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BACKGROUND: Education is crucial for empowering women in the labour market. However, the choice of occupation may not always necessarily be determined by their level of education. OBJECTIVE: The present study seeks to answer two important questions: (1) Does education help in determining occupational choices of women working in the informal sector? (2) How do factors like number of dependents in the family, woman staying with family, migration status of a woman and the use of networks in the job-search process, affect the occupational choices of women working in the informal sector? METHOD: The study utilised the data collected from 476 women, working in the urban informal sector of three northern States of India. We have estimated multinomial Probit model to analyse the data. RESULTS: This paper argues that education level cannot be the sole premise for a woman to have a better occupational choice within the informal sector. Other factors play a relatively significant role in determining the choice of occupation by a woman. CONCLUSION: Working women in the urban informal sector have less autonomy in the choice of their occupation and the education levels alone cannot help in providing access to more hygienic, career enriching and well-paying options to them. [ABSTRACT FROM AUTHOR]

In numerous real-world healthcare applications, handling incomplete medical data poses significant challenges for missing value imputation and subsequent clustering or classification tasks. Traditional approaches often rely on statistical methods for imputation, which may yield suboptimal results and be computationally intensive. This paper aims to integrate imputation and clustering techniques to enhance the classification of incomplete medical data with improved accuracy. Conventional classification methods are ill-suited for incomplete medical data. To enhance efficiency without compromising accuracy, this paper introduces a novel approach that combines imputation and clustering for the classification of incomplete data. Initially, the linear interpolation imputation method alongside an iterative Fuzzy c-means clustering method is applied and followed by a classification algorithm. The effectiveness of the proposed approach is evaluated using multiple performance metrics, including accuracy, precision, specificity, and sensitivity. The encouraging results demonstrate that our proposed method surpasses classical approaches across various performance criteria. [ABSTRACT FROM AUTHOR]

This paper looks into the role of announcement effect and liquidity effect in monetary transmission in Indian money market. It first looks into the impact of these two effects on the operational target that is, overnight call money market and then applies the same framework on other short term money market rates. The time period considered is from May 2011, when the new monetary policy framework was introduced till Feb 2021. The results show the presence of both the effects on the overnight call money rate and also on other short term rates. In addition to this, there is a presence of asymmetry in monetary transmission which is more prominent after fine-tuning of RBI's monetary operations. [ABSTRACT FROM AUTHOR]

Abscisic acid (ABA) is a plant growth regulator known for its functions, especially in seed maturation, seed dormancy, adaptive responses to biotic and abiotic stresses, and leaf and bud abscission. ABA activity is governed by multiple regulatory pathways that control ABA biosynthesis, signal transduction, and transport. The transport of the ABA signaling molecule occurs from the shoot (site of synthesis) to the fruit (site of action), where ABA receptors decode information as fruit maturation begins and is significantly promoted. The maximum amount of ABA is exported by the phloem from developing fruits during seed formation and initiation of fruit expansion. In the later stages of fruit ripening, ABA export from the phloem decreases significantly, leading to an accumulation of ABA in ripening fruit. Fruit growth, ripening, and senescence are under the control of ABA, and the mechanisms governing these processes are still unfolding. During the fruit ripening phase, interactions between ABA and ethylene are found in both climacteric and non-climacteric fruits. It is clear that ABA regulates ethylene biosynthesis and signaling during fruit ripening, but the molecular mechanism controlling the interaction between ABA and ethylene has not yet been discovered. The effects of ABA and ethylene on fruit ripening are synergistic, and the interaction of ABA with other plant hormones is an essential determinant of fruit growth and ripening. Reaction and biosynthetic mechanisms, signal transduction, and recognition of ABA receptors in fruits need to be elucidated by a more thorough study to understand the role of ABA in fruit ripening. Genetic modifications of ABA signaling can be used in commercial applications to increase fruit yield and quality. This review discusses the mechanism of ABA biosynthesis, its translocation, and signaling pathways, as well as the recent findings on ABA function in fruit development and ripening. [ABSTRACT FROM AUTHOR]

Semi-supervised clustering technique partitions the unlabeled data based on prior knowledge of labeled data. Most of the semi-supervised clustering algorithms exist only for the clustering of complete data, i.e., the data sets with no missing features. In this paper, an effort has been made to check the effectiveness of semi-supervised clustering when applied to incomplete data sets. The novelty of this approach is that it considers the missing features along with available knowledge (labels) of the data set. The linear interpolation imputation technique initially imputes the missing features of the data set, thus completing the data set. A semi-supervised clustering is now employed on this complete data set, and missing features are regularly updated within the clustering process. In the proposed work, the labeled percentage range used is 30, 40, 50, and 60% of the total data. Data is further altered by arbitrarily eliminating certain features of its components, which makes the data incomplete with partial labeling. The proposed algorithm utilizes both labeled and unlabeled data, along with certain missing values in the data. The proposed algorithm is evaluated using three performance indices, namely the misclassification rate, random index metric, and error rate. Despite the additional missing features, the proposed algorithm has been successfully implemented on real data sets and showed better/competing results than well-known standard semi-supervised clustering methods. [ABSTRACT FROM AUTHOR]

