dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, the tokens # and LoveIsLove are merged and replaced with a TOPIC token. • Emoji's with text were divided into two tokens. For example, he becomes he and. • If a token contains more than one emoji, each emoji was considered as a token. For example, becomes and. Embeddings: Following Collobert et al. (  2011 ), a lot of authors argued that word embedding plays a vital role to improve natural language task performance. Hence, we experimented the use of word embeddings to improve the performance of our proposed models. Using the fastText  (Bojanowski et al. , 2017) , we prepared two embedding models: Skip-gram and Cbow. After empirically evaluating the performance on validation set, the embeddings' dimensionality was set to 300 for all the embeddings. The embeddings are trained on training data using the parameters: lr=0.
Results and Analysis As mentioned in the previous section, we carried out experiments with our proposed model using two settings. All the presented experiments are evaluated on the test data for the given task. The performance of the systems were evaluated using F1 averaged across the positive, negative and the neutral. The organizer baseline F1 scores for the validation and test data are 0. 58 and 0. 654 respectively. The details of the baseline are given in  (Patwa et al. , 2020). The obtained results with our submitted runs are given in Table  1. For SkipGRun, we achieved 0. 6913 of F1 score with 0. 6952 and 0. 6893 of precision and recall respectively. The SkipGRun outperformed the CbowRun by around 0. 40 in terms of F1 score. CbowRun outperformed the organizers' baseline by a slight margin, however, SkipGRun outperformed the baseline by around 0.
Acknowledgements The research work of the first four authors was supported by  ERA-Net CHIST-ERA   LIHLITH Project  funded by  ANR  ( France)  project  ANR-17-CHR2-0001-03. The research work of the last author was partially funded by the  Spanish MICINN  under the project  MISMIS-FAKEnHATE on Misinformation and Miscommunication in social media: FAKE news and HATE speech  ( PGC2018-096212-B-C31 ).
Introduction Plant breeding has experienced an explosion of advances in the last 30 years, with the development and release of molecular, genomic, bioinformatic and technological tools and resources that accelerate crop improvement  [1]. Not all areas developed, however, at the same pace. Currently, phenotyping represents the main bottleneck for effective selection of interesting genotypes. This is true for all breeding methods, but especially for the increasingly used genomic selection, for which phenotyping is key to train prediction models  [2]. When following a genomic selection strategy, precision-phenotyping of the training population is most important, because that dataset provides the basis for developing the statistical model that is then used to predict phenotypic performance in related members of a breeding population  [3].
The PhotosynQ app (https://photosynq. org), developed in the laboratory of David Kramer (Michigan State University, Michigan, USA), is an open access collaborative platform that allows the plant research community to collect, analyze, discuss and share plant photosynthetic-related data using a low-cost handheld device, the MultispeQ  [13]. This instrument combines a pulse-amplitude-modulated fluorimeter, a chlorophyll meter, and a spectrometer into one. Therefore, it provides valuable information about crop status and photosynthetic performance in a single reading in a time scale of seconds. It provides indirect measurements of a number of parameters, which have been benchmarked against standard laboratory devices  [13]. Traditionally, leaf photosynthetic properties have been measured using slow and laborious gas exchange methods, unfit for testing the large populations managed in breeding programs.
Samplings Pulse-amplitude modulation (PAM) fluorimetry, chlorophyll content, and leaf temperature differential (LTD) measurements were carried out using a MultispeQ v1. 0 device controlled by the PhotosynQ platform software  [13]. Measurements were taken in two relevant moments for the crop physiology, after a period of water stress (5 April 2018), and after an episode of abundant rain  (24 April 2018). Table  1  shows a summary of climatic variables that characterize the two sampling dates. The first sampling, hereinafter "water-stressed", was characterized by a lower precipitation accumulated prior the sampling, whereas the second sampling date, hereinafter "unstressed", was characterized by a higher precipitation accumulated prior the sampling date and higher irradiance and mean temperature during the sampling date.
