dataesr/scientific-paragraphs-categorization huggingface dataset:
This dataset is based on an extension (G3PO+) of the gene prediction benchmark G3PO. Since high-quality, genome-wide annotations are not available for the 147 species in this dataset, we developed a protocol based on expert-guided comparative sequence analysis in order to identify reliable SS in a subset of genes. Since the G3PO+ gene sequences are evolutionarily related, we eliminated redundant sequences, which could cause potential bias of sequence over-representation and thus a risk of overfitting. We also made an effort to respect the proportions of non-canonical SS (2. 2% donor and 1. 3% acceptor) found in real-world data  [21]. To investigate the impact of the quality of the initial training data on the CNN models, we extracted data from public databases such as Ensembl  [4]  and UniProt  [68] , where it has been estimated that many proteins (with the exception of Swiss-Prot, which represents 0.
3% of UniProt) have errors  [69]. We then built a dataset called ' All Sequences' (AS), that includes some badly predicted gene sequences  [45]  and thus introduces noise in the form of wrong or missing SS. We compared the CNN model trained on the AS dataset with a second model trained on a 'Gold Standard' (GS) dataset, which was cleaned by removing all error-prone sequences. Since our results showed that the quality of the data had a significant impact on the accuracy of the models, we conclude that quality control and data cleaning steps are essential in order to obtain better results. We also tested the impact of other parameters, such as the length of the input sequences. It is important to carefully select the size of the genomic sequence in order to take into account different important elements such as regulatory elements (ESE, ESS, ISE or ISS  [70] ), the BPS  [71]  and the PPT  [22]  that can be kept and help the algorithm to generalize.
All these elements constitute intrinsic signals that are indispensable for the spliceosome to accurately recognize the SS. In order to include enough cis elements without introducing too much noise, we chose an input sequence length of 200 nt for both donor and acceptor models. Unfortunately, many other external signals impacting SS recognition by the spliceosome cannot be detected by current methods such as the secondary structure of RNA  [72] , or the transcription speed of polymerase II  [73]. Finally, we investigated the impact of the negative examples and the use of balanced or unbalanced datasets, in terms of the ratio of positive to negative examples. SS prediction is an inherently unbalanced problem, because the number of nucleotides involved in a SS is much smaller than the number of non-SS nucleotides.
The results confirmed the hypothesis that an unbalanced dataset was more prone to overfitting because one of the classes is overrepresented  [66]. Moreover, the heterogeneous negative examples provided better performance. All these tests allowed us to optimize our method and improve prediction performance. In order to estimate the performance of Spliceator on independent genome data, we used six different benchmarks from diverse organisms and compared Spliceator with a number of state-of-the art programs, including two other CNN-based methods. As expected, the more recent CNN-based methods generally achieved higher performance metrics than the older prediction methods that used either neural networks or maximum entropy distributions approaches. We calculated a number of different performance metrics, since the most suitable metric to measure 'good' performance will depend on the specific user application.
Conclusions Here, we present a new approach to train Spliceator, a universal splice site prediction program based on a high-quality dataset from diverse eukaryotic organisms (from primates to protists). We highlighted the inherent link between data quality and the performance of prediction programs based on machine learning algorithms. We also showed that including high quality multi-species data can result in accuracy equivalent to other state-of-the-art SS prediction programs. In the future, it would be interesting to include more data from other species, but also to test other types of network architecture in order to extract new high-level features. Moreover, as some of the extracted features are highly conserved, it would be interesting to use our model to perform transfer learning for gene annotation of other organisms.
Data collection Initial datasets were constructed for each type of SS, in order to establish two separate models: one to predict donor SS and one to predict acceptor SS. The models developed in this study are based on supervised learning allowing the classification of entries in two classes (0: nucleotide not involved in SS, and 1: nucleotide involved in SS). Thus, each dataset is constructed from a positive subset containing SS sequences and a negative subset containing non-SS sequences. To build the positive and negative subsets, gene sequences from the multi-species benchmark G3PO  [45]  were used. G3PO is based on 147 phylogenetically disperse organisms and contains 1793 sequences including 20 human Bardet-Biedl Syndrome (BBS) genes (Additional file 1: Table  S4 ) and their orthologous sequences (ranging from primates to protists) extracted from the OrthoInspector database v3.
