bluuebunny/crossref_metadata_2025_split huggingface dataset:
Nanoscopic lignin mapping on cellulose nanofibers via scanning transmission electron microscopy and atomic force microscopy
Authors: Morita Kenta , Takenaka Musashi , Tomita Kohei , Ishii Jun , Kawaguchi Hideo , Murakami Daisuke , Amo Hikaru , Fujii Miku , Maruyama Tatsuo , Matsumoto Takuya , Nishino Takashi , Ogino Chiaki 
Published: November 2023

AbstractCellulose has been developed as an alternative to petrochemical materials. By comparison with refined nanofibers (RCNFs), lignocellulose nanofibers (LCNFs) show particular promise because it is produced from biomass using only mild pretreatment. The mechanical properties of LCNFs depend on the contained lignin. However, the microscopic location of the lignin contained in LCNFs has not been determined. Thus, we developed two methods to detect and visualize lignin. One uses a scanning transmission electron microscope (STEM) equipped with an energy dispersive X-ray spectroscopy detector. The other method uses an atomic force microscope (AFM) equipped with a cantilever coated with an aromatic molecule. Both methods revealed that the lignin in LCNFs covers a thin cellulose fiber and is precipitated in a grained structure. In particular, the AFM system was able to determine the nanoscopic location of lignin-rich areas. The present study establishes a strong tool for analyzing the characteristics of lignin-containing materials.
Graphical abstract
Hemicellulose-rich paper-grade pulp as raw material for regenerated fibres in an ionic liquid-based process
Authors: Spönla Elisa , Hannula Simo , Kamppuri Taina , Holopainen-Mantila Ulla , Sulaeva Irina , Potthast Antje , Harlin Ali , Grönqvist Stina , Rahikainen Jenni 
Published: November 2023

AbstractHemicellulose-rich pulp raw materials are avoided in the production of man-made cellulosic textile fibres due to hemicellulose reactivity with the currently used industrial solvent systems. Incorporation of hemicelluloses in regenerated fibres could, however, increase the share of used wood biomass and thus improve the environmental footprint of regenerated fibre products. Superbase ionic liquids have shown potential in dissolving and regenerating all the major wood polymers i.e. cellulose, hemicellulose and lignin into regenerated products. In this work, regenerated fibres were spun from hemicellulose-rich softwood and eucalyptus paper-grade pulps and eucalyptus dissolving pulp using a superbase ionic liquid [mTBNH][OAc]. Before dissolution and spinning, intrinsic viscosities of the paper-grade pulps were adjusted either enzymatically or by using a mild acid-treatment to improve dope rheology for dry-jet wet spinning. In fibre spinning, hemicellulose was found to regenerate in high yield and the obtained regenerated fibres had high dry tenacities (5.3 to 15 cN/dtex). The best mechanical properties were measured from fibres with high hemicellulose content (17.3% (w/w)). Pulp pretreatment was found to be critical for achieving good mechanical properties. Acid-pretreatment, which modified both cellulose and hemicellulose, yielded regenerated fibres with better mechanical properties compared to an enzyme-pretreatment which did not alter the hemicellulose structure. Removal of hemicellulose substituents and hydrolysis of hemicellulose backbone in acid-pretreatment may be the key to improved mechanical properties in hemicellulose-containing regenerated fibres. Enzymatic peeling and imaging with a xylan-specific monoclonal antibody (CCRC-M138) suggest that hemicelluloses were enriched to the outermost layers of the regenerated fibres.
The expression characteristics and allelic variation of AnVromindoline-1/3 in Avena nuda L.
Authors: An Jiang-hong , Nan Jin-sheng , Liu Hui-yan , Chai Ming-na , Han Bing , Yang Yan 
Published: November 2023

