Omecamtiv Mecarbil in the treatment of heart failure: the past, the present, and the future.

Heart failure, a prevailing global health issue, imposes a substantial burden on both healthcare systems and patients worldwide. With an escalating prevalence of heart failure, prolonged survival rates, and an aging demographic, an increasing number of individuals are progressing to more advanced phases of this incapacitating ailment. Against this backdrop, the quest for pharmacological agents capable of addressing the diverse subtypes of heart failure becomes a paramount pursuit. From this viewpoint, the present article focuses on Omecamtiv Mecarbil (OM), an emerging chemical compound said to exert inotropic effects without altering calcium homeostasis. For the first time, as a review, the present article uniquely started from the very basic pathophysiology of heart failure, its classification, and the strategies underpinning drug design, to on-going debates of OM's underlying mechanism of action and the latest large-scale clinical trials. Furthermore, we not only saw the advantages of OM, but also exhaustively summarized the concerns in sense of its effects. These of no doubt make the present article the most systemic and informative one among the existing literature. Overall, by offering new mechanistic insights and therapeutic possibilities, OM has carved a significant niche in the treatment of heart failure, making it a compelling subject of study.

Differentially private knowledge transfer for federated learning.

Extracting useful knowledge from big data is important for machine learning. When data is privacy-sensitive and cannot be directly collected, federated learning is a promising option that extracts knowledge from decentralized data by learning and exchanging model parameters, rather than raw data. However, model parameters may encode not only non-private knowledge but also private information of local data, thereby transferring knowledge via model parameters is not privacy-secure. Here, we present a knowledge transfer method named PrivateKT, which uses actively selected small public data to transfer high-quality knowledge in federated learning with privacy guarantees. We verify PrivateKT on three different datasets, and results show that PrivateKT can maximally reduce 84% of the performance gap between centralized learning and existing federated learning methods under strict differential privacy restrictions. PrivateKT provides a potential direction to effective and privacy-preserving knowledge transfer in machine intelligent systems.

Frequency-domain compression imaging for extending the field of view of infrared thermometers.

We propose a computational imaging technique for expanding the field of view of infrared thermometers. The contradiction between the field of view and the focal length has always been a chief problem for researchers, especially in infrared optical systems. Large-area infrared detectors are expensive and technically arduous to be manufactured, which enormously limits the performance of the infrared optical system. On the other hand, the extensive use of infrared thermometers in COVID-19 has created a considerable demand for infrared optical systems. Therefore, improving the performance of infrared optical systems and increasing the utilization of infrared detectors is vital. This work proposes a multi-channel frequency-domain compression imaging method based on point spread function (PSF) engineering. Compared with conventional compressed sensing, the submitted method images once without an intermediate image plane. Furthermore, phase encoding is used without loss of illumination of the image surface. These facts can significantly reduce the volume of the optical system and improve the energy efficiency of the compressed imaging system. Therefore, its application in COVID-19 is of great value. We design a dual-channel frequency-domain compression imaging system to verify the proposed method's feasibility. Then, the wavefront coded PSF and optical transfer function (OTF) are used, and the two-step iterative shrinkage/thresholding (TWIST) algorithm is used to restore the image to get the final result. This compression imaging method provides a new idea for the large field of view monitoring systems, especially in infrared optical systems.

A new lightweight deep neural network for surface scratch detection.

This paper aims to develop a lightweight convolutional neural network, WearNet, to realise automatic scratch detection for components in contact sliding such as those in metal forming. To this end, a large surface scratch dataset obtained from cylinder-on-flat sliding tests was used to train the WearNet with appropriate training parameters such as learning rate, gradient algorithm and mini-batch size. A comprehensive investigation on the network response and decision mechanism was also conducted to show the capability of the developed WearNet. It was found that compared with the existing networks, WearNet can realise an excellent classification accuracy of 94.16% with a much smaller model size and faster detection speed. Besides, WearNet outperformed other state-of-the-art networks when a public image database was used for network evaluation. The application of WearNet in an embedded system further demonstrated such advantages in the detection of surface scratches in sheet metal forming processes.

Cell cycle expression of FLJ25439, a cytokinesis-associated protein, is mediated by D-box recognition and APC/C-Cdc20 regulated degradation.

The midbody is a transient structure forming out of the central spindle at late telophase. Both the midbody and central spindle have important functions ensuring completion of cytokinesis and defects in this process may lead to genetic diseases, including cancer. Thus, understanding the mechanisms that control cytokinesis during mitosis can reveal the key components taking part in some of the processes that promote accurate cell division. Our previous study showed that overexpression of FLJ25439 causes cytokinesis defect with midbody arrest and induces tetraploids with prolonged cell growth/cell cycle progression (Pan et al., 2015). Here, we extend our investigation with regard to the expression profile/regulation and cellular localization/function of FLJ25439 during mitosis/cytokinesis. Using a monoclonal antibody 2A4 we found that FLJ25439 expression is cell cycle-dependent and subjected to APC/C complex regulation. Furthermore, it is a novel substrate for the APC/C-Cdc20 complex and its degradation is proteasome-dependent through D-box recognition during mitotic exit. Immunofluorescence microscopy showed it is distributed at the central spindle and midbody, two structures considered important for completion of cell division, in telophase and cytokinesis, respectively, during cell cycle progression. Depletion of FLJ25439 expression revealed defects in chromosome alignment/segregation and delayed mitosis/cytokinesis progression. We thus conclude that FLJ25439 is a hitherto undiscovered factor involved in cytokinesis regulation.

