bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning based urban land cover classification using PolInSAR data: a study with ALOS-2 and RADARSAT-2 datasets
Authors: Attri Luvkesh , Kumar Shashi , Maithani Sandeep 
Published: July 2024

AbstractA substantial variation in the land cover dynamics has been observed as a consequence of increasing urban expansion. Polarimetric synthetic aperture radar (PolSAR) data is widely being used for land cover studies in urban areas due to its all-weather, day-and-night imaging capabilities. However, in densely built-up areas, challenge arises with buildings having large Azimuth Orientation Angles (AOAs). These buildings are often misclassified as vegetation due to the depolarization of radar signal causing volumetric scattering response from the structures. This study addresses this issue by proposing an approach that integrates polarimetric information with interferometric SAR (InSAR) coherence to improve the differentiation between urban structures and vegetated areas, enhancing the accuracy of urban land-cover classification. Vegetated areas exhibit lower temporal coherence due to changes in the orientation of its leaves and branches caused by wind, seasonal variations, growth phenology, and other factors. In contrast, urban structures, being relatively stable targets, maintain high temporal coherence values. In present research various decomposition and scattering parameters were evaluated, along with PolInSAR coherence derived from L-band (ALOS-2) and C-band (RADARSAT-2), using two machine learning algorithms namely, Random Forest (RF) and Convolutional Neural Network (CNN). The C-band RADARSAT-2 data, particularly with six-component decomposition parameters, performed better, achieving an overall accuracy as 85.85% using RF algorithm. To further improve classification results, optical datasets from Landsat constellation were fused with SAR parameters using Gram-Schmidt fusion technique. This fusion led to significant improvements, achieving an overall accuracy of 94.50% and kappa statistics of 0.92, when CNN algorithm was applied to the fused optical and C-band RADARSAT-2 dataset. These results demonstrate the effectiveness of combining PolInSAR and optical data for more accurate urban land-cover classification, particularly in complex urban environments.
Increasing the Reproducibility and Replicability of Supervised AI/ML in the Earth Systems Science by Leveraging Social Science Methods
Authors: Wirz Christopher D. , Sutter Carly , Demuth Julie L. , Mayer Kirsten J. , Chapman William E. , Cains Mariana Goodall , Radford Jacob , Przybylo Vanessa , Evans Aaron , Martin Thomas , Gaudet Lauriana C. , Sulia Kara , Bostrom Ann , Gagne David John , Bassill Nick , Schumacher Andrea , Thorncroft Christopher 
Published: July 2024

AbstractArtificial intelligence (AI) and machine learning (ML) pose a challenge for achieving science that is both reproducible and replicable. The challenge is compounded in supervised models that depend on manually labeled training data, as they introduce additional decision‐making and processes that require thorough documentation and reporting. We address these limitations by providing an approach to hand labeling training data for supervised ML that integrates quantitative content analysis (QCA)—a method from social science research. The QCA approach provides a rigorous and well‐documented hand labeling procedure to improve the replicability and reproducibility of supervised ML applications in Earth systems science (ESS), as well as the ability to evaluate them. Specifically, the approach requires (a) the articulation and documentation of the exact decision‐making process used for assigning hand labels in a “codebook” and (b) an empirical evaluation of the reliability” of the hand labelers. In this paper, we outline the contributions of QCA to the field, along with an overview of the general approach. We then provide a case study to further demonstrate how this framework has and can be applied when developing supervised ML models for applications in ESS. With this approach, we provide an actionable path forward for addressing ethical considerations and goals outlined by recent AGU work on ML ethics in ESS.
Evaluation and Uncertainty Analysis of the Land Surface Hydrology in LS3MIP Models Over China
Authors: Ma Xin , Wang Aihui 
Published: July 2024

