Adolescent BMI trajectories with clusters of physical activity and sedentary behavior: An exploratory analysis

Objective: The purpose of this study is to identify distinct body mass index (BMI) trajectories associated with weight classification, and to examine demographic characteristics and clusters of obesogenic behaviours in adolescents with these trajectories. Methods: Data were extracted from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development (n = 1,006, Grades 5–8). The independent variables were physical activity (accelerometer and child report), sports participation, television/video watching time and recreational computer use. The dependent variable was raw BMI. Growth mixture modelling, mixture modelling and independent t-test analyses were used. Results: Two distinct BMI trajectories were identified – one with the mean BMI within the Overweight–Obese classification (≥85th percentile) and the other within the healthy weight classification (5th– 84th percentile). Two clusters of physical and sedentary behaviours were identified in adolescents with the Overweight–Obese BMI trajectory. These clusters differed in the type of sedentary behaviour (computer vs. television/video). Three clusters were identified in adolescents with the Healthy Weight BMI trajectory. These clusters differed in levels of physical activity and types of sedentary behaviour. Conclusion: This study contributes to the understanding of multi-dimensional obesogenic behavioural patterns and highlights the importance of understanding types of sedentary behaviour in adolescents

Structural and Therapeutic Insights from the HIV-1 RNA Genome

Infection with HIV currently affects an estimated 30-36 million people throughout the world. Due in part to the poor replication fidelity of this RNA virus, resistance to antiretrovirals develops rapidly. Finding new ways of targeting HIV is therefore an ever urgent need. However, despite the wealth of ongoing research in HIV drug development, most new drug candidates continue to target only a few well-defined protein domains, chosen for their functional importance in HIV replication. Targeting the RNA genome itself in a structure-directed manner presents an opportunity to greatly expand the repertoire of potential target sites for anti-HIV therapeutics. We use a high-resolution SHAPE-directed secondary structure model of an entire HIV-1 RNA genome (1) to refine existing models of the Gag-Pol frameshift element, an important regulatory element and promising therapeutic target, and (2) to investigate the structural determinants for RNAi-based inhibition of HIV-1. We show that the Gag-Pol frameshift element folds into a complex structure that is distinct from currently accepted models and capable of switching between two different conformations. Additionally, we discovered that there exists a strong correlation between shRNA-mediated inhibition of HIV-1 production in a quantitative cell-based assay and very simple thermodynamic features in the SHAPE-directed RNA genome structure model. Both of these results are highly dependent on having an accurate secondary structural model, as obtained by SHAPE data. We anticipate that these results will be broadly applicable to RNA-directed antiretroviral development efforts.Doctor of Philosoph

Image Recognition of Disease-Carrying Insects: A System for Combating Infectious Diseases Using Image Classification Techniques and Citizen Science

We propose a system that assists infectious disease experts in the rapid identification of potential outbreaks resulting from arboviruses (mosquito, ticks, and other arthropod-borne viruses). The proposed system currently identifies mosquito larvae in images received from citizen scientists. Mosquito-borne viruses, such as the recent outbreak of Zika virus, can have devastating consequences in affected communities. We describe the first implemented prototype of our system, which includes modules for image collection, training of image classifiers, specimen recognition, and expert validation and analytics. The results of the recognition of specimens in images provided by citizen scientists can be used to generate visualizations of geographical regions of interest where the threat of an arbovirus may be imminent. Our system uses state-of-the-art image classification algorithms and a combination of mobile and desktop applications to ensure that crucial information is shared appropriately and accordingly among its users

SHAPE-directed RNA secondary structure prediction

The diverse functional roles of RNA are determined by its underlying structure. Accurate and comprehensive knowledge of RNA structure would inform a broader understanding of RNA biology and facilitate exploiting RNA as a biotechnological tool and therapeutic target. Determining the pattern of base pairing, or secondary structure, of RNA is a first step in these endeavors. Advances in experimental, computational, and comparative analysis approaches for analyzing secondary structure have yielded accurate structures for many small RNAs, but only a few large (>500 nts) RNAs. In addition, most current methods for determining a secondary structure require considerable effort, analytical expertise, and technical ingenuity. In this review, we outline an efficient strategy for developing accurate secondary structure models for RNAs of arbitrary length. This approach melds structural information obtained using SHAPE chemistry with structure prediction using nearest-neighbor rules and the dynamic programming algorithm implemented in the RNAstructure program. Prediction accuracies reach ≥95% for RNAs on the kilobase scale. This approach facilitates both development of new models and refinement of existing RNA structure models, which we illustrate using the Gag-Pol frameshift element in an HIV-1 M-group genome. Most promisingly, integrated experimental and computational refinement brings closer the ultimate goal of efficiently and accurately establishing the secondary structure for any RNA sequence

