dataesr/scientific-paragraphs-categorization huggingface dataset:
The rma. glmm() function in the metafor package with the measure set to ''PLO'' (Peto's log odds ratio) was employed. The logit-transformed rates were back-transformed to probabilities for interpretation. The statistical analysis was conducted in Rstudio using the General Package for Meta-Analysis (meta) version 6. 1-0 and the Meta-analysis for R (metafor) version 3. 8-1. The summary estimates with corresponding 95% CIs were depicted in forest plots. Study heterogeneity for randomeffect models was assessed using Cochran's Q test. Publication bias was assessed using funnel plots for PFS and ADT-FS, and Peters' linear regression test for funnel plot asymmetry was performed when at least ten studies were included in the meta-analysis. All tests were two sided, and p < 0. 05 was considered significant.
following or FDG PET (100%) (100%) local control, O'Shaughnessy (2017) [26] STOMP; NCT01558427 Ost (2018) [42]; updated results: Deek (2022) [45] POPSTAR; U1111-1140- SA RCT SA ADT (LHRH analogue) or SBRT alone ADT, local-regional treatment (RP with (r) PLND ± RT), and SBRT in 12 out of 15 patients with bone metastases SBRT or metastasectomy to all lesions SRS to all Other schemes: 30 Gy in 3 fractions 20-30 Gy in 1-5 fractions 30 Gy in 3 fractions 20 Gy in 1 fraction 1-3 bone or lymph biochemical recurrence after curative treatment 1-10 bone or nonpelvic lymph node metastases 1-3 extracranial metastases, asymptomatic biochemical recurrence following curative treatment (no ADT, no local relapse, serum testosterone levels >50 ng/ml); ECOG 1 Conventional imaging Choline PET NaF-PET 20 (17 received MDT) 31/31 33 mHSPC (100%) mHSPC (100%) mHSPC Oligometastases (100%) Oligorecurrence (100%) Oligorecurrence 25 54.
9%) and were cognitively healthy as measured by the Addenbrookes Cognitive Examination-III (ACE-III) (Total score 95. 6 ±4. 06). Mean white matter hyperintensity (WMH) volume at recruitment was 2. 26 ± 2. 77 ml (median = 1. 39ml), with hippocampal volume 8. 15 ± 0. 79ml. There was good representation of known dementia risk factors in the cohort. The PREVENT cohort offers a novel dataset to explore midlife risk factors and early signs of neurodegenerative disease. Data are available open access at no cost via the Alzheimer's Disease Data Initiative (ADDI) platform and Dementia Platforms UK (DPUK) platform pending approval of the data access request from the PREVENT steering group committee.
Introduction The PREVENT dementia programme (PREVENT) was initiated in 2014 as a single-site study based in West London. It has subsequently expanded to become a multi-centre study, opening sites in Edinburgh (2015), Oxford (2017), Cambridge (2017) and Dublin (2018). The aims of PREVENT are to profile midlife risk factors for later-life neurodegeneration and to identify the earliest indices heralding neurodegenerative disease in advance of clinically diagnosable dementia (particularly Alzheimer's disease (AD)). The original baseline protocol for the pilot site is described elsewhere  [1, 2] , with this current paper serving to provide an update on the protocol, detailing a multitude of sub-studies supplementing the main study, and provide an overview of the baseline dataset.
PREVENT has also collaborated with a number of sister projects since its inception. The TriBEKa collaboration (http://tribeka. org/) was established in 2017 between the Barcelona Beta Brain Research centre (the ALFA project  [3] ), University of Edinburgh (PREVENT) and the Karolinska Institute, with the aim of supporting ongoing cohorts of healthy adults at a spectrum of risk for dementia with a focus on neuroimaging data collection. The aim of the collaboration is to harmonise neuroimaging datasets where appropriate and support the addition of rich neuroimaging data from the cohorts to the Global Alzheimer's Association Interactive Network (GAAIN) and Alzheimer's Disease Data Initiative (ADDI) portals for worldwide academic access. In addition to TriBEKa, PREVENT was associated with the European Prevention of Alzheimer's Dementia (EPAD) programme  [4] [5] [6].
Biosamples All participants were asked to provide blood, urine and saliva samples, with an option to undergo a lumbar puncture for cerebrospinal fluid. Approximately 50 ml of blood were collected from overnight fasted participants. Clinical samples were analysed immediately for standard biochemistry and haematology measures at local laboratories, with results entered into the participant database. Research samples were processed and prepared for long term storage as plasma, buffy coat, serum and whole blood samples (for DNA extraction) and stored at -80°C. Saliva samples were also collected from all participants on two different days across eight time points. The first day of sample collection, termed a controlled stress day, was the day of their study visit when the clinical and cognitive assessments were completed.
