Cell type classifiers for breast cancer microscopic images based on fractal dimension texture analysis of image color layers

The Fractal Viewpoint of Tumors and Nanoparticles

Even though the promising therapies against cancer are rapidly improved, the oncology patients population has seen exponential growth, placing cancer in 5th place among the ten deadliest diseases. Efficient drug delivery systems must overcome multiple barriers and maximize drug delivery to the target tumors, simultaneously limiting side effects. Since the first observation of the quantum tunneling phenomenon, many multidisciplinary studies have offered quantum-inspired solutions to optimized tumor mapping and efficient nanodrug design. The property of a wave function to propagate through a potential barrier offer the capability of obtaining 3D surface profiles using imaging of individual atoms on the surface of a material. The application of quantum tunneling on a scanning tunneling microscope offers an exact surface roughness mapping of tumors and pharmaceutical particles. Critical elements to cancer nanotherapeutics apply the fractal theory and calculate the fractal dimension for efficient tumor surface imaging at the atomic level. This review study presents the latest biological approaches to cancer management based on fractal geometry.

Image Layout and Schema Analysis of Chinese Traditional Woodblock Prints Based on Texture and Color Texture Characteristics in the Environment of Few Samples

Technology is the means by which all arts, including woodblock prints, are realized. The “kinship” with modern science and technology makes the development history of woodcut art that can also be understood as a technology history. The purpose of the texture expression produced in the creation of contemporary woodcut is to explore the rich texture expression forms made by contemporary representative painters using special material materials and tools in artistic creation, form a painting technique of personalized words, add new aesthetic meaning to art, and lay a foundation for the formation of unique style of Contemporary Art and the creation and development of woodcut texture. With the development of the times and the change of the public's aesthetic taste, the traditional pattern of printmaking needs to be properly transformed if it is to adapt to the modern humanistic environment, which also involves the importance of screen layout and pattern analysis of Chinese traditional woodcut. Based on the analysis of texture and color texture features in a few-sample environment, this paper proposes an automatic classification method for vignetting texture pictures by extracting the corresponding vignetting coefficients, and through experiments to verify that the proposed SILCO has good generalization sex. In the algorithm designed in this paper, the experiment shows that the accuracy P has a 64.7% improvement effect, and the recall r has a 67.8% performance improvement. On the whole, the experimental data show that the comprehensive classification accuracy is more than 57.4%.

RETRACTED: Research on Classification of Remote Sensing Images Based on Artificial Intelligence

With the rapid development of image processing technology, remote sensing technology has received increasing attention. Relying on artificial intelligence technology and using the advantages of principal component analysis (PCA) to reduce the dimensionality of features, this paper proposes a remote sensing image classification method based on SVM. First, LBP operator is used to extract remote sensing image features, and then PCA is used to perform remote sensing image features. The dimensionality reduction process reduces the feature dimensionality and eliminates feature redundant information, and obtains features that have a large contribution to the classification result. Finally, SVM is used for remote sensing image classification. The results show that PCA-SVM improves the efficiency and accuracy of remote sensing image classification.

Small Cell Lung Cancer Therapeutic Responses Through Fractal Measurements: From Radiology to Mitochondrial Biology

Small cell lung cancer (SCLC) is an aggressive neuroendocrine disease with an overall 5 year survival rate of ~7%. Although patients tend to respond initially to therapy, therapy-resistant disease inevitably emerges. Unfortunately, there are no validated biomarkers for early-stage SCLC to aid in early detection. Here, we used readouts of lesion image characteristics and cancer morphology that were based on fractal geometry, namely fractal dimension (FD) and lacunarity (LC), as novel biomarkers for SCLC. Scanned tumors of patients before treatment had a high FD and a low LC compared to post treatment, and this effect was reversed after treatment, suggesting that these measurements reflect the initial conditions of the tumor, its growth rate, and the condition of the lung. Fractal analysis of mitochondrial morphology showed that cisplatin-treated cells showed a discernibly decreased LC and an increased FD, as compared with control. However, treatment with mdivi-1, the small molecule that attenuates mitochondrial division, was associated with an increase in FD as compared with control. These data correlated well with the altered metabolic functions of the mitochondria in the diseased state, suggesting that morphological changes in the mitochondria predicate the tumor’s future ability for mitogenesis and motogenesis, which was also observed on the CT scan images. Taken together, FD and LC present ideal tools to differentiate normal tissue from malignant SCLC tissue as a potential diagnostic biomarker for SCLC.

6-Gingerol stabilized the p-VEGFR2/VE-cadherin/β-catenin/actin complex promotes microvessel normalization and suppresses tumor progression

BACKGROUND

Anti-angiogenic therapies demonstrate anti-tumor effects by decreasing blood supply to tumors and inhibiting tumor growth. However, anti-angiogenic therapy may leads to changes in tumor microenvironment and increased invasiveness of tumor cells, which in turn promotes distant metastasis and increased drug resistance.

