The dynamic resilience of urban labour networks.

Both cities and markets are well understood as complex systems which are amenable to analysis using physically inspired methods. Cities have shown fascinating universality with size, while labour markets modelled as networks have considerable explanatory power. Labour markets are a particularly attractive domain of study in this context due to societal importance, the influx of high-resolution data as well as exogenous influence of automation. While much previous work has studied the economic characteristics of cities as a function of size and examined the exposure of urban economies to automation, this has often been from a static perspective. In this work, we examine the diffusive properties of labour markets and examine their variance across cities. More specifically, we identify the occupations which are most important in promoting the diffusion of beneficial or deleterious properties. To this end, we propose a new measure of node centrality empSI. We find that these properties of influence vary considerably with city size.

A Hydrotalcite-Based PET Composites with Enhanced Properties for Liquid Milk Packaging Applications.

In the present work, the two-phase mixture (HTLc) of hydrotalcite and its oxide were used to improve the barrier properties, UV resistance and antimicrobial activity of Poly(ethylene terephthalate) (PET) for their application in liquid milk packaging. Firstly, CaZnAl-CO3-LDHs with a two-dimensional layered structure were synthesized by hydrothermal method. CaZnAl-CO3-LDHs precursors were characterized by XRD, TEM, ICP and dynamic light scattering. A series of PET/HTLc composite films were then prepared, characterized by XRD, FTIR and SEM, and a possible mechanism of the composite films with hydrotalcite was proposed. Barrier properties to water vapor and oxygen have been studied in PET nanocomposites, as well as their antibacterial efficacy by the colony technique and their mechanical properties after exposure to UV irradiation for 24 h. By the presence of 1.5 wt% HTLc in the PET composite film, the oxygen transmission rate (OTR) was reduced by 95.27%, the water vapor transmission rate was reduced by 72.58% and the inhibition against Staphylococcus aureus and Escherichia coli was 83.19% and 52.75%. Moreover, a simulation of the migration process in dairy products was used to prove the relative safety. This research first proposes a safe technique for fabricating hydrotalcite-based polymer composites with a high gas barrier, UV resistance and effective antibacterial activity.

Dynamic aspiration based on Win-Stay-Lose-Learn rule in spatial prisoner's dilemma game.

Prisoner's dilemma game is the most commonly used model of spatial evolutionary game which is considered as a paradigm to portray competition among selfish individuals. In recent years, Win-Stay-Lose-Learn, a strategy updating rule base on aspiration, has been proved to be an effective model to promote cooperation in spatial prisoner's dilemma game, which leads aspiration to receive lots of attention. In this paper, according to Expected Value Theory and Achievement Motivation Theory, we propose a dynamic aspiration model based on Win-Stay-Lose-Learn rule in which individual's aspiration is inspired by its payoff. It is found that dynamic aspiration has a significant impact on the evolution process, and different initial aspirations lead to different results, which are called Stable Coexistence under Low Aspiration, Dependent Coexistence under Moderate aspiration and Defection Explosion under High Aspiration respectively. Furthermore, a deep analysis is performed on the local structures which cause defectors' re-expansion, the concept of END- and EXP-periods are used to justify the mechanism of network reciprocity in view of time-evolution, typical feature nodes for defectors' re-expansion called Infectors, Infected nodes and High-risk cooperators respectively are found. Compared to fixed aspiration model, dynamic aspiration introduces a more satisfactory explanation on population evolution laws and can promote deeper comprehension for the principle of prisoner's dilemma.

Bayesian Scalar on Image Regression With Nonignorable Nonresponse.

Medical imaging has become an increasingly important tool in screening, diagnosis, prognosis, and treatment of various diseases given its information visualization and quantitative assessment. The aim of this article is to develop a Bayesian scalar-on-image regression model to integrate high-dimensional imaging data and clinical data to predict cognitive, behavioral, or emotional outcomes, while allowing for nonignorable missing outcomes. Such a nonignorable nonresponse consideration is motivated by examining the association between baseline characteristics and cognitive abilities for 802 Alzheimer patients enrolled in the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI1), for which data are partially missing. Ignoring such missing data may distort the accuracy of statistical inference and provoke misleading results. To address this issue, we propose an imaging exponential tilting model to delineate the data missing mechanism and incorporate an instrumental variable to facilitate model identifiability followed by a Bayesian framework with Markov chain Monte Carlo algorithms to conduct statistical inference. This approach is validated in simulation studies where both the finite sample performance and asymptotic properties are evaluated and compared with the model with fully observed data and that with a misspecified ignorable missing mechanism. Our proposed methods are finally carried out on the ADNI1 dataset, which turns out to capture both of those clinical risk factors and imaging regions consistent with the existing literature that exhibits clinical significance. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Functional Linear Regression Models for Nonignorable Missing Scalar Responses.