The second criterion was to exclude the individual with the poorer genotype call rate in male-male or female-female pairings. We also removed 20 ancestry outliers (i. e. of non-European ancestry), leaving 596 samples in our most stringent dataset. Relatedness was estimated via an identity-by-descent coefficient ≥0. 1875, which represents the halfway point between second and third degree relatives. Ancestry outliers were identified by principal component analyses on the PREVENT genotype dataset merged with HapMap III reference data. PREVENT genotypes were also imputed against European sample data from the Haplotype Reference Consortium (HRC) build release 1. 1 (GRCh37/hg19), 1000 Genomes Phase 3 (version 5), and the TOPMed r2 reference panel  [11] [12] [13]. There were 8,651,773, 9,803,244 and 10,082,029 imputed, autosomal SNPs for HRC, 1000G and TOPMed panels, respectively (imputation quality score R 2 ≥ 0.
Imaging Six hundred and sixty six brain imaging datasets were collected using 3T Siemens Magnetic Resonance Imaging (MRI) scanners (specific models: Verio, PRISMA, Prisma Fit, Skyra). Image processing of the T1-weighted structural scans was carried out using FreeSurfer version 7. 1. 0 following correction for field inhomogeneities using the N4 algorithm  [14, 15]. In particular, using the recon-all pipeline, global volumetrics, cortical thickness and hippocampal volume were measured. Manual corrections were conservatively applied to the recon-all outputs where appropriate by trained operators. Structural MRI scans were also used for the quantification of cerebral small vessel disease (SVD) markers. White matter hyperintensity (WMH) volumes were quantified from lesion masks obtained from FLAIR MRI using an automated script on SPM8.
Lesion maps obtained from the segmentation procedure were used as starting points for manual WMH delineation. Details on the procedures involved on all volumetric analyses have been described previously  [16] [17] [18] [19]. The diffusion weighted imaging (DWI) datasets were first carefully examined for sufficient coverage and minimal eddy-current distortions and preprocessed using MRTRIX (https://www. mrtrix. org/) and FSL (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FDT/UserGuide). Diffusion tensor imaging (DTI) parameters such as fractional anisotropy (FA) and mean diffusivity (MD) are derived using the dtifit function in FSL. Please refer to Table  1  and Figure  1  for acquisition parameters of all scan sequences and details on SVD quantification methods, respectively. Resting state BOLD functional MRI (fMRI) of approximately 10 minutes was acquired from each participant who was instructed to keep their eyes closed and not to think about anything specific.
Data access Open data access is an underpinning principle of the PREVENT dementia programme, with ambitions that data collected through this study will be critical to understanding brain health in the mid-life period. The dataset has already been highly requested and resulted in several publications from outside the core study team (see Table  3 ). The addition of the data to the ADDI platform is anticipated to increase the accessibility and use of this novel dataset especially to Low and Middle-Income countries (LMICs).
Discussion The PREVENT dementia programme is a multi-site study with a comprehensive and deeply phenotyped baseline dataset from 700 participants recruited in midlife, an estimated 24 Given the early age and minimal cerebrovascular burden in the PREVENT cohort, it is well suited to delineate some of the earliest changes associated with risk factors of APOE4 and family history while mitigating risks of confounds from co-morbidity. The cognitive data presented in the manuscript showed no significant difference by a-priori risk groupings, but did suggest a number of sex differences in cognitive performance. As this manuscript was designed to be descriptive rather than hypothesis driven, the analysis were not designed to test any hypothesis regarding sex differences in midlife cognition, however the findings suggest further research in this topic is warranted, particularly given the emerging evidence in sex differences in the accumulation of AD pathology  [56].
Data Availability The baseline dataset is available to access through a data request on the study website (www. preventdementia. co. uk); on the Alzheimer's Disease Data Initiative (ADDI) platform baseline dataset DOI: https://doi. org/10. 34688/PREVENTMAIN_BASELINE_700V1; Dementia Platforms UK (DPUK); and the Global Alzheimer's Association Network (GAAIN). For imaging data a number of derived variables (for example the global volumetrics and WMH volume) are available in the ADDI dataset, with raw structural data available to access upon request following defacing.
