bluuebunny/crossref_metadata_2025_split huggingface dataset:
Molecular insights into substrate translocation in an elevator-type metal transporter
Authors: Zhang Yao , Jafari Majid , Zhang Tuo , Sui Dexin , Sagresti Luca , Merz Kenneth M. , Hu Jian 
Published: September 2024

SummaryThe Zrt/Irt-like protein (ZIP) metal transporters are key players in maintaining the homeostasis of a panel of essential microelements. The prototypical ZIP fromBordetella bronchiseptica(BbZIP) is an elevator transporter, but how the metal substrate moves along the transport pathway and how the transporter changes conformation to allow alternating access remain to be elucidated. Here, we combined structural, biochemical, and computational approaches to investigate the process of metal substrate translocation along with the global structural rearrangement. Our study revealed an upward hinge motion of the transport domain in a high-resolution crystal structure of a cross-linked variant, elucidated the mechanisms of metal release from the transport site into the cytoplasm and activity regulation by a cytoplasmic metal-binding loop, and unraveled an unusual elevator mode in enhanced sampling simulations that distinguishes BbZIP from other elevator transporters. This work provides important insights into the metal transport mechanism of the ZIP family.
scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery
Authors: Jogani Saiyam , Pol Anand Santosh , Prajapati Mayur , Samal Amit , Bhatia Kriti , Parmar Jayendra , Patel Urvik , Shah Falak , Vyas Nisarg , Gupta Saurabh 
Published: September 2024

AbstractPurposeSingle-cell RNA sequencing (scRNA-seq) is producing vast amounts of individual cell profiling data. Analysis of such datasets presents a significant challenge in accurately annotating cell types and their associated biomarkers. scRNA-seq datasets analysis will help us understand diseases such as Alzheimer’s, Cancer, Diabetes, Coronavirus disease 2019 (COVID-19), Systemic Lupus Ery-thematosus (SLE), etc. Recently different pipelines based on machine learning (ML) and Deep Neural Network (DNN) methods have been employed to tackle these issues utilizing scRNA-seq datasets. These pipelines have arisen as a promising resource and are capable of extracting meaningful and concise features from noisy, diverse, and high-dimensional data to enhance annotations and subsequent analysis. Existing tools require high computational resources to execute large sample datasets.MethodsWe have developed a cutting-edge platform known as scaLR (Single Cell Analysis using Low Resource) that efficiently processes data in batches, and reduces the required resources for processing large datasets and running NN models. scaLR is equipped with data processing, feature extraction, training, evaluation, and downstream analysis. The data processing module consists of sample-wise & standard scaler normalization and splitting of data. Its novel feature extraction algorithm, first trains the model on a feature subset and stores feature importance for all the features in that subset. At the end of this process, top K features are selected based on their importance. The model is trained on top K features, its performance evaluation and associated downstream analysis provide significant biomarkers for different cell types and diseases/traits.ResultsTo showcase the capabilities of scaLR, we utilized several scRNA-seq datasets of Peripheral Blood Mononuclear Cells (PBMCs), Alzheimer’s patients, and large datasets from human and mouse embryonic development. Our findings indicate that scaLR offers comparable prediction accuracy and requires less model training time and compute resources than existing Python-based pipelines and frameworks. Moreover, scaLR efficiently handles large sample datasets (>11.4 million cells) with minimal resource usage (29GB RAM, 12GB GPU, and 8 CPUs) while maintaining high prediction accuracy and being capable of ranking the biomarker association with specific cell types and diseases.ConclusionWe present scaLR a Python-basedplatform, engineered to utilize minimal computational resources while maintaining comparable execution times to existing frameworks. It is highly scalable and capable of efficiently handling datasets containing millions of cell samples and providing their classification and important biomarkers.
Glucomannan engineering highlights the role of galactosyl modification in fine-tuning cellulose-glucomannan interaction in Arabidopsis cell walls
Authors: Yoshimi Yoshihisa , Yu Li , Cresswell Rosalie , Guo Xinyu , Echevarría-Poza Alberto , Lyczakowski Jan J. , Dupree Ray , Kotake Toshihisa , Dupree Paul 
Published: September 2024