AbstractOat is a worldwide cultivated crop with nutritional, ecological and economic value. The hardness of oat is closely related to the damage to the grain during harvesting, washing, threshing and hulling, and has an important impact on the processing quality and eating quality of oats. Vromindolines are a group of proteins specific to the Avena genus and are responsible for the softness of oat endosperm. Avena nuda is an allohexaploid, and is an important grain and feed crop in China. Vromindolines have been studied in the genus Oat, but have not been reported in Avena nuda. In this study, we performed paraffin sectioning and scanning electron microscopy analysis on oat kernels with different hardness, and the sequence characteristics, allelic variation, expression patterns and subcellular localization of Vromindoline-1 and Vromindoline-3 genes in Avena nuda were also analyzed. The results showed that the cell structure and ultrastructure of soft oat variety grains are significantly different from those of hard oats variety. PCR amplification and sequence analysis showed that the lengths of AnVin-1 and AnVin-3 genes were 444 and 429 bp, respectively, and AnVin-1 existed in the oat A, C and D genomes. Expression analysis in different tissues showed that AnVin-1A, AnVin-1C, AnVin-1D and AnVin-3C were expressed to varying degree in roots, stems and leaves. Expression analysis at the panicle developmental stage showed that the expression levels of these four genes first increased and then decreased, with the highest expression levels at 14 days after pollination. In addition, the expression levels of AnVin-1C and AnVin-3C in soft oat variety were higher than those in hard oats at 14 DAP. Among the single nucleotide polymorphisms among 18 AnVin-1C and 9 AnVin-3C gene sequences, AnVin-1C6 and AnVin-3C2 genes had three haplotypes in a oat natural population. Based on the allelic variation sites of AnVin-1C6, a cleavage amplified polymorphic sequence marker was established, which explained 1.44% of the variation in hardness. The above results of this study indicated that AnVin-1 and AnVin-3 genes were involved in kernel development and might affect kernel firmness.
Rigidity of geometric structures
Authors: Hamenstädt Ursula , Jäckel Frieder 
Published: November 2023

AbstractGeometric structures on a manifold M arise from a covering of M by charts with values in a homogeneous space G/H, with chart transitions restrictions of elements of G. If M is aspherical, then such geometric structures are given by a homomorphism of the fundamental group of M into G. Rigidity of such structures means that the conjugacy class of the homomorphism can be reconstructed from topological or geometric information on M. We give an overview of such rigidity results, focusing on topological type and length functions.
The Biophilia Reactivity Hypothesis: biophilia as a temperament trait, or more precisely, a domain specific attraction to biodiversity
Authors: Woods Vanessa , Knuth Melinda 
Published: November 2023

AbstractE.O. Wilson proposed the concept of biophilia as “the innately emotional affiliation of human beings with other living organisms.” While the idea has gained traction in diverse fields, including architecture and horticulture, the few empirical tests of the biophilia hypothesis are either inconclusive or fail to provide support. This paper reviews fundamental flaws in Wilson's biophilia hypothesis and proposes “the Biophilia Reactivity Hypothesis”: biophilia as a temperament trait, a theory that offers a falsifiable version of Wilson’s original concept.
Designing an adaptive and deep learning based control framework for modular production systems
Authors: Panzer Marcel , Gronau Norbert 
Published: November 2023

AbstractIn today’s rapidly changing production landscape with increasingly complex manufacturing processes and shortening product life cycles, a company’s competitiveness depends on its ability to design flexible and resilient production processes. On the shop-floor, in particular, the production control plays a crucial role in coping with disruptions and maintaining system stability and resilience. To address challenges arising from volatile sales markets or other factors, deep learning algorithms have been increasingly applied in production to facilitate fast-paced operations. In particular deep reinforcement learning frequently surpassed conventional and intelligent approaches in terms of performance and computational efficiency and revealed high levels of control adaptability. However, existing approaches were often limited in scope and scenario-specific, which hinders a seamless transition to other control optimization problems. In this paper, we propose a flexible framework that integrates a deep learning based hyper-heuristic into modular production to optimize pre-defined performance indicators. The framework deploys a module recognition and agent experience sharing, enabling a fast initiation of multi-level production systems as well as resilient control strategies. To minimize computational and re-training efforts, a stack of trained policies is utilized to facilitate an efficient reuse of previously trained agents. Benchmark results reveal that our approach outperforms conventional rules in terms of multi-objective optimization. The simulation framework further encourages research in deep-learning-based control approaches to leverage explainability.
GMMSampling: a new model-based, data difficulty-driven resampling method for multi-class imbalanced data
Authors: Naglik Iwo , Lango Mateusz 
Published: November 2023

