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The existing biclustering algorithms often depend on assumptions like monotonicity or linearity of feature relations for finding biclusters. Though a few algorithms overcome this problem using density-based methods, they tend to miss out many biclusters because they use global criteria for identifying dense regions. The proposed method, PF-RelDenBi, uses local variations in marginal and joint densities for each pair of features to find the subset of observations, forming the basis of the relation between them. It then finds the set of features connected by a common set of observations using a non-linear feature relation index, resulting in a bicluster. This approach allows us to find biclusters based on feature relations, even if the relations are non-linear or non-monotonous. Additionally, the proposed method does not require the user to provide any parameters, allowing its application to datasets from different domains. To study the behaviour of PF-RelDenBi on datasets with different properties, experiments were carried out on sixteen simulated datasets and the performance has been compared with eleven state-of-the-art algorithms. The proposed method is seen to produce better results for most of the simulated datasets. Experiments were conducted with five benchmark datasets and biclusters were detected using PF-RelDenBi. For the first two datasets, the detected biclusters were used to generate additional features that improved classification performance. For the other three datasets, the performance of PF-RelDenBi was compared with the eleven state-of-the-art methods in terms of accuracy, NMI and ARI. The proposed method is seen to detect biclusters with greater accuracy. The proposed technique has also been applied to the COVID-19 dataset to identify some demographic features that are likely to affect the spread of COVID-19., Competing Interests: The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Susmita Ghosh, one of the authors of the article ”A Parameter free Relative Density based Biclustering Method for identifying non-linear feature relations” is an associate editor of Heliyon. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Herein, blue-emitting carbon nanoparticles (CNPs) were synthesized using the Madhuca longifolia flower for the highly selective and sensitive detection of Cr 6+ ions in aqueous media using a simple, green, and cost-effective approach, and computational experiments were also performed. The prepared CNPs were well-dispersed in water with an average diameter of 12 nm and functionalized with carbonyl, hydroxyl and carboxylic acid groups. The decrease in the fluorescence intensity of the CNPs with an increase in the content of Cr 6+ provided an important signal for the sensitive and selective detection of Cr 6+ in aqueous media. The limit of detection for Cr 6+ in an aqueous medium was found to be 103 ppb, which is more sensitive in comparison with the previously reported study. Furthermore, the validation of the proposed higher sensing feature and more selective nature of the CNPs towards Cr 6+ was also explained using an in silico approach. The results from the theoretical calculations based on the DFT approach demonstrated that the binding energy (BE) of the CNPs with three transition metal (TM) cations (Cr 6+ , Fe 3+ , and Hg 2+ ) follows the order of Cr 6+ > Fe 3+ > Hg 2+ , where the Cr 6+ TM cation associated with the CNPs possesses the highest valence state, showing the highest sensing feature and highest selectivity among the investigated ions, as expected. The metal ions associated with the CNPs having a higher charge and a smaller radius indicated a higher BE and larger degree of deformation of the CNPs. Moreover, to achieve new insights into the structural, stability/energetics, and electronic features, some useful tools, such as NCI-plot, HOMO-LUMO gap, MESP, and QTAIM analysis were employed, which facilitated noteworthy outcomes., Competing Interests: The authors declare that they have no conflict of interest., (This journal is © The Royal Society of Chemistry.)

The COVID-19 pandemic has put a huge challenge on the Indian health infrastructure. With a larger number of people getting affected during the second wave, hospitals were overburdened, running out of supplies and oxygen. Hence, predicting new COVID-19 cases, new deaths, and total active cases multiple days in advance can aid better utilization of scarce medical resources and prudent pandemic-related decision-making. The proposed method uses gated recurrent unit networks as the main predicting model. A study is conducted by building four models pre-trained on COVID-19 data from four different countries (United States of America, Brazil, Spain, and Bangladesh) and fine-tuned on India's data. Since the four countries chosen have experienced different types of infection curves, the pre-training provides a transfer learning to the models incorporating diverse situations into account. Each of the four models then gives 7-day ahead predictions using the recursive learning method for the Indian test data. The final prediction comes from an ensemble of the predictions of the different models. This method with two countries, Spain and Bangladesh, is seen to achieve the best performance amongst all the combinations as well as compared to other traditional regression models., (© 2023. The Author(s).)

