Solvent Extraction of Cd(II) with N-Butylaniline in Chloroform from a Mixture of Potassium Iodide & Sulphuric Acid

819-82

Relation of vertebral deformities to bone density, structure, and strength.

Because they are not reliably discriminated by areal bone mineral density (aBMD) measurements, it is unclear whether minimal vertebral deformities represent early osteoporotic fractures. To address this, we compared 90 postmenopausal women with no deformity (controls) with 142 women with one or more semiquantitative grade 1 (mild) deformities and 51 women with any grade 2-3 (moderate/severe) deformities. aBMD was measured by dual-energy X-ray absorptiometry (DXA), lumbar spine volumetric bone mineral density (vBMD) and geometry by quantitative computed tomography (QCT), bone microstructure by high-resolution peripheral QCT at the radius (HRpQCT), and vertebral compressive strength and load-to-strength ratio by finite-element analysis (FEA) of lumbar spine QCT images. Compared with controls, women with grade 1 deformities had significantly worse values for many bone density, structure, and strength parameters, although deficits all were much worse for the women with grade 2-3 deformities. Likewise, these skeletal parameters were more strongly associated with moderate to severe than with mild deformities by age-adjusted logistic regression. Nonetheless, grade 1 vertebral deformities were significantly associated with four of the five main variable categories assessed: bone density (lumbar spine vBMD), bone geometry (vertebral apparent cortical thickness), bone strength (overall vertebral compressive strength by FEA), and load-to-strength ratio (45-degree forward bending ÷ vertebral compressive strength). Thus significantly impaired bone density, structure, and strength compared with controls indicate that many grade 1 deformities do represent early osteoporotic fractures, with corresponding implications for clinical decision making

'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems

An examination of object recognition challenge leaderboards (ILSVRC, PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small differences amongst themselves in terms of error rate/mAP. To better differentiate the top performers, additional criteria are required. Moreover, the (test) images, on which the performance scores are based, predominantly contain fully visible objects. Therefore, `harder' test images, mimicking the challenging conditions (e.g. occlusion) in which humans routinely recognize objects, need to be utilized for benchmarking. To address the concerns mentioned above, we make two contributions. First, we systematically vary the level of local object-part content, global detail and spatial context in images from PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12. Second, we propose an object-part based benchmarking procedure which quantifies classifiers' robustness to a range of visibility and contextual settings. The benchmarking procedure relies on a semantic similarity measure that naturally addresses potential semantic granularity differences between the category labels in training and test datasets, thus eliminating manual mapping. We use our procedure on the PPSS-12 dataset to benchmark top-performing classifiers trained on the ILSVRC-2012 dataset. Our results show that the proposed benchmarking procedure enables additional differentiation among state-of-the-art object classifiers in terms of their ability to handle missing content and insufficient object detail. Given this capability for additional differentiation, our approach can potentially supplement existing benchmarking procedures used in object recognition challenge leaderboards.Comment: Extended version of our ACCV-2016 paper. Author formatting modifie

Analysis of MAGSAT data of the Indian region

Progress in the development of software for reading MAGSAT data tapes and for the reduction of anomaly data, and in the preparation of data for magnetic anomaly maps is reported

Age-Dependence of Femoral Strength in White Women and Men

Although age-related variations in areal bone mineral density (aBMD) and the prevalence of osteoporosis have been well characterized, there is a paucity of data on femoral strength in the population. Addressing this issue, we used finite-element analysis of quantitative computed tomographic scans to assess femoral strength in an age-stratified cohort of 362 women and 317 men, aged 21 to 89 years, randomly sampled from the population of Rochester, MN, and compared femoral strength with femoral neck aBMD. Percent reductions over adulthood were much greater for femoral strength (55% in women, 39% in men) than for femoral neck aBMD (26% in women, 21% in men), an effect that was accentuated in women. Notable declines in strength started in the mid-40s for women and one decade later for men. At advanced age, most of the strength deficit for women compared with men was a result of this decade-earlier onset of strength loss for women, this factor being more important than sex-related differences in peak bone strength and annual rates of bone loss. For both sexes, the prevalence of “low femoral strength” (<3000 N) was much higher than the prevalence of osteoporosis (femoral neck aBMD T-score of −2.5 or less). We conclude that age-related declines in femoral strength are much greater than suggested by age-related declines in femoral neck aBMD. Further, far more of the elderly may be at high risk of hip fracture because of low femoral strength than previously assumed based on the traditional classification of osteoporosis. © 2010 American Society for Bone and Mineral Research

Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet

Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https://saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.Comment: This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_1

Dynamical cluster-decay model for hot and rotating light-mass nuclear systems, applied to low-energy 32^{32}S + 24^{24}Mg →56\to ^{56}Ni reaction

The dynamical cluster-decay model (DCM) is developed further for the decay of hot and rotating compound nuclei (CN) formed in light heavy-ion reactions. The model is worked out in terms of only one parameter, namely the neck-length parameter, which is related to the total kinetic energy TKE(T) or effective Q-value $Q_{eff}(T)$ at temperature T of the hot CN, defined in terms of the both the light-particles (LP), with $A \leq$ 4, Z $\leq$ 2, as well as the complex intermediate mass fragments (IMF), with $4 2$, is considered as the dynamical collective mass motion of preformed clusters through the barrier. Within the same dynamical model treatment, the LPs are shown to have different characteristics as compared to the IMFs. The systematic variation of the LP emission cross section $\sigma_{LP}$, and IMF emission cross section $\sigma_{IMF}$, calculated on the present DCM match exactly the statistical fission model predictions. It is for the first time that a non-statistical dynamical description is developed for the emission of light-particles from the hot and rotating CN. The model is applied to the decay of $^{56}$Ni formed in the $^{32}$S + $^{24}$Mg reaction at two incident energies E$_{c.m.}$ = 51.6 and 60.5 MeV. Both the IMFs and average $\bar{TKE}$ spectra are found to compare reasonably nicely with the experimental data, favoring asymmetric mass distributions. The LPs emission cross section is shown to depend strongly on the type of emitted particles and their multiplicities