Revisiting investability of heritage properties through indexation and portfolio frontier analysis

In recent years, the soaring prices of heritage properties in Georgetown, Penang have gained the attention of practitioners and investors. The practitioners claim that the prices of heritage properties within the core and buffer zones in Georgetown have increased more than 300% since the city was recognized as a UNESCO World Heritage site in 2008. Such heritage properties containing historical or art elements that lead to forming a diversified portfolio could exert a low correlation of returns with conventional assets. In addition, rehabilitation of heritage properties requires high restoration costs and conversion fees. Despite the above claims, there is an absence of empirical studies relating to heritage investability, particularly to prove whether the heritage properties are truly worth investing in. Thus, this study incorporates a self-developed heritage properties Index (PIHPI_HR) into the conventional investment portfolio for assessing diversification effects. This study has collected 853 units of transacted properties for constructing a 10-year price index (PIHPI_HR). Subsequently, its diversification effect was examined through the Efficient Frontier (EF), derived from the Modern Portfolio Theory (MPT). The findings have proven the optimization of the conventional portfolio by enabling investments in heritage properties where the return is higher than other investment assets at the same risk level. This study also unveiled the price movement of heritage properties together with their investment value, which is deemed to be useful for institutional investors and the public to formulate sustainable investment strategies in the future

A novel approach to simulate gene-environment interactions in complex diseases

Background: Complex diseases are multifactorial traits caused by both genetic and environmental factors. They represent the major part of human diseases and include those with largest prevalence and mortality (cancer, heart disease, obesity, etc.). Despite a large amount of information that has been collected about both genetic and environmental risk factors, there are few examples of studies on their interactions in epidemiological literature. One reason can be the incomplete knowledge of the power of statistical methods designed to search for risk factors and their interactions in these data sets. An improvement in this direction would lead to a better understanding and description of gene-environment interactions. To this aim, a possible strategy is to challenge the different statistical methods against data sets where the underlying phenomenon is completely known and fully controllable, for example simulated ones. Results: We present a mathematical approach that models gene-environment interactions. By this method it is possible to generate simulated populations having gene-environment interactions of any form, involving any number of genetic and environmental factors and also allowing non-linear interactions as epistasis. In particular, we implemented a simple version of this model in a Gene-Environment iNteraction Simulator (GENS), a tool designed to simulate case-control data sets where a one gene-one environment interaction influences the disease risk. The main aim has been to allow the input of population characteristics by using standard epidemiological measures and to implement constraints to make the simulator behaviour biologically meaningful. Conclusions: By the multi-logistic model implemented in GENS it is possible to simulate case-control samples of complex disease where gene-environment interactions influence the disease risk. The user has full control of the main characteristics of the simulated population and a Monte Carlo process allows random variability. A knowledge-based approach reduces the complexity of the mathematical model by using reasonable biological constraints and makes the simulation more understandable in biological terms. Simulated data sets can be used for the assessment of novel statistical methods or for the evaluation of the statistical power when designing a study

Ibrutinib restores immune cell numbers and function in first-line and relapsed/refractory chronic lymphocytic leukemia

© 2020 The Authors Ibrutinib positively modulates many T-cell subsets in chronic lymphocytic leukemia (CLL). To understand ibrutinib\u27s effects on the broader landscape of immune cell populations, we comprehensively characterized changes in circulating counts of 21 immune blood cell subsets throughout the first year of treatment in patients with relapsed/refractory (R/R) CLL (n = 55, RESONATE) and previously untreated CLL (n = 50, RESONATE-2) compared with untreated age-matched healthy donors (n = 20). Ibrutinib normalized abnormal immune cell counts to levels similar to those of age-matched healthy donors. Ibrutinib significantly decreased pathologically high circulating B cells, regulatory T cells, effector/memory CD4+ and CD8+ T cells (including exhausted and chronically activated T cells), natural killer (NK) T cells, and myeloid-derived suppressor cells; preserved naive T cells and NK cells; and increased circulating classical monocytes. T-cell function was assessed in response to T-cell receptor stimulation in patients with R/R CLL (n = 21) compared with age-matched healthy donors (n = 18). Ibrutinib significantly restored T-cell proliferative ability, degranulation, and cytokine secretion. Over the same period, ofatumumab or chlorambucil did not confer the same spectrum of normalization as ibrutinib in multiple immune subsets. These results establish that ibrutinib has a significant and likely positive impact on circulating malignant and nonmalignant immune cells and restores healthy T-cell function

