bluuebunny/crossref_metadata_2025_split huggingface dataset:
The influence of anthropogenic regulation and evaporite dissolution on earthquake-triggered ground failure
Authors: Bürgi Paula , Thompson Eric M. , Allstadt Kate E. , Murray Kyle D. , Mason H. Benjamin , Ahdi Sean K. , Katzenstein Devin 
Published: March 2024

AbstractRemote sensing observations of Searles Lake following the 2019 moment magnitude 7.1 Ridgecrest, California, earthquake reveal an area where surface ejecta is arranged in a repeating hexagonal pattern that is collocated with a solution-mining operation. By analyzing geologic and geotechnical data, here we show that the hexagonal surface ejecta is likely not a result of liquefaction. Instead, we propose dissolution cavity collapse (DCC) as an alternative driving mechanism. We support this theory with pre-event Interferometric Synthetic Aperture Radar data, which reveals differential subsidence patterns and the creation of subsurface void space. We also find that DCC is likely triggered at a lower shaking threshold than classical liquefaction. This and other unknown mechanisms can masquerade as liquefaction, introducing bias into liquefaction prediction models that rely on liquefaction inventories. This paper also highlights the opportunities and drawbacks of using remote sensing data to disentangle the complex factors that influence earthquake-triggered ground failure.
Reshaped three-body interactions and the observation of an Efimov state in the continuum
Authors: Yudkin Yaakov , Elbaz Roy , D’Incao José P. , Julienne Paul S. , Khaykovich Lev 
Published: March 2024

AbstractEfimov trimers are exotic three-body quantum states that emerge from the different types of three-body continua in the vicinity of two-atom Feshbach resonances. In particular, as the strength of the interaction is decreased to a critical point, an Efimov state merges into the atom-dimer threshold and eventually dissociates into an unbound atom-dimer pair. Here we explore the Efimov state in the vicinity of this critical point using coherent few-body spectroscopy in7Li atoms using a narrow two-body Feshbach resonance. Contrary to the expectation, we find that the7Li Efimov trimer does not immediately dissociate when passing the threshold, and survives as a metastable state embedded in the atom-dimer continuum. We identify this behavior with a universal phenomenon related to the emergence of a repulsive interaction in the atom-dimer channel which reshapes the three-body interactions in any system characterized by a narrow Feshbach resonance. Specifically, our results shed light on the nature of7Li Efimov states and provide a path to understand various puzzling phenomena associated with them.
Electric vehicle battery chemistry affects supply chain disruption vulnerabilities
Authors: Cheng Anthony L. , Fuchs Erica R. H. , Karplus Valerie J. , Michalek Jeremy J. 
Published: March 2024

AbstractWe examine the relationship between electric vehicle battery chemistry and supply chain disruption vulnerability for four critical minerals: lithium, cobalt, nickel, and manganese. We compare the nickel manganese cobalt (NMC) and lithium iron phosphate (LFP) cathode chemistries by (1) mapping the supply chains for these four materials, (2) calculating a vulnerability index for each cathode chemistry for various focal countries and (3) using network flow optimization to bound uncertainties. World supply is currently vulnerable to disruptions in China for both chemistries: 80% [71% to 100%] of NMC cathodes and 92% [90% to 93%] of LFP cathodes include minerals that pass through China. NMC has additional risks due to concentrations of nickel, cobalt, and manganese in other countries. The combined vulnerability of multiple supply chain stages is substantially larger than at individual steps alone. Our results suggest that reducing risk requires addressing vulnerabilities across the entire battery supply chain.
microRNA-33 controls hunger signaling in hypothalamic AgRP neurons
Authors: Price Nathan L. , Fernández-Tussy Pablo , Varela Luis , Cardelo Magdalena P. , Shanabrough Marya , Aryal Binod , de Cabo Rafael , Suárez Yajaira , Horvath Tamas L. , Fernández-Hernando Carlos 
Published: March 2024