AbstractLearning from multi-class imbalanced data has still received limited research attention. Most of the proposed methods focus on the global class imbalance ratio only. In contrast, experimental studies demonstrated that the imbalance ratio itself is not the main difficulty in the imbalanced learning. It is the combination of the imbalance ratio with other data difficulty factors, such as class overlapping or minority class decomposition into various subconcepts, that significantly affects the classification performance. This paper presents GMMSampling—a new resampling method that exploits information about data difficulty factors to clear class overlapping regions from majority class instances and to simultaneously oversample each subconcept of the minority class. The experimental evaluation demonstrated that the proposed method achieves better results in terms of G-mean, balanced accuracy, macro-AP, MCC and F-score than other related methods.
Active learning for data streams: a survey
Authors: Cacciarelli Davide , Kulahci Murat 
Published: November 2023

AbstractOnline active learning is a paradigm in machine learning that aims to select the most informative data points to label from a data stream. The problem of minimizing the cost associated with collecting labeled observations has gained a lot of attention in recent years, particularly in real-world applications where data is only available in an unlabeled form. Annotating each observation can be time-consuming and costly, making it difficult to obtain large amounts of labeled data. To overcome this issue, many active learning strategies have been proposed in the last decades, aiming to select the most informative observations for labeling in order to improve the performance of machine learning models. These approaches can be broadly divided into two categories: static pool-based and stream-based active learning. Pool-based active learning involves selecting a subset of observations from a closed pool of unlabeled data, and it has been the focus of many surveys and literature reviews. However, the growing availability of data streams has led to an increase in the number of approaches that focus on online active learning, which involves continuously selecting and labeling observations as they arrive in a stream. This work aims to provide an overview of the most recently proposed approaches for selecting the most informative observations from data streams in real time. We review the various techniques that have been proposed and discuss their strengths and limitations, as well as the challenges and opportunities that exist in this area of research.
Quantifying the Consistency and Characterizing the Confidence of Coronal Holes Detected by Active Contours Without Edges (ACWE)
Authors: Grajeda Jeremy A. , Boucheron Laura E. , Kirk Michael S. , Leisner Andrew , Arge C. Nick 
Published: November 2023

AbstractCoronal Holes (CHs) are regions of open magnetic-field lines, resulting in high-speed solar wind. Accurate detection of CHs is vital for space-weather prediction. This paper presents an intramethod ensemble for coronal-hole detection based on the Active Contours Without Edges (ACWE) segmentation algorithm. The purpose of this ensemble is to develop a confidence map that defines, for all ondisk regions of a solar extreme ultraviolet (EUV) image, the likelihood that each region belongs to a CH based on that region’s proximity to, and homogeneity with, the core of identified CH regions. By relying on region homogeneity, and not intensity (which can vary due to various factors, including line-of-sight changes and stray light from nearby bright regions), to define the final confidence of any given region, this ensemble is able to provide robust, consistent delineations of the CH regions. Using the metrics of global consistency error (GCE), local consistency error (LCE), intersection over union (IOU), and the structural similarity index measure (SSIM), the method is shown to be robust to different spatial resolutions maintaining a median IOU $>0.75$
>
0.75
and minimum SSIM $>0.93$
>
0.93
even when the segmentation process was performed on an EUV image decimated from $4096\times 4096$
4096
×
4096
pixels down to $512\times 512$
512
×
512
pixels. Furthermore, using the same metrics, the method is shown to be robust across short timescales, producing segmentation with a mean IOU of 0.826 from EUV images taken at a 1-h cadence, and showing a smooth decay in similarity across all metrics as a function of time, indicating self-consistent segmentations even when corrections for exposure time have not been applied to the data. Finally, the accuracy of the segmentations and confidence maps are validated by considering the skewness (i.e., unipolarity) of the underlying magnetic field.
Regularity of Sets Under a Reformulation in a Product Space with Reduced Dimension
Authors: Campoy Rubén 
Published: November 2023

