bluuebunny/crossref_metadata_2025_split huggingface dataset:
Single-Label Multi-modal Field of Research Classification
Authors: Ruosch Florian , Vasu Rosni , Wang Ruijie , Rossetto Luca , Bernstein Abraham 
Published: August 2024

AbstractThe automated field of research classification for scientific papers is still challenging, even with modern tools such as large language models. As part of a shared task tackling this problem, this paper presents our contribution SLAMFORC, an approach to single-label classification using multi-modal data. We combined the metadata of papers with their full text and, where available, images into a pipeline to predict their field of research with an ensemble voting on traditional classifiers and large language models. We evaluated our approach on the shared task dataset and scored the highest values for two of the four metrics used in the evaluation of the competition, with the other two being the second highest.
A Technical/Scientific Document Management Platform
Authors: Lemos Melissa , García Grettel M. , Izquierdo Yenier T. , Oliveira Cleber , de Sousa Jefferson Alves , Rezende Bernardo Florindo Mortari , Novelli Bruno , Casanova Marco A. 
Published: August 2024

AbstractThis paper first presents DANKE, a data and knowledge management platform that allows users to submit keyword queries to a centralized database. DANKE uses a knowledge graph to provide a semantic view of the centralized database in a vocabulary familiar to the users. The paper then describes DANKE-U, a specialized module that enables DANKE to handle unstructured data, including scientific and engineering documents. Lastly, the paper presents a real use case from the oil and gas industry, involving technical/scientific documents.
Characterization of flag leaf morphology identifies a major genomic region controlling flag leaf angle in the US winter wheat (Triticum aestivum L.)
Authors: Kumar Pradeep , Gill Harsimardeep S. , Singh Mandeep , Kaur Karanjot , Koupal Dante , Talukder Shyamal , Bernardo Amy , Amand Paul St. , Bai Guihua , Sehgal Sunish K. 
Published: August 2024

Abstract
Key message
Multi-environmental characterization of flag leaf morphology traits in the US winter wheat revealed nine stable genomic regions for different flag leaf-related traits including a major region governing flag leaf angle.

Abstract
Flag leaf in wheat is the primary contributor to accumulating photosynthetic assimilates. Flag leaf morphology (FLM) traits determine the overall canopy structure and capacity to intercept the light, thus influencing photosynthetic efficiency. Hence, understanding the genetic control of these traits could be useful for breeding desirable ideotypes in wheat. We used a panel of 272 accessions from the hard winter wheat (HWW) region of the USA to investigate the genetic architecture of five FLM traits including flag leaf length (FLL), width (FLW), angle (FLANG), length–width ratio, and area using multilocation field experiments. Multi-environment GWAS using 14,537 single-nucleotide polymorphisms identified 36 marker-trait associations for different traits, with nine being stable across environments. A novel and major stable region for FLANG (qFLANG.1A) was identified on chromosome 1A accounting for 9–13% variation. Analysis of spatial distribution for qFLANG.1A in a set of 2354 breeding lines from the HWW region showed a higher frequency of allele associated with narrow leaf angle. A KASP assay was developed for allelic discrimination of qFLANG.1A and was used for its independent validation in a diverse set of spring wheat accessions. Furthermore, candidate gene analysis for two regions associated with FLANG identified seven putative genes of interest for each of the two regions. The present study enhances our understanding of the genetic control of FLM in wheat, particularly FLANG, and these results will be useful for dissecting the genes underlying canopy architecture in wheat facilitating the development of climate-resilient wheat varieties.
Nitrogen Fixation and Microbial Communities Associated with Decomposing Seagrass Leaves in Temperate Coastal Waters
Authors: Papazachariou Vasiliki , Fernández-Juárez Victor , Parfrey Laura Wegener , Riemann Lasse 
Published: August 2024

AbstractSeagrass meadows play pivotal roles in coastal biochemical cycles, with nitrogen fixation being a well-established process associated with living seagrass. Here, we tested the hypothesis that nitrogen fixation is also associated with seagrass debris in Danish coastal waters. We conducted a 52-day in situ experiment to investigate nitrogen fixation (proxied by acetylene reduction) and dynamics of the microbial community (16S rRNA gene amplicon sequencing) and the nitrogen fixing community (nifH DNA/RNA amplicon sequencing) associated with decomposing Zostera marina leaves. The leaves harboured distinct microbial communities, including distinct nitrogen fixers, relative to the surrounding seawater and sediment throughout the experiment. Nitrogen fixation rates were measurable on most days, but highest on days 3 (dark, 334.8 nmol N g−1 dw h−1) and 15 (light, 194.6 nmol N g−1 dw h−1). Nitrogen fixation rates were not correlated with the concentration of inorganic nutrients in the surrounding seawater or with carbon:nitrogen ratios in the leaves. The composition of nitrogen fixers shifted from cyanobacterial Sphaerospermopsis to heterotrophic genera like Desulfopila over the decomposition period. On the days with highest fixation, nifH RNA gene transcripts were mainly accounted for by cyanobacteria, in particular by Sphaerospermopsis and an unknown taxon (order Nostocales), alongside Proteobacteria. Our study shows that seagrass debris in temperate coastal waters harbours substantial nitrogen fixation carried out by cyanobacteria and heterotrophic bacteria that are distinct relative to the surrounding seawater and sediments. This suggests that seagrass debris constitutes a selective environment where degradation is affected by the import of nitrogen via nitrogen fixation.
In search of epigenetic hallmarks of different tissues: an integrative omics study of horse liver, lung, and heart
Authors: Semik-Gurgul Ewelina , Pawlina-Tyszko Klaudia , Gurgul Artur , Szmatoła Tomasz , Rybińska Justyna , Ząbek Tomasz 
Published: August 2024

