Nuclear and isotopic techniques for addressing nutritional problems, with special reference to current applications in developing countries

Nuclear and isotopic techniques are valuable tools in human nutritional research studies. Isotopes, both radioactive and nonradioactive, enable detailed evaluations of nutrient intake, body composition, energy expenditure, status of micronutrients, and nutrient bioavailability. In recent times, isotopic methods have been widely used in a number of coordinated research projects and technical cooperation projects of the International Atomic Energy Agency's Nutrition Programme. The doubly labeled water technique combines the use of the stable isotopes oxygen-18 and hydrogen-2 (deuterium) to measure total energy expenditure in free-living human subjects, and to investigate the magnitude and causes of both undernutrition and the emergence of obesity in developing countries. The deuterium dilution technique is a reliable tool to measure breastmilk intake and thereby infant growth and development. In collaboration with the World Health Organization's Growth Monitoring Program, this technique is being used to generate new data on growth standards for children in developing countries. This technique is also used in the measurement of body composition by the estimation of lean body mass and fat mass in individuals. Stable isotopes of iron and zinc have been successfully used to assess the nutritional impact of several nationwide food supplementation-programs conducted on pregnant and lactating women and children in both industrialized and developing countries. Isotopic techniques are especially suitable for monitoring changes in body composition, energy metabolism, and mineral status (with particular reference to osteoporosis) in the elderly. Nuclear methods have also served to develop models for a physiological reference man in Asia in support of radiological health and safety issues, for establishing elemental composition of foods, and for measurement of pollutants in the environment.     

Tomato redness for assessing ozone treatment to extend the shelf life

Ozone could be seen as an alternative to refrigeration in order to enhance tomato shelf life in areas where cold facilities are not available. However, the effect of ozone on fruit ripening and quality is still unclear. From the other side, it is well known that tomato ripening can be correlated to the development of red color. Therefore, experiments were carried out to develop a redness index to characterize the dynamics of ripening which was further used to characterize the effect of ozone on storage and ripening. Several gaseous ozone treatments were applied. Color changes from green to red were monitored. Ozone treatment delayed both the development of red color as well as of rotting. Color development and rotting followed a trend like that described by Hill’s equation. Shelf life was enhanced by 12 days when treated tomatoes were stored at 15 °C. The longer shelf like was mainly due to a reduction in surface microbial count. Analysis through mathematical modeling allowed establishing the dynamics of shelf life as a function of red color development.     

Tracking toxaphene in the North American Great Lakes basin. 1. Impact of toxaphene residues in United States soils

A coupled atmospheric transport model was employed to study six scenarios to assess the contribution of reemission and long-range transport of toxaphene from different sources in the United States to its environmental fate in the Great Lakes ecosystem in the year 2000. Modeled air concentrations at the first model level (1.5 m) range from less than 5 pg m(-3) over the upper Great lakes (Lakes Superior and Huron) to several tens of picograms per cubic meter over the lower Great Lakes (Lakes Erie and Ontario) in the summer but drop off to the range from 0.05 to 2 pg m(-3) in the wintertime. The modeled toxaphene depositions to the lakes suggest a decreasing trend from the mid-1990s to 2000. Modeling results showed that, on an annual basis, for the Great Lakes basin as a whole, the southeast U.S. sources made the largest contribution to the toxaphene air concentrations and dry and wet depositions at 72%, 78%, and 88% respectively. The model results also showed that a significant proportion of these contributions occur during relatively short episodic events due primarily to the interseasonal changes in atmospheric circulation patterns.     

Enhancement of fuel cell properties in polyethersulfone and sulfonated poly (ether ether ketone) membranes using metal oxide nanoparticles for proton exchange membrane fuel cell

The proton exchange membrane (PEM) was synthesized using polyethersulfone (PES), sulfonated poly (ether ether ketone) (SPEEK) and nanoparticles. The metal oxide nanoparticles such as Fe₃O₄, TiO₂ and MoO₃ were added individually to the polymer blend (PES and SPEEK). The polymer composite membranes exhibit excellent features regarding water uptake, ion exchange capacity and proton conductivity than the pristine PES membrane. Since the presence of sulfonic acid groups provides by added SPEEK and the unique properties of inorganic nanoparticles (Fe₃O₄, TiO₂ and MoO₃) helps to interconnect the ionic domain by the absorption of more water molecules thereby enhance the conductivity value. The proton conductivity of PES, SPEEK, PES/SPEEK/Fe₃O₄, PES/SPEEK/TiO₂ and PES/SPEEK/MoO₃ membranes were 0.22 × 10^?4 S/cm, 5.18 × 10^?4 S/cm, 3.57 × 10^?4 S/cm, 4.57 × 10^?4 S/cm and 2.67 × 10^?4 S/cm respectively. Even though the blending of PES with SPEEK has reduced the conductivity value to a lesser extent, hydrophobic PES has vital role in reducing the solvent uptake, swelling ratio and improves hydrolytic stability. Glass transition temperature (T_g) of the membranes were determined from DSC thermogram and it satisfies the operating condition of fuel cell system which guarantees the thermal stability of the membrane for fuel cell application.     

