Measuring institutional research productivity for the life sciences: the importance of accounting for the order of authors in the byline

Accurate measurement of institutional research productivity should account for the real contribution of the research staff to the output produced in collaboration with other organizations. In the framework of bibliometric measurement, this implies accounting for both the number of co-authors and each individual’s real contribution to scientific publications. Common practice in the life sciences is to indicate such contribution through the order of author names in the byline. In this work, we measure the distortion introduced to university-level bibliometric productivity rankings when the number of co-authors or their position in the byline is ignored. The field of observation consists of all Italian universities active in the life sciences (Biology and Medicine). The analysis is based on the research output of the university staff over the period 2004–2008. Based on the results, we recommend against the use of bibliometric indicators that ignore co-authorship and real contribution of each author to research outputs.     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.     

Short‐term effects in a reduced flow stretch: The case of the Antas River in South Brazil

The building of adduction channels (penstocks) that conduct water from reservoirs to turbines, which are located kilometres from the dam, is becoming common, optimizing the electricity generation in small dams. This design creates a river stretch with reduced discharge between the dam and the powerhouse. This study evaluates the short‐term impacts of the below‐dam decrease in river flow on fish assemblages. Samples were collected in the reduced flow stretch of the Castro Alves Hydropower Plant (Antas River, Rio Grande do Sul, Brazil) before the reservoir started operating (January 2008; mean discharge of 103.7 m³/s) and immediately after operation began (March 2008; mean discharge of 12.4 m³/s). Sampling was conducted in distinct habitats of the reduced flow stretch (slow waters—gillnets, sand beaches—seining nets, structured littoral—electrofishing, and fast waters—cast nets) with a strongly standardized effort. The attributes of the fish assemblages were not negatively affected by the flow reduction in any habitat sampled. However, distinct changes in the spatial structure were observed considering the different types of habitat predominantly used by the species, which represents an entire reorganization of the fish assemblages in the short term. It is fundamental that these short‐term aspects be considered in the licensing of hydropower plants in addition to the long‐term changes.     

The Role of Extracellular Vesicles in the Progression of Human Neuroblastoma

The long-underestimated role of extracellular vesicles in cancer is now reconsidered worldwide by basic and clinical scientists, who recently highlighted novel and crucial activities of these moieties. Extracellular vesicles are now considered as king transporters of specific cargoes, including molecular components of parent cells, thus mediating a wide variety of cellular activities both in normal and neoplastic tissues. Here, we discuss the multifunctional activities and underlying mechanisms of extracellular vesicles in neuroblastoma, the most frequent common extra-cranial tumor in childhood. The ability of extracellular vesicles to cross-talk with different cells in the tumor microenvironment and to modulate an anti-tumor immune response, tumorigenesis, tumor growth, metastasis and drug resistance will be pinpointed in detail. The results obtained on the role of extracellular vesicles may represent a panel of suggestions potentially useful in practice, due to their involvement in the response to chemotherapy, and, moreover, their ability to predict resistance to standard therapies—all issues of clinical relevance.     

Double Mutant Cycles as a Tool to Address Folding,Binding, and Allostery

Quantitative measurement of intramolecular and intermolecular interactions in protein structure is an elusive task, not easy to address experimentally. The phenomenon denoted ‘energetic coupling’ describes short- and long-range interactions between two residues in a protein system. A powerful method to identify and quantitatively characterize long-range interactions and allosteric networks in proteins or protein–ligand complexes is called double-mutant cycles analysis. In this review we describe the thermodynamic principles and basic equations that underlie the double mutant cycle methodology, its fields of application and latest employments, and caveats and pitfalls that the experimentalists must consider. In particular, we show how double mutant cycles can be a powerful tool to investigate allosteric mechanisms in protein binding reactions as well as elusive states in protein folding pathways.     

Sustainable control strategies for plant protection and food packaging sectors by natural substances and novel nanotechnological approaches

An overview is provided of the current technological strategies (also at the nanoscale level) recently involved in plant and/or food protection. In addition, the potential use of natural and sustainable substances, instead of traditional synthesized molecules or chemical-based compounds, is addressed both with respect to packaging systems and novel pesticide formulations. In this context, nanotechnological approaches represent promising strategies for the entire agriculture industry chain, from the field to consumers. Traditional plant protection strategies are often insufficient and the application of chemical-based pesticides has negative effects on animals, humans and the environment. Novel greener tools could represent efficient alternatives for the management of plant diseases using promising strategies; the use of nanotechnologies allows the promotion of the more efficient assembly and subsequent release of environmentally sustainable active principles, limiting the use of chemicals in terms of economic losses. At the same time, new sustainable, antimicrobial and antioxidant systems have been rapidly promoted and investigated in the food packaging sector as a valid eco-friendly possibility for improving the safety and quality of food products and reducing and/or limiting the environmental impact with respect to traditional materials. Together, the scientific community and the growing interest of consumers have promoted the development of new edible and eco-friendly packaging that reduces waste and any environmental impact. In this context, the aim is to provide evidence of the usefulness of strategies aiming to limit agrochemicals, as well as the potential of nanomaterials, in sustainable plant and food protection for agriculture management and the packaging sector. © 2018 Society of Chemical Industry     

Hydrogen production by silica membrane reactor during dehydrogenation of methylcyclohexane: CFD analysis

A comprehensive computational fluid dynamic model has been developed using COMSOL Multiphysics 5.4 software to predict the behavior of a membrane reactor in dehydrogenation of methylcyclohexane for hydrogen production. A reliable reaction kinetic of dehydrogenation reaction and a permeation mechanism of hydrogen through silica membrane have been used in computational fluid dynamic modeling. For performance comparison, an equivalent traditional fixed bed reactor without hydrogen removal has been also modeled. After model validation, it has been used to evaluate the operating parameters effect on the performance of both the silica membrane reactor and the equivalent traditional reactor as well. The operating temperature ranged between 473 and 553 K, pressure between 1 and 2.5 bar, sweep factor from ?6.22 to 25 and feed flow rate from 1 to 5 × 10^?6 mol/s. The membrane reactor performed better than the equivalent traditional reactor, achieving as best result complete methylcyclohexane conversion and 96% hydrogen recovery.