Testing a Landsat-based approach for mapping disturbance causality in U.S. forests

Objective To preliminarily analyze the prediction efficiency of multimodal MRI-based radiomics model for preoperative glioma IDH1 gene expression type. Methods The MRI data of 108 surgery-proven glioma patients from May 2015 to January 2019 were retrospectively analyzed, and the MRI data included axial T1WI,T2WI,fluid attenuated inversion recovery (FLAIR),DWI imaging and enhanced T1WI sequence.Forty-seven cases were IDH1 mutant type, and 61 cases were IDH1 wild type. All patients were divided into training and validation groups according to the 7∶3 ratio of the random forest model. Seventy-three cases were in training group, and 35 cases were in validation group. Independent predictors of IDH1 mutation were screened by univariate analysis combined with multivariate logistic regression analysis (P<0.05) in order to construct a random forest diagnosis model of general clinical information and conventional MRI morphological features.General clinical information and conventional MRI morphological features included gender, age, umbers of cases of left and right hemispheres, location of tumors, maximum diameter of tumors, peritumoral edema, intratumoral cystic degeneration, enhancement and ADC value of tumors. The ROI was manually outlined by MaZda software in the most obvious level of 5 sequences of tumor mass and the radiomics features were extracted, including the gray-level co-occurrence matrix(GLCM), the run-length matrix(RUN), the absolute gradient(GRA),the auto-regressive model(ARM) and wavelets transform(WAV). The least absolute shrinkage and selection operator (LASSO)regression were used to select image radiomics features with a method of 10 fold cross -validation and to reduce the dimensions. The screened image radiomics labels were combined with the conventional morphological feature independent predictors to construct a multimodal MRI-based random forest model, and the validation data set was used to evaluate the accuracy and diagnostic efficiency of each model. The sensitivity and specificity of conventional MRI morphological feature model and multimodal MRI-based radiomics prediction model were evaluated dynamically by drawing ROC curves, and the prediction efficiency of the two models was quantified by using AUC statistical indicators. The model classification error rate under different outcomes and the classification error rate of out of bag(OOB)were used to evaluate the stability of the multimodal MRI-based random forest model. The contribution rate of each variable to the model was reflected by the characteristic variables importance assessment map. Results Univariate regression analysis of the conventional MRI morphological characteristics showed that peritumoral edema, cystic degeneration and enhancement were the three independent predictors of IDH1 gene expression (P<0.01). LASSO algorithm and 10-fold cross-validation identified six robust radiomic features including high frequency coefficients of wavelet transform (WavEnHH_s-4) of T2WI, S(4,4) inverse difference of gray uniformity measurement (InvDfMom),S(5,0) Entropy (entropy),WavEnHH_s-4 of T1WI enhancement, S(1,1) InvDfMom,S(1, -1) Entropy Difference (DifEntrp)of Flair.The error rate of classification for different outcomes and classification error rate of random forest OOB data of multimodal MRI radiomics diagnosis model finally stabilized at 10%. The results of Characteristic Variable Importance Assessment Map: Mean Decrease Accuracy and Mean Decrease Gini index were consistent, which showed that besides three conventional MRI morphological predictors peritumoral edema, enhancement and cystic degeneration, the radiomics labels also played a key role in the model. The results of ROC curve showed that the accuracy, specificity, sensitivity and AUC of conventional MRI morphological feature model were 82.7%, 68.4%, 90.9% and 0.835, respectively, and those of multimodal MRI-based radiomics model were 88.5%, 89.5%, 87.8% and 0.956 respectively. Conclusion Multimodal MRI-based radiomics random forest model can improve the predictive efficiency of preoperative glioma IDH1 gene expression type more quantitatively. Key words: Glioma; Texture; Radiomics; Magnetic resonance imaging

Chapter 2 - Historical perspectives on climate change and its influence on nature

The stable isotope compositions of atmospheric CO2 can provide useful insight into various geochemical processes and carbon cycles on Earth, which is critical for understanding of Earth's changing climate. Here, we present a simple and cost-effective analytical method for the collection and measurement of carbon and oxygen isotope compositions of atmospheric CO2. Air samples of ~150 mL were collected individually or collectively using our simple active air collection system and then extracted on a vacuum purification line to remove noncondensable gases and atmospheric water vapor. The efficiency of removing atmospheric water vapor was tested by using a magnesium perchlorate desiccant trap and a dry ice/ethanol trap. Lastly, a "J-Cut tube sealing/cracking method" was developed to store and transfer purified atmospheric CO2 to the GasBench II and CF-IRMS system for δ13C and δ18O measurements. The collective active air collection method combined with the full sample air extraction method for a 3-min transfer time or "Full 3m TE" yields the best analytical precision of 0.07‰ (δ13C) and 0.04‰ (δ18O). Removing atmospheric water vapor from air samples is not necessary for δ13C, but essential for δ18O measurements. The J-Cut tube sealing/cracking method shows a near 100% effectiveness for the storage and transfer of atmospheric or any CO2. A simple and cost-effect method was developed for the collection, purification, storage, and isotopic analysis of indoor/outdoor atmospheric CO2 samples for general users. This method utilizes a popular headspace gas sample preparation system for CF-IRMS and an easy-to-build vacuum purification line without involving complex and high-cost devices for the preparation of atmospheric CO2.

