bluuebunny/crossref_metadata_2025_split huggingface dataset:
Loess deposits in the low latitudes of East Asia reveal the ~20-kyr precipitation cycle
Authors: Li Xusheng , Zhou Yuwen , Han Zhiyong , Yuan Xiaokang , Yi Shuangwen , Zeng Yuqiang , Qin Lisha , Lu Ming , Lu Huayu 
Published: February 2024

AbstractThe cycle of precipitation change is key to understanding the driving mechanism of the East Asian summer monsoon (EASM). However, the dominant cycles of EASM precipitation revealed by different proxy indicators are inconsistent, leading to the “Chinese 100 kyr problem”. In this study, we examine a high-resolution, approximately 350,000-year record from a low-latitude loess profile in China. Our analyses show that variations in the ratio of dithionite−citrate−bicarbonate extractable iron to total iron are dominated by the ~20-kyr cycle, reflecting changes in precipitation. In contrast, magnetic susceptibility varies with the ~100-kyr cycle and may be mainly controlled by temperature-induced redox processes or precipitation-induced signal smoothing. Our results suggest that changes in the EASM, as indicated by precipitation in this region, are mainly forced by precession-dominated insolation variations, and that precipitation and temperature may have varied with different cycles over the past ~350,000 years.
RETRACTED ARTICLE: Systems engineering of Escherichia coli for high-level glutarate production from glucose
Authors: Zhang Zhilan , Chu Ruyin , Wei Wanqing , Song Wei , Ye Chao , Chen Xiulai , Wu Jing , Liu Liming , Gao Cong 
Published: February 2024

AbstractGlutarate is a key monomer in polyester and polyamide production. The low efficiency of the current biosynthetic pathways hampers its production by microbial cell factories. Herein, through metabolic simulation, a lysine-overproducing E. coli strain Lys5 is engineered, achieving titer, yield, and productivity of 195.9 g/L, 0.67 g/g glucose, and 5.4 g/L·h, respectively. Subsequently, the pathway involving aromatic aldehyde synthase, monoamine oxidase, and aldehyde dehydrogenase (AMA pathway) is introduced into E. coli Lys5 to produce glutarate from glucose. To enhance the pathway’s efficiency, rational mutagenesis on the aldehyde dehydrogenase is performed, resulting in the development of variant Mu5 with a 50-fold increase in catalytic efficiency. Finally, a glutarate tolerance gene cbpA is identified and genomically overexpressed to enhance glutarate productivity. With enzyme expression optimization, the glutarate titer, yield, and productivity of E. coli AMA06 reach 88.4 g/L, 0.42 g/g glucose, and 1.8 g/L·h, respectively. These findings hold implications for improving glutarate biosynthesis efficiency in microbial cell factories.
Oral and middle ear delivery of otitis media standard of care antibiotics, but not biofilm-targeted antibodies, alter chinchilla nasopharyngeal and fecal microbiomes
Authors: Duff Audrey F. , Jurcisek Joseph A. , Kurbatfinski Nikola , Chiang Tendy , Goodman Steven D. , Bakaletz Lauren O. , Bailey Michael T. 
Published: February 2024

