dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction It is now well-established that the sea state displays variability on inter-annual to decadal timescales. Various datasets based on observations or on numerical models have been used to study this variability. The advantages and disadvantages of each dataset will be reviewed in this article. This slow variability is due to the fluctuations of the atmospheric circulation, such as the North Atlantic Oscillation, that modifies the trajectories of extra tropical storms in the North Atlantic (NA, a glossary of acronyms is given in Table  1 ) and thus the associated sea state. The relationship between the surface atmospheric field and the sea state is, however, not straightforward, because sea states consist of locally (wind wave) and remotely (swell) forced waves. In particular, sea states at mid-and low latitudes in the Atlantic are strongly influenced by swell originating from higher latitudes, forced by extra tropical storms, and might also be influenced by swell originating as far as the Southern Ocean  [1].
The remainder of this manuscript is organized as follows. In Section 2, we begin by reviewing the different datasets used in the literature to study the inter-annual to decadal variability of sea states: visual and instrumental in situ observations, satellite altimetry, seismic data, and model reanalysis. In Section 3, we review our current understanding on the link between sea state and atmospheric variability by first reviewing the various statistical methods used to establish this link, and then by describing climate modes' effects on the North Atlantic sea wave height variability. In Section 4 we review the current understanding of the projected change in sea states due to climate change. In Section 5, we summarise and conclude.
Another limitation of VOS data is the spatial sampling which is constrained to the major navigation routes across ocean basins. However, in the North Atlantic, given the dense marine traffic between the European and American continents, a large part of the basin is covered with sea state conditions reported approximately 10 to 50 times per month over 2 × 2 • bins, and up to 350 times per month along the US and European coastlines (see VOS density map on Figure  1 ). Regarding historical instrumental observations, most of the Ocean Weather Stations regularly visited by weather ships during the second half of the 20th century were discontinued during the 1980s, as unmanned weather buoys represented a much cheaper opportunity to continuously record met-oceanic conditions. Still, these stations represent the locations with the highest number of collocated instrumental and visual observations in the deep North Atlantic (NA hereafter) ocean (see Figure  1 ).
In the NA, we identified in the Copernicus Marine Service In Situ database (http://www. marineinsitu. eu/ (accessed on 10 October 2021)) 33 moored wave platforms with more than 25 years of data, with a minimum time coverage of 80% (red squares in Figure  1 ). These stations are mostly located in the Gulf of Mexico, along the US and Canadian east coast (operated by NDBC and MEDS), and along the Atlantic coast of Europe (operated by Met Office and Puerto del Estado).
However, multi-mission altimeter records aggregate data from various platforms and instruments and may suffer from heterogeneities in the radar technologies and processing techniques, which have evolved over time, in the reference dataset used for calibration, and/or in the sampling characteristics of each mission (see  [18]  for a comparison of trends between different multi-platform altimetry products). Regarding the sampling issues, Jiang  [19]  combined the track information of altimeter missions with model reanalysis outputs to investigate the impact of altimeter undersampling on wind and wave height trends. This author showed that the increasing number of in-orbit altimeters (only one altimeter mission was in orbit in 1985, while more than five were simultaneously in orbit in the late 2010s) led to very large over-estimations of the trends of extreme values computed over the period 1985-2018.
Seismic Data Another source of sea state information at multi-decadal scales may be derived from the seismic stations deployed inland and on the ocean floor to monitor earthquake activity. Indeed, background noise with frequencies between 0. 05-0. 3 Hz-known as microseismis present in every seismic record on Earth, and is predominantly caused by ocean waves. The most energetic microseism signal is around a period of 5 s, and its source is proportional to the product of wave energies propagating in opposing directions with twice that period. Moreover, acoustic resonance may amplify the microseism amplitudes at specific water depths  [25]. As a result, there is no general simple relation between microseismic energy and wave heights, although increasing wave heights will generally yield increasing microseismc energy, either related to wave reflection off the coast, rapidly turning winds (such as in a tropical storm), or the presence of waves from two independent storms  [26].
