Adaptive sampling for spatial prediction in wireless sensor networks

University of Technology, Sydney. Faculty of Engineering and Information Technology.Networks of wireless sensors are increasingly exploited in crucial applications of monitoring spatially correlated environmental phenomena such as temperature, rainfall, soil ingredients, and air pollution. Such networks enable efficient monitoring and measurements can be included in developing models of the environmental fields even at unobserved locations. This requires determining the number of sensors and their sampling locations which minimize the uncertainty of predictions. Therefore, the aim of this thesis is to present novel, efficient and practically feasible approaches to sample the environments, so that the uncertainties at unobserved locations are minimized. Gaussian process (GP) is utilized to statistically model the spatial field. This thesis includes both stationary wireless sensor networks (SWSNs) and mobile robotic wireless sensor networks (MRWSNs), and thus the issues are correspondingly formulated into sensor selection and sensor placement problems, respectively. In the first part of the thesis, a novel performance metric for the sensor selection in the SWSNs, named average root mean square error, which reflects the average uncertainty of each predicted location, is proposed. In order to minimize this NP-hard and combinatorial optimization problem, a simulated annealing based algorithm is proposed; and the sensor selection problem is effectively addressed. Particularly, when considering the sensor selection in constrained environments, e.g. gas phase hydrogen sulphide in a sewage system, a modified GP with an improved covariance function is developed. An efficient mutual information maximization criterion suitable for this particular scenario is also presented to select the most informative gaseous sensor locations along the sewer system. The second part of this thesis introduces centralized and distributed methods for spatial prediction over time in the MRWSNs. For the purpose of finding the optimal sampling paths of the mobile wireless sensors to take the most informative observations at each time iteration, a sampling strategy is proposed based on minimizing the uncertainty at all unobserved locations. A novel and very efficient optimality criterion for the adaptive sampling problem is then presented so that the minimization can be addressed by a greedy algorithm in polynomial time. The solution is proven to be bounded; and computational time of the proposed algorithm is illustrated to be practically feasible for the resource-constrained MRWSNs. In order to enhance the issue of computational complexity, Gaussian Markov random field (GMRF) is utilized to model the spatial field exploiting sparsity of the precision matrix. A new GMRF optimality criterion for the adaptive navigation problem is also proposed such that computational complexity of a greedy algorithm to solve the resulting optimization is deterministic even with increasing number of measurements. Based on the realistic simulations conducted using the pre-published data sets, it has shown that the proposed algorithms are superior with appealing results

Machine-learning of atomic-scale properties based on physical principles

We briefly summarize the kernel regression approach, as used recently in materials modelling, to fitting functions, particularly potential energy surfaces, and highlight how the linear algebra framework can be used to both predict and train from linear functionals of the potential energy, such as the total energy and atomic forces. We then give a detailed account of the Smooth Overlap of Atomic Positions (SOAP) representation and kernel, showing how it arises from an abstract representation of smooth atomic densities, and how it is related to several popular density-based representations of atomic structure. We also discuss recent generalisations that allow fine control of correlations between different atomic species, prediction and fitting of tensorial properties, and also how to construct structural kernels---applicable to comparing entire molecules or periodic systems---that go beyond an additive combination of local environments

A Condensation-Ordering Mechanism in Nanoparticle-Catalyzed Peptide Aggregation

Nanoparticles introduced in living cells are capable of strongly promoting the aggregation of peptides and proteins. We use here molecular dynamics simulations to characterise in detail the process by which nanoparticle surfaces catalyse the self- assembly of peptides into fibrillar structures. The simulation of a system of hundreds of peptides over the millisecond timescale enables us to show that the mechanism of aggregation involves a first phase in which small structurally disordered oligomers assemble onto the nanoparticle and a second phase in which they evolve into highly ordered beta-sheets as their size increases

Learning Latent Distribution for Distinguishing Network Traffic in Intrusion Detection System

© 2019 IEEE. We develop a novel deep learning model, Multi-distributed Variational AutoEncoder (MVAE), for the network intrusion detection. To make the traffic more distinguishable, MVAE introduces the label information of data samples into the Kullback-Leibler (KL) term of the loss function of Variational AutoEncoder (VAE). This label information allows MVAEs to force/partition network data samples into different classes with different regions in the latent feature space. As a result, the network traffic samples are more distinguishable in the new representation space (i.e., the latent feature space of MVAE), thereby improving the accuracy in detecting intrusions. To evaluate the efficiency of the proposed solution, we carry out intensive experiments on two popular network intrusion datasets, i.e., NSL-KDD and UNSW-NB15 under four conventional classifiers including Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). The experimental results demonstrate that our proposed approach can significantly improve the accuracy of intrusion detection algorithms up to 24.6% compared to the original one (using area under the curve metric)

