dataesr/scientific-paragraphs-categorization huggingface dataset:
The equations in this problem are identical to those defining measurements, such as eqs 1-4. The numerical solution minimizes the cost function T(x) defined as the sum of squared residuals. Each residual 𝑢 𝑖 (𝑥) is calculated as the difference between experimental measurement i (𝑦 𝑒𝑥𝑝 𝑖 ) and its value calculated from estimated isotopomers Obvious constraints of necessarily non-negative values that add up to 1 are applied to the solution. This NLS problem with inequality and equality constraint is solved using the NLSIC algorithm  23. A chi-square test is performed to determine if the fit is satisfactory (based on a 95 % confidence threshold). Discrepancies indicate inconsistencies between the different datasets. Finally, IsoSolve estimates the precision of the abundance of each isotopomer, cumomer and EMU by propagating measurement uncertainties.
Overall, this framework provides a clear audit of the isotopic information that can actually be measured on proteinogenic amino acids. Data integration and consolidation. To quantitatively evaluate the proposed integrative framework, we produced a reference sample of 13 C-labeled proteinogenic amino acids with controlled and predictable labeling patterns in which all isotopic species are present in equal amounts  9. This sample was analyzed by NMR (ZQF-TOCSY, HSQC, HNCA and HACO-DIPSY experiments) and MS (GC-MS and LC-MS), yielding eight independent datasets (D1-D8 in Figure  2 ) containing a total of 21 isotopic measurements for alanine (Supporting information S1). For all combinations of datasets identified as providing complete isotopic coverage of alanine (Figure  3 ), we used IsoSolve to determine the abundance of each isotopomer, cumomer and EMU.
CONCLUSION The complementarity of different (MS and/or NMR) approaches dedicated to isotopic analyses is often highlighted, but the lack of a generic integrative framework able to deal with heterogeneous, partial isotopic measurements has meant that this has never been evaluated in detail. The proposed framework fills this conceptual gap by allowing any type of isotopic measurements (MS, MS/MS, 1 H-NMR, 13 C-NMR,  15  N-NMR, etc) to be included. The framework is agnostic to the analytical platform, the labeled element, the tracer isotope, and the molecule. It can also be applied to double-labeling approaches (e. g. 13  C and 15 N). This framework has been implemented as an open source Python program, IsoSolve, which is available as a command-line interface and as a Python library to streamline its integration into existing data analysis pipelines.
This framework may thus support structural investigations (e. g. metabolite identification, spectra annotation, validation of MS/MS fragmentation patterns, development of stand-ardized databases for deposition of isotopic datasets based on isotopically-resolved InChIs) as well as functional investigations of metabolic systems (e. g. experimental design, data consolidation, conversion between isotopic representations in 13 C-fluxomics workflows). It should also make isotope labeling experiments more accessible to the wider biological community. Beyond isotopic studies, this framework may prove equally valuable in other fields dealing with the analysis of combinatorial states of biological entities. This is the case in proteomics for instance, for the analysis of post-translational modifications (e.
ACKNOWLEDGMENT The authors thank  MetaboHub-MetaToul  (Metabolomics &  Fluxomics facilities, Toulouse , France, http://www. metatoul. fr), which is part of the  French National Infrastructure  for Metabolomics and Fluxomics (www. metabohub. fr), funded by the  ANR  ( Metab-oHUB-ANR-11-INBS-0010 ), for access to NMR and MS facilities. JCP is grateful for funding from  INSERM  for his temporary fulltime researcher position.
Such an approach has been considered in the context of shape optimization in mechanics in  [40] , and has to the best of our knowledge, only been considered very briefly in the context of shape optimization with IGA in  [13]. It remains a question whether this approach performs comparably to state of the art methods, and the aim of the work is to investigate exactly this question. Different frameworks for shape optimization with IGA exist in the literature. For example in  [29]  a tetrahedral mesh is used to represent the interior of the computational domain, while still representing the boundary using splines. The authors rely on external mesh generator software, while we in this work aim to avoid mesh generation by instead maintaining a parametrization of the interior. In  [15]  the shape is represented implicitly as the level set of a function.
The PDE is then posed on a design domain, which remains constant during the optimization, and the level set function enters the formulation by the ersatz material approach. This framework has the advantage that it allows for changes in the topology of the shape, however at the same time the final shape is represented as a level set and therefore post processing is required to represent this shape using splines, which is necessary for importing the shape into CAD software. In our work the optimization is performed directly on the spline representation and thus the result can be readily imported into CAD software after the optimization. Another possible approach is to use the isogeometric representation of geometry combined with the boundary element method (IGABEM)  [28, 6]. In IGABEM the PDE is reduced to an integral formulation on the boundary of the domain, and therefore maintaining a parametrization of the interior of the domain is avoided altogether.
