Search

Food quality and dietary habits have a significant impact on human health. Growing evidence suggests that miRNAs have been extensively detected in various dietary sources, including plants and animals. miRNAs are a class of small non-coding RNA that regulates gene expression in a sequence-specific manner and modulates various biological processes. These dietary miRNAs are transported through the circulatory system and affect the expression of the gene in the recipient cells through a process called cross-kingdom regulation of the gene. This review will provide insight into the role of dietary miRNAs, their stability and transport mechanisms, as well as their impact on human health. Likewise, we update and discuss the future consequences of dietary miRNAs and their possible use in the treatment of various human diseases.

In this paper, utilizing the theory of Watson transform and Watson convolution, we explore the Watson wavelet convolution product and its related properties. The relation between the Watson Wavelet convolution product and Watson convolution is also computed. Watson wavelet transform and its inversion formula are analyzed heuristically. Watson two-wavelet multipliers and its trace class are derived from Watson wavelet convolution product

In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.

ObjectiveTo generate comparative efficacy evidence of belimumab versus anifrolumab in SLE that can inform treatment practices.MethodsThe SLE Responder Index (SRI)-4 response at 52 weeks of belimumab versus anifrolumab was evaluated with an indirect treatment comparison. The evidence base consisted of randomised trials that were compiled through a systemic literature review.A feasibility assessment was performed to comprehensively compare the eligible trials and to determine the most appropriate indirect treatment comparison analysis method. A multilevel network meta-regression (ML-NMR) was implemented that adjusted for differences across trials in four baseline characteristics: SLE Disease Activity Index-2K, anti–double-stranded DNA antibody positive, low complement (C)3 and low C4. Additional analyses were conducted to explore if the results were robust to different sets of baseline characteristics included for adjustment, alternative adjustment methods and changes to the trials included in the evidence base.ResultsThe ML-NMR included eight trials: five belimumab trials (BLISS-52, BLISS-76, NEA, BLISS-SC, EMBRACE) and three anifrolumab trials (MUSE, TULIP-1, TULIP-2). Belimumab and anifrolumab were comparable in terms of SRI-4 response (OR (95% credible interval), 1.04 (0.74–1.45)), with the direction of the point estimate slightly favouring belimumab. Belimumab had a 0.58 probability of being the more effective treatment. The results were highly consistent across all analysis scenarios.ConclusionsOur results suggest that the SRI-4 response of belimumab and anifrolumab are similar at 52 weeks in the general SLE population, but the level of uncertainty around the point estimate means we cannot rule out the possibility of a clinically meaningful benefit for either treatment. It remains to be seen if specific groups of patients could derive a greater benefit from anifrolumab or from belimumab, and there is certainly an unmet need to identify robust predictors towards more personalised selection of available biological agents in SLE.

Adapting the profound, deep convolutional neural network models for large image classification can result in the layout of network architectures with a large number of learnable parameters and tuning of those varied parameters can considerably grow the complexity of the model. To address this problem a convolutional Deep-Net Model based on the extraction of random patches and enforcing depth-wise convolutions is proposed for training and classification of widely known benchmark Breast Cancer histopathology images. The classification result of these patches is aggregated using majority vote casting in deciding the final image classification type. It has been observed that the proposed Deep-Net model implementation results when compared with classification results of the VGG Net(16 layers) learned features, outclasses in terms of accuracy when applied to breast tumor Histopathology images. The objective of this work is to examine and comprehensively analyze the sub-class classification performance of the proposed model across all optical magnification frontiers.

This chapter proposes a scheme for the identification of copy-move forgery by inducing adaptive over-segmentation and matching of feature points with the help of discrete cosine transform (DCT). Copy-move forging is an image tampering technique that involves concealing undesired things or recreating desirable elements within the same image to create modified tampered images. Traditional methods added a large number of false matches. To conquer this problem, a new algorithm is proposed to incorporate an adaptive threshold method. So, the block feature matching mechanism is used, and the matching feature blocks classify the feature points using patch matching and Hough transform. Forged regions are detected with the help of the newly proposed algorithm. The results of the proposed method show that it can substantially reduce the number of false matches that lead to improvements in both performance and computational costs. This demonstrates the suggested algorithm's resistance against a variety of known attacks. Comparative results are presented for a better evaluation.

