Abstract

Understanding the spatiotemporal distribution of blue-green infrastructures in urban environment is a critical element in the landscape-level planning. Asian cities, which are rapidly undergoing transformational changes in its land cover, predominantly feature urban signatures at the expenses of natural ecosystems. The rapid urbanization in conjunction with mega developmental projects and industrialization requires sophisticated analytical tools in the synoptic assessments and impact measurements. Remote sensing together with geographical information system (GIS) and Global Positioning Systems (GPS) plays an important role in developing the spatial maps and assessing the dynamics of land cover in the urban environment. This chapter reviews various remote sensing instruments available to characterize vegetation and water in complex urban environments. The progress with respect to diverse instruments such as panchromatic, multispectral, hyperspectral, radar, and LiDAR in mapping the blue-green infrastructures is reviewed in this chapter. The chapter helps in understanding the diverse initiatives and instruments in mapping the extent and quality of blue-green infrastructures which are vital for the spatial planning of urban landscapes.

Abstract

Understanding the nexus between land use land cover (LULC) and land surface temperature (LST) of a rapidly growing city may help planners mitigate the effects of uncontrolled urbanization on the micro- and macro-environment. The primary focus of the study is to monitor the transient LULC of Surat, one of the rapidly growing cities in India. To comprehend the urban dynamics, the study analyses the tri-decadal LULC of Surat using temporal Landsat imagery corresponding to 1990, 2001, 2009, and 2020. Besides classification of satellite data to derive LULC using the maximum likelihood algorithm, emphasis has been given to evaluate the normalized difference vegetation index and normalized difference built-up index, which help in differentiating vegetation and built-up from other land-use types. In addition, the LST of Surat is computed, and zonal analysis is performed to examine its association with LULC. Results show that the built-up area of Surat increased by 3.22 times during the considered time, while the aerial extent of vegetation decreased by 1.58 times. Future land-use dynamics are predicted using the Markov model. Findings revealed that the built-up area is expected to increase by 20% between 2020 and 2030, while the vegetation area is likely to decrease by 13%. The developed model attained an accuracy of 52.08%, which is in agreement with the past studies. The findings of this study help urban planners and stakeholders to devise effective policies that can mitigate the

Abstract

The last two decades have witnessed unprecedented advancements in computational techniques and artificial intelligence. These new developments will greatly impact biological data analyses for the health care system. The availability of large scale, high-throughput, biomedical data sets offers fertile ground for the application of AI-based techniques to extract valuable information that can be harnessed in the diagnosis and treatment of disease. This chapter provides a comprehensive review of the computational tools and online resources for the high-throughput analysis of biomedical data. It focuses on single-cell RNA sequencing data, multi-omics data integration, AI drug design, medical imaging data analysis, and the IoT. After providing a brief overview of fundamental biological terms, we describe a variety of research problems in the health care system and how high-throughput data can help. Next, we provide an in depth overview of machine learning techniques in computing and learning methods that can be used in sequencing data analyses (including profiles of traditional mass sequencing, single-cell data, interaction data, line-cell drugs, and medical imaging data). Finally, applications of IoT methods are discussed for biomedical research in detail. In conclusion, the review delineates the overall scope of AI-based computational techniques in biomedical research.

Abstract

A total of 2835 overseas patients from 55 countries received cancer treatment in two hospitals in Delhi, India between November 2013 and April 2019. Of these, 937 patients participated in a 30-point questionnaire on patient reported outcomes. The questionnaire included clinical information, difficulties, expenditure, financial toxicity (FT), education, profession/earning, family/emotional issues, and the reason for the choice of Delhi.

Abstract

Nucleocytoplasmic shuttling of viral elements, supported by several host factors, is essential for the replication of the human immunodeficiency virus (HIV). HIV-1 uses a nuclear RNA export pathway mediated by viral protein Rev to transport its Rev response element (RRE)-containing partially spliced and unspliced transcripts aided by the host nuclear RNA export protein CRM1. The factor(s) interacting with the CRM1-Rev complex are potential antiretroviral target(s) and could serve as a retroviral model system to study nuclear export machinery adapted by these viruses. We earlier reported that cellular Staufen-2 interacts with Rev, facilitating viral-RNA export. Here, we identified the formation of a complex between Staufen-2, CRM1 and Rev. Molecular docking and simulations mapped the interacting residues in the RNA-binding Domain 4 of Staufen-2 as R336 and R337, which were experimentally verified to be critical for interactions among Staufen-2, CRM1 and Rev by mutational analysis. Staufen-2 mutants defective in interaction with CRM1 or Rev failed to supplement the Rev-RNA export activity and viral production, demonstrating the importance of these interactions. Rev-dependent reporter assays and proviral DNA-construct transfection-based studies in Staufen-2 knockout cells in the presence of leptomycin-B (LMB) revealed a significant reduction in CRM1-mediated Rev-dependent RNA export with decreased virus production as compared to Staufen-2 knockout background or LMB treatment alone, suggesting the relevance of these interactions in augmenting RNA export activity of Rev. Our observations provide further insights into the mechanistic intricacies of unspliced viral-RNA export to the cytoplasm and support the notion that abrogating such interactions can reduce HIV-1 proliferation.

