Explore the vast range of titles available.

Background China’s smart home for elderly care emerged in 2008, and had went through four developmental stages which consists of seed stage, start-up stage, development stage and popularization stage. Main text The status quo and development of smart home for elderly care in China is reviewed, and suggestions are provided on how to further develop China’s smart home for elderly care. The focus of China’s policies on smart home for elderly care were different during those four developmental stages. Compared with Western countries, China’s smart home for elderly care is a policy-driven product rather than technology-driven or demand-driven one. In addition, it is quasi-public goods rather than private goods. These unique characteristics of China’s smart home for elderly care not only become the driving force of its rapid development, but also bring many challenges to its development, such as the insufficient demand, the disorderly development, and the waste of public and private resources. Conclusions Although great progress has been made in China’s smart home care, much efforts are still needed to further advance its development. The technical standards for the elderly care services should be formulated as soon as possible and the existing public and private smart home for elderly care platforms should be combined. Enterprises involved in smart home care services should be encouraged to develop new technologies to reduce the cost of products and services provided by smart home for elderly care.

Agricultural products are pivotal to the national economy, and a comprehensive analysis of brand competitiveness significantly contributes to the support of agricultural structural adjustment and modernization. Focusing on the Yangtze River Delta region of China, this study develops an evaluation index system encompassing four dimensions: core brand competitiveness, brand management, market competitiveness, and innovation in branding. Utilizing a DEMATEL-ISM model, this research elucidates the intrinsic relationships among factors that influence brand competitiveness, resulting in a four-tier hierarchical model. The analysis delineates key factors at superficial, intermediate, and profound levels that influence brand competitiveness. Notably, regional production bases, along with innovations in brand technology and systems, emerge as profound influencers. Drawing on these findings, the study recommends strategies to enhance production foundations, accurately define development trajectories, spearhead technological innovation to foster collective reform efforts, and advocate for institutional advancements to bolster healthy brand growth.

Accurately extracting organs from medical images provides radiologist with more comprehensive evidences to clinical diagnose, which offers up a higher accuracy and efficiency. However, the key to achieving accurate segmentation lies in abundant clues for contour distinction, which has a high demand for the network architecture design and its practical training status. To this end, we design auxiliary and refined constraints to optimize the energy function by supplying additional guidance in training procedure, thus promoting model’s ability to capture information. Specifically, for the auxiliary constraint, a set of convolutional structures are involved into a conventional network to act as a discriminator, then adversarial network is established. Based on the obtained architecture, we further build adversarial mechanism by introducing a second discriminator into segmentor for refinement. The involvement of refined constraint contributes to ameliorate training situation, optimize model performance, and boost its ability of collecting information for segmentation. We evaluate the proposed framework on two public databases (NIH Pancreas-CT and MICCAI Sliver07). Experimental results show that the proposed network achieves comparable performance to current pancreas segmentation algorithms and outperforms most state-of-the-art liver segmentation methods. The obtained results on public datasets sufficiently demonstrate the effectiveness of the proposed model for organ segmentation.

Triggering receptors expressed on myeloid cells 2 (TREM2) is considered a protective factor to protect host from bacterial infection, while how it elicits this role is unclear. In the present study, we demonstrate that deficiency of triggering receptors expressed on myeloid cells 2 (TREM2) significantly enhanced macrophage pyroptosis induced by four common pyogenic bacteria including Staphylococcus aureus , Pseudomonas aeruginosa , Streptococcus pneumoniae , and Escherichia coli . TREM2 deficiency also decreased bacterial killing ratio of macrophage, while Caspase-1 or GSDMD inhibition promoted macrophage-mediated clearance to these bacteria. Further study demonstrated that the effect of TREM2 on macrophage pyroptosis and bacterial eradication mainly dependents on the activated status of NLRP3 inflammasome. Moreover, as the key downstream of TREM2, β-catenin phosphorylated at Ser675 by TREM2 signal and accumulated in nucleus and cytoplasm. β-catenin mediated the effect of TREM2 on NLRP3 inflammasome and macrophage pyroptosis by reducing NLRP3 expression, and inhibiting inflammasome complex assembly by interacting with ASC. Collectively, TREM2/β-catenin inhibits NLRP3 inflammasome to regulate macrophage pyroptosis, and enhances macrophage-mediated pyogenic bacterial clearance.

