Haahrkarlsson9162

The synthesis of a triazole appended dinucleating bisphosphine 1,4-bis(5-(diisopropylphosphaneyl)-1-phenyl-1H-1,2,3-triazol-4-yl)benzene (2) and its coinage metal complexes are described. The dinucleating bisphosphine 2 was obtained by the temperature-controlled lithiation of 1,4-bis(1-phenyl-1H-1,2,3-triazol-4-yl)benzene (1a) and 1,4-bis(1-(2-bromophenyl)-1H-1,2,3-triazol-4-yl)benzene (1b) followed by the reaction with iPr2PCl. The reactions of 2 with copper(I) halides in 1  2 molar ratios yielded the [Cu(μ2-X)]2 dimeric complexes [Cu(μ2-X)2(PiPr2N3PhC2)2C6H4] (3, X = Cl; 4, X = Br; and 5, X = I), whereas the reaction of 2 with AgBr resulted in the formation of hetero-cubane complex [Ag4(μ3-Br)4(PiPr2N3PhC2)2C6H42] (7). Similar reactions of 2 with AgX in 1  2 molar ratios yielded disilver complexes [Ag(μ2-X)2(PiPr2N3PhC2)2C6H4] (6, X = Cl and 8, X = I). Treatment of 2 with AgOAc in a 1  2 molar ratio afforded a dinuclear complex [Ag2(μ2-OAc)2(PiPr2N3PhC2)2(C6H4)] (9) with one of the acetate ligands bridging the two metal centres in the side-on mode, whereas the other one adopting the end-on mode keeping the >CO group uncoordinated. The reaction of 2 with two equivalents of [AuCl(SMe2)] afforded the digold complex [(AuClPiPr2N3PhC2)2C6H4] (10). The molecular structures of 2-5 and 7-10 were confirmed by single crystal X-ray analysis. Non-covalent interactions between Cu and Carene were observed in the molecular structures of 3, 4 and 5. These weak interactions were also assessed by DFT calculations in terms of their non-covalent interaction plots (NCI) and QTAIM analyses.An F-atom with ∼1 eV translational energy was aimed at a line of fluorocarbon adsorbates on Cu(110). Sequential 'knock-on' of F-atom products was observed by STM to propagate along the 1D fluorocarbon line. Hot F-atoms travelling along the line in six successive 'to-and-fro' cycles paralleled the rocking of a macroscopic Newton's cradle.Model explainability is essential for the creation of trustworthy Machine Learning models in healthcare. An ideal explanation resembles the decision-making process of a domain expert and is expressed using concepts or terminology that is meaningful to the clinicians. To provide such explanation, we first associate the hidden units of the classifier to clinically relevant concepts. We take advantage of radiology reports accompanying the chest X-ray images to define concepts. We discover sparse associations between concepts and hidden units using a linear sparse logistic regression. To ensure that the identified units truly influence the classifier's outcome, we adopt tools from Causal Inference literature and, more specifically, mediation analysis through counterfactual interventions. Finally, we construct a low-depth decision tree to translate all the discovered concepts into a straightforward decision rule, expressed to the radiologist. We evaluated our approach on a large chest x-ray dataset, where our model produces a global explanation consistent with clinical knowledge.Vessel segmentation is an essential task in many clinical applications. Although supervised methods have achieved state-of-art performance, acquiring expert annotation is laborious and mostly limited for two-dimensional datasets with a small sample size. On the contrary, unsupervised methods rely on handcrafted features to detect tube-like structures such as vessels. However, those methods require complex pipelines involving several hyper-parameters and design choices rendering the procedure sensitive, dataset-specific, and not generalizable. We propose a self-supervised method with a limited number of hyper-parameters that is generalizable across modalities. Our method uses tube-like structure properties, such as connectivity, profile consistency, and bifurcation, to introduce inductive bias into a learning algorithm. To model those properties, we generate a vector field that we refer to as a flow. Our experiments on various public datasets in 2D and 3D show that our method performs better than unsupervised methods while learning useful transferable features from unlabeled data. Unlike generic self-supervised methods, the learned features learn vessel-relevant features that are transferable for supervised approaches, which is essential when the number of annotated data is limited.

American Indians and Alaska Native (AI/ANs) peoples experience significant health disparities compared to the U.S. Proteasome assay general population. We report comorbidities among AI/ANs with diabetes to guide efforts to improve their health status.

Drawing upon data for over 640,000 AI/ANs who used services funded by the Indian Health Service, we identified 43,518 adults with diabetes in fiscal year 2010. We reported the prevalence of comorbidities by age and cardiovascular disease (CVD) status. Generalized linear models were estimated to describe associations between CVD and other comorbidities.

Nearly 15% of AI/AN adults had diabetes. Hypertension, CVD and kidney disease were comorbid in 77.9%, 31.6%, and 13.3%, respectively. Nearly 25% exhibited a mental health disorder; 5.7%, an alcohol or drug use disorder. Among AI/ANs with diabetes absent CVD, 46.9% had 2 or more other chronic conditions; the percentage among adults with diabetes and CVD was 75.5%. Hypertension and tobacco use disorders were associated with a 71% (95% CI for prevalence ratio 1.63 - 1.80) and 33% (1.28 - 1.37) higher prevalence of CVD, respectively, compared to adults without these conditions.