Using this setup, we characterized aerosols during two distinct events of Saharan dust and dust smoke aerosols transported over Lille in spring 2021 20 and summer 2022. For validation purposes, comparisons against the Raman lidar were performed, demonstrating good agreement in aerosols properties with relative differences of up to 12 % in the depolarization measurements. Moreover, a first dataset of CE376 lidar and photometer performing on-road measurements was obtained during the FIREX-AQ (Fire Influence on Regional to Global Environments and Air Quality) field campaign, deployed in summer 2019 over the Northwestern USA. By lidar and photometer mapping in 3D, we investigated the transport of released smoke from active fire spots at William Flats (North East 25 WA, USA). Despite the extreme environmental conditions, our study enabled the investigation of aerosol optical properties near the fire source, distinguishing the influence of diffuse, convective, and residual smoke.
Backscatter, extinction profiles, and column-integrated lidar ratios at 532 and 808 nm were retrieved for a quality-assured dataset. Additionally, Extinction Angstrom Exponent (EAE), Color Ratio (CR), Attenuated Color Ratio (ACR) and Particle Linear Depolarization Ratio (PLDR) were derived. In this study, we discuss the capabilities (and limitations) of the CE376 lidar in bridging observational gaps in aerosol monitoring, 30 providing valuable insights for future research in this field.
Moreover, the aerosols retrievals are validated through comparison with LILAS Raman lidar and we establish the reliability of our results. Our study not only outlines the findings but discusses the limitations and future implications 80 of our approach. A first dataset of co-located CE376 lidar and photometer mobile observations has been obtained during the FIREX-AQ field campaign, organized over the Northwestern US in summer 2019  (FIREX-AQ white paper, 2019). This campaign, led by NASA and NOAA, focused on investigating the chemistry and transport of smoke from wildfires and agricultural burning, in addition to the multiple in-situ instruments deployed in fixed platforms around the region and aboard aircrafts  (Warneke et al. , 2023). Remote 85 sensing instruments were installed in both stationary and mobile DRAGON payloads (Distributed Regional Aerosol Gridded Observations Networks;  Holben et al.
The VSD distributions during the event (Fig. 7 ) showed the predominance of three aerosols sizes, one in the fine mode centered at 445 0. 11 μm radius, and two in the coarse mode centered at 1. 7 μm and 5 μm. On 18 July 2022 (Fig. 7b ) 5 VSD were retrieved, all having higher concentration than the day before (Fig. 7a ), only one VSD in the morning is offset to higher values (0. 15 μm) for the fine mode peak. On 19 July 2022 (Fig. 7c ), 7 VSD were retrieved, 4 of them in the morning showing the same shape as the ones from 18 July. The rest of the VSD show higher contribution at 5 μm size, representing the conditions after 15:00 on 19 July In this section, we present a first dataset obtained with the CE376 lidar and photometer on-board a mobile platform during the FIREX-AQ campaign in summer 2019.
While in the morning the river basin acted almost independently, channeling smoke northward, we noticed how the evolving boundary layer is coupled to the mountain winds systems. The diffused smoke is mixed and subsided along the valleys, with higher aerosols loading closer to the fire downwind. Moreover, fire emissions get stronger while temperatures rise up, permitting the convective loft of the smoke above the mountain ridges. On 7 August 2019, the convective smoke evolved into the formation of pyrocumulus clouds. For further analysis, in the following section we present 630 aerosol retrievals of selected datasets from the trajectories presented here. France" (project CLIMIBIO) and the FEDER (European Funds for Regional Economic Development) for the financial support. We also thank H2020/GRASP-ACE Marie-Curie project for funding.
Acknowledgements The authors would like to thank  ANRT (Association Nationale Recherche Technologie) France  and  CIMEL Electronique  for supporting the research development in the framework of  CIFRE  (n° 2020/0442) thesis of MFSB. We acknowledge the  CaPPA project (Chemical and Physical Properties of the Atmosphere ), funded by the  ANR (French National Research Agency)  through the  PIA (Programme d'Investissement d'Avenir)  under contract " ANR-11LABX-0005-01 ", the  Regional Council "Hauts-de-.
