Communicative and Integrative Biology最新文献

Fundamental tensions exist between formal-logical approaches and cognitive approaches to linguistic meaning. The divergence arises from the fundamental differences in nature and form between formal/mathematical structures of natural language meaning and their cognitive representations. While the former are abstract and logical categories of representations, the latter are ultimately embodied and grounded in sensory-motor systems of the brain. This article aims to motivate a unifying theory/formalism of linguistic meaning from a general biologically integrative perspective in the context of current theorizing in linguistics, neurobiology and cognitive sciences on human language meaning within which two divergent approaches for the mathematical and cognitive aspects of linguistic meaning exist. The tensions can be somewhat neutralized if formal-mathematical structures and cognitive representations of natural language meaning can be shown to have representational duality and unity in brain dynamics. This work shows a broad outline of one, if not the only one, path toward this vision.

Cellular measurement is a crucial faculty in living systems, and exaptations are acknowledged as a significant source of evolutionary innovation. However, the possibility that the origin of biological order is predicated on an exaptation of the measurement of information from the abiotic realm has not been previously explored. To support this hypothesis, the existence of a universal holographic relational information space-time matrix is proposed as a scale-free unification of abiotic and biotic information systems. In this framework, information is a universal property representing the interactions between matter and energy that can be subject to observation. Since observers are also universally distributed, information can be deemed the fundamental fabric of the universe. The novel concept of compartmentalizing this universal N-space information matrix into separate N-space partitions as nodes of informational density defined by Markov blankets and boundaries is introduced, permitting their applicability to both abiotic and biotic systems. Based on these N-space partitions, abiotic systems can derive meaningful information from the conditional settlement of quantum entanglement asymmetries and coherences between separately bounded quantum informational reference frames sufficient to be construed as a form of measurement. These conditional relationships are the precursor of the reiterating nested architecture of the N-space-derived information fields that characterize life and account for biological order. Accordingly, biotic measurement and biological N-space partitioning are exaptations of preexisting information processes within abiotic systems. Abiotic and biotic states thereby reconcile as differing forms of measurement of fundamental universal information. The essential difference between abiotic and biotic states lies within the attributes of the specific observer/detectors, thereby clarifying several contentious aspects of self-referential consciousness.

Power is an all-pervasive, and fundamental force in human relationships and plays a valuable role in social, political, and economic interactions. Power differences are important in social groups in enhancing group functioning. Most people want to have power and there are many benefits to having power. However, power is a corrupting force and this has been a topic of interest for centuries to scholars from Plato to Lord Acton. Even with increased knowledge of power's corrupting effect and safeguards put in place to counteract such tendencies, power abuse remains rampant in society suggesting that the full extent of this effect is not well understood. In this paper, an effort is made to improve understanding of power's corrupting effects on human behavior through an integrated and comprehensive synthesis of the neurological, sociological, physiological, and psychological literature on power. The structural limits of justice systems' capability to hold powerful people accountable are also discussed.

The initiation of fever has been a matter of controversy. Based on observations of little or no induction of prostaglandin synthesizing enzymes in the brain during the first phase of fever it was suggested that fever is initiated by prostaglandin released into the circulation from cells in the liver and lungs. Here we show in the mouse that prostaglandin synthesis is rapidly induced in the brain after immune challenge. These data are consistent with our recent findings in functional experiments that prostaglandin production in brain endothelial cells is both necessary and sufficient for the generation of all phases of fever.

In order to understand the mechanism of desiccation tolerance in Xerophyta schlechteri, we carried out an in silico study to identify hub proteins and functional modules in the nuclear proteome of the leaves. Protein-protein interaction networks were constructed and analyzed from proteome data obtained from Abdalla and Rafudeen. We constructed networks in Cytoscape using the GeneMania software and analyzed them using a Network Analyzer. Functional enrichment analysis of key proteins in the respective networks was done using GeneMania network enrichment analysis, and GO (Gene Ontology) terms were summarized using REViGO. Also, community analysis of differentially expressed proteins was conducted using the Cytoscape Apps, GeneMania and ClusterMaker. Functional modules associated with the communities were identified using an online tool, ShinyGO. We identified HSP 70-2 as the super-hub protein among the up-regulated proteins. On the other hand, 40S ribosomal protein S2-3 (a protein added by GeneMANIA) was identified as a super-hub protein associated with the down-regulated proteins. For up-regulated proteins, the enriched biological process terms were those associated with chromatin organization and negative regulation of transcription. In the down-regulated protein-set, terms associated with protein synthesis were significantly enriched. Community analysis identified three functional modules that can be categorized as chromatin organization, anti-oxidant activity and metabolic processes.

