Motivating factors of farmers' adaptation behaviors to climate change in China: A meta-analysis.

Adapting to climate change is critical to building sustainable and resilient agricultural systems. Understanding farmers' perceptions of climate change has become the key to the effective implementation of climate change adaptation policies. This research draws multidisciplinary attention to how farmers participate in decision-making on adaptation behaviors and provides useful insights for realizing synergies between environmental change and agricultural production. In this work, we conducted a meta-analysis of 63 quantitative studies on Chinese farmers' adaptation to climate change to assess the relationship between motivational factors and adaptation behavior. Our analysis highlights that farmers' perceptions of precipitation changes are often inaccurate; however, other psychological factors, such as perception, experience, and risk attitude, significantly positively impact their adaptation behavior. In addition, different climate regions are the main source of high heterogeneity in inter-study comparisons of climate change perception, and the effect of climate regions may therefore constitute a moderating factor that weakens the positive relationship between climate change perception and adaptive behavior. Furthermore, this study highlights the need to intervene at the household level to enhance farmers' adaptability to climate change, which includes providing support through income diversification, early warning information services, training, assistance, credit, subsidies, and other resources. In the future, research on how perception, experience, and risk interact to affect adaptive behavior should be strengthened.

Soil respiration and its response to climate change and anthropogenic factors in a karst plateau wetland, southwest China.

It is important to investigate the responses of greenhouse gases to climate change (temperature, precipitation) and anthropogenic factors in plateau wetland. Based on the DNDC model, we used meteorological, soil, and land cover data to simulate the soil CO2 emission pattern and its responses to climate change and anthropogenic factors in Guizhou, China. The results showed that the mean soil CO2 emission flux in the Caohai Karst Plateau Wetland was 5.89 ± 0.17 t·C·ha-1·yr-1 from 2000 to 2019, and the annual variation showed an increasing trend with the rate of 23.02 kg·C·ha-1·yr-1. The soil total annual mean CO2 emissions were 70.62 ± 2.04 Gg·C·yr-1 (annual growth rate was 0.28 Gg·C·yr-1). Caohai wetland has great spatial heterogeneity. The emissions around Caohai Lake were high (the areas with high, middle, and low values accounted for 3.07%, 70.96%, and 25.97%, respectively), and the emission pattern was characterized by a decrease in radiation from Caohai Lake to the periphery. In addition, the cropland and forest areas exhibited high intensities (7.21 ± 0.15 t·C·ha-1·yr-1 and 6.73 ± 0.58 t·C·ha-1·yr-1, respectively) and high total emissions (54.97 ± 1.16 Gg·C·yr-1 and 10.24 ± 0.88 Gg·C·yr-1, respectively). Croplands and forests were the major land cover types controlling soil CO2 emissions in the Caohai wetland, while anthropogenic factors (cultivation) significantly increased soil CO2 emissions. Results showed that the soil CO2 emissions were positively correlated with temperature and precipitation; and the temperature change had a greater impact on soil respiration than the change in precipitation. Our results indicated that future climate change (increased temperature and precipitation) may promote an increase in soil CO2 emissions in karst plateau wetlands, and reasonable control measures (e.g. returning cropland to lakes and reducing anthropogenic factors) are the keys to controlling CO2 emissions.

Looking through the imaging perspective: the importance of imaging necrosis in glioma diagnosis and prognostic prediction - single centre experience.

BACKGROUND: The aim of the study was to investigate the diagnostic value of imaging necrosis (Imnecrosis) in grading, predict the genotype and prognosis of gliomas, and further assess tumor necrosis by dynamic contrast-enhanced MR perfusion imaging (DCE-MRI). PATIENTS AND METHODS: We retrospectively included 150 patients (104 males, mean age: 46 years old) pathologically proved as adult diffuse gliomas and all diagnosis was based on the 2021 WHO central nervous system (CNS) classification. The pathological necrosis (Panecrosis) and gene mutation information were collected. All patients underwent conventional and DCE-MRI examinations and had been followed until May 31, 2021. The Imnecrosis was determined by two experienced neuroradiologists. DCE-MRI derived metric maps have been post-processed, and the mean value of each metric in the tumor parenchyma, peritumoral and contralateral area were recorded. RESULTS: There was a strong degree of inter-observer agreement in defining Imnecrosis (Kappa = 0.668, p < 0.001) and a strong degree of agreement between Imnecrosis and Panecrosis (Kappa = 0.767, p < 0.001). Compared to low-grade gliomas, high-grade gliomas had more Imnecrosis (85.37%, p < 0.001), and Imnecrosis significantly increased with the grade of gliomas increasing. And Imnecrosis was significantly more identified in IDH-wildtype, 1p19q-non-codeletion, and CDKN2A/B-homozygous-deletion gliomas. Using multivariate Cox regression analysis, Imnecrosis was an independent and unfavorable prognosis factor (Hazard Ratio = 2.113, p = 0.046) in gliomas. Additionally, extravascular extracellular volume fraction (ve) in tumor parenchyma derived from DCE-MRI demonstrated the highest diagnostic efficiency in identifying Panecrosis and Imnecrosis with high specificity (83.3% and 91.9%, respectively). CONCLUSIONS: Imnecrosis can provide supplementary evidence beyond Panecrosis in grading, predicting the genotype and prognosis of gliomas, and ve in tumor parenchyma can help to predict tumor necrosis with high specificity.