AbstractOtitis media (OM) is one of the most globally pervasive pediatric conditions. Translocation of nasopharynx-resident opportunistic pathogens like nontypeableHaemophilus influenzae(NTHi) assimilates into polymicrobial middle ear biofilms, which promote OM pathogenesis and substantially diminish antibiotic efficacy. Oral or tympanostomy tube (TT)-delivered antibiotics remain the standard of care (SOC) despite consequences including secondary infection, dysbiosis, and antimicrobial resistance. Monoclonal antibodies (mAb) against two biofilm-associated structural proteins, NTHi-specific type IV pilus PilA (anti-rsPilA) and protective tip-region epitopes of NTHi integration host factor (anti-tip-chimer), were previously shown to disrupt biofilms and restore antibiotic sensitivity in vitro. However, the additional criterion for clinical relevance includes the absence of consequential microbiome alterations. Here, nine chinchilla cohorts (n = 3/cohort) without disease were established to evaluate whether TT delivery of mAbs disrupted nasopharyngeal or fecal microbiomes relative to SOC-OM antibiotics. Cohort treatments included a 7d regimen of oral amoxicillin-clavulanate (AC) or 2d regimen of TT-delivered mAb, AC, Trimethoprim-sulfamethoxazole (TS), ofloxacin, or saline. Fecal and nasopharyngeal lavage (NPL) samples were collected before and several days post treatment (DPT) for 16S sequencing. While antibiotic-treated cohorts displayed beta-diversity shifts (PERMANOVA,P < 0.05) and reductions in alpha diversity (q < 0.20) relative to baseline, mAb antibodies failed to affect diversity, indicating maintenance of a eubiotic state. Taxonomic and longitudinal analyses showed blooms in opportunistic pathogens (ANCOM) and greater magnitudes of compositional change (P < 0.05) following broad-spectrum antibiotic but not mAb treatments. Collectively, results showed broad-spectrum antibiotics induced significant fecal and nasopharyngeal microbiome disruption regardless of delivery route. Excitingly, biofilm-targeting antibodies had little effect on fecal and nasopharyngeal microbiomes.
A dynamic Bayesian optimized active recommender system for curiosity-driven partially Human-in-the-loop automated experiments
Authors: Biswas Arpan , Liu Yongtao , Creange Nicole , Liu Yu-Chen , Jesse Stephen , Yang Jan-Chi , Kalinin Sergei V. , Ziatdinov Maxim A. , Vasudevan Rama K. 
Published: February 2024

AbstractOptimization of experimental materials synthesis and characterization through active learning methods has been growing over the last decade, with examples ranging from measurements of diffraction on combinatorial alloys at synchrotrons, to searches through chemical space with automated synthesis robots for perovskites. In virtually all cases, the target property of interest for optimization is defined a priori with the ability to shift the trajectory of the optimization based on human-identified findings during the experiment is lacking. Thus, to highlight the best of both human operators and AI-driven experiments, here we present the development of a human–AI collaborated experimental workflow, via a Bayesian optimized active recommender system (BOARS), to shape targets on the fly with human real-time feedback. Here, the human guidance overpowers AI at early iteration when prior knowledge (uncertainty) is minimal (higher), while the AI overpowers the human during later iterations to accelerate the process with the human-assessed goal. We showcase examples of this framework applied to pre-acquired piezoresponse force spectroscopy of a ferroelectric thin film, and in real-time on an atomic force microscope, with human assessment to find symmetric hysteresis loops. It is found that such features appear more affected by subsurface defects than the local domain structure. This work shows the utility of human–AI approaches for curiosity driven exploration of systems across experimental domains.
Systematic review of the use of ultrasound for venous assessment and venous thrombosis screening in spaceflight
Authors: Elias Antoine , Weber Tobias , Green David A. , Harris Katie M. , Laws Jonathan M. , Greaves Danielle K. , Kim David S. , Mazzolai-Duchosal Lucia , Roberts Lara , Petersen Lonnie G. , Limper Ulrich , Bergauer Andrej , Elias Michael , Winnard Andrew , Goswami Nandu 
Published: February 2024

AbstractThe validity of venous ultrasound (V-US) for the diagnosis of deep vein thrombosis (DVT) during spaceflight is unknown and difficult to establish in diagnostic accuracy and diagnostic management studies in this context. We performed a systematic review of the use of V-US in the upper-body venous system in spaceflight to identify microgravity-related changes and the effect of venous interventions to reverse them, and to assess appropriateness of spaceflight V-US with terrestrial standards. An appropriateness tool was developed following expert panel discussions and review of terrestrial diagnostic studies, including criteria relevant to crew experience, in-flight equipment, assessment sites, ultrasound modalities, and DVT diagnosis. Microgravity-related findings reported as an increase in internal jugular vein (IJV) cross-sectional area and pressure were associated with reduced, stagnant, and retrograde flow. Changes were on average responsive to venous interventions using lower body negative pressure, Bracelets, Valsalva and Mueller manoeuvres, and contralateral IJV compression. In comparison with terrestrial standards, spaceflight V-US did not meet all appropriateness criteria. In DVT studies (n = 3), a single thrombosis was reported and only ultrasound modality criterion met the standards. In the other studies (n = 15), all the criteria were appropriate except crew experience criterion, which was appropriate in only four studies. Future practice and research should account for microgravity-related changes, evaluate individual effect of venous interventions, and adopt Earth-based V-US standards.
Ultrafast readout, crosstalk suppression iontronic array enabled by frequency-coding architecture
Authors: Li Zhibin , Yang Jing , Zhang Yuxuan , Geng Peiyan , Feng Jiansong , Chen Bin , Zhang Xu , Yuan Guojiang , Chen Xiaolong , Wang Taihong 
Published: February 2024

