The ADAM17 sheddase complex regulator iTAP/Frmd8 modulates inflammation and tumor growth

The metalloprotease ADAM17 is a sheddase of key molecules, including TNF and epidermal growth factor receptor ligands. ADAM17 exists within an assemblage, the "sheddase complex," containing a rhomboid pseudoprotease (iRhom1 or iRhom2). iRhoms control multiple aspects of ADAM17 biology. The FERM domain-containing protein iTAP/Frmd8 is an iRhom-binding protein that prevents the precocious shunting of ADAM17 and iRhom2 to lysosomes and their consequent degradation. As pathophysiological role(s) of iTAP/Frmd8 have not been addressed, we characterized the impact of iTAP/Frmd8 loss on ADAM17-associated phenotypes in mice. We show that iTAP/Frmd8 KO mice exhibit defects in inflammatory and intestinal epithelial barrier repair functions, but not the collateral defects associated with global ADAM17 loss. Furthermore, we show that iTAP/Frmd8 regulates cancer cell growth in a cell-autonomous manner and by modulating the tumor microenvironment. Our work suggests that pharmacological intervention at the level of iTAP/Frmd8 may be beneficial to target ADAM17 activity in specific compartments during chronic inflammatory diseases or cancer, while avoiding the collateral impact on the vital functions associated with the widespread inhibition of ADAM17.

Epidemiology and impact of frailty in patients with atrial fibrillation in Europe

BACKGROUND

Frailty is a medical syndrome characterised by reduced physiological reserve and increased vulnerability to stressors. Data regarding the relationship between frailty and atrial fibrillation (AF) are still inconsistent.

OBJECTIVES

We aim to perform a comprehensive evaluation of frailty in a large European cohort of AF patients.

METHODS

A 40-item frailty index (FI) was built according to the accumulation of deficits model in the AF patients enrolled in the ESC-EHRA EORP-AF General Long-Term Registry. Association of baseline characteristics, clinical management, quality of life, healthcare resources use and risk of outcomes with frailty was examined.

RESULTS

Among 10,177 patients [mean age (standard deviation) 69.0 (11.4) years, 4,103 (40.3%) females], 6,066 (59.6%) were pre-frail and 2,172 (21.3%) were frail, whereas only 1,939 (19.1%) were considered robust. Baseline thromboembolic and bleeding risks were independently associated with increasing FI. Frail patients with AF were less likely to be treated with oral anticoagulants (OACs) (odds ratio 0.70, 95% confidence interval 0.55-0.89), especially with non-vitamin K antagonist OACs and managed with a rhythm control strategy, compared with robust patients. Increasing frailty was associated with a higher risk for all outcomes examined, with a non-linear exponential relationship. The use of OAC was associated with a lower risk of outcomes, except in patients with very/extremely high frailty.

CONCLUSIONS

In this large cohort of AF patients, there was a high burden of frailty, influencing clinical management and risk of adverse outcomes. The clinical benefit of OAC is maintained in patients with high frailty, but not in very high/extremely frail ones.

On the Latent Variable Interpretation in Sum-Product Networks

One of the central themes in Sum-Product networks (SPNs) is the interpretation of sum nodes as marginalized latent variables (LVs). This interpretation yields an increased syntactic or semantic structure, allows the application of the EM algorithm and to efficiently perform MPE inference. In literature, the LV interpretation was justified by explicitly introducing the indicator variables corresponding to the LVs' states. However, as pointed out in this paper, this approach is in conflict with the completeness condition in SPNs and does not fully specify the probabilistic model. We propose a remedy for this problem by modifying the original approach for introducing the LVs, which we call SPN augmentation. We discuss conditional independencies in augmented SPNs, formally establish the probabilistic interpretation of the sum-weights and give an interpretation of augmented SPNs as Bayesian networks. Based on these results, we find a sound derivation of the EM algorithm for SPNs. Furthermore, the Viterbi-style algorithm for MPE proposed in literature was never proven to be correct. We show that this is indeed a correct algorithm, when applied to selective SPNs, and in particular when applied to augmented SPNs. Our theoretical results are confirmed in experiments on synthetic data and 103 real-world datasets.

