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2. Real-time Speech Enhancement and Separation with a Unified Deep Neural Network for Single/Dual Talker Scenarios

Author

Patel, Kashyap, Kovalyov, Anton, and Panahi, Issa

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound
Abstract

This paper introduces a practical approach for leveraging a real-time deep learning model to alternate between speech enhancement and joint speech enhancement and separation depending on whether the input mixture contains one or two active speakers. Scale-invariant signal-to-distortion ratio (SI-SDR) has shown to be a highly effective training measure in time-domain speech separation. However, the SI-SDR metric is ill-defined for zero-energy target signals, which is a problem when training a speech separation model using utterances with varying numbers of talkers. Unlike existing solutions that focus on modifying the loss function to accommodate zero-energy target signals, the proposed approach circumvents this problem by training the model to extract speech on both its output channels regardless if the input is a single or dual-talker mixture. A lightweight speaker overlap detection (SOD) module is also introduced to differentiate between single and dual-talker segments in real-time. The proposed module takes advantage of the new formulation by operating directly on the separated masks, given by the separation model, instead of the original mixture, thus effectively simplifying the detection task. Experimental results show that the proposed training approach outperforms existing solutions, and the SOD module exhibits high accuracy., Comment: 6 Pages, Accepted at IEEE Asilomar

Published
2023

3. Whole-genome sequencing in 333,100 individuals reveals rare non-coding single variant and aggregate associations with height

Author

Hawkes, Gareth, Beaumont, Robin N., Li, Zilin, Mandla, Ravi, Li, Xihao, Albert, Christine M., Arnett, Donna K., Ashley-Koch, Allison E., Ashrani, Aneel A., Barnes, Kathleen C., Boerwinkle, Eric, Brody, Jennifer A., Carson, April P., Chami, Nathalie, Chen, Yii-Der Ida, Chung, Mina K., Curran, Joanne E., Darbar, Dawood, Ellinor, Patrick T., Fornage, Myrian, Gordeuk, Victor R., Guo, Xiuqing, He, Jiang, Hwu, Chii-Min, Kalyani, Rita R., Kaplan, Robert, Kardia, Sharon L. R., Kooperberg, Charles, Loos, Ruth J. F., Lubitz, Steven A., Minster, Ryan L., Naseri, Take, Viali, Satupa’itea, Mitchell, Braxton D., Murabito, Joanne M., Palmer, Nicholette D., Psaty, Bruce M., Redline, Susan, Shoemaker, M. Benjamin, Silverman, Edwin K., Telen, Marilyn J., Weiss, Scott T., Yanek, Lisa R., Zhou, Hufeng, Liu, Ching-Ti, North, Kari E., Justice, Anne E., Locke, Jonathan M., Owens, Nick, Murray, Anna, Patel, Kashyap, Frayling, Timothy M., Wright, Caroline F., Wood, Andrew R., Lin, Xihong, Manning, Alisa, and Weedon, Michael N.

Published
2024
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7. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine

Author

Tobias, Deirdre K, Merino, Jordi, Ahmad, Abrar, Aiken, Catherine, Benham, Jamie L, Bodhini, Dhanasekaran, Clark, Amy L, Colclough, Kevin, Corcoy, Rosa, Cromer, Sara J, Duan, Daisy, Felton, Jamie L, Francis, Ellen C, Gillard, Pieter, Gingras, Véronique, Gaillard, Romy, Haider, Eram, Hughes, Alice, Ikle, Jennifer M, Jacobsen, Laura M, Kahkoska, Anna R, Kettunen, Jarno LT, Kreienkamp, Raymond J, Lim, Lee-Ling, Männistö, Jonna ME, Massey, Robert, Mclennan, Niamh-Maire, Miller, Rachel G, Morieri, Mario Luca, Most, Jasper, Naylor, Rochelle N, Ozkan, Bige, Patel, Kashyap Amratlal, Pilla, Scott J, Prystupa, Katsiaryna, Raghavan, Sridharan, Rooney, Mary R, Schön, Martin, Semnani-Azad, Zhila, Sevilla-Gonzalez, Magdalena, Svalastoga, Pernille, Takele, Wubet Worku, Tam, Claudia Ha-ting, Thuesen, Anne Cathrine B, Tosur, Mustafa, Wallace, Amelia S, Wang, Caroline C, Wong, Jessie J, Yamamoto, Jennifer M, Young, Katherine, Amouyal, Chloé, Andersen, Mette K, Bonham, Maxine P, Chen, Mingling, Cheng, Feifei, Chikowore, Tinashe, Chivers, Sian C, Clemmensen, Christoffer, Dabelea, Dana, Dawed, Adem Y, Deutsch, Aaron J, Dickens, Laura T, DiMeglio, Linda A, Dudenhöffer-Pfeifer, Monika, Evans-Molina, Carmella, Fernández-Balsells, María Mercè, Fitipaldi, Hugo, Fitzpatrick, Stephanie L, Gitelman, Stephen E, Goodarzi, Mark O, Grieger, Jessica A, Guasch-Ferré, Marta, Habibi, Nahal, Hansen, Torben, Huang, Chuiguo, Harris-Kawano, Arianna, Ismail, Heba M, Hoag, Benjamin, Johnson, Randi K, Jones, Angus G, Koivula, Robert W, Leong, Aaron, Leung, Gloria KW, Libman, Ingrid M, Liu, Kai, Long, S Alice, Lowe, William L, Morton, Robert W, Motala, Ayesha A, Onengut-Gumuscu, Suna, Pankow, James S, Pathirana, Maleesa, Pazmino, Sofia, Perez, Dianna, Petrie, John R, Powe, Camille E, Quinteros, Alejandra, Jain, Rashmi, Ray, Debashree, and Ried-Larsen, Mathias

Subjects
Health Services and Systems, Biomedical and Clinical Sciences, Health Sciences, Health Services, Diabetes, Clinical Research, Pediatric, Metabolic and endocrine, Good Health and Well Being, Humans, Precision Medicine, Consensus, Diabetes Mellitus, Evidence-Based Medicine, Medical and Health Sciences, Immunology, Biomedical and clinical sciences, Health sciences
Abstract

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

Published
2023

8. Accurate Real-Time Estimation of 2-Dimensional Direction of Arrival using a 3-Microphone Array

Author

Kovalyov, Anton, Patel, Kashyap, and Panahi, Issa

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound
Abstract

This paper presents a method for real-time estimation of 2-dimensional direction of arrival (2D-DOA) of one or more sound sources using a nonlinear array of three microphones. 2D-DOA is estimated employing frame-level time difference of arrival (TDOA) measurements. Unlike conventional methods, which infer location parameters from TDOAs using a theoretical model, we propose a more practical approach based on supervised learning. The proposed model employs nearest neighbor search (NNS) applied to a spherical Fibonacci lattice consisting of TDOA to 2D-DOA mappings learned directly in the field. Filtering and clustering post-processors are also introduced for improved source detection and localization robustness., Comment: 5 pages, 6 figures

Published
2023

9. DFSNet: A Steerable Neural Beamformer Invariant to Microphone Array Configuration for Real-Time, Low-Latency Speech Enhancement

Author

Kovalyov, Anton, Patel, Kashyap, and Panahi, Issa

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound
Abstract

Invariance to microphone array configuration is a rare attribute in neural beamformers. Filter-and-sum (FS) methods in this class define the target signal with respect to a reference channel. However, this not only complicates formulation in reverberant conditions but also the network, which must have a mechanism to infer what the reference channel is. To address these issues, this study presents Delay Filter-and-Sum Network (DFSNet), a steerable neural beamformer invariant to microphone number and array geometry for causal speech enhancement. In DFSNet, acquired signals are first steered toward the speech source direction prior to the FS operation, which simplifies the task into the estimation of delay-and-summed reverberant clean speech. The proposed model is designed to incur low latency, distortion, and memory and computational burden, giving rise to high potential in hearing aid applications. Simulation results reveal comparable performance to noncausal state-of-the-art., Comment: 5 pages, 1 figure, 2 tables

