Detection of dynamic primary user with cooperative spectrum sensing

In this paper we demonstrate that existing cooperative spectrum sensing formulated for static primary users cannot accurately detect dynamic primary users regardless of the information fusion method. Performance error occurs as the sensing parameters calculated by the conventional detector result in sensing performance that violates the sensing requirements. Furthermore, the error is accumulated and compounded by the number of cooperating nodes. To address this limitation, we design and implement the duty cycle detection model for the context of cooperative spectrum sensing to accurately calculate the sensing parameters that satisfy the sensing requirements. We show that longer sensing duration is required to compensate for dynamic primary user traffic.

Modelling total duration of traffic incidents including incident detection and recovery time

Traffic incidents are key contributors to non-recurrent congestion, potentially generating significant delay. Factors that influence the duration of incidents are important to understand so that effective mitigation strategies can be implemented. To identify and quantify the effects of influential factors, a methodology for studying total incident duration based on historical data from an ‘integrated database’ is proposed. Incident duration models are developed using a selected freeway segment in the Southeast Queensland, Australia network. The models include incident detection and recovery time as components of incident duration. A hazard-based duration modelling approach is applied to model incident duration as a function of a variety of factors that influence traffic incident duration. Parametric accelerated failure time survival models are developed to capture heterogeneity as a function of explanatory variables, with both fixed and random parameters specifications. The analysis reveals that factors affecting incident duration include incident characteristics (severity, type, injury, medical requirements, etc.), infrastructure characteristics (roadway shoulder availability), time of day, and traffic characteristics. The results indicate that event type durations are uniquely different, thus requiring different responses to effectively clear them. Furthermore, the results highlight the presence of unobserved incident duration heterogeneity as captured by the random parameter models, suggesting that additional factors need to be considered in future modelling efforts.

Mechanisms of mono- and poly-ubiquitination : ubiquitination specificity depends on compatibility between the E2 catalytic core and amino acid residues proximal to the lysine

Ubiquitination involves the attachment of ubiquitin to lysine residues on substrate proteins or itself, which can result in protein monoubiquitination or polyubiquitination. Ubiquitin attachment to different lysine residues can generate diverse substrate-ubiquitin structures, targeting proteins to different fates. The mechanisms of lysine selection are not well understood. Ubiquitination by the largest group of E3 ligases, the RING-family E3 s, is catalyzed through co-operation between the non-catalytic ubiquitin-ligase (E3) and the ubiquitin-conjugating enzyme (E2), where the RING E3 binds the substrate and the E2 catalyzes ubiquitin transfer. Previous studies suggest that ubiquitination sites are selected by E3-mediated positioning of the lysine toward the E2 active site. Ultimately, at a catalytic level, ubiquitination of lysine residues within the substrate or ubiquitin occurs by nucleophilic attack of the lysine residue on the thioester bond linking the E2 catalytic cysteine to ubiquitin. One of the best studied RING E3/ E2 complexes is the Skp1/Cul1/F box protein complex, SCFCdc4, and its cognate E2, Cdc34, which target the CDK inhibitor Sic1 for K48-linked polyubiquitination, leading to its proteasomal degradation. Our recent studies of this model system demonstrated that residues surrounding Sic1 lysines or lysine 48 in ubiquitin are critical for ubiquitination. This sequence-dependence is linked to evolutionarily conserved key residues in the catalytic region of Cdc34 and can determine if Sic1 is mono- or poly-ubiquitinated. Our studies indicate that amino acid determinants in the Cdc34 catalytic region and their compatibility to those surrounding acceptor lysine residues play important roles in lysine selection. This may represent a general mechanism in directing the mode of ubiquitination in E2 s.

Molecular basis for lysine specificity in the yeast ubiquitin-conjugating enzyme Cdc34

Ubiquitin (Ub)-conjugating enzymes (E2s) and ubiquitin ligases (E3s) catalyze the attachment of Ub to lysine residues in substrates and Ub during monoubiquitination and polyubiquitination. Lysine selection is important for the generation of diverse substrate-Ub structures, which provides versatility to this pathway in the targeting of proteins to different fates. The mechanisms of lysine selection remain poorly understood, with previous studies suggesting that the ubiquitination site(s) is selected by the E2/E3-mediated positioning of a lysine(s) toward the E2/E3 active site. By studying the polyubiquitination of Sic1 by the E2 protein Cdc34 and the RING E3 Skp1/Cul1/F-box (SCF) protein, we now demonstrate that in addition to E2/E3-mediated positioning, proximal amino acids surrounding the lysine residues in Sic1 and Ub are critical for ubiquitination. This mechanism is linked to key residues composing the catalytic core of Cdc34 and independent of SCF. Changes to these core residues altered the lysine preference of Cdc34 and specified whether this enzyme monoubiquitinated or polyubiquitinated Sic1. These new findings indicate that compatibility between amino acids surrounding acceptor lysine residues and key amino acids in the catalytic core of ubiquitin-conjugating enzymes is an important mechanism for lysine selection during ubiquitination.

