NAD tagSeq reveals that NAD+-capped RNAs are mostly produced from a large number of protein-coding genes in Arabidopsis.

The 5' end of a eukaryotic mRNA transcript generally has a 7-methylguanosine (m7G) cap that protects mRNA from degradation and mediates almost all other aspects of gene expression. Some RNAs in Escherichia coli, yeast, and mammals were recently found to contain an NAD+ cap. Here, we report the development of the method NAD tagSeq for transcriptome-wide identification and quantification of NAD+-capped RNAs (NAD-RNAs). The method uses an enzymatic reaction and then a click chemistry reaction to label NAD-RNAs with a synthetic RNA tag. The tagged RNA molecules can be enriched and directly sequenced using the Oxford Nanopore sequencing technology. NAD tagSeq can allow more accurate identification and quantification of NAD-RNAs, as well as reveal the sequences of whole NAD-RNA transcripts using single-molecule RNA sequencing. Using NAD tagSeq, we found that NAD-RNAs in Arabidopsis were produced by at least several thousand genes, most of which are protein-coding genes, with the majority of these transcripts coming from <200 genes. For some Arabidopsis genes, over 5% of their transcripts were NAD capped. Gene ontology terms overrepresented in the 2,000 genes that produced the highest numbers of NAD-RNAs are related to photosynthesis, protein synthesis, and responses to cytokinin and stresses. The NAD-RNAs in Arabidopsis generally have the same overall sequence structures as the canonical m7G-capped mRNAs, although most of them appear to have a shorter 5' untranslated region (5' UTR). The identification and quantification of NAD-RNAs and revelation of their sequence features can provide essential steps toward understanding the functions of NAD-RNAs

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

High-order wavelet reconstruction for multi-scale edge aware tone mapping

This paper presents a High Order Reconstruction (HOR) method for improved multi-scale edge aware tone mapping. The study aims to contribute to the improvement of edge-aware techniques for smoothing an input image, while keeping its edges intact. The proposed HOR methods circumvent limitations of the existing state of the art methods, e.g., altering the image structure due to changes in contrast; remove artefacts around edges; as well as reducing computational complexity in terms of implementation and associated computational costs. In particular, the proposed method aims at reducing the changes in the image structure by intrinsically enclosing an edge-stop mechanism whose computational cost is comparable to the state-of-the-art multi-scale edge aware techniques

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

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

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

Continual Learning for Abdominal Multi-Organ and Tumor Segmentation

The ability to dynamically extend a model to new data and classes is critical for multiple organ and tumor segmentation. However, due to privacy regulations, accessing previous data and annotations can be problematic in the medical domain. This poses a significant barrier to preserving the high segmentation accuracy of the old classes when learning from new classes because of the catastrophic forgetting problem. In this paper, we first empirically demonstrate that simply using high-quality pseudo labels can fairly mitigate this problem in the setting of organ segmentation. Furthermore, we put forward an innovative architecture designed specifically for continuous organ and tumor segmentation, which incurs minimal computational overhead. Our proposed design involves replacing the conventional output layer with a suite of lightweight, class-specific heads, thereby offering the flexibility to accommodate newly emerging classes. These heads enable independent predictions for newly introduced and previously learned classes, effectively minimizing the impact of new classes on old ones during the course of continual learning. We further propose incorporating Contrastive Language-Image Pretraining (CLIP) embeddings into the organ-specific heads. These embeddings encapsulate the semantic information of each class, informed by extensive image-text co-training. The proposed method is evaluated on both in-house and public abdominal CT datasets under organ and tumor segmentation tasks. Empirical results suggest that the proposed design improves the segmentation performance of a baseline neural network on newly-introduced and previously-learned classes along the learning trajectory.Comment: MICCAI-202