Can Stablecoins Foster Cryptocurrencies adoption? A Preliminary Study from the Push-Pull-Mooring Model Perspective

Pegging to fiat currencies, stablecoins are cryptocurrencies with stable prices. Theoretically, stablecoins may mediate price volatility issues, which have been resisting the wider adoption of cryptocurrencies. And yet, their impact on fostering individuals’ cryptocurrencies adoptions remains unclear. In this study, we adopted hot cryptocurrency wallets (hot-wallets) as the context and Push-Pull-Mooring (PPM) model as the theoretical foundations to test these impacts. Our preliminary results showed that less experienced cryptocurrency users may not understand immediately upon learning about stablecoins. They may even feel confused and become less motivated to adopt cryptocurrencies. Conversely, more experienced users may recognize the importance of stablecoins. Hot-wallets that have included stablecoins are more likely to be used by these experienced users. The significant difference between users in terms of experience has hinted that hot-wallet service providers may need to adopt more diversified strategies to engage different potential users

USE JD-R THEORY TO EXPLORE THE RELATIONSHIP BETWEEN EMPLOYEE EXPERIENCE AND EMPLOYEE ENGAGEMENT—TAKING JOB DEMANDS AS THE MODERATING VARIABLE

Past research has proven that employee experience has a positive impact on employee engagement. Based on the conceptual framework of Job Demands-Resources model (JD-R) model, this study regards efficient employee experience as a job resource to explore the impact of "employee experience" and” job demands” on employee engagement in organizations. Work requirements are further divided into challenge demand and hindrance demand. This study adopts the experimental design of the scenario method and uses two two-factor independent sample designs, namely 2x2(employee experience is high / employee experience is low x challenging job demands is high / challenging job demands is low) and 2x2(employee experience is high / employee experience is low x hindering job demands is high / hindering job demands is low).A total of 176 valid questionnaires were collected. The research results found that when employee experience is high, employee engagement is higher than when employee experience is low. Employee experience and job demands have an interactive effect on employee engagement. When employee experience is high, employee engagement will be higher when challenging job demands are added than when hindering job demands are added. It is expected that the results of this study can help in theoretical and practical application

MeInfoText 2.0: gene methylation and cancer relation extraction from biomedical literature

<p>Abstract</p> <p>Background</p> <p>DNA methylation is regarded as a potential biomarker in the diagnosis and treatment of cancer. The relations between aberrant gene methylation and cancer development have been identified by a number of recent scientific studies. In a previous work, we used co-occurrences to mine those associations and compiled the MeInfoText 1.0 database. To reduce the amount of manual curation and improve the accuracy of relation extraction, we have now developed MeInfoText 2.0, which uses a machine learning-based approach to extract gene methylation-cancer relations.</p> <p>Description</p> <p>Two maximum entropy models are trained to predict if aberrant gene methylation is related to any type of cancer mentioned in the literature. After evaluation based on 10-fold cross-validation, the average precision/recall rates of the two models are 94.7/90.1 and 91.8/90% respectively. MeInfoText 2.0 provides the gene methylation profiles of different types of human cancer. The extracted relations with maximum probability, evidence sentences, and specific gene information are also retrievable. The database is available at <url>http://bws.iis.sinica.edu.tw:8081/MeInfoText2/</url>.</p> <p>Conclusion</p> <p>The previous version, MeInfoText, was developed by using association rules, whereas MeInfoText 2.0 is based on a new framework that combines machine learning, dictionary lookup and pattern matching for epigenetics information extraction. The results of experiments show that MeInfoText 2.0 outperforms existing tools in many respects. To the best of our knowledge, this is the first study that uses a hybrid approach to extract gene methylation-cancer relations. It is also the first attempt to develop a gene methylation and cancer relation corpus.</p

Learning to predict expression efficacy of vectors in recombinant protein production

<p>Abstract</p> <p>Background</p> <p>Recombinant protein production is a useful biotechnology to produce a large quantity of highly soluble proteins. Currently, the most widely used production system is to fuse a target protein into different vectors in <it>Escherichia coli </it>(<it>E. coli</it>). However, the production efficacy of different vectors varies for different target proteins. Trial-and-error is still the common practice to find out the efficacy of a vector for a given target protein. Previous studies are limited in that they assumed that proteins would be over-expressed and focused only on the solubility of expressed proteins. In fact, many pairings of vectors and proteins result in no expression.</p> <p>Results</p> <p>In this study, we applied machine learning to train prediction models to predict whether a pairing of vector-protein will express or not express in <it>E. coli</it>. For expressed cases, the models further predict whether the expressed proteins would be soluble. We collected a set of real cases from the clients of our recombinant protein production core facility, where six different vectors were designed and studied. This set of cases is used in both training and evaluation of our models. We evaluate three different models based on the support vector machines (SVM) and their ensembles. Unlike many previous works, these models consider the sequence of the target protein as well as the sequence of the whole fusion vector as the features. We show that a model that classifies a case into one of the three classes (no expression, inclusion body and soluble) outperforms a model that considers the nested structure of the three classes, while a model that can take advantage of the hierarchical structure of the three classes performs slight worse but comparably to the best model. Meanwhile, compared to previous works, we show that the prediction accuracy of our best method still performs the best. Lastly, we briefly present two methods to use the trained model in the design of the recombinant protein production systems to improve the chance of high soluble protein production.</p> <p>Conclusion</p> <p>In this paper, we show that a machine learning approach to the prediction of the efficacy of a vector for a target protein in a recombinant protein production system is promising and may compliment traditional knowledge-driven study of the efficacy. We will release our program to share with other labs in the public domain when this paper is published.</p

