adaptive variation, the virus is probably under selective pressure as a result of the interspecies jump to a new host. This adaptive process should be particularly evident in the proteins involved in the pathogenicity of the virus and in non-structural proteins that interact with the host's immune system. Our data showed that the S gene has the highest density of nonsynonymous variants of the four genes analyzed. The aminoacid substitution G446V was identified in the RBD domain of the S protein involved in binding to the human ACE2 receptor.