bluuebunny/crossref_metadata_2025_split huggingface dataset:
Elite Capture and Corruption: The Influence of Elite Collusion on Village Elections and Rural Land Development in China
Authors: Ruan Ji , Wang Peng 
Published: September 2022

AbstractThis article presents a qualitative empirical study of elite collusion and its influence on village elections and rural land development in China. Drawing on ethnographic data collected from two Chinese villages, it investigates how village cadres collude with other rural elites, using bribery, gift-giving and lavish banquets, to establish reciprocal ties with township officials and other public officials. Meanwhile, the officials make use of formal organizations to corruptly obtain profits and form alliances with village elites. The article examines how rural elites, especially village cadres, use this collusion to profit from the misuse of villagers’ collectively owned assets, the manipulation of village elections and the suppression of anti-corruption protests. It also offers new descriptive evidence of how recent reforms designed to strengthen the Party's overall leadership in rural governance may have actually facilitated elite capture and grassroots corruption.
Computationally Efficient Handling of Partially Coherent Electron Sources in (S)TEM Image Simulations via Matrix Diagonalization
Authors: Li Zhongbo , Rose Harald , Madsen Jacob , Biskupek Johannes , Susi Toma , Kaiser Ute 
Published: September 2022

Abstract
We introduce a novel method to improve the computational efficiency for (S)TEM image simulation by employing matrix diagonalization of the mixed envelope function (MEF). The MEF is derived by taking the finite size and the energy spread of the effective electron source into account, and is a component of the transmission cross-coefficient that accounts for the correlation between partially coherent waves. Since the MEF is a four-dimensional array and its application in image calculations is time-consuming, we reduce the computation time by using its eigenvectors. By incorporating the aperture function into the matrix diagonalization, only a small number of eigenvectors are required to approximate the original matrix with high accuracy. The diagonalization enables for each eigenvector the calculation of the corresponding image by employing the coherent model. The individual images are weighted by the corresponding eigenvalues and then summed up, resulting in the total partially coherent image.
Heat Stress During Arba’een Foot‐Pilgrimage (World’s Largest Gathering) Projected to Reach “Dangerous” Levels Due To Climate Change
Authors: Choi Yeon‐Woo , Eltahir Elfatih A. B. 
Published: September 2022

AbstractArba'een, a Muslim pilgrimage, is one of the largest annual mass gatherings in the world, with a date fixed according to the lunar calendar. Most pilgrims start their long walking journey from Basra/Najaf toward Karbala (about 70–500 km) and are significantly affected by outdoor weather conditions during this period. Here, based on simulations performed using carefully‐selected climate models, we project that heat stress during the pilgrimage is likely to reach a “dangerous” level, defined according to the US National Weather Service criteria, by the end of this century. Moreover, a significant increase in consecutive occurrence of hot days and hot nights is expected within the coming decades, which may cause a high incidence of heat‐related disorders as the human body may not recover from the daytime heat loads. Our study suggests that sound adaptation measures and stringent mitigation actions must be established to ensure a safe pilgrimage in the future.
Energy Partition at Collisionless Supercritical Quasi‐Perpendicular Shocks
Authors: Schwartz Steven J. , Goodrich Katherine A. , Wilson Lynn B. , Turner Drew L. , Trattner Karlheinz J. , Kucharek Harald , Gingell Imogen , Fuselier Stephen A. , Cohen Ian J. , Madanian Hadi , Ergun Robert E. , Gershman Daniel J. , Strangeway Robert J. 
Published: September 2022

