dataesr/scientific-paragraphs-categorization huggingface dataset:
Farther north, Cyclone Surigae brought record rainfall to the Philippines (NOAA 2021c) and parts of Cambodia and Vietnam (Floodlist 2021d,e) in April. An area of positive anomalies over northern and eastern China appears consistent across indices and datasets  (Plate 2. 1l, , reflecting a series of extreme rainfall events; Typhoon In-fa generated several days of heavy rain in Henan Province in mid-July. Nearby Shanxi and Shaanxi Provinces also saw over 60 gauges reporting record accumulations in October (Floodlist 2021f) as moisture was transported from the South China Sea and Bay of Bengal. Many areas with significant events in 2021 also have positive anomalies in GHCNDEX R95p (Fig. 2. 26b), showing the important contribution of extremes to total precipitation. In GPCC, R95p also shows a strong contribution from extreme events over parts of Brazil and southern Africa.
Its occurrence can be apparent in reduced river discharge, soil moisture, and/or groundwater storage, depending on season and duration of the event. Here, a simple estimate of drought called the self-calibrating Palmer Drought Severity Index  (Wells et al. 2004; van der Schrier et al. 2013 ) is presented, using global precipitation and Penman-Monteith Potential ET from an early update of the CRU TS 4. 06 dataset  (Harris et al. 2020). Moisture categories are calibrated over the complete 1901-2021 period to ensure that "extreme" drought and pluvial (wet periods) relate to events that do not occur more frequently than in approximately 2% of the months. This calibration affects direct comparison with other hydrological cycle variables in Plate 2. 1r that use a different baseline period. This year's update is based on an extensively revised dataset that incorporates new estimates of some variables in CRU TS4.
2. 38), which persisted at most latitudes throughout the entire year (Fig. 2. 39). The positive anomalies in Southern Hemisphere mean evaporation-that were also reflected in the global means-are a characteristic signature of La Niña conditions  (Miralles et al. 2014; Martens et al. 2018) , such as those that prevailed during 2020 and 2021 (see SOI in Fig. 2. 38). This ENSO-induced variability in evaporation is superimposed upon the long-term trend, which is attributed to increasing global temperatures  (Miralles et al. 2014 ) and terrestrial greening  (Cheng et al. 2017). The evaporation data that serve as the basis for these results come from version 3. 6 of the Global Land Evaporation Amsterdam Model (GLEAM;  Miralles et al. 2011 ). GLEAM v3. 6 is based on microwave observations (of surface soil moisture, vegetation optical depth, and snow water equivalent), a precipitation dataset produced by blending gauge, satellite, and reanalysis sources  (Beck et al.
2019) , and a new bias-corrected, reanalysis-based product as atmospheric forcing  (Beck et al. 2022). The accuracy of GLEAM v3 has been reported to be in the order of 0. 7 mm day -1 (unbiased root mean square error), and its correlation against in situ eddy-covariance measurements is around 0. 8 on average  (Martens et al. 2017). While the ability to routinely monitor evaporation dynamics is critical for agriculture and water management, as well as to diagnose climate changes, global evaporation datasets (such as GLEAM v3. 6) are still subject to model assumptions and uncertainties derived from parameterizations. In particular, the accuracy of the reported long-term trends is affected by the indirect representation of the influence of CO 2 and atmospheric aridity on stomatal conductance  (Martens et al.
2007 ) observed before the 2010s  (McVicar et al. 2012 ) ceased in the last decade, with a weak reversal or stabilization of surface winds globally  (Zeng et al. 2019 ) and regionally  (Minola et al. 2022; Fig. 2. 42a). Two thresholds of wind intensities (i. e. , > 3 m s -1 and > 10 m s -1 ) show an opposite trend behavior with no trends (or even positive ones) for moderate winds (> 3 m s -1 ; Fig. 2. 42c) and moderate slowdowns for strong winds speeds (> 10 m s -1 ; Fig. 2. 42d), e. g. , in South This annual land surface wind report for 1979-2021 is based on 1) global in situ anemometer observations from the Had-ISD3 dataset  (1973 -2021; Dunn et al. 2012 Dunn et al. , 2016 Dunn et al. , 2019) )  and 2) two gridded reanalysis products:  ERA5 (1979 ERA5 ( -2021;; Hersbach et al. 2020 ) and  MERRA-2 (1980 MERRA-2 ( -2021;; Gelaro et al.
