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1. Can Transformers Smell Like Humans?

Author

Taleb, Farzaneh, Vasco, Miguel, Ribeiro, Antônio H., Björkman, Mårten, and Kragic, Danica

Subjects
Computer Science - Machine Learning
Abstract

The human brain encodes stimuli from the environment into representations that form a sensory perception of the world. Despite recent advances in understanding visual and auditory perception, olfactory perception remains an under-explored topic in the machine learning community due to the lack of large-scale datasets annotated with labels of human olfactory perception. In this work, we ask the question of whether pre-trained transformer models of chemical structures encode representations that are aligned with human olfactory perception, i.e., can transformers smell like humans? We demonstrate that representations encoded from transformers pre-trained on general chemical structures are highly aligned with human olfactory perception. We use multiple datasets and different types of perceptual representations to show that the representations encoded by transformer models are able to predict: (i) labels associated with odorants provided by experts; (ii) continuous ratings provided by human participants with respect to pre-defined descriptors; and (iii) similarity ratings between odorants provided by human participants. Finally, we evaluate the extent to which this alignment is associated with physicochemical features of odorants known to be relevant for olfactory decoding., Comment: Spotlight paper at NeurIPS 2024

Published
2024

2. Efficient Optimization Algorithms for Linear Adversarial Training

Author

RIbeiro, Antônio H., Schön, Thomas B., Zahariah, Dave, and Bach, Francis

Subjects
Statistics - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Machine Learning, Mathematics - Optimization and Control
Abstract

Adversarial training can be used to learn models that are robust against perturbations. For linear models, it can be formulated as a convex optimization problem. Compared to methods proposed in the context of deep learning, leveraging the optimization structure allows significantly faster convergence rates. Still, the use of generic convex solvers can be inefficient for large-scale problems. Here, we propose tailored optimization algorithms for the adversarial training of linear models, which render large-scale regression and classification problems more tractable. For regression problems, we propose a family of solvers based on iterative ridge regression and, for classification, a family of solvers based on projected gradient descent. The methods are based on extended variable reformulations of the original problem. We illustrate their efficiency in numerical examples.

Published
2024

3. Influence of Baseline User Characteristics and Early Use Patterns (24-Hour) on Long-Term Adherence and Effectiveness of a Web-Based Weight Loss Randomized Controlled Trial: Latent Profile Analysis

Author

Andrade, Andre Q, Beleigoli, Alline, Diniz, Maria De Fatima, and Ribeiro, Antonio Luiz

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundLow adherence to real-world online weight loss interventions reduces long-term efficacy. Baseline characteristics and use patterns are determinants of long-term adherence, but we lack cohesive models to guide how to adapt interventions to users’ needs. We also lack information whether very early use patterns (24 hours) help describe users and predict interventions they would benefit from. ObjectiveWe aim to understand the impact of users’ baseline characteristics and early (initial 24 hours) use patterns of a web platform for weight loss on user adherence and weight loss in the long term (24 weeks). MethodsWe analyzed data from the POEmaS randomized controlled trial, a study that compared the effectiveness of a weight loss platform with or without coaching and a control approach. Data included baseline behavior and use logs from the initial 24 hours after platform access. Latent profile analysis (LPA) was used to identify classes, and Kruskal-Wallis was used to test whether class membership was associated with long-term (24 weeks) adherence and weight loss. ResultsAmong 828 participants assigned to intervention arms, 3 classes were identified through LPA: class 1 (better baseline health habits and high 24-hour platform use); class 2 (better than average health habits, but low 24-hour platform use); class 3 (worse baseline health habits and low 24-hour platform use). Class membership was associated with long-term adherence (P

Published
2021
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8. Development and Implementation of a Decision Support System to Improve Control of Hypertension and Diabetes in a Resource-Constrained Area in Brazil: Mixed Methods Study

Author

Marcolino, Milena Soriano, Oliveira, João Antonio Queiroz, Cimini, Christiane Corrêa Rodrigues, Maia, Junia Xavier, Pinto, Vânia Soares Oliveira Almeida, Sá, Thábata Queiroz Vivas, Amancio, Kaique, Coelho, Lissandra, Ribeiro, Leonardo Bonisson, Cardoso, Clareci Silva, and Ribeiro, Antonio Luiz

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundThe low levels of control of hypertension and diabetes mellitus are a challenge that requires innovative strategies to surpass barriers of low sources, distance, and quality of health care. ObjectiveThe aim of this study is to develop a clinical decision support system (CDSS) for diabetes and hypertension management in primary care, to implement it in a resource-constrained region, and to evaluate its usability and health care practitioner satisfaction. MethodsThis mixed methods study is a substudy of HealthRise Brazil Project, a multinational study designed to implement pilot programs to improve screening, diagnosis, management, and control of hypertension and diabetes among underserved communities. Following the identification of gaps in usual care, a team of clinicians established the software functional requirements. Recommendations from evidence-based guidelines were reviewed and organized into a decision algorithm, which bases the CDSS reminders and suggestions. Following pretesting and expert panel assessment, pilot testing was conducted in a quasi-experimental study, which included 34 primary care units of 10 municipalities in a resource-constrained area in Brazil. A Likert-scale questionnaire evaluating perceived feasibility, usability, and utility of the application and professionals’ satisfaction was applied after 6 months. In the end-line assessment, 2 focus groups with primary care physicians and nurses were performed. ResultsA total of 159 reminders and suggestions were created and implemented for the CDSS. At the 6-month assessment, there were 1939 patients registered in the application database and 2160 consultations were performed by primary care teams. Of the 96 health care professionals who were invited for the usability assessment, 26% (25/96) were physicians, 46% (44/96) were nurses, and 28% (27/96) were other health professionals. The questionnaire included 24 items on impressions of feasibility, usability, utility, and satisfaction, and presented global Cronbach α of .93. As for feasibility, all professionals agreed (median scores of 4 or 5) that the application could be used in primary care settings and it could be easily incorporated in work routines, but physicians claimed that the application might have caused significant delays in daily routines. As for usability, overall evaluation was good and it was claimed that the application was easy to understand and use. All professionals agreed that the application was useful (score 4 or 5) to promote prevention, assist treatment, and might improve patient care, and they were overall satisfied with the application (median scores between 4 and 5). In the end-line assessment, there were 4211 patients (94.82% [3993/4211] with hypertension and 24.41% [1028/4211] with diabetes) registered in the application’s database and 7960 consultations were performed by primary health care teams. The 17 participants of the focus groups were consistent to affirm they were very satisfied with the CDSS. ConclusionsThe CDSS was applicable in the context of primary health care settings in low-income regions, with good user satisfaction and potential to improve adherence to evidence-based practices.

Published
2021
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9. Personalized Web-Based Weight Loss Behavior Change Program With and Without Dietitian Online Coaching for Adults With Overweight and Obesity: Randomized Controlled Trial

Author

Beleigoli, Alline, Andrade, Andre Q, Diniz, Maria De Fatima, and Ribeiro, Antonio Luiz

