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The Façade Opening Rate (FOR) reflects a building’s capacity to withstand seismic loads, serving as a crucial foundation for seismic risk assessment and management. However, FOR data are often outdated or nonexistent in rural areas, which are particularly vulnerable to earthquake damage. This paper proposes a practical framework for estimating FORs from real-scene 3D models derived from UAV photogrammetry. The framework begins by extracting individual buildings from 3D models using annotated roof outlines. The known edges of the roof outline are then utilized to sample and generate orthogonally projected front-view images for each building façade, enabling undistorted area measurements. Next, a modified convolutional neural network is employed to automatically extract opening areas (windows and doors) from the front-view façade images. To enhance the accuracy of opening area extraction, a vanishing point correction method is applied to open-source street-view samples, aligning their style with the front-view images and leveraging street-view-labeled samples. Finally, the FOR is estimated for each building by extracting the façade wall area through simple spatial analysis. Results on two test datasets show that the proposed method achieves high accuracy in FOR estimation. Regarding the mean relative error (MRE), a critical evaluation metric which measures the relative difference between the estimated FOR and its ground truth, the proposed method outperforms the closest baseline by 5%. Moreover, on the façade images we generated, the MRE of our method was improve by 1% and 2% compared to state-of-the-art segmentation methods. These results demonstrate the effectiveness of our framework in accurately estimating FORs and highlight its potential for improving seismic risk assessment in rural areas.

Background It is unclear whether Saccharomyces boulardii ( S. boulardii ) supplementation in standard triple therapy (STT) is effective in eradicating Helicobacter pylori ( H. pylori ) infection in children. We therefore conducted a meta-analysis of randomized controlled trials (RCTs) to assess the effect of S. boulardii supplementation on H. pylori eradication in children. Methods We conducted electronic searches in PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure and Wanfang database from the beginning up to September 2023. A random-effects model was employed to calculate the pooled relative risk (RR) with 95% confidence intervals (CI) through a meta-analysis. Results Fifteen RCTs (involving 2156 patients) were included in our meta-analysis. Results of the meta-analysis indicated that S. boulardii in combination with STT was more effective than STT alone (intention-to-treat analysis : 87.7% vs. 75.9%, RR = 1.14, 95% CI: 1.10–1.19, P < 0.00001; per-protocol analysis : 88.5% vs. 76.3%, RR = 1.15, 95% CI: 1.10–1.19, P < 0.00001). The S. boulardii supplementation group had a significantly lower incidence of total adverse events (n = 6 RCTs, 9.2% vs. 29.2%, RR = 0.32, 95% CI: 0.21–0.48, P < 0.00001), diarrhea (n = 13 RCTs, 14.7% vs. 32.4%, RR = 0.46, 95% CI: 0.37–0.56, P < 0.00001), and nausea (n = 11 RCTs, 12.7% vs. 21.3%, RR = 0.53, 95% CI: 0.40–0.72, P < 0.0001) than STT group alone. Similar results were also observed in the incidence of vomiting, constipation, abdominal pain, abdominal distention, epigastric discomfort, poor appetite and stomatitis. Conclusions Current evidence indicated that S. boulardii supplementing with STT could improve the eradication rate of H. pylori , and concurrently decrease the incidence of total adverse events and gastrointestinal adverse events in children.

Compared with commercial lithium batteries with liquid electrolytes, all‐solid‐state lithium batteries (ASSLBs) possess the advantages of higher safety, better electrochemical stability, higher energy density, and longer cycle life; therefore, ASSLBs have been identified as promising candidates for next‐generation safe and stable high‐energy‐storage devices. The design and fabrication of solid‐state electrolytes (SSEs) are vital for the future commercialization of ASSLBs. Among various SSEs, solid polymer composite electrolytes (SPCEs) consisting of inorganic nanofillers and polymer matrix have shown great application prospects in the practice of ASSLBs. The incorporation of inorganic nanofillers into the polymer matrix has been considered as a crucial method to achieve high ionic conductivity for SPCE. In this review, the mechanisms of Li+ transport variation caused by incorporating inorganic nanofillers into the polymer matrix are discussed in detail. On the basis of the recent progress, the respective contributions of polymer chains, passive ceramic nanofillers, and active ceramic nanofillers in affecting the Li+ transport process of SPCE are reviewed systematically. The inherent relationship between the morphological characteristics of inorganic nanofillers and the ionic conductivity of the resultant SPCE is discussed. Finally, the challenges and future perspectives for developing high‐performance SPCE are put forward. This review aims to provide possible strategies for the further improvement of ionic conductivity in inorganic nanoscale filler‐reinforced SPCE and highlight their inspiration for future research directions. The incorporation of inorganic nanofillers into the polymer matrix has been considered as a crucial method to achieve high ionic conductivity for solid polymer composite electrolyte (SPCE). The mechanisms of Li+ transport variation caused by incorporating inorganic nanofillers into the polymer matrix are discussed in detail. This review aims to provide possible strategies for the further improvement of ionic conductivity in inorganic nanoscale filler‐reinforced SPCE.

