A review of historical reconstruction methods of land use/land cover

Understanding long-term human-environment interactions requires historical reconstruction of past land-use and land-cover changes. Most reconstructions have been based primarily on consistently available and relatively standardized information from historical sources. Based on available data sources and a retrospective research, in this paper we review the approaches and methods of the digital reconstruction and analyze their advantages and possible constraints in the following aspects: (1) Historical documents contain qualitative or semi-quantitative information about past land use, which also usually include land-cover data, but preparation of archival documents is very time-consuming. (2) Historical maps and pictures offer visual and spatial quantitative land-cover information. (3) Natural archive has significant advantages as a method for reconstructing past vegetation and has its unique possibilities especially when historical records are missing or lacking, but it has great limits of rebuilding certain land-cover types. (4) Historical reconstruction models have been gradually developed from empirical models to mechanistic ones. The method does not only reconstruct the quantity of land use/cover in historical periods, but it also reproduces the spatial distribution. Yet there are still few historical land-cover datasets with high spatial resolution. (5) Reconstruction method based on multiple-source data and multidisciplinary research could build historical land-cover from multiple perspectives, complement the missing data, verify reconstruction results and thus improve reconstruction accuracy. However, there are challenges that make the method still in the exploratory stage. This method can be a long-term development goal for the historical land-cover reconstruction. Researchers should focus on rebuilding historical land-cover dataset with high spatial resolution by developing new models so that the study results could be effectively applied in simulations of climatic and ecological effects

The impact of gene polymorphism and hepatic insufficiency on voriconazole dose adjustment in invasive fungal infection individuals

Voriconazole (VRZ) is a broad-spectrum antifungal medication widely used to treat invasive fungal infections (IFI). The administration dosage and blood concentration of VRZ are influenced by various factors, posing challenges for standardization and individualization of dose adjustments. On the one hand, VRZ is primarily metabolized by the liver, predominantly mediated by the cytochrome P450 (CYP) 2C19 enzyme. The genetic polymorphism of CYP2C19 significantly impacts the blood concentration of VRZ, particularly the trough concentration (Ctrough), thereby influencing the drug’s efficacy and potentially causing adverse drug reactions (ADRs). Recent research has demonstrated that pharmacogenomics-based VRZ dose adjustments offer more accurate and individualized treatment strategies for individuals with hepatic insufficiency, with the possibility to enhance therapeutic outcomes and reduce ADRs. On the other hand, the security, pharmacokinetics, and dosing of VRZ in individuals with hepatic insufficiency remain unclear, making it challenging to attain optimal Ctrough in individuals with both hepatic insufficiency and IFI, resulting in suboptimal drug efficacy and severe ADRs. Therefore, when using VRZ to treat IFI, drug dosage adjustment based on individuals’ genotypes and hepatic function is necessary. This review summarizes the research progress on the impact of genetic polymorphisms and hepatic insufficiency on VRZ dosage in IFI individuals, compares current international guidelines, elucidates the current application status of VRZ in individuals with hepatic insufficiency, and discusses the influence of CYP2C19, CYP3A4, CYP2C9, and ABCB1 genetic polymorphisms on VRZ dose adjustments and Ctrough at the pharmacogenomic level. Additionally, a comprehensive summary and analysis of existing studies’ recommendations on VRZ dose adjustments based on CYP2C19 genetic polymorphisms and hepatic insufficiency are provided, offering a more comprehensive reference for dose selection and adjustments of VRZ in this patient population

Mindfulness for mediating the relationship between self-control and alexithymia among Chinese medical students: A structural equation modeling analysis

BackgroundsMedical students are prone to experience alexithymia due to academic work overload, which could increase the prevalence of mental illness such as anxiety and depression. The purpose of our study was to estimate the levels of alexithymia and to explore the relationships between alexithymia, self-control, and mindfulness among medical students.Materials and methodsFrom March 18th, 2021 to April 9th, 2021, a cross-sectional study with stratified sampling was carried out in China Medical University, Liaoning Province, China. A total of 1,013 medical students participated in this study. The questionnaires pertaining to the Toronto Alexithymia Scale (TAS-26), the Five Facet Mindfulness Questionnaire (FFMQ), and the Self-control Scale (SCS) were used to assess the levels of alexithymia, mindfulness and self-control. We used Hierarchical Multiple Regression (HMR) and structural equation modeling to explore the mediating role of mindfulness between self-control and alexithymia.ResultsThe mean score of alexithymia in medical students was 69.39 ± 9.9. After controlling for confounders, males were more likely to experience alexithymia. Self-control, acting with awareness, describing, and observing in mindfulness were negatively associated with alexithymia (P < 0.01). Mindfulness mediated the relationship between self-control and alexithymia (a*b = −0.06, BCa 95% CI: −0.09 to −0.031, Percentile 95% CI: −0.089 to −0.031).ConclusionChinese medical students experienced high levels of alexithymia. Self-control could directly attenuate alexithymia for medical students and indirectly affect alexithymia through the mediating path of mindfulness. Initiatives for self-control ability enhancement should be provided to medical students to combat alexithymia. And interventions on mindfulness training should be developed to prevent from alexithymia and promote their mental health

Monitoring of the central blood pressure waveform via a conformal ultrasonic device.

