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7,945 results on '"Dias, A. M."'

1. Optimizing Photoplethysmography-Based Sleep Staging Models by Leveraging Temporal Context for Wearable Devices Applications

Author

Quino, Joseph A. P., Cardenas, Diego A. C., Toledo, Marcelo A. F., Dias, Felipe M., Ribeiro, Estela, Krieger, Jose E., and Gutierrez, Marco A.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Human-Computer Interaction, Computer Science - Machine Learning
Abstract

Accurate sleep stage classification is crucial for diagnosing sleep disorders and evaluating sleep quality. While polysomnography (PSG) remains the gold standard, photoplethysmography (PPG) is more practical due to its affordability and widespread use in wearable devices. However, state-of-the-art sleep staging methods often require prolonged continuous signal acquisition, making them impractical for wearable devices due to high energy consumption. Shorter signal acquisitions are more feasible but less accurate. Our work proposes an adapted sleep staging model based on top-performing state-of-the-art methods and evaluates its performance with different PPG segment sizes. We concatenate 30-second PPG segments over 15-minute intervals to leverage longer segment contexts. This approach achieved an accuracy of 0.75, a Cohen's Kappa of 0.60, an F1-Weighted score of 0.74, and an F1-Macro score of 0.60. Although reducing segment size decreased sensitivity for deep and REM stages, our strategy outperformed single 30-second window methods, particularly for these stages., Comment: 11 pages, 5 figures, 1 table

Published
2024

2. The impact of higher derivative corrections to General Relativity on black hole mergers

Author

Dias, João M., Frassino, Antonia M., Lopes, David C., Paccoia, Valentin D., and Rocha, Jorge V.

Subjects
General Relativity and Quantum Cosmology
Abstract

The merging of two black holes is a notoriously difficult process to describe exactly. Nevertheless, the hindrances posed by gravity's nonlinearity can be circumvented by focusing on the strict extreme mass ratio limit, in which one of the black holes is infinitely larger than the other. Such an approach has been developed by Emparan and Mart\'inez and applied within General Relativity to investigate the time evolution of event horizons melding, using nothing but elementary concepts in gravitational physics and simple integrations of geodesics. We apply this strategy to study black hole mergers in higher derivative gravity, in order to assess how the defining characteristics of the fusion process change as the gravitational theory is modified. We adopt the case of Einsteinian cubic gravity for concreteness, and determine how the mergers' duration and the relative area increment change as the theory's single coupling parameter is varied., Comment: v2: 13 pages, 11 figures, minor modifications and an added subsection about entropy and area growth (including two new figures)

Published
2024

3. Exploring the limitations of blood pressure estimation using the photoplethysmography signal

Author

Dias, Felipe M., Cardenas, Diego A. C., Toledo, Marcelo A. F., Oliveira, Filipe A. C., Ribeiro, Estela, Krieger, Jose E., and Gutierrez, Marco A.

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

Hypertension, a leading contributor to cardiovascular morbidity, underscores the need for accurate and continuous blood pressure (BP) monitoring. Photoplethysmography (PPG) presents a promising approach to this end. However, the precision of BP estimates derived from PPG signals has been the subject of ongoing debate, necessitating a comprehensive evaluation of their effectiveness and constraints. We developed a calibration-based Siamese ResNet model for BP estimation, using a signal input paired with a reference BP reading. We compared the use of normalized PPG (N-PPG) against the normalized Invasive Arterial Blood Pressure (N-IABP) signals as input. The N-IABP signals do not directly present systolic and diastolic values but theoretically provide a more accurate BP measure than PPG signals since it is a direct pressure sensor inside the body. Our strategy establishes a critical benchmark for PPG performance, realistically calibrating expectations for PPG's BP estimation capabilities. Nonetheless, we compared the performance of our models using different signal-filtering conditions to evaluate the impact of filtering on the results. We evaluated our method using the AAMI and the BHS standards employing the VitalDB dataset. The N-IABP signals meet with AAMI standards for both Systolic Blood Pressure (SBP) and Diastolic Blood Pressure (DBP), with errors of 1.29+-6.33mmHg for systolic pressure and 1.17+-5.78mmHg for systolic and diastolic pressure respectively for the raw N-IABP signal. In contrast, N-PPG signals, in their best setup, exhibited inferior performance than N-IABP, presenting 1.49+-11.82mmHg and 0.89+-7.27mmHg for systolic and diastolic pressure respectively. Our findings highlight the potential and limitations of employing PPG for BP estimation, showing that these signals contain information correlated to BP but may not be sufficient for predicting it accurately., Comment: 17 pages, 7 figures, 3 tables

Published
2024

8. Triangle singularity in the $J/\psi \to \phi \pi^+ a_0^-(\pi^- \eta),\; \phi \pi^- a_0^+(\pi^+ \eta)$ decays

Author

Xiao, C. W., Dias, J. M., Dai, L. R., Liang, W. H., and Oset, E.

Subjects
High Energy Physics - Phenomenology
Abstract

We study the $J/\psi \to \phi \pi^+ a_0(980)^- (a_0^- \to \pi^- \eta)$ decay, evaluating the double mass distribution in terms of the $\pi^- \eta$ and $\pi^+ a^-_0$ invariant masses. We show that the $\pi^- \eta$ mass distribution exhibits the typical cusp structure of the $a_0(980)$ seen in recent high statistics experiments, and the $\pi^+ a^-_0$ spectrum shows clearly a peak around $M_{\rm inv}(\pi^+ a^-_0)=1420 \,{\rm MeV}$, corresponding to a triangle singularity. When integrating over the two invariant masses we find a branching ratio for this decay of the order of $10^{-5}$, which is easily accessible in present laboratories. We also call attention to the fact that the signal obtained is compatible with a bump experimentally observed in the $\eta \pi^+\pi^-$ mass distribution in the $J/\psi \to \phi \eta \pi^+\pi^-$ decay and encourage further analysis to extract from there the $\phi \pi^+ a_0^-$ and $\phi \pi^- a_0^+$ decay modes., Comment: 8 pages, 3 figures; V2: discussion added, references added, version to appear in Phys. Rev. D

Published
2024

9. $D^+ \to K_s^0 \pi^+ \eta$ reaction and $a_0(980)^+$

Author

Ikeno, Natsumi, Dias, Jorgivan M., Liang, Wei-Hong, and Oset, Eulogio

Subjects
High Energy Physics - Phenomenology
Abstract

We study the $D^+ \to \bar K^0 \pi^+ \eta$ reaction where the $a_0(980)$ excitation plays a dominant role. We consider mechanisms of external and internal emission at the quark level, hadronize the $q \bar q$ components into two mesons and allow these mesons to undergo final state interaction where the $a_0(980)$ state is generated. While the $a_0(980)$ production is the dominant term, we also find other terms in the reaction that interfere with this production mode and, through interference with it, lead to a shape of the $a_0(980)$ significantly different from the one observed in other experiments, with an apparently much larger width., Comment: 8 pages, 12 figures

Published
2024

11. A unique serum IgG glycosylation signature predicts development of Crohn’s disease and is associated with pathogenic antibodies to mannose glycan

Author

Gaifem, Joana, Rodrigues, Cláudia S., Petralia, Francesca, Alves, Inês, Leite-Gomes, Eduarda, Cavadas, Bruno, Dias, Ana M., Moreira-Barbosa, Catarina, Revés, Joana, Laird, Renee M., Novokmet, Mislav, Štambuk, Jerko, Habazin, Siniša, Turhan, Berk, Gümüş, Zeynep H., Ungaro, Ryan, Torres, Joana, Lauc, Gordan, Colombel, Jean-Frederic, Porter, Chad K., and Pinho, Salomé S.

