dataesr/scientific-paragraphs-categorization huggingface dataset:
Data were normalized to time-point 0 (n = 3). c Immunocytochemistry with the neuronal differentiation markers βIII tubulin and MAP2 on undifferentiatied and differentiated SH-SY5Y cells at 9 days. d RT-qPCR analysis showing effective depletion of COSMOC in SH-SY5Y cells during the first 5 days of neuronal differentiation. SH-SY5Y cells treated with si-COSMOC were compared with cells incubated with scrambled siRNA and data were normalized to time-point 0 (n = 3). e, f RT-qPCR confirmation of effective neuronal differentiation of SH-SY5Y depleted in COSMOC expression during the first 5 DIV. The expected inverse correlation between CDK1 expression (e) and MAPT expression (f) was observed in SH-SY5Y treated with si-COSMOC and those treated with scrambled siRNA. Data were normalized to time-point 0 (n = 3).
g Immunocytochemistry showing βIII tubulin and MAP2 staining of SH-SY5Y cells treated with siCOSMOC, siMOCOS or control siRNA at 5 DIV. h Analysis of cell death by nuclear staining with Hoechst. Example of a nucleus counted as apoptotic after staining with Hoechst (leftmost) and compared with two adjacent normal nuclei. i Quantification of apoptotic nuclei at 5 DIV in SH-SY5Y knockdown for COSMOC, MOCOS or control siRNA (n = 4; Mann-Whitney test: *p < 0. 05). Representative Western blot (j) and quantification (k) of cleaved products of caspase-3 (17 and 19 KDa) at 5 DIV in SH-SY5Y cells depleted in COSMOC, MOCOS or control siRNA. GAPDH was used as control. (n = 4; Mann-Whitney test: *p < 0. 05). Data are represented as mean ± SEM. For all figures, n stands for number of biological replicates of SH-SY5Y cells.
These results are strengthened by the increased expression of PTBP2, a multifunctional splicing factor known to promote a switch in the expression of glycolytic enzymes  [37]. Altogether, these data indicate that ASD patients of our cohort display metabolic disorders and suggest that COSMOC might modulate redox metabolism in stem cells. These findings are unsurprising as ASD are associated with impairments in basic physiological processes such as redox homeostasis. Increased levels of ROS, advanced glycation end products (AGEs) and lowered levels of antioxidants (such as glutathione peroxidase, superoxide dismutase, reduced glutathione) are indeed frequently reported in ASD children  [10, 38]. Moreover, clinical trials have also reported an improved behavior in ASD patients receiving antioxidant therapy  [39].
Elucidating how COSMOC affects lipid metabolism and homeostasis may help unify our understanding of the complex autism disorders. Our data also revealed the control of COSMOC on the splicing of PTBP2, a RNA-binding proteins essential in post-transcriptional regulatory events. This is of major interest as PTBP2 is primarily expressed in the brain and, plays a crucial role in a splicing program required for neuronal differentiation  [25, 26]. As a matter of fact, PTBP2 binds to intronic polypyrimidine clusters in pre-mRNA molecules and participates to the assembly of other splicingregulatory proteins. Its association with autism is based on two studies reporting indel and missense variants in ASD probands  [4, 45]. Interestingly, beside PTBP2, key regulators of alternative splicing, including the RBFOX1 family of proteins and SR100 are linked to neurodevelopmental disorders, including autism  [2, 15].
In addition, alterations in the noncoding transcriptome and splicing of activity-dependent neuronal genes were reported in the cortex of ASD patients  [15]. Supporting this, our data indicate that PSD95 expression, an important regulator of synaptic structure and plasticity, is affected by COSMOC, which may probably mediate through PTBP2 splicing defects. Altogether, the action of COSMOC on PTBP2 may play a wider action on numerous key cellular processes. ASD are highly heritable with a number of risk loci ranging from hundreds to >1000. However, at best, each of them explains less than 1% of the cases and the vast majority of ASD cases are idiopathic. We report here on the identification of a new lncRNA, COSMOC, and its underexpression in nearly all autistic patients of our cohort. Misexpression of COSMOC and its adjacent partner MOCOS that is also found deregulated in most (80%) patients of our cohort  [8] , may be one of the common mechanisms contributing to numerous ASD cases and part of the dysfunctions observed in ASD.
Acknowledgements We gratefully acknowledge  David Laurenceau  (from the  Genetic department of the Regional Hospital of Orleans ) and  Delphine Stephan  (from  INP of Marseille ) for technical assistance. The study was financed by the  CNRS, Orleans University, Aix-Marseille University and the European funding in Region Centre-Val de Loire  ( FEDER N°2016-00110366=BIO-TARGET 2015-2018  and  FEDER N°EX005756=BIO-TARGETII ).
