A 1000 hour endurance test of a glass-coated accelerator grid on a 15-centimeter-diameter Kaufman thruster

Endurance test of glass coated accelerator grid on 15-centimeter-diameter Kaufman thruste

Long-Term Potentiation: One Kind or Many?

Do neurobiologists aim to discover natural kinds? I address this question in this chapter via a critical analysis of classification practices operative across the 43-year history of research on long-term potentiation (LTP). I argue that this 43-year history supports the idea that the structure of scientific practice surrounding LTP research has remained an obstacle to the discovery of natural kinds

Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest

In this paper, we present the scientific outcomes of the 2017 Data Fusion Contest organized by the Image Analysis and Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society. The 2017 Contest was aimed at addressing the problem of local climate zones classification based on a multitemporal and multimodal dataset, including image (Landsat 8 and Sentinel-2) and vector data (from OpenStreetMap). The competition, based on separate geographical locations for the training and testing of the proposed solution, aimed at models that were accurate (assessed by accuracy metrics on an undisclosed reference for the test cities), general (assessed by spreading the test cities across the globe), and computationally feasible (assessed by having a test phase of limited time). The techniques proposed by the participants to the Contest spanned across a rather broad range of topics, and of mixed ideas and methodologies deriving from computer vision and machine learning but also deeply rooted in the specificities of remote sensing. In particular, rigorous atmospheric correction, the use of multidate images, and the use of ensemble methods fusing results obtained from different data sources/time instants made the difference

Potentiality in Biology

We take the potentialities that are studied in the biological sciences (e.g., totipotency) to be an important subtype of biological dispositions. The goal of this paper is twofold: first, we want to provide a detailed understanding of what biological dispositions are. We claim that two features are essential for dispositions in biology: the importance of the manifestation process and the diversity of conditions that need to be satisfied for the disposition to be manifest. Second, we demonstrate that the concept of a disposition (or potentiality) is a very useful tool for the analysis of the explanatory practice in the biological sciences. On the one hand it allows an in-depth analysis of the nature and diversity of the conditions under which biological systems display specific behaviors. On the other hand the concept of a disposition may serve a unificatory role in the philosophy of the natural sciences since it captures not only the explanatory practice of biology, but of all natural sciences. Towards the end we will briefly come back to the notion of a potentiality in biology

Modeling Life as Cognitive Info-Computation

This article presents a naturalist approach to cognition understood as a network of info-computational, autopoietic processes in living systems. It provides a conceptual framework for the unified view of cognition as evolved from the simplest to the most complex organisms, based on new empirical and theoretical results. It addresses three fundamental questions: what cognition is, how cognition works and what cognition does at different levels of complexity of living organisms. By explicating the info-computational character of cognition, its evolution, agent-dependency and generative mechanisms we can better understand its life-sustaining and life-propagating role. The info-computational approach contributes to rethinking cognition as a process of natural computation in living beings that can be applied for cognitive computation in artificial systems.Comment: Manuscript submitted to Computability in Europe CiE 201

Vacuole Formation in Wheat Starchy Endosperm

The formation of vacuoles in wheat (Triticum aestivum cv. Highbury) starchy endosperm cells was studied using electron microscopy. Some vacuoles were always present, even in the coenocytic cytoplasm. The first formed endosperm cells were highly vacuolated, but became filled with cytoplasm as they grew older. Various-sized pieces of cytoplasm were found in vacuoles of developing endosperm cells, probably as a result of autophagic sequestration. The membranes of the autographic vacuoles appeared to originate from the rough endoplasmic reticulum and from extensions of already-formed vacuoles. Autographic activity was confirmed by localizing the hydrolytic enzyme acid phosphatase within the vacuoles. The rough endoplasmic reticulum (RER) also stained positive for this enzyme

Associations of Starch Gel Hardness, Granule Size, Waxy Allelic Expression, Thermal Pasting, Milling Quality, and Kernel Texture of 12 Soft Wheat Cultivars

