Social Media and General Elections in Malaysia 2018 and Indonesia 2019

In 2018 and 2019, Malaysian and Indonesian eligible voters cast their ballots  for prime minister and president and vice president respectively.  Especially in Indonesia, the eligible voters also  elected members of the Regional Representative Council,   the  House of Representatives,  Provincial Legislative Council and District/Municipal Legislative Council. Prior to the polls, the issue of who would run for presidency and vice presidency had become a hot and interesting topic of conversation among Indonesian citizens, with many of them using social media to express it. However, when the society talked too much about politics on the cyber media, the problem is whether they could come up with constructive rather than destructive content of discussion without destroying democracy.  The methodology of this research is library research in which the author collected a number of library materials containing in-depth study of a subject, and found relevant keywords in the catalogs, indexes, search engines, and various scientific journals. The newer the sources, the more up-to-date references and quotations will be.  To search a database effectively, the author started the search by finding keywords, seeking relevant records, and then narrowing  the keywords to focus on the search. The author later evaluated carefully each source found

Engineering DNA polymerases for application in DNB-based sequencing technology

DNA polymerases serve as the core engine to afford sequence information in sequencing technologies that have revolutionized modern biological research. For application in the DNB-based sequencing platform, an assemblage of DNA polymerases was engineered to catalyze the requisite biochemical reaction. In the process, naturally occurring polymerases were tapped into through deep-learning algorithms for constraints between individual protein residues to narrow down the protein sequence space and to annotate protein sequences in light of their catalytic properties. And the constraints were subsequently applied in designing potential polymerase candidates with the guidance of the sequence annotations. Additionally, ancestral protein sequences were estimated to expand the candidate repertoire. Furthermore, the candidates were subjected to in silico screening before examined by an HTS methodology based on fluorescence signal. Finally, the resulting proteins were expressed and purified for testing in the DNB-based sequencing platform. Our sequencing data suggested that these proteins behave better than their existing counterparts

SeqRate: sequence-based protein folding type classification and rates prediction

Protein folding rate is an important property of a protein. Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input. And most methods do not distinguish the different kinetic nature (two-state folding or multi-state folding) of the proteins. Here we developed a method, SeqRate, to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using sequence length, amino acid composition, contact order, contact number, and secondary structure information predicted from only protein sequence with support vector machines.We systematically studied the contributions of individual features to folding rate prediction. On a standard benchmark dataset, the accuracy of folding kinetic type classification is 80%. The Pearson correlation coefficient and the mean absolute difference between predicted and experimental folding rates (sec-1) in the base-10 logarithmic scale are 0.81 and 0.79 for two-state protein folders, and 0.80 and 0.68 for three-state protein folders. SeqRate is the first sequence-based method for protein folding type classification and its accuracy of fold rate prediction is improved over previous sequence-based methods. Its performance can be further enhanced with additional information, such as structure-based geometric contacts, as inputs.Both the web server and software of predicting folding rate are publicly available at http://casp.rnet.missouri.edu/fold_rate/index.html

Robust Topology Optimization Based on Stochastic Collocation Methods under Loading Uncertainties

A robust topology optimization (RTO) approach with consideration of loading uncertainties is developed in this paper. The stochastic collocation method combined with full tensor product grid and Smolyak sparse grid transforms the robust formulation into a weighted multiple loading deterministic problem at the collocation points. The proposed approach is amenable to implementation in existing commercial topology optimization software package and thus feasible to practical engineering problems. Numerical examples of two- and three-dimensional topology optimization problems are provided to demonstrate the proposed RTO approach and its applications. The optimal topologies obtained from deterministic and robust topology optimization designs under tensor product grid and sparse grid with different levels are compared with one another to investigate the pros and cons of optimization algorithm on final topologies, and an extensive Monte Carlo simulation is also performed to verify the proposed approach

The Effect of Normal Force on the Coupled Temperature Field of Metal Impregnation Carbon/Stainless Steel under the Friction and Wear with Electric Current

AbstractTemperature field model for aluminum-stainless steel composite conductor rail (stainless steel)/collector shoe (metal impregnation carbon) under the coupling of contact resistor-friction thermal was established by FE software ANSYS. The temperature field distribution model of the friction pair was simulated and the maximum coupled temperature changing with different normal force was researched. The results show that the maximum coupled temperatures decrease firstly and then rise with the increasing of normal force under the constant displacement, current and relative sliding speed. There is an optimal normal force making the maximum coupled temperature to be the lowest for the friction pair of the metal impregnation carbon and stainless steel. The normal force can be used as the working normal force in order to reduce the abrasion induced by temperature rising

Prediction of IDH1 gene mutation by a nomogram based on multiparametric and multiregional MR images

Objective: To investigate the value of a nomogram based on multiparametric and multiregional MR images to predict Isocitrate Dehydrogenase-1 (IDH1) gene mutations in glioma. Data and methods: The authors performed a retrospective analysis of 110 MR images of surgically confirmed pathological gliomas; 33 patients with IDH1 gene Mutation (IDH1-M) and 77 patients with Wild-type IDH1 (IDH1-W) were divided into training and validation sets in a 7:3 ratio. The clinical features were statistically analyzed using SPSS and R software. Three glioma regions (rCET, rE, rNEC) were outlined using ITK-SNAP software and projected to four conventional sequences (T1, T2, Flair, T1C) for feature extraction using AI-Kit software. The extracted features were screened using R software. A logistic regression model was established, and a nomogram was generated using the selected clinical features. Eight models were developed based on different sequences and ROIs, and Receiver Operating Characteristic (ROC) curves were used to evaluate the predictive efficacy. Decision curve analysis was performed to assess the clinical usefulness. Results: Age was selected with Radscore to construct the nomogram. The Model 1 AUC values based on four sequences and three ROIs were the highest in these models, at 0.93 and 0.89, respectively. Decision curve analysis indicated that the net benefit of model 1 was higher than that of the other models for most Pt-values. Conclusion: A nomogram based on multiparametric and multiregional MR images can predict the mutation status of the IDH1 gene accurately