During the water-stressed sampling, all plots were at the growth stage of stem elongation (Z34-Z37  [29] ), and during the unstressed sampling plants were at booting stage (Z39-Z47). Samplings were performed on clear days, during day central hours around solar midday. Two MultispeQ devices were used to reduce the sampling time. Measurements were taken on four horizontal upper-most fully expanded leaves per plot, two with each device. We designed a route of genotype sampling across the trial to avoid time and light intensity effects affecting differentially to parents and hybrids. The route drove us across the trial, interspersing the upper and bottom rows, as well as the different genotypic sets. Moreover, to reduce the error derived of the under-representation of the female set compared to the hybrid and male set, we measured each female plot several times (32 data points per plot), intercalated in time between the rest of the genotypes.
e. a roughness penalty), by taking advantage of the additional correlated data points available on the pressure surfaces above and below the surface of interest. We have applied this technique to the trend analysis of the vertically correlated time series of tropospheric ozone observations from 1) IAGOS (Inservice Aircraft for a Global Observing System) commercial aircraft profiles above Europe and China, and 2) NOAA GMD's (Global Monitoring Division) ozonesonde records at Hilo, Hawaii and Trinidad Head, California. We illustrate the ability of this technique to detect a consistent trend estimate, and its effectiveness for reducing the associated uncertainty in the noisy profile data due to low sampling frequency. We also conducted a sensitivity analysis of frequent IAGOS profiles above Europe (approximately 120 profiles per month) to determine how many profiles in a month are required for reliable long-term trend detection.
We demonstrate that this method reduces the noise in an irregular and/or sparse time series by taking advantage of the additional information in adjacent layers of the vertical profiles, and yields a trend with reduced uncertainty. While our method was developed for tropospheric ozone profiles it can also be applied to the stratosphere, profiles of other trace gases or atmospheric thermodynamic properties, or to profiles of oceanic or geologic properties. Section 2 begins with a brief introduction of the time series decomposition and smoothing spline ANOVA framework, we then propose a statistical methodology to assess the profile variability. Section 3 applies this methodology to two tropospheric ozone profile datasets measured by 1) IAGOS (In-service Aircraft for a Global Observing System) commercial aircraft above Europe and China (including a probability test of how the trend precision and accuracy change according to sampling frequency); and 2) ozonesondes launched from Hilo, Hawaii and Trinidad Head, California, by NOAA's Global Monitoring Division (GMD).
Note that the focus of this study is jointly modeling multiple correlated time series at different heights above the same region, which differs from a trend study of a series of measurements with uneven temporal sampling at a single altitude  (Tiao et al. , 1990; Rehfeld et al. , 2011; Weatherhead et al. , 2017) , or irregularly distributed observations from a monitoring network  (Stein, 1999; Chang et al. , 2015 Chang et al. , , 2017)). Our problem can be seen as decomposing a two dimensional signal according to the associated variability on a regular grid cell (i. e. , based on the monthly and annual index at a fixed altitude bin). Specifically, we implemented the penalized regression splines decomposition using the thin-plate splines available from the R package mgcv  (Wood, 2006; R Core Team, 2013).
Ozone profile observations Our new trend calculation methodology is applied to the long-term tropospheric ozone vertical profile datasets measured by 1) IAGOS commercial aircraft above Western Europe and China, at 50 hPa intervals from 950 hPa to 250 hPa for the period 1994-2017; and 2) ozonesondes launched from  Hilo, Hawaii (1982 -2018 ) and Trinidad Head, California (1997 -2018) , with a vertical resolution of 100 meters from the surface to 15 km. For consistency the vertical coordinate of the ozonesondes is converted from meters to pressure levels. The IAGOS program has measured ozone worldwide since 1994, using instruments onboard commercial aircraft of internationally operating airlines  (Marenco et al. , 1998; Petzold et al. , 2015 , http://www. iagos. org). This analysis utilizes the same subset of IAGOS ozone profiles above Western Europe reported by  Cooper et al.
