ATSN: Attention-Based Temporal Segment Network for Action Recognition

In human action recognition, a reasonable video representation is still a problem to be solved. For humans, it is easy to focus on the prominent areas of the image in the video, focusing on the part of interest. Inspired by this, we proposed a deep Temporal Segment Network based on visual attention-ATSN. By lightly modifying the model structure, ATSN integrates the human attention mechanism into the Temporal Segment Networks, can effectively add a weight to the video representation features, pays attention to the beneficial regions in the features, and achieves more accurate action recognition. We conducted the Oilfield-7 dataset for human actions on the oilfield. The experimental results on HMDB51 and Oilfield-7 show that the ATSN had achieved excellent performance

Machine Learning Techniques for Screening and Diagnosis of Diabetes: a Survey

Diabetes has become one of the major causes of national disease and death in most countries. By 2015, diabetes had affected more than 415 million people worldwide. According to the International Diabetes Federation report, this figure is expected to rise to more than 642 million in 2040, so early screening and diagnosis of diabetes patients have great significance in detecting and treating diabetes on time. Diabetes is a multifactorial metabolic disease, its diagnostic criteria is difficult to cover all the ethology, damage degree, pathogenesis and other factors, so there is a situation for uncertainty and imprecision under various aspects of medical diagnosis process. With the development of Data mining, researchers find that machine learning is playing an increasingly important role in diabetes research. Machine learning techniques can find the risky factors of diabetes and reasonable threshold of physiological parameters to unearth hidden knowledge from a huge amount of diabetes-related data, which has a very important significance for diagnosis and treatment of diabetes. So this paper provides a survey of machine learning techniques that has been applied to diabetes data screening and diagnosis of the disease. In this paper, conventional machine learning techniques are described in early screening and diagnosis of diabetes, moreover deep learning techniques which have a significance of biomedical effect are also described

Channel Modeling for Terahertz Communications in Rain

Terahertz (THz) communication channels, integral to outdoor applications, are critically influenced by natural factors like rainfall. Our research focused on the nuanced effects of rain on these channels, employing an advanced rainfall emulation system. By analyzing key parameters such as rain rate, altitude based variations in rainfall, and diverse raindrop sizes, we identified the paramount significance of the number of raindrops in the THz channel, particularly in scenarios with constant rain rates but varying drop sizes. Central to our findings is a novel model grounded in Mie scattering theory, which adeptly incorporates the variability of raindrop size distributions at different altitudes. This model has displayed strong congruence with our experimental results. In essence, our study underscores the inadequacy of solely depending on a fixed ground-based rain rate and emphasizes the imperative of calibrating distribution metrics to cater to specific environmental and operational contexts.Comment: submitted to IEEE Transactions on Antennas and Propagatio

Risk factors of CVD mortality among the elderly in Beijing, 1992 - 2009: An 18-year cohort study

Few researchers have examined the effects of multiple risk factors of cardiovascular disease (CVD) mortality simultaneously. This study was to determine the associations of combined lifestyle and other factors with CVD mortality among the elderly (n = 3,257), in Beijing, China, through data mining of the Beijing Longitudinal Study of Aging (BLSA). BLSA is a representative cohort study from 1992 to 2009, hosted by Xuan Wu Hospital. Competing risk survival analysis was conducted to explore the association between risk factors and CVD mortality. The factors focused mainly on lifestyle, physical condition, and the model was adjusted for age and gender. There were 273 of the 1,068 recorded deaths caused by CVD among the 2010 participants. Living in a suburban area (HR = 0.614, 95% CI: 0.410-0.921) was associated with lower CVD mortality. Increasing age (66-75: HR = 1.511, 95% CI: 1.111-2.055; ≥76: HR = 1.847, 95% CI: 1.256-2.717), high blood pressure (HR = 1.407, 95% CI: 1.031-1.920), frequent consumption of meat (HR = 1.559, 95% CI: 1.079-2.254) and physical inactivity (p = 0.046) were associated with higher CVD mortality. The study provides an instructional foundation for the control and prevention of CVD in Beijing, China

Ultrafast carrier dynamics in pristine and FeCl3-intercalated bilayer graphene

Ultrafast carrier dynamics of pristine bilayer graphene (BLG) and bilayer graphene intercalated with FeCl3 (FeCl3-G), were studied using time-resolved transient differential reflection (delta R/R). Compared to BLG, the FeCl3-G data showed an opposite sign of delta R/R, a slower rise time, and a single (instead of double) exponential relaxation. We attribute these differences in dynamics to the down-shifting of the Fermi level in FeCl3-G, as well as the formation of numerous horizontal bands arising from the d-orbitals of Fe. Our work shows that intercalation can dramatically change the electronic structure of graphene, and its associated carrier dynamics.Comment: 14 pages, 4 figure

