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1,786 results on '"Mahindra, A"'

1. Time-inversion of spatiotemporal beam dynamics using uncertainty-aware latent evolution reversal

Author

Rautela, Mahindra, Williams, Alan, and Scheinker, Alexander

Subjects
Physics - Accelerator Physics, Computer Science - Machine Learning
Abstract

Charged particle dynamics under the influence of electromagnetic fields is a challenging spatiotemporal problem. Many high performance physics-based simulators for predicting behavior in a charged particle beam are computationally expensive, limiting their utility for solving inverse problems online. The problem of estimating upstream six-dimensional phase space given downstream measurements of charged particles in an accelerator is an inverse problem of growing importance. This paper introduces a reverse Latent Evolution Model (rLEM) designed for temporal inversion of forward beam dynamics. In this two-step self-supervised deep learning framework, we utilize a Conditional Variational Autoencoder (CVAE) to project 6D phase space projections of a charged particle beam into a lower-dimensional latent distribution. Subsequently, we autoregressively learn the inverse temporal dynamics in the latent space using a Long Short-Term Memory (LSTM) network. The coupled CVAE-LSTM framework can predict 6D phase space projections across all upstream accelerating sections based on single or multiple downstream phase space measurements as inputs. The proposed model also captures the aleatoric uncertainty of the high-dimensional input data within the latent space. This uncertainty, which reflects potential uncertain measurements at a given module, is propagated through the LSTM to estimate uncertainty bounds for all upstream predictions, demonstrating the robustness of the LSTM against in-distribution variations in the input data., Comment: arXiv admin note: text overlap with arXiv:2403.13858

Published
2024

2. Towards latent space evolution of spatiotemporal dynamics of six-dimensional phase space of charged particle beams

Author

Rautela, Mahindra, Williams, Alan, and Scheinker, Alexander

Subjects
Physics - Accelerator Physics, Physics - Computational Physics, Physics - Data Analysis, Statistics and Probability
Abstract

Addressing the charged particle beam diagnostics in accelerators poses a formidable challenge, demanding high-fidelity simulations in limited computational time. Machine learning (ML) based surrogate models have emerged as a promising tool for non-invasive charged particle beam diagnostics. Trained ML models can make predictions much faster than computationally expensive physics simulations. In this work, we have proposed a temporally structured variational autoencoder model to autoregressively forecast the spatiotemporal dynamics of the 15 unique 2D projections of 6D phase space of charged particle beam as it travels through the LANSCE linear accelerator. In the model, VAE embeds the phase space projections into a lower dimensional latent space. A long-short-term memory network then learns the temporal correlations in the latent space. The trained network can evolve the phase space projections across further modules provided the first few modules as inputs. The model predicts all the projections across different modules with low mean squared error and high structural similarity index., Comment: arXiv admin note: substantial text overlap with arXiv:2403.13858

Published
2024
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3. Accelerator system parameter estimation using variational autoencoded latent regression

Author

Rautela, Mahindra, Williams, Alan, and Scheinker, Alexander

Subjects
Physics - Accelerator Physics, Physics - Data Analysis, Statistics and Probability
Abstract

Particle accelerators are time-varying systems whose components are perturbed by external disturbances. Tuning accelerators can be a time-consuming process involving manual adjustment of multiple components, such as RF cavities, to minimize beam loss due to time-varying drifts. The high dimensionality of the system ($\sim$100 amplitude and phase RF settings in the LANSCE accelerator) makes it difficult to achieve optimal operation. The time-varying drifts and the dimensionality make system parameter estimation a challenging optimization problem. In this work, we propose a Variational Autoencoded Latent Regression (VALeR) model for robust estimation of system parameters using 2D unique projections of a charged particle beam's 6D phase space. In VALeR, VAE projects the phase space projections into a lower-dimensional latent space, and a dense neural network maps the latent space onto the space of system parameters. The trained network can predict system parameters for unseen phase space projections. Furthermore, VALeR can generate new projections by randomly sampling the latent space of VAE and also estimate the corresponding system parameters.

Published
2024
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4. A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators

Author

Rautela, Mahindra, Williams, Alan, and Scheinker, Alexander

Subjects
Physics - Accelerator Physics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due to limited non-destructive measurements, computationally demanding simulations, and inherent uncertainties in the system. We propose a two-step unsupervised deep learning framework named as Conditional Latent Autoregressive Recurrent Model (CLARM) for learning the spatiotemporal dynamics of charged particles in accelerators. CLARM consists of a Conditional Variational Autoencoder (CVAE) transforming six-dimensional phase space into a lower-dimensional latent distribution and a Long Short-Term Memory (LSTM) network capturing temporal dynamics in an autoregressive manner. The CLARM can generate projections at various accelerator modules by sampling and decoding the latent space representation. The model also forecasts future states (downstream locations) of charged particles from past states (upstream locations). The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.

Published
2024

5. Bayesian optimized physics-informed neural network for estimating wave propagation velocities

Author

Rautela, Mahindra, Gopalakrishnan, S., and Senthilnath, J.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Physics - Data Analysis, Statistics and Probability
Abstract

In this paper, we propose a novel inverse parameter estimation approach called Bayesian optimized physics-informed neural network (BOPINN). In this study, a PINN solves the partial differential equation (PDE), whereas Bayesian optimization (BO) estimates its parameter. The proposed BOPINN estimates wave velocity associated with wave propagation PDE using a single snapshot observation. An objective function for BO is defined as the mean squared error (MSE) between the surrogate displacement field and snapshot observation. The inverse estimation capability of the proposed approach is tested in three different isotropic media with different wave velocities. From the obtained results, we have observed that BOPINN can accurately estimate wave velocities with lower MSE, even in the presence of noisy conditions. The proposed algorithm shows robust predictions in limited iterations across different runs.

Published
2023

6. Deep generative models for unsupervised delamination detection using guided waves

Author

Rautela, Mahindra, Maghareh, Amin, Dyke, Shirley, and Gopalakrishnan, S.

