Repression of glucocorticoid-stimulated angiopoietin-like 4 gene transcription by insulin.

Angiopoietin-like 4 (Angptl4) is a glucocorticoid receptor (GR) primary target gene in hepatocytes and adipocytes. It encodes a secreted protein that inhibits extracellular LPL and promotes adipocyte lipolysis. In Angptl4 null mice, glucocorticoid-induced adipocyte lipolysis and hepatic steatosis are compromised. Markedly, insulin suppressed glucocorticoid-induced Angptl4 transcription. To unravel the mechanism, we utilized small molecules to inhibit insulin signaling components and found that phosphatidylinositol 3-kinase and Akt were vital for the suppression in H4IIE cells. A forkhead box transcription factor response element (FRE) was found near the 15 bp Angptl4 glucocorticoid response element (GRE). Mutating the Angptl4 FRE significantly reduced glucocorticoid-induced reporter gene expression in cells. Moreover, chromatin immunoprecipitation revealed that GR and FoxO1 were recruited to Angptl4 GRE and FRE in a glucocorticoid-dependent manner, and cotreatment with insulin abolished both recruitments. Furthermore, in 24 h fasted mice, significant occupancy of GR and FoxO1 at the Angptl4 GRE and FRE was found in the liver. In contrast, both occupancies were diminished after 24 h refeeding. Finally, overexpression of dominant negative FoxO1 mutant abolished glucocorticoid-induced Angptl4 expression, mimicking the insulin suppression. Overall, we demonstrate that both GR and FoxO1 are required for Angptl4 transcription activation, and that FoxO1 negatively mediates the suppressive effect of insulin

Harnessing Complexity in High Performance Computing Ecosystems: A Complex Adaptive Systems Framework

The use of high performance computing (HPC) has been generating influential scientific breakthroughs since the twentieth century. Yet there have been few studies of the complex socio-technical systems formed by these supercomputers and the humans who operate and use them. In this paper, we describe the first complex adaptive systems (CAS) analysis of the dynamics of HPC ecosystems. We conducted an 18-month ethnographic study that included scientific collaborations that use an HPC research center and examined the processes in HPC socio-technical systems via CAS theory to devise organizational designs and strategies that take advantage of system complexity. We uncovered several significant mismatches in the variation and adaptation processes within subsystems and conclude with three potential design directions for management and organization of HPC socio-technical ecosystems

Development of Fuzzy Logic Forecast Models for Location-Based Parking Finding Services

Park-and-ride (PnR) facilities provided by Australian transport authorities have been an effective way to encourage car drivers to use public transport such as trains and buses. However, as populations grow and vehicle running costs increase, the demand for more parking spaces has escalated. Often, PnR facilities are filled to capacity by early morning and commuters resort to parking illegally in streets surrounding stations. This paper reports on the development of a location-based parking finding service for PnR users. Based on their current location, the system can inform users which is the best station to park their cars during peak period. Two criteria—parking availability and the shortest travel time—were used to evaluate the best station. Fuzzy logic forecast models were used to estimate the uncertainty of parking availability during the peak parking demand period. A prototype using these methods has been developed based on a case study of the Oats Street and Carlisle PnR facilities in Perth, Western Australia. The system has proved to be efficacious and has the potential to be applied to other parking systems

Flow-matching -- efficient coarse-graining of molecular dynamics without forces

Coarse-grained (CG) molecular simulations have become a standard tool to study molecular processes on time- and length-scales inaccessible to all-atom simulations. Parameterizing CG force fields to match all-atom simulations has mainly relied on force-matching or relative entropy minimization, which require many samples from costly simulations with all-atom or CG resolutions, respectively. Here we present flow-matching, a new training method for CG force fields that combines the advantages of both methods by leveraging normalizing flows, a generative deep learning method. Flow-matching first trains a normalizing flow to represent the CG probability density, which is equivalent to minimizing the relative entropy without requiring iterative CG simulations. Subsequently, the flow generates samples and forces according to the learned distribution in order to train the desired CG free energy model via force matching. Even without requiring forces from the all-atom simulations, flow-matching outperforms classical force-matching by an order of magnitude in terms of data efficiency, and produces CG models that can capture the folding and unfolding transitions of small proteins

On the uniqueness of sign changing bound state solutions of a semilinear equation

We establish the uniqueness of the higher radial bound state solutions of \Delta u +f(u)=0,\quad x\in \RR^n. \leqno(P) We assume that the nonlinearity $f\in C(-\infty,\infty)$ is an odd function satisfying some convexity and growth conditions, and either has one zero at $b>0$, is non positive and not identically 0 in $(0,b)$, and is differentiable and positive $[b,\infty)$, or is positive and differentiable in $[0,\infty)$

Evaluation of tip capacity analysis model for drilled shafts in gravelly soils

This paper examines an analysis model for predicting the tip capacity of drilled shaft foundations under gravelly soils. Forty one static compression load test data are utilized for this purpose. Comparison of predicted and measured results demonstrates that the prediction model greatly overestimates the tip capacity of drilled shafts. Further assessment on the model reveals a greater variation in three coefficients, including the effective overburden pressure ( q ), the overburden bearing capacity factor ( q N ), and the bearing capacity modifier for soil rigidity ( qr ζ ). These factors are modified from the back-analysis of the drilled shaft load test results. Varying effective shaft depths are considered for the back-calculation to evaluate their effects on capacity behavior. Based on the analyses, the recommended effective shaft depth for the evaluation of effective overburden pressure is limited to 15B (B = shaft diameter). The q N and qr ζ are enhanced while maintaining their basic relationship with the soil effective friction angle ( ), φ in which the q N increases and qr ζ decreases as φ increases. Specific design recommendations for the tip bearing capacity analysis of drilled shafts in gravelly soils are given for engineering practice

Machine learning implicit solvation for molecular dynamics

Accurate modeling of the solvent environment for biological molecules is crucial for computational biology and drug design. A popular approach to achieve long simulation time scales for large system sizes is to incorporate the effect of the solvent in a mean-field fashion with implicit solvent models. However, a challenge with existing implicit solvent models is that they often lack accuracy or certain physical properties compared to explicit solvent models as the many-body effects of the neglected solvent molecules are difficult to model as a mean field. Here, we leverage machine learning (ML) and multi-scale coarse graining (CG) in order to learn implicit solvent models that can approximate the energetic and thermodynamic properties of a given explicit solvent model with arbitrary accuracy, given enough training data. Following the previous ML–CG models CGnet and CGSchnet, we introduce ISSNet, a graph neural network, to model the implicit solvent potential of mean force. ISSNet can learn from explicit solvent simulation data and be readily applied to molecular dynamics simulations. We compare the solute conformational distributions under different solvation treatments for two peptide systems. The results indicate that ISSNet models can outperform widely used generalized Born and surface area models in reproducing the thermodynamics of small protein systems with respect to explicit solvent. The success of this novel method demonstrates the potential benefit of applying machine learning methods in accurate modeling of solvent effects for in silico research and biomedical applications