AbstractWidely found in most plant lineages, β-mannans are structurally diverse polysaccharides that can bind to cellulose fibrils to form the complex polysaccharide architecture of the cell wall. Glucomannan backbones acquire variable patterns of galactosyl substitutions, depending on plant developmental stage and species. How the changes in polysaccharide structure influence its cell wall solubility or promote appropriate interaction with cellulose fibrils is poorly understood. Here, we show that fine-tuning of galactosyl modification on glucomannans is achieved by the differing acceptor recognition of mannan α-galactosyltransferases (MAGTs). Biochemical analysis and13C solid-state nuclear magnetic resonance spectroscopy of Arabidopsis with cell wall glucomannan engineered by MAGTs revealed that the degree of galactosylation strongly affects the interaction with cellulose. The findings indicate that plants tailor galactosyl modification on glucomannans for constructing an appropriate cell wall architecture, paving the way to convert properties of lignocellulosic biomass for better use.
Impact ofO-GlcNAcylation elevation on mitophagy and glia in the dentate gyrus
Authors: Kramer Joshua , Chatham John C , Young Martin E , Darley-Usmar Victor , Zhang Jianhua 
Published: September 2024

AbstractO-GlcNAcylation is a dynamic and reversible protein post-translational modification of serine or threonine residues which modulates the activity of transcriptional and signaling pathways and controls cellular responses to metabolic and inflammatory stressors. We and others have shown thatO-GlcNAcylation has the potential to regulate autophagy and mitophagy to play a critical role in mitochondrial quality control, but this has not been assessedin vivoin the brain. This is important since mitochondrial dysfunction contributes to the development of neurodegenerative disease. We used mito-QC reporter mice to assess mitophagy in diverse cells in the dentate gyrus in response to pharmacological inhibition of OGA with thiamet G which leads to elevation of proteinO-GlcNAcylation. We demonstrate that mitophagy occurs predominantly in the GFAP positive astrocytes and is significantly decreased in response to elevatedO-GlcNAcylation. Furthermore, with increasedO-GlcNAcylation, the levels of astrocyte makers GFAP and S100B, and microglial cell marker IBA1 were decreased in the dentate gyrus, while the levels of microglial cell marker TMEM119 were increased, indicating significant changes in glia homeostasis. These results provide strong evidence of the regulation of mitophagy and glia signatures by theO-GlcNAc pathway.
Local optogenetic NMYII activation within the zebrafish neural rod results in long-range, asymmetric force propagation
Authors: Crellin Helena A , Zhu Chengxi , Serrano-Nájera Guillermo , Race Amelia , O’Holleran Kevin , Lenz Martin O , Buckley Clare E 
Published: September 2024

AbstractHow do cellular forces propagate through tissue to allow large-scale morphogenetic events? To investigate this question, we used anin vivooptogenetic approach to reversibly manipulate actomyosin contractility at depth within the developing zebrafish neural rod. Contractility was induced along the lateral cortices of a small patch of developing neural epithelial progenitor cells, resulting in a shortening of these cells along their mediolateral axis. Imaging the immediate response of surrounding tissue uncovered a long-range, tangential, and elastic tissue deformation along the anterior-posterior axis. Unexpectedly, this was highly asymmetric, propagating in either the anterior or the posterior direction in response to local gradients in optogenetic activation. The degree of epithelialisation did not have a significant impact on the extent of force propagation via lateral cortices. We also uncovered a dynamic oscillatory expansion and contraction of the tissue along the anterior-posterior axis, with wavelength within the range of rhombomere length. Together, this study shows dynamic and wave-like propagation of force along the anterior-posterior axis. It also suggests that cell generated forces are actively propagated over long distances within the tissue, and that local anisotropies in tissue organisation and contractility are sufficient to drive directional force propagation.
InTraSeq: A Multimodal Assay that Uncovers New Single-Cell Biology and Regulatory Mechanisms
Authors: Ariss Majd M. , Huang Linglin , Ding Xiaokai , Sheth Shivani , Levy Tyler , Fisher Jeremy , Loebelenz Jean , Arlotta Keith , Dixon Karen , Polakiewicz Roberto , Kuchroo Vijay K. , Beausoleil Sean A. 
Published: September 2024

