dataesr/scientific-paragraphs-categorization huggingface dataset:
This issue has been already described in previous studies  [3, 4]. The main limitation of the ESPRIT study is that information on the patient health statuses remains limited. However, the literature on PSIs in primary care has already shown that factors such as an older patient age and an increased number of comorbidities have been associated with an increased risk for adverse events. Regarding our work, the coding of the PSIs was performed by only 2 investigators. However, they have been previously trained to use the CADYA grid, and they regularly compared their coding results. The lack of information in some clinical descriptions, as well as the absence of time constraints to code the PSIs, may have also constituted a subjective bias in coding. For that reason, an expert validated the consistency of all the codes.
When applied to 279 individuals sequenced as part of the Simons Genome Diversity Project, the pipeline retrieves most of the dimorphic variants previously reported for the HERV-K(HML2) subfamily as well as dozens of additional candidates, including members of the HERV-H and HERV-W families. We experimentally validate several of these candidates, including the first reported instance of an unfixed HERV-W provirus. These data indicate that human proviral content exhibit more extensive interindividual variation than previously recognized. These findings have important implications for our understanding of the contribution of HERVs to human physiology and disease.
Strategy for identification of proviral allele when the reference allele is a solo LTR We developed a pipeline called findprovirus to mine whole genome resequencing data to detect a proviral allele of a locus annotated as a solo LTR in the reference genome (Figure  1D ). The prediction is that a fraction of the read mates to the reads mapping to the annotated solo LTR should be derived from internal sequences of the provirus allele. When mapped to the reference genome, these events should be identified as discordant read mates mapping elsewhere in the reference genome as they may frequently map to the internal region of non-allelic proviral copies. The pipeline extracts reads mapped to the solo LTR and mates of discordant reads to conduct homology-based searches using the discordant read mates as queries against the consensus sequence of the internal region of the respective provirus as defined in the Repbase database  (56)  (see also Methods).
Known and new dimorphic HERVs predicted through the findprovirus pipeline The findprovirus pipeline was used to identity dimorphic candidates for HERV-K(HML2), (hereafter simply noted as HERV-K), HERV-H, and HERV-W families in a dataset consisting of whole genome sequence data for 279 individuals from the SGDP  (55). Solo LTRs annotated in the hg38 reference genome for HERV-K (LTR5_Hs) (n= 553), HERV-H (LTR7) (n= 689) and HERV-W (LTR17) (n= 476) were used as initial queries (see Methods). The pipeline reports the following results: (i) number of discordant reads mapping to the region; (ii) number of informative discordant reads (i. e. their mates have a significant hit with the respective HERV coding sequence); (iii) percentage of reference solo LTR allele aligned to de novo assembled contigs from the reads; (iv) ratio of average read depth of the element to the average read depth at all solo LTRs of that individual; (v) average mappability of regions where informative discordant reads are mapped; and (vi) prediction on the presence or absence of the provirus allele.
The candidates are then visually inspected using Integrative Genomics Viewer (IGV) for the presence of nested polymorphic transposable element (TE) insertion or presence of internal region of same HERV nearby that could result in false positives. After in silico inspection, we identify three strong candidate loci for HERV-K, two for HERV-H, and one for HERV-W (see    S1 ). Two of the three HERV-K candidates have been previously identified and experimentally validated as dimorphic in prior studies  (27, 41, 43)  (Table  1 ). For these two loci, we also identified genomic sequences of the corresponding proviral alleles from the Nucleotide collection (nr/nt) database at National Centre for Biotechnology Information (NCBI) through homology-based searches (see methods) (Table  S1 ). The novel dimorphic candidate that we identified for HERV-K (5q11.