Subjects
Electrical Engineering and Systems Science - Signal Processing
Abstract

With the rising demands for robust structural health monitoring procedures for aerospace structures, the scope of intelligent algorithms and learning techniques is expanding. Supervised algorithms have shown promising results in the field provided a large, balanced, and labeled amount of data for training. For some applications like aerospace, the data collection process is cumbersome, time-taking, and costly. Also, generating possible damage scenarios in a laboratory setup is challenging because of the complexity of the damage initiation and failure mechanism. Besides this, the uncertainties of the real-time operation restrict the online prediction accuracy with supervised learning. In this paper, deep generative models are proposed for unsupervised delamination prediction as an anomaly detection problem. In this one-class-based model, the deep learning network is trained to learn the distribution of baseline signals. In the testing phase, damage signals and unseen baseline signals are fed into the trained network to predict the state of the structure, i.e., healthy or unhealthy (delamination). It is seen that the proposed method can successfully predict the delamination with high accuracy.

Published
2023

7. A conditional latent autoregressive recurrent model for generation and forecasting of beam dynamics in particle accelerators

Author

Mahindra Rautela, Alan Williams, and Alexander Scheinker

Subjects
Medicine, Science
Abstract

Abstract Particle accelerators are complex systems that focus, guide, and accelerate intense charged particle beams to high energy. Beam diagnostics present a challenging problem due to limited non-destructive measurements, computationally demanding simulations, and inherent uncertainties in the system. We propose a two-step unsupervised deep learning framework named as Conditional Latent Autoregressive Recurrent Model (CLARM) for learning the spatiotemporal dynamics of charged particles in accelerators. CLARM consists of a Conditional Variational Autoencoder transforming six-dimensional phase space into a lower-dimensional latent distribution and a Long Short-Term Memory network capturing temporal dynamics in an autoregressive manner. The CLARM can generate projections at various accelerator modules by sampling and decoding the latent space representation. The model also forecasts future states (downstream locations) of charged particles from past states (upstream locations). The results demonstrate that the generative and forecasting ability of the proposed approach is promising when tested against a variety of evaluation metrics.

Published
2024
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8. Statistical Modeling of Bacterial Culture Medium Composition Affecting Mineralization in Ferrochrome Ash

Author

Kothuri, Mahindra, Devatha, C. P., Pisello, Anna Laura, Editorial Board Member, Hawkes, Dean, Editorial Board Member, Bougdah, Hocine, Editorial Board Member, Rosso, Federica, Editorial Board Member, Abdalla, Hassan, Editorial Board Member, Boemi, Sofia-Natalia, Editorial Board Member, Mohareb, Nabil, Editorial Board Member, Mesbah Elkaffas, Saleh, Editorial Board Member, Bozonnet, Emmanuel, Editorial Board Member, Pignatta, Gloria, Editorial Board Member, Mahgoub, Yasser, Editorial Board Member, De Bonis, Luciano, Editorial Board Member, Kostopoulou, Stella, Editorial Board Member, Pradhan, Biswajeet, Editorial Board Member, Abdul Mannan, Md., Editorial Board Member, Alalouch, Chaham, Editorial Board Member, Gawad, Iman O., Editorial Board Member, Nayyar, Anand, Editorial Board Member, Amer, Mourad, Series Editor, Ksibi, Mohamed, editor, Negm, Abdelazim, editor, Hentati, Olfa, editor, Ghorbal, Achraf, editor, Sousa, Arturo, editor, Rodrigo-Comino, Jesus, editor, Panda, Sandeep, editor, Lopes Velho, José, editor, El-Kenawy, Ahmed M., editor, and Perilli, Nicola, editor

Published
2024
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9. Towards deep generation of guided wave representations for composite materials

Author

Rautela, Mahindra, Senthilnath, J., Huber, Armin, and Gopalakrishnan, S.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Audio and Speech Processing, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Laminated composite materials are widely used in most fields of engineering. Wave propagation analysis plays an essential role in understanding the short-duration transient response of composite structures. The forward physics-based models are utilized to map from elastic properties space to wave propagation behavior in a laminated composite material. Due to the high-frequency, multi-modal, and dispersive nature of the guided waves, the physics-based simulations are computationally demanding. It makes property prediction, generation, and material design problems more challenging. In this work, a forward physics-based simulator such as the stiffness matrix method is utilized to collect group velocities of guided waves for a set of composite materials. A variational autoencoder (VAE)-based deep generative model is proposed for the generation of new and realistic polar group velocity representations. It is observed that the deep generator is able to reconstruct unseen representations with very low mean square reconstruction error. Global Monte Carlo and directional equally-spaced samplers are used to sample the continuous, complete and organized low-dimensional latent space of VAE. The sampled point is fed into the trained decoder to generate new polar representations. The network has shown exceptional generation capabilities. It is also seen that the latent space forms a conceptual space where different directions and regions show inherent patterns related to the generated representations and their corresponding material properties.

Published
2022

10. Real-time rapid leakage estimation for deep space habitats using exponentially-weighted adaptively-refined search

Author

Rautela, Mahindra, Mirfarah, Motahareh, Silva, Christian, Dyke, Shirley, Maghareh, Amin, and Gopalakrishnan, S.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Systems and Control
Abstract

The recent accelerated growth in space-related research and development activities makes the near-term need for long-term extraterrestrial habitats evident. Such habitats must operate under continuous disruptive conditions arising from extreme environments like meteoroid impacts, extreme temperature fluctuations, galactic cosmic rays, destructive dust, and seismic events. Loss of air or atmospheric leakage from a habitat poses safety challenges that demand proper attention. Such leakage may arise from micro-meteoroid impacts, crack growth, bolt/rivet loosening, and seal deterioration. In this paper, leakage estimation in deep space habitats is posed as an inverse problem. A forward pressure-based dynamical model is formulated for atmospheric leakage. Experiments are performed on a small-scaled pressure chamber where different leakage scenarios are emulated and corresponding pressure values are measured. An exponentially-weighted adaptively-refined search (EWARS) algorithm is developed and validated for the inverse problem of real-time leakage estimation. It is demonstrated that the proposed methodology can achieve real-time estimation and tracking of constant and variable leaks with accuracy.

