dataesr/scientific-paragraphs-categorization huggingface dataset:
VDAC1 and VDAC3 may be non-expressed without providing a lethal phenotype (at least in mouse,  [16] [17]  while deletion or inactivation of the gene for VDAC2 is lethal  (18). This indicates that, in addition to the overall gene regulation activated in mitochondrial biogenesis, there must be regulatory mechanisms peculiar to each gene. The elucidation of these features may lead to an understanding of even the most characteristic roles of each isoform. In this work we begin studying the regulatory elements of VDAC gene expression starting from the most represented protein, VDAC1. An up-to-date bioinformatic analysis based on the most relevant public databases was performed. A list of TFBS was drawn and a correlation with cellular metabolisms and pathways was attempted. Since VDAC1 expression is crucial in tumour and in neurodegenerative diseases enough to be considered a marker for these pathologies  (19) (20) (21) , we decided to study the activity of the VDAC1 promoter in cell stress conditions, typical of metabolic reprogramming in tumour and neurodegenerative diseases.
Cell viability assay HeLa cells viability was estimated by 3-(4,5-dimethylthiazolyl-2)-2,5-diphenyltetrazoliumbromide (MTT) method. 1x10 4 cells/well of 96-well plates were seeded and, four hours before ending treatment, 10 μl of MTT (5 mg/ml) were added to cell culture. At the end of incubation, medium was removed and 100 μl DMSO was added for each well. Optical density was read at 570 nm. Percentage of viable cells was calculated compared to untreated cells and normalized to medium alone. Three independent experiments were performed and data were statistically analyzed by oneway ANOVA. A value of P<0,05 was considered significant.
Analysis of mitochondrial membrane potential (ΔΨ) 2×10 4 HeLa cells per well were seeded in a 96-well plate. After 24 hours, cell medium was replaced with complete medium (DMEM + 10% FBS) or without a nutrient (w/o FBS, w/o Gln or w/o Pyr). After 24 and 48 hours, cells were loaded with 10 nM Tetramethyl Rhodamine Methyl Ester (TMRM) (Molecular Probes) and incubated at 37°C for 30 min. TMRM accumulates in mitochondria and its signal intensity is a function of mitochondrial membrane potential. Dye accumulation was assessed by using Incucyte S3 platform (Essen BioSciences) with a 20x Objective. The relative intensity of red fluorescent signal was analysed by Incucyte S3 Software (Essen BioSciences). Quantitative Real-time PCR 0. 6 x10 6 cells were plated in 25 cm 2 flasks. After 24h and 48h of incubation in nutrient depleted media or in 1% O 2 , total RNA was extracted using "ReliaPrep RNA cell mini-prep system" (Promega) according to manufacturer's instructions.
Western blotting analysis For western blotting analysis, 0. 6 × 10 6 HeLa cells were plated in 25 cm 2 flasks. After 24 hours, cells were exposed to nutrient or O 2 deprivation as previously described for 24 and 48 hours. Cells were lysed and 80 µg of protein extract of each sample were separated using SDS/PAGE electrophoresis and transferred to a PVDF membrane. The membrane was blotted with primary antibodies anti-VDAC1 1:1000 (ab34726, Abcam) and anti-α-Tubulin 1:6000 (T9026, Sigma-Aldrich) at 4°C overnight and after the washing step with a secondary antibody IRDye® 680RD Donkey anti-Mouse IgG 1:20000 (AB_2814912, Li-Cor Biosciences 925-68072). The membrane was scanned using the Odyssey CLx imaging system (Li-Cor Biosciences). Data were analyzed with Image J software (NIH) and protein expression was measured using integrated intensity readings in regions around protein bands.
In silico VDAC1 gene promoter analysis The human VDAC1 sequence used in the work is identified by the accession number NM_003374. A genomic sequence, (chromosome 5: 133340586-133341185 (GRCh37/hg19)) of 600 bp encompassing the identified TSS, 400 bp upstream and 200 bp downstream, was manually retrieved from GeneBank database of NCBI in human genome assembly chr5:133340586-133341185 (GRCh37/hg19) and was analyzed by Genomatix software (Genomatix GmbH). This VDAC1 promoter sequence was analyzed using Gene Regulation module of Genomatix software suite. In particular, MatInspector TFs application were used to identify potential binding site for transcription factors (TFBS) in input sequence. The database performes the analysis using a Matrix Family Library for core promoter elements in vertebrates with a fixed matrix similarity threshold of 0.