A major issue in longterm model reanalysis is the changing (increasing) numbers of assimilated observations throughout the years, which may produce spurious trends. The Stopa and Cheung  [41]  intercompared wind and wave reanalysis datasets from ECMWF ERA-I and CFSR, utilizing the same set of altimetry and buoy observations and error metrics to assess their consistency in time and space. These authors showed evidence of prominent discontinuities in the H s 95 percentiles in 1993-1994 for the Southern Hemisphere, which can be attributed to the introduction of SSM/I winds into the assimilation in 1994, and an increasing trend in both datasets after 2001, which might be due to assimilated data in the atmospheric model common to both. The Meucci et al. [42]  intercompared several 20th century reanalyses (that assimilate in situ observations of surface pressure and marine surface wind, but no satellite or in situ ocean wave height measurements) with a model-only integration, which did not assimilate any data.
, A N t ) and B = (B 1 , B 2 ,. , B N t ) with N t the number of time records in the dataset and where the overbar represents the time mean. These 2D maps are useful to understand the region of influence of each index and are often complemented with 2D maps of the regression coefficients to give the amplitude of the wave parameters' anomaly that is associated with the studied index. For instance, the 2D maps of the regression coefficient between the normalized NAO and H S is: R H s ,NAO (x, y) = std(H s (x, y, t))corr(H s (x, y, t), NAO(t)), (3) where corr(A, B) is given by Equation (2) and where the time standard deviation of A std(A) is: std(A) = ∑ i (A i -A) 2 1 2. ( 4 ) To illustrate this method, panel D of Figure  2  shows the regression map (called "projection" on the figure) of H s on the normalized NAO index taken from the NOAA website (shown on the bottom left panel of Figure  2 ).
Canonical Correlation Analysis (CCA) The CCA analysis first finds the leading principal components (PCs) of each dataset (H s and SLP, for instance) separately and retains only the first few modes to explain a given percentage of the variance (typically 90%). The linear combination of these PCs that maximizes the temporal correlation between the two variables  [48]  is then obtained using a singular value decomposition. The time correlation between the CCA PC of H s and SLP is optimized by construction, but this optimization is generally made at the expense of the explained variance of each mode. Figure  2  shows the leading CCA mode for SLP and H s (panels E and F, respectively). The H s and SLP patterns look similar to the leading EOF patterns, but the correlation between their PC is higher (0.
How is the Seasonal Cycle Removed? The most simple method to remove the seasonal cycle is to subtract the time mean over each season or over each month within a year. In the Woolf  [15] , the seasonal cycle in monthly averaged H s and pressure fields was removed by subtracting for each month the monthly mean over the entire period of the dataset  (1985) (1986) (1987) (1988) (1989) (1990) (1991) (1992) (1993) (1994) (1995) (1996) (1997). PC analysis was then applied on the pooled 41 winter months (December-March) of years December 1985 to December 1997 (no data for winters 1989/1990 and 1990/1991). The Shimura et al. [49]  argued that the months within the same year cannot be assumed independent, and thus considered the 3 months within a year as one unit. Another method used to account for the seasonal cycle is to fit an analytical model to represent the seasonal cycle variations.
Other authors have used compiled databases of instrumental station pressure, temperature, and precipitation measurements and proxy data, such as tree rings, in order to reconstruct the multi-centennial NAO index (see, for example, the  [62] ). A disadvantage of station-based indices over the EOF approach is that, since the station's positions are fixed in time, their signal contains noise due to transient meteorological phenomena not related to NAO, while the PC time-series gives a better representation of NAO spatial pattern. However, an advantage of station-based indices is that they can be computed on much longer time-series. Figure  6  shows stationbased and EOF-based indices starting in 1865 and 1950, respectively. Although there are some differences between the two indices, there is good agreement between the long-term variations.
