dataesr/scientific-paragraphs-categorization huggingface dataset:
Several packages modeling both hardware platforms and software applications are involved in the extension. Corresponding MARTE stereotypes are then sufficiently refined for precise and complete DPR and SCA domainspecific concept modeling. In addition, MARTE stereotypes have been further specialized to bridge the gap between higher-level specifications and lower-level SystemC/TLM implementation giving rise to the MARTE4SCTLM profile while ensuring consistency from design to runtime. Concretely, to provide automation for modeling reconfigurable SoCs and speed up simulation model generation, a completely integrated Eclipse toolchain was built to ensure support for creating and editing models, applying profiles, and executing transformations for code generation. Moreover, to perform the required model transformations, a set of rules expressed in ATL and specified in declarative style was provided.
e final output is produced instantly through an Acceleo template-based code generation. In order to assess the proposed framework, our choice fell on the specification of a reconfigurable active 3-way crossover. Particular emphasis was given to the modeling of FIR filter reconfiguration features through different views and detail levels as well as code generation. Profile extensions supply the standard with a complete set of stereotypes and tagged values, undeniably bringing more expressiveness In future work, we aim to improve our framework by IP-XACT modeling capabilities for component description which is very useful for dynamic service discovery and service reuse modeling. e conflict between predictability and dynamism in service component architecture will be considered to ensure system correctness.
Naturally, having more detailed models at lower levels of abstraction will come at the cost of faster simulation speed and earlier A. 2. Business Logic Code Generation. At elaboration time, modules, ports, primitive channels, and simulation processes are created and connectivity is established. us, SystemC does not support the dynamic creation or modification of the module hierarchy once the elaboration phase has been completed. When called from the callback end_of_elaboration or during simulation, the sc_spawn function allows the creation of dynamic process instances; these can also be dynamically destroyed during simulation. With dynamic process facility, it would no longer be necessary to preallocate a number of statically defined processes to support the maximum number of possible configurations  [44].
Using the target trial method  2, 3  of conducting observational studies that aim to mimic RCTs, we emulated the same target trial in two distinct EHR systems. We reasoned that replicating results across two samples from vastly different settings-one a healthcare system anchored in two large tertiary care hospitals and another a nation-wide primary care network-would provide a more robust estimate of the generalizability of the drug's effect  4, 5. Moreover, differences in medical practice, data collection, timing and length of follow-up, patterns of missingness, and known and unknown sources of bias between two EHR databases bolster any signal observed in both samples. In this study, we emulated a target trial to estimate the effects of metformin compared to sulfonylureas on the risk of death and dementia.
Metformin is a first-line antidiabetic drug with additional properties that may slow biological aging  6, 7  , including some evidence of increased survival  8. Since the risk of dementia rises very steeply with age  9  , it has been hypothesized that metformin would reduce such risk. Clinical studies of metformin, however, have had mixed results in older adults with incident dementia  10, 11. To address the potentially opposing influences of metformin on dementia-that it might reduce the hazard of death and therefore put more people at risk of developing dementia while reducing the hazard of dementia by slowing biological aging, we used a competing risks analysis framework  12. We used the target trial method  2  to emulate a trial in the Research Patient Data Registry (US RPDR  13  ) at Mass General Brigham (formerly Partners) Health Care system in the US and the UK Clinical Practice Research Datalink (UK CPRD  14  ) database among initiators of metformin vs.
Target trial emulation in the EHR from the US RPDR and UK CPRD We emulated the target trial in cohorts from the US RPDR and UK CPRD EHR databases (Table  1 ) with a one-year run-in period. Our target trial outcomes were time to first diagnosis of dementia or death in type II diabetics over age 50, starting on metformin-or sulfonylurea-monotherapy, and followed for at least one year. Of note, the one-year run-in period was selected to ensure sufficient drug exposure before measuring outcomes. While the duration of a clinical trial is usually fixed, the duration of followup in the emulated trial is often much longer (US RPDR median: 5. 0 years (max 12 years); UK CPRD median: 6. 0 years (max 16 years)). The US RPDR cohort, which was drawn from patients receiving primary care at an academic health care system, included 13,191 patients who started on metformin- (11,229; 85%) or sulfonylureamonotherapy (1,962; 15%) (Fig.