85  (26). Overrepresented TFBS and modules applications were used to associate a statistical analysis to transcription factors binding sites and to regulative units composed by two transcription factors called modules. The assessment of TFBS predicted by MatInspector, and the functional analysis of transcriptioncontrolling elements, such as the CpG island mapping and active chromatin domains was performed also using UCSC Genome Browser (https://genome. ucsc. edu). In order to evaluate the effectiveness of the promoter prediction, the h1prom0 sequence was examined comparing it with experimentally validated promoters of VDAC1 gene generated by Eukaryotic Promoter Database (EPD, https://epd. epfl. ch) (27). TSS assembly pipeline of EPDnew version 004 for H. sapiens is available at the custom track of UCSC Genome Browser.
Structure of VDAC1 gene: transcripts variants and promoter. VDAC1 is the most abundant and characterized isoform expressed in mitochondrial outer membrane. The human VDAC1 gene sequence is located in the chromosome 5 (GRCh37/hg19). In Reported transcripts for VDAC1 are also indicated. They include 7 different splice variants. Four of them encode proteins, two are processed transcripts and in one of them a retained intron is present. Three transcripts have the same exons composition but differ in the lenght of the 5' and 3' UTRs. The transcript variant coding the functional protein is reported with the code ENS00000265333. 8 corresponding to NM_003374. 2 in the refseq database of NCBI. It is not known whether the other splice variants identified have any biological role, however, gene expression data collected from NIH Genotype-Tissue Expression project (GTEX), report the expression of them, including the non protein coding transcripts.
In Fig. 2A  the distribution of transcription factors binding sites (TFBS) and general regulatory elements in the h1prom0 promoter sequence was obtained by using Genomatix software (Genomatix GmbH). The statistical significant binding regions were selected by the Overrepresented TFBS application. The most represented are V$E2FF TF binding sites, a family of transcription factors involved in cell cycle regulation and proliferation; next V$NRF-1 TF binding sites, that are considered as the master regulator of mitochondrial biogenesis  (28, 29). Among the transcription factors binding sites (TFBS) predicted by MatInspector application in h1prom0 promoter sequence, in Fig. 2B  are reported TFBS experimentally validated by ChIp-Seq analysis (ENCODE project). The ChIp-Seq Peaks of V$E2FF and V$NRF-1 transcription factors confirm the importance of these factors highlighted by the bioinformatic prediction, suggesting their central role in VDAC1 gene expression regulation.
NRF-1 and HIFF are main regulators of VDAC1 gene expression We aimed to investigate the molecular mechanism responsible of VDAC1 expression increase in stress condition due to promoter activation we reported above. In particular, we focused our analysis on NRF-1 and HIFF TFs, known to be activated in nutrient depletion and hypoxic conditions  (38) (39) (40). In Fig. 7A  a magnification of VDAC1 promoter region analyzed from UCSC Genome Browser is overlapped with experimental data proving the transcriptional activity of this genomic region, as discussed above in the text. In the same graphical view, the TFBS of NRF-1 in VDAC1 promoter, identified by ChIP-Seq Peaks in ENCODE project, are reported for 6 different cell lines. The two major peaks found for NRF-1 recognition sites are located in overlapping positions of the promoter for any kind of cell.
Moreover, these validated TFBS for NRF-1 fall in the genomic region corresponding to VDAC1 promoter sequence studied in this work (h1prom0). At the moment no information in this sequence is available for HIF-1 validated binding sites by the ENCODE project. We also used bioinformatic tools to identify TFBS and regulative modules in VDAC1 gene promoter. MatInspector and Overrepresented TF applications of Genomatix software allowed to detect the distribution of TFBS. In addition, Overrepresented modules application looks for regulative units defined by Genomatix as modules of two clustered TFBS: these modules probably contribute more than the single elements to the promoter activity. 8 NRF-1 and 2 HIFF binding sites were found in the sequence of the promoter h1prom0. We noticed that most of NRF-1 binding sites are located in proximity or over the transcription starting site (TSS), a typical promoter organization found for nuclear genes coding mitochondrial proteins  (41) (42) (43).