AbstractSingle-cell RNA sequencing (scRNA-seq) has revolutionized cell biology by enabling the profiling of transcriptomes at a single-cell resolution, leading to important discoveries that have advanced our understanding of cellular and tissue heterogeneity, developmental trajectories, and disease progression. Despite these important advances, scRNA-seq is limited to measuring the transcriptome providing a partial view of cellular function. To address this limitation, multimodal scRNA-seq assays have emerged, allowing for the simultaneous measurement of RNA expression and protein. Intracellular Transcriptomic and Protein Sequencing (InTraSeq), a novel multimodal scRNA-seq technology described here, enables the concurrent measurement of mRNA, surface markers, cytoplasmic proteins, and nuclear proteins within individual cells through oligo-barcoded antibodies. This method offers a comprehensive approach to studying cellular function by combining RNA and protein profiling from the same sample and utilizing a relatively simple protocol. The InTraSeq method enables researchers to expand their view of critical intracellular protein expression including post-translational modifications (PTMs) and transcription factors, allowing for the identification of novel cellular subtypes and states that may be obscured by RNA-based analyses alone. This is particularly valuable in understanding the heterogeneity of cell populations and identifying distinct functional states. In this report, we used InTraSeq to characterize the complex cellular states and regulatory mechanisms during Th17 cell differentiation. We simultaneously profiled RNA and protein expression in over 85,000 cells, capturing transcriptional changes, changes in protein expression and the dynamics of signaling pathways at a high resolution. Our results revealed novel insights into Th17 cell differentiation, including the identification of key regulatory factors and their target genes. By simultaneously measuring mRNA, extra and intra-cellular proteins, signaling proteins, and PTMs, InTraSeq offers a comprehensive understanding of cellular processes and enables the identification of novel regulatory mechanisms.
Elicitor specific mechanisms of defence priming in oak seedlings against powdery mildew
Authors: Sanchez-Lucas Rosa , Bosanquet Jack L. , Henderson James , Catoni Marco , Pastor Victoria , Luna Estrella 
Published: September 2024

AbstractDefence priming sensitises plant defences to enable a faster and/or stronger response to subsequent stress. Various chemicals can trigger priming; however, the response remains unexplored in oak. Here, we characterise salicylic acid (SA)-, jasmonic acid (JA)-, and β-aminobutyric acid (BABA)-induced priming of oak seedlings against the causal agent of powdery mildew (Erysiphe alphitoides,PM). Whilst JA had no effects, BABA and SA enhanced resistance by priming callose deposition and SA-dependent gene expression, respectively. Untargeted transcriptome and metabolome analyses revealed genes and metabolites uniquely primed by BABA, SA, and JA. Enrichment analyses demonstrated a limited number of pathways differentiating the three treatments or the resistance-inducing elicitors BABA and SA. However, a similar mode of action between BABA and JA was identified. Moreover, our analyses revealed a lack of crosstalk between SA and JA. Interestingly, priming by BABA was linked to alkaloid, lignan, phenylpropanoid, and indolitic compounds biosynthesis. Moreover, integration of the omics analyses revealed the role of ubiquitination and protein degradation in priming by BABA. Our results confirm the existence of chemical-induced priming in oak and has identified specific molecular markers associated with well-characterised elicitor.
Investigation of plant virus-like particle formation in bacterial and yeast expression systems
Authors: Balke Ina , Resevica Gunta , Zeltina Vilija , Silamikelis Ivars , Liepa Elva , Liepa Reinis , Kalnciema Ieva , Radovica-Spalvina Ilze , Gudra Dita , Pjalkovskis Janis , Freivalds Janis , Kazaks Andris , Zeltins Andris 
Published: September 2024