LenSiam: Self-Supervised Learning on Strong Gravitational Lens Images

Self-supervised learning has been known for learning good representations from data without the need for annotated labels. We explore the simple siamese (SimSiam) architecture for representation learning on strong gravitational lens images. Commonly used image augmentations tend to change lens properties; for example, zoom-in would affect the Einstein radius. To create image pairs representing the same underlying lens model, we introduce a lens augmentation method to preserve lens properties by fixing the lens model while varying the source galaxies. Our research demonstrates this lens augmentation works well with SimSiam for learning the lens image representation without labels, so we name it LenSiam. We also show that a pre-trained LenSiam model can benefit downstream tasks. We open-source our code and datasets at https://github.com/kuanweih/LenSiam .Comment: 5 pages, 2 figures. Accepted by NeurIPS 2023 AI for Science Worksho

First- and Second-trimester Down Syndrome Screening: Current Strategies and Clinical Guidelines

SummaryDown syndrome (DS) is the most common human disease caused by a structural chromosome defect. The original screening test for DS was maternal age or a history of a previously affected infant. Maternal serum screening has been incorporated into routine prenatal checkup in Taiwan since 1994. We used free β-human chorionic gonadotropin and α-fetoprotein (double test) as the serum markers, and this was carried out between the 15 to 20th week of gestation. The overall detection rate was 56% and was compatible with studies of Caucasian populations. The impact of double tests in Taiwan has shown itself by a dramatic lowering of the rate of DS live birth from 0.63 before screening to 0.16 per 1,000 live births at present. However, because of its relatively low detection rate and poor cost-effectiveness, the double test is not justified as a routine screening tool currently. First-trimester combined test is now becoming more widely available and provides increased sensitivity when detecting DS; it has a detection rate of approximately 85% with a false-positive rate of 5%. Nuchal translucency measurement requires ongoing quality control and sufficient certificated obstetricians; therefore, first-trimester ultrasound is limited only in designated centers. The quadruple test, having comparable detection rate, should be considered for incorporation into second-trimester screening in Taiwan in the near future. Other screening approaches and combinations have also been utilized in the Western countries. In this review, we outline the various options with respect to DS screening and hope that this will provide practical information for physicians offering such screenings. [Taiwan J Obstet Cynecol 2008;47(2):157-1 62

Strong Gravitational Lensing Parameter Estimation with Vision Transformer

Quantifying the parameters and corresponding uncertainties of hundreds of strongly lensed quasar systems holds the key to resolving one of the most important scientific questions: the Hubble constant ($H_{0}$) tension. The commonly used Markov chain Monte Carlo (MCMC) method has been too time-consuming to achieve this goal, yet recent work has shown that convolution neural networks (CNNs) can be an alternative with seven orders of magnitude improvement in speed. With 31,200 simulated strongly lensed quasar images, we explore the usage of Vision Transformer (ViT) for simulated strong gravitational lensing for the first time. We show that ViT could reach competitive results compared with CNNs, and is specifically good at some lensing parameters, including the most important mass-related parameters such as the center of lens $\theta_{1}$ and $\theta_{2}$, the ellipticities $e_1$ and $e_2$, and the radial power-law slope $\gamma'$. With this promising preliminary result, we believe the ViT (or attention-based) network architecture can be an important tool for strong lensing science for the next generation of surveys. The open source of our code and data is in \url{https://github.com/kuanweih/strong_lensing_vit_resnet}.Comment: Accepted by ECCV 2022 AI for Space Worksho

Structural and cognitive deficits in chronic carbon monoxide intoxication: a voxel-based morphometry study

BACKGROUND: Patients with carbon monoxide (CO) intoxication may develop ongoing neurological and psychiatric symptoms that ebb and flow, a condition often called delayed encephalopathy (DE). The association between morphologic changes in the brain and neuropsychological deficits in DE is poorly understood. METHODS: Magnetic resonance imaging and neuropsychological tests were conducted on 11 CO patients with DE, 11 patients without DE, and 15 age-, sex-, and education-matched healthy subjects. Differences in gray matter volume (GMV) between the subgroups were assessed and further correlated with diminished cognitive functioning. RESULTS: As a group, the patients had lower regional GMV compared to controls in the following regions: basal ganglia, left claustrum, right amygdala, left hippocampus, parietal lobes, and left frontal lobe. The reduced GMV in the bilateral basal ganglia, left post-central gyrus, and left hippocampus correlated with decreased perceptual organization and processing speed function. Those CO patients characterized by DE patients had a lower GMV in the left anterior cingulate and right amygdala, as well as lower levels of cognitive function, than the non-DE patients. CONCLUSIONS: Patients with CO intoxication in the chronic stage showed a worse cognitive and morphologic outcome, especially those with DE. This study provides additional evidence of gray matter structural abnormalities in the pathophysiology of DE in chronic CO intoxicated patients