Published
2024
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15. A machine-learning sleep-wake classification model using a reduced number of features derived from photoplethysmography and activity signals

Author

Almeida, Douglas A., Dias, Felipe M., Toledo, Marcelo A. F., Cardenas, Diego A. C., Oliveira, Filipe A. C., Ribeiro, Estela, Krieger, Jose E., and Gutierrez, Marco A.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Sleep is a crucial aspect of our overall health and well-being. It plays a vital role in regulating our mental and physical health, impacting our mood, memory, and cognitive function to our physical resilience and immune system. The classification of sleep stages is a mandatory step to assess sleep quality, providing the metrics to estimate the quality of sleep and how well our body is functioning during this essential period of rest. Photoplethysmography (PPG) has been demonstrated to be an effective signal for sleep stage inference, meaning it can be used on its own or in a combination with others signals to determine sleep stage. This information is valuable in identifying potential sleep issues and developing strategies to improve sleep quality and overall health. In this work, we present a machine learning sleep-wake classification model based on the eXtreme Gradient Boosting (XGBoost) algorithm and features extracted from PPG signal and activity counts. The performance of our method was comparable to current state-of-the-art methods with a Sensitivity of 91.15 $\pm$ 1.16%, Specificity of 53.66 $\pm$ 1.12%, F1-score of 83.88 $\pm$ 0.56%, and Kappa of 48.0 $\pm$ 0.86%. Our method offers a significant improvement over other approaches as it uses a reduced number of features, making it suitable for implementation in wearable devices that have limited computational power., Comment: 8 pages, 3 figures

Published
2023

16. Machine Learning-Based Diabetes Detection Using Photoplethysmography Signal Features

Author

Oliveira, Filipe A. C., Dias, Felipe M., Toledo, Marcelo A. F., Cardenas, Diego A. C., Almeida, Douglas A., Ribeiro, Estela, Krieger, Jose E., and Gutierrez, Marco A.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Signal Processing
Abstract

Diabetes is a prevalent chronic condition that compromises the health of millions of people worldwide. Minimally invasive methods are needed to prevent and control diabetes but most devices for measuring glucose levels are invasive and not amenable for continuous monitoring. Here, we present an alternative method to overcome these shortcomings based on non-invasive optical photoplethysmography (PPG) for detecting diabetes. We classify non-Diabetic and Diabetic patients using the PPG signal and metadata for training Logistic Regression (LR) and eXtreme Gradient Boosting (XGBoost) algorithms. We used PPG signals from a publicly available dataset. To prevent overfitting, we divided the data into five folds for cross-validation. By ensuring that patients in the training set are not in the testing set, the model's performance can be evaluated on unseen subjects' data, providing a more accurate assessment of its generalization. Our model achieved an F1-Score and AUC of $58.8\pm20.0\%$ and $79.2\pm15.0\%$ for LR and $51.7\pm16.5\%$ and $73.6\pm17.0\%$ for XGBoost, respectively. Feature analysis suggested that PPG morphological features contains diabetes-related information alongside metadata. Our findings are within the same range reported in the literature, indicating that machine learning methods are promising for developing remote, non-invasive, and continuous measurement devices for detecting and preventing diabetes., Comment: 11 pages, 6 figures

Published
2023

17. Quality Assessment of Photoplethysmography Signals For Cardiovascular Biomarkers Monitoring Using Wearable Devices

Author

Dias, Felipe M., Toledo, Marcelo A. F., Cardenas, Diego A. C., Almeida, Douglas A., Oliveira, Filipe A. C., Ribeiro, Estela, Krieger, Jose E., and Gutierrez, Marco A.

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Electrical Engineering and Systems Science - Signal Processing
Abstract

Photoplethysmography (PPG) is a non-invasive technology that measures changes in blood volume in the microvascular bed of tissue. It is commonly used in medical devices such as pulse oximeters and wrist worn heart rate monitors to monitor cardiovascular hemodynamics. PPG allows for the assessment of parameters (e.g., heart rate, pulse waveform, and peripheral perfusion) that can indicate conditions such as vasoconstriction or vasodilation, and provides information about microvascular blood flow, making it a valuable tool for monitoring cardiovascular health. However, PPG is subject to a number of sources of variations that can impact its accuracy and reliability, especially when using a wearable device for continuous monitoring, such as motion artifacts, skin pigmentation, and vasomotion. In this study, we extracted 27 statistical features from the PPG signal for training machine-learning models based on gradient boosting (XGBoost and CatBoost) and Random Forest (RF) algorithms to assess quality of PPG signals that were labeled as good or poor quality. We used the PPG time series from a publicly available dataset and evaluated the algorithm s performance using Sensitivity (Se), Positive Predicted Value (PPV), and F1-score (F1) metrics. Our model achieved Se, PPV, and F1-score of 94.4, 95.6, and 95.0 for XGBoost, 94.7, 95.9, and 95.3 for CatBoost, and 93.7, 91.3 and 92.5 for RF, respectively. Our findings are comparable to state-of-the-art reported in the literature but using a much simpler model, indicating that ML models are promising for developing remote, non-invasive, and continuous measurement devices., Comment: 9 pages

Published
2023

18. Isometric Exercise Training and Arterial Hypertension: An Updated Review

Author

Edwards, Jamie J., Coleman, Damian A., Ritti-Dias, Raphael M., Farah, Breno Q., Stensel, David J., Lucas, Sam J. E., Millar, Philip J., Gordon, Ben D. H., Cornelissen, Véronique, Smart, Neil A., Carlson, Debra J., McGowan, Cheri, Swaine, Ian, Pescatello, Linda S., Howden, Reuben, Bruce-Low, Stewart, Farmer, Christopher K. T., Leeson, Paul, Sharma, Rajan, and O’Driscoll, Jamie M.

Published
2024
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22. Black hole-wormhole collisions and the emergence of islands

Author

Dias, João M., Frassino, Antonia M., Paccoia, Valentin D., and Rocha, Jorge V.