Tumor Multiregional Mean Apparent Propagator (MAP) Features in Evaluating Gliomas-A Comparative Study With Diffusion Kurtosis Imaging (DKI).

BACKGROUND: Glioma classification affects treatment and prognosis. Reliable imaging methods for preoperatively evaluating gliomas are essential. PURPOSE: To evaluate tumor multiregional mean apparent propagator (MAP) features in glioma diagnosis and to compare those with diffusion-kurtosis imaging (DKI). STUDY TYPE: Retrospective study. SUBJECTS: 70 untreated glioma patients (31 LGGs (low-grade gliomas), 34 women; mean age, 47 ± 12 years, training (60%, n = 42) and testing cohorts (40%, n = 28)). FIELD STRENGTH/SEQUENCE: 3-T, diffusion-MRI using q-space Cartesian grid sampling with 11 different b-values. ASSESSMENT: Tumor multiregional MAP (mean squared displacement (MSD); q-space inverse variance (QIV); non-Gaussianity (NG); axial/radial non-Gaussianity (NGAx, NGRad); return-to-origin/axis/plane probability (RTOP, RTAP, and RTPP)); and DKI metrics (axial/mean/radial kurtosis (AK, MK, and RK)) on tumor parenchyma (TP) and peritumoral areas (PT) in histopathologically gliomas grading and genotyping were assessed. STATISTICAL TESTS: Mann-Whitney U; Kruskal-Wallis; Benjamini-Hochberg; Bonferroni-correction; receiver operating curve (ROC) and area under curve (AUC); DeLong's test; Random Forest (RF). P value<0.05 was considered statistically significant after multiple comparisons correction. RESULTS: Compared with LGGs, MSD, and QIV were significantly lower in TP, whereas NG, NGAx, NGRad, RTOP, RTAP, RTPP, and DKI metrics were significantly higher in HGGs (high-grade gliomas) (P ≤ 0.007), as well as in isocitrate-dehydrogenase (IDH)-mutated than IDH-wildtype gliomas (P ≤ 0.039). These trends were reversed for PT (tumor grades, P ≤ 0.011; IDH-mutation status, P ≤ 0.012). ROC analysis showed that, in TP, DKI metrics performed best in TP (AUC 0.83), whereas in PT, RTPP performed best (AUC 0.77) in glioma grading. AK performed best in TP (AUC 0.77), whereas MSD and RTPP performed best in PT (AUC 0.73) in IDH genotyping. Further RF analysis with DKI and MAP demonstrated good performance in grading (AUC 0.91, Accuracy 82%) and IDH genotyping (AUC 0.87, Accuracy 79%). DATA CONCLUSION: Tumor multiregional MAP features could effectively evaluate gliomas. The performance of MAP may be similar to DKI in TP, while in PT, MAP may outperform DKI. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 2.

Quantitative susceptibility mapping evaluation of glioma.

OBJECTIVES: To comprehensively evaluate the glioma using quantitative susceptibility mapping (QSM). MATERIALS AND METHODS: Forty-two patients (18 women; mean age, 45 years) with pathologically confirmed gliomas were retrospectively included. All the patients underwent conventional and advanced MRI examinations (QSM, DWI, MRS, etc.). Five patients underwent paired QSM (pre- and post-enhancement). Four Visually Accessible Rembrandt Image (VASARI) features and intratumoural susceptibility signal (ITSS) were observed. Three ROIs each were manually drawn separately in the tumour parenchyma with relatively high and low magnetic susceptibility. The association between the tumour's magnetic susceptibility and other MRI parameters was also analysed. RESULTS: Morphologically, gliomas with heterogeneous ITSS were more similar to high-grade gliomas (p = 0.006, AUC: 0.72, sensitivity: 70%, and specificity: 73%). Heterogeneous ITSS was significantly associated with tumour haemorrhage, necrosis, diffusion restriction, and avid enhancement but did not change between pre- and post-enhanced QSM. Quantitatively, tumour parenchyma magnetic susceptibility had limited value in grading gliomas and identifying IDH mutation status, whereas the relatively low magnetic susceptibility of the tumour parenchyma helped identify oligodendrogliomas in IDH mutated gliomas (AUC = 0.78) with high specificity (100%). The relatively high tumour magnetic susceptibility significantly increased after enhancement (p = 0.039). Additionally, we found that the magnetic susceptibility of the tumour parenchyma was significantly correlated with ADC (r = 0.61) and Cho/NAA (r = 0.40). CONCLUSIONS: QSM is a promising candidate for the comprehensive evaluation of gliomas, except for IDH mutation status. The magnetic susceptibility of tumour parenchyma may be affected by tumour cell proliferation. KEY POINTS: • Morphologically, gliomas with a heterogeneous intratumoural susceptibility signal (ITSS) are more similar to high-grade gliomas (p = 0.006; AUC, 0.72; sensitivity, 70%; and specificity, 73%). Heterogeneous ITSS was significantly associated with tumour haemorrhage, necrosis, diffusion restriction, and avid enhancement but did not change between pre- and post-enhanced QSM. • Tumour parenchyma's relatively low magnetic susceptibility helped identify oligodendroglioma with high specificity. • Tumour parenchyma magnetic susceptibility was significantly correlated with ADC (r = 0.61) and Cho/NAA (r = 0.40).

Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability.

OBJECTIVES: Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting IDH-1 mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data. METHODS: Sixty-six patients (29 females; median age, 45 years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed. RESULTS: The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (p < 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the IDH-1 mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81). CONCLUSIONS: Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma. KEY POINTS: • The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods. • DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma. • The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.

Whole brain volume and cortical thickness abnormalities in Wilson's disease: a clinical correlation study.

Wilson's disease (WD) is an inherited autosomal recessive disorder of copper metabolism, and its neurological and neuropsychiatric manifestations are associated with copper accumulation in brain. A few neuroimaging studies have shown that gray matter atrophy in WD affects both subcortical structures and cortex. This study aims to quantitatively evaluate the morphometric brain abnormalities in patients with WD in terms of whole brain volume and cortical thickness and their associations with clinical severity of WD. Thirty patients clinically diagnosed as WD with neurological manifestations and 25 healthy controls (HC) were recruited. 3D T1-weighted images were segmented into 276 whole-brain regions of interest (ROIs) and 68 cortical ROIs. WD-vs-HC group comparisons were then conducted for each ROI. The associations between those morphometric measurements and the Global Assessment Scale (GAS) score for WD were analyzed. Compared with HC, significant WD-related volumetric decreases were found in the bilateral subcortical nuclei (putamen, globus pallidus, caudate nucleus, substantia nigra, red nucleus and thalamus), diffuse white matter and several gray matter regions. WD patients showed reduced cortical thickness in the left precentral gyrus and the left insula. Further, the volumes of the right globus pallidus, bilateral putamen, right external capsule and left superior longitudinal fasciculus were negatively correlated with GAS. Our results indicated that significant WD-related morphometric abnormalities were quantified in terms of whole-brain volumes and cortical thicknesses, some of which correlated significantly to the clinical severity of WD. Those morphometrics may provide a potentially effective biomarker of WD.

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis.

OBJECTIVES: To assess the diagnostic accuracy of machine learning (ML) in predicting isocitrate dehydrogenase (IDH) mutations in patients with glioma and to identify potential covariates that could influence the diagnostic performance of ML. METHODS: A systematic search of PubMed, Web of Science, and the Cochrane library up to 1 August 2019 was conducted to collect all the articles investigating the diagnostic performance of ML for prediction of IDH mutation in glioma. The search strategy combined synonyms for 'machine learning', 'glioma', and 'IDH'. Pooled sensitivity, specificity, and their 95% confidence intervals (CIs) were calculated, and the area under the receiver operating characteristic curve (AUC) was obtained. RESULTS: Nine original articles assessing a total of 996 patients with glioma were included. Among these studies, five divided the participants into training and validation sets, while the remaining four studies only had a training set. The AUC of ML for predicting IDH mutation in the training and validation sets was 93% (95% CI 91-95%) and 89% (95% CI 86-92%), respectively. The pooled sensitivity and specificity were, respectively, 87% (95% CI 82-91%) and 88% (95% CI 83-92%) in the training set and 87% (95% CI 76-93%) and 90% (95% CI 72-97%) in the validation set. In subgroup analyses in the training set, the combined use of clinical and imaging features with ML yielded higher sensitivity (90% vs. 83%) and specificity (90% vs. 82%) than the use of imaging features alone. In addition, ML performed better for high-grade gliomas than for low-grade gliomas, and ML that used conventional MRI sequences demonstrated higher specificity for predicting IDH mutation than ML using conventional and advanced MRI sequences. CONCLUSIONS: ML demonstrated an excellent diagnostic performance in predicting IDH mutation of glioma. Clinical information, MRI sequences, and glioma grade were the main factors influencing diagnostic specificity. KEY POINTS: • Machine learning demonstrated an excellent diagnostic performance for prediction of IDH mutation in glioma (the pooled sensitivity and specificity were 88% and 87%, respectively). • Machine learning that used conventional MRI sequences demonstrated higher specificity in predicting IDH mutation than that based on conventional and advanced MRI sequences (89% vs. 85%). • Integration of clinical and imaging features in machine learning yielded a higher sensitivity (90% vs. 83%) and specificity (90% vs. 82%) than that achieved by using imaging features alone.