Notes

Gem structure and Hirshfeld evaluation involving diethyl (2E,2'E)-3,3'-[1-(8-phenyl-isoquinolin-1-yl)-1H-indole-2,7-di-yl]diacrylate.
Empirically, we compare our method with state-of-the-art instance-independent and instance-dependent PU algorithms on a wide range of synthetic, benchmark and real-world datasets, and the experimental results firmly demonstrate the advantage of the proposed method over the existing PU approaches.Existing face hallucination methods based on convolutional neural networks (CNNs) have achieved impressive performance on low-resolution (LR) faces in a normal illumination condition. However, their performance degrades dramatically when LR faces are captured in non-uniform illumination conditions. This paper proposes a Recursive Copy and Paste Generative Adversarial Network (Re-CPGAN) to recover authentic high-resolution (HR) face images while compensating for non-uniform illumination. To this end, we develop two key components in our Re-CPGAN internal and recursive external Copy and Paste networks (CPnets). Our internal CPnet exploits facial self-similarity information residing in the input image to enhance facial details; while our recursive external CPnet leverages an external guided face for illumination compensation. Specifically, our recursive external CPnet stacks multiple external Copy and Paste (EX-CP) units in a compact model to learn normal illumination and enhance facial details recursively. By doing so, our method offsets illumination and upsamples facial details progressively in a coarse-to-fine fashion, thus alleviating the ambiguity of correspondences between LR inputs and external guided inputs. Furthermore, a new illumination compensation loss is developed to capture illumination from the external guided face image effectively. Extensive experiments demonstrate that our method achieves authentic HR images in a uniform illumination condition with a 16x magnification factor and outperforms state-of-the-art methods qualitatively and quantitatively.Domain Adaptation aims at adapting the knowledge learned from a domain (source-domain) to another (target-domain). Existing approaches typically require a portion of task-relevant target-domain data a priori. We propose an approach, zero-shot deep domain adaptation (ZDDA), which uses paired dual-domain task-irrelevant data to eliminate the need for task-relevant target-domain training data. ZDDA learns to generate common representations for source and target domains data. Then, either domain representation is used later to train a system that works on both domains or having the ability to eliminate the need to either domain in sensor fusion settings. Two variants of ZDDA have been developed ZDDA for classification task (ZDDA-C) and ZDDA for metric learning task (ZDDA-ML). Another limitation in Existing approaches is that most of them are designed for the closed-set classification task, i.e., the sets of classes in both the source and target domains are "known." However, ZDDA-C is also applicable to the open-set classification task where not all classes are "known" during training. Moreover, the effectiveness of ZDDA-ML shows ZDDA's applicability is not limited to classification tasks. ZDDA-C and ZDDA-ML are tested on classification and metric-learning tasks, respectively. Under most experimental conditions, ZDDA outperforms the baseline without using task-relevant target-domain-training data.Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recommendation, community detection). The key problem in graph node embedding lies in how to define the dependence to neighbors. Existing approaches specify (either explicitly or implicitly) certain dependencies on neighbors, which may lead to loss of subtle but important structural information within the graph and other dependencies among neighbors. This intrigues us to ask the question can we design a model to give the adaptive flexibility of dependencies to each node's neighborhood. In this paper, we propose a novel graph node embedding method (named PINE) via a novel notion of partial permutation invariant set function, to capture any possible dependence. Our method 1) can learn an arbitrary form of the representation function from the neighborhood, without losing any potential dependence structures, and 2) is applicable to both homogeneous and heterogeneous graph embedding, the latter of which is challenged by the diversity of node types. Furthermore, we provide theoretical guarantee for the representation capability of our method for general homogeneous and heterogeneous graphs. Empirical evaluation results on benchmark data sets show that our proposed PINE method outperforms the state-of-the-art approaches on producing node vectors for various learning tasks of both homogeneous and heterogeneous graphs.Natural Language Video Localization (NLVL) aims to locate a target moment from an untrimmed video that semantically corresponds to a text query. Existing approaches mainly solve the NLVL problem from the perspective of computer vision by formulating it as ranking, anchor, or regression tasks. These methods suffer from large performance degradation when localizing on long videos. In this work, we address the NLVL from a new perspective, ie span-based question answering (QA), by treating the input video as a text passage. We propose a video span localizing network (VSLNet), on top of the standard span-based QA framework (named VSLBase), to address NLVL. VSLNet tackles the differences between NLVL and span-based QA through a simple yet effective query-guided highlighting (QGH) strategy. QGH guides VSLNet to search for the matching video span within a highlighted region. To address the performance degradation on long videos, we further extend VSLNet to VSLNet-L by applying a multi-scale split-and-concatenation strategy to locate the target moment accurately. Extensive experiments show that the