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The occurrence and severity of mental health issues among university students is increasing globally. India is also on the same platform when it comes to alarming mental health issues among students. However, most studies so far have been limited to assessing the prevalence and outcomes of psychological issues, and a paucity of studies investigating the protective factors for students’ mental health was observed. With the advent of the salutogenic approach, the research interest in health-protective factors, as opposed to risk factors, has heightened drastically. Given this, the present work investigated the role of personality traits and self-compassion in university students’ mental health. A correlational research design was employed. Participants included university students assessed on Costa and McCrae’s (1992) NEO-Five Factor Inventory, Self-Compassion Scale (Neff, 2003), and Mental Health Inventory (Jagadish and Srivastava, 1983). Data analysis employed Pearson’s correlation and multiple linear regression analysis. Extraversion, agreeableness, and self-compassion were positively associated with mental health, whereas neuroticism was negatively associated. The OCEAN personality traits and self-compassion explained significant variance in mental health, and only extraversion positively and significantly predicted mental health. OCEAN Personality Trait Extraversion is a protective factor for university students’ mental health.

PurposeIn an emerging economy like India, the contribution of Indians in the stock market is very low, despite having the highest percentage of savings. The research tries to look for the variables which influence the investor's intentions to invest in the Indian stock market, by considering the theory of planned behavior (TPB). Moreover, the study incorporates financial literacy (FL) in the model to examine its influence on investors’ investment intention and also examine the moderation effect of financial literacy.Design/methodology/approachData were collected using a structured questionnaire from a sample of 393 respondents by using the convenience sampling method which is followed by the snowball sampling technique. For testing the research hypotheses, SEM and PROCESS macro v3.0 for SPSS were taken into consideration.FindingsThe results explain that factors of TPB i.e. attitude (AT), subjective norms (SNs) and perceived behavioral control (PBC) are significantly associated with investment intentions (IIs). Furthermore, along with the original components of the TPB model, Financial Literacy (FL) was also incorporated in the model, which predicted the investors' intention better. The results also stated that FL has a positive impact on AT, PBC and II. Moreover, results reveal that FL moderates the association between AT, PBC and II.Research limitations/implicationsThe study describes that financial literacy can help in increasing the participation of investors in the stock market. Therefore, in this situation, the current research permits the Security Exchange Board of India (SEBI), governments and financial institutions (FIs) to plan and design seminars or courses, programs, to enhance FL among individuals and promote individuals in making well-organized and efficient investment decisions in stock markets that will in turn upsurge individual investors participation. The study contributes to the existing literature of investment behavior by incorporating FL as a moderator. Research avoids considering actual investment behavior. The study also neglects demographic and socio-psychological factors which are the major factor that affects an investment decision. Furthermore, the research has only considered the objective dimension of FL.Originality/valueThe current research tries to incorporate FL in TPB model. Moreover, tries to examine the moderation effect of FL. The research is one of its kind as the past research neglect to examine the moderation effect of FL in relationship between AT, PBC and investment intension to investment in stock market. The research helps to understand how FL encourages investors to invest in the Indian stock market.

Among all the nutrients, nitrogen (N) and phosphorous (P) are the most limiting factors reducing wheat production and productivity world-wide. These macronutrients are directly applied to soil in the form of fertilizers. However, only 30–40% of these applied fertilizers are utilized by crop plants, while the rest is lost through volatilization, leaching, and surface run off. Therefore, to overcome the deficiency of N and P, it becomes necessary to improve their use efficiency. Marker-assisted selection (MAS) combined with traditional plant breeding approaches is considered best to improve the N and P use efficiency (N/PUE) of wheat varieties. In this study, we developed and evaluated a total of 98 simple sequence repeat (SSR) markers including 66 microRNAs and 32 gene-specific SSRs on a panel of 10 (N and P efficient/deficient) wheat genotypes. Out of these, 35 SSRs were found polymorphic and have been used for the study of genetic diversity and population differentiation. A set of two SSRs, namely miR171a and miR167a were found candidate markers able to discriminate contrasting genotypes for N/PUE, respectively. Therefore, these two markers could be used as functional markers for characterization of wheat germplasm for N and P use efficiency. Target genes of these miRNAs were found to be highly associated with biological processes (24 GO terms) as compared to molecular function and cellular component and shows differential expression under various P starving conditions and abiotic stresses.

