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1. AdvBDGen: Adversarially Fortified Prompt-Specific Fuzzy Backdoor Generator Against LLM Alignment

Author

Pathmanathan, Pankayaraj, Sehwag, Udari Madhushani, Panaitescu-Liess, Michael-Andrei, and Huang, Furong

Subjects
Computer Science - Machine Learning
Abstract

With the growing adoption of reinforcement learning with human feedback (RLHF) for aligning large language models (LLMs), the risk of backdoor installation during alignment has increased, leading to unintended and harmful behaviors. Existing backdoor triggers are typically limited to fixed word patterns, making them detectable during data cleaning and easily removable post-poisoning. In this work, we explore the use of prompt-specific paraphrases as backdoor triggers, enhancing their stealth and resistance to removal during LLM alignment. We propose AdvBDGen, an adversarially fortified generative fine-tuning framework that automatically generates prompt-specific backdoors that are effective, stealthy, and transferable across models. AdvBDGen employs a generator-discriminator pair, fortified by an adversary, to ensure the installability and stealthiness of backdoors. It enables the crafting and successful installation of complex triggers using as little as 3% of the fine-tuning data. Once installed, these backdoors can jailbreak LLMs during inference, demonstrate improved stability against perturbations compared to traditional constant triggers, and are more challenging to remove. These findings underscore an urgent need for the research community to develop more robust defenses against adversarial backdoor threats in LLM alignment., Comment: Published at the Neurips Safe Generative AI Workshop 2024

Published
2024

2. A Feature Generator for Few-Shot Learning

Author

Kanagalingam, Heethanjan, Pathmanathan, Thenukan, Ketheeswaran, Navaneethan, Vathanakumar, Mokeeshan, Afham, Mohamed, and Rodrigo, Ranga

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Few-shot learning (FSL) aims to enable models to recognize novel objects or classes with limited labelled data. Feature generators, which synthesize new data points to augment limited datasets, have emerged as a promising solution to this challenge. This paper investigates the effectiveness of feature generators in enhancing the embedding process for FSL tasks. To address the issue of inaccurate embeddings due to the scarcity of images per class, we introduce a feature generator that creates visual features from class-level textual descriptions. By training the generator with a combination of classifier loss, discriminator loss, and distance loss between the generated features and true class embeddings, we ensure the generation of accurate same-class features and enhance the overall feature representation. Our results show a significant improvement in accuracy over baseline methods, with our approach outperforming the baseline model by 10% in 1-shot and around 5% in 5-shot approaches. Additionally, both visual-only and visual + textual generators have also been tested in this paper. The code is publicly available at https://github.com/heethanjan/Feature-Generator-for-FSL., Comment: 16 pages, Accepted to ACCV 2024

Published
2024

3. Spatial Principal Component Analysis and Moran Statistics for Multivariate Functional Areal Data

Author

Pathmanathan, Dharini, Dabo, Issa-Mbenard, Khoo, Tzung Hsuen, Ali-Hassan, Alaa, and Dabo-Niang, Sophie

Subjects
Statistics - Methodology
Abstract

In this article, we present the bivariate and multivariate functional Moran's I statistics and multivariate functional areal spatial principal component analysis (mfasPCA). These methods are the first of their kind in the field of multivariate areal spatial functional data analysis. The multivariate functional Moran's I statistic is employed to assess spatial autocorrelation, while mfasPCA is utilized for dimension reduction in both univariate and multivariate functional areal data. Through simulation studies and real-world examples, we demonstrate that the multivariate functional Moran's I statistic and mfasPCA are powerful tools for evaluating spatial autocorrelation in univariate and multivariate functional areal data analysis.

Published
2024

4. Can Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?

Author

Panaitescu-Liess, Michael-Andrei, Che, Zora, An, Bang, Xu, Yuancheng, Pathmanathan, Pankayaraj, Chakraborty, Souradip, Zhu, Sicheng, Goldstein, Tom, and Huang, Furong

Subjects
Computer Science - Machine Learning
Abstract

Large Language Models (LLMs) have demonstrated impressive capabilities in generating diverse and contextually rich text. However, concerns regarding copyright infringement arise as LLMs may inadvertently produce copyrighted material. In this paper, we first investigate the effectiveness of watermarking LLMs as a deterrent against the generation of copyrighted texts. Through theoretical analysis and empirical evaluation, we demonstrate that incorporating watermarks into LLMs significantly reduces the likelihood of generating copyrighted content, thereby addressing a critical concern in the deployment of LLMs. Additionally, we explore the impact of watermarking on Membership Inference Attacks (MIAs), which aim to discern whether a sample was part of the pretraining dataset and may be used to detect copyright violations. Surprisingly, we find that watermarking adversely affects the success rate of MIAs, complicating the task of detecting copyrighted text in the pretraining dataset. Finally, we propose an adaptive technique to improve the success rate of a recent MIA under watermarking. Our findings underscore the importance of developing adaptive methods to study critical problems in LLMs with potential legal implications., Comment: 21 pages, 6 figures

Published
2024

5. Forecasting mortality rates with functional signatures

Author

Yap, Zhong Jing, Pathmanathan, Dharini, and Niang, Sophie Dabo

Subjects
Statistics - Methodology
Abstract

This study introduces an innovative methodology for mortality forecasting, which integrates signature-based methods within the functional data framework of the Hyndman-Ullah (HU) model. This new approach, termed the Hyndman-Ullah with truncated signatures (HUts) model, aims to enhance the accuracy and robustness of mortality predictions. By utilizing signature regression, the HUts model aims to capture complex, nonlinear dependencies in mortality data which enhances forecasting accuracy across various demographic conditions. The model is applied to mortality data from 12 countries, comparing its forecasting performance against classical models like the Lee-Carter model and variants of the HU models across multiple forecast horizons. Our findings indicate that overall the HUts model not only provides more precise point forecasts but also shows robustness against data irregularities, such as those observed in countries with historical outliers. The integration of signature-based methods enables the HUts model to capture complex patterns in mortality data, making it a powerful tool for actuaries and demographers. Prediction intervals are also constructed using bootstrapping methods., Comment: 46 pages, 24 figures, 9 tables

Published
2024

6. Generalized functional dynamic principal component analysis

Author

Khoo, Tzung Hsuen, Dabo, Issa-Mbenard, Pathmanathan, Dharini, and Dabo-Niang, Sophie

Subjects
Statistics - Methodology
Abstract

In this paper, we explore dimension reduction for time series of functional data within both stationary and non-stationary frameworks. We introduce a functional framework of generalized dynamic principal component analysis (GDPCA). The concept of GDPCA aims for better adaptation to possible nonstationary features of the series. We define the functional generalized dynamic principal component (GDPC) as static factor time series in a functional dynamic factor model and obtain the multivariate GDPC from a truncation of the functional dynamic factor model. GDFPCA uses a minimum squared error criterion to evaluate the reconstruction of the original functional time series. The computation of GDPC involves a two-step estimation of the coefficient vector of the loading curves in a basis expansion. We provide a proof of the consistency of the reconstruction of the original functional time series with GDPC converging in mean square to the original functional time series. Monte Carlo simulation studies indicate that the proposed GDFPCA is comparable to dynamic functional principal component analysis (DFPCA) when the data generating process is stationary, and outperforms DFPCA and FPCA when the data generating process is non-stationary. The results of applications to real data reaffirm the findings in simulation studies.

Published
2024

7. Is poisoning a real threat to LLM alignment? Maybe more so than you think

Author

Pathmanathan, Pankayaraj, Chakraborty, Souradip, Liu, Xiangyu, Liang, Yongyuan, and Huang, Furong

Subjects
Computer Science - Machine Learning, Computer Science - Computation and Language, Computer Science - Cryptography and Security
Abstract

Recent advancements in Reinforcement Learning with Human Feedback (RLHF) have significantly impacted the alignment of Large Language Models (LLMs). The sensitivity of reinforcement learning algorithms such as Proximal Policy Optimization (PPO) has led to new line work on Direct Policy Optimization (DPO), which treats RLHF in a supervised learning framework. The increased practical use of these RLHF methods warrants an analysis of their vulnerabilities. In this work, we investigate the vulnerabilities of DPO to poisoning attacks under different scenarios and compare the effectiveness of preference poisoning, a first of its kind. We comprehensively analyze DPO's vulnerabilities under different types of attacks, i.e., backdoor and non-backdoor attacks, and different poisoning methods across a wide array of language models, i.e., LLama 7B, Mistral 7B, and Gemma 7B. We find that unlike PPO-based methods, which, when it comes to backdoor attacks, require at least 4\% of the data to be poisoned to elicit harmful behavior, we exploit the true vulnerabilities of DPO more simply so we can poison the model with only as much as 0.5\% of the data. We further investigate the potential reasons behind the vulnerability and how well this vulnerability translates into backdoor vs non-backdoor attacks.

Published
2024

8. How Deep Is Your Gaze? Leveraging Distance in Image-Based Gaze Analysis

Author

Koch, Maurice, Pathmanathan, Nelusa, Weiskopf, Daniel, and Kurzhals, Kuno

Subjects
Computer Science - Human-Computer Interaction
Abstract

Image thumbnails are a valuable data source for fixation filtering, scanpath classification, and visualization of eye tracking data. They are typically extracted with a constant size, approximating the foveated area. As a consequence, the focused area of interest in the scene becomes less prominent in the thumbnail with increasing distance, affecting image-based analysis techniques. In this work, we propose depth-adaptive thumbnails, a method for varying image size according to the eye-to-object distance. Adjusting the visual angle relative to the distance leads to a zoom effect on the focused area. We evaluate our approach on recordings in augmented reality, investigating the similarity of thumbnails and scanpaths. Our quantitative findings suggest that considering the eye-to-object distance improves the quality of data analysis and visualization. We demonstrate the utility of depth-adaptive thumbnails for applications in scanpath comparison and visualization.

Published
2024
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11. Using Curiosity for an Even Representation of Tasks in Continual Offline Reinforcement Learning

Author

Pathmanathan, Pankayaraj, Díaz-Rodríguez, Natalia, and Del Ser, Javier

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

In this work, we investigate the means of using curiosity on replay buffers to improve offline multi-task continual reinforcement learning when tasks, which are defined by the non-stationarity in the environment, are non labeled and not evenly exposed to the learner in time. In particular, we investigate the use of curiosity both as a tool for task boundary detection and as a priority metric when it comes to retaining old transition tuples, which we respectively use to propose two different buffers. Firstly, we propose a Hybrid Reservoir Buffer with Task Separation (HRBTS), where curiosity is used to detect task boundaries that are not known due to the task agnostic nature of the problem. Secondly, by using curiosity as a priority metric when it comes to retaining old transition tuples, a Hybrid Curious Buffer (HCB) is proposed. We ultimately show that these buffers, in conjunction with regular reinforcement learning algorithms, can be used to alleviate the catastrophic forgetting issue suffered by the state of the art on replay buffers when the agent's exposure to tasks is not equal along time. We evaluate catastrophic forgetting and the efficiency of our proposed buffers against the latest works such as the Hybrid Reservoir Buffer (HRB) and the Multi-Time Scale Replay Buffer (MTR) in three different continual reinforcement learning settings. Experiments were done on classical control tasks and Metaworld environment. Experiments show that our proposed replay buffers display better immunity to catastrophic forgetting compared to existing works in most of the settings.

Published
2023
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12. A Markov-switching spatio-temporal ARCH model

Author

Khoo, Tzung Hsuen, Pathmanathan, Dharini, Otto, Philipp, and Dabo-Niang, Sophie

Subjects
Statistics - Methodology
Abstract

Stock market indices are volatile by nature, and sudden shocks are known to affect volatility patterns. The autoregressive conditional heteroskedasticity (ARCH) and generalized ARCH (GARCH) models neglect structural breaks triggered by sudden shocks that may lead to an overestimation of persistence, causing an upward bias in the estimates. Different regime-switching models that have abrupt regime-switching governed by a Markov chain were developed to model volatility in financial time series data. Volatility modelling was also extended to spatially interconnected time series, resulting in spatial variants of ARCH models. This inspired us to propose a Markov switching framework of the spatio-temporal log-ARCH model. In this article, we discuss the Markov-switching extension of the model, the estimation procedure and the smooth inferences of the regimes. The Monte-Carlo simulation studies show that the maximum likelihood estimation method for our proposed model has good finite sample properties. The proposed model was applied to 28 stock indices data that were presumably affected by the 2015-2016 Chinese stock market crash. The results showed that our model is a better fit compared to that of the one-regime counterpart. Furthermore, the smoothed inference of the data indicated the approximate periods where structural breaks occurred. This model can capture structural breaks that simultaneously occur in nearby locations.

