Circular stationary solutions in two-dimensional neural fields

 Stationary solutions are studied in two-dimensional homogeneous neural fields of the lateral-inhibition type. It is shown that in extending the one-dimensional theory to two dimensions, new phenomena arise. We discuss the conditions for the existence of localized solutions analogous to the one-dimensional theory and show that they are no longer sufficient in two dimensions. We give indications for the existence of mono- and bistable dynamics as known from the one-dimensional theory and, additionally, a tri-stable type of dynamic in two-dimensional neural fields, where, depending on the input, excitation dies out, spreads without limit, or causes a stable localized excitation. Received: 19 July 2000 / Accepted in revised form: 18 December 2000     

Three-dimensional-segmentation of the spine with specially adapted tools]

Segmented 3-D data of the spine form the basis for various modern clinical applications. Among these, multisegmental image fusion, image registration and finite element modeling for biomechanical analysis are promising innovative tools capable of facilitating treatment decisions and optimization of individual therapy in the future. However, the complex anatomy of the spine and the often extensive degenerative deformation presenting in clinical practice, generally limit the application of fully automated segmentation. A newly developed software system is presented that meets the complex requirements for image segmentation of the spine through the use of specially adapted interactive tools that take account of its axial skeletal structure. Furthermore, a standardized protocol is introduced that combines the newly developed interactive tools (rotation transformation, warped dissection plane) and standard segmentation tools to permit rapid and accurate segmentation. To date, the software environment presented herein has been applied with success to the segmentation of cervical, thoracic and lumbar spine.     

The effect of culture conditions on the morphology of the dimorphic yeast Kluyveromyces marxianus var. marxianus NRRLy2415: a study incorporating image analysis

The effect of changing environmental conditions on the morphology of the yeast Kluyveromyces marxianus var. marxianus NRRLy2415 was investigated in batch and continuous culture, using a previously developed computer-aided image analysis protocol [O'Shea and Walsh (1996) Biotechnol Bioeng 51: 679–690]. The morphology of the organism is primarily controlled by the specific growth rate, μ. This finding was contrary to a previous investigation [Walker and O'Neill (1990) J Chem Tech Biotechnol 49: 75–89]. When the organism is cultured in batch with excess oxygen, μ can approach the maximum specific growth rate, μ_m, and the primary morphology of the culture is yeast-like. However, if the organism is cultured in a chemostat, thereby controlling the growth rate, the morphology reverts to a pseudohyphal form. This response is thought to be an adaptation by the organism to its environment, whereby it assumes a foraging form under adverse environmental conditions. The use of computer-aided image analysis made possible the discrimination of subtle morphological differences between samples and the determination of the relationship between morphology and growth rate. Received: 12 April 1999 / Accepted: 10 October 1999     

Daphnia revisited: local stability and bifurcation theory for physiologically structured population models explained by way of an example

We consider the interaction between a general size-structured consumer population and an unstructured resource. We show that stability properties and bifurcation phenomena can be understood in terms of solutions of a system of two delay equations (a renewal equation for the consumer population birth rate coupled to a delay differential equation for the resource concentration). As many results for such systems are available (Diekmann et al. in SIAM J Math Anal 39:1023–1069, 2007), we can draw rigorous conclusions concerning dynamical behaviour from an analysis of a characteristic equation. We derive the characteristic equation for a fairly general class of population models, including those based on the Kooijman–Metz Daphnia model (Kooijman and Metz in Ecotox Env Saf 8:254–274, 1984; de Roos et al. in J Math Biol 28:609–643, 1990) and a model introduced by Gurney–Nisbet (Theor Popul Biol 28:150–180, 1985) and Jones et al. (J Math Anal Appl 135:354–368, 1988), and next obtain various ecological insights by analytical or numerical studies of special cases.     

Image analysis in fluorescence microscopy: bacterial dynamics as a case study

Fluorescence microscopy is the primary tool for studying complex processes inside individual living cells. Technical advances in both molecular biology and microscopy have made it possible to image cells from many genetic and environmental backgrounds. These images contain a vast amount of information, which is often hidden behind various sources of noise, convoluted with other information and stochastic in nature. Accessing the desired biological information therefore requires new tools of computational image analysis and modeling. Here, we review some of the recent advances in computational analysis of images obtained from fluorescence microscopy, focusing on bacterial systems. We emphasize techniques that are readily available to molecular and cell biologists but also point out examples where problem-specific image analyses are necessary. Thus, image analysis is not only a toolkit to be applied to new images but also an integral part of the design and implementation of a microscopy experiment.     

