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We investigated interindividual variability in gene expression in abdominal subcutaneous (SC) and omental (OM) adipose tissue of 10 massively obese men. Affymetrix human U133A microarrays were used to measure gene expression levels. A total of 6811 probesets generated significant signal in both depots in all samples. Interindividual variability in gene expression was rather low, with more than 90% of transcripts showing a coefficient of variation (CV) lower than 23.6% and 21.7% in OM and SC adipose tissues, respectively. The distributions of CV were similar between the two fat depots. A set of highly variable genes was identified for both tissues on the basis of a high CV and elevated gene expression level. Among the set of highly regulated genes, 18 transcripts were involved in lipid metabolism and 28 transcripts were involved in cell death for SC and OM samples, respectively. In conclusion, gene expression interindividual variability was rather low and globally similar between fat compartments, and the adipose tissue transcriptome appeared as relatively stable, although specific pathways were found to be highly variable in SC and OM depots.

Generating natural and accurate descriptions in image cap-tioning has always been a challenge. In this paper, we pro-pose a novel recall mechanism to imitate the way human con-duct captioning. There are three parts in our recall mecha-nism : recall unit, semantic guide (SG) and recalled-wordslot (RWS). Recall unit is a text-retrieval module designedto retrieve recalled words for images. SG and RWS are de-signed for the best use of recalled words. SG branch cangenerate a recalled context, which can guide the process ofgenerating caption. RWS branch is responsible for copyingrecalled words to the caption. Inspired by pointing mecha-nism in text summarization, we adopt a soft switch to balancethe generated-word probabilities between SG and RWS. Inthe CIDEr optimization step, we also introduce an individualrecalled-word reward (WR) to boost training. Our proposedmethods (SG+RWS+WR) achieve BLEU-4 / CIDEr / SPICEscores of 36.6 / 116.9 / 21.3 with cross-entropy loss and 38.7 /129.1 / 22.4 with CIDEr optimization on MSCOCO Karpathytest split, which surpass the results of other state-of-the-artmethods., Comment: Published in AAAI 2020

Images captured under low-light conditions often suffer from (partially) poor visibility. Besides unsatisfactory lightings, multiple types of degradations, such as noise and color distortion due to the limited quality of cameras, hide in the dark. In other words, solely turning up the brightness of dark regions will inevitably amplify hidden artifacts. This work builds a simple yet effective network for \textbf{Kin}dling the \textbf{D}arkness (denoted as KinD), which, inspired by Retinex theory, decomposes images into two components. One component (illumination) is responsible for light adjustment, while the other (reflectance) for degradation removal. In such a way, the original space is decoupled into two smaller subspaces, expecting to be better regularized/learned. It is worth to note that our network is trained with paired images shot under different exposure conditions, instead of using any ground-truth reflectance and illumination information. Extensive experiments are conducted to demonstrate the efficacy of our design and its superiority over state-of-the-art alternatives. Our KinD is robust against severe visual defects, and user-friendly to arbitrarily adjust light levels. In addition, our model spends less than 50ms to process an image in VGA resolution on a 2080Ti GPU. All the above merits make our KinD attractive for practical use.

Most attention-based image captioning models attend to the image once per word. However, attending once per word is rigid and is easy to miss some information. Attending more times can adjust the attention position, find the missing information back and avoid generating the wrong word. In this paper, we show that attending more times per word can gain improvements in the image captioning task, without increasing the number of parameters. We propose a flexible two-LSTM merge model to make it convenient to encode more attentions than words. Our captioning model uses two LSTMs to encode the word sequence and the attention sequence respectively. The information of the two LSTMs and the image feature are combined to predict the next word. Experiments on the MSCOCO caption dataset show that our method outperforms the state-of-the-art. Using bottom up features and self-critical training method, our method gets BLEU-4, METEOR, ROUGE-L, CIDEr and SPICE scores of 0.381, 0.283, 0.580, 1.261 and 0.220 on the Karpathy test split.

Xiang Zhu,* Yuxi Liu,* Hongfang Huang, Yonghua Zhang, Saisai Huang, Weiwei Zhou, Xiaocui Bian, Shiren Shen, Su Cao Department of Anesthesiology, Affiliated Hospital of Nantong University, Nantong, Jiangsu 226001, People’s Republic of China *These authors contributed equally to this work Purpose: Postoperative pain is a common clinical problem. In this study, we aimed to investigate the role of protein kinase C bII (PKCβII) in the progression of postoperative pain following skin/muscle incision and retraction (SMIR) surgery. Materials and methods: SMIR postoperative pain model was established in rats, akin to a clinical procedure. The expression level and location of p-PKCβII were observed in dorsal root ganglion (DRG) or spinal cord from SMIR-operated rats by Western blotting and immunofluorescence. In addition, the effects of PKCβII on the expression of protein gene product 9.5 (PGP9.5) or vascular endothelial growth factor (VEGF) were assessed by using pharmacological activator and inhibitor of PKCβII. Moreover, mechanical withdrawal threshold (MWT) was assessed before or after SMIR-operated rats were treated with inhibitor or activator of PKCβII.  Results: The expression of PKCβII in DRG and spinal cord was significantly increased after SMIR surgery (P < 0.001, P < 0.01) and expression of PKCβII was located in the neurons of the spinal cord, and magnocellular neurons, non-peptide neurons, and peptide neurons in DRG. Besides, compared with skin/muscle incision group, retraction caused a marked increase in the expression of PKCβII and a significant decrease of MWT (P < 0.001, P < 0.05). The activator of PKCβII greatly increased the expression of PGP9.5 and VEGF (P < 0.05, P < 0.01) and enhanced MWT (P < 0.001), while inhibitor of PKCβII decreased the expression of PGP9.5 and VEGF and attenuated MWT (P

We investigated interindividual variability in gene expression in abdominal subcutaneous (SC) and omental (OM) adipose tissue of 10 massively obese men. Affymetrix human U133A microarrays were used to measure gene expression levels. A total of 6811 probesets generated significant signal in both depots in all samples. Interindividual variability in gene expression was rather low, with more than 90% of transcripts showing a coefficient of variation (CV) lower than 23.6% and 21.7% in OM and SC adipose tissues, respectively. The distributions of CV were similar between the two fat depots. A set of highly variable genes was identified for both tissues on the basis of a high CV and elevated gene expression level. Among the set of highly regulated genes, 18 transcripts were involved in lipid metabolism and 28 transcripts were involved in cell death for SC and OM samples, respectively. In conclusion, gene expression interindividual variability was rather low and globally similar between fat compartments, and the adipose tissue transcriptome appeared as relatively stable, although specific pathways were found to be highly variable in SC and OM depots.