These differences between the two cohorts could potentially be explained by different population structures, particularly their baseline age distribution (Extended Data Fig. 1 ). The UK CPRD cohort had a higher death rate than the US RPDR one, affecting the total number of patients at risk over time (Extended Data Fig. 2 ). The comparison of the absolute cumulative hazards of dementia and death in a competing risks approach revealed additional differences between the UK CPRD and US RPRD cohorts, in terms of both the magnitude and trajectory of diagnosed dementia (Extended Data Fig. 3 ). In the US RPDR cohort, the rates of diagnosed dementia in both treatment arms were higher than the death rates, whereas the opposite pattern was observed in the UK CPRD. These underlying differences likely explain the differing risk curves for death and dementia between the two cohorts.
Overall, the benefits of metformin observed here can be interpreted in terms of both delaying dementia onset (Fig. 4 , blue curves) and prolongating life without dementia (Fig. 4 , orange curves). Notably, the risk difference for death was of larger magnitude in the UK CPRD than in the US RPDR cohort (respectively, a 10% vs. 5% reduction in risk after an average of 12 years of follow-up, Figs. 4c-d ). This was a result of a higher overall death rate in the UK CPRD cohort, relative to the rate of dementia onset. Conversely, in the US RPDR cohort, the overall rate of death was lower than the rate of dementia onset (Extended Data Fig. 3 ). To assess the robustness of our results to modeling choices, we conducted a sensitivity analysis by using a nonparametric approach, thereby relaxing the proportional hazards assumption.
62%) and the older stratum had a shorter length of follow-up (median: 4. 1 vs. 5. 6 years;   total: 22,960 vs. 48,231 person-years). Nevertheless, the age-specific finding in the US RPDR cohort suggests that metformin may be especially beneficial for those who initiate treatment at a younger age. The difference in treatment effect between age groups was less clear in the larger UK CPRD cohort, with a HR of 0. 82 (95% CI: [0. 67;0. 99]) in patients aged ≤70, and of 0. 88 (95% CI: [0. 77;0. 99]) in those aged >70 (Fig. 3b ). Similar results were obtained using the risk difference: a stronger effect of metformin on dementia onset was observed in the US RPDR cohort, as compared to the UK CPRD, in patients who initiated treatment before age 70 (Extended Data Fig. 6 ). Difference in post-treatment HbA1C levels was not clinically significant for metformin vs.
sulfonylurea initiators Since baseline HbA1C levels did not modify the effect of metformin, we also explored whether the drug acted primarily by a better control of blood sugar. For this, we applied a repeated measures mixed effects model on all HbA1C values recorded three months after treatment initiation and beyond. In the US RPDR cohort, 10,180 (77%) patients had HbA1C data available: 8,794 (78%) and 1,386 (71%) among metformin and sulfonylurea initiators, respectively. Interestingly, we found that although the average level of HbA1C was lower (p < 0. 00001) in metformin vs. sulfonylurea initiators, the effect size was not clinically significant (95% CI: [-0. 2601;-0. 1511], see Extended Data Table  5 ). This suggests that the putative effect of metformin on dementia risk is likely through mechanisms other than the control of blood sugar.
Pathway analysis revealed that metformin-altered genes were enriched in pathways related to the extracellular matrix, whereas glyburide-altered genes were enriched in pathways related to cholesterol metabolism (Extended Data Table  6 ). Next, we limited the analysis to elements of the human secretome, since they are measurable in the CSF 21 (Fig. 5c , Extended Data Table  7 ). To reflect subacute drug-induced profiles, we considered the 72-hour timeframe. Osteopontin (SPP1) emerged as the secreted protein with the greatest reduction in RNA levels at 72 hours and the greatest change overall. Previous work reported elevated levels of osteopontin in the serum of aging individuals 22 and in the CSF of AD patients, correlating with cognitive decline 22. Moreover, elevated levels of SPP1 in microglia were detected in AD mouse models and human brains  23.
