Handling Sequential Observations in Intelligent Surveillance

Demand for intelligent surveillance in public transport systems is growing due to the increased threats of terrorist attack, vandalism and litigation. The aim of intelligent surveillance is in-time reaction to information received from various monitoring devices, especially CCTV systems. However, video analytic algorithms can only provide static assertions, whilst in reality, many related events happen in sequence and hence should be modeled sequentially. Moreover, analytic algorithms are error-prone, hence how to correct the sequential analytic results based on new evidence (external information or later sensing discovery) becomes an interesting issue. In this paper, we introduce a high-level sequential observation modeling framework which can support revision and update on new evidence. This framework adapts the situation calculus to deal with uncertainty from analytic results. The output of the framework can serve as a foundation for event composition. We demonstrate the significance and usefulness of our framework with a case study of a bus surveillance project.

A Continuous Time Markov Chain Based Sequential Analytical Approach for Composite Power System Reliability Assessment

This paper proposes a continuous time Markov chain (CTMC) based sequential analytical approach for composite generation and transmission systems reliability assessment. The basic idea is to construct a CTMC model for the composite system. Based on this model, sequential analyses are performed. Various kinds of reliability indices can be obtained, including expectation, variance, frequency, duration and probability distribution. In order to reduce the dimension of the state space, traditional CTMC modeling approach is modified by merging all high order contingencies into a single state, which can be calculated by Monte Carlo simulation (MCS). Then a state mergence technique is developed to integrate all normal states to further reduce the dimension of the CTMC model. Moreover, a time discretization method is presented for the CTMC model calculation. Case studies are performed on the RBTS and a modified IEEE 300-bus test system. The results indicate that sequential reliability assessment can be performed by the proposed approach. Comparing with the traditional sequential Monte Carlo simulation method, the proposed method is more efficient, especially in small scale or very reliable power systems.

The reliability of R50 as a measure of vulnerability of food webs to sequential species deletions

Recent studies predict elevated and accelerating rates of species extinctions over the 21st century, due to climate change and habitat loss. Considering that such primary species loss may initiate cascades of secondary extinctions and push systems towards critical tipping points, we urgently need to increase our understanding of if certain sequences of species extinctions can be expected to be more devastating than others Most theoretical studies addressing this question have used a topological (non-dynamical) approach to analyse the probability that food webs will collapse, below a fixed threshold value in species richness, when subjected to different sequences of species loss. Typically, these studies have neither considered the possibility of dynamical responses of species, nor that conclusions may depend on the value of the collapse threshold. Here we analyse how sensitive conclusions on the importance of different species are to the threshold value of food web collapse. Using dynamical simulations, where we expose model food webs to a range of extinction sequences, we evaluate the reliability of the most frequently used index, R<inf>50</inf>, as a measure of food web robustness. In general, we find that R<inf>50</inf> is a reliable measure and that identification of destructive deletion sequences is fairly robust, within a moderate range of collapse thresholds. At the same time, however, focusing on R<inf>50</inf> only hides a lot of interesting information on the disassembly process and can, in some cases, lead to incorrect conclusions on the relative importance of species in food webs.

Sequential treatment with flavopiridol synergistically enhances pyrrolo-1,5-benzoxazepine-induced apoptosis in human chronic myeloid leukaemia cells including those resistant to imatinib treatment

The Bcr-Abl kinase inhibitor, imatinib mesylate, is the front line treatment for chronic myeloid leukaemia (CML), but the emergence of imatinib resistance has led to the search for alternative drug treatments and the examination of combination therapies to overcome imatinib resistance. The pro-apoptotic PBOX compounds are a recently developed novel series of microtubule targeting agents (MTAs) that depolymerise tubulin. Recent data demonstrating enhanced MTA-induced tumour cell apoptosis upon combination with the cyclin dependent kinase (CDK)-1 inhibitor flavopiridol prompted us to examine whether this compound could similarly enhance the effect of the PBOX compounds. We thus characterised the apoptotic and cell cycle events associated with combination therapy of the PBOX compounds and flavopiridol and results showed a sequence dependent, synergistic enhancement of apoptosis in CML cells including those expressing the imatinib-resistant T315I mutant. Flavopiridol reduced the number of polyploid cells formed in response to PBOX treatment but only to a small extent, suggesting that inhibition of endoreplication was unlikely to play a major role in the mechanism by which flavopiridol synergistically enhanced PBOX-induced apoptosis. The addition of flavopiridol following PBOX-6 treatment did however result in an accelerated exit from the G2/M transition accompanied by an enhanced downregulation and deactivation of the CDK1/cyclin B1 complex and an enhanced degradation of the inhibitor of apoptosis protein (IAP) survivin. In conclusion, results from this study highlight the potential of these novel series of PBOX compounds, alone or in sequential combination with flavopiridol, as an effective therapy against CML.

