The beneficiation of low-grade iron ores to meet the growing demand for iron and steel is an important research topic. However, certain ores do not qualify to be treated in physical separation ...processes, and hence, alternative strategies are being looked into for upgrading their iron content. The objective of this review article is to compile and amalgamate the crucial information regarding the beneficiation of low-grade iron ores using carbothermic reduction followed by magnetic separation, which is a promising technique to treat iron ores with complex mineralogy and liberation issues. Reduction roasting studies done for different types low-grade iron ores including oolitic iron ores, banded iron ores, iron ore slimes and tailings, and industrial wastes have been discussed. The large scale technological advancements in reduction roasting and the possibilities of the application of alternative reductants as substitutes for coal have also been highlighted.
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The emergence of Coronavirus Disease 2019 (COVID-19) in early December 2019 has caused immense damage to health and global well-being. Currently, there are approximately five million confirmed cases ...and the novel virus is still spreading rapidly all over the world. Many hospitals across the globe are not yet equipped with an adequate amount of testing kits and the manual Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test is time-consuming and troublesome. It is hence very important to design an automated and early diagnosis system which can provide fast decision and greatly reduce the diagnosis error. The chest X-ray images along with emerging Artificial Intelligence (AI) methodologies, in particular Deep Learning (DL) algorithms have recently become a worthy choice for early COVID-19 screening. This paper proposes a DL assisted automated method using X-ray images for early diagnosis of COVID-19 infection. We evaluate the effectiveness of eight pre-trained Convolutional Neural Network (CNN) models such as AlexNet, VGG-16, GoogleNet, MobileNet-V2, SqueezeNet, ResNet-34, ResNet-50 and Inception-V3 for classification of COVID-19 from normal cases. Also, comparative analyses have been made among these models by considering several important factors such as batch size, learning rate, number of epochs, and type of optimizers with an aim to find the best suited model. The models have been validated on publicly available chest X-ray images and the best performance is obtained by ResNet-34 with an accuracy of 98.33%. This study will be useful for researchers to think for the design of more effective CNN based models for early COVID-19 detection.
•A deep learning assisted automated method is proposed for COVID-19 diagnosis.•A comprehensive study among eight pre-trained CNN models is performed.•The impact of several hyper-parameters have been analyzed.•The best performing model is obtained and compared with the state-of-the-art methods.•The model could assist radiologists for accurate and stable COVID-19 diagnosis.
This paper presents an automatic classification system for segregating pathological brain from normal brains in magnetic resonance imaging scanning. The proposed system employs contrast limited ...adaptive histogram equalization scheme to enhance the diseased region in brain MR images. Two-dimensional stationary wavelet transform is harnessed to extract features from the preprocessed images. The feature vector is constructed using the energy and entropy values, computed from the level- 2 SWT coefficients. Then, the relevant and uncorrelated features are selected using symmetric uncertainty ranking filter. Subsequently, the selected features are given input to the proposed AdaBoost with support vector machine classifier, where SVM is used as the base classifier of AdaBoost algorithm. To validate the proposed system, three standard MR image datasets, Dataset-66, Dataset-160, and Dataset- 255 have been utilized. The 5 runs of k-fold stratified cross validation results indicate the suggested scheme offers better performance than other existing schemes in terms of accuracy and number of features. The proposed system earns ideal classification over Dataset-66 and Dataset-160; whereas, for Dataset- 255, an accuracy of 99.45% is achieved.
Background
Atypical teratoid/rhabdoid tumours (AT/RT) are uncommon but aggressive, malignant tumours in the paediatric age group. Presentation of concomitant supratentorial and infratentorial lesions ...in an infant is extremely rare. We discuss an infant diagnosed with such lesions. Systematic PubMed search was conducted using keywords ‘atypical teratoid /rhabdoid tumor’, ‘paediatric’ and ‘multifocal’. Reports were included for patients younger than 18 years with two or more lesions. The search yielded additional five cases and were tabulated. Age, sex, location, treatment given and survival/outcome were noted.
Case report
A 10-month-old child presented with complaints of drowsiness and intractable vomiting. Imaging showed multifocal supra- and infratentorial lesions with obstructive hydrocephalus. The child underwent ventriculoperitoneal shunt followed by surgical removal of the posterior fossa lesion. Histopathological features were consistent with AT/RT.
Conclusions
Multifocal AT/RT are very rare. The impact of multifocality in the outcome is not known as very few reports are available. Newer targeted therapies may offer insight in improving outcomes in the future.