(2019). Ozone is measured using a dual-beam UV-absorption monitor (time resolution of 4 seconds) with an accuracy estimated at about ± (2 nmol mol -1 + 2 %)  (Thouret et al. , 1998; Nédélec et al. , 2015). Because most IAGOS aircraft have belonged to airlines based in Europe since the program began in 1994, Western Europe is the most frequently profiled region in the world. Above Western Europe (0 The IAGOS dataset in western Europe is a regional aggregation of profiles from several airports. The profiles are measured by several aircraft and on average there are approximately four profiles per day within the study region. With such a high sampling frequency, the data are expected to be far less noisy than a sparsely sampled ozonesonde time series (one to three profiles per week), and highly representative of the true ozone variability above Western Europe.
The IAGOS dataset in China is also a regional aggregation, but the time series has a period of low sampling frequency (2007-2010), with many months containing no data. In contrast, the ozonesonde records at Hilo and Trinidad Head are expected to have greater uncertainty, due to the lower sampling rate of one profile per week. Our first task was an evaluation of our method against the standard linear model using the IAGOS data above Europe, to determine if our method yields trend values with reduced uncertainty. The next step was to examine the impact of sampling frequency on the trend estimate, based on the IAGOS profiles above Western Europe. Finally, we applied our method to the sparsely sampled Hilo and Trinidad Head ozonesonde records to demonstrate the improved trend quantification. Using the interannual component in Figure  2 (b) we calculate the trend value for each individual pressure layer.
Application of the integrated fit to IAGOS trends above NE China IAGOS aircraft have measured ozone profiles above northeastern (NE) China and Seoul, South Korea since 1994, but at a much lower sampling rate than Western Europe. While Western Europe has 36298 profiles during 1994-2017, NE China (including profiles from Seoul, South Korea) only has 1636 profiles with many months containing sparse data during 2007-2010. The lower sampling frequency and the data gap mean that the NE China IAGOS dataset can provide a further test of the advantages of the integrated fit method. The seasonal and interannual components of the integrated fit to NE China are shown in Figure 8. Compared to Western Europe the seasonal component appears noisier in the vertical, partially due to a lower sampling frequency, however a clear seasonal peak can be seen in June in the upper troposphere and in April-May-June in the lower troposphere.
Acknowledgements. We thank  Toshihiro Kuwayama  (Toshihiro. Kuwayama@arb. ca. gov) and the  California Air Resources Board  for supporting the ozonesonde program at Trinidad Head in recent years, and  Michael Ives  at  Humboldt State University  for his many years of collaboration with NOAA GMD and his dedication to launching weekly, and sometimes daily, ozonesondes from  Trinidad Head. The IA-GOS program acknowledges the  European Commission  for its support of the  MOZAIC  project (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003) the preparatory phase of IAGOS (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013) and IGAS ( 2013IGAS ( -2016 )); the partner institutions of the  IAGOS Research Infrastructure (FZJ, DLR ,  MPI ,  KIT in Germany, CNRS ,  Météo-France ,  Université Paul Sabatier in France , and  University of Manchester, UK) ; the  French Atmospheric Data Center AERIS  for hosting the database; and the participating airlines (Lufthansa,  Air France ,  China Airlines, Iberia, Cathay Pacific, Hawaiian Airlines ) for transporting the instrumentation free of charge.
Code and data availability. The IAGOS data are publicly available at https://doi. org/10. 25326/20. The ozonesonde data can be found in the NOAA ESRL Global Monitoring Division (GMD) database at ftp://aftp. cmdl. noaa. gov/data/ozwv/Ozonesonde/. The R code for the proposed approach is available on GitHub at https://github. com/Kai-LanChang/VerticalProfileStudy.
3. 3 3 Application of the integrated fit to IAGOS trends above NE China IAGOS aircraft have measured ozone profiles above northeastern (NE) China and Seoul, South Korea since 1994, but at a much lower sampling rate than Western Europe. While Western Europe has 36298 profiles during 1994-2017, NE China (including profiles from Seoul, South Korea) only has 1636 profiles with many months containing sparse data during 2007-2010. The lower sampling frequency and the data gap mean that the NE China IAGOS dataset can provide a further test of the advantages of the integrated fit method. The seasonal and interannual components of the integrated fit to NE China are shown in Figure 8. Compared to Western Europe the seasonal component appears noisier in the vertical, partially due to a lower sampling frequency, however a clear seasonal peak can be seen in June in the upper troposphere and in April-May-June in the lower troposphere.