However, trends computed from reanalysis should be interpreted with caution; indeed, the Meucci et al. [42]  showed that spurious trends in H s are present in the entire period of 1901-2010 due to an increasing number of observations and change in the data quality. Using satellite data from various missions covering the period of 1985-2008, the Young et al. [93]  and Young and Ribal  [17]  found positive trends in wind speed over the North Atlantic, and stronger positive trends for the 90th percentile, indicating that extreme values of wind speed are increasing faster than the mean. Using an updated version of their multi-satellite sea-state database covering the period 1985-2018, the  [17]  found weak positive trends for the mean H s , of the order of 0. 3 cm year -1 and larger positive trends for the 90th percentile of H s in the mid to high latitudes of the NA (∼0.
8 cm year -1 ). No significant trends of H s (mean or 90th percentile) were observed in the equatorial region. Because the trends computed from multi-mission satellite data might be quite sensitive to the method used to calibrate the various satellite missions, the Timmermans et al. [18]  compared the JFM linear trends for the period 1992-2017 for four different datasets: the Ribal and Young  [94]  database, the ESA Sea State Climate Change Initiative database, and two ERA5-based reanalysis and hindcast surface wave products from ECMWF. They showed that significant differences in trend magnitude and pattern in the NA existed, particularly at low latitudes, where the Ribal and Young  [94]  database gives negative trends, while the trend is positive in the three other databases. Nonetheless, some patterns seem to appear in all databases, such as the positive trend of approximately 2 cm year -1 off the northeastern U.
S. coast and the negative trend (2-4 cm year -1 ) in the northeastern Atlantic at high latitudes. Figure  9  shows the H s JFM trends computed in the NA over the period 1993-2018 from the ESA Sea State CCI v1 database using a simple linear regression. Negative values, up to -4 cm year -1 , were found in the Norwegian Sea and are statistically significant at the 5% level. Strong positive trends of H s , up to 4 cm year -1 , were found in the western part of the Mediterranean sea. Some significant positive trends can also be found along the eastern U. S. coast and at low latitudes.
Conclusions In this article, we have first reviewed the main datasets used to study the sea state on inter-annual to decadal time-scales. The VOS dataset  [10]  provides unique information on the historical sea state (since 1853) but suffers from low coding precision and is spatially constrained to the major navigation routes. Decadal time-series of sea state data can also be retrieved from instrumental in situ observations, but periodic changes of the wave instruments and relocations have impacted the long-term consistency of these data. Radar Altimetry has now almost 30 years of continuous data with a global scale coverage, and is thus particularly suited for the study of the long-term sea state variability. However, some limitations arise because of the low revisit time period of altimeter missions and the need to aggregate multi-mission records with efficient inter-calibration strategies to reduce the impact of data heterogeneities.
, 2021). Indeed, spectral wave parameters are key components of the sea state description, yet they are still poorly constrained in model reanalysis due to a lack of observations, particularly in the southern hemisphere (Aouf et al. , 2021). The analysis of historical seismic time-series also represents a promising avenue for exploring sea state conditions in the late 19th and early 20th centuries that have not been observed otherwise. Moreover, the recent explosion of in situ measurement through low-cost buoys (Raghukumar et al. 2019) also provides potential future opportunities for tracking change, though with potential unknown issues, such as homogeneity between technologies or public/private data-sharing agreements. Maps of standard deviation of wave height computed on each month of the altimeter dataset for the period 1993-2018 in the NA (Figure  5 ) show that inter-annual variability is larger during winter months and for latitudes north of 40°N.
The small-sized shearwaters are completely absent from the study region during winter. An extension of the present study to other seasons would mostly benefit to black-legged kittiwakes, auks and northern gannets, as their difference in distribution is in part due to the individuals present in the study area during winter being of different population than during summer. Unfortunately however, we still lack dataset with sufficient temporal depth to replicate the present study during other seasons. Given the observed range of habitat strategies exhibited by taxa studied during their breeding period, we found various levels of temporal variability in aggregation and location of core areas according to the species. The relationship between the proportion of population and surface clearly identified several species with aggregated distributions on small areas, such as bottlenose dolphins, kittiwakes and auks (75% of the population was concentrated over 22% of the area, in average), and other species with broader distributions, such as northern gannet (75% of the population was spread over 50% of the area, in average).