To our knowledge, this work is the first attempt to comprehensively address competing death in a study of metformin and dementia, with a rigorous causal framework harmonized across two EHR databases. The risk difference offers a nuanced view over time that is not captured by the time-invariant HR metric, which was similar across the two cohorts. An additional value of this observational study for planning future clinical trials is that it explores the source of the signal in subpopulations defined by criteria that could readily be implemented as inclusion and exclusion criteria. Our age-stratified analysis indeed demonstrated that type II diabetic patients aged 70 or younger at treatment initiation benefited most from metformin's effect on cognitive health. Further, our analysis of HbA1C over time in the US RPDR showed a clinically negligible benefit among metformin vs.
Lowering SPP1 levels in the CNS may thus be a unique mechanism of neuroprotection by metformin. Clinical investigators may consider adding elevated levels of SPP1 in the CSF as inclusion criteria for a clinical trial of metformin in subjects with preclinical AD biomarkers. There are many strengths in our DRIAD-EHR approach. First, in this observational study, our two samples were followed for up to 16 years. Given that the preclinical stages of dementia can last 20 years 32 , this study is examining a therapeutically relevant timeframe which is not feasible in randomized clinical trials. Second, we harmonized our analyses in two distinct EHR databases. The concordance of the hazard ratio estimates for both the survival and dementia outcomes across these two distinct patient populations indicates robust signals  4, 5.
In particular, the level of education was systematically unavailable in either dataset, of concern since it is known to affect both the exposure and outcomes of interest in this study. Additionally, relevant lifestyle factors like diet and physical activity 33 were unavailable. Furthermore, the strong effect of age, the changes in prescribing patterns of sulfonylureas and metformin over the observation period  34, 35  , and the complex differences observed in age at baseline, length of follow-up, and calendar time across the two treatments raises the possibility of residual confounding. Beyond this, in EHR, data missingness is very often informative and can lead to biases in study results  [36] [37] [38]. Second, the absence of linkage to claims data in the US RPDR cohort prevented us from verifying that patients were truly treatment initiators, or from verifying the length of exposure by confirming that prescriptions were filled and refilled at the expected rate.
In other cases, dementia might be overcoded  43, 44. Hence, in the future, we aim to deploy text mining and natural language processing techniques on clinical notes, radiologic image analysis, and other clinical data to better identify subjects with dementia and more precisely determine the timing of disease onset. Fifth, while our study was conducted in two different populations, both are primarily white and have access to health care. While the UK CPRD population is fairly representative of the UK population, the US RPDR population is limited to a single region, less diverse, and more advantaged than the US population as a whole. Sixth, in our mechanistic studies, we approximated chronic exposure to metformin and glyburide through relatively short durations, in cultured human neural cells that did not include all the cell types in the brain, including microglia.
Besides, metformin might act by chronic changes in signaling networks in unrepresented cell types that were not evaluated in these studies. Applying a causal competing risks framework to estimate the effects of metformin compared to sulfonylureas among type II diabetic patients showed consistent findings across two disparate datasets. These robust EHR findings were buttressed by in-vitro analyses of human neural cells at pharmacologically relevant concentrations that revealed novel actions of metformin, relative to glyburide and to its actions in other cell types. Together, this multi-dimensional DRIAD-EHR approach uncovered four pragmatic insights that can be incorporated into clinical trial design: first, an estimate of efficacy among asymptomatic individuals that is validated in two distinct databases; second, the importance of treating mortality as a competing risk for clinical trials that last three to five years; third, enrolling people aged less than 70 (or perhaps less than 75 in non-diabetics, among whom the onset of dementia is, on average, later); and fourth, a novel, mechanistically relevant CSF biomarker, SPP1, for recruitment into the clinical trial that can also serve as a pharmacodynamic mechanism of action.
