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1. Key Activating and Inhibitory Ligands Involved in the Mobilization of Natural Killer Cells for Cancer Immunotherapies

Author

Karmakar S, Pal P, and Lal G

Subjects
natural killer cells, innate immune system, oncogenic transformation, cancer, activating and inhibitory receptors, Immunologic diseases. Allergy, RC581-607
Abstract

Surojit Karmakar, Pradipta Pal, Girdhari Lal National Centre for Cell Science (NCCS), Pune, MH, 411007, IndiaCorrespondence: Girdhari LalNational Centre for Cell Science (NCCS), NCCS Complex, Ganeshkhind, Pune, MH, 411007, IndiaTel +91-20-2570-8292Fax +91-20-2568-2259Email glal@nccs.res.inAbstract: Natural killer (NK) cells are the most potent arm of the innate immune system and play an important role in immunity, alloimmunity, autoimmunity, and cancer. NK cells recognize “altered-self” cells due to oncogenic transformation or stress due to viral infection and target to kill them. The effector functions of NK cells depend on the interaction of the activating and inhibitory receptors on their surface with their cognate ligand expressed on the target cells. These activating and inhibitory receptors interact with major histocompatibility complex I (MHC I) expressed on the target cells and make decisions to mount an immune response. NK cell immune response includes cytolytic activity and secretion of cytokines to help with the ongoing immune response. The advancement of our knowledge on the expression of inhibitory and activating molecules led us to exploit these molecules in the treatment of cancer. This review discusses the importance of activating and inhibitory receptors on NK cells and their clinical importance in cancer immunotherapy.Keywords: natural killer cells, innate immune system, oncogenic transformation, cancer, activating and inhibitory receptors

Published
2021

2. Composite carotid intima-media thickness as a risk predictor of coronary heart disease in a selected population in Sri Lanka

Author

Visula Abeysuriya, Nirmala A. I. Wijesinha, Prakash P. Priyadharshan, Lal G. Chandrasena, and Ananda Rajitha Wickremasinghe

Subjects
Medicine, Science
Abstract

Background Segment-specific variations of carotid intima-media thickness (CIMT) have not been assessed in South Asian populations. The purpose of this study was to determine if segment-specific CIMTs or a composite-CIMT score is a better risk predictor of coronary heart disease in South Asian populations. Methods A comparative prospective study was conducted from November 2019 to October 2020 in a hospital in Colombo, Sri Lanka. Based on pre-defined inclusion and exclusion criteria, cases (having a diagnosis of Coronary Heart Disease (CHD), n = 338) and controls (non-CHD group, n = 356) were recruited. Ultrasound examination of the common carotid (CCA), the carotid bulb (CB) and the internal carotid segments (ICA) of the carotid vessels was conducted by a radiologist, and CIMTs were measured. A composite-CIMT score defined as the average value of all six segments of the left and right sides was derived. Results 694 participants were enrolled (male n = 399, 57.5%). The mean (±SD) age of the study sample was 60.2 (±9.86) years. There were variations in segment-specific values between the left and right vessels. The mean composite-CIMT value of the CHD group was significantly higher than that of the non-CHD group. A composite-CIMT score of 0.758 had a sensitivity of 98.4% and a specificity of 64.6% in distinguishing CHD from non-CHD groups (Area under the curve (AUC): 0.926). Conclusions Carotid artery segment-specific CIMT variations were present in this population. The composite CIMT score is better than segment-specific CIMTs in predicting CHD and may be used to predict CHD in this population.

Published
2022

3. Screening of Coriander Genotypes for their Relative Susceptibility against Aphids under Field Conditions

Author

Meena N K, Lal G, Meena S S, Kant K, and Meena R D

Subjects
aphids, coriander genotypes, semi-arid region, susceptibility, Plant culture, SB1-1110
Abstract

The field experiments were conducted during Rabi 2013-14 and 2014-15 to screen out twelve varieties/entries of coriander (Coriandrum sativum L.) for their relative susceptibility against aphids. None of the varieties/entries escaped the infestation of aphids. The build-up of aphid infestation started from second half of December and reached to its maximum in the first to third week of February in both years and then gradually declined. On the basis of grade index of mean aphid population, coriander varieties RCr- 684 (25.45 aphids/plant), RCr-446 (26.45 aphids/plant), ACr-1 (26.60 aphids/plant), RCr- 436 (41.75 aphids/plant), Gujarat Coriander-2 (42.45 aphids/plant), Pant Haritma (43.50 aphids/plant) and Gujarat Coriander-1 (43.70 aphids/plant) were categorized as least susceptible, Rajendra Swati and RCr-41 were moderately susceptible, whereas, Swati (CS-6), Sadhna (CS-4) and Sindhu (CS-2), 73.88, 70.60 and 69.50 aphids/plant, respectively were categorized as highly susceptible varieties of coriander against aphids under field conditions. Coriander variety RCr-684 received maximum yield (16.82 and 16.63 q/ha) for both the years followed by ACr-1 and RCr-446.

