Showing papers by "Indian Institute of Technology Guwahati published in 2020"

The Natural History, Pathobiology, and Clinical Manifestations of SARS-CoV-2 Infections.

Abstract: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the etiological agent of coronavirus disease 2019 (COVID-19). SARS-CoV-2, is a positive-sense single-stranded RNA virus with epithelial cell and respiratory system proclivity. Like its predecessor, SARS-CoV, COVID-19 can lead to life-threatening disease. Due to wide geographic impact affecting an extremely high proportion of the world population it was defined by the World Health Organization as a global public health pandemic. The infection is known to readily spread from person-to-person. This occurs through liquid droplets by cough, sneeze, hand-to-mouth-to-eye contact and through contaminated hard surfaces. Close human proximity accelerates SARS-CoV-2 spread. COVID-19 is a systemic disease that can move beyond the lungs by blood-based dissemination to affect multiple organs. These organs include the kidney, liver, muscles, nervous system, and spleen. The primary cause of SARS-CoV-2 mortality is acute respiratory distress syndrome initiated by epithelial infection and alveolar macrophage activation in the lungs. The early cell-based portal for viral entry is through the angiotensin-converting enzyme 2 receptor. Viral origins are zoonotic with genomic linkages to the bat coronaviruses but without an identifiable intermediate animal reservoir. There are currently few therapeutic options, and while many are being tested, although none are effective in curtailing the death rates. There is no available vaccine yet. Intense global efforts have targeted research into a better understanding of the epidemiology, molecular biology, pharmacology, and pathobiology of SARS-CoV-2. These fields of study will provide the insights directed to curtailing this disease outbreak with intense international impact. Graphical Abstract.

Measurement of R (D) and R (D?) with a Semileptonic Tagging Method

Abstract: The experimental results on the ratios of branching fractions $\mathcal{R}(D) = {\cal B}(\bar{B} \to D \tau^- \bar{ u}_{\tau})/{\cal B}(\bar{B} \to D \ell^- \bar{ u}_{\ell})$ and $\mathcal{R}(D^*) = {\cal B}(\bar{B} \to D^* \tau^- \bar{ u}_{\tau})/{\cal B}(\bar{B} \to D^* \ell^- \bar{ u}_{\ell})$, where $\ell$ denotes an electron or a muon, show a long-standing discrepancy with the Standard Model predictions, and might hint to a violation of lepton flavor universality. We report a new simultaneous measurement of $\mathcal{R}(D)$ and $\mathcal{R}(D^*)$, based on a data sample containing $772 \times 10^6$ $B\bar{B}$ events recorded at the $\Upsilon(4S)$ resonance with the Belle detector at the KEKB $e^+ e^-$ collider. In this analysis the tag-side $B$ meson is reconstructed in a semileptonic decay mode and the signal-side $\tau$ is reconstructed in a purely leptonic decay. The measured values are $\mathcal{R}(D)= 0.307 \pm 0.037 \pm 0.016$ and $\mathcal{R}(D^*) = 0.283 \pm 0.018 \pm 0.014$, where the first uncertainties are statistical and the second are systematic. These results are in agreement with the Standard Model predictions within $0.2$, $1.1$ and $0.8$ standard deviations for $\mathcal{R}(D)$, $\mathcal{R}(D^*)$ and their combination, respectively. This work constitutes the most precise measurements of $\mathcal{R}(D)$ and $\mathcal{R}(D^*)$ performed to date as well as the first result for $\mathcal{R}(D)$ based on a semileptonic tagging method.