AbstractDifferent notions on regularity of sets and of collection of sets play an important role in the analysis of the convergence of projection algorithms in nonconvex scenarios. While some projection algorithms can be applied to feasibility problems defined by finitely many sets, some other require the use of a product space reformulation to construct equivalent problems with two sets. In this work we analyze how some regularity properties are preserved under a reformulation in a product space of reduced dimension. This allows us to establish local linear convergence of parallel projection methods which are constructed through this reformulation.
Meaningfulness and grief: you don’t know what you got till it’s gone
Authors: Matey Jennifer 
Published: November 2023

AbstractWhat makes a life meaningful and how do we know when our lives have meaning? This paper draws on the experience of grief to provide answer to these questions. It is argued that grief is a unique kind of transformative experience that gives us access to facts about the depth of meaningfulness and value that even the most seemingly mundane aspects of our day-to-day lives have had which often goes largely unrecognized until the source of that meaning is lost.
Efficient Performance Prediction of End-to-End Autonomous Driving Under Continuous Distribution Shifts Based on Anomaly Detection
Authors: Luan Siyu , Gu Zonghua , Wan Shaohua 
Published: November 2023

AbstractA Deep Neural Network (DNN)’s prediction may be unreliable outside of its training distribution despite high levels of accuracy obtained during model training. The DNN may experience different degrees of accuracy degradation for different levels of distribution shifts, hence it is important to predict its performance (accuracy) under distribution shifts. In this paper, we consider the end-to-end approach to autonomous driving of using a DNN to map from an input image to the control action such as the steering angle. For each input image with possible perturbations that cause distribution shifts, we design a Performance Prediction Module to compute its anomaly score, and use it to predict the DNN’s expected prediction error, i.e., its expected deviation from the ground truth (optimal) control action, which is not available after deployment. If the expected prediction error is too large, then the DNN’s prediction may no longer be trusted, and remedial actions should be taken to ensure safety. We consider different methods for computing the anomaly score for the input image, including using the reconstruction error of an Autoencoder, or applying an Anomaly Detection algorithm to a hidden layer of the DNN. We present performance evaluation of the different methods in terms of both prediction accuracy and execution time on different hardware platforms, in order to provide a useful reference for the designer to choose among the different methods.
Development of a highly tolerant bacterial consortium for asphaltene biodegradation in soils
Authors: Navas-Cáceres Oscar Daniel , Parada Mayra , Zafra German 
Published: November 2023

AbstractAsphaltenes are the most polar and heavy fraction of petroleum, and their complex structure and toxicity make them resistant to biodegradation. The ability to tolerate high asphaltene concentrations is crucial to reducing the toxicity-related inhibition of microbial growth and improving their capacity for adaptation, survival, and biodegradation in soils highly contaminated with asphaltenes. This study developed a highly tolerant consortium for efficient asphaltene biodegradation in soils from 22 bacterial isolates obtained from heavy-crude oil-contaminated soils. Isolates corresponded to the Rhodococcus, Bacillus, Stutzerimonas, Cellulosimicrobium, Pseudomonas, and Paenibacillus genera, among others, and used pure asphaltenes and heavy crude oil as the only carbon sources. Surface plate assays were used to evaluate the tolerance of individual isolates to asphaltenes, and the results showed variations in the extension and inhibition rates with maximum tolerance levels at 60,000 mg asphaltenes l−1. Inhibition assays were used to select non-antagonistic bacterial isolates among those showing the highest tolerance levels to asphaltenes. A consortium made up of the five most tolerant and non-antagonistic bacterial isolates was able to degrade up to 83 wt.% out of 10,000 mg asphaltenes kg−1 in the soil after 52 days. Due to its biological compatibility, high asphaltene tolerance, and ability to utilise it as a source of energy, the degrading consortium developed in this work has shown a high potential for soil bioremediation and is a promising candidate for the treatment of aged soil areas contaminated with heavy and extra-heavy crude oil. This would be the first research to assess and consider extreme bacterial tolerance and microbial antagonism between individual degrading microbes, leading to the development of an improved consortium capable of efficiently degrading high amounts of asphaltenes in soil.
On the relationship between oil market and European stock returns
Authors: Magazzino Cosimo , Shahbaz Muhammad , Adamo Massimiliano 
Published: November 2023