0  [74]. Following the same methodology implemented in G3PO, we extended the original dataset by adding 948 sequences from 25 human genes responsible for myopathies and their orthologs from 47 metazoans (Additional file 1: Figure  S2 ). The 948 protein sequences in the extended dataset (called G3PO+) were analyzed according to the G3PO protocol in order to classify them into two categories: those without gene prediction errors (called 'Confirmed') and those that contain at least one error (called 'Unconfirmed'). Errors include insertions, deletions and mismatches in the N-terminal, C-terminal or internal regions. This protocol allows to verify the quality of the data and ensures that the SS present in the 'Confirmed' sequences are biologically true, i. e. they are recognized by the spliceosome.
Positive subsets The 2741 G3PO+ sequences are classified into 2 categories, either 'Confirmed' (1361 sequences) because they were annotated 'error-free' , or 'Unconfirmed' (1380 sequences) because they contained at least one gene prediction error. For each G3PO+ sequence, genomic sequences and exon maps were retrieved from the Ensembl database  [4]  release 87, and the SS were extracted, flanked by a ± 300 nt environment. A verification was made to ensure that no sequences containing undetermined nucleotides (noted 'N') were selected. The GS datasets contain only SS from the 1361 'Confirmed' sequences. Thus, the same positive subset for each GS dataset (GS_0, GS_1, GS_2 and GS_10) contains 10,973 donor and 11,179 acceptor SS sequences, where each sequence is of length 600 nt with the GT (donor) or AG (acceptor) dinucleotide in the central position (301 and 302).
G3PO Extension G3PO+ AS datasets contain SS from all 2741 G3PO+ sequences, including both 'Confirmed' and 'Unconfirmed' sequences. Thus, the AS datasets are representative of the data present in the public databases, as no pre-processing has been performed on the data, and therefore they include a certain number of errors. To eliminate any bias from the size of the datasets, an equivalent number of SS sequences were used (12,000 donor and 12,000 acceptor) to form the same positive subset for each AS dataset (AS_0, AS_1, AS_2 and AS_10). In order to test the impact of the genomic context, each dataset is provided in six different versions, according to the defined length of the input sequences. Sequence lengths selected for this study are 20, 80, 140, 200, 400 and 600 nt, where the SS is always in the central position.
To reduce redundancy, for each sequence length, duplicate sequences are removed. As the length of the sequences decreases, the number of duplicates increases, reducing the size of the data sets (especially for 20 nt sequences). Figure  9  (Additional file 1: Table  S5 ) summarizes the composition of the AS and GS positive subsets according to sequence length. Each SS is described according to its type, either canonical (i. e. GT for donor site and AG for acceptor site) or non-canonical. The number of non-canonical donor and acceptor SS present in each AS and GS dataset for each sequence length is also shown in Fig. 9  (Additional file 1: Table  S5 ). In addition, Fig. 10  shows the sequence logos of the canonical and non-canonical donor and acceptor SS motifs from the AS and GS dataset sequences.
Negative subsets Two negative subsets were first constructed. The first one is composed only of FP sequences, i. e. randomly selected regions in the G3PO+ sequences in both 'Confirmed' and 'Unconfirmed' sequences (for AS dataset) or only 'Confirmed' sequences (for GS dataset), with a GT or AG dinucleotide (depending on the type of SS), in the central position (e. g. 301-302 for length = 600 nt) that do not correspond to a SS identified in the positive subsets. The second type of negative subset is composed of 3 categories of sequences extracted from the G3PO+ dataset:  • Exon sequences: randomly selected exon regions, • Intron sequences: randomly selected intron regions, • False positive SS: randomly selected GT or AG dinucleotides. Negative subsets were also constructed with different numbers of sequences depending on the size of the positive subset: (i) with a ratio 1:1 we have the same number of positive and negative sequences, (ii) with a ratio 1:2 we have twice as many negative sequences as positive sequences and (iii) with a ratio 1:10 we have ten times more negative sequences than positive sequences.