AbstractThe development of iontronic skin (I-skin) capable of ultrafast sensing in a wide pressure range, comparable to human skin, is of paramount importance for intelligent robotics. However, this remains a major challenge due to the lack of iontronic array architectures that can achieve ultrafast readout and crosstalk-free under large capacitance response generated within a wide pressure range. Here, we report a frequency-coding architecture of artificial ion mechanoreceptor skin (AIM-skin) that can provide a universal mode of iontronic array sensing and bypass the dependence of complex integrated back-end interface electronics. Notably, the successful implementation of orthogonal frequency coding in the AIM-skin with high sensitivity and ultrawide pressure range achieve ultrafast parallel readout for the spatiotemporal mechanical stimuli. Furthermore, the parallel zero-potential mechanism (PZPM) of the architecture effectively mitigates electrical crosstalk between sensing units. We have demonstrated that combhination of proposed device and deep learning has a broad application prospect in intelligent human-machine interaction and real-time dynamic robotic manipulation.
Impact of the aqueous corrosion induced alteration layer on mechanical properties of pharmaceutical glasses
Authors: Zheng Qiuju , Ma Xinlin , Ma Youze , Yu Jiaxin , Yue Yuanzheng , He Hongtu 
Published: February 2024

AbstractIt is known that network modifying ions (such as alkali or alkaline earth ions) make glasses susceptible to aqueous corrosion, resulting in the alteration of their surface layers. However, the effect of the altered layers on the mechanical properties of glasses has not been well understood. In this work we study this effect using the pharmaceutical boroaluminosilicate (BAS) glasses as objects by performing nano- and macroscale mechanical tests. The results show that extending the corrosion time increases the thickness of the alteration layer of the BAS glass. The water-related species in the alteration layer lowers the nanohardness, the reduced modulus, the nanowear resistance and Vickers hardness. The corrosion-induced “silica-like” structure in alteration layer benefits the densification of the subsurface caused by nanoindentation and nanowear, and thereby enhances the fracture toughness of the BAS glass. The correlation between the water content in the alteration layer and the mechanical properties has been revealed. This work is instrumental in the design of the next generation of pharmaceutical glasses with higher toughness.
High temperature spin selectivity in a quantum dot qubit using reservoir spin accumulation
Authors: Jansen R. , Yuasa S. 
Published: February 2024

AbstractEmploying spins in quantum dots for fault-tolerant quantum computing in large-scale qubit arrays with on-chip control electronics requires high-fidelity qubit operation at elevated temperature. This poses a challenge for single spin initialization and readout. Existing schemes rely on Zeeman splitting or Pauli spin blockade with typical energy scales of 0.1 or 1 meV for electron-based qubits, so that sufficient fidelity is obtained only at temperatures around or below 0.1 or 1 K, respectively. Here we describe a method to achieve high temperature spin selectivity in a quantum dot using a reservoir with a spin accumulation, which deterministically sets the spin of a single electron on the dot. Since spin accumulation as large as 10 meV is achievable in silicon, spin selection with electrically adjustable error rates below 10−4 is possible even in a liquid He bath at 4 K. Via the reservoir spin accumulation, induced and controlled by a nearby ferromagnet, classical information (magnetization direction) is mapped onto a spin qubit. These features provide the prospect of spin qubit operation at elevated temperatures and connect the worlds of quantum computing and spintronics.
The gut virome is associated with stress-induced changes in behaviour and immune responses in mice
Authors: Ritz Nathaniel L. , Draper Lorraine A. , Bastiaanssen Thomaz F. S. , Turkington Christopher J. R. , Peterson Veronica L. , van de Wouw Marcel , Vlckova Klara , Fülling Christine , Guzzetta Katherine E. , Burokas Aurelijus , Harris Hugh , Dalmasso Marion , Crispie Fiona , Cotter Paul D. , Shkoporov Andrey N. , Moloney Gerard M. , Dinan Timothy G. , Hill Colin , Cryan John F. 
Published: February 2024