The competition between unretarded dispersion interactions between molecules prevailing at medium range order length scales and their phonon induced coupling at larger scales leads to appearance of nano-scale sub structures in amorphous systems. The complexity of intermolecular interactions gives rise to randomization of their operators. Based on a random matrix modelling of the Hamiltonian and its linear response to an external strain field, we show that the ultrasonic attenuation coefficient can be expressed as a ratio of two crucial length-scales related to molecular dynamics. A nearly constant value of the ratio for a wide range of materials then provides a theoretical explanation of the experimentally observed qualitative universality of the ultrasonic attenuation coefficient at low temperatures.

Contrary to previous studies based on bulk materials, we analyze the behavior of the vibrational density of states (VDOS) and specific heat of an amorphous solid of nano-scales (∼3 nm) at low temperature. With Hamiltonian formulation based on the intermolecular dispersion forces, our analysis indicates a universal semi-circle form of the average VDOS in the bulk of the spectrum along with a super-exponentially increasing behavior in its edge. The latter in turn leads to a specific heat with a superlinear temperature (T) dependence belowT< 1Keven at nano-scales, and, surprisingly agreeing with the experiments although the latter are carried out at macroscopic scales. The omnipresence of dispersion forces at microscopic scales indicates the application of our results to other disordered materials too.

The probability distribution of the magnitude C of the curvature 2-form, that underlies the quantum geometric phase and the reaction force of geometric magnetism, is calculated for an ensemble of three-parameter Hamiltonians represented by the gaussian unitary ensemble of N × N matrices. The distributions are determined analytically: exactly for N = 2 and approximately for N ≥ 3, and compared with simulations. The distributions decay asymptotically as 1/C5/2; this is a consequence of the codimension of energy-level degeneracies in the ensemble.

Aims: The aim of this study was to compare concurrent chemoradiation along with neoadjuvant and adjuvant chemotherapy versus concurrent chemoradiation alone in locally advanced cervical cancer regarding treatment response and toxicities. Subjects and Methods: A randomized control study was done on 116 patients with locally advanced carcinoma cervix (Stage IIB to IIIB) registered between January 2014 and February 2015. Patients were randomly divided to receive either one cycle of cisplatin/5-fluorouracil neoadjuvant chemotherapy and two cycles of the same adjuvant chemotherapy with concurrent chemoradiation with weekly cisplatin (Arm A) or only concurrent chemoradiation (Arm B). All patients received three fractions of high-dose-rate intracavitary brachytherapy after completion of the external radiation. Results: A higher proportion of the patients of chemotherapy arm achieved complete response (94%) as compared to the nonchemotherapy arm (56%), and this was statistically significant. There was a trend toward more treatment-related acute toxicity with chemotherapy. Conclusions: These results have corroborated the view that if neoadjuvant and adjuvant chemotherapies are added to concurrent chemoradiation, it could further the effects of concurrent chemoradiation for patients with locally advanced cancer of the uterine cervix.

Globally, cancer is the second deadliest disease in the world. The most familiar type of cancers affecting human beings is lung, colorectal, breast, cervical, stomach, thyroid, and stomach cancer. An individual affected by cancer suffers from physical, emotional, and financial restrain. The proper maintenance of the health system with early detection reduces the cancer death rate. Breast cancer is the most serious crisis that affects a major percentage of women. The superfluous intensification of cells in the breast is the main reason for breast cancer. The histopathological images are very useful in the diagnosis of breast cancer. However, it is a time-intensive and tedious task requiring long years of pathologist training. Today’s digital advancements can be exploited for automating this task. This paper presents a new learning approach for automating breast cancer Classifying is histopathological images into magnification independent (MI) and magnification dependent (MD) eight-class and binary classifying. For learning, we use the deep neural network-ResNet-18 and this is provided with Image Net training. The proposed learning technique mainly depends on the accurate tuning of the block-wise approach in which the deep network model's two terminal residual blocks can be educated. Additionally, we reinforced the proposed approach’s adaptability with Global Contrast Normalization and a three-fold rise in details concerning training data. The evaluation is carried out on Break His dataset. The results protrude our proposed technique to be effective in classifying breast cancer histopathological images into MI and MD eight-class and binary classes. It outperformed 11 renowned existing techniques.