AbstractAgRP neurons drive hunger, and excessive nutrient intake is the primary driver of obesity and associated metabolic disorders. While many factors impacting central regulation of feeding behavior have been established, the role of microRNAs in this process is poorly understood. Utilizing unique mouse models, we demonstrate that miR-33 plays a critical role in the regulation of AgRP neurons, and that loss of miR-33 leads to increased feeding, obesity, and metabolic dysfunction in mice. These effects include the regulation of multiple miR-33 target genes involved in mitochondrial biogenesis and fatty acid metabolism. Our findings elucidate a key regulatory pathway regulated by a non-coding RNA that impacts hunger by controlling multiple bioenergetic processes associated with the activation of AgRP neurons, providing alternative therapeutic approaches to modulate feeding behavior and associated metabolic diseases.
Viscosity-dependent control of protein synthesis and degradation
Authors: Chen Yuping , Huang Jo-Hsi , Phong Connie , Ferrell James E. 
Published: March 2024

AbstractIt has been proposed that the concentration of proteins in the cytoplasm maximizes the speed of important biochemical reactions. Here we have used Xenopus egg extracts, which can be diluted or concentrated to yield a range of cytoplasmic protein concentrations, to test the effect of cytoplasmic concentration on mRNA translation and protein degradation. We find that protein synthesis rates are maximal in ~1x cytoplasm, whereas protein degradation continues to rise to a higher optimal concentration of ~1.8x. We show that this difference in optima can be attributed to a greater sensitivity of translation to cytoplasmic viscosity. The different concentration optima could produce a negative feedback homeostatic system, where increasing the cytoplasmic protein concentration above the 1x physiological level increases the viscosity of the cytoplasm, which selectively inhibits translation and drives the system back toward the 1x set point.
Data-driven energy landscape reveals critical genes in cancer progression
Authors: Liu Juntan , Li Chunhe 
Published: March 2024

AbstractThe evolution of cancer is a complex process characterized by stable states and transitions among them. Studying the dynamic evolution of cancer and revealing the mechanisms of cancer progression based on experimental data is an important topic. In this study, we aim to employ a data-driven energy landscape approach to analyze the dynamic evolution of cancer. We take Kidney renal clear cell carcinoma (KIRC) as an example. From the energy landscape, we introduce two quantitative indicators (transition probability and barrier height) to study critical shifts in KIRC cancer evolution, including cancer onset and progression, and identify critical genes involved in these transitions. Our results successfully identify crucial genes that either promote or inhibit these transition processes in KIRC. We also conduct a comprehensive biological function analysis on these genes, validating the accuracy and reliability of our predictions. This work has implications for discovering new biomarkers, drug targets, and cancer treatment strategies in KIRC.
Putative correlates of protection against shigellosis assessing immunomarkers across responses to S. sonnei investigational vaccine
Authors: Conti Valentino , Rossi Omar , Clarkson Kristen A. , Mancini Francesca , Nakakana Usman N. , Sarakinou Eleanna , Callegaro Andrea , Ferruzzi Pietro , Acquaviva Alessandra , Arora Ashwani Kumar , Marchetti Elisa , Necchi Francesca , Frenck Robert W. , Martin Laura B. , Kaminski Robert W. , Podda Audino , Micoli Francesca 
Published: March 2024

AbstractShigella spp. are a leading bacterial cause of diarrhea. No widely licensed vaccines are available and there is no generally accepted correlate of protection. We tested a S. sonnei Generalized Modules for Membrane Antigen (GMMA)-based vaccine (1790GAHB) in a phase 2b, placebo-controlled, randomized, controlled human infection model study (NCT03527173) enrolling healthy United States adults aged 18–50 years. We report analyses evaluating immune responses to vaccination, with the aim to identify correlates of risk for shigellosis among assessed immunomarkers. We found that 1790GAHB elicited S. sonnei lipopolysaccharide specific α4β7+ immunoglobulin (Ig) G and IgA secreting B cells which are likely homing to the gut, indicating the ability to induce a mucosal in addition to a systemic response, despite parenteral delivery. We were unable to establish or confirm threshold levels that predict vaccine efficacy facilitating the evaluation of vaccine candidates. However, serum anti-lipopolysaccharide IgG and bactericidal activity were identified as potential correlates of risk for shigellosis.
Combined advanced oxidation dye-wastewater treatment plant: design and development with data-driven predictive performance modeling
Authors: Chauhan Pankaj Singh , Singh Kirtiman , Choudhary Aditya , Brighu Urmila , Singh S. K. , Bhattacharya Shantanu 
Published: March 2024

