Species richness, abundance and phenology of fungal fruit bodies over 21 years in a Swiss forest plot

Fungal fruit bodies were surveyed on a plot area of 1500 m2 from 1975¿99 (excluding 1980¿83) in the fungal reserve La Chaneaz in western Switzerland. Fruit bodies were identified and counted on a weekly basis. Species richness and abundances varied strongly between years. More than 400 species were encountered. Many species were transient; particularly rich years showed species occurring for only one year. This indicates that the number of species will substantially increase if the survey is continued. Within years, the species richness, abundances and periods of fruiting were tightly correlated. The abundance data of species within a year seemed symmetrically distributed over their fruiting period. The relation between species richness and abundances within years was studied by fitting species-abundance plots, known from numerical ecology. The surface area under the curves was taken as a parameter for ecological/fungal diversity. Productivity was correlated with the precipitation from June until October. The time of fruit body appearance was correlated with the temperatures in July and August. As groups, mycorrhizal and saprotrophic species behaved similarly over the years. The productivity of species was compared with their distribution in The Netherlands indicating a correlation between the level of local abundance and the geographic range of specie

QuantiMus: A Machine Learning-Based Approach for High Precision Analysis of Skeletal Muscle Morphology.

Skeletal muscle injury provokes a regenerative response, characterized by the de novo generation of myofibers that are distinguished by central nucleation and re-expression of developmentally restricted genes. In addition to these characteristics, myofiber cross-sectional area (CSA) is widely used to evaluate muscle hypertrophic and regenerative responses. Here, we introduce QuantiMus, a free software program that uses machine learning algorithms to quantify muscle morphology and molecular features with high precision and quick processing-time. The ability of QuantiMus to define and measure myofibers was compared to manual measurement or other automated software programs. QuantiMus rapidly and accurately defined total myofibers and measured CSA with comparable performance but quantified the CSA of centrally-nucleated fibers (CNFs) with greater precision compared to other software. It additionally quantified the fluorescence intensity of individual myofibers of human and mouse muscle, which was used to assess the distribution of myofiber type, based on the myosin heavy chain isoform that was expressed. Furthermore, analysis of entire quadriceps cross-sections of healthy and mdx mice showed that dystrophic muscle had an increased frequency of Evans blue dye+ injured myofibers. QuantiMus also revealed that the proportion of centrally nucleated, regenerating myofibers that express embryonic myosin heavy chain (eMyHC) or neural cell adhesion molecule (NCAM) were increased in dystrophic mice. Our findings reveal that QuantiMus has several advantages over existing software. The unique self-learning capacity of the machine learning algorithms provides superior accuracy and the ability to rapidly interrogate the complete muscle section. These qualities increase rigor and reproducibility by avoiding methods that rely on the sampling of representative areas of a section. This is of particular importance for the analysis of dystrophic muscle given the "patchy" distribution of muscle pathology. QuantiMus is an open source tool, allowing customization to meet investigator-specific needs and provides novel analytical approaches for quantifying muscle morphology

Comparative analysis of extracted heights from topographic maps and measured reduced levels in Kumasi, Ghana

No other mapping product has had widespread applications in developmental planning than the topographic map. Topographic maps represent the three-dimensional landscape by providing relief information in the form of contours in addition to plan information on which natural and man-made landmarks are quite accurately represented. Height information, extractible from topographic maps, comes in handy for most land use planning. However, generalizations during contouring and the need to interpolate between successive contours for specific grid nodes introduce errors in extracted heights. There is therefore, the necessity to use some mathematical modeling to remove discrepancies in the interpolation process to improve elevation data extracted from topographic maps. In this study, the accuracy of spot heights derived from interpolated and extracted heights from topographical maps is assessed. Two different mathematical models - a third degree polynomial regression model and the Thompson’s Multiple Variable Polynomial regression models, were respectively used to model the relationship between extracted heights and ground reduced levels. Results from the two models indicate that, the latter presents better refinements to converting extracted heights into reduced levels with a coefficient of determination value of 95.9%, although further research is recommended to investigate numerical techniques that could improve the solution to the Thompson’s polynomial. The Thompson’s model was implemented as a crude height refiner program that receives extracted heights to return corrected heights. The implication of these results for the mapping community is that, it is possible to model a correction function that can help obtain reasonably accurate heights from topographic maps. This will reduce the necessity of always going back to the field for field surveys in spite of the fact that topographic maps covering an area already exists.Keywords: Topographic maps, Interpolation, Thompson’s polynomial, Levelling, Terrain model

Phase fluctuations in the ABC model

We analyze the fluctuations of the steady state profiles in the modulated phase of the ABC model. For a system of $L$ sites, the steady state profiles move on a microscopic time scale of order $L^3$. The variance of their displacement is computed in terms of the macroscopic steady state profiles by using fluctuating hydrodynamics and large deviations. Our analytical prediction for this variance is confirmed by the results of numerical simulations