A review of deep learning with special emphasis on architectures, applications and recent trends

Abstract: Deep learning (DL) has solved a problem that a few years ago was thought to be intractable — the automatic recognition of patterns in spatial and temporal data with an accuracy superior to that of humans. It has solved problems beyond the realm of traditional, hand-crafted machine learning algorithms and captured the imagination of practitioners who are inundated with all types of data. As public awareness of the efficacy of DL increases so does the desire to make use of it. But even for highly trained professionals it can be daunting to approach the rapidly increasing body of knowledge in the field. Where does one start? How does one determine if a particular DL model is applicable to their problem? How does one train and deploy them? With these questions in mind, we present an overview of some of the key DL architectures. We also discuss some new automatic architecture optimization protocols that use multi-agent approaches. Further, since guaranteeing system uptime is critical to many applications, a section dwells on using DL for fault detection and mitigation. This is followed by an exploratory survey of several areas where DL emerged as a game-changer: fraud detection in financial applications, financial time-series forecasting, predictive and prescriptive analytics, medical image processing, power systems research and recommender systems. The thrust of this review is to outline emerging applications of DL and provide a reference to researchers seeking to use DL in their work for pattern recognition with unparalleled learning capacity and the ability to scale with data.

Volume I. Introduction to DUNE

Abstract: The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay—these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. The Deep Underground Neutrino Experiment (DUNE) is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. This TDR is intended to justify the technical choices for the far detector that flow down from the high-level physics goals through requirements at all levels of the Project. Volume I contains an executive summary that introduces the DUNE science program, the far detector and the strategy for its modular designs, and the organization and management of the Project. The remainder of Volume I provides more detail on the science program that drives the choice of detector technologies and on the technologies themselves. It also introduces the designs for the DUNE near detector and the DUNE computing model, for which DUNE is planning design reports. Volume II of this TDR describes DUNE's physics program in detail. Volume III describes the technical coordination required for the far detector design, construction, installation, and integration, and its organizational structure. Volume IV describes the single-phase far detector technology. A planned Volume V will describe the dual-phase technology.

ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations

Abstract: Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification. There has been some recent progress in defining the notion of pooling in graphs whereby the model tries to generate a graph level representation by downsampling and summarizing the information present in the nodes. Existing pooling methods either fail to effectively capture the graph substructure or do not easily scale to large graphs. In this work, we propose ASAP (Adaptive Structure Aware Pooling), a sparse and differentiable pooling method that addresses the limitations of previous graph pooling architectures. ASAP utilizes a novel self-attention network along with a modified GNN formulation to capture the importance of each node in a given graph. It also learns a sparse soft cluster assignment for nodes at each layer to effectively pool the subgraphs to form the pooled graph. Through extensive experiments on multiple datasets and theoretical analysis, we motivate our choice of the components used in ASAP. Our experimental results show that combining existing GNN architectures with ASAP leads to state-of-the-art results on multiple graph classification benchmarks. ASAP has an average improvement of 4%, compared to current sparse hierarchical state-of-the-art method. We make the source code of ASAP available to encourage reproducible research 1.

Engineered nanomaterials and their surface functionalization for the removal of heavy metals: A review

Abstract: Water is polluted by various harmful toxic chemical substances, precisely heavy metals, dyes, and pathogens, which have a disastrous effect on the ecological balance of human as well as animal life. Therefore, there is an urgent need for new technologies to remove these noxious pollutants from water/wastewater. Adsorption is one of the predominant method among all the reported techniques for the removal of heavy metals. Nano technological applications in this direction, i.e. development of nano-sized materials, tubes and composites as adsorbents have engrossed rapidly. In this review, current trends of nanomaterials for the removal of heavy metals from water/wastewater are concisely discussed. Applications of different engineered nanomaterials based on iron oxide, titanium oxide, silica, carbon, graphene oxide, and bio-nanomaterials are largely focused. Different strategies and surface modifiers have been used for the surface modifications/functionalization of these nanomaterials, which in turn enhanced the adsorption capacities by many folds. Toxic effects of various nanoparticles are also discussed, and various strategies like embedding, encapsulation to prevent their releases into the environment are deliberated. In addition, the selection of NPs for the removal of various metals has also been discussed in the respective sections. This review will provide an insight into the latest researches, which is expected to offer worthy implications to academicians and industry professionals working in environmental engineering domain for the removal of heavy metals from water/wastewater.

