bluuebunny/crossref_metadata_2025_split huggingface dataset:
Broadly conserved FlgV controls flagellar assembly and Borrelia burgdorferi dissemination in mice
Authors: Zamba-Campero Maxime , Soliman Daniel , Yu Huaxin , Lasseter Amanda G. , Chang Yuen-Yan , Silberman Julia L. , Liu Jun , Aravind L. , Jewett Mollie W. , Storz Gisela , Adams Philip P. 
Published: November 2024

AbstractFlagella propel pathogens through their environments, yet are expensive to synthesize and are immunogenic. Thus, complex hierarchical regulatory networks control flagellar gene expression. Spirochetes are highly motile bacteria, but peculiarly, the archetypal flagellar regulator σ28 is absent in the Lyme spirochete Borrelia burgdorferi. Here, we show that gene bb0268 (flgV) in B. burgdorferi, previously and incorrectly annotated to encode the RNA-binding protein Hfq, is instead a structural flagellar component that modulates flagellar assembly. The flgV gene is broadly conserved in the flagellar superoperon alongside σ28 in many Spirochaetae, Firmicutes and other phyla, with distant homologs in Epsilonproteobacteria. We find that B. burgdorferi FlgV is localized within flagellar basal bodies, and strains lacking flgV produce fewer and shorter flagellar filaments and are defective in cell division and motility. During the enzootic cycle, flgV-deficient B. burgdorferi survive and replicate in Ixodes ticks but are attenuated for infection and dissemination in mice. Our work defines infection timepoints when spirochete motility is most crucial and implicates FlgV as a broadly distributed structural flagellar component that modulates flagellar assembly.
Multimodal analysis of RNA sequencing data powers discovery of complex trait genetics
Authors: Munro Daniel , Ehsan Nava , Esmaeili-Fard Seyed Mehdi , Gusev Alexander , Palmer Abraham A. , Mohammadi Pejman 
Published: November 2024

AbstractRNA sequencing has the potential to reveal many modalities of transcriptional regulation, such as various splicing phenotypes, but studies on gene regulation are often limited to gene expression due to the complexity of extracting and analyzing multiple RNA phenotypes. Here, we present Pantry, a framework to efficiently generate diverse RNA phenotypes from RNA sequencing data and perform downstream integrative analyses with genetic data. Pantry generates phenotypes from six modalities of transcriptional regulation (gene expression, isoform ratios, splice junction usage, alternative TSS/polyA usage, and RNA stability) and integrates them with genetic data via QTL mapping, TWAS, and colocalization testing. We apply Pantry to Geuvadis and GTEx data, finding that 4768 of the genes with no identified eQTL in Geuvadis have QTL in at least one other transcriptional modality, resulting in a 66% increase in genes over eQTL mapping. We further found that the QTL exhibit modality-specific functional properties that are further reinforced by joint analysis of different RNA modalities. We also show that generalizing TWAS to multiple RNA modalities approximately doubles the discovery of unique gene-trait associations, and enhances identification of regulatory mechanisms underlying GWAS signal in 42% of previously associated gene-trait pairs.
Integrated ab initio modelling of atomic ordering and magnetic anisotropy for design of FeNi-based magnets
Authors: Woodgate Christopher D. , Lewis Laura H. , Staunton Julie B. 
Published: November 2024

AbstractWe describe an integrated modelling approach to accelerate the search for novel, single-phase, multicomponent materials with high magnetocrystalline anisotropy (MCA). For a given system we predict the nature of atomic ordering, its dependence on the magnetic state, and then proceed to describe the consequent MCA, magnetisation, and magnetic critical temperature (Curie temperature). Crucially, within our modelling framework, the same ab initio description of a material’s electronic structure determines all aspects. We demonstrate this holistic method by studying the effects of alloying additions in FeNi, examining systems with the general stoichiometries Fe4Ni3X and Fe3Ni4X, for additives including X = Pt, Pd, Al, and Co. The atomic ordering behaviour predicted on adding these elements, fundamental for determining a material’s MCA, is rich and varied. Equiatomic FeNi has been reported to require ferromagnetic order to establish the tetragonal L10 order suited for significant MCA. Our results show that when alloying additions are included in this material, annealing in an applied magnetic field and/or below a material’s Curie temperature may also promote tetragonal order, along with an appreciable effect on the predicted hard magnetic properties.
Mass cytometry reveals cellular correlates of immune response heterogeneity to SARS-CoV-2 vaccination in the elderly
Authors: Mukherjee Ratnadeep , Eggesbø Linn Margrethe , Wolf Asia-Sophia , Kjønstad Ingrid Fadum , Solum Guri , Ravussin Anthony , Bhandari Sabin , Robertson Anna Hayman , Magnus Per , Trogstad Lill , Kristoffersen Anja Bråthen , Nygaard Unni Cecilie , Mjaaland Siri 
Published: November 2024

