Üner Tan Syndrome: Review and Emergence of Human Quadrupedalism in Self-Organization,\ud Attractors and Evolutionary Perspectives\ud

The first man reported in the world literature exhibiting habitual quadrupedal locomotion was discovered by a British traveler and writer on the famous Baghdat road near Havsa/Samsun on the middle Black-Sea coast of Turkey (Childs, 1917). Interestingly, no single case with human quadrupedalism was reported in the scientific literature after Child's first description in 1917 until the first report on the Uner Tan syndrome (UTS: quadrupedalism, mental retardation, and impaired speech or no speech)in 2005 (Tan, 2005, 2006). Between 2005 and 2010, 10 families exhibiting the syndrome were discovered in Turkey with 33 cases: 14 women (42.4%) and 19 men (57.6%). Including a few cases from other countries, there were 25 men (64.1%)and 14 women (35.9%). The number of men significantly exceeded the number of women (p < .05). Genetics alone did not seem to be informative for the origins of many syndromes, including the Uner Tan syndrome. From the viewpoint of dynamical systems theory, there may not be a single factor including the neural and/or genetic codes that predetermines the emergence of the human quadrupedalism.Rather, it may involve a self-organization process, consisting of many decentralized and local interactions among neuronal, genetic, and environmental subsystems. The most remarkable characteristic of the UTS, the diagonal-sequence quadrupedalism is well developed in primates. The evolutionarily advantage of this gait is not known. However, there seems to be an evolutionarily advantage of this type of locomotion for primate evolution, with regard to the emergence of complex neural circuits with related highly complex structures. Namely, only primates with diagonal-sequence quadrupedal locomotion followed an evolution favoring larger brains, highly developed cognitive abilities with hand skills, and language, with erect posture and bipedal locomotion, creating the unity of human being. It was suggested that UTS may be considered a further example for Darwinian diseases, which may be associated with an evolutionary understanding of the disorders using evolutionary principles, such as the natural selection. On the other hand, the human quadrupedalism was proposed to be a phenotypic example of evolution of reverse, i.e., the reacquisition by derived populations of the same character states as those of ancestor populations. It was also suggested that the emergence of the human quadrupedalism may be related to self-organizing processes occurring in complex systems, which select or attract one preferred behavioral state or locomotor trait out of many possible attractor states. Concerning the locomotor patterns, the dynamical systems in brain and body of the developing child may prefer some kind of locomotion, according to interactions of the internal components and the environmental conditions, without a direct role of any causative factor(s), such as genetic or neural codes, consistent with the concept of self-organization, suggesting no single element may have a causal priority

Outlook Magazine, Autumn 2018

https://digitalcommons.wustl.edu/outlook/1205/thumbnail.jp

Rhythmic dynamics and synchronization via dimensionality reduction : application to human gait

Reliable characterization of locomotor dynamics of human walking is vital to understanding the neuromuscular control of human locomotion and disease diagnosis. However, the inherent oscillation and ubiquity of noise in such non-strictly periodic signals pose great challenges to current methodologies. To this end, we exploit the state-of-the-art technology in pattern recognition and, specifically, dimensionality reduction techniques, and propose to reconstruct and characterize the dynamics accurately on the cycle scale of the signal. This is achieved by deriving a low-dimensional representation of the cycles through global optimization, which effectively preserves the topology of the cycles that are embedded in a high-dimensional Euclidian space. Our approach demonstrates a clear advantage in capturing the intrinsic dynamics and probing the subtle synchronization patterns from uni/bivariate oscillatory signals over traditional methods. Application to human gait data for healthy subjects and diabetics reveals a significant difference in the dynamics of ankle movements and ankle-knee coordination, but not in knee movements. These results indicate that the impaired sensory feedback from the feet due to diabetes does not influence the knee movement in general, and that normal human walking is not critically dependent on the feedback from the peripheral nervous system

Understanding from Machine Learning Models

Simple idealized models seem to provide more understanding than opaque, complex, and hyper-realistic models. However, an increasing number of scientists are going in the opposite direction by utilizing opaque machine learning models to make predictions and draw inferences, suggesting that scientists are opting for models that have less potential for understanding. Are scientists trading understanding for some other epistemic or pragmatic good when they choose a machine learning model? Or are the assumptions behind why minimal models provide understanding misguided? In this paper, using the case of deep neural networks, I argue that it is not the complexity or black box nature of a model that limits how much understanding the model provides. Instead, it is a lack of scientific and empirical evidence supporting the link that connects a model to the target phenomenon that primarily prohibits understanding

A dynamic network approach for the study of human phenotypes

The use of networks to integrate different genetic, proteomic, and metabolic datasets has been proposed as a viable path toward elucidating the origins of specific diseases. Here we introduce a new phenotypic database summarizing correlations obtained from the disease history of more than 30 million patients in a Phenotypic Disease Network (PDN). We present evidence that the structure of the PDN is relevant to the understanding of illness progression by showing that (1) patients develop diseases close in the network to those they already have; (2) the progression of disease along the links of the network is different for patients of different genders and ethnicities; (3) patients diagnosed with diseases which are more highly connected in the PDN tend to die sooner than those affected by less connected diseases; and (4) diseases that tend to be preceded by others in the PDN tend to be more connected than diseases that precede other illnesses, and are associated with higher degrees of mortality. Our findings show that disease progression can be represented and studied using network methods, offering the potential to enhance our understanding of the origin and evolution of human diseases. The dataset introduced here, released concurrently with this publication, represents the largest relational phenotypic resource publicly available to the research community.Comment: 28 pages (double space), 6 figure