Published
2023

17. Assessing the reliability of medical resource demand models in the context of COVID-19

Author

Kimberly Dautel, Ephraim Agyingi, and Pras Pathmanathan

Subjects
Mathematical modeling, Epidemiology, Medical device, Validation, Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Background Numerous medical resource demand models have been created as tools for governments or hospitals, aiming to predict the need for crucial resources like ventilators, hospital beds, personal protective equipment (PPE), and diagnostic kits during crises such as the COVID-19 pandemic. However, the reliability of these demand models remains uncertain. Methods Demand models typically consist of two main components: hospital use epidemiological models that predict hospitalizations or daily admissions, and a demand calculator that translates the outputs of the epidemiological model into predictions for resource usage. We conducted separate analyses to evaluate each of these components. In the first analysis, we validated various hospital use epidemiological models using a recent validation framework designed for epidemiological models. This allowed us to quantify the accuracy of the models in predicting critical aspects such as the date and magnitude of local COVID-19 peaks, among other factors. In the second analysis, we evaluated a range of demand calculators for ventilators, medical gowns, and COVID-19 test kits. To achieve this, we decoupled these demand calculators from the underlying epidemiological models and provided ground truth data for their inputs. This approach enabled a direct comparison of the demand calculators, comparing them against each other and actual usage data when available. The code is available at https://doi.org/10.5281/zenodo.13712387 . Results Performance varied greatly across the epidemiological models, with greater variability in COVID-19 hospital use predictions than for COVID-19 deaths as analyzed previously. Some models did not have any peaks. Among those that did, the models under-estimated date of peak approximately as often as they over-estimated, but were more likely to under-estimate magnitude of peak, with typical relative errors around 50%. Regarding demand calculator predictions, there was significant variability, including five-fold differences in predictions for gown models. Validation against actual or surrogate usage data illustrated the potential value of demand models while demonstrating their limitations. Conclusions The emerging field of demand modeling holds promise in averting medical resource shortages during future public health emergencies. However, achieving this potential necessitates focused efforts on standardization, transparency, and rigorous model validation before placing reliance on demand models in critical public health decision-making.

Published
2024
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18. Economic Evaluation of Nirmatrelvir/Ritonavir Among Adults Against Hospitalization During the Omicron Dominated Period in Malaysia: A Real-World Evidence Perspective

Author

Low, Ee Vien, Teh, Hoon Shien, Hing, Nicholas Yee Liang, Chidambaram, Suresh Kumar, Pathmanathan, Mohan Dass, Kim, Wee Ric, Lee, Wei Jia, Teh, Zhi Wei, Appannan, Maheshwara Rao, Zin, Shahanizan Mohd, Zin, Faizah Muhamad, Amin, Samha Bashirah Mohamed, Ismail, Mastura, Samad, Azah Abdul, and M. Peariasamy, Kalaiarasu

Published
2024
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19. Nirmatrelvir/ritonavir treatment and the risk of post-COVID condition over 180 days in Malaysia

Author

Ee Vien Low, Mohan Dass Pathmanathan, Yi Yang Ten, Suresh Kumar Chidambaram, Wee Ric Kim, Wei Jia Lee, Zhi Wei Teh, Maheshwara Rao Appannan, Mastura Ismail, Azah Abdul Samad, and Kalaiarasu M. Peariasamy

Subjects
Nirmatrelvir/ritonavir, post-COVID condition, Long COVID, Malaysia, Infectious and parasitic diseases, RC109-216
Abstract

Abstract Background The effect of nirmatrelvir/ritonavir on preventing post-COVID condition (PCC) in the BA4, BA5, and XBB Omicron predominant periods is not well understood. The purpose of this study was to assess how nirmatrelvir/ritonavir treatment affected both PCC and health-related quality of life. Methods This retrospective cohort study enrolled 2,524 adults aged 18 years and older who were eligible for nirmatrelvir/ritonavir between July 14 to November 14, 2022. All outcomes were observed from the patient’s first visit to the primary health clinic, 1 week, 1 month, 3 months, and 6 months after testing positive for COVID-19. The primary outcome was the presence of PCC. Secondary outcomes included the effects on health-related quality of life, such as walking, bathing and dressing, activities, cause adverse emotions or signs that prevent individuals from leading normal lives over a 180-day observation period. Results There were no significant differences observed between the nirmatrelvir/ritonavir and those not administered (control group) in terms of PCC symptoms at 3 months (OR 0.71 95% CI 0.31, 1.64) and 6 months (OR 1.30 95% CI 0.76, 2.21). At 3 months, the use of nirmatrelvir/ritonavir was associated with a 26% reduction in symptoms causing negative emotions (OR 0.74 95% CI 0.60, 0.92) and an increased likelihood of symptoms limiting walking (OR 1.58 95% CI 1.10, 2.27). However, there were no significant differences between the nirmatrelvir/ritonavir and the control group in terms of the impact of PCC on health-related quality of life at 6 months. Conclusions Our study indicates that the administration of nirmatrelvir/ritonavir does not significantly reduce PCC after 3 months and 6 months in a population with high vaccination coverage.

Published
2024
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20. Gene expression signature for predicting homologous recombination deficiency in triple-negative breast cancer

Author

Jia-Wern Pan, Zi-Ching Tan, Pei-Sze Ng, Muhammad Mamduh Ahmad Zabidi, Putri Nur Fatin, Jie-Ying Teo, Siti Norhidayu Hasan, Tania Islam, Li-Ying Teoh, Suniza Jamaris, Mee-Hoong See, Cheng-Har Yip, Pathmanathan Rajadurai, Lai-Meng Looi, Nur Aishah Mohd Taib, Oscar M. Rueda, Carlos Caldas, Suet-Feung Chin, Joanna Lim, and Soo-Hwang Teo

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Triple-negative breast cancers (TNBCs) are a subset of breast cancers that have remained difficult to treat. A proportion of TNBCs arising in non-carriers of BRCA pathogenic variants have genomic features that are similar to BRCA carriers and may also benefit from PARP inhibitor treatment. Using genomic data from 129 TNBC samples from the Malaysian Breast Cancer (MyBrCa) cohort, we developed a gene expression-based machine learning classifier for homologous recombination deficiency (HRD) in TNBCs. The classifier identified samples with HRD mutational signature at an AUROC of 0.93 in MyBrCa validation datasets and 0.84 in TCGA TNBCs. Additionally, the classifier strongly segregated HRD-associated genomic features in TNBCs from TCGA, METABRIC, and ICGC. Thus, our gene expression classifier may identify triple-negative breast cancer patients with homologous recombination deficiency, suggesting an alternative method to identify individuals who may benefit from treatment with PARP inhibitors or platinum chemotherapy.

Published
2024
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21. Functional data geometric morphometrics with machine learning for craniodental shape classification in shrews

Author

Aneesha Balachandran Pillay, Dharini Pathmanathan, Sophie Dabo-Niang, Arpah Abu, and Hasmahzaiti Omar

Subjects
Functional data analysis, Principal component analysis, Linear discriminant analysis, Landmarks, Shrews, Geometric morphometrics, Medicine, Science
Abstract

Abstract This work proposes a functional data analysis approach for morphometrics in classifying three shrew species (S. murinus, C. monticola, and C. malayana) from Peninsular Malaysia. Functional data geometric morphometrics (FDGM) for 2D landmark data is introduced and its performance is compared with classical geometric morphometrics (GM). The FDGM approach converts 2D landmark data into continuous curves, which are then represented as linear combinations of basis functions. The landmark data was obtained from 89 crania of shrew specimens based on three craniodental views (dorsal, jaw, and lateral). Principal component analysis and linear discriminant analysis were applied to both GM and FDGM methods to classify the three shrew species. This study also compared four machine learning approaches (naïve Bayes, support vector machine, random forest, and generalised linear model) using predicted PC scores obtained from both methods (a combination of all three craniodental views and individual views). The analyses favoured FDGM and the dorsal view was the best view for distinguishing the three species.

Published
2024
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23. Association of the CHEK2 c.1100delC variant, radiotherapy, and systemic treatment with contralateral breast cancer risk and breast cancer‐specific survival

Author

Morra, Anna, Schreurs, Maartje AC, Andrulis, Irene L, Anton‐Culver, Hoda, Augustinsson, Annelie, Beckmann, Matthias W, Behrens, Sabine, Bojesen, Stig E, Bolla, Manjeet K, Brauch, Hiltrud, Broeks, Annegien, Buys, Saundra S, Camp, Nicola J, Castelao, Jose E, Cessna, Melissa H, Chang‐Claude, Jenny, Chung, Wendy K, Sahlberg, Kristine K, Børresen‐Dale, Anne‐Lise, Gram, Inger Torhild, Olsen, Karina Standahl, Engebråten, Olav, Naume, Bjørn, Geisler, Jürgen, OSBREAC, Alnæs, Grethe I Grenaker, Colonna, Sarah V, Couch, Fergus J, Cox, Angela, Cross, Simon S, Czene, Kamila, Daly, Mary B, Dennis, Joe, Devilee, Peter, Dörk, Thilo, Dunning, Alison M, Dwek, Miriam, Easton, Douglas F, Eccles, Diana M, Eriksson, Mikael, Evans, D Gareth, Fasching, Peter A, Fehm, Tanja N, Figueroa, Jonine D, Flyger, Henrik, Gabrielson, Marike, Gago‐Dominguez, Manuela, García‐Closas, Montserrat, García‐Sáenz, José A, Genkinger, Jeanine, Grassmann, Felix, Gündert, Melanie, Hahnen, Eric, Haiman, Christopher A, Hamann, Ute, Harrington, Patricia A, Hartikainen, Jaana M, Hoppe, Reiner, Hopper, John L, Houlston, Richard S, Howell, Anthony, Clarke, Christine, Marsh, Deborah, Scott, Rodney, Baxter, Robert, Yip, Desmond, Carpenter, Jane, Davis, Alison, Pathmanathan, Nirmala, Simpson, Peter, Graham, J Dinny, Sachchithananthan, Mythily, Amor, David, Andrews, Lesley, Antill, Yoland, Balleine, Rosemary, Beesley, Jonathan, Bennett, Ian, Bogwitz, Michael, Botes, Leon, Brennan, Meagan, Brown, Melissa, Buckley, Michael, Burke, Jo, Butow, Phyllis, Caldon, Liz, Campbell, Ian, Cao, Michelle, Chakrabarti, Anannya, Chauhan, Deepa, Chauhan, Manisha, Chenevix‐Trench, Georgia, Christian, Alice, Cohen, Paul, Colley, Alison, Crook, Ashley, Cui, James, Courtney, Eliza, Cummings, Margaret, and Dawson, Sarah‐Jane

Subjects
Biomedical and Clinical Sciences, Oncology and Carcinogenesis, Women's Health, Breast Cancer, Clinical Research, Prevention, Cancer, Female, Humans, Breast Neoplasms, Checkpoint Kinase 2, Genetic Predisposition to Disease, Germ-Line Mutation, Heterozygote, Proportional Hazards Models, CHEK2 c.1100delC germline genetic variant, contralateral breast cancer risk, radiotherapy, survival, systemic treatment, NBCS Collaborators, ABCTB Investigators, kConFab Investigators, Biochemistry and Cell Biology, Oncology and carcinogenesis
Abstract

BackgroundBreast cancer (BC) patients with a germline CHEK2 c.1100delC variant have an increased risk of contralateral BC (CBC) and worse BC-specific survival (BCSS) compared to non-carriers.AimTo assessed the associations of CHEK2 c.1100delC, radiotherapy, and systemic treatment with CBC risk and BCSS.MethodsAnalyses were based on 82,701 women diagnosed with a first primary invasive BC including 963 CHEK2 c.1100delC carriers; median follow-up was 9.1 years. Differential associations with treatment by CHEK2 c.1100delC status were tested by including interaction terms in a multivariable Cox regression model. A multi-state model was used for further insight into the relation between CHEK2 c.1100delC status, treatment, CBC risk and death.ResultsThere was no evidence for differential associations of therapy with CBC risk by CHEK2 c.1100delC status. The strongest association with reduced CBC risk was observed for the combination of chemotherapy and endocrine therapy [HR (95% CI): 0.66 (0.55-0.78)]. No association was observed with radiotherapy. Results from the multi-state model showed shorter BCSS for CHEK2 c.1100delC carriers versus non-carriers also after accounting for CBC occurrence [HR (95% CI): 1.30 (1.09-1.56)].ConclusionSystemic therapy was associated with reduced CBC risk irrespective of CHEK2 c.1100delC status. Moreover, CHEK2 c.1100delC carriers had shorter BCSS, which appears not to be fully explained by their CBC risk.

