Progress Towards Modeling the Ablation Response of NuSil-Coated PICA

The Mars Science Laboratory (MSL) Entry, Descent and Landing Instrumentation (MEDLI) collected in-flight data largely used by the ablation community to verify and validate physics-based models for the response of the Phenolic Impregnated Carbon Ablator (PICA) material [1-4]. MEDLI data were recently used to guide the development of NASAs high-fidelity material response models for PICA, implemented in the Porous material Analysis Toolbox based on OpenFOAM (PATO) software [5-6]. A follow-up instrumentation suite, MEDLI2, is planned for the upcoming Mars 2020 mission [7] after the large scientific impact of MEDLI. Recent analyses performed as part of MEDLI2 development draw the attention to significant effects of a protective coating to the aerothermal response of PICA. NuSil, a silicone-based overcoat sprayed onto the MSL heatshield as contamination control, is currently neglected in PICA ablation models. To mitigate the spread of phenolic dust from PICA, NuSil was applied to the entire MSL heatshield, including the MEDLI plugs. NuSil is a space grade designation of the siloxane copolymer, primarily used to protect against atomic oxygen erosion in the Low Earth Orbit environment. Ground testing of PICA-NuSil (PICA-N) models all exhibited surface temperature jumps of the order of 200 K due to oxide scale formation and subsequent NuSil burn-off. It is therefore critical to include a model for the aerothermal response of the coating in ongoing code development and validation efforts

A model of primitive streak initiation in the chick embryo

Initiation of the primitive streak in avian embryos provides a well-studied example of a pattern-forming event that displays a striking capacity for regulation. The mechanisms underlying the regulative properties are, however, poorly understood and are not easily accounted for by traditional models of pattern formation, such as reaction–diffusion models. In this paper, we propose a new activator–inhibitor model for streak initiation. We show that the model is consistent with experimental observations, both in its pattern-forming properties and in its ability to form these patterns on the correct time-scales for biologically realistic parameter values. A key component of the model is a travelling wave of inhibition. We present a mathematical analysis of the speed of such waves in both diffusive and juxtacrine relay systems. We use the streak initiation model to make testable predictions. By varying parameters of the model, two very different types of patterning can be obtained, suggesting that our model may be applicable to other processes in addition to streak initiation

Human Amniocytes Are Receptive to Chemically Induced Reprogramming to Pluripotency

Restoring pluripotency using chemical compounds alone would be a major step forward in developing clinical-grade pluripotent stem cells, but this has not yet been reported in human cells. We previously demonstrated that VPA_ AFS cells, human amniocytes cultivated with valproic acid (VPA) acquired functional pluripotency while remaining distinct from human embryonic stem cells (hESCs), questioning the relationship between the modulation of cell fate and molecular regulation of the pluripotency network. Here, we used single-cell analysis and functional assays to reveal that VPA treatment resulted in a homogeneous population of self-renewing non-transformed cells that fulfill the hallmarks of pluripotency, i.e., a short G1 phase, a dependence on glycolytic metabolism, expression of epigenetic modifications on histones 3 and 4, and reactivation of endogenous OCT4 and downstream targets at a lower level than that observed in hESCs. Mechanistic insights into the process of VPA-induced reprogramming revealed that it was dependent on OCT4 promoter activation, which was achieved independently of the PI3K (phosphatidylinositol 3-kinase)/ AKT/ mTOR (mammalian target of rapamycin) pathway or GSK3 beta inhibition but was concomitant with the presence of acetylated histones H3K9 and H3K56, which promote pluripotency. Our data identify, for the first time, the pluripotent transcriptional and molecular signature and metabolic status of human chemically induced pluripotent stem cells

A description of n-ary semigroups polynomial-derived from integral domains

We provide a complete classification of the n-ary semigroup structures defined by polynomial functions over infinite commutative integral domains with identity, thus generalizing G{\l}azek and Gleichgewicht's classification of the corresponding ternary semigroups

Altered Activation Of The Rostral Anterior Cingulate Cortex In The Context Of Emotional Face Distractors In Children And Adolescents With Anxiety Disorders