Biosensor nanoengineering: Design, operation, and implementation for biomolecular analysis

Abstract: Robust, reliable, and quantitative detection of biomarkers at ultra-low concentration is of great importance in clinical settings. Biosensor, an analytical device used for sensitive and selective detection of biomarkers offers various advantages over the conventional clinical diagnosis, which is both time consuming and not suitable for point of care/onsite diagnosis. A revolution in the understanding and synthesis of nanomaterials in the last couple of decades contributed significantly to the development of the biosensors in terms of sensitivity, catalytic activity, biocompatibility, and robustness. Additionally, nanomaterials help in miniaturization of the sensing platform and helping in the commercial success of portable biosensor kits. Surface engineering equally contributed to the biosensor development by ensuring a reproducible and stable sensing surface, efficient analyte-biorecognition element interaction, and reduced fouling effect in biological solution. Due to nanomaterial integration and surface engineering, biosensors are now equally sensitive to the lab-based sophisticated instruments to detect a wide range of molecules of clinical significance. In this review, various types of biosensors, their designs, and their working principles have been discussed. A detailed account of various types of nanomaterials, their functionalization and characterization have also been discussed. The analytical performances of biosensors for both clinical validation and analyte detection have been incorporated here. The recent trends in advanced biosensors, such as smartphone interface for biosensing, nanozymes, lab-on-a-chip based detection methods have been discussed.

Erratum: The Belle II Physics Book (Progress of Theoretical and Experimental Physics (2019) 2019 (123C01) DOI: 10.1093/ptep/ptz106)

Abstract: In the original version of this manuscript, an error was introduced on pp352. '2.7nb:1.6nb' has been corrected to '2.4nb:1.3nb' in the current online and printed version. doi:10.1093/ptep/ptz106.

Rotate to Attend: Convolutional Triplet Attention Module

Abstract: Benefiting from the capability of building inter-dependencies among channels or spatial locations, attention mechanisms have been extensively studied and broadly used in a variety of computer vision tasks recently. In this paper, we investigate light-weight but effective attention mechanisms and present triplet attention, a novel method for computing attention weights by capturing cross-dimension interaction using a three-branch structure. For an input tensor, triplet attention builds inter-dimensional dependencies by the rotation operation followed by residual transformations and encodes inter-channel and spatial information with negligible computational overhead. Our method is simple as well as efficient and can be easily plugged into classic backbone networks as an add-on module. We demonstrate the effectiveness of our method on various challenging tasks including image classification on ImageNet-1k and object detection on MSCOCO and PASCAL VOC datasets. Furthermore, we provide extensive in-sight into the performance of triplet attention by visually inspecting the GradCAM and GradCAM++ results. The empirical evaluation of our method supports our intuition on the importance of capturing dependencies across dimensions when computing attention weights. Code for this paper can be publicly accessed at this https URL

Irradiation induced modification of structural and optical properties of potassium sodium niobate thin films

Abstract: In the present study, the effects of swift heavy ion induced modification on the structural, morphological and optical properties of potassium sodium niobate (KNN) thin films have been investigated. KNN thin films were deposited using RF magnetron sputtering onto Si and quartz substrates. Subsequently, as-deposited films were annealed at 700 °C in air ambience for crystallization. Eventually, these crystalline films were irradiated using 100 MeV Ag ions at various fluences ranging from 1 × 1012 to 1 × 1013 ions/cm2. The crystalline and irradiated films were characterized using various techniques such as X-ray diffraction (XRD), atomic force microscopy (AFM), Raman spectroscopy, and UV–Vis spectroscopy. XRD results reveal that the crystallinity of films decreases drastically upon irradiation and almost disappeared at 1 × 1013 ions/cm2. Raman spectra show the different vibration modes of NbO6 octahedra. Raman peaks intensity is decreased and the peaks get broadened due to irradiation which indicates the amorphous nature of films. Variation in surface morphology and roughness of films before and after irradiation is studied using AFM. The minimum value of roughness is observed at 5 × 1012 ions/cm2. Ion beam irradiation results in the variation of transmittance and optical band gap of the films. The optical band gap of crystalline KNN film is found to be 3.82 eV which decreased to 3.72 eV upon irradiation at 5 × 1012 ions/cm2. The monotonous decrease in the refractive index and packing density of films is also observed with ion fluence.

