dataesr/scientific-paragraphs-categorization huggingface dataset:
Nevertheless, it is essential to take the climate events into consideration when analysing this type of correlation on a local space scale. This is one of the first agronomical analysis on the Physiocap data which is being enlarged every year. On the basis of these preliminary results, Physiocap, coupled with other tools and indicators such as soil analysis, soil tillage and fertilization strategies as well as accurate climate data, seems to be a central component of a multidimensional and innovative vine management.
Scavenger Identification Primary scavenger taxa from observational studies were identified (Table  7. A in the Online Supplement). This list of scavenger taxa complements the species enumeration from  Britton and Morton (1994)  with recent studies (1990-2017) but requires caution in determining the relative importance of different scavenger groups as consumers of marine carrion, because the different techniques used have technical constraints in determining species composition. The retention efficiency and mesh sizes of the different types of traps, for instance, may cause bias in the observed abundances. The list of scavenger species from observational studies were complemented using data from commercial baited fisheries. Species were listed as scavenger species when annual landings between 2003 and 2016 exceeded 1 ton in ICES Subdivisions IV and VII a, d, f and VIIg.
Acknowledgments Part of this work has received funding from the  Horizon 2020 Programme  under grant agreement DiscardLess number  633680. This support is gratefully acknowledged. TM and AS acknowledge support by the  Fundação para a Ciência e Tecnologia (FCT)  strategic project  UID/MAR/04292/2013  granted to MARE. TM is supported by the  Program Investigador FCT  ( IF/01194/2013/CP1199/CT0002 ).
Acknowledgments We appreciate fruitful discussions with our Los Alamos colleagues  Marcus Daniels ,  Seth Lloyd ,  Scott Pakin , and  Rolando Somma , as well as  Edward Dahl  and  Trevor Lanting of D-Wave Systems. Work of J. D. and B. G. was supported by  Narodowe Centrum Nauki (National Science Center)  under Project No. 2016/23/B/ST3/00830  and  2016/20/S/ST2/00152 , respectively. Work of W. H. Z. was supported by the  US Department of Energy  under the  Los Alamos LDRD program. This research was supported in part by  PL-Grid Infrastructure.
sorafenib in European patients with advanced HCC, who did not have prior systemic treatment (NCT01004003; 1199. 37). Since different risk factors between Western and Asian populations, such as incidences of hepatitis B virus (HBV) and hepatitis C virus (HCV), may influence responses to targeted therapies, an identically designed study was conducted in parallel in Asian patients  14, 15  and is reported separately (NCT00987935; 1199. 39).
Extensive plasma and urine sampling was performed during the first treatment cycle to describe the PK characteristics of nintedanib and its main metabolites BIBF 1202 and BIBF 1202 glucuronide. Assessments were performed according to the schedule shown in the protocol. Antiviral treatment was recommended for patients with chronic HBV infection. For phase-II, 93 patients were randomised in a 2:1 ratio to receive nintedanib 200 mg bid (n = 62) or sorafenib 400 mg bid (n = 31) continuously, in 28-day cycles, until intolerable AEs or disease progression (PD). A randomised design was used in favour of performing an uncontrolled trial, which was common at the time the trial was conceived, to permit a contemporaneous comparison against sorafenib. A 2:1 randomisation ratio was chosen to generate more data on nintedanib, especially regarding safety, because data for sorafenib were already extensively defined.
Thus, the recommended phase-II dose was determined to be 200 mg bid for group I and II patients. DLTs experienced after the MTD determination period are described in supplementary Data. Pharmacokinetics PK results appear in supplementary Data. In brief, nintedanib was rapidly absorbed followed by at least biphasic disposition kinetics (supplementary Figure  S2 ). Maximum plasma concentrations were achieved around a median of 2 h and the gMean half-life ranged between 18. 3 and 33. 0 h (supplementary Table  S4 ). We observed a trend towards an increased gMean exposure to nintedanib, BIBF 1202 and BIBF 1202 glucuronide in group II patients compared with those of group I (supplementary Table  S5 ). However, the range of individual values strongly overlapped when data from both groups were compared.