AbstractVirus-like particles (VLPs) have garnered significant attention due to their potential applications in various fields, particularly in biomedicine, where they are used for drug delivery, vaccine development, and as diagnostic tools. Plant virus-derived VLPs have been especially successful, leading to a surge in research aimed at developing new VLPs. Despite this growing interest, not all attempts at VLP formation are successful. Understanding the factors that contribute to successful VLP assembly is crucial for advancing this field. In this study, we focus on the coat proteins (CPs) of three plant viruses belonging to the Sobemovirus genus as a model to investigate the process of VLP formation. Our findings demonstrated that the strong binding of CPs to ssDNA can be a major reason for unsuccessful VLP production. This issue can potentially be overcome by transitioning from an episomal to a chromosome-integrated expression system. By doing so, we aim to elucidate the mechanisms that facilitate successful VLP assembly. Our findings provide valuable insights into the properties of sobemovirus CPs and highlight their potential for encapsulating foreign nucleic acids, thereby providing additional applications as nanocontainers or vaccine platforms with interior modification capabilities for enhanced immune response. Moreover, our RNA-Seq data indicates that sobemovirus-derived VLPs predominantly package their CP mRNA, irrespective of the expression system used. This characteristic can be utilized for the functional encapsulation of nucleic acids, enhancing the versatility and utility of these VLPs in various biotechnological applications.
Gauged Gaussian projected entangled pair states: A high dimensional tensor network formulation for lattice gauge theories
Authors: Kelman Ariel , Borla Umberto , Gomelski Itay , Elyovich Jonathan , Roose Gertian , Emonts Patrick , Zohar Erez 
Published: September 2024

Gauge theories form the basis of our understanding of modern physics—ranging from the description of quarks and gluons to effective models in condensed matter physics. In the nonperturbative regime, gauge theories are conventionally treated discretely as lattice gauge theories. The resulting systems are evaluated with path-integral based Monte Carlo methods. These methods, however, can suffer from the sign problem and do not allow for a direct evaluation of real-time dynamics. In this work, we present a unified and comprehensive framework for gauged Gaussian projected entangled pair states (PEPS), a variational ansatz based on tensor networks. We review the construction of Hamiltonian lattice gauge theories, explain their similarities to PEPS, and detail the construction of the ansatz state. The estimation of ground states is based on a variational Monte Carlo procedure with the PEPS as an ansatz state. This sign-problem-free ansatz can be efficiently evaluated in any dimension with arbitrary gauge groups, and can include dynamical fermionic matter, suggesting new options for the simulation of nonperturbative regimes of gauge theories, including quantum chromodynamics.

Published by the American Physical Society
2024
A new combined finite-discrete element method for stability analysis of soil-rock mixture slopes
Authors: Deng Penghai , Liu Quansheng , Lu Haifeng 
Published: September 2024

PurposeThe purpose of this paper is to propose a new combined finite-discrete element method (FDEM) to analyze the mechanical properties, failure behavior and slope stability of soil rock mixtures (SRM), in which the rocks within the SRM model have shape randomness, size randomness and spatial distribution randomness.Design/methodology/approachBased on the modeling method of heterogeneous rocks, the SRM numerical model can be built and by adjusting the boundary between soil and rock, an SRM numerical model with any rock content can be obtained. The reliability and robustness of the new modeling method can be verified by uniaxial compression simulation. In addition, this paper investigates the effects of rock topology, rock content, slope height and slope inclination on the stability of SRM slopes.FindingsInvestigations of the influences of rock content, slope height and slope inclination of SRM slopes showed that the slope height had little effect on the failure mode. The influences of rock content and slope inclination on the slope failure mode were significant. With increasing rock content and slope dip angle, SRM slopes gradually transitioned from a single shear failure mode to a multi-shear fracture failure mode, and shear fractures showed irregular and bifurcated characteristics in which the cut-off values of rock content and slope inclination were 20% and 80°, respectively.Originality/valueThis paper proposed a new modeling method for SRMs based on FDEM, with rocks having random shapes, sizes and spatial distributions.
Casson-Williamson ternary hybrid nanofluid flow over a yawed cylinder with the impacts of multiple slips
Authors: Patil Prabhugouda Mallanagouda , Goudar Bharath , Momoniat Ebrahim 
Published: September 2024

Purpose
Many industries use non-Newtonian ternary hybrid nanofluids (THNF) because of how well they control rheological and heat transport. This being the case, this paper aims to numerically study the Casson-Williamson THNF flow over a yawed cylinder, considering the effects of several slips and an inclined magnetic field. The THNF comprises Al2O3-TiO2-SiO2 nanoparticles because they improve heat transmission due to large thermal conductivity.