Over 700 GP practices (8% of total GP practices) • have contributed data to CPRD and the mean follow-up time for patients included in CPRD is around eight years. Death records are updated periodically from the UK Office of National Statistics (ONS). Additionally, data linkages were established with secondary care data from Hospital Episode Statistics (HES) as well as with small-area measures of social deprivation (2001-2017). Use of CPRD data for this study was approved by the Independent Scientific Advisory Committee (ISAC) for Medicines and Healthcare products Regulatory Agency (MHRA) database research (protocol number: 19_065R). Both the US RPDR and the UK CPRD data includes patient demographics; encounter details such as dates, providers, diagnoses, and procedures; medical notes, drug prescriptions, and laboratory test results.
III. Exposure assessment In both cohorts, any patients in combination therapy at baseline, including with insulin, were excluded. We first identified individuals who met the eligibility criteria and assigned them to the treatment indicated in their medical record at baseline. The sulfonylurea monotherapy group included patients who were exposed only to sulfonylureas, including first generation (tolbutamide, chlorpropamide, tolazamide, or acetohexamide) and second generation (gliclazide, glibenclamide, glipizide, glimepiride, gliquidone, glibornuride, or glymidine sodium). Similarly, the metformin monotherapy group included those who were exposed only to metformin (Extended Data Figs. 8-9).
US RPDR. In RPDR, dementia incidence was defined by the presence of either one or several dementia diagnosis codes (expertly curated list of International Classification of Diseases (ICD) codes including: a) ICD10 codes: 290. X, 294. X, and 331. X; b) ICD9 codes: 780. 93, G30. X, and G31. X (Extended Data Table  8 ), and/or by the initiation of drugs primarily used for dementia (Donepezil, Galantamine, Rivastigmine, and their respective brand names Aricept, Razadyne, Exelon (Extended Data Table  9 )). UK CPRD. In CPRD, dementia incidence was defined by the presence of either one or several dementia diagnosis codes (expertly curated list including: a) a selected set of CPRD Medcodes: see Extended Data Table  10  for the detailed code list; b) ICD 9/10 codes in linked HES or ONS databases: see Extended Data Table  11  for the detailed code list), and/or by the initiation of drugs primarily used for dementia (Donepezil, Galantamine, Rivastigmine, or Memantine (Extended Data Table  11 )).
The choice was based on previous literature, which suggested that accuracy is highest with two instances of cancer ICD codes  56. In both cohorts, information on the following covariates before the baseline date was extracted: age at the first prescription, sex, SES (index of multiple deprivation (IMD) in the UK CPRD and median annual household income by zip code in the US RPDR), BMI (<25, 25-30, ≥30 kg/m 2 , or missing), HbA1C (<7%, 7-10%, >10%, or missing), and comorbidities (hypertension, CVD, stroke, and cancer). Additional covariates which were not available in the US RPDR dataset were extracted in the UK CPRD cohort, including smoking status (non-smoker, current smoker, ex-smoker, or missing), and presence of chronic obstructive pulmonary disease (COPD) before baseline. Whereas the US RPDR cohort mainly includes patients living in the Boston area, the UK CPRD cohort is representative of patients nationally.
US RPDR. In the RPDR cohort, there were missing values in both the BMI (32%) and HbA1C (38%) variables at antidiabetic treatment initiation. Missingness affected sulfonylurea-more than metformininitiators, as 42% of them were missing BMI information at baseline and 52% did not have an HbA1C measure (Table  2 ). In the propensity score model, we treated missing data as a separate category, for both the baseline BMI and HbA1C variables. Further, we combined the two categories of missing BMI and BMI<25 into one, assuming that the baseline BMI would be more likely to be captured in the medical records if it were >25. Additionally, we noted that the effects of missing BMI and BMI<25 indicators on treatment assignment were similar, and thus we collapsed the two into one reference category. UK CPRD.