Subjects
General Relativity and Quantum Cosmology, High Energy Physics - Theory
Abstract

We use ray-tracing techniques to determine the evolution of the event horizon of a large black hole that ``gobbles'' a tiny, traversable wormhole. This calculation has physical meaning in the extreme mass ratio limit. Two setups are considered: a single-mouth wormhole connecting two otherwise independent universes, and a double-mouth zero-length wormhole within the same universe. In the first setting it turns out that, at early times, there exist two disconnected horizons, one in each universe, which then merge as the wormhole falls into the large black hole. In the second setup, we observe the appearance of an `island', a region of spacetime that is spatially disconnected from the exterior of the black hole, but in causal contact with future null infinity. The island shrinks as time evolves and eventually disappears after sufficient time has elapsed, as compared to the distance between the two mouths. This provides a communication channel with the interior of the large black hole for a certain time interval. We compute numerically the lifetime of the island and verify that it depends linearly on the inter-mouth distance. Extending the analysis to wormholes with finite length, we show that the achronal averaged null energy condition prevents the appearance of islands., Comment: 15 pages, 10 figures; v2: minor corrections, matches published version

Published
2023
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23. STCF Conceptual Design Report: Volume 1 -- Physics & Detector

Author

Achasov, M., Ai, X. C., Aliberti, R., An, L. P., An, Q., Bai, X. Z., Bai, Y., Bakina, O., Barnyakov, A., Blinov, V., Bobrovnikov, V., Bodrov, D., Bogomyagkov, A., Bondar, A., Boyko, I., Bu, Z. H., Cai, F. M., Cai, H., Cao, J. J., Cao, Q. H., Cao, Z., Chang, Q., Chao, K. T., Chen, D. Y., Chen, H., Chen, H. X., Chen, J. F., Chen, K., Chen, L. L., Chen, P., Chen, S. L., Chen, S. M., Chen, S., Chen, S. P., Chen, W., Chen, X. F., Chen, X., Chen, Y., Chen, Y. Q., Cheng, H. Y., Cheng, J., Cheng, S., Dai, J. P., Dai, L. Y., Dai, X. C., Dedovich, D., Denig, A., Denisenko, I., Ding, D. Z., Dong, L. Y., Dong, W. H., Druzhinin, V., Du, D. S., Du, Y. J., Du, Z. G., Duan, L. M., Epifanov, D., Fan, Y. L., Fang, S. S., Fang, Z. J., Fedotovich, G., Feng, C. Q., Feng, X., Feng, Y. T., Fu, J. L., Gao, J., Ge, P. S., Geng, C. Q., Geng, L. S., Gilman, A., Gong, L., Gong, T., Gradl, W., Gu, J. L., Escalante, A. G., Gui, L. C., Guo, F. K., Guo, J. C., Guo, J., Guo, Y. P., Guo, Z. H., Guskov, A., Han, K. L., Han, L., Han, M., Hao, X. Q., He, J. B., He, S. Q., He, X. G., He, Y. L., He, Z. B., Heng, Z. X., Hou, B. L., Hou, T. J., Hou, Y. R., Hu, C. Y., Hu, H. M., Hu, K., Hu, R. J., Hu, X. H., Hu, Y. C., Hua, J., Huang, G. S., Huang, J. S., Huang, M., Huang, Q. Y., Huang, W. Q., Huang, X. T., Huang, X. J., Huang, Y. B., Huang, Y. S., Hüsken, N., Ivanov, V., Ji, Q. P., Jia, J. J., Jia, S., Jia, Z. K., Jiang, H. B., Jiang, J., Jiang, S. Z., Jiao, J. B., Jiao, Z., Jing, H. J., Kang, X. L., Kang, X. S., Ke, B. C., Kenzie, M., Khoukaz, A., Koop, I., Kravchenko, E., Kuzmin, A., Lei, Y., Levichev, E., Li, C. H., Li, C., Li, D. Y., Li, F., Li, G., Li, H. B., Li, H., Li, H. N., Li, H. J., Li, H. L., Li, J. M., Li, J., Li, L., Li, L. Y., Li, N., Li, P. R., Li, R. H., Li, S., Li, T., Li, W. J., Li, X. H., Li, X. Q., Li, Y., Li, Y. Y., Li, Z. J., Liang, H., Liang, J. H., Liao, G. R., Liao, L. Z., Liao, Y., Lin, C. X., Lin, X. S., Liu, B. J., Liu, C. W., Liu, D., Liu, F., Liu, G. M., Liu, H. B., Liu, J., Liu, J. J., Liu, J. B., Liu, K., Liu, K. Y., Liu, L., Liu, Q., Liu, S. B., Liu, T., Liu, X., Liu, Y. W., Liu, Y., Liu, Y. L., Liu, Z. Q., Liu, Z. Y., Liu, Z. W., Logashenko, I., Long, Y., Lu, C. G., Lu, N., Lü, Q. F., Lu, Y., Lv, Z., Lukin, P., Luo, F. J., Luo, T., Luo, X. F., Lyu, H. J., Lyu, X. R., Ma, J. P., Ma, P., Ma, Y., Maas, F., Malde, S., Matvienko, D., Meng, Z. X., Mitchell, R., Dias, J. M., Nefediev, A., Nefedov, Y., Olsen, S. L., Ouyang, Q., Pakhlov, P., Pakhlova, G., Pan, X., Pan, Y., Passemar, E., Pei, Y. P., Peng, H. P., Peng, L., Peng, X. Y., Peng, X. J., Peters, K., Pivovarov, S., Pyata, E., Qi, B. B., Qi, Y. Q., Qian, W. B., Qian, Y., Qiao, C. F., Qin, J. J., Qin, L. Q., Qin, X. S., Qiu, T. L., Rademacker, J., Redmer, C. F., Sang, H. Y., Saur, M., Shan, W., Shan, X. Y., Shang, L. L., Shao, M., Shekhtman, L., Shen, C. P., Shen, J. M., Shen, Z. T., Shi, H. C., Shi, X. D., Shwartz, B., Sokolov, A., Song, J. J., Song, W. M., Song, Y., Song, Y. X., Sukharev, A., Sun, J. F., Sun, L., Sun, X. M., Sun, Y. J., Sun, Z. P., Tang, J., Tang, S. S., Tang, Z. B., Tian, C. H., Tian, J. S., Tikhonov, Y., Todyshev, K., Uglov, T., Vorobyev, V., Wan, B. D., Wang, B. L., Wang, B., Wang, D. Y., Wang, G. Y., Wang, G. L., Wang, H. L., Wang, J., Wang, J. H., Wang, J. C., Wang, M. L., Wang, R., Wang, S. B., Wang, W., Wang, W. P., Wang, X. C., Wang, X. D., Wang, X. L., Wang, X. P., Wang, X. F., Wang, Y. D., Wang, Y. P., Wang, Y. Q., Wang, Y. L., Wang, Y. G., Wang, Z. Y., Wang, Z. L., Wang, Z. G., Wei, D. H., Wei, X. L., Wei, X. M., Wen, Q. G., Wen, X. J., Wilkinson, G., Wu, B., Wu, J. J., Wu, L., Wu, P. W., Wu, T. W., Wu, Y. S., Xia, L., Xiang, T., Xiao, C. W., Xiao, D., Xiao, M., Xie, Y. H., Xing, Y., Xing, Z. Z., Xiong, X. N., Xu, F. R., Xu, J., Xu, L. L., Xu, Q. N., Xu, X. C., Xu, X. P., Xu, Y. C., Xu, Y. P., Xu, Y., Xu, Z. Z., Xuan, D. W., Xue, F. F., Yan, L., Yan, M. J., Yan, W. B., Yan, W. C., Yan, X. S., Yang, B. F., Yang, C., Yang, H. J., Yang, H. R., Yang, H. T., Yang, J. F., Yang, S. L., Yang, Y. D., Yang, Y. H., Yang, Y. S., Yang, Y. L., Yang, Z. Y., Yao, D. L., Yin, H., Yin, X. H., Yokozaki, N., You, S. Y., You, Z. Y., Yu, C. X., Yu, F. S., Yu, G. L., Yu, H. L., Yu, J. S., Yu, J. Q., Yuan, L., Yuan, X. B., Yue, Y. F., Zeng, M., Zeng, S., Zhang, A. L., Zhang, B. W., Zhang, G. Y., Zhang, G. Q., Zhang, H. J., Zhang, H. B., Zhang, J. Y., Zhang, J. L., Zhang, J., Zhang, L., Zhang, L. M., Zhang, R., Zhang, S. L., Zhang, T., Zhang, X., Zhang, Y., Zhang, Y. X., Zhang, Y. T., Zhang, Y. F., Zhang, Y. C., Zhang, Y. M., Zhang, Y. L., Zhang, Z. H., Zhang, Z. Y., Zhao, H. Y., Zhao, J., Zhao, L., Zhao, M. G., Zhao, Q., Zhao, R. G., Zhao, R. P., Zhao, Z. G., Zhao, Z. X., Zhemchugov, A., Zheng, B., Zheng, L., Zheng, Q. B., Zheng, R., Zheng, Y. H., Zhong, X. H., Zhou, H. J., Zhou, H. Q., Zhou, H., Zhou, S. H., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, Y. L., Zhou, Y., Zhou, Y. X., Zhou, Z. Y., Zhu, J. Y., Zhu, K., Zhu, R. D., Zhu, R. L., Zhu, S. H., Zhu, Y. C., Zhu, Z. A., Zhukova, V., Zhulanov, V., Zou, B. S., and Zuo, Y. B.