Published
2022
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12. Inverse characterization of composites using guided waves and convolutional neural networks with dual-branch feature fusion

Author

Rautela, Mahindra, Huber, Armin, Senthilnath, J., and Gopalakrishnan, S.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

In this work, ultrasonic guided waves and a dual-branch version of convolutional neural networks are used to solve two different but related inverse problems, i.e., finding layup sequence type and identifying material properties. In the forward problem, polar group velocity representations are obtained for two fundamental Lamb wave modes using the stiffness matrix method. For the inverse problems, a supervised classification-based network is implemented to classify the polar representations into different layup sequence types (inverse problem - 1) and a regression-based network is utilized to identify the material properties (inverse problem - 2)

Published
2022
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13. Delamination prediction in composite panels using unsupervised-feature learning methods with wavelet-enhanced guided wave representations

Author

Rautela, Mahindra, Senthilnath, J., Monaco, Ernesto, and Gopalakrishnan, S.

Subjects
Electrical Engineering and Systems Science - Signal Processing, Electrical Engineering and Systems Science - Audio and Speech Processing, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

With the introduction of damage tolerance-based design philosophies, the demand for reliable and robust structural health monitoring (SHM) procedures for aerospace composite structures is increasing rapidly. The performance of supervised learning algorithms for SHM depends on the amount of labeled and balanced datasets. Apart from this, collecting datasets accommodating all possible damage scenarios is cumbersome, costly, and inaccessible for aerospace applications. In this paper, we have proposed two different unsupervised-feature learning approaches where the algorithms are trained only on the baseline scenarios to learn the distribution of baseline signals. The trained unsupervised feature learner is used for delamination prediction with an anomaly detection philosophy. In the first approach, we have combined dimensionality reduction techniques (principal component analysis and independent component analysis) with a one-class support vector machine. In another approach, we have utilized deep learning-based deep convolutional autoencoders (CAE). These state-of-the-art algorithms are applied on three different guided wave-based experimental datasets. The raw guided wave signals present in the datasets are converted into wavelet-enhanced higher-order representations for training unsupervised feature-learning algorithms. We have also compared different techniques, and it is seen that CAE generates better reconstructions with lower mean squared error and can provide higher accuracy on all the datasets.

Published
2022
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14. Studi Organisme Pengganggu Tanaman pada Bit Merah (Beta vulgaris L.) yang Dibudidayakan Dalam Greenhouse Dataran Rendah

Author

Mahindra Dewi Nur Aisyah, Enik Akhiriana, and Mahmudah Hamawi

Subjects
beet, sugar, pest, disease, Technology
Abstract

Beet (Beta vulgaris L) is a potential plant for producing sugar (sugar beet) and dye (beetroot). Therefore, there is a great opportunity to be cultivated in Indonesia because of the suitable climate. Beet plants can harvest twice a year and have a higher yield than in areas with subtropical climates. However, the presence of plant pests and disease organism attacks can be a limiting factor in beet production. This study aims to observe pests and diseases attack beet cultivated in greenhouses. The observation conducted for five months, from September 2022 to January 2023. The results showed that symptoms of pest and disease attacks beet are Spodoptera sp., Lyriomyza sp., tungau, Cercospora sp., Rhozoctonia sp., and Erysiphe sp.

Published
2023
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15. Educated by Hitting: Examining Authoritarian Parenting Patterns in the Batak Toba Ethnic Community in the Samosir Area, Ambarita Village

Author

Annisa Wilda, Artha Mahindra Diputera, Herlina Deborah Victoria, Permata Sari Br Siregar, Hani Eklesia Sidauruk, and Ribka Julianta Br Sinuraya

Subjects
authoritarian, batak toba ethnic, parenting, Theory and practice of education, LB5-3640
Abstract

This research aims to find out how parenting patterns have developed in the Batak Toba ethnic in the Samosir Region and whether the Toba Batak tribe community in the Samosir Region uses a parenting style that involves hitting (Authoritarian). This research was conducted in Ambarita Village, Simanindo District, Samosir Regency with a population of 400 heads of families. The sample used was 80 parents. This research uses a combination method (Mix method) using a Sequential Explanatory research approach. Data processing techniques are carried out by distributing questionnaires and conducting interviews. Based on the research results, it can be concluded that the parenting pattern applied by the Toba Batak tribe is 85% democratic parenting, parenting patterns authoritarian 15%, permissive parenting is 0%, that is, there are no parents who apply permissive parenting (abandoning the child). 1) parents who do not give freedom to children and restrain children 2) parents who give punishment by hitting or pinching when the child makes a mistake. This research will have an impact on parents in communities who have children to apply parenting patterns that are in accordance with the characteristics of children and provide input and encouragement to children to find the best way to achieve their goals

Published
2023
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19. An Intelligent Garbage Management System: An IoT-Based Application

Author

Mahindra Guptha, K., Radhika, S., Sateesh, B., Pavan Aditya, M., Sai Lokesh, J., Chaari, Fakher, Series Editor, Gherardini, Francesco, Series Editor, Ivanov, Vitalii, Series Editor, Cavas-Martínez, Francisco, Editorial Board Member, di Mare, Francesca, Editorial Board Member, Haddar, Mohamed, Editorial Board Member, Kwon, Young W., Editorial Board Member, Trojanowska, Justyna, Editorial Board Member, Xu, Jinyang, Editorial Board Member, Venkata Rao, Ravipudi, editor, and Taler, Jan, editor

Published
2023
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20. Using a Modified Delphi Panel to Estimate Health Service Utilization for Patients with Advanced and Non-Advanced Systemic Light Chain Amyloidosis

Author

Gertz M, Abonour R, Gibbs SN, Finkel M, Landau H, Lentzsch S, Lin G, Mahindra A, Quock T, Rosenbaum C, Rosenzweig M, Sidana S, Tuchman SA, Witteles R, Yermilov I, and Broder MS

Subjects
consensus, hematology, outcomes research, mayo stage, cardiac failure, Medicine (General), R5-920, Therapeutics. Pharmacology, RM1-950
Abstract