AbstractHeterogeneity in vaccine response, particularly in vulnerable populations like the elderly, represents a significant public health challenge. We conducted an in-depth examination of immune cell profiles before and after SARS-CoV-2 vaccination utilizing mass cytometry in a cohort of healthy Norwegian seniors (65–80 years). We have demonstrated that higher pre-vaccination frequencies of CD27+IgD- class-switched memory B cells and subsets of CD27-CD24+CD38+ transitional B cells were associated with a robust vaccine response. Post-vaccination, high responders exhibited increased frequencies of IFN-γ+CD4+ T cells with antigen recall and a concurrent decrease in CCR6(+) TH cell subset frequencies compared to low responders. The presence of a γδ T cell subset displaying polyfunctional cytokine responses was also associated with better vaccine response in the elderly. This in-depth profiling sheds light on inherent differences in immune cell frequencies and functions that may offer insights for targeted vaccination strategies in older populations.
Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine
Authors: Muduli Debendra , Kumari Rani , Akhunzada Adnan , Cengiz Korhan , Sharma Santosh Kumar , Kumar Rakesh Ranjan , Sah Dinesh Kumar 
Published: November 2024

AbstractGlaucoma is defined as progressive optic neuropathy that damages the structural appearance of the optic nerve head and is characterized by permanent blindness. For mass fundus image-based glaucoma classification, an improved automated computer-aided diagnosis (CAD) model performing binary classification (glaucoma or healthy), allowing ophthalmologists to detect glaucoma disease correctly in less computational time. We proposed learning technique called fast discrete curvelet transform with wrapping (FDCT-WRP) to create feature set. This method is entitled extracting curve-like features and creating a feature set. The combined feature reduction techniques named as principal component analysis and linear discriminant analysis, have been applied to generate prominent features and decrease the feature vector dimension. Lastly, a newly improved learning algorithm encompasses a modified pelican optimization algorithm (MOD-POA) and an extreme learning machine (ELM) for classification tasks. In this MOD-POA+ELM algorithm, the modified pelican optimization algorithm (MOD-POA) has been utilized to optimize the parameters of ELM’s hidden neurons. The effectiveness has been evaluated using two standard datasets called G1020 and ORIGA with the $$10 \times 5$$-fold stratified cross-validation technique to ensure reliable evaluation. Our employed scheme achieved the best results for both datasets obtaining accuracy of 93.25% (G1020 dataset) and 96.75% (ORIGA dataset), respectively. Furthermore, we have utilized seven Explainable AI methodologies: Vanilla Gradients (VG), Guided Backpropagation (GBP ), Integrated Gradients ( IG), Guided Integrated Gradients (GIG), SmoothGrad, Gradient-weighted Class Activation Mapping (GCAM), and Guided Grad-CAM (GGCAM) for interpretability examination, aiding in the advancement of dependable and credible automation of healthcare detection of glaucoma.
Versatile unsupervised design of antennas using flexible parameterization and computational intelligence methods
Authors: Koziel Slawomir , Pietrenko-Dabrowska Anna , Szczepanski Stanislaw 
Published: November 2024