Subjects
High Energy Physics - Experiment, High Energy Physics - Phenomenology, Physics - Instrumentation and Detectors
Abstract

The Super $\tau$-Charm facility (STCF) is an electron-positron collider proposed by the Chinese particle physics community. It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of $0.5\times 10^{35}{\rm cm}^{-2}{\rm s}^{-1}$ or higher. The STCF will produce a data sample about a factor of 100 larger than that by the present $\tau$-Charm factory -- the BEPCII, providing a unique platform for exploring the asymmetry of matter-antimatter (charge-parity violation), in-depth studies of the internal structure of hadrons and the nature of non-perturbative strong interactions, as well as searching for exotic hadrons and physics beyond the Standard Model. The STCF project in China is under development with an extensive R\&D program. This document presents the physics opportunities at the STCF, describes conceptual designs of the STCF detector system, and discusses future plans for detector R\&D and physics case studies.

Published
2023
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24. Radiative decays of the spin-$2$ partner of $X(3872)$

Author

Shi, Pan-Pan, Dias, Jorgivan M., and Guo, Feng-Kun

Subjects
High Energy Physics - Phenomenology, High Energy Physics - Experiment, Nuclear Experiment, Nuclear Theory
Abstract

It has been generally expected that the $X(3872)$ has a spin-2 partner, $X_2$, with quantum numbers $J^{PC}=2^{++}$. In the hadronic molecular model, its mass was predicted to be below the $D^*\bar D^*$ threshold, and the new structure reported in the $\gamma \psi(2S)$ invariant mass distribution by the Belle Collaboration with mass $M= (4014.3 \pm 4.0 \pm 1.5)$ MeV and decay width $\Gamma= (4 \pm 11 \pm 6)$ MeV, with a global significance of 2.8 $\sigma$, is a nice candidate for it. We consider the radiative decay widths for the $X_{2}\to \gamma\psi$ with $\psi=J/\psi, \psi(2S)$ treating the $X_2$ as a $D^*\bar{D}^*$ shallow bound state, and estimate the events of $X_2$ in two-photon collisions that can be collected in the $\gamma J/\psi\to\gamma\ell^+\ell^-$ ($\ell=e,\mu$) final states at Belle. Based on the upper limit for the ratio of decay widths of $X(3872)\to \gamma \psi(2S)$ and $X(3872)\to \gamma J/\psi$ measured by BESIII, we predict the similar ratio $\Gamma(X_2\to \gamma \psi(2S))/\Gamma(X_2\to \gamma J/\psi)$ to be smaller than $1.0$. We suggest searching for the $X_2$ signal in the $\gamma J/\psi$ invariant mass distribution via two-photon fusions. The results will lead to insights into both the $X(3872)$ and the new structure observed by Belle., Comment: 21 pages, 3 figures. Version to appear in PLB

Published
2023
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25. On the $\eta_1(1855)$, $\pi_1(1400)$ and $\pi_1(1600)$ as dynamically generated states and their SU(3) partners

Author

Yan, Mao-Jun, Dias, Jorgivan M., Guevara, Adolfo, Guo, Feng-Kun, and Zou, Bing-Song

Subjects
High Energy Physics - Phenomenology
Abstract

In this work, we interpret the newly observed $\eta_1(1855)$ resonance with exotic $J^{PC}=1^{-+}$ quantum numbers in the $I=0$ sector, reported by the BESIII Collaboration, as a dynamically generated state from the interaction between the lightest pseudoscalar mesons and axial-vector mesons. The interaction is derived from the lowest order chiral Lagrangian from which the Weinberg-Tomozawa term is obtained, describing the transition amplitudes among the relevant channels, which are then unitarized using the Bethe-Salpeter equation, according to the chiral unitary approach. We evaluate the $\eta_1(1855)$ decays into the $\eta\eta^{\prime}$ and $K\bar{K}^*\pi$ channels and find that the latter has a larger branching fraction. We also investigate its SU(3) partners, and according to our findings, the $\pi_1(1400)$ and $\pi_1(1600)$ structures may correspond to dynamically generated states, with the former one coupled mostly to the $b_1\pi$ component and the latter one coupled to the $K_1(1270)\bar{K}$ channel. In particular, our result for the ratio $\Gamma(\pi_1(1600)\to f_1(1285)\pi)/ \Gamma(\pi_1(1600)\to \eta^{\prime}\pi)$ is consistent with the measured value, which supports our interpretation for the higher $\pi_1$ state. We also report two poles with a mass about 1.7~GeV in the $I=1/2$ sector, which may be responsible for the $K^*(1680)$. We suggest searching for two additional $\eta_1$ exotic mesons with masses around 1.4 and 1.7~GeV. In particular, the predicted $\eta_1(1700)$ is expected to have a width around 0.1~GeV and can decay easily into $K\bar K\pi\pi$., Comment: Accepted for publication in Universe with a DOI link https://doi.org/10.3390/universe9020109