The HERV-W candidate, 18q21. 1_W2 displayed up to 33 discordant mates aligned to HERV-W internal sequence in a given individual (Figure  S2 ). The findprovirus pipeline predicted that 194 of 279 individuals had at least one proviral allele of 18q21. 1_W2, suggesting that this is a common allele in the human population (Table  S1 ). To experimentally validate these three candidates (Table  1 ), we used Polymerase Chain Reaction (PCR) to genotype a panel of individuals from the SGDP predicted to include a mixture of genotypes. Primers were designed in the flanking regions and used as a pair to detect the solo LTR allele or in combination with an internal primer (located in gag and/or env region) to detect the proviral allele (see Methods). The PCR products were analyzed by gel electrophoresis and their identity was confirmed by Sanger sequencing (Supplementary Data 1).
The results validated that each of the three loci exist as proviral and solo LTR alleles in the human population (Figure  2 A-C )(Table  1 ). In addition, we also identified seven FOSMID clones in the nr/nt database at NCBI supporting the presence of proviral alleles (Table  S1 ). Altogether these data strongly support the dimorphic HERV-H and HERV-W calls made through our findprovirus pipeline.
After in silico inspection, we retained 12 HERV-K candidates, 67 HERV-H candidates, and no HERV-W candidate (Table  1 , Table  S2 ). In the case of HERV-K, eight of the 12 candidate loci were previously reported to be dimorphic, and some were known to be also insertionally polymorphic, i. e. a pre-integration 'empty' allele has also been reported  (25, 27, 35, 40, 43)  (see Table  1 , Table  S2 ). The pipeline predicts four novel HERV-K loci to be dimorphic in the population. For HERV-H, we observe that many of the predicted solo LTR allele occurs at low frequency in the SGDP dataset, being predicted in only a few individuals (Table  1 , Table  S2 ). This might be expected if these alleles arose from relatively recent recombination events. Alternatively, they may represent false positives.
To corroborate the findsoloLTR results, we interrogated the Database of Genomic Variants (DGV)  (59)  to assess whether any of the candidate dimorphic HERV-K or HERV-H loci had been previously predicted as copy number variants in the human population. The DGV systematically catalogs structural variants in human genomes reported in prior studies, but importantly it does not yet include data collected from the SDGP (55), thereby potentially serving as independent validation of our predictions from that dataset. We found that two of the four HERV-K candidates and more than half (35 out of 67) of the HERV-H candidates were catalogued in DGV as putative deletion variants (Table  1 , Table  S2 ). One of the HERV-K-associated deletions and twenty of the 35 HERV-H-associated deletions were inferred to have breakpoints mapping within the proviral LTRs, consistent with the idea that LTR recombination events caused these deletions (Table  1 ).
While the SDGP represents a fairly diverse sampling of the human population compared to those previously surveyed for HERV polymorphisms such as the 1000 Genome Project, it still remains minuscule. As sequencing efforts continue to intensify worldwide, our pipeline brings a valuable addition to the toolbox for cataloguing structural variants. We were intrigued to discover a dimorphic element for the HERV-W family (18q21. 1_W2). This element is represented as a solo LTR in the reference genome, but our data clearly show that it also occurs as a provirus segregating in South Asian populations (Figure  2A ) and likely in other diverse populations (our pipeline predicted a provirus allele in 194 out of 279 individuals surveyed, Table  1 , Table  S1 ). To the best of our knowledge, this is the first HERV-W locus reported to show any type of dimorphism.
Classification of proviruses and solo LTRs in the reference genome The repeats annotated as LTR5-Hs and HERV-K-int (HERV-K(HML2 family)), as LTR17 and HERV17-int (HERV-W family) and as LTR7 and HERV-H-int (HERV-H family) are extracted from the RepeatMasker annotation of the human reference (GRCh38/hg38) assembly (RepeatMasker open-4. 0. 5 -Repeat Library 20140131 available at http://www. repeatmasker. org/). The extracted RepeatMasker data is parsed to identify potentially full-length proviruses and solo LTRs using the tool "One Code to Find Them All" (66). Using a custom script, (https://github. com/jainy/dimorphicERV) each copy in the parsed output is further classified as a provirus containing (i) 2 LTRs and internal region (ii) 1 LTR and internal region (iii) only internal region or as a solo LTR.
