dataesr/scientific-paragraphs-categorization huggingface dataset:
HUMAnN3 (v3. 0. 0. alpha. 3)  55 was used to pro le the potential metabolic functions of the metagenomes, revealing 507,517 UniRef90 gene families that were grouped into 361 MetaCyc functional pathways and 5,564 KOs. Based on this UniRef90 pathway data, contributional diversity (Gini-Simpson for withinsample and Bray-Curtis for between-sample diversity) 26 was calculated for each cutotype and compared by selecting pathways detected in > 75% of samples within each cutotype. Antimicrobial resistance gene family markers were detected using ShortBRED (v0. 9. 3) 56. The associations of the taxonomic, functional, and antimicrobial resistance data with host and environmental factors, including PAH exposure, were determined using MaAsLin2  57  , with city, acne onset, wrinkle grade, facial pigmentation frequency, and exposure to PAHs as xed effects and BMI and age as random effects.
An adjusted p-value (i. e. , q-value) ≤ 0. 05 was considered statistically signi cant in this analysis. The composition differences (β-diversity) between samples were analyzed based on the Bray-Curtis dissimilarity using the function "vegdist" in the R package "vegan," and the permutational multivariate analysis of variance (PERMANOVA) test was applied using the "adonis" function in the R package "vegan" with 999 permutations to test the in uence of factors including cutotype, city, acne onset, age group, and facial pigmentation frequency. The effects of PAHs on community differentiation by cutotype were assessed with distance-based redundancy analysis using the "capscale" function in the R package "vegan. " The Procrustes test was performed to determine congruency between taxonomic (Bracken) and functional (HUMAnN) composition data using the "protest" function in the R package "vegan.
" To assess α-diversity, samples were rst subsampled using seqtk (v1. 3) to a read depth of 313,504 reads per sample, which corresponded to the sequencing depth of the sample with the lowest number of reads. This depth was representative of the total specieslevel taxon richness of the metagenomes (Supplementary Fig. 3 ). Shannon diversity was calculated for the subsampled dataset in "vegan. " Statistical signi cance of comparisons between two and more than two groups were performed using the Mann-Whitney and Kruskal-Wallis tests, respectively, in the R package "stats" (v3. 6. 1). Community-strain-level in situ growth rate estimation Growth rate estimation of the community was performed using the Growth Rate Index (GRiD) algorithm 8 with a coverage cutoff (-c) of 0. 2, and ambiguous reads were re-assigned using Pathoscope (v2.
Correlation Analysis Of The Multivariate Datasets For each (Bracken) and functional (HUMAnN) features of the microbiome as well as the measured skin metabolites and PAHs were incorporated into a multivariate analysis to identify the correlating features between the datasets. Residualized values of all features from each type of data were determined based on a linear mixed-effects model using the R package "lme4. " The model included city, acne onset, and facial pigmentation frequency as xed effects and age group as a random effect. The resulting residualized datasets were subjected to pairwise hierarchical all-against-all association (HAllA) analysis (v0. 8. 18) as previously described  59. Spearman's rank correlations with a Benjamini-Hochberg false discovery rate-corrected p-value (i. e.
, q-value) ≤ 0. 25 were considered statistically signi cant  59. Spearman's correlations <|0. 25| were considered weak and were removed from the output. A sub-network containing 500 correlations of the lowest q-value within each HAllA analysis between two datasets was constructed for each cutotype. The sub-networks for the two cutotypes were merged and visualized in Cytoscape (v3. 8. 2) to identify both correlations shared between the cutotypes and cutotypespeci c correlations 60.
Data and script availability The raw paired-end metagenomics sequences have been deposited in NCBI's BioProject under accession number PRJNA730653. Custom scripts and input and output les generated for analysis and gures as well as the reference genomes used for decontamination are available at GitHub page https://github. com/mhyleung/skin_microbiota_pah2. Requests for access to the datasets used for gures should be directed to PKHL.
Background France's influenza surveillance system, led by Santé Publique France, the national public health agency, is based on several sources of inpatient and outpatient data  [1]. The severity and impact of seasonal influenza on the health system are routinely monitored using two indicators: the number of patients visiting emergency departments (ED) who are subsequently hospitalized, and cases admitted to intensive care units (ICU). Although these two indicators allow real-time monitoring during the influenza season, they only reflect part of the burden of influenza on the hospital system. This is because of the data sources used, and a definition limited to clinical influenza diagnosis  [2]. The French national hospital discharge database (PMSI) records information on all patients hospitalized a posteriori.