ACKNOWLEDGMENTS We thank  Alexander Soukas ,  Victor Castro , and  Peter K. Sorger  for helpful discussions. The authors thank  Shawn Murphy ,  Henry Chueh , and the  Partners Health Care Research Patient Data Registry group  for facilitating use of their database. This study is based in part on data from the Clinical Practice Research Datalink database obtained under license from the  UK Medicines and Healthcare products Regulatory Agency. However , the interpretation and conclusions contained in this article are those of the authors alone and not necessarily those of the  NHS , the  NIHR , or the  Department of Health. The results published here are in part based on data obtained from the  AMP-AD Knowledge Portal  (doi: 10. 7303/syn2580853 ). These data were generated from postmortem brain tissue collected through the  Mount Sinai VA Medical Center Brain Bank , led by  Dr.
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules. Using an open-source dataset, we worked with Kaggle to design and host a 3-month competition which received 47,800 ML model predictions from 2,700 teams in 84 countries. Within 3 weeks, the Kaggle community produced models with comparable accuracy to our best previously published 'in-house' efforts.
Introduction The rise of machine learning (ML) in the physical sciences has created a number of notable successes, (1-7) and the number of published outputs is increasing substantially. (8)  This explosion is perhaps not entirely surprising, given that ML 'search space' is effectively infinite. For example, the performance of a particular ML algorithm strategy depends sensitively on at least four components: (a) the dataset used for training (and the corresponding methodology used for dataset curation); (b) the feature selection used to construct ML inputs; (c) the choice of ML algorithm; and (d) the values of the optimal constituent hyperparameters. For components (b) and (c), the space of possibilities is continually expanding; for components (a) and (d), the space of possibilities is potentially infinite.
The so-called 'Champs Kaggle Competition' (CKC) ran from 29-May-19 through 28-Aug-19. The 5 models which achieved the highest accuracy were awarded respective prize money of $12. 5k, $7. 5k, $5k, $3k and $2k. Over ~13 weeks, the CKC received 47,800 model predictions from 2,700 teams in 84 countries  (Fig 1a) , representing the most exhaustive search to date of ML strategies aimed at predicting QM NMR properties from 3D molecular structure information. The number of participants who engaged with the CKC was amongst the highest for any physical science challenge which Kaggle has hosted to date. Fig  1b  and 1c  show a steady increase in the number of participants who joined the CKC versus time. CKC participants reported being drawn to the competition because it: (a) facilitated progress on an important research problem; (b) involved a rich, noise-less dataset whose structure was easy to understand; and (c) had a dataset which was manageable using standard data processing tools, workflows, and hardware.
Competition Design 2. 1. Domain NMR is the dominant spectroscopic technique for determining 2D and 3D molecular structure in solution. Amongst the most important data obtained in an NMR spectrum are the chemical shifts (which describe the position/frequency of a signal in the spectrum) and the scalar couplings (which determine the splitting/shape of the signal in the spectrum). ML methods to predict NMR properties are established in academic and commercial workflows for determining 2D molecular structure from experimental NMR datasets. (25) (26) (27) (28)  Despite this success, these 2D approaches often fail when the NMR properties are affected by 3D structure, for example atoms are separated by several bonds yet remain close in 3D space (ring current effects, hydrogen bonding etc. ). This is an inherently difficult problem as the 3D molecular structure is simply not well described by 2D representations and there are not enough highquality experimental data available to accurately infer most 3D relationships from a 2D structural representation alone.
The most accurate computed predictions of NMR properties use QM methods like density functional theory (DFT) to get a one-toone mapping between a 3D structure and the contribution it has to the experimentally observed NMR property. Accurate QM methods for NMR property predictions are powerful but expensive. Recent work has thus focussed on developing ML algorithms which can efficiently reproduce the results of costly QM methods, achieving results in seconds rather than hours or days. (24, 29)  ML approaches have the added appeal that they can be trained using large datasets of DFT-computed NMR parameters, which are not limited to experimental structural observations. With a large enough training database, we have shown in previous work that an ML strategy can approach the accuracy of DFT calculations of atom-centered NMR parameters such as chemical shift for 3D structure analysis, but with several orders of magnitude reduction in time.