This article presents a novel ultrasound image standardization approach. This method aims to preserve the non-linear relationship in the echo-textures while ensuring the endurance in the transformed image. This is achieved by utilizing the concept of histogram specification. A reference cumulative distribution function (CDF) of a considered distribution is used to process the test images. Initially, the shape and scale parameter of the distribution are estimated for each type of echo-texture from the reference ultrasound images of a particular organ. These parameters are used to estimate the prototype parameter set. The obtained prototype parameter set, along with a distribution function, is then used to construct a reference CDF. This CDF, in turn, is used as a transfer function in the histogram specification technique for standardizing the given input image. The efficiency and stability of the proposed approach are investigated and compared with the linear scaling technique. Four measures are used to evaluate the algorithms on three data sets. The results show that the proposed approach provides better standardization of images and is invariant to the gain of the scanning device as opposed to linear scaling., Competing Interests: Declaration of Competing Interest No author associated with this paper has disclosed any potential or pertinent conflicts which may be perceived to have impending conflict with this work. For full disclosure statements refer to https://doi.org/10.1016/j.compbiomed.2020.103746., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

In this paper, a novel context-dependent fuzzy set associated statistical model-based intensity inhomogeneity correction technique for magnetic resonance image (MRI) is proposed. The observed MRI is considered to be affected by intensity inhomogeneity and it is assumed to be a multiplicative quantity. In the proposed scheme the intensity inhomogeneity correction and MRI segmentation is considered as a combined task. The maximum a posteriori probability (MAP) estimation principle is explored to solve this problem. A fuzzy set associated Gibbs' Markov random field (MRF) is considered to model the spatio-contextual information of an MRI. It is observed that the MAP estimate of the MRF model does not yield good results with any local searching strategy, as it gets trapped to local optimum. Hence, we have exploited the advantage of variable neighborhood searching (VNS)-based iterative global convergence criterion for MRF-MAP estimation. The effectiveness of the proposed scheme is established by testing it on different MRIs. Three performance evaluation measures are considered to evaluate the performance of the proposed scheme against existing state-of-the-art techniques. The simulation results establish the effectiveness of the proposed technique.

Most of the supervised dimensionality reduction (DR) methods design interclass scatter as the separability between the class means, which may force to assume unimodal Gaussian likelihoods and their projection space trends toward the class means. This paper presents a novel DR approach, norm discriminant eigenspace transform (NDET), in which average norms ( l 2 ) of classes have been utilized to characterize the interclass separability and the within-class distance characterizes the intraclass compactness. NDET is intended to accommodate data distributions that may be multimodal and non-Gaussian. We derive an upper bound for NDET, and a specific solution space to attain this bound. Existence of the specific solution is very unwonted, thereby we have considered the solution space of upper bound to achieve better reduction of dimensionality and discrimination of classes. Also, a nonlinear version of NDET (kernel NDET) is developed to model nonlinear relationships between the features. We show, experimentally (on synthetic data) that NDET effectively overcomes the limitations, which arise due to unimodal and data distribution assumptions of the traditional algorithms. Extensive empirical studies are made; and the proposed method is compared with closely related state-of-the-art schemes on UCI machine learning repository and face recognition data sets, to establish its novelty.

In this article, a statistical fusion based segmentation technique is proposed to identify different abnormality in magnetic resonance images (MRI). The proposed scheme follows seed selection, region growing-merging and fusion of multiple image segments. In this process initially, an image is divided into a number of blocks and for each block we compute the phase component of the Fourier transform. The phase component of each block reflects the gray level variation among the block but contains a large correlation among them. Hence a singular value decomposition (SVD) technique is adhered to generate a singular value of each block. Then a thresholding procedure is applied on these singular values to identify edgy and smooth regions and some seed points are selected for segmentation. By considering each seed point we perform a binary segmentation of the complete MRI and hence with all seed points we get an equal number of binary images. A parcel based statistical fusion process is used to fuse all the binary images into multiple segments. Effectiveness of the proposed scheme is tested on identifying different abnormalities: prostatic carcinoma detection, tuberculous granulomas identification and intracranial neoplasm or brain tumor detection. The proposed technique is established by comparing its results against seven state-of-the-art techniques with six performance evaluation measures., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Protein-protein interaction (PPI) networks are believed to be important sources of information related to biological processes and complex metabolic functions of the cell. Identifying protein complexes is of great importance for understanding cellular organization and functions of organisms. In this work, a method is proposed, referred to as MIPCE, to find protein complexes in a PPI network based on mutual information.MIPCE has been biologically validated by GO-based score and satisfactory results have been obtained. We have also compared our method with some wellknown methods and obtained better results in terms of various parameters such as precession, recall and F-measure.

Construction of co-expression network and extraction of network modules have been an appealing area of bioinformatics research. This article presents a co-expression network construction and a biologically relevant network module extraction technique based on fuzzy set theoretic approach. The technique is able to handle both positive and negative correlations among genes. The constructed network for some benchmark gene expression datasets have been validated using topological internal and external measures. The effectiveness of network module extraction technique has been established in terms of well-known p-value, Q-value and topological statistics.

