[FeIII(TF4DMAP)OTf] catalysed anti-Markovnikov oxidation of terminal aryl alkenes to aldehydes and transformation of methyl aryl tertiary amines to formamides with H2O2 as a terminal oxidant

Anti-Markovnikov oxidation of terminal aryl alkenes to aldehydes and transformation of N-methyl aryl tertiary amines to formamides with H2O2 as a terminal oxidant under mild conditions have been achieved with moderate to good product yields using [FeIII(TF4DMAP)OTf] as catalyst.postprin

Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing

Within the context of autonomous driving a model-based reinforcement learning algorithm is proposed for the design of neural network-parameterized controllers. Classical model-based control methods, which include sampling- and lattice-based algorithms and model predictive control, suffer from the trade-off between model complexity and computational burden required for the online solution of expensive optimization or search problems at every short sampling time. To circumvent this trade-off, a 2-step procedure is motivated: first learning of a controller during offline training based on an arbitrarily complicated mathematical system model, before online fast feedforward evaluation of the trained controller. The contribution of this paper is the proposition of a simple gradient-free and model-based algorithm for deep reinforcement learning using task separation with hill climbing (TSHC). In particular, (i) simultaneous training on separate deterministic tasks with the purpose of encoding many motion primitives in a neural network, and (ii) the employment of maximally sparse rewards in combination with virtual velocity constraints (VVCs) in setpoint proximity are advocated.Comment: 10 pages, 6 figures, 1 tabl

'Part'ly first among equals: Semantic part-based benchmarking for state-of-the-art object recognition systems

An examination of object recognition challenge leaderboards (ILSVRC, PASCAL-VOC) reveals that the top-performing classifiers typically exhibit small differences amongst themselves in terms of error rate/mAP. To better differentiate the top performers, additional criteria are required. Moreover, the (test) images, on which the performance scores are based, predominantly contain fully visible objects. Therefore, `harder' test images, mimicking the challenging conditions (e.g. occlusion) in which humans routinely recognize objects, need to be utilized for benchmarking. To address the concerns mentioned above, we make two contributions. First, we systematically vary the level of local object-part content, global detail and spatial context in images from PASCAL VOC 2010 to create a new benchmarking dataset dubbed PPSS-12. Second, we propose an object-part based benchmarking procedure which quantifies classifiers' robustness to a range of visibility and contextual settings. The benchmarking procedure relies on a semantic similarity measure that naturally addresses potential semantic granularity differences between the category labels in training and test datasets, thus eliminating manual mapping. We use our procedure on the PPSS-12 dataset to benchmark top-performing classifiers trained on the ILSVRC-2012 dataset. Our results show that the proposed benchmarking procedure enables additional differentiation among state-of-the-art object classifiers in terms of their ability to handle missing content and insufficient object detail. Given this capability for additional differentiation, our approach can potentially supplement existing benchmarking procedures used in object recognition challenge leaderboards.Comment: Extended version of our ACCV-2016 paper. Author formatting modifie

Chinese herbal medicine for infertility with anovulation: a systematic review.

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Uncoupling Protein-2 Mediates DPP-4 Inhibitor-Induced Restoration of Endothelial Function in Hypertension Through Reducing Oxidative Stress

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Reply to Nomograms need to be presented in full

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Vapor-Phase Stabilization of Biomass Pyrolysis Vapors Using Mixed-Metal Oxide Catalysts

