Broad Band Polarimetry of Supernovae: SN1994D, SN1994Y, SN1994ae, SN1995D and SN 1995H

We have made polarimetric observations of three Type Ia supernovae (SN Ia) and two type II supernovae (SN II). No significant polarization was detected for any of the SN Ia down to the level of 0.2\%, while polarization of order $1.0\%$ was detected for the two SN II 1994Y and 1995H. A catalog of all the SNe with polarization data is compiled that shows a distinct trend that all the 5 SN II with sufficient polarimetric data show polarizations at about 1\%, while none of the 9 SN Ia in the sample show intrinsic polarization. This systematic difference in polarization of supernovae, if confirmed, raises many interesting questions concerning the mechanisms leading to supernova explosions. Our observations enhance the use of SN Ia as tools for determining the distance scale through various techniques, but suggest that one must be very cautious in utilizing Type II for distance determinations. However, we caution that the link between the asphericity of a supernova and the measured ``intrinsic'' polarization is complicated by reflected light from the circumstellar material and the intervening interstellar material, the so-called light echo. This effect may contribute more substantially to SN II than to SN Ia. The tight limits on polarization of SN Ia may constrain progenitor models with extensive scattering nebulae such as symbiotic stars and other systems of extensive mass loss.Comment: 27 pages, 3 Postscript figure

Design, purification and assessment of GRP78 binding peptide-linked Subunit A of Subtilase cytotoxic for targeting cancer cells

The sequence of primers for GBP-SubA and optimization of E. coli strain and vector of GBP-SubA expression. (DOC 710 kb

What Makes a Helpful Online Review When Information Overload Exists?

With the increasing of online reviews, information overload has become a major problem in online community. What makes a helpful online review when information overload exists? In this study, the research model is developed to examine the helpfulness of online consumer reviews when information overload exists. Information quality is measured by review length and pictures in the model. The result is showed the relationship between review length and review helpfulness is usually described as an inverted U curve. The impact of review length and picture review on helpfulness is stronger when information overload exists. The impact of is also stronger with negative reviews than without negative reviews. As a result, our findings help extend the literature on information diagnosticity within the context of information overload

The Evaluation of E-commerce Efficiency in China using DEA-Tobit model: evidence from Taobao data

Using the analytical framework of DEA-Tobit, this paper investigates the efficiency of e-commerce in China\u27s provinces based on the cross-section data of 31 provinces in China and the data of e-commerce service providers from Taobao’s open platform. The data envelopment analysis (DEA) is used to calculate the technical efficiency and scale efficiency. Furthermore the paper gives an empirical test on the relationship between the scale efficiency and influencing factors by using the censored Tobit model. The results show there are significant regional differences in the efficiency of e-commerce services in provinces of China, and the Real GDP per capita, the seller number on e-commerce platform, the retail sales and wholesale are important reasons for the different efficiency in each province of China. This study provides a domain-specific, integrative approach in evaluating the E-commerce development combining macro data from National Bureau of Statistics of China and micro data from taobao.com

The Progenitor of Supernova 2004dj in a Star Cluster

The progenitor of type II-plateau supernova (SN) 2004dj is identified with a supergiant in a compact star cluster known as "Sandage Star 96" (S96) in the nearby spiral galaxy NGC 2403, which was fortuitously imaged as part of the Beijing-Arizona-Taiwan-Connecticut (BATC) Multicolor Sky Survey from Feb 1995 to Dec 2003 prior to SN 2004dj. The superior photometry of BATC images for S96, taken with 14 intermediate-band filters covering 3000-10000\AA, unambiguously establishes the star cluster nature of S96 with an age of $\sim 20$Myr, a reddening of $\hbox{E}(B-V)\sim 0.35$ mag and a total mass of $\sim 96,000$M$_{\odot}$. The compact star cluster nature of S96 is also consistent with the lack of light variations in the past decade. The SN progenitor is estimated to have a main-sequence mass of $\sim$12M$_{\odot}$. The comparison of our intermediate-band data of S96 with the post-outburst photometry obtained as the SN has significantly dimmed, may hopefully conclusively establish the nature of the progenitor.Comment: 4 pages; 3 figures. To accept for Publications in ApJ Letters, but slightly longer in this perprin