ACKNOWLEDGEMENTS We thank the patients and their families and the investigators who participated in this trial, as well as all the members of the study team. We also thank  Rolf Kaiser  and  Arne Wenz  for their contributions during trial preparation and conduct, in addition to their thorough reviews of the manuscript. Medical writing services and editorial support, supported financially by  Boehringer Ingelheim , were provided by  Steven Tresker  of Cactus Communications during the preparation of this report. Funding: this work was supported by  Boehringer Ingelheim. This work was supported by the  NIHR Liverpool Biomedical Research Unit  [to D. H. P], the  Liverpool Experimental Cancer Medicine Centre  [to D. H. P. ], the  NIHR/Wellcome UCLH Clinical Research Facility  [to T. M. ], the  NIHR UCL Hospitals Biomedical Research Centre  [to T.
ACKNOWLEDGEMENTS We thank  Marina Wolf ,  Oliver Schlüter , and  Leszek Kaczmarek  for helpful comments. M. N. -S. , A. B. , and K. R. designed the experiments; M. N. -S. , A. B. , Z. M. , M. Z. , A. T. , R. P. , J. K. , C. R. and K. K. performed and analyzed the experiments; M. N. -S. , A. B. and K. R. wrote the manuscript. We thank  Karolina Kuchniak ,  Jakub Dzik , and  Chelsea Reeve  for technical assistance. This work was supported by a  National Science Centre (Poland)  (Grant No. 2013/08/W/NZ4/00691  and  2015/19/B/NZ4/03163 ) to K. R. and  Foundation for Polish Science HOMING  Grant (no. Homing/2016-1/10 ) to A. B.
Objective-We sought to describe a large, international cohort of patients diagnosed with primary mucinous ovarian carcinoma (PMOC) across three tertiary medical centers to evaluate differences in patient characteristics, surgical/adjuvant treatment strategies, and oncologic outcomes. Methods-This was a retrospective review spanning 1976-2014. All tumors were centrally reviewed by an expert gynecologic pathologist. Each center used a combination of clinical and histologic criteria to confirm a PMOC diagnosis. Data were abstracted from medical records, and a de-identified dataset was compiled and processed at a single institution. Appropriate statistical tests were performed. Results-222 patients with PMOC were identified; all had undergone primary surgery. Disease stage distribution was as follows: stage I, 163 patients (74%); stage II, 8 (4%); stage III, 40 (18%); and stage IV, 10 (5%).
METHODS This was an institutional review board-approved retrospective review. We identified all cases of PMOC using institutional datasets of patients treated at Memorial Sloan Kettering Cancer Center (MSK) in the United States from 1998-2013, Rigshospitalet (DEN) in Denmark from 2005-2013, and Gustave Roussy Cancer Campus (GRCC) in France from 1976-2014. Cases of pure borderline pathology, intraepithelial carcinoma, or metastatic mucinous carcinoma from another site were excluded. All tumors were reviewed by an expert gynecologic pathologist within each respective center. Each center used identical, mutually agreed upon clinical and histologic criteria to confirm a PMOC diagnosis; patients who presented with a unilateral ovarian tumor >10 cm in size were included, and in patients with bilateral tumors or a tumor <10 cm in size, a GI work-up often was performed to rule out a GI primary.
CK-7, CK-20, and caudal-type homeobox 2 (CDX2) staining, if available, were used to support the diagnosis. Clinical and pathologic data were abstracted from medical records, and a de-identified dataset was compiled and processed at a single institution. MSK and GRCC data were retrospectively collected from patients' electronic medical records. DEN data were retrieved from a national clinical quality data registry that includes many clinical variables but has incomplete treatment data  (11). Patients were staged according to the 2014 International Federation of Gynecology and Obstetrics (FIGO) classification system. Histologic grading was performed according to the Prat grading system for GRCC and DEN cases, and the Shimizu and Silverberg system  (12)  for MSK cases.