Published
2023
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26. Sovereign Citizen on Digital Regulated Services Ecosystem

Author

Osório, A. Luís, Camarinha-Matos, Luis M., Gonçalves, Carlos, Dias, Tiago M., Rannenberg, Kai, Editor-in-Chief, Soares Barbosa, Luís, Editorial Board Member, Carette, Jacques, Editorial Board Member, Tatnall, Arthur, Editorial Board Member, Neuhold, Erich J., Editorial Board Member, Stiller, Burkhard, Editorial Board Member, Stettner, Lukasz, Editorial Board Member, Pries-Heje, Jan, Editorial Board Member, M. Davison, Robert, Editorial Board Member, Rettberg, Achim, Editorial Board Member, Furnell, Steven, Editorial Board Member, Mercier-Laurent, Eunika, Editorial Board Member, Winckler, Marco, Editorial Board Member, Malaka, Rainer, Editorial Board Member, Camarinha-Matos, Luis M., editor, Ortiz, Angel, editor, Boucher, Xavier, editor, and Barthe-Delanoë, Anne-Marie, editor

Published
2024
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27. Assessing the Impact of Wetland Conversion to Unregulated Urban Development on Flood Risk: A GIS-Based Analysis of the Meda Ela and Pinga Oya Catchments in Kandy District

Author

Dias, W. M. S. S., Dissanayake, D. M. L., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Dissanayake, Ranjith, editor, Mendis, Priyan, editor, De Silva, Sudhira, editor, Fernando, Shiromal, editor, Konthesingha, Chaminda, editor, Attanayake, Upul, editor, and Gajanayake, Pradeep, editor

Published
2024
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28. Highway Solarisation in Sri Lanka: A GIS-Based Evaluation of Solar Energy Potential on the Southern Expressway

Author

Dias, W. M. S. S., Basnayaka, W. B. M. R. S., di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Cui, Zhen-Dong, Series Editor, Dissanayake, Ranjith, editor, Mendis, Priyan, editor, De Silva, Sudhira, editor, Fernando, Shiromal, editor, Konthesingha, Chaminda, editor, Attanayake, Upul, editor, and Gajanayake, Pradeep, editor

Published
2024
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29. Development of a New Bicycle Drive Hub

Author

Pereira, António B., Dias, Pedro M., Ramos, António M., Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Haddar, Mohamed, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Rui, Xiaoting, editor, and Liu, Caishan, editor

Published
2024
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30. Exploring the molecular pathways of the activation process in PPARγ recurrent bladder cancer mutants.

Author

de Oliveira, Vinícius M., Malospirito, Caique C., da Silva, Fernando B., Videira, Natália B., Dias, Marieli M. G., Sanches, Murilo N., Leite, Vitor B. P., and Figueira, Ana Carolina M.

Subjects
GAIN-of-function mutations, BLADDER cancer, ADIPOGENESIS, CHEMINFORMATICS, HOMEOSTASIS
Abstract

The intricate involvement of Peroxisome Proliferator-Activated Receptor Gamma (PPARγ) in glucose homeostasis and adipogenesis is well-established. However, its role in cancer, particularly luminal bladder cancer, remains debated. The overexpression and activation of PPARγ are implicated in tumorigenesis. Specific gain-of-function mutations (M280I, I290M, and T475M) within the ligand-binding domain of PPARγ are associated with bladder cancer and receptor activation. The underlying molecular pathways prompted by these mutations remain unclear. We employed a dual-basin structure-based model (db-SBM) to explore the conformational dynamics between the inactive and active states of PPARγ and examined the effects of the M280I, I290M, and T475M mutations. Our findings, consistent with the existing literature, reveal heightened ligand-independent transcriptional activity in the I290M and T475M mutants. Both mutants showed enhanced stabilization of the active state compared to the wild-type receptor, with the I290M mutation promoting a specific transition route, making it a prime candidate for further study. Electrostatic analysis identified residues K303 and E488 as pivotal in the I290M activation cascade. Biophysical assays confirmed that disrupting the K303–E488 interaction reduced the thermal stabilization characteristic of the I290M mutation. Our study demonstrates the predictive capabilities of combining simulation and cheminformatics methods, validated by biochemical experiments, to gain insights into molecular activation mechanisms and identify target residues for protein modulation. [ABSTRACT FROM AUTHOR]

Published
2024
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32. Active Gaze Control for Foveal Scene Exploration

Author

Dias, Alexandre M. F., Simões, Luís, Moreno, Plinio, and Bernardino, Alexandre

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Systems and Control
Abstract

Active perception and foveal vision are the foundations of the human visual system. While foveal vision reduces the amount of information to process during a gaze fixation, active perception will change the gaze direction to the most promising parts of the visual field. We propose a methodology to emulate how humans and robots with foveal cameras would explore a scene, identifying the objects present in their surroundings with in least number of gaze shifts. Our approach is based on three key methods. First, we take an off-the-shelf deep object detector, pre-trained on a large dataset of regular images, and calibrate the classification outputs to the case of foveated images. Second, a body-centered semantic map, encoding the objects classifications and corresponding uncertainties, is sequentially updated with the calibrated detections, considering several data fusion techniques. Third, the next best gaze fixation point is determined based on information-theoretic metrics that aim at minimizing the overall expected uncertainty of the semantic map. When compared to the random selection of next gaze shifts, the proposed method achieves an increase in detection F1-score of 2-3 percentage points for the same number of gaze shifts and reduces to one third the number of required gaze shifts to attain similar performance., Comment: 6 pages, 8 figures, ICDL 2022 (International Conference on Development and Learning, formerly ICDL-EpiRob)

Published
2022

47. Perceptions and Determinants of Adopting Sustainable Eating Behaviours among University Students in Canada: A Qualitative Study Using Focus Group Discussions

Author

Mollaei, Sadaf, Minaker, Leia M., Lynes, Jennifer K., and Dias, Goretty M.