AbstractThe microbiota–gut–brain axis has been shown to play an important role in the stress response, but previous work has focused primarily on the role of the bacteriome. The gut virome constitutes a major portion of the microbiome, with bacteriophages having the potential to remodel bacteriome structure and activity. Here we use a mouse model of chronic social stress, and employ 16S rRNA and whole metagenomic sequencing on faecal pellets to determine how the virome is modulated by and contributes to the effects of stress. We found that chronic stress led to behavioural, immune and bacteriome alterations in mice that were associated with changes in the bacteriophage classCaudoviricetesand unassigned viral taxa. To determine whether these changes were causally related to stress-associated behavioural or physiological outcomes, we conducted a faecal virome transplant from mice before stress and autochthonously transferred it to mice undergoing chronic social stress. The transfer of the faecal virome protected against stress-associated behaviour sequelae and restored stress-induced changes in select circulating immune cell populations, cytokine release, bacteriome alterations and gene expression in the amygdala. These data provide evidence that the virome plays a role in the modulation of the microbiota–gut–brain axis during stress, indicating that these viral populations should be considered when designing future microbiome-directed therapies.
A framework for automated scalable designation of viral pathogen lineages from genomic data
Authors: McBroome Jakob , de Bernardi Schneider Adriano , Roemer Cornelius , Wolfinger Michael T. , Hinrichs Angie S. , O’Toole Aine Niamh , Ruis Christopher , Turakhia Yatish , Rambaut Andrew , Corbett-Detig Russell 
Published: February 2024

AbstractPathogen lineage nomenclature systems are a key component of effective communication and collaboration for researchers and public health workers. Since February 2021, the Pango dynamic lineage nomenclature for SARS-CoV-2 has been sustained by crowdsourced lineage proposals as new isolates were sequenced. This approach is vulnerable to time-critical delays as well as regional and personal bias. Here we developed a simple heuristic approach for dividing phylogenetic trees into lineages, including the prioritization of key mutations or genes. Our implementation is efficient on extremely large phylogenetic trees consisting of millions of sequences and produces similar results to existing manually curated lineage designations when applied to SARS-CoV-2 and other viruses including chikungunya virus, Venezuelan equine encephalitis virus complex and Zika virus. This method offers a simple, automated and consistent approach to pathogen nomenclature that can assist researchers in developing and maintaining phylogeny-based classifications in the face of ever-increasing genomic datasets.
A probabilistic computation framework to estimate the dawn phenomenon in type 2 diabetes using continuous glucose monitoring
Authors: Barua Souptik , Glantz Namino , Larez Arianna , Bevier Wendy , Sabharwal Ashutosh , Kerr David 
Published: February 2024

AbstractIn type 2 diabetes (T2D), the dawn phenomenon is an overnight glucose rise recognized to contribute to overall glycemia and is a potential target for therapeutic intervention. Existing CGM-based approaches do not account for sensor error, which can mask the true extent of the dawn phenomenon. To address this challenge, we developed a probabilistic framework that incorporates sensor error to assign a probability to the occurrence of dawn phenomenon. In contrast, the current approaches label glucose fluctuations as dawn phenomena as a binary yes/no. We compared the proposed probabilistic model with a standard binary model on CGM data from 173 participants (71% female, 87% Hispanic/Latino, 54 ± 12 years, with either a diagnosis of T2D for six months or with an elevated risk of T2D) stratified by HbA1c levels into normal but at risk for T2D, with pre-T2D, or with non-insulin-treated T2D. The probabilistic model revealed a higher dawn phenomenon frequency in T2D [49% (95% CI 37–63%)] compared to pre-T2D [36% (95% CI 31–48%), p = 0.01] and at-risk participants [34% (95% CI 27–39%), p < 0.0001]. While these trends were also found using the binary approach, the probabilistic model identified significantly greater dawn phenomenon frequency than the traditional binary model across all three HbA1c sub-groups (p < 0.0001), indicating its potential to detect the dawn phenomenon earlier across diabetes risk categories.
Seasonal variation of net ecosystem carbon exchange and gross primary production over a Loess Plateau semi-arid grassland of northwest China
Authors: Zhang Xueteng , Bi Jianrong , Zhu Di , Meng Zhaozhao 
Published: February 2024