Design/methodology/approach
Applying suitable nonsimilarity variables transforms the coupled highly dimensional nonlinear partial differential equations (PDEs) into a system of nondimensional PDEs. To accomplish the goal of achieving the solution, an implicit finite difference approach is used in conjunction with Quasilinearization. With the assistance of a script written in MATLAB, the numerical results and the graphical representation of those solutions were ascertained.

Findings
As the Casson parameter

β increases, there is an improvement in the velocity profiles in both chord and span orientations, while the gradients

Re1/2Cf, Re1/2C¯f reduce for the same variations of

β. The velocities of Casson THNF are greater than those of Casson-Williamson THNF. Approximately, a 202% and a 32% ascension are remarked in the magnitudes of

Re1/2Cf and

Re1/2C¯f for Casson-Williamson THNF than the Casson THNF only. When velocity slip attribute

S1 jumps to 1 from 0.5, magnitude of both

F(ξ,η) and

Re1/2Cf fell down and it is reflected to be 396% at

ξ=1,

Wi=1 and

β=1. An augmentation in thermal jump results in advanced fluid temperature and lower

Re−1/2Nu. In particular, about 159% of down drift is detected when

S2 taking 1.

Originality/value
There is no existing research on the effects of Casson-Williamson THNF flow over a yawed cylinder with multiple slips and an angled magnetic field, according to the literature.
A modified fuzzy PFMEA model for risk-centric Six Sigma assessment under the paradigm of Quality 4.0
Authors: Kar Anwesa , Rai Rajiv Nandan 
Published: September 2024

Purpose
The purpose of the study is to examine how risk factors contribute to the occurrence of defects in a process. By analyzing these risk factors in relation to process quality, the study aims to help organizations prioritize their resources and efforts toward addressing the most significant risks. These challenges, integrated with the emerging concept of Quality 4.0, necessitate a comprehensive risk assessment technique.

Design/methodology/approach
Fuzzy logic integrated with an analytic network process is used in the process failure mode and effects analysis for conducting risk identification and assessment under uncertainty. Through a mathematical model, the linkage of risk with Six Sigma is established and, finally, a value–risk matrix is developed for illustrating and analysing risk impact on process quality.

Findings
A case study on fused filament fabrication demonstrates the proposed methodology’s applicability. The results show its effectiveness in assessing risk factors’ impact on Six Sigma metrics: defects per million opportunities/sigma level.

Practical implications
By integrating qualitative assessments and leveraging available data, this approach enables a more comprehensive understanding of risks and their utilization for an organization’s quality improvement initiatives.

Originality/value
This approach establishes a risk-centric Six Sigma assessment method in accordance with the requirement of ISO 9001:2015 and in the context of Quality 4.0.
Comparing human and synthetic data in service research: using augmented language models to study service failures and recoveries
Authors: Bickley Steven J. , Chan Ho Fai , Dao Bang , Torgler Benno , Tran Son , Zimbatu Alexandra 
Published: September 2024

Purpose
This study aims to explore Augmented Language Models (ALMs) for synthetic data generation in services marketing and research. It evaluates ALMs' potential in mirroring human responses and behaviors in service scenarios through comparative analysis with five empirical studies.

Design/methodology/approach
The study uses ALM-based agents to conduct a comparative analysis, leveraging SurveyLM (Bickley et al., 2023) to generate synthetic responses to the scenario-based experiment in Söderlund and Oikarinen (2018) and four more recent studies from the Journal of Services Marketing. The main focus was to assess the alignment of ALM responses with original study manipulations and hypotheses.

Findings
Overall, our comparative analysis reveals both strengths and limitations of using synthetic agents to mimic human-based participants in services research. Specifically, the model struggled with scenarios requiring high levels of visual context, such as those involving images or physical settings, as in the Dootson et al. (2023) and Srivastava et al. (2022) studies. Conversely, studies like Tariq et al. (2023) showed better alignment, highlighting the model's effectiveness in more textually driven scenarios.

Originality/value
To the best of the authors’ knowledge, this research is among the first to systematically use ALMs in services marketing, providing new methods and insights for using synthetic data in service research. It underscores the challenges and potential of interpreting ALM versus human responses, marking a significant step in exploring AI capabilities in empirical research.