AbstractDeveloping contemporary antennas is a challenging endeavor that requires considerable engineering insight. The most laborious stage is to devise an antenna architecture that delivers the required functionalities, e.g., multiband operation. Iterative by nature (hands-on topology modifications, parametric studies, trial-and-error geometry selection), it typically takes many weeks and requires considerable engagement from a human expert. Consequently, only a few possible design options concerning the fundamental antenna geometry may be considered. Automated topology rendition and geometry parameter optimization are highly relevant, especially from the industrial perspective. Therein, reducing time-to-market and limiting the involvement of trained experts is critical. This research proposes an innovative procedure for unsupervised development of planar antennas. Our method leverages flexible antenna parameterization based on re-sizable elliptical patches. It permits the realization of a massive number of geometries of diverse shapes and complexities using a small number of decision variables. Computational intelligence methods are employed to conduct antenna evolution exclusively based on specifications and possible constraints (e.g., maximum size). Fine-tuning of the structure geometry is achieved through low-cost local search routines. Our methodology is demonstrated by designing several antennas featuring distinct characteristics (broadband, single-, dual- and triple-band). The obtained results, supported by experimental data, underscore the presented approach’s versatility and capability to render unconventional topologies at reasonably low computational expenses. As mentioned earlier, the design process is fully automated without human expert involvement.
The impact of mechanical tuning on the printability of decellularized amniotic membrane bioinks for cell-laden bioprinting of soft tissue constructs
Authors: Kafili Golara , Tamjid Elnaz , Simchi Abdolreza 
Published: November 2024

AbstractDecellularized extracellular matrix (dECM) bioinks hold significant potential in the 3D bioprinting of tissue-engineered constructs (TECs). While 3D bioprinting allows for the creation of custom-designed TECs, the development of bioinks based solely on dAM, without the inclusion of supporting agents or chemical modifications, remains underexplored. In this study, we present the concentration-dependent printability and rheological properties of dAM bioinks, along with an analysis of their in vitro cellular responses. Our findings demonstrate that increasing dAM concentrations, within the range of 1 to 3% w/v, enhances the mechanical moduli of the bioinks, enabling the 3D printing of flat structures with superior shape fidelity. In vitro assays reveal high cell viability across all dAM bioink formulations; however, at 3% w/v, the bioink tends to impede fibroblast proliferation, resulting in round cell morphology. We propose that bioinks containing 2% w/v dAM strike an optimal balance, providing fine-resolved features and a supportive microenvironment for fibroblasts, promoting elongated spindle-like morphology and enhanced proliferation. These results underscore the importance of dAM concentration in regulating the properties and performance of bioinks, particularly regarding cell viability and morphology, for the successful 3D bioprinting of soft tissues.
Analysis of detoxification kinetics and end products of furan aldehydes in Acinetobacter baylyi ADP1
Authors: Liu Changshuo , Efimova Elena , Santala Ville , Santala Suvi 
Published: November 2024

AbstractThe efficient utilization of lignocellulosic hydrolysates in bioprocesses is impeded by their complex composition and the presence of toxic compounds, such as furan aldehydes, formed during lignocellulose pretreatment. Biological detoxification of these furan aldehydes offers a promising solution to enhance the utilization of lignocellulosic hydrolysates. Acinetobacter baylyi ADP1 is known to metabolize furan aldehydes, yet the complete spectrum of reaction products and dynamics remains unclear. Here, we determined the detoxification metabolites of furfural and 5-hydroxymethylfurfural in A. baylyi ADP1 and studied the kinetics of detoxification. The results indicate that detoxification in A. baylyi ADP1 follows a typical alcohol-aldehyde-acid scheme, with furoic acid and 5-hydroxymethyl-2-furancarboxylic acid as the final products for furfural and 5-hydroxymethylfurfural, respectively. Both end products were found to be less toxic for cells than their unmodified forms. These findings underscore the potential of A. baylyi ADP1 in detoxifying lignocellulosic hydrolysates for bioprocess applications.
Hybrid taguchi-grey relational analysis approach for optimizing cutter operational parameters in selective cauliflower harvesting
Authors: Kushwah Ajay , Sharma P. K. , Kushwaha H. L. , Sharma Brij Bihari , Shrivastava A. K. , Nag Ramineni Harsha , Chowdhury Manojit , Carpenter Gopal , Yadav Rashmi 
Published: November 2024

