Multiple Functions for Secondary Metabolites in Encrusting Marine Invertebrates

We used three chemical fractions (spanning a wide range of polarities) from the extracts of four marine invertebrates, the spongesCrambe crambe andHemimycale columella and the ascidiansCystodytes dellechiajei andPolysyncraton lacazei, to test inhibition of cell division, photosynthesis, and settlement. We used assay organisms from the same habitat, seeking to determine whether a species may display diverse, ecologically relevant bioac-tivities and, if so, whether the same types of compound may be responsible for such activities. Cell division was strongly inhibited by the spongeC. crambe. A dichloromethane fraction fromC. crambe prevented development of sea urchinParacentrotus lividus eggs at a concentration of 10 μg/ml, as did the butanolic fraction, but at higher concentrations (50 and 100 μg/ml). At 50 μg/ml, the aqueous fraction ofC. crambe allowed cell division but prevented eggs from developing beyond the gastrula stage. Similar results were recorded with the dichloromethane fraction ofP. lacazei and from the aqueous fraction ofH. columella. Photosynthesis was unaffected by any of the species at 50 μg/ml. Larval settlement was inhibited by one or another fraction from the four species surveyed at a concentration of 50 μg/ml, althoughC. crambe exhibited the greatest amount of activity. We therefore found that various fractions displayed the same type of bioactivity, while compounds from the same fraction were responsible for multiple activities, suggesting that secondary metabolites are multiple-purpose tools in nature, which is relevant to our understanding of species ecology and evolution. Moreover, results showed that the assessment of the role of chemical compounds is significantly influenced by the assay organism, fractionation procedure, concentration, and duration of experiments. All these factors should be carefully considered when testing ecological hypotheses of the roles of chemically-mediated bioactivities.     

Superior corrosion protection performance of polypdopamine-intercalated CeO2/polyurethane nanocomposite coatings on steel in 3.5% NaCl solution

Journal of Applied Electrochemistry - Newly synthesized polyurethane (PU)–polydopamine (Pda)/CeO2 (cerium oxide) nanocomposite-coated mild steel was investigated for its corrosion protection...     

Assessment of crowdsourcing and gamification loss in user-assisted object segmentation

There has been a growing interest in applying human computation – particularly crowdsourcing techniques – to assist in the solution of multimedia, image processing, and computer vision problems which are still too difficult to solve using fully automatic algorithms, and yet relatively easy for humans. In this paper we focus on a specific problem – object segmentation within color images – and compare different solutions which combine color image segmentation algorithms with human efforts, either in the form of an explicit interactive segmentation task or through an implicit collection of valuable human traces with a game. We use Click’n’Cut, a friendly, web-based, interactive segmentation tool that allows segmentation tasks to be assigned to many users, and Ask’nSeek, a game with a purpose designed for object detection and segmentation. The two main contributions of this paper are: (i) We use the results of Click’n’Cut campaigns with different groups of users to examine and quantify the crowdsourcing loss incurred when an interactive segmentation task is assigned to paid crowd-workers, comparing their results to the ones obtained when computer vision experts are asked to perform the same tasks. (ii) Since interactive segmentation tasks are inherently tedious and prone to fatigue, we compare the quality of the results obtained with Click’n’Cut with the ones obtained using a (fun, interactive, and potentially less tedious) game designed for the same purpose. We call this contribution the assessment of the gamification loss, since it refers to how much quality of segmentation results may be lost when we switch to a game-based approach to the same task. We demonstrate that the crowdsourcing loss is significant when using all the data points from workers, but decreases substantially (and becomes comparable to the quality of expert users performing similar tasks) after performing a modest amount of data analysis and filtering out of users whose data are clearly not useful. We also show that – on the other hand – the gamification loss is significantly more severe: the quality of the results drops roughly by half when switching from a focused (yet tedious) task to a more fun and relaxed game environment.     

Rehabilitating rivers and enhancing ecosystem services in a water-scarcity context: the Yarqon River

After the creation of the state of Israel in 1948, the increase in urban and agricultural water demand and the increased flow of poorly treated sewage had a severe impact in one of the most ecologically important rivers in Israel, the Yarqon. The attempt to reverse this situation began with the creation of the Yarqon River Authority in 1988. In the last 20 years, the authority has implemented or collaborated in several projects that have significantly contributed to enhancing the provision of ecological services. This article aims to analyze the case of the pollution and subsequent rehabilitation of aquatic ecosystem services in the Israeli water-scarcity context.     

Detecting end-effectors on 2.5D data using geometric deformable models: Application to human pose estimation

End-effectors are usually related to the location of limbs, and their reliable detection enables robust body tracking as well as accurate pose estimation. Recent innovation in depth cameras has re-stated the pose estimation problem. We focus on the information provided by these sensors, for which we borrow the name 2.5D data from the Graphics community. In this paper we propose a human pose estimation algorithm based on topological propagation. Geometric Deformable Models are used to carry out such propagation, implemented according to the Narrow Band Level Set approach. A variant of the latter method is proposed, including a density restriction which helps preserving the topological properties of the object under analysis. Principal end-effectors are extracted from a directed graph weighted with geodesic distances, also providing a skeletal-like structure describing human pose. An evaluation against reference methods is performed with promising results. The proposed solution allows a frame-wise end-effector detection, with no temporal tracking involved, which may be generalized to the tracking of other objects beyond human body.     

Design of artificial neuron controller for STATCOM in dSPACE environment

Reactive power compensation is an important issue in the control of electric power system. Reactive power from the source increases the transmission losses and reduces the power transmission capability of the transmission lines. Moreover, reactive power should not be transmitted through the transmission line to a longer distance. Hence Flexible AC Transmission Systems (FACTS) devices such as static compensator (STATCOM) unified power flow controller (UPFC) and static volt–ampere compensator (SVC) are used to alleviate these problems. In this paper, a voltage source converter (VSC) based STATCOM is developed with PI and Artificial Neural Network Controller (ANNC). The conventional PI controller has more tuning difficulties while the system parameter changes, whereas a trained neural network requires less computation time. The ANNC has the ability to generalize and can interpolate in between the training data. The ANNC designed was tested on a 75 V, ±3KVAR STATCOM in real time environment via state-of-the-art of digital signal processor advanced control engineering (dSPACE) DS1104 board and it was found that it was producing better results than the PI controller.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

Workload assignment for global real-time scheduling on unrelated clustered platforms

Real-Time Systems - Heterogeneous MPSoCs are being used more and more, from cellphones to critical embedded systems. Most of those systems offer heterogeneous sets of identical cores. In this...