bluuebunny/crossref_metadata_2025_split huggingface dataset:
Influence of Pore Fluid on Grain‐Scale Interactions and Mobility of Granular Flows of Differing Volume
Authors: Taylor‐Noonan Alexander M. , Bowman Elisabeth T. , McArdell Brian W. , Kaitna Roland , McElwaine Jim N. , Take W. Andy 
Published: November 2022

AbstractThe presence of a pore fluid is recognized to significantly increase the mobility of saturated over dry granular flows. However, the mechanisms through which pore fluid increases mobility may not be captured in experimental flows of small volume typical of laboratory conditions. Here we present the results of dry and initially fluid saturated or “wet” experimental flows of near‐monodisperse coarse‐grained ceramic particles in a large laboratory flume for five source volumes of 0.2–1.0 m3. Measurements include flow height, velocity profile, pore pressure, and evolving solid volume fraction, as well as the final deposit shape. The dry experiments constrain the frictional properties of the common granular material and comparison with wet flows permits an independent evaluation of the interstitial fluid effects. These results demonstrate that flow dilation and strong variation in the velocity profile are directly linked to a greatly increased mobility for wet granular flows compared to dry, and a significant influence of scale as controlled by source volume on flow behavior. Excess pore pressure need not be present for these effects to occur.
Effects of Bed Hydrophobicity on Post‐Fire Debris Flow Entrainment and Momentum Growth
Authors: Ng Charles W. W. , Zheng Min , Liu Haiming , Poudyal Sunil 
Published: November 2022

AbstractForest wildfires leave vast areas with burned topsoil that is water repellent, susceptible to erosion by surface runoff and a potential source of debris flow. To mitigate post‐fire debris flow hazards, debris resisting barriers are usually constructed in the flow path. However, the fundamental interaction mechanisms of debris flow on a water repellent bed and the momentum exchange process that governs the destructive potential of debris flows have yet to be elucidated. This study investigates the influence of bed sediment hydrophobicity on entrainment by debris flows and the impact force on a downstream barrier using a series of physical flume tests. Bed sediment with increasing contact angles (50°–130°) were used to model bed hydrophobicity from wettable to hydrophobic conditions, and volumetric water content (0%–30%) to simulate the natural unsaturated condition after rainfall in fire‐burned hillslopes. Compared to a wettable bed, a hydrophobic bed exhibits a failure pattern with slab‐by‐slab entrainment and en masse failure with a sixfold increase in the average erosion depth. A new dimensionless number is proposed to quantify the effects of contact angle on soil erosion. The flow momentum increases up to 21% after entrainment of hydrophobic bed compared to non‐erodible bed. The peak impact force on the downstream barrier can increase up to 80% for hydrophobic bed as a consequence of momentum gains after entrainment. The hydrodynamic coefficient for estimating impact force reveals the inadequacy of current design criteria of debris resisting barriers for fire‐prone vegetated hillslopes, suggesting further investigations are needed.
P2X1 enhances leukemogenesis through PBX3-BCAT1 pathways
Authors: He Xiaoxiao , Xu Yilu , Huang Dan , Yu Zhuo , Yu Jing , Xie Li , Liu Ligen , Yu Ye , Chen Chiqi , Wan Jiangbo , Zhang Yaping , Zheng Junke 
Published: November 2022

AbstractHow bone marrow niches regulate leukemogenic activities of leukemia-initiating cells (LICs) is unclear. The present study revealed that the metabolic niche component, ATP, efficiently induced ion influx in LICs through its ligand-gated ion channel, P2X1. P2X1 deletion impaired LIC self-renewal capacities and resulted in an approximately 8-fold decrease in functional LIC numbers in a murine acute myeloid leukemia (AML) model without affecting normal hematopoiesis. P2X1 phosphorylation at specific sites of S387 and T389 was essential for sustaining its promoting effects on leukemia development. ATP-P2X1-mediated signaling upregulated the PBX3 level to transactivate BCAT1 to maintain LIC fates. P2X1 knockdown inhibited the proliferation of both human AML cell lines and primary cells. The P2X1 antagonist sufficiently suppressed AML cell proliferation. These results provided a unique perspective on how metabolic niche factor ATP fine-tunes LIC activities, which may benefit the development of strategies for targeting LICs or other cancer stem cells.
Dual-function perovskite light-emitting/sensing devices for optical interactive display
Authors: Ju Songman , Zhu Yangbin , Hu Hailong , Liu Yang , Xu Zhongwei , Zheng Jinping , Mao Chaomin , Yu Yongshen , Yang Kaiyu , Lin Lihua , Guo Tailiang , Li Fushan 
Published: November 2022

