Subhajit Dutta

Dr. B.C. Roy Engineering College | BCREC · Civil Engineering

Telomeres are crucial for cancer progression. Immune signalling in the tumour microenvironment has been shown to be very important in cancer prognosis. However, the mechanisms by which telomeres might affect tumour immune response remain poorly understood. Here, we observed that interleukin-1 signalling is telomere-length dependent in cancer cells....

Telomeres are crucial for cancer progression. Immune signalling in the tumour microenvironment has been shown to be very important in cancer prognosis. However, the mechanisms by which telomeres might affect tumour immune response remain poorly understood. Here, we observed that interleukin-1 signalling is telomere-length dependent in cancer cells....

Trans-Himalayan hot spring waters rich in boron, chlorine, sodium and sulfur (but poor in calcium and silicon) are known based on PCR-amplified 16S rRNA gene sequence data to harbor high diversities of infiltrating bacterial mesophiles. Yet, little is known about the community structure and functions, primary productivity, mutual interactions, and...

Sediments underlying marine hypoxic zones are huge sinks of unreacted complex organic matter, where despite acute O2 limitation, obligately aerobic bacteria thrive, and steady depletion of organic carbon takes place within a few meters below the seafloor. However, little knowledge exists about the sustenance and complex carbon degradation potential...

Telomeres are crucial for cancer progression. Immune signalling in the tumour microenvironment has been shown to be very important in cancer prognosis. However, the mechanisms by which telomeres might affect tumour immune response remain poorly understood. Here, we observed that interleukin-1 signalling is telomere-length dependent in cancer cells....

Telomeres are crucial for cancer progression. Immune signalling in the tumour microenvironment has been shown to be very important in cancer prognosis. However, the mechanisms by which telomeres might affect tumour immune response remain poorly understood. Here, we observed that interleukin-1 signalling is telomere-length dependent in cancer cells....

Sediments underlying marine hypoxias are huge sinks of unreacted complex organic matter, where despite acute O2-limitation aerobic bacterial communities thrive, and near-complete depletion of organic carbon takes place within a few meters below the seafloor. However, little knowledge exists about how aerobic chemoorganotrophs survive in these sulfi...

The function of the human telomerase reverse transcriptase ( hTERT ) in the synthesis and maintenance of chromosome ends, or telomeres, is widely understood. Whether and how telomeres, on the other hand, influence hTERT regulation is relatively less studied. We found hTERT was transcriptionally up/downregulated depending on telomere length (TL). Th...

Classification of high-dimensional low sample size (HDLSS) data poses a challenge in a variety of real-world situations, such as gene expression studies, cancer research, and medical imaging. This article presents the development and analysis of some classifiers that are specifically designed for HDLSS data. These classifiers are free of tuning par...

Little is known about the relative abundance and metabolisms of microorganisms present in the vent-waters of Trans-Himalayan hot springs. This study revealed a Bacteria-dominated microbiome in the ∼84 ° C vent-water (microbial cell density ∼8.5 × 10 ⁴ mL ⁻¹ ; live:dead cell ratio 17:10) of a sulfur-borax spring called Lotus Pond, situated at 4436 m...

Classification of high-dimensional low sample size (HDLSS) data poses a challenge in a variety of real-world situations, such as gene expression studies, cancer research, and medical imaging. This article presents the development and analysis of some classifiers that are specifically designed for HDLSS data. These classifiers are free of tuning par...

Higher‐order kernels have been widely implemented for nonparametric function estimation, mainly due to their faster asymptotic rates of convergence and interpretability. This article constructs a novel higherorder kernel of any pre‐specified even order by using Shannon’s formula. The proposed kernels have a closed form expression and are easy to im...

For a holistic understanding of microbial life’s high-temperature adaptation, it is imperative to explore the biology of the phylogenetic relatives of mesophilic bacteria which get stochastically introduced to geographically and geologically diverse hot spring systems by local geodynamic forces. Here, in vitro endurance of high heat up to the exten...

The Mardia measures of multivariate skewness and kurtosis summarize the respective characteristics of a multivariate distribution with two numbers. However, these measures do not reflect the sub-dimensional features of the distribution. Consequently, testing procedures based on these measures may fail to detect skewness or kurtosis present in a sub...

High temperature growth/survival was revealed in a phylogenetic relative (strain SMMA_5) of the mesophilic Paracoccus isolated from the 78-85°C water of a Trans- Himalayan sulfur-borax spring. After 12 h at 50°C, or 45 minutes at 70°C, in mineral salts thiosulfate (MST) medium, SMMA_5 retained ∼2% colony-forming units (CFUs), whereas comparator Par...

In this article, we propose a new model-free feature screening method based on energy distances for ultrahigh-dimensional binary classification problems. Unlike existing methods, the cut-off involved in our procedure is data adaptive. With a high probability, the screened set retains only features after discarding all the noise variables. The propo...

Using notions of depth functions in the multivariate setting, we have constructed several new multivariate goodness of fit (GoF) tests based on existing univariate GoF tests. Since the exact computation of depth is difficult, depth is estimated based on a large random sample drawn from the null distribution. It has been shown that test statistics b...

In high dimension, low sample size (HDLSS) settings, distance concentration phenomena affects the performance of several popular classifiers which are based on Euclidean distances. The behaviour of these classifiers in high dimensions is completely governed by the first and second order moments of the underlying class distributions. Moreover, the c...