Published
2019
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5. Comparative Analysis on Speech Driven Gesture Generation

Author

Unnikrishnan, Pranav, Logesh, K. S. R., Sivakumar, Abinesh, Karun, R. Manesh, Santhosh, Jyothis V., Lal, G. Jyothish, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, K, Hemachandran, editor, Rodriguez, Raul Villamarin, editor, Rege, Manjeet, editor, Piuri, Vincenzo, editor, Xu, Guandong, editor, and Ong, Kok-Leong, editor

Published
2024
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6. Understanding Individual Emotional Responses: Analyzing Variations and Introducing Personal Emotional Bias in Kannada Opinion Data Set

Author

Kadakol, Satish, Sanjanasri, J. P., Jyothish Lal, G., 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, Kumar, Sandeep, editor, K., Balachandran, editor, Kim, Joong Hoon, editor, and Bansal, Jagdish Chand, editor

Published
2024
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7. Mean Teacher Model with Consistency Regularization for Semi-supervised Detection of COVID-19 Using Cough Recordings

Author

Dinesh Kumar, M. R., Paval, K. S., Sanghamitra, Shreya, Shrish Surya, N. T., Jyothish Lal, G., Ravi, Vinayakumar, 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, Kumar, Sandeep, editor, K., Balachandran, editor, Kim, Joong Hoon, editor, and Bansal, Jagdish Chand, editor

Published
2024
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8. Bridging Language Barriers: Exploring Hindi-to-English Speech-to-Speech Translation for Multilingual Communication

Author

Phogat, Divith, Prashanth, Karnati Sai, Rishith, Mangamuru Sai, Sai, Rachure Charith, Karthikeya, Sajja Bala, Jyothish Lal, G., Premjith, B., 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, Kumar, Sandeep, editor, K., Balachandran, editor, Kim, Joong Hoon, editor, and Bansal, Jagdish Chand, editor

Published
2024
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10. NN2Rules: Extracting Rule List from Neural Networks

Author

Lal, G Roshan and Mithal, Varun

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

We present an algorithm, NN2Rules, to convert a trained neural network into a rule list. Rule lists are more interpretable since they align better with the way humans make decisions. NN2Rules is a decompositional approach to rule extraction, i.e., it extracts a set of decision rules from the parameters of the trained neural network model. We show that the decision rules extracted have the same prediction as the neural network on any input presented to it, and hence the same accuracy. A key contribution of NN2Rules is that it allows hidden neuron behavior to be either soft-binary (eg. sigmoid activation) or rectified linear (ReLU) as opposed to existing decompositional approaches that were developed with the assumption of soft-binary activation.

Published
2022

11. TE2Rules: Explaining Tree Ensembles using Rules

Author

Lal, G Roshan, Chen, Xiaotong, and Mithal, Varun

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

Tree Ensemble (TE) models, such as Gradient Boosted Trees, often achieve optimal performance on tabular datasets, yet their lack of transparency poses challenges for comprehending their decision logic. This paper introduces TE2Rules (Tree Ensemble to Rules), a novel approach for explaining binary classification tree ensemble models through a list of rules, particularly focusing on explaining the minority class. Many state-of-the-art explainers struggle with minority class explanations, making TE2Rules valuable in such cases. The rules generated by TE2Rules closely approximate the original model, ensuring high fidelity, providing an accurate and interpretable means to understand decision-making. Experimental results demonstrate that TE2Rules scales effectively to tree ensembles with hundreds of trees, achieving higher fidelity within runtimes comparable to baselines. TE2Rules allows for a trade-off between runtime and fidelity, enhancing its practical applicability. The implementation is available here: https://github.com/linkedin/TE2Rules.