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundThe effect of computer- or human-delivered personalized feedback on the effectivess of web-based behavior change platforms for weight loss is unclear. ObjectiveWe aimed to compare the effectiveness of a web-based behavior change intervention personalized through either computerized or human-delivered feedback with a nonpersonalized intervention in promoting weight loss in community-based adults with overweight or obesity. MethodsThis pragmatic, 3-group, parallel-arm, randomized trial recruited students and staff in a Brazilian public university who were aged 18 to 60 years, had a BMI of ≥25 kg/m2, and were not pregnant. Participants were allocated to one of 3 groups: platform only (24-week behavior change program delivered using a web platform with personalized computer-delivered feedback), platform plus coaching (same 24-week web-based behavior change program plus 12 weeks of personalized feedback delivered online by a dietitian), or waiting list (nonpersonalized dietary and physical activity recommendations delivered through an e-booklet and videos). Self-reported weight at 24 weeks was the primary outcome. Changes in dietary and physical activity habits within 24 weeks were secondary outcomes. ResultsAmong the 1298 participants, 375 (28.89%) were lost to follow-up. In the intention-to-treat analysis, the platform-only and platform plus coaching groups had greater mean weight loss than the waiting-list group at 24 weeks (–1.08 kg, 95% CI –1.41 to –0.75 vs –1.57 kg, 95% CI –1.92 to –1.22 vs –0.66 kg, 95% CI –0.98 to –0.34, respectively). The platform-only and platform plus coaching groups, compared with the waiting list group, had a greater increase in the consumption of vegetables (3%, 95% CI 1% to 6% vs 5%, 95% CI 2% to 8% vs –3%, 95% CI –5% to 0%) and fruits (9%, 95% CI 6% to 12% vs 6%, 95% CI 2% to 9% vs 2%, 95% CI 0% to 6%) and a larger reduction in ultraprocessed food intake (–18%, 95% CI –23% to –13% vs –25%, 95% CI –30% to –20% vs –12%, 95% CI –16% to –8%). Changes in physical activity did not differ across the groups. Engagement was higher in the platform plus coaching group than in the platform-only group (7.6 vs 5.2 completed sessions; P=.007). Longer usage of the platform was associated with clinically meaningful (≥5%) weight loss (odds ratio 1.02, 95% CI 1.01 to 1.04). ConclusionsThe web-based behavior change programs with computer- and human-delivered personalized feedback led to greater, albeit small-magnitude, weight loss within 24 weeks. Improvement in multiple dietary habits, but not physical activity, were also greater in the personalized programs compared with the nonpersonalized one. The human-delivered personalized feedback by the online dietitian coach increased user engagement with the program and was associated with a significantly higher chance of clinically meaningful weight loss. Trial RegistrationClinicalTrials.gov NCT03435445; https://clinicaltrials.gov/ct2/show/NCT03435445 International Registered Report Identifier (IRRID)RR2-10.2196/10.1186/s12889-018-5882-y

Published
2020
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10. Development and Evaluation of a Mobile Decision Support System for Hypertension Management in the Primary Care Setting in Brazil: Mixed-Methods Field Study on Usability, Feasibility, and Utility

Author

Silveira, Daniel Vitório, Marcolino, Milena Soriano, Machado, Elaine Leandro, Ferreira, Camila Gonçalves, Alkmim, Maria Beatriz Moreira, Resende, Elmiro Santos, Carvalho, Bárbara Couto, Antunes, André Pires, and Ribeiro, Antonio Luiz Pinho

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundDespite being an important cardiovascular risk factor, hypertension has low control levels worldwide. Computerized clinical decision support systems (CDSSs) might be effective in reducing blood pressure with a potential impact in reducing cardiovascular risk. ObjectiveThe goal of the research was to evaluate the feasibility, usability, and utility of a CDSS, TeleHAS (tele–hipertensão arterial sistêmica, or arterial hypertension system), in the care of patients with hypertension in the context of a primary care setting in a middle-income country. MethodsThe TeleHAS app consists of a platform integrating clinical and laboratory data on a particular patient, from which it performs cardiovascular risk calculation and provides evidence-based recommendations derived from Brazilian and international guidelines for the management of hypertension and cardiovascular risk. Ten family physicians from different primary care units in the city of Montes Claros, Brazil, were randomly selected to use the CDSS for the care of hypertensive patients for 6 months. After 3 and 6 months, the feasibility, usability, and utility of the CDSS in the routine care of the health team was evaluated through a standardized questionnaire and semistructured interviews. ResultsThroughout the study, clinicians registered 535 patients with hypertension, at an average of 1.24 consultations per patient. Women accounted for 80% (8/10) of participant doctors, median age was 31.5 years (interquartile range 27 to 59 years). As for feasibility, 100% of medical users claimed it was possible to use the app in the primary care setting, and for 80% (8/10) of them it was easy to incorporate its use into the daily routine and home visits. Nevertheless, 70% (7/10) of physicians claimed that the time taken to fill out the CDSS causes significant delays in service. Clinicians evaluated TeleHAS as good (8/10, 80% of users), with easy completion and friendly interface (10/10, 100%) and the potential to improve patients’ treatment (10/10, 100%). A total of 90% (9/10) of physicians had access to new knowledge about cardiovascular risk and hypertension through the app recommendations and found it useful to promote prevention and optimize treatment. ConclusionsIn this study, a CDSS developed to assist the management of patients with hypertension was feasible in the context of a primary health care setting in a middle-income country, with good user satisfaction and the potential to improve adherence to evidence-based practices.

Published
2019
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11. End-to-end Risk Prediction of Atrial Fibrillation from the 12-Lead ECG by Deep Neural Networks

Author

Habineza, Theogene, Ribeiro, Antônio H., Gedon, Daniel, Behar, Joachim A., Ribeiro, Antonio Luiz P., and Schön, Thomas B.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Quantitative Biology - Quantitative Methods, Statistics - Applications
Abstract

Background: Atrial fibrillation (AF) is one of the most common cardiac arrhythmias that affects millions of people each year worldwide and it is closely linked to increased risk of cardiovascular diseases such as stroke and heart failure. Machine learning methods have shown promising results in evaluating the risk of developing atrial fibrillation from the electrocardiogram. We aim to develop and evaluate one such algorithm on a large CODE dataset collected in Brazil. Results: The deep neural network model identified patients without indication of AF in the presented ECG but who will develop AF in the future with an AUC score of 0.845. From our survival model, we obtain that patients in the high-risk group (i.e. with the probability of a future AF case being greater than 0.7) are 50% more likely to develop AF within 40 weeks, while patients belonging to the minimal-risk group (i.e. with the probability of a future AF case being less than or equal to 0.1) have more than 85% chance of remaining AF free up until after seven years. Conclusion: We developed and validated a model for AF risk prediction. If applied in clinical practice, the model possesses the potential of providing valuable and useful information in decision-making and patient management processes., Comment: 16 pages with 7 figures

Published
2023
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16. Regularization properties of adversarially-trained linear regression

Author

Ribeiro, Antônio H., Zachariah, Dave, Bach, Francis, and Schön, Thomas B.

Subjects
Statistics - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Machine Learning, Mathematics - Optimization and Control
Abstract

State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to defend against it. Formulated as a min-max problem, it searches for the best solution when the training data were corrupted by the worst-case attacks. Linear models are among the simple models where vulnerabilities can be observed and are the focus of our study. In this case, adversarial training leads to a convex optimization problem which can be formulated as the minimization of a finite sum. We provide a comparative analysis between the solution of adversarial training in linear regression and other regularization methods. Our main findings are that: (A) Adversarial training yields the minimum-norm interpolating solution in the overparameterized regime (more parameters than data), as long as the maximum disturbance radius is smaller than a threshold. And, conversely, the minimum-norm interpolator is the solution to adversarial training with a given radius. (B) Adversarial training can be equivalent to parameter shrinking methods (ridge regression and Lasso). This happens in the underparametrized region, for an appropriate choice of adversarial radius and zero-mean symmetrically distributed covariates. (C) For $\ell_\infty$-adversarial training -- as in square-root Lasso -- the choice of adversarial radius for optimal bounds does not depend on the additive noise variance. We confirm our theoretical findings with numerical examples., Comment: Accepted (spotlight) NeurIPS 2023; A preliminary version of this work titled: "Surprises in adversarially-trained linear regression" was made available under a different identifier: arXiv:2205.12695

Published
2023

17. Bacterial population-level trade-offs between drought tolerance and resource acquisition traits impact decomposition

Author

Malik, Ashish A, Martiny, Jennifer BH, Ribeiro, Antonio, Sheridan, Paul O, Weihe, Claudia, Brodie, Eoin L, and Allison, Steven D

Subjects
Climate Change Impacts and Adaptation, Biological Sciences, Ecology, Environmental Sciences, Human Genome, Genetics, Droughts, Soil Microbiology, Bacteria, Ecosystem, Plant Leaves, Metagenome, Stress, Physiological, Soil, Drought Resistance, bacteria, drought, genomics, litter decomposition, microbial traits, population ecology, Technology, Microbiology, Biological sciences, Environmental sciences
Abstract

Microbes drive fundamental ecosystem processes, such as decomposition. Environmental stressors are known to affect microbes, their fitness, and the ecosystem functions that they perform; yet, understanding the causal mechanisms behind this influence has been difficult. We used leaf litter on soil surface as a model in situ system to assess changes in bacterial genomic traits and decomposition rates for 18 months with drought as a stressor. We hypothesized that genome-scale trade-offs due to investment in stress tolerance traits under drought reduce the capacity for bacterial populations to carry out decomposition, and that these population-level trade-offs scale up to impact emergent community traits, thereby reducing decomposition rates. We observed drought tolerance mechanisms that were heightened in bacterial populations under drought, identified as higher gene copy numbers in metagenome-assembled genomes. A subset of populations under drought had reduced carbohydrate-active enzyme genes that suggested-as a trade-off-a decline in decomposition capabilities. These trade-offs were driven by community succession and taxonomic shifts as distinct patterns appeared in populations. We show that trait-trade-offs in bacterial populations under drought could scale up to reduce overall decomposition capabilities and litter decay rates. Using a trait-based approach to assess the population ecology of soil bacteria, we demonstrate genome-level trade-offs in response to drought with consequences for decomposition rates.

