arXiv subjects

Bradly Alicea

Publications and source records attributed to Bradly Alicea.

27 records · Page 2Linked to original sources

arXiv2013

A Semi-automated Peer-review System

A semi-supervised model of peer review is introduced that is intended to overcome the bias and incompleteness of traditional peer review. Traditional approaches are reliant on human biases, while consensus decision-making is constrained by sparse information. Here, the architecture for one potential improvement (a semi-supervised, human-assisted classifier) to the traditional approach will be introduced and evaluated. To evaluate the potential advantages of such a system, hypothetical receiver operating characteristic (ROC) curves for both approaches will be assessed. This will provide more specific indications of how automation would be beneficial in the manuscript evaluation process. In conclusion, the implications for such a system on measurements of scientific impact and improving the quality of open submission repositories will be discussed.

cs.DL↗

arXiv2013

Cellular decision-making bias: the missing ingredient in cell functional diversity

Cell functional diversity is a significant determinant on how biological processes unfold. Most accounts of diversity involve a search for sequence or expression differences. Perhaps there are more subtle mechanisms at work. Using the metaphor of information processing and decision-making might provide a clearer view of these subtleties. Understanding adaptive and transformative processes (such as cellular reprogramming) as a series of simple decisions allows us to use a technique called cellular signal detection theory (cellular SDT) to detect potential bias in mechanisms that favor one outcome over another. We can apply method of detecting cellular reprogramming bias to cellular reprogramming and other complex molecular processes. To demonstrate scope of this method, we will critically examine differences between cell phenotypes reprogrammed to muscle fiber and neuron phenotypes. In cases where the signature of phenotypic bias is cryptic, signatures of genomic bias (pre-existing and induced) may provide an alternative. The examination of these alternates will be explored using data from a series of fibroblast cell lines before cellular reprogramming (pre-existing) and differences between fractions of cellular RNA for individual genes after drug treatment (induced). In conclusion, the usefulness and limitations of this method and associated analogies will be discussed.

q-bio.QM↗

arXiv2013

The Emergence of Animal Social Complexity: theoretical and biobehavioral evidence

This paper will introduce a theory of emergent animal social complexity using various results from computational models and empirical results. These results will be organized into a vertical model of social complexity. This will support the perspective that social complexity is in essence an emergent phenomenon while helping to answer two interrelated questions. The first of these involves how behavior is integrated at units of analysis larger than the individual organism. The second involves placing aggregate social events into the context of processes occurring within individual organisms over time (e.g. genomic and physiological processes). By using a complex systems perspective, five principles of social complexity can be identified. These principles suggest that lower-level mechanisms give rise to high-level mechanisms, ultimately resulting in metastable networks of social relations. These network structures then constrain lower-level phenomena ranging from transient, collective social groups to physiological regulatory mechanisms within individual organisms. In conclusion, the broader implications and drawbacks of applying the theory to a diversity of natural populations will be discussed.

q-bio.PE↗

arXiv2013

Collective properties of cellular identity: a computational approach

Cell type (e.g. pluripotent cell, fibroblast) is the end result of many complex processes that unfold due to evolutionary, developmental, and transformational stimuli. A cell's phenotype and the discrete, a priori states that define various cell subtypes (e.g. skin fibroblast, embryonic stem cell) are ultimately part of a continuum that may predict changes and systematic variation in cell subtypes. These features can be both observable in existing cellular states and hypothetical (e.g. unobserved). In this paper, a series of approaches will be used to approximate the continuous diversity of gene expression across a series of pluripotent, totipotent, and fibroblast cellular subtypes. We will use a series of previously-collected datasets and analyze them using three complementary approaches: the computation of distances based on the subsampling of diversity, assessing the separability of individual genes for a specific cell line both within and between cell types, and a hierarchical soft classification technique that will assign a membership value for specific genes in specific cell types given a number of different criteria. These approaches will allow us to assess the observed gene-expression diversity in these datasets, as well as assess how well a priori cell types characterize their constituent populations. In conclusion, the application of these findings to a broader biological context will be discussed.

