Evolving to the optoelectronic mouse for phycotoxin analysis in shellfish

Despite ethical and technical concerns, the in vivo method, or more commonly referred to mouse bioassay (MBA), is employed globally as a reference method for phycotoxin analysis in shellfish. This is particularly the case for paralytic shellfish poisoning (PSP) and emerging toxin monitoring. A high-performance liquid chromatography method (HPLC-FLD) has been developed for PSP toxin analysis, but due to difficulties and limitations in the method, this procedure has not been fully implemented as a replacement. Detection of the diarrhetic shellfish poisoning (DSP) toxins has moved towards LC-mass spectrometry (MS) analysis, whereas the analysis of the amnesic shellfish poisoning (ASP) toxin domoic acid is performed by HPLC. Although alternative methods of detection to the MBA have been described, each procedure is specific for a particular toxin and its analogues, with each group of toxins requiring separate analysis utilising different extraction procedures and analytical equipment. In addition, consideration towards the detection of unregulated and emerging toxins on the replacement of the MBA must be given. The ideal scenario for the monitoring of phycotoxins in shellfish and seafood would be to evolve to multiple toxin detection on a single bioanalytical sensing platform, i.e. 'an artificial mouse'. Immunologically based techniques and in particular surface plasmon resonance technology have been shown as a highly promising bioanalytical tool offering rapid, real-time detection requiring minimal quantities of toxin standards. A Biacore Q and a prototype multiplex SPR biosensor have been evaluated for their ability to be fit for purpose for the simultaneous detection of key regulated phycotoxin groups and the emerging toxin palytoxin. Deemed more applicable due to the separate flow channels, the prototype performance for domoic acid, okadaic acid, saxitoxin, and palytoxin calibration curves in shellfish achieved detection limits (IC20) of 4,000, 36, 144 and 46 μg/kg of mussel, respectively. A one-step extraction procedure demonstrated recoveries greater than 80 % for all toxins. For validation of the method at the 95 % confidence limit, the decision limits (CCα) determined from an extracted matrix curve were calculated to be 450, 36 and 24 μg/kg, and the detection capability (CCβ) as a screening method is ≤10 mg/kg, ≤160 μg/kg and ≤400 μg/kg for domoic acid, okadaic acid and saxitoxin, respectively.

Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

A tree augmented classifier based on Extreme Imprecise Dirichlet Model

We present TANC, a TAN classifier (tree-augmented naive) based on imprecise probabilities. TANC models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM). A first contribution of this paper is the experimental comparison between EDM and the global Imprecise Dirichlet Model using the naive credal classifier (NCC), with the aim of showing that EDM is a sensible approximation of the global IDM. TANC is able to deal with missing data in a conservative manner by considering all possible completions (without assuming them to be missing-at-random), but avoiding an exponential increase of the computational time. By experiments on real data sets, we show that TANC is more reliable than the Bayesian TAN and that it provides better performance compared to previous TANs based on imprecise probabilities. Yet, TANC is sometimes outperformed by NCC because the learned TAN structures are too complex; this calls for novel algorithms for learning the TAN structures, better suited for an imprecise probability classifier.

Bayesian network data imputation with application to survival tree analysis

Retrospective clinical datasets are often characterized by a relatively small sample size and many missing data. In this case, a common way for handling the missingness consists in discarding from the analysis patients with missing covariates, further reducing the sample size. Alternatively, if the mechanism that generated the missing allows, incomplete data can be imputed on the basis of the observed data, avoiding the reduction of the sample size and allowing methods to deal with complete data later on. Moreover, methodologies for data imputation might depend on the particular purpose and might achieve better results by considering specific characteristics of the domain. The problem of missing data treatment is studied in the context of survival tree analysis for the estimation of a prognostic patient stratification. Survival tree methods usually address this problem by using surrogate splits, that is, splitting rules that use other variables yielding similar results to the original ones. Instead, our methodology consists in modeling the dependencies among the clinical variables with a Bayesian network, which is then used to perform data imputation, thus allowing the survival tree to be applied on the completed dataset. The Bayesian network is directly learned from the incomplete data using a structural expectation–maximization (EM) procedure in which the maximization step is performed with an exact anytime method, so that the only source of approximation is due to the EM formulation itself. On both simulated and real data, our proposed methodology usually outperformed several existing methods for data imputation and the imputation so obtained improved the stratification estimated by the survival tree (especially with respect to using surrogate splits).

A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.

NP-hardness of MAP in Ternary Tree Bayesian Networks

This paper strengthens the NP-hardness result for the (partial) maximum a posteriori (MAP) problem in Bayesian networks with topology of trees (every variable has at most one parent) and variable cardinality at most three. MAP is the problem of querying the most probable state configuration of some (not necessarily all) of the network variables given evidence. It is demonstrated that the problem remains hard even in such simplistic networks.

The Shifting Battleground of Article 4(2) TEU: Evolving National Identities and the corresponding need for EU management?