Morie Gertz,1,* Rafat Abonour,2,* Sarah N Gibbs,3,* Muriel Finkel,4,* Heather Landau,5,* Suzanne Lentzsch,6,* Grace Lin,7,* Anuj Mahindra,8,* Tiffany Quock,9,* Cara Rosenbaum,10,* Michael Rosenzweig,11,* Surbhi Sidana,12,* Sascha A Tuchman,13,* Ronald Witteles,12,* Irina Yermilov,3,* Michael S Broder3,* 1Department of Medicine, Mayo Clinic, Rochester, MN, USA; 2Department of Medicine, Indiana University School of Medicine; Director, Multiple Myeloma, Waldenstrom’s Disease and Amyloidosis Program, Indianapolis, IN, USA; 3PHAR (Partnership for Health Analytic Research), Beverly Hills, CA, USA; 4Amyloidosis Support Groups Inc, Wood Dale, IL, USA; 5Memorial Sloan-Kettering Cancer Center, New York, NY, USA; 6Multiple Myeloma and Amyloidosis Program, Columbia University Medical Center, New York, NY, USA; 7Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA; 8Malignant Hematology, Scripps Clinic MD Anderson Cancer Center, La Jolla, CA, USA; 9Health Economics and Outcomes Research, Prothena Biosciences Ltd., South San Francisco, CA, USA; 10Department of Medicine, Hematology/Oncology, Weill Cornell Medical College, New York, NY, USA; 11Division of Multiple Myeloma, Department of Hematology and Hematopoietic Cell Transplantation, City of Hope, Duarte, CA, USA; 12Division of Cardiovascular Medicine, Stanford School of Medicine, Palo Alto, CA, USA; 13Division of Hematology, Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA*These authors contributed equally to this workCorrespondence: Michael S Broder, PHAR (Partnership for Health Analytic Research), 280 S Beverly Drive, Suite 404, Beverly Hills, CA, 90212, USA, Tel +1 310 858 9555, Email mbroder@pharllc.comPurpose: Patients with diagnosed with systemic light chain (AL) amyloidosis at advanced Mayo stages have greater morbidity and mortality than those diagnosed at non-advanced stages. Estimating service use by severity is difficult because Mayo stage is not available in many secondary databases. We used an expert panel to estimate healthcare utilization among advanced and non-advanced AL amyloidosis patients.Patients and Methods: Using the RAND/UCLA modified Delphi method, expert panelists completed 180 healthcare utilization estimates, consisting of inpatient and outpatient visits, testing, chemotherapy, and procedures by disease severity and organ involvement during two treatment phases (the 1 year after starting first line [1L] therapy and 1 year following treatment [post-1L]). Estimates were also provided for post-1L by hematologic treatment response (complete or very good partial response [CR/VGPR], partial, no response or relapse [PR/NR/R]). Areas of disagreement were discussed during a meeting, after which ratings were completed a second time.Results: During 1L therapy, 55% of advanced patients had ≥ 1 hospitalization and 38% had ≥ 2 admissions. Rates of hematopoietic stem cell transplant (HSCT) in advanced patients were 5%, while pacemaker or implantable cardioverter defibrillator (ICD) placement were 15%. During post-1L therapy, rates of hospitalization in advanced patients remained high (≥ 1 hospitalization: 20-43%, ≥ 2 hospitalizations: 10-20%), and up to 10% of advanced patients had a HSCT. Ten percent of these patients underwent pacemaker/ICD placement.Conclusion: Experts estimated advanced patients, who would not be good candidates for HSCT, would have high rates of hospitalization (traditionally the most expensive type of healthcare utilization) and other health service use. The development of new treatment options that can facilitate organ recovery and improve function may lead to decreased utilization.Keywords: consensus, hematology, outcomes research, mayo stage, cardiac failure

Published
2023

21. Paper card-based vs application-based vaccine credentials: a comparison

Author

Mahindra, Aryan, Sharma, Anshuman, Katiyar, Priyanshi, Sukumaran, Rohan, Singh, Ishaan, Johnson, Albert, Jakimowicz, Kasia, Venkatasubramanian, Akarsh, CV, Chandan, Advani, Shailesh, Iyer, Rohan, Shankar, Sheshank, Srivastava, Saurish, TV, Sethuraman, Singh, Abhishek, and Raskar, Ramesh

Subjects
Computer Science - Computers and Society
Abstract

In this early draft, we provide an overview on similarities and differences in the implementation of a paper card-based vaccine credential system and an app-based vaccine credential system. A vaccine credential's primary goal is to regulate entry and ensure safety of individuals within densely packed public locations and workspaces. This is critical for containing the rapid spread of Covid-19 in densely packed public locations since a single individual can infect a large majority of people in a crowd. A vaccine credential can also provide information such as an individual's Covid-19 vaccination history and adverse symptom reaction history to judge their potential impact on the overall health of individuals within densely packed public locations and workspaces. After completing the comparisons, we believe a card-based implementation will benefit regions with less socioeconomic mobility, limited resources, and stagnant administrations. An app-based implementation on the other hand will benefit regions with equitable internet access and lower technological divide. We also believe an interoperable system of both credential systems will work best for regions with enormous working-class populations and dense housing clusters., Comment: 10 pages, 4 figures

Published
2021

22. MIT SafePaths Card (MiSaCa): Augmenting Paper Based Vaccination Cards with Printed Codes

Author

Bae, Joseph, Sukumaran, Rohan, Shankar, Sheshank, Srivastava, Saurish, Iyer, Rohan, Mahindra, Aryan, Mirza, Qamil, Arseni, Maurizio, Sharma, Anshuman, Agrawal, Saras, Mukhopadhyay, Orna, Kang, Colin, Katiyar, Priyanshi, Shekhar, Apurv, Hasan, Sifat, Dasgupta, Krishnendu, Gandhi, Darshan, TV, Sethuramen, Patwa, Parth, Singh, Ishaan, Singh, Abhishek, and Raskar, Ramesh

Subjects
Computer Science - Computers and Society, Computer Science - Cryptography and Security
Abstract

In this early draft, we describe a user-centric, card-based system for vaccine distribution. Our system makes use of digitally signed QR codes and their use for phased vaccine distribution, vaccine administration/record-keeping, immunization verification, and follow-up symptom reporting. Furthermore, we propose and describe a complementary scanner app system to be used by vaccination clinics, public health officials, and immunization verification parties to effectively utilize card-based framework. We believe that the proposed system provides a privacy-preserving and efficient framework for vaccine distribution in both developed and developing regions., Comment: 8 pages, 4 Figures, 1 Table