It was also reported the number of regulative units, or modules, involving the two considered TFBS: they are 189 for NRF-1 and 65 for HIFF. The whole list of NRF-1 and HIFF TFBS found on VDAC1 promoter is also reported in Fig. 7A , including the results'statistical significance (p-value, expected score, Overrepresentation score and Z-score). These data suggest a relevant role of these factors in VDAC1 gene expression regulation. Moreover, the detection of many modules containing either NRF-1 or HIFF TFBS in VDAC1 promoter suggests that they may have a significant role in the regulation of this gene by promoting a larger cooperation with other TFs in metabolic stress conditions. Analyzing the results of MatInspector TF application in details, we found that the two binding sites of HIFF transcription factors family identified on h1prom0 sequence correspond to recognition sites for HIF-1 factor, one of the main regulators of hypoxia cell response.
Other TFs involved in mitochondrial biogenesis were predicted to contribute to the regulation of VDAC1 gene expression in stress conditions To extend our view of the cooperation between NRF-1, HIF-1 and other transcription factors in VDAC1 gene regulation in stress, we deepened our bioinformatic prediction analysis on VDAC1 promoter, looking for other TFBS. In particular, we focused our attention to TFs involved in mitochondrial biogenesis, since VDAC1 gene expression should parallel the increase of mitochondrial mass in the cell, a mechanism used to face cellular stress conditions  (29). Based on previous information  (38, 46) , we searched binding sites for CREB, ETSF, SP1F, YY1F and for modules composed by them together with those for NRF-1. In Fig. 10A  a bioinformatic quest, in UCS Genome Browser, showed a zooming into the VDAC1 promoter region analyzed (chromosome 5: 133340586-133341185 (GRCh37/hg19)) with the distribution of TFBS of regulative factors (CREB, SP1F, ETS, CREM, ATF7, NFE2L2, E2F1) involved in mitochondrial biogenesis and identified by ChIP-Seq Peaks data in ENCODE project.
Probably the regulation of VDAC1 gene expression driven by NRF-1 might be triggered in combination with other factors when specific cellular mechanisms are activated in stress conditions. In Fig. 10B , the number and location of these factors is reported in VDAC1 promoter structure, showing that most of them are in close proximity to NRF-1 binding sites. A search for modules containing, as mandatory condition, one of the transcription factor involved in biogenesis in combination with NRF-1, was performed and it was found that high statistical significance is associated to regulative units presenting NRF-1/CREB, NRF-1/SP1F and NRF-1/ETSF. These data corroborate the idea that NRF-1 and HIF1a are not the only necessary TFs to regulate VDAC1 promoter under the stress condition studied, but VDAC1 gene is under a more complex control of the expression.
CONCLUSIONS VDAC1 is a pivotal protein in the regulation of mitochondrial metabolism and energetic balance in normal conditions but also in tumorigenesis and neurodegeneration. However, the information on VDAC genes expression available in the literature are mainly derived from high-throughput approach and database elaboration data. In particular, it is very common to find correlation of VDAC1 expression changes with proliferative pathways as a main outcome arising from the analysis of many pathologies  (47, 48). We therefore undertook a systematic study of the region upstream of the site where the transcription of VDAC1 mRNA in humans begins, starting with a review of the main public sources and by processing with bioinformatics tools the chosen sequence. From the in silico analysis of human VDAC1 gene in UCSC genome browser, it appears that 7 different splicing variant mRNAs were identified.
This variety does not affect the coding region but mainly modifies 5'-UTR and 3'-UTR length. These sequence maybe target of translation regulation by miRNA or interference. A number of publications indeed reported about the identification of miRNA molecules targeting VDAC1. Among them, miR-7 and miR-320 down-regulate VDAC1 to restore mitochondrial functionality in cellular models of neurodegenerative disease  (49)  to reduce cell proliferation, migration, invasion  (50, 51) , or to protect cells from apoptosis in various pathologies  (52). To understand and clarify this aspect, an important starting point is to know how and when the expression of these transcripts is regulated. However very little information is available in the literature regarding VDAC1 gene regulation. We thus cross-checked the public data available on VDAC1 promoter in the Genome browser of UCSC with data extracted by Genomatix suite of software to identify the most relevant transcription factor binding sites (TFBS) located in VDAC1 promoter.
