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This study identifies the anti-inflammatory, antioxidant, and immunomodulatory potential of a fatty acid methyl ester segregated from the brown algae Turbinaria ornata and identified by nuclear magnetic resonance and mass spectrometry as methyl 6,12-dimethyltridecanoate (ET). Antioxidant and anti-inflammatory effects of ET were studied on lipopolysaccharide (LPS)-induced inflammatory reaction in RAW 264.7 macrophages. Moreover, in silico docking studies of isolated ET with inflammatory markers TNFα, NFκB, and COX-2 showed potent binding scores suggesting anti-inflammatory potential. ET significantly reduced LPO and increased LPS-induced SOD, catalase, and GSH levels. Molecular docking results were further confirmed by checking mRNA levels of selected cytokines (IL6 and IL10), followed by protein expression of iNOS and NFκB in LPS-induced macrophages. ET significantly upregulated the expression of IL10 and downregulated the expression of IL6, iNOS, and NFκB, confirming the inhibition of LPS-induced inflammation via the iNOS/NFκB pathway. [ABSTRACT FROM AUTHOR]

The article titled "Effective offline handwritten text recognition model based on a sequence-to-sequence approach with CNN-RNN networks" has been retracted by the Editor-in-Chief and the publisher of Neural Computing & Applications. The retraction was made due to concerns raised during an investigation, which found issues such as unsupported citations, non-standard phrasing, and image irregularities. The Editor-in-Chief no longer has confidence in the results and conclusions of the article. The authors of the article disagree with the retraction. Springer Nature, the publisher, remains neutral regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

This document is a retraction note for an article titled "Performance analysis of pre-cancerous mammographic image enhancement feature using non-subsampled shearlet transform." The article has been retracted by the publisher due to concerns regarding compromised editorial handling and peer review process, inappropriate or irrelevant references, and not being in scope of the journal or guest-edited issue. The authors have not responded to correspondence regarding this retraction. Springer Nature, the publisher, remains neutral with regard to jurisdictional claims and institutional affiliations. [Extracted from the article]

In a decentralized network every user makes use of personal identity details at different places for various services and these details are shared with third-parties without their consent and stored at an unknown location. Organizations like government, banks and social platforms are considered to be the weakest area in the current identity management system as they are vulnerable which in turn leads to compromising billions of user identity data. Block chain based User Identity Management is a solution which provides a decentralized environment that manages the user identity data and their related Know-Your-Customer documents in a distributed ledger. All the transactions of the network are stored in the block, which is a type of a data structure and these blocks are validated using the powerful consensus algorithms and linked to form a block chain. Smart contracts will act as an interface between the client and the block chain network. User's information cannot be provided to any third party vendors without the explicit consent of the user. This paper proposes a framework for User Identity Management using Block chain technology in a decentralized network. The proposed framework ensures a high level privacy and security for the personal identity details and the documents. In addition to that the performance analysis of the framework is presented in terms of Transaction immutability, Mining Resource, Memory utilization and Difficulty Variation. The results of the analysis proves that the proposed framework performs better in terms of all these factors. [ABSTRACT FROM AUTHOR]

Automatic text recognition system might serve as an important factor in creating a paperless environment through digitizing and processing the existing paper documents in the upcoming days. Handwritten recognition using deep learning methods has been widely explored by many researchers. The existence of large quantity of data and a variety of algorithmic innovations enable the ease of training deep neural networks. Different techniques have been initiated in the literature for recognizing text from handwritten documents. This paper proposes a hybrid handwritten text recognition (H2TR) model using deep neural networks that use the sequence-to-sequence (Seq2Seq) approach. This hybrid model makes use of the salient features of convolution neural network (CNN) and recurrent neural network (RNN) with long–short-term memory network (LSTM). It uses CNN to extract the features from the handwritten image. The features that are extracted are later modelled with a sequence-to-sequence approach and fed to RNN–LSTM for encoding the visual features and decoding the sequence of letters that are available in the handwritten image. The proposed model is tested with IAM and RIMES handwritten databases, which shows competitive letter accuracy and word accuracy results. [ABSTRACT FROM AUTHOR]