METHODS

The CO-IP assays, N-STORM and cytoskeleton analysis were used to confirm the mechanism that p-VEGFR2/VE-cadherin/β-catenin/actin complex regulates vascular remodeling and improves the tumor microenvironment. 6-gingerol (6G), the major bioactive component in ginger, stabilized this complex by enhancing the binding of VEGFa to VEGFR2 with non-pathway dependent. Biacore, pull down and molecular docking were employed to confirm the interaction between 6G and VEGFR2 and enhancement of VEGFa binding to VEGFR2.

RESULTS

Here, we report that microvascular structural entropy (MSE) may be a prognostic factor in several tumor types and have potential as a biomarker in the clinic. 6G regulates the structural organization of the microvascular bed to decrease MSE via the p-VEGFR2/VE-cadherin/β-catenin/actin complex and inhibit tumor progression. 6G promotes the normalization of tumor vessels, improves the tumor microenvironment and decreases MSE, facilitating the delivery of chemotherapeutic agents into the tumor core and thereby reducing tumor growth and metastasis.

CONCLUSIONS

This study demonstrated the importance of vascular normalization in tumor therapy and elucidated the mechanism of action of ginger, a medicinal compound that has been used in China since ancient times.

Navigating features: a topologically informed chart of electromyographic features space

The success of biological signal pattern recognition depends crucially on the selection of relevant features. Across signal and imaging modalities, a large number of features have been proposed, leading to feature redundancy and the need for optimal feature set identification. A further complication is that, due to the inherent biological variability, even the same classification problem on different datasets can display variations in the respective optimal sets, casting doubts on the generalizability of relevant features. Here, we approach this problem by leveraging topological tools to create charts of features spaces. These charts highlight feature sub-groups that encode similar information (and their respective similarities) allowing for a principled and interpretable choice of features for classification and analysis. Using multiple electromyographic (EMG) datasets as a case study, we use this feature chart to identify functional groups among 58 state-of-the-art EMG features, and to show that they generalize across three different forearm EMG datasets obtained from able-bodied subjects during hand and finger contractions. We find that these groups describe meaningful non-redundant information, succinctly recapitulating information about different regions of feature space. We then recommend representative features from each group based on maximum class separability, robustness and minimum complexity.

Postnatal Developmental Changes in Fractal Complexity of Giemsa-Stained Chromatin in Mice Spleen Follicular Cells

Although there are numerous recent works focusing on fractal properties of DNA and chromatin, many issues regarding changes in chromatin fractality during physiological aging remain unclear. In this study, we present results indicating that in mice, there is an age-related reduction of chromatin fractal complexity in a population of spleen follicular cells (SFCs). Spleen tissue was obtained from 16 mice and fixated in Carnoy solution. The youngest animal was newborn, and each animal was exactly 1 month older than the previous. We performed fractal analysis of SFC chromatin structure, stained using Giemsa technique. Fractal analysis was done in a plugin algorithm of ImageJ software. We also performed gray-level co-occurrence matrix (GLCM) analysis of all chromatin structures with the calculation of parameters such as angular second moment and inverse difference moment. Giemsa-stained SFC chromatin exhibited an age-dependent reduction of fractal dimension with statistically significant (p<0.01) linear trend. Moreover, there was a statistically significant increase of SFC chromatin lacunarity. The chromatin GLCM parameters did not significantly change. To our knowledge, this is the first study to perform fractal and GLCM analyses of SFC chromatin and to investigate potential changes of fractal parameters during postnatal development.

Analysis of Big Data in Gait Biomechanics: Current Trends and Future Directions

The increasing amount of data in biomechanics research has greatly increased the importance of developing advanced multivariate analysis and machine learning techniques, which are better able to handle “big data”. Consequently, advances in data science methods will expand the knowledge for testing new hypotheses about biomechanical risk factors associated with walking and running gait-related musculoskeletal injury. This paper begins with a brief introduction to an automated three-dimensional (3D) biomechanical gait data collection system: 3D GAIT, followed by how the studies in the field of gait biomechanics fit the quantities in the 5 V’s definition of big data: volume, velocity, variety, veracity, and value. Next, we provide a review of recent research and development in multivariate and machine learning methods-based gait analysis that can be applied to big data analytics. These modern biomechanical gait analysis methods include several main modules such as initial input features, dimensionality reduction (feature selection and extraction), and learning algorithms (classification and clustering). Finally, a promising big data exploration tool called “topological data analysis” and directions for future research are outlined and discussed.