However, the over-expression of NRF-1 further enhanced VDAC1 promoter activation when cells are exposed to serum depletion, while any/no additional effect was registered on VDAC1 promoter when cells were devoid of glutamine. This result confirms that NRF-1 contributes to transcriptional activation of VDAC1 gene in stress conditions that immediately compromise cells proliferation and mitochondrial functionality, as supported by the data obtained on cells viability and mitochondrial membrane potential. VDAC1 thus might behave as the subset of genes regulated by NRF-1 and targeted by E2F, growth regulatory transcription factor family identified by association of chromatin immunoprecipitation with microarrays data (ChIP-on-chip). Most of them are indeed implicated in mitochondrial biogenesis and metabolism, other than mitosis, cytokinesis (65).
At last, when we exposed cells to hypoxia a strong activation of the VDAC1 promoter was triggered by HIF-1 over-expression which further enhanced the high activity already registered in oxygen reduction. However, the over-expression of exogenous HIF-1 induced transcriptional activation not only of VDAC1 wt promoter but also of VDAC1 promoter mutated for the HIF-1 binding sites. These data suggest that probably other factors are involved in this pathway and trigger VDAC1 response to promptly adapt mitochondrial functionality to oxygen deprivation. We might suppose that in this condition as well as in nutrient depletion, mitochondrial biogenesis is activated. The trend of VDAC1 transcripts expression, in hypoxic condition, is similar to that observed when cells were exposed to starvation.
They have also been identified as a reservoir for Nipah viruses that induce severe encephalitis in humans with high fatality rates  [37, 38]. The ecology of most Pteropus species is relatively poorly known, although some taxa have evidently adapted to anthropogenic environments. For instance, some species populations mainly forage in human-shaped landscapes  [39]  and include cultivated or exotic plant species in their diet  [40, 41]. We employed GPS (Gobal Positioning System) devices to collect spatio-temporal data on individual P. lylei and investigate their foraging ecology in a highly anthropogenic and heterogeneous environment in Cambodia. Based on GPS locations (presence data), random locations (pseudo-absence data) and environmental factors, a previous study investigated habitat selection in the same population to predict its overall distribution in the study region  [42].
Spatial data from the GPS loggers were collected for several consecutive nights (18:00-05:00) for each bat and were retrieved at the roost site in daytime using a receiving station with a maximum transfer distance of 20 m (these data are available in movebank. org; study name: "Foraging movements of Lyle's flying foxes in Cambodia":  [42] ). Acquisition rates for spatial data were heterogeneous among individuals  [42]. Because  [49]  found that Pteropus spp. spent an average 12. 0 min (range: 1-67 min) and 25. 0 min (range: 10-40 min) searching for and consuming fruits in fig and mango trees respectively, only GPS devices recording one location every 5 min were considered. The present analyses involved seven males and one female for which data were obtained for 3 to 12 consecutive nights from 22nd April and 17th May (one additional bat was excluded due to insufficient data).
The number of nights for which data were collected from a given GPS device depended on several factors including battery life and proximity of bats to the receiving station. The first night of data was excluded from analyses to avoid potential behavioral biases related to the capture event and because the GPS devices were programmed to record at a lower rate on the first night  [42]. Seven nights that had more than 10% of values missing and five nights where the intervals between two locations were greater than 5 min were also excluded. For the remainder of the dataset, occasional missing values (< 2% of all data) were interpolated using continuous-time correlated random walk modelling  ([50] ; 'crawl' R package) to maintain temporal regularity between locations. The nightly movements of bats are presented in the supplementary material (Fig.
72  An analysis of the textiles from Hampshire was undertaken by Sue Harrington. 73  The evidence does not permit the southern Hampshire and northern Hampshire sites to be distinguished as separate areas. Although two of the rare types of weave, the twill mixed spin and plain mixed spin, were only found in the cemeteries of Apple Down, Droxford and Worthy Park, the other rare types did not group together in any meaningful way. It is notable that cloth types were identified that are unusual for Hampshire in all the five sites and these do record a higher frequency in East Kent although this could be explained by biases in the dataset. It seems that it was the actual style rather than the cloth itself that carried symbolic meaning. As has been mentioned above, the creation of ethnic groups is often dependent upon cultural changes provoked through, for example, the interaction of competing groups.