In this paper, a spatiocontextual unsupervised change detection technique for multitemporal, multispectral remote sensing images is proposed. The technique uses a Gibbs Markov random field (GMRF) to model the spatial regularity between the neighboring pixels of the multitemporal difference image. The difference image is generated by change vector analysis applied to images acquired on the same geographical area at different times. The change detection problem is solved using the maximum a posteriori probability (MAP) estimation principle. The MAP estimator of the GMRF used to model the difference image is exponential in nature, thus a modified Hopfield type neural network (HTNN) is exploited for estimating the MAP. In the considered Hopfield type network, a single neuron is assigned to each pixel of the difference image and is assumed to be connected only to its neighbors. Initial values of the neurons are set by histogram thresholding. An expectation-maximization algorithm is used to estimate the GMRF model parameters. Experiments are carried out on three-multispectral and multitemporal remote sensing images. Results of the proposed change detection scheme are compared with those of the manual-trial-and-error technique, automatic change detection scheme based on GMRF model and iterated conditional mode algorithm, a context sensitive change detection scheme based on HTNN, the GMRF model, and a graph-cut algorithm. A comparison points out that the proposed method provides more accurate change detection maps than other methods.

A 25-year femalepresented at the eye OPD with the complaint of some white thing in her right eye. She had visual acuity of 6/18 in the right eye and 6/6 in the left one. Slit lamp examination showed she had a live larva actively moving in the anterior chamber of the right eye. The patient was started on oral steroid, topical moxifloxacin with steroid, cyclopentolate and planned for surgery. Exploration of lerva was done by visco-expressive method. Histopathology showed if to be a cysticercus lerva. Her visual acurity was 6/6 in the affected eye after one month.

Polycyclic aromatic hydrocarbons of tobacco require activation by phase I enzymes, such as cytochrome-P4501A1 (CYP1A1) to become an ultimate carcinogen, which are subjected to detoxification by phase II enzymes, especially glutathione S-transferases (GSTs). A study was designed to find whether genetic predisposition are risk modifiers of oral pathologies. The study included 102 cases with Oral Cancers (OCs), 68 cases with nonmalignant pathologies, 100 cases as control group. GSTM1 null genotype was associated with increased risk of OCs but not with benign pathologies. Deleted GSTT1 was associated with all pathologies. Both m1m2 and m2m2 polymorphisms of CYP1A1 were associated with oral pathologies.

Oral cancer is a lifestyle-related cancer, with tobacco as a primary factor. Progression of oral cancer develops over several years from the stage of leukoplakia, erythroplakia, etc. A micronucleus test was applied to oral mucosal cells, considering them as the target site for carcinogens and cytogenetic damage. The test has been established as a reliable biomarker for differential prevalence of MN indices among oral cancers, pre-cancers, non-malignant oral pathologies, and healthy controls for the first time. Buccal scrapings were collected from 63 patients with cancer and pre-cancerous lesions, 42 with non-malignant oral problems, and 100 healthy controls. The analysis revealed that MN frequencies in cancer and pre-cancerous cases were 4-fold elevated (p < 0.001) and 3.87-fold (p < 0.002) elevated for other non-malignant pathologies. Significant associations between use of tobacco in various forms and development of oral pathologies are also established. The relative cancer risk for smoking healthy controls with a definite MN frequency was also found to be significant. The results indicate the validity of the MN test as a cytogenetic marker for the development of several oral pathologies.

A new model for neuro-fuzzy (NF) classification systems is proposed. The motivation is to utilize the feature-wise degree of belonging of patterns to all classes that are obtained through a fuzzification process. A fuzzification process generates a membership matrix having total number of elements equal to the product of the number of features and classes present in the data set. These matrix elements are the input to neural networks. The effectiveness of the proposed model is established with four benchmark data sets (completely labeled) and two remote sensing images (partially labeled). Different performance measures such as misclassification, classification accuracy and kappa index of agreement for completely labeled data sets, and beta index of homogeneity and Davies-Bouldin (DB) index of compactness for remotely sensed images are used for quantitative analysis of results. All these measures supported the superiority of the proposed NF classification model. The proposed model learns well even with a lower percentage of training data that makes the system fast.

In September 1963, I came to the famous Photosynthesis Laboratory of Eugene Rabinowitch (1901-1973) at the University of Illinois at Urbana, Illinois, after submitting my doctoral thesis, under Professor Pasupati Mukerjee, in physical chemistry, then at the Indian Association for the Cultivation of Science, Calcutta, India. I present here my personal impressions, my research and interactions at the then International Center of Photosynthesis at Urbana, Illinois. A brief mention is made of research of others at this center, my collaboration with Govindjee (Urbana) and with John Olson (at Brookhaven National Laboratory).