Background Nitrogen (N) and phosphorous (P) play a very important role in growth and development of wheat as well as major constituents of biological membrane. To meet the plant nutritional demand these nutrients applied in the form of fertilizers. But plant can utilize only half of the applied fertilizer whereas rest is lost thorough surface run off, leaching and volatilization. Thus, to overcome the N/P loss we need to elucidate the molecular mechanism behind the N/P uptake. Methods In our study, we used DBW16 (low NUE), and WH147 (high NUE) wheat genotypes under different doses of N, whereas HD2967 (low PUE) and WH1100 (high PUE) genotypes were studied under different doses of P. To check the effect of different doses of N/P, the physiological parameters like Total chlorophyll content, net photosynthetic rate, N/P content, and N/PUE of these genotypes were calculated. In addition, gene expression of various genes involved in N/P uptake, utilization, and acquisition were studied by quantitative real time- PCR. Results Statistical analysis revealed a lower percent reduction in TCC, NPR, N/P content in N/P efficient wheat genotypes (WH147 & WH1100). A significant increase in relative fold expression of genes under low N/P concentration was observed in N/P efficient genotypes as compared to N/P deficient genotypes. Conclusion Significant differences in physiological data and gene expression among N/ P efficient and deficient wheat genotypes could be useful for future improvement of N/P use efficiency.

In wheat, lodging is affected by anatomical and chemical characteristics of the stem cell wall. Plant characteristics determining the stem strength were measured in lodging tolerant mutant (PMW-2016-1) developed through mutation breeding utilizing hexaploid wheat cultivar, DPW-621-50. Various anatomical features, chemical composition, and mechanical strength of the culms of newly developed lodging-tolerant mutant (PMW-2016-1) and parent (DPW-621-50), were examined by light microscopy, the Klason method, prostate tester coupled with a Universal Tensile Machine, and Fourier Transform Infrared Spectroscopy. Significant changes in the anatomical features, including the outer radius of the stem, stem wall thickness, and the proportions of various tissues, and vascular bundles were noticed. Chemical analysis revealed that the lignin level in the PMW-2016-1 mutant was higher and exhibited superiority in stem strength compared to the DPW-621-50 parent line. The force (N) required to break the internodes of mutant PMW-2016-1 was higher than that of DPW-621-50. The results suggested that the outer stem radius, stem wall thickness, the proportion of sclerenchyma tissues, the number of large vascular bundles, and lignin content are important factors that affect the mechanical strength of wheat stems, which can be the key parameters for the selection of varieties having higher lodging tolerance. Preliminary studies on the newly identified mutant PMW-2016-1 suggested that this mutant may possess higher lodging tolerance because it has a higher stem strength than DPW-621-50 and can be used as a donor parent for the development of lodging-tolerant wheat varieties.

PurposeThe purpose of the study is to examine the impact of behavioural biases (i.e. overconfidence, risk-aversion, herding and disposition) on investment decisions amongst gender. The authors further examine the moderation effect of financial literacy in the relationship between behaviour biases and investment decisions amongst gender.Design/methodology/approachThe study considered a cross-sectional research design. For this survey, the data have been collected through a structured questionnaire from 253 individual investors of the Delhi-NCR region. To analyse the validity and reliability, the Pearson correlation and Cronbach's alpha test have been taken into account respectively. For testing the hypothesis, hierarchical regression analysis has been used in the study.FindingsThe results of the study reveal that amongst male investors, the influence of risk-aversion and herding on investment decision was negative and statistically significant, while the influence of overconfidence on investment decision was positive and significant. However, the influence of disposition was found statistically insignificant. The results stated that amongst female investors the effect of risk-aversion and herding on investment decision was negative and statistically significant. However, the effect of overconfidence and disposition was statistically insignificant influence the investment decision. It has been observed that financial literacy has significantly influenced investment decisions amongst male and female investors. The results of the interaction effect amongst male investors stated that the interaction between overconfidence and investment decision was significantly influenced by financial literacy. However, the interaction of financial literacy with the remaining three biases, i.e. risk-aversion, herding and disposition was found insignificant. The results for the interaction effect of financial literacy with overconfidence, risk-aversion, disposition and herding were found statistically significant amongst female investors.Research limitations/implicationsBased on this present research finding, the study is more productive for the portfolio manager and policymakers at the time of making an investment portfolio for the investors based on their behavioural biases. The study recommends that investors need training programmes, workshops and seminars that enhance financial literacy and financial knowledge of investors which helps them to overcome the behavioural biases while making an investment decision.Originality/valueThe current study aims to explore whether several behavioural biases can affect investment decisions amongst gender. Moreover, the authors would like to examine whether these associations are moderated by financial literacy. In this sense, financial literacy might also show a substantial part in the prediction of investments. The current study might be of the first study that examines the moderation effect financial literacy amongst male and female investors.