Plasmodium falciparum parasites lacking histidine-rich protein 2 and 3 : a review and recommendations for accurate reporting

Malaria rapid diagnostic tests (RDTs) play a critical role in malaria case management, surveillance and case investigations. Test performance is largely determined by design and quality characteristics, such as detection sensitivity, specificity, and thermal stability. However, parasite characteristics such as variable or absent expression of antigens targeted by RDTs can also affect RDT performance. Plasmodium falciparum parasites lacking the PfHRP2 protein, the most common target antigen for detection of P. falciparum, have been reported in some regions. Therefore, accurately mapping the presence and prevalence of P. falciparum parasites lacking pfhrp2 would be an important step so that RDTs targeting alternative antigens, or microscopy, can be preferentially selected for use in such regions. Herein the available evidence and molecular basis for identifying malaria parasites lacking PfHRP2 is reviewed, and a set of recommended procedures to apply for future investigations for parasites lacking PfHRP2, is proposed.

Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, k -nearest neighbor ( k -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

Topic dependent language modelling for spoken term detection

This paper investigates the effect of topic dependent language models (TDLM) on phonetic spoken term detection (STD) using dynamic match lattice spotting (DMLS). Phonetic STD consists of two steps: indexing and search. The accuracy of indexing audio segments into phone sequences using phone recognition methods directly affects the accuracy of the final STD system. If the topic of a document in known, recognizing the spoken words and indexing them to an intermediate representation is an easier task and consequently, detecting a search word in it will be more accurate and robust. In this paper, we propose the use of TDLMs in the indexing stage to improve the accuracy of STD in situations where the topic of the audio document is known in advance. It is shown that using TDLMs instead of the traditional general language model (GLM) improves STD performance according to figure of merit (FOM) criteria.

Unusual event detection in crowded scenes

Novel computer vision techniques have been developed to automatically detect unusual events in crowded scenes from video feeds of surveillance cameras. The research is useful in the design of the next generation intelligent video surveillance systems. Two major contributions are the construction of a novel machine learning model for multiple instance learning through compressive sensing, and the design of novel feature descriptors in the compressed video domain.

Detection of rain in acoustic recordings of the environment

Environmental monitoring has become increasingly important due to the significant impact of human activities and climate change on biodiversity. Environmental sound sources such as rain and insect vocalizations are a rich and underexploited source of information in environmental audio recordings. This paper is concerned with the classification of rain within acoustic sensor re-cordings. We present the novel application of a set of features for classifying environmental acoustics: acoustic entropy, the acoustic complexity index, spectral cover, and background noise. In order to improve the performance of the rain classification system we automatically classify segments of environmental recordings into the classes of heavy rain or non-rain. A decision tree classifier is experientially compared with other classifiers. The experimental results show that our system is effective in classifying segments of environmental audio recordings with an accuracy of 93% for the binary classification of heavy rain/non-rain.

A new Salmonella typhimurium NM5004 strain expressing rat glutathione S-transferase 5-5 : use in detection of genotoxicity of dihaloalkanes using an SOS/umu test system

The Escherichia coli mu operon was subcloned into a pKK233-2 vector containing rat glutathione S-transferase (GST) 5-5 cDNA and the plasmid thus obtained was introduced into Salmonella typhimurium TA1535. The newly developed strain S.typhimurium NM5004, was found to have 52-fold greater GST activity than the original umu strain S.typhimurium TA1535/pSK1002. We compared sensitivities of these two tester strains, NM5004 and TA1535/ pSK1002, for induction of umuC gene expression with several dihaloalkanes which are activated or inactivated by GST 5-5 activity. The induction of umuC gene expression by these chemicals was monitored by measuring the cellular P-galactosidase activity produced by umuC'lacZ fusion gene in these two tester strains. Ethylene dibromide, 1-bromo-2-chloroethane, 1,2-dichloroethane, and methylene dichloride induced umuC gene expression more strongly in the NM5004 strain than the original strain, 4-Nitroquinoline 1-oxide and N-methyl-N'-nitro-N-nitrosoguanidine were found to induce umuC gene expression to similar extents in both strains. In the case of 1-nitropyrene and 2-nitrofluorene, however, NM5004 strain showed weaker umuC gene expression responses than the original TA1535/ pSK1002 strain, 1,2-Epoxy-3-(4'-nitrophenoxy)propane, a known substrate for GST 5-5, was found to inhibit umuC induction caused by 1-bromo-2-chloroethane. These results indicate that this new tester NM5004 strain expressing a mammalian GST theta class enzyme may be useful for studies of environmental chemicals proposed to be activated or inactivated by GST activity.