Overall cohort The median age of the overall cohort was 49 years (range, 15-89 years). Fifty-one (49%) of 104 patients had an elevated CA-125 level (>35 U/mL) at diagnosis. The median tumor size was 15 cm (range, 5-36 cm). In the 141 patient records with laterality data available, 134 (95%) unilateral tumors were documented. Among unilateral tumors with tumor size data available, 107 (88%) of 122 were ≥10 cm. Of the 177 tumors that were histologically graded, 109 (62%) were grade 1 and 68 (38%) were grade 2 or 3. Thirty-seven (26%) and 41 (28%) of 145 patients underwent upper or lower endoscopy, respectively. Any endoscopic evaluation was performed in 48 (33%) of 145 patients. Of the 221 patients with documented staging details, 163 had stage I (74%), 8 had stage II (4%), 40 had stage III (18%), and 10 had stage IV (5%) disease.
At MSK, more lymph node dissections were performed. This heterogeneity also introduces referral bias, which can limit comparisons across centers. Although our series is large and from three high-volume specialized centers, specific data capture and retrieval methods differ across institutions  (12). Some data were recorded in a prospective and compulsory manner but were not all inclusive of the many data points of interest for this rare tumor type. Most data were captured retrospectively. An unanswered question of our series is the impact of lymphadenectomy on oncologic outcome in clinically apparent stage I PMOC. This is an important question. Lymphadenectomy was not routinely performed across the institutions. Of the 99 patients who underwent any form of lymphadenectomy, 5 patients had positive pelvic nodes and 7 had positive para-aortic nodes.
However, it is critical that expert pathologists carefully review all banked tumor specimens with well-annotated correlative clinical data, particularly in stage II and greater cases, to ensure the tumor studied is indeed a PMOC. Tumor registries and institutional collaborations, in combination with a practical and cooperative effort to include the numerous more contemporary variables that allow for uniform and accurate data capture and analysis of rare tumors, are needed for the study of PMOC. Cooperative databases of clinical and immunohistochemistry data, scanned slide sets for review, and a GI/imaging work-up would further support recruitment of accurately diagnosed cases. With a diminishing likelihood of prospective study of PMOCs, the many questions investigators seek to ask at the trial level will need to be creatively addressed with collaborative, accurate, and comprehensive data sharing.
In this work, the use of cluster analysis algorithms, widely applied in the field of big data, is proposed to explore and analyse electron energy loss spectroscopy (EELS) data sets. Three different data clustering approaches have been tested both with simulated and experimental data from Fe 3 O 4 /Mn 3 O 4 core/shell nanoparticles. The first method consists on applying data clustering directly to the acquired spectra. A second approach is to analyse spectral variance with principal component analysis (PCA) within a given data cluster. Lastly, data clustering on PCA score maps is discussed. The advantages and requirements of each approach are studied. Results demonstrate how clustering is able to recover compositional and oxidation state information from EELS data with minimal user input, giving great prospects for its usage in EEL spectroscopy.
Introduction The introduction of the first commercial electron energy loss spectrometer in 1986, model 666 by Gatan, set a huge milestone in the history of materials science. Since then, electron energy loss spectroscopy (EELS) has evolved from acquiring a single spectrum in several seconds  1  with a spatial resolution of tens of nanometers to atomic resolution spectrum images (SI) with thousands of pixels acquired in milliseconds  2. Not only that, but the development of EELS tomography  3, 4  at increasingly higher spatial and energy resolutions is starting to generate so-called EEL spectrum volumes of millions of spectra. In parallel, the development of automated electron tomography  5, 6  and the foreseeable coalescence into automated EELS tomography might produce even larger EELS datasets.
EELS is, therefore, at a point where it faces new challenges and prospects of great developments. How to efficiently extract relevant information from enormous datasets is, nonetheless, a question that has been extensively addressed in the big data, data mining, machine learning and other data-science (DS) fields. Clearly, the microscopy community can benefit from the methods developed in these areas. A prominent example is the widespread use of principal component analysis (PCA) in EELS  3, 7, 8  , either for reducing noise without loss of spatial resolution or for data exploration, i. e. , the identification of different phases or compositions in a sample without thorough modelling of the spectra. This has enabled the mapping of energy loss near edge structure (ELNES) related properties from relatively noisy datasets  7  or even 3D reconstruction of oxidation states at the nanoscale  9.