Published
2022

14. Brain Signal Classification Using Decomposition Techniques and Deep Learning

Author

Karthik, T. R., Ramakrishnan, Rohith, Vadakedath, Anirudh, Sowmya, V., Gopalakrishnan, E. A., Jyothish Lal, G., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Tan, Kay Chen, Series Editor, Chakraborty, Basabi, editor, Biswas, Arindam, editor, and Chakrabarti, Amlan, editor

Published
2023
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15. Multimodal Approach for Code-Mixed Speech Sentiment Classification

Author

Keshav, S., Jyothish Lal, G., Premjith, B., Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Chakravarthy, V.V.S.S.S., editor, Bhateja, Vikrant, editor, Flores Fuentes, Wendy, editor, Anguera, Jaume, editor, and Vasavi, K. Padma, editor

Published
2023
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16. Speech Emotion Recognition Using CNN-LSTM and Vision Transformer

Author

Kumar, C S Ayush, Maharana, Advaith Das, Krishnan, Srinath Murali, Hanuma, Sannidhi Sri Sai, Lal, G. Jyothish, Ravi, Vinayakumar, 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, Abraham, Ajith, editor, Bajaj, Anu, editor, Gandhi, Niketa, editor, Madureira, Ana Maria, editor, and Kahraman, Cengiz, editor

Published
2023
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17. Fairness-Aware Online Personalization

Author

Lal, G Roshan, Geyik, Sahin Cem, and Kenthapadi, Krishnaram

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

Decision making in crucial applications such as lending, hiring, and college admissions has witnessed increasing use of algorithmic models and techniques as a result of a confluence of factors such as ubiquitous connectivity, ability to collect, aggregate, and process large amounts of fine-grained data using cloud computing, and ease of access to applying sophisticated machine learning models. Quite often, such applications are powered by search and recommendation systems, which in turn make use of personalized ranking algorithms. At the same time, there is increasing awareness about the ethical and legal challenges posed by the use of such data-driven systems. Researchers and practitioners from different disciplines have recently highlighted the potential for such systems to discriminate against certain population groups, due to biases in the datasets utilized for learning their underlying recommendation models. We present a study of fairness in online personalization settings involving the ranking of individuals. Starting from a fair warm-start machine-learned model, we first demonstrate that online personalization can cause the model to learn to act in an unfair manner if the user is biased in his/her responses. For this purpose, we construct a stylized model for generating training data with potentially biased features as well as potentially biased labels and quantify the extent of bias that is learned by the model when the user responds in a biased manner as in many real-world scenarios. We then formulate the problem of learning personalized models under fairness constraints and present a regularization based approach for mitigating biases in machine learning. We demonstrate the efficacy of our approach through extensive simulations with different parameter settings. Code: https://github.com/groshanlal/Fairness-Aware-Online-Personalization, Comment: Accepted in RecSys 2020, FAccTRec Workshop: Responsible Recommendation

Published
2020

20. Early Warning Indicators for Financial Crisis During Covid-19

Author

Modi, Aakash Jignesh, Jyothish Lal, G., Gopalakrishnan, E. A., Sowmya, V., Soman, K. P., Vinayakumar, R., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Garg, Deepak, editor, Jagannathan, Sarangapani, editor, Gupta, Ankur, editor, Garg, Lalit, editor, and Gupta, Suneet, editor

Published
2022
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21. A Deep Recurrent Framework for Cleaning Motion Capture Data

Author

Mall, Utkarsh, Lal, G. Roshan, Chaudhuri, Siddhartha, and Chaudhuri, Parag

Subjects
Computer Science - Graphics, Computer Science - Computer Vision and Pattern Recognition
Abstract

We present a deep, bidirectional, recurrent framework for cleaning noisy and incomplete motion capture data. It exploits temporal coherence and joint correlations to infer adaptive filters for each joint in each frame. A single model can be trained to denoise a heterogeneous mix of action types, under substantial amounts of noise. A signal that has both noise and gaps is preprocessed with a second bidirectional network that synthesizes missing frames from surrounding context. The approach handles a wide variety of noise types and long gaps, does not rely on knowledge of the noise distribution, and operates in a streaming setting. We validate our approach through extensive evaluations on noise both in joint angles and in joint positions, and show that it improves upon various alternatives.

Published
2017

22. Long-term Experiment on Fennel-cluster Bean Cropping System as Affected by Production Systems: A Strategy for Improving Yield, Quality and Soil Fertility.

Author

Lal, S., Lal, G., Meena, N. K., Jangir, C. K., Choudhary, M. K., Chaudhary, N., Meena, R. D., Meena, S. S., Meena, M. D., and Aishwath, O. P.