Article 4(2) TEU requires that the European Union (EU) respect the Member States’ national identities, creating a legal obligation enforceable before the CJEU and valuable in political negotiations. However, the concept of national identities is unclear, leaving open questions about the scope or parameters of the provision and its applicability. The CJEU appears likely to take a relatively flexible approach in light of Article 4(2) TEU’s relationship with national constitutional courts’ reserves. This flexible approach would enable Member States to rely upon a range of aspects as part of their national identity, including ones that were previously unidentified. This is a crucial feature if one considers that national identities may evolve gradually or even dramatically, including where Member States purposefully attempt to develop their national identities further. This possibility of an evolved national identity is exemplified by the French Charte de l’Environnement. It may thereby be possible for Member States to stretch the scope and application of Article 4(2) TEU through reference to these evolving national identities. This potential raises significant challenges for the EU regarding the management of Article 4(2) TEU, which it will need to address if it wishes to ensure harmonisation and uniformity in the relevant areas.

Evolving southwest African response to abrupt deglacial North Atlantic climate change events

Climate change during the last deglaciation was strongly influenced by the „bipolar seesaw‟, producing antiphase climate responses between the North and South Atlantic. However, mounting evidence demands refinements of this model, with the occurrence of abrupt events in southern low to mid latitudes occurring in-phase with North Atlantic climate. Improved constraints on the north-south phasing and spatial extent of these events are therefore critical to
understanding the mechanisms that propagate abrupt events within the climate system. We present a 19,400 year multi-proxy record of climate change obtained from a rock hyrax midden in southernmost Africa. Arid anomalies in phase with the Younger Dryas and 8.2 ka events are apparent, indicating a clear shift in the influence of the bipolar seesaw, which diminished as the Earth warmed, and was succeeded after ~14.6 ka by the emergence of a dominant interhemispheric atmospheric teleconnection.

Massive stars exploding in a He-rich circumstellar medium - V. Observations of the slow-evolving SN Ibn OGLE-2012-SN-006

We present optical observations of the peculiar Type Ibn supernova (SN Ibn) OGLE-2012-SN-006, discovered and monitored by the Optical Gravitational Lensing Experiment-IV survey, and spectroscopically followed by Public ESO Spectroscopic Survey of Transient Objects (PESSTO) at late phases. Stringent pre-discovery limits constrain the explosion epoch with fair precision to JD = 245 6203.8 +/- 4.0. The rise time to the I-band light-curve maximum is about two weeks. The object reaches the peak absolute magnitude M-I = -19.65 +/- 0.19 on JD = 245 6218.1 +/- 1.8. After maximum, the light curve declines for about 25 d with a rate of 4 mag (100 d)(-1). The symmetric I-band peak resembles that of canonical Type Ib/c supernovae (SNe), whereas SNe Ibn usually exhibit asymmetric and narrower early-time light curves. Since 25 d past maximum, the light curve flattens with a decline rate slower than that of the Co-56-Fe-56 decay, although at very late phases it steepens to approach that rate. However, other observables suggest that the match with the Co-56 decay rate is a mere coincidence, and the radioactive decay is not the main mechanism powering the light curve of OGLE-2012-SN-006. An early-time spectrum is dominated by a blue continuum, with only a marginal evidence for the presence of He I lines marking this SN type. This spectrum shows broad absorptions bluewards than 5000 angstrom, likely O II lines, which are similar to spectral features observed in superluminous SNe at early epochs. The object has been spectroscopically monitored by PESSTO from 90 to 180 d after peak, and these spectra show the typical features observed in a number of SN 2006jc-like events, including a blue spectral energy distribution and prominent and narrow (v(FWHM) approximate to 1900 km s(-1)) He I emission lines. This suggests that the ejecta are interacting with He-rich circumstellar material. The detection of broad (10(4) km s(-1)) O I and Ca II features likely produced in the SN ejecta (including the [OI] lambda lambda 6300,6364 doublet in the latest spectra) lends support to the interpretation of OGLE-2012-SN-006 as a core-collapse event.

The Robustness of Periodic Orchestrations in Uncertain Evolving Environments

A framework for assessing the robustness of long-duration repetitive orchestrations in uncertain evolving environments is proposed. The model assumes that service-based evaluation environments are stable over short time-frames only; over longer periods service-based environments evolve as demand fluctuates and contention for shared resources varies. The behaviour of a short-duration orchestration E in a stable environment is assessed by an uncertainty profile U and a corresponding zero-sum angel-daemon game Γ(U) [2]. Here the angel-daemon approach is extended to assess evolving environments by means of a subfamily of stochastic games. These games are called strategy oblivious because their transition probabilities are strategy independent. It is shown that the value of a strategy oblivious stochastic game is well defined and that it can be computed by solving a linear system. Finally, the proposed stochastic framework is used to assess the evolution of the Gabrmn IT system.