Published
2024

19. The Impact of mHealth Interventions: Systematic Review of Systematic Reviews

Author

Marcolino, Milena Soriano, Oliveira, João Antonio Queiroz, D'Agostino, Marcelo, Ribeiro, Antonio Luiz, Alkmim, Maria Beatriz Moreira, and Novillo-Ortiz, David

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundMobile phone usage has been rapidly increasing worldwide. mHealth could efficiently deliver high-quality health care, but the evidence supporting its current effectiveness is still mixed. ObjectiveWe performed a systematic review of systematic reviews to assess the impact or effectiveness of mobile health (mHealth) interventions in different health conditions and in the processes of health care service delivery. MethodsWe used a common search strategy of five major scientific databases, restricting the search by publication date, language, and parameters in methodology and content. Methodological quality was evaluated using the Measurement Tool to Assess Systematic Reviews (AMSTAR) checklist. ResultsThe searches resulted in a total of 10,689 articles. Of these, 23 systematic reviews (371 studies; more than 79,665 patients) were included. Seventeen reviews included studies performed in low- and middle-income countries. The studies used diverse mHealth interventions, most frequently text messaging (short message service, SMS) applied to different purposes (reminder, alert, education, motivation, prevention). Ten reviews were rated as low quality (AMSTAR score 0-4), seven were rated as moderate quality (AMSTAR score 5-8), and six were categorized as high quality (AMSTAR score 9-11). A beneficial impact of mHealth was observed in chronic disease management, showing improvement in symptoms and peak flow variability in asthma patients, reducing hospitalizations and improving forced expiratory volume in 1 second; improving chronic pulmonary diseases symptoms; improving heart failure symptoms, reducing deaths and hospitalization; improving glycemic control in diabetes patients; improving blood pressure in hypertensive patients; and reducing weight in overweight and obese patients. Studies also showed a positive impact of SMS reminders in improving attendance rates, with a similar impact to phone call reminders at reduced cost, and improved adherence to tuberculosis and human immunodeficiency virus therapy in some scenarios, with evidence of decrease of viral load. ConclusionsAlthough mHealth is growing in popularity, the evidence for efficacy is still limited. In general, the methodological quality of the studies included in the systematic reviews is low. For some fields, its impact is not evident, the results are mixed, or no long-term studies exist. Exceptions include the moderate quality evidence of improvement in asthma patients, attendance rates, and increased smoking abstinence rates. Most studies were performed in high-income countries, implying that mHealth is still at an early stage of development in low-income countries.

Published
2018
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21. Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study

Author

Sau, Arunashis, Pastika, Libor, Sieliwonczyk, Ewa, Patlatzoglou, Konstantinos, Ribeiro, Antônio H, McGurk, Kathryn A, Zeidaabadi, Boroumand, Zhang, Henry, Macierzanka, Krzysztof, Mandic, Danilo, Sabino, Ester, Giatti, Luana, Barreto, Sandhi M, Camelo, Lidyane do Valle, Tzoulaki, Ioanna, O'Regan, Declan P, Peters, Nicholas S, Ware, James S, Ribeiro, Antonio Luiz P, Kramer, Daniel B, Waks, Jonathan W, and Ng, Fu Siong

Published
2024
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22. Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images.

Author

Sangha, Veer, Nargesi, Arash, Dhingra, Lovedeep, Khunte, Akshay, Mortazavi, Bobak, Ribeiro, Antônio, Banina, Evgeniya, Adeola, Oluwaseun, Garg, Nadish, Brandt, Cynthia, Miller, Edward, Ribeiro, Antonio, Velazquez, Eric, Giatti, Luana, Barreto, Sandhi, Foppa, Murilo, Ouyang, David, Krumholz, Harlan, Khera, Rohan, and Yuan, Neal

Subjects
artificial intelligence, biomedical technology, electrocardiography, heart failure, machine learning, ventricular dysfunction, left, Adult, Humans, Prospective Studies, Longitudinal Studies, Electrocardiography, Ventricular Dysfunction, Left, Ventricular Function, Left
Abstract

BACKGROUND: Left ventricular (LV) systolic dysfunction is associated with a >8-fold increased risk of heart failure and a 2-fold risk of premature death. The use of ECG signals in screening for LV systolic dysfunction is limited by their availability to clinicians. We developed a novel deep learning-based approach that can use ECG images for the screening of LV systolic dysfunction. METHODS: Using 12-lead ECGs plotted in multiple different formats, and corresponding echocardiographic data recorded within 15 days from the Yale New Haven Hospital between 2015 and 2021, we developed a convolutional neural network algorithm to detect an LV ejection fraction 27-fold higher odds of LV systolic dysfunction on transthoracic echocardiogram (odds ratio, 27.5 [95% CI, 22.3-33.9] in the held-out set). Class-discriminative patterns localized to the anterior and anteroseptal leads (V2 and V3), corresponding to the left ventricle regardless of the ECG layout. A positive ECG screen in individuals with an LV ejection fraction ≥40% at the time of initial assessment was associated with a 3.9-fold increased risk of developing incident LV systolic dysfunction in the future (hazard ratio, 3.9 [95% CI, 3.3-4.7]; median follow-up, 3.2 years). CONCLUSIONS: We developed and externally validated a deep learning model that identifies LV systolic dysfunction from ECG images. This approach represents an automated and accessible screening strategy for LV systolic dysfunction, particularly in low-resource settings.

Published
2023

23. On Merging Feature Engineering and Deep Learning for Diagnosis, Risk-Prediction and Age Estimation Based on the 12-Lead ECG

Author

Zvuloni, Eran, Read, Jesse, Ribeiro, Antônio H., Ribeiro, Antonio Luiz P., and Behar, Joachim A.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, I.2.0, J.3
Abstract

Objective: Machine learning techniques have been used extensively for 12-lead electrocardiogram (ECG) analysis. For physiological time series, deep learning (DL) superiority to feature engineering (FE) approaches based on domain knowledge is still an open question. Moreover, it remains unclear whether combining DL with FE may improve performance. Methods: We considered three tasks intending to address these research gaps: cardiac arrhythmia diagnosis (multiclass-multilabel classification), atrial fibrillation risk prediction (binary classification), and age estimation (regression). We used an overall dataset of 2.3M 12-lead ECG recordings to train the following models for each task: i) a random forest taking the FE as input was trained as a classical machine learning approach; ii) an end-to-end DL model; and iii) a merged model of FE+DL. Results: FE yielded comparable results to DL while necessitating significantly less data for the two classification tasks and it was outperformed by DL for the regression task. For all tasks, merging FE with DL did not improve performance over DL alone. Conclusion: We found that for traditional 12-lead ECG based diagnosis tasks DL did not yield a meaningful improvement over FE, while it improved significantly the nontraditional regression task. We also found that combining FE with DL did not improve over DL alone which suggests that the FE were redundant with the features learned by DL. Significance: Our findings provides important recommendations on what machine learning strategy and data regime to chose with respect to the task at hand for the development of new machine learning models based on the 12-lead ECG.

Published
2022

24. Deep networks for system identification: a Survey

Author

Pillonetto, Gianluigi, Aravkin, Aleksandr, Gedon, Daniel, Ljung, Lennart, Ribeiro, Antônio H., and Schön, Thomas B.