However, PCA comes with its own problems, such as artefacts from ignoring too many components, obtaining components not related to physical properties of the sample or, simply, components that do not represent the data adequately. This stems from a fundamental flaw of applying PCA to EELS data. PCA solves the problem of finding an orthogonal coordinate system that maximizes the variance along its axes  10. In this description it is implicitly assumed that the data treated follows a multivariate normal distribution  11. However, this is not true in general for EEL SI. For this reason, usually PCA is combined with other data processing techniques, such as Independent Component Analysis (ICA)  [12] [13] [14]. Bayesian Linear Unmixing (BLU) must also be mentioned as an alternative method that has also been used in EELS  8, 15.
All these spectral decomposition methods have yielded excellent results with EELS. However, they rely on the ability of the scientist to make a physical interpretation of the output components, something that is not always easy. It must be kept in mind that the goal of EEL spectrum imaging and the related data analysis techniques is mapping the spatial distribution of properties reflected in the shape of individual EEL spectra. In many cases, this implies finding a way to segment a given SI into different regions, where each corresponds to a different material or has a different property. This can be thought of as classifying spectra into groups with similar characteristics, such as spectra with a peak at the same energy position or spectra showing two edges with a similar intensity ratio.
Therefore, if the last l=n-h links would occur across distances higher than the threshold, l+1 clusters would be obtained. The way to choose an adequate value for d threshold is to consider how min(d ij ) evolves with each iteration. Usually it will be small until a certain iteration from which it grows steeply. Defining the min(d ij ) value before that significant increase will yield an adequate threshold. The by considering the spectra in a SI an as a collection of p-dimensional (p=E) points the algorithm can be easily applied to EELS data. However, clustering performed this way is merely a segmentation method and some spectral variance might still be present in a given cluster. Nonetheless, there is no hindrance to perform PCA on each cluster, obtaining scores and factors that represent the data on that region.
This can also be understood in the clustering nomenclature as having n=X•Y objects each with c attributes (the scores of each PCA component). This great reduction in the number of attributes means that the clustering algorithm will now deal with a much smaller, mostly noise-free dataset, improving the computational time and accuracy in the cluster formation. In this work, the three proposed strategies to apply clustering to EEL SI are considered: performing data clustering on the raw data, performing PCA within data clusters, and finally, performing clustering on the score maps of an initial PCA decomposition. The different methods will be tested out both with simulated and experimental EELS data from Fe 3 O 4 /Mn 3 O 4 core/shell nanoparticles, to study how they perform in different scenarios and what preprocessing is necessary in each case.
Results and Discussion Spectrum image simulation. To demonstrate the application of data clustering to EELS datasets an artificial EELS SI was generated. The 128x128 pixels SI contain a 1024 channel spectrum in each one, which reflect the composition of the model shown in Figure  1A. It was chosen to be a particle with four main regions: a FeO core of constant composition, a FeCoO region (as if it was a precipitate), an internal void and, finally, a shell with varying Fe/O ratio. The intensity of the iron edge in this shell increases linearly from the edge of the particle to the core region, while the oxygen edge intensity decreases in the same proportion. For each of these regions, the spectrum in each pixel was built as a linear model comprised of several factors. First, a power-law background was used.
The corresponding hydrogenic cross-sections for each element were added, with intensity proportional to the chosen concentration of the element. Two gaussian curves were used to model the white lines for Fe (at 710 eV and 724eV) and Co (at 781 eV and 796 eV). Finally, gaussian and poissonian noise with a signal-to-noise ratio (SNR) of 10:1 were added, resulting in the spectra in figure  1C  for the positions highlighted in figure  1B. In Figure  1D  the intensity of the channel corresponding to an energy loss of 781 eV is plotted against the intensity of channel 710 eV, where each simulated spectrum is represented by a single point. The latter panel serves to discuss the spectral variance distribution within an SI. It is clear that the data form groups related to the different compositions present in the sample, even with the presence of noise, demonstrating that the EELS data does not follow a normal, PCA-suitable distribution.