As an important part of modern health care, medical imaging data, which can be regarded as densely sampled functional data, have been widely used for diagnosis, screening, treatment, and prognosis, such as finding breast cancer through mammograms. The aim of this paper is to propose a functional linear regression model for using functional (or imaging) predictors to predict clinical outcomes (e.g., disease status), while addressing missing clinical outcomes. We introduce an exponential tilting semiparametric model to account for the nonignorable missing data mechanism. We develop a set of estimating equations and its associated computational methods for both parameter estimation and the selection of the tuning parameters. We also propose a bootstrap resampling procedure for carrying out statistical inference. Under some regularity conditions, we systematically establish the asymptotic properties (e.g., consistency and convergence rate) of the estimates calculated from the proposed estimating equations. Simulation studies and a real data analysis are used to illustrate the finite sample performance of the proposed methods.

Optimal stabilization of Boolean networks through collective influence.

Boolean networks have attracted much attention due to their wide applications in describing dynamics of biological systems. During past decades, much effort has been invested in unveiling how network structure and update rules affect the stability of Boolean networks. In this paper, we aim to identify and control a minimal set of influential nodes that is capable of stabilizing an unstable Boolean network. For locally treelike Boolean networks with biased truth tables, we propose a greedy algorithm to identify influential nodes in Boolean networks by minimizing the largest eigenvalue of a modified nonbacktracking matrix. We test the performance of the proposed collective influence algorithm on four different networks. Results show that the collective influence algorithm can stabilize each network with a smaller set of nodes compared with other heuristic algorithms. Our work provides a new insight into the mechanism that determines the stability of Boolean networks, which may find applications in identifying virulence genes that lead to serious diseases.

Low Tube Voltage and Iterative Model Reconstruction in Follow-up CT Angiography After Thoracic Endovascular Aortic Repair: Ultra-low Radiation Exposure and Contrast Medium Dose.

RATIONALE AND OBJECTIVES: This study aimed to investigate the feasibility of reducing radiation exposure and contrast medium (CM) dose in follow-up computed tomography angiography (CTA) after thoracic endovascular aortic repair (TEVAR) using low tube voltage and knowledge-based iterative model reconstruction (IMR). MATERIALS AND METHODS: Thirty-six patients that required follow-up CTA after TEVAR were included in this intra-individual study. The conventional protocol with standard tube voltage of 120 kVp and CM volume of 70 mL was applied in the first follow-up CTA of all the patients (control group A). The ultra-low CM dose protocol with low tube voltage of 80 kVp and weight-adapted CM volume of 0.4 mL/kg was utilized in the second follow-up CTA (study group B). Set A.FBP (group A filtered back-projection) contained images for group A that were reconstructed through FBP method. Three sets (B.FBP, B.HIR, and B.IMR) for group B were reconstructed using three methods, FBP, hybrid iterative reconstruction (HIR), and IMR, respectively. Objective measurements including aortic attenuations, image noise, contrast-to-noise ratios (CNRs), and figure of merit of CNR (FOMCNR), and subjective rating scores of the four image sets were compared. RESULTS: Compared to the images in set A.FBP, the images in set B.IMR had better quality in terms of equivalent attenuation values, equivalent subjective scores, lower noise, higher or equivalent CNRs, and higher FOMCNR. The quality of images in sets B.FBP and B.HIR was unacceptable. The radiation exposure and CM dose in group B were 1.94 mGy and 28 ± 5 mL, respectively, representing reductions of 77.6% (P < .001) and 60% (P < .001) as compared to those in group A. CONCLUSIONS: In follow-up examinations after TEVAR, CTA with ultra-low radiation exposure and CM dose is feasible using low tube voltage and IMR for nonobese patients.

Iterative reconstruction in single-source dual-energy CT angiography: feasibility of low and ultra-low volume contrast medium protocols.

OBJECTIVE: To evaluate the feasibility of using contrast medium (CM) of low and ultra-low volumes and injection rates in aortic CT angiography (CTA) through the joint application of single-source dual-energy CT (ssDECT) and adaptive statistical iterative reconstruction (ASIR). METHODS: 120 patients with known or suspected aortic dissection underwent aortic CTA and were equally divided into 3 groups. Conventional 120-kVp scan with a CM volume of 70 ml and an injection rate of 5 ml s-1 was performed on Group A. Groups B and C underwent ssDECT scan with CM volumes of 0.6 and 0.4 ml kg-1, respectively. 40% and 50% ASIR algorithms were applied for Groups B and C, respectively. A five-point grading scheme was utilized to subjectively evaluate the image quality, and the CT value and contrast-to-noise ratio were recorded as objective measures. The radiation dose was also evaluated. RESULTS: Groups B and C had equivalent subjective scores and CT values as Group A, whereas they had higher or equivalent contrast-to-noise ratios. Group B had 40.1% and 30% reductions on CM volume and injection rate, respectively, than Group A. Group C further resulted in 19.2% and 22% lesser CM volume and injection rate than Group B. The average effective radiation doses for the study groups were 22.5-24.5% lower than the control group. CONCLUSION: With the aid of ASIR and ssDECT for aortic CTA, it is feasible to adopt low and ultra-low CM volumes and injection rates while obtaining good quality images. Advances in knowledge: Low and ultra-low CM volumes and injection rates are feasible in CTA through the joint application of ssDECT and ASIR.

