Low Thermal Conductivity Silicone Aerogels Tuned by Composite Surfactants.

The excellent flexibility of silicone aerogels has attracted attention. However, the thermal conductivity of silicone aerogels is higher than that of inorganic silicone aerogels. Thermal conductivity can be reduced by increasing the thermal insulation of silicone aerogels while maintaining their flexibility.

Label refinement network from synthetic error augmentation for medical image segmentation.

Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconnected cylindrical structures in the segmentation of tree-like structures such as airways or blood vessels.

Siamese model for collateral score prediction from computed tomography angiography images in acute ischemic stroke.

Introduction: Imaging biomarkers, such as the collateral score as determined from Computed Tomography Angiography (CTA) images, play a role in treatment decision making for acute stroke patients. In this manuscript, we present an end-to-end learning approach for automatic determination of a collateral score from a CTA image. Our aim was to investigate whether such end-to-end learning approaches can be used for this classification task, and whether the resulting classification can be used in existing outcome prediction models.

Synthesis of novel 5-amido-2-carboxypyrazines as inhibitors of the type three secretion system of Salmonella enterica serovar Typhimurium.

A series of novel 5-amido-2-carboxypyrazine derivatives were designed, synthesized and evaluated for the inhibitory activities against the T3SS of Salmonella enterica serovar Typhimurium. Preliminary results displayed that the compounds 2f, 2g, 2h and 2i showed potent inhibitory activities against T3SS. Compound 2h was identified as the most potent T3SS inhibitor and the SPI-1 effector secretion was strongly inhibited by 2h in a dose-dependent manner.

Deep reinforcement learning for cerebral anterior vessel tree extraction from 3D CTA images.

Extracting the cerebral anterior vessel tree of patients with an intracranial large vessel occlusion (LVO) is relevant to investigate potential biomarkers that can contribute to treatment decision making. The purpose of our work is to develop a method that can achieve this from routinely acquired computed tomography angiography (CTA) and computed tomography perfusion (CTP) images. To this end, we regard the anterior vessel tree as a set of bifurcations and connected centerlines.

Inter-rater reliability for assessing intracranial collaterals in patients with acute ischemic stroke: comparing 29 raters and an artificial intelligence-based software.

Purpose: Outcome of endovascular treatment in acute ischemic stroke patients is depending on the collateral circulation maintaining blood flow to the ischemic territory. We evaluated the inter-rater reliability and accuracy of raters and an automated algorithm for assessing the collateral score (CS, range: 0-3) in acute ischemic stroke patients.

Methods: Baseline CTA scans with an intracranial anterior occlusion from the MR CLEAN study (n=500) were used.

A one-pot synthesis of AgBr/AgPO composite photocatalysts.

In this study, an AgBr/AgPO (ABAP) photocatalyst has been prepared a facile one-pot anion-exchange method. SEM, XRD, XPS and UV-Vis DRS characterization techniques are carried out to study the structural and physicochemical characteristics of the AgBr/AgPO composites. The ABAP photocatalyst exhibited outstanding photocatalytic capability for the photodegradation of rhodamine B (RhB) under visible light irradiation.

Automatic Collateral Scoring From 3D CTA Images.

The collateral score is an important biomarker in decision making for endovascular treatment (EVT) of patients with ischemic stroke. The existing collateral grading systems are based on visual inspection and prone to subjective interpretation and interobserver variation. The purpose of our work is the development of an automatic collateral scoring method.

Optimization of database for identification need of unknown spent nuclear fuel samples.

Recent years, the work of nuclear forensics has been greatly promoted in many aspects, and an important work is the establishment and application of nuclear forensic database. For now, this research is mainly based on uranium ore, because there is plenty of uranium ore information that is open and is easy to form database available for attribution. However, as the potential threat of spent nuclear fuel gets more and more attention, the number of researches on the identification of spent nuclear fuel via database is increasing.

Effect of amlodipine besylate combined with acupoint application of traditional Chinese medicine nursing on the treatment of renal failure and hypertension by the PI3K/AKT pathway.

To study the effect and molecular mechanisms of amlodipine besylate combined with acupoint application of traditional Chinese medicine nursing on the treatment methods of renal failure and hypertension. A total of 60 cases of renal failure hypertension were randomly divided into the Control group and the Treatment group. The control group was treated with amlodipine besylate, while the treatment group was treated with amlodipine besylate combined with acupoint application of traditional Chinese medicine nursing.

Modes of Antiviral Action of Chemical Portions and Constituents from Woad Root Extract against Influenza Virus A FM1.

Woad root has been used for the prevention of influenza for hundreds of years in many Asian countries. In this study, the antiviral modes of clemastanin B (CB), epigoitrin, phenylpropanoid portion (PEP), and the mixture of phenylpropanoids, alkaloids, and organic acid portions (PEP + ALK + OA) from wood root extract against influenza virus A FM1 were investigated. The results revealed that CB, epigoitrin, PEP, and PEP + ALK + OA exert their anti-influenza activity via inhibiting the virus multiplication, prophylaxis, and blocking the virus attachment.

Identification of Gene Biomarkers for Distinguishing Small-Cell Lung Cancer from Non-Small-Cell Lung Cancer Using a Network-Based Approach.

Lung cancer consists of two main subtypes: small-cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC) that are classified according to their physiological phenotypes. In this study, we have developed a network-based approach to identify molecular biomarkers that can distinguish SCLC from NSCLC. By identifying positive and negative coexpression gene pairs in normal lung tissues, SCLC, or NSCLC samples and using functional association information from the STRING network, we first construct a lung cancer-specific gene association network.