FeNi-based magnetoimpedance multilayers: Tailoring of the softness by magnetic spacers

The microstructure and magnetic properties of sputtered permalloy films and FeNi(170 nm)/X/FeNi(170 nm) (X=Co, Fe, Gd, Gd-Co) sandwiches were studied. Laminating of the thick FeNi film with various spacers was done in order to control the magnetic softness of FeNi-based multilayers. In contrast to the Co and Fe spacers, Gd and Gd-Co magnetic spacers improved the softness of the FeNi/X/FeNi sandwiches. The magnetoimpedance responses were measured for [FeNi/Ti(6 nm)] 2/FeNi and [FeNi/Gd(2 nm)] 2/FeNi multilayers in a frequency range of 1-500 MHz: for all frequencies under consideration the highest magnetoimpedance variation was observed for [FeNi/Gd(2 nm)] 2/FeNi multilayers. © 2012 American Institute of Physics

Iron overload causes endolysosomal deficits modulated by NAADP-regulated 2-pore channels and RAB7A

Various neurodegenerative disorders are associated with increased brain iron content. Iron is known to cause oxidative stress, which concomitantly promotes cell death. Whereas endolysosomes are known to serve as intracellular iron storage organelles, the consequences of increased iron on endolysosomal functioning, and effects on cell viability upon modulation of endolysosomal iron release remain largely unknown. Here, we show that increasing intracellular iron causes endolysosomal alterations associated with impaired autophagic clearance of intracellular protein aggregates, increased cytosolic oxidative stress and increased cell death. These effects are subject to regulation by NAADP, a potent second messenger reported to target endolysosomal TPCNs (2-pore channels). Consistent with endolysosomal iron storage, cytosolic iron levels are modulated by NAADP, and increased cytosolic iron is detected when overexpressing active, but not inactive TPCNs, indicating that these channels can modulate endolysosomal iron release. Cell death triggered by altered intralysosomal iron handling is abrogated in the presence of an NAADP antagonist or when inhibiting RAB7A activity. Taken together, our results suggest that increased endolysosomal iron causes cell death associated with increased cytosolic oxidative stress as well as autophagic impairments, and these effects are subject to modulation by endolysosomal ion channel activity in a RAB7A-dependent manner. These data highlight alternative therapeutic strategies for neurodegenerative disorders associated with increased intracellular iron load

A framework for the development of biomedical text mining software tools

Over the last few years, a growing number of techniques has been successfully proposed to tackle diverse challenges in the Biomedical Text Mining (BioTM) arena. However, the set of available software tools to researchers has not grown in a similar way. This work makes a contribution to close this gap, proposing a framework to ease the development of user-friendly and interoperable applications in this field, based on a set of available modular components. These modules can be connected in diverse ways to create applications that fit distinct user roles. Also, developers of new algorithms have a framework that allows them to easily integrate their implementations with state-of-the-art BioTM software for related tasks.This work was supported in part by the research projects recSysBio (ref. POCI/BIO/60139/2004) and MOBioPro (ref. POSC/EW59899/2004) of the University of Minho, financed by the Portuguese Fundaao para a Ciencia e Tecnologia. The work of SC is supported by a PhD grant from the same institution (ref. SFRH/BD/22863/2005)

BioDR : semantic indexing networks for biomedical document retrieval

In Biomedical research, retrieving documents that match an interesting query is a task performed quite frequently. Typically, the set of obtained results is extensive containing many non-interesting documents and consists in a flat list, i.e., not organized or indexed in any way. This work proposes BioDR, a novel approach that allows the semantic indexing of the results of a query, by identifying relevant terms in the documents. These terms emerge from a process of Named Entity Recognition that annotates occurrences of biological terms (e.g. genes or proteins) in abstracts or full-texts. The system is based on a learning process that builds an Enhanced Instance Retrieval Network (EIRN) from a set of manually classified documents, regarding their relevance to a given problem. The resulting EIRN implements the semantic indexing of documents and terms, allowing for enhanced navigation and visualization tools, as well as the assessment of relevance for new documents.Fundação para a Ciência e a Tecnologia (FCT)Maria Barbeito” contract XuntaHUELLA financed by the Consellería de Sanidade (Xunta de Galicia de Galicia

Biomedical text mining applied to document retrieval and semantic indexing

In Biomedical research, the ability to retrieve the adequate information from the ever growing literature is an extremely important asset. This work provides an enhanced and general purpose approach to the process of document retrieval that enables the filtering of PubMed query results. The system is based on semantic indexing providing, for each set of retrieved documents, a network that links documents and relevant terms obtained by the annotation of biological entities (e.g. genes or proteins). This network provides distinct user perspectives and allows navigation over documents with similar terms and is also used to assess document relevance. A network learning procedure, based on previous work from e-mail spam filtering, is proposed, receiving as input a training set of manually classified documents

RAB8, RAB10 and RILPL1 contribute to both LRRK2 kinase-mediated centrosomal cohesion and ciliogenesis deficits