Starches were isolated from 12 soft wheat (Triticum aestivum L.) cultivars and were characterized for waxy (Wx) allelic expression, thermal pasting characteristics, and starch granule size. Gels were produced from the thermally degraded starches and were evaluated using large deformation rheological measurements. Data were compared with cultivar kernel texture, milling characteristics, starch chemical analyses, and flour pasting characteristics. Larger flour yields were produced from cultivars that had larger starch granules. Flour yield also was correlated with lower amylose content and greater starch content. Harder starch gels were correlated with higher levels of amylose content and softer kernel texture. The cultivar Fillmore, which had a partial waxy mutation at the B locus, produced the highest peak pasting viscosity and the lowest gel hardness. Softer textured wheats had greater lipid‐complexed amylose and starch phosphorus contents and had less total starch content. Among these wheats of the soft market class, softer textured wheats had larger starch granules and harder textured wheats had smaller starch granules. In part, this may explain why soft wheats vary in texture. The smaller granules have larger surface area available for noncovalent bonding with the endosperm protein matrix and they also may pack more efficiently, producing harder endosperm.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141588/1/cche0163.pd

Inhibiting ERK Activation with CI-1040 Leads to Compensatory Upregulation of Alternate MAPKs and Plasminogen Activator Inhibitor-1 following Subtotal Nephrectomy with No Impact on Kidney Fibrosis

Extracellular-signal regulated kinase (ERK) activation by MEK plays a key role in many of the cellular processes that underlie progressive kidney fibrosis including cell proliferation, apoptosis and transforming growth factor β1-mediated epithelial to mesenchymal transition. We therefore assessed the therapeutic impact of ERK1/2 inhibition using a MEK inhibitor in the rat 5/6 subtotal nephrectomy (SNx) model of kidney fibrosis. There was a twentyfold upregulation in phospho-ERK1/2 expression in the kidney after SNx in Male Wistar rats. Rats undergoing SNx became hypertensive, proteinuric and developed progressive kidney failure with reduced creatinine clearance. Treatment with the MEK inhibitor, CI-1040 abolished phospho- ERK1/2 expression in kidney tissue and prevented phospho-ERK1/2 expression in peripheral lymphocytes during the entire course of therapy. CI-1040 had no impact on creatinine clearance, proteinuria, glomerular and tubular fibrosis, and α-smooth muscle actin expression. However, inhibition of ERK1/2 activation led to significant compensatory upregulation of the MAP kinases, p38 and JNK in kidney tissue. CI-1040 also increased the expression of plasminogen activator inhibitor-1 (PAI-1), a key inhibitor of plasmin-dependent matrix metalloproteinases. Thus inhibition of ERK1/2 activation has no therapeutic effect on kidney fibrosis in SNx possibly due to increased compensatory activation of the p38 and JNK signalling pathways with subsequent upregulation of PAI-1

Liebermannite, KAlSi_3O_8, a new shock-metamorphic, high-pressure mineral from the Zagami Martian meteorite

In this paper, we discuss the occurrence of liebermannite (IMA 2013-128), KAlSi_3O_8, a new, shock-generated, high-pressure tetragonal hollandite-type structure silicate mineral, in the Zagami basaltic shergottite meteorite. Liebermannite crystallizes in space group I4/m with Z = 2, cell dimensions of a = 9.15 ± 0.14 (1σ) Å, c = 2.74 ± 0.13 Å, and a cell volume of 229 ± 19 Å^3 (for the type material), as revealed by synchrotron diffraction. In Zagami, liebermannite likely formed via solid-state transformation of primary igneous K-feldspar during an impact event that achieved pressures of ~20 GPa or more. The mineral name is in honor of Robert C. Liebermann, a high-pressure mineral physicist at Stony Brook University, New York, USA

BiOnt: Deep Learning using Multiple Biomedical Ontologies for Relation Extraction

Successful biomedical relation extraction can provide evidence to researchers and clinicians about possible unknown associations between biomedical entities, advancing the current knowledge we have about those entities and their inherent mechanisms. Most biomedical relation extraction systems do not resort to external sources of knowledge, such as domain-specific ontologies. However, using deep learning methods, along with biomedical ontologies, has been recently shown to effectively advance the biomedical relation extraction field. To perform relation extraction, our deep learning system, BiOnt, employs four types of biomedical ontologies, namely, the Gene Ontology, the Human Phenotype Ontology, the Human Disease Ontology, and the Chemical Entities of Biological Interest, regarding gene-products, phenotypes, diseases, and chemical compounds, respectively. We tested our system with three data sets that represent three different types of relations of biomedical entities. BiOnt achieved, in F-score, an improvement of 4.93 percentage points for drug-drug interactions (DDI corpus), 4.99 percentage points for phenotype-gene relations (PGR corpus), and 2.21 percentage points for chemical-induced disease relations (BC5CDR corpus), relatively to the state-of-the-art. The code supporting this system is available at https://github.com/lasigeBioTM/BiOnt.Comment: ECIR 202