© 2019 American Chemical Society. Mixed-metal oxides possess a wide range of tunability and show promise for catalytic stabilization of biomass pyrolysis products. For materials derived from layered double hydroxides, understanding the effect of divalent cation species and divalent/trivalent cation stoichiometric ratio on catalytic behavior is critical to their successful implementation. In this study, four mixed-metal oxide catalysts consisting of Al, Zn, and Mg in different stoichiometric ratios were synthesized and tested for ex-situ catalytic fast pyrolysis (CFP) using pine wood as feedstock. The catalytic activity and deactivation behavior of these catalysts were monitored in real-time using a lab-scale pyrolysis reactor and fixed catalyst bed coupled with a molecular beam mass spectrometer (MBMS), and data were analyzed by multivariate statistical approaches. In the comparison between Mg-Al and Zn-Al catalyst materials, we demonstrated that the Mg-Al materials possessed greater quantities of basic sites, which we attributed to their higher surface areas, and they produced upgraded pyrolysis vapors which contained less acids and more deoxygenated aromatic hydrocarbons such as toluene and xylene. However, detrimental impacts on carbon yields were realized via decarbonylation and decarboxylation reactions and coke formation. Given that the primary goals of catalytic upgrading of bio-oil are deoxygenation, reduction of acidity, and high carbon yield, these results highlight both promising catalytic effects of mixed-metal oxide materials and opportunities for improvement

Dynamical modeling of collective behavior from pigeon flight data: flock cohesion and dispersion

Several models of flocking have been promoted based on simulations with qualitatively naturalistic behavior. In this paper we provide the first direct application of computational modeling methods to infer flocking behavior from experimental field data. We show that this approach is able to infer general rules for interaction, or lack of interaction, among members of a flock or, more generally, any community. Using experimental field measurements of homing pigeons in flight we demonstrate the existence of a basic distance dependent attraction/repulsion relationship and show that this rule is sufficient to explain collective behavior observed in nature. Positional data of individuals over time are used as input data to a computational algorithm capable of building complex nonlinear functions that can represent the system behavior. Topological nearest neighbor interactions are considered to characterize the components within this model. The efficacy of this method is demonstrated with simulated noisy data generated from the classical (two dimensional) Vicsek model. When applied to experimental data from homing pigeon flights we show that the more complex three dimensional models are capable of predicting and simulating trajectories, as well as exhibiting realistic collective dynamics. The simulations of the reconstructed models are used to extract properties of the collective behavior in pigeons, and how it is affected by changing the initial conditions of the system. Our results demonstrate that this approach may be applied to construct models capable of simulating trajectories and collective dynamics using experimental field measurements of herd movement. From these models, the behavior of the individual agents (animals) may be inferred

Haemodynamic changes in visceral hybrid repairs of type III and type V thoracoabdominal aortic aneurysms

The visceral hybrid procedure combining retrograde visceral bypass grafting and completion endovascular stent grafting is a feasible alternative to conventional open surgical or wholly endovascular repairs of thoracoabdominal aneurysms (TAAA). However, the wide variability in visceral hybrid configurations means that a priori prediction of surgical outcome based on haemodynamic flow profiles such as velocity pattern and wall shear stress post repair remain challenging. We sought to appraise the clinical relevance of computational fluid dynamics (CFD) analyses in the setting of visceral hybrid TAAA repairs. Two patients, one with a type III and the other with a type V TAAA, underwent successful elective and emergency visceral hybrid repairs, respectively. Flow patterns and haemodynamic parameters were analysed using reconstructed pre- and post-operative CT scans. Both type III and type V TAAAs showed highly disturbed flow patterns with varying helicity values preoperatively within their respective aneurysms. Low time-averaged wall shear stress (TAWSS) and high endothelial cell action potential (ECAP) and relative residence time (RRT) associated with thrombogenic susceptibility was observed in the posterior aspect of both TAAAs preoperatively. Despite differing bypass configurations in the elective and emergency repairs, both treatment options appear to improve haemodynamic performance compared to preoperative study. However, we observed reduced TAWSS in the right iliac artery (portending a theoretical risk of future graft and possibly limb thrombosis), after the elective type III visceral hybrid repair, but not the emergency type V repair. We surmise that this difference may be attributed to the higher neo-bifurcation of the aortic stent graft in the type III as compared to the type V repair. Our results demonstrate that CFD can be used in complicated visceral hybrid repair to yield potentially actionable predictive insights with implications on surveillance and enhanced post-operative management, even in patients with complicated geometrical bypass configurations

Metabonomics and Intensive Care

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency medicine 2016. Other selected articles can be found online at http://www.biomedcentral.com/collections/annualupdate2016. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901