Abstract

The present report compares the antibacterial properties of unique (sphere+rod)-shaped gold nanostar with its structural components, i.e., gold nanorods and nanosphere. Gold nanostars, nanorods, and nanospheres have been synthesized using literature reported protocols. Clinical isolates of gram-negative E. coli and gram-positive S. aureus bacteria have been used as model pathogens for the antibacterial studies. The antibacterial activities of the nanoparticles have been assessed using various techniques. To compare the antimicrobial activity of the nanoparticles, we have investigated the effects of varying gold concentrations of different nanoparticles on bacterial growth kinetics. The impacts of the leached/residual gold ions and the surface capping agent and charge of the particles on the antibacterial activity have also been assessed. Studies of the nanoparticle’s dose and exposure time show the highest antibacterial activity for nanostars and lowest for nanospheres for a given quantity of gold. Bradford’s Reagent Assay and confocal fluorescent microscopy studies confirm the trend of the particle-specific antibacterial activities. SEM/TEM and DNA extraction studies show that the bactericidal activities involve a combination of effects like the destruction of cell membrane, fragmentation of bacterial DNA, internalization of particles, etc. To assess the antibacterial impacts of the amount of leached/residual gold ions, their concentrations have been determined by inductively coupled plasma - optical emission (ICP-OES) and mass (ICP-MS) spectroscopic studies as well as the effects of the added gold ions have been studied. No discernible effects have been observed due to the variation in the leached/residual gold ions amount. The DCHF-DA fluorescence assay has been employed to determine the levels of reactive oxygen species (ROS) produced by different nanoparticle systems, which corresponds to the trend in antibacterial activities observed. The nature and magnitude of the surface charge have been determined by zeta potential measurements. Analyses of the impacts of various parameters/factors like gold ions, capping agents, surface charges, ROS levels, etc., show that the antibacterial activities of the gold nanoparticles of different morphology arise from both morphology-specific physical and chemical effects of the gold nanoparticles.

Abstract

Parkinson’s disease (PD) is a speech disorder caused by the effect of neurological damage on motor control activity of speech production. Due to the noninvasive nature, detection of PD from the speech is remarkable. This study proposes the use of Long-Term Average Spectral (LTAS) feature to detect PD from speech using different filterbank techniques and classifiers on the PC-GITA dataset. The fact that PD can be perceived from long-temporal information has motivated us to explore the LTAS features for PD detection. Performance of LTAS features is compared to baseline features, namely statistical and openSMILE features (ComParE, GeMAPS, and eGeMAPS). The performance of these features is validated using five different classifiers. The LTAS features, which are obtained with auditory filterbanks (Constant Q Transform and Gammatone), showed better performance as compared to the statistical features (MFCC_Stat and LPCC_Stat) and openSMILE features (ComParE, GeMAPS, and eGeMAPS). The combination of the Gammatone-based LTAS feature with eGeMAPS further improved the detection accuracy of the PD detection system on the PC-GITA database.

Abstract

Coherence is an important aspect of text quality and is crucial for ensuring its readability. It is essential for outputs from text generation systems like summarization, question answering, machine translation, question generation, table-to-text, etc. An automated coherence scoring model is also helpful in essay scoring or providing writing feedback. A large body of previous work has leveraged entity-based methods, syntactic patterns, discourse relations, and traditional deep learning architectures for text coherence assessment. However, these approaches do not consider factual information present in the documents. The transitions of facts associated with entities across sentences could help capture the essence of textual coherence better. We hypothesize that coherence assessment is a cognitively complex task that requires deeper fact-aware models and can benefit from other related tasks. In this work, we propose a novel deep learning model that fuses document-level information with factual information to improve coherence modeling. We further enhance the model efficacy by training it simultaneously with Natural Language Inference task in multi-task learning setting, taking advantage of inductive transfer between the two tasks. Our experiments with popular benchmark datasets across multiple domains demonstrate that the proposed model achieves state-of-the-art results on a synthetic coherence evaluation task and two real-world tasks involving prediction of varying degrees of coherence.

Abstract

Body shaming, a criticism based on the body’s shape, size, or appearance, has become a dangerous act on social media. With a rise in the reporting of body shaming experiences on the web, automated monitoring of body shaming posts will help rescue individuals, especially adolescents, from the emotional anguish they experience. To the best of our knowledge, this is the first work on body shaming detection, and we contribute the dataset in which the posts are tagged as body shaming or non-body shaming. We use transformer-based language models to detect body shaming posts. Further, we leverage unlabeled data in a semi-supervised manner using the GAN-BERT model, as it was developed for tasks where labeled data is scarce and unlabeled data is abundant. The findings of the experiments reveal that the algorithm learns valuable knowledge from the unlabeled dataset and outperforms many deep learning and conventional machine learning baselines.

Abstract

Pattern mining from graph transactional data (GTD) is an active area of research with applications in the domains of bioinformatics, chemical informatics and social networks. Existing works address the problem of mining frequent subgraphs from GTD. However, the knowledge concerning the coverage aspect of a set of subgraphs is also valuable for improving the performance of several applications. In this regard, we introduce the notion of subgraph coverage patterns (SCPs). Given a GTD, a subgraph coverage pattern is a set of subgraphs subject to relative frequency, coverage and overlap constraints provided by the user. We propose the Subgraph ID-based Flat Transactional (SIFT) framework for the efficient extraction of SCPs from a given GTD. Our performance evaluation using three real datasets demonstrates that our proposed SIFT framework is indeed capable of efficiently extracting SCPs from GTD. Furthermore, we demonstrate the effectiveness of SIFT through a case study in computer-aided drug design.