Structural plasticity in RNA and its role in the regulation of protein translation in coliphage Q beta

We have analyzed both conformational and functional changes caused by two large cis-acting deletions (delta 159 and delta 549) located within the read-through domain, a 850 nucleotide hairpin, in coliphage Q beta genomic RNA. Studies in vivo show that co-translational regulation of the viral coat and replicase genes has been uncoupled in viral genomes carrying deletion delta 159. Translational regulation is restored in deletion delta 549, a naturally evolved pseudorevertant. Structural analysis by computer modeling shows that structural features within the read-through domain of delta 159 RNA are less well determined than they are in the read-through domain of wild-type RNA, whereas predicted structure in the read-through domain of evolved pseudorevertant delta 549 is unusually well determined. Structural analysis by electron microscopy of the genomic RNAs shows that several long range helices at the base of the read-through domain, that suppress translational initiation of the viral replicase gene in the wild-type genome, have been destabilized in delta 159 RNA. In addition, the structure of local hairpins within the read-through region is more variable in delta 159 RNA than in wild-type RNA. Stable RNA secondary structure is restored in the read-through domain of delta 549 RNA. Our analyses suggest that structure throughout the read-through domain affects the regulation of viral replicase expression by altering the likelihood that long-range interactions at the base of the domain will form. We discuss possible kinetic and equilibrium models that can explain this effect, and argue that observed changes in structural plasticity within the read-through domain of the mutant genomes are key in understanding the process. During the course of these studies, we became aware of the importance of the information contained in the energy dot plot produced by the RNA secondary structure prediction program mfold. As a result, we have improved the graphical representation of this information through the use of color annotation in the predicted optimal folding. The method is presented here for the first time.     

A comparison of statistical relational learning and graph neural networks for aggregate graph queries

Statistical relational learning (SRL) and graph neural networks (GNNs) are two powerful approaches for learning and inference over graphs. Typically, they are evaluated in terms of simple metrics such as accuracy over individual node labels. Complex aggregate graph queries (AGQ) involving multiple nodes, edges, and labels are common in the graph mining community and are used to estimate important network properties such as social cohesion and influence. While graph mining algorithms support AGQs, they typically do not take into account uncertainty, or when they do, make simplifying assumptions and do not build full probabilistic models. In this paper, we examine the performance of SRL and GNNs on AGQs over graphs with partially observed node labels. We show that, not surprisingly, inferring the unobserved node labels as a first step and then evaluating the queries on the fully observed graph can lead to sub-optimal estimates, and that a better approach is to compute these queries as an expectation under the joint distribution. We propose a sampling framework to tractably compute the expected values of AGQs. Motivated by the analysis of subgroup cohesion in social networks, we propose a suite of AGQs that estimate the community structure in graphs. In our empirical evaluation, we show that by estimating these queries as an expectation, SRL-based approaches yield up to a 50-fold reduction in average error when compared to existing GNN-based approaches.

     

Hybrid predictive dynamics: a new approach to simulate human motion

A new methodology, called hybrid predictive dynamics (HPD), is introduced in this work to simulate human motion. HPD is defined as an optimization-based motion prediction approach in which the joint angle control points are unknowns in the equations of motion. Some of these control points are bounded by the experimental data. The joint torque and ground reaction forces are calculated by an inverse algorithm in the optimization procedure. Therefore, the proposed method is able to incorporate motion capture data into the formulation to predict natural and subject-specific human motions. Hybrid predictive dynamics includes three procedures, and each is a sub-optimization problem. First, the motion capture data are transferred from Cartesian space into joint space by using optimization-based inverse kinematics (IK) methodology. Secondly, joint profiles obtained from IK are interpolated by B-spline control points by using an error-minimization algorithm. Third, boundaries are built on the control points to represent specific joint profiles from experiments, and these boundaries are used to guide the predicted human motion. To predict more accurate motion, the boundaries can also be built on the kinetic variables if the experimental data are available. The efficiency of the method is demonstrated by simulating a box-lifting motion. The proposed method takes advantage of both prediction and tracking capabilities simultaneously, so that HPD has more applications in human motion prediction, especially towards clinical applications.     

Predictive simulation of human walking transitions using an optimization formulation

A general optimization formulation for transition walking prediction using 3D skeletal model is presented. The formulation is based on a previously presented one-step walking formulation (Xiang et al., Int J Numer Methods Eng 79:667–695, 2009b). Two basic transitions are studied: walk-to-stand and slow-to-fast walk. The slow-to-fast transition is used to connect slow walk to fast walk by using a step-to-step transition formulation. In addition, the speed effects on the walk-to-stand motion are investigated. The joint torques and ground reaction forces (GRF) are recovered and analyzed from the simulation. For slow-to-fast walk transition, the predicted ground reaction forces in step transition is even larger than that of the fast walk. The model shows good correlation with the experimental data for the lower extremities except for the standing ankle profile. The optimal solution of transition simulation is obtained in a few minutes by using predictive dynamics method.     

Simultaneous analysis and design optimization of nonlinear response

The optimal structural design requiring nonlinear analysis and design sensitivity analysis can be an enormous computational task. It is extremely important to explore ways to reduce the computational effort so that more realistic and larger-scale structures can be optimized. The optimal design process is iterative requiring response analysis of the structure for each design improvement. A recent study has shown that up to 90 percent of the total computational effort is spent in computing the nonlinear response of the structure during the optimal design process. Thus, efficiency of the optimization process for nonlinear structures can be substantially improved if numerical effort for analyzing the structure can be reduced. This paper explores the idea of using design sensitivity coefficients (computed at each iteration to improve design) to predict displacement response of the structure at a changed design. The iterative procedure for nonlinear analysis of the structure is then started from the predicted response. This optimization procedure is called mixed and the original procedure where sensitivity information is not used is called the conventional approach. The numerical procedures for the two approaches are developed and implemented. They are compared on some truss type structures by including both geometric and material nonlinearities. Stress, strain, displacement, and buckling load constraints are imposed. The study shows the mixed method to be numerically stable and efficient.     