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Systems and Control
Abstract

Deep learning is a topic of considerable current interest. The availability of massive data collections and powerful software resources has led to an impressive amount of results in many application areas that reveal essential but hidden properties of the observations. System identification learns mathematical descriptions of dynamic systems from input-output data and can thus benefit from the advances of deep neural networks to enrich the possible range of models to choose from. For this reason, we provide a survey of deep learning from a system identification perspective. We cover a wide spectrum of topics to enable researchers to understand the methods, providing rigorous practical and theoretical insights into the benefits and challenges of using them. The main aim of the identified model is to predict new data from previous observations. This can be achieved with different deep learning based modelling techniques and we discuss architectures commonly adopted in the literature, like feedforward, convolutional, and recurrent networks. Their parameters have to be estimated from past data trying to optimize the prediction performance. For this purpose, we discuss a specific set of first-order optimization tools that is emerged as efficient. The survey then draws connections to the well-studied area of kernel-based methods. They control the data fit by regularization terms that penalize models not in line with prior assumptions. We illustrate how to cast them in deep architectures to obtain deep kernel-based methods. The success of deep learning also resulted in surprising empirical observations, like the counter-intuitive behaviour of models with many parameters. We discuss the role of overparameterized models, including their connection to kernels, as well as implicit regularization mechanisms which affect generalization, specifically the interesting phenomena of benign overfitting ...

Published
2023

25. ECG-Based Electrolyte Prediction: Evaluating Regression and Probabilistic Methods

Author

Von Bachmann, Philipp, Gedon, Daniel, Gustafsson, Fredrik K., Ribeiro, Antônio H., Lampa, Erik, Gustafsson, Stefan, Sundström, Johan, and Schön, Thomas B.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Objective: Imbalances of the electrolyte concentration levels in the body can lead to catastrophic consequences, but accurate and accessible measurements could improve patient outcomes. While blood tests provide accurate measurements, they are invasive and the laboratory analysis can be slow or inaccessible. In contrast, an electrocardiogram (ECG) is a widely adopted tool which is quick and simple to acquire. However, the problem of estimating continuous electrolyte concentrations directly from ECGs is not well-studied. We therefore investigate if regression methods can be used for accurate ECG-based prediction of electrolyte concentrations. Methods: We explore the use of deep neural networks (DNNs) for this task. We analyze the regression performance across four electrolytes, utilizing a novel dataset containing over 290000 ECGs. For improved understanding, we also study the full spectrum from continuous predictions to binary classification of extreme concentration levels. To enhance clinical usefulness, we finally extend to a probabilistic regression approach and evaluate different uncertainty estimates. Results: We find that the performance varies significantly between different electrolytes, which is clinically justified in the interplay of electrolytes and their manifestation in the ECG. We also compare the regression accuracy with that of traditional machine learning models, demonstrating superior performance of DNNs. Conclusion: Discretization can lead to good classification performance, but does not help solve the original problem of predicting continuous concentration levels. While probabilistic regression demonstrates potential practical usefulness, the uncertainty estimates are not particularly well-calibrated. Significance: Our study is a first step towards accurate and reliable ECG-based prediction of electrolyte concentration levels., Comment: Code and trained models are available at https://github.com/philippvb/ecg-electrolyte-regression

Published
2022

26. Prognostic Significance and Associations of Neural Network–Derived Electrocardiographic Features

Author

Sau, Arunashis, Ribeiro, Anto[Combining Circumflex Accent]nio H., McGurk, Kathryn A., Pastika, Libor, Bajaj, Nikesh, Gurnani, Mehak, Sieliwonczyk, Ewa, Patlatzoglou, Konstantinos, Ardissino, Maddalena, Chen, Jun Yu, Wu, Huiyi, Shi, Xili, Hnatkova, Katerina, Zheng, Sean L., Britton, Annie, Shipley, Martin, Andršová, Irena, Novotný, Tomáš, Sabino, Ester C., Giatti, Luana, Barreto, Sandhi M., Waks, Jonathan W., Kramer, Daniel B., Mandic, Danilo, Peters, Nicholas S., O’Regan, Declan P., Malik, Marek, Ware, James S., Ribeiro, Antonio Luiz P., and Ng, Fu Siong

Published
2024
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30. Abstract 4143130: Regression of Inflammation in Chronic Chagas Disease Using Specific Anti-Parasitic Therapy Is Associated With an Activated Immune Profile Before Therapy and Increased Levels of IL-17 After Therapy

Author

Silva, Silvana, Dutra, Walderez, Barros, Marcio, Nunes, Maria, Ribeiro, Antonio, Sabino, Ester Cerdeira, Oliveira-da Silva, Léa, Koh, Carolina, Carvalho, Andrea, Araujo, Fernanda, Pacheco, Iago, Muniz Meira, Geanne, Silva Ambrósio Vieira, Gustavo Henrique, Ferreira, Iago, Machado, Jean, Nyssen, Jeroen, Zanetti, Sofia, and dos Santos, Tiago

Published
2024
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33. Abstract 4138273: Acceptability and Gain of Knowledge of Community Educational Tools About Rheumatic Heart Disease Integrated With Screening In Low-Income Settings

Author

Abrams, Jessica, Sable, Craig, Spaziani, Alison, Zuhlke, Liesl, Cardoso, Clareci, Vinhal, Wanessa, Ribeiro, Isabely, Oliveira, Kaciane, Amaral, Ingred Beatriz, Nunes, Maria, Diniz, Marina, Fraga, Lucas, Paula, Luiza, Coelho, Cecilia, TACURI CHAVEZ, LUZ MARINA, Lemos, Larissa, Correia, Julliane, Ribeiro, Antonio, and Nascimento, Bruno

Published
2024
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37. Genetic architecture of spatial electrical biomarkers for cardiac arrhythmia and relationship with cardiovascular disease.

Author

Young, William J, Haessler, Jeffrey, Benjamins, Jan-Walter, Repetto, Linda, Yao, Jie, Isaacs, Aaron, Harper, Andrew R, Ramirez, Julia, Garnier, Sophie, van Duijvenboden, Stefan, Baldassari, Antoine R, Concas, Maria Pina, Duong, ThuyVy, Foco, Luisa, Isaksen, Jonas L, Mei, Hao, Noordam, Raymond, Nursyifa, Casia, Richmond, Anne, Santolalla, Meddly L, Sitlani, Colleen M, Soroush, Negin, Thériault, Sébastien, Trompet, Stella, Aeschbacher, Stefanie, Ahmadizar, Fariba, Alonso, Alvaro, Brody, Jennifer A, Campbell, Archie, Correa, Adolfo, Darbar, Dawood, De Luca, Antonio, Deleuze, Jean-François, Ellervik, Christina, Fuchsberger, Christian, Goel, Anuj, Grace, Christopher, Guo, Xiuqing, Hansen, Torben, Heckbert, Susan R, Jackson, Rebecca D, Kors, Jan A, Lima-Costa, Maria Fernanda, Linneberg, Allan, Macfarlane, Peter W, Morrison, Alanna C, Navarro, Pau, Porteous, David J, Pramstaller, Peter P, Reiner, Alexander P, Risch, Lorenz, Schotten, Ulrich, Shen, Xia, Sinagra, Gianfranco, Soliman, Elsayed Z, Stoll, Monika, Tarazona-Santos, Eduardo, Tinker, Andrew, Trajanoska, Katerina, Villard, Eric, Warren, Helen R, Whitsel, Eric A, Wiggins, Kerri L, Arking, Dan E, Avery, Christy L, Conen, David, Girotto, Giorgia, Grarup, Niels, Hayward, Caroline, Jukema, J Wouter, Mook-Kanamori, Dennis O, Olesen, Morten Salling, Padmanabhan, Sandosh, Psaty, Bruce M, Pattaro, Cristian, Ribeiro, Antonio Luiz P, Rotter, Jerome I, Stricker, Bruno H, van der Harst, Pim, van Duijn, Cornelia M, Verweij, Niek, Wilson, James G, Orini, Michele, Charron, Philippe, Watkins, Hugh, Kooperberg, Charles, Lin, Henry J, Wilson, James F, Kanters, Jørgen K, Sotoodehnia, Nona, Mifsud, Borbala, Lambiase, Pier D, Tereshchenko, Larisa G, and Munroe, Patricia B

