Volatility clustering in land markets

Purpose– The purpose of this paper is to investigate the volatility clustering in the return of land markets through both theoretical and empirical approaches.Design/methodology/approach– Using extensive monthly panel data at the provincial level from 1986 to 2013, the authors identify the existence of time-correlated and time-varying returns in Canadian land markets.Findings– Consistent with the proposed theory, volatility clustering in land markets tends to be observed in more populated areas.Originality/value– The result has significant implications for portfolio management, economic theory and government policy by revealing the systematic pattern of volatility clustering in land markets.This is the author accepted manuscript. The final version is available from Emerald via http://dx.doi.org/10.1108/PM-02-2014-000

Computational network design from functional specifications

Connectivity and layout of underlying networks largely determine agent behavior and usage in many environments. For example, transportation networks determine the flow of traffic in a neighborhood, whereas building floorplans determine the flow of people in a workspace. Designing such networks from scratch is challenging as even local network changes can have large global effects. We investigate how to computationally create networks starting from only high-level functional specifications. Such specifications can be in the form of network density, travel time versus network length, traffic type, destination location, etc. We propose an integer programming-based approach that guarantees that the resultant networks are valid by fulfilling all the specified hard constraints and that they score favorably in terms of the objective function. We evaluate our algorithm in two different design settings, street layout and floorplans to demonstrate that diverse networks can emerge purely from high-level functional specifications

Slow cooling and efficient extraction of C-exciton hot carriers in MoS2 monolayer

In emerging optoelectronic applications, such as water photolysis, exciton fission and novel photovoltaics involving low-dimensional nanomaterials, hot-carrier relaxation and extraction mechanisms play an indispensable and intriguing role in their photo-electron conversion processes. Two-dimensional transition metal dichalcogenides have attracted much attention in above fields recently; however, insight into the relaxation mechanism of hot electron-hole pairs in the band nesting region denoted as C-excitons, remains elusive. Using MoS2 monolayers as a model two-dimensional transition metal dichalcogenide system, here we report a slower hot-carrier cooling for C-excitons, in comparison with band-edge excitons. We deduce that this effect arises from the favourable band alignment and transient excited-state Coulomb environment, rather than solely on quantum confinement in two-dimension systems. We identify the screening-sensitive bandgap renormalization for MoS2 monolayer/graphene heterostructures, and confirm the initial hot-carrier extraction for the C-exciton state with an unprecedented efficiency of 80%, accompanied by a twofold reduction in the exciton binding energy

Data fusion-based structural damage detection under varying temperature conditions

Author name used in this manuscript: Yuequan BaoAuthor name used in this manuscript: You-Lin Xu2012-2013 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Depression and anxiety in relation to cancer incidence and mortality: a systematic review and meta-analysis of cohort studies

The link between depression and anxiety status and cancer outcomes has been well-documented but remains unclear. We comprehensively quantified the association between depression and anxiety defined by symptom scales or clinical diagnosis and the risk of cancer incidence, cancer-specific mortality, and all-cause mortality in cancer patients. Pooled estimates of the relative risks (RRs) for cancer incidence and mortality were performed in a meta-analysis by random effects or fixed effects models as appropriate. Associations were tested in subgroups stratified by different study and participant characteristics. Fifty-one eligible cohort studies involving 2,611,907 participants with a mean follow-up period of 10.3 years were identified. Overall, depression and anxiety were associated with a significantly increased risk of cancer incidence (adjusted RR: 1.13, 95% CI: 1.06–1.19), cancer-specific mortality (1.21, 1.16–1.26), and all-cause mortality in cancer patients (1.24, 1.13–1.35). The estimated absolute risk increases (ARIs) associated with depression and anxiety were 34.3 events/100,000 person years (15.8–50.2) for cancer incidence and 28.2 events/100,000 person years (21.5–34.9) for cancer-specific mortality. Subgroup analyses demonstrated that clinically diagnosed depression and anxiety were related to higher cancer incidence, poorer cancer survival, and higher cancer-specific mortality. Psychological distress (symptoms of depression and anxiety) was related to higher cancer-specific mortality and poorer cancer survival but not to increased cancer incidence. Site-specific analyses indicated that overall, depression and anxiety were associated with an increased incidence risks for cancers of the lung, oral cavity, prostate and skin, a higher cancer-specific mortality risk for cancers of the lung, bladder, breast, colorectum, hematopoietic system, kidney and prostate, and an increased all-cause mortality risk in lung cancer patients. These analyses suggest that depression and anxiety may have an etiologic role and prognostic impact on cancer, although there is potential reverse causality; Furthermore, there was substantial heterogeneity among the included studies, and the results should be interpreted with caution. Early detection and effective intervention of depression and anxiety in cancer patients and the general population have public health and clinical importance

