NONPARAMETRIC MANOVA APPROACHES FOR NON-NORMAL MULTIVARIATE OUTCOMES WITH MISSING VALUES

Machine learning to mechanically assess 2D and 3D biomimetic electrospun scaffolds for tissue engineering applications: Between the predictability and the interpretability

Currently, the use of autografts is the gold standard for the replacement of many damaged biological tissues. However, this practice presents disadvantages that can be mitigated through tissue-engineered implants. The aim of this study is to explore how machine learning can mechanically evaluate 2D and 3D polyvinyl alcohol (PVA) electrospun scaffolds (one twisted filament, 3 twisted filament and 3 twisted/braided filament scaffolds) for their use in different tissue engineering applications. Crosslinked and non-crosslinked scaffolds were fabricated and mechanically characterised, in dry/wet conditions and under longitudinal/transverse loading, using tensile testing. 28 machine learning models (ML) were used to predict the mechanical properties of the scaffolds. 4 exogenous variables (structure, environmental condition, crosslinking and direction of the load) were used to predict 2 endogenous variables (Young's modulus and ultimate tensile strength). ML models were able to identify 6 structures and testing conditions with comparable Young's modulus and ultimate tensile strength to ligamentous tissue, skin tissue, oral and nasal tissue, and renal tissue. This novel study proved that Classification and Regression Trees (CART) models were an innovative and easy to interpret tool to identify biomimetic electrospun structures; however, Cubist and Support Vector Machine (SVM) models were the most accurate, with R2 of 0.93 and 0.8, to predict the ultimate tensile strength and Young's modulus, respectively. This approach can be implemented to optimise the manufacturing process in different applications.

Multisystemic supports and adolescent resilience to depression over time: A South African mixed methods study

In sub-Saharan countries, like South Africa, there is scant understanding of adolescent resilience to depression over time; the multisystemic resource combinations that support such resilience; and whether more diverse resource combinations yield better mental health dividends. In response, we conducted a longitudinal concurrent nested mixed methods study with 223 South African adolescents (mean age: 17.16 years, SD = 1.73; 64.60% girls; 81.60% Black). Using longitudinal mixture modeling, the quantitative study identified trajectories of depression and associations between trajectory membership and resource diversity. Using a draw-and-write methodology and reflexive thematic analyses, the qualitative study explored the resource diversity associated with each trajectory. Taken together, these studies identified four depression trajectories (Stable Low; Declining; Worsening; Chronic High) with varying resource diversity at baseline and over time. Resource diversity was inclusive of personal, relational, contextual, and culturally valued resources in both the Stable Low and Declining trajectories, with emphasis on relational supports. Personal resources were emphasized in the Worsening and Chronic High trajectories, and culturally valued and contextual resources de-emphasized. In summary, resource constellations characterized by within and across system diversity and cultural responsiveness are more protective and will be key to advancing sub-Saharan adolescent mental health.

Spatial and temporal parasite dynamics: microhabitat preferences and infection progression of two co-infecting gyrodactylids

Background

Mathematical modelling of host-parasite systems has seen tremendous developments and broad applications in theoretical and applied ecology. The current study focuses on the infection dynamics of a gyrodactylid-fish system. Previous experimental studies have explored the infrapopulation dynamics of co-infecting ectoparasites, Gyrodactylus turnbulli and G. bullatarudis, on their fish host, Poecilia reticulata, but questions remain about parasite microhabitat preferences, host survival and parasite virulence over time. Here, we use more advanced statistics and a sophisticated mathematical model to investigate these questions based on empirical data to add to our understanding of this gyrodactylid-fish system.

Methods

A rank-based multivariate Kruskal-Wallis test coupled with its post-hoc tests and graphical summaries were used to investigate the spatial and temporal parasite distribution of different gyrodactylid strains across different host populations. By adapting a multi-state Markov model that extends the standard survival models, we improved previous estimates of survival probabilities. Finally, we quantified parasite virulence of three different strains as a function of host mortality and recovery across different fish stocks and sexes.

Results

We confirmed that the captive-bred G. turnbulli and wild G. bullatarudis strains preferred the caudal and rostral regions respectively across different fish stocks; however, the wild G. turnbulli strain changed microhabitat preference over time, indicating microhabitat preference of gyrodactylids is host and time dependent. The average time of host infection before recovery or death was between 6 and 14 days. For this gyrodactylid-fish system, a longer period of host infection led to a higher chance of host recovery. Parasite-related mortalities are host, sex and time dependent, whereas fish size is confirmed to be the key determinant of host recovery.