SNPInterForest: A new method for detecting epistatic interactions

<p>Abstract</p> <p>Background</p> <p>Multiple genetic factors and their interactive effects are speculated to contribute to complex diseases. Detecting such genetic interactive effects, i.e., epistatic interactions, however, remains a significant challenge in large-scale association studies.</p> <p>Results</p> <p>We have developed a new method, named SNPInterForest, for identifying epistatic interactions by extending an ensemble learning technique called random forest. Random forest is a predictive method that has been proposed for use in discovering single-nucleotide polymorphisms (SNPs), which are most predictive of the disease status in association studies. However, it is less sensitive to SNPs with little marginal effect. Furthermore, it does not natively exhibit information on interaction patterns of susceptibility SNPs. We extended the random forest framework to overcome the above limitations by means of (i) modifying the construction of the random forest and (ii) implementing a procedure for extracting interaction patterns from the constructed random forest. The performance of the proposed method was evaluated by simulated data under a wide spectrum of disease models. SNPInterForest performed very well in successfully identifying pure epistatic interactions with high precision and was still more than capable of concurrently identifying multiple interactions under the existence of genetic heterogeneity. It was also performed on real GWAS data of rheumatoid arthritis from the Wellcome Trust Case Control Consortium (WTCCC), and novel potential interactions were reported.</p> <p>Conclusions</p> <p>SNPInterForest, offering an efficient means to detect epistatic interactions without statistical analyses, is promising for practical use as a way to reveal the epistatic interactions involved in common complex diseases.</p

Electrical control over single hole spins in nanowire quantum dots

Single electron spins in semiconductor quantum dots (QDs) are a versatile platform for quantum information processing, however controlling decoherence remains a considerable challenge. Recently, hole spins have emerged as a promising alternative. Holes in III-V semiconductors have unique properties, such as strong spin-orbit interaction and weak coupling to nuclear spins, and therefore have potential for enhanced spin control and longer coherence times. Weaker hyperfine interaction has already been reported in self-assembled quantum dots using quantum optics techniques. However, challenging fabrication has so far kept the promise of hole-spin-based electronic devices out of reach in conventional III-V heterostructures. Here, we report gate-tuneable hole quantum dots formed in InSb nanowires. Using these devices we demonstrate Pauli spin blockade and electrical control of single hole spins. The devices are fully tuneable between hole and electron QDs, enabling direct comparison between the hyperfine interaction strengths, g-factors and spin blockade anisotropies in the two regimes

Ligand-Receptor Interactions

The formation and dissociation of specific noncovalent interactions between a variety of macromolecules play a crucial role in the function of biological systems. During the last few years, three main lines of research led to a dramatic improvement of our understanding of these important phenomena. First, combination of genetic engineering and X ray cristallography made available a simultaneous knowledg of the precise structure and affinity of series or related ligand-receptor systems differing by a few well-defined atoms. Second, improvement of computer power and simulation techniques allowed extended exploration of the interaction of realistic macromolecules. Third, simultaneous development of a variety of techniques based on atomic force microscopy, hydrodynamic flow, biomembrane probes, optical tweezers, magnetic fields or flexible transducers yielded direct experimental information of the behavior of single ligand receptor bonds. At the same time, investigation of well defined cellular models raised the interest of biologists to the kinetic and mechanical properties of cell membrane receptors. The aim of this review is to give a description of these advances that benefitted from a largely multidisciplinar approach

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum