An evolve-then-correct reduced order model for hidden fluid dynamics

In this paper, we put forth an evolve-then-correct reduced order modeling approach that combines intrusive and nonintrusive models to take hidden physical processes into account. Specifically, we split the underlying dynamics into known and unknown components. In the known part, we first utilize an intrusive Galerkin method projected on a set of basis functions obtained by proper orthogonal decomposition. We then formulate a recurrent neural network emulator based on the assumption that observed data is a manifestation of all relevant processes. We further enhance our approach by using an orthonormality conforming basis interpolation approach on a Grassmannian manifold to address off-design conditions. The proposed framework is illustrated here with the application of two-dimensional co-rotating vortex simulations under modeling uncertainty. The results demonstrate highly accurate predictions underlining the effectiveness of the evolve-then-correct approach toward realtime simulations, where the full process model is not known a priori

Heterojunction Hybrid Devices from Vapor Phase Grown MoS2_{2}

We investigate a vertically-stacked hybrid photodiode consisting of a thin n-type molybdenum disulfide (MoS$_{2}$) layer transferred onto p-type silicon. The fabrication is scalable as the MoS$_{2}$ is grown by a controlled and tunable vapor phase sulfurization process. The obtained large-scale p-n heterojunction diodes exhibit notable photoconductivity which can be tuned by modifying the thickness of the MoS$_{2}$ layer. The diodes have a broad spectral response due to direct and indirect band transitions of the nanoscale MoS$_{2}$. Further, we observe a blue-shift of the spectral response into the visible range. The results are a significant step towards scalable fabrication of vertical devices from two-dimensional materials and constitute a new paradigm for materials engineering.Comment: 23 pages with 4 figures. This article has been published in Scientific Reports. (26 June 2014, doi:10.1038/srep05458

A deep learning enabler for non-intrusive reduced order modeling of fluid flows

In this paper, we introduce a modular deep neural network (DNN) framework for data-driven reduced order modeling of dynamical systems relevant to fluid flows. We propose various deep neural network architectures which numerically predict evolution of dynamical systems by learning from either using discrete state or slope information of the system. Our approach has been demonstrated using both residual formula and backward difference scheme formulas. However, it can be easily generalized into many different numerical schemes as well. We give a demonstration of our framework for three examples: (i) Kraichnan-Orszag system, an illustrative coupled nonlinear ordinary differential equations, (ii) Lorenz system exhibiting chaotic behavior, and (iii) a non-intrusive model order reduction framework for the two-dimensional Boussinesq equations with a differentially heated cavity flow setup at various Rayleigh numbers. Using only snapshots of state variables at discrete time instances, our data-driven approach can be considered truly non-intrusive, since any prior information about the underlying governing equations is not required for generating the reduced order model. Our \textit{a posteriori} analysis shows that the proposed data-driven approach is remarkably accurate, and can be used as a robust predictive tool for non-intrusive model order reduction of complex fluid flows.Comment: 36 pages, 21 figure

Peroxisome proliferator-activated receptors and thiazolidinediones in diabetic nephropathy

Diabetic nephropathy is global problem with several drugs into trial without much success the current article highlights the role of thiazolidinedione’s in diabetic nephropathy by scrutinizing and reconnoitring the cellular and intracellular mechanism and shielding action and the role of peroxisome proliferator-activated gamma receptors (PPARγ) receptors. Not only anti-diabetic action but renal protective effect with evidence based study has been highlighted. PPAR γ-is versatile target having numerous benefits and mainly preventing fibrosis in diabetic experimental model and some clinical case report yet, the benefits are not up to mark, since renal failure itself causes volume expansion and the thiazolidinedione’s (TZDs) also preserve salt and water and lead to congestive heart failure which constraints its clinical application. Dual activators and balaglitazone selective PPAR modulator are having upcoming potential for treatment of diabetic nephropathy. Further detail investigation on such drug is needed to explore. However adverse effect like heart failure, osteoporosis and volume expansion effect over-rides the beneficial effect thus limiting its clinical use of currently available TZDs

Effects of Anatomy and Particle Size on Nasal Sprays and Nebulizers

To study the effects of nasal deformity on aerosol penetration past the nasal valve (NV) for varying particle sizes using sprays or nebulizers

Physiological and behavioral effects of animal-assisted interventions for therapy dogs in pediatric oncology settings

Over the past two decades, animal-assisted interventions (AAIs), defined as the purposeful incorporation of specially trained animals in services to improve human health, have become increasingly popular in clinical settings. However, to date, there have been few rigorously-designed studies aimed at examining the impact of AAIs on therapy animals, despite a notable potential for stress. The current study measured physiological and behavioral stress indicators in therapy dogs who participated in AAI sessions in pediatric oncology settings, while also examining the psychosocial effects for patients and their parents. This manuscript describes the study’s canine stress findings. Methods: A total of 26 therapy dog-handler teams were paired with newly diagnosed children with cancer at five children’s hospitals in the United States. These teams provided regular AAI visits to the child and his/her parent(s) for a period of four months. The teams completed a demographic form, the Canine Behavioral Assessment & Research Questionnaire (C-BARQ), and a self-report survey to document the types of activities that occurred during each session. Canine saliva was also collected at five baseline time points and 20 minutes after the start of study sessions for cortisol analysis, and all study sessions were video recorded to document the dog’s behavior via an ethogram measure. Results: Data showed no significant differences in salivary cortisol levels between baseline (0.51µg/dL) and AAI sessions (0.44µg/dL), p = 0.757. Higher salivary cortisol was significantly associated with a higher number of stress behaviors per session (p = 0.039). There was a significant relationship between stress and affiliative session behaviors (pConclusions:Results show that therapy dogs did not have significantly increased physiological stress responses, nor did they exhibit significantly more stress-related behaviors than affiliative-related behaviors, while participating in AAIs in pediatric oncology settings. The significant relationship between canine cortisol and behavior, thus strengthening the argument for the use of cortisol in canine well-being research. This study discusses the importance of further investigation to confirm these findings, which may lead to enhanced canine involvement in hospital settings

Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa

Over 200 million malaria cases globally lead to half-million deaths annually. The development of malaria prevalence prediction systems to support malaria care pathways has been hindered by lack of data, a tendency towards universal "monolithic" models (one-size-fits-all-regions) and a focus on long lead time predictions. Current systems do not provide short-term local predictions at an accuracy suitable for deployment in clinical practice. Here we show a data-driven approach that reliably produces one-month-ahead prevalence prediction within a densely populated all-year-round malaria metropolis of over 3.5 million inhabitants situated in Nigeria which has one of the largest global burdens of P. falciparum malaria. We estimate one-month-ahead prevalence in a unique 22-years prospective regional dataset of > 9 × 10^{4} participants attending our healthcare services. Our system agrees with both magnitude and direction of the prediction on validation data achieving MAE ≤ 6 × 10^{-2}, MSE ≤ 7 × 10^{-3}, PCC (median 0.63, IQR 0.3) and with more than 80% of estimates within a (+ 0.1 to - 0.05) error-tolerance range which is clinically relevant for decision-support in our holoendemic setting. Our data-driven approach could facilitate healthcare systems to harness their own data to support local malaria care pathways

Computed nasal resistance compared with patient-reported symptoms in surgically treated nasal airway passages: A preliminary report