Published
2023

24. A unified approach for morphometrics and functional data analysis with machine learning for craniodental shape quantification in shrew species

Author

Pillay, Aneesha Balachandran, Pathmanathan, Dharini, Dabo-Niang, Sophie, Abu, Arpah, and Omar, Hasmahzaiti

Subjects
Quantitative Biology - Quantitative Methods, I.5.3
Abstract

This work proposes a functional data analysis approach for morphometrics with applications in classifying three shrew species (S. murinus, C. monticola and C. malayana) based on the images. The discrete landmark data of craniodental views (dorsal, jaw and lateral) are converted into continuous curves where the curves are represented as linear combinations of basis functions. A comparative study based on four machine learning algorithms such as naive Bayes, support vector machine, random forest, and generalized linear models was conducted on the predicted principal component scores obtained from the FDA approach and classical approach (combination of all three craniodental views and individual views). The FDA approach produced better results in separating the three clusters of shrew species compared to the classical method and the dorsal view gave the best representation in classifying the three shrew species. Overall, based on the FDA approach, GLM of the predicted PCA scores was the most accurate (95.4% accuracy) among the four classification models., Comment: 24 pages,4 Figures,2 tables,Dharini Pathmanathan (corresponding author)

Published
2023

27. Economic Evaluation of Nirmatrelvir/Ritonavir Among Adults Against Hospitalization During the Omicron Dominated Period in Malaysia: A Real-World Evidence Perspective

Author

Ee Vien Low, Hoon Shien Teh, Nicholas Yee Liang Hing, Suresh Kumar Chidambaram, Mohan Dass Pathmanathan, Wee Ric Kim, Wei Jia Lee, Zhi Wei Teh, Maheshwara Rao Appannan, Shahanizan Mohd Zin, Faizah Muhamad Zin, Samha Bashirah Mohamed Amin, Mastura Ismail, Azah Abdul Samad, and Kalaiarasu M. Peariasamy

Subjects
Therapeutics. Pharmacology, RM1-950, Pharmacy and materia medica, RS1-441
Abstract

Abstract Background and objectives Nirmatrelvir/ritonavir was administered orally to manage mild to moderate symptoms of COVID-19 in adult patients. The objectives of this study were to (i) evaluate the cost-effectiveness of prescribing nirmatrelvir/ritonavir within 5 days of a COVID-19 illness in order to avert hospitalization within a 30-day period in the Malaysia setting; (ii) determine how variations in pricing and hospitalization rates will affect the cost-effectiveness of nirmatrelvir/ritonavir. Methods The 30-day hospitalization related to COVID-19 was determined using 1 to 1 propensity score-matched real-world data in Malaysia from 14 July 2022 to 14 November 2022. To determine the total per-person costs related to COVID-19, we added the cost of drug (nirmatrelvir/ritonavir or control), clinic visits and inpatient care. Incremental cost-effectiveness ratio (ICER) per hospitalization averted was calculated. Results Our cohort included 31,487 patients. The rate of hospitalization within 30 days was found to be 0.35% for the group treated with nirmatrelvir/ritonavir, and 0.52% for the control group. The nirmatrelvir/ritonavir group cost an additional MYR 1,625.72 (USD 358.88) per patient. This treatment also resulted in a reduction of 0.17% risk for hospitalization, which corresponded to an ICER of MYR 946,801.26 (USD 209,006.90) per hospitalization averted. Conclusion In Malaysia, where vaccination rates were high, nirmatrelvir/ritonavir has been shown to be beneficial in the outpatient treatment of adults with COVID-19 who have risk factors; however, it was only marginally cost effective against hospitalization for healthy adults during the Omicron period.

Published
2024
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28. Clustering of HR + /HER2− breast cancer in an Asian cohort is driven by immune phenotypes

Author

Jia-Wern Pan, Mohana Ragu, Wei-Qin Chan, Siti Norhidayu Hasan, Tania Islam, Li-Ying Teoh, Suniza Jamaris, Mee-Hoong See, Cheng-Har Yip, Pathmanathan Rajadurai, Lai-Meng Looi, Nur Aishah Mohd Taib, Oscar M. Rueda, Carlos Caldas, Suet-Feung Chin, Joanna Lim, and Soo-Hwang Teo

Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Abstract Breast cancer exhibits significant heterogeneity, manifesting in various subtypes that are critical in guiding treatment decisions. This study aimed to investigate the existence of distinct subtypes of breast cancer within the Asian population, by analysing the transcriptomic profiles of 934 breast cancer patients from a Malaysian cohort. Our findings reveal that the HR + /HER2− breast cancer samples display a distinct clustering pattern based on immune phenotypes, rather than conforming to the conventional luminal A-luminal B paradigm previously reported in breast cancers from women of European descent. This suggests that the activation of the immune system may play a more important role in Asian HR + /HER2− breast cancer than has been previously recognized. Analysis of somatic mutations by whole exome sequencing showed that counter-intuitively, the cluster of HR + /HER2− samples exhibiting higher immune scores was associated with lower tumour mutational burden, lower homologous recombination deficiency scores, and fewer copy number aberrations, implicating the involvement of non-canonical tumour immune pathways. Further investigations are warranted to determine the underlying mechanisms of these pathways, with the potential to develop innovative immunotherapeutic approaches tailored to this specific patient population.

Published
2024
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29. Evaluating Situated Visualization in AR with Eye Tracking

Author

Kurzhals, Kuno, Becher, Michael, Pathmanathan, Nelusa, and Reina, Guido

Subjects
Computer Science - Human-Computer Interaction, Computer Science - Graphics
Abstract

Augmented reality (AR) technology provides means for embedding visualization in a real-world context. Such techniques allow situated analyses of live data in their spatial domain. However, as existing techniques have to be adapted for this context and new approaches will be developed, the evaluation thereof poses new challenges for researchers. Apart from established performance measures, eye tracking has proven to be a valuable means to assess visualizations qualitatively and quantitatively. We discuss the challenges and opportunities of eye tracking for the evaluation of situated visualizations. We envision that an extension of gaze-based evaluation methodology into this field will provide new insights on how people perceive and interact with visualizations in augmented reality.

Published
2022

30. Failure assessment of deteriorated steel light poles

Author

Hendrik Wijaya, Sahan Bandara, Pathmanathan Rajeev, Emad Gad, and Johnny Shan

Subjects
Failure assessment, Deterioration, Corrosion, Power distribution structure, Condition assessment, Reliability, Technology
Abstract

Steel poles are extensively used as street light posts and various other applications within the power distribution network. For partially buried pole, the effect of corrosion below and at the ground level significantly controls the overall load capacity. In this study, number of in-service steel pole specimens extracted from field were assessed for the corrosion damage. The level of corrosion was estimated by remaining wall thickness measured using ultrasonic testing and the surface corrosion was estimated using 3D laser scanning to adopt an appropriate existing model for buried steel structures. A relationship between maximum and average corrosion pit depth and loss of embedment depth was then developed. Finally, the corrosion parameters, combined with uncertainty in loading and soil properties were employed to perform time-dependent reliability assessment for steel light poles which can be used to assist the asset maintenance team in planning the replacement or strengthening works for steel pole.

Published
2024
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31. Credibility assessment of in silico clinical trials for medical devices.

Author

Pras Pathmanathan, Kenneth Aycock, Andreu Badal, Ramin Bighamian, Jeff Bodner, Brent A Craven, and Steven Niederer

Subjects
Biology (General), QH301-705.5
Abstract

In silico clinical trials (ISCTs) are an emerging method in modeling and simulation where medical interventions are evaluated using computational models of patients. ISCTs have the potential to provide cost-effective, time-efficient, and ethically favorable alternatives for evaluating the safety and effectiveness of medical devices. However, ensuring the credibility of ISCT results is a significant challenge. This paper aims to identify unique considerations for assessing the credibility of ISCTs and proposes an ISCT credibility assessment workflow based on recently published model assessment frameworks. First, we review various ISCTs described in the literature, carefully selected to showcase the range of methodological options available. These studies cover a wide variety of devices, reasons for conducting ISCTs, patient model generation approaches including subject-specific versus 'synthetic' virtual patients, complexity levels of devices and patient models, incorporation of clinician or clinical outcome models, and methods for integrating ISCT results with real-world clinical trials. We next discuss how verification, validation, and uncertainty quantification apply to ISCTs, considering the range of ISCT approaches identified. Based on our analysis, we then present a hierarchical workflow for assessing ISCT credibility, using a general credibility assessment framework recently published by the FDA's Center for Devices and Radiological Health. Overall, this work aims to promote standardization in ISCTs and contribute to the wider adoption and acceptance of ISCTs as a reliable tool for evaluating medical devices.

Published
2024
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32. Toward trustworthy medical device in silico clinical trials: a hierarchical framework for establishing credibility and strategies for overcoming key challenges

Author

Kenneth I. Aycock, Tom Battisti, Ashley Peterson, Jiang Yao, Steven Kreuzer, Claudio Capelli, Sanjay Pant, Pras Pathmanathan, David M. Hoganson, Steve M. Levine, and Brent A. Craven

Subjects
In silico clinical trial, ISCT, model credibility, computational modeling and simulation, hierarchical verification and validation, Medicine (General), R5-920
Abstract

Computational models of patients and medical devices can be combined to perform an in silico clinical trial (ISCT) to investigate questions related to device safety and/or effectiveness across the total product life cycle. ISCTs can potentially accelerate product development by more quickly informing device design and testing or they could be used to refine, reduce, or in some cases to completely replace human subjects in a clinical trial. There are numerous potential benefits of ISCTs. An important caveat, however, is that an ISCT is a virtual representation of the real world that has to be shown to be credible before being relied upon to make decisions that have the potential to cause patient harm. There are many challenges to establishing ISCT credibility. ISCTs can integrate many different submodels that potentially use different modeling types (e.g., physics-based, data-driven, rule-based) that necessitate different strategies and approaches for generating credibility evidence. ISCT submodels can include those for the medical device, the patient, the interaction of the device and patient, generating virtual patients, clinical decision making and simulating an intervention (e.g., device implantation), and translating acute physics-based simulation outputs to health-related clinical outcomes (e.g., device safety and/or effectiveness endpoints). Establishing the credibility of each ISCT submodel is challenging, but is nonetheless important because inaccurate output from a single submodel could potentially compromise the credibility of the entire ISCT. The objective of this study is to begin addressing some of these challenges and to identify general strategies for establishing ISCT credibility. Most notably, we propose a hierarchical approach for assessing the credibility of an ISCT that involves systematically gathering credibility evidence for each ISCT submodel in isolation before demonstrating credibility of the full ISCT. Also, following FDA Guidance for assessing computational model credibility, we provide suggestions for ways to clearly describe each of the ISCT submodels and the full ISCT, discuss considerations for performing an ISCT model risk assessment, identify common challenges to demonstrating ISCT credibility, and present strategies for addressing these challenges using our proposed hierarchical approach. Finally, in the Appendix we illustrate the many concepts described here using a hypothetical ISCT example.

Published
2024
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33. CacaoFIT: the network of cacao field trials in Latin America and its contribution to sustainable cacao farming in the region

Author

Luis Orozco-Aguilar, Arlene Lopez-Sampson, Rolando H. Cerda, Fernando Casanoves, Oscar Ramirez-Argueta, Javier Diaz Matute, Juan Carlos Suárez Salazar, Johanna Rüegg, Stephane Saj, Joaquin Milz, Ulf Schneidewind, Argenis Mora Garces, Eliana Baez Daza, Jairo Rojas Molina, Yeirme Jaimes Suarez, Genaro A. Agudelo-Castañeda, Olivier Deheuvels, Enelvi Brito Sosa, Jaime Hinojosa Gómez, Ramon E. Jaimez, Sophya Reyes Espinoza, Melanie Bordeaux, Carlos Caicedo Vargas, Leider Tinoco, Geover Peña Monserrate, Julian Perez Flores, Alfonso Azpeitia Morales, Cesar O. Arevalo-Hernandez, Enrique Arevalo Gardini, Luis E. Pocasangre, Osmary Araque, Athina Koutouleas, Eufemia Segura Magaña, Omar Dominguez, Paula Arenas, Lorena Sotopinto, Marisela Salgado-Mora, Antonio Gama-Rodrigues, Emanuela Gama-Rodrigues, Annelle Holder, Gideon Ramtahal, Pathmanathan Umaharan, Manfred Willy Muller, Fernando Texeira Mendes, and Eduardo Somarriba

Subjects
agroforestry, cacao trials, on-farm research, perennial crops, sustainability, Nutrition. Foods and food supply, TX341-641, Food processing and manufacture, TP368-456
Abstract

A network of agronomists, researchers, and practitioners associated with cacao farming provided open access to their independent field trials across Latin America and the Caribbean (LAC). A centralized dataset was assembled using qualitative and quantitative data from 25 experimental field trials (hereafter referred to as “CacaoFIT”) spanning several LAC agroecosystems. This dataset was used to document the main traits and agroclimatic attributes of the cacao cultivation model being tested within the CacaoFIT network. By synthesizing data from an entire network of cacao trials, this study aimed to highlight specific design features and management practices that may contribute to better cacao farming sustainability. The CacaoFIT network comprises 200 ha of field trials testing over 150 cacao genotypes and set up under different shade canopy design, management, and research goals. Small-sized trials were common across Mesoamerica, whereas medium to large-size trials were distinct to South America. Cacao trials were 15 years old (on average) and ranged from 3 to 25 years of establishment. Most cacao trials were managed conventionally (i.e., 55%), while 20% were under organic practices, and the remaining 25% presented both conventional and organic management approaches. Most field trials (ca. 60%) planted an average of 10 international clones or national cultivars at high (1,230–1,500 plants ha−1) and medium density (833–1,111 plants ha−1). Mixed shade canopies were the dominant agroforestry model, while timber vs. leguminous shade canopies were also common. The diversity and depth of research domains examined across the CacaoFIT network varied widely. Agronomy and agroforestry topics dominated the research agenda across all trials, followed by environmental services domains. Cacao physiology and financial performance were researched to a lesser extent within the network. Five featured field trials from CacaoFIT offered technical guidelines to inform cacao farming within similar contexts. This collaborative work is a scaffold to encourage public–private partnerships, capacity building, and data sharing amongst cacao researchers across the tropics.

Published
2024
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34. Weekly ultra-hypofractionated radiotherapy in localised prostate cancer

Author

Nora Sundahl, Douglas Brand, Chris Parker, David Dearnaley, Alison Tree, Angela Pathmanathan, Yae-eun Suh, Nicholas Van As, Rosalind Eeles, Vincent Khoo, Robert Huddart, and Julia Murray

Subjects
Prostate cancer, Radiotherapy, Ultra-hypofractionation, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Background: Moderately hypofractionated radiotherapy regimens or stereotactic body radiotherapy (SBRT) are standard of care for localised prostate cancer. However, some patients are unable or unwilling to travel daily to the radiotherapy department and do not have access to, or are not candidates for, SBRT. For many years, The Royal Marsden Hospital NHS Foundation Trust has offered a weekly ultra-hypofractionated radiotherapy regimen to the prostate (36 Gy in 6 weekly fractions) to patients unable/unwilling to travel daily. Methods: The current study is a retrospective analysis of all patients with non-metastatic localised prostate cancer receiving this treatment schedule from 2010 to 2015. Results: A total of 140 patients were included in the analysis, of whom 86 % presented with high risk disease, with 31 % having Gleason Grade Group 4 or 5 disease and 48 % T3 disease or higher. All patients received hormone treatment, and there was often a long interval between start of hormone treatment and start of radiotherapy (median of 11 months), with 34 % of all patients having progressed to non-metastatic castrate-resistant disease prior to start of radiotherapy. Median follow-up was 52 months. Median progression-free survival (PFS) and overall survival (OS) for the whole group was 70 months and 72 months, respectively. PFS and OS in patients with hormone-sensitive disease at time of radiotherapy was not reached and 75 months, respectively; and in patients with castrate-resistant disease at time of radiotherapy it was 20 months and 61 months, respectively. Conclusion: Our data shows that a weekly ultra-hypofractionated radiotherapy regimen for prostate cancer could be an option in those patients for whom daily treatment or SBRT is not an option.

Published
2024
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35. Clinical impact of Prostate-Specific Membrane Antigen Positron Emission Tomography (PET) on intensification or deintensification of advanced renal cell carcinoma management

Author

Pathmanathan, Shivanshan, Tariq, Arsalan, Pearce, Adam, Rhee, Handoo, Kyle, Samuel, Raveenthiran, Sheliyan, Wong, David, McBean, Rhiannon, Marsh, Phillip, Goodman, Steven, Dhiantravan, Nattakorn, Esler, Rachel, Dunglison, Nigel, Navaratnam, Anojan, Yaxley, John, Thomas, Paul, Pattison, David A., Goh, Jeffrey C., Gan, Chun Loo, and Roberts, Matthew J.

Published
2023
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38. Characteristics and outcomes of COVID-19 patients admitted to hospital with and without respiratory symptoms