proposed methods outperform the state-of-the-art methods; VSLNet-L addresses the issue of performance degradation on long videos. Our study suggests that the span-based QA framework is an effective strategy to solve the NLVL problem.Left ventricular assist devices (LVADs) are mechanical pumps, which can be used to support heart failure (HF) patients as bridge to transplant and destination therapy. To automatically adjust the LVAD speed, a physiological control system needs to be designed to respond to variations of patient hemodynamics across a variety of clinical scenarios. These control systems require pressure feedback signals from the cardiovascular system. However, there are no suitable long-term implantable sensors available. In this study, a novel real-time deep convolutional neural network (CNN) for estimation of preload based on the LVAD flow was proposed. A new sensorless adaptive physiological control system for an LVAD pump was developed using the full dynamic form of model free adaptive control (FFDL-MFAC) and the proposed preload estimator to maintain the patient conditions in safe physiological ranges. The CNN model for preload estimation was trained and evaluated through 10-fold cross validation on 100 different patient conditions and the proposed sensorless control system was assessed on a new testing set of 30 different patient conditions across six different patient scenarios. The proposed preload estimator was extremely accurate with a correlation coefficient of 0.97, root mean squared error of 0.84 mmHg, reproducibility coefficient of 1.56 mmHg, coefficient of variation of 14.44%, and bias of 0.29 mmHg for the testing dataset. The results also indicate that the proposed sensorless physiological controller works similarly to the preload-based physiological control system for LVAD using measured preload to prevent ventricular suction and pulmonary congestion. This study shows that the LVADs can respond appropriately to changing patient states and physiological demands without the need for additional pressure or flow measurements.Many patients with obstructive sleep apnea (OSA) experience excessive daytime sleepiness (EDS), which can negatively affect daily functioning, cognition, mood, and other aspects of well-being. Although EDS can be reduced with primary OSA treatment, such as continuous positive airway pressure (CPAP) therapy, a significant proportion of patients continue to experience EDS despite receiving optimized therapy for OSA. This article reviews the pathophysiology and clinical evaluation and management of EDS in patients with OSA. The mechanisms underlying EDS in CPAP-treated patients remain unclear. Experimental risk factors include chronic intermittent hypoxia and sleep fragmentation, which lead to oxidative injury and changes in neurons and brain circuit connectedness involving noradrenergic and dopaminergic neurotransmission in wake-promoting regions of the brain. In addition, neuroimaging studies have shown alterations in the brain's white matter and gray matter in patients with OSA and EDS. Clinical management of EDS begins with ruling out other potential causes of EDS and evaluating its severity. Tools to evaluate EDS include objective and self-reported assessments of sleepiness, as well as cognitive assessments. Patients who experience residual EDS despite primary OSA therapy may benefit from wake-promoting pharmacotherapy. Agents that inhibit reuptake of dopamine or of dopamine and norepinephrine (modafinil/armodafinil and solriamfetol, respectively) have demonstrated efficacy in reducing EDS and improving quality of life in patients with OSA. Additional research is needed on the effects of wake-promoting treatments on cognition in these patients and to identify individual or disorder-specific responses.In this study, an amino-functionalized magnetic silica microsphere material (Fe3O4-SiO2-NH2) was prepared. Using glutaraldehyde as a cross-linking agent, Trametes versicolor laccase was adsorbed-covalently bonded and immobilized on the material to prepare Laccase @ Fe3O4-SiO2. In addition, the materials were characterized and analysed by SEM, TEM, XRD, FT-IR and VSM. Finally, the thermal inactivation dynamics of immobilized laccase in polar/non-polar/toxic systems and the adsorption and degradation of 2,4-DCP were studied. The results showed that Laccase @ Fe3O4-SiO2 under the optimal conditions (pH 6, temperature 65°C, initial concentration of 2,4-DCP 10 mg/L), the removal rate was as high as 81.6%. Pyrrolidinedithiocarbamate ammonium in vivo Moreover, compared with free laccase, immobilized laccase had good tolerance under low pH and high-temperature conditions, and storage stability was also greatly improved. After repeated use for 7 times, Laccase @ Fe3O4-SiO2 can still maintain 59% removal rate of 2,4-DCP, which gives it the potential for industrial applications.The evolution of pleural disease imaging modalities through the years has helped the scientific community understand and treat various disease states. Ultrasound (US) has been an image modality that has reigned superior to those used in the past such as chest X-ray and computed tomographic scan in terms of cost effectiveness, portability, and reduction in unwarranted radiation exposure to patients. Here we provide a succinct review of US use in pleural disease including imaging techniques, identifying safe pleural space for access, and predicting pleural fluid volume and etiology along with specificities regarding trapped lung identification and pleural mass biopsy. We believe bedside chest US is an adjunct to the physical exam adding superior diagnostic abilities. Further research is warranted in more specific aspects of sonographic use such as in fibrinolytic therapy management, evaluation for trapped lung, and the utility of specific modes like the color flow Doppler.
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