The impact of foreign trading information on emerging futures markets: a study of Taiwan's unique data set

Using a unique dataset from the Taiwan Futures Exchange, this paper investigates whether trading imbalances by foreign investors affect emerging Taiwan futures market in terms of returns and volatility. First, this evidence demonstrates a positive relation between contemporaneous futures returns and net purchases by foreign investors when other market factor effects are controlled. Second, this failure to detect price reversals is inconsistent with the price pressure hypothesis. Third, foreign investors do not exhibit positive feedback trading patterns. Fourth, a bi-directional Granger-causality relationship exists between futures volatility and foreign trading flows. As found for other stock or foreign exchange markets, our empirical results demonstrate that foreign trading flows do have impacts on the return and volatility of developing futures market, suggesting that trading by foreign investors may enhance the information flow of the local futures market.Foreign trading

POSTURAL STABILITY PERFORMANCE BETWEEN SEDENTARY AND ACTIVE SUBJECTS WITH THE BIODEX STABILITY SYSTEM

INTRODUCTION: Postural stability (PS) has been defined as the ability to maintain an upright posture within the base of support (Lee and Lin, 2007) and is considered to be an important indicator of musculoskeletal health and physical performance. This study examined the PS performance between sedentary and active subjects using the Biodex Balance System (BBS) with well intraclass correlation coefficient (Hinman, 2000)

Scalable Content-Based Analysis of Images in Web Archives with TensorFlow and the Archives Unleashed Toolkit

We demonstrate the integration of the Archives Unleashed Toolkit, a scalable platform for exploring web archives, with Google's TensorFlow deep learning toolkit to provide scholars with content-based image analysis capabilities. By applying pretrained deep neural networks for object detection, we are able to extract images of common objects from a 4TB web archive of GeoCities, which we then compile into browsable collages. This case study illustrates the types of interesting analyses enabled by combining big data and deep learning capabilities.This work was primarily supported by the Natural Sciences and Engineering Research Council of Canada. Additional funding for this project has come from the Andrew W. Mellon Foundation. Our sincerest thanks to the Internet Archive for providing us with the GeoCities web archive

Repurposing tofacitinib as an anti-myeloma therapeutic to reverse growth-promoting effects of the bone marrow microenvironment.

The myeloma bone marrow microenvironment promotes proliferation of malignant plasma cells and resistance to therapy. Activation of JAK/STAT signaling is thought to be a central component of these microenvironment-induced phenotypes. In a prior drug repurposing screen, we identified tofacitinib, a pan-JAK inhibitor Food and Drug Administration (FDA) approved for rheumatoid arthritis, as an agent that may reverse the tumor-stimulating effects of bone marrow mesenchymal stromal cells. Herein, we validated in vitro, in stromal-responsive human myeloma cell lines, and in vivo, in orthotopic disseminated xenograft models of myeloma, that tofacitinib showed efficacy in myeloma models. Furthermore, tofacitinib strongly synergized with venetoclax in coculture with bone marrow stromal cells but not in monoculture. Surprisingly, we found that ruxolitinib, an FDA approved agent targeting JAK1 and JAK2, did not lead to the same anti-myeloma effects. Combination with a novel irreversible JAK3-selective inhibitor also did not enhance ruxolitinib effects. Transcriptome analysis and unbiased phosphoproteomics revealed that bone marrow stromal cells stimulate a JAK/STAT-mediated proliferative program in myeloma cells, and tofacitinib reversed the large majority of these pro-growth signals. Taken together, our results suggest that tofacitinib reverses the growth-promoting effects of the tumor microenvironment. As tofacitinib is already FDA approved, these results can be rapidly translated into potential clinical benefits for myeloma patients

Crystallographic origin of cycle decay of the high-voltage LiNi\u3csub\u3e0.5\u3c/sub\u3eMn\u3csub\u3e1.5\u3c/sub\u3eO\u3csub\u3e4\u3c/sub\u3e spinel lithium-ion battery electrode