Grain yield is one of the most important aims for combating the needs of the growing world population. The role of development and nutrient transfer in flag leaf for higher yields at the grain level is well known. It is a great challenge to properly exploit this knowledge because all the processes, starting from the emergence of the flag leaf to the grain filling stages of wheat (

Nutrient deficiency in food crops severely compromises human health, particularly in under privileged communities. Globally, billions of people, particularly in developing nations, have limited access to nutritional supplements and fortified foods, subsequently suffering from micronutrient deficiency leading to a range of health issues. The green revolution enhanced crop production and provided food to billions of people but often falls short with respect to the nutritional quality of that food. Plants may assimilate nutrients from synthetic chemical fertilizers, but this approach generally has low nutrient delivery and use efficiency. Further, the overexposure of chemical fertilizers may increase the risk of neoplastic diseases, render food crops unfit for consumption and cause environmental degradation. Therefore, to address these challenges, more research is needed for sustainable crop yield and quality enhancement with minimum use of chemical fertilizers. Complex nutritional disorders and 'hidden hunger' can be addressed through biofortification of food crops. Nanotechnology may help to improve food quality via biofortification as plants may readily acquire nanoparticle-based nutrients. Nanofertilizers are target specific, possess controlled release, and can be retained for relatively long time periods, thus prevent leaching or run-off from soil. This review evaluates the recent literature on the development and use of nanofertilizers, their effects on the environment, and benefits to food quality. Further, the review highlights the potential of nanomaterials on plant genetics in biofortification, as well as issues of affordability, sustainability, and toxicity.

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the effectiveness of interventions. Asymptomatic breakthrough infections have been a major problem during the ongoing surge of Delta variant globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines used in the higher-income regions. Here, we show for the first time how statistical and machine learning (ML) approaches can discriminate SARS-CoV-2 infection from immune response to an inactivated whole virion vaccine (BBV152, Covaxin, India), thereby permitting real-world vaccine effectiveness assessments from cohort-based serosurveys in Asia and Africa where such vaccines are commonly used. Briefly, we accessed serial data on Anti-S and Anti-NC antibody concentration values, along with age, sex, number of doses, and number of days since the last vaccine dose for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine (SVM) model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, 724 were classified as infected. Since the vaccine contains wild-type virus and the antibodies induced will neutralize wild type much better than Delta variant, we determined the relative ability of a random subset of such samples to neutralize Delta versus wild type strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, Delta variant, was neutralized more effectively than the wild type, which cannot happen without infection. The fraction rose to 71.8% (28 of 39) in subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period.

OBJECTIVES Pneumocystis pneumonia (PCP) remains a debilitating cause of death among HIV-infected patients. The combination trimethoprim/sulfamethoxazole (SXT) is the most effective anti-Pneumocystis treatment and prophylaxis. However, long-term use of this combination has raised alarms about the emergence of resistant organisms. This study was performed to investigate mutations in the dihydropteroate synthase (DHPS) gene and their clinical consequences in HIV-infected patients with PCP. METHODS A total of 76 clinically suspected cases of PCP among HIV-seropositive adult patients from March 2014 to March 2017 were included. Clinical samples (bronchoalveolar lavage fluid and sputum) were investigated for the detection of Pneumocystis jirovecii using both microscopy and nested PCR. DHPS genotyping and mutational analyses were performed and the data were correlated with clinical characteristics. RESULTS Among the 76 enrolled HIV-positive patients, only 17 (22.4%) were positive for P. jirovecii. DHPS gene sequencing showed a novel nucleotide substitution at position 288 (Val96Ile) in three patients (3/12; 25.0%). Patients infected with the mutant P. jirovecii genotype had severe episodes of PCP, did not respond to SXT and had a fatal outcome (P=0.005). All three patients had a CD4+ T-cell count

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the vaccine effectiveness. Asymptomatic breakthrough infections have been a major problem in assessing vaccine effectiveness in populations globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines since whole virion vaccines generate antibodies against all the viral proteins. Here, we show how a statistical and machine learning (ML) based approach can be used to discriminate between SARS-CoV-2 infection and immune response to an inactivated whole virion vaccine (BBV152, Covaxin). For this, we assessed serial data on antibodies against Spike and Nucleocapsid antigens, along with age, sex, number of doses taken, and days since last dose, for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, our ensemble ML model classified 724 to be infected. For method validation, we determined the relative ability of a random subset of samples to neutralize Delta versus wild-type strain using a surrogate neutralization assay. We worked on the premise that antibodies generated by a whole virion vaccine would neutralize wild type more efficiently than delta strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, neutralization against Delta strain was more effective, indicating infection. We found 71.8% subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period. Our approach will help in real-world vaccine effectiveness assessments where whole virion vaccines are commonly used.

