Revisiting the Major Mental Disorders and Updating the Nosological Schema: A Synthesis

Recently, conciliating findings from molecular genetics, evolutionary biology, along with empirical clinical evidence regarding the major mental disorders (MMDs) namely bipolar affective disorder (BPAD), schizophrenia, obsessive compulsive disorder (OCD), the anxieties with depression, autism and attention deficit/hyperactivity disorder (ADHD) all point to a common neural-developmental origin. Genetic loci associated with schizophrenia do not directly lead to the disorder;instead, they code for the expression of lopsided, temperamental, variants in individuals that originate mainly from one part of our human nature which applies also, to the rest of the MMDs. These individuals contribute to the flexibility, robustness, and creative input of our species, concomitantly, they incur vulnerability to the development of a MMD as an evolutionary trade off. MMDs initially, are expressed as periodic epiphenomena on the underlying temperamental extreme variants of brain function. Their expressions tend to become permanent. Underlying, aberrant traits remain unaltered. Their clinical expressions are characterized by “either-or”, antithetical substitutes, in addition to co-occurring psychosis. The latter is a common occurrence to other assaults on brain function. Characteristic, “ether-or” symptoms are the result of a disturbed, overall, coordinating property of brain function, normally responsive to the smooth, synchronizing expression of all higher mental faculties. Clinical findings point to the need of modifying the current schema in order to better reflect their collective significance in order to help guide research to a new, more promising direction in elucidating their triggers, development, and mechanisms whereby opening a new horizon for therapy and treatment.

Crop adaptation to climate change:An evolutionary perspective

The disciplines of evolutionary biology and plant and animal breeding have been intertwined throughout their development,with responses to artificial selection yielding insights into the action of natural selection and evolutionary biology providing statistical and conceptual guidance for modern breeding.Here we offer an evolutionary perspective on a grand challenge of the 21st century:feeding humanity in the face of climate change.We first highlight promising strategies currently under way to adapt crops to current and future climate change.These include methods to match crop varieties with current and predicted environments and to optimize breeding goals,management practices,and crop microbiomes to enhance yield and sustainable production.We also describe the promise of crop wild relatives and recent technological innovations such as speed breeding,genomic selection,and genome editing for improving environmental resilience of existing crop varieties or for developing new crops.Next,we discuss how methods and theory from evolutionary biology can enhance these existing strategies and suggest novel approaches.We focus initially on methods for reconstructing the evolutionary history of crops and their pests and symbionts,because such historical information provides an overall framework for crop-improvement efforts.We then describe how evolutionary approaches can be used to detect and mitigate the accumulation of deleterious mutations in crop genomes,identify alleles and mutations that underlie adaptation(and maladaptation)to agricultural environments,mitigate evolutionary trade-offs,and improve critical proteins.Continuing feedback between the evolution and crop biology communities will ensure optimal design of strategies for adapting crops to climate change.

Discrete Circular Distributions with Applications to Shared Orthologs of Paired Circular Genomes

For structural comparisons of paired prokaryotic genomes,an important topic in synthetic and evolutionary biology,the locations of shared orthologous genes(henceforth orthologs)are observed as binned data.This and other data,e.g.,wind directions recorded at monitoring sites and intensive care unit arrival times on the 24-hour clock,are counted in binned circular arcs,thus modeling them by discrete circular distributions(DCDs)is required.We propose a novel method to construct a DCD from a base continuous circular distribution(CCD).The probability mass function is defined to take the normalized values of the probability density function at some pre-fixed equidistant points on the circle.Five families of constructed DCDs which have normalizing constants in closed form are presented.Simulation studies show that DCDs outperform the corresponding CCDs in modeling grouped(discrete)circular data,and minimum chi-square estimation outperforms maximum likelihood estimation for parameters.We apply the constructed DCDs,invariant wrapped Poisson and wrapped discrete skew Laplace to compare the structures of paired bacterial genomes.Specifically,discrete four-parameter wrapped Cauchy(nonnegative trigonometric sums)distribution models multi-modal shared orthologs in Clostridium(Sulfolobus)better than the others considered,in terms of AIC and Freedman’s goodness-of-fit test.The result that different DCDs fit the shared orthologs is consistent with the fact they belong to two kingdoms.Nevertheless,these prokaryotes have a common favored site around 70°on the unit circle;this finding is important for building synthetic prokaryotic genomes in synthetic biology.These DCDs can also be applied to other binned circular data.

Populations with individual variation in dispersal in heterogeneous environments: Dynamics and competition with simply diffusing populations

We consider a model for a population in a heterogeneous environment, with logistic-type local population dynamics, under the assumption that individuals can switch between two different nonzero rates of diffusion. Such switching behavior has been observed in some natural systems. We study how environmental heterogeneity and the rates of switching and diffusion affect the persistence of the population. The reactiondiffusion systems in the models can be cooperative at some population densities and competitive at others. The results extend our previous work on similar models in homogeneous environments. We also consider competition between two populations that are ecologically identical, but where one population diffuses at a fixed rate and the other switches between two different diffusion rates. The motivation for that is to gain insight into when switching might be advantageous versus diffusing at a fixed rate. This is a variation on the classical results for ecologically identical competitors with differing fixed diffusion rates, where it is well known that "the slower diffuser wins".

MTMO： an efficient network-centric algorithm for subtree counting and enumeration

Background： The frequency of small subtrees in biological, social, and other types of networks could shed light into the structure, function, and evolution of such networks. However, counting all possible subtrees of a prescribed size can be computationally expensive because of their potentially large number even in small, sparse networks. Moreover, most of the existing algorithms for subtree counting belong to the subtree-centric approaches, which search for a specific single subtree type at a time, potentially taking more time by searching again on the same network. Methods： In this paper, we propose a network-centric algorithm （MTMO） to efficiently count k-size subtrees. Our algorithm is based on the enumeration of all connected sets of k-1 edges, incorporates a labeled rooted tree data structure in the enumeration process to reduce the number of isomorphism tests required, and uses an array-based indexing scheme to simplify the subtree counting method. Results： The experiments on three representative undirected complex networks show that our algorithm is roughly an order of magnitude faster than existing subtree-centric approaches and base network-centric algorithm which does not use rooted tree, allowing for counting larger subtrees in larger networks than previously possible. We also show major differences between unicellular and multicellular organisms. In addition, our algorithm is applied to find network motifs based on pattern growth approach. Conclusions： A network-centric algorithm which allows for a This enables us to count larger motif in larger networks than faster counting of non-induced subtrees is proposed previously.