The availability of wind observations from the Aeolus instrument has been shown to constrain the uncertainties of notoriously difficult upper level divergence patterns and tropical weather forecast errors on short timescales  (Rennie et al. 2021; Martin et al. 2021). Since those data will be used in future reanalyses (ERA5 does not yet assimilate Aeolus data), we looked at systematic impacts on seasonal means of winds and divergence to assess the potential for temporal discontinuities in these datasets. The rootmean square (rms) difference between velocity potentials from ECMWF assimilation experiments with/without Aeolus reaches only 2% of the rms value of the anomaly diagnostic in Fig. 2. 46. Similar analysis of upper tropospheric zonal mean cross-equatorial velocity and divergence showed only negligible systematic differences, although on shorter time scales (sub-monthly) differences can become considerable.
9) for this period. Throughout 2021, the global monthly mean TOA OLR anomaly varied between positive and negative values (Fig. 2. 48). OLR anomalies peaked at +0. 7 W m -2 in February, but otherwise fluctuated between ±0. 5 W m -2 the rest of the year. These results are consistent with NOAA HIRS (Lee and NOAA CDR Program 2011) and NASA AIRS  (Susskind et al. 2012 ) OLR datasets (not shown). For the year as a whole, the 2021 global annual mean TOA OLR anomaly was +0. 05 W m -2. The global monthly mean TOA absorbed solar radiation (ASR, determined from TSI minus RSW) anomaly remained positive throughout 2021, peaking at +1. 4 W m -2 in June. For the year as a whole, the 2021 global annual mean TOA ASR anomaly was +0. 75 W m -2. The global monthly mean TOA total net anomaly, which is calculated from ASR anomaly minus OLR anomaly, also stayed positive throughout 2021, topping out at +1.
8 W m -2 in June. For the year as a whole, the 2021 global annual mean TOA total net anomaly was +0. 70 W m -2. The Earth energy imbalance (EEI) that had been observed prior to 2020 grew even larger during 2021, so that the doubling in observed EEI between 2005 and 2019 from both CERES and in situ observations  (Loeb et al. 2021 ) continued to substantially increase, primarily because of an increase in ASR radiation. It should be noted that a 21-year trend is not necessarily indicative of a longer-term trend. At this timescale, internal variability in the climate system can offset or augment any forced, longer-term trend in net radiation. The TSI data were obtained from the Total Irradiance Monitor aboard the Solar Radiation and Climate Experiment (SORCE) mission  (Kopp and Lean 2011) , the Royal Meteorological Institute of Belgium composite dataset  (Dewitte et al.
La Soufriere erupted at least 20 times on four consecutive days. Its plume was observed as high as 20 km by MISR, well into the lower stratosphere (LS), and HYSPLIT trajectory analysis showed that it reached Mauna Loa one month later. CALIPSO detected a strong signal between 21 and 23 km in mid-August between the equator and 35°N. Lidar observations on Mauna Loa detected a large signal just above 21 km on 20 August, and SAGE tracked that same signal in the stratosphere through October. Balloon-borne aerosol concentration measurements in Kansas (United States) in August and later in France detected high aerosol concentrations at 20-21 km. Similar observations in Boulder, Colorado, show near-normal concentrations in the LS through mid-April, followed by elevated values between 15 and 19 km through the end of the year, albeit decreasing a bit in December.
Even if lightning activity remained constant in the future, its societal impact will increase dramatically. If lightning also increases with climate change, this impact may worsen. (Mühle et al. 2010; Miller et al. 2010). Changes indicated in brackets are the differences between the 2021 and 2020 means. These values differ from the growth rates reported in the main text and figures, which represent the net addition of a LLGHG to the atmosphere over the course of a year, calculated by differencing (approximately) the 1 Jan atmospheric abundances in successive years. All values are preliminary and subject to minor updates. c Global mean estimates derived from multiple NOAA measurement programs ("Combined Dataset"). with emissions (including ~5% from cement production) increasing from 3. 0 ± 0.
The 2021 study merged 24 years of global FAPAR monthly products based on three optical sensors from 1998 to 2021  (Gobron et al. 2010; Pinty et al. 2011; Gobron and Robustelli 2013). Uncertainties of each dataset were derived through various means, such as error propagation technique and comparisons against multiple proxies using ground measurements and radiative transfer simulations, that all provide an estimate of the uncertainties and biases. This long-term FAPAR dataset presents an estimated global average uncertainty close to 5%-10% when comparing to groundbased measurements.