Given an undirected connected graph \(G\), the profile minimization problem (PMP) is to place the vertices of \(G\) in a linear layout (labeling) in such a way that the sum of profiles of the vertices in \(G\) is minimized, where the profile of a vertex is the difference of its labeling with the labeling of its left most neighbor in the layout. It is an NP-complete problem and has applications in various areas such as numerical analysis, fingerprinting, and information retrieval. In this paper, we design a tabu-embedded simulated annealing algorithm for profile reduction (TSAPR) for PMP which uses a well-known spectral sequencing method to generate an initial solution. An efficient technique is employed to compute the profile of a neighbor of a solution. The experiments are conducted on different classes of graphs such as \(T^{4}\)-trees, tensor product of graphs, complete bipartite graphs, triangulated triangle graphs, and a subset of Harwell–Boeing graphs. The computational results demonstrate an improvement in the existing results by TSAPR in most of the cases.

For a given graph \(G\), minimum stretch spanning tree problem (MSSTP) seeks for a spanning tree of \(G\) such that the distance between the farthest pair of adjacent vertices of \(G\) in tree is minimized. It is an NP-hard problem with applications in communication networks. In this paper, a general variable neighborhood search (GVNS) algorithm is developed for MSSTP in which initial solution is generated using four well-known heuristics and a problem-specific construction heuristic. Six neighborhood strategies are designed to explore the search space. The experiments are conducted on various classes of graphs for which optimal results are known. Computational results show that the proposed algorithm is better than the artificial bee colony (ABC) algorithm which is adapted by us for MSSTP.

The profile minimization problem is a graph layout problem that consists of finding a linear arrangement (labeling) of vertices of a graph such that the sum of profiles of all vertices is minimum. The profile of a vertex can be defined as the difference of the position of its left most neighbor and the position of that vertex in the linear arrangement. It is an NP-complete problem with applications in the areas where the reordering of rows and columns of a sparse matrix is required in order to reduce the storage space. In this work, we propose a hybrid simulated annealing algorithm with the aim of profile reduction of a given graph by incorporating spectral sequencing for generating the initial solution. Experiments conducted on some benchmark graphs show that our algorithm outperforms the best existing algorithm in some cases and is comparable for rest of the instances. It also attains the optimal values for some classes of graphs with known optimal results.

Introduction:Head and neck lesions are commonly encountered in patients of all age group including skin, soft tissue, thyroid, lymph node, salivary gland, eye, nose, oral, otologic, etc, include a spectrum of lesions ranging from simple benign to highly malignant, contribute to significant morbidity and mortility. Aims & Objective: To determine histopathological subtypes, frequency of head & neck lesion, age, gender distribution and site & organ wise distribution Methodology: A one year retrospective study was designed to study of various biopsies from head and neck region, sent for HPE at department of pathology, SSMC, Rewa, M.P. from 1st Nov 2018 to 31st Oct 2019 Result:Total 186 cases from the head and neck region were analyzed .Age range was 4 years to 89 years. With maximum cases were in the age group of 11-30 years (60.2%).M:F ratio was 1.16:1. In our study, 55.9% benign, 33.3% inflammatory,6.5% malignant lesion. Squamous cell carcinoma was the most common malignant lesion Conclusion:We conclude that site specific data like this is helpful in evaluating patterns of head and neck lesions and augment the baseline data of institute and region.

Data on the genetic diversity of Pneumocystis jirovecii causing Pneumocystis pneumonia (PCP) among children are still limited, and there are no available data from the Indian subcontinent, particularly associations between genotypes and clinical characteristics. A total of 37 children (62 days-12 years [median 5.5 years]) were included in this study. Pneumocystis was diagnosed by microscopy using Grocott-Gomori methenamine silver stain in 12 cases and by nested PCR using mtLSUrRNA in 25 cases. Genotyping was performed using three different genes, mitochondrial large subunit ribosomal RNA (mtLSUrRNA), dihydropteroate synthase (DHPS) and dihydrofolate reductase (DHFR). mtLSUrRNA genotype 3 and novel mutations at the gene target DHFR (401 T > C) and DHPS 96/98 were frequently observed and clinically associated with severe PCP and treatment failure. Phylogenetic analyses revealed 13 unique sequence types (STs). Two STs (i) 3-DHFR 401 T > C-DHPS 96/98 – PJ1 and (ii) 3-DHFR 401 T > C-DHPS 96- PJ3 were significantly associated with treatment failure and high mortality among PCP-positive patients. In conclusion, the present study strongly suggests the emergence of virulent P. jirovecii strains or genetic polymorphisms, leading to treatment failure and high mortality. Our study is the first of its kind from the Indian subcontinent and has highlighted the genetic diversity of Pneumocystis jirovecii among children and their clinical outcomes. These findings emphasize the need to focus more on genotypes to better understand the epidemiology of Pneumocystis pneumonia.