Subjects
*GUAR, *CROPPING systems, *FENNEL, *AGRICULTURAL productivity, *CARBON in soils
Abstract

Background: The rise in unprecedented pressure of producing enough food for an ever-growing human population resulted in the use of considerable amounts of agrochemicals in crop production. To overcome this, inclusion of annual pulse crops such as cluster beans (Cyamopsis tetragonoloba L.) in cropping systems is an alternative technique for enhancing output while minimising environmental impact. Therefore, to assess the influence of different crop production systems on yield, quality and soil fertility attributes of fennel (Foeniculum vulgare L.)- cluster bean (Cyamopsis tetragonoloba L.) crop sequence, a long term field experiment was conducted. Methods: A field experiment was conducted on fennel (Foeniculum vulgare L.) cluster bean (Cyamopsis tetragonoloba L.) crop sequence under AI-NPOF Project during 2016-17 to 2020-21 at ICAR-National Research Centre on Seed Spices, Ajmer (Rajasthan), India. The experiment was laid out as a factorial design based on randomized complete blocks (RCBD) with three replications. The first factor consisted of six production systems viz., (PS1) - 100% organic, (PS2) - 75% organic + 25% innovative practice (Compost extract, cattle urine), (PS3) - Integrated (50% organic + 50% inorganic), (PS4) - Integrated (75% organic + 25% inorganic), (PS5) -100% inorganic nutrient sources and (PS6) - State recommendation] including fennel (Rabi season) - cluster bean (Kharif season) cropping sequence, whereas the second factor included the environment over the period. Result: The outcomes of study revealed that production system (PS4) - Integrated (75% organic + 25% inorganic) resulted in the maximum seed yields and yield attributes of cluster bean (1501.87 kg ha-1) and fennel (1970.19 kg ha-1). The study shows that the production system (PS4) had a 17.29 and 30.75% increment of protein as compared to PS5 and PS1 in fennel and cluster beans, respectively. Under production system PS1, the per cent increment of soil organic carbon (~25.2%) and readily available N (~9%), P (~38.5%) and K (~Non-significant) were noted over the years respectively, compared with production system (PS5). A significant positive association was observed between fennel and cluster bean grain yield and soil organic carbon (P<0.01) and the Shannon- Wiener indexes (R² = 0.75, R² = 0.69), respectively. The combined assessment of yield, quality, soil organic carbon and soil sustainability shows that rotations based on legume crops performed better and provided a model of sustainable crop intensification. Thus, adding legume to the cropping system and implementing integrated nutrient management (INM) could gradually improve soil quality in agroecologies where fennel predominates. [ABSTRACT FROM AUTHOR]

Published
2024
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40. TE2Rules: Extracting Rule Lists from Tree Ensembles

Author

Lal, G Roshan, Chen, Xiaotong, and Mithal, Varun

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Machine Learning (cs.LG)
Abstract

Tree Ensemble (TE) models (e.g. Gradient Boosted Trees and Random Forests) often provide higher prediction performance compared to single decision trees. However, TE models generally lack transparency and interpretability, as humans have difficulty understanding their decision logic. This paper presents a novel approach to convert a TE trained for a binary classification task, to a rule list (RL) that is a global equivalent to the TE and is comprehensible for a human. This RL captures all necessary and sufficient conditions for decision making by the TE. Experiments on benchmark datasets demonstrate that, compared to state-of-the-art methods, (i) predictions from the RL generated by TE2Rules have high fidelity with respect to the original TE, (ii) the RL from TE2Rules has high interpretability measured by the number and the length of the decision rules, (iii) the run-time of TE2Rules algorithm can be reduced significantly at the cost of a slightly lower fidelity, and (iv) the RL is a fast alternative to the state-of-the-art rule-based instance-level outcome explanation techniques.

Published
2022

50. Methane Emission Studies and Estimate from Indian Paddy Fields

Author

Parashar, D. C., Mitra, A. P., Gupta, Prabhat K., Rai, J., Sharma, R. C., Singh, N., Kaul, S., Ray, H. S., Das, S. N., Parida, K. M., Rao, S. B., Kanungo, S. P., Ramasami, T., Nair, B. U., Swami, M., Singh, G., Gupta, S. K., Singh, A. R., Saikia, B. K., Barua, A. K. S., Pathak, M. G., Iyer, C. S. P., Gopalakrishnan, M., Sane, P. V., Singh, S. N., Banerjee, R., Sethunathan, N., Adhya, T. K., Rao, V. R., Palit, P., Saha, A. K., Purkait, N. N., Chaturvedi, G. S., Sen, S. P., Sen, M., Sarkar, B., Banik, A., Subharaya, B. H., Lal, S., Venkatramani, S., Lal, G., Chaudhary, A., Sinha, S. K., van Ham, J., editor, Janssen, L. J. H. M., editor, and Swart, R. J., editor

Published
1994
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