AbstractCollisionless shocks in astrophysical plasmas are important thermalizers, converting some of the incident flow energy into thermal energy, and non‐thermalizers, partitioning that energy in unequal ways to different particle species, subpopulations thereof, and field components. This partition problem, or equivalently the shock equation of state, lies at the heart of shock physics. Here, we employ systematically a framework to capture all the incident and downstream energy fluxes at two example traversals of the Earth's bow shock by the Magnetospheric Multiscale Mission. Here and traditionally, such data has to be augmented by information from other spacecraft, for example, to provide more accurate measurements of the cold solar wind beam. With some care and fortuitous choices, the total energy flux is conserved, including instantaneous measurements through the shock layer. The dominant incident proton ram energy is converted primarily into downstream proton enthalpy flux, the majority of which is actually carried by a small fraction of suprathermal protons. Fluctuations include both real and instrumental effects. Separating these, resolving the solar wind beam, and other considerations point the way to a dedicated mission to solve this energy partition problem across a full range of plasma and shock conditions.
Investigation of the Post‐Sunset Extra Electron Density Peak Poleward of the Equatorial Ionization Anomaly Southern Crest
Authors: Cai Xuguang , Qian Liying , Wang Wenbin , McInerney Joseph M. , Liu Han‐Li , Eastes Richard W. 
Published: September 2022

AbstractThe Global‐scale observation of limb and disk mission observed an extra electron density (Ne) peak after sunset at approximately 30°S near 40°W on 4 November 2019, which is poleward and immediately next to the southern equatorial ionization anomaly (EIA) crest. This Ne peak is different from previously reported mid‐latitude peaks that occur at all local times. The Whole Atmosphere Community Climate Model‐eXtended captures this phenomenon. Model diagnostic analysis reveals that the decrease of Ne and hmF2 between 15° and 25°S makes Ne near 30°S appear as an extra density peak relative to the southern EIA crest. Transport by poleward meridional wind decreases Ne between 15° and 25°S. Moreover, the upward E × B drifts due to pre‐reversal enhancement lift the plasma between the dip equator and 15°S but do not affect Ne much between 15° and 25°S with a low drift speed. Comparison with days without the extra peak shows the importance of E × B drift latitudinal variations on the extra peak formation. This study provides new insights into the dynamic variability of the nighttime ionosphere.
Multi‐Ion Oscillitons—Origin of Coherent Magnetospheric EMIC Waves
Authors: Sauer Konrad , Dubinin Eduard 
Published: September 2022

AbstractThe recent spacecraft observations by MMS and Van Allen Probes associated with electromagnetic ion cyclotron (EMIC) waves in the Earth magnetosphere emphasize the important role of multi‐ion plasma composition for generation and characteristics of these emissions. We show that main properties of the coherent EMIC waves can be explained with the concept of “multi‐ion oscillitons” (Sauer et al., 2001, https://doi.org/10.1029/2001GL013047). In a plasma with two types of ions of different masses (e.g., protons and oxygen ions), oscillitons arise from the exchange of momentum and energy between the two ion components, with the electromagnetic field acting as a mediator. At frequencies near cross‐over frequencies of different wave modes in the multi‐ion plasma the nonlinear resonance which strongly amplifies the seed unstable mode can be excited. A small phase difference in oscillations of different ion species leads to a nonlinear wave beating and generation of wave packets. The “resonance” frequency is characterized by a local maximum of the phase velocity and the coincidence of phase and group velocity. It is suggested that the oscillitons are triggered by the instability due to the proton temperature anisotropy and may survive outside the source region for long distances. The generation of coherent waves by oscillitons is of a general nature and may contribute to understand the manifold of phenomena in other space plasma environments in which the dynamics of minor ion admixtures cannot be neglected. The concept of oscillitons can also be applied to the momentum exchange between particle groups of the same mass, but different temperature.
Validation of the DSCOVR Spacecraft Mission Space Weather Solar Wind Products
Authors: Loto'aniu Paul T. M. , Romich K. , Rowland W. , Codrescu S. , Biesecker D. , Johnson J. , Singer H. J. , Szabo A. , Stevens M. 
Published: September 2022

