The Lecythidaceae family tree, Couroupita guianensis Aubl, popularly known as Nagpushpa, is a widely cultivated ornamental tree with several uses in traditional medicine. The tree is revered as highly sacred in Indian traditional culture due to its uniquely shaped, fragrant flowers. Considering the significance, we were prompted to carry out the metabolite and transcriptome analysis of Nagapushpa. The flower, petals, stamen, stem and leaf of C. guianensis were metabolically profiled, and it was discovered that the flower tissue contained the highest terpenoid reservoir. A number of terpenoid pathway transcripts were also found in the flower tissue after transcriptome profiling. KEGG pathway mapping was carried out to correlate transcript sequences with the biosynthesis of different types of terpenes. We were able to clone three full-length terpene synthase gene candidates, i.e. monoterpene ocimene synthase, diterpene ent-kaurene synthase and sesquiterpene farnesene synthase. The transcript expression of selected terpene synthase genes was also verified in flower tissue. These cloned sequences were used for in silico structural investigations and protein function prediction at the level of 3D structure. The data presented in this study provide a comprehensive resource for the metabolic and transcriptomic profiles of C. guianensis. The study paves the way towards the elucidation of terpene biosynthetic pathway in C. guianensis and heterologous production of useful terpenoids in the future.

Stimulated Raman forward scattering (SRFS) of an intense X-mode laser pump in a preformed parabolic plasma density profile is investigated. The laser pump excites a plasma wave and one/two electromagnetic sideband waves. In Raman forward scattering, the growth rate of the parametric instability scales as two-third powers of the pump amplitude and increases linearly with cyclotron frequency.

High mortality rates and poor outcomes from eating disorders, especially anorexia nervosa, are largely preventable and require urgent action. A national strategy to address this should include prevention; early detection; timely access to integrated physical and psychological treatments; safe management of emergencies; suicide prevention; and investment in training, services and research.

The difficulty of diagnosing prostate cancer (PC) with the available biomarkers frequently leads to over-diagnosis and overtreatment of PC, underscoring the need for novel molecular signatures. The purpose of this review is to provide a summary of the currently available cellular metabolomics for PC molecular signatures. A comprehensive search on PubMed was conducted to find studies published between January 2004 and August 2022 that reported biomarkers for PC detection, development, aggressiveness, recurrence and treatment response. Although potential studies have reported the presence of distinguishing molecules that can distinguish between benign and cancerous prostate tissue. However, there are few studies looking into signature molecules linked to disease development, therapy response or tumour recurrence. The majority of these studies use high-dimensional datasets, and the number of potential metabolites investigated frequently exceeds the size of the available samples. In light of this, pre-analytical, statistical, methodological and confounding factors such as antiandrogen therapy (NAT) may also be linked to the identified chemometric multivariate differences between PC and relevant control samples in the datasets. Despite the methodological and procedural challenges, a range of methodological groups and processes have consistently identified a number of signature metabolites and pathways that appear to imply a substantial involvement in the cellular metabolomics of PC for tumour formation and recurrence.

Eggnog, a dairy-based beverage, comprises both milk and egg proteins. We aimed at optimizing the eggnog formulation using Box–Behnken design of response surface methodology. The combined effects of milk (60–75), cream (25–35) and eggnog base (6–8, all three as g 100/ml) were investigated on heat coagulation time, viscosity and thermal gelation temperature. ANOVA indicated that experimental data were well explained by a quadratic model with high check values (R2 > 0.94) and non-significant lack of fit tests. Based on the responses, an optimized formulation of eggnog with 60.0 milk, 25.0 cream and 6.50 eggnog base (as g 100/ml), could be considered best for manufacturing eggnog with desired attributes. This optimized formulation was characterized for physico-chemical, microbial and sensory attributes and the results indicated significantly higher fat and protein content than control formulation, but lesser lactose and total sugar content. Significantly higher viscosity, heat stability and lower thermal gelation temperature were also observed for the optimized formulation. Coliform, yeast and mold, E. coli and Salmonella counts were not detected in any sample but a significantly lower total plate count was observed for the optimized formulation.

