Research utility of noninvasive methods for measurement of cardiac output

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109853/1/cptclpt198751.pd

Generalized Heisenberg algebras and k-generalized Fibonacci numbers

It is shown how some of the recent results of de Souza et al. [1] can be generalized to describe Hamiltonians whose eigenvalues are given as k-generalized Fibonacci numbers. Here k is an arbitrary integer and the cases considered by de Souza et al. corespond to k=2.Comment: 8 page

Intraocular pressure measurement in the conscious rat

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74866/1/j.1600-0420.1999.770108.x.pd

Precision medicine for suicidality: from universality to subtypes and personalization

Suicide remains a clear, present and increasing public health problem, despite being a potentially preventable tragedy. Its incidence is particularly high in people with overt or un(der)diagnosed psychiatric disorders. Objective and precise identification of individuals at risk, ways of monitoring response to treatments and novel preventive therapeutics need to be discovered, employed and widely deployed. We sought to investigate whether blood gene expression biomarkers for suicide (that is, a ‘liquid biopsy’ approach) can be identified that are more universal in nature, working across psychiatric diagnoses and genders, using larger cohorts than in previous studies. Such markers may reflect and/or be a proxy for the core biology of suicide. We were successful in this endeavor, using a comprehensive stepwise approach, leading to a wealth of findings. Steps 1, 2 and 3 were discovery, prioritization and validation for tracking suicidality, resulting in a Top Dozen list of candidate biomarkers comprising the top biomarkers from each step, as well as a larger list of 148 candidate biomarkers that survived Bonferroni correction in the validation step. Step 4 was testing the Top Dozen list and Bonferroni biomarker list for predictive ability for suicidal ideation (SI) and for future hospitalizations for suicidality in independent cohorts, leading to the identification of completely novel predictive biomarkers (such as CLN5 and AK2), as well as reinforcement of ours and others previous findings in the field (such as SLC4A4 and SKA2). Additionally, we examined whether subtypes of suicidality can be identified based on mental state at the time of high SI and identified four potential subtypes: high anxiety, low mood, combined and non-affective (psychotic). Such subtypes may delineate groups of individuals that are more homogenous in terms of suicidality biology and behavior. We also studied a more personalized approach, by psychiatric diagnosis and gender, with a focus on bipolar males, the highest risk group. Such a personalized approach may be more sensitive to gender differences and to the impact of psychiatric co-morbidities and medications. We compared testing the universal biomarkers in everybody versus testing by subtypes versus personalized by gender and diagnosis, and show that the subtype and personalized approaches permit enhanced precision of predictions for different universal biomarkers. In particular, LHFP appears to be a strong predictor for suicidality in males with depression. We also directly examined whether biomarkers discovered using male bipolars only are better predictors in a male bipolar independent cohort than universal biomarkers and show evidence for a possible advantage of personalization. We identified completely novel biomarkers (such as SPTBN1 and C7orf73), and reinforced previously known biomarkers (such as PTEN and SAT1). For diagnostic ability testing purposes, we also examined as predictors phenotypic measures as apps (for suicide risk (CFI-S, Convergent Functional Information for Suicidality) and for anxiety and mood (SASS, Simplified Affective State Scale)) by themselves, as well as in combination with the top biomarkers (the combination being our a priori primary endpoint), to provide context and enhance precision of predictions. We obtained area under the curves of 90% for SI and 77% for future hospitalizations in independent cohorts. Step 5 was to look for mechanistic understanding, starting with examining evidence for the Top Dozen and Bonferroni biomarkers for involvement in other psychiatric and non-psychiatric disorders, as a mechanism for biological predisposition and vulnerability. The biomarkers we identified also provide a window towards understanding the biology of suicide, implicating biological pathways related to neurogenesis, programmed cell death and insulin signaling from the universal biomarkers, as well as mTOR signaling from the male bipolar biomarkers. In particular, HTR2A increase coupled with ARRB1 and GSK3B decreases in expression in suicidality may provide a synergistic mechanistical corrective target, as do SLC4A4 increase coupled with AHCYL1 and AHCYL2 decrease. Step 6 was to move beyond diagnostics and mechanistical risk assessment, towards providing a foundation for personalized therapeutics. Items scored positive in the CFI-S and subtypes identified by SASS in different individuals provide targets for personalized (psycho)therapy. Some individual biomarkers are targets of existing drugs used to treat mood disorders and suicidality (lithium, clozapine and omega-3 fatty acids), providing a means toward pharmacogenomics stratification of patients and monitoring of response to treatment. Such biomarkers merit evaluation in clinical trials. Bioinformatics drug repurposing analyses with the gene expression biosignatures of the Top Dozen and Bonferroni-validated universal biomarkers identified novel potential therapeutics for suicidality, such as ebselen (a lithium mimetic), piracetam (a nootropic), chlorogenic acid (a polyphenol) and metformin (an antidiabetic and possible longevity promoting drug). Finally, based on the totality of our data and of the evidence in the field to date, a convergent functional evidence score prioritizing biomarkers that have all around evidence (track suicidality, predict it, are reflective of biological predisposition and are potential drug targets) brought to the fore APOE and IL6 from among the universal biomarkers, suggesting an inflammatory/accelerated aging component that may be a targetable common denominator