Multivariate optimisation of Cr (VI), Co (III) and Cu (II) adsorption onto nanobentonite incorporated nanocellulose/chitosan aerogel using response surface methodology

Abstract: In this study, tailor made Nanobentonite incorporated Nanocellulose/Chitosan aerogel (NCNB) has been used to study its metal adsorption capacity from simulated wastewater. The aerogel was characterised with FESEM, AFM, EDX, XRD, FTIR, Raman spectroscopy and found the successful conversion to the desired adsorbent in nano dimension. This NCNB was used for optimisation of Chromium, Cobalt and Copper adsorption. Response Surface Methodology (RSM) was implemented to derive the binary correlation between various experimental parameters such as initial metal concentration, pH, adsorbent mass and temperature using Central Composite Design (CCD). The regression coefficients were evaluated which validated second order polynomial equation for the remediation of Cr, Co and Cu with NCNB. The maximum adsorption efficiencies under the optimum condition of the process parameters for Cr, Co and Cu removal were found to be 98.90, 97.45 and 99.01 % respectively. Co efficient of determination, R2 for Cr, Co and Cu were 0.9796, 0.8973 and 0.9321 respectively. Halsey isotherm and pseudo-second-order kinetic model agreed well with the experimental data for all the three metals. Thermodynamics study showed the metal adsorption was spontaneous, endothermic and feasible in nature. These data imperatively show that use of this aerogel can be a good alternative to the expensive methods of heavy metal remediation.

Targeting AKT/mTOR in Oral Cancer: Mechanisms and Advances in Clinical Trials.

Abstract: Oral cancer (OC) is a devastating disease that takes the lives of lots of people globally every year. The current spectrum of treatment modalities does not meet the needs of the patients. The disease heterogeneity demands personalized medicine or targeted therapies. Therefore, there is an urgent need to identify potential targets for the treatment of OC. Abundant evidence has suggested that the components of the protein kinase B (AKT)/ mammalian target of rapamycin (mTOR) pathway are intrinsic factors for carcinogenesis. The AKT protein is central to the proliferation and survival of normal and cancer cells, and its downstream protein, mTOR, also plays an indispensable role in the cellular processes. The wide involvement of the AKT/mTOR pathway has been noted in oral squamous cell carcinoma (OSCC). This axis significantly regulates the various hallmarks of cancer, like proliferation, survival, angiogenesis, invasion, metastasis, autophagy, and epithelial-to-mesenchymal transition (EMT). Activated AKT/mTOR signaling is also associated with circadian signaling, chemoresistance and radio-resistance in OC cells. Several miRNAs, circRNAs and lncRNAs also modulate this pathway. The association of this axis with the process of tumorigenesis has culminated in the identification of its specific inhibitors for the prevention and treatment of OC. In this review, we discussed the significance of AKT/mTOR signaling in OC and its potential as a therapeutic target for the management of OC. This article also provided an update on several AKT/mTOR inhibitors that emerged as promising candidates for therapeutic interventions against OC/head and neck cancer (HNC) in clinical studies.