High-voltage spinel LiNi0.5Mn1.5O4 (LNMO) is considered a potential high-power-density positive electrode for lithium-ion batteries, however, it suffers from capacity decay after extended charge-discharge cycling, severely hindering commercial application. Capacity fade is thought to occur through the significant volume change of the LNMO electrode occurring on cycling, and in this work we use operando neutron powder diffraction to compare the structural evolution of the LNMO electrode in an as-assembled 18650-type battery containing a Li4Ti5O12 negative electrode with that in an identical battery following 1000 cycles at high-current. We reveal that the capacity reduction in the battery post cycling is directly proportional to the reduction in the maximum change of the LNMO lattice parameter during its evolution. This is correlated to a corresponding reduction in the MnO6 octahedral distortion in the spinel structure in the cycled battery. Further, we find that the rate of lattice evolution, which reflects the rate of lithium insertion and removal, is ∼9 and ∼10% slower in the cycled than in the as-assembled battery during the Ni2+/Ni3+ and Ni3+/Ni4+ transitions, respectively

Development of an orally administered nanoparticle vaccine to combat clostridioides difficile infections

Clostridioides difficile is a gram+, spore forming, toxin producing anaerobe that is found throughout the environment. C. difficile is the leading agent of hospital acquired infections. Symptoms of C. difficile infection can range from diarrhea to pseudomembranous colitis and if left untreated can lead to death. C. difficile is currently only treated with 3 antibiotics Metronidazole, Vancomycin, and Fidaxomicin. All these antibiotics are non-specific to C. difficile and have the side effect of killing the normal microbiome of the gut. This microbiome helps to keep the body resistant to C. difficile infections, and its destruction can lead to relapses of disease. Ongoing work in our lab is looking at preventing C. difficile infections using a nanoparticle based oral vaccine. In a mouse model of C. difficile infections, we previously demonstrated that intraperitoneal injection of a vaccine composed of the receptor-binding domain of C. difficile toxin B (TcdB) with chitosan and poly-g-glutamic acid were effective in inducing antigen-specific IgA and IgG antibodies. Continuing this research, we later demonstrated that rTcdB encapsulated in a polypeptide-based polymer and delivered orally produces a long lasting and robust antibody response. These robust antibody responses to the C. difficile toxin were enough to prevent disease within a mouse model, however, it was not able to reduce bacteria burden leaving the potential for asymptomatic spread and relapses of disease. In my project, I wish to continue this research in two ways. First, by continuing the optimization of the nanoparticle delivery system. To do this we propose using a nanoparticle polymer that specifically targets M-cells lining the intestine and be pH activated. We hypothesize that this will improve antigen immunogenicity and provide better protection against C. difficile infections. Second, evaluate several C. difficile surface proteins immunogenicity in a mouse model. We hypothesize that a two-target approach may decrease the bacterial load and lead to complete protection against C. difficile infections

Genome-Wide Gene-Environment Interaction Analysis Using Set-Based Association Tests

The identification of gene-environment interactions (G × E) may eventually guide health-related choices and medical interventions for complex diseases. More powerful methods must be developed to identify G × E. The “adaptive combination of Bayes factors method” (ADABF) has been proposed as a powerful genome-wide polygenic approach to detect G × E. In this work, we evaluate its performance when serving as a gene-based G × E test. We compare ADABF with six tests including the “Set-Based gene-EnviRonment InterAction test” (SBERIA), “gene-environment set association test” (GESAT), etc. With extensive simulations, SBERIA and ADABF are found to be more powerful than other G × E tests. However, SBERIA suffers from a power loss when 50% SNP main effects are in the same direction with the SNP × E interaction effects while 50% are in the opposite direction. We further applied these seven G × E methods to the Taiwan Biobank data to explore gene× alcohol interactions on blood pressure levels. The ADAMTS7P1 gene at chromosome 15q25.2 was detected to interact with alcohol consumption on diastolic blood pressure (p = 9.5 × 10−7, according to the GESAT test). At this gene, the P-values provided by other six tests all reached the suggestive significance level (p < 5 × 10−5). Regarding the computation time required for a genome-wide G × E analysis, SBERIA is the fastest method, followed by ADABF. Considering the validity, power performance, robustness, and computation time, ADABF is recommended for genome-wide G × E analyses