PV structure Its similarities with Earth are also reflected in its potential vorticity structure, as can be seen e. g. in Figure  13 , which shows the zonal mean zonal wind and QGPV gradient for a typical atmospheric state during northern hemisphere winter on Mars, from data obtained from the Mars Climate Database (e. g. Lewis et al. , 1999, ;  see also http://www-mars. lmd. jussieu. fr). As can be seen in Fig. 13 (a), zonal winds are typically concentrated into deep, mid-latitude jet streams during winter and the adjacent seasons, with strong vertical and horizontal shears and a pronounced latitudinal tilt with altitude. The corresponding structure in QGPV (see Fig. 13(b )) shows ∂Q/∂y (∂Q/∂φ) positive in the jet itself (cf Fig. 13 (a)) but with weaker reversals in sign on either side. Together with the positive equatorward temperature gradient at the surface in the north, this would indicate that the CSP conditions can be satisfied in this season either by criterion (i) or (iii), much as on Earth.
Figure13. Zonal mean sections of (a) zonal wind (m s -1 ) and (b) ∂Q/∂φ (s -1 rad -1 ) typical of Martian northern hemisphere winter. Data were taken from the Mars Climate Database (e. g. Lewis et al. , 1999 , see also http://www-mars. lmd. jussieu. fr/mars/access. html) for Ls = 270 • using the standard Mars climatology. Contour intervals are (a) 15 m s -1 and (b) 0. 0002 s -1 rad -1 and negative contours are shown dashed.
Acknowledgements. PLR, FT-V and RMBY acknowledge support from the  UK Science and Technology Research Council  during the course of this research under grants  ST/K502236/1 ,  ST/K00106X/1  and  ST/I001948/1. This article originated from a lecture prepared by  PLR  for the  Summer School on Waves, Instability and Turbulence in Geophysical and Astrophysical Flows in Cargèse, Corsica in July 2019. We are grateful to  Prof. G. Vallis  and an anonymous referee for their comments on an earlier version of this manuscript.
Data availability. Data on the Martian atmosphere can be obtained from the Mars Climate Database website http://www-mars. lmd. jussieu. fr/mars/access. html. Data from the model simulations of Young et al. (2019a)  can be obtained from Oxford University's Research Archive at https://doi. org/10. 5287/bodleian:PyYbbxpk2. Data from laboratory experiments reported in Section 2 can be obtained from the authors on request.
Population at risk The population at risk in each site during the recruitment period was estimated from the most accurate local routine demographic data, stratified by age (5-year bands), sex, country or region of origin or ethnicity. The statistical bureaus in France, Italy and Spain identify migrants using registered country of birth. Thus, in the catchment areas of these countries, we compared the rate of psychosis for foreign-born individuals to that for native-born individuals. Since denominator information on parental country of birth was lacking, the numbers of second-generation migrants were necessarily included in the reference population. The classification of region of birth of the French bureau is detailed in the online Supplementary eMethods. For the Netherlands, we used population data on country of birth and parental countries of birth to distinguish between the native Dutch (Dutch-born individuals born to Dutch-born parents), first-and second-generation migrants.
A Dutch-born individual is considered a second-generation migrant if at least one of the parents was born abroad. At Dutch sites, we compared the incidence rate of psychosis for first-and second-generation migrants combined with that for the native Dutch. For the UK we used data from the 2011 census which recognizes a range of categories (see online Supplementary eMethods), based on self-assigned ethnicity and country of birth (the UK or not). We compared the incidence rate for minorities in the UK to that for the White British. Thus, denominator information on minorities born in the country of destination was available in the Netherlands (second generation) and in the UK (second generation or higher), not in France, Italy or Spain (online Supplementary eTable S1).
We considered individuals from categories 2 through 7 as non-Western migrants. This classification did not entirely overlap with that used by the UK census, but several comparisons were possible. The details are summarized in the online Supplementary eMethods. There were too few people from the Maghreb or Latin America in the UK for meaningful analyses. For this reason, we excluded the UK from the comparison of rates for individuals from these regions. Likewise, persons from the Middle-East in Spain were not included as separate denominator category. Since the French data did not allow the delineation of migrants from the Middle East, Asia, or Latin America, the French sites could not be included in these comparisons.