This paper aims at presenting a complete picture of advances till now in the field of computer-aided detection of Pulmonary Tuberculosis using Chest X-ray Images. Advances are analyzed in chronological order as they happen and are divided into three phases in which technology shifted into new paradigms. Study concludes that although techniques that use Machine learning based methods for segmentation and classification are prevailing for the moment in terms of flexibility for very particular feature extraction in borderline cases where probabilistic methods can be tweaked according to requirements and accuracy, Deep Convolutional Neural Network based technique will secure higher standings as the computational capability and dataset management improves. Finally, briefly an attempt at using visualization techniques for borderline cases is discussed.

Brain tumor consists of malignant cells that harm brain, and hence its diagnosis and treatment are required for human wellness. In computer vision, tumor detection algorithms have been developed for identifying the tumor cells from normal brain cells. This work proposed a magnetic resonance image segmentation-based classification model for brain tumor detection. Training data for Convolutional Neural Network (CNN) was obtained from Intelligent Water Drop (IWD) genetic algorithm. IWD provides segmented cluster center pixel values of MRI image. Image was segmented as per cluster center pixels and blocked for training by CNN. Classification process was performed by CNN. Experiment was done on real brain tumor dataset. This model was compared with FCM clustering algorithm with rough set where obtained accuracy was 92.57%. Obtained accuracy of proposed model is 95.24% and hence result shows that proposed IWDCNN model has increased the pixel segmentation-based classification accuracy. Evaluated average values of some other metrics are precision = 0.971486, recall = 0.979, and F-measure = 0.975.

Malignant cells get created in the brain in case of brain cancer. Brain controls all the functions of the human body hence detection accuracy of brain cancer is helpful for human wellness. This paper proposed a model to identify brain tumors using a combination of Biogeography algorithm and Convolutional Neural Network (CNN). Here brain MRI images are supplied to Biogeography algorithm which generates pixel value sets for tumor and non-tumor region. Once sets get identified then training of CNN is performed. Trained CNN model directly segments the image into respective diagnosis classes. Hybridization of Biogeography and Convolutional Neural Network decides tumorous and non-tumorous brains. Proposed model increases accuracy of disease diagnosis and proved better than when compared with similar dataset handled by FCM clustering algorithm and rough set. Our model gives the average accuracy 94.83%, precision value 0.96916, Recall value 0.98 and F–Measure value 0.97.

In last decade, LDA is extensively used for unsupervised topic modeling, and various extension of LDA has also been proposed. This paper presents a semi-supervised extension IN-LDA, which uses very few influential words related to the domain for providing supervision in the topic generation process. IN-LDA also improves the performance of the LDA generation process in two ways. First, it deals with multi-aspect terms by passing N-grams vectors, and second simulated annealing-based algorithm is used for tuning hyperparameters of LDA for more coherent output. The experiment is conducted on two popular datasets, movie reviews and 20Newsgroup. IN-LDA is showing improved results when compared with others on coherence value. It also shows a better interpretation of output due to influential words.

In noninteracting limit, the density of states of a many body system can be expressed as the convolution of single body density of states of its subunits. Here we use the formulation to derive the ensemble averaged many body density of states for the cases in which subunits can be modelled by Gaussian or Wishart random matrix ensembles., 18 pages (double spacing), no figures

A canonical superoscillatory function, defined on the circle, satisfies a set of uncertainty inequalities for periodic functions, strengthened by an anticommutator term introduced by Schrödinger. The superoscillations can be made to dominate the uncertainty inequality by selecting them with a sufficiently narrow window. The uncertainty product, and its minimum value, are calculated as functions of the window width, as well as their asymptotics for narrow and wide windows. The function has minimum uncertainty only for very narrow windows.

Our theoretical analysis reveals the existence of a previously unknown universality, namely, a dimensionless length scale, related to a ratio of medium and short range order structures of amorphous systems that originates from a conspiracy between dispersion forces among molecules and their phonon mediated coupling. The scale in turn gives rise to the structure lying underneath the low temperature universalities of many other properties e.g. specific heat, internal friction, boson peak characteristics, Meissner–Berret ratio etc.

