(Section A: Planning Strategies and Design Concepts)

This paper uses four years of ecosystem classification data, from 2000, 2005, 2010 and 2015, to analyse the spatiotemporal variation characteristics of the ecosystems of counties and cities in the Min Delta urban agglomeration over 15 years across four aspects, including changes in the ecosystem area for each period, a transfer matrix of the counties and cities, the comprehensive dynamic ecosystem index, and the forces driving these changes. The results show that: (1) from 2000 to 2015, the total area of farmland, forest and shrub ecosystems in the Min Delta urban agglomeration decreased, while the total area of urban, wetland and grassland ecosystems has increased. There are spatiotemporal differences and patterns in the area change and transfer of various ecosystems. The series of scales and proportion of ecosystem types in the counties and cities of the Min Triangle show that there is a two-way transfer between farmland and urban ecosystems. In addition, there are spatiotemporal differences in the transfer of these two ecosystems. Forest ecosystems are transferred into farmland, urban and grassland ecosystems at different levels. In the eastern part of the Min Triangle, wetlands are mostly transferred to urban ecosystems, and the western regions are mostly transferred to forests and farmland. (2) For the comprehensive dynamic index of the Min Delta urban agglomeration, from 2000 to 2015, the degree of ecosystem dynamics was higher in each period than the previous, and the dynamics in the eastern and central parts were higher than those in the west and south for the same period. From 2000 to 2005, the comprehensive dynamic index was below 0.2%. The dynamic index of Longhai in Xiamen and Zhangzhou increased significantly from 2005 to 2010 from that of the previous period, and their values all exceeded 0.9%. From 2010 to 2015, the area with a large change in the dynamic index expanded to the east and south from the central area of Xiamen. The dynamics in the northwest did not sufficiently increase. (3) The GDP, value of agricultural production, forestry, and fisheries, secondary and tertiary industries, urbanization rate, and permanent residents are important factors influencing ecosystems. The driving effects of these socioeconomic indicators and urban population development have different degrees of significance on farmland, urban, forest and wetland ecosystems during different periods of the Delta\u27s urban agglomeration

Association between (ΔPaO2/FiO2)/PEEP and in-hospital mortality in patients with COVID-19 pneumonia: A secondary analysis.

BackgroundThe arterial pressure of oxygen (PaO2)/inspiratory fraction of oxygen (FiO2) is associated with in-hospital mortality in patients with Coronavirus Disease 2019 (COVID-19) pneumonia. ΔPaO2/FiO2 [the difference between PaO2/FiO2 after 24 h of invasive mechanical ventilation (IMV) and PaO2/FiO2 before IMV] is associated with in-hospital mortality. However, the value of PaO2 can be influenced by the end-expiratory pressure (PEEP). To the best of our knowledge, the relationship between the ratio of (ΔPaO2/FiO2)/PEEP and in-hospital mortality remains unclear. This study aimed to evaluate their association.MethodsThe study was conducted in southern Peru from April 2020 to April 2021. A total of 200 patients with COVID-19 pneumonia requiring IMV were included in the present study. We analyzed the association between (ΔPaO2/FiO2)/PEEP and in-hospital mortality by Cox proportional hazards regression models.ResultsThe median (ΔPaO2/FiO2)/PEEP was 11.78 mmHg/cmH2O [interquartile range (IQR) 8.79-16.08 mmHg/cmH2O], with a range of 1 to 44.36 mmHg/cmH2O. Patients were divided equally into two groups [low group (ConclusionsThe (ΔPaO2/FiO2)/PEEP ratio was associated with in-hospital mortality in patients with COVID-19 pneumonia. (ΔPaO2/FiO2)/PEEP might be a marker of disease severity in COVID-19 patients

Estimates of Daily PM2.5 Exposure in Beijing Using Spatio-Temporal Kriging Model

Excessive exposure to ambient (outdoor) air pollution may greatly increase the incidences of respiratory and cardiovascular diseases. Accurate reports of the spatial-temporal distribution characteristics of daily PM2.5 exposure can effectively prevent and reduce the harm caused to humans. Based on the daily average concentration data of PM2.5 in Beijing in May 2014 and the spatio-temporal kriging (STK) theory, we selected the optimal STK fitting model and compared the spatial-temporal prediction accuracy of PM2.5 using the STK method and ordinary kriging (OK) method. We also reveal the spatial-temporal distribution characteristics of the daily PM2.5 exposure in Beijing. The results show the following: (1) The fitting error of the Bilonick model (BM) model which is the smallest (0.00648), and the fitting effect of the prediction model of STK is the best for daily PM2.5 exposure. (2) The cross-examination results show that the STK model (RMSE = 8.90) has significantly lower fitting errors than the OK model (RMSE = 10.70), so its simulation prediction accuracy is higher. (3) According to the interpolation of the STK model, the daily exposure of PM2.5 in Beijing in May 2014 has good continuity in both time and space. The overall air quality is good, and overall the spatial distribution is low in the north and high in the south, with the highest concentration in the southwestern region. (4) There is a certain degree of spatial heterogeneity in the cumulative duration at the good, moderate, and polluted grades of China National Standard. The areas with the longest cumulative duration at the good, moderate and polluted grades are in the north, southeast, and southwest of the study area, respectively

A Video Target Re-Recognition Method Based on Adaptive Attention Enhancement and Multi-Scale Feature Fusion

