Persistent azulene α-carbocations:synthesis from aldehydes, spectroscopic and crystallographic properties

The non-benzenoid aromatic system azulene is sufficiently nucleophilic at C1 that it can react with a protonated aldehyde to form an α-azulenyl alcohol. This in turn may be protonated and undergo loss of water to give an azulene α-carbocation. We report the isolation of such azulenyl cations as salts with non-coordinating anions. The salts have been characterised by NMR, UV/Vis absorption and (in certain cases) X-ray crystallography. Reduction of representative salts to afford azulenyl(aryl) methylenes has been demonstrated.</p

Persistent azulene α-carbocations:synthesis from aldehydes, spectroscopic and crystallographic properties

The non-benzenoid aromatic system azulene is sufficiently nucleophilic at C1 that it can react with a protonated aldehyde to form an α-azulenyl alcohol. This in turn may be protonated and undergo loss of water to give an azulene α-carbocation. We report the isolation of such azulenyl cations as salts with non-coordinating anions. The salts have been characterised by NMR, UV/Vis absorption and (in certain cases) X-ray crystallography. Reduction of representative salts to afford azulenyl(aryl) methylenes has been demonstrated.</p

Stabilization of protein-protein interactions in drug discovery

Introduction: PPIs are involved in every disease and specific modulation of these PPIs with small molecules would significantly improve our prospects of developing therapeutic agents. Both industry and academia have engaged in the identification and use of PPI inhibitors. However in comparison, the opposite strategy of employing small-molecule stabilizers of PPIs is underrepresented in drug discovery. Areas covered: PPI stabilization has not been exploited in a systematic manner. Rather, this concept validated by a number of therapeutically used natural products like rapamycin and paclitaxel has been shown retrospectively to be the basis of the activity of synthetic molecules originating from drug discovery projects among them lenalidomide and tafamidis. Here, the authors cover the growing number of synthetic small-molecule PPI stabilizers to advocate for a stronger consideration of this as a drug discovery approach. Expert opinion: Both the natural products and the growing number of synthetic molecules show that PPI stabilization is a viable strategy for drug discovery. There is certainly a significant challenge to adapt compound libraries, screening techniques and downstream methodologies to identify, characterize and optimize PPI stabilizers, but the examples of molecules reviewed here in our opinion justify these efforts.</p

User's guide for GAPCON-THERMAL-2: A computer program for calculating the thermal behavior of an oxide fuel rod

This report is being published as a user's manual for GAPCON-THERMAL-2 and provides a general description of the code and instructions for its use. The GAPCON-THERMAL-2 code was developed for the Regulatory Staff, NRC, to use as a tool in estimating fuel-cladding gap conductances and fuel stored energy and represents a modification of the GAPCON-THERMAL-1 code. The goal of the modifications was to reduce uncertainties associated with calculating power history and burnup effects and yet retain a relatively flexible and fast running code for parametric studies. 15 references (auth

Stabilization of protein-protein interactions in drug discovery

Introduction: PPIs are involved in every disease and specific modulation of these PPIs with small molecules would significantly improve our prospects of developing therapeutic agents. Both industry and academia have engaged in the identification and use of PPI inhibitors. However in comparison, the opposite strategy of employing small-molecule stabilizers of PPIs is underrepresented in drug discovery. Areas covered: PPI stabilization has not been exploited in a systematic manner. Rather, this concept validated by a number of therapeutically used natural products like rapamycin and paclitaxel has been shown retrospectively to be the basis of the activity of synthetic molecules originating from drug discovery projects among them lenalidomide and tafamidis. Here, the authors cover the growing number of synthetic small-molecule PPI stabilizers to advocate for a stronger consideration of this as a drug discovery approach. Expert opinion: Both the natural products and the growing number of synthetic molecules show that PPI stabilization is a viable strategy for drug discovery. There is certainly a significant challenge to adapt compound libraries, screening techniques and downstream methodologies to identify, characterize and optimize PPI stabilizers, but the examples of molecules reviewed here in our opinion justify these efforts.</p