Background:Nasal airway obstruction (NAO) is a common health condition impacting mood, energy, recreation, sleep, and overall quality of life. Nasal surgery often addresses NAO but the results are sometimes unsatisfactory. Evaluating surgical treatment efficacy could be improved if objective tests were available that correlated with patient-reported measures of symptoms. The goal of this study was to develop methods for comparing nasal resistance computed by computational fluid dynamics (CFD) models with patient-reported symptoms of NAO using early data from a 4-year prospective study.Methods:Computed tomography (CT) scans and patient-reported scores from the Nasal Obstruction Symptom Evaluation (NOSE) scale and a visual analog scale (VAS) measuring unilateral airflow sensation were obtained pre- and postoperatively in two NAO patients showing no significant mucosal asymmetry who were successfully treated with functional nasal surgery, including septoplasty. Pre- and postsurgery CFD models were created from the CT scans. Numerical simulation of steady-state inspiratory airflow was used to calculate bilateral and unilateral CFD-derived nasal resistance (CFD-NR).Results:In both subjects, NOSE and VAS scores improved after surgery, bilateral CFD-NR decreased, and unilateral CFD-NR decreased on the affected side. In addition, NOSE and VAS scores tracked with unilateral CFD-NR on the affected side.Conclusion:These preliminary results suggest a possible correlation between unilateral NR and patient-reported symptoms and imply that analysis of unilateral obstruction should focus on the affected side. A formal investigation of unilateral CFD-NR and patient-reported symptoms in a series of NAO patients is needed to determine if these variables are correlated

Tissue Microenvironments Define and Get Reinforced by Macrophage Phenotypes in Homeostasis or during Inflammation, Repair and Fibrosis

Current macrophage phenotype classifications are based on distinct in vitro culture conditions that do not adequately mirror complex tissue environments. In vivo monocyte progenitors populate all tissues for immune surveillance which supports the maintenance of homeostasis as well as regaining homeostasis after injury. Here we propose to classify macrophage phenotypes according to prototypical tissue environments, e.g. as they occur during homeostasis as well as during the different phases of (dermal) wound healing. In tissue necrosis and/or infection, damage- and/or pathogen-associated molecular patterns induce proinflammatory macrophages by Toll-like receptors or inflammasomes. Such classically activated macrophages contribute to further tissue inflammation and damage. Apoptotic cells and antiinflammatory cytokines dominate in postinflammatory tissues which induce macrophages to produce more antiinflammatory mediators. Similarly, tumor-associated macrophages also confer immunosuppression in tumor stroma. Insufficient parenchymal healing despite abundant growth factors pushes macrophages to gain a profibrotic phenotype and promote fibrocyte recruitment which both enforce tissue scarring. Ischemic scars are largely devoid of cytokines and growth factors so that fibrolytic macrophages that predominantly secrete proteases digest the excess extracellular matrix. Together, macrophages stabilize their surrounding tissue microenvironments by adapting different phenotypes as feed-forward mechanisms to maintain tissue homeostasis or regain it following injury. Furthermore, macrophage heterogeneity in healthy or injured tissues mirrors spatial and temporal differences in microenvironments during the various stages of tissue injury and repair. Copyright (C) 2012 S. Karger AG, Base

Prediction of peptide and protein propensity for amyloid formation

Understanding which peptides and proteins have the potential to undergo amyloid formation and what driving forces are responsible for amyloid-like fiber formation and stabilization remains limited. This is mainly because proteins that can undergo structural changes, which lead to amyloid formation, are quite diverse and share no obvious sequence or structural homology, despite the structural similarity found in the fibrils. To address these issues, a novel approach based on recursive feature selection and feed-forward neural networks was undertaken to identify key features highly correlated with the self-assembly problem. This approach allowed the identification of seven physicochemical and biochemical properties of the amino acids highly associated with the self-assembly of peptides and proteins into amyloid-like fibrils (normalized frequency of β-sheet, normalized frequency of β-sheet from LG, weights for β-sheet at the window position of 1, isoelectric point, atom-based hydrophobic moment, helix termination parameter at position j+1 and ΔGº values for peptides extrapolated in 0 M urea). Moreover, these features enabled the development of a new predictor (available at http://cran.r-project.org/web/packages/appnn/index.html) capable of accurately and reliably predicting the amyloidogenic propensity from the polypeptide sequence alone with a prediction accuracy of 84.9 % against an external validation dataset of sequences with experimental in vitro, evidence of amyloid formation