Data availability Data from photometer are available at AERONET website (https://aeronet. gsfc. nasa. gov, last access: 23 October 2023). 710 Radiosonde data are accessible at the Wyoming University database (https://weather. uwyo. edu/upperair/sounding. html, last access: 23 October 2023), and Meteo-France database (https://donneespubliques. meteofrance. fr, last access: 23 October 2023). The data of DEM from SRTM are available at Earth Explorer interface of USGS (https://earthexplorer. usgs. gov/, last access: 23 October 2023). The MODIS thermal anomalies product is available at NASA Worldview (https://wvs. earthdata. nasa. gov, last access: 23 October 2023). Lidar data used in this paper are available upon request to the corresponding author.
Beauvechain (50. 78° N, 4. 76° E, Belgium) and Herstmonceux (50. 90° N, 0. 32° E, England) from Wyoming University database 355 (https://weather. uwyo. edu/upperair/sounding. html, last access: 23 October 2023), and Trappes (48. 77° N, 1. 99° E, France) from Meteo-France database (https://donneespubliques. meteofrance. fr, last access: 23 October 2023). Beauvechain is the closest site, about 120 km away from Lille, Herstmonceux is 200 km and Trappes is 240 km far from Lille.
± 330 Mm -1 (with LR= 82 ± 2 sr) for extinction at 532 nm was observed. Inside the plume, a decrease of the PLDR (Fig. 12l) from 0. 05 ± 0. 01 to 0. 03 ± 0. 01 is detected, in addition to values progressively increasing from 0. 4 ± 0. 1 to 0. 9 ± 0. 1 for ACR (Fig. 12n). Both parameters suggest the predominance of big spherical particles towards the smoke layer top, which could be related to the fast increase in the coating mass of soot particles within minutes from emission. In contrast, dataset D showed in Fig. 12 panels.
Due to climate change, decreasing ice cover and increasing industrial activities, Arctic marine ecosystems are expected to face higher levels of anthropogenic stress. To sustain healthy and productive ocean ecosystems, it is imperative to build baseline data to assess future climatic and environmental changes. Herein, a natural oil seep site offshore western Svalbard (Prins Karls Forland, PKF, 80-100 m water depth), discovered using satellite radar images, was investigated using an extensive multiscale and multisource geospatial dataset collected by satellite, aerial, floating, and underwater platforms. The investigated PKF seep area covers roughly a seafloor area of.
Uncrewed Aerial Vehicles (UAVs) for local sea surface inspection To locate the oil slick, we surveyed the area of interest, previously identified through the analysis of SAR data, using a consumer-grade UAV, equipped with a high-resolution RGB camera. For this purpose, we employed a DJI Mavic 2 Pro equipped with a 1" CMOS camera sensor (20 MP) that can collect images with a resolution (R) of 5472 × 3648 pixels, and an integrated GPS/GLONASS system. We deployed the UAV from the deck of R/V Kronprins Haakon in May 2021  (Bünz and Panieri, 2021) , to collect high resolution imagery data and video (i. e. , at the centimeter level over an area of 0. 1 km 2 ) by flying at an altitude from the sea surface of ca. 25 m. Oil slicks were detected from the point where oil droplets emerged at the sea surface to approximately 1 km downstream.
ROV settings for underwater photogrammetric data acquisition and Structure from Motion (SfM) processing The AEgir6000, a work-class ROV, equipped with two forwardlooking (45 • and 60 • ) cameras and one downward-looking HD video camera, filmed the seafloor along four, 100 m parallel transects that were spaced 2 m apart, at a constant speed (0. 3 Knots) and altitude (2 m). Additional ROV surveys were performed to collect sediment and fauna samples. Accurate ROV positioning was based on Ultra-Short Base-Line (USBL) navigation, using a dead-reckoning based on an inertial navigation system complemented with a Doppler Velocity Log. Based on videos obtained from the nadiral camera, a frame every second was automatically extracted in order to apply Structure from Motion (SfM) using Agisoft Metashape®  (Agisoft, 2018)  following a wellestablished photogrammetric workflow  (Fallati et al.
Mega-epifauna annotation, habitat, and morphometric analyses The identification and quantification of all mega-epifaunal, morphologically distinct taxa (designated as morphospecies), as well as microbial mats, were annotated in orthomosaics using the opensourced software PAPARA(ZZ)I  (Marcon and Purser, 2017). Megaepifaunal abundances were expressed as individuals per m 2 (ind. m 2 ). Microbial mat patches were counted, the areal extension was measured on ROV-based photomosaics, and coverage was quantified using the average nearest neighbor analysis performed in ArcGIS® 10. automate the recognition of microbial mats on the orthomosaics and detect spatial patterns at the seafloor through Object-Based Image Analysis (OBIA) (Table  S2 ).
