Free trade as domestic, economic, and strategic issues: a big data analytics approach.

This article examines the engagement of domestic actors in public conversation surrounding free trade negotiations with a focus on the framing of these negotiations as economic, strategic or domestic issues. To analyse this topic, this article utilises the use of Twitter as a barometer of public sentiment toward the Regional Comprehensive Economic Partnership (RCEP). We employ topic classification and sentiment analysis to understand how RCEP is discussed in 345,015 tweets. Our findings show that the overall sentiment score towards RCEP is neutral. However, we find that when RCEP is discussed as a strategic issue, the sentiment is slightly more negative than when discussed as a domestic or economic issue. This article further suggests that discussion of RCEP is driven by the fear of China's geopolitical ambitions, domestic protectionist agendas, and impact of RCEP on the domestic economy. This article contributes to the growing use of big data in understanding trade negotiations. Furthermore, it contributes to the study of free trade negotiation by examining how domestic political actors frame free trade negotiations.

Dietary Intake as Determinant Nongenetic Factors to Colorectal Cancer Incidence and Staging Progression: A Study in South Sulawesi Population, Indonesia.

Reports from various population-based studies indicate that the incidence of colorectal cancer may be strongly affected by dietary patterns of the respective populations. The nature of dietary patterns of specific Indonesia population on the risk of colorectal cancer might differ from previously published data with the global population. Therefore, we conducted a study where the dietary pattern in colorectal cancer patient cohorts was compared to age- and population-matched control. We documented 89 colorectal cancer cases and among 173 individuals from the South Sulawesi population. A series of logistic regression and Fisher's exact tests were utilized to test associations of dietary intakes and colorectal cancer risk as well as colorectal cancer staging. Our data demonstrate that vegetable (p-value = 8.70 × 10-26, OR = 0.49) and fruit (p-value = 7.59x10-5, OR = 0.70) intakes are associated with the reduced risk of colorectal cancer incidence. Conversely, acidic food, reheated food, meat, spicy food, and alcohol are associated with the increment case of cancer. Moreover, meat intake (p-value < 0.01) shows a significant association with cancer staging progression. Common dietary pattern is a determinant aspect to the colorectal cancer incidence as well as its staging progression.

Real-Time Cuffless Continuous Blood Pressure Estimation Using Deep Learning Model.

Blood pressure monitoring is one avenue to monitor people's health conditions. Early detection of abnormal blood pressure can help patients to get early treatment and reduce mortality associated with cardiovascular diseases. Therefore, it is very valuable to have a mechanism to perform real-time monitoring for blood pressure changes in patients. In this paper, we propose deep learning regression models using an electrocardiogram (ECG) and photoplethysmogram (PPG) for the real-time estimation of systolic blood pressure (SBP) and diastolic blood pressure (DBP) values. We use a bidirectional layer of long short-term memory (LSTM) as the first layer and add a residual connection inside each of the following layers of the LSTMs. We also perform experiments to compare the performance between the traditional machine learning methods, another existing deep learning model, and the proposed deep learning models using the dataset of Physionet's multiparameter intelligent monitoring in intensive care II (MIMIC II) as the source of ECG and PPG signals as well as the arterial blood pressure (ABP) signal. The results show that the proposed model outperforms the existing methods and is able to achieve accurate estimation which is promising in order to be applied in clinical practice effectively.