Randomized Phase II trial of nintedanib, afatinib and sequential combination in castration-resistant prostate cancer

AIMS: The aim of this article was to evaluate afatinib (BIBW 2992), an ErbB family blocker, and nintedanib (BIBF 1120), a triple angiokinase inhibitor, in castration-resistant prostate cancer patients.

PATIENTS & METHODS: Patients were randomized to receive nintedanib (250 mg twice daily), afatinib (40 mg once daily [q.d.]), or alternating sequential 7-day nintedanib (250 mg twice daily) and afatinib (70 mg q.d. [Combi70]), which was reduced to 40 mg q.d. (Combi40) due to adverse events. The primary end point was progression-free rate at 12 weeks.

RESULTS: Of the 85 patients treated 46, 20, 16 and three received nintedanib, afatinib, Combi40 and Combi70, respectively. At 12 weeks, the progression-free rate was 26% (seven out of 27 patients) for nintedanib, and 0% for afatinib and Combi40 groups. Two patients had a ≥50% decline in PSA (nintedanib and the Combi40 groups). The most common drug-related adverse events were diarrhea, nausea, vomiting and lethargy.

CONCLUSION: Nintedanib and/or afatinib demonstrated limited anti-tumor activity in unselected advanced castration-resistant prostate cancer patients.

Modelling Duqu 2.0 Malware using Attack Trees with Sequential Conjunction

In this paper we identify requirements for choosing a threat modelling formalisation for modelling sophisticated malware such as Duqu 2.0. We discuss the gaps in current formalisations and propose the use of Attack Trees with Sequential Conjunction when it comes to analysing complex attacks. The paper models Duqu 2.0 based on the latest information sourced from formal and informal sources. This paper provides a well structured model which can be used for future analysis of Duqu 2.0 and related attacks.

k-fold Subsampling based Sequential Backward Feature Elimination

We present a new wrapper feature selection algorithm for human detection. This algorithm is a hybrid featureselection approach combining the benefits of filter and wrapper methods. It allows the selection of an optimalfeature vector that well represents the shapes of the subjects in the images. In detail, the proposed featureselection algorithm adopts the k-fold subsampling and sequential backward elimination approach, while thestandard linear support vector machine (SVM) is used as the classifier for human detection. We apply theproposed algorithm to the publicly accessible INRIA and ETH pedestrian full image datasets with the PASCALVOC evaluation criteria. Compared to other state of the arts algorithms, our feature selection based approachcan improve the detection speed of the SVM classifier by over 50% with up to 2% better detection accuracy.Our algorithm also outperforms the equivalent systems introduced in the deformable part model approach witharound 9% improvement in the detection accuracy

A Molecular Mechanism for Sequential Activation of a G Protein-Coupled Receptor

Ligands targeting G protein-coupled receptors (GPCRs) are currently classified as either orthosteric, allosteric, or dualsteric/bitopic. Here, we introduce a new pharmacological concept for GPCR functional modulation: sequential receptor activation. A hallmark feature of this is a stepwise ligand binding mode with transient activation of a first receptor site followed by sustained activation of a second topographically distinct site. We identify 4-CMTB (2-(4-chlorophenyl)-3-methyl-N-(thiazol-2-yl)butanamide), previously classified as a pure allosteric agonist of the free fatty acid receptor 2, as the first sequential activator and corroborate its two-step activation in living cells by tracking integrated responses with innovative label-free biosensors that visualize multiple signaling inputs in real time. We validate this unique pharmacology with traditional cellular readouts, including mutational and pharmacological perturbations along with computational methods, and propose a kinetic model applicable to the analysis of sequential receptor activation. We envision this form of dynamic agonism as a common principle of nature to spatiotemporally encode cellular information.

Sediment metal bioavailability in Lake Taihu, China: evaluation of sequential extraction, DGT, and PBET techniques

The European “Community Bureau of Reference” (BCR) sequential extraction procedure, diffusive gradient in thin-films technique (DGT), and physiologically based extraction test were applied to assess metal bioavailability in sediments of Lake Taihu (n = 13). Findings from the three methods showed that Cd was a significant problem in the western lake whereas Cu, Zn, and Ni pollution was most severe in the northern lake. Results from the sequential extraction revealed that more than 50 % of the Cu and Zn were highly mobile and defined within the extractable fraction (AS1 + FM2 + OS3) in the majority of the sediments, in contrast extractable fractions of Ni and Cd were lower than 50 % in most of the sampling sites. Average Cu, Zn, Ni, and Cd bioaccessibilities were <50 % in the gastric phase. Zn and Cd bioaccessibility in the intestinal phase was ∼50 % lower than the gastric phase while bioaccessibilities of Cu and Ni were 47–57 % greater than the gastric phase. Linear regression analysis between DGT and BCR measurements indicated that the extractable fractions (AS1 + FM2 + OS3) in the reducing environment were the main source of DGT uptake, suggesting that DGT is a good in situ evaluation tool for metal bioavailability in sediments.