Detailed information on the morbidity burden of AI/ANs with diabetes may inform enhancements to strategies implemented to prevent and treat CVD and other comorbidities.

Detailed information on the morbidity burden of AI/ANs with diabetes may inform enhancements to strategies implemented to prevent and treat CVD and other comorbidities.Effectively monitoring the dynamics of human mobility is of great importance in urban management, especially during the COVID-19 pandemic. Traditionally, the human mobility data is collected by roadside sensors, which have limited spatial coverage and are insufficient in large-scale studies. With the maturing of mobile sensing and Internet of Things (IoT) technologies, various crowdsourced data sources are emerging, paving the way for monitoring and characterizing human mobility during the pandemic. This paper presents the authors' opinions on three types of emerging mobility data sources, including mobile device data, social media data, and connected vehicle data. We first introduce each data source's main features and summarize their current applications within the context of tracking mobility dynamics during the COVID-19 pandemic. Then, we discuss the challenges associated with using these data sources. Based on the authors' research experience, we argue that data uncertainty, big data processing problems, data privacy, and theory-guided data analytics are the most common challenges in using these emerging mobility data sources. Last, we share experiences and opinions on potential solutions to address these challenges and possible research directions associated with acquiring, discovering, managing, and analyzing big mobility data.Walk-sharing is a cost-effective and proactive approach that promises to improve pedestrian safety and has been shown to be technically (theoretically) viable. Yet, the practical viability of walk-sharing is largely dependent on community acceptance, which has not, until now, been explored. Gaining useful insights on the community's spatio-temporal and social preferences in regard to walk-sharing will ensure the establishment of practical viability of walk-sharing in a real-world urban scenario. We aim to derive practical viability using defined performance metrics (waiting time, detour distance, walk-alone distance and matching rate) and by investigating the effectiveness of walk-sharing in terms of its major objective of improving pedestrian safety and safety perception. We make use of the results from a web-based survey on the public perception on our proposed walk-sharing scheme. Findings are fed into an existing agent-based walk-sharing model to investigate the performance of walk-sharing and deduce its practical viability in urban scenarios.Gauging viral transmission through human mobility in order to contain the COVID-19 pandemic has been a hot topic in academic studies and evidence-based policy-making. Although it is widely accepted that there is a strong positive correlation between the transmission of the coronavirus and the mobility of the general public, there are limitations to existing studies on this topic. For example, using digital proxies of mobile devices/apps may only partially reflect the movement of individuals; using the mobility of the general public and not COVID-19 patients in particular, or only using places where patients were diagnosed to study the spread of the virus may not be accurate; existing studies have focused on either the regional or national spread of COVID-19, and not the spread at the city level; and there are no systematic approaches for understanding the stages of transmission to facilitate the policy-making to contain the spread. To address these issues, we have developed a new methodological framework for COVID-19 transmission analysis based upon individual patients' trajectory data. By using innovative space-time analytics, this framework reveals the spatiotemporal patterns of patients' mobility and the transmission stages of COVID-19 from Wuhan to the rest of China at finer spatial and temporal scales. It can improve our understanding of the interaction of mobility and transmission, identifying the risk of spreading in small and medium-sized cities that have been neglected in existing studies. This demonstrates the effectiveness of the proposed framework and its policy implications to contain the COVID-19 pandemic.

Inhaled budesonide benefits patients with COVID-19.

enables the sustained, low dose administration of budesonide within a delivery vehicle similar to lung surfactant.

may offer anti-inflammatory and protective effects to the lung in COVID-19, yet it's effect on SARS-CoV-2 replication is unknown.

To determine the efficacy of

against SARS-CoV-2-infection

, evaluate its ability to decrease inflammation, and airway hyperresponsiveness in an animal model of lung inflammation.

SARS-CoV-2-infected Vero 76 cells were treated with

([0.03-100 µg/ml]) for 3 days, and virus yield in the supernatant was measured. Ovalbumin-sensitized C57BL/6 mice received aerosolized (a)

weekly, (b) only budesonide, either daily or weekly, or (c) weekly empty

(without budesonide). All treatment groups were compared to sensitized untreated, or normal mice using histopathologic examination, electron microscopy (EM), airway hyperresponsiveness (AHR) to Methacholine (Mch) challenge, and eosinophil peroxidase activity (EPO) measurements in bronchioalveolar lavage (BAL).

showed significant inhibition of viral replication of SARS-CoV-2-infected cells with the selectivity index (SI) value >24. Weekly

and daily budesonide therapy significantly decreased lung inflammation and EPO in BAL.

localized in type II pneumocytes, and was the only group to significantly decrease AHR, and EPO in BAL with Mch challenge.

significantly inhibited viral replication in SARS-CoV-2-infected cells. It localized into type II pneumocytes, decreased lung inflammation, AHR and EPO activity with Mch challenge. This novel drug formulation may offer a potential inhalational treatment for COVID-19.

ProLung™-budesonide significantly inhibited viral replication in SARS-CoV-2-infected cells. It localized into type II pneumocytes, decreased lung inflammation, AHR and EPO activity with Mch challenge. This novel drug formulation may offer a potential inhalational treatment for COVID-19.