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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.

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.

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

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.

Intestinal parasitic infections are one of the major causes of diarrhea in human immunodeficiency virus (HIV) seropositive individuals. Antiretroviral therapy has markedly reduced the incidence of many opportunistic infections, but parasite-related diarrhea still remains frequent and often underestimated especially in developing countries. The present hospital-based study was conducted to determine the spectrum of intestinal parasitosis in adult HIV/AIDS (acquired immunodeficiency syndrome) patients with or without diarrhea with the levels of CD4(+) T-cell counts. A total of 400 individuals were enrolled and were screened for intestinal parasitosis. Of these study population, 200 were HIV seropositives, and the remaining 200 were HIV uninfected individuals with or without diarrhea. Intestinal parasites were identified by using microscopy as well as PCR assay. A total of 130 (32.5%) out of 400 patients were positive for any kinds of intestinal parasites. The cumulative number of parasite positive patients was 152 due to multiple infections. A significant association of Cryptosporidium (P