Abstract

Purpose: University students are a unique population with great potential to adopt eating habits that promote positive human and planetary health outcomes. The purpose of this study is to illustrate the current perceptions of sustainable eating behaviours among the students and to examine the determinants of sustainable eating behaviours. Design/methodology/approach: Data were collected from December 2020 to May 2021 through focus group discussions among university students in Ontario, facilitated through synchronous online sessions. There were 21 student participants during the course of five focus group sessions (4-5 participants per session) from various departments within the university. The discussions were transcribed and analyzed for main themes and concepts using open coding; deductive coding based on the framework by Deliens et al. as well as the literature; and inductive coding for emerging themes. Findings: The students had different perceptions about what constituted sustainable eating behaviours, some of which were not based on fact. A variety of individual, environmental (macro, micro and social) and university characteristics were mentioned as factors influencing sustainable food choices, with "food literacy" and "campus food" being the top two factors. Originality/value: This study presents a novel and holistic overview of how sustainable eating behaviours and sustainable foods are perceived among university students and identifies the perceived determinants of adopting sustainable eating behaviours. This study helps with identifying opportunities to promote sustainable eating behaviours among university students and the design/implementation of informed interventions and policies aimed at improving eating behaviours.

Published
2023
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49. General and abdominal adiposity and hypertension in eight world regions: a pooled analysis of 837 population-based studies with 7·5 million participants