Two French studies have recently used this database to estimate the epidemiological and economic burdens of severe influenza in the country  [3]  and to describe the characteristics and severity of patients hospitalized with diagnosed influenza  [2]. However, many hospitalizations for respiratory infections (e. g. , pneumonia, bronchitis) occur without virological influenza concurrently diagnosed  [4]. In order to compensate for this lack of virological confirmation for atypical forms of influenza or forms with complications, the World Health Organization (WHO) has proposed broadening influenza surveillance by monitoring severe acute respiratory infections (SARI)  [5]. This provides a more representative assessment of the true burden of influenza epidemics on the hospital system. We aimed to estimate the burden of influenza-attributable SARI on the hospital system in metropolitan France over six annual epidemics (2012 to 2018) using two regression models, specifically a periodic regression model and a generalized linear model (GLM).
Hospitalization data The PMSI database is part of France's larger National Health Data System (Système national des données de santé, SNDS) database. From the PMSI, we extracted all hospitalizations with a diagnosis of SARI (ICD-10 code: J09-J20) occurring between 1 July 2012 and 30 June 2018. For each hospitalization, we obtained the following information: age group, week of admission, total length of stay (in days), admission to an intensive care unit (yes/no), and death (yes/no).
Periodic regression To estimate the proportion of influenza-attributable SARI, a periodic regression method was applied to the weekly number of hospitalizations for pneumonia and bronchitis  [7]. We modelled the baseline level with a multivariable linear regression model using the following equation: E (y w ) = α 0 + α 1 w + γ 1 cos (2πw/52. 17) +δ 1 sin (2πw/52. 17) + γ 2 cos (4πw/52. 17) +δ 2 sin (4πw/52. 17) , where y w is, for week w, the number of pneumonia and bronchitis hospitalizations. to a change in the dynamics of winter respiratory epidemics. The co-circulation of the three current major respiratory viruses (influenza, SARS-Cov-2, and RSV) and the evolution of diagnostic confirmation practices must now be taken into account when analyzing SARI. Keywords Influenza, Severe acute respiratory infections (SARI), Surveillance, Hospital, Burden The baseline level in a given week w was obtained by fitting the model to the observations from 1/07/2012 to 30/06/2018 from which we removed the presence of influenza and bronchiolitis epidemics (Additional file 1) in the training dataset from the series.
Generalized linear model We also performed a generalized linear model (GLM) as a complementary approach to validate the results of the periodic regression method. Specifically, the GLM was performed to estimate the number of influenza-associated hospitalizations during the epidemic periods by using overall pneumonia and bronchitis (PD) hospitalizations, assuming negative-binomial distributed errors with a logarithmic link function. Two model parameters were used: indicators of influenza activity (including influenza-like illness (ILI) incidence data and virological data), and morbidity data for RSV. Weekly ILI incidence data were obtained from the French general practitioner network Sentinelles. Percentages of nasopharyngeal samples testing positive for influenza in France were obtained from the National Reference Center for Influenza.
Swabs were performed by practitioners in the Regional Groups for Influenza Surveillance Network (GROG) (until the 2013/2014 influenza season) and by members of the Sentinelles network (from the 2014/2015 influenza season onward). These data were stratified by influenza type and subtype: A(H1N1)pdm09, A(H3N2) and B. For each influenza type and subtype, the product of ILI incidence and the percentage of samples testing positive was used as an indicator of influenza activity. As a proxy for the circulation of RSV, the proportions of consultations for bronchiolitis were obtained from computerized medical records completed during consultations at emergency departments participating in the OSCOUR® network (representing from approximatively 50% of national emergency department activity in 2010-11 to 90% in 2016-17).