An Investigation into the Densification-Affected Deformation and Fracture in Fused Silica under Contact Sliding.

Subsurface damage of fused silica optics is one of the major factors restricting the performance of optical systems. The densification-affected deformation and fracture in fused silica under a sliding contact are investigated in this study, via three-dimensional finite element analysis (FEA). The finite element models of scratching with 70.3° conical and Berkovich indenters are established. A refined elliptical constitutive model is used to consider the influence of densification. The finite element models are experimentally verified by elastic recovery, and theoretically verified by hardness ratio. Results of densification and plastic deformation distributions indicate that the accuracy of existent sliding stress field models may be improved if the spherical/cylindrical yield region is replaced by an ellipsoid/cylindroid, and the embedding of the yield region is considered. The initiation sequence, and the locations and stages of radial, median, and lateral cracks are discussed by analyzing the predicted sliding stress fields. Median and radial cracks along the sliding direction tend to be the first cracks that emerge in the sliding and unloading stages, respectively. They coalesce to form a big median-radial crack that penetrates through the entire yield region. The fracture behavior of fused silica revealed in this study is essential in the low-damage machining of fused silica optics.

A federated graph neural network framework for privacy-preserving personalization.

Graph neural network (GNN) is effective in modeling high-order interactions and has been widely used in various personalized applications such as recommendation. However, mainstream personalization methods rely on centralized GNN learning on global graphs, which have considerable privacy risks due to the privacy-sensitive nature of user data. Here, we present a federated GNN framework named FedPerGNN for both effective and privacy-preserving personalization. Through a privacy-preserving model update method, we can collaboratively train GNN models based on decentralized graphs inferred from local data. To further exploit graph information beyond local interactions, we introduce a privacy-preserving graph expansion protocol to incorporate high-order information under privacy protection. Experimental results on six datasets for personalization in different scenarios show that FedPerGNN achieves 4.0% ~ 9.6% lower errors than the state-of-the-art federated personalization methods under good privacy protection. FedPerGNN provides a promising direction to mining decentralized graph data in a privacy-preserving manner for responsible and intelligent personalization.

Communication-efficient federated learning via knowledge distillation.

Federated learning is a privacy-preserving machine learning technique to train intelligent models from decentralized data, which enables exploiting private data by communicating local model updates in each iteration of model learning rather than the raw data. However, model updates can be extremely large if they contain numerous parameters, and many rounds of communication are needed for model training. The huge communication cost in federated learning leads to heavy overheads on clients and high environmental burdens. Here, we present a federated learning method named FedKD that is both communication-efficient and effective, based on adaptive mutual knowledge distillation and dynamic gradient compression techniques. FedKD is validated on three different scenarios that need privacy protection, showing that it maximally can reduce 94.89% of communication cost and achieve competitive results with centralized model learning. FedKD provides a potential to efficiently deploy privacy-preserving intelligent systems in many scenarios, such as intelligent healthcare and personalization.

The Homeless People in China During the COVID-19 Pandemic: Victims of the Strict Pandemic Control Measures of the Government.

Background: By implementing aggressive control measures, China has rapidly and effectively controlled the Coronavirus disease 2019 (COVID-19) pandemic. However, the neglected homeless population may become victims of that perceived success. Due to political sensitivity, we know little about them. Aims: This study aimed to investigate how the pandemic and the pandemic control strategies of the government affected the lives of the homeless people in mainland China. Methods: A total of 103 eligible participants experiencing absolute homelessness were recruited from Guangzhou City during July and August 2020. Surveys measured demographic characteristics including health status, changes in daily living, and actions of the government toward the homeless during the pandemic. Sankey diagrams and ordered logit regression models were used to examine the impact on the homeless of inhumane government efforts to drive the homeless away. Qualitative materials were analyzed by using an inductive approach to provide more details. Results: First, the homeless people in Guangzhou tended to be male, aged 40 to 64 years, less well-educated, and they originated from outside Guangdong although they were living in the city center. Most had little connection with their families. After a long period of homelessness, almost half of the participants were in poor health with various conditions, which made them extremely vulnerable to COVID-19. Second, the pandemic caused a substantial decline in incomes of homeless people, had less of a negative impact on their food intake, and affected sleep time in different ways. Third, during the COVID-19 pandemic, humanitarian aid from local governments of China decreased, whereas inhumane efforts to drive the homeless away intensified. Fourth, quantitative models and qualitative materials demonstrate the devastating effect of the strict pandemic control strategy of the government on the lives of homeless people, which may further cause their health problems. Conclusion: This study for the first time illustrated the characteristic features of the street homeless population in mainland China and their living situation during the COVID-19 pandemic, and most importantly demonstrated the devastating effect of the strict pandemic control of the government, which has been considered a great success in previous studies, on lives of homeless people. Urgent measures should be taken to ensure the protection of the homeless population and prevent an impending humanitarian crisis.