Clones having an insert of the expected size were sequenced using M13 primers (F-5 0 -TGTAAAACGACGGCCAGT-3 0 and R-5 0 AGGAAACAGCTATGACCATG-3 0 ; Promega). Cycling parameters were denaturation at 94°C for 10 min, followed by 35 cycles at 94°C for 60 s, 53°C for 60 s, and 72°C for 3 min, with a final extension at 72°C for 20 min. Amplicon size was confirmed by agarose gel electrophoresis, as described above. Cloned amplicons were subjected to bidirectional capillary DNA sequencing (Sanger) on an Applied Biosystems ABI 3730XL DNA analyzer (The University of Arizona Genomics Core; http://uagc. arizona. edu/). The sequence reads were manually edited and assembled into contigs using the SeqMan Pro software, available in DNASTAR Lasergene v14. 0 package (DNAS-TAR, Madison, Wisconsin, USA). The contigs were exported as fasta files and annotated using Blast2GO software  (Conesa et al.
Phylogenetic analysis Mitotype identification was based on phylogenetic analysis using representative B. tabaci reference COI sequences available in GenBank  (Benson et al. 2005)  or the Brown laboratory reference COI database (J. K. Brown, unpublished data) that contains sequences for B. tabaci field samples initially identified based on morphological characters (Mr. Ray Gill, California Department of Food and Agriculture, Sacramento, California, USA). The greenhouse whitefly, Trialeurodes vaporariorum (Westwood; Brown laboratory database), was used as the outgroup to root the tree. The sequences (n = 573) were aligned using MUSCLE v3. 8. 31  (Edgar 2004 ), implemented in the Align Multiple Sequences tool available in Mesquite v2. 75  (Maddison and Maddison 2018). The ends were trimmed to 832 nt in length, and the terminal gaps were treated as missing data.
6  (Rambaut et al. 2018) , and the effective sample size was confirmed to be >200. The MCMC runs were summarized using sump and sumt commands in MrBayes v3. 2. 5  (Huelsenbeck and Ronquist 2001). Trees in the first 3 9 10 7 generations per replicate were discarded as burn-ins. The majority-rule consensus tree was drawn using FigTree v1. 4. 2 (http://tree. bio. ed. ac. uk/software/figtree/). The within-and between-mitotype divergence was estimated by pairwise distance analysis using the PAUP v4. 0a software  (Swofford 2003). Distances were corrected to account for multiple substitutions at a single site based on the GTR model of evolution.
Niche range prediction The climate niche range was predicted based on presence-only records for the predominant native mitotype ECU1 and introduced B mitotype. Because of the small number of records available for the ECU2 and ECU3, at 1 and 2, respectively, it was not possible to predict the distribution of these rare mitotypes. Niche range prediction analysis considered only the geographic limits of continental Ecuador with geographic range boundaries set at 82°W, 75°E, 5°S , and 2°N. All of the climate variables used for the niche range predictions were cropped to this geographic extent using the crop and extent functions of the package raster in R  (Hijmans et al. 2019a). A set of environmental variables that define habitat suitability was downloaded from the WorldClim v2 website (https://www.
worldclim. org/data/worldclim21. html). The World-Clim dataset comprises nineteen climate layers (BIO1-BIO19) with average climate predictors combined from the 1970-2000 period. The variables used for species distribution predictions were annual mean temperature (BIO1), mean diurnal temperature range (BIO2), isothermality (BIO3), temperature seasonality (BIO4), maximum temperature of the warmest month (BIO5), minimum temperature of coldest month (BIO6), temperature annual range (BIO7), mean temperature of wettest quarter (BIO8), mean temperature of driest quarter (BIO9), mean temperature of warmest quarter (BIO10), mean temperature of coldest quarter (BIO11), annual precipitation (BIO12), precipitation of wettest month (BIO13), precipitation of the driest month (BIO14), precipitation seasonality (BIO15), precipitation of wettest quarter (BIO16), precipitation of driest quarter (BIO17), precipitation of warmest quarter (BIO18), and precipitation of coldest quarter (BIO19).
Layers were rasterized using the band interleaved by line (BIL) format using the rasterize function of the package raster in R  (Hijmans et al. 2019a ). The geographic boundaries and coordinates of all layers were standardized, and the cell size resolution was set at 0. 05 9 0. 05 pixels. The headers of raster files were modified as needed, and the vector of bioclimatic variables was created by stacking BIL files using the stack function available in the raster package in R  (Hijmans et al. 2019a). Niche range predictions were carried out using the MaxEnt v3. 4. 1 software  (Phillips et al. 2017 ) that estimates and compares occurrence probability densities, based on the vector relating the nineteen environmental covariates relevant to whitefly habitat suitability  (Elith et al. 2011). MaxEnt was selected because it utilizes presenceonly records and produces accurate predictions for relatively small sample sizes, for example, as few as five records  (Hernandez et al.