AbstractCauliflower is an important winter crop grown in India, its curds are rich in nutritional profile, containing valuable minerals and vitamins. However, cauliflower harvesting is mainly accomplished by hands, which is time-consuming and requires a high labour force. On the other hand, most developed cauliflower harvesters are once over or single pass type, which harvests all plants irrespective of their maturity. So, the selective harvester could improve the cauliflower curds yield, and then decrease the labour requirement. To improve the cutting performance of the selective cauliflower harvester, the working parameters of the chainsaw cutting mechanism need to be considered and optimized. This research investigates the impact of cutting height, feed (push) force, and cutting speed on the efficiency of the cutter during harvest. The Taguchi approach, together with grey relational analysis (GRA), was employed to identify the most favorable combination of operational parameters. In addition, the variance analysis was conducted to statistically examine the impact of multiple parameters. The findings indicated that the feed force was the major parameter that influenced the cutting force, splitting failure levels, and cutting time. The most effective parameter combination consisted of a cutting height of 15 mm, a feed force of 10 N, and a cutting speed of 5 m/s. The grey relational grade of the ideal parameter combination has shown a 0.322 increase in comparison to the grade achieved with the initially selected parameter combination. This setting was further incorporated in the developed selective cauliflower harvester to improve the performance of its cutting mechanism.
An optimal antibiotic selection framework for Sepsis patients using Artificial Intelligence
Authors: Wendland Philipp , Schenkel-Häger Christof , Wenningmann Ingobert , Kschischo Maik 
Published: November 2024

AbstractIn this work we present OptAB, the first completely data-driven online-updateable antibiotic selection model based on Artificial Intelligence for Sepsis patients accounting for side-effects. OptAB performs an iterative optimal antibiotic selection for real-world Sepsis patients focussing on minimizing the Sepsis-related organ failure score (SOFA-Score) as treatment success while accounting for nephrotoxicity and hepatotoxicity as serious antibiotic side-effects. OptAB provides disease progression forecasts for (combinations of) the antibiotics Vancomycin, Ceftriaxone and Piperacillin/Tazobactam and learns realistic treatment influences on the SOFA-Score and the laboratory values creatinine, bilirubin total and alanine-transaminase indicating possible side-effects. OptAB is based on a hybrid neural network differential equation algorithm and can handle the special characteristics of patient data including irregular measurements, a large amount of missing values and time-dependent confounding. OptAB’s selected optimal antibiotics exhibit faster efficacy than the administered antibiotics.
Anisotropic magnetic interactions in a candidate Kitaev spin liquid close to a metal-insulator transition
Authors: Ma Zeyu , Ni Danrui , Kaib David A. S. , MacFarquharson Kylie , Pearce John S. , Cava Robert J. , Valentí Roser , Coldea Radu , Coldea Amalia I. 
Published: November 2024

AbstractIn the Kitaev honeycomb model, spins coupled by strongly-frustrated anisotropic interactions do not order at low temperature but instead form a quantum spin liquid with spin fractionalisation into Majorana fermions and static fluxes. The realization of such a model in crystalline materials could lead to major breakthroughs in understanding entangled quantum states, however achieving this in practice is a very challenging task. The recently synthesized honeycomb material RuI3 shows no long-range magnetic order down to the lowest probed temperatures and has been theoretically proposed as a quantum spin liquid candidate material on the verge of an insulator to metal transition. Here we report a comprehensive study of the magnetic anisotropy in un-twinned single crystals via torque magnetometry and detect clear signatures of strongly anisotropic and frustrated magnetic interactions. We attribute the development of sawtooth and six-fold torque signal to strongly anisotropic, bond-dependent magnetic interactions by comparing to theoretical calculations. As a function of magnetic field strength at low temperatures, torque shows an unusual non-parabolic dependence suggestive of a proximity to a field-induced transition. Thus, RuI3, without signatures of long-range magnetic order, displays key hallmarks of an exciting candidate for extended Kitaev magnetism with enhanced quantum fluctuations.
THz generation by exchange-coupled spintronic emitters
Authors: Adam Roman , Cao Derang , Bürgler Daniel E. , Heidtfeld Sarah , Wang Fangzhou , Greb Christian , Cheng Jing , Chakraborty Debamitra , Komissarov Ivan , Büscher Markus , Mikulics Martin , Hardtdegen Hilde , Sobolewski Roman , Schneider Claus M. 
Published: November 2024

