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Introduction: Mass media advertisements have claimed health benefits of body inversion for relaxation and cardiovascular conditioning. We conducted a preliminary study to evaluate real time physiological changes and responses to mediation, Hatha yoga, and specifically inversion and standing postures to determine the O2 consumption recorded by a wearable metabolic device and cardiovascular measures. Methods: Healthy study volunteers executed a sequence of yoga postures that included inversions of whole body while wearing a Cosmed K5 portable metabolic backpack. We obtained brachial blood pressure during the last 30 seconds of each posture. Each trial began seated, followed by a warm-up consisting of gentle flow yoga and ending with relaxation. Results: Twelve experienced yoga practitioners (mean age 44 years, 58% female) participated in 17 trials. Over all trials, mean VO2 for Sirsasana as compared with the supported inversion posture decreased from 8.4 to 4.9 (ml/kg/min). Conclusions: Conflicting findings exist in the literature concerning inversion physiology. Cardiac output response to inversion is not consistent in scientific reports. Participants responded differently under a variety of circumstances in previous studies, making comparisons to this and existing research challenging. We find sufficient cause for further research and suggest that some forms of inversion may be beneficial to heart failure patients. Keywords: yoga, inversion, cardiopulmonary, heart failure, physiology

Background Apelin is an adipokine directly associated with adiposity, insulin resistance, and decreased blood pressure. Urinary 8-isoprostane is a marker of chronic oxidative endothelial stress. Visfatin, an adipokine that acts by binding and activating the insulin receptor, has been associated with hypertension. As severe hypertension (SH) is highly prevalent among African Americans (AA), we aimed to assess the association of these biomarkers with SH status. Methods A sample of 250 AA participants (134 normotensive controls and 116 with SH (including 98 treatment controlled, SCH: severe controlled hypertension, and 18 treatment resistant, SRH: severe resistant hypertension)) from the Minority Health Genomics and Translational Research Bio-Repository Database (MH-GRID) in metro Atlanta had blood analyzed for apelin and visfatin and urine for 8-isoprostane. T-tests, sex-specific age-adjusted correlation coefficients, and multivariable logistic regression models were used to assess the association of biomarkers with hypertensive status. Results Levels of apelin and 8-isoprostane were not statistically different between controls and SCH or SRH. Statistically significant differences were present in levels of visfatin between controls (1.03±0.84 pg/ml), SCH (1.34±1.14 pg/ml), and SRH (1.59±0.85 pg/ml). After multivariable adjustment, categorization in the middle 2 quartiles of urinary 8-isoprostane were associated with SH. In similar models, categorization into the highest quartile of visfatin was associated with SH (odds ratio = 2.80; 95% confidence interval: 1.02-7.02). A continuous association of visfatin with SH was present. Conclusion In our community sample of AA, there were increased odds of SH with increased levels of urinary 8-isoprostane and visfatin, but not with apelin.

RNAs have been shown to exhibit differential enrichment between nuclear, cytoplasmic, and exosome fractions. A current fundamental question asks why non-coding RNA partition into different spatial compartments. We report on the analysis of cellular compartment models with miRNA data sources for spatial-mechanistic modeling to address the broad area of multi-scalar cellular communication by miRNAs. We show that spatial partitioning of miRNAs is related to sequence similarity to the overall transcriptome. This has broad implications in biological informatics for gene regulation and provides a deeper understanding of nucleotide sequence structure and RNA language meaning for human pathologies resulting from changes in gene expression.

Background: Complementary and alternative medicine is a rapidly growing area of biomedical inquiry. Yoga has emerged in the forefront of holistic medical care due to its long history of linking physical, mental, and spiritual well-being. Research in yoga therapy (YT) has associated improved cardiovascular and quality of life (QoL) outcomes for the special needs of heart failure (HF) patients. Aim: The aim of this study is to review yoga intervention studies on HF patients, discuss proposed mechanisms, and examine yoga's effect on physiological systems that have potential benefits for HF patients. Second, to recommend future research directions to find the most effective delivery methods of yoga to medically stable HF patients. Methods: The authors conducted a systematic review of the medical literature for RCTs involving HF patients as participants in yoga interventions and for studies utilizing mechanistic theories of stretch and new technologies. We examined physical intensity, mechanistic theories, and the use of the latest technologies. Conclusions: Based on the review, there is a need to further explore yoga mechanisms and research options for the delivery of YT. Software apps as exergames developed for use at home and community activity centers may minimize health disparities and increase QoL for HF patients.