Breast cancer is the second highest death causing disease in female. From the data it's clear that one of the eight women is affected by breast cancer. The most effective way to detect the cancer is by using mammography technique and also it detects masses and abnormal conditions. The cluster shaped white spots on mammograms are used the cancer in the early stages. The importance of breast cancer detection to reduce different noises which are commonly found in mammogram images and provides better image quality. Here the multi-scale and the multi-directional analysis would produce optimal approximation and detail coefficients of Non-subsampled shearlet transform (NSST). Hence in this paper, the noisy image is amended into piecewise smooth function in different subbands of frequency. NSST coefficients are amended into information and noise related coefficients are noise is removed by using the adaptive threshold. To evaluate the performance of this proposed algorithm, Mean Square Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) are used. Testing these experimental results show that the proposed algorithm can preserve the edges and textures very well while weakening the noise can obtain better suppressed noise and enhances the objective of evaluations than other noise removal methods. [ABSTRACT FROM AUTHOR]

Cryptography is one of the most accepted and successful data security methods used at present by most of the organizations. Encrypting the message using natural languages reduces the encryption time and improves the performance. Tamil is a most ancient Dravidian language spoken in the southern part of India. Here we use Tamil language for encrypting the text named as Tamilian Cryptography. Since Tamil language has 247 characters it is very difficult to crack the message. The message to be encrypted is translated to Tamil language, this translated text is then mapped to a randomly generated 2-bit combination of English alphabets. The result of this process is called intermediate cipher (inter cipher). This is an hybrid algorithm since the inter cipher is encrypted using Advanced Encryption Standard algorithm to enhance the confidentiality of the text. Since the Tamilian Cryptography makes use of three phases namely Translation, Mapping and Encryption, it makes the data much more secure than the existing algorithms like blowfish and Data Encryption Standard (DES) and also the person who tries to decrypt must have the knowledge of Tamil language as well the mapping details to see the original data and This algorithm exhibits stronger avalanche effect of 95% which is greater than blowfish and DES. The evaluation of the proposed algorithm shows that it executes faster and has comparatively lesser encryption time, less memory overhead. [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published: https://doi.org/10.1007/s11042-021-11211-5 [ABSTRACT FROM AUTHOR]

Cancer is the uncontrolled growth of abnormal cells; if not detected and treated at the early stage cancer would become fatal. Breast cancer remains the second leading cause of cancer induced deaths, despite the advances taken place in treatment methods. The incidence of breast cancer in India is on the rise that one in 22 women in India is likely to suffer from breast cancer during her lifetime. One in eight women is affected by this dangerous disease in America. Cancer always a major problem to the world however various methods are available. Some of methods for breast cancer detection are X-ray mammography, MRI and ultrasound. However, they have some limitations to detect the breast cancer but microwave imaging is a safer method as compared to the X-ray. In this Paper cancer tumour in the breast is detected by I-shaped dual C-slotted microstrip patch antenna is designed for 10 GHz. The proposed antenna has a bandwidth of 7.51 dB covering the frequency range 4.9–7.89 GHz, the return loss below − 37 dB and antenna gain of 2.45 dB. The flexible this patch antenna holds good efficiency of 87.1%. [ABSTRACT FROM AUTHOR]

Breast cancer is the most invasive cancer in women and second leading cause of mortality in women. Advances in detection and treatment have improved survival rates dramatically in patients. Now-a-days mammographic image based analysis is widely used to predict the breast cancer. The diagnosis is simple and survivals of the patient are high if the breast cancer is predicted at the early stage accurately. This paper heavily focuses on methodologies which provides accurate detection of cancerous tissues and thereby reduces the death rates. The algorithms are non-subsampled Shearlet transform (NSST) for image preprocessing, Adaptive clustering for image segmentation and finally adaptive neuro-fuzzy inference system (ANFIS) for image classification. NSST is an extension of wavelet transform in multidimensional and multidirectional case wherein the components are processed with the help of thresholding scheme. Adaptive clustering algorithm separates the pixels in the image into clusters based on both their intensity and sensitivity and thereby makes the segmentation much simpler and easier. ANFIS algorithm is the classifier which is a combination of both neural network adaptive capabilities and the fuzzy logic qualitative approach which is used to classify the normal and abnormal image accurately. The robustness of the proposed methodology is examined using classification accuracy, sensitivity and specificity. [ABSTRACT FROM AUTHOR]

