Migration, Social Security, and Economic Growth

This paper studies the effect of population aging on economic performance in an overlapping-generations model with international migration. Fertility is endogenized so that immigrants and natives can have different fertility rates. Fertility is an important determinant to the tax burden of social security since it affects the quantity and quality of future tax payers. We find that introducing immigrants into the economy can reduce the tax burden of social security. If life expectancy (or the replacement ratio) is high enough, the growth rate of GDP per worker for an economy with international migration will be higher than for a closed economy. Regarding migration policies, our numerical results indicate that economic growth rate of GDP per worker will first decrease then increase as the flow of immigrants increases. Increasing the quality of immigrants will enhance economic growth.Economic growth; Fertility; Migration; Social security.

Plastic design of steel frames for minimum weight

The purpose of this study was to develop a method for weight minimization of the plastically designed frame, braced normal to its plane of action, and composed of prismatic steel members. The method accounts for the non-linear relationship between weight and moment capacity for both beams and columns. Reduction in the pure-bending fully-plastic moment in the presence of axial loading and both beam-column instability and overall frame instability due to sidesway are taken into account. Provision is made for minimization of frames employing standard sections as well as for frames whose built-up members may be chosen from a continuous spectrum. An initial solution to the minimization problem is obtained by the Simplex Method of linear programming, after which a check procedure is used to explore variations in the initial solution to determine if it can be improved. Simple portal frames with fixed end legs and hinged end legs are considered as a model. Design charts are established --Abstract, page ii

GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization

Bioinformatics tools have been developed to interpret gene expression data at the gene set level, and these gene set based analyses improve the biologists' capability to discover functional relevance of their experiment design. While elucidating gene set individually, inter gene sets association is rarely taken into consideration. Deep learning, an emerging machine learning technique in computational biology, can be used to generate an unbiased combination of gene set, and to determine the biological relevance and analysis consistency of these combining gene sets by leveraging large genomic data sets. In this study, we proposed a gene superset autoencoder (GSAE), a multi-layer autoencoder model with the incorporation of a priori defined gene sets that retain the crucial biological features in the latent layer. We introduced the concept of the gene superset, an unbiased combination of gene sets with weights trained by the autoencoder, where each node in the latent layer is a superset. Trained with genomic data from TCGA and evaluated with their accompanying clinical parameters, we showed gene supersets' ability of discriminating tumor subtypes and their prognostic capability. We further demonstrated the biological relevance of the top component gene sets in the significant supersets. Using autoencoder model and gene superset at its latent layer, we demonstrated that gene supersets retain sufficient biological information with respect to tumor subtypes and clinical prognostic significance. Superset also provides high reproducibility on survival analysis and accurate prediction for cancer subtypes.Comment: Presented in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA and published in BMC Systems Biology 2018, 12(Suppl 8):14

Predicting drug response of tumors from integrated genomic profiles by deep neural networks

The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However, due to essential differences between cell lines and tumors, the translation into predicting drug response in tumors remains challenging. Here we proposed a DNN model to predict drug response based on mutation and expression profiles of a cancer cell or a tumor. The model contains a mutation and an expression encoders pre-trained using a large pan-cancer dataset to abstract core representations of high-dimension data, followed by a drug response predictor network. Given a pair of mutation and expression profiles, the model predicts IC50 values of 265 drugs. We trained and tested the model on a dataset of 622 cancer cell lines and achieved an overall prediction performance of mean squared error at 1.96 (log-scale IC50 values). The performance was superior in prediction error or stability than two classical methods and four analog DNNs of our model. We then applied the model to predict drug response of 9,059 tumors of 33 cancer types. The model predicted both known, including EGFR inhibitors in non-small cell lung cancer and tamoxifen in ER+ breast cancer, and novel drug targets. The comprehensive analysis further revealed the molecular mechanisms underlying the resistance to a chemotherapeutic drug docetaxel in a pan-cancer setting and the anti-cancer potential of a novel agent, CX-5461, in treating gliomas and hematopoietic malignancies. Overall, our model and findings improve the prediction of drug response and the identification of novel therapeutic options.Comment: Accepted for presentation in the International Conference on Intelligent Biology and Medicine (ICIBM 2018) at Los Angeles, CA, USA. Currently under consideration for publication in a Supplement Issue of BMC Genomic

Toward better intelligent learning (iLearning) performance:what makes iLearning work for students in a university setting?

We explored the critical factors associated with iLearning that impact students’ learning performance and identified the factors with a notable influence to help managers in higher education institutions increase the effectiveness of iLearning for students. We initially synthesised 4 main dimensions (including 26 criteria): performance expectancy, lecturers’ influence, quality of service, and personal innovativeness. Subsequently, we conducted surveys in two stages. First, by studying a group of students with experience using iLearning at Taiwanese universities, we extracted 5 critical dimensions (including 18 criteria) through a factor analysis. Second, by studying a group of senior educators and practitioners in Taiwan, we prioritised the dimensions and criteria through the analytic hierarchy process (AHP). We found that performance expectancy is the top critical dimension, and the top five critical criteria pertain to enhancing the learning performance, increasing the learning participation, altering learning habits, ensuring access at all times, and enabling prompt use of learning resources. Moreover, we recommend several suggestions for the relevant managers to enhance the students’ iLearning performance

On the Pitfalls of Resource Augmentation Factors and Utilization Bounds in Real-Time Scheduling