Acetylation modification regulates GRP78 secretion in colon cancer cells

High glucose-regulated protein 78 (GRP78) expression contributes to the acquisition of a wide range of phenotypic cancer hallmarks, and the pleiotropic oncogenic functions of GRP78 may result from its diverse subcellular distribution. Interestingly, GRP78 has been reported to be secreted from solid tumour cells, participating in cell-cell communication in the tumour microenvironment. However, the mechanism underlying this secretion remains elusive. Here, we report that GRP78 is secreted from colon cancer cells via exosomes. Histone deacetylase (HDAC) inhibitors blocked GRP78 release by inducing its aggregation in the ER. Mechanistically, HDAC inhibitor treatment suppressed HDAC6 activity and led to increased GRP78 acetylation; acetylated GRP78 then bound to VPS34, a class III phosphoinositide-3 kinase, consequently preventing the sorting of GRP78 into multivesicular bodies (MVBs). Of note, we found that mimicking GRP78 acetylation by substituting the lysine at residue 633, one of the deacetylated sites of HDAC6, with a glutamine resulted in decreased GRP78 secretion and impaired tumour cell growth in vitro. Our study thus reveals a hitherto-unknown mechanism of GRP78 secretion and may also provide implications for the therapeutic use of HDAC inhibitors

Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data

Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and localization. However, with limited resources, it is challenging to determine the best type of annotations when annotating massive amounts of unlabeled data. To address this issue, we focus on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans; both applications require significant effort and time for pixel-wise annotation due to the high dimensional nature of the data, involving either temporary or spatial dimensions. In this paper, we develop a new annotation strategy, termed Drag&Drop, which simplifies the annotation process to drag and drop. This annotation strategy is more efficient, particularly for temporal and volumetric imaging, than other types of weak annotations, such as per-pixel, bounding boxes, scribbles, ellipses, and points. Furthermore, to exploit our Drag&Drop annotations, we develop a novel weakly supervised learning method based on the watershed algorithm. Experimental results show that our method achieves better detection and localization performance than alternative weak annotations and, more importantly, achieves similar performance to that trained on detailed per-pixel annotations. Interestingly, we find that, with limited resources, allocating weak annotations from a diverse patient population can foster models more robust to unseen images than allocating per-pixel annotations for a small set of images. In summary, this research proposes an efficient annotation strategy for tumor detection and localization that is less accurate than per-pixel annotations but useful for creating large-scale datasets for screening tumors in various medical modalities.Comment: Published in Machine Intelligence Researc

Efficient Bi-Level Optimization for Recommendation Denoising

The acquisition of explicit user feedback (e.g., ratings) in real-world recommender systems is often hindered by the need for active user involvement. To mitigate this issue, implicit feedback (e.g., clicks) generated during user browsing is exploited as a viable substitute. However, implicit feedback possesses a high degree of noise, which significantly undermines recommendation quality. While many methods have been proposed to address this issue by assigning varying weights to implicit feedback, two shortcomings persist: (1) the weight calculation in these methods is iteration-independent, without considering the influence of weights in previous iterations, and (2) the weight calculation often relies on prior knowledge, which may not always be readily available or universally applicable. To overcome these two limitations, we model recommendation denoising as a bi-level optimization problem. The inner optimization aims to derive an effective model for the recommendation, as well as guiding the weight determination, thereby eliminating the need for prior knowledge. The outer optimization leverages gradients of the inner optimization and adjusts the weights in a manner considering the impact of previous weights. To efficiently solve this bi-level optimization problem, we employ a weight generator to avoid the storage of weights and a one-step gradient-matching-based loss to significantly reduce computational time. The experimental results on three benchmark datasets demonstrate that our proposed approach outperforms both state-of-the-art general and denoising recommendation models. The code is available at https://github.com/CoderWZW/BOD.Comment: 11pages, 5 figures, 6 table