The C10+ range is; generally, little affected when care is taken not to evaporate extracts to dryness, as is performed at APT (e. g. , the methylnaphthalene data is considered reliable). The age-diagnostic biomarkers analysis provided clear constraints on the age of oil samples. The Nordiacholestane ratio (NDR), believed to originate from diatoms, radiated rapidly for the Cretaceous and Tertiary  (Holba et al. , 1998 ). An NDR above 0. 25 indicates a post-Jurassic age, while values above 0. 5 indicate a Tertiary or younger age. Oleanane originates from flowering land plants, and its presence in samples indicates a Cretaceous or younger age  (Peters et al. , 2005). Oleanane is commonly quantified using the Oleanane index. Values for oleanane above 0. 0 indicate a post-Jurassic age, while values above 0.
Authigenic carbonate isotopic analyses (δ 13 C, δ 18 O, U/Th) To observe microstructures and select target phases for isotope analyses and U/Th dating (Fig. 3 ), the carbonate concretion collected from the microbial mats (PusC-03) at 4 cmbsf was cut into thin sections. Two sub-samples were obtained via micro-drilling from matrix micrite and aragonitic cavity-filling cement. Following a reaction with anhydrous phosphoric acid for 3 h at 50 • C, the stable isotope composition (δ 13 C, δ 18 O) was measured on a Thermo Scientific Gasbench II coupled to a Finnigan MAT 253 triple collector isotope ratio mass spectrometer at the Stable Isotope Lab-SIL, at UiT. Data are reported in ‰ notation relative to VPDB. The analytical error was better than 0. 1 ‰ (1SD) for both carbon and oxygen. Uranium and Thorium were measured on a Thermo Neptune Plus multi-collector inductively coupled plasma-mass spectrometer (ICP-MS) using a 236 U-229 Th isotopic tracer at the British Geological Survey, Geochronology and Tracers Facility (UK).
g. , following  (Higgins and Thiel, 1988). Nematodes and benthic foraminifera, the dominant meiofauna taxa, were further investigated for the 0-1 cm sediment fraction. Whenever possible, identifications were conducted to the genus level. In total, 150 nematodes were randomly picked from each replicate and mounted on permanent glycerin slides following stepwise dehydration in a graded series of ethanol: glycerin mixtures  (Seinhorst, 1959). Nematodes were then identified under the microscope down to the lowest possible taxonomic level (i. e. , genus or species) using pictorial keys  (Platt and Warwick, 1988; Warwick et al. , 1998)  and literature available within the Nemys Database  (Bezerra et al. , 2022). Living foraminifera were classified at the genus level according to (Alfred R. Loeblich, 1988).
Proteomics data processing DIA-NN 87 (version 1. 8. 1) was used to search the raw files from each run. A predicted spectral library was made using the swissprot mouse FASTA database and in silico labelled to have mTRAQ as a fixed mod (+140. 0949630177) on each trypsin digested peptide. Peak height was used for quantification with a scan window of 1, mass accuracy of 10 ppm and MS1 accuracy of 5 ppm, MBR was enabled and search outputs were filtered at 1% Q value. The following commands were employed by use of the additional commands dialogue: -fixed-mod mTRAQ 140. 0949630177, nK, -channels mTRAQ, 0, nK, 0:0; mTRAQ, 4, nK, 4. 0070994:4. 0070994; mTRAQ, 8, nK, 8. 0141988132:8. 0141988132},peak-translation, -ms1-isotope-quant, -ms1-base-profile, -ms1-subtract 2. The report file containing filtered peptide level output was processed using R.
Acknowledgments The authors thank the  Institute for Chemical Imaging and Living Systems (CILS)  for their technical assistance with the confocal microscope and flow cytometer. The authors would like to thank  Dr. James Sherley  for his insightful feedback throughout this project. S. A. B gratefully acknowledges the financial support from the  National Institutes of Health  ( 1R01EB027705 ) and the  National Science Foundation  ( DMR-1847843  and  TI-2141019 ).
(E) Heat maps of selected proteins from Tricarboxylic acid (TCA) cycle and electron transport chain (ETC) processes for Hypoxia vs. Normoxia and Anoxia vs. Normoxia. PSEA using the Reactome database of TCA and ETC pathways for Hypoxia vs. Normoxia, Anoxia vs. Normoxia (left), and Hypoxia vs. Anoxia (right). NES = normalized enrichment score. N = 3 biological replicates and N = 2-3 technical replicates per condition.