Identification of novel proteins regulating lipid droplet biogenesis in filamentous fungi.

Lipid droplets (LDs) are storage organelles for neutral lipids which are critical for lipid homeostasis. Current knowledge of fungal LD biogenesis is largely limited to budding yeast, while LD regulation in multinucleated filamentous fungi which exhibit considerable metabolic activity remains unexplored. In this study, 19 LD-associated proteins were identified in the multinucleated species Aspergillus oryzae using a colocalization screening of a previously established enhanced green fluorescent protein (EGFP) fusion library. Functional screening identified 12 lipid droplet-regulating (LDR) proteins whose loss of function resulted in irregular LD biogenesis, particularly in terms of LD number and size. Bioinformatics analysis, targeted mutagenesis, and microscopy revealed four LDR proteins that localize to LD via the putative amphipathic helices (AHs). Further analysis revealed that LdrA with an Opi1 domain is essential for cytoplasmic and nuclear LD biogenesis involving a novel AH. Phylogenetic analysis demonstrated that the patterns of gene evolution were predominantly based on gene duplication. Our study identified a set of novel proteins involved in the regulation of LD biogenesis, providing unique molecular and evolutionary insights into fungal lipid storage.

Public vs. private job dilemma: Influencing factors in career selection for university graduates.

The unprecedented growth of educated workforce following the economic development and diversity in workplace has widened the career choices of young people in Bangladesh. However, it intensifies a dilemma among the job seekers about determining their career goals, because career goals are often influenced by certain socio-demographic and cultural aspects. Hence, this cross-sectional study was designed to investigate the career choices of university students in Bangladesh and to identify its determinants. Administering a self-administered questionnaire (SAQ), data were collected from 422 students at a public university using the multistage stratified sampling. Data were analyzed by bivariate (chi-square) and multivariate (exploratory factor analysis and binary logistic regression) analyses. Findings suggest that the career choices of students vary regarding their gender, religion, and academic track. For example, female (AOR: 0.281; 95% CI: 0.144 to 0.547) and Muslim (AOR: 3.648; 95% CI: 1.765 to 7.542) students preferred public jobs, whereas students of commerce (AOR: 0.344; 95% CI: 0.144 to 0.820) went for private ones. Among socioeconomic issues, only the father's occupation had a substantial effect on career decisions (AOR: 0.347; 95% CI: 0.144 to 0.820). The career choice was also determined by the job prospects (AOR: 1.251; 95% CI: 1.161 to 1.347), preference of family (AOR: 1.238; 95% CI: 1.099 to 1.394), as well as job diversity (AOR: 0.879; 95% CI: 0.795 to 0.972). Based on the findings of this study, it is recommended that the government should address the trends and patterns of career choices of students through empirical research when formulating future educational and career-related policies in Bangladesh.

Content-based image retrieval system for solid waste bin level detection and performance evaluation.

This paper presents a CBIR system to investigate the use of image retrieval with an extracted texture from the image of a bin to detect the bin level. Various similarity distances like Euclidean, Bhattacharyya, Chi-squared, Cosine, and EMD are used with the CBIR system for calculating and comparing the distance between a query image and the images in a database to obtain the highest performance. In this study, the performance metrics is based on two quantitative evaluation criteria. The first one is the average retrieval rate based on the precision-recall graph and the second is the use of F1 measure which is the weighted harmonic mean of precision and recall. In case of feature extraction, texture is used as an image feature for bin level detection system. Various experiments are conducted with different features extraction techniques like Gabor wavelet filter, gray level co-occurrence matrix (GLCM), and gray level aura matrix (GLAM) to identify the level of the bin and its surrounding area. Intensive tests are conducted among 250 bin images to assess the accuracy of the proposed feature extraction techniques. The average retrieval rate is used to evaluate the performance of the retrieval system. The result shows that, the EMD distance achieved high accuracy and provides better performance than the other distances.