Ripple modulated electronic structure of a 3D topological insulator

3D topological insulators, similar to the Dirac material graphene, host linearly dispersing states with unique properties and a strong potential for applications. A key, missing element in realizing some of the more exotic states in topological insulators is the ability to manipulate local electronic properties. Analogy with graphene suggests a possible avenue via a topographic route by the formation of superlattice structures such as a moir\'e patterns or ripples, which can induce controlled potential variations. However, while the charge and lattice degrees of freedom are intimately coupled in graphene, it is not clear a priori how a physical buckling or ripples might influence the electronic structure of topological insulators. Here we use Fourier transform scanning tunneling spectroscopy to determine the effects of a one-dimensional periodic buckling on the electronic properties of Bi2Te3. By tracking the spatial variations of the scattering vector of the interference patterns as well as features associated with bulk density of states, we show that the buckling creates a periodic potential modulation, which in turn modulates the surface and the bulk states. The strong correlation between the topographic ripples and electronic structure indicates that while doping alone is insufficient to create predetermined potential landscapes, creating ripples provides a path to controlling the potential seen by the Dirac electrons on a local scale. Such rippled features may be engineered by strain in thin films and may find use in future applications of topological insulators.Comment: Nature Communications (accepted

Predicting disease-associated substitution of a single amino acid by analyzing residue interactions

<p>Abstract</p> <p>Background</p> <p>The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues.</p> <p>Results</p> <p>We found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively.</p> <p>Conclusions</p> <p>The satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.</p

Improving the prediction of disease-related variants using protein three-dimensional structure

Background: Single Nucleotide Polymorphisms (SNPs) are an important source of human genome variability. Non-synonymous SNPs occurring in coding regions result in single amino acid polymorphisms (SAPs) that may affect protein function and lead to pathology. Several methods attempt to estimate the impact of SAPs using different sources of information. Although sequence-based predictors have shown good performance, the quality of these predictions can be further improved by introducing new features derived from three-dimensional protein structures.Results: In this paper, we present a structure-based machine learning approach for predicting disease-related SAPs. We have trained a Support Vector Machine (SVM) on a set of 3,342 disease-related mutations and 1,644 neutral polymorphisms from 784 protein chains. We use SVM input features derived from the protein's sequence, structure, and function. After dataset balancing, the structure-based method (SVM-3D) reaches an overall accuracy of 85%, a correlation coefficient of 0.70, and an area under the receiving operating characteristic curve (AUC) of 0.92. When compared with a similar sequence-based predictor, SVM-3D results in an increase of the overall accuracy and AUC by 3%, and correlation coefficient by 0.06. The robustness of this improvement has been tested on different datasets and in all the cases SVM-3D performs better than previously developed methods even when compared with PolyPhen2, which explicitly considers in input protein structure information.Conclusion: This work demonstrates that structural information can increase the accuracy of disease-related SAPs identification. Our results also quantify the magnitude of improvement on a large dataset. This improvement is in agreement with previously observed results, where structure information enhanced the prediction of protein stability changes upon mutation. Although the structural information contained in the Protein Data Bank is limiting the application and the performance of our structure-based method, we expect that SVM-3D will result in higher accuracy when more structural date become available. \ua9 2011 Capriotti; licensee BioMed Central Ltd

Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information

<p>Abstract</p> <p>Background</p> <p>As the number of non-synonymous single nucleotide polymorphisms (nsSNPs), also known as single amino acid polymorphisms (SAPs), increases rapidly, computational methods that can distinguish disease-causing SAPs from neutral SAPs are needed. Many methods have been developed to distinguish disease-causing SAPs based on both structural and sequence features of the mutation point. One limitation of these methods is that they are not applicable to the cases where protein structures are not available. In this study, we explore the feasibility of classifying SAPs into disease-causing and neutral mutations using only information derived from protein sequence.</p> <p>Results</p> <p>We compiled a set of 686 features that were derived from protein sequence. For each feature, the distance between the wild-type residue and mutant-type residue was computed. Then a greedy approach was used to select the features that were useful for the classification of SAPs. 10 features were selected. Using the selected features, a decision tree method can achieve 82.6% overall accuracy with 0.607 Matthews Correlation Coefficient (MCC) in cross-validation. When tested on an independent set that was not seen by the method during the training and feature selection, the decision tree method achieves 82.6% overall accuracy with 0.604 MCC. We also evaluated the proposed method on all SAPs obtained from the Swiss-Prot, the method achieves 0.42 MCC with 73.2% overall accuracy. This method allows users to make reliable predictions when protein structures are not available. Different from previous studies, in which only a small set of features were arbitrarily chosen and considered, here we used an automated method to systematically discover useful features from a large set of features well-annotated in public databases.</p> <p>Conclusion</p> <p>The proposed method is a useful tool for the classification of SAPs, especially, when the structure of the protein is not available.</p