Author

Barbara Wanjiru Citarella, Christiana Kartsonaki, Elsa D. Ibáñez-Prada, Bronner P. Gonçalves, Joaquin Baruch, Martina Escher, Mark G. Pritchard, Jia Wei, Fred Philippy, Andrew Dagens, Matthew Hall, James Lee, Demetrios James Kutsogiannis, Evert-Jan Wils, Marília Andreia Fernandes, Bharath Kumar Tirupakuzhi Vijayaraghavan, Prasan Kumar Panda, Ignacio Martin-Loeches, Shinichiro Ohshimo, Arie Zainul Fatoni, Peter Horby, Jake Dunning, Jordi Rello, Laura Merson, Amanda Rojek, Michel Vaillant, Piero Olliaro, Luis Felipe Reyes, S.A. Moharam, Sabriya Abdalasalam, Alaa Abdalfattah Abdalhadi, Naana Reyam Abdalla, Walaa Abdalla, Almthani Hamza Abdalrheem, Ashraf Abdalsalam, Saedah Abdeewi, Esraa Hassan Abdelgaum, Mohamed Abdelhalim, Mohammed Abdelkabir, Israa Abdelrahman, Sheryl Ann Abdukahil, Lamees Adil Abdulbaqi, Salaheddin Abdulhamid, Widyan Abdulhamid, Nurul Najmee Abdulkadir, Eman Abdulwahed, Rawad Abdunabi, Ryuzo Abe, Laurent Abel, Ahmed Mohammed Abodina, Amal Abrous, Lara Absil, Kamal Abu Jabal, Nashat Abu Salah, Abdurraouf Abusalama, Tareg Abdallah Abuzaid, Subhash Acharya, Andrew Acker, Elisabeth Adam, Safia Adem, Manuella Ademnou, Francisca Adewhajah, Diana Adrião, Anthony Afum-Adjei Awuah, Melvin Agbogbatey, Saleh Al Ageel, Aya Mustafa Ahmed, Musaab Mohammed Ahmed, Shakeel Ahmed, Zainab Ahmed Alaraji, Abdulrahman Ahmed Elhefnawy Enan, Reham Abdelhamid Ahmed Khalil, Ali Mostafa Ahmed Mohamed Abdelaziz, Kate Ainscough, Eka Airlangga, Tharwat Aisa, Ali Aisha, Bugila Aisha, Ali Ait Hssain, Younes Ait Tamlihat, Takako Akimoto, Ernita Akmal, Chika Akwani, Eman Al Qasim, Ahmed Alajeeli, Ahmed Alali, Razi Alalqam, Aliya Mohammed Alameen, Mohammed Al-Aquily, Zinah A. Alaraji, Khalid Albakry, Safa Albatni, Angela Alberti, Osama Aldabbourosama, Tala Al-dabbous, Amer Aldhalia, Abdulkarim Aldoukali, Senthilkumar Alegesan, Marta Alessi, Beatrice Alex, Kévin Alexandre, Abdulrahman Al-Fares, Asil Alflite, Huda Alfoudri, Qamrah Alhadad, Hoda Salem Alhaddad, Maali Khalid Mohamed Abdalla Alhasan, Ahmad Nabil Alhouri, Hasan Alhouri, Adam Ali, Imran Ali, Maha TagElser Mohammed Ali, Syed Ali Abbas, Yomna Ali Abdelghafar, Naseem Ali Sheikh, Kazali Enagnon Alidjnou, Mahmoud Aljadi, Sarah Aljamal, Mohammed Alkahlout, Akram Alkaseek, Qabas Alkhafajee, Clotilde Allavena, Nathalie Allou, Lana Almasri, Abdulrahman Almjersah, Raja Ahmed Alqandouz, Walaa Alrfaea, Moayad Alrifaee, Rawan Alsaadi, Yousef Al-Saba'a, Entisar Alshareea, Eslam Alshenawy, Aneela Altaf, João Melo Alves, João Alves, Rita Alves, Joana Alves Cabrita, Maria Amaral, Amro Essam Amer, Nur Amira, Amos Amoako Adusei, John Amuasi, Roberto Andini, Claire Andrejak, Andrea Angheben, François Angoulvant, Sophia Ankrah, Séverine Ansart, Sivanesen Anthonidass, Massimo Antonelli, Carlos Alexandre Antunes de Brito, Ardiyan Apriyana, Yaseen Arabi, Irene Aragao, Francisco Arancibia, Carolline Araujo, Antonio Arcadipane, Patrick Archambault, Lukas Arenz, Jean-Benoît Arlet, Christel Arnold-Day, Lovkesh Arora, Rakesh Arora, Elise Artaud-Macari, Diptesh Aryal, Angel Asensio, Elizabeth A. Ashley, Muhammad Ashraf, Muhammad Sheharyar Ashraf, Abir Ben Ashur, Franklin Asiedu-Bekoe, Namra Asif, Mohammad Asim, Grace Assi, Jean Baptiste Assie, Amirul Asyraf, Fouda Atangana, Ahmed Atia, Minahel Atif, Asia Atif Abdelrhman Abdallahrs, Anika Atique, Moad Atlowly, AM Udara Lakshan Attanyake, Johann Auchabie, Hugues Aumaitre, Adrien Auvet, Abdelmalek Awad Ali Mohammed, Eyvind W. Axelsen, Ared Ayad, Ahmed Ayman Hassan Helmi, Laurène Azemar, Mohammed Azizeldin, Cecile Azoulay, Hakeem Babatunde, Benjamin Bach, Delphine Bachelet, Claudine Badr, Roar Bævre-Jensen, Nadia Baig, John Kenneth Baillie, J Kevin Baird, Erica Bak, Agamemnon Bakakos, Nazreen Abu Bakar, Hibah Bileid Bakeer, Ashraf Bakri, Andriy Bal, Mohanaprasanth Balakrishnan, Irene Bandoh, Firouzé Bani-Sadr, Renata Barbalho, Nicholas Yuri Barbosa, Wendy S. Barclay, Saef Umar Barnett, Michaela Barnikel, Helena Barrasa, Cleide Barrigoto, Marie Bartoli, Joaquín Baruch, Romain Basmaci, Muhammad Fadhli Hassin Basri, AbdAlkarim Batool, Denise Battaglini, Jules Bauer, Diego Fernando Bautista Rincon, Denisse Bazan Dow, Abigail Beane, Alexandra Bedossa, Ker Hong Bee, Husna Begum, Sylvie Behilill, Albertus Beishuizen, Aleksandr Beljantsev, David Bellemare, Anna Beltrame, Beatriz Amorim Beltrão, Marine Beluze, Nicolas Benech, Lionel Eric Benjiman, Suzanne Bennett, Luís Bento, Jan-Erik Berdal, Lamis Berdeweel, Delphine Bergeaud, Hazel Bergin, Giulia Bertoli, Lorenzo Bertolino, Simon Bessis, Sybille Bevilcaqua, Karine Bezulier, Amar Bhatt, Krishna Bhavsar, Isabella Bianchi, Claudia Bianco, Sandra Bichoka, Farah Nadiah Bidin, Felwa Bin Humaid, Mohd Nazlin Bin Kamarudin, Muhannud Binnawara, Zeno Bisoffi, Patrick Biston, Laurent Bitker, Mustapha Bittaye, Jonathan Bitton, Pablo Blanco-Schweizer, Catherine Blier, Frank Bloos, Mathieu Blot, Filomena Boccia, Laetitia Bodenes, Debby Bogaert, Anne-Hélène Boivin, Ariel Bolanga, Isabela Bolaños, Pierre-Adrien Bolze, François Bompart, Aurelius Bonifasius, Joe Bonney, Diogo Borges, Raphaël Borie, Hans Martin Bosse, Elisabeth Botelho-Nevers, Lila Bouadma, Olivier Bouchaud, Sabelline Bouchez, Damien Bouhour, Kévin Bouiller, Laurence Bouillet, Camile Bouisse, Latsaniphone Bountthasavong, Anne-Sophie Boureau, John Bourke, Maude Bouscambert, Aurore Bousquet, Marielle Boyer-Besseyre, Maria Boylan, Fernando Augusto Bozza, Axelle Braconnier, Cynthia Braga, Timo Brandenburger, Filipa Brás Monteiro, Luca Brazzi, Dorothy Breen, Patrick Breen, David Brewster, Kathy Brickell, Tessa Broadley, Helen Brotherton, Alex Browne, Nicolas Brozzi, Sonja Hjellegjerde Brunvoll, Marjolein Brusse-Keizer, Petra Bryda, Nina Buchtele, Polina Bugaeva, Marielle Buisson, Danilo Buonsenso, Erlina Burhan, Donald Buri, Aidan Burrell, Ingrid G. Bustos, Denis Butnaru, André Cabie, Susana Cabral, Joana Cabrita, Eder Caceres, Cyril Cadoz, Rui Caetano Garcês, Kate Calligy, Jose Andres Calvache, João Camões, Valentine Campana, Paul Campbell, Josie Campisi, Cecilia Canepa, Mireia Cantero, Janice Caoili, Pauline Caraux-Paz, Sheila Cárcel, Filipa Cardoso, Filipe Cardoso, Nelson Cardoso, Sofia Cardoso, Simone Carelli, Nicolas Carlier, Thierry Carmoi, Gayle Carney, Inês Carqueja, Marie-Christine Carret, François Martin Carrier, Ida Carroll, Gail Carson, Maire-Laure Casanova, Mariana Cascão, Siobhan Casey, José Casimiro, Bailey Cassandra, Silvia Castañeda, Nidyanara Castanheira, Guylaine Castor-Alexandre, Ivo Castro, Ana Catarino, François-Xavier Catherine, Paolo Cattaneo, Roberta Cavalin, Giulio Giovanni Cavalli, Alexandros Cavayas, Adrian Ceccato, Masaneh Ceesay, Shelby Cerkovnik, Minerva Cervantes-Gonzalez, Muge Cevik, Anissa Chair, Catherine Chakveatze, Adrienne Chan, Meera Chand, Jean-Marc Chapplain, Charlotte Charpentier, Julie Chas, Muhammad Mobin Chaudry, Jonathan Samuel Chávez Iñiguez, Anjellica Chen, Yih-Sharng Chen, Léo Chenard, Matthew Pellan Cheng, Antoine Cheret, Thibault Chiarabini, Julian Chica, Suresh Kumar Chidambaram, Leong Chin Tho, Catherine Chirouze, Davide Chiumello, Sung-Min Cho, Bernard Cholley, Danoy Chommanam, Marie-Charlotte Chopin, Yock Ping Chow, Ting Soo Chow, Nathaniel Christy, Hiu Jian Chua, Jonathan Chua, Jose Pedro Cidade, José Miguel Cisneros Herreros, Anna Ciullo, Jennifer Clarke, Rolando Claure-Del Granado, Sara Clohisey, Cassidy Codan, Caitriona Cody, Jennifer Coles, Megan Coles, Gwenhaël Colin, Michael Collins, Pamela Combs, Jennifer Connolly, Marie Connor, Anne Conrad, Elaine Conway, Graham S. Cooke, Hugues Cordel, Amanda Corley, Sabine Cornelis, Alexander Daniel Cornet, Arianne Joy Corpuz, Andrea Cortegiani, Grégory Corvaisier, Camille Couffignal, Sandrine Couffin-Cadiergues, Roxane Courtois, Stéphanie Cousse, Juthaporn Cowan, Rachel Cregan, Gloria Crowl, Jonathan Crump, Claudina Cruz, Marc Csete, Ailbhe Cullen, Matthew Cummings, Gerard Curley, Elodie Curlier, Colleen Curran, Paula Custodio, Ana da Silva Filipe, Charlene Da Silveira, Al-Awwab Dabaliz, John Arne Dahl, Darren Dahly, Umberto D'Alessandro, Peter Daley, Zaina Dalloul, Heidi Dalton, Jo Dalton, Seamus Daly, Juliana Damas, Joycelyn Dame, Cammandji Damien, Nick Daneman, Jorge Dantas, Frédérick D'Aragon, Gillian de Loughry, Diego de Mendoza, Etienne De Montmollin, Rafael Freitas de Oliveira França, Ana Isabel de Pinho Oliveira, Rosanna De Rosa, Cristina De Rose, Thushan de Silva, Peter de Vries, Jillian Deacon, David Dean, Alexa Debard, Bianca DeBenedictis, Marie-Pierre Debray, Nathalie DeCastro, William Dechert, Romain Decours, Eve Defous, Isabelle Delacroix, Alexandre Delamou, Eric Delaveuve, Karen Delavigne, Nathalie M. Delfos, Ionna Deligiannis, Andrea Dell'Amore, Christelle Delmas, Pierre Delobel, Corine Delsing, Elisa Demonchy, Emmanuelle Denis, Dominique Deplanque, Pieter Depuydt, Diane Descamps, Mathilde Desvallées, Santi Dewayanti, Pathik Dhangar, Alpha Diallo, Souleymane Taran Diallo, Sylvain Diamantis, André Dias, Fernanda Dias Da Silva, Rodrigo Diaz, Juan Jose Diaz, Priscila Diaz, Bakary K. Dibba, Kévin Didier, Jean-Luc Diehl, Wim Dieperink, Jérôme Dimet, Vincent Dinot, Fara Diop, Alphonsine Diouf, Yael Dishon, Cedric Djadda, Félix Djossou, Annemarie B. Docherty, Helen Doherty, Arjen M. Dondorp, Christl A. Donnelly, Yoann Donohue, Sean Donohue, Peter Doran, Céline Dorival, Eric D'Ortenzio, Yash Doshi, Phouvieng Douangdala, James Joshua Douglas, Renee Douma, Nathalie Dournon, Joanne Downey, Mark Downing, Thomas Drake, Aoife Driscoll, Ibrahim Kwaku Duah, Claudio Duarte Fonseca, Vincent Dubee, François Dubos, Audrey Dubot-Pérès, Alexandre Ducancelle, Toni Duculan, Susanne Dudman, Abhijit Duggal, Paul Dunand, Mathilde Duplaix, Emanuele Durante-Mangoni, Lucian Durham, III, Bertrand Dussol, Juliette Duthoit, Xavier Duval, Anne Margarita Dyrhol-Riise, Sim Choon Ean, Ada Ebo, Marco Echeverria-Villalobos, Michael Edelstein, Siobhan Egan, Linn Margrete Eggesbø, Khadeja Ehzaz, Carla Eira, Mohammed El Sanharawi, Marwan El Sayed, Mohammed Elabid, Mohamed Bashir Elagili, Subbarao Elapavaluru, Mohammad Elbahnasawy, Sohail Elboshra, Brigitte Elharrar, Jacobien Ellerbroek, Merete Ellingjord-Dale, Hamida ELMagrahi, Mohammad Muatasm Elmubark, Loubna Elotmani, Lauren Eloundou, Philippine Eloy, Basma Elshaikhy, Tarek Elshazly, Wafa Elsokni, Aml Ahmed Eltayeb, Iqbal Elyazar, Zarief Kamel Emad, Hussein Embarek, Isabelle Enderle, Tomoyuki Endo, Gervais Eneli, Chan Chee Eng, Ilka Engelmann, Vincent Enouf, Olivier Epaulard, Haneen Esaadi, Mariano Esperatti, Hélène Esperou, Catarina Espírito Santo, Marina Esposito-Farese, Rachel Essaka, Lorinda Essuman, João Estevão, Manuel Etienne, Anna Greti Everding, Mirjam Evers, Isabelle Fabre, Marc Fabre, Ismaila Fadera, Asgad Osman Abdalla Fadlalla, Amna Faheem, Arabella Fahy, Cameron J. Fairfield, Zul Fakar, Komal Fareed, Pedro Faria, Ahmed Farooq, Hanan Fateena, Mohamed Fathi, Salem Fatima, Karine Faure, Raphaël Favory, Mohamed Fayed, Niamh Feely, Jorge Fernandes, Susana Fernandes, François-Xavier Ferrand, Eglantine Ferrand Devouge, Joana Ferrão, Mário Ferraz, Benigno Ferreira, Isabel Ferreira, Bernardo Ferreira, Sílvia Ferreira, Nicolas Ferriere, Céline Ficko, Claudia Figueiredo-Mello, William Finlayson, Thomas Flament, Tom Fletcher, Aline-Marie Florence, Letizia Lucia Florio, Brigid Flynn, Deirdre Flynn, Jean Foley, Victor Fomin, Tatiana Fonseca, Patricia Fontela, Karen Forrest, Simon Forsyth, Denise Foster, Giuseppe Foti, Berline Fotso, Erwan Fourn, Robert A. Fowler, Marianne Fraher, Diego Franch-Llasat, Christophe Fraser, John F. Fraser, Marcela Vieira Freire, Ana Freitas Ribeiro, Craig French, Caren Friedrich, Ricardo Fritz, Stéphanie Fry, Nora Fuentes, Masahiro Fukuda, G. Argin, Valérie Gaborieau, Rostane Gaci, Massimo Gagliardi, Jean-Charles Gagnard, Amandine Gagneux-Brunon, Abdou Gai, Sérgio Gaião, Linda Gail Skeie, Adham Mohamed Galal Mohamed Ramadan, Phil Gallagher, Carrol Gamble, Yasmin Gani, Arthur Garan, Rebekha Garcia, Julia Garcia-Diaz, Esteban Garcia-Gallo, Navya Garimella, Denis Garot, Valérie Garrait, Basanta Gauli, Anatoliy Gavrylov, Alexandre Gaymard, Johannes Gebauer, Eva Geraud, Louis Gerbaud Morlaes, Nuno Germano, Malak Ghemmeid, Praveen Kumar Ghisulal, Jade Ghosn, Marco Giani, Tristan Gigante, Elaine Gilroy, Guillermo Giordano, Michelle Girvan, Valérie Gissot, Gezy Giwangkancana, Daniel Glikman, Petr Glybochko, Eric Gnall, Geraldine Goco, François Goehringer, Siri Goepel, Jean-Christophe Goffard, Jin Yi Goh, Brigitta Golács, Jonathan Golob, Kyle Gomez, Joan Gómez-Junyent, Marie Gominet, Alicia Gonzalez, Patricia Gordon, Isabelle Gorenne, Laure Goubert, Cécile Goujard, Tiphaine Goulenok, Margarite Grable, Jeronimo Graf, Edward Wilson Grandin, Pascal Granier, Giacomo Grasselli, Lorenzo Grazioli, Christopher A. Green, Courtney Greene, William Greenhalf, Segolène Greffe, Domenico Luca Grieco, Matthew Griffee, Fiona Griffiths, Ioana Grigoras, Albert Groenendijk, Fassou Mathias Grovogui, Heidi Gruner, Yusing Gu, Jérémie Guedj, Martin Guego, Anne-Marie Guerguerian, Daniela Guerreiro, Romain Guery, Anne Guillaumot, Laurent Guilleminault, Maisa Guimarães de Castro, Thomas Guimard, Marieke Haalboom, Daniel Haber, Ali Hachemi, Abdurrahman Haddud, Nadir Hadri, Wael Hafez, Fakhir Raza Haidri, Fatima Mhd Rida Hajij, Sheeba Hakak, Adam Hall, Sophie Halpin, Shaher Hamdan, Abdelhafeez Hamdi, Jawad Hameed, Ansley Hamer, Raph L. Hamers, Rebecca Hamidfar, Bato Hammarström, Naomi Hammond, Terese Hammond, Lim Yuen Han, Matly Hanan, Rashan Haniffa, Kok Wei Hao, Hayley Hardwick, Ewen M. Harrison, Janet Harrison, Samuel Bernard Ekow Harrison, Alan Hartman, Sulieman Hasan, Mohammad Ali Nabil Hasan, Mohd Shahnaz Hasan, Junaid Hashmi, Madiha Hashmi, Amoni Hassan, Ebtisam Hassanin, Muhammad Hayat, Ailbhe Hayes, Leanne Hays, Jan Heerman, Lars Heggelund, Ahmed Helmi, Ross Hendry, Martina Hennessy, Aquiles Rodrigo Henriquez-Trujillo, Maxime Hentzien, Diana Hernandez, Andrew Hershey, Liv Hesstvedt, Astarini Hidayah, Eibhlin Higgins, Rupert Higgins, Samuel Hinton, Hiroaki Hiraiwa, Haider Hirkani, Hikombo Hitoto, Antonia Ho, Yi Bin Ho, Alexandre Hoctin, Isabelle Hoffmann, Wei Han Hoh, Oscar Hoiting, Rebecca Holt, Jan Cato Holter, Juan Pablo Horcajada, Ikram Houas, Mabrouka Houderi, Catherine L. Hough, Stuart Houltham, Jimmy Ming-Yang Hsu, Jean-Sébastien Hulot, Abby Hurd, Iqbal Hussain, Aliae Mohamed Hussein, Mahmood Hussein, Fatima Ibrahim, Bashir Ibran, Samreen Ijaz, M. Arfan Ikram, Carlos Cañada Illana, Patrick Imbert, Muhammad Imran Ansari, Rana Imran Sikander, Hugo Inácio, Carmen Infante Dominguez, Yun Sii Ing, Mariachiara Ippolito, Vera Irawany, Sarah Isgett, Tiago Isidoro, Nadiah Ismail, Margaux Isnard, Mette Stausland Istre, Junji Itai, Daniel Ivulich, Danielle Jaafar, Salma Jaafoura, Hamza Jaber, Julien Jabot, Clare Jackson, Abubacarr Jagne, Stéphane Jaureguiberry, Denise Jaworsky, Florence Jego, Anilawati Mat Jelani, Synne Jenum, Ruth Jimbo-Sotomayor, Ong Yiaw Joe, Ruth Noemí Jorge García, Silje Bakken Jørgensen, Cédric Joseph, Mark Joseph, Swosti Joshi, Mercé Jourdain, Philippe Jouvet, Anna Jung, Hanna Jung, Dafsah Juzar, Ouifiya Kafif, Florentia Kaguelidou, Neerusha Kaisbain, Thavamany Kaleesvran, Sabina Kali, Karl Trygve Kalleberg, Smaragdi Kalomoiri, Muhammad Aisar Ayadi Kamaluddin, Armand Saloun Kamano, Zul Amali Che Kamaruddin, Nadiah Kamarudin, Kavita Kamineni, Darshana Hewa Kandamby, Kong Yeow Kang, Darakhshan Kanwal, Dyah Kanyawati, Mohamed Karghul, Pratap Karpayah, Todd Karsies, Daisuke Kasugai, Kevin Katz, Christy Kay, Lamees Kayyali, Seán Keating, Pulak Kedia, Andrea Kelly, Aoife Kelly, Claire Kelly, Niamh Kelly, Sadie Kelly, Yvelynne Kelly, Maeve Kelsey, Kalynn Kennon, Sommay Keomany, Maeve Kernan, Younes Kerroumi, Sharma Keshav, Shams Khail, Sarah Khaled, Imrana Khalid, Antoine Khalil, Irfan Khan, Quratul Ain Khan, Sushil Khanal, Abid Khatak, Krish Kherajani, Michelle E. Kho, Denisa Khoo, Ryan Khoo, Saye Khoo, Muhammad Nasir Khoso, Amin Khuwaja, Khor How Kiat, Yuri Kida, Peter Kiiza, Beathe Kiland Granerud, Anders Benjamin Kildal, Jae Burm Kim, Antoine Kimmoun, Detlef Kindgen-Milles, Nobuya