Continuous monitoring of the central-blood-pressure waveform from deeply embedded vessels, such as the carotid artery and jugular vein, has clinical value for the prediction of all-cause cardiovascular mortality. However, existing non-invasive approaches, including photoplethysmography and tonometry, only enable access to the superficial peripheral vasculature. Although current ultrasonic technologies allow non-invasive deep-tissue observation, unstable coupling with the tissue surface resulting from the bulkiness and rigidity of conventional ultrasound probes introduces usability constraints. Here, we describe the design and operation of an ultrasonic device that is conformal to the skin and capable of capturing blood-pressure waveforms at deeply embedded arterial and venous sites. The wearable device is ultrathin (240 μm) and stretchable (with strains up to 60%), and enables the non-invasive, continuous and accurate monitoring of cardiovascular events from multiple body locations, which should facilitate its use in a variety of clinical environments

Outpatient depression current care expenditure changes in Liaoning Province from 2015 to 2020: a study based on the “system of health accounts 2011”

Introduction: Depression is the leading cause of disability worldwide and has become a health issue of global concern. Based on the “System of Health Accounts 2011” (SHA 2011) for patients with depression, this paper studies the changes in the current curative expenditure (CCE) of outpatient depression in Liaoning Province, China, and provides policy recommendations.Method: A stratified multistage random sample of 56,994 patients with depression included from 1,227 healthcare facilities in Liaoning Province were included. The significance of differences in variables within groups was analyzed by univariate analysis (including descriptive statistics analysis, Mann-Whitney U test and Kruskal–Wallis H test), and factors influencing depression outpatient CCE were analyzed by multiple linear regression analysis and constructing structural equation models (SEM).Results: The CCE of outpatient depression was ranging from CNY 75.57 million to CNY 100.53 million in 2015–2020, with the highest of CNY 100.53 million in 2018, CNY 103.28 million in 2019. Medical expenditures are mainly concentrated in general hospitals and provincial healthcare institutions, accounting for about 90% of all provincial scope expenditures. The multiple regression results show that provincial healthcare institutions, purchase of drug, select medical treatment for depression, general hospitals and urban employees’ health insurance are the main influencing factors for depression outpatient CCE. The results of SEM show that insurance status negative impact outpatient expenditure.Conclusion: Health insurance is an important factor in equitable access to healthcare resources for patients, and medication expenditure is the influential factor affecting the high expenditure of outpatient clinics. It is of great importance to reduce the medical burden of patients by increasing the coverage of medical insurance, increasing the proportion of bills that are eligible for reimbursement, and improving the system by guaranteeing the supply of psychotropic medication

ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family

IntroductionInsect pests from the family Papilionidae (IPPs) are a seasonal threat to citrus orchards, causing damage to young leaves, affecting canopy formation and fruiting. Existing pest detection models used by orchard plant protection equipment lack a balance between inference speed and accuracy.MethodsTo address this issue, we propose an adaptive spatial feature fusion and lightweight detection model for IPPs, called ASFL-YOLOX. Our model includes several optimizations, such as the use of the Tanh-Softplus activation function, integration of the efficient channel attention mechanism, adoption of the adaptive spatial feature fusion module, and implementation of the soft Dlou non-maximum suppression algorithm. We also propose a structured pruning curation technique to eliminate unnecessary connections and network parameters.ResultsExperimental results demonstrate that ASFL-YOLOX outperforms previous models in terms of inference speed and accuracy. Our model shows an increase in inference speed by 29 FPS compared to YOLOv7-x, a higher mAP of approximately 10% than YOLOv7-tiny, and a faster inference frame rate on embedded platforms compared to SSD300 and Faster R-CNN. We compressed the model parameters of ASFL-YOLOX by 88.97%, reducing the number of floating point operations per second from 141.90G to 30.87G while achieving an mAP higher than 95%.DiscussionOur model can accurately and quickly detect fruit tree pest stress in unstructured orchards and is suitable for transplantation to embedded systems. This can provide technical support for pest identification and localization systems for orchard plant protection equipment

Non-destructive detection of kiwifruit soluble solid content based on hyperspectral and fluorescence spectral imaging

The soluble solid content (SSC) is one of the important parameters depicting the quality, maturity and taste of fruits. This study explored hyperspectral imaging (HSI) and fluorescence spectral imaging (FSI) techniques, as well as suitable chemometric techniques to predict the SSC in kiwifruit. 90 kiwifruit samples were divided into 70 calibration sets and 20 prediction sets. The hyperspectral images of samples in the spectral range of 387 nm~1034 nm and the fluorescence spectral images in the spectral range of 400 nm~1000 nm were collected, and their regions of interest were extracted. Six spectral pre-processing techniques were used to pre-process the two spectral data, and the best pre-processing method was selected after comparing it with the predicted results. Then, five primary and three secondary feature extraction algorithms were used to extract feature variables from the pre-processed spectral data. Subsequently, three regression prediction models, i.e., the extreme learning machines (ELM), the partial least squares regression (PLSR) and the particle swarm optimization - least square support vector machine (PSO-LSSVM), were established. The prediction results were analyzed and compared further. MASS-Boss-ELM, based on fluorescence spectral imaging technique, exhibited the best prediction performance for the kiwifruit SSC, with the Rp2, Rc2 and RPD of 0.8894, 0.9429 and 2.88, respectively. MASS-Boss-PLSR based on the hyperspectral imaging technique showed a slightly lower prediction performance, with the Rp2, Rc2, and RPD of 0.8717, 0.8747, and 2.89, respectively. The outcome presents that the two spectral imaging techniques are suitable for the non-destructive prediction of fruit quality. Among them, the FSI technology illustrates better prediction, providing technical support for the non-destructive detection of intrinsic fruit quality