A generalized Gaussian process model for computer experiments with binary time series

Non-Gaussian observations such as binary responses are common in some computer experiments. Motivated by the analysis of a class of cell adhesion experiments, we introduce a generalized Gaussian process model for binary responses, which shares some common features with standard GP models. In addition, the proposed model incorporates a flexible mean function that can capture different types of time series structures. Asymptotic properties of the estimators are derived, and an optimal predictor as well as its predictive distribution are constructed. Their performance is examined via two simulation studies. The methodology is applied to study computer simulations for cell adhesion experiments. The fitted model reveals important biological information in repeated cell bindings, which is not directly observable in lab experiments.Comment: 49 pages, 4 figure

Enhanced production of bacterial cellulose by using a biofilm reactor and its material property analysis

Bacterial cellulose has been used in the food industry for applications such as low-calorie desserts, salads, and fabricated foods. It has also been used in the paper manufacturing industry to enhance paper strength, the electronics industry in acoustic diaphragms for audio speakers, the pharmaceutical industry as filtration membranes, and in the medical field as wound dressing and artificial skin material. In this study, different types of plastic composite support (PCS) were implemented separately within a fermentation medium in order to enhance bacterial cellulose (BC) production by Acetobacter xylinum. The optimal composition of nutritious compounds in PCS was chosen based on the amount of BC produced. The selected PCS was implemented within a bioreactor to examine the effects on BC production in a batch fermentation. The produced BC was analyzed using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), thermogravimetric analysis (TGA), and dynamic mechanical analysis (DMA). Among thirteen types of PCS, the type SFYR+ was selected as solid support for BC production by A. xylinum in a batch biofilm reactor due to its high nitrogen content, moderate nitrogen leaching rate, and sufficient biomass attached on PCS. The PCS biofilm reactor yielded BC production (7.05 g/L) that was 2.5-fold greater than the control (2.82 g/L). The XRD results indicated that the PCS-grown BC exhibited higher crystallinity (93%) and similar crystal size (5.2 nm) to the control. FESEM results showed the attachment of A. xylinum on PCS, producing an interweaving BC product. TGA results demonstrated that PCS-grown BC had about 95% water retention ability, which was lower than BC produced within suspended-cell reactor. PCS-grown BC also exhibited higher Tmax compared to the control. Finally, DMA results showed that BC from the PCS biofilm reactor increased its mechanical property values, i.e., stress at break and Young's modulus when compared to the control BC. The results clearly demonstrated that implementation of PCS within agitated fermentation enhanced BC production and improved its mechanical properties and thermal stability

08031 Abstracts Collection -- Software Engineering for Self-Adaptive Systems

From 13.01. to 18.01.2008, the Dagstuhl Seminar 08031 ``Software Engineering for Self-Adaptive Systems\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

An Empirical Evaluation of Deep Learning on Highway Driving

Numerous groups have applied a variety of deep learning techniques to computer vision problems in highway perception scenarios. In this paper, we presented a number of empirical evaluations of recent deep learning advances. Computer vision, combined with deep learning, has the potential to bring about a relatively inexpensive, robust solution to autonomous driving. To prepare deep learning for industry uptake and practical applications, neural networks will require large data sets that represent all possible driving environments and scenarios. We collect a large data set of highway data and apply deep learning and computer vision algorithms to problems such as car and lane detection. We show how existing convolutional neural networks (CNNs) can be used to perform lane and vehicle detection while running at frame rates required for a real-time system. Our results lend credence to the hypothesis that deep learning holds promise for autonomous driving.Comment: Added a video for lane detectio

Spatiotemporal Landslide Activity Derived from Tree-rings: The Tieliku Mingsui Landslide, Northern Taiwan

Spatiotemporal landslide activity records are reconstructed for the Tieliku Mingsui landslide. Periods and the extent of scar activity at the foot of the landslide body are estimated from satellite and aerial photo records. The location of landslide features at the densely forested head of the landslide body are surveyed in the field using a VBS-RTK survey and periods of activity are inferred from growth disturbances recorded in 14 conifer and broadleaf trees growing adjacent to the features. Together, image and growth disturbance records produce a detailed spatiotemporal landslide activity record that spans 34 years and includes 8 years of activity. A comparison of landslide activity records with rainfall data collected near the landslide reveals that years of landslide activity coincide with years of high summer season and event accumulated rainfall

Massive Shift in Gene Expression during Transitions between Developmental Stages of the Gall Midge, Mayetiola Destructor

Citation: Chen, M. S., Liu, S. Z., Wang, H. Y., Cheng, X. Y., El Bouhssini, M., & Whitworth, R. J. (2016). Massive Shift in Gene Expression during Transitions between Developmental Stages of the Gall Midge, Mayetiola Destructor. PLoS One, 11(5), 1-19. https://doi.org/10.1371/journal.pone.0155616Mayetiola destructor is a destructive pest of wheat and has six developmental stages. Molecular mechanisms controlling the transition between developmental stages remain unknown. Here we analyzed genes that were expressed differentially between two successive developmental stages, including larvae at 1, 3, 5, and 7 days, pupae, and adults. A total of 17,344 genes were expressed during one or more of these studied stages. Among the expressed genes, 38-68% were differently expressed between two successive stages, with roughly equal percentages of up-and down-regulated genes. Analysis of the functions of the differentially expressed genes revealed that each developmental stage had some unique types of expressed genes that are characteristic of the physiology at that stage. This is the first genome-wide analysis of genes differentially expressed in different stages in a gall midge. The large dataset of up-and down-regulated genes in each stage of the insect shall be very useful for future research to elucidate mechanisms regulating insect development and other biological processes