Children and adolescents weathering the storm: Resilience in the presence of bullying victimization, harassment, and pandemic lockdown in northern Norway

Resilience is a concept of growing interest because it can systematically inform prevention measures and psychosocial interventions for children and adolescents. The aim of this study was to explore resilience factors among young people who are victims of bullying and harassment (age 9 to 16 years old). In 2021 the burden of the pandemic lockdown became an additional adversity. The study used a repeated cross-sectional design. Two datasets with a total of 2,211 participants from 2017 (N = 972) and 2021 (N = 1,239) were included. The strengths and difficulties questionnaire (SDQ) was applied to define the resilient and non-resilient groups, and the quality-of-life questionnaire (KINDL) was used to map resilience factors. A total of 227 participants reported that they were being bullied, and 604 participants reported harassments from their peers. We used correlation and regression analyses to identify which factors predicted the highest resistance to the negative effects of bullying and harassment. The results were that 77.2% of the participants stayed resilient when facing these maladjustments, but this dropped to 61.7% during the pandemic. The most important resilience factors before the pandemic were the school environment, emotional well-being, and good relations with their friends. The impact of these predictors changed during the pandemic. Emotional well-being increased in strength, school environment was reduced, and friends did not predict resilience anymore. The effect sizes were generally large to medium. As it is common to experience adversity at some stage in life, it is vital for families, schools, social and healthcare workers to be aware of the factors associated with resilience. The results of this study may contribute towards an evidence base for developing plans to increase the capacity of resilience among young people.

The Exact Stochastic Process of the Haploid Multi-Allelic Wright-Fisher Mutation Model

Diffusion models are widely applied in population genetics, but their approximate solutions may not accurately capture the exact stochastic process. Nevertheless, this practice was necessary due to computing limitations, particularly for large populations. In this article, we develop the exact Markov chain algebra (MCA) for a discrete haploid multi-allelic Wright-Fisher model (MA-WFM) with a full mutation matrix to address this challenge. A special case of nonzero mutations between multiple alleles have not been captured by previous bi-allelic models. We formulated the mean allele frequencies for asymptotic equilibrium analytically for the tri- and quad-allelic case. We also evaluated the exact time-dependent Markov model numerically, presenting it concisely in terms of diffusion variables. The convergence with increasing population size to a diffusion limit is demonstrated for the population composition distribution. Our model shows that there will never be exact irreversible extinction when there are nonzero mutation rates into each allele and never be an exact irreversible fixation when there are nonzero mutation rates out of each allele. We only present results where there is no complete extinction and no complete fixation. Finally, we present detailed computations for the full Markov process, exposing the behavior near the boundaries for the compositional domains, which are non-singular boundaries according to diffusion theory.

The severe impact of the COVID-19 pandemic on bullying victimization, mental health indicators and quality of life

Children and adolescents have been severely affected by the COVID-19 pandemic. The aim of this study was to explore the prevalence of traditional and digital bullying and mental health problems a year into the pandemic. Further, how anxiety level, loneliness, and self-perceived school functioning have influenced the quality of life. A comprehensive questionnaire was administered (N = 1239) in the city of Tromsø and compared to a similar data collection (N = 972) conducted in the same schools in 2017. The main findings were increased prevalence in bullying, more mental health problems and significantly reduced quality of life compared to before the pandemic. Implications and the importance of implementing anti-bullying and psychosocial measures after the pandemic are discussed.

Using statistical methods to model the fine-tuning of molecular machines and systems

Fine-tuning has received much attention in physics, and it states that the fundamental constants of physics are finely tuned to precise values for a rich chemistry and life permittance. It has not yet been applied in a broad manner to molecular biology. However, in this paper we argue that biological systems present fine-tuning at different levels, e.g. functional proteins, complex biochemical machines in living cells, and cellular networks. This paper describes molecular fine-tuning, how it can be used in biology, and how it challenges conventional Darwinian thinking. We also discuss the statistical methods underpinning fine-tuning and present a framework for such analysis.

A Mutation Model from First Principles of the Genetic Code

The paper presents a neutral Codons Probability Mutations (CPM) model of molecular evolution and genetic decay of an organism. The CPM model uses a Markov process with a 20-dimensional state space of probability distributions over amino acids. The transition matrix of the Markov process includes the mutation rate and those single point mutations compatible with the genetic code. This is an alternative to the standard Point Accepted Mutation (PAM) and BLOcks of amino acid SUbstitution Matrix (BLOSUM). Genetic decay is quantified as a similarity between the amino acid distribution of proteins from a (group of) species on one hand, and the equilibrium distribution of the Markov chain on the other. Amino acid data for the eukaryote, bacterium, and archaea families are used to illustrate how both the CPM and PAM models predict their genetic decay towards the equilibrium value of 1. A family of bacteria is studied in more detail. It is found that warm environment organisms on average have a higher degree of genetic decay compared to those species that live in cold environments. The paper addresses a new codon-based approach to quantify genetic decay due to single point mutations compatible with the genetic code. The present work may be seen as a first approach to use codon-based Markov models to study how genetic entropy increases with time in an effectively neutral biological regime. Various extensions of the model are also discussed.