Mycobacterium tuberculosis is a hard-to-eradicate intracellular pathogen that infects one-third of the global population. It can live within macrophages owning to its ability to arrest phagolysosome biogenesis. Autophagy has recently been identified as an effective way to control the intracellular mycobacteria by enhancing phagosome maturation. In the present study, we demonstrate a novel role of miR-155 in regulating the autophagy-mediated anti-mycobacterial response. Both in vivo and in vitro studies showed that miR-155 expression was significantly enhanced after mycobacterial infection. Forced expression of miR-155 accelerated the autophagic response in macrophages, thus promoting the maturation of mycobacterial phagosomes and decreasing the survival rate of intracellular mycobacteria, while transfection with miR-155 inhibitor increased mycobacterial survival. However, macrophage-mediated mycobacterial phagocytosis was not affected after miR-155 overexpression or inhibition. Furthermore, blocking autophagy with specific inhibitor 3-methyladenine or silencing of autophagy related gene 7 (Atg7) reduced the ability of miR-155 to promote autophagy and mycobacterial elimination. More importantly, our study demonstrated that miR-155 bound to the 3'-untranslated region of Ras homologue enriched in brain (Rheb), a negative regulator of autophagy, accelerated the process of autophagy and sequential killing of intracellular mycobacteria by suppressing Rheb expression. Our results reveal a novel role of miR-155 in regulating autophagy-mediated mycobacterial elimination by targeting Rheb, and provide potential targets for clinical treatment.

Background A novel multi-level pyramidal pooling residual U-Net with adversarial mechanism was proposed for organ segmentation from medical imaging, and was conducted on the challenging NIH Pancreas-CT dataset. Methods The 82 pancreatic contrast-enhanced abdominal CT volumes were split via four-fold cross validation to test the model performance. In order to achieve accurate segmentation, we firstly involved residual learning into an adversarial U-Net to achieve a better gradient information flow for improving segmentation performance. Then, we introduced a multi-level pyramidal pooling module (MLPP), where a novel pyramidal pooling was involved to gather contextual information for segmentation, then four groups of structures consisted of a different number of pyramidal pooling blocks were proposed to search for the structure with the optimal performance, and two types of pooling blocks were applied in the experimental section to further assess the robustness of MLPP for pancreas segmentation. For evaluation, Dice similarity coefficient (DSC) and recall were used as the metrics in this work. Results The proposed method preceded the baseline network 5.30% and 6.16% on metrics DSC and recall, and achieved competitive results compared with the-state-of-art methods. Conclusions Our algorithm showed great segmentation performance even on the particularly challenging pancreas dataset, this indicates that the proposed model is a satisfactory and promising segmentor.

The accurate detection of leukocytes is the basis for the diagnosis of blood system diseases. However, diagnosing leukocyte disorders by doctors is time-consuming and requires extensive experience. Automated detection methods with high accuracy can improve detection efficiency and provide recommendations to inexperienced doctors. Current methods and instruments either fail to automate the identification process fully or have low performance and need suitable leukocyte data sets for further study. To improve the current status, we need to develop more intelligent strategies. This paper investigates fulfilling high-performance automatic detection for leukocytes using a deep learning-based method. We established a new dataset more suitable for leukocyte detection, containing 6273 images (8595 leukocytes) and considering nine common clinical interference factors. Based on the dataset, the performance evaluation of six mainstream detection models is carried out, and a more robust ensemble model is proposed. The mean of average precision (mAP) @IoU = 0.50:0.95 and mean of average recall (mAR)@IoU = 0.50:0.95 of the ensemble model on the test set are 0.853 and 0.922, respectively. The detection performance of poor-quality images is robust. For the first time, it is found that the ensemble model yields an accuracy of 98.84% for detecting incomplete leukocytes. In addition, we also compared the test results of different models and found multiple identical false detections of the models, then provided correct suggestions for the clinic.

