St Joseph in Early Christianity

In die eerste millennium is gegewens oor Josef, die man van Maria, baie skaars. In sy werk, St Joseph in Early Christianity, bied Lienhard belangrike inligting wat die Nuwe-Testamentiese Apokriewe in die tweede eeu, en die kerkvaders tot aan die einde van die Romeinse Ryk (c600) oor hom verstrek

Efficient approach for maximizing lifespan in wireless sensor networks by using mobile sinks

Recently, sink mobility has been shown to be highly beneficial in improving network lifetime in wireless sensor networks (WSNs). Numerous studies have exploited mobile sinks (MSs) to collect sensed data in order to improve energy efficiency and reduce WSN operational costs. However, there have been few studies on the effectiveness of MS operation on WSN closed operating cycles. Therefore, it is important to investigate how data is collected and how to plan the trajectory of the MS in order to gather data in time, reduce energy consumption, and improve WSN network lifetime. In this study, we combine two methods, the cluster-head election algorithm and the MS trajectory optimization algorithm, to propose the optimal MS movement strategy. This study aims to provide a closed operating cycle for WSNs, by which the energy consumption and running time of a WSN is minimized during the cluster election and data gathering periods. Furthermore, our flexible MS movement scenarios achieve both a long network lifetime and an optimal MS schedule. The simulation results demonstrate that our proposed algorithm achieves better performance than other well-known algorithms

Towards Better Dynamic Graph Learning: New Architecture and Unified Library

We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning. DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop interactions by: (1) a neighbor co-occurrence encoding scheme that explores the correlations of the source node and destination node based on their historical sequences; (2) a patching technique that divides each sequence into multiple patches and feeds them to Transformer, allowing the model to effectively and efficiently benefit from longer histories. We also introduce DyGLib, a unified library with standard training pipelines, extensible coding interfaces, and comprehensive evaluating protocols to promote reproducible, scalable, and credible dynamic graph learning research. By performing exhaustive experiments on thirteen datasets for dynamic link prediction and dynamic node classification tasks, we find that DyGFormer achieves state-of-the-art performance on most of the datasets, demonstrating its effectiveness in capturing nodes' correlations and long-term temporal dependencies. Moreover, some results of baselines are inconsistent with previous reports, which may be caused by their diverse but less rigorous implementations, showing the importance of DyGLib. All the used resources are publicly available at https://github.com/yule-BUAA/DyGLib.Comment: Accepted at NeurIPS 202

Flexural properties of fiber reinforced root canal posts.

AbstractOBJECTIVES: Fiber-reinforced composite (FRC) root canal posts have been introduced to be used instead of metal alloys and ceramics. The aim of this study was to investigate the flexural properties of different types of FRC posts and compare those values with a novel FRC material for dental applications.METHODS: Seventeen different FRC posts of various brands (Snowpost, Carbopost, Parapost, C-post, Glassix, Carbonite) and diameters, (1.0-2.1 mm) and a continuous unidirectional E-glass FRC polymerized by light activation to a cylindrical form (everStick, diameter 1.5 mm) as a control material were tested. The posts (n=5) were stored at room's humidity or thermocycled (12.000 x, 5 degrees C/55 degrees C) and stored in water for 2 weeks before testing. A three-point bending test (span=10 mm) was used to measure the flexural strength and modulus of FRC post specimens.RESULTS: Analysis of ANOVA revealed that thermocycling, brand of material and diameter of specimen had a significant effect (p<0.001) on the fracture load and flexural strength. The highest flexural strength was obtained with the control material (everStick, 1144.9+/-99.9 MPa). There was a linear relationship between fracture load and diameter of posts for both glass fiber and carbon fiber posts. Thermocycling decreased the flexural modulus of the tested specimens by approximately 10%. Strength and fracture load decreased approximately 18% as a result of thermocycling.SIGNIFICANCE: Considerable variation can be found in the calculated strength values of the studied post brands. Commercial prefabricated FRC posts showed lower flexural properties than an individually polymerised FRC material.</div

The transcriptome of the novel dinoflagellate Oxyrrhis marina (Alveolata: Dinophyceae): response to salinity examined by 454 sequencing