2006 , Pearson et al. 2007 ). The niche range was predicted using the R computing environment and the maxent function available in the dismo package  (Hijmans et al. 2019b)  with Java (Urbanek 2019). The occurrence records were divided into two groups, with 75% used to train the model and 25% to test the model, randomized for 10 independent runs. To reduce the likelihood of spurious predictions potentially resulting from sampling bias, a target-group approach was used  (Phillips et al. 2017) , which consisted of randomly selected geographic coordinates that might or might not overlap (non-focal species records) with the focal species records were used as background records, for example, random geographic locations where a particular mitotype occurs. The non-focal species records were analyzed in R to evaluate the model using the function evaluate from the dismo package  (Hijmans et al.
The continuous probabilities were plotted using the function dismo:predict  (Hijmans et al. 2019b) , and the binary maps were produced using the function threshold from the dismo package  (Hijmans et al. 2019b ), using the threshold for which the sum of the sensitivity (true positive rate) and specificity (true negative rate) was greatest. The distribution maps for the major phylogenetic clades of B. tabaci were exported as raster files and visualized with the Quantum Geographic Information System (QGIS) software v 3. 8. 1 (QGIS Development Team 2017). The permutation importance of each variable, which equals to the contribution by each of bioclimatic variable to the model, was based on the effects a variable contributes to reducing the AUC when it is randomly permuted during training, with a large decrease indicating high reliance of the model on a particular climatic variable.
Identification of mitotypes Five hundred and seventy-three high-quality sequences were obtained by PCR amplification of a 1015-bp fragment of the mtCOI sequence (3 0end of the mitochondrial COI, 949 bp) and contiguous tRNAleu, 66 bp. Sequences were trimmed to 832 bases and redundant haplotypes were removed, leaving 262 unique haplotypes for the analyses. The haplotype sequences are available in GenBank (Data S1). Based on the phylogenetic analysis, three native mitotypes were identified in Ecuador, hereafter referred to as ECU1, ECU2, and ECU3 (Fig. 1 ). The ECU1-2 mitotypes clustered within one of the two previously established major clades, AMTROP VI and VII clades (AMTROP species, de Moya et al. 2019; previously, New World-1 and 2,  Dinsdale et al. 2010 , Boykin et al. 2012) , and the most divergent mitotype, ECU3, also grouped within the AMTROP major clade.
The B mitotype grouped within the NAF-MEDME III major clade, representing B-type clades, which are sister to previously identified Q mitotypes. Several COI sequences for whiteflies collected from cassava and other wild plant species were identified as Bemisia tuberculata (Bondar), and belonged to a distinct clade, itself also a sister to the B. tabaci sibling species group. All of the main branches of the tree were well supported by having posterior probabilities (pp) of >95% (Fig. 1 ). Five subclades within the AMTROP VI-VIII major clade (AMTROP species; de Moya et al. 2019) were identified. One subclade, AMTROP (VI)-Ecuador and Colombia, contained ECU1 and previously reported mitotypes from Colombia  (Quintero et al. 2001). The second, AMTROP (VI)-North-Central-America/Caribbean, harbored mitotypes previously reported or archived in Brown laboratory database, from Belize, Guatemala, Honduras, Mexico, Panama, and the United States  (Brown and Idris 2005) , but contained no haplotypes from Ecuador.
The third, AMTROP (VI)-Puerto Rico, represented a single exemplar from Puerto Rico, known as the Jatropha (race) mitotype (GenBank accession no. KX397317). The fourth, AMTROP (VII)-South America (4;  Viscarret et al. 2003 , Marubayashi et al. 2013) , contained ECU2 and haplotypes previously reported from Argentina, Bolivia, and Brazil (Lourenc ßão and Nagai 1994,  Lima et al. 2002 , Viscarret et al. 2003 , Marubayashi et al. 2013 , Barbosa et al. 2014 , Queiroz et al. 2016 , Quintela et al. 2016 ). The fifth subclade, AMTROP (VIII)-Ecuador, is a newly identified clade thus far represented only by the ECU3 mitotype. The three AMTROP major clades harboring the ECU-B. tabaci mitotypes from Ecuador therefore represent three of eight recognized groups (major clades) known thus far, worldwide. These three clades, AMTROP VI, VII, and VIII, collectively, comprise the AMTROP putative cryptic species (de Moya.