Published
2021

23. COVID-19 Tests Gone Rogue: Privacy, Efficacy, Mismanagement and Misunderstandings

Author

Morales, Manuel, Barbar, Rachel, Gandhi, Darshan, Landage, Sanskruti, Bae, Joseph, Vats, Arpita, Kothari, Jil, Shankar, Sheshank, Sukumaran, Rohan, Mathur, Himi, Misra, Krutika, Saxena, Aishwarya, Patwa, Parth, V., Sethuraman T., Arseni, Maurizio, Advani, Shailesh, Jakimowicz, Kasia, Anand, Sunaina, Katiyar, Priyanshi, Mehra, Ashley, Iyer, Rohan, Murali, Srinidhi, Mahindra, Aryan, Dmitrienko, Mikhail, Srivastava, Saurish, Gangavarapu, Ananya, Penrod, Steve, Sharma, Vivek, Singh, Abhishek, and Raskar, Ramesh

Subjects
Computer Science - Computers and Society
Abstract

COVID-19 testing, the cornerstone for effective screening and identification of COVID-19 cases, remains paramount as an intervention tool to curb the spread of COVID-19 both at local and national levels. However, the speed at which the pandemic struck and the response was rolled out, the widespread impact on healthcare infrastructure, the lack of sufficient preparation within the public health system, and the complexity of the crisis led to utter confusion among test-takers. Invasion of privacy remains a crucial concern. The user experience of test takers remains low. User friction affects user behavior and discourages participation in testing programs. Test efficacy has been overstated. Test results are poorly understood resulting in inappropriate follow-up recommendations. Herein, we review the current landscape of COVID-19 testing, identify four key challenges, and discuss the consequences of the failure to address these challenges. The current infrastructure around testing and information propagation is highly privacy-invasive and does not leverage scalable digital components. In this work, we discuss challenges complicating the existing covid-19 testing ecosystem and highlight the need to improve the testing experience for the user and reduce privacy invasions. Digital tools will play a critical role in resolving these challenges., Comment: 22 pages, 2 figures

Published
2021

24. Spatial K-anonymity: A Privacy-preserving Method for COVID-19 Related Geospatial Technologies

Author

Iyer, Rohan, Rex, Regina, McPherson, Kevin P., Gandhi, Darshan, Mahindra, Aryan, Singh, Abhishek, and Raskar, Ramesh

Subjects
Computer Science - Cryptography and Security
Abstract

There is a growing need for spatial privacy considerations in the many geo-spatial technologies that have been created as solutions for COVID-19-related issues. Although effective geo-spatial technologies have already been rolled out, most have significantly sacrificed privacy for utility. In this paper, we explore spatial k-anonymity, a privacy-preserving method that can address this unnecessary tradeoff by providing the best of both privacy and utility. After evaluating its past implications in geo-spatial use cases, we propose applications of spatial k-anonymity in the data sharing and managing of COVID-19 contact tracing technologies as well as heat maps showing a user's travel history. We then justify our propositions by comparing spatial k-anonymity with several other spatial privacy methods, including differential privacy, geo-indistinguishability, and manual consent based redaction. Our hope is to raise awareness of the ever-growing risks associated with spatial privacy and how they can be solved with Spatial K-anonymity.

Published
2021

25. Digital Landscape of COVID-19 Testing: Challenges and Opportunities

Author

Gandhi, Darshan, Sukumaran, Rohan, Katiyar, Priyanshi, Radunsky, Alex, Anand, Sunaina, Advani, Shailesh, Kothari, Jil, Jakimowicz, Kasia, Shankar, Sheshank, V., Sethuraman T., Misra, Krutika, Saxena, Aishwarya, Landage, Sanskruti, Sonker, Richa, Patwa, Parth, Mahindra, Aryan, Dmitrienko, Mikhail, Vaish, Kanishka, Mehra, Ashley, Murali, Srinidhi, Iyer, Rohan, Bae, Joseph, Sharma, Vivek, Singh, Abhishek, Barbar, Rachel, and Raskar, Ramesh

Subjects
Computer Science - Computers and Society
Abstract

The COVID-19 Pandemic has left a devastating trail all over the world, in terms of loss of lives, economic decline, travel restrictions, trade deficit, and collapsing economy including real-estate, job loss, loss of health benefits, the decline in quality of access to care and services and overall quality of life. Immunization from the anticipated vaccines will not be the stand-alone guideline that will help surpass the pandemic and return to normalcy. Four pillars of effective public health intervention include diagnostic testing for both asymptomatic and symptomatic individuals, contact tracing, quarantine of individuals with symptoms or who are exposed to COVID-19, and maintaining strict hygiene standards at the individual and community level. Digital technology, currently being used for COVID-19 testing include certain mobile apps, web dashboards, and online self-assessment tools. Herein, we look into various digital solutions adapted by communities across universities, businesses, and other organizations. We summarize the challenges experienced using these tools in terms of quality of information, privacy, and user-centric issues. Despite numerous digital solutions available and being developed, many vary in terms of information being shared in terms of both quality and quantity, which can be overwhelming to the users. Understanding the testing landscape through a digital lens will give a clear insight into the multiple challenges that we face including data privacy, cost, and miscommunication. It is the destiny of digitalization to navigate testing for COVID-19. Block-chain based systems can be used for privacy preservation and ensuring ownership of the data to remain with the user. Another solution involves having digital health passports with relevant and correct information. In this early draft, we summarize the challenges and propose possible solutions to address the same., Comment: 28 pages, 4 figures

Published
2020

26. Clinical Landscape of COVID-19 Testing: Difficult Choices

Author

Gandhi, Darshan, Landage, Sanskruti, Bae, Joseph, Shankar, Sheshank, Sukumaran, Rohan, Patwa, Parth, T V, Sethuraman, Katiyar, Priyanshi, Advani, Shailesh, Iyer, Rohan, Anand, Sunaina, Mahindra, Aryan, Barbar, Rachel, Singh, Abhishek, and Raskar, Ramesh

Subjects
Quantitative Biology - Other Quantitative Biology
Abstract

The coronavirus disease 2019 (COVID-19) pandemic has spread rapidly across the world, leading to enormous amounts of human death and economic loss. Until definitive preventive or curative measures are developed, policies regarding testing, contact tracing, and quarantine remain the best public health tools for curbing viral spread. Testing is a crucial component of these efforts, enabling the identification and isolation of infected individuals. Differences in testing methodologies, time frames, and outcomes can have an impact on their overall efficiency, usability and efficacy. In this early draft, we draw a comparison between the various types of diagnostic tests including PCR, antigen, and home tests in relation to their relative advantages, disadvantages, and use cases. We also look into alternative and unconventional methods. Further, we analyze the short-term and long-term impacts of the virus and its testing on various verticals such as business, government laws, policies, and healthcare., Comment: 9 pages, 12 figures