In this paper, we take a careful look at speedup factors, utilization bounds, and capacity augmentation bounds. These three metrics have been widely adopted in real-time scheduling research as the de facto standard theoretical tools for assessing scheduling algorithms and schedulability tests. Despite that, it is not always clear how researchers and designers should interpret or use these metrics. In studying this area, we found a number of surprising results, and related to them, ways in which the metrics may be misinterpreted or misunderstood. In this paper, we provide a perspective on the use of these metrics, guiding researchers on their meaning and interpretation, and helping to avoid pitfalls in their use. Finally, we propose and demonstrate the use of parametric augmentation functions as a means of providing nuanced information that may be more relevant in practical settings

Dual task measures in older adults with and without cognitive impairment: Response to simultaneous cognitive-exercise training and minimal clinically important difference estimates

BACKGROUND: Responsiveness and minimal clinically important difference (MCID) are critical indices to understand whether observed improvement represents a meaningful improvement after intervention. Although simultaneous cognitive-exercise training (SCET; e.g., performing memory tasks while cycling) has been suggested to enhance the cognitive function of older adults, responsiveness and MCID have not been established. Hence, we aimed to estimate responsiveness and MCIDs of two dual task performance involving cognition and hand function in older adults with and without cognitive impairment and to compare the differences in responsiveness and MCIDs of the two dual task performance between older adults with and without cognitive impairment. METHODS: A total of 106 older adults completed the Montreal Cognitive Assessment and two dual tasks before and after SCET. One dual task was a combination of Serial Sevens Test and Box and Block Test (BBT), and the other included frequency discrimination and BBT. We used effect size and standardized response mean to indicate responsiveness and used anchor- and distribution-based approaches to estimating MCID ranges. When conducting data analysis, all participants were classified into two cognitive groups, cognitively healthy (Montreal Cognitive Assessment ≥ 26) and cognitively impaired (Montreal Cognitive Assessment \u3c 26) groups, based on the scores of the Montreal Cognitive Assessment before SCET. RESULTS: In the cognitively healthy group, Serial Seven Test performance when tasked with BBT and BBT performance when tasked with Serial Seven Test were responsive to SCET (effect size = 0.18-0.29; standardized response mean = 0.25-0.37). MCIDs of Serial Seven Test performance when tasked with BBT ranged 2.09-2.36, and MCIDs of BBT performance when tasked with Serial Seven Test ranged 3.77-5.85. In the cognitively impaired group, only frequency discrimination performance when tasked with BBT was responsive to SCET (effect size = 0.37; standardized response mean = 0.47). MCIDs of frequency discrimination performance when tasked with BBT ranged 1.47-2.18, and MCIDs of BBT performance when tasked with frequency discrimination ranged 1.13-7.62. CONCLUSIONS: Current findings suggest that a change in Serial Seven Test performance when tasked with BBT between 2.09 and 2.36 corrected number (correct responses - incorrect responses) should be considered a meaningful change for older adults who are cognitively healthy, and a change in frequency discrimination performance when tasked with BBT between 1.47 and 2.18 corrected number (correct responses - incorrect responses) should be considered a meaningful change for older adults who are cognitively impaired. Clinical practitioners may use these established MCIDs of dual tasks involving cognition and hand function to interpret changes following SCET for older adults with and without cognitive impairment. TRIAL REGISTRATION: NCT04689776, 30/12/2020

Comparison of the Prevalence of Metabolic Syndrome Between the Criteria for Taiwanese and Japanese and the Projected Probability of Stroke in Elderly Hypertensive Taiwanese

SummaryBackgroundThe cutoff of abdominal circumference for metabolic syndrome (MS) defined by the Bureau of Health Promotion (BHP) of Taiwan for Taiwanese (men, 90cm; women, 80cm) and by the International Diabetes Federation (IDF) for Japanese (men, 85cm; women, 90cm) differs. This study aimed to examine the impact of this difference on the prevalence of MS and the impact of an MS diagnosis on the projected risk of stroke in hypertensive Taiwanese.MethodsMS was examined in a sample of 3,472 hypertensive patients (aged 55–80 years; 1,709 women) across Taiwan. The 10-year probability of stroke estimated from the Framingham equation was compared between MS and non-MS patients.ResultsThe prevalence of MS using the BHP criteria was 59.2% using the BHP criteria (95% confidence interval, CI, 57.6–60.8%; men, 52.5%; women, 66.1%) and 48.9% by the IDF criteria (95% CI, 47.2–50.5%; men, 61.3%; women, 36.1%). Both criteria showed that, compared with non-MS, MS has higher predicted 10-year probability of stroke (BHP, 0.153 ± 0.115 vs. 0.133 ± 0.105; IDF, 0.159 ± 0.109 vs. 0.132 ± 0.112; both p < 0.001) because of the difference in women (BHP, 0.143 ± 0.124 vs. 0.102 ± 0.091; IDF, 0.147 ± 0.121 vs. 0.118 ± 0.110; both p < 0.001) rather than men (BHP, p = 0.21; IDF, p = 0.29).ConclusionBoth criteria demonstrate that MS is highly prevalent in elderly hypertensive patients in Taiwan. Additionally in women, but not men, the predicted probability of stroke is higher in MS than in non-MS patients. The diagnosis of MS is potentially useful for identifying elderly hypertensive females with an elevated risk of stroke in Taiwan