Modified DeeplabV3+ with multi-level context attention mechanism for colonoscopy polyp segmentation.

The development of automated methods for analyzing medical images of colon cancer is one of the main research fields. A colonoscopy is a medical treatment that enables a doctor to look for any abnormalities like polyps, cancer, or inflammatory tissue inside the colon and rectum. It falls under the category of gastrointestinal illnesses, and it claims the lives of almost two million people worldwide. Video endoscopy is an advanced medical imaging approach to diagnose gastrointestinal disorders such as inflammatory bowel, ulcerative colitis, esophagitis, and polyps. Medical video endoscopy generates several images, which must be reviewed by specialists. The difficulty of manual diagnosis has sparked research towards computer-aided techniques that can quickly and reliably diagnose all generated images. The proposed methodology establishes a framework for diagnosing coloscopy diseases. Endoscopists can lower the risk of polyps turning into cancer during colonoscopies by using more accurate computer-assisted polyp detection and segmentation. With the aim of creating a model that can automatically distinguish polyps from images, we presented a modified DeeplabV3+ model in this study to carry out segmentation tasks successfully and efficiently. The framework's encoder uses a pre-trained dilated convolutional residual network for optimal feature map resolution. The robustness of the modified model is tested against state-of-the-art segmentation approaches. In this work, we employed two publicly available datasets, CVC-Clinic DB and Kvasir-SEG, and obtained Dice similarity coefficients of 0.97 and 0.95, respectively. The results show that the improved DeeplabV3+ model improves segmentation efficiency and effectiveness in both software and hardware with only minor changes.

Numerical analysis of a grating embedded bidirectional integrated optical coupler pressure sensor.

A numerical analysis of a grating embedded bidirectional optical coupled waveguide structure is presented for the first time, to our knowledge. A finite difference method (FDM) based scheme is devised to extract the allowed eigen TE and TM modes of the structure. Sensing characteristics of the grating employed between two high refractive index couplers are then explored. The influence of strain on the composite structure is numerically analyzed for better understanding of guiding phenomena. A numerical method based on a three-point central finite difference scheme with proper boundary conditions at the point of discontinuity is developed. For an accurate sensitivity analysis, a large number of mesh points (N=1000) are used in the FDM algorithm, while the whole analysis is done on MATLAB software. To the best of the authors' knowledge, Bragg grating sensitivities of individual TE and TM modes have been estimated for the first time. It is found that higher order TE and TM modes show improved sensitivity performance. The physics behind the improved sensitivity of the proposed structure is correlated with existing cases. The proposed technique is based on effective refractive index theory, and hence it is easy to implement. This work can be easily extended to obtain temperature, humidity, and vibration sensitivities of other novel structures.

Surface plasmon biosensor based on Bi2Te3 antimonene heterostructure for the detection of cancer cells.

This theoretical investigation proposes the preliminary stage detection of cancer cells using a prism-based surface plasmon resonance biosensor. The surface plasmon resonance (SPR) biosensor is label-free and rapid; it supports real-time investigations, including greater sensitivity, and it has an enormous possibility for detecting different forms of cancer. The refractive index alteration of cancer cells is 1.360-1.401. The performance of the SPR sensor has been analyzed. The different types of cancer cells for skin, cervical, blood, adrenal, and breast (type I and II) have been analyzed, and sensitivities of 292, 384, 229, 242, 254, and 261 (degree/RIU) have been achieved, respectively. A high figure of merit of 115.2RIU-1 has been achieved for HeLa cells and achieved detection accuracy of 0.32deg-1 for basal cells.

On strong KKT type sufficient optimality conditions for multiobjective semi-infinite programming problems with vanishing constraints.

In this paper, we consider a nonsmooth multiobjective semi-infinite programming problem with vanishing constraints (MOSIPVC). We introduce stationary conditions for the MOSIPVCs and establish the strong Karush-Kuhn-Tucker type sufficient optimality conditions for the MOSIPVC under generalized convexity assumptions.

Highly selective colorimetric and fluorometric chemosensor for cyanide on silica gel and DMSO/H2O (7:3 v/v) mixed solvent and its imaging in living cells.

The chemosensor 2,3-bis((E)-((2-hydroxynaphthalen-1-yl)methylene)amino)maleonitrile (1) has been synthesized using 2-hydroxy-1-napthaldehyde and 2,3-diaminomaleonitrile and characterized. Sensor 1 exhibits selective binding with CN(-) in dimethyl sulfoxide (DMSO)/H2O (7:3 v/v) and DMSO/aqueous Tris (Tris(hydroxymethyl)aminomethane) buffer (7:3 v/v, 10 mM, pH 7.2) media with significant changes in its UV-visible and fluorescence spectra. Titration of 1-Zn(II) complex with CN(-) ion in DMSO/aqueous Tris buffer (7:3 v/v, 10 mM, pH 7.2) regenerates the free sensor 1, as supported by UV-visible spectra. (1)H NMR titration of 1 with CN(‒) in (CD3)2SO confirms the hydrogen-bonding interaction between the two OH groups of the former and the latter in bidentate manner. Sensor 1 impregnated on silica gel thin layer chromatography (TLC) strip followed by dipping in anion solutions in DMSO/H2O (7:3 v/v) generates yellow to red spectacular colour change with CN(-) ion selectively which can be exploited as potential tool for ready-made detection of toxic CN(-) ion in environmental and analytical chemistry. Similar visual change in colour for 1 is observed selectively with CN(-) also when both of them are taken in DMSO/H2O (7:3 v/v) solution. Sensor 1 is used as an imaging reagent for detection of the cellular uptake of CN(-) ion in Baby Hamster Kidney (BHK-21) cells.