2018). To differentiate interannual variability from long-term change, one would need to detrend the data first  (Moesinger et al. 2022 ). An alternative view is provided by the difference in VOD between the years 2021 and 2020 (Appendix Fig. A2. 14), which reveals, for example, that although Kenya in 2021 had above-average vegetation activity, it was below that of 2020. The VOD anomalies were computed from the VOD Climate Archive (VODCA;  Moesinger et al. 2020). VODCA blends VOD observations retrieved with the Land Parameter Retrieval Model  (Meesters et al. 2005; van der Schalie et al. 2017 ) from several space-borne radiometers, including SSM/I, TRMM, Windsat, AMSR-E, and AMSR2, into a harmonized long-term dataset. VODCA contains individual datasets for Ku-band (covering the period 1987-2021),  X-band (1997-2021), and C-band (2002-21)  at 0.
25° spatial and daily temporal resolutions. Here, we used the VODCA Kuband dataset, the longest available record, to compute anomalies from the long-term (1991-2020) climatology. Despite its theoretically higher sensitivity to the upper canopy, intra and interannual dynamics of Ku-band observations strongly agree with those of X-band and C-band  (Moesinger et al. 2022). VOD cannot be retrieved over frozen or snow-covered areas for which they are masked in winter (Appendix Fig. A2. 13).
2. c. 2 Rock Glacier Velocity Rock glacier monitoring at Hinteres Langtalkar and Dösen rock glaciers (AT) is supported by the Hohe Tauern National Park Carinthia through its long-term permafrost monitoring program. Laurichard (FR) survey is supported by CryobsClim "long-term Observation and Experimentation System for Environmental Research" (SOERE/All'envi-OZCAR Research Infrastructure) and the PermaFrance observatory "monitoring the mountain permafrost in the French Alps" as well as French National Research Agency in the framework of the Investissements d'Avenir programs: Risk@UGA (ANR-15-IDEX-02) and LabEx OSUG@2020 (ANR10 LABX56). The Ecrins National Park helps field surveys since the early 2000s. The Swiss Permafrost Monitoring Network PERMOS is financially supported by MeteoSwiss in the framework of GCOS Switzerland, the Federal Office for the Environment, and the Swiss Academy of Sciences.
2. c. 4 Lake Ice Cover We thank Alexander Mills, Huaxia Yao, Lars Rudstam, Mr. Miyasaka, Ichiro Matsuzaki, Greg Sass, Don Pierson, the Minnesota Department of Natural Resources, North Temperate Lakes Long-Term Ecological Research Network, and citizen scientist contributors from the Community Lake Ice Collaboration for providing ice phenology records for their local lakes each winter. Funding was provided by Natural Sciences Engineering and Research Council Discovery Grant to S. Sharma. T. Nõges was supported by the Estonian Environment Agency and Estonian Research Council project PRG709.
3 annual mean precipitation anomalies (mm yr -1 ); (l) GPCC maximum 1-day (Rx1day) annual precipitation anomalies (mm); (m) MODIS Aqua C6. 1 cloud fraction annual anomalies (%); (n) ELSE (Ensemble Land State Estimator) global distribution of runoff anomalies (mm yr -1 ); Plate 2. 1. (cont. ) (o) ELSE global distribution of river discharge anomalies (m 3 s -1 ); (p) GRACE and GRACE-FO difference in annual-mean terrestrial water storage between 2021 and 2020 (cm); (q) ESA CCI average surface soil moisture anomalies (m 3 m -3 ); (r) Mean scPDSI for 2021. Droughts are indicated by negative values (brown), wet episodes by positive values (green); (s) GLEAM land evaporation anomalies (mm yr -1 ); (t) ERA5 sea level pressure anomalies (hPa); Plate 2. 1. (cont. ) (u) Surface wind speed anomalies (m s -1 ) from the observational HadISD3 dataset (land, circles), the ERA5 reanalysis output (land, shaded areas), and RSS satellite observations (ocean, shaded areas); (v) ERA5 850-hPa eastward wind speed anomalies (m s -1 ); (w) Total aerosol optical depth (AOD) anomalies at 550 nm; (x) Ratio of total AOD at 550 nm in 2021 relative to 2003-20; (y) Number of days with AOD above the 99.
9th percentile. Areas with zero days appear as the white/gray background; (z) GOME-2C total column ozone anomalies relative to the 1998-2008 mean from GSG merged dataset (DU); (aa) OMI /MLS tropospheric ozone column anomalies for 60°S-60°N (DU); Plate 2. 1. (cont. ) (ab) CAMS reanalysis total column CO anomalies (%); (ac) Land surface visible broadband albedo anomalies (%); (ad) Land surface near-infrared albedo anomalies (%); (ae) FAPAR anomalies; (af) GFASv1. 4 carbonaceous emission anomalies (g C m -2 yr -1 ) from biomass burning; (ag) VODCA Ku-band VOD anomalies.