Kitamura, Eyrun Floerecke Kjetland Kjetland, Paul Klenerman, Rob Klont, Gry Kloumann Bekken, Stephen R. Knight, Robin Kobbe, Paa Kobina Forson, Chamira Kodippily, Malte Kohns Vasconcelos, Sabin Koirala, Mamoru Komatsu, Franklina Korkor Abebrese, Volkan Korten, Stephanie Kouba, Mohamed Lamine Kourouma, Karifa Kourouma, Arsène Kpangon, Karolina Krawczyk, Ali Kredan, Vinothini Krishnan, Sudhir Krishnan, Oksana Kruglova, Anneli Krund, Pei Xuan Kuan, Ashok Kumar, Deepali Kumar, Ganesh Kumar, Mukesh Kumar, Dinesh Kuriakose, Ethan Kurtzman, Demetrios Kutsogiannis, Galyna Kutsyna, Ama Kwakyewaa Bedu-Addo, Sylvie Kwedi, Konstantinos Kyriakoulis, Marie Lachatre, Marie Lacoste, John G. Laffey, Nadhem Lafhej, Marie Lagrange, Fabrice Laine, Olivier Lairez, Sanjay Lakhey, Marc Lambert, François Lamontagne, Marie Langelot-Richard, Vincent Langlois, Eka Yudha Lantang, Marina Lanza, Cédric Laouénan, Samira Laribi, Delphine Lariviere, Stéphane Lasry, Sakshi Lath, Naveed Latif, Youssef Latifeh, Odile Launay, Didier Laureillard, Yoan Lavie-Badie, Andy Law, Cassie Lawrence, Teresa Lawrence, Minh Le, Clément Le Bihan, Cyril Le Bris, Georges Le Falher, Lucie Le Fevre, Quentin Le Hingrat, Marion Le Maréchal, Soizic Le Mestre, Gwenaël Le Moal, Vincent Le Moing, Hervé Le Nagard, Ema Leal, Marta Leal Santos, Biing Horng Lee, Heng Gee Lee, Su Hwan Lee, Jennifer Lee, Todd C. Lee, Yi Lin Lee, Gary Leeming, Bénédicte Lefebvre, Laurent Lefebvre, Benjamin Lefèvre, Sylvie LeGac, Merili-Helen Lehiste, Jean-Daniel Lelievre, François Lellouche, Adrien Lemaignen, Véronique Lemee, Anthony Lemeur, Gretchen Lemmink, Ha Sha Lene, Jenny Lennon, Rafael León, Marc Leone, Tanel Lepik, Quentin Lepiller, François-Xavier Lescure, Olivier Lesens, Mathieu Lesouhaitier, Amy Lester-Grant, Andrew Letizia, Sophie Letrou, Bruno Levy, Yves Levy, Claire Levy-Marchal, Katarzyna Lewandowska, Erwan L'Her, Gianluigi Li Bassi, Janet Liang, Ali Liaquat, Geoffrey Liegeon, Kah Chuan Lim, Wei Shen Lim, Chantre Lima, Bruno Lina, Lim Lina, Andreas Lind, Maja Katherine Lingad, Guillaume Lingas, Sylvie Lion-Daolio, Keibun Liu, Marine Livrozet, Patricia Lizotte, Antonio Loforte, Navy Lolong, Leong Chee Loon, Diogo Lopes, Dalia Lopez-Colon, Anthony L. Loschner, Paul Loubet, Bouchra Loufti, Guillame Louis, Silvia Lourenco, Lara Lovelace-Macon, Lee Lee Low, Marije Lowik, Jia Shyi Loy, Jean Christophe Lucet, Carlos M. Luna, Olguta Lungu, Miles Lunn, Liem Luong, Nestor Luque, Dominique Luton, Olavi Maasikas, Moïse Machado, Sara Machado, Gabriel Macheda, Mustafa Magzoub, Rafael Mahieu, Sophie Mahy, Ana Raquel Maia, Lars S. Maier, Oumou Maiga Ascofare, Mylène Maillet, Thomas Maitre, Nimisha Abdul Majeed, Maximilian Malfertheiner, Nadia Malik, Paddy Mallon, Fernando Maltez, Denis Malvy, Victoria Manda, Laurent Mandelbrot, Frank Manetta, Julie Mankikian, Edmund Manning, Aldric Manuel, Veronika Maráczi, Ceila Maria Sant′Ana Malaque, Flávio Marino, Samuel Markowicz, Ana Marques, Catherine Marquis, Laura Marsh, Brian Marsh, Megan Marshal, John Marshall, Celina Turchi Martelli, Dori-Ann Martin, Emily Martin, Guillaume Martin-Blondel, Alessandra Martinelli, F. Eduardo Martinez, Martin Martinot, Alejandro Martín-Quiros, Ana Martins, João Martins, Nuno Martins, Caroline Martins Rego, Gennaro Martucci, Olga Martynenko, Eva Miranda Marwali, Marsilla Marzukie, David Maslove, Sabina Mason, Sobia Masood, Fatma Masoud, Moise Massoma, Palmer Masumbe, Mohd Basri Mat Nor, Moshe Matan, Henrique Mateus Fernandes, Meghena Mathew, Christina Mathew, Mathieu Mattei, Laurence Maulin, Juergen May, Javier Maynar, Mayfong Mayxay, Thierry Mazzoni, Lisa Mc Sweeney, Colin McArthur, Naina McCann, Peter McCanny, Aine McCarthy, Anne McCarthy, Colin McCloskey, Rachael McConnochie, Sherry McDermott, Sarah E. McDonald, Aine McElroy, Samuel McElwee, Natalie McEvoy, Allison McGeer, Kenneth A. McLean, Paul McNally, Bairbre McNicholas, Edel Meaney, Cécile Mear-Passard, Maggie Mechlin, Nastia Medombou, Omar Mehkri, Ferruccio Mele, Luis Melo, Kashif Ali Memon, João João Mendes, Ogechukwu Menkiti, Kusum Menon, France Mentré, Alexander J. Mentzer, Emmanuelle Mercier, Noémie Mercier, Antoine Merckx, Mayka Mergeay-Fabre, Blake Mergler, António Mesquita, Roberta Meta, Osama Metwally, Agnès Meybeck, Dan Meyer, Alison M. Meynert, Vanina Meysonnier, Mehdi Mezidi, Céline Michelanglei, Isabelle Michelet, Efstathia Mihelis, Vladislav Mihnovit, Duha Milad Abdullah, Jennene Miller, Hugo Miranda-Maldonado, Nor Arisah Misnan, Nik Nur Eliza Mohamed, Nouralsabah Mohamed, Tahira Jamal Mohamed, Alaa Mohamed Ads, Ahmed Reda Mohamed Elsayed Abdelhalim, Libya Mohammed, Shrouk Fawze Mohammed Mostafa, Manahil Omer Abdelrahman Mohammedahmed, Omer Abdullah Mohammedelhassan, Asma Moin, Walaa Mokhtar, Elena Molinos, Brenda Molloy, Mary Mone, Agostinho Monteiro, Claudia Montes, Giorgia Montrucchio, Sarah Moore, Shona C. Moore, Lina Morales Cely, Marwa Morgom, Lucia Moro, Catherine Motherway, Ana Motos, Hugo Mouquet, Clara Mouton Perrot, Julien Moyet, Suleiman Haitham Mualla, Mohamed Muftah, Aisha Kalsoom Mufti, Ng Yong Muh, Mo'nes Muhaisen, Dzawani Muhamad, Jimmy Mullaert, Fredrik Müller, Karl Erik Müller, Daniel Munblit, Syed Muneeb Ali, Nadeem Munir, Laveena Munshi, Aisling Murphy, Patrick Murray, Marlène Murris, Srinivas Murthy, Himed Musaab, Alamin Mustafa, Mus'ab Mustafa, Dana Mustafa, Himasha Muvindi, Dimitra Melia Myrodia, Farah Nadia Mohd-Hanafiah, Behzad Nadjm, Dave Nagpal, Alex Nagrebetsky, Blanka Nagybányai-Nagy, Herwin Nanda Boudoin, Mangala Narasimhan, Nageswaran Narayanan, Prashant Nasa, Rashid Nasim Khan, Ahmad Nasrallah, Adel Gerges Nassif Metri, Alasdair Nazerali-Maitland, Nadège Neant, Holger Neb, Nikita Nekliudov, Matthew Nelder, Erni Nelwan, Raul Neto, Emily Neumann, Wing Yiu Ng, Pauline Yeung Ng, Anthony Nghi, Duc Nguyen, Orna Ni Choileain, Niamh Ni Leathlobhair, Nerissa Niba, Alistair D. Nichol, Prompak Nitayavardhana, Stephanie Nonas, Nurul Amani Mohd Noordin, Nurul Faten Izzati Norharizam, Anita North, Alessandra Notari, Mahdad Noursadeghi, Adam Nowinski, Saad Nseir, Leonard Numfor, Nurnaningsih Nurnaningsih, Dwi Utomo Nusantara, Elsa Nyamankolly, Anders Benteson Nygaard, Fionnuala O. Brien, Annmarie O. Callaghan, Annmarie O'Callaghan, Giovanna Occhipinti, Derbrenn OConnor, Max O'Donnell, Lawrence Ofori-Boadu, Tawnya Ogston, Takayuki Ogura, Tak-Hyuk Oh, Sophie O'Halloran, Katie O'Hearn, Sally-Ann Ohene, João Oliveira, Larissa Oliveira, Piero L. Olliaro, Cinderella Omar Rageh Elnaggar, Alsarrah Ali Mohammed Omer, Pierre Ondobo, David S.Y. Ong, Jee Yan Ong, Wilna Oosthuyzen, Anne Opavsky, Peter Openshaw, Saijad Orakzai, Claudia Milena Orozco-Chamorro, Jamel Ortoleva, Mohamed Osama Elsayed Soliman, Javier Osatnik, Linda O'Shea, Miriam O'Sullivan, Eman Othman, Siti Zubaidah Othman, Nadia Ouamara, Rachida Ouissa, Micheal Owusu, Ama Akyampomaa Owusu-Asare, Eric Oziol, Maïder Pagadoy, Justine Pages, Amanda Palacios, Massimo Palmarini, Giovanna Panarello, Hem Paneru, Lai Hui Pang, Mauro Panigada, Nathalie Pansu, Aurélie Papadopoulos, Rachael Parke, Melissa Parker, Jérémie Pasquier, Bruno Pastene, Fabian Patauner, Drashti Patel, Mohan Dass Pathmanathan, Luís Patrão, Patricia Patricio, Lisa Patterson, Rajyabardhan Pattnaik, Christelle Paul, Mical Paul, Jorge Paulos, William A. Paxton, Jean-François Payen, Sandra L. Peake, Kalaiarasu Peariasamy, Giles J. Peek, Florent Peelman, Nathan Peiffer-Smadja, Vincent Peigne, Mare Pejkovska, Paolo Pelosi, Ithan D. Peltan, Rui Pereira, Daniel Perez, Thomas Perpoint, Antonio Pesenti, Vincent Pestre, Lenka Petrou, Michele Petrovic, Ventzislava Petrov-Sanchez, Frank Olav Pettersen, Gilles Peytavin, Richard Odame Philips, Ooyanong Phonemixay, Soulichanya Phoutthavong, Michael Piagnerelli, Walter Picard, Olivier Picone, Maria de Piero, Djura Piersma, Carlos Pimentel, Raquel Pinto, Catarina Pires, Lionel Piroth, Ayodhia Pitaloka, Chiara Piubelli, Riinu Pius, Simone Piva, Laurent Plantier, Hon Shen Png, Julien Poissy, Ryadh Pokeerbux, Sergio Poli, Georgios Pollakis, Diane Ponscarme, Diego Bastos Porto, Andra-Maris Post, Douwe F. Postma, Pedro Povoa, Diana Póvoas, Jeff Powis, Sofia Prapa, Viladeth Praphasiri, Sébastien Preau, Christian Prebensen, Jean-Charles Preiser, Anton Prinssen, Gamage Dona Dilanthi Priyadarshani, Lucia Proença, Sravya Pudota, Bambang Pujo Semedi, Mathew Pulicken, Peter Puplampu, Gregory Purcell, Luisa Quesada, Vilmaris Quinones-Cardona, Else Quist-Paulsen, Mohammed Quraishi, Fadi Qutishat, Maia Rabaa, Christian Rabaud, Ebenezer Rabindrarajan, Aldo Rafael, Marie Rafiq, Abdelrahman Ragab, Mutia Rahardjani, Arslan Rahat Ullah, Ahmad Kashfi Haji Ab Rahman, Rozanah Abd Rahman, Fernando Rainieri, Giri Shan Rajahram, Pratheema Ramachandran, Nagarajan Ramakrishnan, José Ramalho, Ahmad Afiq Ramli, Blandine Rammaert, Grazielle Viana Ramos, Asim Rana, Rajavardhan Rangappa, Ritika Ranjan, Christophe Rapp, Aasiyah Rashan, Thalha Rashan, Ghulam Rasheed, Menaldi Rasmin, Indrek Rätsep, Cornelius Rau, Tharmini Ravi, Ali Raza, Andre Real, Stanislas Rebaudet, Sarah Redl, Brenda Reeve, Attaur Rehman, Muhammad Osama Rehman Khalid, Dag Henrik Reikvam, Renato Reis, Jonathan Remppis, Martine Remy, Hongru Ren, Hanna Renk, Anne-Sophie Resseguier, Matthieu Revest, Oleksa Rewa, Maria Ines Ribeiro, Antonia Ricchiuto, David Richardson, Denise Richardson, Laurent Richier, Siti Nurul Atikah Ahmad Ridzuan, Ana L. Rios, Asgar Rishu, Patrick Rispal, Karine Risso, Maria Angelica Rivera Nuñez, Chiara Robba, André Roberto, Stephanie Roberts, Charles Roberts, David L. Robertson, Olivier Robineau, Anna Roca, Ferran Roche-Campo, Paola Rodari, Simão Rodeia, Bernhard Roessler, Claire Roger, Pierre-Marie Roger, Roberto Roncon-Albuquerque, Jr., Mélanie Roriz, Manuel Rosa-Calatrava, Michael Rose, Dorothea Rosenberger, Andrea Rossanese, Matteo Rossetti, Patrick Rossignol, Carine Roy, Benoît Roze, Desy Rusmawatiningtyas, Clark D. Russell, Maeve Ryan, Steffi Ryckaert, Aleksander Rygh Holten, Isabela Saba, Sairah Sadaf, Musharaf Sadat, Valla Sahraei, Abdurraouf Said, Nadia Saidani, Pranya Sakiyalak, Fodé Bangaly Sako, Moamen Salah, Ali Alaa Salah Eldin Mohamed Abbas, Nawal Salahuddin, Leonardo Salazar, Jodat Saleem, Mohammed Saleh Alyasiri, Talat Ahmed Abu Salem, Gabriele Sales, Charlotte Salmon Gandonniere, Hélène Salvator, Dana Samardali, Shaden Samardali, Yehia Samir Shaaban Aly Orabi, Emely Sanchez, Olivier Sanchez, Kizy Sanchez de Oliveira, Angel Sanchez-Miralles, Vanessa Sancho-Shimizu, Gyan Sandhu, Zulfiqar Sandhu, Pierre-François Sandrine, Oana Săndulescu, Marlene Santos, Shirley Sarfo-Mensah, Bruno Sarmento Banheiro, Iam Claire E. Sarmiento, Benjamine Sarton, Ankana Satya, Sree Satyapriya, Rumaisah Satyawati, Egle Saviciute, Yen Tsen Saw, Justin Schaffer, Tjard Schermer, Arnaud Scherpereel, Marion Schneider, János Schnur, Stephan Schroll, Michael Schwameis, Gary Schwartz, Janet T. Scott, James Scott-Brown, Nicholas Sedillot, Tamara Seitz, Jaganathan Selvanayagam, Mageswari Selvarajoo, Malcolm G. Semple, Rasidah Bt Senian, Eric Senneville, Claudia Sepulveda, Filipa Sequeira, Tânia Sequeira, Ary Serpa Neto, Ellen Shadowitz, Syamin Asyraf Shahidan, Hamza Shahla, Laila Shalabi, Haitam Shames, Anuraj Shankar, Shaikh Sharjeel, Pratima Sharma, Catherine A. Shaw, Victoria Shaw, John Robert Sheenan, Dr. Rajesh Mohan Shetty, Rohan Shetty, Mohiuddin Shiekh, Nobuaki Shime, Keiki Shimizu, Sally Shrapnel, Shubha Kalyan Shrestha, Pramesh Sundar Shrestha, Hoi Ping Shum, Nassima Si Mohammed, Ng Yong Siang, Moses Siaw-Frimpong, Jeanne Sibiude, Bountoy Sibounheuang, Nidhal Siddig, Atif Siddiqui, Maqsood Ahmed Siddiqui, Louise Sigfrid, Fatoumata Sillah, Piret Sillaots, Catarina Silva, Maria Joao Silva, Rogério Silva, Benedict Sim Lim Heng, Wai Ching Sin, Dario Sinatti, Mahendra Singh, Punam Singh, Pompini Agustina Sitompul, Karisha Sivam, Vegard Skogen, Sue Smith, Benjamin Smood, Coilin Smyth, Morgane Snacken, Dominic So, Tze Vee Soh, Lene Bergendal Solberg, Joshua Solomon, Tom Solomon, Emily Somers, Agnès Sommet, Myung Jin Song, Rima Song, Tae Song, Jack Song Chia, Arne Søraas, Albert Sotto, Edouard Soum, Ana Chora Sousa, Marta Sousa, Maria Sousa Uva, Vicente Souza-Dantas, Mamadou Saliou Sow, Alexandra Sperry, Elisabetta Spinuzza, B. P. Sanka Ruwan Sri Darshana, Shiranee Sriskandan, Sarah Stabler, Thomas Staudinger, Stephanie-Susanne Stecher, Trude Steinsvik, Ymkje Stienstra, Birgitte Stiksrud, Eva Stolz, Amy Stone, Anca Streinu-Cercel, Adrian Streinu-Cercel, Geoff Strong, Ami Stuart, David Stuart, Richa Su, Decy Subekti, Gabriel Suen, Jacky Y. Suen, Prasanth Sukumar, Asfia Sultana, Charlotte Summers, Dubravka Supic, Deepashankari Suppiah, Magdalena Surovcová, Atie Suwarti, Andrey Svistunov, Sarah Syahrin, Augustina Sylverken, Konstantinos Syrigos, Jaques Sztajnbok, Konstanty Szuldrzynski, Shirin Tabrizi, Fabio S. Taccone, Lysa Tagherset, Shahdattul Mawarni Taib, Sara Taleb, Cheikh Talla, Jelmer Talsma, Renaud Tamisier, Maria Lawrensia Tampubolon, Kim Keat Tan, Yan Chyi Tan, Hiroyuki Tanaka, Taku Tanaka, Hayato Taniguchi, Huda Taqdees, Arshad Taqi, Coralie Tardivon, Yousef Tarek Kamal Mostafa, Ali Tarhabat, Pierre Tattevin, M Azhari Taufik, Hassan Tawfik, Tze Yuan Tee, João Teixeira, Sofia Tejada, Marie-Capucine Tellier, Sze Kye Teoh, Vanessa Teotonio, François Téoulé, Olivier Terrier, Nicolas Terzi, Hubert Tessier-Grenier, Adrian Tey, Alif Adlan Mohd Thabit, Anand Thakur, Zhang Duan Tham, Suvintheran Thangavelu, Elmi Theron, Vincent Thibault, Simon-Djamel Thiberville, Benoît Thill, Jananee Thirumanickam, Niamh Thompson, Shaun Thompson, Emma C. Thomson, David Thomson, Mathew Thorpe, Surain Raaj Thanga Thurai, Ryan S. Thwaites, Paul Tierney, Vadim Tieroshyn, Peter S. Timashev, Jean-François Timsit, Noémie Tissot, Fiona Toal, Jordan Zhien Yang Toh, Maria Toki, Kristian Tonby, Sia Loong Tonnii, Marta Torre, Antoni Torres, Margarida Torres, Rosario Maria Torres Santos-Olmo, Hernando Torres-Zevallos, Aboubacar Tounkara, Michael Towers, Fodé Amara Traoré, Tony Trapani, Cécile Tromeur, Ioannis Trontzas, Tiffany Trouillon, Jeanne Truong, Christelle Tual, Sarah Tubiana, Helen Tuite, Alexis F. Turgeon, Jean-Marie Turmel, Lance C.W. Turtle, Anders Tveita, Pawel Twardowski, Makoto Uchiyama, PG Ishara Udayanga, Andrew Udy, Roman Ullrich, Alberto Uribe, Asad Usman, Effua Usuf, Timothy M. Uyeki, Cristinava Vajdovics, Piero Valentini, Luís Val-Flores, Stijn Van de Velde, Marcel van den Berge, Machteld van der Feltz, Job van der Palen, Paul van der Valk, Nicky Van Der Vekens, Peter Van der Voort, Sylvie Van Der Werf, Laura van Gulik, Jarne Van Hattem, Carolien van Netten, Ilonka van Veen, Noémie Vanel, Henk Vanoverschelde, Michael Varrone, Shoban Raj Vasudayan, Charline Vauchy, Pavan Kumar Vecham, Shaminee Veeran, Aurélie Veislinger, Sebastian Vencken, Sara Ventura, Annelies Verbon, José Ernesto Vidal, César Vieira, Deepak Vijayan, Judit Villar, Pierre-Marc Villeneuve, Andrea Villoldo, Gayatri Vishwanathan, Benoit Visseaux, Hannah Visser, Chiara Vitiello, Manivanh Vongsouvath, Harald Vonkeman, Fanny Vuotto, Suhaila Abdul Wahab, Noor Hidayu Wahab, Nadirah Abdul Wahid, Marina Wainstein, Laura Walsh, Wan Fadzlina Wan Muhd Shukeri, Chih-Hsien Wang, Steve Webb, Katharina Weil, Tan Pei Wen, Hassi Wesam, Sanne Wesselius, T. Eoin West, Murray Wham, Bryan Whelan, Nicole White, Paul Henri Wicky, Aurélie Wiedemann, Surya Otto Wijaya, Keith Wille, Sue Willems, Bailey Williams, Patricia J. Williams, Virginie Williams, Jessica Wittman, Calvin Wong, Xin Ci Wong, Yew Sing Wong, Teck Fung Wong, Natalie Wright, Lim Saio Xian, Ioannis Xynogalas, Siti Rohani Binti Mohd Yakop, Masaki Yamazaki, Elizabeth Yarad, Yazdan Yazdanpanah, Nicholas Yee Liang Hing, Abdelrahman Yehia Mahmoud Abdelaal, Cécile Yelnik, Chian Hui Yeoh, Stephanie Yerkovich, Touxiong Yiaye, Toshiki Yokoyama, Hodane Yonis, Obada Yousif, Saptadi Yuliarto, Akram Zaaqoq, Marion Zabbe, Gustavo E. Zabert, Kai Zacharowski, Masliza Zahid, Maram Zahran, Nor Zaila Binti Zaidan, Maria Zambon, Miguel Zambrano, Alberto Zanella, Nurul Zaynah, Hiba Zayyad, Alexander Zoufaly, and David Zucman