Acknowledgements CL was funded by the  French ministry in charge of research (Ministère de l'Enseigmenent Supérieur et de la Recherche, MESR ) during her PhD. We are indebted to crew members on-board the R/V Thalassa, to all observers who participated in the surveys as well as to the  French Office of Biodiversity (Office Français de la Biodiversité, OFB)  and  French ministry of environment (Ministère de la Transition Écologique et Solidaire)  who funded the megafauna observers. Paul Bourriau  and  Martin Huret  are thanked for taking care of the CTD casts and maintaining the hydrological sensors,  Matthieu Doray  for his coordination of the survey. Version 3 of this preprint has been peerreviewed and recommended by  Peer Community In Ecology  (https://doi. org/10. 24072/pci. ecology. 100048).
Patients Five patients with high CTC counts were selected from the Augusta study (approved by the Ethics Committee of the Medical Faculty of the Heinrich Heine University Düsseldorf; Ref-No: 3430) and DETECT III study (NCT01619111; https://clinicaltrials. gov/ ct2/show/NCT01619111 accessed on 26 November 2021). All patients provided their informed consent for the use of their blood samples for CTC analysis and for translational research projects. Clinical patient data are shown in Table  1. By whole exome sequencing of the CTCs, mutations targetable with drugs approved by the FDA were detected in four patients. From those patients, one was selected to further validate the treatment recommendation by a detailed multiparametric liquid biopsy analysis over a period of more than three years. The Caucasian 65-year-old female patient 1 was first diagnosed with a multicentric right-sided breast cancer.
The integrity of extracted DNA from PTs of patients 2, 4, and 5 was not sufficient to perform WES or no tissue was available. The libraries were sequenced on either a HiSeq 3000 system (Illumina) or a NovaSeq 6000 system (Illumina). After sequencing, data were uploaded to the Molecular Health Guide platform (Molecular Health, Heidelberg, Germany) and identified single nucleotide variants were analyzed. Variants exceeding a variant allele frequency (VAF) of 5% and exceeding a coverage of 100× with a coverage of that position exceeding 40× in the reference sample were considered reliable.
Sequencing of PIK3CA Hotspots by ddPCR Droplet digital PCR of PTs and single isolated cells was performed by using a QX200™ Droplet Digital PCR system (Bio-Rad Laboratories, Hercules, CA, USA). Primers and probes are shown in Supplementary Tables  S5  and S6. For droplet generation the QX200 droplet generator (Bio-Rad Laboratories) was used. Samples were subjected into the Droplet Reader (Bio-Rad Laboratories). Data were analyzed using QuantaSoft analysis software (Bio-Rad Laboratories). MCF7 and T-47D cells were used as a negative and positive control to gate the threshold for finding mutation. Negative controls with no DNA were included at each run.
Array Comparative Genome Hybridization WGA products were processed for aCGH as previously described  [23] ; 1 µg DNA was processed for aCGH. As reference, the WGA product of a single GM14667 cell or DNA from matched normal tissue was used. For data analysis, the output image files were normalized and fluorescence ratios for each probe were determined using Feature Extraction software (Agilent; version 10. 7. 3. 1, Protocol CGH_1105_Oct09). Data were visualized and analyzed with the Genomic Workbench 6. 5. 0. 18 software by applying the ADM-2 algorithm with a threshold of 6. 0. The centralization algorithm was set to a threshold of 4. 0 with a bin size of 10. To identify copy number alterations, an aberration filter with a minimum log 2 ratio of ±0. 3 and a minimum of 100 consecutive probes was set.
Multiplex ESR1-ddPCR from cfDNA and Data Analysis For screening of ESR1 mutations plasma was collected from EDTA vacutainers (Becton, Dickinson and Company, Franklin Lakes, NJ, USA) immediately after blood draw and cryoconserved. The cfDNA was extracted from 2 mL plasma using the QIAamp Circulating Nucleic Acid Kit (Qiagen). Screening for ESR1 mutations was performed as previously described by using a multiplex ddPCR  [24]. Samples were considered positive if the merged replicates presented a minimum of 3 E380Q mutant droplets or 8 exon 8 mutant droplets and if the average mutant allele frequency was higher than 0. 1%.