Model parameters are described in Additional file 2. The GLM model analysis could only be performed on the five seasons from 2013 to 14 to 2017-18 because of the lack of availability of virological data for influenza before 2013. The model was adjusted for temporal trend and RSV circulation. We selected lags on influenza activity indicators (lags retained: 0, 1) and the RSV indicator (lags 0, 1), based on the Akaike Information Criterion (AIC). In order to take into account the nonlinear relationship between hospitalizations for pneumonia and bronchitis and covariables, we used b-splines (with three degrees of freedom) on each component  [9]. Population figures were introduced into the model as an offset. The following regression model was used: log(E(Y t /P op)) = β 0 + β 1 bs (t) +β 8 bs(f lag(ILI H1N1t )) +β 9 bs(f lag(ILI H3N2t )) +β 10 bs(f lag(ILI Bt )) +β 11 bs(f lag(Bronchio t )) where Y t was the number of pneumonia and bronchitis hospitalizations predicted by the model, P op was the off- set corresponding to the population size, t was the time, bs a function of b-splines with three degrees of freedom, and f lag a function of lag.
Discussion The analysis of SARI hospitalization data from the PMSI database allowed us to provide a more representative estimate of the true burden of influenza on France's hospital system, as it took into account the non-visible part of influenza. Using periodic regression, we estimated that between the 2013-2014 and 2017-2018 influenza epidemics, 60/100,000 inhabitants were hospitalized per season. This estimate was confirmed by GLM, which provided a similar hospitalization rate of 64/100,000. In contrast, the hospitalization rate of patients with diagnosis of influenza alone was only two-thirds of the rate for influenza-attributable SARI. A previous study estimated the influenza hospitalization rate in metropolitan France at 38/100,000 per season over the period 2013 to 2018  [2, 10]. International comparisons are complicated by the diversity of definitions, sources, methods and study periods, as highlighted in the literature review by Roguski et, al.
The periodic regression method used in this study made it possible to estimate the impact of influenza at the regional level and to confirm the existence of regional disparities in the use of hospitalizations for influenza across metropolitan France. This was previously observed in an earlier study on influenza  [18]  and more recently during the COVID-19 pandemic  [19, 20]. It confirms that monitoring data on a sub-regional scale is essential to quantify the burden of seasonal epidemics, including influenza, and to adapt the care offer locally with a view to improving patient management. Our study has the usual limitations associated with using medico-administrative databases whose coding rules are based on economic and not epidemiological criteria. We assumed that the coding of influenza diagnosis corresponded to virological confirmation.
Conclusion The present study's analysis of the PMSI medico-administrative database helped provide a more accurate estimation of the burden of influenza-associated SARI on France's hospital system, particularly in the elderly. Moreover, this work complements an initial study that was limited to hospitalizations with a diagnosis of influenza, its objective was to describe characteristics and severity  [18]. Both studies contribute to the overall evaluation of the socioeconomic impact of influenza in France. Their findings provide us with a better understanding of the burden of influenza on the hospital system, and should help raise the general public and health professionals' awareness of the importance of barrier measures and influenza vaccination. We only focused on hospitalizations in this study; other studies investigated overall (i.
Data Availability The datasets used and/or analyzed during the current study are not publicly available for legal and ethical reasons. The datasets are available from the corresponding author on reasonable request and only in compliance with the legal provisions allowing access to France's most comprehensive health database, the National Health Data System (Système national des données de santé). More specifically,approval from the CESREES (Comité éthique et scientifique pour les recherches, les études et les évaluations dans le domaine de la santé) ethics committee and from the French data protection authority is needed.
Single cell RNA sequencing and data analysis For cell cultures GS359, GS785, and GS789 the cells sequenced comprised five conditions: prior treatment (T0), after the first TMZ exposure when reached the confluency (T1-TMZ and T1-CTRL) and after the last TMZ treatment (T2-TMZ and T2-CTRL). For cell culture GS772, the cell sequenced comprised three conditions: T0, T1-CTRL, and T1-TMZ. Cell cultures were washed with PBS and incubated with Accutase (Invitrogen) until detachment, and collected in HBSS (Gibco, Thermo Fisher Scientific). Subsequently, the cells were centrifuged and resuspended in HBSS and sorted (FACSAria II, BD Biosciences) into 384-well plates based on single cells selection gating. Library preparation was carried out according to the CELseq2 protocol  18. Cells were then subjected to single cell transcriptomic sequencing (SORT-seq, ~100k reads/cell).