The paper examines the market microstructure - A descriptive study of the nature and structure of the Indian money market. For the empirical analysis we have studied each of the four sub-markets of the money market - Commercial Paper (CP), Certificate of Deposit (CD), Call rate (CMM)and T-Bills. Our paper looks at the qualitative aspects, the granular view of the Indian money market. It studies the overall trends, growth rates and index of interest rates of the four submarkets of the Indian money market. The paper clearly shows that the short-term interest rate follows almost the same pattern; breaks occur in a similar fashion. We attribute this decline to change in market microstructure which is a consequence to the liberalization of the money market. The growth analysis is done with the help of semi-log equations, structural breaks to study the overall growth rates for different markets. It is evident from the paper that on the whole, there has been a declining trend in the money market rates, with some differences between sub-periods. The overall and period-wise trends and growth rates also exhibit similar patterns amongst the four short-term interest rates, where all rates are falling. [ABSTRACT FROM AUTHOR]

Wheat cultivars are genetically crossed to improve end-use quality for traits as per demands of baking industry and broad consumer preferences. The processing and baking qualities of bread wheat are influenced by a variety of genetic make-ups, environmental factors and their interactions. Two wheat cultivars, WL711 and C306, derived recombinant inbred lines (RILs) with a population of 206, were used for phenotyping of quality-related traits. The genetic analysis of quality traits showed considerable variation for measurable quality traits, with normal distribution and transgressive segregation across the years. From the 206 RILs, few RILs were found to be superior to those of the parental cultivars for key quality traits, indicating their potential use for the improvement of end-use quality and suggesting the probability of finding new alleles and allelic combinations from the RIL population. Mapping analysis identified 38 putative QTLs for 13 quality-related traits, with QTLs explaining 7.9–16.8% phenotypic variation spanning over 14 chromosomes, i.e., 1A, 1B, 1D, 2A, 2D, 3B, 3D, 4A, 4B, 4D, 5D, 6A, 7A and 7B. In-silico analysis based on homology to the annotated wheat genes present in database, identified six putative candidate genes within QTL for total grain protein content, qGPC.1B.1 region. Major QTL regions for other quality traits such as TKW have been identified on 1B, 2A, and 7A chromosomes in the studied RIL population. This study revealed the importance of the combination of stable QTLs with region-specific QTLs for better phenotyping, and the QTLs presented in our study will be useful for the improvement of wheat grain and bread-making quality. [ABSTRACT FROM AUTHOR]

BACKGROUND Hexaploid wheat ( Triticum aestivum L.) dominates the list of the most important human food sources ever. Its complex genetic background is the reason behind the wide diversity that exists in nutritional as well as food end-product quality. High-molecular-weight glutenin sub-units ( HMW-GS) are the main grain storage proteins in the endosperm of wheat and related species. It is well established that the composition and quantity of allelic variation in HMW-GS genes substantially affect the taste and appearance of dough products and therefore work in this area is highly desired. RESULTS A significant positive effect on wheat dough quality traits was observed among near isogenic lines of HMW-GS sub-units 20 and 2.2 in wheat variety HD2329 during quality evaluation of data generated over 2 years. A remarkably significant ( P < 0.01) effect was observed on dough quality parameters like ratio of wet gluten/dry gluten, SDS sedimentation, farinogram parameters, and bread/chapatti traits whereas flour protein and dry gluten content showed an insignificant effect. CONCLUSION HMW-GS 20 was found to be superior to HMW-GS 2.2 in terms of dough quality and both the near isogenic lines developed by us were found to be highly superior to the recurrent parent HD2329. As we know that the improvement of flour quality based on superior HMW-GS alleles is necessary to meet changing consumer demand, the study can be of immense use to future researchers who can target these HMW sub-units 20 and 2.2 in breeding programmes for the improvement of wheat end-product quality. © 2017 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Context: Globally, it is estimated that by 2020, noncommunicable diseases (NCDs) will account for 73% of deaths and 60% of disease burden. India is in the midst of an epidemiological transition leading to increasing the prevalence of NCDs. Targeting the risk factors for NCDs is recognized as an essential preventive strategy. Aims: The rationale of this study was to decipher the baseline data on the prevalence of NCD risk factors among the rural population. Settings and Design: A community-based cross-sectional study was conducted among 207 adults in the rural catchment area of the Department of Community Medicine. Subjects and Methods: The World Health Organization STEPS approach was employed which includes three sequential phases: Collection of information on sociodemographic variables and behavioral risk factors (STEP 1), obtaining physical measurements (STEP 2), and acquiring biochemical measurements (STEP 3). Statistical Analysis: Data were analyzed using the SPSS version 16. P < 0.05 was considered to be statistically significant. Results: Among the study subjects, the prevalence of smoking, smokeless tobacco use, alcohol use, and sedentary lifestyle was 26.0%, 35.1%, 16.9%, and 9.6%, respectively, in males and 4.6%, 15.4%, 0.0%, and 19.0%, respectively, in females. The prevalence of hypertension and diabetes was 15.6% and 13.0% respectively among males and 20.0% and 7.7% respectively among females. Hypercholesterolemia and Hypertriglyceridemia were observed in 5.2% men and 13.8% women and 22.1% men and 16.9% women, respectively. Conclusions: The study reveals high burden of NCD risk factors in rural areas as well and reiterates the need to take preventive measures. [ABSTRACT FROM AUTHOR]