Risk factors for cerebrovascular disease mortality among the elderly in Beijing: A competing risk analysis

Objective: To examine the associations of combined lifestyle factors and physical conditions with cerebrovascular diseases (CBVD) mortality, after accounting for competing risk events, including death from cardiovascular diseases, cancers and other diseases. Methods: Data on 2010 subjects aged over 55 years were finally analyzed using competing risk models. All the subjects were interviewed by the Beijing Longitudinal Study of Aging (BLSA), in China, between 1 January 1992 and 30 August 2009. Results: Elderly females were at a lower risk of death from CBVD than elderly males (HR = 0.639, 95% CI = 0.457-0.895). Increasing age (HR = 1.543, 95% CI = 1.013-2.349), poor self-rated health (HR = 1.652, 95% CI = 1.198-2.277), hypertension (HR = 2.201, 95% CI = 1.524-3.178) and overweight (HR = 1.473, 95% CI = 1.013-2.142) or obesity (HR = 1.711, 95% CI = 1.1754-2.490) was associated with higher CBVD mortality risk. Normal cognition function (HR = 0.650, 95% CI = 0.434-0.973) and living in urban (HR = 0.456, 95% CI = 0.286-0.727) was associated with lower CBVD mortality risk. Gray\u27s test also confirmed the cumulative incidence (CIF) of CBVD was lower in the \u27married\u27 group than those without spouse, and the mortality was lowest in the \u27nutrition sufficient\u27 group among the \u27frequent consumption of meat group\u27 and the \u27medial type group\u27 (P valu

Enhancement of TKI Sensitivity in Lung Adenocarcinoma through m6A-dependent Translational Repression of Wnt Signaling by circ-FBXW7

BACKGROUND: Tyrosine kinase inhibitors (TKIs) that specifically target mutational points in the EGFR gene have significantly reduced suffering and provided greater relief to patients with lung adenocarcinoma (LUAD). The third-generation EGFR-TKI, Osimertinib, has been successfully employed in clinical treatments to overcome resistance to both original and acquired T790M and L858R mutational points. Nevertheless, the issue of treatment failure response has emerged as an insurmountable problem. METHODS: By employing a combination of multiple and integrated approaches, we successfully identified a distinct population within the tumor group that plays a significant role in carcinogenesis, resistance, and recurrence. Our research suggests that addressing TKI resistance may involve targeting the renewal and repopulation of stem-like cells. To investigate the underlying mechanisms, we conducted RNA Microarray and m6A Epi-Transcriptomic Microarray analyses, followed by assessment of transcription factors. Additionally, we specifically designed a tag to detect the polypeptide circRNA-AA, and its expression was confirmed through m6A regulations. RESULTS: We initially identified unique molecular signatures present in cancer stem cells that contributed to poor therapeutic responses. Activation of the alternative Wnt pathway was found to sustain the renewal and resistant status of these cells. Through bioinformatics analysis and array studies, we observed a significant decrease in the expression of circFBXW7 in Osimertinib-resistant cell lines. Notably, the abnormal expression pattern of circFBXW7 determined the cellular response to Osimertinib. Functional investigations revealed that circFBXW7 inhibits the renewal of cancer stem cells and resensitizes both resistant LUAD cells and stem cells to Osimertinib. In terms of the underlying mechanism, we discovered that circFBXW7 can be translated into short polypeptides known as circFBXW7-185AA. These polypeptides interact with β-catenin in an m6A-dependent manner. This interaction leads to reduced stability of β-catenin by inducing subsequent ubiquitination, thereby suppressing the activation of canonical Wnt signaling. Additionally, we predicted that the m6A reader, YTHDF3, shares common binding sites with hsa-Let-7d-5p. Enforced expression of Let-7d post-transcriptionally decreases the levels of YTHDF3. The repression of Let-7d by Wnt signaling releases the stimulation of m6A modification by YTHDF3, promoting the translation of circFBXW7-185AA. This creates a positive feedback loop contributing to the cascade of cancer initiation and promotion. CONCLUSIONS: Our bench study, in vivo experiments, and clinical validation have unequivocally shown that circFBXW7 effectively inhibits the abilities of LUAD stem cells and reverses resistance to TKIs by modulating Wnt pathway functions through the action of circFBXW7-185AA on β-catenin ubiquitination and inhibition. The regulatory role of circRNA in Osimertinib treatment has been rarely reported, and our findings reveal that this process operates under the influence of m6A modification. These results highlight the tremendous potential of this approach in enhancing therapeutic strategies and overcoming resistance to multiple TKI treatments