A revision of the Sclerocoelus galapagensis group (Diptera: Sphaeroceridae: Limosininae)

The Sclerocoelus galapagensis group is defined and revised, including the description of S. galapagensis new species from the Galapagos Islands; S. caribensis new species from the Caribbean and adjacent areas; S. brasilensis new species from Brazil, Ecuador, Colombia, and Panama; S. hemorrhoidal is new species from Ecuador and Venezuela; and S. andensis new species from Argentina, Bolivia, and Venezuela. The south Atlantic species Sclerocoelus subbrevipennis (Frey), new combination, is redescribed as a member of the S. galapagensis group, and is considered the sister species to the rest of the species group. A key to species, character matrix, and cladogram are provided

Inferring context-sensitive probablistic boolean networks from gene expression data under multi-biological conditions

In recent years biological microarrays have emerged as a high-throughput data acquisition technology in bioinformatics. In conjunction with this, there is an increasing need to develop frameworks for the formal analysis of biological pathways. A modeling approach defined as Probabilistic Boolean Networks (PBNs) was proposed for inferring genetic regulatory networks [1]. This technology, an extension of Boolean Networks [2], is able to capture the time-varying dependencies with deterministic probabilities for a series of sets of predictor functions

A fast method for computing the output of rank order filters within arbitrarily shaped windows

Rank order filters are used in a multitude of image processing tasks. Their application can range from simple preprocessing tasks which aim to reduce/remove noise, to more complex problems where such filters can be used to detect and segment image features. There is, therefore, a need to develop fast algorithms to compute the output of this class of filter. A number of methods for efficiently computing the output of specific rank order filters have been proposed [1]. For example, numerous fast algorithms exist that can be used for calculating the output of the median filter. Fast algorithms for calculating morphological erosions and dilations - which are also a special case of the more general rank order filter - have also been proposed. In this paper we present an extension of a recently introduced method for computing fast morphological operators to the more general case of rank order filters. Using our method, we are able to efficiently compute any rank, using any arbitrarily shaped window, such that it is possible to quickly compute the output of any rank order filter. We demonstrate the usefulness and efficiency of our technique by implementing a fast method for computing a recent generalisation of the morphological Hit-or-Miss Transform which makes it more robust in the presence of noise. We also compare the speed and efficiency of this routine with similar techniques that have been proposed in the literature

Metalogue: trying to talk about (un)sustainability - a reflection on experience

This paper considers dilemmas for organization and management scholars studying and writing about environmental sustainability. It suggests that sustainability requires new ways of thinking which in turn require new forms of representation to help foster their emergence. Consequently, the paper partly takes the experimental form of a ‘metalogue’ (Bateson, 1972), in which the structure of the conversation between the authors is intended to be reflective of the content of the problematic subject discussed, in this case their experiences of trying to raise critical questions about scholarship for sustainability. This experimental form, which invites the reader to eschew expectations of typical points of orientation, enables an appreciation of how forms of argument seem to replicate epistemological challenges in the sustainability field. The paper shows how metaloguing becomes not only an alternative form but also an inquiry process for considering sustainability that can support embodied reflexivity, critical questioning and appreciation of entanglements of people-scholars

The Academy of Dental Materials: Providing roots and wings.

ObjectivesThe long history of the Academy of Dental Materials (ADM) is documented with its strategies (a) to rapidly communicate science among its members, (b) to establish special awards to stimulate new science, and (c) to develop new dental materials scientists.MethodsWe searched the history of the last 35 years of the ADM newsletters, transactions, journals, and officer notes. We document the (a) presidents, (b) meeting history, (c) membership growth, and (d) development of special awards through 2019 with the recent creation of the ADM Marshall Post-Doctoral Award.ResultsThere are 36 years of recent ADM history, 42 international meetings, membership growth to 400 individuals from 15 countries, service of 19 presidents, Paffenbarger annual Awardees since 1989, induction of >200 fellows, and recognition of the first winner of Marshall Post-Doctoral Award in 2018. New directions for recruiting members are suggested. Three potential new thrusts for the organization are presented: artificial intelligence, genetic engineering, and intensive member mentoring.SignificanceThese suggestions for the ADM provide a path for the ADM to continue to adapt to the ever changing scientific landscape

Medical image enhancement using threshold decomposition driven adaptive morphological filter

One of the most common degradations in medical images is their poor contrast quality. This suggests the use of contrast enhancement methods as an attempt to modify the intensity distribution of the image. In this paper, a new edge detected morphological filter is proposed to sharpen digital medical images. This is done by detecting the positions of the edges and then applying a class of morphological filtering. Motivated by the success of threshold decomposition, gradientbased operators are used to detect the locations of the edges. A morphological filter is used to sharpen these detected edges. Experimental results demonstrate that the detected edge deblurring filter improved the visibility and perceptibility of various embedded structures in digital medical images. Moreover, the performance of the proposed filter is superior to that of other sharpener-type filters

Classification of ordered texture images using regression modelling and granulometric features

Structural information available from the granulometry of an image has been used widely in image texture analysis and classification. In this paper we present a method for classifying texture images which follow an intrinsic ordering of textures, using polynomial regression to express granulometric moments as a function of class label. Separate models are built for each individual moment and combined for back-prediction of the class label of a new image. The methodology was developed on synthetic images of evolving textures and tested using real images of 8 different grades of cut-tear-curl black tea leaves. For comparison, grey level co-occurrence (GLCM) based features were also computed, and both feature types were used in a range of classifiers including the regression approach. Experimental results demonstrate the superiority of the granulometric moments over GLCM-based features for classifying these tea images

Morphological granulometry for classification of evolving and ordered texture images.

In this work we investigate the use of morphological granulometric moments as texture descriptors to predict time or class of texture images which evolve over time or follow an intrinsic ordering of textures. A cubic polynomial regression was used to model each of several granulometric moments as a function of time or class. These models are then combined and used to predict time or class. The methodology was developed on synthetic images of evolving textures and then successfully applied to classify a sequence of corrosion images to a point on an evolution time scale. Classification performance of the new regression approach is compared to that of linear discriminant analysis, neural networks and support vector machines. We also apply our method to images of black tea leaves, which are ordered according to granule size, and very high classification accuracy was attained compared to existing published results for these images. It was also found that granulometric moments provide much improved classification compared to grey level co-occurrence features for shape-based texture images

Modeling of evolving textures using granulometries

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Challenges of mentorship

Mentorship is the fourteenth series of ‘Midwifery basics’ targeted at practising midwives. It aims to provide information to raise awareness of the impact of the work of midwives on student learning and ultimately on women’s experience and encourage midwives to seek further information through a series of activities. In this sixth article Charlotte Kenyon, Stephen Hogarth and Joyce Marshall consider some of the challenges to mentorship and possible solutions to these