Published
2023

10. Closed-Form Solution for Scaling a Wireless Acoustic Sensor Network

Author

Patel, Kashyap, Kovalyov, Anton, and Panahi, Issa

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

This study presents a closed-form solution for localizing and synchronizing an acoustic sensor node with respect to a Wireless Acoustic Sensor Network (WASN). The aim is to allow efficient scaling of a WASN by individually calibrating newly joined sensor nodes instead of recalibrating the entire array. A key contribution is that the sensor to be calibrated does not need to include a built-in emitter. The proposed method uses signals emitted from spatially distributed sources to compute time difference of arrival (TDOA) measurements between the existing WASN and a new sensor. The problem is then modeled as a set of multivariate nonlinear TDOA equations. Through a simple transformation, the nonlinear TDOA equations are converted into a system of linear equations. Then, weighted least squares (WLS) is applied to find an accurate estimate of the calibration parameters. Signal sources can either be known emitters within the existing WASN or arbitrary sources in the environment, thus allowing for flexible applicability in both active and passive calibration scenarios. Simulation results under various conditions show high joint localization and synchronization performance, often comparable to the Cram\'er-Rao lower bound (CRLB)., Comment: To be submitted to the Journal of Acoustical Society of America for publication

Published
2023

12. A Secure Health Monitoring Model for Prediction of Heart Disease Detection Using Machine Learning

Author

Padhya, Bhargav P., Dharwa, Jyotindra N., Patel, Himanshu N., Patel, Kashyap C., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Joshi, Amit, editor, Mahmud, Mufti, editor, Ragel, Roshan G., editor, and Karthik, S., editor

Published
2024
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13. UX-NET: Filter-and-Process-based Improved U-Net for Real-time Time-domain Audio Separation

Author

Patel, Kashyap, Kovalyov, Anton, and Panahi, Issa

Subjects
Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Sound
Abstract

This study presents UX-Net, a time-domain audio separation network (TasNet) based on a modified U-Net architecture. The proposed UX-Net works in real-time and handles either single or multi-microphone input. Inspired by the filter-and-process-based human auditory behavior, the proposed system introduces novel mixer and separation modules, which result in cost and memory efficient modeling of speech sources. The mixer module combines encoded input in a latent feature space and outputs a desired number of output streams. Then, in the separation module, a modified U-Net (UX) block is applied. The UX block first filters the encoded input at various resolutions followed by aggregating the filtered information and applying recurrent processing to estimate masks of separated sources. The letter 'X' in UX-Net is a name placeholder for the type of recurrent layer employed in the UX block. Empirical findings on the WSJ0-2mix benchmark dataset show that one of the UX-Net configurations outperforms the state-of-the-art Conv-TasNet system by 0.85 dB SI-SNR while using only 16% of the model parameters, 58% fewer computations, and maintaining low latency., Comment: Submitted to ICASSP 2023

Published
2022

15. Population pharmacokinetic analysis of sirolimus in Japanese pediatric and adult subjects receiving tablet or granule formulations

Author

Miyazaki, Taichi, Hayashi, Daichi, Nozawa, Akifumi, Yasue, Shiho, Endo, Saori, Ohnishi, Hidenori, Asada, Ryuta, Kato, Mototoshi, Fujino, Akihiro, Kuroda, Tatsuo, Maekawa, Takanobu, Fumino, Shigehisa, Kawakubo, Naonori, Tajiri, Tatsuro, Shimizu, Kenji, Sanada, Chihiro, Hamada, Izumi, Ishikawa, Yuko, Hasegawa, Mayumi, Patel, Kashyap, Xie, Yehua, and Ozeki, Michio

Published
2024
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17. Galectin-4 Antimicrobial Activity Primarily Occurs Through its C-Terminal Domain

Author

Jan, Hau-Ming, Wu, Shang-Chuen, Stowell, Carter J., Vallecillo-Zúniga, Mary L., Paul, Anu, Patel, Kashyap R., Muthusamy, Sasikala, Lin, Hsien-Ya, Ayona, Diyoly, Jajosky, Ryan Philip, Varadkar, Samata P., Nakahara, Hirotomo, Chan, Rita, Bhave, Devika, Lane, William J., Yeung, Melissa Y., Hollenhorst, Marie A., Rakoff-Nahoum, Seth, Cummings, Richard D., Arthur, Connie M., and Stowell, Sean R.