Health-care initiatives are pushing the development and utilization of clinical data for medical discovery and translational research studies. Machine learning tools implemented for Big Data have been applied to detect patterns in complex diseases. This study focuses on hypertension and examines phenotype data across a major clinical study called Minority Health Genomics and Translational Research Repository Database composed of self-reported African American (AA) participants combined with related cohorts. Prior genome-wide association studies for hypertension in AAs presumed that an increase of disease burden in susceptible populations is due to rare variants. But genomic analysis of hypertension, even those designed to focus on rare variants, has yielded marginal genome-wide results over many studies. Machine learning and other nonparametric statistical methods have recently been shown to uncover relationships in complex phenotypes, genotypes, and clinical data. We trained neural networks with phenotype data for missing-data imputation to increase the usable size of a clinical data set. Validity was established by showing performance effects using the expanded data set for the association of phenotype variables with case/control status of patients. Data mining classification tools were used to generate association rules.

An in silico study of mRNA secondary structure has found a bias within the coding sequences of genes that favors "in-frame" pairing of nucleotides. This pairing of codons, each with its reverse-complement, partitions the 20 amino acids into three subsets. The genetic code can therefore be represented by a three-component graph. The composition of proteins in terms of amino acid membership in the three subgroups has been measured, and sequence runs of members within the same subgroup have been analyzed using a runs statistic based on Z-scores. In a GENBANK database of over 416,000 protein sequences, the distribution of this runs-test statistic is negatively skewed. To assess whether this statistical bias was due to a chance grouping of the amino acids in the real genetic code, several alternate partitions of the genetic code were examined by permuting the assignment of amino acids to groups. A metric was constructed to define the difference, or "distance", between any two such partitions, and an exhaustive search was conducted among alternate partitions maximally distant from the natural partition of the genetic code, to select sets of partitions that were also maximally distant from one another. The statistical skewness of the runs statistic distribution for native protein sequences were significantly more negative under the natural partition than they were under all of the maximally different partition of codons, although for all partitions, including the natural one, the randomized sequences had quite similar skewness. Hence under the natural graph theory partition of the genetic code there is a preference for more protein sequences to contain fewer runs of amino acids, than they do under the other partitions, meaning that the average run must be longer under the natural partition. This suggests that a corresponding bias may exist in the coding sequences of the actual genes that code for these proteins.

Proper spindle positioning and orientation are essential for accurate mitosis which requires dynamic interactions between microtubule and actin filament (F-actin). Although mounting evidence demonstrates the role of F-actin in cortical cytoskeleton dynamics, it remains elusive as to the structure and function of F-actin-based networks in spindle geometry. Here we showed a ring-like F-actin structure surrounding the mitotic spindle which forms since metaphase and maintains in MG132-arrested metaphase HeLa cells. This cytoplasmic F-actin structure is relatively isotropic and less dynamic. Our computational modeling of spindle position process suggests a possible mechanism by which the ring-like F-actin structure can regulate astral microtubule dynamics and thus mitotic spindle orientation. We further demonstrated that inhibiting Plk1, Mps1 or Myosin, and disruption of microtubules or F-actin polymerization perturbs the formation of the ring-like F-actin structure and alters spindle position and symmetric division. These findings reveal a previously unrecognized but important link between mitotic spindle and ring-like F-actin network in accurate mitosis and enables the development of a method to theoretically illustrate the relationship between mitotic spindle and cytoplasmic F-actin.

