A workflow for the automatic segmentation of organelles in electron microscopy image stacks.

Electron microscopy (EM) facilitates analysis of the form, distribution, and functional status of key organelle systems in various pathological processes, including those associated with neurodegenerative disease. Such EM data often provide important new insights into the underlying disease mechanisms. The development of more accurate and efficient methods to quantify changes in subcellular microanatomy has already proven key to understanding the pathogenesis of Parkinson's and Alzheimer's diseases, as well as glaucoma. While our ability to acquire large volumes of 3D EM data is progressing rapidly, more advanced analysis tools are needed to assist in measuring precise three-dimensional morphologies of organelles within data sets that can include hundreds to thousands of whole cells. Although new imaging instrument throughputs can exceed teravoxels of data per day, image segmentation and analysis remain significant bottlenecks to achieving quantitative descriptions of whole cell structural organellomes. Here, we present a novel method for the automatic segmentation of organelles in 3D EM image stacks. Segmentations are generated using only 2D image information, making the method suitable for anisotropic imaging techniques such as serial block-face scanning electron microscopy (SBEM). Additionally, no assumptions about 3D organelle morphology are made, ensuring the method can be easily expanded to any number of structurally and functionally diverse organelles. Following the presentation of our algorithm, we validate its performance by assessing the segmentation accuracy of different organelle targets in an example SBEM dataset and demonstrate that it can be efficiently parallelized on supercomputing resources, resulting in a dramatic reduction in runtime

An Adaptive Neuro Fuzzy Inference System for Supply chain Agility Evaluation

Nowadays, in turbulent and violate global markets, agility has been considered as a fundamental characteristic of a supply chain needed for survival. To achieve the competitive edge, companies must align with suppliers and customers to streamline operations, as well as agility beyond individual companies. Consequently Agile Supply Chain (ASC) is considered as a dominant competitive advantage.  However, so far a little effort has been made for designing, operating and evaluating agile supply chain in recent years. Therefore, in this study a new approach has been developed based on Adaptive Neuro Fuzzy Inference System (ANFIS) for evaluating agility in supply chain considering agility capabilities such as Flexibility, Competency, Cost, Responsiveness and Quickness. This evaluation helps managers to perform gap analysis between existent agility level and the desired one and also provides more informative and reliable information for decision making. Finally the proposed model has been applied to a leading car manufacturing company in Iran to prove the applicability of the model

A workflow for the automatic segmentation of organelles in electron microscopy image stacks.

Electron microscopy (EM) facilitates analysis of the form, distribution, and functional status of key organelle systems in various pathological processes, including those associated with neurodegenerative disease. Such EM data often provide important new insights into the underlying disease mechanisms. The development of more accurate and efficient methods to quantify changes in subcellular microanatomy has already proven key to understanding the pathogenesis of Parkinson's and Alzheimer's diseases, as well as glaucoma. While our ability to acquire large volumes of 3D EM data is progressing rapidly, more advanced analysis tools are needed to assist in measuring precise three-dimensional morphologies of organelles within data sets that can include hundreds to thousands of whole cells. Although new imaging instrument throughputs can exceed teravoxels of data per day, image segmentation and analysis remain significant bottlenecks to achieving quantitative descriptions of whole cell structural organellomes. Here, we present a novel method for the automatic segmentation of organelles in 3D EM image stacks. Segmentations are generated using only 2D image information, making the method suitable for anisotropic imaging techniques such as serial block-face scanning electron microscopy (SBEM). Additionally, no assumptions about 3D organelle morphology are made, ensuring the method can be easily expanded to any number of structurally and functionally diverse organelles. Following the presentation of our algorithm, we validate its performance by assessing the segmentation accuracy of different organelle targets in an example SBEM dataset and demonstrate that it can be efficiently parallelized on supercomputing resources, resulting in a dramatic reduction in runtime

Tip-induced domain structures and polarization switching in ferroelectric amino acid glycine

Bioorganic ferroelectrics and piezoelectrics are becoming increasingly important in view of their intrinsic compatibility with biological environment and biofunctionality combined with strong piezoelectric effect and a switchable polarization at room temperature. Here, we study tip-induced domain structures and polarization switching in the smallest amino acid beta-glycine, representing a broad class of non-centrosymmetric amino acids. We show that beta-glycine is indeed a room-temperature ferroelectric and polarization can be switched by applying a bias to non-polar cuts via a conducting tip of atomic force microscope (AFM). Dynamics of these in-plane domains is studied as a function of an applied voltage and pulse duration. The domain shape is dictated by polarization screening at the domain boundaries and mediated by growth defects. Thermodynamic theory is applied to explain the domain propagation induced by the AFM tip. Our findings suggest that the properties of beta-glycine are controlled by the charged domain walls which in turn can be manipulated by an external bias. (C) 2015 AIP Publishing LLC

Piezoelectricity and ferroelectricity in biomaterials: Molecular modeling and piezoresponse force microscopy measurements

Piezoelectricity is one of the important functional properties inherent to many biomaterials. It stems from the non-centrosymmetric crystal structure of most biopolymers including proteins, polysaccharides, and lipids. Understanding the relationship between the generated electric field and applied mechanical stress has become the main motivation to studying piezoelectricity in biological systems and artificial biomaterials at the nanoscale. In this work, we present a review of the piezoelectric and ferroelectric properties of several molecular systems and nanomaterials revealed by Piezoresponse Force Microscopy (PFM) and compare the results with molecular modeling and computer simulations. Experimentally observed by PFM and calculated dielectric, piezoelectric, and ferroelectric properties of these materials are analyzed in the context of their possible role in functionality of biological systems. (C) 2014 AIP Publishing LLC

Green Supply Chain, Logistics, and Transportation

This chapter presents the concepts of green supply chain network, green supply chain management, and green logistics . Increasing environmental concerns requires companies to become more responsive to products that either has been returned or that are at the end of their useful lives. Organization’s responsiveness and their reactions toward life cycles of products are critical to achieve sustained success once fluctuations are recurrent and the business environments are turbulent. Life cycles are getting shorter, and effective managing can save large amounts of cash as many materials can be extracted, reused, and redistributed. Alongside this context, this chapter focuses on a general overview toward closed-loop supply chains and offers a generalized optimization model . In addition, incentive approaches for an optimal recovery plan in a closed-loop supply chain are discussed in this chapter