AbstractThe recalcitrant nature of the industrial dyes poses a significant challenge to existing treatment technologies due to the stringent environmental regulations. This combined with the inefficiency of a single treatment method has led to the implementation of the combination of primary, secondary, and tertiary treatment processes, which fails during complex secondary aeration processes due to variable pH loads of industrial effluent wastewater. This article presents a modified design methodology of a pilot-scale micro-pre-treatment unit using a solar-triggered advanced oxidation process reactor that both effectively controls the influent variability at the source and mitigates textile effluents for making the discharge reusable for different industrial purposes. The proposed modified combination technique of controlled serial processes inclusive of primary, secondary, and tertiary treatment steps with ZnO/ZnO-GO NanoMat-based advanced oxidation process demonstrates complete remediation of industrial grade effluent with effective reuse of the discharge. Further, a reliable prediction model for estimating water quality parameter using machine learning models are proposed. Multi-linear regression and Artificial Neural network modeling provide simple, accurate, and robust prediction capabilities, which are evaluated for the efficiency of the processes. The generated prediction models capture the output parameters within an acceptable level of accuracy $$({{\boldsymbol{R}}}_{{adj}}^{{\bf{2}}}\, >\, 0.90)$$

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and allow compliance with the discharge Inland Water Discharge Standards (IWDS).
Porphyrin-fused graphene nanoribbons
Authors: Chen Qiang , Lodi Alessandro , Zhang Heng , Gee Alex , Wang Hai I. , Kong Fanmiao , Clarke Michael , Edmondson Matthew , Hart Jack , O’Shea James N. , Stawski Wojciech , Baugh Jonathan , Narita Akimitsu , Saywell Alex , Bonn Mischa , Müllen Klaus , Bogani Lapo , Anderson Harry L. 
Published: March 2024

AbstractGraphene nanoribbons (GNRs), nanometre-wide strips of graphene, are promising materials for fabricating electronic devices. Many GNRs have been reported, yet no scalable strategies are known for synthesizing GNRs with metal atoms and heteroaromatic units at precisely defined positions in the conjugated backbone, which would be valuable for tuning their optical, electronic and magnetic properties. Here we report the solution-phase synthesis of a porphyrin-fused graphene nanoribbon (PGNR). This PGNR has metalloporphyrins fused into a twisted fjord-edged GNR backbone; it consists of long chains (>100 nm), with a narrow optical bandgap (~1.0 eV) and high local charge mobility (>400 cm2 V–1 s–1 by terahertz spectroscopy). We use this PGNR to fabricate ambipolar field-effect transistors with appealing switching behaviour, and single-electron transistors displaying multiple Coulomb diamonds. These results open an avenue to π-extended nanostructures with engineerable electrical and magnetic properties by transposing the coordination chemistry of porphyrins into graphene nanoribbons.
Structure of the human KMN complex and implications for regulation of its assembly
Authors: Polley Soumitra , Raisch Tobias , Ghetti Sabrina , Körner Marie , Terbeck Melina , Gräter Frauke , Raunser Stefan , Aponte-Santamaría Camilo , Vetter Ingrid R. , Musacchio Andrea 
Published: March 2024

AbstractBiorientation of chromosomes during cell division is necessary for precise dispatching of a mother cell’s chromosomes into its two daughters. Kinetochores, large layered structures built on specialized chromosome loci named centromeres, promote biorientation by binding and sensing spindle microtubules. One of the outer layer main components is a ten-subunit assembly comprising Knl1C, Mis12C and Ndc80C (KMN) subcomplexes. The KMN is highly elongated and docks on kinetochores and microtubules through interfaces at its opposite extremes. Here, we combine cryogenic electron microscopy reconstructions and AlphaFold2 predictions to generate a model of the human KMN that reveals all intra-KMN interfaces. We identify and functionally validate two interaction interfaces that link Mis12C to Ndc80C and Knl1C. Through targeted interference experiments, we demonstrate that this mutual organization strongly stabilizes the KMN assembly. Our work thus reports a comprehensive structural and functional analysis of this part of the kinetochore microtubule-binding machinery and elucidates the path of connections from the chromatin-bound components to the force-generating components.
A haplotype-resolved genome provides insight into allele-specific expression in wild walnut (Juglans regia L.)
Authors: Han Liqun , Luo Xiang , Zhao Yu , Li Ning , Xu Yuhui , Ma Kai 
Published: March 2024

