Synthetic and spectroscopic studies on the structures of uniflorines A and B: structural revision to 1,2,6,7-tetrahydroxy-3-hydroxymethylpyrrolizidine alkaloids

The diastereoselective synthesis of the C-2 epimer and the C-1, C-2 di-epimers of the putative structure of the alkaloid uniflorine A has been achieved. The synthesis of the latter di-epimers employed a novel pyrrolo[1,2-c]oxazin-1-one precursor to allow for the reversal of π-facial diastereoselectivity in an osmium(VIII)-catalyzed syn-dihydroxylation (DH) reaction. The NMR spectral data of these epimeric compounds and that of related isomers did not match that of the natural product. From a comparison of the NMR data of uniflorines A and B with that of casuarine and the known synthetic 1,2,6,7-tetrahydroxy-3-hydroxymethylpyrrolizidine isomers we concluded unequivocally that uniflorine B is the known alkaloid casuarine. Although we cannot unequivocally prove the structure of uniflorine A, without access to the original material and data, the published data suggest that the natural product is also a 1,2,6,7-tetrahydroxy-3-hydroxymethylpyrrolizidine with the same relative C-7–C-7a–C-1–C-2–C-3 stereochemistry as casuarine. We thus suggest that uniflorine A is 6-epi-casuarine

Second Order Perturbations of Flat Dust FLRW Universes with a Cosmological Constant

We summarize recent results concerning the evolution of second order perturbations in flat dust irrotational FLRW models with $\Lambda\ne 0$. We show that asymptotically these perturbations tend to constants in time, in agreement with the cosmic no-hair conjecture. We solve numerically the second order scalar perturbation equation, and very briefly discuss its all time behaviour and some possible implications for the structure formation.Comment: 6 pages, 1 figure. to be published in "Proceedings of the 5th Alexander Friedmann Seminar on Gravitation and Cosmology", Int. Journ. Mod. Phys. A (2002). Macros: ws-ijmpa.cls, ws-p9-75x6-50.cl

A Mixture of Regressions Model of COVID-19 Death Rates and Population Comorbidities

As the COVID-19 pandemic spread worldwide, it has become clearer that prevalence of certain comorbidities in a given population could make it more vulnerable to serious outcomes of that disease, including fatality. Indeed, it might be insightful from a health policy perspective to identify clusters of populations in terms of the associations between their prevalent comorbidities and the observed COVID-19 specific death rates. In this study, we described a mixture of polynomial time series (MoPTS) model to simultaneously identify (a) three clusters of 86 U.S. cities in terms of their dynamic death rates, and (b) the different associations of those rates with 5 key comorbidities among the populations in the clusters. We also described an EM algorithm for efficient maximum likelihood estimation of the model parameters

Partial identification in the statistical matching problem

The statistical matching problem involves the integration of multiple datasets where some variables are not observed jointly. This missing data pattern leaves most statistical models unidentifiable. Statistical inference is still possible when operating under the framework of partially identified models, where the goal is to bound the parameters rather than to estimate them precisely. In many matching problems, developing feasible bounds on the parameters is equivalent to finding the set of positive-definite completions of a partially specified covariance matrix. Existing methods for characterising the set of possible completions do not extend to high-dimensional problems. A Gibbs sampler to draw from the set of possible completions is proposed. The variation in the observed samples gives an estimate of the feasible region of the parameters. The Gibbs sampler extends easily to high-dimensional statistical matching problems.Daniel Ahfock, Saumyadipta Pyne, Sharon X. Lee, Geoffrey J. McLachla

Immune and acute phase markers in exercising adults

Many reports have documented the anti-inflammatory effects of regular exercise in adults. However clinicians and researchers remain uncertain on selecting specific biomarkers that are useful for predicting or monitoring chronic inflammatory states and/or disease. Clearer identification of markers (or clusters of markers) in physically active individuals that vary from established references ranges will indicate the extent of the purported anti-inflammatory effect of regular exercise. Physically active adults were recruited from the community to participate in a prospective study comparing self-reported health outcomes and exercise activity across 150 days of dietary intervention. Of the 450 participants recruited, 64 males (mean age 37.4 y, mean BMI = 25.3) and 59 females (mean age 40.4y, mean BMI= 23.4) agreed to supply a baseline blood sample taken at rest. A total of 187 analytes were measured by standard techniques on these pre-intervention samples including 11 immune markers (cell-types and immunoglobulins) and 11 acute phase reactants (WCC, albumin, haptoglobin, CRP, C3, C4, IGF-1, transferrin, iron, ferritin & ceruloplasmin). We compared baseline values with relevent hospital reference range (RR) values where these are assumed to be more reflective of a much less physically-active community population. A total of 5 out of 11 of the acute phase reactants (Hapt, C3, Fer, Trf (for females), and ceruloplasmin (for males)) had \u3e10% of values below the low ‘cut-off end’ of the relevant RR. Three immune cell-types (CD19, CD8 & CD16/56) had \u3e10% of values below the ‘low-cut-off end’ of the relevant RR. In contrast 25% of subjects had an IgE value that exceeded the RR. Collectively our results support the notion that regular exercise or physical activity exerts an anti-inflammatory affect. The results suggest putative roles for a host of exercise associated adaptive mechanisms beyond the generally accepted role for IL-6 derived from skeletal muscle and\or visceral fat. We conclude that across a host of measures, exercising adults have values for immune and acute phase reactants largely within, but at the non-inflammatory ‘end’ of clinical reference ranges

A computational framework to emulate the human perspective in flow cytometric data analysis

