We propose a network for Congested Scene Recognition called CSRNet to provide a data-driven and deep learning method that can understand highly congested scenes and perform accurate count estimation as well as present highquality density maps. The proposed CSRNet is composed of two major components: a convolutional neural network (CNN) as the front-end for 2D feature extraction and a dilated CNN for the back-end, which uses dilated kernels to deliver larger reception fields and to replace pooling operations. CSRNet is an easy-trained model because of its pure convolutional structure. We demonstrate CSRNet on four datasets (ShanghaiTech dataset, the UCF CC 50 dataset, the WorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art performance. In the Shang-haiTech Part B dataset, CSRNet achieves 47.3% lower Mean Absolute Error (MAE) than the previous state-of-theart method. We extend the targeted applications for counting other objects, such as the vehicle in TRANCOS dataset. Results show that CSRNet significantly improves the output quality with 15.4% lower MAE than the previous state-ofthe-art approach.

The Visual Object Tracking challenge VOT2018 is the sixth annual tracker benchmarking activity organized by the VOT initiative. Results of over eighty trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis and a "real-time" experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. A long-term tracking subchallenge has been introduced to the set of standard VOT sub-challenges. The new subchallenge focuses on long-term tracking properties, namely coping with target disappearance and reappearance. A new dataset has been compiled and a performance evaluation methodology that focuses on long-term tracking capabilities has been adopted. The VOT toolkit has been updated to support both standard short-term and the new longterm tracking subchallenges. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website 60 .

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Hepatocellular carcinoma (HCC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. Ferroptosis, an iron-dependent form of regulated cell death, can be induced by sorafenib. However, the prognostic value of ferroptosis-related genes in HCC remains to be further elucidated. In this study, the mRNA expression profiles and corresponding clinical data of HCC patients were downloaded from public databases. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct a multigene signature in the TCGA cohort. HCC patients from the ICGC cohort were used for validation. Our results showed that most of the ferroptosis-related genes (81.7%) were differentially expressed between HCC and adjacent normal tissues in the TCGA cohort. Twenty-six differentially expressed genes (DEGs) were correlated with overall survival (OS) in the univariate Cox regression analysis (all adjusted P< 0.05). A 10-gene signature was constructed to stratify patients into two risk groups. Patients in the high-risk group showed significantly reduced OS compared with patients in the low-risk group (P < 0.001 in the TCGA cohort and P = 0.001 in the ICGC cohort). The risk score was an independent predictor for OS in multivariate Cox regression analyses (HR> 1, P< 0.01). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Functional analysis revealed that immune-related pathways were enriched, and immune status were different between two risk groups. In conclusion, a novel ferroptosis-related gene signature can be used for prognostic prediction in HCC. Targeting ferroptosis may be a therapeutic alternative for HCC.

Progress toward understanding the pathogenesis of cystic fibrosis (CF) and developing effective therapies has been hampered by lack of a relevant animal model. CF mice fail to develop the lung and pancreatic disease that cause most of the morbidity and mortality in patients with CF. Pigs may be better animals than mice in which to model human genetic diseases because their anatomy, biochemistry, physiology, size, and genetics are more similar to those of humans. However, to date, gene-targeted mammalian models of human genetic disease have not been reported for any species other than mice. Here we describe the first steps toward the generation of a pig model of CF. We used recombinant adeno-associated virus (rAAV) vectors to deliver genetic constructs targeting the CF transmembrane conductance receptor (CFTR) gene to pig fetal fibroblasts. We generated cells with the CFTR gene either disrupted or containing the most common CF-associated mutation (ΔF508). These cells were used as nuclear donors for somatic cell nuclear transfer to porcine oocytes. We thereby generated heterozygote male piglets with each mutation. These pigs should be of value in producing new models of CF. In addition, because gene-modified mice often fail to replicate human diseases, this approach could be used to generate models of other human genetic diseases in species other than mice.