Darwin's perplexing paradox: intelligent design in nature

Today, many would assume that Charles Darwin absolutely rejected any claim of intelligent design in nature. However, review of his initial writings reveals that Darwin accepted some aspects of this view. His conceptualization of design was founded on both the cosmological and the teleological ideas from classical natural theology. When Darwin discovered the dynamic process of natural selection, he rejected the old teleological argument as formulated by William Paley. However, he was never able to ignore the powerful experience of the beauty and complexity of an intelligently designed universe, as a whole. He corresponded with Asa Gray on religious themes, particularly touching the problem of pain and intelligent design in nature. The term "intelligent design" was probably introduced by William Whewell. Principally for theological and philosophical reasons, Darwin could only accept the concept for the universe as a whole, not with respect to individual elements of the living world.

Molecular characterization of cold adaptation of membrane proteins in the Vibrionaceae core-genome

Cold-adaptation strategies have been studied in multiple psychrophilic organisms, especially for psychrophilic enzymes. Decreased enzyme activity caused by low temperatures as well as a higher viscosity of the aqueous environment require certain adaptations to the metabolic machinery of the cell. In addition to this, low temperature has deleterious effects on the lipid bilayer of bacterial membranes and therefore might also affect the embedded membrane proteins. Little is known about the adaptation of membrane proteins to stresses of the cold. In this study we investigate a set of 66 membrane proteins from the core genome of the bacterial family Vibrionaceae to identify general characteristics that discern psychrophilic and mesophilic membrane proteins. Bioinformatical and statistical methods were used to analyze the alignments of the three temperature groups mesophilic, intermediate and psychrophilic. Surprisingly, our results show little or no adaptation to low temperature for those parts of the proteins that are predicted to be inside the membrane. However, changes in amino acid composition and hydrophobicity are found for complete sequences and sequence parts outside the lipid bilayer. Among others, the results presented here indicate a preference for helix-breaking and destabilizing amino acids Ile, Asp and Thr and an avoidance of the helix-forming amino acid Ala in the amino acid composition of psychrophilic membrane proteins. Furthermore, we identified a lower overall hydrophobicity of psychrophilic membrane proteins in comparison to their mesophilic homologs. These results support the stability-flexibility hypothesis and link the cold-adaptation strategies of membrane proteins to those of loop regions of psychrophilic enzymes.

A TUTORIAL ON MARKOV MODELS BASED ON MENDEL'S CLASSICAL EXPERIMENTS

Hidden Markov Models (HMM) can be extremely useful tools for the analysis of data from biological sequences, and provide a probabilistic model of protein families. Most reviews and general introductions follow the excellent tutorial by Rabiner,1 where the focus is outside biology. Mendel's famous experiments in plant hybridisation were published in 1866 and are often considered the icebreaking work of modern genetics. He had no prior knowledge of the dual nature of genes, but through a series of experiments he was able to anticipate the hidden concept and name it "Elemente". In this paper we present the background, theory and algorithms of HMM based on examples from Mendel's experiments, and introduce the toolbox "mendelHMM". This approach is considered to have some intuitive advantages in a biological and bioinformatical setting. Applications to analysing bio-sequences like nucleic acids and proteins are also discussed.

DeltaProt: a software toolbox for comparative genomics

Background

Statistical bioinformatics is the study of biological data sets obtained by new micro-technologies by means of proper statistical methods. For a better understanding of environmental adaptations of proteins, orthologous sequences from different habitats may be explored and compared. The main goal of the DeltaProt Toolbox is to provide users with important functionality that is needed for comparative screening and studies of extremophile proteins and protein classes. Visualization of the data sets is also the focus of this article, since visualizations can play a key role in making the various relationships transparent. This application paper is intended to inform the reader of the existence, functionality, and applicability of the toolbox.

Results

We present the DeltaProt Toolbox, a software toolbox that may be useful in importing, analyzing and visualizing data from multiple alignments of proteins. The toolbox has been written in MATLAB™ to provide an easy and user-friendly platform, including a graphical user interface, while ensuring good numerical performance. Problems in genome biology may be easily stated thanks to a compact input format. The toolbox also offers the possibility of utilizing structural information from the SABLE or other structure predictors. Different sequence plots can then be viewed and compared in order to find their similarities and differences. Detailed statistics are also calculated during the procedure.

Conclusions

The DeltaProt package is open source and freely available for academic, non-commercial use. The latest version of DeltaProt can be obtained from http://services.cbu.uib.no/software/deltaprot/. The website also contains documentation, and the toolbox comes with real data sets that are intended for training in applying the models to carry out bioinformatical and statistical analyses of protein sequences.