The present study was carried out with the objectives of genotyping Pneumocystis jirovecii at three distinct loci, to identify the single nucleotide polymorphisms (SNPs), and to study its clinical implications in patients with Pneumocystis pneumonia (PCP). Analysis of genetic diversity in P. jirovecii from immunocompromised patients was carried out by genotyping at three distinct loci encoding mitochondrial large subunit rRNA (mtLSU rRNA), cytochrome b (CYB), and superoxide dismutase (SOD) using polymerase chain reaction (PCR) assays followed by direct DNA sequencing. Of the 300 patients enrolled in the present study, 31 (10.33%) were positive for PCP by a specific mtLSU rRNA nested PCR assay, whereas only 15 P. jirovecii could be amplified at the other two loci (SOD and CYB). These positives were further subjected to sequence typing. Important genotypic combinations between four SNPs (mt85, SOD110, SOD215, and CYB838) and clinical outcomes could be observed in the present study, and mt85A, mt85T, and SOD110C/SOD215T were frequently associated with “negative follow-up”. These SNPs were also noted to be relatively more prevalent amongst circulating genotypes in our study population. The present study is the first of its kind from the Indian subcontinent and demonstrated that potential SNPs of P. jirovecii may possibly be attributed to the clinical outcome of PCP episodes in terms of severity or fatality in different susceptible populations likely to develop PCP during their course of illness.

Introduction: Pneumocystis pneumonia (PCP) is an opportunistic life-threatening infection, especially for immunocompromised individuals. A trimethoprim-sulfamethoxazole (TMP-SMX) combination is commonly used for the treatment of PCP, targeting both dihydrofolate reductase (DHFR) and dihydropteroate synthase (DHPS) enzymes. Several studies have already shown that polymorphisms in the DHPS gene are associated with drug resistance. The present study analyzed DHFR gene polymorphisms in Pneumocystis jirovecii recovered from clinical samples from patients admitted to a tertiary care health center in New Delhi, India. Methodology: Detection of P. jirovecii was performed using Gomori methenamine silver staining (GMS) and nested polymerase chain reaction (PCR) assay targeting the mitochondrial large subunit ribosomal RNA (mt LSU rRNA) gene. The DHFR gene was amplified using nested PCR protocol and was sequenced for detection of polymorphisms. Results: Of 180 clinical samples, only 4% (7/180) were positive by GMS staining, and 10% (18/180) were positive by mt LSU rRNA PCR assay. Of these 18 positive samples, only 77% (14/18) were amplified by the DHFR gene PCR assay. A total of 16 nucleotide substitutions were observed in 42% (6/14) samples targeted for the DHFR gene, of which 8 nucleotide substitutions were synonymous and the rest were non-synonymous. Conclusions: The DHFR gene mutations found in this study may possibly indicate an association of process likely to contribute to therapeutic failure or an evolutionary process, and warrant continuous monitoring.

Objectives: Cultivation and identification of mycobacteria to species level remains difficult and time-consuming. Hence, easy and rapid diagnostic methods are necessary for the differentiation of Mycobacterium tuberculosis (MTB) from non-tuberculous mycobacteria (NTM). The present study aims to detect and differentiate MTB from NTM isolated from clinical samples by immunochromatographic test (ICT) and polymerase chain reaction (PCR). Methods: Over a period of 1 year, clinical samples (n=496) received from suspected cases of TB, at the Department of Microbiology, Kasturba Medical College Hospital, Mangalore were cultured to isolate Mycobacterium spp. Identification of all the isolates was done by conventional biochemical technique, ICT, and PCR. Results: Among the 496 samples processed, 49 (9.87%) were acid-fast bacilli smear positive and 59 (11.89%) samples showed the growth of Mycobacterium spp. Among these, 10 were rapid growers, 49 were slow-growing mycobacteria, out of which 30 were MTB as identified by conventional biochemical reaction. Out of 59 Mycobacterial isolates subjected to ICT for the detection of MPT 64 antigen, only 28 were identified as MTB. However, all the 30 isolates were correctly identified as MTB by PCR. Conclusion: Hence, PCR is essential for rapid differentiation of non-tuberculous Mycobacterium from MTB. False negative results seen with immunochromatographic MPT 64 antigen assay could be due to mutations within the mpt64 gene. Further studies are necessary to characterize these PCR-positive and immunochromatographic assay negative MTB isolates.