Subjects
COVID-19, Non-respiratory symptoms, Respiratory symptoms, Risk factors, Mortality, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Background: COVID-19 is primarily known as a respiratory illness; however, many patients present to hospital without respiratory symptoms. The association between non-respiratory presentations of COVID-19 and outcomes remains unclear. We investigated risk factors and clinical outcomes in patients with no respiratory symptoms (NRS) and respiratory symptoms (RS) at hospital admission. Methods: This study describes clinical features, physiological parameters, and outcomes of hospitalised COVID-19 patients, stratified by the presence or absence of respiratory symptoms at hospital admission. RS patients had one or more of: cough, shortness of breath, sore throat, runny nose or wheezing; while NRS patients did not. Results: Of 178,640 patients in the study, 86.4 % presented with RS, while 13.6 % had NRS. NRS patients were older (median age: NRS: 74 vs RS: 65) and less likely to be admitted to the ICU (NRS: 36.7 % vs RS: 37.5 %). NRS patients had a higher crude in-hospital case-fatality ratio (NRS 41.1 % vs. RS 32.0 %), but a lower risk of death after adjusting for confounders (HR 0.88 [0.83–0.93]). Conclusion: Approximately one in seven COVID-19 patients presented at hospital admission without respiratory symptoms. These patients were older, had lower ICU admission rates, and had a lower risk of in-hospital mortality after adjusting for confounders.