Local Moments in an Interacting Environment

We discuss how local moment physics is modified by the presence of interactions in the conduction sea. Interactions in the conduction sea are shown to open up new symmetry channels for the exchange of spin with the localized moment. We illustrate this conclusion in the strong-coupling limit by carrying out a Schrieffer Wolff transformation for a local moment in an interacting electron sea, and show that these corrections become very severe in the approach to a Mott transition. As an example, we show how the Zhang Rice reduction of a two-band model is modified by these new effects.Comment: Latex file with two postscript figures. Revised version, with more fully detailed calculation

Conservation of Distinct Genetically-Mediated Human Cortical Pattern

The many subcomponents of the human cortex are known to follow an anatomical pattern and functional relationship that appears to be highly conserved between individuals. This suggests that this pattern and the relationship among cortical regions are important for cortical function and likely shaped by genetic factors, although the degree to which genetic factors contribute to this pattern is unknown. We assessed the genetic relationships among 12 cortical surface areas using brain images and genotype information on 2,364 unrelated individuals, brain images on 466 twin pairs, and transcriptome data on 6 postmortem brains in order to determine whether a consistent and biologically meaningful pattern could be identified from these very different data sets. We find that the patterns revealed by each data set are highly consistent (p<10−3), and are biologically meaningful on several fronts. For example, close genetic relationships are seen in cortical regions within the same lobes and, the frontal lobe, a region showing great evolutionary expansion and functional complexity, has the most distant genetic relationship with other lobes. The frontal lobe also exhibits the most distinct expression pattern relative to the other regions, implicating a number of genes with known functions mediating immune and related processes. Our analyses reflect one of the first attempts to provide an assessment of the biological consistency of a genetic phenomenon involving the brain that leverages very different types of data, and therefore is not just statistical replication which purposefully use very similar data sets.publishedVersio

Integrated computational framework for the design and fabrication of bending-active structures made from flat sheet material

This paper introduces an integrated computational design framework for the design and realization of arbitrarily-curved bending-active architectural structures. The developed framework consists of a series of methods that enable the production of a complex 3D structures composed of a set of flat 2D panels whose mechanical properties are locally tuned by varying the shape of embedded spiraling patterns. The resulting panels perform as variable stiffness elements, and they are optimized to match a desired target shape once assembled together. The presented framework includes all the steps for the physical delivery of architectural objects, including conception, static assessment, and digital fabrication. The developed framework has been applied to an architectural scale prototype, which demonstrates the potential of integrating architectural design, computational simulation, structural engineering, and digital fabrication, opening up several possible novel applications in the building sector.</p

Co-operative Kondo Effect in the two-channel Kondo Lattice

We discuss the possibility of a co-operative Kondo effect driven by channel interference in a Kondo lattice where local moments are coupled to a single Fermi sea via two orthogonal scattering channels. In this situation, the channel quantum number is not conserved. We argue that the absence of channel conservation causes the Kondo effect in the two channels to constructively interfere, giving rise to a superconducting condensate of composite pairs, formed between the local moments and the conduction electrons. Our arguments are based on the observation that a heavy Fermi surface gives rise to zero modes for Kondo singlets to fluctuate between screening channels of different symmetry, producing a divergent composite pair susceptibility. Secondary screening channels couple to these divergent fluctuations, promoting an instability into a state with long-range composite order. We present detailed a detailed mean-field theory for this superconducting phase, and discuss the possible implications for heavy fermion physics.Comment: 23 double column pages. 9 fig

Preprocessing and Quality Control Strategies for Illumina DASL Assay-Based Brain Gene Expression Studies with Semi-Degraded Samples

Available statistical preprocessing or quality control analysis tools for gene expression microarray datasets are known to greatly affect downstream data analysis, especially when degraded samples, unique tissue samples, or novel expression assays are used. It is therefore important to assess the validity and impact of the assumptions built in to preprocessing schemes for a dataset. We developed and assessed a data preprocessing strategy for use with the Illumina DASL-based gene expression assay with partially degraded postmortem prefrontal cortex samples. The samples were obtained from individuals with autism as part of an investigation of the pathogenic factors contributing to autism. Using statistical analysis methods and metrics such as those associated with multivariate distance matrix regression and mean inter-array correlation, we developed a DASL-based assay gene expression preprocessing pipeline to accommodate and detect problems with microarray-based gene expression values obtained with degraded brain samples. Key steps in the pipeline included outlier exclusion, data transformation and normalization, and batch effect and covariate corrections. Our goal was to produce a clean dataset for subsequent downstream differential expression analysis. We ultimately settled on available transformation and normalization algorithms in the R/Bioconductor package lumi based on an assessment of their use in various combinations. A log2-transformed, quantile-normalized, and batch and seizure-corrected procedure was likely the most appropriate for our data. We empirically tested different components of our proposed preprocessing strategy and believe that our results suggest that a preprocessing strategy that effectively identifies outliers, normalizes the data, and corrects for batch effects can be applied to all studies, even those pursued with degraded samples

Kondo Effect in a Metal with Correlated Conduction Electrons: Diagrammatic Approach

We study the low-temperature behavior of a magnetic impurity which is weakly coupled to correlated conduction electrons. To account for conduction electron interactions a diagrammatic approach in the frame of the 1/N expansion is developed. The method allows us to study various consequences of the conduction electron correlations for the ground state and the low-energy excitations. We analyse the characteristic energy scale in the limit of weak conduction electron interactions. Results are reported for static properties (impurity valence, charge susceptibility, magnetic susceptibility, and specific heat) in the low-temperature limit.Comment: 16 pages, 9 figure