Subjects
Humans, Cardiovascular Diseases, Electrocardiography, Risk Factors, Arrhythmias, Cardiac, Atrioventricular Block, Genome-Wide Association Study, Biomarkers, Human Genome, Cardiovascular, Genetics, Heart Disease
Abstract

The 3-dimensional spatial and 2-dimensional frontal QRS-T angles are measures derived from the vectorcardiogram. They are independent risk predictors for arrhythmia, but the underlying biology is unknown. Using multi-ancestry genome-wide association studies we identify 61 (58 previously unreported) loci for the spatial QRS-T angle (N = 118,780) and 11 for the frontal QRS-T angle (N = 159,715). Seven out of the 61 spatial QRS-T angle loci have not been reported for other electrocardiographic measures. Enrichments are observed in pathways related to cardiac and vascular development, muscle contraction, and hypertrophy. Pairwise genome-wide association studies with classical ECG traits identify shared genetic influences with PR interval and QRS duration. Phenome-wide scanning indicate associations with atrial fibrillation, atrioventricular block and arterial embolism and genetically determined QRS-T angle measures are associated with fascicular and bundle branch block (and also atrioventricular block for the frontal QRS-T angle). We identify potential biology involved in the QRS-T angle and their genetic relationships with cardiovascular traits and diseases, may inform future research and risk prediction.

Published
2023

41. Surprises in adversarially-trained linear regression

Author

Ribeiro, Antônio H., Zachariah, Dave, and Schön, Thomas B.

Subjects
Statistics - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Mathematics - Statistics Theory
Abstract

State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to defend against such examples. It is formulated as a min-max problem, searching for the best solution when the training data was corrupted by the worst-case attacks. For linear regression problems, adversarial training can be formulated as a convex problem. We use this reformulation to make two technical contributions: First, we formulate the training problem as an instance of robust regression to reveal its connection to parameter-shrinking methods, specifically that $\ell_\infty$-adversarial training produces sparse solutions. Secondly, we study adversarial training in the overparameterized regime, i.e. when there are more parameters than data. We prove that adversarial training with small disturbances gives the solution with the minimum-norm that interpolates the training data. Ridge regression and lasso approximate such interpolating solutions as their regularization parameter vanishes. By contrast, for adversarial training, the transition into the interpolation regime is abrupt and for non-zero values of disturbance. This result is proved and illustrated with numerical examples.

Published
2022

44. Burden of disease attributable to risk factors: Estimates of the Global Burden of Disease from 1990 to 2021

Author

Malta, Deborah Carvalho, Gomes, Crizian Saar, Veloso, Guilherme Augusto, Felisbino-Mendes, Mariana Santos, Brant, Luisa Campos Caldeira, Teixeira, Renato Azeredo, Prates, Elton Junio Sady, Flor, Luísa Sorio, Stein, Caroline, Vasconcelos, Nádia Machado de, Machado, Ísis Eloah, da Silva, Alanna Gomes, Naghavi, Mohsen, and Pinho Ribeiro, Antônio Luiz

Published
2024
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45. Prevalence of Chagas disease among Latin American immigrants in non-endemic countries: an updated systematic review and meta-analysis

Author

Nepomuceno de Andrade, Gisele, Bosch-Nicolau, Pau, Nascimento, Bruno R., Martins-Melo, Francisco Rogerlândio, Perel, Pablo, Geissbühler, Yvonne, Demacq, Caroline, Quijano, Monica, Mosser, Jonathan F., Cousin, Ewerton, Machado, Ísis Eloah, Rodrigues, Matheus Lucca A.C., Ribeiro, Antonio Luiz P., and Molina, Israel

Published
2024
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46. Noncommunicable disease burden in Brazil and its states from 1990 to 2021, with projections for 2030

Author

Malta, Deborah Carvalho, Gomes, Crizian Saar, Veloso, Guilherme Augusto, Teixeira, Renato Azeredo, Felisbino Mendes, Mariana Santos, Brant, Luisa Campos Caldeira, Prates, Elton Junio Sady, Silva, Alanna Gomes, Souza, Juliana Bottoni de, Duncan, Bruce Bartholow, Schmidt, Maria Inês, Souza, Maria de Fátima Marinho de, Vasconcelos, Ana Maria Nogales, Szwarcwald, Célia Landmann, Velásquez Meléndez, Jorge Gustavo, Machado, Ísis Eloah, Naghavi, Mohsen, and Ribeiro, Antônio Luiz Pinho

Published
2024
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48. Overparameterized Linear Regression under Adversarial Attacks

Author

Ribeiro, Antônio H. and Schön, Thomas B.

Subjects
Statistics - Machine Learning, Computer Science - Cryptography and Security, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Mathematics - Statistics Theory
Abstract

We study the error of linear regression in the face of adversarial attacks. In this framework, an adversary changes the input to the regression model in order to maximize the prediction error. We provide bounds on the prediction error in the presence of an adversary as a function of the parameter norm and the error in the absence of such an adversary. We show how these bounds make it possible to study the adversarial error using analysis from non-adversarial setups. The obtained results shed light on the robustness of overparameterized linear models to adversarial attacks. Adding features might be either a source of additional robustness or brittleness. On the one hand, we use asymptotic results to illustrate how double-descent curves can be obtained for the adversarial error. On the other hand, we derive conditions under which the adversarial error can grow to infinity as more features are added, while at the same time, the test error goes to zero. We show this behavior is caused by the fact that the norm of the parameter vector grows with the number of features. It is also established that $\ell_\infty$ and $\ell_2$-adversarial attacks might behave fundamentally differently due to how the $\ell_1$ and $\ell_2$-norms of random projections concentrate. We also show how our reformulation allows for solving adversarial training as a convex optimization problem. This fact is then exploited to establish similarities between adversarial training and parameter-shrinking methods and to study how the training might affect the robustness of the estimated models.

Published
2022
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49. LA CONTRIBUCIÓN DEL BALANCED SCORECARD COMO INSTRUMENTO DE GESTIÓN ESTRATÉGICA EN EL APOYO A LA GERENCIA

Author

Scaramussa, Sadi A, Reisdorfer, Vitor K., and Ribeiro, Antonio A.

Subjects
Balanced Scorecard, Herramienta de Gestión, Estrategia y Planeamiento., Management. Industrial management, HD28-70, Business, HF5001-6182
Abstract

El balanced scorecard - BSC - tratado inicialmente como un sistema de medición de desempeño con base en indicadores, que con el pasar de los tiempos mejoró y hoy es posible afirmar que se trata de una herramienta de gestión. Presenta un orden de conceptos e ideas preexistentes de una manera lógica, objetiva e inteligente. Su correcta aplicación implica una serie de ventajas, como la integración de medidas financieras y no financieras, la comunicación feedback de la estrategia, el vínculo con planeamiento y presupuesto, mayor foco en alinear la organización.Los beneficios de la implantación son los más variables, donde podemos apuntar que el BSC ofrece una metodología de gestión empresarial que se propone no solamente como indicadores financieros de medición al desempeño operacional. La aplicación estratégica transforma esa metodología en herramienta gerencial y disponible para toda la organización.

