On directed information theory and Granger causality graphs

Directed information theory deals with communication channels with feedback. When applied to networks, a natural extension based on causal conditioning is needed. We show here that measures built from directed information theory in networks can be used to assess Granger causality graphs of stochastic processes. We show that directed information theory includes measures such as the transfer entropy, and that it is the adequate information theoretic framework needed for neuroscience applications, such as connectivity inference problems.Comment: accepted for publications, Journal of Computational Neuroscienc

Dissociation Between the Growing Opioid Demands and Drug Policy Directions Among the U.S. Older Adults with Degenerative Joint Diseases

We aim to examine temporal trends of orthopedic operations and opioid-related hospital stays among seniors in the nation and states of Oregon and Washington where marijuana legalization was accepted earlier than any others. As aging society advances in the United States (U.S.), orthopedic operations and opioid-related hospital stays among seniors increase in the nation. A serial cross-sectional cohort study using the healthcare cost and utilization project fast stats from 2006 through 2015 measured annual rate per 100,000 populations of orthopedic operations by age groups (45–64 vs 65 and older) as well as annual rate per 100,000 populations of opioid-related hospital stays among 65 and older in the nation, Oregon and Washington states from 2008 through 2017. Orthopedic operations (knee arthroplasty, total or partial hip replacement, spinal fusion or laminectomy) and opioid-related hospital stays were measured. The compound annual growth rate (CAGR) was used to quantify temporal trends of orthopedic operations by age groups as well as opioid-related hospital stays and was tested by Rao–Scott correction of χ2 for categorical variables. The CAGR (4.06%) of orthopedic operations among age 65 and older increased (P...) (See full abstract in article

Deriving a mutation index of carcinogenicity using protein structure and protein interfaces

With the advent of Next Generation Sequencing the identification of mutations in the genomes of healthy and diseased tissues has become commonplace. While much progress has been made to elucidate the aetiology of disease processes in cancer, the contributions to disease that many individual mutations make remain to be characterised and their downstream consequences on cancer phenotypes remain to be understood. Missense mutations commonly occur in cancers and their consequences remain challenging to predict. However, this knowledge is becoming more vital, for both assessing disease progression and for stratifying drug treatment regimes. Coupled with structural data, comprehensive genomic databases of mutations such as the 1000 Genomes project and COSMIC give an opportunity to investigate general principles of how cancer mutations disrupt proteins and their interactions at the molecular and network level. We describe a comprehensive comparison of cancer and neutral missense mutations; by combining features derived from structural and interface properties we have developed a carcinogenicity predictor, InCa (Index of Carcinogenicity). Upon comparison with other methods, we observe that InCa can predict mutations that might not be detected by other methods. We also discuss general limitations shared by all predictors that attempt to predict driver mutations and discuss how this could impact high-throughput predictions. A web interface to a server implementation is publicly available at http://inca.icr.ac.uk/

Reconfigurable superconducting vortex pinning potential for magnetic disks in hybrid structures

High resolution scanning Hall probe microscopy has been used to directly visualise the superconducting vortex behavior in hybrid structures consisting of a square array of micrometer-sized Py ferromagnetic disks covered by a superconducting Nb thin film. At remanence the disks exist in almost fully flux-closed magnetic vortex states, but the observed cloverleaf-like stray fields indicate the presence of weak in-plane anisotropy. Micromagnetic simulations suggest that the most likely origin is an unintentional shape anisotropy. We have studied the pinning of added free superconducting vortices as a function of the magnetisation state of the disks, and identified a range of different phenomena arising from competing energy contributions. We have also observed clear differences in the pinning landscape when the superconductor and the ferromagnet are electron ically coupled or insulated by a thin dielectric layer, with an indication of non-trivial vortex-vortex interactions. We demonstrate a complete reconfiguration of the vortex pinning potential when the magnetisation of the disks evolves from the vortex-like state to an onion-like one under an in-plane magnetic field. Our results are in good qualitative agreement with theoretical predictions and could form the basis of novel superconducting devices based on reconfigurable vortex pinning sites

Therapeutic opportunities within the DNA damage response

The DNA damage response (DDR) is essential for maintaining the genomic integrity of the cell, and its disruption is one of the hallmarks of cancer. Classically, defects in the DDR have been exploited therapeutically in the treatment of cancer with radiation therapies or genotoxic chemotherapies. More recently, protein components of the DDR systems have been identified as promising avenues for targeted cancer therapeutics. Here, we present an in-depth analysis of the function, role in cancer and therapeutic potential of 450 expert-curated human DDR genes. We discuss the DDR drugs that have been approved by the US Food and Drug Administration (FDA) or that are under clinical investigation. We examine large-scale genomic and expression data for 15 cancers to identify deregulated components of the DDR, and we apply systematic computational analysis to identify DDR proteins that are amenable to modulation by small molecules, highlighting potential novel therapeutic targets