This study aimed at the performance evaluation of a closed flock of Marwari sheep and also to study the effect of accumulated inbreeding on the growth traits using a linear mixed model methodology. The data generated for 39 years (1981 to 2020) on Marwari sheep maintained at ICAR-Central Sheep & Wool Research Institute, Arid Region Campus (CSWRI, ARC), Bikaner, Rajasthan, India were used for analysis on growth traits. The overall least-squares means (LSM) of live weights at birth (BWT), weaning (3MWT), 6 months (6MWT), 9 months (9MWT) and 12 months (12MWT) were observed to be 3.02 ± 0.01, 14.30 ± 0.04, 20.12 ± 0.05, 23.68 ± 0.06 and 26.39 ± 0.07 kg, respectively. Overall LSM for average daily gain from birth to 3 months (ADG1), 3 to 6 months (ADG2) and 6 to 12 months (ADG3) were 125.44 ± 0.40, 67.37 ± 0.40 and 35.83 ± 0.29 g/day, respectively. Kleiber ratio (KR) from birth to 3 months (KR1), 3 to 6 months (KR2), and 6 to 12 months (KR3) were 16.78 ± 0.02, 6.58 ± 0.04 and 3.05 ± 0.02, respectively. Results revealed a 4.36, 25.83, 36.33, 31.50 and 28.99% improvement in the live weights since the inception of the improvement programme. This is also reflected by a significant effect of sire on all the growth traits. The estimate of inbreeding in the flock was 1.55%. Highly inbred animals were 5.13% (>5% Fi). The study revealed the non-significant effect of inbreeding level on all growth traits except for BWT and KR3. For BWT, inbreeding classes had variation; however, a negative effect was not seen. The inbreeding class (>5% Fi) was reduced by 0.05 units for KR3 as against its preceding class. Dam’s age at lambing and weight influenced the birth weight and subsequent weights. The study concluded that the selection programme of Marwari sheep is in the right direction; however, regular monitoring of inbreeding is necessary and factors affecting growth must be monitored to attain better growth rates in the nucleus.

Prostate cancer (PC) presents great challenges in early diagnosis and often leads to unnecessary invasive procedures as well as over diagnosis and treatment, thus highlighting the need for promising early diagnostic biomarkers. The aim of this review is to provide an up-to-date summary of chronologically existing metabolomics PC biomarkers, their potential to improve clinical PC diagnosis and to reduce the proliferation and monitoring of PC. The systematic research was conducted on PubMed in accordance with PRISMA guidelines to report PC biomarkers. The majority of the studies distinguished malignant from benign prostate and few explored the biomarkers associated with the progression of PC. The present review summarises the primary outcomes of most significant studies to extend our knowledge of PC metabolomics biomarkers. We observed divergent inter-laboratory technical procedures employing different statistical approaches produced abundant information regarding PC metabolites perturbation. Since PC metabolomics is still in its early phase, it is vital that we dig out the most specific, sensitive and accurate metabolic signatures and conduct more studies with milestone findings with comparable sample sizes to validate and corroborate the findings.

The work presented in this research communication was carried out to prepare low calorie synbiotic milk beverage by optimizing water and sugar level and to investigate the effect on its storage ability of different packaging materials (polypropylene, high impact polystyrene, high-density polyethylene and glass). Addition of both water and sugar significantly (P < 0.05) affected the viscosity, probiotic count and sensory properties. Based on the findings, 40% water and 8% sugar level were optimized for the preparation of the beverage. Apparent viscosity and acidity increased whilst pH and probiotic counts declined during storage, irrespective of packaging materials. The prepared beverage remained most acceptable at refrigeration temperature up to a period of 15 and 12 d when packaged in glass and high impact polystyrene, respectively. Furthermore, it retained a minimum recommended level of probiotic (7 log cfu/ml) during storage for 15 d at 4 °C.