Raw data clustering The simulated SI was transformed into a matrix of 128x128=16384 rows and 1024 columns according to the previously discussed data interpretation. The simulated data was then fed to the hierarchical agglomerative clustering algorithm. The code, implemented mainly with the Scipy  20  and Hyperspy 21 python libraries, is available as Supplementary information. An interesting way to visualize the clustering process is to plot the so called linkage tree of the clustering process. The clusters are laid along the x-axis of the plot, sorted by an arbitrary id number. The y-axis represents distance as defined by (1). A vertical line arises from each cluster. These vertical lines are linked by horizontal lines at a height which corresponds to the min(d ij ) at which they were merged together in the clustering process.
Discussion The presented results show that data clustering is a suitable analysis tool for EELS data sets. It is able to identify regions of different composition without any prior assumption of the data. This is certainly an advantage over the mentioned spectra modelling, which is limited to wellunderstood spectral features such as white lines. This advantage is shared with PCA and ICA. ICA in particular is expected to yield physically meaningful components, but that is not always the case. In the present case ICA was applied to the data for completion, yielding the results in figure  S6. Although some components can berelated to the different oxidation states of manganese and to the iron oxide core, a direct interpretation is not possible since all of them have non-physical features (negative edges, negative background, detector intensity steps…).
Conclusions By means of data clustering, SI segmentation according to chemical composition is obtained for EEL spectra, even recovering ELNES information. The authors believe that the three different strategies presented here will enable the use of data clustering in a wide range of problems, allowing easy and fast EELS data exploration. Importantly, it should be noted that this method is not limited to SI and could also be potentially used in EELS tomography in order to provide straightforward volume segmentation. The obtained results clearly show the usefulness and robustness of data clustering methods to deal with large EELS datasets.
Acknowledgements First and foremost, the authors ackowledge  Pedro Delicado  from the  department of Statiscs of the Universitat Politècnica de Catalunya  for fruitful discussions and valuable advice. We also acknowlege  Josep Nogués  from the catalan institue of nanoscience and technology (ICN2) for his colaboration in the sinthesis of the studied nanoparticles. EELS data from the sample of Figures 678was acquired at the  Oak Ridge National Laboratory. The authors acknowledge the financial support from the  Spanish Ministry of Economy, Industry and Competitiveness  through the projects  MAT2016-79455-P  and  MAT2015-66888-C3-3-R  with support of  FEDER  funds.
Professor Silverstein, Rutgers University, USA, will present new ERG findings in schizophrenia, and data on the relationships between ERG anomalies, symptoms, retinal structural abnormalities as measured with optical coherence tomography, and antipsychotic medication use. Based on empirical data, the symposium will offer an integrated view as to: i) how non-invasive measurements of retinal structure and function show consistent anomalies in schizophrenia, bipolar disorder, major depression and ADHD; ii) how findings from children and adolescents at high genetic risk not only indicate a neurodevelopmental process, but also suggest that retinal anomalies in patients are not due to medication use or degenerative effects; iii) how ERG can be administered to adults and children at low cost in clinical studies; iv) how to integrate findings in the staging of the risk status of children at genetic risk.
g. , diabetes, hypertension); and 5) examine potential medication effects on these findings. Methods: We have assessed 25 patients with schizophrenia and 25 controls who are free of medical comorbidity with fERG and measures of visual function and symptom severity, and data collection is ongoing with patients and controls with diabetes and/or hypertension using these same measures. In addition, we are in the process of completing data collection with two additional groups of patients and controls, one with fERG and OCT (n=12 to date), and another with fERG and VEPs (n=13 to date). fERG data are being collected under both light-and dark-adapted conditions, using a range of flash intensities, backgrounds, and temporal frequencies. The primary fERG variables of interest are a-wave and b-wave amplitudes, which reflect photoreceptor and bipolar cell responses, respectively, and the photopic negative response (PhNR), which reflects ganglion cell activity.