Explore the vast range of titles available.

Research on video smoke detection has become a hot topic in fire disaster prevention and control as it can realize early detection. Conventional methods use handcrafted features rely on prior knowledge to recognize whether a frame contains smoke. Such methods are often proposed for fixed fire scene and sensitive to the environment resulting in false alarms. In this paper, we use convolutional neural networks (CNN), which are state-of-the-art for image recognition tasks to identify smoke in video. We develop a joint detection framework based on faster RCNN and 3D CNN. An improved faster RCNN with non-maximum annexation is used to realize the smoke target location based on static spatial information. Then, 3D CNN realizes smoke recognition by combining dynamic spatial–temporal information. Compared with common CNN methods using image for smoke detection, 3D CNN improved the recognition accuracy significantly. Different network structures and data processing methods of 3D CNN have been compared, including Slow Fusion and optical flow. Tested on a dataset that comprises smoke video from multiple sources, the proposed frameworks are shown to perform very well in smoke location and recognition. Finally, the framework of two-stream 3D CNN performs the best, with a detection rate of 95.23% and a low false alarm rate of 0.39% for smoke video sequences.

Alzheimer's disease is the most common cause of dementia. It is an increasingly serious global health problem and has a significant impact on individuals and society. However, the precise cause of Alzheimer's disease is still unknown. In this study, 11,748 Web-of-Science-indexed manuscripts regarding Alzheimer's disease, all published from 2015 to 2019, and their 693,938 references were analyzed. A document co-citation network map was drawn using CiteSpace software. Research frontiers and development trends were determined by retrieving subject headings with apparent changing word frequency trends, which can be used to forecast future research developments in Alzheimer's disease.

There are remarkable disparities among patients of different races with prostate cancer; however, the mechanism underlying this difference remains unclear. Here, we present a comprehensive landscape of the transcriptome profiles of 14 primary prostate cancers and their paired normal counterparts from the Chinese population using RNA-seq, revealing tremendous diversity across prostate cancer transcriptomes with respect to gene fusions, long noncoding RNAs （long ncRNA）, alternative splicing and somatic mutations. Three of the 14 tumors （21.4%） harbored a TM- PRSS2-ERG fusion, and the low prevalence of this fusion in Chinese patients was further confirmed in an additional tumor set （10/54=18.5%）. Notably, two novel gene fusions, CTAGE5-KHDRBS3 （20/54=37%） and USP9Y-TTTY15 （19/54=35.2%）, occurred frequently in our patient cohort. Further systematic transcriptional profiling identified nu- merous long ncRNAs that were differentially expressed in the tumors. An analysis of the correlation between expres- sion of long ncRNA and genes suggested that long ncRNAs may have functions beyond transcriptional regulation. This study yielded new insights into the pathogenesis of prostate cancer in the Chinese population.

Theory predicts that biological processes of aging may contribute to poor mental health in late life. To test this hypothesis, we evaluated prospective associations between biological age and incident depression and anxiety in 424,299 UK Biobank participants. We measured biological age from clinical traits using the KDM-BA and PhenoAge algorithms. At baseline, participants who were biologically older more often experienced depression/anxiety. During a median of 8.7 years of follow-up, participants with older biological age were at increased risk of incident depression/anxiety (5.9% increase per standard deviation [SD] of KDM-BA acceleration, 95% confidence intervals [CI]: 3.3%–8.5%; 11.3% increase per SD of PhenoAge acceleration, 95% CI: 9.%–13.0%). Biological-aging-associated risk of depression/anxiety was independent of and additive to genetic risk measured by genome-wide-association-study-based polygenic scores. Advanced biological aging may represent a potential risk factor for incident depression/anxiety in midlife and older adults and a potential target for risk assessment and intervention. Theory indicates that biological aging may contribute to poor mental health in late life. Here, authors show advanced biological aging may represent a potential risk factor for incident depression/anxiety in midlife and older adults and a potential target for risk assessment and intervention.

This study aimed to determine the levels of health-related behaviours (physical activity, screen exposure and sleep status) among Chinese students from primary, secondary and high schools during the pandemic of COVID-19, as well as their changes compared with their status before the pandemic. A cross-sectional online survey of 10,933 students was conducted among 10 schools in Guangzhou, China, between 8th and 15th March, 2020. After getting the informed consent from student’s caregivers, an online questionnaire was designed and used to obtain time spending on health-related behaviours during the pandemic of COVID-19, as well as the changes compared with 3 months before the pandemic, which was completed by students themselves or their caregivers. Students were stratified by regions (urban, suburban, exurban), gender (boys and girls), and grades (lower grades of primary school, higher grades of primary schools, secondary schools and high schools). Data were expressed as number and percentages and Chi-square test was used to analyse difference between groups. Overall, the response rate of questionnaire was 95.3% (10,416/10,933). The median age of included students was 13.0 (10.0, 16.0) years and 50.1% (n = 5,219) were boys. 41.4%, 53.6% and 53.7% of total students reported less than 15 min per day in light, moderate and vigorous activities and 58.7% (n = 6,113) reported decreased participation in physical activity compared with the time before pandemic. Over 5 h of screen time spending on online study was reported by 44.6% (n = 4,649) of respondents, particular among high school students (81.0%). 76.9% of students reported increased screen time compared with the time before pandemic. Inadequate sleep was identified among 38.5% of students and the proportion was highest in high school students (56.9%). Our study indicated that, during the COVID-19 pandemic, the school closure exerted tremendous negative effects on school-aged children’s health habits, including less physical activity, longer screen exposure and irregular sleeping pattern.