Cardiac hypertrophy (CH) is an augmentation of either the right ventricular or the left ventricular mass in order to compensate for the increase of work load on the heart. Metabolic abnormalities lead to histological changes of cardiac myocytes and turn into CH. The molecular mechanisms that lead to initiate CH have been of widespread concern, hence the development of the new field of research, metabolomics: one ‘omics’ approach that can reveal comprehensive information of the paradigm shift of metabolic pathways network in contrast to individual enzymatic reaction-based metabolites, have attempted and until now only 19 studies have been conducted using experimental animal and human specimens. Nuclear magnetic resonance spectroscopy and mass spectrometry-based metabolomics studies have found that CH is a metabolic disease and is mainly linked to the harmonic imbalance of glycolysis, citric acid cycle, amino acids and lipid metabolism. The current review will summarise the main outcomes of the above mentioned 19 studies that have expanded our understanding of the molecular mechanisms that may lead to CH and eventually to heart failure.

In this paper, a new method is proposed to find a feasible energy-efficient path between an initial point and goal point on uneven terrain and then to optimally traverse the path. The path is planned by integrating the geometric features of the uneven terrain and the biped robot dynamics. This integrated information of biped dynamics and associated cost (energy) for moving toward the goal point is used to define the value of a new speed function at each point on the discretized surface of the terrain. The value is stored as a matrix called the dynamic transport cost (DTC). The path is obtained by solving the Eikonal equation numerically by fast marching method (FMM) on an orthogonal grid, by using the information stored in the DTC matrix. One step of walk on uneven terrain is characterized by 10 footstep parameters (FSPs); these FSPs represent the position of swinging foot at the starting and ending time of the walk, orientation, and state (left or right) of support foot. A walking dataset was created for different walking conditions (FSPs), which the biped robot is likely to encounter when it has to walk on the uneven terrain. The corresponding energy optimal hip and foot trajectory parameters (HFTPs) are obtained by optimization using a genetic algorithm (GA). The created walk dataset is generalized by training a feedforward neural network (NN) using the scaled conjugate gradient (SCG) algorithm. The Foot placement planner gives a sequence of foot positions and orientations along the obtained path, which is followed by the biped robot by generating real-time optimal foot and hip trajectories using the learned NN. Simulation results on different types of uneven terrains validate the proposed method.

This study used a series of individual-level datasets from National Family Health Surveys conducted in 1998–99, 2005–06 and 2015–16 to assess the factors behind the reduction in childhood stunting and underweight in India between the years 1998–99 and 2015–16. A multivariable decomposition regression analysis was performed. Results showed that the prevalence of childhood stunting declined from 49.4% in 1998–99 to 34.9% in 2015–16. Over the same period, the prevalence of childhood underweight declined from 41.9% in 1998–99 to 33.1% in 2015–16. The reduction in the prevalence of stunting was found to be contributed largely by a reduction in the combined prevalence of stunting and underweight (60%), followed by stunted only (21%) and the combined prevalence of stunting, underweight and wasting (19%). Likewise, the reduction in the prevalence of underweight was contributed by a reduction in the combined prevalence of stunting and underweight and the combined prevalence of stunting, underweight and wasting. Results of the decomposition analysis showed that over the period 1998–99 to 2015–16, improvement in wealth status and maternal education led to 13% and 12% declines, respectively, in childhood stunting and to 31% and 19% declines, respectively, in childhood underweight. Furthermore, reductions in childhood stunting and underweight were due to an increased average number of antenatal care visits, lower average birth order, decreased share of children with below-average birth size, increased use of clean fuel for cooking and a reduction in the practice of open defecation. These findings suggest that further reduction in the prevalence of childhood stunting and underweight could be attained through more equitable household economic growth, investment in girl’s education, greater access to improved toilet facilities, more widespread use of clean fuel for cooking, reduction in average birth order, increased antenatal care visits and greater consumption of IFA tablets by pregnant women. Policymakers need to prioritize these measures to further reduce malnutrition among Indian children.

The artificial magnetic conductor (AMC) formed by meta-materials is used in the micro-strip antenna design for microwave sensor applications. The AMC structures with two uniplanar layers, which include modified square split-ring resonator cells, are repeated and formed into a 5 × 3 array. The antenna's operational bandwidth result is significant, and it is in the range of 2.8–10 GHz with a maximum gain of 11.8 dB. Cancer or malignant tumor cells have entirely different electrical properties than healthy breast tissue. Principal component analysis and specific absorption rate (SAR) are used as a parameter to identify cancer or malignant tumor cells in the breast. The SAR values have been calculated for each layer of the 3D breast model. The results show a difference in the SAR values based on the size and tumors' locations.