Department of Computer Science & Engineering

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The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

School of Artificial Intelligence and Data Science (AIDE)

A new family of attitudinal discrete choice models is proposed by considering the attitudinal character and the weight vector, both of which are specific to a decision maker (DM). Given the attribute values of different alternatives, the proposed models give varying choice probabilities, as per the DM's-specific attitudinal character and the weight vector. It is also shown that the conventional discrete choice models are the special cases of the proposed attitudinal models. The proposed choice models are also generalized through an additional parameter to add to their capabilities. An application on real data is included to demonstrate their usefulness in the real world. © 2019 Wiley Periodicals, Inc.

International Journal of Intelligent Systems

Attitudinal choice models with applications in human decision making

Over the last decade there has been growing interest in understanding the brain activity, in the absence of any task or stimulus, captured by the resting-state functional magnetic resonance imaging (rsfMRI). The resting state patterns have been observed to be exhibiting complex spatio-temporal dynamics and substantial effort has been made to characterize the dynamic functional connectivity (dFC) configurations. However, the dynamics governing the state transitions that the brain undergoes and their relationship to stationary functional connectivity still remains an open problem. One class of approaches attempts to characterize the dynamics in terms of finite number of latent brain states, however, such attempts are yet to amalgamate the underlying anatomical structural connectivity (SC) with the dynamics. Another class of methods links individual dynamic FCs with the underlying SC but does not characterize the temporal evolution of FC. Further, the latent states discovered by previous approaches could not be directly linked to the SC, thereby motivating us to discover the underlying lower-dimensional manifold that represents the temporal structure. In the proposed approach, the discovered manifold is further parameterized as a set of local density distributions, or latent transient states. We propose an innovative method that learns parameters specific to the latent states using a graph-theoretic model (temporal Multiple Kernel Learning, tMKL) that inherently links dynamics to the structure and finally predicts the grand average FC of the test subjects by leveraging a state transition Markov model. The proposed solution does not make strong assumptions about the underlying data and is generally applicable to resting or task data for learning subject-specific state transitions and for successfully characterizing SC-dFC-FC relationship through a unifying framework. Training and testing were done using the rs-fMRI data of 46 healthy participants. tMKL model performs significantly better than the existing models for predicting resting state functional connectivity based on whole-brain dynamic mean-field model (DMF), single diffusion kernel (SDK) model and multiple kernel learning (MKL) model. Further, the learned model was tested on an independent cohort of 100 young, healthy participants from the Human Connectome Project (HCP) and the results establish the generalizability of the proposed solution. More importantly, the model retains sensitivity toward subject-specific anatomy, a unique contribution towards a holistic approach for SC-FC characterization. © 2018 Elsevier Inc.

World University root node

NeuroImage

Resting state dynamics meets anatomical structure: Temporal multiple kernel learning (tMKL) model

RNA viruses are known to modulate host microRNA (miRNA) machinery for their own benefit. Japanese encephalitis virus (JEV), a neurotropic RNA virus, has been reported to manipulate several miRNAs in neurons or microglia. However, no report indicates a complete sketch of the miRNA profile of neural stem/progenitor cells (NSPCs), hence the focus of our current study. We used an miRNA array of 84 miRNAs in uninfected and JEV-infected human neuronal progenitor cells and primary neural precursor cells isolated from aborted fetuses. Severalfold downregulation of hsa-miR-9-5p, hsa-miR-22-3p, hsa-miR-124-3p, and hsa-miR-132-3p was found postinfection in both of the cell types compared to the uninfected cells. Subsequently, we screened for the target genes of these miRNAs and looked for the biological pathways that were significantly regulated by the genes. The target genes involved in two or more pathways were sorted out. Protein-protein interaction (PPI) networks of the miRNA target genes were formed based on their interaction patterns. A binary adjacency matrix for each gene network was prepared. Different modules or communities were identified in those networks by community detection algorithms. Mathematically, we identified the hub genes by analyzing their degree centrality and participation coefficient in the network. The hub genes were classified as either provincial (P<0.4) or connector (P>0.4) hubs. We validated the expression of hub genes in both cell line and primary cells through qRT-PCR after JEV infection and respective miR mimic transfection. Taken together, our findings highlight the importance of specific target gene networks of miRNAs affected by JEV infection in NSPCs. © 2019 Mukherjee et al.

mSphere

Identification and classification of hubs in microRNA target gene networks in human neural stem/progenitor cells following Japanese encephalitis virus infection

Essays in the Application of Machine Learning in Development Economics

Background: The Quantitative structure activity relationship for thirty two novel substituted quinoxalines was performed for their antitubercular (Mycobacterium tuberculosis H37Rv) and antileptospiral (Leptospirainterrogans) activities. The quinoxalines were substituted with azetidinones, thiazolidinones and fluoroquinolones. Several compounds exhibited good activity against both the infections and they all possess fluoroquinolone moiety with the quinoxaline. Methods: The models developed showed good linear relationship (r2 = 0.71-0.88), with an internal predictive ability (q2&gt; 0.61) and good external predictive ability (pred_r2&gt;0.71). The compounds were separated into a training set on which regression was performed and a test set on which the predictive ability of the model was tested. Other statistical parameters including Ro2, Ro’2, k, k’ and Z-score were in the acceptable range. Results and Conclusion: The descriptors obtained explained the necessity of spatial orientation of atoms including branching and adjacency, presence of electronegative groups, balance between lipophilic elements and their binding strengths. © 2019 Bentham Science Publishers.