Clinical manifestations Clinical features of +GT and -GT groups are detailed in Table  2. In the univariable analysis, the presence of musculoskeletal pain, premature tooth loss, dental abnormalities, metatarsal stress fractures and history of orthopedic surgery were significantly higher in the +GT group. While metatarsal stress fractures were detected in four subjects in the + GT group, no subject reported this type of fracture in the -GT group. Data from the knee and/or pelvis X-ray was available for 11 subjects: 5 in the +GT group and 6 in the -GT group. Among them, two subjects in the former had radiographic chondrocalcinosis signs (one of them with a previous diagnosis of pyrophosphate arthropathy), contrasting with a negative previous history in the -GT group. A higher VAS was observed in the +GT group  [3 (2-5)  vs 1 (0-5); p < 0.
We identified four previously unreported variants based on the American College of Medical Genetics and Genomics (ACMG) classification criteria  [20]. The majority of the variants were missense in nature and were located in exons five, six and nine, as has been commonly observed in public databases and previous reports  [9, 18]. According to our results, approximately 50% of persistently low APL levels signify a different underlying etiology than HPP. Therefore, genetic confirmation by ALPL genetic testing is required. Nevertheless, in clinical practice, access to such testing is not always available or rapid testing is required. Therefore, it would be very useful to identify the clinical and routine biochemical characteristics of the disease and its predictive value for a proper and early diagnosis, in order to avoid erroneous therapeutic decisions.
Clarifying this aspect is an important issue from the diagnostic point of view, given the high clinical variability of the disease  [22] , the evidence of normal substrates in some adults carrying a defective ALPL allele and the limitations of conventional genetic studies  [18, 23]. The main weaknesses of our study are the lack of pediatric medical histories, the use of a clinical questionnaire for identifying clinical symptoms and limited data available regarding certain variables, such as radiographic records. As a limitation, the design of the study does not allow us to establish a definitive clinical diagnosis of HPP: although a detailed questionnaire was designed for the evaluation of subjects included, a complete visit and complementary tests targeted to study clinical features or laboratory abnormalities were not performed, so that some subjects with variants in ALPL could be carriers.
Furthermore, ALP substrates and the analysis of the dominant negative effect of the variants have not been performed due to limitations in their determination. The main strengths are the thorough analysis of medical records, which yielded a considerably large sample size for a rare disease, the integration of data across a wide range of variables, the analysis of information obtained through a detailed clinical questionnaire and the genetic analysis encompassing this large population. In addition, to the best of our knowledge, this is the first time that ALP cut-off levels are used to identify a positive ALPL genetic test, although these results need further investigation.
Study population and design This cross-sectional study was performed at La Paz University Hospital (Madrid, Spain). The study adhered to the tenets of the Declaration of Helsinki and approval was obtained from its ethics committee. Each subject provided written informed consent prior to inclusion. First, 1,536,711 laboratory records including ALP serum levels values from 386,353 subjects were screened to identify abnormally low ALP results. These values were recorded in the biochemical database of the hospital from   [11].
Collected data Eligible subjects completed a questionnaire to report family or personal history of musculoskeletal pain, muscle weakness, fractures, premature dental loss and previous orthopedic surgery, among other clinical features classically associated with HPP. Additionally, radiographic data were retrieved from medical records. Musculoskeletal pain was considered when symptoms were recurring or chronic (> 6 months) and not when transient and muscle weakness was defined by a chronic subjective perception of a decrease in muscle strength, but no physical examination was performed. Dental abnormalities were defined as tooth shape abnormalities, structure and colour abnormalities of enamel or dentin, thin enamel, late teeth eruption or severe/recurrent cavities and early loss of permanent teeth was defined as the loss of several teeth or extraction (> 10) due to tooth abnormalities in the past, prior to the age of 50.