Published
2020

27. Evaluation of vaccine storage and distribution practices in rural healthcare facilities in Kenya

Author

Dennis Kipkoech Sinnei, Peter Ndirangu Karimi, Shital Mahindra Maru, Stephen Karengera, and Thomas Bizimana

Subjects
Vaccines, Cold chain, Rural health facilities, Distribution, Storage, Therapeutics. Pharmacology, RM1-950, Pharmacy and materia medica, RS1-441
Abstract

Abstract Background Vaccines require cold chain storage conditions, and good distribution practices throughout the supply chain to maintain their quality and potency. However, in the last mile of the vaccines supply chain, these requirements may not be guaranteed resulting in reduced effectiveness which could lead to an upsurge in vaccine preventable morbidity and mortality. The aim of this research was to evaluate vaccine storage and distribution practices in the last mile of vaccine supply chain in Turkana County. Methods A descriptive cross-sectional study was conducted from January 2022 to February 2022 across seven sub-counties in Turkana County, Kenya, to assess vaccine storage and distribution practices. The study sample size was 128 county health professionals across 4 hospitals, 9 health centers, and 115 dispensaries. The respondents were selected using simple random sampling within the facilities strata. Data were collected using a structured questionnaire, adapted, and adopted from a standardized WHO questionnaire on effective vaccines management and administered to one healthcare personnel working in the immunization supply chain per facility. Data were analyzed using excel and presented as percentages in table forms. Results A total of 122 health care workers participated in the study. Most respondents (89%, n = 109) had utilized a vaccine forecasting sheet, but only 81% did have an established maximum–minimum level inventory control system. Many of the respondents had sufficient knowledge of ice pack conditioning although 72% had adequate vaccine carriers and ice packs. Only 67% of respondents had a complete set of twice-daily manual temperature records at the facility. Most refrigerators complied with the WHO specifications but only 80% of them had functional fridge-tags. The number of facilities that had a routine maintenance plan was below average while only 65% had an adequate contingency plan. Conclusion Rural health facilities have suboptimal supply of vaccine carriers and icepacks for effective storage and distribution of vaccines. In addition, some vaccine fridges lack functional fridge-tags for proper temperature monitoring. Routine maintenance and contingency plans remain a challenge to ensure optimal service delivery.

Published
2023
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28. Factors affecting elective cesarean section in women with multiple pregnancy in Caruban, Indonesia [version 3; peer review: 2 approved]

Author

Muhammad Pradhiki Mahindra, Mahendra Tri Arif Sampurna, Muhammad Pradhika Mapindra, Apriska Mega Sutowo Putri, Aries Krisbiyantoro, and Rozi Aditya Aryananda

Subjects
Research Article, Articles, cesarean section, complication, multiple pregnancy, vaginal birth
Abstract

Background: Caesarean sections have become the most popular method for delivering twin babies because of the safety concerns associated with a natural birth. This study aims to identify the maternal characteristics and obstetric parameters that serve as risk factors influencing caesarean delivery in twin pregnancies by comparing women delivering via caesarean section and vaginal birth. Methods: A retrospective chart review design was used to analyse 47 women with multiple pregnancies from the medical records at a primary referral hospital in East Java, Indonesia. Women delivering vaginally were then compared with women who underwent a caesarean section to identify any differences between the groups. Results: In our study, more women delivered by caesarean section (n=35) than by vaginal birth (n=12). Women were more likely to undergo a caesarean section if they had a previous history of undergoing a caesarean section (OR 16.5; 95% CI 1.91-142.49; p=0.02). Similar to previous studies, we found that foetal malpresentation significantly increase the risk of caesarean delivery (OR 8.25; 95%CI 0.95-71.09; p=0.03), while labour augmentation decrease the likelihood of caesarean section (OR 0.20; 95% CI 0.49-0.81; p=0.03). There was also a significant older patients in the caesarean section groups (OR 1.26; 95% CI 1.09-1.45 ; p=0.00). Conclusions: The percentage of multiple pregnancies delivered via caesarean section is quite high. Other larger cohort study are warranted, since many factors were involved in the decision of caesarean section.

Published
2023
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30. Towards efficiently solving the rubik’s cube with deep reinforcement learning and recursion

Author

Roshan M. Mahindra, Rakesh S., Gnana Guru T. Sri, Rohith B., and Hemalatha J.

Subjects
cubics, recursion, deep learning model, reinforcement learning, training, gpu, Environmental sciences, GE1-350
Abstract

The Rubik’s cube is a prototypical combinatorial puzzle that has a large state space with a single goal state. The goal state is unlikely to be retrieved using orders of randomly generated moves, posing unique challenges for machine learning. The proposed work is above to solve the Rubik’s cube with recursion and DeepCubeA, a deep reinforcement learning approach that learns how to solve increasingly difficult states in reverse from the goal state without any specific domain knowledge. DeepCubeA solves 100% of all test patterns, finding a shortest path to the goal state 60.3% of the time. Deep Cube A generalizes to other combinatorial puzzles andis able to solve the 15 puzzle, 24 puzzle, 35 puzzle, 48 puzzle, Lights Out and Sokoban, finding a shortest path in the majority of verifiable cases. These models were trained with 1 4 GPUs and 20 30 CPUs. This varies throughout training as the training is often stopped and started again to make room for other processes. Further our experimentation compares the results of Rubik’s cube solving among both recursion and DeepCubeA and also with the state of art models. Later, we intend to develop a new deep learning model with an application.