AbstractInteractive display devices integrating multiple functions have become a development trend of display technology. The excellent luminescence properties of perovskite quantum dots (PQDs) make it an ideal luminescent material for the next generation of wide-color gamut displays. Here we design and fabricate dual-function light-sensing/displaying light-emitting devices based on PQDs. The devices can display information as an output port, and simultaneously sense outside light signals as an input port and modulate the display information in a non-contact mode. The dual functions were attributed to the device designs: (1) the hole transport layer in the devices also acts as the light-sensing layer to absorb outside light signals; (2) the introduced hole trapping layer interface can trap holes originating from the light-sensing layer, and thus tune the charge transport properties and the light-emitting intensities. The sensing and display behavior of the device can be further modulated by light signals with different time and space information. This fusion of sensing and display functions has broad prospects in non-contact interactive screens and communication ports.
Effectiveness of KarXT (xanomeline-trospium) for cognitive impairment in schizophrenia: post hoc analyses from a randomised, double-blind, placebo-controlled phase 2 study
Authors: Sauder Colin , Allen Luke A. , Baker Elizabeth , Miller Andrew C. , Paul Steven M. , Brannan Stephen K. 
Published: November 2022

AbstractThe muscarinic receptor agonist xanomeline improved cognition in phase 2 trials in Alzheimer’s disease and schizophrenia. We present data on the effect of KarXT (xanomeline–trospium) on cognition in schizophrenia from the 5-week, randomised, double-blind, placebo-controlled EMERGENT-1 trial (NCT03697252). Analyses included 125 patients with computerised Cogstate Brief Battery (CBB) subtest scores at baseline and endpoint. A post hoc subgroup analysis evaluated the effects of KarXT on cognitive performance in patients with or without clinically meaningful cognitive impairment at baseline, and a separate outlier analysis excluded patients with excessive intraindividual variability (IIV) across cognitive subdomains. ANCOVA models assessed treatment effects for completers and impairment subgroups, with or without removal of outliers. Sample-wide, cognitive improvement was numerically but not statistically greater with KarXT (n = 60) than placebo (n = 65),p = 0.16. However, post hoc analyses showed 65 patients did not exhibit clinically meaningful cognitive impairment at baseline, while eight patients had implausibly high IIV at one or both timepoints. Significant treatment effects were observed after removing outliers (KarXTn = 54, placebon = 63;p = 0.04). Despite the small sample size, a robust (d = 0.50) and significant effect was observed among patients with cognitive impairment (KarXTn = 23, placebon = 37;p = 0.03). These effects did not appear to be related to improvement in PANSS total scores (linear regression,R2 = 0.03). Collectively, these findings suggest that KarXT may have a separable and meaningful impact on cognition, particularly among patients with cognitive impairment.
A CRISPR endonuclease gene drive reveals distinct mechanisms of inheritance bias
Authors: Verkuijl Sebald A. N. , Gonzalez Estela , Li Ming , Ang Joshua X. D. , Kandul Nikolay P. , Anderson Michelle A. E. , Akbari Omar S. , Bonsall Michael B. , Alphey Luke 
Published: November 2022