Current Computer-Aided Drug Design

2D QSAR analysis of substituted quinoxalines for their antitubercular and antileptospiral activities

Attitudinal Choquet integral (ACI) extends Choquet integral (CI) through a consideration of a decision-maker's (DM's) attitudinal character. In this paper, we generalize ACI, and the resulting operator is termed as generalized ACI (GACI). GACI adds to the efficacy of the ACI in the representation of a DM's unique and complex attitudinal character. It also generates a vast range of exponential ACI operators, such as harmonic ACI, ACI, quadratic ACI, to name a few. We further present induced GACI to consider additional information that may be associated with the arguments of aggregation. The special cases of the proposed operators are investigated. The usefulness of the proposed operators in modelling human decision behavior is shown through a case study. © 2018 Wiley Periodicals, Inc.

Generalized attitudinal Choquet integral

In this chapter, the information set theory is extended with a moderator’s information about the elements of the information set. The role of moderator is to provide a second opinion about the validity of the information values comprising the information set. The properties of the proposed moderator information sets (MIS) are also investigated. Many illustrative examples are included to show the usefulness of MIS in the real world decision-making. © 2019, Springer Nature Switzerland AG.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Moderated Information Sets

We introduce a novel entropy framework for the computation of utility on the basis of an agent’s subjective evaluation of the granularised information source values. A concept of evaluating agent as an information gain function of this entropy framework is presented, which takes as its arguments both an information source value and the agent’s evaluation of the same. A method to model the agent’s perceived utility values is proposed. Based on these values, several new measures are designed for the evaluation of the information source values, perceived utilities, and the evaluating agent. A real application is included. © 2018, © 2018 Operational Research Society.

Journal of the Operational Research Society

Modelling subjective utility through entropy

Freezing of gait (FOG) is a mysterious clinical phenomenon seen in Parkinson’s disease (PD) patients, a neurodegenerative disorder of the basal ganglia (BG), where there is cessation of locomotion under specific contexts. These contexts could include motor initiation, i.e., when starting movement, passing through narrow passages and corridors, while making a turn and as they are about to reach a destination. We have developed computational models of the BG which explains the freezing behavior seen in PD. The model uses reinforcement learning framework, incorporating Actor–Critic architecture, to aid learning of a virtual subject to navigate through these specific contexts. The model captures the velocity changes (slowing down) seen in PD freezers upon encountering a doorway, turns, and under the influence of cognitive load compared to PD non-freezers and healthy controls. The model throws interesting predictions about the pathology of freezing suggesting that dopamine, a key neurochemical deficient in PD, might not be the only reason for the occurrences of such freeze episodes. Other neuromodulators which are involved in action exploration and risk sensitivity influence these motor arrests. Finally, we have incorporated a network model of the BG to understand the network level parameters which influence contextual motor freezing. © Springer Nature Singapore Pte Ltd. 2018.

Cognitive Science and Technology

A basal ganglia model of freezing of gait in parkinson’s disease

Objective: Effective connectivity (EC) is the methodology for determining functional-integration among the functionally active segregated regions of the brain. By definition [1] EC is 'the causal influence exerted by one neuronal group on another' which is constrained by anatomical connectivity (AC) (axonal connections). AC is necessary for EC but does not fully determine it, because synaptic communication occurs dynamically in a context-dependent fashion. Although there is a vast emerging evidence of structure-function relationship using multimodal imaging studies, till date only a few studies have done joint modeling of the two modalities: functional MRI (fMRI) and diffusion tensor imaging (DTI). We aim to propose a unified probabilistic framework that combines information from both sources to learn EC using dynamic Bayesian networks (DBNs). Method: DBNs are probabilistic graphical temporal models that learn EC in an exploratory fashion. Specifically, we propose a novel anatomically informed (AI) score that evaluates fitness of a given connectivity structure to both DTI and fMRI data simultaneously. The AI score is employed in structure learning of DBN given the data. Results: Experiments with synthetic-data demonstrate the face validity of structure learning with our AI score over anatomically uninformed counterpart. Moreover, real-data results are cross-validated by performing classification-experiments. Conclusion: EC inferred on real fMRI-DTI datasets is found to be consistent with previous literature and show promising results in light of the AC present as compared to other classically used techniques such as Granger-causality. Significance: Multimodal analyses provide a more reliable basis for differentiating brain under abnormal/diseased conditions than the single modality analysis. © 1964-2012 IEEE.