Data composition strategies By combining the same positive subset with different negative subsets, a number of datasets were constructed in order to measure the impact of different parameters, including the type of negative examples used (heterogeneous = exons, introns and FP or homogeneous = only FP), the use of balanced or unbalanced datasets defined by the ratio of positive to negative examples, and data quality (AS vs. GS). In total, eight datasets were established, summarized in Table  4.
Convolutional Neural Network Models were constructed and trained for each type of SS (donor or acceptor) independently. The models result from supervised learning, where genomic sequences (input) are coupled with class labels 0: non-SS and 1: SS. The CNN then applies filters on each input sequence and tries to modify the weights of these filters to improve the predictions thanks to the back-propagation algorithm. The filters allow to extract pertinent features/ patterns within the input data. The output of the CNN is a vector of size 2, corresponding to the non-SS (0)/SS classes  (1). The implementation of the CNN, as well as the training of the models, was done in Python v3. 7, with Tensorflow v2. 4. 1  [76] , the API Keras v2. 3. 1 and the Scikit-learn library v0. 23. 2  [77].
The reference sequences are the cytoplasmic tryptophanyl-tRNA synthetase of Paramecium tetraurelia (A0D783_PARTE) and the tryptophan-tRNA ligase of Arabidopsis thaliana (SYWM_ARATH). All orthologs were extracted from the OrthoInspector database version 3  [74] , multiple sequence alignments were obtained with PipeAlign version 2  [80] , and manually analyzed to identify 62 'Confirmed' sequences (33 plants and 29 protists). Finally, the benchmark contains 692 (with 21 non-canonical) donor SS and 714 (with 18 non-canonical) acceptor SS, and the same number of non-SS sequences to balance the dataset. The benchmarks were used to compare Spliceator with other existing SS prediction methods, including NNSplice, MaxEntScan, DSSP and SpliceFinder. Note that SpliceRover was not included in these large-scale benchmark tests, since the method is only available as a web server.
Composition of the 8 datasetsComposition of the 8 datasets used to study the impact of (i) the type of negative examples (only FP sequences vs. heterogeneous data with exons, introns and FP sequences), (ii) the ratio of positive to negative examples (1:1, 1:2 and 1:10), (iii) data quality ('Confirmed' and 'Unconfirmed' sequences in the AS datasets vs. only Confirmed sequences in the GS datasets FP, False Positive; GS, Gold Standard; AS, All Sequences.
Funding This work was supported by  French Infrastructure Institut Français de Bioinformatique (IFB)   ANR-11-INBS-0013 , and the  ANR projects Elixir-Excelerate :  GA-676559  and  RAinRARE :  ANR-18-RAR3-0006-02 , and Institute funds from the  French Centre National de la Recherche Scientifique , the  University of Strasbourg. The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data or in writing the manuscript.
Preparation of datasets for CNN Training and test sets For each dataset described above, a random selection was performed to form a training set containing 80% of the sequences and a test set containing 20% of the sequences. Since there are 10 different negative datasets, there are 10 different training and test sets. Figure11(Additional file 1: TableS6A and B) summarizes the average number of %Identity = Number of identical nucleotide Length of sequence *  100.
, 2010; Wang et al. , 2013). Higher-resolution local-scale (∼ 1 km) UHI effects can also be explored in detail with urban energy balance models, such as the Surface Urban Energy and Water Balance (SUEWS) model  (Alexander et al. , 2015) , which incorporates detailed land cover data to calculate local perturbations to surface heat fluxes  (Grimmond et al. , 2010). However, the availability of land cover information in developing countries, where heat-related mortality rates in cities are highest  (Kjellstrom et al. , 2009) , often restricts such studies. In fact, the Intergovernmental Panel on Climate Change's (IPCC) fifth assessment report specifically highlighted the lack of detailed global urban land use datasets  (IPCC, 2014). Stewart and Oke's (2012)  thermal classification scheme for neighbourhoods, termed local climate zones (LCZs), provides one solution.
