Social Determinants of Mental Health During a Year of the COVID-19 Pandemic

Belonging is a basic human need, with social isolation signaling a threat to biological fitness. Sensitivity to ostracism varies across individuals and the lifespan, peaking in adolescence. Government-imposed restrictions upon social interactions during COVID-19 may therefore be particularly detrimental to young people and those most sensitive to ostracism. Participants (N = 2367; 89.95% female, 11-100 years) from three countries with differing levels of government restrictions (Australia, UK, and USA) were surveyed trice at three-month intervals (May 2020 – April 2021). Young people, and those living under the tightest government restrictions, reported the worst mental health, with these inequalities in mental health remaining constant throughout the study period. Further dissection of these results revealed that young people high on social rejection sensitivity reported the most mental health problems at the final assessment. These findings help account for the greater impact of enforced social isolation on young people’s mental health, and open novel avenues for intervention.

Ontology and crow optimization-based deep belief network for privacy preservation of medical data

Medical data classification is used to find the hidden patterns of data by training a large amount of patient data collected from the providers. As the medical data is very sensitive, it must be a safeguard from all the noncollaborative means. Thus, it is important to take steps to preserve the confidential medical data. Accordingly, this paper proposes a classification method termed as crow optimization-based deep belief neural network (CS-DBN) to preserve the privacy of confidential medical data automatically. This classifier works based on three phases, including generation of the privacy-preserved data, construction of ontology, and classification. The Deep convolutional kernel approach is used to provide data confidentiality using the optimal coefficients. The construction of ontology is done with the cardiac heart disease terms used in the medical field for classification. Finally, the classification is performed using the deep belief network (DBN), which is trained using the crow search algorithm (CSA). The performance is analyzed in terms of the metrics, namely, accuracy, fitness, sensitivity, and specificity. The proposed CS-DBN method produces higher fitness, accuracy, sensitivity, and specificity of 0.9007, 0.8842, 1, and 0.8408, respectively.

Mobility and its sensitivity to fitness differences determine consumer–resource distributions

An animal's movement rate (mobility) and its ability to perceive fitness gradients (fitness sensitivity) determine how well it can exploit resources. Previous models have examined mobility and fitness sensitivity separately and found that mobility, modelled as random movement, prevents animals from staying in high-quality patches, leading to a departure from an ideal free distribution (IFD). However, empirical work shows that animals with higher mobility can more effectively collect environmental information and better sense patch quality, especially when the environment is frequently changed by human activities. Here, we model, for the first time, this positive correlation between mobility and fitness sensitivity and measure its consequences for the populations of a consumer and its resource. In the absence of consumer demography, mobility alone had no effect on system equilibria, but a positive correlation between mobility and fitness sensitivity could produce an IFD. In the presence of consumer demography, lower levels of mobility prevented the system from approaching an IFD due to the mixing of consumers between patches. However, when positively correlated with fitness sensitivity, high mobility led to an IFD. Our study demonstrates that the expected covariation of animal movement attributes can drive broadly theorized consumer–resource patterns across space and time and could underlie the role of consumers in driving spatial heterogeneity in resource abundance.

Exploring the Mode of Action of Antimicrobial Peptide MUC7 12-Mer by Fitness Profiling of Saccharomyces cerevisiae Genomewide Mutant Collection

ABSTRACT The MUC7 12-mer (RKSYKCLHKRCR) is a cationic antimicrobial peptide derived from the human salivary mucin MUC7. To study its effect/mechanism of action on fungi, we performed a fitness screen of a tagged, diploid, homozygous gene deletion mutant pool of the yeast Saccharomyces cerevisiae grown in the presence of the MUC7 peptide. Forty-five strains exhibiting reduced fitness and 13 strains exhibiting increased fitness (sensitivity or resistance, respectively) were identified by hybridization intensities to tag arrays. The strongest fitness defects were observed with deletions in genes encoding elements of the RIM101 signaling pathway (regulating response to alkaline and neutral pH and other environmental conditions) and of the endosomal sorting complex required for transport (ESCRT; functioning mainly in protein sorting for degradation, but also required for activation of the RIM101 pathway). Other deletions identified as conferring fitness defect or gain are in genes associated with a variety of functions, including transcription regulation, protein trafficking, transport, metabolism, and others. The results of the pool fitness screen were validated by a set of mutant strains tested individually in the presence of the MUC7 12-mer. All tested RIM101-related deletion strains showing fitness defects confirmed their sensitivities. Taken together, the results led us to conclude that deletions of genes associated with the RIM101 pathway confer sensitivity to the peptide by preventing activation of this pathway and that this stress response plays a major role in the protection of S. cerevisiae against damage inflicted by the MUC7 12-mer peptide.