AbstractThe Land Surface, Snow and Soil moisture Model Intercomparison Project (LS3MIP) offers valuable land surface hydrology products from the land modules of current Earth system models (ESMs). Historical hydrological variables from six ESMs driven by four meteorological forcing data sets (GSWP, WFDEI, CRU‐NCEP, and Princeton) in Land Model Intercomparison Project (LMIP) have been extensively evaluated with various high‐quality reference data sets over Chinese mainland. Compared with the reference data sets, the multi‐model ensemble means (MMEs) of most hydrological variables are underestimated, while their annual trends show high spatial consistency, with sign consistency over 56%–85% of land area. After computing and ranking four statistical metrics (bias, correlation coefficient, normalized standard deviation, and unbiased root‐mean‐square biases) between simulations and references, it is found that the CLM5 has the best performance, while the GSWP3 exhibits the highest quality. Furthermore, the analysis of variance method (ANOVA) is then used to trace sources (model, atmospheric forcing data sets and their interactions) of the uncertainty of those modeling hydrological variables for 1900–2012 (1948–2012 for runoff) over China. The results indicate that the total uncertainty and its composition vary with time and decrease significantly in recent decades, reflecting the enhanced forcing data quality. Larger forcing uncertainty existed during the early twentieth century because less available observation data sets have been adopted to constrain climate variables. For all modeling hydrological variables, the model uncertainty plays the dominant role, suggesting that the quality of LMIP products largely relies on Land surface models.
How Critical Is the Accuracy of the Atmospheric Transport Modeling to Improve the Urban CO2 Emission in India?—A Lagrangian‐Based Approach
Authors: Sukumaran Jithin , Pillai Dhanyalekshmi , Thilakan Vishnu , Lekshmi Saradambal , Udayakumar Gokul , Mathew Thara Anna , Ravi Aparnna , MG Manoj 
Published: July 2024

AbstractAnthropogenic CO2 emission reduction strategies depend on how well we track emission enhancements at the urban scale. The estimation system, based on inverse modeling, relies on our knowledge of atmospheric transport and prior flux distributions. Hence, the analysis framework must account for uncertainties associated with each component in order to interpret the variations in observed CO2. Using an ensemble of simulations, we quantify the uncertainties in simulating anthropogenic CO2 mixing ratio enhancements at 15 locations in India. Differences in the representation of transport mechanisms and prior emission in the forward model induce a consistently large model spread of 62.2% and 41.9%, respectively, in the simulated mixing ratio over cities. The analysis reports an average uncertainty of 2.2 ppm with a maximum of 8 ppm for representing diurnally averaged anthropogenic CO2 enhancement. Diurnal variations in emissions and transport induce a rectification effect in those enhancements. The outcome of this study can thus inform future atmospheric CO2 inversion modeling at an urban scale on the expected forward model uncertainties, which are the essential components in the Bayesian inversion framework, typically lacking in the Indian region. The first‐order inversion experiments show that the change in the transport model induces significant uncertainty (up to 84.9%) in anthropogenic CO2 flux estimation at the national scale. Hence, the confidence level of inverse‐based emission estimation in India depends considerably on the accuracy of atmospheric transport modeling.
Exploring the Behaviors of Initiated Progressive Failure and Slow‐Moving Landslides in Los Angeles Using Satellite InSAR and Pixel Offset Tracking
Authors: Li Xiang , Handwerger Alexander L. , Peltzer Gilles , Fielding Eric 
Published: July 2024

AbstractCatastrophic landslides are often preceded by slow, progressive, accelerating deformation that differs from the persistent motion of slow‐moving landslides. Here, we investigate the motion of a landslide that damaged 12 homes in Rolling Hills Estates (RHE), Los Angeles, California on 8 July 2023, using satellite‐based synthetic aperture radar interferometry (InSAR) and pixel tracking of satellite‐based optical images. To better understand the precursory motion of the RHE landslide, we compared its behavior with local precipitation and with several slow‐moving landslides nearby. Unlike the slow‐moving landslides, we found that RHE was a first‐time progressive failure that failed after one of the wettest years on record. We then applied a progressive failure model to interpret the failure mechanisms and further predict the failure time from the pre‐failure movement of RHE. Our work highlights the importance of monitoring incipient slow motion of landslides, particularly where no discernible historical displacement has been observed.
Co‐Benefits of Mitigating Aerosol Pollution to Future Solar and Wind Energy in China Toward Carbon Neutrality
Authors: Ren Lili , Yang Yang , Wang Hailong , Wang Pinya , Yue Xu , Liao Hong 
Published: July 2024