The ability of enzymes to access various conformational states is often essential for their catalytic activity. Lysine 5,6-aminomutase (5,6-LAM), a pyridoxal 5′-phosphate (PLP) and 5′-deoxyadenosylcobalamin (dAdoCbl)dependent enzyme, catalyzes 1,2-amino shift in lysine isomers by shuttling between an open conformational state and a closed conformational state. Nevertheless, suicide inactivation of 5,6-LAM is an obstacle to the realization of its potential as a biocatalyst. In this work, the fate of the reaction of 5-hydroxylysine, an analogue of lysine, is investigated using spectroscopic and computational methods. Although 5-hydroxylysine does not afford any product, results obtained from UV–visible and electron paramagnetic resonance (EPR) spectroscopies demonstrate that initial steps of the catalytic cycle are performed with it. Simulation of the weakly spin-coupled spectrum estimates an intermediate distance between the PLP substrate-based radical and Co(II) in comparison to the that in the open state and the closed state. This distinct conformational state, different from the open state and the closed state, is alluded to in its putative role in suicide inactivation and denoted as the suicidally-inactivated state. Our findings highlight the emergence of EPR spectroscopy as a powerful tool to uncover the hidden conformations in radical enzymes. These results provide new insights into the suicide inactivation of dAdoCbl-dependent enzymes.

Fluorescent imaging has been expanded, as a non-invasive diagnostic modality for cancers, in recent years. Fluorescent probes in the near-infrared window can provide high sensitivity, resolution, and signal-to-noise ratio, without the use of ionizing radiation. Some fluorescent compounds with low molecular weight, such as rhodamine B (RhB) and indocyanine green (ICG), have been used in fluorescent imaging to improve imaging contrast and sensitivity; however, since these probes are excreted from the body quickly, they possess significant restrictions for imaging. To find a potential solution to this, this work investigated the synthesis and properties of novel macromolecular fluorescent compounds. Herein, water-soluble dextran fluorescent compounds (SD-Dextran-RhB) were prepared by the attachment of RhB and sulfadiazine (SD) derivatives to dextran carrier. These fluorescent compounds were then characterized through IR, 1H NMR, 13C NMR, UV, GPC, and other methods. Assays of their cellular uptake and cell cytotoxicity and fluorescent imaging were also performed. Through this study, it was found that SD-Dextran-RhB is sensitive to acidic conditions and possesses low cell cytotoxicities compared to normal 293 cells and HepG2 and HeLa tumor cells. Moreover, SD-Dextran-RhB demonstrated good fluorescent imaging in HepG2 and HeLa cells. Therefore, SD-Dextran-RhB is suitable to be potentially applied as a probe in the fluorescent imaging of tumors.

Subjective cognitive function in patients with advanced cancer pain is frequently overlooked in clinical practice. The study employs latent profile analysis to categorize subjective cognitive function subtypes in patients suffering from advanced cancer pain and examines the factors influencing these subtypes. A survey involving 220 patients with advanced cancer pain utilized a general information questionnaire, the functional assessment of cancer therapy-cognitive (FACT-Cog) scale, the numerical rating scale (NRS), the patient health questionnaire-9 (PHQ-9), and the generalized anxiety disorder-7 (GAD-7). Latent profile analysis was applied to classify subjective cognitive function, and multiple logistic regression analysis was used to determine the influencing factors for each category. Out of 220 questionnaires, all were valid. The average FACT-Cog score was 90.20 (SD = 22.69), with 24.5% of patients exhibiting cognitive impairment. The PHQ-9 score averaged 9.21 (SD = 6.17), and the GAD-7 score averaged 8.02 (SD = 4.16). Latent profile analysis identified three subjective cognitive function groups: High subjective cognitive function group (7 patients, 3.2%), medium subjective cognitive function group (162 patients, 73.6%), and low subjective cognitive function group (51 patients, 23.2%). Depression, anxiety, age, education level, average 24-hour NRS score, and breakthrough pain frequency were identified as significant influencing factors across the subjective cognitive function profiles. The subjective cognitive function of patients with advanced cancer pain demonstrates distinct classification characteristics, primarily influenced by depression, anxiety, age, education level, pain scores, and the frequency of breakthrough pain. When devising cancer pain management strategies, it is crucial to develop targeted subjective cognitive interventions tailored to the specific profiles of these patients.