, 2020; Squires et al. , 2020; Zhao et al. , 2020). ADMS-Urban studies in London focused only on specific urban developments  (Hamilton et al. , 2014)  and building materials  (Aktas et al. , 2017) , whereas in Kuala Lumpur the effects of AHEs were excluded (K. Wang et al. , 2019). Several previous studies have implemented LCZs for UHI simulations, including in Dublin  (Alexander et al. , 2015) , Madrid  (Brousse et al. , 2016)  and Singapore  (Mughal et al. , 2019). We build on this earlier work by combining a LCZ map for Beijing with the locations of fine-scale green spaces, waterways and buildings from OpenStreetMap (OSM). Detailed descriptions of the ADMS-Urban model, its meteorological inputs and the development of surface parameter and AHE datasets are provided in Sect. 2. In Sect. 3, simulated neighbourhood-scale spatiotemporal near-surface air temperature variations across urban Beijing are evaluated using in situ near-surface air temperature measurements and satellite-derived LSTs, with the impacts of extreme temperature events on Beijing's UHI also explored.
Ideally, we would use several rural meteorological sites distributed around the model domain, selecting the air temperature measurement from the site coinciding with upwind conditions; an upwind measurement is desirable so that the air temperature sensor is not strongly influenced by the advection of urban heat. Air temperatures measured at the airport site were not deemed appropriate for this study given that the local environment is likely heavily affected by the warming effects of surrounding urban surface thermal properties and nearby AHEs. For this study, the only accessible upwind air temperature measurement data were from the rural field campaign site at Pinggu. However, during the winter period, the predominant wind direction was from the north-east, situating the model domain downwind of the rural site.
ADMS-Urban surface parameters Near-surface air temperatures are modelled across the area contained within Beijing's Sixth Ring Road (Fig. 1 ); the resolution of the model calculation grid is ∼ 105 m. Thermal and morphological properties covering the modelled urban area and surrounding suburban regions are derived from Open-StreetMap (OSM) data (https://openstreetmap. org/, last access: 9 March 2020) and LCZs mapped by the World Urban Database and Access Portal Tools (WUDAPT) project (http://www. wudapt. org/cities/in-asia/, last access: 19 May 2020). The function and plan area of specific buildings, green spaces and waterways are obtained from OSM (Fig. 2 ). Table A1 provides a full list of the OSM land use types and the thermal admittance, surface resistance to evaporation and albedo values assigned to each, based on data reported in the literature  (Oke, 1982; Cox et al.
Code availability. ADMS-Urban is a commercial product; therefore the underlying software code is not made publicly available. Further information on the ADMS-Urban model development and license availability may be obtained by contacting Cambridge Environmental Research Consultants (CERC). Data availability. All modelled output data presented here may be accessed by contacting the authors. Measurement data are available via the Centre for Environmental Data Analysis (CEDA) depository at https: //catalogue. ceda. ac. uk/uuid/7ed9d8a288814b8b85433b0d3fec0300 by contacting our APHH-China partners.
et al. [14]  investigate carrier transport through AlN from a different doping nature of Si(111) substrates. In this work, we elaborate on a normal ON AIGaN/GaN metal-oxidesemiconductor high-electron-mobility transistor (MOS-HEMT) device on-Si for switching electronics and we investigate, using ATLAS-SILVACO software, simulations of the premature breakdown in the device. We show that eliminating leakage at AlN/Si interface makes it possible to increase the breakdown voltages of the fabricated transistors based on a GaN-on-Silicon substrate. Our approach is to use a SiNx layer for cost effective leakage reduction.
Li et al. [14]  investigate carrier transport through AlN from a different doping nature of Si(111) substrates. In this work, we elaborate on a normal ON AIGaN/GaN metal-oxide-semiconductor high-electron-mobility transistor (MOS-HEMT) device on-Si for switching electronics and we investigate, using ATLAS-SILVACO software, simulations of the premature breakdown in the device. We show that eliminating leakage at AlN/Si interface makes it possible to increase the breakdown voltages of the fabricated transistors based on a GaN-on-Silicon substrate. Our approach is to use a SiNx layer for cost effective leakage reduction.