AbstractIn this paper, we present a statistical validation of the Deep Space Climate Observatory (DSCOVR) solar wind data in the operational space weather archive. The DSCOVR observations of the interplanetary magnetic field (IMF), solar wind velocity, density, and temperature were hourly averaged and compared to measurements from NASA's Advanced Composition Explorer (ACE) and Wind spacecraft. Hourly averages, in general, show good correlations between the satellites for the IMF, solar wind velocity Geocentric Solar Ecliptic (GSE) vx‐component, and density. During the period covered by this study (spanning from late July 2016, when DSCOVR went operational, to the end of 2020), the DSCOVR products show no clear evidence of permanent degradation. However, for plasma parameters, there were periods of disagreement with ACE and Wind. The correlation coefficients (Pearson's r) calculated over the entire study period were similar or the same between DSCOVR versus Wind and DSCOVR versus ACE. For comparisons between DSCOVR and Wind, the IMF Bx and By GSE r were 0.94 and 0.96, respectively, while r for the IMF GSE Bz‐component was 0.88. For solar wind velocity, r was found to be 0.96 for the GSE vx‐component, compared with 0.30 for vy and 0.33 for vz. For density, r was found to be 0.84. DSCOVR density observations tend to overestimate compared to Wind values when the solar wind densities are low (below ∼5/cc), while the agreement between the two spacecraft on IMF measurements tends to increase with decreasing spatial separation.
Targeted inhibition of tumor-derived exosomes as a novel therapeutic option for cancer
Authors: Li Ye , Chen Zhuo-Kun , Duan Xu , Zhang He-Jing , Xiao Bo-Lin , Wang Kui-Ming , Chen Gang 
Published: September 2022

AbstractMounting evidence indicates that tumor-derived exosomes (TDEs) play critical roles in tumor development and progression by regulating components in the tumor microenvironment (TME) in an autocrine or paracrine manner. Moreover, due to their delivery of critical molecules that react to chemotherapy and immunotherapy, TDEs also contribute to tumor drug resistance and impede the effective response of antitumor immunotherapy, thereby leading to poor clinical outcomes. There is a pressing need for the inhibition or removal of TDEs to facilitate the treatment and prognosis of cancer patients. Here, in the present review, we systematically overviewed the current strategies for TDE inhibition and clearance, providing novel insights for future tumor interventions in translational medicine. Moreover, existing challenges and potential prospects for TDE-targeted cancer therapy are also discussed to bridge the gaps between progress and promising applications.
A novel role of follicle-stimulating hormone (FSH) in various regeneration-related functions of endometrial stem cells
Authors: Park Se-Ra , Kim Soo-Rim , Kim Seong-Kwan , Park Jeong-Ran , Hong In-Sun 
Published: September 2022

AbstractFollicle-stimulating hormone (FSH) promotes the production and secretion of estrogen, which in turn stimulates the growth and maturation of ovarian follicles. Therefore, consecutive FSH treatment to induce ovarian hyperstimulation (superovulation) is still considered the most cost-effective option for the majority of assisted reproductive technologies (ARTs). However, a relatively high cancellation rate and subsequent low pregnancy outcomes (approximately 15%) are the most challenging aspects of this FSH-based ART. Currently, the main cause for this low implantation rate of FSH-based ART has not yet been revealed. Therefore, we hypothesized that these high cancellation rates with FSH-based superovulation protocols might be associated with the harmful effects of consecutive FSH treatment. Importantly, several recent studies have revealed that tissue-resident stem cell deficiency can significantly reduce cyclic endometrial regeneration and subsequently decrease the pregnancy outcome. In this context, we investigated whether FSH treatment could directly inhibit endometrial stem cell functions and consequently suppress endometrial regeneration. Consistent with our hypothesis, our results revealed for the first time that FSH could inhibit various regeneration-associated functions of endometrial stem cells, such as self-renewal, migration, and multilineage differentiation capacities, via the PI3K/Akt and ERK1/2 signaling pathways both in vitro and in vivo.
In vivo tractography of human locus coeruleus—relation to 7T resting state fMRI, psychological measures and single subject validity
Authors: Liebe Thomas , Kaufmann Jörn , Hämmerer Dorothea , Betts Matthew , Walter Martin 
Published: September 2022