Published
2010

50. Global, regional, and national burden of stroke and its risk factors, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Author

Feigin, Valery L, Abate, Melsew Dagne, Abate, Yohannes Habtegiorgis, Abd ElHafeez, Samar, Abd-Allah, Foad, Abdelalim, Ahmed, Abdelkader, Atef, Abdelmasseh, Michael, Abd-Elsalam, Sherief, Abdi, Parsa, Abdollahi, Arash, Abdoun, Meriem, Abd-Rabu, Rami, Abdulah, Deldar Morad, Abdullahi, Auwal, Abebe, Mesfin, Abeldaño Zuñiga, Roberto Ariel, Abhilash, E S, Abiodun, Olugbenga Olusola, Abiodun, Olumide, Abo Kasem, Rahim, Aboagye, Richard Gyan, Abouzid, Mohamed, Abreu, Lucas Guimarães, Abrha, Woldu Aberhe, Abtahi, Dariush, Abu Rumeileh, Samir, Abualhasan, Ahmed, Abualruz, Hasan, Abu-Gharbieh, Eman, Abukhadijah, Hana J, Abu-Rmeileh, Niveen ME, Aburuz, Salahdein, Abu-Zaid, Ahmed, Acuna, Juan Manuel, Adane, Denberu Eshetie, Adane, Mesafint Molla, Addo, Isaac Yeboah, Adedoyin, Rufus Adesoji, Adegboye, Oyelola A, Adekanmbi, Victor, Adhikari, Kishor, Adnani, Qorinah Estiningtyas Sakilah, Adra, Saryia, Adzigbli, Leticia Akua, Afify, Abdelrahman Yousry, Afolabi, Aanuoluwapo Adeyimika, Afrashteh, Fatemeh, Afzal, Muhammad Sohail, Afzal, Saira, Aghamiri, Shahin, Agyemang-Duah, Williams, Ahinkorah, Bright Opoku, Ahmad, Aqeel, Ahmad, Muayyad M, Ahmad, Sajjad, Ahmad, Shahzaib, Ahmad, Tauseef, Ahmadzade, Amir Mahmoud, Ahmed, Ali, Ahmed, Ayman, Ahmed, Haroon, Ahmed, Syed Anees, Ajami, Marjan, Aji, Budi, Akara, Essona Matatom, Akinyemi, Rufus Olusola, Akkaif, Mohammed Ahmed, Akrami, Ashley E, Al Awaidy, Salah, Al Hamad, Hanadi, Al Hasan, Syed Mahfuz, Al Qadire, Mohammad, Al Ta'ani, Omar, Al-Ajlouni, Yazan, Alalalmeh, Samer O, Alalwan, Tariq A, Al-Aly, Ziyad, Al-amer, Rasmieh Mustafa, Aldhaleei, Wafa A, Aldossary, Mohammed S, Alemohammad, Seyedeh Yasaman, Al-Fatly, Bassam, Al-Gheethi, Adel Ali Saeed, Alhalaiqa, Fadwa Naji, Alharrasi, Maryam, Ali, Abid, Ali, Mohammed Usman, Ali, Rafat, Ali, Syed Shujait, Ali, Waad, Al-Ibraheem, Akram, Alif, Sheikh Mohammad, Aljunid, Syed Mohamed, Almahmeed, Wael, Al-Marwani, Sabah, Alomari, Mahmoud A, Alonso, Jordi, Alqahtani, Jaber S, Al-Raddadi, Rajaa M Mohammad, Alrawashdeh, Ahmad, Alsabri, Mohammed A, Alshahrani, Najim Z, Altaany, Zaid, Altaf, Awais, Al-Tammemi, Alaa B, Altwalbeh, Diala, Alvis-Guzman, Nelson, Alwafi, Hassan, Al-Wardat, Mohammad, Al-Worafi, Yaser Mohammed, Aly, Hany, Aly, Safwat, Alyahya, Mohammad Sharif Ibrahim, Alzoubi, Karem H, Al-Zyoud, Walid Adnan, Amani, Reza, Amegbor, Prince M, Amera, Tewodros Getnet, Amin, Tarek Tawfik, Amindarolzarbi, Alireza, Amiri, Sohrab, Amu, Hubert, Amugsi, Dickson A, Amusa, Ganiyu Adeniyi, Ancuceanu, Robert, Anderlini, Deanna, Angappan, Dhanalakshmi, Anil, Abhishek, Ansari, Mohammed Tahir Tahir, Ansari-Moghaddam, Alireza, Ansong, Rockson, Anvari, Saeid, Anwar, Saleha, Anwar, Sumadi Lukman, Anyabolo, Ekenedilichukwu Emmanuel, Anyasodor, Anayochukwu Edward, Apostol, Geminn Louis Carace, Appiah, Francis, Aqeel, Muhammad, Arabloo, Jalal, Arabzadeh Bahri, Razman, Arafat, Mosab, Aravkin, Aleksandr Y, Ardekani, Ali, Areda, Demelash, Aregawi, Brhane Berhe, Aregu, Getnet Mesfin, Aremu, Olatunde, Arifin, Hidayat, Ärnlöv, Johan, Artamonov, Anton A, Arulappan, Judie, Aryal, Umesh Raj, Aryan, Zahra, Asbeutah, Akram M, Asemahagn, Mulusew A, Asemu, Mulu Tiruneh, Asghari-Jafarabadi, Mohammad, Ashemo, Mubarek Yesse, Ashraf, Tahira, Aslani, Armin, Asmerom, Haftu Asmerom, Astell-Burt, Thomas, Athari, Seyyed Shamsadin, Atorkey, Prince, Atout, Maha Moh'd Wahbi, Atreya, Alok, Aujayeb, Avinash, Ausloos, Marcel, Avan, Abolfazl, Awad, Hamzeh, Awotidebe, Adedapo Wasiu, Ayana, Lemessa Assefa A, Aychiluhm, Setognal Birara, Aynalem, Amdehiwot A, Aynalem, Zewdu Bishaw, Azadnajafabad, Sina, Azami, Hiva, Aziz, Shahkaar, Azzam, Ahmed Y, Babu, Abraham Samuel, Babu, Giridhara Rathnaiah, Badar, Muhammad, Badiye, Ashish D, Bahrami Taghanaki, Pegah, Bahramian, Saeed, Bai, Ruhai, Baig, Atif Amin, Bakkannavar, Shankar M, Bako, Abdulaziz T, Baltatu, Ovidiu Constantin, Bam, Kiran, Banach, Maciej, Banakar, Morteza, Bandyopadhyay, Soham, Banik, Palash Chandra, Bansal, Kannu, Bao, Yanping, Barboza, Miguel A, Bardhan, Mainak, Barengo, Noel C, Barker-Collo, Suzanne Lyn, Bärnighausen, Till Winfried, Barqawi, Hiba Jawdat, Barrow, Amadou, Barua, Lingkan, Bashiri, Azadeh, Bashiru, Hameed Akande, Basiru, Afisu, Bastan, Mohammad-Mahdi, Basu, Sanjay, Basu, Saurav, Batra, Kavita, Begde, Ahmet, Behnam, Babak, Behnoush, Amir Hossein, Belayneh, Melesse B Y, Belingheri, Michael, Bello, Umar Muhammad, Bennett, Derrick A, Bensenor, Isabela M, Berhe, Fentaw Tadese, Bermudez, Amiel Nazer C, Beyene, Habtamu B B, Beyene, Kebede A, Bhagat, Devidas S, Bhagavathula, Akshaya Srikanth, Bhala, Neeraj, Bhalla, Ashish, Bhardwaj, Nikha, Bhardwaj, Pankaj, Bhaskar, Sonu, Bhat, Ajay Nagesh, Bhat, Vivek, Bhatti, Gurjit Kaur, Bhatti, Jasvinder Singh Singh, Bhuiyan, Mohiuddin Ahmed, Bhusal, Subarna, Bikbov, Boris, Bilgin, Cem, Biondi, Antonio, Bishaw, Keralem Anteneh, Biswas, Atanu, Biswas, Bijit, Bodhare, Trupti, Bogale, Eyob Ketema, Boloor, Archith, Bonakdar Hashemi, Milad, Bonny, Aime, Bora Basara, Berrak, Borhany, Hamed, Bosoka, Samuel Adolf, Bouaoud, Souad, Bouyahya, Abdelhakim, Boyko, Edward J, Bozic, Marija M, Braithwaite, Dejana, Breitner, Susanne, Brenner, Hermann, Britton, Gabrielle, Brunoni, Andre R, Bryazka, Dana, Bugiardini, Raffaele, Bulto, Lemma