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109274/1/da22289.pd

Weighted network modules

The inclusion of link weights into the analysis of network properties allows a deeper insight into the (often overlapping) modular structure of real-world webs. We introduce a clustering algorithm (CPMw, Clique Percolation Method with weights) for weighted networks based on the concept of percolating k-cliques with high enough intensity. The algorithm allows overlaps between the modules. First, we give detailed analytical and numerical results about the critical point of weighted k-clique percolation on (weighted) Erdos-Renyi graphs. Then, for a scientist collaboration web and a stock correlation graph we compute three-link weight correlations and with the CPMw the weighted modules. After reshuffling link weights in both networks and computing the same quantities for the randomised control graphs as well, we show that groups of 3 or more strong links prefer to cluster together in both original graphs.Comment: 19 pages, 7 figure

Neural correlates of explicit and implicit emotion processing in relation to treatment response in pediatric anxiety

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136676/1/jcpp12658_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136676/2/jcpp12658.pd

\u3ci\u3eMedicine Meets Virtual Reality 17\u3c/i\u3e

Chapter, A Virtual Reality Training Program for Improvement of Robotic Surgical Skills, co-authored by Mukul Mukherjee and Nicholas Stergiou, UNO faculty members. Chapter, Consistency of Performance of Robot-Assisted Surgical Tasks in Virtual Reality, co-authored by Mukul Mukherjee and Nicholas Stergiou, UNO faculty members. The 17th annual Medicine Meets Virtual Reality (MMVR17) was held January 19-22, 2009, in Long Beach, CA, USA. The conference is well established as a forum for emerging data-centered technologies for medical care and education. Each year, it brings together an international community of computer scientists and engineers, physicians and surgeons, medical educators and students, military medicine specialists and biomedical futurists. MMVR emphasizes inter-disciplinary collaboration in the development of more efficient and effective physician training and patient care. The MMVR17 proceedings collect 108 papers by conference lecture and poster presenters. These papers cover recent developments in biomedical simulation and modeling, visualization and data fusion, haptics, robotics, sensors and other related information-based technologies. Key applications include medical education and surgical training, clinical diagnosis and therapy, physical rehabilitation, psychological assessment, telemedicine and more. From initial vision and prototypes, through assessment and validation, to clinical and academic utilization and commercialization - MMVR explores the state-of-the-art and looks toward healthcare’s future. The proceedings volume will interest physicians, surgeons and other medical professionals interested in emerging and future tools for diagnosis and therapy; educators responsible for training the next generation of doctors and scientists; IT and medical device engineers creating state-of-the-art and next-generation simulation, imaging, robotics and communication systems; data technologists creating systems for gathering, processing and distributing medical intelligence; military medicine specialists addressing the challenges of warfare and defense health needs; and biomedical futurists and investors who want to understand where the field is headed.https://digitalcommons.unomaha.edu/facultybooks/1233/thumbnail.jp

Associative polynomial functions over bounded distributive lattices

The associativity property, usually defined for binary functions, can be generalized to functions of a given fixed arity n>=1 as well as to functions of multiple arities. In this paper, we investigate these two generalizations in the case of polynomial functions over bounded distributive lattices and present explicit descriptions of the corresponding associative functions. We also show that, in this case, both generalizations of associativity are essentially the same.Comment: Final versio

Identifying dynamical modules from genetic regulatory systems: applications to the segment polarity network

BACKGROUND It is widely accepted that genetic regulatory systems are 'modular', in that the whole system is made up of smaller 'subsystems' corresponding to specific biological functions. Most attempts to identify modules in genetic regulatory systems have relied on the topology of the underlying network. However, it is the temporal activity (dynamics) of genes and proteins that corresponds to biological functions, and hence it is dynamics that we focus on here for identifying subsystems. RESULTS Using Boolean network models as an exemplar, we present a new technique to identify subsystems, based on their dynamical properties. The main part of the method depends only on the stable dynamics (attractors) of the system, thus requiring no prior knowledge of the underlying network. However, knowledge of the logical relationships between the network components can be used to describe how each subsystem is regulated. To demonstrate its applicability to genetic regulatory systems, we apply the method to a model of the Drosophila segment polarity network, providing a detailed breakdown of the system. CONCLUSION We have designed a technique for decomposing any set of discrete-state, discrete-time attractors into subsystems. Having a suitable mathematical model also allows us to describe how each subsystem is regulated and how robust each subsystem is against perturbations. However, since the subsystems are found directly from the attractors, a mathematical model or underlying network topology is not necessarily required to identify them, potentially allowing the method to be applied directly to experimental expression data