Markers of temozolomide resistance are found in recurrent glioblastoma tumors. To validate the clinical relevance and identify a common pattern in the ADA phenotype, we conducted a study using the GLASS database to investigate the expression of ADA temozolomide resistant markers in recurrent glioblastoma samples. We considered all subpopulations to determine the overall markers of temozolomide resistance (Supplementary Table  4 ). As shown in Figure  3A , we found that 37 genes (18 up-regulated and 19 down-regulated) were commonly shared between GS359 and GS789 (Supplementary Table  5 ). While these genes may not be directly implicated in a particular biological process, the upregulated genes exhibited significant enrichment in cellular components such as collagens, junction complexes, synaptic membranes, filopodia and lamellipodia protrusions, adhesions, and ribosomes (Figure  3B ).
This implies their potential involvement in essential functions like migration, cell communication, and protein synthesis. Conversely, the downregulated genes were enriched for spindle and endoplasmic reticulum (ER) and vesicle compartments (Figure  3B ). Network graphs displaying the enriched cellular compartments identified in the common 37 genes of temozolomide resistant (T2-TMZ) ADA cell cultures. On the left, the network graph is related to the upregulated genes, while on the right the network graph refers to the downregulated genes. (C) Volcano plot representing the differentially expressed genes between primary and recurrent glioblastomas derived from the GLASS dataset. Positive log2 fold changes refer to the upregulation in the recurrent tumors compared to the primary glioblastomas.
The overlapping significant up-and down-regulated genes with temozolomide resistant (T2-TMZ) GS359 are displayed in the white boxes. (D) Venn diagrams displaying the amount of overlapping up-(left) and down-(right) regulated genes between recurrent and temozolomide resistant (T2-TMZ) GS359, and the percentage of the genes related to the two dominant clusters. (E) Volcano plot representing the differentially expressed genes between primary and recurrent glioblastomas derived from the GLASS dataset. The overlapping significant up-and downregulated genes with temozolomide resistant (T2-TMZ) GS789 are displayed in the white boxes. (F) Venn diagrams displaying the amount of overlapping up-(left) and down-(right) regulated genes between recurrent and temozolomide resistant (T2-TMZ) GS789, and the percentage of the genes related to the dominant cluster.
Acknowledgments The authors gratefully acknowledge the patients who kindly donated tumor tissue and data, making this study possible. In addition, we thank  Iris Verploegh  for the helpful discussions. M. P. C. acknowledges support from the  Oncode Institute, Stichting Ammodo and NWO (Netherlands Organization for Scientific Research)   Vidi Grant. M. P. C. appreciates  Josephine Nefkens Stichting 's support on the UFO microscope.
Data availability Bulk RNA sequencing data of the cell lines (n=21) applied in this article is available in the Gene Expression Omnibus Repository: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE232173, Accession number: GSE232173. Single cell RNA sequencing data is available in the Gene Expression Omnibus Repository: https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE226202, Accession number: GSE226202.
4  and Supplementary Data 2). The alteration of diopside was widely observed in tuffs via optical microscopy, alongside some evidence for olivine alteration. There were many irregular fractures in altered minerals surrounding by clay minerals (Supplementary Fig. 5 ). SEM-EDS and Raman spectrometry were used to further characterize the microstructure and mineral alteration in the tuffs. SEM-EDS showed the coupling of Fe, Mg and C of filamentous heamatite, surrounded by multiple apatite crystals with particle size of 1-4 μm (Fig. 1a, b, ). Characteristic peaks of heamatite (1328 cm -1 ) and carbonaceous matter (CM, D band at ~1358 cm -1 and G band at ~1576 cm -1 ) were found concurrently in the Raman spectrum (Fig. 1m ). Higher resolution elemental analysis was mapped using nano SIMS. The results show that carbon (C), nitrogen (N) and sulfur (S) were enriched around the altered hematite veins.
The Gibbs free energy was calculated based on the pressure (P = 250 bar) and temperature (T = 10 °C) at the average sampling depth (2500 m) of the tuffs. 32, 39  ; and indigo circles represent Berau delta sediments, data from ref. 72. The dark and light orange color represent tuffs and marine samples, respectively; while samples from chemolithotrophic systems and land are represented by yellow and gray, respectively. Data sources: tuffs and background sediments from this study, Mariana Trench sediments  32  , altered rocks and sediments  25, 26, 35  , hydrothermal deposits  28, [42] [43] [44] [45]  , cold seep carbonates  27  , serpentinites  37  , and the data source of global soil and peats datasets please see Supplementary References 9-24. The calculation results showed that the chemical energy released by water rock reactions in tuffs could provide 1.