Published
2024
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32. Factors affecting elective cesarean section in women with multiple pregnancy in Caruban, Indonesia [version 3; peer review: 2 approved]

Author

Apriska Mega Sutowo Putri, Aries Krisbiyantoro, Mahendra Tri Arif Sampurna, Muhammad Pradhika Mapindra, Rozi Aditya Aryananda, and Muhammad Pradhiki Mahindra

Subjects
cesarean section, complication, multiple pregnancy, vaginal birth, eng, Medicine, Science
Abstract

Background: Caesarean sections have become the most popular method for delivering twin babies because of the safety concerns associated with a natural birth. This study aims to identify the maternal characteristics and obstetric parameters that serve as risk factors influencing caesarean delivery in twin pregnancies by comparing women delivering via caesarean section and vaginal birth. Methods: A retrospective chart review design was used to analyse 47 women with multiple pregnancies from the medical records at a primary referral hospital in East Java, Indonesia. Women delivering vaginally were then compared with women who underwent a caesarean section to identify any differences between the groups. Results: In our study, more women delivered by caesarean section (n=35) than by vaginal birth (n=12). Women were more likely to undergo a caesarean section if they had a previous history of undergoing a caesarean section (OR 16.5; 95% CI 1.91-142.49; p=0.02). Similar to previous studies, we found that foetal malpresentation significantly increase the risk of caesarean delivery (OR 8.25; 95%CI 0.95-71.09; p=0.03), while labour augmentation decrease the likelihood of caesarean section (OR 0.20; 95% CI 0.49-0.81; p=0.03). There was also a significant older patients in the caesarean section groups (OR 1.26; 95% CI 1.09-1.45 ; p=0.00). Conclusions: The percentage of multiple pregnancies delivered via caesarean section is quite high. Other larger cohort study are warranted, since many factors were involved in the decision of caesarean section.

Published
2023
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34. Lateral Placement of Vehicles Under Mixed Traffic in Indian Urban Scenario

Author

Deshmukh, Mahindra, Dhamaniya, Ashish, di Prisco, Marco, Series Editor, Chen, Sheng-Hong, Series Editor, Vayas, Ioannis, Series Editor, Kumar Shukla, Sanjay, Series Editor, Sharma, Anuj, Series Editor, Kumar, Nagesh, Series Editor, Wang, Chien Ming, Series Editor, Maurya, Akhilesh Kumar, editor, Maitra, Bhargab, editor, Rastogi, Rajat, editor, and Das, Animesh, editor

Published
2022
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35. Resume Screening Using Machine Learning

Author

Vamsi Naidu, P., Bommu, Venkata Mahindra Reddy, Pallapothu, Vasavi Durga, Janapamula, Sai Ramesh Naidu, Kommuri, Naga Lakshmi, 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, Chen, Joy Iong-Zong, editor, Tavares, João Manuel R. S., editor, Iliyasu, Abdullah M., editor, and Du, Ke-Lin, editor

Published
2022
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36. Effectiveness of Participation in Pregnancy Classes to Reduce the Incidence of Obstetric Labor Complications and Cesarean Section

Author

Apriska M. S. Putri, Muhammad P. Mahindra, Mahendra T. A. Sampurna, Rozi A. Aryananda, and Fitria K. Khotimah

Subjects
Gynecology and obstetrics, RG1-991
Abstract

Objective : to assess the effectiveness of participation in pregnancy classes to inform such a prenatal program for physicians and midwives, focusing to reduce the incidence of obstetric labor complications and cesarean section. Methods : This study was done using an analytic observational approach, using a cross-sectional study and consecutive sampling. One hundred and ninety-four participants of this study were women aged 20–35 years who gave birth between January and November 2019 in balarejo sub-district, East Java, Indonesia. The subjects were divided into two groups: participation (four times) and non-participation (less than four times) in the pregnancy classes. The analyzed outcomes are labor complications (prolonged labor, postpartum hemorrhage, postpartum fever, premature rupture of membrane), and cesarean section birth.. Results: The results of this study indicate that participants of pregnancy classes have a significantly lower probability of suffering prolonged labor (OR=0.05, 95% CI=0.01-0.23, p

Published
2022
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38. Management of chronic Achilles tendon tear by reverse gastrocnemius turndown flap and augmentation with FHL tendon – A case series

Author

Devarshi Rastogi, Mayank Mahindra, Shah Wali Ullah, Sanjiv Kumar, and Shitanshu Kumar Singh

Subjects
achilles tendon total rupture score, american orthopaedic foot and ankle society score, plantar flexion, Medicine
Abstract

Background: Chronic Achilles tendon ruptures are defined as tears that occur 4–6 weeks after injury and also those that fail to heal at 6–12 weeks. Reduced plantar flexion, discomfort, trouble walking, chronic pain, poor wound healing, and rerupture are all problems of chronic tears. The V-Y flap, turndown flap, peroneus tendon graft, flexor digitorum graft, and flexor hallucis longus (FHL) graft are just a few of the operations proposed and claimed to have great post-operative clinical results. Aims and Objectives: This study aims to evaluate the functional outcome of tendon debridement, gastrocnemius turndown flap, and augmentation with FHL tendon to reconstruct a chronic Achilles tendon tear. Materials and Methods: This study included 12 patients with chronic Achilles tendon rupture. The gastrocnemius turndown flap, which was augmented with FHL tendon, was used to treat all of the patients in the study. The American Orthopaedic Foot and Ankle Society (AOFAS) score, Achilles Tendon Total Rupture Score (ATRS) score, and ability to do repetitive single affected side heel raises were the outcomes evaluated in this study. Results: After a year of follow-up, the mean AOFAS score increased from 61.17 points preoperatively to 86.67 points. The mean ATRS improved significantly from 41.91 points preoperatively to 84.41 points at the most recent follow-up (P

Published
2022
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41. Determinants of neonatal deaths in Indonesia: A national survey data analysis of 10,838 newborns

Author

Mahendra Tri Arif Sampurna, Kartika Dharma Handayani, Martono Tri Utomo, Dina Angelika, Risa Etika, Agus Harianto, Muhammad Pradhika Mapindra, Muhammad Pradhiki Mahindra, Ferry Efendi, Risma Kerina Kaban, Rinawati Rohsiswatmo, Visuddho Visuddho, and Putu Bagus Dharma Permana