The ability to recognize faces is a fundamental skill in human social interaction. While much research has focused on the recognition of familiar faces, there is growing interest in understanding the cognitive processes underlying the recognition of unfamiliar faces. Previous studies have suggested that both semantic knowledge and physical features play a role in unfamiliar face recognition, but the nature of their relationship is not well understood. This study examines the relationship between unfamiliar face recognition ability and the encoding abilities of semantic knowledge and physical features for famous faces. Using the Gorilla platform, a large group of participants (N = 66) with a broad age range completed three tasks: a challenging unfamiliar face matching task and Famous People Recognition Tests 1 and 2 to evaluate semantic and physical feature encoding abilities, respectively. Results indicate positive correlations between encoding abilities for both semantic knowledge and physical features of familiar faces with Model Face Matching Task scores. Additionally, the encoding ability for semantic knowledge was found to be positively associated with that of physical features.

Since humans still outperform artificial neural networks on many tasks, drawing inspiration from the brain may help to improve current machine learning algorithms. Contrastive Hebbian learning (CHL) and equilibrium propagation (EP) are biologically plausible algorithms that update weights using only local information (without explicitly calculating gradients) and still achieve performance comparable to conventional backpropagation. In this study, we augmented CHL and EP with Adjusted Adaptation, inspired by the adaptation effect observed in neurons, in which a neuron's response to a given stimulus is adjusted after a short time. We add this adaptation feature to multilayer perceptrons and convolutional neural networks trained on MNIST and CIFAR-10. Surprisingly, adaptation improved the performance of these networks. We discuss the biological inspiration for this idea and investigate why Neuronal Adaptation could be an important brain mechanism to improve the stability and accuracy of learning.

Hypotheses have historically linked the emergence and evolution of defining human characteristics such as bipedal walking to ground-dwelling, envisioning our earliest ancestors as living in treeless savannahs (i.e. the traditional savannah hypothesis). However, over the last two decades, evidence from the fossil record combined with comparative studies of extant apes have challenged this hypothesis, instead favoring the importance of arboreality during key phases of hominin evolutionary history. Here we review some of these studies, including a recent study of savannah chimpanzees that provides the first model of how bipedalism could have been adaptive as an arboreal locomotor behavior in early hominins, even after the forests receded during the early Miocene-Pliocene transition. We suggest that whilst a shift to exploiting open habitats catalyzed hominin divergence from great apes, adaptations to arboreal living have been key in shaping what defines humans today, in counter to the traditional savannah hypothesis. Future comparative studies within and between great ape species will be instrumental to understanding variation in arboreality in extant apes, and thus the processes shaping human evolution over the last 3-7 million years.

Epilepsy is one of the dreaded conditions that had taken billions of people under its cloud worldwide. Detecting the seizure at the correct time in an individual is something that medical practitioners focus in order to help people save their lives. Analysis of the Electroencephalogram (EEG) signal from the scalp area of the human brain can help in detecting the seizure beforehand. This paper presents a novel classification technique to classify EEG brain signals for epilepsy identification based on Discrete Wavelet Transform and Moth Flame Optimization-based Extreme Learning Machine (DM-ELM). ELM is a very popular machine learning method based on Neural Networks (NN) where the model is trained rigorously to get the minimized error rate and maximized accuracy. Here we have used several experimental evaluations to compare the performance of basic ELM and DM-ELM and it has been experimentally proved that DM-ELM outperforms basic ELM but with few time constraints.

This paper argues that all scientific research is framed by one of two organizing principles that underpin and shape almost every aspect of scientific research as well as nonscientific inquiry. The most commonly employed principle within mainstream science is content determines content. This is a closed, circular principle that is usually unstated within hypotheses but plays a major role in developing methodologies and arriving at conclusions. The second more open principle is context determines content. This principle represents the implied background embedded within hypotheses. The difference between these two principles revolves around the issue of context, with the first principle closing off contexts by ignoring, erasing, or devaluing them, while the second more holistic principle explicitly takes them into account. Each of these research principles has a focus on the explicit detailed nature of 'content' while differing in relation to the source and cause of such content. We argue that the more open and holistic principle of context determines that content is superior in producing reliable evidence, results and conclusions.