Background Low temperature is a major factor influencing the growth and development of Chinese jujube ( Ziziphus jujuba Mill.) in cold winter and spring. Little is known about the molecular mechanisms enabling jujube to cope with different freezing stress conditions. To elucidate the freezing-related molecular mechanism, we conducted comparative transcriptome analysis between ‘Dongzao’ (low freezing tolerance cultivar) and ‘Jinsixiaozao’ (high freezing tolerance cultivar) using RNA-Seq. Results More than 20,000 genes were detected at chilling (4 °C) and freezing (− 10 °C, − 20 °C, − 30 °C and − 40 °C) stress between the two cultivars. The numbers of differentially expressed genes (DEGs) between the two cultivars were 1831, 2030, 1993, 1845 and 2137 under the five treatments. Functional enrichment analysis suggested that the metabolic pathway, response to stimulus and catalytic activity were significantly enriched under stronger freezing stress. Among the DEGs, nine participated in the Ca 2+ signal pathway, thirty-two were identified to participate in sucrose metabolism, and others were identified to participate in the regulation of ROS, plant hormones and antifreeze proteins. In addition, important transcription factors ( WRKY , AP2 / ERF , NAC and bZIP ) participating in freezing stress were activated under different degrees of freezing stress. Conclusions Our research first provides a more comprehensive understanding of DEGs involved in freezing stress at the transcriptome level in two Z. jujuba cultivars with different freezing tolerances. These results may help to elucidate the molecular mechanism of freezing tolerance in jujube and also provides new insights and candidate genes for genetically enhancing freezing stress tolerance.

Despite timely immunization programs, and efficacious vaccines conveying protection against SARS-CoV-2 infection, breakthrough infections in vaccinated individuals have been reported. The Delta variant of concern (VOC) outbreak in Guangzhou resulted in local transmission in vaccinated and non-vaccinated residents, providing a unique opportunity to study the protective effects of the inactivated vaccines in breakthrough infection. Here, we find that the 2-dose vaccinated group has similar peak viral titers and comparable speeds of viral RNA clearance to the non-vaccinated group but accelerated viral suppression in the middle course of the disease. We quantitatively demonstrate that peak viral pneumonia is significantly mitigated in the 2-dose vaccine group (median 0.298%) compared with the non-vaccinated (5.77%) and 1-dose vaccine (3.34%) groups. Pneumonia absorbance is approximately 6 days ahead in the 2-dose group (median 10 days) than in the non-vaccinated group (16 days) ( p  = 0.003). We also observe reduced cytokine inflammation and markedly undisturbed gene transcription profiles of peripheral blood mononuclear cells (PBMCs) in the 2-dose group. In short, our study demonstrates that prior vaccination substantially restrains pneumonia development, reduces cytokine storms, and facilitates clinical recovery. SARS-CoV-2 breakthrough infections in vaccinated individuals are a public health concern. Here, the authors analyse the clinical characteristics and profile immune alterations among vaccinated and non-vaccinated residents with Delta SARS-CoV-2 infection in Guangzhou.

Diffuse axonal injury (DAI) is characterized by delayed axonal disconnection. Although the effect of DAI on axonal pathology has been well documented, there is limited information regarding the role of myelin in the pathogenesis of DAI. We used a modified Marmarou method to create a moderate DAI model in adult rat and examined the corpus callosum and brain stem for myelin pathology and dynamic glial responses to DAI. During the first week following DAI, Luxol Fast Blue staining and western blot analysis for MBP showed significant loss of myelin in the corpus callosum and the brain stem. Increased apoptosis of mature oligodendrocyte, as depicted by its marker CC-1, was observed. Conversely, there was an increased number of Olig2-positive cells accompanied by hypertrophic microglia/macrophage and mild reactive astrocytes. Electron microscopy revealed degenerating axons in the corpus callosum and marked myelin abnormalities in the brain stem in the early stage of DAI. Brain stem regions exhibited myelin intrusions or external protrusions with widespread delamination and myelin collapse, leading to degeneration of accompanying axons. Our results show distinct pathologic processes involving axon and myelin between the corpus callosum and the brain stem in DAI. Oligodendrocyte selective vulnerability and subsequent demyelination may contribute to axonal degeneration in the brain stem. Defining the cause of ongoing oligodendrocyte death and promoting myelin regeneration may provide important targets for therapeutic interventions of DAI.