Ensemble of Local Texture Descriptor for Accurate Breast Cancer Detection from Histopathologic Images

Histopathological analysis is important for detection of the breast cancer (BC). Computer-aided diagnosis and detection systems are developed to assist the radiologist in the diagnosis process and to relieve the patient from unnecessary pain. In this study, a computer-aided diagnosis system for the early detection of benign and malignant breast cancer is proposed. The proposed system consists of feature extraction, feature ensemble, and classification stages. Various preprocessing steps such as grayscale conversion, noise filtering, and image resizing are employed on the input histopathological images. The local texture descriptors namely Local Binary Pattern (LBP), Frequency Decoded LBP (FDLBP), Binary Gabor Pattern (BGP), Local Phase Quantization (LPQ), Binarized Statistical Image Features (BSIF), CENsus TRansform hISTogram (CENTRIST), and Pyramid Histogram of Oriented Gradients (PHOG) are employed for feature extraction from the histopathologic images. The obtained features are then concatenated for the construction of the ensemble of the features. Three classifiers namely Support Vector Machines (SVM), K-nearest neighbor (KNN), and Neural Networks (NN) are used in the detection of the BC and the classification accuracy score is used for performance evaluation. A dataset called BreaKHis is used in the studies. There are 9109 microscopic pictures in BreaKHis, with 2480 benign samples and 5429 malignant samples. During the collection of the data, 82 patients\u27 breast tumor tissues were envisioned using various magnification factors such as 40X, 100X, 200X, and 400X. The accuracy score is used to assess the acquired findings. The results show that the proposed method has the potential to use accurate BC detection

CyanoCyc cyanobacterial web portal

CyanoCyc is a web portal that integrates an exceptionally rich database collection of information about cyanobacterial genomes with an extensive suite of bioinformatics tools. It was developed to address the needs of the cyanobacterial research and biotechnology communities. The 277 annotated cyanobacterial genomes currently in CyanoCyc are supplemented with computational inferences including predicted metabolic pathways, operons, protein complexes, and orthologs; and with data imported from external databases, such as protein features and Gene Ontology (GO) terms imported from UniProt. Five of the genome databases have undergone manual curation with input from more than a dozen cyanobacteria experts to correct errors and integrate information from more than 1,765 published articles. CyanoCyc has bioinformatics tools that encompass genome, metabolic pathway and regulatory informatics; omics data analysis; and comparative analyses, including visualizations of multiple genomes aligned at orthologous genes, and comparisons of metabolic networks for multiple organisms. CyanoCyc is a high-quality, reliable knowledgebase that accelerates scientists’ work by enabling users to quickly find accurate information using its powerful set of search tools, to understand gene function through expert mini-reviews with citations, to acquire information quickly using its interactive visualization tools, and to inform better decision-making for fundamental and applied research

Diffusional Interactions among Marine Phytoplankton and Bacterioplankton: Modelling H2O2 as a Case Study

Marine phytoplankton vary widely in size across taxa, and in cell suspension densities across habitats and growth states. Cell suspension density and total biovolume determine the bulk influence of a phytoplankton community upon its environment. Cell suspension density also determines the intercellular spacings separating phytoplankton cells from each other, or from co-occurring bacterioplankton. Intercellular spacing then determines the mean diffusion paths for exchanges of solutes among co-occurring cells. Marine phytoplankton and bacterioplankton both produce and scavenge reactive oxygen species (ROS), to maintain intracellular ROS homeostasis to support their cellular processes, while limiting damaging reactions. Among ROS, hydrogen peroxide (H2O2) has relatively low reactivity, long intracellular and extracellular lifetimes, and readily crosses cell membranes. Our objective was to quantify how cells can influence other cells via diffusional interactions, using H2O2 as a case study. To visualize and constrain potentials for cell-to-cell exchanges of H2O2, we simulated the decrease of [H2O2] outwards from representative phytoplankton taxa maintaining internal [H2O2] above representative seawater [H2O2]. [H2O2] gradients outwards from static cell surfaces were dominated by volumetric dilution, with only a negligible influence from decay. The simulated [H2O2] fell to background [H2O2] within ~3.1 µm from a Prochlorococcus cell surface, but extended outwards 90 µm from a diatom cell surface. More rapid decays of other, less stable ROS, would lower these threshold distances. Bacterioplankton lowered simulated local [H2O2] below background only out to 1.2 µm from the surface of a static cell, even though bacterioplankton collectively act to influence seawater ROS. These small diffusional spheres around cells mean that direct cell-to-cell exchange of H2O2 is unlikely in oligotrophic habits with widely spaced, small cells; moderate in eutrophic habits with shorter cell-to-cell spacing; but extensive within phytoplankton colonies

Potential modifications of structure-function of casein micelles

Potential modifications of structure-function of casein micelles. 4th International Conference of Food Industrie

Potential modifications of structure-function of casein micelles

Potential modifications of structure-function of casein micelles. 4th International Conference of Food Industrie

Histogram of occurrences of number of total genes, in a genome or transcriptome, (y axis) that code for the production of enzymes that produce or scavenge H₂O₂, O2•− or ^•NO in vivo.

Symbol color corresponds to taxon lineage (‘Taxa’). (TIF)</p

Comparison of log₁₀ (Total number of genes encoding O2•− metabolizing enzymes (‘SupOx_count’) normalized to the total number of genes present in each organism (‘GeneModels_count’)) vs. the log₁₀ (median cell radius in μm (‘log_Radius_um’)).

Poisson (solid line) or Quasi-Poisson (dashed line) regressions fitted to data ± Standard Error (dotted line). Regressions were run with (black line) or without (blue line) ‘Colony’ and ‘Flagella’ as co-variates. Selected prokaryote genomes are presented for comparison, but excluded from the presented regressions. Symbol color corresponds to taxon lineage (‘Phylum’).</p

Enzyme commission number, kegg orthology number, enzyme name and ROS substrate metabolized.

Enzyme commission number, kegg orthology number, enzyme name and ROS substrate metabolized.</p

Comparison of log₁₀ (Total number of genes encoding H₂O₂, O2•− or ^•NO metabolizing enzymes normalized to the total number of genes present in each Ochrophyte) vs. the log₁₀(median cell radius in μm).

Poisson (solid line) or Quasi-Poisson (dashed line) regressions fitted to data ± Standard Error (dotted line). Regressions were run without (blue line) ‘Colony’ and ‘Flagella’ as co-variates. Citations for data sources are in S3 Table. (TIF)</p

Variable names, definitions, units, and first location of occurrence in code, used for our data.

Variable names, definitions, units, and first location of occurrence in code, used for our data.</p