Clinical significance of VEGF-A, -C and -D expression in esophageal malignancies

Vascular endothelial growth factors ( VEGF)- A, - C and - D are members of the proangiogenic VEGF family of glycoproteins. VEGF-A is known to be the most important angiogenic factor under physiological and pathological conditions, while VEGF-C and VEGF-D are implicated in the development and sprouting of lymphatic vessels, so called lymphangiogenesis. Local tumor progression, lymph node metastases and hematogenous tumor spread are important prognostic factors for esophageal carcinoma ( EC), one of the most lethal malignancies throughout the world. We found solid evidence in the literature that VEGF expression contributes to tumor angiogenesis, tumor progression and lymph node metastasis in esophageal squamous cell carcinoma ( SCC), and many authors could show a prognostic value for VEGF-assessment. In adenocarcinoma (AC) of the esophagus angiogenic properties are acquired in early stages, particularly in precancerous lesions like Barrett's dysplasia. However, VEGF expression fails to give prognostic information in AC of the esophagus. VEGF-C and VEGF-D were detected in SCC and dysplastic lesions, but not in normal mucosa of the esophagus. VEGF-C expression might be associated with lymphatic tumor invasion, lymph node metastases and advanced disease in esophageal SCC and AC. Therapeutic interference with VEGF signaling may prove to be a promising way of anti-angiogenic co-treatment in esophageal carcinoma. However, concrete clinical data are still pending

Direct comparison of methionine restriction with leucine restriction on the metabolic health of C57BL/6J mice

EKL was the recipient of a BBSRC postgraduate studentship. This work was funded by Tenovus Scotland project grant to MD and NM (G13/07) and BBSRC DTG. MD is also supported by the British Heart Foundation (PG/09/048/27675, PG/11/8/28703 and PG/14/43/30889) and Diabetes UK (14/0004853). NM is funded by British Heart Foundation (PG/16/90/32518).Peer reviewedPublisher PD

Effects of Payena dasyphylla (Miq.) on hyaluronidase enzyme activity and metalloproteinases protein expressions in interleukin-1beta stimulated human chondrocytes cells

Background: Hyaluronidases have been found as the target enzymes in the development of osteoarthritis (OA) disease. While there is still no curative treatment for this disease, recent studies on the treatment of OA were focused on the effectiveness of natural products which are expected to improve the symptoms with minimal side effects. The aim of this study was to screen selected Malaysian plants on their anti-hyaluronidase activity as well as to evaluate the active plant and its derived fractions on its potential anti-arthritic and antioxidant activities.Methods: A total of 20 methanolic crude extracts (bark and leaf) from ten different plants were screened using a colorimetric hyaluronidase enzymatic assay. The active plant extract (Payena dasyphylla) was then studied for its hyaluronidase inhibitory activity in the interleukin-1β (IL-1β) stimulated human chondrocytes cell line (NHAC-kn) using zymography method. The Payena dasyphylla methanolic bark extract was then fractionated into several fractions in where the ethyl acetate (EA) fraction was evaluated for its inhibitory effects on the HYAL1 and HYAL2 gene expressions using reverse transcription-polymerase chain reaction (RT-PCR) technique. While the MMP-3 and MMP-13 protein expressions were evaluated using western blot method. The phenolic and flavonoid contents of the three fractions as well as the antioxidant property of the EA fraction were also evaluated.Results: Bark extract of Payena dasyphylla (100 μg/ml) showed the highest inhibitory activity against bovine testicular hyaluronidase with 91.63%. The plant extract also inhibited hyaluronidase expression in the cultured human chondrocyte cells in response to IL-1β (100 ng/ml). Similarly, treatment with Payena dasyphylla ethyl acetate (EA) fraction (100 μg/ml) inhibited the HYAL1 and HYAL2 mRNA gene expressions as well as MMP-3 and MMP-13 protein expression in a dose dependent manner. Payena dasyphylla EA fraction has demonstrated the highest amount of phenolic and flavonoid content with 168.62 ± 10.93 mg GAE/g and 95.96 ± 2.96 mg RE/g respectively as compared to water and hexane fractions. In addition, the Payena dasyphylla EA fraction showed strong antioxidant activity with IC50 value of 11.64 ± 1.69 μg/mL.Conclusion: These findings have shown that Payena dasyphylla might contained potential phenolic compounds that inhibiting the key enzyme in osteoarthritis development, which is the hyaluronidase enzyme through interruption of HYAL1 and HYAL1 gene expressions. The degradation of cartilage could also be inhibited by the plant through suppression of MMP-3 and MMP-13 protein expressions. We also reported that the inhibitory effect of Payena dasyphylla on hyaluronidase activity and expression might be due to its anti-oxidant property