The role of telomeres in sustained tumor growth is well understood. However, mechanisms of how telomeres might impact the tumor microenvironment (TME) are not clear. Upon examining tumor associated macrophages (TAMs) in 94 hormone-negative (triple-negative) breast cancer (TNBC) cases we found infiltration of TAMs to be telomere sensitive: Tumors wi...

Mardia's measures of multivariate skewness and kurtosis summarize the respective characteristics of a multivariate distribution with two numbers. However, these measures do not reflect the sub-dimensional features of the distribution. Consequently, testing procedures based on these measures may fail to detect skewness or kurtosis present in a sub-d...

This paper establishes the algorithmic principle of alternating projections onto incoherent low-rank subspaces (APIS) as a unifying principle for designing robust regression algorithms that offer consistent model recovery even when a significant fraction of training points are corrupted by an adaptive adversary. APIS offers the first algorithm for...

Using the fact that some depth functions characterize certain family of distribution functions, and under some mild conditions, distribution of the depth is continuous, we have constructed several new multivariate goodness of fit tests based on existing univariate GoF tests. Since exact computation of depth is difficult, depth is computed with resp...

In high dimension, low sample size (HDLSS)settings, the simple average distance classifier based on the Euclidean distance performs poorly if differences between the locations get masked by the scale differences. To rectify this issue, modifications to the average distance classifier was proposed by Chan and Hall (2009). However, the existing class...

In high dimension, low sample size (HDLSS) settings, Euclidean distance based classifiers suffer from curse of dimensionality if the competing populations are similar in terms of their location and scale parameters. In this article, we propose a classifier which can discriminate between populations having different marginal distributions. A general...

We construct a d-dimensional discrete multivariate distribution for which any proper subset of its components belongs to a specific family of distributions. However, the joint d-dimensional distribution fails to belong to that family and in other words, it is ‘inconsistent’ with the distribution of these subsets. We also address preservation of thi...

A robust method for multivariate regression is developed based on robust estimators of the joint location and scatter matrix of the explanatory and response variables using the notion of data depth. The multivariate regression estimator possesses desirable affine equivariance properties, achieves the best breakdown point of any affine equivariant e...

In this article, we develop and investigate a new classifier based on features extracted using spatial depth. Our construction is based on fitting a generalized additive model to the posterior probabilities of the different competing classes. To cope with possible multi-modal as well as non-elliptic population distributions, we develop a localized...

In this article, we use L$_p$ depth for classification of multivariate data, where the value of $p$ is chosen adaptively using observations from the training sample. While many depth based classifiers are constructed assuming elliptic symmetry of the underlying distributions, our proposed L$_p$ depth classifiers cater to a larger class of distribut...

For data with more variables than the sample size, phenomena like concentration of pairwise distances, violation of cluster assumptions and presence of hubness often have adverse effects on the performance of the classic nearest neighbor classifier. To cope with such problems, some dimension reduction techniques like those based on random linear pr...

This paper explores the use of visualization through animations, coined visuanimation, in the field of statistics. In particular, it illustrates the embedding of animations in the paper itself and the storage of larger movies in the online supplemental material. We present results from statistics research projects using a variety of visuanimations,...

Several fascinating examples of non-Gaussian bivariate distributions which have marginal distribution functions to be Gaussian have been proposed in the literature. These examples often clarify several properties associated with the normal distribution. In this paper, we generalize this result in the sense that we construct a pp-dimensional distrib...

Classification of character sequences, where the characters come from a finite set, arises in disciplines such as molecular biology and computer science. For discriminant analysis of such character sequences, the Bayes classifier based on Markov models turns out to have class boundaries defined by linear functions of occurrences of words in the seq...

Mahalanobis distance and Fisher's linear discriminant analysis are fundamentally related to each other, and both the ideas have been extensively used in the classification literature. Fisher's linear and quadratic discriminant functions are known to possess Bayes risk optimality when the class distributions are Gaussian. However, they may have poor...

This article uses projection depth (PD) for robust classification of multivariate data. Here we consider two types of classifiers, namely, the maximum depth classifier and the modified depth-based classifier. The latter involves kernel density estimation, where one needs to choose the associated scale of smoothing. We consider both the single scale...

For multivariate data, Tukey’s half-space depth is one of the most popular depth functions available in the literature. It is conceptually simple and satisfies several desirable properties of depth functions. The Tukey median, the multivariate median associated with the half-space depth, is also a well-known measure of center for multivariate data...

Internet of Things (IoT) has been recognized as a part of future internet and ubiquitous computing. It creates a true ubiquitous or smart environment. It demands a complex distributed architecture with numerous diverse components, including the end devices and application and association with their context. This article provides the significance of...

In this paper, a module has been presented which can perform a seamless interoperation between Bluetooth and Wi-Fi interfaces. The work is really challenging to achieve as both the interfaces work in the frequency band of 2.4 GHz creating a high possibility of interference. The challenge of implementing such an interoperable application atop OSGi f...

This paper provides a survey of middleware system for Internet of Things (IoT). IoT is considered as a part of future internet and ubiquitous computing, and it creates a true ubiquitous or smart environment. The middleware for IoT acts as a bond joining the heterogeneous domains of applications communicating over heterogeneous interfaces. Comprehen...

In the recent past, several depth-based classifiers have been proposed in the literature for classification of multivariate data. In this article, we use L p depth for this purpose, where p is chosen adaptively using the training data. While other depth-based classifiers have the Bayes risk consistency only under elliptic symmetry, the proposed cla...