Measurement Errors and their Propagation in the Registration of Remote Sensing Images (?)

Reference control points (RCPs) used in establishing the regression model in the registration or geometric correction of remote sensing images are generally assumed to be ?perfect?. That is, the RCPs, as explanatory variables in the regression equation, are accurate and the coordinates of their locations have no errors. Thus ordinary least squares (OLS) estimator has been applied extensively to the registration or geometric correction of remotely sensed data. However, this assumption is often invalid in practice because RCPs always contain errors. Moreover, the errors are actually one of the main sources which lower the accuracy of geometric correction of an uncorrected image. Under this situation, the OLS estimator is biased. It cannot handle explanatory variables with errors and cannot propagate appropriately errors from the RCPs to the corrected image. Therefore, it is essential to develop new feasible methods to overcome such a problem. In this paper, we introduce the consistent adjusted least squares (CALS) estimator and propose a relaxed consistent adjusted least squares (RCALS) method, with the latter being more general and flexible, for geometric correction or registration. These estimators have good capability in correcting errors contained in the RCPs, and in propagating appropriately errors of the RCPs to the corrected image with and without prior information. The objective of the CALS and our proposed RCALS estimators is to improve the accuracy of measurement value by weakening the measurement errors. The validity of the CALS and RCALS estimators are first demonstrated by applying them to perform geometric corrections of controlled simulated images. The conceptual arguments are further substantiated by a real-life example. Compared to the OLS estimator, the CALS and RCALS estimators give a superior overall performances in estimating the regression coefficients and variance of measurement errors. Keywords: error propagation, geometric correction, ordinary least squares, registration, relaxed consistent adjusted least squares, remote sensing images.

Large Language Models at Work in China's Labor Market

This paper explores the potential impacts of large language models (LLMs) on the Chinese labor market. We analyze occupational exposure to LLM capabilities by incorporating human expertise and LLM classifications, following Eloundou et al. (2023)'s methodology. We then aggregate occupation exposure to the industry level to obtain industry exposure scores. The results indicate a positive correlation between occupation exposure and wage levels/experience premiums, suggesting higher-paying and experience-intensive jobs may face greater displacement risks from LLM-powered software. The industry exposure scores align with expert assessments and economic intuitions. We also develop an economic growth model incorporating industry exposure to quantify the productivity-employment trade-off from AI adoption. Overall, this study provides an analytical basis for understanding the labor market impacts of increasingly capable AI systems in China. Key innovations include the occupation-level exposure analysis, industry aggregation approach, and economic modeling incorporating AI adoption and labor market effects. The findings will inform policymakers and businesses on strategies for maximizing the benefits of AI while mitigating adverse disruption risks

Chronic oleoylethanolamide treatment attenuates diabetes-induced mice encephalopathy by triggering peroxisome proliferator-activated receptor alpha in the hippocampus.

Brain is a site of diabetic end-organ damage. Diabetes-associated cognitive dysfunction, referred as "diabetic encephalopathy" (DE) has been coined for the patients with type 2 diabetes mellitus showing decline in their cognitive function, especially weak episodic memory, cognitive inflexibility and poor psychomotor performance leading towards Alzheimer’s disease. Current evidence supported that aberrant synapses, energy metabolism imbalance, advanced glycation end products (AGEs) accumulation and Tau hyperphosphorylation are associated with cognition deficits induced by diabetes. Oleoylethanolamide (OEA), an endogenous peroxisome proliferator-activated receptor alpha (PPARα) agonist, has anti-hyperlipidemia, anti-inflammatory and neuroprotective activities. However, the effect of OEA on DE is unknown. Therefore, we tested its influence against cognitive dysfunction in high fat diet and streptozotocin (HFD + STZ)-induced diabetic C57BL/6J and PPARα--/- mice using Morris water maze (MWM) test. Neuron staining, dementia markers and neuroplasticity in the hippocampus were assessed to evaluate the neuropathological changes. The results showed that chronic OEA treatment significantly lowered hyperglycemia, recovered cognitive performance, reduced dementia markers, and inhibited hippocampal neuron loss and neuroplasticity impairments in diabetic mice. In contrast, the changes in MWM performance and neuron loss were not observed in PPARα knockout mice via OEA administration. These results indicated that OEA may provide a potential alternative therapeutic for DE by activating PPARα signaling