Dehydrozaluzanin C (DC) is a sesquiterpene lactone isolated from Asteraceae plant Ainsliaea macrocephala . To investigate the antitumor effects of DC and possible molecular mechanisms for treating cancer. The antitumor effect of DC was studied using HT-29 and HCT-116 human colon tumor cell lines and Balb/c nude mice models. The anti-proliferative, proapoptotic effects, and cycle arrest of DC were observed by cell viability, colony formation, apoptosis, and cycle assays. The changes of protein expression level were examined by Western blot analysis. The transcription activity of PPARγ was determined by Luciferase reporter assay. The role of PPARγ activation in the antitumor activity of DC was verified using PPARγ antagonist GW9662 and si-PPARγ HT-29 cells. DC treatment significantly decreased colon tumor cell viability, cell clone number, and increased apoptosis rate and arrested cell cycle at S phase. Furthermore, DC treatment significantly decreased Bcl-2, CDK2, and cyclin A2 protein levels while increasing the expression of cleaved caspase 3 and Bax in HT-29 and HCT-116 cells. Further investigations indicated that cell survival, induction of apoptosis, and cycle arrest by DC could be significantly reversed following treatment with the PPARγ antagonist GW9662 or in si-PPARγ cells. In vivo , DC treatment significantly decreased the weight and volume of xenograft tumor tissues in mice and apoptosis-related protein levels. The results suggest that DC effectively inhibits colon tumor cell proliferation, clone formation, apoptosis, and cell cycle arrest through PPARγ activation. These results support the potential of DC as an anti-tumor lead compound for further investigation.

Most distributed stream processing engines (DSPEs) do not support online task management and cannot adapt to time-varying data flows. Recently, some studies have proposed online task deployment algorithms to solve this problem. However, these approaches do not guarantee the Quality of Service (QoS) when the task deployment changes at runtime, because the task migrations caused by the change of task deployments will impose an exorbitant cost. We study one of the most popular DSPEs, Apache Storm, and find out that when a task needs to be migrated, Storm has to stop the resource (implemented as a process of Worker in Storm) where the task is deployed. This will lead to the stop and restart of all tasks in the resource, resulting in the poor performance of task migrations. Aiming to solve this problem, in this paper, we propose N-Storm (Nonstop Storm), which is a task-resource decoupling DSPE. N-Storm allows tasks allocated to resources to be changed at runtime, which is implemented by a thread-level scheme for task migrations. Particularly, we add a local shared key/value store on each node to make resources aware of the changes in the allocation plan. Thus, each resource can manage its tasks at runtime. Based on N-Storm, we further propose Online Task Deployment (OTD). Differing from traditional task deployment algorithms that deploy all tasks at once without considering the cost of task migrations caused by a task re-deployment, OTD can gradually adjust the current task deployment to an optimized one based on the communication cost and the runtime states of resources. We demonstrate that OTD can adapt to different kinds of applications including computation- and communication-intensive applications. The experimental results on a real DSPE cluster show that N-Storm can avoid the system stop and save up to 87% of the performance degradation time, compared with Apache Storm and other state-of-the-art approaches. In addition, OTD can increase the average CPU usage by 51% for computation-intensive applications and reduce network communication costs by 88% for communication-intensive applications.

There are no models for assessing the factors that determine moderate to poor performance status in patients with cancer after chemotherapy. This study investigated the influencing factors and identified the best model for predicting moderate–poor performance status. A convenience sampling method was used. Demographic and clinical data and evaluation results for fatigue, pain, quality of life and Eastern Cooperative Oncology Group status were collected three days after the end of chemotherapy. Decision tree, random forest and logistic regression models were constructed. Ninety-four subjects in the case group had moderate to poor performance status, and 365 subjects in the control group had no or mild activity disorders. The random forest model was the most accurate model. Physical function, total protein, general quality of life within one week before chemotherapy, hemoglobin, pain symptoms and globulin were the main factors. Total protein and hemoglobin levels reflect nutritional status, and globulin levels are an index of liver function. Therefore, physical function, nutritional status, general quality of life and pain symptoms within one week before chemotherapy and liver function can be used to predict moderate–poor performance status. Nurses should pay more attention to patients with poor physical function, poor nutritional status, lower quality of life and pain symptoms after chemotherapy.