A computational framework for topographies of cortical areas

Self-organizing feature maps (SOFMs) represent a dimensionality-reduction algorithm that has been used to replicate feature topographies observed experimentally in primary visual cortex (V1). We used the SOFM algorithm to model possible topographies of generic sensory cortical areas containing up to five arbitrary physiological features. This study explored the conditions under which these multi-feature SOFMs contained two features that were mapped monotonically and aligned orthogonally with one another (i.e., “globally orthogonal”), as well as the conditions under which the map of one feature aligned with the longest anatomical dimension of the modeled cortical area (i.e., “dominant”). In a single SOFM with more than two features, we never observed more than one dominant feature, nor did we observe two globally orthogonal features in the same map in which a dominant feature occurred. Whether dominance or global orthogonality occurred depended upon how heavily weighted the features were relative to one another. The most heavily weighted features are likely to correspond to those physical stimulus properties transduced directly by the sensory epithelium of a particular sensory modality. Our results imply, therefore, that in the primary cortical area of sensory modalities with a two-dimensional sensory epithelium, these two features are likely to be organized globally orthogonally to one another, and neither feature is likely to be dominant. In the primary cortical area of sensory modalities with a one-dimensional sensory epithelium, however, this feature is likely to be dominant, and no two features are likely to be organized globally orthogonally to one another. Because the auditory system transduces a single stimulus feature (i.e., frequency) along the entire length of the cochlea, these findings may have particular relevance for topographic maps of primary auditory cortex. This research was supported by The McDonnell Center for Higher Brain Function, The Wallace H. Coulter Foundation and NIH grant DC008880.     

Influence of aquatic macrophyte habitat complexity on invertebrate abundance and richness in tropical lagoons

1. Aquatic plants are a key component of spatial heterogeneity in a waterscape, contributing to habitat complexity and helping determine diversity at various spatial scales. Theoretically, the more complex a habitat, the higher the number of species present. 2. Few empirical data are available to test the hypothesis that complexity increases diversity in aquatic communities (e.g. Jeffries, 1993 ). Fractal dimension has become widely applied in ecology as a tool to quantify the degree of complexity at different scales. 3. We investigated the hypothesis that complexity in vegetated habitat in two tropical lagoons mediates littoral invertebrate number of taxa (S) and density (N). Aquatic macrophyte habitat complexity was defined using a fractal dimension and a gradient of natural plant complexities. We also considered plant area, plant identity and, only for S, invertebrate density as additional explanatory variables. 4. Our results indicate that habitat complexity provided by the different architectures of aquatic plants, significantly affects both S and total N. However, number of individuals (as a result of passive sampling) also helps to account for S and, together with plant identity and area, contributes to the determination of N. We suggest that measurements of structural complexity, measured through fractal geometry, should be included in studies aimed at explaining attributes of attached invertebrates at small (e.g. plant or leaf) scales.     

CO-WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image

The most significant groupings of cold-blooded creatures are the fish family. It is crucial to recognize and categorize the most significant species of fish since various species of seafood diseases and decay exhibit different symptoms. Systems based on enhanced deep learning can replace the area's currently cumbersome and sluggish traditional approaches. Although it seems straightforward, classifying fish images is a complex procedure. In addition, the scientific study of population distribution and geographic patterns is important for advancing the field's present advancements. The goal of the proposed work is to identify the best performing strategy using cutting-edge computer vision, the Chaotic Oppositional Based Whale Optimization Algorithm (CO-WOA), and data mining techniques. Performance comparisons with leading models, such as Convolutional Neural Networks (CNN) and VGG-19, are made to confirm the applicability of the suggested method. The suggested feature extraction approach with Proposed Deep Learning Model was used in the research, yielding accuracy rates of 100 %. The performance was also compared to cutting-edge image processing models with an accuracy of 98.48 %, 98.58 %, 99.04 %, 98.44 %, 99.18 % and 99.63 % such as Convolutional Neural Networks, ResNet150V2, DenseNet, Visual Geometry Group-19, Inception V3, Xception. Using an empirical method leveraging artificial neural networks, the Proposed Deep Learning model was shown to be the best model.     