Subjects
Determinant, IDHS, Maternal factors, Neonatal death, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Background: Neonatal mortality is one of the key impediments in achieving global sustainable development goals, especially in lower middle income countries (LMICs). As an LMIC with the highest reported neonatal mortality rate in Southeast Asia, Indonesia faces inequitable distribution of health facilities across the archipelago. Therefore, in this paper, we aim to evaluate the determinants of neonatal mortality rate in Indonesia to search for better strategies to overcome this problem. Methods: We conducted an analysis of the 2017 Indonesia Demographic Health Survey dataset of 10,838 live-born infants born from singleton pregnancies in 2017. Using a hierarchical approach, multivariate analysis was conducted to identify potential factors (including socioeconomic, household, and proximate determinants) that contributed to neonatal mortality. Results: The lack of participation in postnatal care [odds ratio (OR) = 20.394, p = 0.01)] and delivery complications other than prolonged labour (OR = 2.072, p = 0.02) were the maternal factors that significantly associated with increased risk of neonatal death. Regarding neonatal factors, low-birth-weight infants appeared to be more vulnerable to neonatal death (OR = 12.489, p = 0.01). Conclusion: Low participation in postnatal care, development of labour complications, and low birth weight were associated with higher neonatal mortality. It implies that in a limited resource and geographically challenging country such as Indonesia, improving the quality and optimizing services of public hospitals with equitable distribution of quality health care services in all regions should be prioritized in the efforts of reducing neonatal mortality rate.

Published
2023
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45. A prediction model for stillbirth based on first trimester pre‐eclampsia combined screening.

Author

Al‐Fattah, Adly Nanda, Mahindra, Muhammad Pradhiki, Yusrika, Mirani Ulfa, Mapindra, Muhammad Pradhika, Marizni, Shinda, Putri, Vania Permata, Besar, Sadina Pramuktini, Widjaja, Felix Firyanto, Kusuma, Raden Aditya, and Siassakos, Dimitrios

Subjects
*ECLAMPSIA, *STILLBIRTH, *PREECLAMPSIA, *PLACENTAL growth factor, *MEDICAL screening, *PREDICTION models
Abstract

Objective Methods Results Conclusion To evaluate the accuracy of combined models of maternal biophysical factors, ultrasound, and biochemical markers for predicting stillbirths.A retrospective cohort study of pregnant women undergoing first‐trimester pre‐eclampsia screening at 11–13 gestational weeks was conducted. Maternal characteristics and history, mean arterial pressure (MAP) measurement, uterine artery pulsatility index (UtA‐PI) ultrasound, maternal ophthalmic peak ratio Doppler, and placental growth factor (PlGF) serum were collected during the visit. Stillbirth was classified as placental dysfunction‐related when it occurred with pre‐eclampsia or birth weight <10th percentile. Combined prediction models were developed from significant variables in stillbirths, placental dysfunction‐related, and controls. We used the area under the receiver‐operating‐characteristics curve (AUC), sensitivity, and specificity based on a specific cutoff to evaluate the model's predictive performance by measuring the capacity to distinguish between stillbirths and live births.There were 13 (0.79%) cases of stillbirth in 1643 women included in the analysis. The combination of maternal factors, MAP, UtA‐PI, and PlGF, significantly contributed to the prediction of stillbirth. This model was a good predictor for all (including controls) types of stillbirth (AUC 0.879, 95% CI: 0.799–0.959, sensitivity of 99.3%, specificity of 38.5%), and an excellent predictor for placental dysfunction‐related stillbirth (AUC 0.984, 95% CI: 0.960–1.000, sensitivity of 98.5, specificity of 85.7).Screening at 11–13 weeks' gestation by combining maternal factors, MAP, UtA‐PI, and PlGF, can predict a high proportion of stillbirths. Our model has good accuracy for predicting stillbirths, predominantly placental dysfunction‐related stillbirths. [ABSTRACT FROM AUTHOR]

Published
2024
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46. Analytical study of inflammatory cytokines and immunoglobulin expression following dust mite allergen exposure in pregnant mice

Author

Risa Etika, Subijanto Marto Sudarmo, Suwarno Suwarno, Muhammad Pradhika Mapindra, and Muhammad Pradhiki Mahindra

Subjects
mite allergen, pregnant balb/c mice, newborn mice, tolerance, cytokine, Medicine, Pediatrics, RJ1-570
Abstract

Background Allergen tolerability due to allergic immune reactions could be transferred through the placenta from maternal to fetal circulation. Hence, a further investigation regarding the tolerability following mite allergen exposures is desirable. Objective To evaluate various doses of mite allergens and cytokines associated with Th1, Th2, and Treg cells with regards to possible allergic tolerance in neonatal mice. Methods This study used an experimental design with a post-test only control group, to assess the effect of mite allergens on pregnant BALB/C mice and their newborns. In this study female BALB/C mice aged 10 weeks were mated with male mice, then pregnant BALB/C mice were exposed to allergens at 4 weeks gestation. During pregnancy, pregnant females’ blood specimens were taken to measure cytokines and immunoglobulins. Meanwhile, neonatal blood specimens were taken at 2 weeks postnatally to measure cytokines and immunoglobulins. Blood specimens from pregnant BALB/C mice and their newborns were evaluated using ELISA kits for the following cytokines: interleukin (IL)-2, interferon (IFN)-γ, interleukin (IL)-4, IL-5, IL-10, TGF-β1, as well as immunoglobulins (Ig)G-1, IgG-2a, IgG-2b, IgG3 subclass, IgM, IgA, and IgE. The case group was the group that received high and low doses of exposure, while the control group did not get exposure. Results In response to low dose mite allergen exposure, there were significant increases of IL-2, IFN-y, and IL-4, IL-5, and TGF-β1 in mothers and neonates. Pregnant mices that received high doses of allergens, however, had significant increases in IL-5 and TGF-B1; results were likewise for their offspring. Mothers and neonates, had significantly increased expression of IgG subclasses after a low dose of dust mite allergen. Following a ten-fold increase in allergen dose, the mothers showed significant increases in IgA, IgM, IgE, and IgG subclasses, whereas in neonatal mice, those immunoglobulin levels were not significantly different from control mice. Conclusion Exposure to mite allergens can trigger regulatory functions of Th1, Th2, and Tregs cells to activate their cytokines, except IL-10. The regulatory function of Tregs is dominated by TGFβ in maternal and neonatal mice, at low and high doses. Th1 cytokines express cytokines during exposure only to low-dose allergens and Th2 cells regulate IL-5 levels to both low- and high-dose allergens.

Published
2021
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