AbstractThe climate commitment to achieving carbon neutrality before 2060 in China has been announced recently. In the context of pursuing carbon neutrality, sharing similar sources as greenhouse gases, aerosol particle and precursor emissions are projected to substantially decrease in China, which can potentially have a great impact on climate. Here, we investigate the effects of future aerosol reductions, because of achieving carbon neutrality, on solar and wind energy in China by using an earth system model. We show that significant reductions in aerosol emissions, particularly in eastern China, lead to increases in the surface downwelling shortwave radiation, surface air temperature and wind speed, which can further enhance the potential of solar and wind energy production. The findings underline that the pursuit of carbon neutrality can yield co‐benefits of not only mitigating climate change and air pollution but also fortifying the stability of renewable energy sources.
Variability in the Electrodynamics of the Small Scale Auroral Arc
Authors: Krcelic P. , Fear R. C. , Whiter D. , Lanchester B. , Brindley N. 
Published: July 2024

AbstractA statistical study has been made of dynamic small‐scale auroral events in order to understand the drivers of the large variability in the electrodynamics of auroral arcs at fine scales. We used the Auroral Structure and Kinetics (ASK) instrument, located on Svalbard, in order to measure various electrodynamic properties of fine scale auroral arcs. We performed Spearman and Kendall statistical tests and found two significant correlations. The first is between the mean precipitation flux and the variability of flux, which we assume is because of the dynamic and bursty nature of the acceleration mechanism and its dependence on Alfvén waves. The second correlation is between the variability of the precipitating electron flux and the variability of the tangential component of the electric field close to the arc and perpendicular to the magnetic field. We propose that both variabilities occur because of the variability of the upward (field‐aligned) current sheet in and around the arc, which is dynamic and non‐uniform. The correlation between the two variabilities can therefore be explained by their common source.
Evaluation of a New Approach for Entrainment and Detrainment Rate Estimation
Authors: Zhu Lei , Lu Chunsong , Xu Xiaoqi , Li Yabin , Luo Shi , He Xin , Gao Sinan , Li Junjun , Chen Yiqi , Wang Yuan , Liu Yangang 
Published: July 2024

AbstractEntrainment and detrainment rates (ε and δ) constitute the most critical free parameters in mass flux schemes commonly employed for cumulus parameterizations. Recently, Zhu et al. (2021) introduced a new approach that utilizes aircraft observations to simultaneously estimate ε and δ for cumulus clouds, overcoming the limitation of other observation‐based approaches that solely yield ε without offering insights into δ. This study aims to comprehensively evaluate the reliability of this new approach. First, evaluation using an Explicit Mixing Parcel Model demonstrates the capability of the new approach to back‐calculate predetermined ε and δ based on the physical properties before and after the entrainment mixing. Second, evaluation using large‐eddy simulations illustrates that the new approach yields consistent ε and δ profiles compared to the traditional approach. Sensitivity tests indicate a weak sensitivity of the estimated δ with the new approach to the entrained air source. A decrease in the proportion of cloudy air in the assumed detrained air leads to a reduction in the estimated δ, while ε remains unaffected. Finally, the most appropriate assumptions for entrained and detrained air are discussed. Estimating ε for cumulus parameterizations involves acquiring ambient air more than 500 m away from the cloud edge as entrained air. Due to implicit mean field approximations in the traditional approach, determining the optimal assumption for detrained air properties proves challenging. This study confirms the reliability of the new approach in estimating ε and δ, providing confidence in its application to extensive observational data and advancement in parameterization.
HIV-protease inhibitors potentiate the activity of carfilzomib in triple-negative breast cancer
Authors: Besse Andrej , Sedlarikova Lenka , Buechler Lorina , Kraus Marianne , Yang Chieh-Hsiang , Strakova Nicol , Soucek Karel , Navratil Jiri , Svoboda Marek , Welm Alana L. , Joerger Markus , Driessen Christoph , Besse Lenka 
Published: July 2024

Abstract
Background
Resistance to chemotherapy is a major problem in the treatment of patients with triple-negative breast cancer (TNBC). Preclinical data suggest that TNBC is dependent on proteasomes; however, clinical observations indicate that the efficacy of proteasome inhibitors in TNBC may be limited, suggesting the need for combination therapies.