The coordinates at the boundaries of each copy is then extracted from the parsed output. Each HERV locus is then given a unique identifier depending on the cytoband it belonged to and based on the total number of copies of that family found in each band. The positions of cytoband for GRCh38/hg38 is downloaded (http://hgdownload. cse. ucsc. edu/goldenpath/hg38/database/cytoBand. txt. gz). The coordinates of HERV copies marked as proviruses with 2LTRs and internal regions and as solo LTRs are used in the subsequent analysis. For HERV-W, the copies that are generated by retrotransposition mediated by LINE-1 machinery have partial LTRs  (96)  and such copies annotated as pseudogenes  (78)  were excluded from our analysis.
Identification of provirus allele when the reference allele is a solo LTR The findprovirus pipeline identifies solo LTR to provirus variants in the Binary Alignment/Map (bam) format files where paired end reads from whole genome resequencing data are mapped to reference assembly using Burrows-Wheeler Aligner (BWA) (97) (Figure  1D , Figure  S1 ) (https://github. com/jainy/dimorphicERV). The pipeline analyses the coordinates of all solo LTRs obtained from One Code to Find Them All (see methods). The findprovirus pipeline extracts reads mapped to each solo LTR and to a flanking 100-bp region using samtools (version 1. 4. 1) (98). Only reads that are mapped with a mapping quality of 30 or greater (i. e. mapped with >99. 99% probability) are collected and the reads are processed to fasta format using SeqKit  (99).
The discordant reads in the solo LTR and in the flanking 100-bp region are identified using samtools  (98)  and the mates of discordant reads are extracted using picard tools (version 2. 9. 2) (http://broadinstitute. github. io/picard/). Sequence homology of mates of discordant reads to the consensus coding sequence of the respective HERV extracted from the Repbase database (  56 ) is tested using BLASTn (version 2. 6. 0, default parameters) and the number of reads with significant hits (e-value < 0. 0001) are counted. Discordant read mates with significant sequence similarity to an internal HERV sequence suggest the presence of a proviral allele in that individual. Two independent, additional approaches are also used to assess the presence of a proviral allele. The first approach measures the average read depth at the solo LTR using samtools and reads with a mapping quality of 20 or more (mapped with > 99% probability) and reads with a base quality of 20 or more (base call accuracy of > 99%) are counted.
Identification of solo LTR allele when the reference allele is a provirus The findsoloLTR pipeline identifies the provirus to solo LTR variants in bam files (Figure  1E , Figure  S3 ) (https://github. com/jainy/dimorphicERV). It first calculates the read depth across the provirus using samtools  (98). Read depth is calculated for reads with a mapping quality of 30 or more and with a base quality score of 20 or more. Similarly, read depth is calculated across 5' and 3' flanking 250-bp regions. The pipeline then assess the percentage of average read depth across the provirus to the average of read depths across the flanks. Presence of two proviral alleles is inferred when the read depth percentage greater than or equal to 50% and read depth percentage lower than 50% is used to infer the presence of solo LTR allele (Figure  1E ).
The CNVs identified in DGV are visually inspected for the concordance of their breakpoints with the two LTRs, which is suggestive of their origin through a LTR mediated recombination. The CNVs having one or both breakpoints lie outside the LTRs are also identified. The candidates along with 100-bp flanking sequence are also queried against nr/nt database at NCBI to identify the presence of any BAC/FOSMID clones containing corresponding the solo LTR or provirus variant. Experimental validationAfter in silico validation, PCR primers are designed in the regions flanking the LTR and in the gag and/or env regions assembled from the mates of the discordant reads for selected candidates. The solo LTR allele is amplified by primer pairs flanking the solo LTR and the proviral allele is amplified with the internal primer located on the env region or gag region.
Acknowledgments This work was supported by a research agreement between  GeNeuro Innovation SAS  and the  University of Utah  as well as funds from the  National Institutes of Health  to C. F. W e would like to thank  W. Scott Watkins  for providing the 279 SGDP bam files for the analysis and Lynn B. Jorde for providing the DNA samples as well as equipment and infrastructure for performing this study. We also would like to thank  Aurelie Kapusta  and  Clement Goubert  for many helpful discussions.
