Applications of Molecular Dynamics simulations for biomolecular systems and improvements to density-based clustering in the analysis

Molecular Dynamics simulations provide a powerful tool to study biomolecular systems with atomistic detail. The key to better understand the function and behaviour of these molecules can often be found in their structural variability. Simulations can help to expose this information that is otherwise experimentally hard or impossible to attain. This work covers two application examples for which a sampling and a characterisation of the conformational ensemble could reveal the structural basis to answer a topical research question. For the fungal toxin phalloidin—a small bicyclic peptide—observed product ratios in different cyclisation reactions could be rationalised by assessing the conformational pre-organisation of precursor fragments. For the C-type lectin receptor langerin, conformational changes induced by different side-chain protonations could deliver an explanation of the pH-dependency in the protein’s calcium-binding. The investigations were accompanied by the continued development of a density-based clustering protocol into a respective software package, which is generally well applicable for the use case of extracting conformational states from Molecular Dynamics data

The role of heterogeneity in spatial plant population dynamics

Ecological theory names interacting mechanisms that allow competing species to coexist in limited available space, some of them are perceive as antagonistic. Most prominent are niche differentiation, heterogeneity and neutrality (ecological equivalence). Species similarity is also influenced by two mechanisms: Habitat filtering selects for ecologically similar species, while niche differentiation reduces competitive pressure and thus prefers ecologically different species. The spatial arrangement of abiotic resources can determine the spatial pattern and competition framework for a pre-selected tree species ensemble. Spatial occurrence patterns of trees are formed by dispersal, growth and mortality which are influenced by the interacting abiotic and abiotic conditions. The relative impact of these mechanisms are underresearched in temperate forest trees, especially in Europe. We analysed a data set of a temperate old-growth forest with spatially explicit information about more than 15 000 individual trees of six tree species (90 % beech admixed with Ash, Hornbeam, Sycamore, Norway Maple, and Wych Elm) located in the central region of the Hainich National Park in central Germany. We tested space-related coexistence mechanisms under heterogeneous conditions. For this, we employed Point Pattern Analysis for testing several ecological hypotheses on inter- and intraspecific interactions of the species, varying from randomness to strict ecological niche. In order to identify the critical components of possible niches, we collected field data on the abiotic conditions such as the availability of water and light, and considered topography using a Digital Elevation Model. These field data were used for fitting suitability surfaces depending on tree species identity using spatial interpolation methods such as Kriging and Generalised Additive Models. We used Spatial Point Process Models to reconstruct the spatial distribution processes composed of purely biotic, abiotic or mixed covariates of the tree species. We found that spatial heterogeneity was important in all aspects we studied. Both, tree density and the distribution of the abiotic habitat components varied in space. Especially when species interacted with beech, abiotic heterogeneity played an important role: beech outcompeted the admixed species under most prevailing abiotic conditions. This way, beech influenced the spatial pattern of the six studied species by limiting available (niche) space via inter- and intraspecific competition. Here, Beech proved to be the superior competitor with no pronounced abiotic niche, but is mostly excluded from slopes. The remaining available niche space was often occupied by ecologically similar species, which formed typical associations in subregions of the study area less suitable for beech. We found spatial segregation between the three most abundant species Beech, Ash, and Hornbeam, coexistence by niches seem to be rather trait based rather than based on abiotic preferences. Habitat suitability and spatial distribution of Ash, Sycamore, and Norway Maple were more affected by the abiotic environmental condition than Beech, Hornbeam, and Elm. This indicates that the coexistence of rare species seems to be mediated by heterogeneity. Our study revealed that the difference in abiotic conditions, such as soil depth and plant-available water were relevant for habitat suitability at small spatial and temporal scales. When simulating the distribution pattern of the surveyed species, it became apparent that biotic interactions play an important part in shaping the scales at which aggregation or segregation happen in the abiotic environment. Beech and Sycamore both showed endogenous heterogeneity. For both species, point processes models incorporated several different interaction scales of intraspecific interaction. The interspecific interaction played only a minor role compared to the intraspecific one. All results together seem to underline that niche differentiation happens at the level of the individual allowing ecologically similar species to interact de facto neutrally within their niche space and thus, to coexist in presence of a strong competitor

The structure and evolution of story networks

With this study, we advance the understanding about the processes through which stories are retold. A collection of story retellings can be considered as a network of stories, in which links between stories represent pre-textual (or ancestral) relationships. This study provides a mechanistic understanding of the structure and evolution of such story networks: we construct a story network for a large diachronic collection of Dutch literary retellings of Red Riding Hood, and compare this network to one derived from a corpus of paper chain letters. In the analysis, we first provide empirical evidence that the formation of these story networks is subject to age-dependent selection processes with a strong lopsidedness towards shorter time-spans between stories and their pre-texts (i.e. ‘young’ story versions are preferred in producing new versions). Subsequently, we systematically compare these findings with and among predictions of various formal models of network growth to determine more precisely which kinds of attractiveness are also at play or might even be preferred as explicatory models. By carefully studying the structure and evolution of the two story networks, then, we show that existing stories are differentially preferred to function as a new version's pre-text given three types of attractiveness: (i) frequency-based and (ii) model-based attractiveness which (iii) decays in time

Machine Learning Approaches for Heart Disease Detection: A Comprehensive Review

This paper presents a comprehensive review of the application of machine learning algorithms in the early detection of heart disease. Heart disease remains a leading global health concern, necessitating efficient and accurate diagnostic methods. Machine learning has emerged as a promising approach, offering the potential to enhance diagnostic accuracy and reduce the time required for assessments. This review begins by elucidating the fundamentals of machine learning and provides concise explanations of the most prevalent algorithms employed in heart disease detection. It subsequently examines noteworthy research efforts that have harnessed machine learning techniques for heart disease diagnosis. A detailed tabular comparison of these studies is also presented, highlighting the strengths and weaknesses of various algorithms and methodologies. This survey underscores the significant strides made in leveraging machine learning for early heart disease detection and emphasizes the ongoing need for further research to enhance its clinical applicability and efficacy