AbstractThe locus coeruleus (LC) in the brainstem as the main regulator of brain noradrenaline gains increasing attention because of its involvement in neurologic and psychiatric diseases and its relevance in general to brain function. In this study, we created a structural connectome of the LC nerve fibers based on in vivo MRI tractography to gain an understanding into LC connectivity and its impact on LC-related psychological measures. We combined our structural results with ultra-high field resting-state functional MRI to learn about the relationship between in vivo LC structural and functional connections. Importantly, we reveal that LC brain fibers are strongly associated with psychological measures of anxiety and alertness indicating that LC-noradrenergic connectivity may have an important role on brain function. Lastly, since we analyzed all our data in subject-specific space, we point out the potential of structural LC connectivity to reveal individual characteristics of LC-noradrenergic function on the single-subject level.
Pharmacological targeting of CBP/p300 drives a redox/autophagy axis leading to senescence-induced growth arrest in non-small cell lung cancer cells
Authors: Ansari Mohammad Salik Zeya , Stagni Venturina , Iuzzolino Angela , Rotili Dante , Mai Antonello , Del Bufalo Donatella , Lavia Patrizia , Degrassi Francesca , Trisciuoglio Daniela 
Published: September 2022

Abstractp300/CBP histone acetyltransferases (HAT) are critical transcription coactivators involved in multiple cellular activities. They act at multiple levels in non-small cell lung carcinoma (NSCLC) and appear, therefore, as promising druggable targets. Herein, we investigated the biological effects of A-485, the first selective (potent) drug-like HAT catalytic inhibitor of p300/CBP, in human NSCLC cell lines. A-485 treatment specifically reduced p300/CBP-mediated histone acetylation marks and caused growth arrest of lung cancer cells via activation of the autophagic pathway. Indeed, A-485 growth-arrested cells displayed phenotypic markers of cell senescence and failed to form colonies. Notably, disruption of autophagy by genetic and pharmacological approaches triggered apoptotic cell death. Mechanistically, A-485-induced senescence occurred through the accumulation of reactive oxygen species (ROS), which in turn resulted in DNA damage and activation of the autophagic pathway. Interestingly, ROS scavengers were able to revert senescence phenotype and restore cell viability, suggesting that ROS production had a key role in upstream events leading to growth arrest commitment. Altogether, our data provide new insights into the biological effects of the A-485 and uncover the importance of the autophagic/apoptotic response to design a new combinatorial anticancer strategy.
Scalable anisotropic cooling aerogels by additive freeze-casting
Authors: Chan Kit-Ying , Shen Xi , Yang Jie , Lin Keng-Te , Venkatesan Harun , Kim Eunyoung , Zhang Heng , Lee Jeng-Hun , Yu Jinhong , Yang Jinglei , Kim Jang-Kyo 
Published: September 2022

AbstractCooling in buildings is vital to human well-being but inevitability consumes significant energy, adding pressure on achieving carbon neutrality. Thermally superinsulating aerogels are promising to isolate the heat for more energy-efficient cooling. However, most aerogels tend to absorb the sunlight for unwanted solar heat gain, and it is challenging to scale up the aerogel fabrication while maintaining consistent properties. Herein, we develop a thermally insulating, solar-reflective anisotropic cooling aerogel panel containing in-plane aligned pores with engineered pore walls using boron nitride nanosheets by an additive freeze-casting technique. The additive freeze-casting offers highly controllable and cumulative freezing dynamics for fabricating decimeter-scale aerogel panels with consistent in-plane pore alignments. The unique anisotropic thermo-optical properties of the nanosheets combined with in-plane pore channels enable the anisotropic cooling aerogel to deliver an ultralow out-of-plane thermal conductivity of 16.9 mW m−1 K−1 and a high solar reflectance of 97%. The excellent dual functionalities allow the anisotropic cooling aerogel to minimize both parasitic and solar heat gains when used as cooling panels under direct sunlight, achieving an up to 7 °C lower interior temperature than commercial silica aerogels. This work offers a new paradigm for the bottom-up fabrication of scalable anisotropic aerogels towards practical energy-efficient cooling applications.
Tracking changes in SARS-CoV-2 transmission with a novel outpatient sentinel surveillance system in Chicago, USA
Authors: Richardson Reese , Jorgensen Emile , Arevalo Philip , Holden Tobias M. , Gostic Katelyn M. , Pacilli Massimo , Ghinai Isaac , Lightner Shannon , Cobey Sarah , Gerardin Jaline 
Published: September 2022

