Breast density classification for cancer detection using DCT-PCA feature extraction and classifier ensemble

It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN

Microsatellite based genetic diversity and population structure of the endangered Spanish Guadarrama goat breed

<p>Abstract</p> <p>Background</p> <p>Assessing genetic biodiversity and population structure of minor breeds through the information provided by neutral molecular markers, allows determination of their extinction risk and to design strategies for their management and conservation. Analysis of microsatellite loci is known to be highly informative in the reconstruction of the historical processes underlying the evolution and differentiation of animal populations. Guadarrama goat is a threatened Spanish breed which actual census (2008) consists of 3057 females and 203 males distributed in 22 populations more or less isolated. The aim of this work is to study the genetic status of this breed through the analysis of molecular data from 10 microsatellites typed in historic and actual live animals.</p> <p>Results</p> <p>The mean expected heterozygosity across loci within populations ranged from 0.62 to 0.77. Genetic differentiation measures were moderate, with a mean F_STof 0.074, G_STof 0.081 and R_STof 0.085. Percentages of variation among and within populations were 7.5 and 92.5, respectively. Bayesian clustering analyses pointed out a population subdivision in 16 clusters, however, no correlation between geographical distances and genetic differences was found. Management factors such as the limited exchange of animals between farmers (estimated gene flow Nm = 3.08) mostly due to sanitary and social constraints could be the major causes affecting Guadarrama goat population subdivision.</p> <p>Conclusion</p> <p>Genetic diversity measures revealed a good status of biodiversity in the Guadarrama goat breed. Since diseases are the first cause affecting the census in this breed, population subdivision would be an advantage for its conservation. However, to maintain private alleles present at low frequencies in such small populations minimizing the inbreeding rate, it would necessitate some mating designs of animals carrying such alleles among populations. The systematic use of molecular markers will facilitate the comprehensive management of these populations, which in combination with the actual breeding program to increase milk yield, will constitute a good strategy to preserve the breed.</p

Botany, chemistry, and pharmaceutical significance of Sida cordifolia: a traditional medicinal plant

Sida cordifolia Linn. belonging to the family, Malvaceae has been widely employed in traditional medications in many parts of the world including India, Brazil, and other Asian and African countries. The plant is extensively used in the Ayurvedic medicine preparation. There are more than 200 plant species within the genus Sida, which are distributed predominantly in the tropical regions. The correct taxonomic identification is a major concern due to the fact that S. cordifolia looks morphologically similar with its related species. It possesses activity against various human ailments, including cancer, asthma, cough, diarrhea, malaria, gonorrhea, tuberculosis, obesity, ulcer, Parkinson’s disease, urinary infections, and many others. The medical importance of this plant is mainly correlated to the occurrence of diverse biologically active phytochemical compounds such as alkaloids, flavonoids, and steroids. The major compounds include β-phenylamines, 2-carboxylated tryptamines, quinazoline, quinoline, indole, ephedrine, vasicinone, 5-3-isoprenyl flavone, 5,7-dihydroxy-3-isoprenyl flavone, and 6-(isoprenyl)- 3-methoxy- 8-C-β-D-glucosyl-kaempferol 3-O-β-D-glucosyl[1–4]-α-D-glucoside. The literature survey reveals that most of the pharmacological investigations on S. cordifolia are limited to crude plant extracts and few isolated pure compounds. Therefore, there is a need to evaluate many other unexplored bioactive phytoconstituents with evidences so as to justify the traditional usages of S. cordifolia. Furthermore, detailed studies on the action of mechanisms of these isolated compounds supported by clinical research are necessary for validating their application in contemporary medicines. The aim of the present chapter is to provide a detailed information on the ethnobotanical, phytochemical, and pharmacological aspects of S. cordifolia

Global, regional, and national burden of disorders affecting the nervous system, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BackgroundDisorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021.MethodsWe estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined.FindingsGlobally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378–521), affecting 3·40 billion (3·20–3·62) individuals (43·1%, 40·5–45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7–26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6–38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5–32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7–2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer.InterpretationAs the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed

Application of water quality index models to an Irish estuary

The paper investigates the application of different Water Quality Index (WQI) models for to estuarine waters. WQI models are aggregation based mathematical models that convert extensive water quality data into a single value. They typically contain four crucial components with the functions of (1) selecting parameters, (2) developing sub-index rules, (3) generating weighting values, and (4) aggregating the sub-indices. They are attractive because of their relative simplicity and ease of application. However, there is a level of uncertainty in the final aggregated indices due to the potentially large spatial and temporal variations in the input water parameter values. Here we apply seven different WQI models to Cork Harbour, an estuary on the southwest coast of Ireland. The water quality data input data included measurements of nine water quality monitoring parameters from 31 monitoring sites in Cork Harbour. The spatial uncertainty of the WQI models was estimated based on the standard deviation of the computed indices. The spatial uncertainty of the input water quality data was also determined and compared with that of the WQIs for any correlationThis research was funded by the Department of Civil Engineering, NUI Galway. The authors would like to thank the Environmental Protection Agency for water quality data

A review study of water quality index models and their use for assessing surface water quality

The water quality index (WQI) model is a popular tool for evaluating surface water quality. It uses aggregation techniques that allow conversion of extensive water quality data into a single value or index. Globally, the WQI model has been applied to evaluate water quality (surface water and groundwater) based on local water quality criteria. Since its development in the 1960s, it has become a popular tool due to its generalised structure and ease-of-use. Commonly, WQI models involve four consecutive stages; these are (1) selection of the water quality parameters, (2) generation of sub-indices for each parameter (3) calculation of the parameter weighting values, and (4) aggregation of sub-indices to compute the overall water quality index. Several researchers have utilized a range of applications of WQI models to evaluate the water quality of rivers, lakes, reservoirs, and estuaries. Some problems of the WQI model are that they are usually developed based on site-specific guidelines for a particular region, and are therefore not generic. Moreover, they produce uncertainty in the conversion of large amounts of water quality data into a single index. This paper presents a comparative discussion of the most commonly used WQI models, including the different model structures, components, and applications. Particular focus is placed on parameterization of the models, the techniques used to determine the sub-indices, parameter weighting values, index aggregation functions and the sources of uncertainty. Issues affecting model accuracy are also discussed.The authors would like to acknowledge the Hardiman Research Scholarship of the National University of Ireland Galway, which funded the first author as part of his PhD program. The reserach was also supported by MaREI, the SFI Research Centre for Energy, Climate, and Marine [Grant No: 12/RC/2302_P2]