Investigation on the Effect of Shapes on the Drying Kinetics and Sensory Evaluation Study of Dried Jackfruit

Jackfruits are seasonal and highly nutritional fruits indigenous to the Southwestern rainforests of India. However much of the produce are spoilt annually due to poor preservation techniques. Minimal studies have been conducted on the drying kinetics of jackfruit and the effect of shapes on the drying kinetics. In this research, drying curves of three different shaped jackfruit slices were obtained using a convective oven at 40oC, 50oC, 60oC and 70oC. Modified Midilli-Kucuk Model was found to be the best kinetic model for drying of jackfruits. At all temperatures, effective moisture diffusivity values and activation energy varied from 2.66 x 10-10 - 4.85 x 10-10 m2/s and 16.08 - 20.07 kJ/mol respectively. Drying was found to be most efficient at 50oC using the square shaped slices with a R2, RMSE and SSE value of 0.9984, 0.01127 and 0.002668 respectively. Sensory evaluation of untreated and additive-added dried jackfruit slices was conducted by 40 untrained sensory panelists. Jackfruit with ascorbic acid and sugar coating had highest aesthetics value due to better retention of colour by ascorbic acid. However sugar coated jackfruit had the most favorable taste and smell. Further optimization must be done to satisfy consumers collectively to enable a highly marketable product

Topological data analysis of Escherichia coli O157:H7 and non-O157 survival in soils.

Shiga toxin-producing E. coli O157:H7 and non-O157 have been implicated in many foodborne illnesses caused by the consumption of contaminated fresh produce. However, data on their persistence in soils are limited due to the complexity in datasets generated from different environmental variables and bacterial taxa. There is a continuing need to distinguish the various environmental variables and different bacterial groups to understand the relationships among these factors and the pathogen survival. Using an approach called Topological Data Analysis (TDA); we reconstructed the relationship structure of E. coli O157 and non-O157 survival in 32 soils (16 organic and 16 conventionally managed soils) from California (CA) and Arizona (AZ) with a multi-resolution output. In our study, we took a community approach based on total soil microbiome to study community level survival and examining the network of the community as a whole and the relationship between its topology and biological processes. TDA produces a geometric representation of complex data sets. Network analysis showed that Shiga toxin negative strain E. coli O157:H7 4554 survived significantly longer in comparison to E. coli O157:H7 EDL 933, while the survival time of E. coli O157:NM was comparable to that of E. coli O157:H7 EDL 933 in all of the tested soils. Two non-O157 strains, E. coli O26:H11 and E. coli O103:H2 survived much longer than E. coli O91:H21 and the three strains of E. coli O157. We show that there are complex interactions between E. coli strain survival, microbial community structures, and soil parameters

4-(4-Chloro­phen­yl)-4-hy­droxy­piperidinium maleate maleic acid solvate

In the cation of the title compound, C11H15ClNO+·C4H3O4 −·C4H4O4, the dihedral angle between the mean planes of the chlorine-substituted aromatic ring and the 4-hy­droxy­piperidinium ring (C–C–C–C–C–N) is 61.9 (8)°. Intra­molecular O—H⋯O and inter­molecular O—H⋯O and N—H⋯O hydrogen bonding, as well as weak π-stacking inter­actions [centroid–centroid distance = 3.646 (5) Å] help to establish the packing

Increasing the Power to Detect Causal Associations by Combining Genotypic and Expression Data in Segregating Populations