Serous subtype of ovarian cancer is considered to originate from fallopian epithelium mucosa that has been exposed to physiological changes resulting from ovulation. Ovulation influences an increased in inflammation of epithelial ovarian cells as results of constant exposure of cells to ROS. The imbalance between ROS and antioxidant capacities, as well as a disruption of redox signaling, causes a wide range of damage to DNA, proteins, and lipids. This study applied spectrophotometric, dinitrophenylhydrazone (DNPH) assay, two-dimensional gel electrophoresis, and Western blot analyses to assess the levels of oxidatively modified proteins in 100 primary serous epithelial ovarian carcinoma and normal/surrounding tissues. These samples were obtained from 56 Caucasian and 44 African-American patients within the age range of61±10years. Analyses showed that the levels of reactive protein carbonyl groups increased as stages progressed to malignancy. Additionally, the levels of protein carbonyls in serous ovarian carcinoma among African Americans are 40% (P<0.05) higher relative to Caucasian at similar advanced stages. Results suggest that oxidative stress is involved in the modification of carbonyl protein groups, leading to increased aggressiveness of epithelial ovarian tumors and may contribute to the disease's invasiveness among African Americans.

The current study utilized smart and connected technologies to assist difficult to reach populations with healthy lifestyle intervention for cancer prevention. Lack of physical activities and high body mass index are important cancer risk factors. We tested the hypothesis that a low cost gaming platform could be used to supplement therapeutic regimes for life style changes to affect risk factors associated with cancer. Using a 3D room sensor built into the Microsoft Kinect, we analyzed qualitative and quantitative measures of Yoga and Tai Chi postures and dance routines with the Kinect sensor. Specifically, we used Kinect version 2.0 Software Development Kit to capture skeleton image streams composed of X, Y, Z coordinates at 20 frames per second. The instructors performed a series of 17 hatha yoga postures, including a variety of standing and supine positions, 12 Tai Chi motions, or performed traditional African-based dance routines. Inaccuracy of data capture was measured by the number of imputed joint positions in the skeleton stream using two-sample equal variance t-test, along with time-average measurements for the dance routines. The accuracy of skeleton capture for sampled yoga postures was heavily dependent on the yoga practitioner view orientation and the particular yoga posture. Frontal view orientation was slightly more accurate than perpendicular, but not significantly different. Standing poses were significantly more accurate than seated or supine body orientations. Instability of joint positions in the skeleton stream was more severe for seated poses as evidenced by large fluctuations between image frames that resulted in visible displacements of joints that were described as jitter. Tai Chi and African dance routines were significantly more accurate for full range of postures. Multiplayer modes in other exergames show positive effect with motivation and physical exertion. Implementation of multiplayer capacity will help to establish a culturally appropriate electronic cohort focused on cancer prevention. In conclusion, the skeleton movement tracking algorithms embedded in Kinect were not appropriate for body positions that significantly deviated far from upright postures. Future studies will evaluate Tai Chi and African dance as an Exergame for multiple players. This app will be designed by peers of targeted populations at risk for cancer, linking GIS neighborhood characteristics for longitudinal tracking across metropolitan Atlanta over time. Acknowledge partial support from G12MD007602, 8U54MD007588, and U54 CA118638. Citation Format: Blessing Ahabue, Paula Pullen, Afebuameh Ogbesor, William S. Seffens, James W. Lillard. Exergame development for cancer prevention and intervention. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 3740. doi:10.1158/1538-7445.AM2015-3740

Free energies of folding for native mRNA sequences are more negative than calculated free energies of folding for randomized mRNA sequences with the same mononucleotide base composition and length. Randomization only of the coding region of most genes also yields folding free energies of less negative magnitude than those of the original mRNA sequences. For 79 mRNA sequences selected from a yeast SAGE library, the free energy minimization calculations of native mRNA sequences are also usually more negative than randomized mRNA sequences, as above. This difference can be expressed as a bias using standard deviation units. We also observed that if this yeast SAGE data is grouped according to expression levels, the mean folding free energy bias is different between the high, average, and low expression-level genes. A t-Test for paired two-samples of means shows a significant difference in folding free energies between high and low expression yeast genes. Thus the sequences of these yeast genes typically give rise to more stable secondary mRNA structures in high expression genes than in single-copy genes. The results of this study could serve as a foundation for comparison with other genomes, which in turn will allow investigating how the folding bias may be affected by specific characteristics of each organism, such as growth temperature, dinucleotide composition, or GC content of the genome.