The code used for this work can be found at https://github. com/gismo/shapeopt. The paper is organized as follows. In section 2 we outline the relevant notation, and in sections 3 and 4 we describe the two methods we are going to compare. In section 5 we apply these methods to the aforementioned shape optimization problem, and discuss and compare the performance of the two approaches. We end the paper with some discussion and conclusions. Some of the more technical details are presented in the appendices.
Preliminaries and notation Let us consider the following PDE-constrained shape optimization problem: max Ω∈O ad E(Ω, u), (1a) s. t. a Ω (u, v) = Ω (v) for all v ∈ V, (1b) where O ad is a set of admissible shapes, E is the objective and (1b) is the governing PDE in the weak form. Within the IGA framework both Ω and u will be approximated numerically using splines. Namely, we have Ω = G(]0, 1[ d ), with the parametrization G(ξ) = N g i=1 c i R g i (ξ), (2) where c i ∈ R d are the control points, N g is the number of control points, and R g i are the basis (B) splines. In this work, unless specifically stated otherwise, we will use tensor product B-splines. The superscript g indicates that the B-splines R g i (that is, their degrees and knotvectors) are specific to the geometry representation.
This problem can be intensified by using small datasets with different random splits for training and evaluation. Such an approach can lead the optimization algorithm into different local minima and makes it almost impossible for the model to generalize  [2]. It is, therefore, difficult to compare different deep-learning model architectures with each other even when using the same data  [23]. Another challenge is especially prominent in the GNN domain, where the optimal features for node or edge representation are unknown. Deep-learning benchmark studies often use the same data but different representations for their input data which makes it difficult to make a fair comparison between the models  [2, 3]. To mitigate these problems, we use the exact same data split to train, evaluate, and test each of the used models with different node and edge features, as well as learning strategies to obtain an average performance independent of the used features and training approaches.
Experimental Data A total of 10,617 molecules annotated with experimentally derived logD and logP values or logS and logP values were used for model training and predictions. The selected molecules were derived from the Delaney lipophilicity dataset containing experimentally evaluated logD and logP values at pH 7. 4  [26]  and an aqueous solubility set with logS and logP values  [27]. Each dataset was evaluated and molecules were neutralized in both sets. For the aqueous solubility data, salts were stripped off and molecules with logS values lower than -10. 0 or higher than 0. 0 were removed. The original preprocessed and post-processed data can be found in the GitHub repository  [28]. The splitting of each dataset into three subsets for training, evaluation, and testing was completed randomly in a ratio of 81:9:10 for the (training, evaluation, and testing).
The data splitting was performed with the same seed for each of the models to be able to compare them using the exact same training, evaluation, and test data. The minimum value of each of the logD, logP, and logS properties was used as an offset to ensure only positive property values. The resulting lipophilicity dataset consisted of 4174 compounds. In total, 3380 were used for training, 376 for evaluating and model selection, and 418 for testing. The post processed solubility dataset contained 6443 molecules. Overall, 5219 compounds were allocated for training, 579 for evaluation, and model selection, and 645 for testing.
Training Approaches The training strategies differ in the used dataset and the training target (logD, logP, or logS). Under these constraints, seven different types of strategies were used. The first multi-task learning strategy used a combined approach of logD, logP, and logS values referred to as "logD/P/S". Three additional multi-task strategies utilized a combination of two physicochemical properties and are notated as either "logD/P", "logD/S", or "logS/P". Three other single task strategies are only learned on a single physicochemical property and are referred to as either "single task logD", "logP", or "logS". When physicochemical properties from different datasets were used, the individual datasets were first split into training, evaluation, and test sets. Afterwards, each physicochemical property was evaluated and tested individually so that the evaluation and test results of the multi-task learning approaches can be compared to those with a single-task learning strategy.
When testing either single-, or multi-task models, the combined root mean squared error (RMSE) for all properties was calculated as the measure for the best model. For logP, we only used the results from either the first multi-task approach ("multi-task logS/D/P") or the single-task approach with logP values. The reasoning behind this was to use the same test and evaluation data for all models while trying to avoid an unbalanced data bias in favor of logP values. When training with two physicochemical properties where one was logP, we only used the data that had both properties. For example, when training on the lipophilicity dataset which had logP and logD values, we did not include logP compounds from the aqueous solubility dataset and vice versa.
For training, evaluation, and testing we split each of the datasets as described in Section Experimental Data. Each of the GNNs were trained for 1600 epochs and the model with the best performance was identified using RMSE as the evaluation metric on the validation set. To evaluate the model type performance, we used the model with the best RMSE of the two runs performed for each model setting. When evaluating the average model type performance, the average RMSE of the different model settings was used for the calculation. To evaluate models with several properties, we summed all RMSEs. For example, when using logD and logP for training, we summed the RMSE of the logD and logP prediction on the evaluation set to receive a combined RMSE. When the combined RMSE was below the last best combined RMSE, the model weights were saved.