AbstractWild germplasm resources are crucial for gene mining and molecular breeding because of their special trait performance. Haplotype-resolved genome is an ideal solution for fully understanding the biology of subgenomes in highly heterozygous species. Here, we surveyed the genome of a wild walnut tree from Gongliu County, Xinjiang, China, and generated a haplotype-resolved reference genome of 562.99 Mb (contig N50 = 34.10 Mb) for one haplotype (hap1) and 561.07 Mb (contig N50 = 33.91 Mb) for another haplotype (hap2) using PacBio high-fidelity (HiFi) reads and Hi-C technology. Approximately 527.20 Mb (93.64%) of hap1 and 526.40 Mb (93.82%) of hap2 were assigned to 16 pseudochromosomes. A total of 41039 and 39744 protein-coding gene models were predicted for hap1 and hap2, respectively. Moreover, 123 structural variations (SVs) were identified between the two haplotype genomes. Allele-specific expression genes (ASEGs) that respond to cold stress were ultimately identified. These datasets can be used to study subgenome evolution, for functional elite gene mining and to discover the transcriptional basis of specific traits related to environmental adaptation in wild walnut.
MCSdb, a database of proteins residing in membrane contact sites
Authors: Pan Xianrun , Ren Liping , Yang Yu , Xu Yi , Ning Lin , Zhang Yibing , Luo Huaichao , Zou Quan , Zhang Yang 
Published: March 2024

AbstractOrganelles do not act as autonomous discrete units but rather as interconnected hubs that engage in extensive communication by forming close contacts called “membrane contact sites (MCSs)”. And many proteins have been identified as residing in MCS and playing important roles in maintaining and fulfilling specific functions within these microdomains. However, a comprehensive compilation of these MCS proteins is still lacking. Therefore, we developed MCSdb, a manually curated resource of MCS proteins and complexes from publications. MCSdb documents 7010 MCS protein entries and 263 complexes, involving 24 organelles and 44 MCSs across 11 species. Additionally, MCSdb orchestrates all data into different categories with multitudinous information for presenting MCS proteins. In summary, MCSdb provides a valuable resource for accelerating MCS functional interpretation and interorganelle communication deciphering.
Development of a solar powered multirotor micro aerial vehicle
Authors: Abidali Aly , Agha Stephen A. , Munjiza Antonio , Shaheed Mohammad H. 
Published: March 2024

AbstractRotary-wing aerial vehicles offer manoeuvrability and vertical take-off and landing (VTOL) advantages over fixed-wing systems. Rotary-wing systems do however have comparatively higher energy demands and consequently shorter flight times and therefore a greater energy dependence over their fixed-wing counterparts. Advances in photovoltaic technologies have resulted in significant increases in the specific power (power-to-weight-ratio) of solar cells enabling the design of solar-powered rotary-wing aircraft, and now micro-sized variants. The micro aerial vehicle (MAV) presented, the Micro Solarcopter, is a 0.15 m $$\times$$
×
0.15 m $$\times$$
×
0.02 m solar-rechargeable radio-controlled aircraft. The 0.071 kg aircraft can fly for an average time of 3.5 min, recharge in approximately 68 min under 1000 W/m2 irradiance at 25 °C and can hibernate for 38 days without sunlight. The paper explores the use of commercially available photovoltaic cells for the purpose of increasing the energy autonomy of multi-rotor MAVs, by enabling them to stay out in the field without returning to base for charging. A working prototype has been presented which incorporates a battery management system, automatic power on and off, low-power sleep mode, and first-person-view (FPV) camera.
Comparing population-level humoral and cellular immunity to SARS-Cov-2 in Bangalore, India
Authors: Malani Anup , Aiyar Jayashree , Sant Andrea , Kamran Neha , Mohanan Manoj , Taneja Saloni , Woda Bartek , Zhao Wanran , Acharya Anu 
Published: March 2024