Author

Zhou, Bin, Bennett, James E, Wickham, Aidan P, Singleton, Rosie K, Mishra, Anu, Carrillo-Larco, Rodrigo M, Ikeda, Nayu, Jain, Lakshya, Barradas-Pires, Ana, Heap, Rachel A, Lhoste, Victor PF, Sheffer, Kate E, Phelps, Nowell H, Rayner, Archie W, Gregg, Edward W, Woodward, Mark, Stevens, Gretchen A, Iurilli, Maria LC, Danaei, Goodarz, Di Cesare, Mariachiara, Aguilar-Salinas, Carlos A, Ahmad, Noor Ani, Bovet, Pascal, Chen, Zhengming, Damasceno, Albertino, Filippi, Sarah L, Janszky, Imre, Kengne, Andre P, Khang, Young-Ho, Khunti, Kamlesh, Laxmaiah, Avula, Lim, Lee-Ling, Lissner, Lauren, Margozzini, Paula, Mbanya, Jean Claude N, McGarvey, Stephen T, Shaw, Jonathan E, Söderberg, Stefan, Soto-Mota, Luis Adrián, Wang, Junyang, Zaccardi, Francesco, Abarca-Gómez, Leandra, Abbasi-Kangevari, Mohsen, Abdrakhmanova, Shynar, Abdul Ghaffar, Suhaila, Abdul Rahim, Hanan F, Abdurrahmonova, Zulfiya, Abu-Rmeileh, Niveen M, Acosta-Cazares, Benjamin, Adam, Ishag, Adamczyk, Marzena, Aekplakorn, Wichai, Agdeppa, Imelda A, Aghazadeh-Attari, Javad, Agyemang, Charles, Ahmad, Mohamad Hasnan, Ahmadi, Ali, Ahmadi, Naser, Ahmadi, Nastaran, Ahmed, Soheir H, Ahrens, Wolfgang, Aitmurzaeva, Gulmira, Ajlouni, Kamel, Al-Hazzaa, Hazzaa M, Al-Hinai, Halima, Al-Lawati, Jawad A, Al-Raddadi, Rajaa, Al Asfoor, Deena, Al Hourani, Huda M, Alarouj, Monira, AlBuhairan, Fadia, AlDhukair, Shahla, Ali, Mohamed M, Alieva, Anna V, Alkandari, Abdullah, Alkhatib, Buthaina M, Aly, Eman, Amarapurkar, Deepak N, Amiano Etxezarreta, Pilar, Amougou, Norbert, Andersen, Lars Bo, Anderssen, Sigmund A, Androutsos, Odysseas, Anjana, Ranjit Mohan, Ansari-Moghaddam, Alireza, Anufrieva, Elena, Aounallah-Skhiri, Hajer, Aris, Tahir, Arku, Raphael E, Arlappa, Nimmathota, Aryal, Krishna K, Assah, Felix K, Assembekov, Batyrbek, Assunção, Maria Cecília F, Auvinen, Juha, Avdičová, Mária, Azad, Kishwar, Azevedo, Ana, Azimi-Nezhad, Mohsen, Azizi, Fereidoun, Bacopoulou, Flora, Bahijri, Suhad, Bajramovic, Izet, Balakrishna, Nagalla, Bamoshmoosh, Mohamed, Banach, Maciej, Bandosz, Piotr, Banegas, José R, Baran, Rafał, Barbagallo, Carlo M, Barbosa Filho, Valter, Barceló, Alberto, Baretić, Maja, Barnoya, Joaquin, Barrera, Lena, Barros, Aluisio JD, Barros, Mauro Virgílio Gomes, Basit, Abdul, Bastos, Joao Luiz, Batieha, Anwar M, Batista, Aline P, Batista, Rosangela L, Battakova, Zhamilya, Baur, Louise A, Bayauli, Pascal M, Bel-Serrat, Silvia, Belavendra, Antonisamy, Ben Romdhane, Habiba, Benedek, Theodora, Benedics, Judith, Benet, Mikhail, Benitez Rolandi, Gilda Estela, Benzeval, Michaela, Bere, Elling, Berger, Nicolas, Bergh, Ingunn Holden, Berkinbayev, Salim, Bernabe-Ortiz, Antonio, Bettiol, Heloísa, Beybey, Augustin F, Bezerra, Jorge, Bhagyalaxmi, Aroor, Bhargava, Santosh K, Bika Lele, Elysée Claude, Bikbov, Mukharram M, Bista, Bihungum, Bjelica, Dusko J, Bjerregaard, Peter, Bjertness, Espen, Bjertness, Marius B, Björkelund, Cecilia, Bloch, Katia V, Blokstra, Anneke, Bo, Simona, Bobak, Martin, Boddy, Lynne M, Boehm, Bernhard O, Boggia, Jose G, Bogova, Elena, Bonaccio, Marialaura, Bonilla-Vargas, Alice, Borghs, Herman, Botomba, Steve, Bourne, Rupert, Boymatova, Khadichamo, Braeckman, Lutgart, Braithwaite, Tasanee, Brajkovich, Imperia, Branca, Francesco, Brenner, Hermann, Brewster, Lizzy M, Briceño, Yajaira, Brinduse, Lacramioara, Bringolf-Isler, Bettina, Brito, Miguel, Brug, Johannes, Bugge, Anna, Buntinx, Frank, Buoncristiano, Marta, Burns, Con, Cabrera de León, Antonio, Caixeta, Roberta B, Cama, Tilema, Can, Günay, Cândido, Ana Paula C, Cañete, Felicia, Capanzana, Mario V, Čapková, Naděžda, Capuano, Eduardo, Capuano, Rocco, Capuano, Vincenzo, Cardoso, Viviane C, Carlsson, Axel C, Casanueva, Felipe F, Casas, Maribel, Censi, Laura, Cervantes‐Loaiza, Marvin, Chamnan, Parinya, Chamukuttan, Snehalatha, Chan, Queenie, Chaturvedi, Nish, Chen, Fangfang, Chen, Huashuai, Chen, Long-Sheng, Cheng, Yiling J, Cheraghian, Bahman, Chetrit, Angela, Chikova-Iscener, Ekaterina, Chinapaw, Mai JM, Chinnock, Anne, Chiolero, Arnaud, Chirita-Emandi, Adela, Chirlaque, María-Dolores, Chong, Chean Lin, Christofaro, Diego G, Chudek, Jerzy, Cifkova, Renata, Cirillo, Massimo, Claessens, Frank, Clare, Philip, Cohen, Emmanuel, Confortin, Susana C, Coppinger, Tara C, Cortés, Lilia Yadira, Cosmin, Cojocaru R, Costanzo, Simona, Cowan, Melanie J, Cowell, Chris, Crampin, Amelia C, Cross, Amanda J, Crujeiras, Ana B, Cruz, Juan J, Cucu, Alexandra M, Cureau, Felipe V, Cuschieri, Sarah, D'Arrigo, Graziella, d'Orsi, Eleonora, da Silva-Ferreira, Haroldo, Dahm, Christina C, Dallongeville, Jean, Dankner, Rachel, Davletov, Kairat, de Assis Guedes de Vasconcelos, Francisco, de Assis, Maria Alice Altenburg, De Bacquer, Dirk, De Bacquer, Jaco, de Bont, Jeroen, De Curtis, Amalia, de Fragas Hinnig, Patrícia, de Gaetano, Giovanni, De Henauw, Stefaan, De Miguel-Etayo, Pilar, de Oliveira, Paula Duarte, de Paiva, Karina Mary, De Ridder, Karin, de Valois Correia Júnior, Marco Aurélio, Deepa, Mohan, DeGennaro, Vincent Jr, Demarest, Stefaan, Dennison, Elaine, Deschamps, Valérie, Dhimal, Meghnath, Díez Ripollés, María Pilar, Dika, Zivka, Djalalinia, Shirin, Dominguez, Liria, Donati, Maria Benedetta, Donfrancesco, Chiara, Dong, Guanghui, Donoso, Silvana P, Dorobantu, Maria, Dörr, Marcus, Dragano, Nico, Drygas, Wojciech, Du, Shufa, Duante, Charmaine A, Duboz, Priscilla, Duda, Rosemary B, Duleva, Vesselka L, Dushpanova, Anar, Dyussupova, Azhar, Dziankowska-Zaborszczyk, Elzbieta, Ebrahimi, Narges, Echeverría, Guadalupe, Eddie, Ricky, Eftekhar, Ebrahim, Efthymiou, Vasiliki, Egbagbe, Eruke E, Eghtesad, Sareh, Ekelund, Ulf, El-Khateeb, Mohammad, El Ati, Jalila, Elosua, Roberto, Enang, Ofem, Erasmus, Rajiv T, Erem, Cihangir, Ergor, Gul, Eriksen, Louise, Eriksson, Johan G, Escobedo-de la Peña, Jorge, Esmaeili, Ali, Evans, Roger G, Fakhradiyev, Ildar, Fakhretdinova, Albina A, Fall, Caroline H, Faramarzi, Elnaz, Farjam, Mojtaba, Farzadfar, Farshad, Farzi, Yosef, Fattahi, Mohammad Reza, Fawwad, Asher, Felix-Redondo, Francisco J, Ferguson, Trevor S, Fernandes, Romulo A, Fernández-Bergés, Daniel, Fernando, Desha R, Ferrante, Daniel, Ferrari, Gerson, Ferrari, Marika, Ferreccio, Catterina, Ferrer, Eldridge, Figueiró, Thamara Hubler, Fijalkowska, Anna, Fink, Günther, Fisberg, Mauro, Forsner, Maria, Fottrell, Edward F, Fouad, Heba M, Francis, Damian K, Frontera, Guillermo, Fuchs, Flavio D, Fuchs, Sandra C, Furdela, Viktoriya, Furusawa, Takuro, Gabriela, Stefan Adela, Gaciong, Zbigniew, Galán Cuesta, Manuel, Galbarczyk, Andrzej, Galcheva, Sonya