Self-similar correlation function in brain resting-state fMRI

Adaptive behavior, cognition and emotion are the result of a bewildering variety of brain spatiotemporal activity patterns. An important problem in neuroscience is to understand the mechanism by which the human brain's 100 billion neurons and 100 trillion synapses manage to produce this large repertoire of cortical configurations in a flexible manner. In addition, it is recognized that temporal correlations across such configurations cannot be arbitrary, but they need to meet two conflicting demands: while diverse cortical areas should remain functionally segregated from each other, they must still perform as a collective, i.e., they are functionally integrated. Here, we investigate these large-scale dynamical properties by inspecting the character of the spatiotemporal correlations of brain resting-state activity. In physical systems, these correlations in space and time are captured by measuring the correlation coefficient between a signal recorded at two different points in space at two different times. We show that this two-point correlation function extracted from resting-state fMRI data exhibits self-similarity in space and time. In space, self-similarity is revealed by considering three successive spatial coarse-graining steps while in time it is revealed by the 1/f frequency behavior of the power spectrum. The uncovered dynamical self-similarity implies that the brain is spontaneously at a continuously changing (in space and time) intermediate state between two extremes, one of excessive cortical integration and the other of complete segregation. This dynamical property may be seen as an important marker of brain well-being both in health and disease.Comment: 14 pages 13 figures; published online before print September 2

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

Funder: NCI U24CA211006Abstract: The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts

Multi-scale modeling in human systems pharmacology & physiology

Prevention of drug-induced toxicities is a major component in the drug development pipeline and in clinical patient care. The toxicity potential of drugs is routinely accessed with in vitro assays and in vivo animal experiments before the first-in-human administration. Although these experiments produce valuable information, a significant amount of false positive toxicity predictions results when translating these findings to the human situation. The lack of patient-specific in vitro models that allow mimicking the patient-specific in vivo situation, as well as computational methods that translate these model findings to the patient level are missing to date. In this work, the development of computational multi-scale concepts is presented and applied to estimate the impact of drug concentrations-time profiles on the organ-specific cellular biochemistry. The presented concepts are used to estimate drug-induced metabolic perturbations on the cellular level after drug administration, enabling the prediction of to be expected changes of metabolite pools in organs, tissues, and blood plasma. Besides the prediction of drug-induced metabolic perturbations on the cellular biochemistry and metabolite concentrations, metabolic changes on the whole-body level can be estimated and thus allow the mechanistic characterization of the underlaying processes. The presented concepts and approaches allow the integration of time-resolved experimental data from in vitro and in vivo high-throughput experiments together with drug concentration-time profiles. Thereby, supporting a translation from in vitro findings into an in vivo context. The presented computational concepts span multiple orders of biological organization ranging from the whole-body over the organ and tissue scale down to the cellular level. If applied during the drug development process, the presented concepts could help to identify potential toxic compounds early in the drug development pipeline. Therefore, the presented work might help to develop targeted therapies aiming to reduce or even prevent drug-induced toxicities. This work may also contribute to our mechanistic understanding of organ-specific drug-induced metabolic perturbations that can set the molecular base for drug-induced toxicity

Prehistoric Plant Exploitation and Domestication: An Inspiration for the Science of De Novo Domestication in Present Times

De novo domestication is a novel trend in plant genetics, where traits of wild or semi-wild species are changed by the use of modern precision breeding techniques so that they conform to modern cultivation. Out of more than 300,000 wild plant species, only a few were fully domesticated by humans in prehistory. Moreover, out of these few domesticated species, less than 10 species dominate world agricultural production by more than 80% today. Much of this limited diversity of crop exploitation by modern humans was defined early in prehistory at the emergence of sedentary agro-pastoral cultures that limited the number of crops evolving a favorable domestication syndrome. However, modern plant genetics have revealed the roadmaps of genetic changes that led to these domestication traits. Based on such observations, plant scientists are now taking steps towards using modern breeding technologies to explore the potential of de novo domestication of plant species that were neglected in the past. We suggest here that in this process of de novo domestication, the study of Late Paleolithic/Late Archaic and Early Neolithic/Early Formative exploration of wild plants and identification of neglected species can help identify the barriers towards domestication. Modern breeding technologies may then assist us to break these barriers in order to perform de novo domestication to increase the crop species diversity of modern agriculture