Methods
We compared bortezomib and carfilzomib and their combinations with nelfinavir and lopinavir in TNBC cell lines and primary cells with regard to their cytotoxic activity, functional proteasome inhibition, and induction of the unfolded protein response (UPR). Furthermore, we evaluated the involvement of sXBP1, ABCB1, and ABCG2 in the cytotoxic activity of drug combinations.

Results
Carfilzomib, via proteasome β5 + β2 inhibition, is more cytotoxic in TNBC than bortezomib, which inhibits β5 + β1 proteasome subunits. The cytotoxicity of carfilzomib was significantly potentiated by nelfinavir or lopinavir. Carfilzomib with lopinavir induced endoplasmic reticulum stress and pro-apoptotic UPR through the accumulation of excess proteasomal substrate protein in TNBC in vitro. Moreover, lopinavir increased the intracellular availability of carfilzomib by inhibiting carfilzomib export from cells that express high levels and activity of ABCB1, but not ABCG2.

Conclusion
Proteasome inhibition by carfilzomib combined with nelfinavir/lopinavir represents a potential treatment option for TNBC, warranting further investigation.
A novel LGALS1-depended and immune-associated fatty acid metabolism risk model in acute myeloid leukemia stem cells
Authors: Qin Huanhuan , Peng Meixi , Cheng Jingsong , Wang Zhenyu , Cui Yinghui , Huang Yongxiu , Gui Yaoqi , Sun Yanni , Xiang Wenqiong , Huang Xiaomei , Huang Ting , Wang Li , Chen Jieping , Hou Yu 
Published: July 2024

AbstractLeukemia stem cells (LSCs) are recognized as the root cause of leukemia initiation, relapse, and drug resistance. Lipid species are highly abundant and essential component of human cells, which often changed in tumor microenvironment. LSCs remodel lipid metabolism to sustain the stemness. However, there is no useful lipid related biomarker has been approved for clinical practice in AML prediction and treatment. Here, we constructed and verified fatty acid metabolism-related risk score (LFMRS) model based on TCGA database via a series of bioinformatics analysis, univariate COX regression analysis, and multivariate COX regression analysis, and found that the LFMRS model could be an independent risk factor and predict the survival time of AML patients combined with age. Moreover, we revealed that Galectin-1 (LGALS1, the key gene of LFMRS) was highly expressed in LSCs and associated with poor prognosis of AML patients, and LGALS1 repression inhibited AML cell and LSC proliferation, enhanced cell apoptosis, and decreased lipid accumulation in vitro. LGALS1 repression curbed AML progression, lipid accumulation, and CD8+ T and NK cell counts in vivo. Our study sheds light on the roles of LFMRS (especially LGALS1) model in AML, and provides information that may help clinicians improve patient prognosis and develop personalized treatment regimens for AML.
Role of ferroptosis in radiation-induced soft tissue injury
Authors: Berry Charlotte E. , Kendig Carter B. , An Nicholas , Fazilat Alexander Z. , Churukian Andrew A. , Griffin Michelle , Pan Phoebe M. , Longaker Michael T. , Dixon Scott J. , Wan Derrick C. 
Published: July 2024

AbstractIonizing radiation has been pivotal in cancer therapy since its discovery. Despite its therapeutic benefits, IR causes significant acute and chronic complications due to DNA damage and the generation of reactive oxygen species, which harm nucleic acids, lipids, and proteins. While cancer cells are more vulnerable to ionizing radiation due to their inefficiency in repairing damage, healthy cells in the irradiated area also suffer. Various types of cell death occur, including apoptosis, necrosis, pyroptosis, autophagy-dependent cell death, immunogenic cell death, and ferroptosis. Ferroptosis, driven by iron-dependent lipid peroxide accumulation, has been recognized as crucial in radiation therapy’s therapeutic effects and complications, with extensive research across various tissues. This review aims to summarize the pathways involved in radiation-related ferroptosis, findings in different organs, and drugs targeting ferroptosis to mitigate its harmful effects.
Prenatal LPS leads to increases in RAS expression within the PVN and overactivation of sympathetic outflow in offspring rats
Authors: Hao Xueqin , Long Xueting , Fan Lingling , Gou Jijia , Liu Yuchao , Fu Yifan , Zhao Huijuan , Xie Xiaojuan , Wang Dongmei , Liang Gaofeng , Ye Yujia , Wang Jing , Li Sanqiang , Zeng Chunyu 
Published: July 2024