AbstractCRISPR/Cas gene drives can bias transgene inheritance through different mechanisms. Homing drives are designed to replace a wild-type allele with a copy of a drive element on the homologous chromosome. In Aedes aegypti, the sex-determining locus is closely linked to the white gene, which was previously used as a target for a homing drive element (wGDe). Here, through an analysis using this linkage we show that in males inheritance bias of wGDe did not occur by homing, rather through increased propagation of the donor drive element. We test the same wGDe drive element with transgenes expressing Cas9 with germline regulatory elements sds3, bgcn, and nup50. We only find inheritance bias through homing, even with the identical nup50-Cas9 transgene. We propose that DNA repair outcomes may be more context dependent than anticipated and that other previously reported homing drives may, in fact, bias their inheritance through other mechanisms.
Polymeric micelles effectively reprogram the tumor microenvironment to potentiate nano-immunotherapy in mouse breast cancer models
Authors: Panagi Myrofora , Mpekris Fotios , Chen Pengwen , Voutouri Chrysovalantis , Nakagawa Yasuhiro , Martin John D. , Hiroi Tetsuro , Hashimoto Hiroko , Demetriou Philippos , Pierides Chryso , Samuel Rekha , Stylianou Andreas , Michael Christina , Fukushima Shigeto , Georgiou Paraskevi , Papageorgis Panagiotis , Papaphilippou Petri Ch. , Koumas Laura , Costeas Paul , Ishii Genichiro , Kojima Motohiro , Kataoka Kazunori , Cabral Horacio , Stylianopoulos Triantafyllos 
Published: November 2022

AbstractNano-immunotherapy improves breast cancer outcomes but not all patients respond and none are cured. To improve efficacy, research focuses on drugs that reprogram cancer-associated fibroblasts (CAFs) to improve therapeutic delivery and immunostimulation. These drugs, however, have a narrow therapeutic window and cause adverse effects. Developing strategies that increase CAF-reprogramming while limiting adverse effects is urgent. Here, taking advantage of the CAF-reprogramming capabilities of tranilast, we developed tranilast-loaded micelles. Strikingly, a 100-fold reduced dose of tranilast-micelles induces superior reprogramming compared to free drug owing to enhanced intratumoral accumulation and cancer-associated fibroblast uptake. Combination of tranilast-micelles and epirubicin-micelles or Doxil with immunotherapy increases T-cell infiltration, resulting in cures and immunological memory in mice bearing immunotherapy-resistant breast cancer. Furthermore, shear wave elastography (SWE) is able to monitor reduced tumor stiffness caused by tranilast-micelles and predict response to nano-immunotherapy. Micellar encapsulation is a promising strategy for TME-reprogramming and SWE is a potential biomarker of response.
High-precision estimation of emitter positions using Bayesian grouping of localizations
Authors: Fazel Mohamadreza , Wester Michael J. , Schodt David J. , Cruz Sebastian Restrepo , Strauss Sebastian , Schueder Florian , Schlichthaerle Thomas , Gillette Jennifer M. , Lidke Diane S. , Rieger Bernd , Jungmann Ralf , Lidke Keith A. 
Published: November 2022

AbstractSingle-molecule localization microscopy super-resolution methods rely on stochastic blinking/binding events, which often occur multiple times from each emitter over the course of data acquisition. Typically, the blinking/binding events from each emitter are treated as independent events, without an attempt to assign them to a particular emitter. Here, we describe a Bayesian method of inferring the positions of the tagged molecules by exploring the possible grouping and combination of localizations from multiple blinking/binding events. The results are position estimates of the tagged molecules that have improved localization precision and facilitate nanoscale structural insights. The Bayesian framework uses the localization precisions to learn the statistical distribution of the number of blinking/binding events per emitter and infer the number and position of emitters. We demonstrate the method on a range of synthetic data with various emitter densities, DNA origami constructs and biological structures using DNA-PAINT and dSTORM data. We show that under some experimental conditions it is possible to achieve sub-nanometer precision.
Sampling of structure and sequence space of small protein folds
Authors: Linsky Thomas W. , Noble Kyle , Tobin Autumn R. , Crow Rachel , Carter Lauren , Urbauer Jeffrey L. , Baker David , Strauch Eva-Maria 
Published: November 2022

AbstractNature only samples a small fraction of the sequence space that can fold into stable proteins. Furthermore, small structural variations in a single fold, sometimes only a few amino acids, can define a protein’s molecular function. Hence, to design proteins with novel functionalities, such as molecular recognition, methods to control and sample shape diversity are necessary. To explore this space, we developed and experimentally validated a computational platform that can design a wide variety of small protein folds while sampling shape diversity. We designed and evaluated stability of about 30,000 de novo protein designs of eight different folds. Among these designs, about 6,200 stable proteins were identified, including some predicted to have a first-of-its-kind minimalized thioredoxin fold. Obtained data revealed protein folding rules for structural features such as helix-connecting loops. Beyond serving as a resource for protein engineering, this massive and diverse dataset also provides training data for machine learning. We developed an accurate classifier to predict the stability of our designed proteins. The methods and the wide range of protein shapes provide a basis for designing new protein functions without compromising stability.
Forest fire threatens global carbon sinks and population centres under rising atmospheric water demand
Authors: Clarke Hamish , Nolan Rachael H. , De Dios Victor Resco , Bradstock Ross , Griebel Anne , Khanal Shiva , Boer Matthias M. 
Published: November 2022