We have developed a simple, robust environment-friendly and efficient method for ZnO nanoparticles biosynthesis using Dalbergia sissoo fresh leaf extract. Before using these nanoparticles for antimicrobial assay, a detailed characterization was performed using techniques like Ultraviolet/Visible (UV/Vis) spectroscopy, Particle size analysis (PSA), Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), atomic force microscopy (AFM),Transmission electron microscopy (TEM) etc. The average size of biosynthesized ZnO nanoparticles was around 30 nm and they were pure and crystalline by nature. The effectiveness of these biosynthesized nanoparticles were checked against both pathogenic and non-pathogenic microbes. A total of eight bacterial strains- Escherichia coli, Bacillus subtilis, Pseudomonas aeruginosa, Klebsilla pneumoniae, Staphylococcus aureus, Streptococcus entericus, Bacillus cereus, Pantoea cypripedii and three fungal strains- Candida albicans, Aspergilus niger and Aspergilus flavus were studied to have a clear view of the spectrum of ZnO nanoparticles anti-microbial activity. The effectiveness of biosynthesized ZnO nanoparticles against the microbes was found to be better than the standard reference antibiotics used (streptomycin, chloramphenicol and rifampicin). The results seem to be very promising and can be used for some practical applications of ZnO nanoparticles in nearfuture.

Cotton ( Gossypium hirsutum L.), is an important fibre and oilseed crop of the world. India in particular has the largest area under cotton cultivation and around 60% proportion in the raw fibre textile industry is contributed by cotton alone. Cotton is affected by many diseases (bacterial blights, fungal leafspots, mildew) and pests (white flies, bollworms, aphids etc.). The bacterial blight disease caused by Xanthomonas axonopodis pv. malvacearum is considered as one of the most devastating one that cause huge losses in production every year. Due to systemic spread of this bacterial infection, combating this disease is slightly challenging. Spray of toxic chemicals like endosulfan, streptocycline and dimethoate is a common practice in fields but these chemicals are unable to control the disease spread substantially. Nanotechnology is a newly emerging technology that is being extensively exploited in the agriculture sector these days. Past studies have reported the antimicrobial effect of various metallic nanoparticles including zinc oxide nanoparticles which is known to possess antibacterial potential against both gram +ve and gram -ve bacteria. Based upon this, synthesis of ZnO nanoparticles was carried out using Morus alba plant leaf extract and the nanoparticles were characterised in detail using scanning electron microscopy, atomic force microscopy, X-ray diffraction analysis, electron dispersive X-ray spectroscopy study etc. The zinc oxide nanoparticles were found crystalline in nature and the size ranged between 10-50 nanometers. The efficacy of these nanoparticles was checked against Xanthomonas axonopodis pv. malvacearum under in vitro conditions and found to be very effective in controlling the bacterial spread in comparison to streptomycin that was used as control. Our results suggest that ZnO nanoparticles can be used as an effective antibacterial agent to control bacterial blight disease of cotton.

Wheat (Triticum aestivum L.) contributes substantially to global food and nutritional security. With the growing demands under the constraints of depleting natural resources, environmental fluctuation, and increased risk of epidemic outbreaks, the task of increasing wheat production has become daunting. The factors responsible for first green revolution seem to be exhausting rapidly, and there is an immediate need to develop the technologies which can not only increase the wheat production but also sustain the same at a higher level without adversely affecting the natural resources. Understanding abiotic stress factors such as temperature, drought tolerance, and biotic stress tolerance traits such as insect pest and pathogen resistance in combination with high yield in plants is of paramount importance to counter climate change related adverse effects on the productivity of wheat crops. Thus, an important goal of wheat breeding is to develop high-yielding varieties with better nutritional quality and resistance to major diseases. Therefore, in this chapter, we present a judicious mixture of basic as well as applied research outlooks. We trust that the information covered in this chapter would bridge the much-researched area of stress in plants with the information to breed climate-ready crop cultivars to ensure food security in the future.