Change detection (CD) in remote sensing (RS) imagery is a pivotal method for detecting changes in the Earth’s surface, finding wide applications in urban planning, disaster management, and national security. Recently, deep learning (DL) has experienced explosive growth and, with its superior capabilities in feature learning and pattern recognition, it has introduced innovative approaches to CD. This review explores the latest techniques, applications, and challenges in DL-based CD, examining them through the lens of various learning paradigms, including fully supervised, semi-supervised, weakly supervised, and unsupervised. Initially, the review introduces the basic network architectures for CD methods using DL. Then, it provides a comprehensive analysis of CD methods under different learning paradigms, summarizing commonly used frameworks. Additionally, an overview of publicly available datasets for CD is offered. Finally, the review addresses the opportunities and challenges in the field, including: (a) incomplete supervised CD, encompassing semi-supervised and weakly supervised methods, which is still in its infancy and requires further in-depth investigation; (b) the potential of self-supervised learning, offering significant opportunities for Few-shot and One-shot Learning of CD; (c) the development of Foundation Models, with their multi-task adaptability, providing new perspectives and tools for CD; and (d) the expansion of data sources, presenting both opportunities and challenges for multimodal CD. These areas suggest promising directions for future research in CD. In conclusion, this review aims to assist researchers in gaining a comprehensive understanding of the CD field.

N 6 -methyladenosine (m 6 A) messenger RNA methylation is a gene regulatory mechanism affecting cell differentiation and proliferation in development and cancer. To study the roles of m 6 A mRNA methylation in cell proliferation and tumorigenicity, we investigated human endometrial cancer in which a hotspot R298P mutation is present in a key component of the methyltransferase complex (METTL14). We found that about 70% of endometrial tumours exhibit reductions in m 6 A methylation that are probably due to either this METTL14 mutation or reduced expression of METTL3, another component of the methyltransferase complex. These changes lead to increased proliferation and tumorigenicity of endometrial cancer cells, likely through activation of the AKT pathway. Reductions in m 6 A methylation lead to decreased expression of the negative AKT regulator PHLPP2 and increased expression of the positive AKT regulator mTORC2. Together, these results reveal reduced m 6 A mRNA methylation as an oncogenic mechanism in endometrial cancer and identify m 6 A methylation as a regulator of AKT signalling. Liu et al. show that reduced m 6 A mRNA methylation in endometrial cancer is oncogenic. Mechanistically, the AKT pathway is activated in these tumours due to altered expression of AKT regulators carrying m 6 A on their transcripts.

Transcription factor Krüppel‐like factor 5 (KLF5) is a member of the Krüppel‐like factors’ (KLFs) family. KLF5 regulates a number of cellular functions, such as apoptosis, proliferation and differentiation. Therefore, KLF5 can play a role in many diseases, including, cancer, cardiovascular disease and gastrointestinal disorders. An important role for KLF5 in the kidney was recently reported, such that KLF5 regulated podocyte apoptosis, renal cell proliferation, tubulointerstitial inflammation and renal fibrosis. In this review, we have summarized the available information in the literature with a brief description on how transcriptional, post‐transcriptional and post‐translational modifications of KLF5 modulate its function in a variety of organs including the kidney with a focus of its importance on the pathogenesis of various kidney diseases. Furthermore, we also have outlined the current and possible mechanisms of KLF5 activation in kidney diseases. These studies suggest a need for more systemic investigations, particularly for generation of animal models with renal cell‐specific deletion or overexpression of KLF5 gene to examine direct contributions of KLF5 to various kidney diseases. This will promote further experimentation in the development of therapies to prevent or treat various kidney diseases.

Recent studies have demonstrated that immunological disease progression is closely related to abnormal function of the central nervous system (CNS). Rheumatoid arthritis (RA) is a chronic, inflammatory synovitis-based systemic immune disease of unknown etiology. In addition to joint pathological damage, RA has been linked to neuropsychiatric comorbidities, including depression, schizophrenia, and anxiety, increasing the risk of neurodegenerative diseases in life. Immune cells and their secreted immune factors will stimulate the peripheral and central neuronal systems that regulate innate and adaptive immunity. The understanding of autoimmune diseases has largely advanced insights into the molecular mechanisms of neuroimmune interaction. Here, we review our current understanding of CNS comorbidities and potential physiological mechanisms in patients with RA, with a focus on the complex and diverse regulation of mood and distinct patterns of peripheral immune activation in patients with rheumatoid arthritis. And in our review, we also discussed the role that has been played by peripheral neurons and CNS in terms of neuron mechanisms in RA immune challenges, and the related neuron-immune crosstalk.

This paper presents an experimental study on the applicability of microbial induced carbonate precipitation (MICP) to treat municipal solid waste incineration (MSWI) fly ash with high alkalinity and heavy metal toxicity. The experiments were carried out on fly ashes A and B produced from incineration processes of mechanical grate furnace and circulating fluidized bed, respectively. The results showed that both types of fly ashes contained high CaO content, which could supply sufficient endogenous Ca for MICP treatment. Moreover, S. pasteurii can survive from high alkalinity and heavy metal toxicity of fly ash solution. Further, the unconfined compressive strength (UCS) of MICP treated fly ashes A and B reached 0.385MPa and 0.709 MPa, respectively. The MICP treatment also resulted in a reduction in the leaching toxicity of heavy metals, especially for Cu, Pb and Hg. MICP had a higher solidification and stabilization effect on fly ash B, which has finer particle size and higher Ca content. These findings shone a light on the possibility of using MICP technique as a suitable and efficient tool to treat the MSWI fly ash.