On F-theory E_6 GUTs

We approach the Minimum Supersymmetric Standard Model (MSSM) from an E_6 GUT by using the spectral cover construction and non-abelian gauge fluxes in F-theory. We start with an E_6 singularity unfolded from an E_8 singularity and obtain E_6 GUTs by using an SU(3) spectral cover. By turning on SU(2) X U(1)^2 gauge fluxes, we obtain a rank 5 model with the gauge group SU(3) X SU(2) X U(1)^2. Based on the well-studied geometric backgrounds in the literature, we demonstrate several models and discuss their phenomenology.Comment: 42 pages, 17 tables; typos corrected, clarifications added, and references adde

Monotherapy of acetylsalicylic acid or warfarin for prevention of ischemic stroke in low-risk atrial fibrillation: A Easter Asian population-based study

Background: This study aimed to investigate the effectiveness of monotherapy acetylsalicylic acid(ASA) and warfarin for stroke prevention in low-risk atrial fibrillation (AF) by using a population--based cohort study in Taiwan.Methods: A newly diagnosed low-risk AF patient cohort were identified by using National HealthInsurance Research Database (NHIRD) in Taiwan in 2008. The study cohort was observed witha follow-up of 2 years to examine the onset of ischemic stroke (IS) (to 2010). The longitudinal data wereanalyzed by using generalized estimation equations (GEE).Results: A total of 8,065 newly-diagnosed low-risk AF patients were identified in 2008. 7.4% wereprescribed with ASA and 4.6% were prescribed with warfarin. The GEE results showed that low-riskAF patients with hypertension who received warfarin were associated with a statistically significant58.4% reduction of IS risk (OR = 0.416, p = 0.024, 95% CI 0.194–0.891). Additionally, low-risk AFpatients with hyperlipidemia who received warfarin were associated with a 69.3% reduction of IS risk(OR = 0.307, p = 0.044, 95% CI 0.097–0.969).Conclusions: Warfarin is suggested to be prescribed in preventing IS for low-stroke-risk AF patientswith hypertension and hyperlipidemia

T Oligo-Primed Polymerase Chain Reaction (TOP-PCR): A Robust Method for the Amplification of Minute DNA Fragments in Body Fluids.

Body fluid DNA sequencing is a powerful noninvasive approach for the diagnosis of genetic defects, infectious agents and diseases. The success relies on the quantity and quality of the DNA samples. However, numerous clinical samples are either at low quantity or of poor quality due to various reasons. To overcome these problems, we have developed T oligo-primed polymerase chain reaction (TOP-PCR) for full-length nonselective amplification of minute quantity of DNA fragments. TOP-PCR adopts homogeneous "half adaptor" (HA), generated by annealing P oligo (carrying a phosphate group at the 5' end) and T oligo (carrying a T-tail at the 3' end), for efficient ligation to target DNA and subsequent PCR amplification primed by the T oligo alone. Using DNA samples from body fluids, we demonstrate that TOP-PCR recovers minute DNA fragments and maintains the DNA size profile, while enhancing the major molecular populations. Our results also showed that TOP-PCR is a superior method for detecting apoptosis and outperforms the method adopted by Illumina for DNA amplification

Towards Open-Ended Visual Recognition with Large Language Model

Localizing and recognizing objects in the open-ended physical world poses a long-standing challenge within the domain of machine perception. Recent methods have endeavored to address the issue by employing a class-agnostic mask (or box) proposal model, complemented by an open-vocabulary classifier (e.g., CLIP) using pre-extracted text embeddings. However, it is worth noting that these open-vocabulary recognition models still exhibit limitations in practical applications. On one hand, they rely on the provision of class names during testing, where the recognition performance heavily depends on this predefined set of semantic classes by users. On the other hand, when training with multiple datasets, human intervention is required to alleviate the label definition conflict between them. In this paper, we introduce the OmniScient Model (OSM), a novel Large Language Model (LLM) based mask classifier, as a straightforward and effective solution to the aforementioned challenges. Specifically, OSM predicts class labels in a generative manner, thus removing the supply of class names during both training and testing. It also enables cross-dataset training without any human interference, exhibiting robust generalization capabilities due to the world knowledge acquired from the LLM. By combining OSM with an off-the-shelf mask proposal model, we present promising results on various benchmarks, and demonstrate its effectiveness in handling novel concepts. Code/model are available at https://github.com/bytedance/OmniScient-Model

Effective superior vena cava isolation using a novel C-shaped approach

IntroductionSuperior vena cava (SVC) isolation has been proposed as part of the ablation strategy for atrial fibrillation. However, circumferential isolation of the SVC can lead to late-onset complications, such as SVC stenosis.MethodsWe describe a detailed observation of the SVC conduction pattern and present a newly developed approach for SVC isolation that involves creating a C-shaped non-circumferential ablation line while sparing the lateral segment.ResultsTwelve consecutive patients were included in the study, all of whom achieved bidirectional block during the ablation procedure.DiscussionThis approach to SVC isolation is effective and has the potential to reduce ablation related complications; however, larger studies and long-term follow-up is warranted to confirm these findings