DNA repair, genome stability and cancer: a historical perspective

The multistep process of cancer progresses over many years. The prevention of mutations by DNA repair pathways led to an early appreciation of a role for repair in cancer avoidance. However, the broader role of the DNA damage response (DDR) emerged more slowly. In this Timeline article, we reflect on how our understanding of the steps leading to cancer developed, focusing on the role of the DDR. We also consider how our current knowledge can be exploited for cancer therapy

Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges

‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework. Importantly, we explicitly place unexplained residual models in a causal framework using directed acyclic graphs. This allows for theoretical justification of appropriate confounder adjustment and provides a framework for extending our results to more complex scenarios than those examined in this paper. We also discuss several interpretational issues relating to unexplained residual models within a causal framework. We argue that unexplained residual models offer no additional insights compared to traditional regression methods, and, in fact, are more challenging to implement; moreover, they artificially reduce estimated standard errors. Consequently, we conclude that unexplained residual models, if used, must be implemented with great care

The Association Between Pre-pregnancy BMI and Preterm Delivery in a Diverse Southern California Population of Working Women

Whereas preterm birth has consistently been associated with low maternal pre-pregnancy weight, the relationship with high pre-pregnancy weight has been inconsistent. We quantified the pre-pregnancy BMI—preterm delivery (PTD) relationship using traditional BMI categories (underweight, normal weight, overweight and obese) as well as continuous BMI. Eligible women participated in California’s statewide prenatal screening program, worked during pregnancy, and delivered a live singleton birth in Southern California in 2002–2003. The final analytic sample included 354 cases delivering at <37 weeks, as identified by clinical estimate of gestational age from screening records, and 710 term normal-birthweight controls. Multivariable logistic regression models using categorical BMI levels and continuous BMI were compared. In categorical analyses, PTD was significantly associated with pre-pregnancy underweight only. Nonparametric local regression revealed a V-shaped relationship between continuous BMI and PTD, with minimum risk at the high end of normal, around 24 kg/m2. The odds ratio (OR) for PTD associated with low BMI within the normal range (19 kg/m2) was 2.84 (95%CI = 1.61–5.01); ORs for higher BMI in the overweight (29 kg/m2) and obese (34 kg/m2) ranges were 1.42 (95%CI = 1.10–1.84) and 2.01 (95% CI = 1.20–3.39) respectively, relative to 24 kg/m2). BMI categories obscured the preterm delivery risk associated with low-normal, overweight, and obese BMI. We found that higher BMI up to around 24 kg/m2 is increasingly protective of preterm delivery, beyond which a higher body mass index becomes detrimental. Current NHLBI/WHO BMI categories may be inadequate for identifying women at higher risk for PTD

Qualia: The Geometry of Integrated Information

According to the integrated information theory, the quantity of consciousness is the amount of integrated information generated by a complex of elements, and the quality of experience is specified by the informational relationships it generates. This paper outlines a framework for characterizing the informational relationships generated by such systems. Qualia space (Q) is a space having an axis for each possible state (activity pattern) of a complex. Within Q, each submechanism specifies a point corresponding to a repertoire of system states. Arrows between repertoires in Q define informational relationships. Together, these arrows specify a quale—a shape that completely and univocally characterizes the quality of a conscious experience. Φ— the height of this shape—is the quantity of consciousness associated with the experience. Entanglement measures how irreducible informational relationships are to their component relationships, specifying concepts and modes. Several corollaries follow from these premises. The quale is determined by both the mechanism and state of the system. Thus, two different systems having identical activity patterns may generate different qualia. Conversely, the same quale may be generated by two systems that differ in both activity and connectivity. Both active and inactive elements specify a quale, but elements that are inactivated do not. Also, the activation of an element affects experience by changing the shape of the quale. The subdivision of experience into modalities and submodalities corresponds to subshapes in Q. In principle, different aspects of experience may be classified as different shapes in Q, and the similarity between experiences reduces to similarities between shapes. Finally, specific qualities, such as the “redness” of red, while generated by a local mechanism, cannot be reduced to it, but require considering the entire quale. Ultimately, the present framework may offer a principled way for translating qualitative properties of experience into mathematics