Two Hypercard calculators for molecular biology

Two calculators built in Macintosh Hypercard are described.GelFragSizer estimates and plots DNA restriction fragment sizes,using the local reciprocal or cubic spline methods. Enzyme Kineticsestimates the Michaelis–Menton rate parameters for enzyme–catalyzedreactions, and plots data versus estimated curve in a varietyof formats. These stacks help to demonstrate the ease and sophisticationof calculation tools that scientists can develop with Hypercard. Received on January 16, 1990; accepted on January 24, 1990     

Neural field model of binocular rivalry waves

We present a neural field model of binocular rivalry waves in visual cortex. For each eye we consider a one-dimensional network of neurons that respond maximally to a particular feature of the corresponding image such as the orientation of a grating stimulus. Recurrent connections within each one-dimensional network are assumed to be excitatory, whereas connections between the two networks are inhibitory (cross-inhibition). Slow adaptation is incorporated into the model by taking the network connections to exhibit synaptic depression. We derive an analytical expression for the speed of a binocular rivalry wave as a function of various neurophysiological parameters, and show how properties of the wave are consistent with the wave-like propagation of perceptual dominance observed in recent psychophysical experiments. In addition to providing an analytical framework for studying binocular rivalry waves, we show how neural field methods provide insights into the mechanisms underlying the generation of the waves. In particular, we highlight the important role of slow adaptation in providing a “symmetry breaking mechanism” that allows waves to propagate.     

Errors in Quantitative Image Analysis due to Platform-Dependent Image Scaling

PURPOSE: To evaluate the ability of various software (SW) tools used for quantitative image analysis to properly account for source-specific image scaling employed by magnetic resonance imaging manufacturers. METHODS: A series of gadoteridol-doped distilled water solutions (0%, 0.5%, 1%, and 2% volume concentrations) was prepared for manual substitution into one (of three) phantom compartments to create “variable signal,” whereas the other two compartments (containing mineral oil and 0.25% gadoteriol) were held unchanged. Pseudodynamic images were acquired over multiple series using four scanners such that the histogram of pixel intensities varied enough to provoke variable image scaling from series to series. Additional diffusion-weighted images were acquired of an ice-water phantom to generate scanner-specific apparent diffusion coefficient (ADC) maps. The resulting pseudodynamic images and ADC maps were analyzed by eight centers of the Quantitative Imaging Network using 16 different SW tools to measure compartment-specific region-of-interest intensity. RESULTS: Images generated by one of the scanners appeared to have additional intensity scaling that was not accounted for by the majority of tested quantitative image analysis SW tools. Incorrect image scaling leads to intensity measurement bias near 100%, compared to nonscaled images. CONCLUSION: Corrective actions for image scaling are suggested for manufacturers and quantitative imaging community.     

A scale-independent signal processing method for sequence analysis

In this paper, we present methods to detect and localize patternsin biologically related protein sequences (family). The patternscommon to the sequences of the family are detected by usingFourier analysis. No previous scales (codes) are needed, theyare actually produced as a result of the analysis procedure,together with the frequencies of the Fourier decompositions.Characteristic features of the family are thus expressed as(code–frequency) pairs. Various tools are proposed inorder to localize the patterns, to compare the codes, and toevaluate the proximity of an arbitrary sequence to the investigatedfamily. The general strategy is illustrated on a family composedof proteins Received on October 17, 1989; accepted on January 16, 1990     

Brain imaging tools in neurosciences

In this chapter brain imaging tools in neurosciences are presented. These include a brief overview on single-photon emission tomography (SPET) and a detailed focus on positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). In addition, a critical discussion on the advantages and disadvantages of the three diagnostic systems is added.Furthermore, this article describes the image analysis tools from visual analysis over region-of-interest technique up to statistical parametric mapping, co-registration methods, and network analysis. It also compares the newly developed combined PET/CT scanner approach with established image fusion software approaches.There is rapid change: Better scanner qualities, new software packages and scanner concepts are on the road paved for an amply bright future in neurosciences.     

Bayesian image processing of data from constrained source distributions—II. valued,uncorrelated and correlated constraints

Bayesian image processing formalisms which incorporatea priori information about valued-uncorrelated and valued-correlated (patterned) source distributions are introduced and the corresponding iterative algorithms are derived using the EM technique. Striking improvement in image processing is demonstrated when applying these algorithms to Poisson and Gaussian randomized data in one-dimensional cases.     