N, Burkart, Katrin, Bustanji, Yasser, Butt, Zahid A, Caetano dos Santos, Florentino Luciano, Cámera, Luis Alberto, Campos, Luciana Aparecida, Campos-Nonato, Ismael R, Cao, Fan, Capodici, Angelo, Cárdenas, Rosario, Carr, Sinclair, Carreras, Giulia, Carvalho, Andre F, Carvalho, Felix, Castaldelli-Maia, Joao Mauricio, Castañeda-Orjuela, Carlos A, Castelpietra, Giulio, Catapano, Alberico L, Cattaruzza, Maria Sofia, Cegolon, Luca, Cembranel, Francieli, Cenko, Edina, Cerin, Ester, Chadwick, Joshua, Chakraborty, Chiranjib, Chakraborty, Sandip, Chan, Jeffrey Shi Kai, Chandika, Rama Mohan, Chandrasekar, Eeshwar K, Chanie, Gashaw Sisay, Chattu, Vijay Kumar, Chaudhary, Anis Ahmad, Chaurasia, Akhilanand, Chen, Haowei, Chen, Mingling, Chen, Simiao, Chi, Gerald, Chichagi, Fatemeh, Chimoriya, Ritesh, Ching, Patrick R, Chitheer, Abdulaal, Cho, So Mi Jemma, Choi, Dong-Woo, Chong, Bryan, Chong, Chean Lin, Chopra, Hitesh, Choudhari, Sonali Gajanan, Choudhary, Rahul, Chu, Dinh-Toi, Chukwu, Isaac Sunday, Chung, Sheng-Chia, Cindi, Zinhle, Cioffi, Iolanda, Cogen, Rebecca M, Columbus, Alyssa, Costanzo, Simona, Couto, Rosa A S, Criqui, Michael H, Cruz-Martins, Natalia, Cuadra-Hernández, Silvia Magali, da Silva, Alanna Gomes, Dadana, Sriharsha, Dadras, Omid, Dai, Xiaochen, Dalal, Koustuv, Dalli, Lachlan L, Damiani, Giovanni, D'Amico, Emanuele, Dandona, Lalit, Dandona, Rakhi, Darwish, Amira Hamed, Das, Saswati, Dashti, Mohsen, Dashtkoohi, Mohadese, Dashtkoohi, Mohammad, Dastmardi, Maedeh, Davletov, Kairat, De la Cruz-Góngora, Vanessa, DeAngelo, Sean, Debele, Aklilu Tamire, Debopadhaya, Shayom, Delgado-Enciso, Ivan, Demessa, Berecha Hundessa, Demetriades, Andreas K, Denova-Gutiérrez, Edgar, Dervišević, Emina, Desai, Hardik Dineshbhai, Desale, Aragaw Tesfaw, Desta, Fikreab, Devanbu, Vinoth Gnana Chellaiyan, Devegowda, Devananda, Dewan, Syed Masudur Rahman, Dhane, Amol S, Dhimal, Meghnath, Dhulipala, Vishal R, Diaz, Michael J, Diress, Mengistie, Dodangeh, Milad, Doegah, Phidelia Theresa, Dohare, Sushil, Doheim, Mohamed Fahmy, Dokova, Klara Georgieva, Dongarwar, Deepa, D'Oria, Mario, Doshi, Ojas Prakashbhai, Doshi, Rajkumar Prakashbhai, Douiri, Abdel, Dowou, Robert Kokou, Dsouza, Ashel Chelsea, Dsouza, Haneil Larson, Dsouza, Viola Savy, Duncan, Bruce B, Duraes, Andre Rodrigues, Dziedzic, Arkadiusz Marian, Ekholuenetale, Michael, El Bayoumy, Ibrahim Farahat, El Sayed Zaki, Maysaa, Elbarazi, Iffat, El-Dahiyat, Faris, Elgendy, Islam Y, Elhadi, Muhammed, El-Huneidi, Waseem, Elmonem, Mohamed A, Elmoselhi, Adel B, Eltaha, Chadi, Emeto, Theophilus I, Esezobor, Christopher Imokhuede, Esfandiari, Negin, Esmaeili, Zahra, Esposito, Francesco, Etoom, Mohammad, Fabin, Natalia, Fadhil, Ibtihal, Fagbamigbe, Adeniyi Francis, Fagbule, Omotayo Francis, Faghani, Shahriar, Fahim, Ayesha, Fakhradiyev, Ildar Ravisovich, Falzone, Luca, Fareed, Mohammad, Fares, Jawad, Farinha, Carla Sofia e Sá, Faris, MoezAlIslam Ezzat Mahmoud, Faris, Pawan Sirwan, Farjoud Kouhanjani, Mohsen, Faro, Andre, Farrokhpour, Hossein, Fasanmi, Abidemi Omolara, Fauk, Nelsensius Klau, Fazeli, Patrick, Fazylov, Timur, Feizkhah, Alireza, Fekadu, Ginenus, Feng, Xiaoqi, Fereshtehnejad, Seyed-Mohammad, Ferrara, Pietro, Ferreira, Nuno, Fetensa, Getahun, Feyisa, Bikila Regassa, Fischer, Florian, Flor, Luisa S, Foley, Kristen Marie, Fonseca, Ana Catarina, Foroumadi, Roham, Foroutan, Behzad, Fortuna, Daniela, Foschi, Matteo, Franklin, Richard Charles, Fridayani, Ni Kadek Yuni, G, Sridevi, Gaal, Peter Andras, Gaidhane, Abhay Motiramji, Gaipov, Abduzhappar, Galali, Yaseen, Gallus, Silvano, Gandhi, Aravind P, Ganesan, Balasankar, Gasevic, Danijela, Gautam, Prem, Gautam, Rupesh K, Gebregergis, Miglas Welay, Gebrehiwot, Mesfin, Gebrekidan, Kebre Gebrekirstos, Getacher, Lemma, Getahun, Genanew K, Getie, Molla, Ghadimi, Delaram J, Ghadirian, Fataneh, Ghaffari Jolfayi, Amir, Ghafourifard, Mansour, Ghasemi, Mohammad-Reza, Ghasemzadeh, Afsaneh, Ghazy, Ramy Mohamed, Gholami, Ehsan, Gholami, Zainab, Ghozy, Sherief, Giannoni Luza, Stefano, Gilani, Jaleed Ahmed, Gill, Tiffany K, Gillum, Richard F, Gindaba, Ebisa Zerihun, Gnedovskaya, Elena V, Goel, Amit, Goldust, Mohamad, Golechha, Mahaveer, Goleij, Pouya, Golinelli, Davide, Gona, Philimon N, Gorini, Giuseppe, Goulart, Alessandra C, Goulart, Barbara Niegia Garcia, Gouravani, Mahdi, Grivna, Michal, Grosso, Giuseppe, Grover, Ashna, Guan, Shi-Yang, Guarducci, Giovanni, Guha, Avirup, Guicciardi, Stefano, Gulati, Snigdha, Gunawardane, Damitha Asanga, Guo, Cui, Guo, Zhifeng, Gupta, Anish Kumar, Gupta, Bhawna, Gupta, Mohak, Gupta, Rahul, Gupta, Rajat Das, Gupta, Rajeev, Gupta, Sapna, Habibzadeh, Farrokh, Hadi, Najah R, Haghani Dogahe, Mohammad, Haghi-Aminjan, Hamed, Haghmorad, Dariush, Haj-Mirzaian, Arvin, Halimi, Aram, Hamdy, Nadia M, Hamidi, Samer, Hamilton, Erin B, Hanif, Asif, Hanifi, Nasrin, Hankey, Graeme J, Hannan, Md Abdul, Haq, Zaim Anan, Hargono, Arief, Harlianto, Netanja I, Haro, Josep Maria, Has, Eka Mishbahatul Marah, Hasaballah, Ahmed I, Hasan, Ikramul, Hasnain, Md Saquib, Hassan, Ikrama, Hassan Zadeh Tabatabaei, Mahgol Sadat, Haubold, Johannes, Havmoeller, Rasmus J, Hay, Simon I, Hbid, Youssef, Hebert, Jeffrey J, Hegazi, Omar E, Heidari, Mohammad, Hemmati, Mehdi, Heyi, Demisu Zenbaba, Hezam, Kamal, Hiraike, Yuta, Hoan, Nguyen Quoc, Holla, Ramesh, Horita, Nobuyuki, Hossain, Md Mahbub, Hosseinzadeh, Hassan, Hosseinzadeh, Mehdi, Hosseinzadeh