dataesr/scientific-paragraphs-categorization huggingface dataset:
c) Carbon demands As explained above, prokaryotic carbon demand is generally assessed by adding rates of prokaryotic heterotrophic production (PHP) to that of prokaryotic respiration (PR). PHP of non-sinking prokaryotes (that is free-living and attached to suspended particles prokaryotes) are derived from leucine incorporation measurements on seawater samples and are taken from  Fuchs et al. (2022). These data did not permit the separation of the free-living from attached to suspended particles  (Baumas et al. 2021). Hence, in the sequel, we no longer make this https://doi. org/10. 5194/bg-2023-34 Preprint. Discussion started: 16 February 2023 c Author(s) 2023. CC BY 4. 0 License. distinction and group both types under the term "non-sinking prokaryotes". During DY032, Marine Snow Catchers (MSC) were deployed to separate slow and fast-sinking particles from 100L of samples  (Riley et al.
e. 127m -751m). Non-sinking prokaryotes PHP rates were integrated using a piecewise model with a single node on the log-data as described in  Fuchs et al. (2022). Sinking prokaryotes PHP rates were integrated using power law. Sinking PR were integrated using trapeze because data are only available until 500m and without any a priori on the curve shape, this method is certainly the most conservative. Zooplankton activities are known to be related to POC concentration  (Steinberg et al. 2008). Zooplankton respiration data were available only for the cruise D341 when the net POC input into the active mesopelagic layer was 59 mg C m -2 d -1 (including PIPs) instead of 134 mg C m - 2 d -1 for DY032 (see above). For D341, zooplankton respiration integrated within the active mesopelagic zone  (135-726m, Fuchs et al.
2022 ) was 9 mg C m -2 d -1. Zooplankton respiration was integrated using a power law as in  Giering et al. (2014). Zooplankton respiration data are missing for DY032, thus we consider this quantity as a percentage of the POC input that we calculate from the D341 data set, i. e. 14. 67%. The zooplankton respiration value used here is therefore 17 mg C m -2 d -1.
(1992)  and  Grossart and Ploug (2001). Smith et al. (1992)  sampled particles at 25m and showed that 97% of particulate combined amino acids are released in the surrounding water. Later,  Grossart and Ploug (2001)  using aggregates from phytoplankton cultures show a loss of POC of 74%. Relying on these two studies, Anderson and Tang (2010) followed by  Giering et al. (2014)  consider that the value should be lower than that of a fresh detritus and choose a conservative value of 0. 5. In the case of these two experiments, only the amino acids are considered and the experiments were conducted under laboratory-controlled settings. Conversely, we used unpublished data from PEACETIME cruise (see methods details in supp. data) of in situ hydrolysis rates of aminopeptidase and βglucosydase from sinking prokaryotes (which hydrolyze amino acids and sugar, respectively) that we were able to convert into hydrolyzed carbon fluxes (see measurements and calculation details in supp.
087 and therefore produces very consistent results for a post-bloom period at the PAP site. Concerning PGEsinking, too few values are available. To our knowledge, only  Collins et al. (2015)  provided in situ values associated with sinking prokaryotes (from 0. 01 to 0. 03) at 150m. This is the only comparison we have, and our value of 0. 026 matches this order of magnitude. To provide further comparison, the DY032 data before integration allows us to calculate a PGEsinking (using PGEsinking = PHPsinking/(PHPsinking+PRsinking) from del Giorgio and Cole 1998) per PRsinking and PHPsinking of sinking prokaryotes points performed at the same depth. This led to a variation from 0. 033 at 70m to 0. 0013 at 500m. Although the lack of datapoints deeper than 500m and the low number of points forces us to stay cautious about these estimates, it may indicate that PGEsinking is not constant throughout the mesopelagic zone and decreases with depth.
(2014)  who reconcile the C budget of the mesopelagic zone. Although  Giering et al. (2014)  did not take into account sinking prokaryotes from in situ data point of view, their results were mainly due to the difference in CF used, i. e. 0. 44 kg C mol Leu -   Collins et al. 2015). Indeed, if we consider that enzymes account for a large proportion of the proteins produced by cells (see above) the PGEsinking must be low due to the high metabolic cost of their production  (Grossart and Ploug 2000). Finally, the C budget built from a combination of CFs of 0. 5 kg C mol Leu -1 and PGEs revealed by our optimization method seems the most reasonable option (from the three budgets built, Fig. 1 ) with an excess of C input of 40 mg C m -2 d -1. In this case, PGEs were determined by the model, which in addition to PHP and PR of sinking and non-sinking prokaryotes and zooplankton respiration, also accounts for the production of zooplankton biomass into calculations.