AbstractGrassland ecosystems store approximately one-third of the global terrestrial carbon stocks, which play a crucial role in regulating the carbon cycle on regional and global scales, but the current scientific understanding of the variation in net carbon dioxide exchange (NEE) on grassland ecosystems is still limited. Based on the eddy covariance technique, this study investigated the seasonal variation of ecosystem respiration (Reco) and gross primary production (GPP) from 2018 to 2020 in a semi-arid grassland on the Loess Plateau in northwest China. The results indicated that the annual cumulative average NEE value was − 0.778 kg C/m2, the growing season cumulative value accounted for approximately 83.81%, which suggested that the semiarid grassland showed a notable soil carbon sink. The correlation analysis revealed that soil temperature (Ts) (RReco = 0.71, RGPP = 0.61) and soil water content (SWC) (RReco = 0.47, RGPP = 0.44) were the two main driving factors in modulating the variation of daily average GPP and Reco (P < 0.01). Therefore, the monthly average of GPP and Reco increased with the increase in Ts (RGPP = 0.716, P < 0.01; RReco = 0.586, P < 0.05), resulting in an increase in the carbon sequestration capacity of the grass ecosystem. This study also showed that soil moisture has a promoting effect on the response of Reco and GPP to Ts, and the correlation among GPP, Reco, and Ts was much stronger under wet conditions. For instance, the coefficient of determination of Reco and GPP with Ts under wet conditions in 2018 increased by 0.248 and 0.286, respectively, compared to those under droughty conditions. Additionally, the temperature sensitivity of Reco (Q10) increased by 46.13% compared to dry conditions. In addition, carbon exchange models should consider the synergistic effect of Ts and SWC as one of the main driving factors for theoretical interpretation or modeling. Under the potential scenario of future global warming and the frequent extreme weather events, our findings have important implications for predicting future CO2 exchange and establishing an optimal ecological model of carbon flux exchange.
Predicting soil cone index and assessing suitability for wind and solar farm development in using machine learning techniques
Authors: Hassan Marwa , Beshr Eman 
Published: February 2024

AbstractThis study proposes a novel approach that combines machine learning models to predict soil compaction using the soil cone index values. The methodology incorporates support vector regression (SVR) to gather input data on key soil parameters, and the output data from SVR are used as inputs for additional machine learning techniques such as Gradient Boosting, Decision Tree, Artificial Neural Networks, and Adaptive Neuro-Fuzzy Inference System. Evaluation of Artificial Intelligent techniques shows that the XGBoost model outperforms others, exhibiting high accuracy and reliability with low mean square error and high correlation coefficient. The effectiveness of the XGBoost model has implications for soil management, agricultural productivity, and land suitability evaluations, particularly for renewable energy projects. By integrating advanced AI techniques, stakeholders can make informed decisions about land use planning, sustainable farming practices, and the feasibility of renewable energy installations. Overall, this research contributes to soil science by demonstrating the potential of AI techniques, specifically the XGBoost model, in accurately predicting soil compaction and supporting optimal soil management practices.
Intelligent robotics harvesting system process for fruits grasping prediction
Authors: Alaaudeen K. M. , Selvarajan Shitharth , Manoharan Hariprasath , Jhaveri Rutvij H. 
Published: February 2024

AbstractThis paper proposes and executes an in-depth learning-based image processing approach for self-picking apples. The system includes a lightweight one-step detection network for fruit recognition. As well as computer vision to analyze the point class and anticipate a correct approach position for each fruit before grabbing. Using the raw inputs from a high-resolution camera, fruit recognition and instance segmentation are done on RGB photos. The computer vision classification and grasping systems are integrated and outcomes from tree-grown foods are provided as input information and output methodology poses for every apple and orange to robotic arm execution. Before RGB picture data is acquired from laboratory and plantation environments, the developed vision method will be evaluated. Robot harvest experiment is conducted in indoor as well as outdoor to evaluate the proposed harvesting system's performance. The research findings suggest that the proposed vision technique can control robotic harvesting effectively and precisely where the success rate of identification is increased above 95% in case of post prediction process with reattempts of less than 12%.