The majority of security systems for wireless sensor networks are based on symmetric encryption. The main open issue for these approaches concerns the establishment of symmetric keys. A promising key distribution technique is the random pre-distribution of secret keys. Despite its effectiveness, this approach presents considerable memory overheads, in contrast with the limited resources of wireless sensor networks. A new scheme, called Seed based Min–Max Composite (S2MC), is proposed that exploits the best features of random pre-distribution with lower memory requirements and higher resilience. The novelty of the S2MC scheme lies in the organization of the secret material that uses the limited number of light weight pre-distributed seeds in a certain range and generating keys from the seeds during communication. The simulative analysis demonstrates that the introduced approach provides a higher level of security in terms of resilience than the existing schemes since the keys are erased after communication in the presence of compromised nodes. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) is the technique to identify the damage in the building or physical structures. Several structural health monitoring has been proposed in recent years. Increase in number of sensors and its wiring mechanism causes issue in signal processing demand, efficiency, and unreliability. In current state, already present sensors cannot be used for Structural Health Monitoring because of its high cost and low battery capacity, although none of these techniques are capable of monitoring the condition of complex physical structures. The measurement of strain can be done by using patch antenna as strain sensors. In currently available sensors only the wireless range. The main objective of this paper is to investigate the feasibility of using circular and rectangular slotted microstrip patch antenna as a strain sensor for structural health monitoring without these afore mentioned problems. These antenna sensors’ (slotted rectangular and circular microstrip patch antennas) simulation results are confirmed. From the simulation results, the sensor provides information about crack orientation. [ABSTRACT FROM AUTHOR]

The problem of mammographic image classification has been handled using various measures and features. The methods consider only small set of features to perform classification, but still the methods suffer to produce efficient classification accuracy. To overcome the problem of accuracy in mammographic image classification, a region-specific multi-attribute white mass estimation technique is proposed. The method uses the white mass value, density measure, and binding to identify the microcalcification. First, the peak white mass value is identified by visiting throughout the mammogram region. Second, the method splits the mammographic image into a number of small scale integral images. Third, for each integral image, the method computes multi-attribute white mass value, and based on computed white mass value, the method identifies the region being affected by the calcification. The method produces efficient result in mammogram image classification. [ABSTRACT FROM AUTHOR]

Biocompatible ethylene vinyl acetate copolymer (EVA) was utilized to study the release of an antiviral drug (acyclovir (ACY)) and an antimicrobial drug (doxycycline hyclate (DOH)). Release of both drugs from EVA was measured individually and in combination. The effect of drug combination of DOH and ACY is presented. Additionally, the release rate of DOH after coating of the matrix with a different copolymer, in drug-loading with increasing loads of DOH, and with increases in temperature are also presented. The drugs incorporated in EVA films were prepared from the dry sheet obtained by solvent evaporation of polymer casting solutions with drugs. Drug release from the films was examined for about 12 days in distilled water at 37 °C. Changes in optical density were followed spectrophotometrically. The combination of ACY and DOH resulted in an increased release of ACY by about three times ( P < 0.001) while DOH showed a decrease in rate of about two times compared to the individual release rates ( P = 0.008). Increases in drug levels of DOH resulted in increases in drug release rates ( P = 0.001). The release rate of DOH increased with temperature ( P = .001; 27, 32, 37 and 42 °C were studied) and the energy of activation (Δ E ≠ = 56.69 kJ/mol) was calculated using the Arrhenius equation for the diffusion of DOH molecules. Thus, the release rates of drugs were influenced by many factors: drug combination, coating the device, drug-loading, and temperature variation. Therefore it is proposed that controlling these variables should make it possible to obtain therapeutic levels of drugs released from drug loaded polymer, which may be beneficial in treating oral infections. [ABSTRACT FROM AUTHOR]