Acknowledgement We thank the crew and officers of the  R. R. S. DISCOVERY (NERC)  for their help during the PAP DY032 cruise and of the N/ O Pourquoi Pas ? during the PEACETIME cruise. This study is a contribution to the  PEACETIME  project ( MISTRALS CNRS INSU , doi: 10. 17600/17000300 ) managed by  Cécile Guieu  (LOV) and  Karine Desboeufs  (LISA). We warmly thank  F. Van Wambeke ,  S. Guasco  and  B. Zancker  for onboard works (enzymatic activities, sugar and amino acids concentrations measurements) during PEACETIME cruise. We wish to express our gratitude to  F. Van Wambeke ,  S. Giering ,  T. Anderson  and  A. Belcher  for stimulating and informative discussions. This manuscript is a contribution of the  APERO  project funded by the  National Research Agency  under the grant  APERO  [grant number  ANR ANR-21-CE01-0027 ] and by the  French LEFE-Cyber program.
BAHCC1 expression increases during melanoma progression To explore BAHCC1 expression in tumors, we analyzed public transcriptional data and observed first that BAHCC1 was more strongly expressed in melanoma cell lines compared to other tumors cells (Figure  2a ). In agreement, SKCM and UVM tumors from TCGA displayed higher levels of BAHCC1 compared to other tumors and normal tissues (Figure  2b ). Furthermore, BAHCC1 overexpression was a marker of poor prognosis in SKCM or UVM patients (Figure  2c-d ) and its expression was independent from the BRAF, NRAS or NF1 mutation status in SKCM patients (Figure  2e ). To trace the evolution of BAHCC1 expression along melanoma initiation and progression, different published bulk and single-cell transcriptomic datasets coming from normal cells, premalignant and malignant lesions were combined.
Identification of SEs H3K27ac ChIP-seq data from 12 different melanoma cell lines (MM001, MM011, MM031, MM034, MM057, MM074, MM047, MM087, MM099, MM118, SK-MEL-5, 501mel) and 3 melanocytes cell lines (NHEM1, NHEM2, Foreskin) were retrieved from GEO GSE60666, GSM958157 and GSE94488 and mapped to the Homo Sapiens genome (assembly hg19) using Bowtie v1. 0. 0 with default parameters except for "-p 3 -m 1 -strata -best -chunkmbs 128". Normalized BigWig files were generated using Homer makeUCSCfile v4. 9. 1 with the following parameter'-norm 20e6' meaning that data were normalized to 20M reads. The genome was divided into bins of 10Kb long with Deeptools multiBamSummary v2. 5. 0. The number of reads for each bin was computed for each sample. The following figure was made with Deeptools plotCorrelation and shows the pairwise correlation values (Spearman) for all samples of this project.
Single cell data analysis Expression matrix with row reads counts for the single cell experiment was retrieved from GEO GSE116237, GSE115978, GSE151091 and GSE138665 datasets. Then, data were normalized and clustered using the Seurat software package version 3. 1. 4 in R version 3. 6. 1. Data were filtered and only genes detected in at least 3 cells and cells with at least 350 detected genes were kept for further analysis. Expression of 26,661 transcripts in single cells was quantified. To cluster cells, read counts were normalized using the method "LogNormalize" of the Seurat function NormalizeData. It divides gene expression counts by the total expression, multiplies this by a scale factor (10,000 was used), and log-transforms the result. Then, 2000 variable features were selected with the variance stabilizing transformation method using the Seurat function FindVariableGenes with default parameters.
Integrated expression matrices were scaled (linear transformation) followed by principal component analysis (PCA) for linear dimensional reduction. The first 20 principal components (PCs) were used to cluster the cells with a resolution of 0. 5 and as input to tSNE to visualize the dataset in two dimensions. The Bioconductor package AUCell v 1. 6. 1 was used to assess whether some cells from the different datasets were enriched in gene sets of interest.
Other publicly available datasets used in this work BAHCC1 RNA expression was quantified in available datasets including GEO GSE12391, GSE80829, GSE98394, GSE114445, GSE46517, GSE8401 and in the CCLE portal. TCGA and GTEx data were obtained through UCSC Xena browser, cBioPortal and Gene Expression Profiling Interactive Analysis 2 (GEPIA2). PCA profile of TCGA and melanoma cell lines according to their phenotypic profile was obtained from the Graeber Lab software.