Recurrent blooms of Heterosigma akashiwo (Raphidophyceae) in the Piraquê Channel, Rodrigo de Freitas Lagoon, southeast Brazil

Six blooms of Heterosigma akashiwo(Raphidophyceae) were observed from March 2007 through March 2008 in the Rodrigo de Freitas Lagoon, a semi-confined eutrophic system located in Rio de Janeiro state, southeast Brazil. Vegetative cells of H. akashiwo analysed by optical and electron microscopy showed morphology as described in the literature. The blooms (2.8 × 104 to 4 × 108 cell.L–1) were restricted to the middle section of the Piraquê Channel, which is situated in the northeastern part of the lagoon and receives freshwater inflow. The salinity of subsurface water and the channel depth showed significant negative correlations with H. akashiwo abundances, and appeared to restrict the blooms to this compartment of the lagoon. No fish mortality was associated with the H. akashiwo blooms, nor were brevetoxins detected in a cell extract obtained from the bloom observed on 19 March 2007.

Deep Transfer: A Markov Logic Approach

This article argues that currently the largest gap between human and machine learning is learning algorithms' inability to perform deep transfer, that is, generalize from one domain to another domain containing different objects, classes, properties and relations. We argue that second-order Markov logic is ideally suited for this purpose, and propose an approach based on it. Our algorithm discovers structural regularities in the source domain in the form of Markov logic formulas with predicate variables, and instantiates these formulas with predicates from the target domain. Our approach has successfully transferred learned knowledge among molecular biology, Web and social network domains.

Relational Dynamic Bayesian Networks

Stochastic processes that involve the creation of objects and relations over time are widespread, but relatively poorly studied. For example, accurate fault diagnosis in factory assembly processes requires inferring the probabilities of erroneous assembly operations, but doing this efficiently and accurately is difficult. Modeled as dynamic Bayesian networks, these processes have discrete variables with very large domains and extremely high dimensionality. In this paper, we introduce relational dynamic Bayesian networks (RDBNs), which are an extension of dynamic Bayesian networks (DBNs) to first-order logic. RDBNs are a generalization of dynamic probabilistic relational models (DPRMs), which we had proposed in our previous work to model dynamic uncertain domains. We first extend the Rao-Blackwellised particle filtering described in our earlier work to RDBNs. Next, we lift the assumptions associated with Rao-Blackwellization in RDBNs and propose two new forms of particle filtering. The first one uses abstraction hierarchies over the predicates to smooth the particle filter's estimates. The second employs kernel density estimation with a kernel function specifically designed for relational domains. Experiments show these two methods greatly outperform standard particle filtering on the task of assembly plan execution monitoring.

Microcystins (cyanobacteria hepatotoxins) bioaccumulation in fish and crustaceans from Sepetiba Bay (Brasil, RJ)

Blooms of cyanobacteria in water bodies cause serious environmental problems and the occurrence of toxic strains are also related with the human health. Aquatic animals could bioaccumulate microcystins (cyanobacteria hepatotoxins) and so, beyond water, the ingestion of contaminated food represents a human health risk. Recently, WHO recommended a maximum concentration of microcystins (MCYSTs) in drinking water and established the tolerable daily intake (TDI) for consumption of cyanobacteria products contends MCYSTs (0.04 microg(-1) kg(-1) day(-1)). Sepetiba Bay is located in the municipal districts of Rio de Janeiro, Mangaratiba and Itaguai; being an important place of fishing activity. Due to the industrial development in the area, this bay is submitted to different environmental impacts, increasing the organic and industrial pollution. A strain of the nanoplanktonic cyanobacteria Synechocystis aquatilis f. aquatilis that produce MCYSTs was already isolated. In this study, we verified MCYSTs presence in muscle tissue of fish and crustaceans, which were harvested monthly in Sepetiba Bay during 11 months, in order to evaluate the potential risk of their ingestion. MCYSTs were analyzed by immunoassay techniques using the ELISA Microcystin Plate Kit (ENVIROLOGIX INC) and the concentration were expressed as microcystin-LR equivalent. The analyses of seston samples, water, muscle tissues showed the presence of this cyanotoxin in all samples and it was verified that 19% of the animals' samples were above the limit recommended by WHO for human consumption. The maximum value found was of 103.3 microg kg(-1) (TDI 0.52 microg kg(-1) day(-1)) and the minimum, was 0.25 microg kg(-1) in crabs muscle tissue (TDI of 0.001 microg kg(-1) day(-1)). Such data demonstrate that, although in low concentrations, there is already a contamination of fish and crustaceans from Sepetiba Bay. We highlight that the recommended limit refers to healthy adult.

Prospects and challenges for multi-relational data mining

This short paper argues that multi-relational data mining has a key role to play in the growth of KDD, and briefly surveys some of the main drivers, research problems, and opportunities in this emerging field.