Background: In recent years, intense research efforts have focused on developing methods for automated flow cytometric data analysis. However, while designing such applications, little or no attention has been paid to the human perspective that is absolutely central to the manual gating process of identifying and characterizing cell populations. In particular, the assumption of many common techniques that cell populations could be modeled reliably with pre-specified distributions may not hold true in real-life samples, which can have populations of arbitrary shapes and considerable inter-sample variation. <p/>Results: To address this, we developed a new framework flowScape for emulating certain key aspects of the human perspective in analyzing flow data, which we implemented in multiple steps. First, flowScape begins with creating a mathematically rigorous map of the high-dimensional flow data landscape based on dense and sparse regions defined by relative concentrations of events around modes. In the second step, these modal clusters are connected with a global hierarchical structure. This representation allows flowScape to perform ridgeline analysis for both traversing the landscape and isolating cell populations at different levels of resolution. Finally, we extended manual gating with a new capacity for constructing templates that can identify target populations in terms of their relative parameters, as opposed to the more commonly used absolute or physical parameters. This allows flowScape to apply such templates in batch mode for detecting the corresponding populations in a flexible, sample-specific manner. We also demonstrated different applications of our framework to flow data analysis and show its superiority over other analytical methods. <p/>Conclusions: The human perspective, built on top of intuition and experience, is a very important component of flow cytometric data analysis. By emulating some of its approaches and extending these with automation and rigor, flowScape provides a flexible and robust framework for computational cytomics

Clustering patterns connecting COVID-19 dynamics and Human mobility using optimal transport

Social distancing and stay-at-home are among the few measures that are known to be effective in checking the spread of a pandemic such as COVID-19 in a given population. The patterns of dependency between such measures and their effects on disease incidence may vary dynamically and across different populations. We described a new computational framework to measure and compare the temporal relationships between human mobility and new cases of COVID-19 across more than 150 cities of the United States with relatively high incidence of the disease. We used a novel application of Optimal Transport for computing the distance between the normalized patterns induced b

AKT1 and MYC induce distinctive metabolic fingerprints in human prostate cancer

Cancer cells may overcome growth factor dependence by deregulating oncogenic and/or tumor-suppressor pathways that affect their metabolism, or by activating metabolic pathways de novo with targeted mutations in critical metabolic enzymes. It is unknown whether human prostate tumors develop a similar metabolic response to different oncogenic drivers or a particular oncogenic event results in its own metabolic reprogramming. Akt and Myc are arguably the most prevalent driving oncogenes in prostate cancer. Mass spectrometry-based metabolite profiling was performed on immortalized human prostate epithelial cells transformed by AKT1 or MYC, transgenic mice driven by the same oncogenes under the control of a prostate-specific promoter, and human prostate specimens characterized for the expression and activation of these oncoproteins. Integrative analysis of these metabolomic datasets revealed that AKT1 activation was associated with accumulation of aerobic glycolysis metabolites, whereas MYC overexpression was associated with dysregulated lipid metabolism. Selected metabolites that differentially accumulated in the MYC-high versus AKT1-high tumors, or in normal versus tumor prostate tissue by untargeted metabolomics, were validated using absolute quantitation assays. Importantly, the AKT1/MYC status was independent of Gleason grade and pathologic staging. Our fi ndings show how prostate tumors undergo a metabolic reprogramming that refl ects their molecular phenotypes, with implications for the development of metabolic diagnostics and targeted therapeutics.Instituto de Investigaciones Bioquímicas de La PlataFacultad de Ciencias Médica

Evolution of Second-Order Cosmological Perturbations and Non-Gaussianity

We present a second-order gauge-invariant formalism to study the evolution of curvature perturbations in a Friedmann-Robertson-Walker universe filled by multiple interacting fluids. We apply such a general formalism to describe the evolution of the second-order curvature perturbations in the standard one-single field inflation, in the curvaton and in the inhomogeneous reheating scenarios for the generation of the cosmological perturbations. Moreover, we provide the exact expression for the second-order temperature anisotropies on large scales, including second-order gravitational effects and extend the well-known formula for the Sachs-Wolfe effect at linear order. Our findings clarify what is the exact non-linearity parameter f_NL entering in the determination of higher-order statistics such as the bispectrum of Cosmic Microwave Background temperature anisotropies. Finally, we compute the level of non-Gaussianity in each scenario for the creation of cosmological perturbations.Comment: 14 pages, LaTeX file. Further comments adde

A Forum for Business Growth and Workforce Development: Findings and Recommendations

In the fall of 2008, Illinois State University – Extended University (EU) and the Economic Development Council of the Bloomington-Normal Area (EDC) initiated discussions about a community partnership project to identify workforce opportunities and challenges related to economic stabilization and growth in order to gain a better understanding of the state of workforce preparedness in the area. Rapidly changing dynamics in the economy made previous assessments obsolete. Organizations who work toward the promotion of a strong workforce were approached to participate in the project. EU and the EDC were joined in sponsoring a community event by Heartland Community College, Illinois Wesleyan University, Lincoln College – Normal, Regional Office of Education 17, McLean County Chamber of Commerce, CareerLink 16, and the Small Business Development Center at Illinois State University. Project partners designed and developed a series of discussion forums for eight sectors: Agriculture and Energy, Manufacturing, Small Business Retail, Service, Financial Services, Information Technology, Healthcare, and Construction. The Forum for Business Growth and Workforce Development was held from June 8 – 12, 2009 at Illinois State University. Each sector panel discussion was moderated over a ninety minute period and included two to seven panelists from area businesses