TGF-β1-Mediated Leukocyte Cell-Derived Chemotaxin 2 Is Associated With Liver Fibrosis in Biliary Atresia

ObjectiveBiliary atresia (BA) presents as a severe infantile cholangiopathy disease, characterized by progressive liver fibrosis and the resulting poor prognosis. Leukocyte cell-derived chemotaxin 2 (LECT2) was proposed as the key gene associated with hepatic fibrosis in BA, but the molecular mechanism is unclear. This study aims to investigate the function of LECT2 in BA.MethodsA total of 53 patients were enrolled in this study; 36 patients with BA, and 17 control patients with cholestasis, including congenital biliary dilations, biliary hypoplasia, and inspissated bile syndrome. The role of LECT2 in BA was analyzed using histological and cytological tests. The correlation between LECT2 and infiltrating immune cells was further analyzed by bioinformatics. The analyses were conducted using correlational analyses and ROC curves.ResultsLECT2 was highly expressed in infants with BA and positively related with fibrosis (0.1644 ± 0.0608 vs. 0.0779 ± 0.0053, p < 0.0001; rs = 0.85, p < 0.0001). Serum levels of LECT2 showed high distinguishing features for patients with BA having an AUC of 0.95 (95% CI: 0.90–1.00). CD163 was highly expressed in the aggravation of fibrosis (0.158 ± 0.062 vs. 0.29 ± 0.078, p < 0.0001), and the expression of LECT2 was positively correlated with the accumulation of CD163+ macrophages (r = 0.48, p = 0.003). The bioinformatic analysis also showed that LECT2 was positively correlated with macrophage M2 (r = 0.34, p = 0.03). TGF-β1 and CD163 colocalized to the portal area in the livers of patients with BA. Moreover, TGF-β1 upregulated the expression of LECT2.ConclusionLECT2 is highly expressed in both BA liver tissue and serum, and serum LECT2 is a potential diagnostic biomarker of BA. Meanwhile, TGF-β1 is secreted by macrophages to regulate LECT2 associated with BA liver fibrosis

Principles and methods of scaling geospatial Earth science data

The properties of geographical phenomena vary with changes in the scale of measurement. The information observed at one scale often cannot be directly used as information at another scale. Scaling addresses these changes in properties in relation to the scale of measurement, and plays an important role in Earth sciences by providing information at the scale of interest, which may be required for a range of applications, and may be useful for inferring geographical patterns and processes. This paper presents a review of geospatial scaling methods for Earth science data. Based on spatial properties, we propose a methodological framework for scaling addressing upscaling, downscaling and side-scaling. This framework combines scale-independent and scale-dependent properties of geographical variables. It allows treatment of the varying spatial heterogeneity of geographical phenomena, combines spatial autocorrelation and heterogeneity, addresses scale-independent and scale-dependent factors, explores changes in information, incorporates geospatial Earth surface processes and uncertainties, and identifies the optimal scale(s) of models. This study shows that the classification of scaling methods according to various heterogeneities has great potential utility as an underpinning conceptual basis for advances in many Earth science research domains. © 2019 Elsevier B.V

Cervical HPV infection in Yueyang, China: a cross-sectional study of 125,604 women from 2019 to 2022

ObjectiveHuman papillomavirus (HPV) infection is currently the main cause of cervical cancer and precancerous lesions in women. The aim of this study was to investigate the epidemiological characteristics of HPV genotypes among women in Yueyang city and to provide a basis for the prevention and treatment of cervical cancer in this city.MethodsA cross-sectional study was conducted on 125,604 women who had received treatment from eight hospitals in Yueyang city from September 2019 to September 2022. Analysis of the prevalence of HPV in patients.ResultsThe prevalence of HPV was 20.5% (95%CI: 20.2–20.7%), of which the high-risk type (HR-HPV) accounted for 17.5% (95%CI: 17.3–17.7%) and the low-risk type (LR-HPV) accounted for 5.0% (95%CI: 4.9–5.1%). Among the HR-HPV subtypes, the top five in prevalence, from the highest to the lowest, were HPV52 (5.1%), HPV16(2.7%), HPV58 (2.6%), HPV53 (2.4%), and HPV51 (1.7%). The main LR-HPV infection types were HPV81 (2,676 cases, OR = 2.1%; 95%CI, 2.0–2.1%). Among the infected patients, 19,203 cases (OR = 74.3%; 95%CI, 73.8–74.9%) had a single subtype, 4,673 cases (OR = 18.1%; 95%CI, 17.6–18.6%) had two subtypes, and 1957 cases (OR = 7.6%; 95%CI, 7.3–7.9%) had three or more subtypes. HPV prevalence is highest among women <25 years, 55–64 years and ≥ 65 years of age.ConclusionThe prevalence of HPV in women in Yueyang city was 20.5%, with HR-HPV being dominant. As women aged <25 years, 55–64 years, and ≥ 65 years are at a relatively higher risk, more attention should be paid to them for prevention and control of HPV infections