FastFlow: AI for Fast Urban Wind Velocity Prediction

Data-driven approaches, including deep learning, have shown great promise as surrogate models across many domains. These extend to various areas in sustainability. An interesting direction for which data-driven methods have not been applied much yet is in the quick quantitative evaluation of urban layouts for planning and design. In particular, urban designs typically involve complex trade-offs between multiple objectives, including limits on urban build-up and/or consideration of urban heat island effect. Hence, it can be beneficial to urban planners to have a fast surrogate model to predict urban characteristics of a hypothetical layout, e.g. pedestrian-level wind velocity, without having to run computationally expensive and time-consuming high-fidelity numerical simulations. This fast surrogate can then be potentially integrated into other design optimization frameworks, including generative models or other gradient-based methods. Here we present the use of CNNs for urban layout characterization that is typically done via high-fidelity numerical simulation. We further apply this model towards a first demonstration of its utility for data-driven pedestrian-level wind velocity prediction. The data set in this work comprises results from high-fidelity numerical simulations of wind velocities for a diverse set of realistic urban layouts, based on randomized samples from a real-world, highly built-up urban city. We then provide prediction results obtained from the trained CNN, demonstrating test errors of under 0.1 m/s for previously unseen urban layouts. We further illustrate how this can be useful for purposes such as rapid evaluation of pedestrian wind velocity for a potential new layout. It is hoped that this data set will further accelerate research in data-driven urban AI, even as our baseline model facilitates quantitative comparison to future methods

Quality control of cannabis inflorescence and oil products : response factors for the cost-efficient determination of ten cannabinoids by HPLC

The quality control of medicinal cannabis should include quantification of as many cannabinoids as practicable in a routine analytical lab, to accurately reflect the quality of the product. However, the cost and availability of some cannabinoid standards is an impediment to their routine use. This work seeks to overcome this obstacle by analysing samples using relative retention times (RRT) and relative response factors (RRF), relative to CBD and CBDA reference standards which are readily available. A high-performance liquid chromatography-photodiode array method was developed to quantify ten cannabinoids (Δ9 -THC, Δ8 -THC, THCA-A, CBN, CBD, CDBA, CBC, CBDV, CBG, and CBGA) in dried cannabis inflorescence and cannabis oil. This method was validated according to ICH guidelines. The proposed method has detection limits ranging from 20 to 78 µg/g, which provided sufficient sensitivity for the panel of cannabinoids. Non-cannabinoid surrogate matrices were used for spike recovery studies to determine method accuracy – analyte recoveries for the inflorescence and oil ranged from 90.1 to 109.3% (inflorescence mean, 100.9%; oil mean, 99.6%). The RRT and RRF values determined independently by three analysts were comparable, indicating the method is robust. The validity of analysis using RRT and RRF was further confirmed by testing six inflorescence samples, as it was found that concentrations above the order of magnitude of the LoQ agreed satisfactorily (range, 95.0 to 111.9%; mean, 100.0%) with the concentrations obtained through the conventional approach of multipoint calibration using pure standards. The proposed method is therefore suitable for the rapid and simple determination of a panel of ten cannabinoids without having to repeatedly purchase every expensive pure standard. Accordingly, analysts in the medicinal cannabis field may explore the use of RRF and RRT for their methods and instruments

Minimal Determinants for Binding Activated Gα from the Structure of a Gα i1 −Peptide Dimer † , ‡

G-proteins cycle between an inactive GDP-bound state and active GTP-bound state, serving as molecular switches that coordinate cellular signaling. We recently used phage-display to identify a series of peptides that bind Gα subunits in a nucleotide-dependent manner [Johnston, C. A., Willard, F. S., Jezyk, M. R., Fredericks, Z., Bodor, E. T., Jones, M. B., Blaesius, R., Watts, V. J., Harden, T. K., Sondek, J., Ramer, J. K., and Siderovski, D. P. (2005) Structure 13, 1069–1080]. Here we describe the structural features and functions of KB-1753, a peptide that binds selectively to GDP·AlF4−- and GTPγS-bound states of Gαi subunits. KB-1753 blocks interaction of Gαtransducin with its effector, cGMP phosphodiesterase, and inhibits transducin-mediated activation of cGMP degradation. Additionally, KB-1753 interferes with RGS protein binding and resultant GAP activity. A fluorescent KB-1753 variant was found to act as a sensor for activated Gα in vitro. The crystal structure of KB-1753 bound to Gαi1·GDP·AlF4− reveals binding to a conserved hydrophobic groove between switch II and α3 helices, and, along with supporting biochemical data and previous structural analyses, supports the notion that this is the site of effector interactions for Gαi subunits

Fundamental differences between SPH and grid methods

We have carried out a hydrodynamical code comparison study of interacting multiphase fluids. The two commonly used techniques of grid and smoothed particle hydrodynamics (SPH) show striking differences in their ability to model processes that are fundamentally important across many areas of astrophysics. Whilst Eulerian grid based methods are able to resolve and treat important dynamical instabilities, such as Kelvin-Helmholtz or Rayleigh-Taylor, these processes are poorly or not at all resolved by existing SPH techniques. We show that the reason for this is that SPH, at least in its standard implementation, introduces spurious pressure forces on particles in regions where there are steep density gradients. This results in a boundary gap of the size of the SPH smoothing kernel over which information is not transferred.Comment: 15 pages, 13 figures, to be submitted to MNRAS. For high-resolution figures, please see http://www-theorie.physik.unizh.ch/~agertz

cGAS-STING pathway targeted therapies and their applications in the treatment of high-grade glioma

Median survival of patients with glioblastoma (GBM) treated with standard of care which consists of maximal safe resection of the contrast-enhancing portion of the tumor followed by radiation therapy with concomitant adjuvant temozolomide (TMZ) remains 15 months. The tumor microenvironment (TME) is known to contain immune suppressive myeloid cells with minimal effector T cell infiltration. Stimulator of interferon genes (STING) is an important activator of immune response and results in production of Type 1 interferon and antigen presentation by myeloid cells. This review will discuss important developments in STING agonists, potential biomarkers for STING response, and new combinatorial therapeutic approaches in gliomas