Five teacher educators discuss children's literature addressing Earth's changing climate. They present tools for evaluating the quality of resources likely to help teachers and students stimulate conceptual and emotional development rather than anxiety or oversimplification. An annotated selection of current books along with a checklist to evaluate children's literature oriented toward issues of climate change is presented to help teachers choose appropriate literature for facilitating students’ development of scientifically, socially, and ecologically responsible thinking and decision‐making.

Climate change is an issue of growing economic, social, and political concern. Continued rise in the average temperatures of the Earth could lead to drastic climate change or an increased frequency of extreme events, which would negatively affect agriculture, population, and global health. One way of studying the dynamics of the Earth's changing climate is by attempting to identify regions that exhibit similar climatic behavior in terms of long-term variability. Climate networks have emerged as a strong analytics framework for both descriptive analysis and predictive modeling of the emergent phenomena. Previously, the networks were constructed using only one measure of similarity, namely the (linear) Pearson cross correlation, and were then clustered using a community detection algorithm. However, nonlinear dependencies are known to exist in climate, which begs the question whether more complex correlation measures are able to capture any such relationships. In this paper, we present a systematic study of different univariate measures of similarity and compare how each affects both the network structure as well as the predictive power of the clusters. © 2011 IEEE.

The Sun encompasses planet Earth, supplies the heat that warms it, and even shakes it. The United Nation's Intergovernmental Panel on Climate Change (IPCC) assumed that solar influence on Earth's climate is limited to changes in solar irradiance and adopted the consensus opinion of a hydrogen-filled Sun – the Standard Solar Model (SSM). They did not consider the alternative solar model and instead adopted another consensus opinion: Anthropogenic greenhouse gases play a dominant role in climate change. The SSM fails to explain the solar wind, solar cycles, and the empirical link of solar surface activity with Earth's changing climate. The alternative solar model-molded from an embarrassingly large number of unexpected observations that space-age measurements revealed since 1959-explains not only these puzzles but also how closely linked interactions between the Sun and its planets and other celestial bodies induce turbulent cycles of secondary solar characteristics that significantly affect Earth's climate.

Climate Change 3: History and Current State

International cooperation is an essential component of modern scientific research and societal advancement [see Ismail‐Zadeh and Beer, 2009], and scientific ocean drilling represents one of Earth science's longest‐running and most successful international collaborations. The strength of this collaboration and its continued success result from the realization that scientific ocean drilling provides a unique and powerful tool to study the critical processes of both short‐term change and the long‐term evolution of Earth systems. A record of Earth's changing tectonics, climate, ocean circulation, and biota is preserved in marine sedimentary deposits and the underlying basement rocks. And because the ocean floor is the natural site for accumulation and preservation of geological materials, it may preserve a continuous record of these processes.The challenge lies in accessing these records. It was recognized early on that no single country could support such a large effort on the decadal time scale needed. Thus, those planning drilling efforts quickly realized that international resources and commitment are necessary to mounting a significant investigation of the records preserved beneath the oceans on the global scale required. An international program also allows countries that make more limited financial contributions to utilize the full resources provided by the large program and make significant scientific contributions.

The empirical study aimed to explore the relationships among social media exposure, job stress, anxiety, and identity disturbance in a nonclinical setting in the COVID-19 pandemic context. An online questionnaire was administered to 282 participants in the United States of America (USA) during the COVID-19 pandemic. The study utilized a two-step Structural Equation Modeling (SEM) approach consisting of both measurement model and structural model testing. Relationships between the model variables of social media exposure, identity disturbance, anxiety, and job stress were analyzed using standardized beta coefficients, standard errors, t-values, and p-values. The results indicate that both social media exposure and job stress are associated with increased anxiety levels, which, in turn, influence identity disturbance. Moreover, there is a moderating effect of job stress on the relationship between social media exposure and anxiety, as well as the mediating effect of anxiety on the relationship between social media exposure and identity disturbance. The findings are valuable for organizations and can be used to develop programs aimed at mitigating the adverse effects of social media exposure on mental health. Prioritizing employee mental health through awareness and support initiatives is paramount, especially for those facing high stress and extensive social media use.

Experimental fires, of both low and high intensity, were lit during summer 2000 and the following 2 y in the Castel Volturno Nature Reserve, southern Italy. Soil samples were collected Jul 2000–Jul 2002 to analyze the soil fungal community dynamics. Species abundance distribution patterns (geometric, logarithmic, log normal, broken-stick) were compared. We plotted datasets with information both on species richness and abundance for total, xerotolerant and heat-stimulated soil microfungi. The xerotolerant fungi conformed to a broken-stick model for both the low- and high intensity fires at 7 and 84 d after the fire; their distribution subsequently followed logarithmic models in the 2 y following the fire. The distribution of the heat-stimulated fungi changed from broken-stick to logarithmic models and eventually to a log-normal model during the post-fire recovery. Xerotolerant and, to a far greater extent, heat-stimulated soil fungi acquire an important functional role following soil water stress and/or fire disturbance; these disturbances let them occupy unsaturated habitats and become increasingly abundant over time.