A longitudinal resource for population neuroscience of school-age children and adolescents in China

During the past decade, cognitive neuroscience has been calling for population diversity to address the challenge of validity and generalizability, ushering in a new era of population neuroscience. The developing Chinese Color Nest Project (devCCNP, 2013–2022), the first ten-year stage of the lifespan CCNP (2013–2032), is a two-stages project focusing on brain-mind development. The project aims to create and share a large-scale, longitudinal and multimodal dataset of typically developing children and adolescents (ages 6.0–17.9 at enrolment) in the Chinese population. The devCCNP houses not only phenotypes measured by demographic, biophysical, psychological and behavioural, cognitive, affective, and ocular-tracking assessments but also neurotypes measured with magnetic resonance imaging (MRI) of brain morphometry, resting-state function, naturalistic viewing function and diffusion structure. This Data Descriptor introduces the first data release of devCCNP including a total of 864 visits from 479 participants. Herein, we provided details of the experimental design, sampling strategies, and technical validation of the devCCNP resource. We demonstrate and discuss the potential of a multicohort longitudinal design to depict normative brain growth curves from the perspective of developmental population neuroscience. The devCCNP resource is shared as part of the “Chinese Data-sharing Warehouse for In-vivo Imaging Brain” in the Chinese Color Nest Project (CCNP) – Lifespan Brain-Mind Development Data Community (https://ccnp.scidb.cn) at the Science Data Bank

Spatial distribution estimation of malaria in northern China and its scenarios in 2020, 2030, 2040 and 2050

© 2016 The Author(s). Background: Malaria is one of the most severe parasitic diseases in the world. Spatial distribution estimation of malaria and its future scenarios are important issues for malaria control and elimination. Furthermore, sophisticated nonlinear relationships for prediction between malaria incidence and potential variables have not been well constructed in previous research. This study aims to estimate these nonlinear relationships and predict future malaria scenarios in northern China. Methods: Nonlinear relationships between malaria incidence and predictor variables were constructed using a genetic programming (GP) method, to predict the spatial distributions of malaria under climate change scenarios. For this, the examples of monthly average malaria incidence were used in each county of northern China from 2004 to 2010. Among the five variables at county level, precipitation rate and temperature are used for projections, while elevation, water density index, and gross domestic product are held at their present-day values. Results: Average malaria incidence was 0.107 per annum in northern China, with incidence characteristics in significant spatial clustering. A GP-based model fit the relationships with average relative error (ARE) = 8.127 % for training data (R2 = 0.825) and 17.102 % for test data (R2 = 0.532). The fitness of GP results are significantly improved compared with those by generalized additive models (GAM) and linear regressions. With the future precipitation rate and temperature conditions in Special Report on Emission Scenarios (SRES) family B1, A1B and A2 scenarios, spatial distributions and changes in malaria incidences in 2020, 2030, 2040 and 2050 were predicted and mapped. Conclusions: The GP method increases the precision of predicting the spatial distribution of malaria incidence. With the assumption of varied precipitation rate and temperature, and other variables controlled, the relationships between incidence and the varied variables appear sophisticated nonlinearity and spatially differentiation. Using the future fluctuated precipitation and the increased temperature, median malaria incidence in 2020, 2030, 2040 and 2050 would significantly increase that it might increase 19 to 29 % in 2020, but currently China is in the malaria elimination phase, indicating that the effective strategies and actions had been taken. While the mean incidences will not increase even reduce due to the incidence reduction in high-risk regions but the simultaneous expansion of the high-risk areas