ALBERT

Description: SN 2018hti is a Type I superluminous supernova (SLSN I) with an absolute g-band magnitude of −22.2 at maximum brightness, discovered by the Asteroid Terrestrial-impact Last Alert System in a metal-poor galaxy at a redshift of 0.0612. We present extensive photometric and spectroscopic observations of this supernova, covering the phases from ∼−35 d to more than  +340 d from the r-band maximum. Combining our BVgri-band photometry with Swift UVOT optical/ultraviolet photometry, we calculated the peak luminosity as ∼3.5 × 1044 erg s−1. Modelling the observed light curve reveals that the luminosity evolution of SN 2018hti can be produced by an ejecta mass of 5.8 M⊙ and a magnetar with a magnetic field of B = 1.8 × 1013 G having an initial spin period of P0 = 1.8 ms. Based on such a magnetar-powered scenario and a larger sample, a correlation between the spin of the magnetar and the kinetic energy of the ejecta can be inferred for most SLSNe I, suggesting a self-consistent scenario. Like for other SLSNe I, the host galaxy of SN 2018hti is found to be relatively faint (Mg = −17.75 mag) and of low metallicity (Z = 0.3 Z⊙), with a star formation rate of 0.3 M⊙ yr−1. According to simulation results of single-star evolution, SN 2018hti could originate from a massive, metal-poor star with a zero-age main sequence (ZAMS) mass of 25–40 M⊙, or from a less massive rotating star with MZAMS ≈ 16–25 M⊙. For the case of a binary system, its progenitor could also be a star with $M_mathrm{ZAMS} gtrsim 25, mathrm{ M}_odot$.

Description: To improve the video semantic analysis for video surveillance, a new video semantic analysis method based on the kernel discriminative sparse representation (KSVD) and weighted K nearest neighbors (KNN) is proposed in this paper. A discriminative model is built by introducing a kernel discriminative function to the KSVD dictionary optimization algorithm, mapping the sparse representation features into a high-dimensional space. The optimal dictionary is then generated and applied to compute the sparse representations of video features. For video semantic analysis, a weighted KNN algorithm based on the optimal sparse representation is proposed. In the algorithm, a kernel function is introduced to establish discrimination about sparse representation features and the classification vote result is weighted, the purpose of which is to improve the accuracy and rationality for video semantic analysis. The experimental results show that the proposed method significantly improves the discrimination of sparse representation features when compared with the traditional KSVD-based support vector machine method. The method can effectively detect the concept and event, which can be potentially useful for improving the video surveillance.

Description: With the energy and power costs increasing alongside the growth of the IT infrastructures, achieving workload concentration and high availability in cloud computing environments is becoming more and more complex. Virtual machine (VM) migration has become an important approach to address this issue, particularly; live migration of the VMs across the physical servers facilitates dynamic workload scheduling of the cloud services as per the energy management requirements, and also reduces the downtime by allowing the migration of the running instances. However, migration is a complex process affected by several factors such as bandwidth availability, application workload and operating system configurations, which in turn increases the complications in predicting the migration time in order to negotiate the service-level agreements in a real datacenter. In this paper, we propose an adaptive approach named improved MIGration (iMIG), in which we characterize some of the key metrics of the live migration performance, and conduct several experiments to study the impacts of the investigated metrics on the Kernel-based VM (KVM) functionalities, as well as the energy consumed by both the destination and the source hosts. Our results reveal the importance of the configured parameters: speed limit , TCP buffer size and max downtime , along with the VM properties and also their corresponding impacts on the migration process. Improper setting of these parameters may either incur migration failures or causes excess energy consumption. We witness a few bugs in the existing Quick EMUlator (QEMU)/KVM parameter computation framework, which is one of most widely used KVM frameworks based on QEMU. Based on our observations, we develop an analytical model aimed at better predictions of both the migration time and the downtime, during the process of VM deployment. Finally, we implement a suite of profiling tools in the adaptive mechanism based on the qemu-kvm-0.12.5 version, and our experiment results prove the efficiency of our approach in improving the live migration performance. In comparison with the default migration approach, our approach achieves a 40% reduction in the migration latency and a 45% reduction in the energy consumption.

Description: microRNAs (miRNAs) are important gene regulators. They control a wide range of biological processes and are involved in several types of cancers. Thus, exploring miRNA functions is important for diagnostics and therapeutics. To date, there are few feasible experimental techniques for discovering miRNA regulatory mechanisms. Alternatively, predictions of miRNA–mRNA regulatory relationships by computational methods have increasingly achieved promising results. Computational approaches are proving their ability as effective tools in reducing the number of biological experiments that must be conducted and to assist with the design of the experiments. In this review, we categorize and review different computational approaches to identify miRNA activities and functions, including the co-regulation of miRNAs and transcription factors. Our main focuses are on the recent approaches that use multiple data types for exploring miRNA functions. We discuss the remaining challenges in the evaluation and selection of models based on the results from a case study. Finally, we analyse the remaining challenges of each computational approach and suggest some future research directions.

Description: Dicer participates in heterochromatin formation in fission yeast and plants. However, whether it has a similar role in mammals remains controversial. Here we showed that the human Dicer protein interacts with SIRT7, an NAD + -dependent H3K18Ac (acetylated lysine 18 of histone H3) deacetylase, and holds a proportion of SIRT7 in the cytoplasm. Dicer knockdown led to an increase of chromatin-associated SIRT7 and simultaneously a decrease of cytoplasmic SIRT7, while its overexpression induced SIRT7 reduction in the chromatin-associated fraction and increment in the cytoplasm. Furthermore, DNA damaging agents promoted Dicer expression, leading to decreased level of chromatin-associated SIRT7 and increased level of H3K18Ac, which can be alleviated by Dicer knockdown. Taken together with that H3K18Ac was exclusively associated with the chromatin, our findings suggest that Dicer induction by DNA damaging treatments prevents H3K18Ac deacetylation, probably by trapping more SIRT7 in the cytoplasm.