A generic algorithm for finding restriction sites within DNA sequences

This paper describes a generic algorithm for finding restrictionsites within DNA sequences. The ‘genericity’ ofthe algorithm is made possible through the use of set theory.Basic elements of DNA sequences, i.e. nucleotides (bases), arerepresented in sets, and DNA sequences, whether specific, ambiguousor even protein-coding, are represented as sequences of thosesets. The set intersection operation demonstrates its abilityto perform pattern-matching correctly on various DNA sequences.The performance analysis showed that the degree of complexityof the pattern matching is reduced from exponential to linear.An example is given to show the actual and potential restrictionsites, derived by the generic algorithm, in the DNA sequencetemplate coding for a synthetic calmodulin. Received on October 2, 1990; accepted on December 18, 1990     

Using cellular automata images and pseudo amino acid composition to predict protein subcellular location

Summary. The avalanche of newly found protein sequences in the post-genomic era has motivated and challenged us to develop an automated method that can rapidly and accurately predict the localization of an uncharacterized protein in cells because the knowledge thus obtained can greatly speed up the process in finding its biological functions. However, it is very difficult to establish such a desired predictor by acquiring the key statistical information buried in a pile of extremely complicated and highly variable sequences. In this paper, based on the concept of the pseudo amino acid composition (Chou, K. C. PROTEINS: Structure, Function, and Genetics, 2001, 43: 246–255), the approach of cellular automata image is introduced to cope with this problem. Many important features, which are originally hidden in the long amino acid sequences, can be clearly displayed through their cellular automata images. One of the remarkable merits by doing so is that many image recognition tools can be straightforwardly applied to the target aimed here. High success rates were observed through the self-consistency, jackknife, and independent dataset tests, respectively.     

Recent insights in phosphatidylinositol signaling

Studies of phosphatidylinositol signaling pathways are entering a new phase in which molecular genetic techniques are providing powerful tools to dissect the functions of various metabolites and pathways. Studies with phospholipase C are most advanced and clearly indicate that phosphatidylinositol turnover is critical for vision in Drosophila and cell proliferation in various cultured cells. Expression of cDNA constructs and microinjection of PLC or antibodies against it clearly establish a role for PtdIns signaling distinct from its role in calcium mobilization and protein kinase C activation. The importance of inositol cyclic phosphates is also beginning to be realized from the study of cyclic hydrolase using similar techniques. Elucidation of the function of the 3-phosphate inositol phospholipid pathway awaits similar studies. The recent cDNA cloning of inositol monophosphatase (Diehl et al., 1990), Ins(1,4,5)P3 3-kinase (Choi et al., 1990), and inositol polyphosphate 1-phosphatase (York and Majerus, 1991) should provide tools to define further the cell biology of the phosphatidylinositol signaling pathway.     

Standardisation of DNA quantitation by image analysis: quality control of instrumentation.

BACKGROUND: DNA image analysis is frequently performed in clinical practice as a prognostic tool and to improve diagnosis. The precision of prognosis and diagnosis depends on the accuracy of analysis and particularly on the quality of image analysis systems. It has been reported that image analysis systems used for DNA quantification differ widely in their characteristics (Thunissen et al.: Cytometry 27: 21-25, 1997). This induces inter-laboratory variations when the same sample is analysed in different laboratories. In microscopic image analysis, the principal instrumentation errors arise from the optical and electronic parts of systems. They bring about problems of instability, non-linearity, and shading and glare phenomena. METHODS: The aim of this study is to establish tools and standardised quality control procedures for microscopic image analysis systems. Specific reference standard slides have been developed to control instability, non-linearity, shading and glare phenomena and segmentation efficiency. RESULTS: Some systems have been controlled with these tools and these quality control procedures. Interpretation criteria and accuracy limits of these quality control procedures are proposed according to the conclusions of a European project called PRESS project (Prototype Reference Standard Slide). Beyond these limits, tested image analysis systems are not qualified to realise precise DNA analysis. CONCLUSIONS: The different procedures presented in this work determine if an image analysis system is qualified to deliver sufficiently precise DNA measurements for cancer case analysis. If the controlled systems are beyond the defined limits, some recommendations are given to find a solution to the problem.     