Volume IV. The DUNE far detector single-phase technology

Abstract: The preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay—these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. DUNE is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. Central to achieving DUNE's physics program is a far detector that combines the many tens-of-kiloton fiducial mass necessary for rare event searches with sub-centimeter spatial resolution in its ability to image those events, allowing identification of the physics signatures among the numerous backgrounds. In the single-phase liquid argon time-projection chamber (LArTPC) technology, ionization charges drift horizontally in the liquid argon under the influence of an electric field towards a vertical anode, where they are read out with fine granularity. A photon detection system supplements the TPC, directly enhancing physics capabilities for all three DUNE physics drivers and opening up prospects for further physics explorations. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. Volume IV presents an overview of the basic operating principles of a single-phase LArTPC, followed by a description of the DUNE implementation. Each of the subsystems is described in detail, connecting the high-level design requirements and decisions to the overriding physics goals of DUNE.

Investigation of the properties of alkali-activated slag mixes involving the use of nanoclay and nucleation seeds for 3D printing

Abstract: This study investigated the properties of alkali activated slag (AAS) binders formulated for extrusion‐based 3D printing. The fresh properties of AAS mixes were tailored through the use of nanoclay (NC) and nucleation seeds. The printability criteria employed were the ease of extrusion (extrudability) and the stability of the layered structure (buildability). Introduction of 0.4% NC in AAS mixes led to improved thixotropic properties due to the flocculation effect, which accounted for the extrudability and shape fidelity of the binder. Inclusion of 2% hydromagnesite seeds in this mix design provided additional nucleation sites for the increased precipitation of hydrate phases, resulting in denser microstructures. This enhanced the hydration reaction and improved the structural build-up rate necessary for large-scale 3D printing. The developed AAS mix containing 0.4% NC and 2% hydromagnesite seeds was used in the printing of an actual 3D structure to demonstrate its feasibility to be used in 3D printing applications.

Algae based microbial fuel cells for wastewater treatment and recovery of value-added products

Abstract: Microalgae based microbial fuel cells are efficient systems to remove nitrogen, phosphorous and CO2 from wastewater, to produce bioelectricity and value-added products from microalgal biomass. Microalgae can be used in MFCs as algae assisted cathode systems, microbial carbon capture cells or sediment microbial fuel cells as well as photosynthetic microalgae microbial fuel cell. These MFCs are shown efficient for CO2 capture with a low risk of carbon emission, N and P removal via symbiotic interactions of microalgae-bacteria consortia in wastewater treatment along with power generation. The oxygen production by microalgae during the light period reduces the need for external oxygen supply for cathodic reactions, which is advantageous for reducing the aeration cost, as otherwise power needs to be supplied for mechanical aeration. Utilization of algal biomass harvested from the cathodic compartment requires a pretreatment in a biorefinery concept. This still remains a major drawback, but current advances towards the choice of a biofilm on the cathode allow for further recovery of value-added products from algal biomass. Alternatively, the algal biomass can be utilized as the sole feedstock in the anodic compartment. This paper reviews the application of algae based microbial fuel cells for bioelectricity production, mainly focusing on the use of algae in the cathodic compartment, microalgae in the anodic compartment and the main interactions between the compartments affecting the bioelectricity production.

Oral cancer diagnosis and perspectives in India.

Abstract: Globally, oral cancer is the sixth most common type of cancer with India contributing to almost one-third of the total burden and the second country having the highest number of oral cancer cases. Oral squamous cell carcinoma (OSCC) dominates all the oral cancer cases with potentially malignant disorders, which is also recognized as a detectable pre-clinical phase of oral cancer. Tobacco consumption including smokeless tobacco, betel-quid chewing, excessive alcohol consumption, unhygienic oral condition, and sustained viral infections that include the human papillomavirus are some of the risk aspects for the incidence of oral cancer. Lack of knowledge, variations in exposure to the environment, and behavioral risk factors indicate a wide variation in the global incidence and increases the mortality rate. This review describes various risk factors related to the occurrence of oral cancer, the statistics of the distribution of oral cancer in India by various virtues, and the socio-economic positions. The various conventional diagnostic techniques used routinely for detection of the oral cancer are discussed along with advanced techniques. This review also focusses on the novel techniques developed by Indian researchers that have huge potential for application in oral cancer diagnosis.