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IntroductionTo reduce mortality, the Taiwan government has vigorously promoted free cancer screening and preventive health screening services. Cancers are usually advanced by the time they are discovered in the emergency department. Through this study, we aimed to understand the characteristics of cancer patients diagnosed through the emergency department and thus identify high-risk populations by comparing cancer staging and survival rates in patients diagnosed in the emergency department and those diagnosed in the non-emergency department.MethodsThe retrospective study enrolled a total of 389,043 patients over the age of 20 who were newly diagnosed with one of the five major cancers (including lung cancer, colorectal cancer, breast cancer, prostate cancer, and oral cancer) between 2008 and 2017 and analyzed their diagnostic pathway, cancer stage at diagnosis, and survival time.ResultsOf the study participants, 59,423 patients (about 15.3%) were diagnosed with cancer through the emergency department. We found that a sizable proportion of older people and patients with low education and low incomes were diagnosed through emergency department visits, and those with a health condition comorbidity severity of 3 had the highest proportion diagnosed by the emergency department, advanced stages at diagnosis, and risk of death. These can be classified as high-risk groups. In addition, 76.4% of patients diagnosed in the emergency department had advanced cancer, and the risk of death was 1.46 times higher than that of patients diagnosed in the non-emergency department. Although cancer screening is available, it does not reduce the proportion of patients with advanced cancer who are diagnosed through or at the time of diagnosis in the emergency department.ConclusionsThe present study found that the government’s cancer screening did not affect the proportion or number of cancers diagnosed through emergency department visits. Therefore, the government should focus on more cancer screening, health education in high-risk groups, and strengthening the link between emergency and oncology departments to reduce the risk of death for patients diagnosed through emergency department visits.

Chlorophyll a fluorescence, a sensitive and cost-effective probe, is widely used in photosynthetic research. Its rapid phase, occurring within 1 second under intense illumination, displays complex O-J-I-P transients, providing valuable insights into various aspects of photosynthesis. In addition to employing experimental approaches to measure the rapid Fluorescence Induction (FI) kinetics, mathematical modeling serves as a crucial tool for understanding the underlying mechanisms that drive FI dynamics. However, the significant uncertainty and arbitrary nature of selecting model parameters amplify concerns about the effectiveness of modeling tools in aiding photosynthesis research. Therefore, there is a need to gain a deeper understanding of how these models operate and how arbitrary parameter choices may influence their outcomes. In this study, we employed the Morris method, a global Sensitivity Analysis (SA) tool, to assess the significance of rate constants employed in an existing fluorescence model, particularly those linked to the entire electron transport chain, in shaping the rapid FI dynamics. In summary, utilizing the insights gained from the Morris SA allows for targeted refinement of the photosynthesis model, thereby improving our understanding of the complex processes inherent in photosynthetic systems.

Fusing object detection techniques and stochastic variational inference, we proposed a new scheme for lightweight neural network models, which could simultaneously reduce model sizes and raise the inference speed. This technique was then applied in fast human posture identification. The integer-arithmetic-only algorithm and the feature pyramid network were adopted to reduce the computational complexity in training and to capture features of small objects, respectively. Features of sequential human motion frames (i.e., the centroid coordinates of bounding boxes) were extracted by the self-attention mechanism. With the techniques of Bayesian neural network and stochastic variational inference, human postures could be promptly classified by fast resolving of the Gaussian mixture model for human posture classification. The model took instant centroid features as inputs and indicated possible human postures in the probabilistic maps. Our model had better overall performance than the baseline model ResNet in mean average precision (32.5 vs. 34.6), inference speed (27 vs. 48 milliseconds), and model size (46.2 vs. 227.8 MB). The model could also alert a suspected human falling event about 0.66 s in advance.

We investigated potassium (K) accumulation characteristics and expression of K metabolism related genes in one high-K variety (ND202) and a common variety (NC89) of tobacco (Nicotiana tabacum L.). Results showed that K accumulation and leaf K content in ND202 were higher than those in NC89. The distribution rate and K accumulation in the leaves of ND202 increased significantly, while the distribution rate in the roots and stems had lower values. In addition, the maximum K accumulation rate and high-speed K accumulation duration in ND202 were found to be better than those in NC89. The expression of NKT1 in the upper and middle leaves of ND202 had an advantage, and the relative expression of NtKC1 and NtTPK1 in both the upper and middle leaves, as well as the roots, was also significantly upregulated. Conversely, the expression of NTRK1 in the lower leaves and roots of ND202 was weaker. ND202 had significantly greater expression levels of NtHAK1 than NC89 in the upper and middle leaves and roots; moreover, the expression of NtKT12 in the upper leaves and roots of ND202 was also higher. In comparison with common varieties, high-K varieties had a stronger ability to absorb and accumulate K. They also possessed higher expression of K+ channel- and transporter-related genes and showed a superior K accumulation rate and longer duration of high-speed K accumulation. Furthermore, K accumulation rate at 40–60 days can be suggested as an important reference for the selection of high-K tobacco varieties. [ABSTRACT FROM AUTHOR]