RNA Sequencing A total of 40,000 CTCs were sorted from DLA product fixed with 4% paraformaldehyde. RNA sequencing was performed in duplicates using the TruSeq RNA Access Library Kit (Illumina). Libraries were sequenced on a NextSeq 500 system (Illumina). Data were analyzed and TPM values were determined using the CLC Genomics Workbench (Qiagen). Reads were mapped by applying the following parameters: mismatch cost: 2; insertion/deletion cost: 3; length fraction: 0. 8; similarity fraction: 0. 8; global alignment: no; strand specific: both; maximum number of hits per read: 5.
Background: Maintenance of high physical performance during aging might be supported by an adequate dietary intake of niacin, vitamins B-6 and B-12, and folate because these B vitamins are involved in multiple processes related to muscle functioning. However, not much is known about the association between dietary intake of these B vitamins and physical performance. Objectives: The objectives of this study were to investigate the association between dietary intake of niacin, vitamins B-6 and B-12, and folate and physical performance in older adults and to explore mediation by niacin status and homocysteine concentrations. Methods: We used baseline data from the New Dietary Strategies Addressing the Specific Needs of the Elderly Population for Healthy Aging in Europe (NU-AGE) trial, which included n = 1249 healthy older adults (aged 65-79 y) with complete data on dietary intake measured with 7-d food records and questionnaires on vitamin supplement use and physical performance measured with the short physical performance battery and handgrip dynamometry.
In addition, we explore potential mediation by homocysteine concentrations and niacin status. This work was supported by the TIFN research program Mitochondrial Health (ALWTF. 2015. 5). The NU-AGE trial was supported by the European Union's Seventh Framework Program (266486). The funders had no role in the design and implementation of the study nor in data analysis and interpretation. Supplemental Tables 1-4 and Supplemental Figure  1  are available from the "Supplementary data" link in the online posting of the article and from the same link in the online table of contents at https://academic. oup. com/ajcn/. Address correspondence to PG (e-mail: pol. grootswagers@wur. nl.
Study design We used baseline data from the New Dietary Strategies Addressing the Specific Needs of the Elderly Population for Healthy Aging in Europe (NU-AGE) study for this analysis. The NU-AGE study is a dietary intervention trial that was performed in 5 centers in different European countries (France, Italy, the Netherlands, Poland, and the United Kingdom). It is registered at clinicaltrials. gov under NCT01754012. The study design and participant recruitment have been described in detail elsewhere  (22, 23). In summary, 2668 apparently healthy, communitydwelling older adults (aged 65-79 y) were recruited for the trial, of which 1518 attended the screening and 1296 eligible participants were included (for flowchart, see Supplemental Figure  1 ). The primary outcome of the NU-AGE trial was the change in C-reactive protein after 1 y of dietary intervention.
Participants from the French center were excluded for vitamin B-12 analyses due to unreliable intake data for this vitamin. Supplementary vitamin B intake was assessed by means of an additional questionnaire, and participants using vitamin B-containing supplements were identified and excluded from a sensitivity analysis. Niacin equivalents were calculated as follows: niacin (mg) + [tryptophan (mg)/60]. Tryptophan intake was estimated from total protein intake by assuming an average abundance of 1. 1%  (32).
Statistical analysis For descriptive purposes, energy-adjusted tertiles of the 4 vitamins (niacin, B-6, B-12, and folate) were created using the residual method  (38). Descriptive statistics are presented as means ± SDs for normally distributed values and as medians (25th-75th percentile) for skewed variables. Skewed outcome variables (walk time and chair rise test time) were log transformed. All analyses were performed using SAS software version 9. 4 (SAS Institute). Associations between intake of B vitamins and measures of physical performance were assessed via mixed linear models. Continuous, unadjusted measures of the B vitamins were used as exposure, and energy adjustment was performed via adding energy intake to the model. Possible effect modification by physical activity level was tested by adding the interaction term exposure * physical activity to the model.