The ImageJ ecosystem: Open‐source software for image visualization,processing, and analysis

For decades, biologists have relied on software to visualize and interpret imaging data. As techniques for acquiring images increase in complexity, resulting in larger multidimensional datasets, imaging software must adapt. ImageJ is an open‐source image analysis software platform that has aided researchers with a variety of image analysis applications, driven mainly by engaged and collaborative user and developer communities. The close collaboration between programmers and users has resulted in adaptations to accommodate new challenges in image analysis that address the needs of ImageJ's diverse user base. ImageJ consists of many components, some relevant primarily for developers and a vast collection of user‐centric plugins. It is available in many forms, including the widely used Fiji distribution. We refer to this entire ImageJ codebase and community as the ImageJ ecosystem. Here we review the core features of this ecosystem and highlight how ImageJ has responded to imaging technology advancements with new plugins and tools in recent years. These plugins and tools have been developed to address user needs in several areas such as visualization, segmentation, and tracking of biological entities in large, complex datasets. Moreover, new capabilities for deep learning are being added to ImageJ, reflecting a shift in the bioimage analysis community towards exploiting artificial intelligence. These new tools have been facilitated by profound architectural changes to the ImageJ core brought about by the ImageJ2 project. Therefore, we also discuss the contributions of ImageJ2 to enhancing multidimensional image processing and interoperability in the ImageJ ecosystem.     

Assessing impact of forest landscape dynamics on migratory corridors: a case study of two protected areas in Himalayan foothills

Contiguity of protected areas (PAs) is a critical factor to promote well being of the native flora, fauna and life support system to humans. Such contiguity cannot be guaranteed without providing a path or ‘a corridor’ through forested landscapes that includes natural land cover and undisturbed patches. Incidentally, the Himalayan foothills have greater pressure on these landscapes due to high human dependence for livelihood. This pressure is expected to increase in the coming years altering the potential corridors between PAs. The PA managers need flexible processing, modeling and decision tools to propose a range of acceptable corridors between the PAs and ensure their sustainable health. Such flexible tools can be utilized in future to modify for taking decision to conserve the patches connecting patches and adapt as per changing landscapes. This article describes utility of geospatial modeling tools to assess the status of corridors in light of changing landscapes between Rajaji and Jim Corbett National Park, the two most important PAs in the Himalayan foothills. The work has been carried out in four stages, first—using satellite data land use land cover (LULC) maps were prepared for year 1990, 2000 and 2005, second—Land Change Modeler (LCM) was used for LULC change analysis, third—Multi Layer Perceptron Neural Network (MLPNN) was used to predict the status of LULC for 2015 and 2020, and fourth—using temporal morphology of the areas behaving both as barrier and easiness, friction surface cost was calculated to identify least cost pathways (LCPs)/migratory corridors between the PAs. The LULC maps for 1990, 2000 and 2005 were evaluated using accuracy assessment (80%) and Khat statistics (>0.79). The change prediction model was validated by comparing actual LULC of 2005 with predicted LULC of 2005 and the agreement was 71%. The LCP has shifted with the predicted change in the classes. The corridor has shifted by 0.5–3 km towards the south and has come closer to the agriculture fields and river channels.     

Parametric-equation representation of biconcave erythrocytes

The representation of the shape of a biconcave erythrocyte by a set of three parametric equations was achieved by using the expressions that transform the curvilinear coordinates from the disc-cyclide coordinate system [denoted J2R; Moon and Spencer (1988), Field Theory Handbook, Springer-Verlag, Berlin] to Cartesian coordinates. The equations are products of elliptic functions, so the challenge was to relate the three major ’shape-defining’ measurements of the human erythrocyte in Cartesian coordinates to three parameters in the new curvilinear coordinates, to give a realistic representation of the shape of the membrane-surface. The relationships between the coefficients of the Cartesian degree-4 surface that describes the discocyte and the coordinate transformation equations were derived with the aid of Mathematica; and the membrane-surface of the cell was drawn using the ParametricPlot3D function in this ‘package’. By having the erythrocyte shape expressed in its new form it is readily amenable to further transformations that might be used to model those changes in shape that are seen when the cells are immersed in media of various osmolalities, or when they change metabolic ’states’. On the other hand, the relationship between the coefficients of the Cartesian expression for the disc-cyclide surface is relevant to image analysis of erythrocytes, as determined by physical methods that rely on Cartesian imaging ’slices’. These methods include confocal microscopy and various nuclear magnetic resonance microimaging procedures.