Temperature anomalies (°C) and uncertainties (where available) for 2021 (1991-2020 base period). Where uncertainty ranges are provided, the temperature anomalies correspond to the central values of a range of possible estimates. Uncertainty ranges represent a 95% confidence interval. Note that for the HadCRUT5 column, land values were computed using the CRUTEM. 5. 0. 1. 0 dataset (Osborn et al. 2021), ocean values were computed using the HadSST. 4. 0. 1. 0 dataset (Kennedy et al. 2019) and global land and ocean values used the HadCRUT. 5. 0. 1. 0 dataset.
Surface Winds C. Azorin-Molina was supported by CSIC-UV-GVA and funded by RTI2018-095749-A-I00, AICO/2021/023, Leonardo-FBBVA and the CSIC Interdisciplinary Thematic Platform PTI-CLIMA. R. J. H. Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS. L. Ricciardulli was supported by NASA Ocean Vector Wind Science Team grant 80HQTR19C0003.
Tim Osborn received funding from UK NERC (NE/S015582/1). Ian Harris received funding from UK National Centre for Atmospheric Science (NCAS). The research presented in the drought section was carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia. 2. d. 11 Land Evaporation D. G. Miralles acknowledges support from the European Research Council (ERC) under grant agreement no. 715254 (DRY-2-DRY). M. F. McCabe is supported by the King Abdullah University of Science and Technology. David Fereday was supported by the Met Office Hadley Centre Climate Programme funded by BEIS.
2. e. 2 2 Surface Winds C. Azorin-Molina was supported by CSIC-UV-GVA and funded by RTI2018-095749-A-I00, AICO/2021/023, Leonardo-FBBVA and the CSIC Interdisciplinary Thematic Platform PTI-CLIMA. R. J. H. Dunn was supported by the Met Office Hadley Centre Climate Programme funded by BEIS. L. Ricciardulli was supported by NASA Ocean Vector Wind Science Team grant 80HQTR19C0003.
1 Mean Sea Level Pressure and Related Modes of Variability Tim Osborn received funding from UK NERC (NE/S015582/1). Ian Harris received funding from UK National Centre for Atmospheric Science (NCAS). The research presented in the drought section was carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia. 2. d. 11 Land Evaporation D. G. Miralles acknowledges support from the European Research Council (ERC) under grant agreement no. 715254 (DRY-2-DRY). M. F. McCabe is supported by the King Abdullah University of Science and Technology. David Fereday was supported by the Met Office Hadley Centre Climate Programme funded by BEIS. 2. e.
Furthermore, novel ADCs interact with the immune system  [26] ,which plays a crucial role in cancer development. Inflammation could be reflected by pretreatment peripheral leukocyte counts and ratios such as the neutrophil-to-lymphocyte ratio (NLR). This biomarker appears to be an independent prognostic factor in BC patients treated with neoadjuvant chemotherapy (NAC), especially for HR positive BC  [27, 28]. To date, no data are available for HER2-low BC. In early BC, the effectiveness of NAC is evaluated by pathological complete response (pCR) rates. pCR has prognostic value and is one of the main treatment objectives  [29]. Few data concerning pCR rates in patients with HER2-low early BC are available. The primary objective of our study was to explore pCR rates in HER2-low early BC patients treated with NAC, according to HER2 status (score 1 + versus score 2 + , ISH negative).
Data collection and outcomes We collected the following clinicopathological characteristics at baseline: age, ECOG-PS at diagnosis, gender, menopausal status, blood counts, histological subtypes, TNM and histoprognostic factors (grade, HR status, HER2 status, Ki67 and vascular embolism) determined by biopsy. After surgery, assessment of pCR (by Sataloff  [30]  and/or RCB classification  [31] ), TNM, and histoprognostic factors (grade, HR status, HER2 status, Ki67, vascular embolism) determined on the resected specimen were collected, as well as data concerning treatments (surgery, chemotherapy regimen and safety) and follow-up (recurrence of cancer or death). The primary objective of this study was to evaluate pCR rates according to HER2-low status. Secondary objectives were to assess disease free survival (DFS), i.
Discussion HER2-low early stage BC appears to be a distinct biological entity. In this study, we further highlighted this fact by demonstrating that, inside the subgroup of HER 2-low BC, HER2 score 2 + is an independent positive predictive marker for tumor recurrence and survival after neoadjuvant therapy. To date, few data are available on the intrinsic differences inside the HER2-low subgroup in this setting. In our two cancer centers, among the entire population who received neo-adjuvant therapy during the study period, 22. 6% of tumors were classified as HER2-low with 15. 3% HER2 score 1 + and 7. 4% HER2 score 2 +. In comparison, from January 2014 to December 2021, the HER2 French database, a platform collecting data on different tumors from all French comprehensive cancer centers, registered 19.