Comparison of various procedures for estimation of the classification error in discriminance analysis

The practical use of classification methods depends, for the majority of cases, on whether it is possible to obtain evidence how newly appearing objects of unknown classes-membership can be allocated on the basis of a classifier established by a training sample. In the present paper several error estimation methods are presented and compared with each other. It is shown by means of a practical example in which way the number of characteristics used in the discrimination affects the classification error. Discriminance functions calculated from many characteristics yield a higher error rate than those from fewer characteristics. From the comparison of the error rates and computing times involved in the individual methods recommendations for the selection of practicable estimation methods are given.     

Natural history and burrow system architecture of the silvery mole-rat from Brachystegia woodland

The silvery mole-rat Heliophobius argenteocinereus (Bathyergidae) is a solitary subterranean rodent, widely distributed throughout eastern and south-eastern Africa in a variety of habitats. Here, we provide the first data on its biology in a typical natural habitat, the Brachystegia woodland. The population density of mole-rats was low (4.6 ha⁻¹) and its distribution across the study site was random. Contrary to subadults and pups, the sex ratio of adult mole-rats was highly female biased (1:5.75), probably due to the higher mortality of males as a consequence of their mating strategy. Reproduction of Heliophobius is seasonal and pups are born at the beginning of the hot dry season. Burrow systems of the silvery mole-rat were long, highly branched, reticulated and comparable in fractal dimension to systems of social bathyergids. Variability in burrow architecture was related to the body mass of the burrow occupants, soil hardness and food supply when tested together. Burrow systems with a higher fractal dimension had inhabitants that had a greater body mass. Longer systems were less branched. Nests were typically deeper than foraging tunnels and experienced negligible temperature fluctuations. The microenvironmental characteristics of the subterranean niche including temperature, humidity and soil characteristics are provided for purpose of comparison with other mole-rat species.     

Multi-scale trajectory analysis: powerful conceptual tool for understanding ecological change

The model at the basis of trajectory analysis is conceptually simple. When applied to time series vegetation data, the projectile becomes a surrogate for vegetation state, the trajectory for the evolving vegetation process, and the properties of the trajectory for the true process characteristics. Notwithstanding its simplicity, the model is well-defined under natural circumstances and easily adapted to serial vegetation data, irrespective of source. As a major advantage, compared to other models that isolate the elementary processes and probe vegetation dynamics for informative regularities on the elementary level, the trajectory model allows us to probe for regularities on the level of the highest process integrity. Theories and a data analytical methodology developed around the trajectory model are outlined, including many numerical examples. A rich list of key references and volumes of supplementary information supplied in the Web Only Appendices rounds out the presentation.     

SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning

Intrinsically disordered or unstructured proteins (or regions in proteins) have been found to be important in a wide range of biological functions and implicated in many diseases. Due to the high cost and low efficiency of experimental determination of intrinsic disorder and the exponential increase of unannotated protein sequences, developing complementary computational prediction methods has been an active area of research for several decades. Here, we employed an ensemble of deep Squeeze-and-Excitation residual inception and long short-term memory (LSTM) networks for predicting protein intrinsic disorder with input from evolutionary information and predicted one-dimensional structural properties. The method, called SPOT-Disorder2, offers substantial and consistent improvement not only over our previous technique based on LSTM networks alone, but also over other state-of-the-art techniques in three independent tests with different ratios of disordered to ordered amino acid residues, and for sequences with either rich or limited evolutionary information. More importantly, semi-disordered regions predicted in SPOT-Disorder2 are more accurate in identifying molecular recognition features (MoRFs) than methods directly designed for MoRFs prediction. SPOT-Disorder2 is available as a web server and as a standalone program at https://sparks-lab.org/server/spot-disorder2/.     

Influence of river geomorphologic features on hippopotamus density distribution along the Luangwa River,Zambia

River geomorphologic features along a 165‐km stretch of the Luangwa River in eastern Zambia were assessed in 2008 to determine their influence on hippopotamus pattern of density distribution. A fixed high wing Cessna 206 aircraft fitted with geographical positioning system (GPS) was flown over the stretch taking coordinates for each geomorphologic feature, followed by taking length in metres of each geomorphologic feature on the ground. A total hippopotamus count was later carried out along the same stretch. Sixty‐four geomorphologic features were recorded along the stretch of the river. Of the total geomorphologic features recorded, 34 (53%) were river bends, 13 (20%) lagoons and 17 (27%) were confluences. Hippo density was highest in river bends (75.48%), confluences (9.18%), lagoons 6.03% and all the other features put together had only 9.38%. It was concluded that geomorphologic features influenced hippopotamus pattern of density distribution in the Luangwa River.