We used these models to test the model on the test set. Each model was run two times and the results with the best test set performance were taken. Additionally, the 95% confidence interval range was calculated by applying bootstrapping 100 times while leaving out 10% of the test dataset. To generate consensus models between GNN and non-GNN models, we combined the best GNN model for each physicochemical property with the best non-GNN model. We did this by adding the predicted log values of one model with the other and then divided it by two. These hybrid models are then called according to their GNN model type plus consensus (e. g. , D-GIN cons. ).
Other Machine Learning Approaches We used the random forest (RF), support vector machine (SVM), and k-nearest neighbor (K-NN) implementations of scikit-learn (Version 0. 23. 2  [32] ). Default hyperparameters were used. The featurization is described in Table  A7. When using descriptors as input, we standardized them with the scikit-learn StandardScaler. For the fingerprints and descriptors we used version 2020. 09. 2 of the RDKit  [29]  python package. Each of the models were trained in a single-task manner for each of the property values.
154 (but it also had a single run with a RMSE value of 0. 729 making it the model type with the best single run performance and highlighting the importance of multiple repetitions for reporting model type performances). The consensus D-MPNN also outperformed the D-GIN. The consensus D-GIN (average RMSE value of 0. 455 ± 0. 028) and consensus D-MPNN (average RMSE value of 0. 475 ± 0. 027) showed the best average performance for logP prediction (Table  2 , and Figure  2 ). The RF and SVM model types also performed with low minimum RMSE values of 0. 470 and 0. 493, respectively. However, their average RMSE values (RF: 0. 681 ± 0. 224 and SVM: 0. 693 ± 0. 134) were higher than the D-GIN and D-MPNN model types. Consensus models are often used in deep learning applications typically combining either different models that were trained on slightly different training data or multiple model architectures with different strengths and weaknesses.
Impact of Training Strategies The impact of different training strategies are shown in Table  3. The lowest mean logD RMSE can be obtained by a multi-task strategy that involves learning on both logD and logP values. This is similar to the best training strategy for the logS property, which is a multi-task approach including logS and logP properties. As for the logP property, the best approach is a single-task strategy including logP values, however the multi-task approach which combines all physicochemical properties achieves similarly good performance. When analyzing the logD/S/P RMSE predictions with respect to training strategy and model type, Table  5  and Figures  7 8 9 show that there is no particularly favorable learning strategy for any of the model types. The datasets used in this study are specific for one particular physicochemical property.
Data sources: We developed and published a protocol, applying the Navigation Guide as an organizing systematic review framework where feasible. We searched electronic academic databases for potentially relevant records from published and unpublished studies, including the WHO International Clinical Trial Registers Platform, Medline, PubMed, EMBASE, Web of Science, CISDOC and PsycInfo. We also searched grey literature databases, Internet search engines and organizational websites; hand-searched reference lists of previous systematic reviews; and consulted additional experts. Study eligibility and criteria: We included working-age (≥15 years) workers in the formal and informal economy in any WHO and/or ILO Member State but excluded children (aged <15 years) and unpaid domestic workers. We included randomized controlled trials, cohort studies, case-control studies and other non-randomized intervention studies with an estimate of the effect of exposure to long working hours (41-48, 49-54 and ≥55 h/week), compared with exposure to standard working hours (35-40 h/week), on depression (prevalence, incidence and/or mortality).
Electronic academic databases We searched the seven following electronic academic databases: The Ovid MEDLINE search strategy was presented in the protocol  (Rugulies et al. , 2019). The full search strategies for all databases were revised by a research librarian and are presented in Appendix 1 in the Supplementary data. We performed searches in electronic databases operated in the English language using a search strategy in the English language. When we neared completion of the review, we conducted a top-up search of the MEDLINE database on 28 November 2019 to capture the most recent publications (e. g. , publications ahead of print). Deviations from the proposed search strategy and the actual search strategy are documented in Section 8.
Electronic grey literature databases We also searched the two following grey literature databases. • OpenGrey (http://www. opengrey. eu/) (up to 25 July 2018) • Grey Literature Report (http://greylit. org/) (searched on 5 August 2018 but last update of Grey Literature Report database was in January 2017) The full search strategies for the two grey literature databases are presented in Appendix 1 in the Supplementary data.
Hand-searching and expert consultation We hand-searched for potentially eligible studies in: • Reference lists of previous systematic reviews. • Reference lists of all included trials register records. • Study records published over the past 24 months in the three peerreviewed academic journals with the largest number of included studies. • Study records that have cited the included studies (identified in Web of Science citation database). • Collections of the review authors. Additional experts were contacted with a list of included studies, with the request to identify potentially eligible additional studies.
