Boosting systemic risk governance: Perspectives and insights from understanding national systems approaches for dealing with disaster and climate risk. Contributing Paper.

This contributing paper reviews the governance of systemic risk with the aim to identify opportunities and enabling factors for improving governance by managing what are increasingly interdependent risks with the potential for cascading impacts. The researchers use insights from the joint International Institute for Applied Systems Analysis-International Science Council (IIASA-ISC) “Building pathways to sustainability in a post-COVID world” initiative and forensic reviews of disasters, the Post-Event Review Capability (PERC), to illustrate how sub-national and national systems have governed systemic risks. More specifically, we explore risk governance successes and failures with the goal of developing insights on how to bolster systemic risk governance in policy and practice. The study indicates that the governance of systemic risks tends is still siloed in spite of the clear need for cross-sectoral and multi-level initiatives. The examples provided from the PERCs and the IIASA-ISC initiative illustrate how single-focus risk governance limits opportunities for building resilience and effectively addressing the systemic nature of risks. They also highlight clear opportunities for shifting towards more systemic risk governance, one where risk is co-managed across institutional boundaries and risk reduction methods are founded on effective and inclusive communication

Generation of Functional CLL-Specific Cord Blood CTL Using CD40-Ligated CLL APC

PMCID: PMC3526610This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Fifteen-Year Population Attributable Fractions and Causal Pies of Risk Factors for Newly Developed Hepatocellular Carcinomas in 11,801 Men in Taiwan

Development of hepatocellular carcinoma (HCC) is a multi-factorial process. Chronic infections with hepatitis B virus (HBV) and hepatitis C virus (HCV) are important risk factors of HCC. Host factors, such as alcohol drinking, may also play a role. This study aims to provide a synthesis view on the development of HCC by examining multiple risk factors jointly and collectively. Causal-pie modeling technique was applied to analyze a cohort of 11,801 male residents (followed up for 15 years) in Taiwan, during which a total of 298 incident HCC cases were ascertained. The rate ratios adjusted by age were further modeled by an additive Poisson regression. Population attributable fractions (PAFs) and causal-pie weights (CPWs) were calculated. A PAF indicates the magnitude of case-load reduction under a particular intervention scenario, whereas a CPW for a particular class of causal pies represents the proportion of HCC cases attributable to that class. Using PAF we observed a chance to reduce around 60% HCC risk moving from no HBV-related intervention to the total elimination of the virus. An additional ∼15% (or ∼5%) reduction can be expected, if the HBV-related intervention is coupled with an HCV-related intervention (or an anti-drinking campaign). Eight classes of causal pies were found to be significant, including four dose-response classes of HBV (total CPW=52.7%), one independent-effect class of HCV (CPW=14.4%), one HBV-alcohol interaction class (CPW=4.2%), one HBV-HCV interaction class (CPW=1.7%), and one all-unknown class (CPW=27.0%). Causal-pie modeling for HCC helps clarify the relative importance of each viral and host factor, as well as their interactions

Identification of animal movement patterns using tri-axial magnetometry

BackgroundAccelerometers are powerful sensors in many bio-logging devices, and are increasingly allowing researchers to investigate the performance, behaviour, energy expenditure and even state, of free-living animals. Another sensor commonly used in animal-attached loggers is the magnetometer, which has been primarily used in dead-reckoning or inertial measurement tags, but little outside that. We examine the potential of magnetometers for helping elucidate the behaviour of animals in a manner analogous to, but very different from, accelerometers. The particular responses of magnetometers to movement means that there are instances when they can resolve behaviours that are not easily perceived using accelerometers.MethodsWe calibrated the tri-axial magnetometer to rotations in each axis of movement and constructed 3-dimensional plots to inspect these stylised movements. Using the tri-axial data of Daily Diary tags, attached to individuals of number of animal species as they perform different behaviours, we used these 3-d plots to develop a framework with which tri-axial magnetometry data can be examined and introduce metrics that should help quantify movement and behaviour.ResultsTri-axial magnetometry data reveal patterns in movement at various scales of rotation that are not always evident in acceleration data. Some of these patterns may be obscure until visualised in 3D space as tri-axial spherical plots (m-spheres). A tag-fitted animal that rotates in heading while adopting a constant body attitude produces a ring of data around the pole of the m-sphere that we define as its Normal Operational Plane (NOP). Data that do not lie on this ring are created by postural rotations of the animal as it pitches and/or rolls. Consequently, stereotyped behaviours appear as specific trajectories on the sphere (m-prints), reflecting conserved sequences of postural changes (and/or angular velocities), which result from the precise relationship between body attitude and heading. This novel approach shows promise for helping researchers to identify and quantify behaviours in terms of animal body posture, including heading.ConclusionMagnetometer-based techniques and metrics can enhance our capacity to identify and examine animal behaviour, either as a technique used alone, or one that is complementary to tri-axial accelerometry

Sequence- and Interactome-Based Prediction of Viral Protein Hotspots Targeting Host Proteins: A Case Study for HIV Nef

Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk

Lenalidomide after Stem-Cell Transplantation for Multiple Myeloma

Data are lacking on whether lenalidomide maintenance therapy prolongs the time to disease progression after autologous hematopoietic stem-cell transplantation in patients with multiple myeloma

Clinical impact of the loss of chromosome 7q on outcomes of patients with myelodysplastic syndromes treated with allogeneic hematopoietic stem cell transplantation

We conducted a nationwide retrospective study to evaluate the prognostic influence of +1, der(1;7)(q10;p10) [hereafter der(1;7)] and ?7/del(7q) after allogeneic hematopoietic stem cell transplantation (allo-HSCT) for de novo myelodysplastic syndromes (MDS). In this database, 69 MDS patients with der(1;7), 75 with ?7/del(7q), and 511 with normal karyotype (NK) underwent allo-HSCT at advanced disease status. The 3-year overall survival (OS) and cumulative incidence of relapse (CIR) were 50.4 and 19.4% for those with der(1;7), 36.2 and 38.4% for ?7/del(7q),and 51.1 and 20.7% for NK, respectively. In the multivariate analysis, the presence of ?7/del(7q) correlated with a significantly shorter OS (HR [95% CI], 1.38 [1.00?1.89]; P = 0.048) and higher CIR (HR, 2.11 [1.36?3.28]; P = 0.001) than those with NK. There were 23 patients with der(1;7), 29 with ?7/del(7q), and 347 with NK who underwent allo-HSCT at early disease status.The 3-year OS and CIR were as follows: 47.3 and 9.5% for the der(1;7) group, 70.5 and 13.8% for ?7/del(7q), and 70.9 and 5.6% for NK,respectively. No significant differences were observed in OS and CIR among three groups. The impact of the loss of chromosome 7q on OS and CIR may differ based on its type and disease status after allo-HSCT for MDS

Vibrational Analysis of Bat-Ball Collision

The purpose of this project is to develop a model of a bat-ball collision to predict the outgoing velocity of the ball and how it varies as the point of impact along the bat changes. The method we propose is to subtract the kinematic losses due to vibration from the existing rigid body model for bat ball collisions. Our model accounts for the mechanism by which kinetic energy is imparted to the ball and the mechanism by which vibrational energy is imparted to the bat because naturally it deforms after impact. We were able to determine that a “sweet spot” of a bat is the location where outgoing ball velocity is at a maximum due to minimal kinetic losses due to vibration. This sweet spot is different than predicted by the rigid body model,it is not located at any of the nodes of vibration