The mechanisms of compromised mitochondrial function under various pathological conditions, including hypoxia, remain largely unknown. Recent studies have shown that microRNA-210 (miR-210) is induced by hypoxia under the regulation of hypoxia-inducible factor-1a and has an important role in cell survival under hypoxic microenvironment. Hence, we hypothesized that miR-210 has a role in regulating mitochondrial metabolism and investigated miR-210 effects on mitochondrial function in cancer cell lines under normal and hypoxic conditions. Our results demonstrate that miR-210 decreases mitochondrial function and upregulates the glycolysis, thus make cancer cells more sensitive to glycolysis inhibitor. miR-210 can also activate the generation of reactive oxygen species (ROS). ISCU (iron-sulfur cluster scaffold homolog) and COX10 (cytochrome c oxidase assembly protein), two important factors of the mitochondria electron transport chain and the tricarboxylic acid cycle have been identified as potential targets of miR-210. The unique means by which miR-210 regulates mitochondrial function reveals an miRNAmediated link between microenvironmental stress, oxidative phosphorylation, ROS and iron homeostasis.

BackgroundLong non-coding RNAs (lncRNAs) have emerged as critical regulators of tumor progression. However, the role and molecular mechanism of lncRNA XIST in gastric cancer is still unknown.MethodsReal-time PCR analysis was performed to measure the expression levels of lncRNA XIST in gastric cancer tissues and cell lines, the correlation between lncRNA XIST expression and clinicopathological characteristics and prognosis was analyzed in gastric cancer patients. The biological function of lncRNA XIST on gastric cancer cells were determined both in vitro and in vivo. The regulating relationship between lncRNA XIST and miR-101 was investigated in gastric cancer cells.ResultslncRNA XIST was significantly up-regulated in gastric cancer tissues and cell lines. Overexpression of lncRNA XIST was markedly associated with larger tumor size, lymph node invasion, distant metastasis and TNM stage in gastric cancer patients. Functionally, knockdown of lncRNA XIST exerted tumor-suppressive effects by inhibiting cell proliferation, migration and invasion in vitro and tumor growth and metastasis in vivo. Furthermore, an inverse relationship between lncRNA XIST and miR-101 was found. Polycomb group protein enhancer of zeste homolog 2 (EZH2), a direct target of miR-101, could mediated the biological effects that lncRNA XIST exerted.ConclusionslncRNA XIST is up-regulated and is associated with aggressive tumor phenotypes and patient survival in gastric cancer, and the newly identified lncRNA XIST/miR-101/EZH2 axis could be a potential biomarkers or therapeutic targets for gastric cancer patients.

Summary Cucumber, Cucumis sativus L. is the only taxon with 2n = 2x = 14 chromosomes in the genus Cucumis. It consists of two cross‐compatible botanical varieties: the cultivated C. sativus var. sativus and the wild C. sativus var. hardwickii. There is no consensus on the evolutionary relationship between the two taxa. Whole‐genome sequencing of the cucumber genome provides a new opportunity to advance our understanding of chromosome evolution and the domestication history of cucumber. In this study, a high‐density genetic map for cultivated cucumber was developed that contained 735 marker loci in seven linkage groups spanning 707.8 cM. Integration of genetic and physical maps resulted in a chromosome‐level draft genome assembly comprising 193 Mbp, or 53% of the 367 Mbp cucumber genome. Strategically selected markers from the genetic map and draft genome assembly were employed to screen for fosmid clones for use as probes in comparative fluorescence in situ hybridization analysis of pachytene chromosomes to investigate genetic differentiation between wild and cultivated cucumbers. Significant differences in the amount and distribution of heterochromatins, as well as chromosomal rearrangements, were uncovered between the two taxa. In particular, six inversions, five paracentric and one pericentric, were revealed in chromosomes 4, 5 and 7. Comparison of the order of fosmid loci on chromosome 7 of cultivated and wild cucumbers, and the syntenic melon chromosome I suggested that the paracentric inversion in this chromosome occurred during domestication of cucumber. The results support the sub‐species status of these two cucumber taxa, and suggest that C. sativus var. hardwickii is the progenitor of cultivated cucumber.