Published
2024
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39. MRI-guided radiotherapy in twenty fractions for localised prostate cancer; results from the MOMENTUM study

Author

Kobika Sritharan, Lois Daamen, Angela Pathmanathan, Tine Schytte, Floris Pos, Ananya Choudhury, Jochem R.N. van der Voort van Zyp, Linda G.W. Kerkmeijer, William Hall, Emma Hall, Helena M. Verkooijen, Trina Herbert, Shaista Hafeez, Adam Mitchell, and Alison C. Tree

Subjects
MR-Linac, Prostate cancer, MRIgRT, MOMENTUM, Adaptive radiotherapy, Patient-reported outcomes, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Background and purpose: MRI-guided radiotherapy (MRIgRT) offers multiple potential advantages over CT-guidance. This study examines the potential clinical benefits of MRIgRT for men with localised prostate cancer, in the setting of moderately hypofractionated radiotherapy. We evaluate two-year toxicity outcomes, early biochemical response and patient-reported outcomes (PRO), using data obtained from a multicentre international registry study, for the first group of patients with prostate cancer who underwent treatment on a 1.5 T MR-Linac. Materials and methods: Patients who were enrolled within the MOMENTUM study and received radical treatment with 60 Gy in 20 fractions were identified. PSA levels and CTCAE version 5.0 toxicity data were measured at follow-up visits. Those patients who consented to PRO data collection also completed EQ-5D-5L, EORTC QLQ-C30 and EORTC QLQ-PR25 questionnaires. Results: Between November 2018 and June 2022, 146 patients who had MRIgRT for localised prostate cancer on the 1.5 T MR-Linac were eligible for this study. Grade 2 and worse gastro-intestinal (GI) toxicity was reported in 3 % of patients at three months whilst grade 2 and worse genitourinary (GU) toxicity was 7 % at three months. There was a significant decrease in the median PSA at 12 months. The results from both the EQ-5D-5L data and EORTC global health status scale indicate a decline in the quality of life (QoL) during the first six months. The mean change in score for the EORTC scale showed a decrease of 11.4 points, which is considered clinically important. QoL improved back to baseline by 24 months. Worsening of hormonal symptoms in the first six months was reported with a return to baseline by 24 months and sexual activity in all men worsened in the first three months and returned to baseline at 12 months. Conclusion: This study establishes the feasibility of online-MRIgRT for localised prostate on a 1.5 T MR-Linac with low rates of toxicity, similar to that published in the literature. However, the clinical benefits of MRIgRT over conventional radiotherapy in the setting of moderate hypofractionation is not evident. Further research will focus on the delivery of ultrahypofractionated regimens, where the potential advantages of MRIgRT for prostate cancer may become more discernible.

Published
2024
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42. An examination of sleep spindle metrics in the Sleep Heart Health Study: superiority of automated spindle detection over total sigma power in assessing age-related spindle decline

Author

Kalyan Palepu, Kolia Sadeghi, Dave F. Kleinschmidt, Jacob Donoghue, Seth Chapman, Alexander R. Arslan, M. Brandon Westover, Sydney S. Cash, and Jay Pathmanathan

Subjects
Sleep Spindles, Sigma Power, Sigma Coherence, EEG, Spindle coherence, Drug development, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background Sleep spindle activity is commonly estimated by measuring sigma power during stage 2 non-rapid eye movement (NREM2) sleep. However, spindles account for little of the total NREM2 interval and therefore sigma power over the entire interval may be misleading. This study compares derived spindle measures from direct automated spindle detection with those from gross power spectral analyses for the purposes of clinical trial design. Methods We estimated spindle activity in a set of 8,440 overnight electroencephalogram (EEG) recordings from 5,793 patients from the Sleep Heart Health Study using both sigma power and direct automated spindle detection. Performance of the two methods was evaluated by determining the sample size required to detect decline in age-related spindle coherence with each method in a simulated clinical trial. Results In a simulated clinical trial, sigma power required a sample size of 115 to achieve 95% power to identify age-related changes in sigma coherence, while automated spindle detection required a sample size of only 60. Conclusions Measurements of spindle activity utilizing automated spindle detection outperformed conventional sigma power analysis by a wide margin, suggesting that many studies would benefit from incorporation of automated spindle detection. These results further suggest that some previous studies which have failed to detect changes in sigma power or coherence may have failed simply because they were underpowered.

Published
2023
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43. Real-world nirmatrelvir-ritonavir outpatient treatment in reducing hospitalization for high-risk patients with COVID-19 during Omicron BA.4, BA.5 and XBB subvariants dominance in Malaysia: A retrospective cohort study

Author

Ee Vien Low, Mohan Dass Pathmanathan, Suresh Kumar Chidambaram, Wee Ric Kim, Wei Jia Lee, Zhi Wei Teh, Maheshwara Rao Appannan, Shahanizan Mohd Zin, Faizah Muhamad Zin, Samha Bashirah Mohamed Amin, Mastura Ismail, Azah Abdul Samad, and Kalaiarasu M. Peariasamy

Subjects
COVID-19, Nirmatrelvir-ritonavir, Hospitalization, Effectiveness, High-risk, Vaccination, Infectious and parasitic diseases, RC109-216
Abstract

Objective: To determine if nirmatrelvir-ritonavir 300mg/100mg treatment for 5 days in high-risk outpatients with mild to moderate COVID-19 symptoms was associated with a reduction in hospitalization, intensive care unit (ICU) admission, and death. Methods: This 1:1 propensity score matched cohort study from 647 public health clinics in Malaysia included all patients with COVID-19 with positive tests aged 18 years and older, who were eligible for nirmatrelvir-ritonavir treatment within 5 days of illness from July 14, 2022, to November 14, 2022. The exposed group was patients with COVID-19 initiated with nirmatrelvir-ritonavir treatment, whereas those not initiated with the drug served as the control group. Data was analyzed from July 14, 2022 to December 31, 2022. Results: A total of 20,966 COVID-19 high-risk outpatients (n = 10,483 for nirmatrelvir-ritonavir group and n = 10,483 for control group) were included in the study. Nirmatrelvir-ritonavir treatment was associated with a 36% reduction (adjusted hazard ratio 0.64 [95% CI 0.43, 0.94]) in hospitalization compared with those not given the drug. There was a single ICU admission for the control group and one death each was reported in the nirmatrelvir-ritonavir and control group, respectively. Conclusions: Nirmatrelvir-ritonavir treatment was associated with reduced hospitalization in high-risk patients with COVID-19 even in highly vaccinated populations.

Published
2023
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47. Severe hyponatraemia due to high output external biliary drainage corrected with bile refeeding: A case report

Author

Duminda Subasinghe, Preethi Dassanayake, Ravindri Jayasinghe, Sivatharshya Pathmanathan, Vihara Dassanyake, and Sivasuriya Sivaganesh

Subjects
Medicine (General), R5-920
Abstract

Hyponatraemia is an uncommon complication of external biliary drainage. We report on a 62-year-old male with hilar cholangiocarcinoma who developed refractory severe hyponatraemia despite sodium replacement during preoperative external biliary drainage. Nasojejunal bile refeeding restored sodium levels to normal.

Published
2024
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48. Post COVID-19 condition among adults in Malaysia following the Omicron wave: A prospective cohort study.

Author

Peter Seah Keng Tok, Kong Yeow Kang, Sock Wen Ng, Norazida Ab Rahman, Muhammad Aminul Syahmi, Mohan Dass Pathmanathan, Maheshwara Rao Appannan, Kalaiarasu M Peariasamy, and Sheamini Sivasampu

Subjects
Medicine, Science
Abstract

Post COVID-19 condition is an important public health problem as we emerge from the COVID-19 pandemic. In this prospective cohort study, we aimed to determine the prevalence of this condition and assess its associated factors and impact on health-related quality of life in a population setting in Malaysia. Study was conducted from April to June 2022 when the Omicron variant predominated. All individuals testing positive for SARS-CoV-2 infection (RT-PCR, RTK-Ag) were invited for participation. Study questionnaires were delivered via the MySejahtera platform (mobile application). From the total of 44,386 participants who provided responses up to 3-months interval, 1,510 participants (3.4%) fulfilled the post COVID-19 condition criteria. Majority of the affected participants (83.8%, n = 1,265) experienced either cough, fatigue or forgetfulness-the three most common symptoms. Being females, having existing comorbidities, presence of symptoms and requiring hospital admission during the acute illness were associated with higher likelihoods of developing the post COVID-19 condition at 3-months interval. Amongst the 1,510 individuals, one in five had limitations in performing their usual daily activities while at least one in three expressed that their work was affected. Understanding this condition better is essential to guide strategic and responsive plans of action, which may require coordinated multidisciplinary interventions.

Published
2024
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49. Loss Comparison of Electric Vehicle Fuel Cell Integration Methods

Author

Wang, Yuheng, Pathmanathan, Mehanathan, and Lehn, Peter W.

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

This study analyzes and compares the drivetrain losses of two methods of fuel cell integration in electric vehicle drivetrains. The first is a conventional (boosted) two-stage system while the second is a dual inverter based solution. Each source of drivetrain losses is described by mathematical equations and the impact of higher order harmonics are observed through circuit model simulation. The dual inverter system achieves an overall energy efficiency improvement of 5.27% and 10.13% for highway and urban driving compared to the conventional method. The use of lower voltage rated power modules and lower switching frequency in the dual inverter system has significantly reduced the switching losses and improved the driving efficiency.

Published
2020

50. RAMSES: A full-stack application for detecting seizures and reducing data during continuous EEG monitoring

Author

Bernabei, John M., Owoputi, Olaoluwa, Small, Shyon D., Nyema, Nathaniel T., Dumenyo, Elom, Kim, Joongwon, Baldassano, Steven N., Painter, Christopher, Conrad, Erin C., Ganguly, Taneeta M., Balu, Ramani, Davis, Kathryn A., Pathmanathan, Jay, and Litt, Brian

Subjects
Quantitative Biology - Quantitative Methods, Electrical Engineering and Systems Science - Signal Processing
Abstract

Objective: Continuous EEG (cEEG) monitoring is associated with lower mortality in critically ill patients, however it is underutilized due to the difficulty of manually interpreting prolonged streams of cEEG data. Here we present a novel real-time, machine learning-based alerting and monitoring system for epilepsy and seizures (RAMSES) that dramatically reduces the amount of manual EEG review. Methods: We developed a custom data reduction algorithm using a random forest, and deployed it within an online cloud-based platform which streams data and communicates interactively with caregivers via a web interface to display algorithm results. We validate RAMSES on cEEG recordings from 77 patients undergoing routine scalp ICU EEG monitoring. Results: On subjects with seizures we achieved >80% overall data reduction, while detecting a mean of 84% of seizures across all validation patients, with 19/27 patients achieving 100% seizure detection. On seizure free-patients, the majority of cEEG records, we reduced data requiring manual review by >83%. Conclusion: This study validates a platform for machine-learning assisted data reduction. Significance: This work represents a meaningful step towards improving utility and decreasing cost for cEEG monitoring We also make our high-quality annotated dataset of 77 ICU cEEG recordings public for others to validate and improve upon our methods.

Published
2020

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