Published
2024
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18. A Brief Survey on Hyperspectral Sensor

Author

Modi, Ritiksha, Baranwal, Ichchhit, Patel, Kashyap, Nayak, Amit, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Ranganathan, G., editor, Fernando, Xavier, editor, and Piramuthu, Selwyn, editor

Published
2023
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19. A Survey: Secure Cloud Data Storage and Access Control System Using Blockchain

Author

Patel, Kashyap, Modi, Ritiksha, Sharma, Shital, Patel, Minal, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Ranganathan, G., editor, Fernando, Xavier, editor, and Piramuthu, Selwyn, editor

Published
2023
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26. Antibody-mediated antigen loss switches augmented immunity to antibody-mediated immunosuppression

Author

Jajosky, Ryan P., Patel, Kashyap R., Allen, Jerry William L., Zerra, Patricia E., Chonat, Satheesh, Ayona, Diyoly, Maier, Cheryl L., Morais, Dominique, Wu, Shang-Chuen, Luckey, C. John, Eisenbarth, Stephanie C., Roback, John D., Fasano, Ross M., Josephson, Cassandra D., Manis, John P., Chai, Li, Hendrickson, Jeanne E., Hudson, Krystalyn E., Arthur, Connie M., and Stowell, Sean R.

Published
2023
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30. Non-coding variants disrupting a tissue-specific regulatory element in HK1 cause congenital hyperinsulinism

Author

Wakeling, Matthew N., Owens, Nick D. L., Hopkinson, Jessica R., Johnson, Matthew B., Houghton, Jayne A. L., Dastamani, Antonia, Flaxman, Christine S., Wyatt, Rebecca C., Hewat, Thomas I., Hopkins, Jasmin J., Laver, Thomas W., van Heugten, Rachel, Weedon, Michael N., De Franco, Elisa, Patel, Kashyap A., Ellard, Sian, Morgan, Noel G., Cheesman, Edmund, Banerjee, Indraneel, Hattersley, Andrew T., Dunne, Mark J., Richardson, Sarah J., and Flanagan, Sarah E.

Published
2022
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31. Experimental Investigation in Wire Cut EDM of Inconel 718 Superalloy

Author

Joshi, Ayanesh Y., Banker, Vaishal J., Patel, Kenil K., Patel, Kashyap S., Joshi, Devarsh M., Purohit, Madhav R., Cavas-Martínez, Francisco, Series Editor, Chaari, Fakher, Series Editor, di Mare, Francesca, Series Editor, Gherardini, Francesco, Series Editor, Haddar, Mohamed, Series Editor, Ivanov, Vitalii, Series Editor, Kwon, Young W., Series Editor, Trojanowska, Justyna, Series Editor, Dave, Harshit K., editor, Dixit, Uday Shanker, editor, and Nedelcu, Dumitru, editor

Published
2022
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35. Framework From a Multidisciplinary Approach for Transitioning Variants of Unknown Significance From Clinical Genetic Testing in Kidney Disease to a Definitive Classification

Author

Mirshahi, Uyenlinh L., Bhan, Ahana, Tholen, Lotte E., Fang, Brian, Chen, Guoli, Moore, Bryn, Cook, Adam, Anand, Prince Mohan, Patel, Kashyap, Haas, Mary E., Lotta, Luca A., Igarashi, Peter, de Baaij, Jeroen H.F., Ferrè, Silvia, Hoenderop, Joost G.J., Carey, David J., and Chang, Alexander R.

Published
2022
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38. A homozygous TARS2 variant is a novel cause of syndromic neonatal diabetes.

Author

Donis, Russell, Patel, Kashyap A., Wakeling, Matthew N., Johnson, Matthew B., Amoli, Masha M., Yildiz, Melek, Akçay, Teoman, Aspi, Irani, Yong, James, Yaghootkar, Hanieh, Weedon, Michael N., Hattersley, Andrew T., Flanagan, Sarah E., and De Franco, Elisa

Subjects
*MISSENSE mutation, *DEVELOPMENTAL delay, *ETIOLOGY of diabetes, *NUCLEOTIDE sequencing, *DIABETES
Abstract

Aims Methods Results Conclusions Neonatal diabetes is a monogenic condition which can be the presenting feature of complex syndromes. The aim of this study was to identify novel genetic causes of neonatal diabetes with neurological features including developmental delay and epilepsy.We performed genome sequencing in 27 individuals with neonatal diabetes plus epilepsy and/or developmental delay of unknown genetic cause. Replication studies were performed in 123 individuals with diabetes diagnosed aged ≤1 year without a known genetic cause using targeted next‐generation sequencing.Three individuals, all diagnosed with diabetes in the first week of life, shared a rare homozygous missense variant, p.(Arg327Gln), in TARS2. Replication studies identified the same homozygous variant in a fourth individual diagnosed with diabetes at 1 year. One proband had epilepsy, one had development delay and two had both.Biallelic TARS2 variants cause a mitochondrial encephalopathy (COXPD‐21) characterised by severe hypotonia, epilepsy and developmental delay. Diabetes is not a known feature of COXPD‐21. Current evidence suggests that the p.(Arg327Gln) variant disrupts TARS2's regulation of the mTORC1 pathway which is essential for β‐cells.Our findings establish the homozygous p.(Arg327Gln) TARS2 variant as a novel cause of syndromic neonatal diabetes and uncover a role for TARS2 in pancreatic β‐cells. [ABSTRACT FROM AUTHOR]