Adli, Ahmad, Hostiuc, Mihaela, Hostiuc, Sorin, Hu, Ben, Hu, Chengxi, Huang, Junjie, Humayun, Ayesha, Hussain, Salman, Huy, Le Duc, Huynh, Hong-Han, Hwang, Bing-Fang, Ibitoye, Segun Emmanuel, Ikeda, Nayu, Ikiroma, Adalia, Ilesanmi, Olayinka Stephen, Ilic, Irena M, Ilic, Milena D, Imam, Mohammad Tarique, Immurana, Mustapha, Inbaraj, Leeberk Raja, Iqhrammullah, Muhammad, Iradukunda, Arnaud, Irham, Lalu Muhammad, Islam, Md Rabiul, Ismail, Faisal, Ismail, Nahlah Elkudssiah, Iso, Hiroyasu, Isola, Gaetano, Itumalla, Ramaiah, Iwagami, Masao, Iwu, Chidozie Declan CD, J, Vinothini, Jaafari, Jalil, Jacob, Louis, Jafarzadeh, Abdollah, Jahrami, Haitham, Jain, Akhil, Jain, Nityanand, Jairoun, Ammar Abdulrahman, Jaiswal, Abhishek, Jakovljevic, Mihajlo, Jalilzadeh Yengejeh, Reza, Janakiraman, Balamurugan, Jatau, Abubakar Ibrahim, Jayapal, Sathish Kumar, Jayaram, Shubha, Jee, Sun Ha, Jeganathan, Jayakumar, Jegnie, Mihretu, Jema, Alelign Tasew, Jeswani, Bijay Mukesh, Jeyakumar, Angeline, Jha, Anil K, Jha, Ravi Prakash, Ji, Zixiang, Jiang, Heng, Jin, Shuai, Jin, Yingzhao, Jokar, Mohammad, Jonas, Jost B, Joo, Tamas, Jose, Jobinse, Joseph, Nitin, Joshua, Charity Ehimwenma, Joukar, Farahnaz, Jozwiak, Jacek Jerzy, Jürisson, Mikk, Kabir, Ali, Kabir, Md Awal, Kabir, Zubair, Kadashetti, Vidya, Kalani, Rizwan, Kalra, Sanjay, Kamal, Vineet Kumar, Kamireddy, Arun, Kan, Haidong, Kanaan, Mona, Kandel, Himal, Kanmodi, Kehinde Kazeem, Kantar, Rami S, Kapoor, Neeti, Karakasis, Paschalis, Karaye, Ibraheem M, Karch, André, Karimi, Hanie, Karimi, Salah Eddin, Karimi, Yeganeh, Karimi Behnagh, Arman, Karki, Prabin, Kasraei, Hengameh, Kauppila, Joonas H, Kaur, Harkiran, Kaydi, Neda, Kayode, Gbenga A, Kazemi, Foad, Kazemian, Sina, Kesse-Guyot, Emmanuelle, Khader, Yousef Saleh, Khafaie, Morteza Abdullatif, Khaing, Inn Kynn, Khajuria, Himanshu, Khalaji, Amirmohammad, Khalid, Nauman, Khalil, Anees Ahmed, Khan, Asaduzzaman, Khan, Fayaz, Khan, M Nuruzzaman, Khan, Maseer, Khan, Mohammad Jobair, Khan, Moien AB, Khan, Yusra H, Khanmohammadi, Shaghayegh, Khatab, Khaled, Khatatbeh, Haitham, Khatatbeh, Moawiah Mohammad, Khateri, Sorour, Khatib, Mahalaqua Nazli, Khayamzadeh, Maryam, Khayat Kashani, Hamid Reza, Khidri, Feriha Fatima, Khokhar, Manoj, Khosla, Atulya Aman, Khosravi, Majid, Khubchandani, Jagdish, Kian, Saeid, Kim, Kwanghyun, Kim, Min Seo, Kim, Yun Jin, Kimokoti, Ruth W, Kisa, Adnan, Kisa, Sezer, Kolahi, Ali-Asghar, Koly, Kamrun Nahar, Kompani, Farzad, Kondlahalli, Shivakumar KM Marulasiddaiah, Korja, Miikka, Korshunov, Vladimir Andreevich, Korzh, Oleksii, Kosen, Soewarta, Kostev, Karel, Kothari, Nikhil, Kotnis, Ashwin Laxmikant, Koulmane Laxminarayana, Sindhura Lakshmi, Krishan, Kewal, Krishna, Varun, Krishnamoorthy, Vijay, Krishnan, Bindu, Kruja, Jera, Kuate Defo, Barthelemy, Kucuk Bicer, Burcu, Kuddus, Md Abdul, Kuddus, Mohammed, Kugbey, Nuworza, Kulimbet, Mukhtar, Kulkarni, Vishnutheertha, Kumar, Akshay, Kumar, Ashish, Kumar, Dewesh, Kumar, G Anil, Kumar, Nithin, Kumar, Rakesh, Kumaran D, Senthil, Kundu, Amartya, Kundu, Satyajit, Kunutsor, Setor K, Kurmi, Om P, Kusuma, Dian, Kutikuppala, L V Simhachalam, Kuttikkattu, Ambily, Kytö, Ville, La Vecchia, Carlo, Lacey, Ben, Lahariya, Chandrakant, Lal, Dharmesh Kumar, Lallukka, Tea, Lám, Judit, Landires, Iván, Larsson, Anders O, Lasrado, Savita, Latifinaibin, Kaveh, Lauriola, Paolo, Lavados, Pablo M, Lawal, Basira Kankia, Le, Long Khanh Dao, Le, Nhi Huu Hanh, Le, Thao Thi Thu, Le, Trang Diep Thanh, Lee, Paul H, Lee, Seung Won, Lee, Wei-Chen, Lee, Yo Han, Li, Ming-Chieh, Li, Wei, Li, Xiaopan, Li, Yichong, Lim, Lee-Ling, Lim, Stephen S, Lin, John C, Lindholm, Daniel, Linn, Shai, Liu, Gang, Liu, Runben, Liu, Shuke, Liu, Xiaofeng, Liu, Xuefeng, Llanaj, Erand, Lo, Chun-Han, Lo, Warren David, Lohner, Valerie, López-Gil, José Francisco, Lorenzovici, László, Lorkowski, Stefan, Lotufo, Paulo A, Lucchetti, Giancarlo, Luo, Lisha, Lusk, Jay B, Ma, Zheng Feei, Machoy, Monika, Madadizadeh, Farzan, Maddison, Ralph, Mahmoudi, Elham, Mahmoudvand, Golnaz, Makram, Omar M, Malakan Rad, Elaheh, Malhotra, Kashish, Malik, Ahmad Azam, Malik, Iram, Mallhi, Tauqeer Hussain, Malta, Deborah Carvalho, Mamun, Abdullah A, Manla, Yosef, Mansouri, Mohammad Hadi, Mansouri, Pejman, Mansouri, Vahid, Mansournia, Mohammad Ali, Mantovani, Lorenzo Giovanni, Manu, Emmanuel, Marateb, 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Ibrahim, Mohammed, Hussen, Mohammed, Mustapha, Mohammed, Salahuddin, Mohammed, Shafiu, Mokdad, Ali H, Molavi Vardanjani, Hossein, Molokhia, Mariam, Momani, Shaher, Monasta, Lorenzo, Moni, Mohammad Ali, Montazeri, Fateme, Moodi Ghalibaf, AmirAli, Moosazadeh, Mahmood, Moradi, Maryam, Moradi, Yousef, Moraga, Paula, Morawska, Lidia, Moreira, Rafael Silveira, Morrison, Shane Douglas, Mosaddeghi Heris, Reza, Mossialos, Elias, Mousavi, Parsa, Msherghi, Ahmed, Mubarik, Sumaira, Muccioli, Lorenzo, Mulita, Admir, Muniyandi, Malaisamy, Munjal, Kavita, Murillo-Zamora, Efren, Muthu, Sathish, Myung, Woojae, Nabavi, Amin, Nabhan, Ashraf Fawzy, Nafei, Ayoub, Nagarajan, Ahamarshan Jayaraman, Naghavi, Pirouz, Naik, Ganesh R, Naik, Gurudatta, Naimzada, Mukhammad David, Nair, Sanjeev, Nair, Tapas Sadasivan, Najdaghi, Soroush, Najmuldeen, Hastyar Hama Rashid, Nakhostin Ansari, Noureddin, Nangia, Vinay, Narasimha Swamy, Sreenivas, Nargus, Shumaila, Narimani Davani, Delaram, Nascimento, Bruno Ramos, 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