Concern for the psychological health of people affected by the COVID-19 pandemic is necessary. Previous studies suggested that self-compassion contributes to life-satisfaction. However, little is ...known about the mechanism underlying this relation. This study investigated the relationship between self-compassion and life-satisfaction among Chinese self-quarantined residents during the COVID-19 pandemic. Furthermore, we examined the mediating effect of positive coping and the moderating role of gender in this relation. Participants consist of 337 self-quarantined residents (129 men, 208 women) from a community in China, who completed measures of demographic information, Self-Compassion Scale, Satisfaction with Life Scale, and Simplified Coping Style Questionnaire. The results revealed that self-compassion was positively linked with life-satisfaction. Moreover, positive coping partially mediated the relationship between self-compassion and life-satisfaction for males and not females. In the female group, self-compassion was positively linked with positive coping and life-satisfaction; however, positive coping and life-satisfaction were not significantly linked. These findings indicated that intervention focus on self-compassion could increase life-satisfaction in self-quarantined people during the COVID-19, and self-compassion may contribute to life-satisfaction via positive coping only in the male.
Effective integration of contextual information is crucial for salient object detection. To achieve this, most existing methods based on 'skip' architecture mainly focus on how to integrate ...hierarchical features of Convolutional Neural Networks (CNNs). They simply apply concatenation or element-wise operation to incorporate high-level semantic cues and low-level detailed information. However, this can degrade the quality of predictions because cluttered and noisy information can also be passed through. To address this problem, we proposes a global Recurrent Localization Network (RLN) which exploits contextual information by the weighted response map in order to localize salient objects more accurately. Particularly, a recurrent module is employed to progressively refine the inner structure of the CNN over multiple time steps. Moreover, to effectively recover object boundaries, we propose a local Boundary Refinement Network (BRN) to adaptively learn the local contextual information for each spatial position. The learned propagation coefficients can be used to optimally capture relations between each pixel and its neighbors. Experiments on five challenging datasets show that our approach performs favorably against all existing methods in terms of the popular evaluation metrics.
Nanoscale titanium dioxide (nano-TiO
2
) has been widely used in industry and medicine. However, the safety of nano-TiO
2
exposure remains unclear. In this study, we evaluated the liver, brain, and ...embryo toxicity and the underlying mechanism of nano-TiO
2
using mice models. The results showed that titanium was distributed to and accumulated in the heart, brain, spleen, lung, and kidney of mice after intraperitoneal (i.p.) nano-TiO
2
exposure, in a dose-dependent manner. The organ/body weight ratios of the heart, spleen, and kidney were significantly increased, and those of the brain and lung were decreased. High doses of nano-TiO
2
significantly damaged the functions of liver and kidney and glucose and lipid metabolism, as showed in the blood biochemistry tests. Nano-TiO
2
caused damages in mitochondria and apoptosis of hepatocytes, generation of reactive oxygen species, and expression disorders of protective genes in the liver of mice. We found ruptured and cracked nerve cells and inflammatory cell infiltration in the brain. We also found that the activities of constitutive nitric oxide synthases (cNOS), inducible NOS (iNOS), and acetylcholinesterase, and the levels of nitrous oxide and glutamic acid were changed in the brain after nano-TiO
2
exposure. Ex vivo mouse embryo models exhibited developmental and genetic toxicity after high doses of nano-TiO
2
. The size of nano-TiO
2
particles may affect toxicity, larger particles producing higher toxicity. In summary, nano-TiO
2
exhibited toxicity in multiple organs in mice after exposure through i.p. injection and gavage. Our study may provide data for the assessment of the risk of nano-TiO
2
exposure on human health.