Rice exhibits enormous genetic diversity, population structure and molecular marker-traits associated with abiotic stress tolerance to high temperature stress. A set of breeding lines and landraces ...representing 240 germplasm lines were studied. Based on spikelet fertility percent under high temperature, tolerant genotypes were broadly classified into four classes. Genetic diversity indicated a moderate level of genetic base of the population for the trait studied. Wright's F statistic estimates showed a deviation of Hardy-Weinberg expectation in the population. The analysis of molecular variance revealed 25 percent variation between population, 61 percent among individuals and 14 percent within individuals in the set. The STRUCTURE analysis categorized the entire population into three sub-populations and suggested that most of the landraces in each sub-population had a common primary ancestor with few admix individuals. The composition of materials in the panel showed the presence of many QTLs representing the entire genome for the expression of tolerance. The strongly associated marker RM547 tagged with spikelet fertility under stress and the markers like RM228, RM205, RM247, RM242, INDEL3 and RM314 indirectly controlling the high temperature stress tolerance were detected through both mixed linear model and general linear model TASSEL analysis. These markers can be deployed as a resource for marker-assisted breeding program of high temperature stress tolerance.
Alveolar macrophages are terminally differentiated, lung-resident macrophages of prenatal origin. Alveolar macrophages are unique in their long life and their important role in lung development and ...function, as well as their lung-localized responses to infection and inflammation. To date, no unified method for identification, isolation, and handling of alveolar macrophages from humans and mice exists. Such a method is needed for studies on these important innate immune cells in various experimental settings. The method described here, which can be easily adopted by any laboratory, is a simplified approach to harvesting alveolar macrophages from bronchoalveolar lavage fluid or from lung tissue and maintaining them in vitro. Because alveolar macrophages primarily occur as adherent cells in the alveoli, the focus of this method is on dislodging them prior to harvest and identification. The lung is a highly vascularized organ, and various cell types of myeloid and lymphoid origin inhabit, interact, and are influenced by the lung microenvironment. By using the set of surface markers described here, researchers can easily and unambiguously distinguish alveolar macrophages from other leukocytes, and purify them for downstream applications. The culture method developed herein supports both human and mouse alveolar macrophages for in vitro growth, and is compatible with cellular and molecular studies.
High seed vigour ensures good quality seed and higher productivity. Early seedling growth parameters indicate seed vigour in rice. Seed vigour via physiological growth parameters is a complex trait ...controlled by many quantitative trait loci. A panel was prepared representing a population of 274 rice landraces by including genotypes from all the phenotypic groups of sixseedling stage physiological parameters including germination % for association mapping. Wide variations for the six studiedtraits were observed in the population. The population was classified into 3 genetic groups. Fixation indices indicated the presence of linkage disequilibrium in the population. The population was classified into subpopulations and each subpopulation showed correspondence with the 6 physiological traits. A total of 5 reported QTLs viz., qGP8.1 for germination % (GP); qSVII2.1, qSVII6.1 and qSVII6.2 for seed vigour index II (SVII), and qRSR11.1 for root-shoot ratio (RSR) were validated in this mapping population. In addition, 13 QTLs regulating the physiological parameters such as qSVI 11.1 for seed vigour index I; qSVI11.1 and qSVI12.1 for seed vigour index II; qRRG10.1, qRRG8.1, qRRG8.2, qRRG6.1 and qRRG4.1 for rate of root growth (RRG); qRSR2.1, qRSR3.1 and qRSR5.1 for root-shoot ratio (RSR) while qGP6.2 and qGP6.3 for germination %were identified. Additionally, co-localization or co-inheritance of QTLs, qGP8.1 and qSVI8.1 for GP and SVI-1; qGP6.2 and qRRG6.1 for GP and RRG, and qSVI11.1 and qRSR11.1 for SVI and RSR were detected. The QTLs identified in this study will be useful for improvement of seed vigour trait in rice.
Sensorineural hearing loss is correlated to massive neurological or psychiatric disease.
T1-weighted volumetric images were acquired from fourteen subjects with right-sided hearing loss (RHL), ...fifteen subjects with left-sided hearing loss (LHL), and twenty healthy controls (HC).
We treated a three-class classification problem: HC, LHL, and RHL. Stationary wavelet entropy was employed to extract global features from magnetic resonance images of each subject. Those stationary wavelet entropy features were used as input to a single-hidden layer feedforward neuralnetwork classifier.
The 10 repetition results of 10-fold cross validation show that the accuracies of HC, LHL, and RHL are 96.94%, 97.14%, and 97.35%, respectively.
Our developed system is promising and effective in detecting hearing loss.
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