AbstractThe renin-angiotensin system (RAS) and the sympathetic nervous system (SNS) are two major blood pressure-regulating systems. The link between the renal and cerebral RAS axes was provided by reflex activation of renal afferents and efferent sympathetic nerves. There is a self-sustaining enhancement of the brain and the intrarenal RAS. In this study, prenatal exposure to lipopolysaccharide (LPS) led to increased RAS activity in the paraventricular nucleus (PVN) and overactivation of sympathetic outflow, accompanied by increased production of reactive oxygen species (ROS) and disturbances between inhibitory and excitatory neurons in PVN. The AT1 receptor blocker losartan and α2 adrenergic receptor agonist clonidine in the PVN significantly decreased renal sympathetic nerve activity (RSNA) and synchronously reduced systolic blood pressure. Prenatal LPS stimulation caused H3 acetylation at H3K9 and H3K14 in the PVN, which suggested that epigenetic changes are involved in transmitting the prenatal adverse stimulative information to the next generation. Additionally, melatonin treatment during pregnancy reduced RAS activity and ROS levels in the PVN; balanced the activity of inhibitory and excitatory neurons in the PVN; increased urine sodium secretion; reduced RSNA and blood pressure. In conclusion, prenatal LPS leads to increased RAS expression within the PVN and overactivation of the sympathetic outflow, thereby contributing to hypertension in offspring rats. Melatonin is expected to be a promising agent for preventing prenatal LPS exposure-induced hypertension.
Multicolor recordable and erasable photonic crystals based on on-off thermoswitchable mechanochromism toward inkless rewritable paper
Authors: Hu Yang , Qi Chenze , Ma Dekun , Yang Dongpeng , Huang Shaoming 
Published: July 2024

AbstractMechanochromic photonic crystals are attractive due to their force-dependent structural colors; however, showing unrecordable color and unsatisfied performances, which significantly limits their development and expansion toward advanced applications. Here, a thermal-responsive mechanochromic photonic crystal with a multicolor recordability-erasability was fabricated by combining non-close-packing mechanochromic photonic crystals and phase-change materials. Multicolor recordability is realized by pressing thermal-responsive mechanochromic photonic crystals to obtain target colors over the phase-change temperature followed by fixing the target colors and deformed configuration at room temperature. The stable recorded color can be erased and reconfigured by simply heating and similar color-recording procedures respectively due to the thermoswitchable on-off mechanochromism of thermal-responsive mechanochromic photonic crystals along with solid-gel phase transition. These thermal-responsive mechanochromic photonic crystals are ideal rewritable papers for ink-freely achieving multicolor patterns with high resolution, difficult for conventional photonic papers. This work offers a perspective for designing color-recordable/erasable and other stimulus-switchable materials with advanced applications.
Low-input PacBio sequencing generates high-quality individual fly genomes and characterizes mutational processes
Authors: Jia Hangxing , Tan Shengjun , Cai Yingao , Guo Yanyan , Shen Jieyu , Zhang Yaqiong , Ma Huijing , Zhang Qingzhu , Chen Jinfeng , Qiao Gexia , Ruan Jue , Zhang Yong E. 
Published: July 2024

AbstractLong-read sequencing, exemplified by PacBio, revolutionizes genomics, overcoming challenges like repetitive sequences. However, the high DNA requirement ( > 1 µg) is prohibitive for small organisms. We develop a low-input (100 ng), low-cost, and amplification-free library-generation method for PacBio sequencing (LILAP) using Tn5-based tagmentation and DNA circularization within one tube. We test LILAP with two Drosophila melanogaster individuals, and generate near-complete genomes, surpassing preexisting single-fly genomes. By analyzing variations in these two genomes, we characterize mutational processes: complex transpositions (transposon insertions together with extra duplications and/or deletions) prefer regions characterized by non-B DNA structures, and gene conversion of transposons occurs on both DNA and RNA levels. Concurrently, we generate two complete assemblies for the endosymbiotic bacterium Wolbachia in these flies and similarly detect transposon conversion. Thus, LILAP promises a broad PacBio sequencing adoption for not only mutational studies of flies and their symbionts but also explorations of other small organisms or precious samples.