The working conditions were 30 mA and 40 kV with CuK-alpha radiation, ranging from 3 to 85°2 θ. Phase analysis was completed using Jada 5. 0 software. The sample was dissolved in HNO 3 + HF solution on a hot plate. Then the eluted samples were spiked with internal standard In (1 ppm) and diluted with 2% HNO 3. Quantitative analysis of trace elements was done via Agilent 7700X Inductively Coupled Plasma Mass Spectrometer at the State Key Laboratory of Marine Geology, Tongji University, China. After subsampling of fresh tuff samples over drying, the surface of the tuffs was observed under different magnifications using a stereoscope. Thin sections were made, rinsed with Mili-Q water, dried and then blown with nitrogen to remove the contamination on the slices surface before microscopic observation at the Institute of Deep Sea Science and Engineering, CAS.
The sections were first observed by polarizing microscope with mainly transmitted light and supplemented by reflected light. Next, the scanning electron microscope (Thermo ScientificTM Apreo) was used to observe the morphology and structure of minerals under voltage of 15 kV. Element distributions of thin sections were analyzed using energy dispersive spectrometer with 20 kV and high vacuum. Raman spectrometry (WITec alpha 300R) was used to scan the surfaces on tuff thin sections with cumulative time of 1 s × 3. The Project FIVE 5. 2 software was used for subsequent integration and analysis of all Raman spectrum data. The tuff thin sections were finally made into 1-inch tin targets, and was analyzed by nano secondary ion mass spectrometry (Nano SIMS, CAMECA 50L) with a Cs + ion beam intensity of 1.
While DC, #rings tetra and #rings penta were calculated to appraise the degree of cyclization of whole, tetraand penta-methylated brGDGTs, respectively  72, 73. Rings penta-hexa represents the sum of the fractional abundance of cyclic penta-and hexa-methylated brGDGTs  25. MBT ¼ Ia þ Ib þ Ic ∑brGDGTs ð4Þ DC ¼ Ib þ IIb þ IIb0 Ia þ Ib þ IIa þ IIa0 þ IIb þ IIb0 ð5Þ #rings tetra ¼ Ib þ 2 Ic Ia þ Ib þ Ic ð6Þ #rings penta ¼ IIb þ IIb0 þ 2 IIc þ 2 IIC0 IIa þ IIa0 þ IIb þ IIb0 þ IIc þ IIc0 ð7Þ Rings pentaÀhexa ¼ IIb þ IIb0 þ IIc þ IIc0 þ IIIb þ IIIb0 þ IIIc þ IIIc0 ð8Þ Principal component analysis was conducted using Canoco 5. 0 software, and linear discriminant analysis was performed with the SPSS software to provide a general view of the distribution pattern of brGDGTs. Reporting summary. Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Data availability The GDGT, trace elements, and mineral composition data generated from this study can be found in Supplementary Data 1 and 2, and are additionally accessible at Pangaea. de (Deposition title: Inorganic and organic geochemical composition of Mariana Trough tuffs and background sediments; authors: T. L. , J. Li, J. Longman, X. P. ). The relevant data of SEM, Raman spectra, and NanoSIMS in the paper and its Supplementary Information can be found at https://doi. org/10. 6084/m9. figshare. 22014749.
Acknowledgements The authors wish to thank the manned submersible  SHENHAIYONGSHI  and all the participants on the R/V TANSUOYIHAO during the  Mariana Subduction Geological  Expedition in 2018 and 2019. This work was funded by the  National Key Research and Development Program of China  (Grant No. 2022YFC2805500 ), and the  National Natural Science Foundation of China  (Grant Nos. 42176072 ,  41876050 , and  41576038 ).
No. 3572112) at twice the concentration recommended by the manufacturer. The cells were then washed three times with PBS and the fluorescence foci were counted under UV microscope. The virus titration was based on technical duplicates and expressed in fluorescence focus-forming unit per mL (FFU mL -1 ). Cells were stained for 15 min with Hoechst (1:10000) (H1399, Thermo Scientific). After another step of three PBS washes, images were obtained from Opera Phenix (PerkinElmer) with 20× air objective lens and analyzed in Columbus Image Data Management System (PerkinElmer).