Current-Voltage Characteristics Normal ON AIGaN/GaN MOS-HEMT devices were realized on commercial HEMT substrate. The composition of the transistor includes: a highly resistive Si substrate, an AlGaN buffer with a back barrier grown on 40 nm of an AlN nucleation layer, a 150 nm GaN non-intentionally doped channel, a 4 nm Al0. 45Ga0. 55N barrier and 5 nm SiO2 gate oxide. The transistors have the following dimensions: a gate length of 1. 5 µm, 1µm gamma field plate, a source-drain distance (LGS) of 2 µm, and 15 µm gate-drain (LGD) distance. The epitaxial structure used for this study is illustrated in Figure  1. The device was simulated by using ATLAS-SILVACO software. Poisson's and continuity equations, including ionized deep-level terms for electrons and holes and a driftdiffusion model, are used to solve transport equations.
Acknowledgments: Authors would like to acknowledge  Ahmed Chakroun  and all LN2 staff for their contributions in microfabrication process. LN2 is an  International Research Laboratory (IRL)  funded and co-operated by  Université de Sherbrooke (UdeS) ,  Centre National de la Recherche Scientifique (CNRS) ,  Ecole Centrale Lyon (ECL) ,  Institut National des Sciences Appliquées de Lyon (INSA Lyon) , and  Université Grenoble Alpes (UGA). It is also financially supported by the  Fond de Recherche du Québec Nature et Technologies FRQNT.
g. , between older and new satellites). In this study, we will consider two different reanalyses: ERA-Interim  [15]  and ERA5  [18]. Details on the reanalyses are given in the subsection below. The P s and T m data are computed from 6-hourly pressure level data, as described in Reference  [14]  and the subsequent IWV estimates are aggregated into daily estimates, as described in Reference  [17]. The interest of comparing two different reanalysis is that ERA5 is of superior quality due to the assimilation of more observations and of higher spatial resolution, i. e. , providing more accurate pressure and temperature estimates in regions of steep topography. The accuracy of reanalysis estimates of P s and T m , as compared to other data sources, is further discussed in Bock  [27]  and Bock et al.
[28]. The main factors conditioning the accuracy of the ZTD estimates at the GNSS data processing level are, by decreasing order, the hydrostatic and wet mapping functions, the a priori ZHD correction data, and the antenna phase center variation (PCV) models. Any bias in these data and models would map directly into the ZWD estimates. The mapping functions are the most important because they determine how the signals from the satellites at various elevations are mapped into the zenith direction (from a mathematical point of view, they represent the partial derivatives, or regressors, of the ZHD and ZWD parameters). Because the hydrostatic delay is corrected a priori, any bias in the a priori ZHD, or in the hydrostatic mapping function, will also map into the estimated ZWD  [29]. Receiver antenna phase center variations also highly depend on the elevation angle and, to a lesser extent, on the azimuth angle.
atx, at the times when the IGS and CODE data sets used in this study were produced. Absolute calibrations from "Robot" and "Chamber" are the most accurate, while relative calibrations (type "field") and relative converted to absolute (type "converted") are the less accurate  [31]. The impact of satellite and receiver antenna offsets (PCO) and PCVs on geodetic parameters have been mainly investigated for positioning purposes. The impact on ZTD estimates has not been much studied yet, although it is known that it would be tightly correlated with the vertical position component. One of the goals of this paper is, thus, to examine how the change from igs05. atx to igs08. atx impacts the accuracy and homogeneity of the GNSS IWV series and to which extent these differences are detected by our segmentation method.
In addition to the aforementioned factors, there are other errors sources that can impact the accuracy of the ZTD estimates, such as multipath and ambiguity fixing errors, as well as satellite orbits and clock errors, and unmodeled and mis-modeled station displacements at sub-daily time scales (e. g. , tides). However, these are minor errors sources for the purpose of our study here. For further details, see the study of Ning et al. [32]. Of main concern here are the sources of bias and the mechanisms through which these biases can change with time, i. e. , translate into inhomogeneities. In this study, we consider two different GNSS data sets, which are representative of two different generations of reprocessing products delivered by IGS (see Table  1 ). The first one, referred to as IGS repro1, was produced by JPL/NASA in 2010-2011 in precise point positioning (PPP) mode with GIPSY OASIS II software  [33]  as a special release of ZTD estimates.