However, because of the complex nature of human behaviour inside buildings, these methods do not perform well for occupied buildings  [12]. Physical models can be computationally intensive and therefore an exhaustive exploration of parameter space for optimal control strategies could be infeasible. Because of these factors, a physical model of a building/energy system is mostly avoided, and a simpler, efficient and accurate data driven model is created. Data-driven techniques do not require detailed information about building characteristics or heating, ventilation and air conditioning (HVAC) systems. However, the computational cost of learning and hyper-parameters could be high for data-driven models. The prediction accuracy is also influenced by the quantity and quality of the available training dataset.
Mohandes et al. [33]  predicted wind speed and Esen et al. [34]  modeled a ground-coupled heat pump system by using SVM. Support vector machines have recently attracted researchers' focus in the field of building energy prediction. To the authors' knowledge, the first work was reported by  Dong et al. [35]. The authors applied SVM to predict monthly building energy consumption in a tropical region (Singapore). Outside dry-bulb temperature, solar radiation, and relative humidity were considered as input parameters. The weather data were collected from a weather station which was approximately 12 miles away from the buildings under investigation. Although the percent error was small, the inaccuracies in the results due to weather data were not discussed in the paper. Li et al. [36]  and Li et al.
Decision trees (DT) are used to classify a dataset into various predefined target values or classes. A DT based model can be represented by logical statements/rules. Decision tree, tree-based ensemble algorithms, in particular, are less popular in building energy research domains. Decision tree based methods were used by Yu et al. [39]  to predict energy consumption. The authors concluded that decision tree based algorithms could be used to develop reliable models. A decision support model to reduce a school building's electricity was developed by Hong et al. [40]. The authors used decision trees to form a group of educational buildings based on electricity consumption. Hong et al. [41]  used decision trees for clustering a type of multifamily housing complex based on gas consumption. Tso and Yau  [42]  compared regression analysis, DT and artificial neural networks (ANN) for predicting electricity consumption.
Materials and Methods This section introduces the three proposed data-driven techniques for predicting HVAC energy consumption. The section also details training and testing datasets along with the evaluation metrics used for comparing the studied techniques. In this paper, SVR is used for comparison purposes, as it is one of the most widely machine learning techniques in a built environment research domain. Figure  1  illustrates the schematic overview of the proposed research. Historical weather, energy consumption and occupancy data were retrieved from a database for developing prediction models. The data was pre-processed for model development by removing outliers and treating missing values. The data was also normalised for SVR algorithms. Important features were selected by using random forest and extra trees algorithms.
Extremely Randomized Trees Extremely randomized trees or Extra-Trees (ET)  [48]  introduce stochasticity during the induction production of classical decision trees  [49]. ET was developed as an extension of another tree-based ensemble method (random forest) to be a more computationally efficient algorithm. From several experiments, it was found that Extra-Trees (ET) outperform other tree-based methods, including random forests (RF)  [50]. The key difference between RF and ET are highlighted in  [51] : (1) ET uses the entire data set for training a model (i. e. , it does use tree bagging), whereas a random forest algorithm uses a bootstrap replica for training a machine learning model, and (2) ET randomly picks the best feature along with the corresponding value to split the node. Due to these main differences, ET is less likely to overfit a dataset and has reported better performance  [48].
Data Description The historical dataset of HVAC energy consumption was gathered from a hotel building in Madrid, Spain. Social parameters (e. g. , number of rooms booked, the total number of guests on a particular day) were also retrieved from the hotel's reservation system. Weather parameters were also collected from a nearby weather station. As hotels and restaurants do not exhibit clear energy consumption patterns as compared to other building types e. g. , school buildings, which makes prediction a challenging task. Figure  2  shows electricity consumption of a school in Wales, UK and a hotel building in Madrid, Spain. It can be seen that there is a clear energy consumption pattern for the school building as the energy consumption is lower during the night and weekends. On the other hand, this is not the case for the hotel building.