Published
2024
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40. Systematic genetic testing for recessively inherited monogenic diabetes: a cross-sectional study in paediatric diabetes clinics

Author

Patel, Kashyap A., Ozbek, Mehmet N., Yildiz, Melek, Guran, Tulay, Kocyigit, Cemil, Acar, Sezer, Siklar, Zeynep, Atar, Muge, Colclough, Kevin, Houghton, Jayne, Johnson, Matthew B., Ellard, Sian, Flanagan, Sarah E., Cizmecioglu, Filiz, Berberoglu, Merih, Demir, Korcan, Catli, Gonul, Bas, Serpil, Akcay, Teoman, Demirbilek, Huseyin, Weedon, Michael N., and Hattersley, Andrew T.

Published
2022
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50. The use of quantitative clinical pharmacology approaches to support moxidectin dosing recommendations in lactation.

Author

Wood, Nolan D., Smith, Danelle, Kinrade, Sally A., Sullivan, Mark T., Rayner, Craig R., Wesche, David, Patel, Kashyap, and Rowland-Yeo, Karen

Subjects
BREAST milk, ONCHOCERCA volvulus, MOXIDECTIN, ONCHOCERCIASIS, CLINICAL pharmacology
Abstract

Moxidectin is approved by the US Food and Drug Administration (US FDA) for the treatment of onchocerciasis (river-blindness) due to Onchocerca volvulus in patients aged 12 years and older. In onchocerciasis-endemic areas, mass drug administration (MDA) programs with ivermectin, with or without vector control, aim to control the disease, reduce morbidity, interrupt transmission, and more recently, achieve elimination. Moxidectin has the potential to be used in MDA programs. In countries where onchocerciasis is endemic, infants are often breastfed up to the age of 2 years, suggesting that some women are likely to be lactating during such periodic MDA programs. Quantitative analyses of non-clinical and clinical data using non-compartmental analysis and population based pharmacokinetic (popPK) modeling as well as physiologically based pharmacokinetic modeling (PBPK) were performed to determine the amount of moxidectin excreted in breast milk and subsequent exposures in the infant. The results of the analyses were similar. Concentrations of moxidectin in breast milk followed a similar pattern to those in plasma, with maximum concentrations occurring approximately 4 hours after dosing followed by a rapid decline in both breast milk and plasma. As early as two days after dosing, concentrations of moxidectin in breast milk were below the threshold for acceptable daily intake levels established by the European Medicines Agency (EMA) and FDA for secondary exposures from veterinary use, and below the WHO recommended relative infant dose (RID) safety threshold. The analyses were conducted to support prescribers and policy makers on dosing recommendations for moxidectin in lactation. Author summary: Moxidectin is approved to treat onchocerciasis or "river-blindness". For years, mass drug administration programs (MDA) involving at least annual treatment of entire populations within a geographical area have been used as a strategy to control onchocerciasis. The potential treatment population therefore includes pregnant and breast-feeding women. Guidance on the safe and effective use of medicines in these populations is needed but is often insufficient. In this study, we describe two methods to determine the amount of moxidectin administered to breastfeeding infants via breastmilk. Both methods used data from a clinical study in which moxidectin levels were measured in the blood and breast milk of lactating women. In an empirical analysis, the amount of moxidectin in a breast-fed infant was inferred based on observed blood and breast milk levels in the clinical study. Using a physiologically based pharmacokinetic (PBPK) model approach, which considers all prior information on the drug and the physiological variability involved, direct assessments of the amount of drug in the infant were estimated. Both methods established the likely time-course and level of moxidectin exposure in infants via breast milk, highlighting the benefit of an integrated approach to generate dosing recommendations supporting the use of moxidectin in breastfeeding women. [ABSTRACT FROM AUTHOR]

Published
2024
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