Controllable Image-to-Video Translation: A Case Study on Facial Expression Generation

The recent advances in deep learning have made it possible to generate photo-realistic images by using neural networks and even to extrapolate video frames from an input video clip. In this paper, for the sake of both furthering this exploration and our own interest in a realistic application, we study image-to-video translation and particularly focus on the videos of facial expressions. This problem challenges the deep neural networks by another temporal dimension comparing to the image-to-image translation. Moreover, its single input image fails most existing video generation methods that rely on recurrent models. We propose a user-controllable approach so as to generate video clips of various lengths from a single face image. The lengths and types of the expressions are controlled by users. To this end, we design a novel neural network architecture that can incorporate the user input into its skip connections and propose several improvements to the adversarial training method for the neural network. Experiments and user studies verify the effectiveness of our approach. Especially, we would like to highlight that even for the face images in the wild (downloaded from the Web and the authors' own photos), our model can generate high-quality facial expression videos of which about 50\% are labeled as real by Amazon Mechanical Turk workers.Comment: 10 page

Does Notch play a tumor suppressor role across diverse squamous cell carcinomas?

The role of Notch pathway in tumorigenesis is highly variable. It can be tumor suppressive or pro-oncogenic, typically depending on the cellular context. Squamous cell carcinoma (SCC) is a cancer of the squamous cell, which can occur in diverse human tissues. SCCs are one of the most frequent human malignancies for which the pathologic mechanisms remain elusive. Recent genomic analysis of diverse SCCs identified marked levels of mutations in NOTCH1, implicating Notch signaling pathways in the pathogenesis of SCCs. In this review, evidences highlighting NOTCH's role in different types of SCCs are summarized. Moreover, based on accumulating structural information of the NOTCH receptor, the functional consequences of NOTCH1 gene mutations identified from diverse SCCs are analyzed, emphasizing loss of function of Notch in these cancers. Finally, we discuss the convergent view on an intriguing possibility that Notch may function as tumor suppressor in SCCs across different tissues. These mechanistic insights into Notch signaling pathways will help to guide the research of SCCs and development of therapeutic strategies for these cancers

The association of two single nucleotide polymorphisms (SNPs) in growth hormone (GH) gene with litter size and superovulation response in goat-breeds

Two active mutations (A 781 G and A 1575 G) in growth hormone (GH) gene, and their associations with litter size (LS), were investigated in both a high prolificacy (Matou, n = 182) and a low prolificacy breed (Boer, n = 352) by using the PCR-RFLP method. Superovulation experiments were designed in 57 dams, in order to evaluate the effect of different genotypes of the GH gene on superovulation response. Two genotypes (AA and AB, CC and CD) in each mutation were detected in these two goat breeds. Neither BB nor DD homozygous genotypes were observed. The genotypic frequencies of AB and CC were significantly higher than those of AA and CD. In the third parity, Matou dams with AB or CC genotypes had significantly larger litter sizes than those with AA and CD (p < 0.05). On combining the two loci, both Matou and Boer dams with ABCD genotype had the largest litter sizes when compared to the other genotypes (p < 0.05). When undergoing like superovulation treatments, a significantly higher number of corpora lutea and ova, with a lower incidence of ovarian cysts, were harvested in the AB and CC genotypes than in AA and CD. These results show that the two loci of GH gene are highly associated with abundant prolificacy and superovulation response in goat breeds

Chromosome-Wide Characterization of Intragenic Crossover in Shiitake Mushroom, Lentinula edodes

Meiotic crossover plays a critical role in generating genetic variations and is a central component of breeding. However, our understanding of crossover in mushroom-forming fungi is limited. Here, in Lentinula edodes, we characterized the chromosome-wide intragenic crossovers, by utilizing the single-nucleotide polymorphisms (SNPs) datasets of an F1 haploid progeny. A total of 884 intragenic crossovers were identified in 110 single-spore isolates, the majority of which were closer to transcript start sites. About 71.5% of the intragenic crossovers were clustered into 65 crossover hotspots. A 10 bp motif (GCTCTCGAAA) was significantly enriched in the hotspot regions. Crossover frequencies around mating-type A (MAT-A) loci were enhanced and formed a hotspot in L. edodes. Genome-wide quantitative trait loci (QTLs) mapping identified sixteen crossover-QTLs, contributing 8.5&ndash;29.1% of variations. Most of the detected crossover-QTLs were co-located with crossover hotspots. Both cis- and trans-QTLs contributed to the nonuniformity of crossover along chromosomes. On chr2, we identified a QTL hotspot that regulated local, global crossover variation and crossover hotspot in L. edodes. These findings and observations provide a comprehensive view of the crossover landscape in L. edodes, and advance our understandings of conservation and diversity of meiotic recombination in mushroom-forming fungi