For this study, one and a half year data was collected from the hotel's building management system (BMS) with a collection interval of 5 min. The collected dataset contained air temperature, relative humidity, wind speed, dew point air temperature, hour of the day, day of the week, month of the year, total rooms booked, total number of guests and value of HVAC energy consumption. Table  1  summarizes the variables used in the development of prediction models. Figure  3  shows the hourly HVAC consumption values of the studied building from 15 January 2015 to 15 January 2016. Training data is taken as 80% of the dataset such that the first 7680 samples were used in training and validation phases and the remaining 3290 samples composed the test dataset. For each algorithm training, the dataset was shuffled with identical random seeds to ensure that the exact same training set was used.
Model Evaluation Metrics To evaluate the predictive performance of the developed models, four different metrics, i. e. , root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R 2 ) and normalised root mean squared error (NRMSE) were used. Determination coefficient is used to measure the correlation between the actual and predicted HVAC energy consumption values. The remaining three metrics are described as below: RMSE = N ∑ i=1 (y i -ŷi ) 2 N (13) MAE = 1 N N ∑ i=1 |ŷ i -y i | (14) NRMSE = RMSE y maxy min  (15)  where ŷi is the predicted value, y i is the actual value, N is the total number of samples, and y max and y min are the maximum and minimum values of actual HVAC energy consumption, respectively. For this work, the implementation of extra trees and support vector regression included in the scikit-learn (a machine learning library for Python programming language)  [52].
DHN was implemented in Python programming language. All developed models are encapsulated in a Python library to allow online data acquisition and predictions updates. It is worth mentioning that the model presented by Dieleman  [53]  was adapted for this paper. The models were trained and tested on personal computers (Intel Core i7 3. 20 GHz with 32 GB of installed memory (for SVR and ET) and Intel Xeon 32 CPU 1. 30 GHz with 64 GB of installed memory (for DHN models)).
, x n ], n being the number of decision variables; and f (x) is the objective function. In this case, eight decision variables were used i. e. , number of neurons in each hidden layer, number of hidden layers, number of training epochs, learning rate, momentum, output layer activation function, bias value of gate layers neurons, and model inputs. Among the input variables of the dataset, preliminary studies showed that the most impacting variables were: outside temperature, relative humidity, number of guests and HVAC consumption values. The model inputs' decision variable states how many preceding values of each of these four input variables are used as input to the DHN model. Equation (  16 ) is subject to ranges of values for each decision variable listed in Table  2. The objective function of the problem is to minimize the normalized root mean squared error (NRMSE) on a testing dataset.
SVR Hyper-Parametric Tuning For support vector regression, penalty parameter (C) and radius (ǫ) are two important tunable hyper-parameters to achieve better predictive performance. A small value of C results in a small weight on the training dataset, which could result in larger prediction errors on the testing dataset. This means that the trained model will under-fit the training data  [36]. However, a larger value of C would result in over-fitting the training dataset. Larger values of penalty parameter (C) will also reset the objective back to minimising the empirical risk only. On the contrary, larger values of C means a larger range of the value of support vectors, which means more data points can be selected  [35]. ǫ is indirectly related to the number of support vectors, and a larger value of ǫ can result into fewer number of support vectors machines.
In the next step, the first result of C was fixed to find ǫ. It is worth mentioning that stepwise search may result into sub-optimal hyper-parameters as it is assumed that all hyper-parameters are independent from each other. As a first step, the value of ǫ was fixed to 0. 1 and varied C over the range between 2 -7 and 2 7 to train an SVR model over the training dataset. The resulting models were then used to predict on a testing dataset to calculate performance metrics. From results, it was found that a model's performance increases with an increase of C. Initially, the performance of the SVR model increased with an increase of C. However, with higher values of C, the performance of the SVR model was slightly increased. The performance started to decline for values of C higher than 2 6. The higher values of C were also over-fitting the training dataset.