We measured the correlation of phylogenetic distances to morphological and echolocation distances, and tested the relationship between morphology and behavior when the effect of phylogeny is removed. Morphology evolved via a mosaic of convergence and stasis, whereas call design was influenced exclusively through local adaptation and convergent evolution. Furthermore, the frequency of echolocation calls is negatively correlated with the size of the bat, but other characters do not seem to be evolving in concert. We hypothesize that slight variation in both morphology and behaviour among species of the genus might result from niche specialization, and that traits evolve to avoid competition for resources in similar environments.Gillnets made of the biodegradable resin polybutylene succinate co-adipate-co-terephthalate were tested under commercial fishing conditions to compare their fishing performance with that of conventional nylon polyamide (PA) gillnets. Both types of gillnets were made of 0.55 mm Ø monofilaments. However, since the biodegradable nets are weaker than nylon PA nets when using the same monofilament diameter, we also used biodegradable nets made of 0.60 mm Ø monofilament that had a similar tensile strength to the 0.55 mm Ø nylon PA nets. The relative catch efficiency of the different gillnet types was evaluated over the 2018 autumn fishing season for saithe and cod in northern Norway. For cod, both biodegradable gillnets (0.55 and 0.60 mm) had a significantly lower catch efficiency compared to the traditional nylon PA net (0.55 mm) with estimated catch efficiencies of 62.38% (CI 50.55-74.04) and 54.96% (CI 35.42-73.52) compared with the nylon PA net, respectively. BML-284 concentration Similarly for saithe, both biodegradable gillnets (0.55 and 0.60 mm) had a lower estimated catch efficiency compared to the traditional nylon PA net (0.55 mm) with estimated catch efficiencies of 83.40% (71.34-94.86) and 83.87% (66.36-104.92), compared with the nylon PA net, respectively. Tensile strength does not explain the differences in catch efficiency between the two gillnet types, since increasing the twine diameter of the biodegradable gillnets (to match the strength of nylon PA gillnets) did not yield similar catch efficiencies. However, the elasticity and stiffness of the materials may be responsible for the differences in catch efficiency between the nylon PA and biodegradable gillnets.Protein-protein interactions (PPIs) are essential for most biological processes. However, current PPI networks present high levels of noise, sparseness and incompleteness, which limits our ability to understand the cell at the system level from the PPI network. Predicting novel (missing) links in noisy PPI networks is an essential computational method for automatically expanding the human interactome and for identifying biologically legitimate but undetected interactions for experimental determination of PPIs, which is both expensive and time-consuming. Recently, graph convolutional networks (GCN) have shown their effectiveness in modeling graph-structured data, which employ a 1-hop neighborhood aggregation procedure and have emerged as a powerful architecture for node or graph representations. In this paper, we propose a novel node (protein) embedding method by combining GCN and PageRank as the latter can significantly improve the GCN’s aggregation scheme, which has difficulty in extending and exploring topological information of networks across higher-order neighborhoods of each node. Building on this novel node embedding model, we develop a higher-order GCN variational auto-encoder (HO-VGAE) architecture, which can learn a joint node representation of higher-order local and global PPI network topology for novel protein interaction prediction. It is worth noting that our method is based exclusively on network topology, with no protein attributes or extra biological features used. Extensive computational validations on PPI prediction task demonstrate our method without leveraging any additional biological information shows competitive performance-outperforms all existing graph embedding-based link prediction methods in both accuracy and robustness.Mitochondrial DNA variants associated with diseases are widely studied in contemporary populations, but their prevalence has not yet been investigated in ancient populations. The publicly available AmtDB database contains 1443 ancient mtDNA Eurasian genomes from different periods. The objective of this study was to use this data to establish the presence of pathogenic mtDNA variants putatively associated with mitochondrial diseases in ancient populations. The clinical significance, pathogenicity prediction and contemporary frequency of mtDNA variants were determined using online platforms. The analyzed ancient mtDNAs contain six variants designated as being “confirmed pathogenic” in modern patients. The oldest of these, m.7510T>C in the MT-TS1 gene, was found in a sample from the Neolithic period, dated 5800-5400 BCE. All six have well established clinical association, and their pathogenic effect is corroborated by very low population frequencies in contemporary populations. Analysis of the geographic location of the ancient samples, contemporary epidemiological trends and probable haplogroup association indicate diverse spatiotemporal dynamics of these variants. The dynamics in the prevalence and distribution is conceivably result of de novo mutations or human migrations and subsequent evolutionary processes. In addition, ten variants designated as possibly or likely pathogenic were found, but the clinical effect of these is not yet well established and further research is warranted. All detected mutations putatively associated with mitochondrial disease in ancient mtDNA samples are in tRNA coding genes. Most of these mutations are in a mt-tRNA type (Model 2) that is characterized by loss of D-loop/T-loop interaction. Exposing pathogenic variants in ancient human populations expands our understanding of their origin and prevalence dynamics.According to the efficient coding hypothesis, sensory systems are adapted to maximize their ability to encode information about the environment. Sensory neurons play a key role in encoding by selectively modulating their firing rate for a subset of all possible stimuli. This pattern of modulation is often summarized via a tuning curve. The optimally efficient distribution of tuning curves has been calculated in variety of ways for one-dimensional (1-D) stimuli. However, many sensory neurons encode multiple stimulus dimensions simultaneously. It remains unclear how applicable existing models of 1-D tuning curves are for neurons tuned across multiple dimensions. We describe a mathematical generalization that builds on prior work in 1-D to predict optimally efficient multidimensional tuning curves. Our results have implications for interpreting observed properties of neuronal populations. For example, our results suggest that not all tuning curve attributes (such as gain and bandwidth) are equally useful for evaluating the encoding efficiency of a population.