Description: An important topic in systems biology is the reverse engineering of regulatory mechanisms through reconstruction of context-dependent gene networks. A major challenge is to identify the genes and the regulations specific to a condition or phenotype, given that regulatory processes are highly connected such that a specific response is typically accompanied by numerous collateral effects. In this study, we design a multi-layer approach that is able to reconstruct condition-specific genes and their regulation through an integrative analysis of large-scale information of gene expression, protein interaction and transcriptional regulation (transcription factor-target gene relationships). We establish the accuracy of our methodology against synthetic datasets, as well as a yeast dataset. We then extend the framework to the application of higher eukaryotic systems, including human breast cancer and Arabidopsis thaliana cold acclimation. Our study identified TACSTD2 (TROP2) as a target gene for human breast cancer and discovered its regulation by transcription factors CREB, as well as NFkB. We also predict KIF2C is a target gene for ER–/HER2– breast cancer and is positively regulated by E2F1. The predictions were further confirmed through experimental studies. Availability: The implementation and detailed protocol of the layer approach is available at http://www.egr.msu.edu/changroup/Protocols/Three-layer%20approach%20to%20reconstruct%20condition.html . Contact: krischan@egr.msu.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Alveolar epithelial cell (AEC) trans-differentiation is a process where type II alveolar epithelial cells (AEC II) trans-differentiate into type I alveolar epithelial cells (AEC I) during lung recovery after various injuries, in which AEC I are damaged. This process is critical for lung tissue repair. MicroRNAs are a group of small RNAs that regulate gene expression at the post-transcriptional level. They have the potential to regulate almost every aspect of cell physiology. However, whether AEC trans-differentiation is regulated by microRNAs is completely unknown. In this study, we found that miR-375 was downregulated during AEC trans-differentiation. The overexpression of miR-375 with an adenoviral vector inhibited alveolar epithelial trans-differentiation as indicated by an increase in the AEC II marker, surfactant protein C, and decreases in the AEC I markers, T1α and advanced glycosylation end product-specific receptor. miR-375 also inhibited the Wnt/β-catenin pathway. The constitutively activation of Wnt/β-catenin signaling with a stabilized form of β-catenin blocked the miR-375 effects. Frizzled 8 was identified as a target of miR-375. In summary, our results demonstrate that miR-375 regulates AEC trans-differentiation through the Wnt/β-catenin pathway. This discovery may provide new targets for therapeutic intervention to benefit lung recovery from injuries.

Description: Transcription factors (TFs) and epigenetic modifications play crucial roles in the regulation of gene expression, and correlations between the two types of factors have been discovered. However, methods for quantitatively studying the correlations remain limited. Here, we present a computational approach to systematically investigating how epigenetic changes in chromatin architectures or DNA sequences relate to TF binding. We implemented statistical analyses to illustrate that epigenetic modifications are predictive of TF binding affinities, without the need of sequence information. Intriguingly, by considering genome locations relative to transcription start sites (TSSs) or enhancer midpoints, our analyses show that different locations display various relationship patterns. For instance, H3K4me3, H3k9ac and H3k27ac contribute more in the regions near TSSs, whereas H3K4me1 and H3k79me2 dominate in the regions far from TSSs. DNA methylation plays relatively important roles when close to TSSs than in other regions. In addition, the results show that epigenetic modification models for the predictions of TF binding affinities are cell line-specific. Taken together, our study elucidates highly coordinated, but location- and cell type-specific relationships between epigenetic modifications and binding affinities of TFs.

Description: 〈span〉〈div〉Summary〈/div〉We propose a novel approach to compute the gravity field due to density anomaly in both outside and inside of the solid Earth with high accuracy and efficiency. The high accuracy comes from the direct employment of the analytic gravitation solution between any point on a two-dimensional (2D) plane in the horizontal direction and individual mass cubes. The high computational efficiency comes from two aspects: 1, the application of the highly efficient 2D discrete convolution algorithm; and 2, a newly developed algorithm for the optimized computation of the weight coefficient matrix. Numerical examples for applying to compare with analytical solutions demonstrated its excellent accuracy. Comparison with other state-of-the-art gravity modeling algorithm has proved that this algorithm has superior performance in both accuracy and efficiency. Application to analyze real topography demonstrated the practicality. This algorithm will be an attractive candidate for carrying out the forward modeling step in geophysical inversion problems with the claimed and proved advantages.〈/span〉

Description: Seismic data are primarily used in studies of the Earth's lithospheric structure including the Moho geometry. In regions, where seismic data are sparse or completely absent, gravimetric or combined gravimetric-seismic methods could be applied to determine the Moho depth. In this study, we derive and present generalized expressions for solving the Vening Meinesz–Moritz's (VMM) inverse problem of isostasy for a Moho depth determination from gravity and vertical gravity-gradient data. By solving the (non-linear) Fredholm's integral equation of the first kind, the linearized observation equations, which functionally relate the (given) gravity/gravity-gradient data to the (unknown) Moho depth, are derived in the spectral domain. The VMM gravimetric results are validated by using available seismic and gravimetric Moho models. Our results show that the VMM Moho solutions obtained by solving the VMM problem for gravity and gravity-gradient data are almost the same. This finding indicates that in global applications, using the global gravity/gravity-gradient data coverage, the spherical harmonic expressions for the gravimetric forward and inverse modelling yield (theoretically) the same results. Globally, these gravimetric solutions have also a relatively good agreement with the CRUST1.0 and GEMMA GOCE models in terms of their rms Moho differences (4.7 km and 4.1 km, respectively).