AbstractLevels of fire activity and severity that are unprecedented in the instrumental record have recently been observed in forested regions around the world. Using a large sample of daily fire events and hourly climate data, here we show that fire activity in all global forest biomes responds strongly and predictably to exceedance of thresholds in atmospheric water demand, as measured by maximum daily vapour pressure deficit. The climatology of vapour pressure deficit can therefore be reliably used to predict forest fire risk under projected future climates. We find that climate change is projected to lead to widespread increases in risk, with at least 30 additional days above critical thresholds for fire activity in forest biomes on every continent by 2100 under rising emissions scenarios. Escalating forest fire risk threatens catastrophic carbon losses in the Amazon and major population health impacts from wildfire smoke in south Asia and east Africa.
Sub-microscopic magnetite and metallic iron particles formed by eutectic reaction in Chang’E-5 lunar soil
Authors: Guo Zhuang , Li Chen , Li Yang , Wen Yuanyun , Wu Yanxue , Jia Bojun , Tai Kairui , Zeng Xiaojia , Li Xiongyao , Liu Jianzhong , Ouyang Ziyuan 
Published: November 2022

AbstractFerric iron as well as magnetite are rarely found in lunar samples, and their distribution and formation mechanisms on the Moon have not been well studied. Here, we discover sub-microscopic magnetite particles in Chang’E-5 lunar soil. Magnetite and pure metallic iron particles are embedded in oxygen-dissolved iron-sulfide grains from the Chang’E-5 samples. This mineral assemblage indicates a FeO eutectoid reaction (4FeO = Fe3O4 + Fe) for formation of magnetite. The iron-sulfide grains’ morphology features and the oxygen’s distribution suggest that a gas–melt phase reaction occurred during large-impact events. This could provide an effective method to form ubiquitous sub-microscopic magnetite in fine lunar soils and be a contributor to the presentation of ferric iron on the surface of the Moon. Additionally, the formation of sub-microscopic magnetite and metallic iron by eutectoid reaction may provide an alternative way for the formation of magnetic anomalies observed on the Moon.
Modeling COVID-19 transmission between age groups in the United States considering virus mutations, vaccinations, and reinfection
Authors: Roy Jyotirmoy , Heath Samuel M. , Wang Shiyan , Ramkrishna Doraiswami 
Published: November 2022

AbstractThe in-depth understanding of the dynamics of COVID-19 transmission among different age groups is of great interest for governments and health authorities so that strategies can be devised to reduce the pandemic’s detrimental effects. We developed the SIRDV-Virulence (Susceptible-Infected-Recovered-Dead-Vaccinated-Virulence) epidemiological model based on a population balance equation to study the effects virus mutants, vaccination strategies, ‘Anti/Non Vaxxer’ proportions, and reinfection rates to provide methods to mitigate COVID-19 transmission among the United States population. Based on publicly available data, we obtain the key parameters governing the spread of the pandemic. The results show that a large fraction of infected cases comes from the adult and children populations in the presence of a highly infectious COVID-19 mutant. Given the situation at the end of July 2021, the results show that prioritizing children and adult vaccinations over that of seniors can contain the spread of the active cases, thereby preventing the healthcare system from being overwhelmed and minimizing subsequent deaths. The model suggests that the only option to curb the effects of this pandemic is to reduce the population of unvaccinated individuals. A higher fraction of ‘Anti/Non-vaxxers’ and a higher reinfection rate can both independently lead to the resurgence of the pandemic.
Finding patterns in policy questions
Authors: Osman Magda , Cosstick Nick 
Published: November 2022