Genetic analysis Genomic DNA was extracted from peripheral blood with a Chemagic Blood kit (Perkin Elmer, Waltham, MA) and the screening of the exons and intron/exon boundaries of ALPL (NM_000478. 4) was performed by Sanger sequencing. In silico pathogenicity prediction and control population frequency analysis were assessed using Alamut V2. 6 software (Interactive Biosoftware Rouen, France), Varsome (https://varsome. com/), CADD software (http:// cadd. gs. washington. edu) and Silvent et al. criteria  [24]. The allelic frequencies were determined using gnomAD (http://gnomad. broadinstitute. org/) and the in silico tools included CADD V1. 3, DANN, SIFT, Polyphen, Mutation-Tester, Mutation assessor, FATHM and SpliceSiteFinderlike, MaxEntScan, NNSPLICE, GeneSplicer. The ALPL disease-causing variants database (http://www.
Statistical analysis First, descriptive analysis was employed to determine the prevalence of ALPL variants among subjects with persistent hypophosphatasaemia and to compare clinical and laboratory characteristics between individuals with and without ALPL variants. Continuous variables were described as median (interquartile range -IQR-) and categorical variables as an absolute number and relative percentage. Comparisons between two independent groups for continuous variables were performed using the Student's t-test for unpaired data if normally distributed, or a Mann-Whitney U test when not. Statistical significance difference between groups for categorical variables was calculated using the Chi-square test or the Fisher's exact test, as appropriate. Logistic regression models adjusted for confounders were employed to investigate the association between clinical and laboratory characteristics and the genetic status.
The gene balance hypothesis posits that ohnologues persist because the loss of one copy would lead to a detrimental change in the stoichiometry of macromolecular complexes, the interactome and signalling pathways  47. The majority of duplicates, however, are predicted to become non-functional or get lost (degeneration)-for example, the ohnologue retention rate from the teleost WGD in the extant teleosts is estimated to be only 15-20%  48. On the basis of noncoding microsatellites, the sterlet was proposed to have undergone extensive duplicate gene degeneration and has been classified since then even as a functional diploid species  10. To estimate the duplicate retention rate, we identified 9,914 high-fidelity pairs of ohnologues and 4,175 singletons (Supplementary Note 7). This dataset represents a duplicate retention rate of 70% (Supplementary Table  11 ), considerably higher than in all teleosts, including the 4R salmonids (Supplementary Note 8).
Notably, 39 of these singletons are arranged in eight syntenic blocks. A similar phenomenon was seen for the commonly retained ohnologues (Supplementary Figs. 17b  and 19 , and Supplementary Table  14 ). The loss or retention of linked genes after WGDs could be explained by the functional relationships of their gene products-for example, through protein-protein interactions  52. However, a search of singleton genes, embedded in syntenic blocks using the STRING  53  database, did not reveal such protein-protein interactions. An alternative explanation for the conservation of microsynteny is the bystander relationship  54  , where the regulatory region of one gene is located in neighbouring genes. Further studies are required to validate this type of physical association of genes on chromosomes over long evolutionary times rather than functional relationships of their encoded proteins.
Biotinylated DNA fragments were pulled down using M280 Streptavidin Dynabeads (Invitrogen) and ligated to paired-end adaptors (Illumina). The Hi-C library was amplified using paired-end primers (Illumina) with 10 PCR amplification cycles. The library was sequenced using HiSeq3000 (Illumina) generating 150-bp paired-end reads at 20-fold genome coverage. The raw Sequel BAM files were converted into subreads in fasta format using the SMRT Link software package (v. 5. 0. 1) from Pacific Biosciences  86. PacBio reads were assembled with smartdenovo (v. 1. 0) 87 with standard parameters. Contigs were polished with two rounds of racon  88  (v. 1. 3. 1), using long reads aligned with minimap2 (ref. 89 ) (v. 2. 7) and three rounds of pilon  90 (v. 1. 22) , using 42-fold Illumina reads. The Illumina reads were aligned with bwa mem (v.