Nebuliser therapy in the intensive care unit

The relationship between identity, lived experience, sexual practices and the language through which these are conveyed has been widely debated in sexuality literature. For example, ‘coming out’ has famously been conceptualised as a ‘speech act’ (Sedgwick 1990) and as a collective narrative (Plummer 1995), while a growing concern for individuals’ diverse identifications in relations to their sexual and gender practices has produced interesting research focusing on linguistic practices among LGBT-identified individuals (Leap 1995; Kulick 2000; Cameron and Kulick 2006; Farqhar 2000). While an explicit focus on language remains marginal to literature on sexualities (Kulick 2000), issue of language use and translation are seldom explicitly addressed in the growing literature on intersectionality. Yet intersectional perspectives ‘reject the separability of analytical and identity categories’ (McCall 2005:1771), and therefore have an implicit stake in the ‘vernacular’ language of the researched, in the ‘scientific’ language of the researcher and in the relationship of continuity between the two. Drawing on literature within gay and lesbian/queer studies and cross-cultural studies, this chapter revisits debates on sexuality, language and intersectionality. I argue for the importance of giving careful consideration to the language we choose to use as researchers to collectively define the people whose experiences we try to capture. I also propose that language itself can be investigated as a productive way to foreground how individual and collective identifications are discursively constructed, and to unpack the diversity of lived experience. I address intersectional complexity as a methodological issue, where methodology is understood not only as the methods and practicalities of doing research, but more broadly as ‘a coherent set of ideas about the philosophy, methods and data that underlie the research process and the production of knowledge’ (McCall 2005:1774). My points are illustrated with examples drawn from my ethnographic study on ‘lesbian’ identity in urban Russia, interspersed with insights from existing literature. In particular, I aim to show that an explicit focus on language can be a productive way to explore the intersections between the global, the national and the local in cross-cultural research on sexuality, while also addressing issues of positionality and accountability to the communities researched

Deep Generative Model-based Quality Control for Cardiac MRI Segmentation

In recent years, convolutional neural networks have demonstrated promising performance in a variety of medical image segmentation tasks. However, when a trained segmentation model is deployed into the real clinical world, the model may not perform optimally. A major challenge is the potential poor-quality segmentations generated due to degraded image quality or domain shift issues. There is a timely need to develop an automated quality control method that can detect poor segmentations and feedback to clinicians. Here we propose a novel deep generative model-based framework for quality control of cardiac MRI segmentation. It first learns a manifold of good-quality image-segmentation pairs using a generative model. The quality of a given test segmentation is then assessed by evaluating the difference from its projection onto the good-quality manifold. In particular, the projection is refined through iterative search in the latent space. The proposed method achieves high prediction accuracy on two publicly available cardiac MRI datasets. Moreover, it shows better generalisation ability than traditional regression-based methods. Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows

Autologous tooth graft after endodontical treated used for socket preservation: A multicenter clinical study.

The aim of the study was to evaluate the tooth extracted use as autologous tooth graft after endodontic root canal therapies used for socket preservation. To this purpose, the Tooth Transformer shredding and decontamination machine has been used. The graft obtained in this way, was inserted at the time of the extraction or at a second surgery altogether with the chosen regenerative therapy. This clinical trial enrolled patients with post-estractive defects requiring the restoration bone dimension and shape in the maxillary and mandibular zone. In addition, 98 patients with 119 extraction sockets were enrolled across 10 standardized centers. An innovative preparation method, using the dedicated automated device Tooth Transformer, able to transform autologous teeth in suitable grafting material, has been used. The extracted tooth was cleaned and treated using a Tooth Transformer and made a socket preservation. Thirteen Biopsies were realized to analyze the histologic outcomes at the average time of four months to demonstrate that the autologous tooth graft made from root after endodontic therapy should be used in human bone regeneration as graft for dental implant placement

Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging

“The final authenticated version is available online at https://doi.org/10.1007/978-3-030-32245-8_83.”© 2019, Springer Nature Switzerland AG. Recent progress in fully-automated image segmentation has enabled efficient extraction of clinical parameters in large-scale clinical imaging studies, reducing laborious manual processing. However, the current state-of-the-art automatic image segmentation may still fail, especially when it comes to atypical cases. Visual inspection of segmentation quality is often required, thus diminishing the improvements in efficiency. This drives an increasing need to enhance the overall data processing pipeline with robust automatic quality scoring, especially for clinical applications. We present a novel quality control-driven (QCD) framework to provide reliable segmentation using a set of different neural networks. In contrast to the prior segmentation and quality scoring methods, the proposed framework automatically selects the optimal segmentation on-the-fly from the multiple candidate segmentations available, directly utilizing the inherent Dice similarity coefficient (DSC) predictions. We trained and evaluated the framework on a large-scale cardiovascular magnetic resonance aortic cine image sequences from the UK Biobank Study. The framework achieved segmentation accuracy of mean DSC at 0.966, mean prediction error of DSC within 0.015, and mean error in estimating lumen area ≤17.6 mm2 for both ascending aorta and proximal descending aorta. This novel QCD framework successfully integrates the automatic image segmentation along with detection of critical errors on a per-case basis, paving the way towards reliable fully-automatic extraction of clinical parameters for large-scale imaging studies