In contrast, the ECU1, ECU2, and ECU3 mitotypes were collected from the same individual plant. The ECU1 and ECU2 occurred together on wild Solanum sp. , while the ECU1 and ECU3 mitotypes co-associated on W. crispa and C. papaya (Data S1). The whitefly B. tuberculata was represented in 8. 5% of the samples (relative frequency) and was collected from cassava plants, M. esculenta, and to a lesser extent from non-cultivated species. It was found most often on the northwestern slopes of the Andes at an altitude of ~1500 feet. Phylogenetically, B. tuberculata resolved as a sister clade to B. tabaci, and was consistent with the results of previous studies that were based on morphological and phylogenetic differences  (Bellotti and Arias 2001 , Calvert et al. 2001 , Gill and Brown 2010 , Silva et al.
Niche range predictions The niche range prediction for ECU1 and B mitotypes considered 65 and 64 records, respectively. The geographic coordinates associated with each record are shown in Data S1. The probability of whitefly mitotype distribution as a function of microenvironmental conditions was analyzed to examine the potential environmental factors that could influence whitefly physiological adaptability  (Costa et al. 2010 , Elith et al. 2011 , Kumar et al. 2015). The predicted AUC values for the B and ECU1 mitotypes were >0. 9 for the training and testing datasets (Table  1 ), indicating model performance was robust  (Hosmer et al. 2013). The probability distribution threshold for the ECU1 and B mitotype model was 0. 240 and 0. 067, respectively, and maps were constructed to illustrate the distribution ranges (Fig.
Instrument setup To operate the developed device, a control system (LabView Inc. , USA) and solenoid valves (Lee Valve Inc. , USA) were utilized. 28 A house vacuum and pressure were utilized as a pressure source and provided a specic pressure via a LabView-controlled pressure regulator. The solenoid valves controlled the micro valves by opening and closing the connected pressure lines to the house vacuum and pressure. Each solenoid valve was independently actuated via the LabView system with a specic program for valving and pumping operations. Aer interconnecting the solenoid valves with the pressure lines, a custom-made jig was utilized to operate the membrane micro valve by pneumatic control. The holes in the jig were packaged with a rubber O-ring and tightly contacted to the holes at the pneumatic layers to avoid air leakage.
Acknowledgements The authors gratefully acknowledge the support for this work provided by a 2019  Kwangwoon Research Grant. The work reported in this paper was partly conducted during the sabbatical year of Kwangwoon University in 2019. This research was also supported by the  Basic Science Research Program  through the  National Research Foundation of Korea (NRF) , funded by the  Ministry of Education  (no. NRF-2018R1D1A1B07050677 ), and the  Korea Health Technology R&D Project  through the  Korea Health Industry Development Institute (KHIDI) , funded by the  Ministry of Health & Welfare, Republic of Korea  (Grant no. HI18C1839 ).
Taken all together, high haplotype diversity (D-loop=0. 989-1. 000; ND5=0. 848-0. 965), coupled with a low level of nucleotide diversity (D-loop=0. 019-0. 025; ND5=0. 0017-0. 003), "star-like" haplotype network, and unimodal mismatch distribution, suggests a recent population expansion for populations of T. tonggol in Malaysia. Furthermore, neutrality and goodness of fit tests supported the signature of a relatively recent population expansion during the Pleistocene epoch. To provide additional insight into the phylogeographic pattern of the species within the Indo-Pacific Ocean, we included haplotypes from GenBank and a few samples from Taiwan. Preliminary analyses suggest a more complex genetic demarcation of the species than an explicit Indian Ocean versus Pacific Ocean delineation.
, 2012) , meanwhile, moderate genetic differentiation among populations of masu salmon Oncorhynchus masou masou from Japan, Russia, and Korea was apparent based on ND5 and microsatellites markers  (Yu et al. , 2010). However, low genetic differentiation with high gene flow was detected between sampling locations of the Pearse's mudskipper (Periophthalmus novemradiatus) that inhabits Setiu Wetland, Terengganu, Malaysia  (Nabilsyafiq et al. , 2019). The present study aimed at elucidating the population genetics of T. tonggol in Malaysian waters based on mtDNA D-loop and ND5 markers, as well as further investigating the phylogeography of T. tonggol within the Indo-Pacific region, where samples from Taiwan (provided by National Taiwan University) and India (west coast: Kochi, Veraval, and Ratnagiri, and east coast: Andaman Sea; all haplotype sequences retrieved from GenBank) were included in molecular analyses.