Spontaneous Intestinal Perforation: The Long-Term Outcome.

Background/Purpose Evaluating the long-term outcome of spontaneous intestinal perforation (SIP). Methods We studied all patients treated for SIP at our institution between January 1, 2005 and December 31, 2014. Results Twenty-three infants (13 males) with a median gestational age of 26 (range: 23-32) weeks and a median weight of 825 (range: 560-1,965) g composed this cohort. Seventeen (74%) infants had an extremely low birth weight (ELBW); nine (39%) infants were the result of multiple pregnancies.Patent ductus arteriosus (PDA) was present in 16 (70%) infants. Cyclooxygenase inhibitors were administered in 12 (52%) infants.Ten infants (seven males, 44%) were diagnosed with intraventricular hemorrhage (IVH), which was identified in the majority (8/10) at a median of 9 (range: 1-11) days prior to the perforation.All patients presented with pneumoperitoneum and underwent a laparotomy at a median age of 9 (range: 2-16) days. Twenty-one patients had an ileal perforation. A temporary stoma was placed in 21 patients, whereas two got primary anastomosis. Two (8.7%) male infants died. During the long-term follow-up period (median 6 years), six (five males) (26%) infants developed moderate to severe disabilities in combination with cerebral palsy. No surgical complications were observed. Conclusion The most important risk factor for SIP is ELBW (75%). The distal ileum is the most frequent site of perforation (88%). Approximately 40% develop IVH most often prior to the SIP. Moderate to severe neurologic disabilities are seen in more than a quarter of the children. Disability and mortality affect mostly the male sex. Long-term risks of surgical complications are very low.

What is the role of laparoscopic surgery in intussusception?

BACKGROUND: Assessing the role of laparoscopy in the management of intussusception. METHODS: A retrospective review of children aged up to 17 years who had surgery for intussusception at this institution between 1 January 2004 and 31 December 2013. RESULTS: The cohort of 44 individuals (18 females) presented at a median age of 9 months (range 2.5 months-15.75 years) with intussusception; 36 patients had undergone a failed pneumatic reduction. Thirty-seven patients had an initial laparoscopic approach. Conversion was required in 13 individuals: inability to reduce a 'tight' intussusception in seven individuals, limited working space in four individuals, and inadequate tactile response in two individuals. Twenty-four patients (54%) had the laparoscopic approach completed. An open approach was chosen for seven individuals at a median age of 5 (range 4-11) months: three individuals had marked abdominal distension, two individuals had a pneumoperitoneum and two individuals presented with a large central mass. Together with the 13 conversions, a total of 20 patients (46%) underwent an open approach. The more distal the apex of the intussusception, the more likely open surgery was. Hospital stays for the subgroup of patients with successfully completed laparoscopic intervention (n = 24) were shorter than for the open surgery group (n = 20) with P = 0.0145, but the open procedure was used to manage the more challenging cases. The subgroup of seven infants undergoing direct open surgery were significantly younger than the remaining individuals (P = 0.0046). CONCLUSION: Laparoscopic intervention is meaningful in approximately 50% of children requiring a surgical reduction.

Structure detection of semiparametric structural equation models with Bayesian adaptive group lasso.

Structural equation models (SEMs) are widely recognized as the most important statistical tool for assessing the interrelationships among latent variables. This study develops a Bayesian adaptive group least absolute shrinkage and selection operator procedure to perform simultaneous model selection and estimation for semiparametric SEMs, wherein the structural equation is formulated using the additive nonparametric functions of observed and latent variables. We propose the use of basis expansions to approximate the unknown functions. By introducing adaptive penalties to the groups of basis expansions, the nonlinear, linear, or non-existent effects of observed and latent variables in the structural equation can be automatically detected. A simulation study demonstrates that the proposed method performs satisfactorily. This paper presents an application of revealing the observed and latent risk factors of diabetic kidney disease.

Latent variable models with nonparametric interaction effects of latent variables.

Renal disease is one of the common complications of diabetes, especially for Asian populations. Moreover, cardiovascular and renal diseases share common risk factors. This paper proposes a latent variable model with nonparametric interaction effects of latent variables for a study based on the Hong Kong Diabetes Registry, which was established in 1995 as part of a continuous quality improvement program at the Prince of Wales Hospital in Hong Kong. Renal outcome (outcome latent variable) is regressed in terms of cardiac function and diabetes (explanatory latent variables) through an additive structural equation formulated using a series of unspecified univariate and bivariate smooth functions. The Bayesian P-splines approach, along with a Markov chain Monte Carlo algorithm, is proposed to estimate smooth functions, unknown parameters, and latent variables in the model. The performance of the developed methodology is demonstrated via a simulation study. The effect of the nonparametric interaction of cardiac function and diabetes on renal outcome is investigated using the proposed methodology.