q-bio.QM↗

arXiv2012

Naturally Supervised Learning in Manipulable Technologies

The relationship between physiological systems and modern electromechanical technologies is fast becoming intimate with high degrees of complex interaction. It can be argued that muscular function, limb movements, and touch perception serve supervisory functions for movement control in motion and touch-based (e.g. manipulable) devices/interfaces and human-machine interfaces in general. To get at this hypothesis requires the use of novel techniques and analyses which demonstrate the multifaceted and regulatory role of adaptive physiological processes in these interactions. Neuromechanics is an approach that unifies the role of physiological function, motor performance, and environmental effects in determining human performance. A neuromechanical perspective will be used to explain the effect of environmental fluctuations on supervisory mechanisms, which leads to adaptive physiological responses. Three experiments are presented using two different types of virtual environment that allowed for selective switching between two sets of environmental forces. This switching was done in various ways to maximize the variety of results. Electromyography (EMG) and kinematic information contributed to the development of human performance-related measures. Both descriptive and specialized analyses were conducted: peak amplitude analysis, loop trace analysis, and the analysis of unmatched muscle power. Results presented here provide a window into performance under a range of conditions. These analyses also demonstrated myriad consequences for force-related fluctuations on dynamic physiological regulation. The findings presented here could be applied to the dynamic control of touch-based and movement-sensitive human-machine systems. In particular, the design of systems such as human-robotic systems, touch screen devices, and rehabilitative technologies could benefit from this research.

cs.HC↗

arXiv2011

Relativistic virtual worlds: an emerging framework

In this paper, I will attempt to establish a framework for representation in virtual worlds that may allow for input data from many different scales and virtual physics to be merged. For example, a typical virtual environment must effectively handle user input, sensor data, and virtual world physics all in real- time. Merging all of these data into a single interactive system requires that we adapt approaches from topological methods such as n-dimensional relativistic representation. A number of hypothetical examples will be provided throughout the paper to clarify technical challenges that need to be overcome to realize this vision. The long-term goal of this work is that truly invariant representations will ultimately result from establishing formal, inclusive relationships between these different domains. Using this framework, incomplete information in one or more domains can be compensated for by parallelism and mappings within the virtual world representation. To introduce this approach, I will review recent developments in embodiment, virtual world technology, and neuroscience relevant to the control of virtual worlds. The next step will be to borrow ideas from fields such as brain science, applied mathematics, and cosmology to give proper perspective to this approach. A simple demonstration will then be given using an intuitive example of physical relativism. Finally, future directions for the application of this method will be considered.

cs.HC↗

arXiv2009

Performance augmentation in hybrid bionic systems: techniques and experiment

Recent developments in hybrid biological-technological systems (hybrid bionic systems) has made clear the need for evaluating ergonomic fit in such systems, especially as users first become adjusted to using such systems. This training is accompanied by physiological adaptation, and can be thought of computationally as a relative degree of matching between prosthetic devices, physiology, and behavior. Achieving performance augmentation involves two features of performance: a specific form of learning, memory, and mechanotransduction called sensorimotor learning, and physiological adaptation to novel physical information imposed by the augmented environment of hybrid bionic systems. A method borrowed from environmental medicine involving perturbing the environment for a range of internal physiological conditions was used to induce sensorimotor learning and memory associated physiological changes. In addition, features of the adult phenotype were considered as a mitigator of learning-related adaptations. Using a series of statistical tests and techniques, the results demonstrate than three forms of regulation are at work related to morphological, neural, and muscular control. A discussion of the conceptual relationship between homeostasis and adaptation will then be discussed in addition to potential applications to performance augmentation strategies.

q-bio.NC↗

arXiv2009

Hierarchies of Biocomplexity: modeling lifes energetic complexity

In this paper, a model for understanding the effects of selection using systems- level computational approaches is introduced. A number of concepts and principles essential for understanding the motivation for constructing the model will be introduced first. This will be followed by a description of parameters, measurements, and graphical representations used in the model. Four possible outcomes for this model are then introduced and described. In addition, the relationship of relative fitness to selection is described. Finally, the consequences and potential lessons learned from the model are discussed.

q-bio.PE↗

arXiv2008

The adaptability of physiological systems optimizes performance: new directions in augmentation

This paper contributes to the human-machine interface community in two ways: as a critique of the closed-loop AC (augmented cognition) approach, and as a way to introduce concepts from complex systems and systems physiology into the field. Of particular relevance is a comparison of the inverted-U (or Gaussian) model of optimal performance and multidimensional fitness landscape model. Hypothetical examples will be given from human physiology and learning and memory. In particular, a four-step model will be introduced that is proposed as a better means to characterize multivariate systems during behavioral processes with complex dynamics such as learning. Finally, the alternate approach presented herein is considered as a preferable design alternate in human-machine systems. It is within this context that future directions are discussed.

cs.HC↗