Sample collection The specimens were collected from 11 fish landing sites in Malaysia and were morphologically identified following identification keys as described in  Lim et al. (2018). This species can be differentiated from other Thunnus species by having a moderate length pectoral fin, reaching the origin of the second dorsal fin and blackish caudal fin  (Lim et al. , 2018). A small portion of pectoral fin of each individual was cut and preserved in 95% ethanol and stored in a 1. 5 mL centrifuge tube at 4-8 o C until further analysis. Each catch locality was confirmed to be nonoverlapping (discrete geographical entity) based on feedback from the fishermen and was divided into four regions following  Akib et al. (2015) (Table 1, Fig 1A). Additionally, four samples from Taiwan and 153 GenBank sequences of T.
Sequence editing and alignment Multiple sequences were aligned and edited using ClustalW implemented in MEGA 6. 0  (Tamura et al. , 2013). DNA sequences were verified for correct identity by using the Basic Local Alignment Search Tool (BLAST) in the National Center for Biotechnology Information (NCBI) database (http://blast. ncbi. nlm. nih. gov/Blast. cgi) before further analyses. All haplotypes were deposited into GenBank under the accession numbers MK643829-644008 (D-loop) and MN252922-252981 (ND5).
Phylogenetic and population level analyses The phylogenetic relationships among haplotypes were determined based on the Maximum-Likelihood (ML) method implemented in MEGA 6. 0. The best nucleotide substitution models with the lowest BIC score (Bayesian Information Criterion) for the D-loop and ND5 sequences were Tamura 3-parameter (T92)  (Tamura, 1992)  and Hasegawa-Kishino-Yano with Gamma distribution and invariant sites (HKY+G+1), respectively, as identified in MEGA 6. 0. In the case that the T92 and HKY models were unavailable in the BEAST and Arlequin software packages (see below), the TN93 model  (Tamura & Nei, 1993)  was used instead. The confidence level for each node was assessed by 1000 bootstrap replications  (Felsenstein, 1985). The Pacific bluefin tuna T. orientalis (AB933631) was included as an out-group taxon for D-loop sequences, while the Yellowfin tuna, T.
albacares (KM588080) was included as out-group taxon for ND5 gene sequences. To infer the relationships among haplotypes from Malaysian waters, a minimum spanning network (MSN) was constructed by using the median-joining method implemented in NETWORK version 5. 0. 1. 1  (Bandelt et al. , 1999). The population pairwise comparisons Ф ST for both datasets were determined using Arlequin 3. 5 software  (Excoffier & Lischer, 2010)  and the statistically significant pairwise comparisons were tested with 10,000 permutations. Significant probability values were corrected by performing the False Discovery Rate Procedure (FDR) at α=0. 05  (Benjamini & Hochberg, 1995). Further analysis of genetic differentiation among populations was extended for haplotypebased statistics (H ST ), sequence-based statistics (N ST )  (Lynch & Crease, 1990) , and K ST * with significance levels assessed using permutation tests with 1000 replicates  (Hudson et al.
, 1992)  in DnaSP 6. 0. Using the same program, the estimation of gene flow (Nm) based on both haplotype and sequence statistics were calculated following  Nei (1973)  and  Hudson et al. (1992) , respectively. Genetic distance estimates between sampled populations were calculated in MEGA 6. 0. PeerJ reviewing PDF | (2019:08:40038:4:0:NEW 9 Jul 2020) Manuscript to be reviewed Analysis of molecular variance (AMOVA) was performed to infer the population subdivision with three hierarchical levels, including genes within individuals, individuals within demes, and demes within groups of demes  (Excoffier et al. , 2005) , by using Arlequin 3. 5 software. The Mantel test in IBD v 1. 52 (Isolation by Distance)  (Mantel, 1967; Bohonak, 2002)  was used to investigate the correlation between genetic and geographical distance.
Genetic distance was represented by population pairwise Ф ST values while geographical distances between sampling locations were measured by using Google Earth. Geographic distance was ln transformed and the strength of the relationship was examined with reduced major axis regression (10,000 randomizations) in IBD v 1. 52. To understand its phylogeography within the Indo-Pacific region, GenBank sequences of the D-loop obtained from the west coast of the Indian Ocean (WCIO) [Kochi (25 haplotypes) MF592988-593012, Veraval (22) MF593027-593048, Ratnagiri and Veraval (92) KC313300-313393  (Kunal et al. , 2014) ], east coast of the Indian Ocean (ECIO) (Andaman Sea (14) MF593013-593026), and four samples from Taiwan (TW) were included in phylogenetic tree reconstruction, pairwise comparisons Ф ST , AMOVA, and genetic distance analyses.