Plasma miR-203a-3p as a Novel Predictor of Dementia in Patients with Parkinson's Disease.

The early detection of cognitive decline in Parkinson's disease is important for providing drug therapy and non-pharmacological management. The circulating microRNAs present in plasma are promising biomarkers of PD with dementia (PDD) due to their critical roles in synaptic plasticity and the regulation of neurodegeneration-associated proteins. In this study, we aimed to identify plasma microRNAs that may differentiate PD with or without cognitive impairment. Global microRNA expression was obtained from a discovery set of 123 participants who were divided into four groups, namely normal controls (HC), PD with no dementia (PDND), PD with mild cognitive impairment (PD-MCI), and PDD, using next-generation sequencing. The BOLD selector was used for microRNA candidate selection. Six miRNAs, namely miR-203a-3p, miR-626, miR-662, miR-3182, miR-4274, and miR-4295, were clustered as potential candidates for use in identifying PDND from PD-MCI. Another independent cohort of 120 participants was further recruited in a validation step in order to detect candidate microRNAs via droplet digital PCR (ddPCR), which was used for its high sensitivity in detecting low miRNA concentrations. Our results show that the ratio of miR-203a-3p/miR-16-5p, in which miR-16-5p was used as a reference control miRNA, was significantly increased in PDD compared to that seen in PD-MCI and PDND individually, and was negatively correlated with the MoCA scores (r = -0.237, p = 0.024) in patients with PD. However, there was no significant difference in the ratio of miR-203a-3p/miR-16-5p between HC and PDND, PD-MCI, or PDD individually. The ROC curve of the logistic regression model, factoring in the variables of age, the ratio of miR-203a-3p/miR-16-5p, and the UPDRS III score, demonstrated an AUC of 0.883. Our findings suggest that the ratio of miR-203a-3p/miR-16-5p, used with age and motor score, could be a predictor of dementia among PD patients.

An efficient swarm intelligence approach to the optimization on high-dimensional solutions with cross-dimensional constraints, with applications in supply chain management.

Introduction: The Swarm Intelligence Based (SIB) method has widely been applied to efficient optimization in many fields with discrete solution domains. E-commerce raises the importance of designing suitable selling strategies, including channel- and direct sales, and the mix of them, but researchers in this field seldom employ advanced metaheuristic techniques in their optimization problem due to the complexities caused by the high-dimensional problems and cross-dimensional constraints. Method: In this work, we introduce an extension of the SIB method that can simultaneously tackle these two challenges. To pursue faster computing, CPU parallelization techniques are employed for algorithm acceleration. Results: The performance of the SIB method is examined on the problems of designing selling schemes in different scales. It outperforms the Genetic Algorithm (GA) in terms of both the speed of convergence and the optimized capacity as measured using improvement multipliers.

An efficient approach for identifying important biomarkers for biomedical diagnosis.

In this paper, we explore the challenges associated with biomarker identification for diagnosis purpose in biomedical experiments, and propose a novel approach to handle the above challenging scenario via the generalization of the Dantzig selector. To improve the efficiency of the regularization method, we introduce a transformation from an inherent nonlinear programming due to its nonlinear link function into a linear programming framework under a reasonable assumption on the logistic probability range. We illustrate the use of our method on an experiment with binary response, showing superior performance on biomarker identification studies when compared to their conventional analysis. Our proposed method does not merely serve as a variable/biomarker selection tool, its ranking of variable importance provides valuable reference information for practitioners to reach informed decisions regarding the prioritization of factors for further investigations.

Interactive tool for clustering and forecasting patterns of Taiwan COVID-19 spread.

The COVID-19 data analysis is essential for policymakers to analyze the outbreak and manage the containment. Many approaches based on traditional time series clustering and forecasting methods, such as hierarchical clustering and exponential smoothing, have been proposed to cluster and forecast the COVID-19 data. However, most of these methods do not scale up with the high volume of cases. Moreover, the interactive nature of the application demands further critically complex yet compelling clustering and forecasting techniques. In this paper, we propose a web-based interactive tool to cluster and forecast the available data of Taiwan COVID-19 confirmed infection cases. We apply the Model-based (MOB) tree and domain-relevant attributes to cluster the dataset and display forecasting results using the Ordinary Least Square (OLS) method. In this OLS model, we apply a model produced by the MOB tree to forecast all series in each cluster. Our user-friendly parametric forecasting method is computationally cheap. A web app based on R's Shiny App makes it easier for practitioners to find clustering and forecasting results while choosing different parameters such as domain-relevant attributes. These results could help in determining the spread pattern and be utilized by medical researchers.

A Mixture Model of Truncated Zeta Distributions with Applications to Scientific Collaboration Networks.

The degree distribution has attracted considerable attention from network scientists in the last few decades to have knowledge of the topological structure of networks. It is widely acknowledged that many real networks have power-law degree distributions. However, the deviation from such a behavior often appears when the range of degrees is small. Even worse, the conventional employment of the continuous power-law distribution usually causes an inaccurate inference as the degree should be discrete-valued. To remedy these obstacles, we propose a finite mixture model of truncated zeta distributions for a broad range of degrees that disobeys a power-law behavior in the range of small degrees while maintaining the scale-free behavior. The maximum likelihood algorithm alongside the model selection method is presented to estimate model parameters and the number of mixture components. The validity of the suggested algorithm is evidenced by Monte Carlo simulations. We apply our method to five disciplines of scientific collaboration networks with remarkable interpretations. The proposed model outperforms the other alternatives in terms of the goodness-of-fit.

Optimizing Two-level Supersaturated Designs using Swarm Intelligence Techniques.

Supersaturated designs (SSDs) are often used to reduce the number of experimental runs in screening experiments with a large number of factors. As more factors are used in the study, the search for an optimal SSD becomes increasingly challenging because of the large number of feasible selection of factor level settings. This paper tackles this discrete optimization problem via an algorithm based on swarm intelligence. Using the commonly used E(s2) criterion as an illustrative example, we propose an algorithm to find E(s2)-optimal SSDs by showing that they attain the theoretical lower bounds in Bulutoglu and Cheng (2004) and Bulutoglu (2007). We show that our algorithm consistently produces SSDs that are at least as efficient as those from the traditional CP exchange method in terms of computational effort, frequency of finding the E(s2)-optimal SSD and also has good potential for finding D3-, D4- and D5-optimal SSDs.

Analysing the Unequal Effects of Positive and Negative Information on the Behaviour of Users of a Taiwanese On-Line Bulletin Board.

The impact of social influence causes people to adopt the behaviour of others when interacting with other individuals. The effects of social influence can be direct or indirect. Direct social influence is the result of an individual directly influencing the opinion of another, while indirect social influence is a process taking place when an individual's opinion and behaviour is affected by the availability of information about others' actions. Such indirect effect may exhibit a more significant impact in the on-line community because the internet records not only positive but also negative information, for example on-line written text comments. This study focuses on indirect social influence and examines the effect of preceding information on subsequent users' opinions by fitting statistical models to data collected from an on-line bulletin board. Specifically, the different impacts of information on approval and disapproval comments on subsequent opinions were investigated. Although in an anonymous situation where social influence is assumed to be at minimum, our results demonstrate the tendency of on-line users to adopt both positive and negative information to conform to the neighbouring trend when expressing opinions. Moreover, our results suggest unequal effects of the local approval and disapproval comments in affecting the likelihood of expressing opinions. The impact of neighbouring disapproval densities was stronger than that of neighbouring approval densities on inducing subsequent disapproval relative to approval comments. However, our results suggest no effects of global social influence on subsequent opinion expression.