AbstractPublic health indicators typically used for COVID-19 surveillance can be biased or lag changing community transmission patterns. In this study, we investigate whether sentinel surveillance of recently symptomatic individuals receiving outpatient diagnostic testing for SARS-CoV-2 could accurately assess the instantaneous reproductive number R(t) and provide early warning of changes in transmission. We use data from community-based diagnostic testing sites in the United States city of Chicago. Patients tested at community-based diagnostic testing sites between September 2020 and June 2021, and reporting symptom onset within four days preceding their test, formed the sentinel population. R(t) calculated from sentinel cases agreed well with R(t) from other indicators. Retrospectively, trends in sentinel cases did not precede trends in COVID-19 hospital admissions by any identifiable lead time. In deployment, sentinel surveillance held an operational recency advantage of nine days over hospital admissions. The promising performance of opportunistic sentinel surveillance suggests that deliberately designed outpatient sentinel surveillance would provide robust early warning of increasing transmission.
Topological feature engineering for machine learning based halide perovskite materials design
Authors: Anand D. Vijay , Xu Qiang , Wee JunJie , Xia Kelin , Sum Tze Chien 
Published: September 2022

AbstractAccelerated materials development with machine learning (ML) assisted screening and high throughput experimentation for new photovoltaic materials holds the key to addressing our grand energy challenges. Data-driven ML is envisaged as a decisive enabler for new perovskite materials discovery. However, its full potential can be severely curtailed by poorly represented molecular descriptors (or fingerprints). Optimal descriptors are essential for establishing effective mathematical representations of quantitative structure-property relationships. Here we reveal that our persistent functions (PFs) based learning models offer significant accuracy advantages over traditional descriptor based models in organic-inorganic halide perovskite (OIHP) materials design and have similar performance as deep learning models. Our multiscale simplicial complex approach not only provides a more precise representation for OIHP structures and underlying interactions, but also has better transferability to ML models. Our results demonstrate that advanced geometrical and topological invariants are highly efficient feature engineering approaches that can markedly improve the performance of learning models for molecular data analysis. Further, new structure-property relationships can be established between our invariants and bandgaps. We anticipate that our molecular representations and featurization models will transcend the limitations of conventional approaches and lead to breakthroughs in perovskite materials design and discovery.
Explainable machine learning in materials science
Authors: Zhong Xiaoting , Gallagher Brian , Liu Shusen , Kailkhura Bhavya , Hiszpanski Anna , Han T. Yong-Jin 
Published: September 2022

AbstractMachine learning models are increasingly used in materials studies because of their exceptional accuracy. However, the most accurate machine learning models are usually difficult to explain. Remedies to this problem lie in explainable artificial intelligence (XAI), an emerging research field that addresses the explainability of complicated machine learning models like deep neural networks (DNNs). This article attempts to provide an entry point to XAI for materials scientists. Concepts are defined to clarify what explain means in the context of materials science. Example works are reviewed to show how XAI helps materials science research. Challenges and opportunities are also discussed.
Inferring topological transitions in pattern-forming processes with self-supervised learning
Authors: Abram Marcin , Burghardt Keith , Ver Steeg Greg , Galstyan Aram , Dingreville Remi 
Published: September 2022

AbstractThe identification of transitions in pattern-forming processes are critical to understand and fabricate microstructurally precise materials in many application domains. While supervised methods can be useful to identify transition regimes, they need labels, which require prior knowledge of order parameters or relevant microstructures describing these transitions. Instead, we develop a self-supervised, neural-network-based approach that does not require predefined labels about microstructure classes to predict process parameters from observed microstructures. We show that assessing the difficulty of solving this inverse problem can be used to uncover microstructural transitions. We demonstrate our approach by automatically discovering microstructural transitions in two distinct pattern-forming processes: the spinodal decomposition of a two-phase mixture and the formation of binary-alloy microstructures during physical vapor deposition of thin films. This approach opens a path forward for discovering unseen or hard-to-discern transitions and ultimately controlling complex pattern-forming processes.