Acknowledgments We thank the  IGBMC facilities , in particular  Paola Rossolillo  and  Karim Essabri  for the isolation of BAHCC1 cDNA, and  Dr Catherine Birck  and  Dr Nathalie Troffer-Charlier  for the purification of the BAH and Tudor domains. We thank  Prof D. Lipsker  and the staff of the  Strasbourg University Hospital dermatology clinic  for tumour sections and  Prof. G. Ghanem  and  Prof. J-C Marine  for providing us the MM-series melanoma cultures. This study was supported by the  Institut National Du Cancer (INCa)  ( 2017-11537 ), the  Ligue contre le Cancer  (Equipe labélisée 2022, FC and ID), the  Fondation ARC , and the  ANR-10-LABX-0030-INRT , a  French State  fund managed by the  Agence Nationale de la Recherche  under the frame program  Investissements d'Avenir   ANR-10-IDEX-0002-02.
Preliminary results from the phase 2 PLEIADES study (median follow-up, 9. 2 months) and the phase 1b EQUULEUS study (median follow-up, 16. 6 months) showed that combining subcutaneous daratumumab (DARA SC) and daratumumab IV, respectively, with Kd (carfilzomib 70 mg/m 2 weekly) was well tolerated and induced deep responses in RRMM patients  [10, 11]. We report final data from PLEIADES and EQUULEUS, with a median follow-up of 12. 4 and 23. 7 months, respectively. METHODS PLEIADES (ClinicalTrials. gov Identifier: NCT03412565) evaluated DARA SC (daratumumab 1800 mg co-formulated with recombinant human hyaluronidase PH20 [rHuPH20; 2000 U/mL; ENHANZE ® drug delivery technology, Halozyme, Inc. , San Diego, CA, USA]) plus weekly Kd (carfilzomib 70 mg/m 2 ; dexamethasone 40 mg) in RRMM patients with 1 prior line of lenalidomide-based therapy.
EQUULEUS (NCT01998971) evaluated daratumumab IV plus weekly Kd (carfilzomib 70 mg/m 2 ; dexamethasone 40 mg) in RRMM patients after 1 to 3 prior lines of therapy (including bortezomib and an immunomodulatory drug). Patients in both studies received 28-day cycles of D-Kd until disease progression. Primary endpoints were the overall response rate (ORR) in PLEIADES and safety and tolerability in EQUULEUS. Supplementary Information includes additional methodology.
Grade 3/4 infections were reported in 18 (21. 2%) patients, most commonly pneumonia (4. 7%). Serious TEAEs occurred in 41 (48. 2%) patients, most commonly basal cell carcinoma, pneumonia, and upper respiratory tract infection (4. 7% each). Five In PLEIADES (median [range] follow-up, 12. 4 [0. 2-20. 6] months), ORR was 84. 8% in the all-treated population and 84. 1% in lenalidomide-refractory patients (Fig. 1 ). ORR was 75. 0% (12/16) and 82. 1% (23/28) in patients with high and standard cytogenetic risk, respectively. The median duration of response was not Fig. 1  Response rates in all patients and lenalidomide-refractory patients treated with D-Kd in PLEIADES or EQUULEUS. Data were shown for the all-treated population. Note: Due to rounding, percentages may not add up to the total percentage for each category.
ACKNOWLEDGEMENTS These studies (ClinicalTrials. gov Identifiers:  NCT03412565  and  NCT01998971 ) were sponsored by  Janssen Research & Development, LLC. We thank the patients participating in the PLEIADES and EQUULEUS studies and their families, the staff members at the study sites, the data and safety monitoring committees, and the staff members who were involved in the data collection and analyses. We also thank  Amgen  for supplying the carfilzomib used in these studies. Medical writing and editorial support were provided by  Lisa Shannon, PharmD , of  Lumanity Communications Inc. , and were funded by  Janssen Global Services, LLC.
microsynth. com/standard-services. html, accessed on 19 April 2023). For taxonomic affiliation, fungal isolates were first identified according to general morphotype principles of fungal classification. Then, ITS sequences were compared against tree databases: the rRNA/ITS databases with fungi (taxid:4751) as organism option using Blastn+  [34] ; the UNITE and the INSD databases using massBLASTer (BLAST+ 2. 13. 0) in PlutoF  [35]. For bacteria, the 16S rDNA sequences were compared to the Refseq genome database using Blastn+  [34]. The 16S rDNA sequences were further aligned with reference sequences and phylogenetic trees using the Maximum Likelihood method, and a Kimura 2-parameter model with 1000 replicates was constructed using Seaview  [36]. Amplicons of laccase-encoding genes were obtained using the degenerate primer pairs Cu1BF/Cu2R targeting laccase-encoding genes from Basidiomycota and Cu1AF/Cu2R targeting Ascomycota ones  [37].