Impact of Novel Bio-Foliar on Natural Cocoons and Silk Quality

Present study was carried out to improve the cocoon and silk quality by nursing with bio-foliar. Five mulberry varieties, namely, S-1, S-146, S-1635, AR-14, and BR-2 were fed to silkworms and cocoons produced by them were used for analysis. Results of the investigation indicated that highest cocoon weight (1.733 g), shell weight (0.349 g), shell ratio (20.144%), effective rate of rearing (97.50), silk filament length (1094.07 m), filament weight (0.315 g), denier (3.071 g/d), renditta (5.505 kg), non-breakable filament length (875.254 m), and silk yield (18.169%) were obtained by enriching BR-2 and S-1635 varieties with 45 ppm of bio-foliar compare to control.     

Environmental performance of algal biofuel technology options

Considerable research and development is underway to produce fuels from microalgae, one of several options being explored for increasing transportation fuel supplies and mitigating greenhouse gas emissions (GHG). This work models life-cycle GHG and on-site freshwater consumption for algal biofuels over a wide technology space, spanning both near- and long-term options. The environmental performance of algal biofuel production can vary considerably and is influenced by engineering, biological, siting, and land-use considerations. We have examined these considerations for open pond systems, to identify variables that have a strong influence on GHG and freshwater consumption. We conclude that algal biofuels can yield GHG reductions relative to fossil and other biobased fuels with the use of appropriate technology options. Further, freshwater consumption for algal biofuels produced using saline pond systems can be comparable to that of petroleum-derived fuels.     

An intelligent hierarchical residual attention learning-based conjoined twin neural network for Alzheimer's stage detection and prediction

Alzheimer's disorder (AD) causes permanent impairment in the brain's memory of the cellular system, leading to the initiation of dementia. Earlier detection of Alzheimer's disease in the initial stages is challenging for researchers. Deep learning and machine learning-based techniques can help resolve many issues associated with brain imaging exploration. Brain MR Images (Brain-MRI) are used to detect Alzheimer's in computable research work. To correctly categorize the stages of Alzheimer's disease, discriminative features need to be extracted from the MR images. Recently, many studies have used deep learning methods for the early detection of this disorder. However, overfitting degrades the deep learning method's performance because the dataset's selection images are smaller and imbalanced. Some studies could not reach more discriminative and effectual attention-aware features for Alzheimer's stage classification to increase the model performance. In this paper, we develop a novel hierarchical residual attention learning-inspired multistage conjoined twin network (HRAL-CTNN) to classify the stages of Alzheimer's. We used augmentation approaches to scale insufficient and imbalanced data. The HRAL-CTNN is efficiently overcoming the issues of not obtaining efficient attention-aware and generative features for Alzheimer's stage classification. The proposed model solved the problem of redundant features by extracting attentive discriminant features, and scaling imbalance data by data augmentation, after that training and validation using HRAL-CTNN. The execution of this proposed work has been performed on the ADNI MRI dataset. This work achieved outstanding accuracy of 99.97 $\pm$ 0.01% and F1 score of 99.30 $\pm$ 0.02% for Alzheimer's stage classification. This model proposed by our group outperformed the existing related studies in terms of the model's performance score.     

Synergistic Excited State Involved Catalytic Reduction of (NH3-trz)[Fe(dipic)2] Complex by SnO2/TiO2 Nanocomposite

In this study, cost-effective, environmentally friendly well-fabricated SnO₂/TiO₂ nanocomposite synthesized via hydrothermal route and the photocatalytic activity was validated using the (NH₃-trz)[Fe(dipic)₂] complex under ultra-violet illumination. The structural features of (NH₃-trz)[Fe(dipic)₂] complex and catalysts were systematically examined by various characteristics. The photoreactivity of the model compound (NH₃-trz)[Fe(dipic)₂] in water/binary solvent systems was investigated. The rate of photoreaction (k) of nanocomposite (0.1432 s^?1) is higher than the SnO₂ (0.0373 s^?1) and TiO₂ (0.1422 s^?1) in H₂O:PrⁱOH (70:30%) than the rest of the solvents system. The pathways, mechanistic feature of accumulated reactive species on nanocomposite to induce adherent [Fe(dipic)₂]^? anion and photo-reductive products were studied.

Graphical Abstract