V, Galfo, Myriam, Garcia-de-la-Hera, Manoli, Garcia, Pablo, Garnett, Sarah P, Gasull, Magda, Gazzinelli, Andrea, Gehring, Ulrike, Gerdts, Eva, Ghaderi, Ebrahim, Ghamari, Seyyed-Hadi, Ghanbari, Ali, Ghasemi, Erfan, Gheorghe-Fronea, Oana-Florentina, Ghimire, Anup, Gialluisi, Alessandro, Giampaoli, Simona, Gianfagna, Francesco, Gironella, Glen, Giwercman, Aleksander, Gkiouras, Konstantinos, Glushkova, Natalya, Godara, Ramesh, Godos, Justyna, Goldberg, Marcel, Gómez, Georgina, Gómez Gómez, Jesús Humberto, Gomez, Luis F, Gómez, Santiago F, Gomula, Aleksandra, Gonçalves Cordeiro da Silva, Bruna, Gonçalves, Helen, Gonçalves, Mauer, González-Alvarez, Ana D, Gonzalez-Chica, David A, González-Gil, Esther M, Gonzalez-Gross, Marcela, González-Rivas, Juan P, Gonzalez, Angel R, Gottrand, Frederic, Grafnetter, Dušan, Grajda, Aneta, Grammatikopoulou, Maria G, Grodzicki, Tomasz, Grøholt, Else Karin, Grøntved, Anders, Guajardo, Viviana, Guallar-Castillón, Pilar, Guerchet, Maëlenn, Guerrero, Ramiro, Guimaraes, Andre L, Gujral, Unjali P, Gulliford, Martin C, Gunter, Marc J, Gupta, Rajeev, Gureje, Oye, Gurinović, Mirjana A, Gurzkowska, Beata, Gutierrez, Laura, Gwee, Xinyi, Haghshenas, Rosa, Hakimi, Hamid, Halkjær, Jytte, Hambleton, Ian R, Hamzeh, Behrooz, Hanekom, Willem A, Hange, Dominique, Hanif, Abu AM, Hantunen, Sari, Hao, Jie, Hardman, Carla Menêses, Hardy, Louise, Hari Kumar, Rachakulla, Harooni, Javad, Hashemi-Shahri, Seyed Mohammad, Hassapidou, Maria, Hata, Jun, Haugsgjerd, Teresa, Heinen, Mirjam, Hendriks, Marleen Elisabeth, Henrique, Rafael dos Santos, Henriques, Ana, Hernandez Cadena, Leticia, Herrala, Sauli, Herrera-Cuenca, Marianella, Herrera, Victor M, Herter-Aeberli, Isabelle, Herzig, Karl-Heinz, Heshmat, Ramin, Hill, Allan G, Ho, Sai Yin, Holdsworth, Michelle, Homayounfar, Reza, Homs, Clara, Hoogendijk, Emiel O, Horimoto, Andrea RVR, Hormiga, Claudia M, Horta, Bernardo L, Houti, Leila, Howitt, Christina, Htay, Thein Thein, Htet, Aung Soe, Htike, Maung Maung Than, Huerta, José María, Huhtaniemi, Ilpo Tapani, Huiart, Laetitia, Huidumac Petrescu, Constanta, Huisman, Martijn, Husseini, Abdullatif, Huybrechts, Inge, Hwalla, Nahla, Iacoviello, Licia, Iakupova, Ellina M, Iannone, Anna G, Igland, Jannicke, Ijoma, Chinwuba, Iotova, Violeta, Irazola, Vilma E, Ishida, Takafumi, Isiguzo, Godsent C, Islam, Muhammad, Islam, Sheikh Mohammed Shariful, Islek, Duygu, Ittermann, Till, Ivanova-Pandourska, Ivaila Y, Iwasaki, Masanori, Jääskeläinen, Tuija, Jackson, Rod T, Jaddou, Hashem Y, Jadoul, Michel, Jafar, Tazeen, Jan, Nataša, Janus, Edward, Jarani, Juel, Jarnig, Gerald, Jarvelin, Marjo-Riitta, Jasienska, Grazyna, Jelaković, Ana, Jelaković, Bojan, Jha, Anjani Kumar, Jimenez, Ramon O, Jöckel, Karl-Heinz, Joffres, Michel, Jokelainen, Jari J, Jonas, Jost B, Joshi, Pradeep, Joshi, Rohina, Josipović, Josipa, Joukar, Farahnaz, Jóźwiak, Jacek J, Juolevi, Anne, Juresa, Vesna, Jureša, Vesna, Kaaks, Rudolf, Kaducu, Felix O, Kadvan, Agnes L, Kafatos, Anthony, Kajantie, Eero O, Kakutia, Natia, Kállayová, Daniela, Kalmatayeva, Zhanna, Kalter-Leibovici, Ofra, Kannan, Srinivasan, Kapantais, Efthymios, Karaglani, Eva, Karakosta, Argyro, Karki, Khem B, Kassi Anicet, Adoubi, Katibeh, Marzieh, Katulanda, Prasad, Katzmarzyk, Peter T, Kauhanen, Jussi, Kazakbaeva, Gyulli M, Kaze, François F, Ke, Calvin, Keinänen-Kiukaanniemi, Sirkka, Kelishadi, Roya, Kelleher, Cecily, Kemper, Han CG, Keramati, Maryam, Kersting, Mathilde, Khader, Yousef Saleh, Khaledifar, Arsalan, Khalili, Davood, Kheiri, Bahareh, Kheradmand, Motahareh, Khosravi, Alireza, Kiechl-Kohlendorfer, Ursula, Kiechl, Sophia J, Kiechl, Stefan, Kim, Hyeon Chang, Klakk, Heidi, Klanarong, Suntara, Klanova, Jana, Klimek, Magdalena, Knoflach, Michael, Kobel, Susanne, Koirala, Bhawesh, Kolle, Elin, Kolsteren, Patrick, König, Jürgen, Korpelainen, Raija, Korrovits, Paul, Korzycka, Magdalena, Kos, Jelena, Koskinen, Seppo, Koussoh Simone, Malik, Kovács, Éva, Kovalskys, Irina, Kowlessur, Sudhir, Koziel, Slawomir, Kratenova, Jana, Kratzer, Wolfgang, Kriemler, Susi, Kristensen, Peter Lund, Krizan, Helena, Kroker-Lobos, Maria F, Krokstad, Steinar, Kruger, Herculina S, Kruger, Ruan, Kryst, Łukasz, Kubinova, Ruzena, Kujala, Urho M, Kujundzic, Enisa, Kulaga, Zbigniew, Kulimbet, Mukhtar, Kumari, Meena, Kunešová, Marie, Kurjata, Pawel, Kyobutungi, Catherine, La, Quang Ngoc, Labadarios, Demetre, Lachat, Carl, Lai, Daphne, Laid, Youcef, Lall, Lachmie, Landaeta Jimenez, Maritza, Landais, Edwige, Lankila, Tiina, Lanska, Vera, Lappas, Georg, Larijani, Bagher, Lateva, Mina P, Latt, Tint Swe, Laurenzi, Martino, Lazo-Porras, Maria, Le Coroller, Gwenaëlle, Le Nguyen Bao, Khanh, Lehtimäki, Terho, Lemogoum, Daniel, Leong, Elvynna, Leszczak, Justyna, Leung, Gabriel M, Li, Yanping, Liivak, Merike, Lim, Charlie, Lim, Wei-Yen, Lima-Costa, M Fernanda, Lin, Hsien-Ho, Lind, Lars, Litwin, Mieczyslaw, Liu, Liping, Liu, Xiaotian, Longo Abril, Guadalupe, Lopes, Oscar, Lopez-Garcia, Esther, López-Gil, José Francisco, Lopez, Tania, Lozano, José Eugenio, Lukrafka, Janice L, Luksiene, Dalia, Lundqvist, Annamari, Lunet, Nuno, Lunogelo, Charles, Lustigová, Michala, M'Buyamba-Kabangu, Jean-René, Machado-Coelho, George LL, Machado-Rodrigues, Aristides M, Macia, Enguerran, Madar, Ahmed A, Maestre, Gladys E, Maggi, Stefania, Magliano, Dianna J, Magnacca, Sara, Magriplis, Emmanuella, Mahasampath, Gowri, Maire, Bernard, Makdisse, Marcia, Malekpour, Mohammad-Reza, Malekzadeh, Fatemeh, Malekzadeh, Reza, Mallikharjuna Rao, Kodavanti, Malyutina, Sofia, Maniego, Lynell V, Manios, Yannis, Mann, Jim I, Mansour-Ghanaei, Fariborz, Manzato, Enzo, Mapatano, Mala Ali, Maria-Magdalena, Rosu, Mariño, Joany, Markaki, Anastasia, Marques, Larissa Pruner, Marrugat, Jaume, Martorell, Reynaldo, Maruszczak, Katharina, Masala, Giovanna, Mascarenhas, Luis P, Masimango Imani, Mannix, Masinaei, Masoud, Mathiesen, Ellisiv B, Matijasevich, Alicia, Matłosz, Piotr, Matsha, Tandi E, Matsudo, Victor, Matteo, Giletta, Maulik, Pallab K, Mavrogianni, Christina, Mc Donald Posso, Anselmo J, McFarlane, Shelly R, McLean, Rachael M, Mediene Benchekor, Sounnia, Mehlig, Kirsten, Mehrparvar, Amir Houshang, Melgarejo, Jesus D, Méndez, Fabián, Mendivil, Carlos O, Mendoza Montano, Carlos, Menezes, Ana Maria B, Mensink, Gert BM, Mereke, Alibek, Meshram, Indrapal I, Meto, Diane T, Meyer, Haakon E, Mi, Jie, Miłkowska, Karolina, Miller, Jody C, Milushkina, Olga, Minderico, Cláudia S, Mini, GK, Miquel, Juan Francisco, 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2024
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