AbstractThe mechanism of THz generation in ferromagnet/metal (F/M) bilayers has been typically ascribed to the inverse spin Hall effect (ISHE). Here, we fabricated Pt/Fe/Cr/Fe/Pt multilayers containing two back-to-back spintronic THz emitters separated by a thin (tCr≤ 3nm) wedge-shaped Cr spacer. In such an arrangement, magnetization alignment of the two Fe films can be controlled by the interplay between Cr-mediated interlayer exchange coupling (IEC) and an external magnetic field. This in turn results in a strong variation of the THz amplitude A, with A↑↓ reaching up to 14 times A↑↑ (arrows indicate the relative alignment of the magnetization of the two magnetic layers). This observed functionality is ascribed to the interference of THz transients generated by two closely spaced THz emitters. Moreover, the magnetic field dependence A(H) shows a strong asymmetry that points to an additional performance modulation of the THz emitter via IEC and multilayer design.
ALBA proteins facilitate cytoplasmic YTHDF-mediated reading of m6A in Arabidopsis
Authors: Reichel Marlene , Tankmar Mathias Due , Rennie Sarah , Arribas-Hernández Laura , Lewinski Martin , Köster Tino , Wang Naiqi , Millar Anthony A , Staiger Dorothee , Brodersen Peter 
Published: November 2024

Abstract
N6-methyladenosine (m6A) exerts many of its regulatory effects on eukaryotic mRNAs by recruiting cytoplasmic YT521-B homology-domain family (YTHDF) proteins. Here, we show that in Arabidopsis thaliana, the interaction between m6A and the major YTHDF protein ECT2 also involves the mRNA-binding ALBA protein family. ALBA and YTHDF proteins physically associate via a deeply conserved short linear motif in the intrinsically disordered region of YTHDF proteins and their mRNA target sets overlap, with ALBA4 binding sites being juxtaposed to m6A sites. These binding sites correspond to pyrimidine-rich elements previously found to be important for m6A binding to ECT2. Accordingly, both the biological functions of ECT2, and its binding to m6A targets in vivo, require ALBA association. Our results introduce the YTHDF-ALBA complex as the functional cytoplasmic m6A-reader in Arabidopsis, and define a molecular foundation for the concept of facilitated m6A reading, which increases the potential for combinatorial control of biological m6A effects.
RGAM: A refined global attention mechanism for medical image segmentation
Authors: Ning Gangjun , Liu Pingping , Dai Chuangye , Sun Mingsi , Zhou Qiuzhan , Li Qingliang 
Published: November 2024

AbstractAttention mechanisms are popular techniques in computer vision that mimic the ability of the human visual system to analyse complex scenes, enhancing the performance of convolutional neural networks (CNN). In this paper, the authors propose a refined global attention module (RGAM) to address known shortcomings of existing attention mechanisms: (1) Traditional channel attention mechanisms are not refined enough when concentrating features, which may lead to overlooking important information. (2) The 1‐dimensional attention map generated by traditional spatial attention mechanisms make it difficult to accurately summarise the weights of all channels in the original feature map at the same position. The RGAM is composed of two parts: refined channel attention and refined spatial attention. In the channel attention part, the authors used multiple weight‐shared dilated convolutions with varying dilation rates to perceive features with different receptive fields at the feature compression stage. The authors also combined dilated convolutions with depth‐wise convolution to reduce the number of parameters. In the spatial attention part, the authors grouped the feature maps and calculated the attention for each group independently, allowing for a more accurate assessment of each spatial position’s importance. Specifically, the authors calculated the attention weights separately for the width and height directions, similar to SENet, to obtain more refined attention weights. To validate the effectiveness and generality of the proposed method, the authors conducted extensive experiments on four distinct medical image segmentation datasets. The results demonstrate the effectiveness of RGAM in achieving state‐of‐the‐art performance compared to existing methods.