Analysis of physical pore space characteristics of two pyrolytic biochars and potential as microhabitat

Background and Aims Biochar amendment to soil is a promising practice of enhancing productivity of agricultural systems. The positive effects on crop are often attributed to a promotion of beneficial soil microorganisms while suppressing pathogens e.g. This study aims to determine the influence of biochar feedstock on (i) spontaneous and fungi inoculated microbial colonisation of biochar particles and (ii) physical pore space characteristics of native and fungi colonised biochar particles which impact microbial habitat quality. Methods Pyrolytic biochars from mixed woods and Miscanthus were investigated towards spontaneous colonisation by classical microbiological isolation, phylogenetic identification of bacterial and fungal strains, and microbial respiration analysis. Physical pore space characteristics of biochar particles were determined by X-ray μ-CT. Subsequent 3D image analysis included porosity, surface area, connectivities, and pore size distribution. Results Microorganisms isolated from Wood biochar were more abundant and proliferated faster than those from the Miscanthus biochar. All isolated bacteria belonged to gram-positive bacteria and were feedstock specific. Respiration analysis revealed higher microbial activity for Wood biochar after water and substrate amendment while basal respiration was on the same low level for both biochars. Differences in porosity and physical surface area were detected only in interaction with biochar-specific colonisation. Miscanthus biochar was shown to have higher connectivity values in surface, volume and transmission than Wood biochars as well as larger pores as observed by pore size distribution. Differences in physical properties between colonised and non-colonised particles were larger in Miscanthus biochar than in Wood biochar. Conclusions Vigorous colonisation was found on Wood biochar compared to Miscanthus biochar. This is contrasted by our findings from physical pore space analysis which suggests better habitat quality in Miscanthus biochar than in Wood biochar. We conclude that (i) the selected feedstocks display large differences in microbial habitat quality as well as physical pore space characteristics and (ii) physical description of biochars alone does not suffice for the reliable prediction of microbial habitat quality and recommend that physical and surface chemical data should be linked for this purpose

Measurement of the top quark mass using the matrix element technique in dilepton final states

We present a measurement of the top quark mass in pp¯ collisions at a center-of-mass energy of 1.96 TeV at the Fermilab Tevatron collider. The data were collected by the D0 experiment corresponding to an integrated luminosity of 9.7  fb−1. The matrix element technique is applied to tt¯ events in the final state containing leptons (electrons or muons) with high transverse momenta and at least two jets. The calibration of the jet energy scale determined in the lepton+jets final state of tt¯ decays is applied to jet energies. This correction provides a substantial reduction in systematic uncertainties. We obtain a top quark mass of mt=173.93±1.84  GeV

Platelet-Rich Plasma Promotes the Proliferation of Human Muscle Derived Progenitor Cells and Maintains Their Stemness

Human muscle-derived progenitor cells (hMDPCs) offer great promise for muscle cell-based regenerative medicine; however, prolonged ex-vivo expansion using animal sera is necessary to acquire sufficient cells for transplantation. Due to the risks associated with the use of animal sera, the development of a strategy for the ex vivo expansion of hMDPCs is required. The purpose of this study was to investigate the efficacy of using platelet-rich plasma (PRP) for the ex-vivo expansion of hMDPCs. Pre-plated MDPCs, myoendothelial cells, and pericytes are three populations of hMDPCs that we isolated by the modified pre-plate technique and Fluorescence Activated Cell Sorting (FACS), respectively. Pooled allogeneic human PRP was obtained from a local blood bank, and the effect that thrombin-activated PRP-releasate supplemented media had on the ex-vivo expansion of the hMDPCs was tested against FBS supplemented media, both in vitro and in vivo. PRP significantly enhanced short and long-term cell proliferation, with or without FBS supplementation. Antibody-neutralization of PDGF significantly blocked the mitogenic/proliferative effects that PRP had on the hMDPCs. A more stable and sustained expression of markers associated with stemness, and a decreased expression of lineage specific markers was observed in the PRP-expanded cells when compared with the FBS-expanded cells. The in vitro osteogenic, chondrogenic, and myogenic differentiation capacities of the hMDPCs were not altered when expanded in media supplemented with PRP. All populations of hMDPCs that were expanded in PRP supplemented media retained their ability to regenerate myofibers in vivo. Our data demonstrated that PRP promoted the proliferation and maintained the multi-differentiation capacities of the hMDPCs during ex-vivo expansion by maintaining the cells in an undifferentiated state. Moreover, PDGF appears to be a key contributing factor to the beneficial effect that PRP has on the proliferation of hMDPCs. © 2013 Li et al