Selected studies Study selection was carried out with the Covidence software (Veritas Health Innovation). All records identified in the search were downloaded, and duplicates were identified and deleted. Afterwards, two review authors independently and in duplicate screened titles and abstracts (step 1) and then full texts (step 2) of potentially relevant records. A third review author resolved any disagreements between the two review authors. If a study record identified in the literature search was authored by a review author assigned to study selection or if an assigned review author was involved in the study, the record was re-assigned to another review author for study selection. We present the study selection in a flow chart, as per PRISMA guidelines  (Liberati et al. , 2009).
Died from depression (depression mortality). We included studies that defined depression in accordance with our standard definition (Table  2 ). Other affective disorders (e. g. , bipolar disorders) were excluded. We did not expect documented ICD-10 diagnostic codes in most studies examining exposure to long working hours and its effect on depression, but expected that depression was assessed with other methods. The following measurements of depression were regarded as eligible, as described in the protocol  (Rugulies et al. , 2019) : i. Psychiatric diagnostic interview. ii. Diagnosis by a physician, psychologist or other qualified health professional. iii. Hospital admission or discharge record. iv. Administrative data (e. g. , disability pensioning with the diagnosis of depression). v. Register data of treatment for depression with one or both of antidepressant medication and psychotherapy (this measurement was only included if there was documentation that the treatment was for depression specifically, and not for other disorders).
Extracted data A standard data extraction form was developed and trialled until data extractors reached convergence and agreement. At least two review authors independently extracted data on study characteristics (including study authors, study year, study country, participants, exposure and outcome), study design (including study type, comparator, epidemiological model(s) used and effect estimate measure) and risk of bias (including source population representation, blinding, exposure assessment, outcome assessment, confounding, incomplete outcome data, selective outcome reporting, conflict of interest and other sources of bias). A third review author resolved conflicts in data extraction. Data were entered into and managed with Excel. We also extracted data on potential conflict of interest in included studies.
, 2011)  and the US Agency for Healthcare Research and Quality  (Viswanathan et al. , 2008). Some further refinements of the Navigation Guide method may be warranted  (Goodman et al. , 2017) , but it has been successfully applied in several completed and ongoing systematic reviews  (Johnson et al. , 2016; Johnson et al. , 2014; Koustas et al. , 2014; Lam et al. , 2016a; Lam et al. , 2014; Lam et al. , 2017; Lam et al. , 2016b; Vesterinen et al. , 2014). In our application of the Navigation Guide method, we drew heavily on one of its latest versions, as presented in the protocol for an ongoing systematic review  (Lam et al. , 2016c). We assessed risk of bias on the individual study level and across the body of evidence for each outcome. The nine risk of bias domains included in the Navigation Guide method for human data were: (i) source population representation; (ii) blinding; (iii) exposure assessment; (iv) outcome assessment; (v) confounding; (vi) incomplete outcome data; (vii) selective outcome reporting; (viii) conflict of interest; and (ix) other sources of bias.
, 2018 -HILDA; Virtanen et al. , 2018 -HRS; Virtanen et al. , 2018 -NLSY; Virtanen et al. , 2018 -PUMA; Virtanen et al. , 2018 -SHARE; Virtanen et al. , 2018 -SLOSH). The remaining eight studies from the  Virtanen et al. (2018)  review were excluded, because their outcome was not depression but distress (n = 7) or they assessed depression with an unvalidated rating scale (n = 1). Further, we included results from an unpublished manuscript  (Zadow et al. , 2019;  note: the article is now accepted  Zadow et al. , 2021)  and from an analysis we (the author group) conducted of the National Longitudinal Survey of Youth (NLSY) open access database  (NLSY OA Cohort, 2019). We excluded 397 study records after full-text screening. For the 40 excluded studies that most closely resembled the included studies, the reasons for their exclusion are listed in Appendix 3.
, 2018  -NLSY from the metaanalyses, because these two studies were based on the same study population as the NLSY open access database cohort that we analysed (NLSY OA Cohort, 2019). We prioritized the open access data analysis over the published analyses by  Dembe and Yao (2016)  and  Virtanen et al. , 2018  -NLSY because in our analyses we could apply all of the standard exposure categories as described in our protocol  (Rugulies et al. , 2019).
Population studied The 22 included studies captured 109,906 workers (51,324 females and 58,582 males). Twenty-one studies included both female and male workers, whereas one study included male workers only  (Kato et al. , 2014). The age range was from 17 to 71 years. When studies reported a mean age, this was most often in the 40s (Table  5  for details). By WHO region, most studies examined populations in the Americas (nine studies of two countries) followed by Europe (eight studies, including seven studies of five countries, plus one study combining data from 28 countries), followed by the Western Pacific (five studies of three countries). The most commonly studied countries were the United States of America (six studies), Canada (three studies), and Australia, Denmark, the Republic of Korea and the United Kingdom of Great Britain and Northern Ireland (two studies each).