Conclusion

From existing empirical data, we provided new insights into the gyrodactylid-fish system. This study could inform the modelling of other host-parasite interactions where the entire infection history of the host is of interest by adapting multi-state Markov models. Such models are under-utilised in parasitological studies and could be expanded to estimate relevant epidemiological traits concerning parasite virulence and host survival.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05471-9.

Comparing Mental Health, Wellbeing and Flourishing in Undergraduate Students Pre- and during the COVID-19 Pandemic

There has been a preponderance of studies on student mental health, wellbeing and flourishing during the COVID-19 pandemic. Few studies have compared data on student mental health and wellbeing before and during the pandemic. The purpose of the current study was to compare mental health and wellbeing in undergraduate students before and during the COVID-19 pandemic. Survey research was conducted with three groups of undergraduate students (n = 905) from diverse scientific fields at a large, urban university in South Africa. Data was collected by means of electronic surveys, combining full-scale items from three instruments, the Mental Health Continuum Short Form, the Flourishing Scale and the Fragility of Happiness Scale. Data was analysed by the Statistical Package for the Social Sciences (SPSS), the Analysis of Moment Structures (AMOS) and R software. The results indicate that while the mental health and wellbeing of students declined during the pandemic concerning their perceived ability to contribute to society, having supportive and rewarding social relationships and them being engaged and interested in their daily activities, it also improved in terms of their perceived ability to manage their daily lives (environmental mastery), being challenged to grow (personal growth) and in terms of their views that society was becoming better (social growth/actualisation).

Evolutionary drivers of morphological differentiation among three bottlenose dolphin lineages, Tursiops spp. (Delphinidae), in the northwest Indian Ocean utilizing linear and geometric morphometric techniques

Local adaptation and adaptive radiations are typically associated with phenotypic variation suited to alternative environments. In the marine environment, the nature of relevant ecological or environmental transitions is poorly understood, especially for highly mobile species. Here we compare three genetic lineages in the genus Tursiops (bottlenose dolphins), using linear measurements and geometric morphometric techniques, in the context of environmental variation in the northwest Indian Ocean. Cranial morphology was clearly differentiated comparing Tursiops truncatus and Tursiops aduncus, while a recently discovered genetic lineage, found in the Arabian Sea, was morphologically most similar to T. aduncus from the same region, but distinct for various measures, particularly metrics associated with the lateral dimension of the skull. The extent of divergence between T. truncatus and T. aduncus compared to differences between the T. aduncus lineages is consistent with the recent phylogeny for these species. Therefore, with the corroboration of genetic and morphological inference, we propose two conservation units of T. aduncus be recognized in the region at a sub-specific level so that their conservation can be managed effectively. We consider possible evolutionary mechanisms associated with regional habitat characteristics and the exploitation of distinct prey resources.

Combination rules for homoscedastic and heteroscedastic MANOVA models from multiply imputed datasets

Classical MANOVA tests do not pose any difficulty when the assumptions on which they are based are satisfied, while the modified Brown-Forsythe (MBF) procedure has low sensitivity to the lack of multivariate normality and homogeneity of covariance matrices. Both methods assume complete data for all subjects. In this paper, we present combination rules for the MANOVA and MBF procedures with multiply imputed datasets. These rules are illustrated by pooling the results obtained with a two-factor multivariate design after applying the two approaches to each of the imputed datasets when the covariance matrices were equal (MI-MANOVA) and when the covariance matrices were unequal (MI-MBF). A Monte-Carlo study was carried out to compare the proposed solution, in terms of type I error rates and statistical power, with the MANOVA and MBF approaches without missing data, and with listwise deletion of missing data followed by the MANOVA approach (LD-MANOVA) and listwise deletion followed by the MBF procedure (LD-MBF). Simulations showed that the type I error rates in all analyses on datasets with missing values (with or without imputation) were well controlled. We also found that the MI-MANOVA approach was substantially more powerful than LD-MANOVA. Moreover, the power of the MI-MANOVA was generally comparable to that of its complete data counterpart. Similar results were obtained for the MI-MBF procedure when covariance matrices were unequal. We conclude, based on the current evidence, that the solution presented performs well and could be of practical use. We illustrate the application of combination rules using a real dataset.