This is the final version of the article. Available from [BioMed Central via the DOI in this record.BACKGROUND: The heterotrophic dinoflagellate Oxyrrhis marina is increasingly studied in experimental, ecological and evolutionary contexts. Its basal phylogenetic position within the dinoflagellates make O. marina useful for understanding the origin of numerous unusual features of the dinoflagellate lineage; its broad distribution has lent O. marina to the study of protist biogeography; and nutritive flexibility and eurytopy have made it a common lab rat for the investigation of physiological responses of marine heterotrophic flagellates. Nevertheless, genome-scale resources for O. marina are scarce. Here we present a 454-based transcriptome survey for this organism. In addition, we assess sequence read abundance, as a proxy for gene expression, in response to salinity, an environmental factor potentially important in determining O. marina spatial distributions. RESULTS: Sequencing generated ~57 Mbp of data which assembled into 7, 398 contigs. Approximately 24% of contigs were nominally identified by BLAST. A further clustering of contigs (at ≥ 90% identity) revealed 164 transcript variant clusters, the largest of which (Phosphoribosylaminoimidazole-succinocarboxamide synthase) was composed of 28 variants displaying predominately synonymous variation. In a genomic context, a sample of 5 different genes were demonstrated to occur as tandem repeats, separated by short (~200-340 bp) inter-genic regions. For HSP90 several intergenic variants were detected suggesting a potentially complex genomic arrangement. In response to salinity, analysis of 454 read abundance highlighted 9 and 20 genes over or under expressed at 50 PSU, respectively. However, 454 read abundance and subsequent qPCR validation did not correlate well - suggesting that measures of gene expression via ad hoc analysis of sequence read abundance require careful interpretation. CONCLUSION: Here we indicate that tandem gene arrangements and the occurrence of multiple transcribed gene variants are common and indicate potentially complex genomic arrangements in O. marina. Comparison of the reported data set with existing O. marina and other dinoflagellates ESTs indicates little sequence overlap likely as a result of the relatively limited extent of genome scale sequence data currently available for the dinoflagellates. This is one of the first 454-based transcriptome surveys of an ancestral dinoflagellate taxon and will undoubtedly prove useful for future comparative studies aimed at reconstructing the origin of novel features of the dinoflagellates.This work was supported by a NERC grant (NE/F005237/1) awarded to PCW, DJSM, and CDL. We would like to thank Dr Margret Hughes of the Liverpool CGR for conducting 454 sequencing, and Dr Kevin Ashelford for invaluable scripting and bioinformatics support

Predicting Temporal Sets with Deep Neural Networks

Given a sequence of sets, where each set contains an arbitrary number of elements, the problem of temporal sets prediction aims to predict the elements in the subsequent set. In practice, temporal sets prediction is much more complex than predictive modelling of temporal events and time series, and is still an open problem. Many possible existing methods, if adapted for the problem of temporal sets prediction, usually follow a two-step strategy by first projecting temporal sets into latent representations and then learning a predictive model with the latent representations. The two-step approach often leads to information loss and unsatisfactory prediction performance. In this paper, we propose an integrated solution based on the deep neural networks for temporal sets prediction. A unique perspective of our approach is to learn element relationship by constructing set-level co-occurrence graph and then perform graph convolutions on the dynamic relationship graphs. Moreover, we design an attention-based module to adaptively learn the temporal dependency of elements and sets. Finally, we provide a gated updating mechanism to find the hidden shared patterns in different sequences and fuse both static and dynamic information to improve the prediction performance. Experiments on real-world data sets demonstrate that our approach can achieve competitive performances even with a portion of the training data and can outperform existing methods with a significant margin.Comment: 9 pages, 6 figures, Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '2020

Real-time visualization of clustering and intracellular transport of gold nanoparticles by correlative imaging.

Mechanistic understanding of the endocytosis and intracellular trafficking of nanoparticles is essential for designing smart theranostic carriers. Physico-chemical properties, including size, clustering and surface chemistry of nanoparticles regulate their cellular uptake and transport. Significantly, even single nanoparticles could cluster intracellularly, yet their clustering state and subsequent trafficking are not well understood. Here, we used DNA-decorated gold (fPlas-gold) nanoparticles as a dually emissive fluorescent and plasmonic probe to examine their clustering states and intracellular transport. Evidence from correlative fluorescence and plasmonic imaging shows that endocytosis of fPlas-gold follows multiple pathways. In the early stages of endocytosis, fPlas-gold nanoparticles appear mostly as single particles and they cluster during the vesicular transport and maturation. The speed of encapsulated fPlas-gold transport was critically dependent on the size of clusters but not on the types of organelle such as endosomes and lysosomes. Our results provide key strategies for engineering theranostic nanocarriers for efficient health management