Results First, 146 fungi and 83 bacteria were isolated from a PCB-contaminated brow taxonomically characterized and assayed for their PCB-transformation capacity. A them, the 29 fungal isolates listed in Table  1  and the 17 bacterial isolates listed in were selected with respect to their taxonomic diversity and for their highest PCB formation efficiency, i. e. presenting more than 30% or 10% of iPCB depletion in 3 or for fungi or bacteria, respectively. Table  1. Identification name (ID), phylum and species blast taxonomic affiliation of the ITS (with percent of identity), the database used for taxonomic affiliation and average degradat ciency ± the standard deviation in percent for each assayed isolate. M ) were obtained by measuring their full oxidation in the concentration range from 10 µM to 1000 µM at the previously mentioned wavelength.
Concerning genomic DNA ligninolytic gene amplification confirmed by sequencing, laccase encoding genes were amplified in only 6 fungal isolates belonging to Dothiora sp. and Dothiora maculans (isolates CRIS_F2 and F13, respectively), Fusarium sp. (CRIS_F6), Alternaria sp. (CRIS_F20 and F22) and Cladosporium sp. (CRIS_F27). In the same way, dye-peroxidase encoding gene was observed for only one fungal isolate (CRIS_F12) belonging to Aspergillus. Concerning the bacterial isolates, no ligninolytic and bph enzyme-encoding gene was amplified. Table  1. Identification name (ID), phylum and species blast taxonomic affiliation of the ITS region (with percent of identity), the database used for taxonomic affiliation and average degradation efficiency ± the standard deviation in percent for each assayed isolate.
Funding: This work was funded by the  French MITI-CNRS  (urban ecosystem- AAP 2019  " EMU-DPCB "), the  French EC2CO-CNRS  ( CNRS INSU -AAP 2020  " LigninoPCB ") and  French Auvergne-Rhône-Alpes AURA Region  ( Pack Ambition Recherche 2021 "FunDePCB") programs. FM PhD was supported by a  Research and Technology French National association ANRT and Envisol fellowship.
Data Availability Statement: All data that support findings of this study have been deposited in National Center for Biotechnology Information (NCBI) under reference PRJNA924245 (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA924245, accessed on 16 January 2023) for fungal isolates and under reference PRJNA924227 (https://www. ncbi. nlm. nih. gov/bioproject/PRJNA924227, accessed on 16 January 2023) for bacterial isolates.
These connected devices generate a wealth of information that can be leveraged to optimise operations, monitor environmental conditions, and ensure quality control. Artificial intelligence (AI) brings powerful capabilities to the wine industry by enabling machines to simulate human intelligence and perform complex tasks  [3]. AI algorithms can analyse large datasets, identify patterns, and make data-driven predictions and recommendations. In the wine industry, AI can be applied to various areas such as vineyard management, production processes, quality control, consumer behaviour analysis, and personalised marketing strategies. Blockchain technology, known for its secure and transparent nature, has the potential to revolutionise supply chain management in the wine industry  [4]. By utilising blockchain, wineries can create an immutable and decentralised ledger that records every transaction and event along the supply chain.
For example, AIpowered analytics platforms have been utilised to analyse large datasets, such as historical weather patterns and grape-growing conditions, to predict wine quality and characteristics. This helps winemakers make informed decisions about grape selection, fermentation techniques, and ageing processes, resulting in wines that meet consumer expectations. Furthermore, AI-driven recommendation systems have been employed to provide personalised wine recommendations to consumers based on their preferences, past purchases, and demographic information. This enhances the consumer experience, improves customer engagement, and increases sales. Despite the potential benefits, there are challenges to AI adoption in the wine industry. One challenge is the availability and quality of data. AI algorithms require large amounts of high-quality data to train and generate accurate predictions or recommendations.
Methods The trial protocol and analysis plan are in Supplement 1. VISION (NCT02864992) was a phase 2, single-arm, openlabel, multicenter nonrandomized clinical trial of tepotinib in patients with METex14-skipping advanced/metastatic NSCLC (cohorts A and C). Cohort C (enrollment: August 2019-May 2021) was an independent cohort, designed to confirm findings from cohort A (enrollment: September 2016-December 2019). Patients with advanced EGFR/ALK wild-type and METex14-skipping NSCLC detected by tissue (TBx) and/or liquid biopsy (LBx) using next-generation sequencing, received tepotinib, 500 mg (450 mg active moiety), once daily. The primary end point was objective response by independent review committee (IRC) using RECIST v1. 1. Secondary end points included duration of response (DOR), progression-free survival (PFS), overall survival (OS), and safety.