Mutations in the LRRK2 kinase are the most common cause of familial Parkinson's disease, and variants increase risk for the sporadic form of the disease. LRRK2 phosphorylates multiple RAB GTPases including RAB8A and RAB10. Phosphorylated RAB10 is recruited to centrosome-localized RILPL1, which may interfere with ciliogenesis in a disease-relevant context. Our previous studies indicate that the centrosomal accumulation of phosphorylated RAB8A causes centrosomal cohesion deficits in dividing cells, including in peripheral patient-derived cells. Here, we show that both RAB8 and RAB10 contribute to the centrosomal cohesion deficits. Pathogenic LRRK2 causes the centrosomal accumulation not only of phosho-RAB8 but also of phospho-RAB10, and the effects on centrosomal cohesion are dependent on RAB8, RAB10 and RILPL1. Conversely, the pathogenic LRRK2-mediated ciliogenesis defects correlate with the centrosomal accumulation of both phospho-RAB8 and phospho-RAB10. LRRK2-mediated centrosomal cohesion and ciliogenesis alterations are observed in patient-derived peripheral cells, as well as in primary astrocytes from mutant LRRK2 mice, and are reverted upon LRRK2 kinase inhibition. These data suggest that the LRRK2-mediated centrosomal cohesion and ciliogenesis defects are distinct cellular readouts of the same underlying phospho-RAB8/RAB10/RILPL1 nexus and highlight the possibility that either centrosomal cohesion and/or ciliogenesis alterations may serve as cellular biomarkers for LRRK2-related PD

An ant colony-based semi-supervised approach for learning classification rules

Semi-supervised learning methods create models from a few labeled instances and a great number of unlabeled instances. They appear as a good option in scenarios where there is a lot of unlabeled data and the process of labeling instances is expensive, such as those where most Web applications stand. This paper proposes a semi-supervised self-training algorithm called Ant-Labeler. Self-training algorithms take advantage of supervised learning algorithms to iteratively learn a model from the labeled instances and then use this model to classify unlabeled instances. The instances that receive labels with high confidence are moved from the unlabeled to the labeled set, and this process is repeated until a stopping criteria is met, such as labeling all unlabeled instances. Ant-Labeler uses an ACO algorithm as the supervised learning method in the self-training procedure to generate interpretable rule-based models—used as an ensemble to ensure accurate predictions. The pheromone matrix is reused across different executions of the ACO algorithm to avoid rebuilding the models from scratch every time the labeled set is updated. Results showed that the proposed algorithm obtains better predictive accuracy than three state-of-the-art algorithms in roughly half of the datasets on which it was tested, and the smaller the number of labeled instances, the better the Ant-Labeler performance

An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile.

A better understanding of the response against Tuberculosis (TB) infection is required to accurately identify the individuals with an active or a latent TB infection (LTBI) and also those LTBI patients at higher risk of developing active TB. In this work, we have used the information obtained from studying the gene expression profile of active TB patients and their infected -LTBI- or uninfected -NoTBI- contacts, recruited in Spain and Mozambique, to build a class-prediction model that identifies individuals with a TB infection profile. Following this approach, we have identified several genes and metabolic pathways that provide important information of the immune mechanisms triggered against TB infection. As a novelty of our work, a combination of this class-prediction model and the direct measurement of different immunological parameters, was used to identify a subset of LTBI contacts (called TB-like) whose transcriptional and immunological profiles are suggestive of infection with a higher probability of developing active TB. Validation of this novel approach to identifying LTBI individuals with the highest risk of active TB disease merits further longitudinal studies on larger cohorts in TB endemic areas

Defective extracellular matrix remodeling in brown adipose tissue is associated with fibro-inflammation and reduced diet-induced thermogenesis.

The relevance of extracellular matrix (ECM) remodeling is reported in white adipose tissue (AT) and obesity-related dysfunctions, but little is known about the importance of ECM remodeling in brown AT (BAT) function. Here, we show that a time course of high-fat diet (HFD) feeding progressively impairs diet-induced thermogenesis concomitantly with the development of fibro-inflammation in BAT. Higher markers of fibro-inflammation are associated with lower cold-induced BAT activity in humans. Similarly, when mice are housed at thermoneutrality, inactivated BAT features fibro-inflammation. We validate the pathophysiological relevance of BAT ECM remodeling in response to temperature challenges and HFD using a model of a primary defect in the collagen turnover mediated by partial ablation of the Pepd prolidase. Pepd-heterozygous mice display exacerbated dysfunction and BAT fibro-inflammation at thermoneutrality and in HFD. Our findings show the relevance of ECM remodeling in BAT activation and provide a mechanism for BAT dysfunction in obesity

Size-induced superantiferromagnetism with reentrant spin-glass behavior in metallic nanoparticles of TbCu2

An unusual 4f -superantiferromagnetic state characterized by simultaneous antiferromagnetic and spin-glass behaviors induced by particle-size reduction is revealed in metallic nanoparticles (≈ 9 nm) of TbCu 2 . The Néel temperature is 46 K and the glassy freezing is below ≈ 9 K and governed by a critical slowing down process. Neutron diffraction at 1.8 K establishes the superantiferromagnetism. The latter is settled by the nanoparticle moments and the freezing mechanism is provided by the surface spins