Complement System in Alzheimer’s Disease

Alzheimer’s disease is a type of dementia characterized by problems with short-term memory, cognition, and difficulties with activities of daily living. It is a progressive, neurodegenerative disorder. The complement system is an ancient part of the innate immune system and comprises of more than thirty serum and membrane-bound proteins. This system has three different activating pathways and culminates into the formation of a membrane attack complex that ultimately causes target cell lysis (usually pathogens) The complement system is involved in several important functions in the central nervous system (CNS) that include neurogenesis, synaptic pruning, apoptosis, and neuronal plasticity. Here, we discuss how the complement system is involved in the effective functioning of CNS, while also contributing to chronic neuroinflammation leading to neurodegenerative disorders such as Alzheimer’s disease. We also discuss potential targets in the complement system for stopping its harmful effects via neuroinflammation and provide perspective for the direction of future research in this field.     

In situ single-grain peak profile measurements on Ti–7Al during tensile deformation

High-energy three-dimensional X-ray diffraction with medium and high reciprocal space resolution was applied to study in situ tensile deformation of Ti–7Al specimens. Samples with planar and random dislocation microstructures were prepared and characterized by electron microscopy. Stress tensors of individual grains were obtained at several loads up to 2% deformation. The stress tensors were found to rotate, and resolved shear stresses were calculated. High-resolution reciprocal space maps of selected grains were recorded. Azimuthal and radial distributions were visualized and discussed in terms of idealized dislocation structures. Heterogeneous grain rotations were observed for the planar microstructure and found to be consistent with activation of the highest stressed basal slip system. Intra-granular strain gradients were detected in excess of the intrinsic radial dislocation peak broadening. The potential of combining the applied techniques with modeling to obtain multiple length-scale information during deformation of bulk specimens is discussed.     

Character Recognition Digit Recognition Pattern Recognition Spatiotemporal Neural Networks Modular Networks Segmentation Problem

We describe an alternate approach to visual recognition of handwritten words, wherein an image is converted into a spatio-temporal signal by scanning it in one or more directions, and processed by a suitable connectionist network. The scheme offers several attractive features including shift-invariance, explication of local spatial geometry along the scan direction, a significant reduction in the number of free parameters, the ability to process arbitrarily long images along the scan direction, and a natural framework for dealing with the segmentation/recognition dilemma. Other salient features of the work include the use of a modular and structured approach for network construction and the integration of connectionist components with a procedural component to exploit the complementary strengths of both techniques. The system consists of two connectionist components and a procedural controller. One network concurrently makes recognition and segmentation hypotheses, and another performs refined recognition of segmented characters. The interaction between the networks is governed by the procedural controller. The system is tested on three tasks: isolated digit recognition, recognition of overlapping pairs of digits and recognition of ZIP codes.     

The application of connectionism to query planning/scheduling in intelligent user interfaces

In the mid-1990s, the Earth Observing System (EOS) will generate an estimated 10 Terabytes of data per day. Making full use of this enormous amount of data will require sophisticated technologies from real-time distributed Artificial Intelligence (AI) and data management. Without regard to the overall problems in distributed AI, this paper focuses on developing efficient models for doing query planning/scheduling in intelligent user interfaces that reside in a network environment. Before intelligent query/planning can be done, a model for real-time AI planning/scheduling must be developed. As connectionist models (CM) have shown promise in decreasing run-times, this paper proposes a connectionist approach to AI planning/scheduling. The solution involves merging a CM rule-based system to a general spreading activation model for the generation and selection of plans. A system has been implemented in the Rochester Connectionist Simulator and runs on a Sun 3/260.     

Optimal control theory for sustainable environmental management

Sustainable ecosystem management aims to promote the structure and operation of the human components of the system while simultaneously ensuring the persistence of the structures and operation of the natural component. Given the complexity of this task owing to the diverse temporal and spatial scales and multidisciplinary interactions, a systems theory approach based on sound mathematical techniques is essential. Two important aspects of this approach are formulation of sustainability-based objectives and development of the management strategies. Fisher information can be used as the basis of a sustainability hypothesis to formulate relevant mathematical objectives for disparate systems, and optimal control theory provides the means to derive time-dependent management strategies. Partial correlation coefficient analysis is an efficient technique to identify the appropriate control variables for policy development. This paper represents a proof of concept for this approach using a model system that includes an ecosystem, humans, a very rudimentary industrial process, and a very simple agricultural system. Formulation and solution of the control problems help in identifying the effective management options which offer guidelines for policies in real systems. The results also emphasize that management using multiple parameters of different nature can be distinctly effective.