The statistics of chiral matrix ensembles with uncorrelated but multivariate Gaussian distributed elements is intuitively expected to be driven by many parameters. Contrary to intuition, however, our theoretical analysis reveals the existence of a single parameter, a function of all ensemble parameters, which governs the dynamics of spectral statistics. The analysis not only extends the similar formulation (known as complexity parameter formulation) for the Hermitian ensembles without chirality to those with it but also reveals the underlying connection between chiral complex systems with seemingly different system conditions as well as to other complex systems e.g. multi-parametric Wishart ensembles as well as generalized Calogero Sutherland Hamiltonian (CSH)., Comment: 28 Pages, 8 figures, new tables added for better parametric details

A family of hetero-trinuclear metal complexes with the general molecular formula [Zn2Dy(L1)2(OAc)4] (X) where X = (NO3)0.92(Br)0.08 (1), ClO4 (2), Cl (3) and PF6 (4) were structurally characterized using a Schiff base ligand (HL1). The Dy(iii) ion in 1-4 exhibits distorted square anti-prism geometry; however, the extent of distortion observed around Dy(iii) in these complexes differ from each other. Consequently, 1-4 show distinct magnetization relaxation dynamics, with the anisotropic energy barrier of 25.4 cm-1, 12.9 cm-1, 14.08 cm-1 and 55.5 cm-1, respectively. The detailed electronic structure of 1-4 and the experimentally observed magnetization relaxation dynamics trends were rationalized using ab initio calculations. The detailed investigation discloses the non-zero influence of the anion themselves and the anion induced geometry change in Zn(ii) affects the electronic structure of Dy(iii), which in turn affects the magnetization relaxation dynamics. Overall, the study unveils an unprecedented methodology i.e. change in geometry of Zn(ii) by altering the anion in the crystal lattice to modulate the relaxation dynamics of Dy(iii).

Image segmentation has key influence in numerous medical imaging uses. An image segmentation model that is based on the particle swarm optimizer (PSO) is developed in this paper for breast cancer histopathology images of different magnification levels (40X, 100X, 200X and 400X), thus simplifying image representation and making it meaningful and easier for future analysis. As lower the magnification level, the bigger is the field of view and as it can provide greater detail, thus more time and care must be taken to use such images. Thus, finding a segmentation method that works equally well for all zoom levels of images is a big challenge. To explicate the better performance of the proposed method and its applicability on breast cancer images, results of applications and tests are augmented which shows PSO image clustering approach using intra-cluster distance as an optimization function, performs superior to cutting edge strategies, namely K-means and genetic algorithm (GA). The algorithms, when given specified number of clusters, find the centroids, thus grouping similar image primitives. The influence of different estimations of PSO control parameters on execution is additionally outlined.

Prediction of the diseases are possible using medical diagnosis system. This type of health care model can be developed using soft computing techniques. Hybrid approaches of data classification and optimization algorithm increases data classification accuracy. This research proposed applications of Moth Flame optimization (MFO) and Fuzzy Min Max Neural Network (FMMNN) for the development of medical data classification system. Here MFO algorithm considers bulk of features from the disease dataset and produces optimized set of features based on fitness function. MFO is able to avoid local minima problem and this is the main cause behind production of optimal set of features. Optimized features are then passed to FMMNN for classification of malignant and benign cases. As classification is concerned, model experiment achieved 97.74% accuracy for Liver Disorders and 86.95 % accuracy for Pima Indian Diabetes dataset. Improving the medical data classification accuracy is directly related to attain good human health.

LDA is a very popular unsupervised model used to find thematic information about the documents. The performance of the LDA greatly depends on its hyper parameter values. If its parameters are tuned properly then LDA may produce much better results. This paper mainly focused on finding good LDA configurations for finding improved LDA output as compare to the traditional LDA model. We have proposed and implemented SA-LDA algorithm that uses Simulated Annealing (SA) to find optimal values of LDA parameters. An empirical evaluation using customer review datasets from three different domains namely Mobile, Hotel and Movie is conducted. The experiment results show that SA-LDA gives better performance when it is evaluated by the coherence score.