Phosphate sorption isotherms in relation to P nutrition of wheat (Triticum aestivum)

The phosphate sorption isotherms are needed to explain differential plant responses to P fertilization in soils. Laboratory and greenhouse experiments investigated the use of phosphorus sorption isotherms in relation to P fertilizer requirement of wheat in ten benchmark soils of Punjab, India. The modified Mitscherlich Equation (3) was used to describe plant response observed in different soils. Maximum obtainable yield (MOY) ranged from 11.6 g pot^–1 in Gurdaspur (I) sandy clay loam to 7.0 g pot^–1 in Nabha sandy clay loam. Response to P applied @ 25 mg P kg^–1 soil was maximum (77%) in Bathinda sand and minimum in Chuharpur clay loam (33%). The response curvature varied from 3.74 × 10^–2 in Nabha sandy clay loam to 4.43 × 10^–2 in Kanjli sandy loam. The soil solution P required to produce optimum yield (90% MOY) varied from 1.61 µg ml^–1 in Bathinda sand to 0.10 µg ml^–1 in Sadhugarh clay. Dry matter yield obtained at 0.2 µg ml^–1 solution P concentration ranged from 55% in Bathinda sand to 85% of MOY in Gurdaspur (II) clay loam. At the same solution P concentration (0.1 µg P ml^–1), dry matter yield was 91% in Sadhugarh clay, 80% in Gurdaspur (II) clay loam and, 43% of MOY in Bathinda sand and eventually coincided with the decreasing maximum buffer capacity (MBC) in these soils. At the same level of sorbed P (100 mg P kg^–1 soil) the yield was observed to be inversely proportional to MBC. The study, therefore, concludes that, soils should be grouped according to their P sorption characteristics and MBC before using critical soil solution P as a criterion for obtaining optimum yields.     

Micro-computed tomography scanning approaches to quantify,parameterize and visualize bioturbation activity in clogged streambeds: A proof of concept

Fine particle clogging and faunal bioturbation are two key processes co-occurring in the hyporheic zone that potentially affect hyporheic exchange through modifications in the sediment structure of streambeds. Clogging results from excessive fine sediment infiltration and deposition in rivers, and it is known to decrease matrix porosity and potentially reduce permeability. Faunal bioturbation activity may compensate for the negative effect of clogging by reworking the sediment, increasing porosity, and preventing further infiltration of fines. Although both processes of clogging and bioturbation have received significant attention in the literature separately, their combined effects on streambed sediment structure are not well understood, mostly due to the lack of a standard methodology for their assessment. Here, we illustrate a novel methodology using X-ray computed tomography (CT), as proof of concept, to investigate how, together, clogging and bioturbation affect streambed porosity in a controlled flow-through flume. By visualising gallery formations of an upward conveyor macroinvertebrate; Lumbriculus variegatus as a model species, we quantified bioturbation activity in a clogged streambed, focusing on orientation, depth, and volume at downwelling and upwelling areas of the flume. Gallery creation increased the porosity of the streambed sediment, suggesting a potential improvement in permeability and a possible offset of clogging effects. We illustrate the promising use of X-ray CT as a tool to assess bioturbation in clogged streambeds, and the potential role of bioturbation activity supporting hyporheic exchange processes in streambeds, warranting further studies to understand the extent of bioturbation impacts in natural systems.     

Comparison of two scaling approaches for the development of biomechanical multi-body human models

Dimensional scaling approaches are widely used to develop multi-body human models in injury biomechanics research. Given the limited experimental data for any particular anthropometry, a validated model can be scaled to different sizes to reflect the biological variance of population and used to characterize the human response. This paper compares two scaling approaches at the whole-body level: one is the conventional mass-based scaling approach which assumes geometric similarity; the other is the structure-based approach which assumes additional structural similarity by using idealized mechanical models to account for the specific anatomy and expected loading conditions. Given the use of exterior body dimensions and a uniform Young’s modulus, the two approaches showed close values of the scaling factors for most body regions, with 1.5 % difference on force scaling factors and 13.5 % difference on moment scaling factors, on average. One exception was on the thoracic modeling, with 19.3 % difference on the scaling factor of the deflection. Two 6-year-old child models were generated from a baseline adult model as application example and were evaluated using recent biomechanical data from cadaveric pediatric experiments. The scaled models predicted similar impact responses of the thorax and lower extremity, which were within the experimental corridors; and suggested further consideration of age-specific structural change of the pelvis. Towards improved scaling methods to develop biofidelic human models, this comparative analysis suggests further investigation on interior anatomical geometry and detailed biological material properties associated with the demographic range of the population.     

Bioenergy,food security and poverty reduction: trade-offs and synergies along the water–energy–food security nexus

This article provides a review of trade-offs and synergies of bioenergy within the water–energy–food security nexus, with emphasis on developing countries. It explores the links of bioenergy with food security, poverty reduction, environmental sustainability, health, and gender equity. It concludes that applying the nexus perspective to analyses of bioenergy widens the scope for achieving multiple-win outcomes along the above aspects.