Within the realm of computer vision, the task of re-identifying targets across multiple video frames has emerged as a pivotal challenge, particularly in domains like video surveillance, smart transportation systems, and pedestrian flow analytics. Conventional re-identification techniques often grapple with constraints stemming from varying camera perspectives, inconsistent lighting conditions, and prevalent occlusions. Addressing these challenges, this research introduces MVF-Re, a sophisticated re-identification approach that synergizes adaptive attention mechanisms with multi-scale feature fusion. Initially, we architect a deep attention-enhanced feature pyramid network, a pioneering framework that dynamically tailors itself to video frame content, thereby capturing intricate target details. Subsequently, we incorporate a multi-input Siamese network, ensuring the derivation of consistent and resilient feature sets across diverse contexts. To augment feature distinctiveness, we conceptualize a context-sensitive dynamic attention mechanism, adept at judiciously allocating weights to individual video frames. Culminating our approach, we deploy an innovative multi-scale feature fusion methodology, offering a holistic and robust target representation. Empirical evaluations on multiple benchmark datasets underscore the superior performance of our methodology, underscoring its proficiency in multi-frame target re-identification

B₆₃: The Most Stable Bilayer Structure with Dual Aromaticity

The emergence of a bilayer B48 cluster, which has been both theoretically predicted and experimentally observed, as well as the recent experimental synthesis of bilayer borophene sheets on Ag and Cu surfaces, has generated tremendous curiosity in the bilayer structures of boron clusters. However, the connection between bilayer boron cluster and bilayer borophene remains unknown. By combining a genetic algorithm and density functional theory calculations, a global search for the low-energy structures of the B63 cluster was conducted, revealing that the Cs bilayer structure with three interlayer B–B bonds is the most stable bilayer structure. This structure was further examined in terms of its structural stability, chemical bonding, and aromaticity. Interestingly, the interlayer bonds induce strong electronegativity and robust aromaticity. Furthermore, the dual aromaticity stems from diatropic currents originating from virtual translational transitions for both σ and π electrons. This unprecedent bilayer boron cluster is anticipated to enrich the concept of dual aromaticity and serve as a potential precursor for bilayer borophene

Prognostic Value of Cancer Stem Cell Marker ALDH1 Expression in Colorectal Cancer: A Systematic Review and Meta-Analysis.

OBJECTIVE:Many studies have indicated the prognostic and clinicopathological value of aldehyde dehydrogenase 1 (ALDH1) in colorectal cancer (CRC) patients still remains controversial. Thus we performed this study to clarify the relationship between high ALDH1 expression in CRC and its impact on survival and clinicopathological features. METHODS:Publications for relevant studies in Pubmed, the Cochrane Library, Embase, and China National Knowledge Infrastructure (CNKI) through April 2015 were identified. Only articles describing ALDH1 antigen with immunohistochemistry in CRC were included. The software RevMan 5.1 was used to analyze the outcomes, including 5-year overall survival (OS), disease-free survival (DFS) and clinicopathological features. RESULTS:9 studies with 1203 patients satisfying the criteria were included. The overall rate of high ALDH1 expression was 46.5% by immunohistochemical staining. High ALDH1 expression as an independent prognostic factor was significantly associated with the 5-year OS and DFS (OR = 0.42, 95%CI: 0.26-0.68, P = 0.0004; OR = 0.38, 95%CI: 0.24-0.59, P 60 years old vs. <60 years old; OR = 1.11, 95%CI: 0.63-1.94, P = 0.72). CONCLUSIONS:High ALDH1 expression indicates a poor prognosis in CRC patients. Moreover, high ALDH1 expression correlates with the T stage, N stage, and tumor differentiation, but not with age

Lgr5+CD44+EpCAM+ Strictly Defines Cancer Stem Cells in Human Colorectal Cancer

Background/Aims: Although EpCAM+CD44+ cells exhibit more stem-like properties than did EpCAM-CD44- cells, the specificity of EpCAM combined with CD44 in defining CSCs needs further improvement. Lgr5 is used as a biomarker to isolate cancer stem cells (CSCs) in colorectal cancer. However, it remains unclear whether Lgr5, along with EpCAM and CD44, can further identify and define CSCs in colorectal cancer. Methods: Lgr5+CD44+EpCAM+, Lgr5+CD44+EpCAM-, Lgr5+CD44-EpCAM+, Lgr5-CD44+EpCAM+, and Lgr5-CD44-EpCAM-cells were separately isolated using fluorescence-activated cell sorting (FACS). Colony formation, self-renewal, differentiation, and tumorigenic properties of these cells were investigated through in vitro experiments and in vivo tumor xenograft models. The expression of stemness genes and CSC- and epithelial-mesenchymal transition (EMT)-related genes, such as KLF4, Oct4, Sox2, Nanog, CD133, CD44, CD166, ALDH1, Lgr5, E-cadherin, ZO-1, Vimentin, Snail, Slug, and Twist, was examined using real-time PCR. Results: Lgr5-positive subpopulations exhibited higher capacities for colony formation, self-renewal, differentiation, and tumorigenicity as well as higher expression of stemness genes and mesenchymal genes and lower expression of epithelial genes than did Lgr5-negative subpopulations. Conclusion: Our data revealed that tumorigenic cells were highly restricted to Lgr5-positive subpopulations. Most importantly, Lgr5+CD44+EpCAM+ cells exhibited more pronounced CSC-like traits than did any other subpopulation, indicating that Lgr5 combined with CD44 and EpCAM can further improve the stem-like traits of CSCs in colorectal cancer