AbstractThis paper investigates the dynamic relationship between the oil market and European stock market returns using monthly data from May 2007 to April 2022 for 27 European Union member countries. A novel approach is adopted by using the time-varying Granger causality test and the structural vector auto-regression model to examine the causal links. Empirical results reveal strong evidence of time-varying causation between the variables, considering the oil market from both the supply-side and demand-side perspectives. In light of these findings, numerous policy considerations emerge, including refining risk management strategies for investors, reformulating economic and energy policies, the potential impact on monetary policy decisions, the need for ad hoc market regulations, facilitating investor education initiatives, promoting international cooperation, and advancing the transition to sustainable energy sources.
Screening for Coronary Artery Disease in Patients with Diabetes
Authors: Saraste Antti , Knuuti Juhani , Bax Jeroen 
Published: November 2023

Abstract
Purpose of Review
The study aims to describe methods for detecting subclinical coronary artery disease (CAD) and their potential implications in asymptomatic patients with diabetes.

Recent Findings
Imaging tools can assess non-invasively the presence and severity of CAD, based on myocardial ischemia, coronary artery calcium score, and coronary computed tomography coronary angiography. Subclinical CAD is common in the general population ageing 50 to 64 years with any coronary atherosclerosis present in 42.1% and obstructive CAD in 5.2%. In patients with diabetes, an even higher prevalence has been noted. The presence of myocardial ischemia, obstructive CAD, and the extent of coronary atherosclerosis provide powerful risk stratification regarding the risk of cardiovascular events. However, randomized trials evaluating systematic screening in the general population or patients with diabetes have demonstrated only moderate impact on management and no significant impact on patient outcomes.

Summary
Despite providing improved risk stratification, systematic screening of CAD is not recommended in patients with diabetes.
Mutant p53 gain-of-function stimulates canonical Wnt signaling via PI3K/AKT pathway in colon cancer
Authors: Alvarado-Ortiz Eduardo , Ortiz-Sánchez Elizabeth , Sarabia-Sánchez Miguel Angel , de la Cruz-López Karen Griselda , García-Carrancá Alejandro , Robles-Flores Martha 
Published: November 2023

AbstractAberrant canonical Wnt signaling is a hallmark of colon cancer. The TP53 tumor suppressor gene is altered in many solid tumors, including colorectal cancer, resulting in mutant versions of p53 (mut-p53) that lose their tumor suppressor capacities and acquire new-oncogenic functions (GOFs) critical for disease progression. Although the mechanisms related to mut-p53 GOF have been explored extensively, the relevance of mut-p53 in the canonical Wnt pathway is not well defined. This work investigated the influence of mut-p53 compared to wt-p53 in β-catenin-dependent Wnt signaling. Using the TCGA public data from Pan-Cancer and the GEPIA2 platform, an in silico analysis of wt-p53 versus mut-p53 genotyped colorectal cancer patients showed that TP53 (p53) and CTNNB1 (β-catenin) are significantly overexpressed in colorectal cancer, compared with normal tissue. Using p53 overexpression or p53 knockdown assays of wt-p53 or mut-p53, we found that while wt-p53 antagonizes canonical Wnt signaling, mut-p53 induces the opposite effect, improving the β-catenin-dependent transcriptional activity and colony formation ability of colon cancer cells, which were both decreased by mut-p53 knockdown expression. The mechanism involved in mut-p53-induced activation of canonical Wnt appears to be via AKT-mediated phosphorylation of Ser 552 of β-catenin, which is known to stabilize and enhance its transcriptional activity. We also found that while wt-p53 expression contributes to 5-FU sensitivity in colon cancer cells, the RITA p53 reactivating molecule counteracted the resistance against 5-FU in cells expressing mut-p53. Our results indicate that mut-p53 GOF acts as a positive regulator of canonical Wnt signaling and participates in the induction of resistance to 5-FU in colon cancer cells.
Graphical abstract