An examination of 51 mRNA sequences in GENBANK has revealed that calculated mRNA folding free energies are more negative than expected. Free energy minimization calculations of native mRNA sequences are more negative than randomized mRNA sequences with the same base composition and length. Randomization only of the coding region of genes also yields folding free energies of less negative magnitude than the original native mRNA sequence. Examination of the predicted basepairing within the coding sequence finds an unequal distribution between the three possible frames. The wobble-to-1 frame, which is ”in-frame”, is preferred significantly compared to randomized sets of mRNA sequences. This suggests that evolution may bias or adjust the local selection of codons to favor the global formation of more mRNA structures. This would result in greater negative folding free energies as seen in the 51 mRNAs examined.

An examination of 51 mRNA sequences in GenBank has revealed that calculated mRNA folding is more stable than expected by chance. Free energy minimization calculations of native mRNA sequences are more negative than randomized mRNA sequences with the same base composition and length. Randomization of the coding region of genes yields folding free energies of less negative magnitude than the original native mRNA sequence. Randomization of codon choice, while still preserving original base composition, also results in less stable mRNAs. This suggests that a bias in the selection of codons favors the potential formation of mRNA structures which contribute to folding stability.

B ENOIT is a fractal analysis software product for Windows 95, Windows 98, or Windows NT used to find order and patterns in seemingly chaotic data, particularly where traditional statistical approaches to data analysis fail. It is widely used in disciplines as diverse as biology, chemistry, physics, economics, medicine, and geology. The U.S. Geological Survey, for example, employs fractal analysis to accurately predict the volume of undiscovered deposits of oil and natural gas, on the basis of data from known deposits. BENOIT measures user-supplied data by standard fractal methods. For a fractal, measures change in value as the scale decreases in size because ever-smaller pieces become included in the analysis. Measures are plotted as a function of ruler size on a log-log plot, and a fractal dimension is calculated from the slope of the resulting line. Users select one of 10 analytical measures with the software. Five of the available measures in the program act upon bitmap images in Windows BMP format. These are described as the “self-similar” or two-dimensional (2D) methods, while the remaining group of routines act upon time-series or 1D data. The latter group requires data to be in a simple but specific data format, such as is available in Excel. The program also features a data generator that produces files with a given fractal dimension. Users may find this useful for testing and control purposes. The self-similar or image methods available in BENOIT measure different characteristics of bitmap objects in ways that should be scale-invariant. A real dataset normally has some fractal limit, and outside the limit, the fractal dimension will return a trivial value (1 for time-series or 2 for image data). Upper and lower fractal limits are controlled by the size of the dataset. Self-similar methods available in BENOIT are well known in fractal analysis: box dimension, perimeter-area dimension, information dimension, and ruler dimension. All methods are explained in standard Help files that contain several pages of information for each topic. The 1D analysis routines use “self-affine” methods of analysis. Self-affine fractals differ from self-similiar fractals in that their parts need to be rescaled by different factors in different coordinates to resemble the original. In the roughness-length method, the root-mean-square variation or roughness of the data is calculated for a variety of horizontal scales. The operation provides an estimate of the Hurst exponent, H , in a log-log plot, which is related to the fractal dimension. Standard self-affine methods available include R/S (Rescaled Range) analysis, power spectrum, roughness-length, variogram, and wavelets. Printing of log-log figures is provided, but without many features that would be found in a spreadsheet. Documentation for the program is available online. BENOIT has a highly visual interface, complete with an animated grid or ruler for self-similar fractal methods, and it gives users control of all calculations that the program performs, unlike other fractal software. Benoit is not without flaws. Some operations, such as name registration with the Windows NT 4.0 taskbar and the Open File requester, do not conform to standard Windows conventions. It would be of help to have an outline or flowchart of the operation of the different routines available in BENOIT for newcomers to fractal analysis. In summary, the variety of fractal analysis methods available in BENOIT, together with generally detailed help files and significant user control of operations, make BENOIT a good resource for learning about and using fractal analysis methods.