Computational Methods for Modeling Aptamers and Designing Riboswitches

Riboswitches, which are located within certain noncoding RNA region perform functions as genetic “switches”, regulating when and where genes are expressed in response to certain ligands. Understanding the numerous functions of riboswitches requires computation models to predict structures and structural changes of the aptamer domains. Although aptamers often form a complex structure, computational approaches, such as RNAComposer and Rosetta, have already been applied to model the tertiary (three-dimensional (3D)) structure for several aptamers. As structural changes in aptamers must be achieved within the certain time window for effective regulation, kinetics is another key point for understanding aptamer function in riboswitch-mediated gene regulation. The coarse-grained self-organized polymer (SOP) model using Langevin dynamics simulation has been successfully developed to investigate folding kinetics of aptamers, while their co-transcriptional folding kinetics can be modeled by the helix-based computational method and BarMap approach. Based on the known aptamers, the web server Riboswitch Calculator and other theoretical methods provide a new tool to design synthetic riboswitches. This review will represent an overview of these computational methods for modeling structure and kinetics of riboswitch aptamers and for designing riboswitches

Understanding the Correlation between Landscape Pattern and Vertical Urban Volume by Time-Series Remote Sensing Data: A Case Study of Melbourne

Urbanization is changing the world&rsquo;s surface pattern more and more drastically, which brings many social and ecological problems. Quantifying the changes in the landscape pattern and 3D structure of the city is important to understand these issues. This research study used Melbourne, a compact city, as a case study, and focused on landscape patterns and vertical urban volume (volume mean (VM), volume standard deviation (VSD)) and investigate the correlation between them from the scope of different scales and functions by Remote Sensing (RS) and Geographic Information System (GIS) techniques. We found: (1) From 2000 to 2012, the landscape pattern had a trend of decreasing fragmentation and increasing patch aggregation. The growth of VM and VSD was more severe than that of landscape metrics, and presented a &ldquo;high&ndash;low&rdquo; situation from the city center to the surroundings, maintaining the structure of &ldquo;large east and small west&rdquo;. (2) Landscape pattern was found closely associated with the urban volume. In the entire study area, landscape pattern patches with low fragmentation and high aggregation were directly proportional to VM with high value, which represented high urbanization, and patches with high connectivity and fragmentation had a positive relationship with high VSD, which represented strong spatial recognition. (3) The urban volumes of different urban functional areas were affected by different landscape patterns, and the analysis based on the local development situation can explain the internal mechanism of the interaction between the landscape pattern and the urban volume

Co-Transcriptional Folding and Regulation Mechanisms of Riboswitches

Riboswitches are genetic control elements within non-coding regions of mRNA. These self-regulatory elements have been found to sense a range of small metabolites, ions, and other physical signals to exert regulatory control of transcription, translation, and splicing. To date, more than a dozen riboswitch classes have been characterized that vary widely in size and secondary structure. Extensive experiments and theoretical studies have made great strides in understanding the general structures, genetic mechanisms, and regulatory activities of individual riboswitches. As the ligand-dependent co-transcriptional folding and unfolding dynamics of riboswitches are the key determinant of gene expression, it is important to investigate the thermodynamics and kinetics of riboswitches both in the presence and absence of metabolites under the transcription. This review will provide a brief summary of the studies about the regulation mechanisms of the pbuE, SMK, yitJ, and metF riboswitches based on the ligand-dependent co-transcriptional folding of the riboswitches

Using a Hidden Markov Model for Improving the Spatial-Temporal Consistency of Time Series Land Cover Classification

Time series land cover maps play a key role in monitoring the dynamic change of land use. To obtain classification maps with better spatial-temporal consistency and classification accuracy, this study used an algorithm that incorporated information from spatial and temporal neighboring observations in a hidden Markov model (HMM) to improve the time series land cover maps initially produced by a support vector machine (SVM). To investigate the effects of different initial distributions and transition probability matrices on the classification of the HMM, we designed different experimental schemes with different input elements to verify this algorithm with Landsat and HJ satellite images. In addition, we introduced spatial weights into the HMM to make effective use of spatial information. The experimental results showed that the HMM considered that spatial weights could eliminate the vast majority of illogical land cover transition that may occur in previous pixel-wise classification, and that this model had obvious advantages in spatial-temporal consistency and classification accuracy over some existing classification models