AbstractTo help advance exchanges between science and policy, a useful first step is to examine the questions which policymakers pose to scientists. The style of a question indicates what the asker is motivated to know, and how they might use that knowledge. Therefore, the aggregate pattern of typical policy inquires can help scientists anticipate what types of information policy audiences desire. A dataset (n = 2972) of questions from policymakers collected over 10 years (2011–2021)—by the Centre for Science and Policy at the University of Cambridge—was classified into one of seven classes. In the main, the most popular questions posed by policymakers—within the public and private sectors—were those whose answers inform how to achieve specific outcomes—whether directly, or by providing a causal analysis which is instrumental to this process. Moreover, this seems to be a general aspect of policymakers’ inquiries, given that it is preserved regardless of the policy issue considered (e.g., Artificial Intelligence, Economy, or Health). Thus, maximizing the usefulness of the information that policymakers receive when engaging with scientists requires informing how to achieve specific outcomes—directly, or by providing a useful causal analysis.
Green synthesis of silver nanoparticles using the extract of spent coffee used for paper-based hydrogen peroxide sensing device
Authors: Srikhao Natwat , Ounkaew Artjima , Kasemsiri Pornnapa , Theerakulpisut Somnuk , Okhawilai Manunya , Hiziroglu Salim 
Published: November 2022

AbstractHydrogen peroxide (H2O2) has attracted considerable attention for use as a disinfectant ingredient for various applications over the decades. The use of H2O2 within the safety regulations can avoid its toxicity to human health and the environment. In this study, a paper-based sensor containing green-synthesized silver nanoparticles (P-AgNPs) was developed for use in a smartphone in the determination of the H2O2 concentration. In the synthesis process, an extract of spent coffee grounds was used as a bioreducing agent. The effects of reaction time and silver nitrate (AgNO3) concentration on the green synthesis of silver nanoparticles (AgNPs) were investigated. The optimum conditions for the preparation of P-AgNPs were determined to be 100 mM AgNO3 (P-AgNPs-100) and 15 h synthesis time. The P-AgNPs-100 sensor exhibited high sensitivity with a detection limit of 1.26 mM H2O2, which might be suitable for the detection of H2O2-based household and beverage sanitizers. The H2O2 detection capability of P-AgNPs-100 was comparable to that of a commercial strip sensor. Furthermore, P-AgNPs-100 had a detection efficiency of more than 95% after long-term storage for 100 days.
Hybrid machine learning approach for landslide prediction, Uttarakhand, India
Authors: Kainthura Poonam , Sharma Neelam 
Published: November 2022

AbstractNatural disasters always have a damaging effect on our way of life. Landslides cause serious damage to both human and natural resources around the world. In this paper, the prediction accuracy of five hybrid models for landslide occurrence in the Uttarkashi, Uttarakhand (India) was evaluated and compared. In this approach, the Rough Set theory coupled with five different models namely Bayesian Network (HBNRS), Backpropagation Neural Network (HBPNNRS), Bagging (HBRS), XGBoost (HXGBRS), and Random Forest (HRFRS) were taken into account. The database for the models development was prepared using fifteen conditioning factors that had 373 landslide and 181 non-landslide locations that were then randomly divided into training and testing locations with a ratio of 75%:25%. The appropriateness and predictability of these conditioning factors were assessed using the multi-collinearity test and the least absolute shrinkage and selection operator approach. The accuracy, sensitivity, specificity, precision, and F-Measures, and the area under the curve (AUC)-receiver operating characteristics curve, were used to evaluate and compare the performance of the individual and hybrid created models. The findings indicate that the constructed hybrid model HXGBRS (AUC = 0.937, Precision = 0.946, F1-score = 0.926 and Accuracy = 89.92%) is the most accurate model for predicting landslides when compared to other models (HBPNNRS, HBNRS, HBRS, and HRFRS). Importantly, when the fusion is performed with the rough set method, the prediction capability of each model is improved. Simultaneously, the HXGBRS model proposed shows superior stability and can effectively avoid overfitting. After the core modules were developed, the user-friendly platform was designed as an integrated GIS environment using dynamic maps for effective landslide prediction in large prone areas. Users can predict the probability of landslide occurrence for selected region by changing the values of a conditioning factors. The created approach could be beneficial for predicting the impact of landslides on slopes and tracking landslides along national routes.