To dissect common human diseases such as obesity and diabetes, a systematic approach is needed to study how genes interact with one another, and with genetic and environmental factors, to determine clinical end points or disease phenotypes. Bayesian networks provide a convenient framework for extracting relationships from noisy data and are frequently applied to large-scale data to derive causal relationships among variables of interest. Given the complexity of molecular networks underlying common human disease traits, and the fact that biological networks can change depending on environmental conditions and genetic factors, large datasets, generally involving multiple perturbations (experiments), are required to reconstruct and reliably extract information from these networks. With limited resources, the balance of coverage of multiple perturbations and multiple subjects in a single perturbation needs to be considered in the experimental design. Increasing the number of experiments, or the number of subjects in an experiment, is an expensive and time-consuming way to improve network reconstruction. Integrating multiple types of data from existing subjects might be more efficient. For example, it has recently been demonstrated that combining genotypic and gene expression data in a segregating population leads to improved network reconstruction, which in turn may lead to better predictions of the effects of experimental perturbations on any given gene. Here we simulate data based on networks reconstructed from biological data collected in a segregating mouse population and quantify the improvement in network reconstruction achieved using genotypic and gene expression data, compared with reconstruction using gene expression data alone. We demonstrate that networks reconstructed using the combined genotypic and gene expression data achieve a level of reconstruction accuracy that exceeds networks reconstructed from expression data alone, and that fewer subjects may be required to achieve this superior reconstruction accuracy. We conclude that this integrative genomics approach to reconstructing networks not only leads to more predictive network models, but also may save time and money by decreasing the amount of data that must be generated under any given condition of interest to construct predictive network models

Influence of powder-bed temperature on the microstructure and mechanical properties of Ti-6Al-4V produced by selective laser melting

Advanced characterisation techniques were used on LPBF Ti-6Al-4V samples produced on a heated base plate. When the substrate temperature is 100{\deg}C the elongation is 6\%, which increases and peaks at 10\% at 570{\deg}C, then sharply decreases to zero ductility at 770{\deg}C. At 100{\deg}C, a heavily strained and twinned microstructure, primarily composed of {\alpha}+{\alpha}', was observed and it was comparable to asbuilt microstructures obtained by conventional LPBF methods. At higher temperatures, twins are no longer present and instead nano-scale {\beta} precipitates are observed within {\alpha}' and {\alpha}, as well as dislocation networks (570{\deg}C) and tangles (770{\deg}C). Solute segregation at crystal defects was observed in all pre-heating conditions. Al and V segregation at microtwins was observed in the 100{\deg}C sample, reporting for the first time `selective' and mutually exclusive Al- and V-rich regions forming in adjacent twins. V segregation at dislocations was observed in the 570{\deg}C and 770{\deg}C samples, consistent with the higher preheating temperatures. High O contents were measured in all samples but with apparent opposing effects. At 100{\deg}C and 570{\deg}C was estimated to be below the critical threshold for O embrittlement and locally aids in maintaining a strength high by solid solution strengthening, whereas at 770{\deg}C it was above the threshold, therefore failing in a brittle fashion. Based on these observations, the initial increase in ductility from 100{\deg}C to 570{\deg}C is attributed to a reduction in microtwins and the dislocation networks acting as `soft barriers' for slip within a coarser microstructure. The lack of ductility at 770{\deg}C was attributed to local solute redistribution causing dislocation pinning and an increase of O content in this sample

Injunctive safety norms, young worker risk-taking behaviors, and workplace injuries

Accepted versionInjunctive safety norms (ISNs) refer to perceptions of others’ expectations of one’s safety-related conduct. Drawing on a sample of Canadian young workers (n = 11,986; M age = 17.90 years; 55% males), we study the relationships among four sources of non-work-related (i.e., parents, siblings, friends, teachers), two sources of work-related (i.e., supervisors, co-workers) ISNs, young workers’ self-reported work-related risk-taking behaviors, and workplace injuries. Structural equation modeling suggests that ISNs from parents, supervisors, and co-workers were related to less frequent work-related risk-taking behaviors, and with fewer workplace injuries via less frequent work-related risk-taking behaviors. In addition, ISNs from supervisors were directly associated with fewer workplace injuries. In contrast, ISNs from teachers and siblings were not associated with work-related risk-taking behaviors, but ISNs from siblings were associated with fewer work injuries. Finally, ISNs from friends were associated with more frequent work-related risk-taking and more frequent work injuries via more frequent work-related risk-taking. This study draws attention to the relative roles of non-work sources of social influence and provides some evidence of how ISNs might be related to young workers’ work-related risk-taking behaviours and their workplace injuries. It also contributes to practice by suggesting specific interventions that parents, supervisors, and co-workers could undertake to reduce young workers’ work-related risk-taking and workplace injuries, namely encouraging youth to be safe at work