Predefined analysis sets for all end points included METex14-skipping detection by TBx (T positive), LBx (L positive), and T positive and/or L positive. 1  An exploratory analysis using modified RANO-BM criteria assessed intracranial activity in patients with brain metastases (BM) and 1 or more evaluable postbaseline tumor assessments. Data cutoff for all analyses was November 20, 2022, except RANO-BM (data cutoff: February 20, 2022). For further details, see eMethods in Supplement 2. ) months, these follow-up outcomes of longer than 18 months for cohort C are consistent with those from its primary analysis (>9 months' follow-up),  4  and were improved compared with primary analysis results for cohort A (>9 months' follow-up),  1  but mostly comparable to those reported herein with longerterm follow-up (>35 months' follow-up; eTable 3, eFigure 2 in Supplement 2).
After electrophoresis, total protein staining was performed using Coomassie Blue (0. 05 %w/v Coomassie Blue R-250) for 40 min and destaining (30 % v/v methanol and 10 %v/v glacial acetic acid) for ≈5 h. Gels were documented using ChemiDoc MP and ImageLab (Bio-Rad, Montréal, Canada). Final image editing and panel design were performed using Adobe (San Jose, CA, USA) Photoshop CS5 and Illustrator 2020. SANS Experiments: SANS was carried out on the Sans2d smallangle diffractometer at the ISIS Pulsed Neutron & Muon Source (STFC Rutherford Appleton Laboratory, Didcot, U. K. , http://www. isis. stfc. ac. uk). [39] A usable q-range of 0. 015-5 nm -1 was achieved utilizing an incident wavelength range of 1. 75-12. 5 Å and employing two detectors at 5 and 12 m from the sample. Each raw scattering data set was corrected for the detector efficiencies, sample transmission, and background scattering from the empty cell and converted to absolute scale with a standard sample (a solid blend of hydrogenous and perdeuterated polystyrene) using the software Mantid (http://www.
mantidproject. org). The first test SANS measurement on native slime was conducted at the SANS-1 instrument operated by Hereon and FRM II at the Heinz Maier-Leibnitz Zentrum (MLZ), Garching, Germany. [40] The obtained data are shown in Figure  S5 , Supporting Information. SAXS Experiments: SAXS measurements were done at the Materials Characterization Laboratory of the ISIS facility on a Nano-inXider instrument (Xenocs, Sassenage, France) using a micro-focus sealedtube Cu 30W/30 µm X-ray source. The scattered X-rays were detected using a Dectris Pilatus 3 hybrid photon counting detector at a distance of 938 mm from the sample stage, and covering a usable q range of 0. 03-2 nm -1. Scattering from the samples was collected in 1-mm borosilicate glass capillaries at room temperature. Data reduction (azimuthal averaging, buffer subtraction, absolute scaling) was done using the Foxtrot software.
Acknowledgements The authors thank the  ISIS Facility  for the provision of beamtime on Sans2d (DOI:  10. 5286/ISIS. E. RB181 0607 ) and the use of the  Materials Characterization Laboratory, MLZ  for allocation of a test beamtime,  S. Busch  for performing first SANS measurements,  I. Marcotte  for laboratory and technical support,  A. A. Arnold  for thoughtful discussion and inputs, and  D. M. Rowell ,  I. S. Oliveira ,  C. Martin , and  I. Schumann  for help with specimen collection. A. B. thanks  Robert J. Kittel  for providing office and laboratory space in Leipzig. Collecting and export permits have been kindly provided by the  Office of Environment & Heritage (NSW National Parks & Wildlife Service)  and the  Department of the Environment of Australia  for the Australian species studied, and by the  National System of Conservation Areas (SINAC, MINAE).
Casual analysis of the proteomic data  (52)  in Ingenuity Pathway Analysis (IPA) software revealed a high probability for the activation of a number of pathways known to influence vascular remodeling (Figure  4F ) with a high degree of significance (Figure  4G ). These pathways included the AngII-mediated (activation z score of 3. 1; P value of overlap = 7. 9 × 10 -17 ) and TGF-β1 signaling pathways (z score = 2. 0; P value of overlap = 6. 3 × 10 -23 ), both of which are associated with vascular remodeling  (43, 53). Interestingly, these data also predicted that the SMAD7 pathway was inhibited (z score = -3. 5; P value of overlap = 1. 4 × 10 -5 ). SMAD7 functions as an inhibitor of TGF-β signaling by associating with the E3 ubiquitin ligase SMURF2, which triggers degradation of the TGF-β receptor type 1  (54, 55).