In the evolution of metal–organic frameworks (MOFs) for carbon capture, a lasting challenge is to strike a balance between high uptake capacity/selectivity and low energy cost for regeneration. ...Meanwhile, these man‐made materials have to survive from practical demands such as stability under harsh conditions and feasibility of scale‐up synthesis. Reported here is a new MOF, Zn(imPim) (aka. MAF‐stu‐1), with an imidazole derivative ligand, featuring binding pockets that can accommodate CO2 molecules in a fit‐like‐a‐glove manner. Such a high degree of shape complementarity allows direct observation of the loaded CO2 in the pockets, and warrants its optimal carbon capture performances exceeding the best‐performing MOFs nowadays. Coupled with the record thermal (up to 680 °C) and chemical stability, as well as rapid large‐scale production, both encoded in the material design, Zn(imPim) represents a most competitive candidate to tackle the immediate problems of carbon dioxide capture.
A perfect fit: The protein‐like pocket–substrate shape complementarity (see picture) in an ultrastable MOF solves the problem of balancing high uptake capacity/selectivity and low energy penalty for CO2 capture from fuel gas.
Conductive polymers have been attracting attention for decades due to their promising applications in photovoltaic cells and thermoelectrics. Among them, poly(3,4-ethylene ...dioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) is the most extensively studied one with the features of high water dispersibility, transparency and thermal stability as well as having relatively high electrical conductivity (EC). Nevertheless, the EC of as-prepared PEDOT:PSS is still unsatisfactory for real applications. Experimental studies on PEDOT:PSS have showed that its low EC could be elevated by more than 3 to 4 orders of magnitude by polar solvent treatment. However, the mechanism of this enhancement remains unclear. In this work, dimethyl sulfoxide (DMSO) treated PEDOT:PSS polymers are studied using multiscale molecular modeling, including density functional theory (DFT) calculations and molecular dynamics (MD) simulations. We elucidate the mechanism of EC enhancement at the molecular level, demonstrating that DMSO dissolves the PSS shell to release the conductive PEDOT in the core for self-aggregation, leading to subsequent phase separation of PEDOT and PSS by charge screening. These findings are important for the selection of alternative solvents for further EC enhancement of PEDOT:PSS in thermoelectric applications.
Since December 2019, an epidemic caused by novel coronavirus (2019-nCoV) infection has occurred unexpectedly in China. As of 8 pm, 31 January 2020, more than 20 pediatric cases have been reported in ...China. Of these cases, ten patients were identified in Zhejiang Province, with an age of onset ranging from 112 days to 17 years. Following the latest
National recommendations for diagnosis and treatment of pneumonia caused by 2019-nCoV
(the 4th edition) and current status of clinical practice in Zhejiang Province, recommendations for the diagnosis and treatment of respiratory infection caused by 2019-nCoV for children were drafted by the National Clinical Research Center for Child Health, the National Children’s Regional Medical Center, Children’s Hospital, Zhejiang University School of Medicine to further standardize the protocol for diagnosis and treatment of respiratory infection in children caused by 2019-nCoV.
This paper proposes a computer-aided cirrhosis diagnosis system to diagnose cirrhosis based on ultrasound images. We first propose a method to extract a liver capsule on an ultrasound image, then, ...based on the extracted liver capsule, we fine-tune a deep convolutional neural network (CNN) model to extract features from the image patches cropped around the liver capsules. Finally, a trained support vector machine (SVM) classifier is applied to classify the sample into normal or abnormal cases. Experimental results show that the proposed method can effectively extract the liver capsules and accurately classify the ultrasound images.
Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the use of tyrosine kinase inhibitors in lung adenocarcinoma. Conventional identification of EGFR ...genotype requires biopsy and sequence testing which is invasive and may suffer from the difficulty of accessing tissue samples. Here, we propose a deep learning model to predict EGFR mutation status in lung adenocarcinoma using non-invasive computed tomography (CT).We retrospectively collected data from 844 lung adenocarcinoma patients with pre-operative CT images, EGFR mutation and clinical information from two hospitals. An end-to-end deep learning model was proposed to predict the EGFR mutation status by CT scanning.By training in 14 926 CT images, the deep learning model achieved encouraging predictive performance in both the primary cohort (n=603; AUC 0.85, 95% CI 0.83-0.88) and the independent validation cohort (n=241; AUC 0.81, 95% CI 0.79-0.83), which showed significant improvement over previous studies using hand-crafted CT features or clinical characteristics (p<0.001). The deep learning score demonstrated significant differences in EGFR-mutant and EGFR-wild type tumours (p<0.001).Since CT is routinely used in lung cancer diagnosis, the deep learning model provides a non-invasive and easy-to-use method for EGFR mutation status prediction.
LiNi0.8Co0.1Mn0.1O2 is considered as a promising cathode material for lithium ion batteries because of its high capacity and low cost. However, the LiNi0.8Co0.1Mn0.1O2 suffers structural instability ...and irreversible phase transition during charge/discharge processes, especially under high voltage, resulting in serious capacity fading and thermal runaway. Here, we propose a simple and effective method of modifying LiNi0.8Co0.1Mn0.1O2 by Mg doping. Benefiting from the pillaring effects of inactive Mg in the crystal structure, Li(Ni0.8Co0.1Mn0.1)1-xMgxO2 materials exhibit low Li+/Ni2+ cation mixing, high structural stability, and improved cyclic stability in the voltage of 3.0–4.5 V. The optimal Li(Ni0.8Co0.1Mn0.1)0.97Mg0.03O2 achieves a high capacity retention of 81% over 350 cycles at 0.5 C and exhibits enhanced thermal stability at 4.5 V. The promotion mechanism is explored systematically by a combination study of electrochemical characterizations, demonstrating the faster Li+ diffusion kinetics, higher electronic conductivity, and stronger structure due to the Mg doping. Moreover, the full cell of Li(Ni0.8Co0.1Mn0.1)0.97Mg0.03O2//mesocarbon microbeads delivers a promising energy density of 595.3 W h kg−1 at 0.5 C (based on the mass of the cathode). The present work demonstrates that moderate Mg doping is a facile yet effective strategy to modify high-performance LiNi0.8Co0.1Mn0.1O2 for high-voltage lithium ion batteries.
•LiNi0.8Co0.1Mn0.1O2 cathode material is modified by Mg doping.•The Li+/Ni2+ mixing of LiNi0.8Co0.1Mn0.1O2 material is reduced by Mg doping.•NCMMg0.03 material exhibits improved cycling retention and thermal stability.•The NCMMg0.03//MCMB full cell delivers an energy density of 595.3 W h kg−1 at 0.5 C.
In class imbalance learning problems, how to better recognize examples from the minority class is the key focus, since it is usually more important and expensive than the majority class. Quite a few ...ensemble solutions have been proposed in the literature with varying degrees of success. It is generally believed that diversity in an ensemble could help to improve the performance of class imbalance learning. However, no study has actually investigated diversity in depth in terms of its definitions and effects in the context of class imbalance learning. It is unclear whether diversity will have a similar or different impact on the performance of minority and majority classes. In this paper, we aim to gain a deeper understanding of if and when ensemble diversity has a positive impact on the classification of imbalanced data sets. First, we explain when and why diversity measured by Q-statistic can bring improved overall accuracy based on two classification patterns proposed by Kuncheva et al. We define and give insights into good and bad patterns in imbalanced scenarios. Then, the pattern analysis is extended to single-class performance measures, including recall, precision, and F-measure, which are widely used in class imbalance learning. Six different situations of diversity's impact on these measures are obtained through theoretical analysis. Finally, to further understand how diversity affects the single class performance and overall performance in class imbalance problems, we carry out extensive experimental studies on both artificial data sets and real-world benchmarks with highly skewed class distributions. We find strong correlations between diversity and discussed performance measures. Diversity shows a positive impact on the minority class in general. It is also beneficial to the overall performance in terms of AUC and G-mean.
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