Equipped with the new algorithms proposed here, DeltaProt serves as an auxiliary analysis tool capable of visualizing and comparing orthologus protein sequences. The framework of the algorithms also enables easy incorporation of extra information on structure, if such data is available.

Wright-Fisher model of social insects with haploid males and diploid females

An inhomogeneous discrete Markov model is formulated for sexual random mating in finite populations of haploid male and diploid female individuals. This is a Wright-Fisher type of model for social insects. The generations are non-overlapping and of given finite sizes. Bottlenecks are included, allowing different sizes to change from generation to generation. Mutations and selection are included in this exact model for the stochastic process. Computations of the exact Markov model are presented, focussing on the sexually asymmetric genetic drift caused by haplodiploidy.

Environmental adaptation of proteins: regression models with simple physicochemical properties

Bio-sequences from ortholog proteins are well suited for statistical inference when the sequences can be divided into ordinal groups based on known environmental features or traits of the host organisms. In this paper two new regression models are described for extracting proteomic trends of extreme environments. The approach is based on physicochemical properties of the amino acids, and may also utilise stratification of the data. We are especially looking for connections of temperature adaptation between the organism and its molecular level. To show the applicability of the methods, we present analyses of genomic data from proteobacteria orders, where we examine the cold adaptation of membrane proteins, intracellular proteins, and the enzyme endonuclease I. Our results confirm earlier findings that redistribution of charge and increase of surface hydrophobicity might be some of the most important signatures for cold adaptation.

Molecular characterization of cold adaptation based on ortholog protein sequences from Vibrionaceae species

A set of 298 protein families from psychrophilic Vibrio salmonicida was compiled to identify genotypic characteristics that discern it from orthologous sequences from the mesophilic Vibrio/Photobacterium branch of the gamma-Proteobacteria (Vibrionaceae family). In our comparative exploration we employed alignment based bioinformatical and statistical methods. Interesting information was found in the substitution matrices, and the pattern of asymmetries in the amino acid substitution process. Together with the compositional difference, they identified the amino acids Ile, Asn, Ala and Gln as those having the most psycrophilic involvement. Ile and Asn are enhanced whereas Gln and Ala are suppressed. The inflexible Pro residue is also suppressed in loop regions, as expected in a flexible structure. The dataset were also classified and analysed according to the predicted subcellular location, and we made an additional study of 183 intracellular and 65 membrane proteins. Our results revealed that the psychrophilic proteins have similar hydrophobic and charge contributions in the core of the protein as mesophilic proteins, while the solvent-exposed surface area is significantly more hydrophobic. In addition, the psychrophilic intracellular (but not the membrane) proteins are significantly more negatively charged at the surface. Our analysis supports the hypothesis of preference for more flexible amino acids at the molecular surface. Life in cold climate seems to be obtained through many minor structural modifications rather than certain amino acids substitutions.

Sequence comparison and environmental adaptation of a bacterial endonuclease

The periplasmic/extracellular bacterial enzyme endonuclease I was chosen as a model system to identify features that might be responsible for temperature- and salt adaptation. A statistical study of amino acid sequence properties belonging to endonuclease I enzymes from three mesophilic habitats (non-marine, brackish water and marine), and three marine temperature groups (psychrophile, intermediate and mesophile) has been conducted. Ten new endonuclease I genes have been sequenced in order to increase the sample size. A bioinformatical method of property dependent statistical analysis of alignments has been applied. To our knowledge this is the first time these methods have been used in order to investigate environmental adaptation of enzymes. Adaptation to low temperature seems to involve increased surface isoelectric point and hydrophobicity in contrast to salt adaptation in which the isoelectric point and hydrophobicity at the surface decreases. Redistribution of charge and hydrophobicity might be the most important signature for cold adaptation and salt adaptation of this enzyme class. The results indicate that general trends of adaptation are possible to elucidate from the amino acid sequences. Also in this paper a new scale of stratified B-factors, derived from the Protein Data Bank, is presented.

A sexually neutral discrete Markov model for given sum males + females

An inhomogeneous discrete Markov model is formulated for sexual random mating with diploid male and female individuals. The generations are nonoverlapping and of given sizes. The genetic variation is in a sexually neutral allele with two varieties, giving three different genotypes. Taking sex as a marker, the Markov model works with six genotypes. The sex of each offspring is random. This implies a probability of extinction, giving the model an algorithmic nature. We compute expected genotype frequencies, their standard deviations and fixation probabilities.

Markov model of haploid random mating with given distribution of population size

An exact Markov chain model is formulated and computed for random mating in a haploid gamete pool. There are two versions of the gamete, and there is a finite number of diploid monoecious organisms. The founder population is given, and the subsequent generations allow a prescribed statistical distribution over different population sizes. The non-homogeneous Markov chain works on the haploid gamete level provided the probability of self-fertilization is 1/n, where n is the number of diploid individuals. Standard deviations of gamete frequencies and fixation probabilities are calculated. Effective population sizes for different population size distributions are estimated, including periodic bottlenecks.