There is excellent agreement (98-99%) in the evaluation of positive HER2 status between biopsy and surgical specimen, but this is not necessarily the case for HER2-low BC. New ADC targeting HER2, such as trastuzumab deruxtecan or trastuzumab duocarmazine, have shown preclinical efficacy in HER2-low breast cancer cells. These effects may be due to a higher drug-antibody ratio, driving a greater amount of cytotoxic payload to the targeted cells and a bystander effect  [23, 24, 45, 46]. Recently, the FDA granted Breakthrough Therapy Designation for trastuzumab deruxtecan in HER2-low metastatic BC following the positive results of the DESTINY-Breast04 trial  [25]. Many studies are in progress and focus on HER2-low tumors (NCT04556773, NCT04494425, NCT04742153 and NCT05165225)  [45]. The aim of these studies is to extend the survival benefits observed with anti-HER2 agents in HER2-positive disease to a greater proportion of patients with HER2-low BC  [45, 46].
Statistical variability of electroencephalography (EEG) between subjects and between sessions is a common problem faced in the field of Brain-Computer Interface (BCI). Such variability prevents the usage of pre-trained machine learning models and requires the use of a calibration for every new session. This paper presents a new transfer learning (TL) method that deals with this variability. This method aims to reduce calibration time and even improve accuracy of BCI systems by aligning EEG data from one subject to the other in the tangent space of the positive definite matrices Riemannian manifold. We tested the method on BCI databases comprising a total of subjects pertaining to three BCI paradigms, namely, event related potentials (ERP), motor imagery (MI), and steady state visually evoked potentials (SSVEP).
We employ a support vector classifier for feature classification. The results demonstrate a significant improvement of classification accuracy, as compared to a classical training-test pipeline, in the case of the ERP paradigm, whereas for both the MI and SSVEP paradigm no deterioration of performance is observed. A global. % accuracy improvement is obtained compared to a previously published Riemannian method, Riemannian Procrustes Analysis (RPA). Interestingly, tangent space alignment has an intrinsic ability to deal with transfer learning for sets of data that have di erent number of channels, naturally applying to inter-dataset transfer learning.
Fourth, it extends naturally to the heterogeneous transfer learning case, i. e. , when the number and/or placements of electrodes is not the same in the source and target data set. In a similar previous attempt the SVD has been applied independently on the source and target dataset and the resulting matrices are then used to align the data  (Sun et al. , 2015). Our method is instead casted as a Procrustes problem and therefore it fulfills a well-known optimality condition for inter-domain alignment. A previous version of our method has been presented in  Bleuze et al. (2021). As compared to that presentation, we have improved it by adding several ways to deal with the rank deficiency of the cross product matrix. Also, here we test it on a very large amount of data, namely, 18 BCI databases comprising a total of 349 subjects.
Following  Zanini et al. (2018) , we recenter both the source and target data sets by setting their global mean at the identity. This is simply obtained by transforming all trials of a dataset such as C rec = M -1 2 CM -1 2 , ( 4 ) where M is the center of mass of the observations and C rec denotes the recentered trial. After recentering all trials their center of mass becomes the identity matrix, corresponding to the "zero" point in an Euclidean space. The logarithmic mapping at the identity simplifies, yielding Log I Nc (C rec ) = I 1/2 N c log I -1/2 N c M -1 2 CM -1 2 I -1/2 N c I 1/2 N c = log M -1 2 CM -1 2. The above recentering followed by tangent space projection was first proposed in the BCI field in  Barachant et al. (2012 Barachant et al. ( , 2013) )  and is nowadays a standard processing procedure, which in this article is carried out systematically, unless explicitly mentioned.
Results The TSA algorithm previously introduced has been tested on three well know BCI paradigms: ERP, MI, and SSVEP. We have analyzed 18 open-access BCI databases available on the Mother Of All BCI Benchmark (MOABB;  Jayaram and Barachant, 2018). Python library, of which five uses the ERP paradigm, 10 the MI paradigm, and 3 the SSVEP paradigm. The 18 databases include a total of 349 subjects with very high variability between and within datasets. We summarize the data in Table  1 , following  Congedo et al. (2019). We execute transfer learning from one subject to the other for all possible source/target pair of subjects within each database. The accuracy is evaluated using balanced accuracy since the number of trials per class is often unbalanced (always for the ERP paradigm). Since the amount of data of all pair-wise comparisons is huge, we start by visually evaluating all balanced accuracies obtained for a given database by means of seriation plots, i.