AbstractTwo types of immunity, humoral and cellular, offer protection against COVID. Humoral protection, contributed by circulating neutralizing antibodies, can provide immediate protection but decays more quickly than cellular immunity and can lose effectiveness in the face of mutation and drift in the SARS-CoV-2 spike protein. Therefore, population-level seroprevalence surveys used to estimate population-level immunity may underestimate the degree to which a population is protected against COVID. In early 2021, before India began its vaccination campaign, we tested for humoral and cellular immunity to SARS-Cov-2 in representative samples of slum and non-slum populations in Bangalore, India. We found that 29.7% of samples (unweighted) had IgG antibodies to the spike protein and 15.5% had neutralizing antibodies, but at up to 46% showed evidence of cellular immunity. We also find that prevalence of cellular immunity is significantly higher in slums than in non-slums. These findings suggest (1) that a significantly larger proportion of the population in Bangalore, India, had cellular immunity to SARS-CoV-2 than had humoral immunity, as measured by serological surveys, and (2) that low socio-economic status communities display higher frequency of cellular immunity, likely because of greater exposure to infection due to population density.
Clinical electromagnetic brain scanner
Authors: Abbosh Amin , Bialkowski Konstanty , Guo Lei , Al-Saffar Ahmed , Zamani Ali , Trakic Adnan , Brankovic Aida , Bialkowski Alina , Zhu Guohun , Cook David , Crozier Stuart 
Published: March 2024

AbstractStroke is a leading cause of death and disability worldwide, and early diagnosis and prompt medical intervention are thus crucial. Frequent monitoring of stroke patients is also essential to assess treatment efficacy and detect complications earlier. While computed tomography (CT) and magnetic resonance imaging (MRI) are commonly used for stroke diagnosis, they cannot be easily used onsite, nor for frequent monitoring purposes. To meet those requirements, an electromagnetic imaging (EMI) device, which is portable, non-invasive, and non-ionizing, has been developed. It uses a headset with an antenna array that irradiates the head with a safe low-frequency EM field and captures scattered fields to map the brain using a complementary set of physics-based and data-driven algorithms, enabling quasi-real-time detection, two-dimensional localization, and classification of strokes. This study reports clinical findings from the first time the device was used on stroke patients. The clinical results on 50 patients indicate achieving an overall accuracy of 98% in classification and 80% in two-dimensional quadrant localization. With its lightweight design and potential for use by a single para-medical staff at the point of care, the device can be used in intensive care units, emergency departments, and by paramedics for onsite diagnosis.
A hybrid modeling framework for generalizable and interpretable predictions of ICU mortality across multiple hospitals
Authors: Samadi Moein E. , Guzman-Maldonado Jorge , Nikulina Kateryna , Mirzaieazar Hedieh , Sharafutdinov Konstantin , Fritsch Sebastian Johannes , Schuppert Andreas 
Published: March 2024

AbstractThe development of reliable mortality risk stratification models is an active research area in computational healthcare. Mortality risk stratification provides a standard to assist physicians in evaluating a patient’s condition or prognosis objectively. Particular interest lies in methods that are transparent to clinical interpretation and that retain predictive power once validated across diverse datasets they were not trained on. This study addresses the challenge of consolidating numerous ICD codes for predictive modeling of ICU mortality, employing a hybrid modeling approach that integrates mechanistic, clinical knowledge with mathematical and machine learning models . A tree-structured network connecting independent modules that carry clinical meaning is implemented for interpretability. Our training strategy utilizes graph-theoretic methods for data analysis, aiming to identify the functions of individual black-box modules within the tree-structured network by harnessing solutions from specific max-cut problems. The trained model is then validated on external datasets from different hospitals, demonstrating successful generalization capabilities, particularly in binary-feature datasets where label assessment involves extrapolation.
Semantic segmentation of thermal defects in belt conveyor idlers using thermal image augmentation and U-Net-based convolutional neural networks
Authors: Siami Mohammad , Barszcz Tomasz , Wodecki Jacek , Zimroz Radoslaw 
Published: March 2024

AbstractThe belt conveyor (BC) is the main means of horizontal transportation of bulk materials at mining sites. The sudden fault in BC modules may cause unexpected stops in production lines. With the increasing number of applications of inspection mobile robots in condition monitoring (CM) of industrial infrastructure in hazardous environments, in this article we introduce an image processing pipeline for automatic segmentation of thermal defects in thermal images captured from BC idlers using a mobile robot. This study follows the fact that CM of idler temperature is an important task for preventing sudden breakdowns in BC system networks. We compared the performance of three different types of U-Net-based convolutional neural network architectures for the identification of thermal anomalies using a small number of hand-labeled thermal images. Experiments on the test data set showed that the attention residual U-Net with binary cross entropy as the loss function handled the semantic segmentation problem better than our previous research and other studied U-Net variations.