AbstractDNA methylation and microRNA (miRNA) expression are epigenetic mechanisms essential for regulating tissue-specific gene expression and metabolic processes. However, high-resolution transcriptome, methylome, or miRNAome data is only available for a few model organisms and selected tissues. Up to date, only a few studies have reported on gene expression, DNA methylation, or miRNA expression in adult equine tissues at the genome-wide level. In the present study, we used RNA-Seq, miRNA-seq, and reduced representation bisulfite sequencing (RRBS) data from the heart, lung, and liver tissues of healthy cold-blooded horses to identify differentially expressed genes (DEGs), differentially expressed miRNA (DE miRNA) and differentially methylated sites (DMSs) between three types of horse tissues. Additionally, based on integrative omics analysis, we described the observed interactions of epigenetic mechanisms with tissue-specific gene expression alterations. The obtained data allowed identification from 4067 to 6143 DMSs, 9733 to 11,263 mRNAs, and 155 to 185 microRNAs, differentially expressed between various tissues. We pointed out specific genes whose expression level displayed a negative correlation with the level of CpG methylation and miRNA expression and revealed biological processes that they enrich. Furthermore, we confirmed and validated the accuracy of the Next-Generation Sequencing (NGS) results with bisulfite sequencing PCR (BSP) and quantitative PCR (qPCR). This comprehensive analysis forms a strong foundation for exploring the epigenetic mechanisms involved in tissue differentiation, especially the growth and development of the equine heart, lungs, and liver.
The influence of seawater pCO2 and temperature on the amino acid composition and aragonite CO3 disorder of coral skeletons
Authors: Allison Nicola , Ross Phoebe , Castillo Alvarez Cristina , Penkman Kirsty , Kröger Roland , Kellock Celeste , Cole Catherine , Clog Matthieu , Evans David , Hintz Chris , Hintz Ken , Finch Adrian A. 
Published: August 2024

AbstractCoral skeletons are composites of aragonite and biomolecules. We report the concentrations of 11 amino acids in massive Porites spp. coral skeletons cultured at two temperatures (25 °C and 28 °C) and 3 seawater pCO2 (180, 400 and 750 µatm). Coral skeletal aspartic acid/asparagine (Asx), glutamic acid/glutamine (Glx), glycine, serine and total amino acid concentrations are significantly higher at 28 °C than at 25 °C. Skeletal Asx, Glx, Gly, Ser, Ala, L-Thr and total amino acid are significantly lower at 180 µatm seawater pCO2 compared to 400 µatm, and Ser is reduced at 180 µatm compared to 750 µatm. Concentrations of all skeletal amino acids are significantly inversely related to coral calcification rate but not to calcification media pH. Raman spectroscopy of these and additional specimens indicates that CO3 disorder in the skeletal aragonite lattice is not affected by seawater pCO2 but decreases at the higher temperature. This is contrary to observations in synthetic aragonite where disorder is positively related to the aragonite precipitation rate mediated by either increasing temperature (this study) or increasing Ω (this study and a previous report) and to the concentration of amino acid in the precipitation media (a previous report). We observe no significant relationship between structural disorder and coral calcification rate or skeletal [amino acid]. Both temperature and seawater pCO2 can significantly affect skeletal amino acid composition, and further work is required to clarify how environmental change mediates disorder.
New Tools to Study 1-11-Representation of Graphs
Authors: Futorny Mikhail , Kitaev Sergey , Pyatkin Artem 
Published: August 2024

AbstractThe notion of a k-11-representable graph was introduced by Jeff Remmel in 2017 and studied by Cheon et al. in 2019 as a natural extension of the extensively studied notion of word-representable graphs, which are precisely 0-11-representable graphs. A graph G is k-11-representable if it can be represented by a word w such that for any edge (resp., non-edge) xy in G the subsequence of w formed by x and y contains at most k (resp., at least $$k+1$$

k
+
1

) pairs of consecutive equal letters. A remarkable result of Cheon at al. is that any graph is 2-11-representable, while it is unknown whether every graph is 1-11-representable. Cheon et al. showed that the class of 1-11-representable graphs is strictly larger than that of word-representable graphs, and they introduced a useful toolbox to study 1-11-representable graphs. In this paper, we introduce new tools for studying 1-11-representation of graphs. We apply them for establishing 1-11-representation of Chvátal graph, Mycielski graph, split graphs, and graphs whose vertices can be partitioned into a comparability graph and an independent set.
Association between gastrointestinal symptoms and specialty care utilization among colon cancer survivors: a cohort study
Authors: Edwards Anya L. , Trang Karen , Tolstykh Irina V. , Van Blarigan Erin L. , Van Loon Katherine , Laffan Angela , Stanfield Dalila , Steiding Paige , Neuhaus John , Atreya Chloe E. , Piawah Sorbarikor , Venook Alan P. , Varma Madhulika G. 
Published: August 2024

Abstract
Purpose
Persistent gastrointestinal (GI) symptoms are frequently experienced by colon cancer survivors and may help identify patients with higher utilization of healthcare services. To assess the relationship between GI symptoms and specialty care utilization among colon cancer survivors.

Methods
A prospective longitudinal cohort study at an academic medical center of 126 adults surgically treated for stage I–IV colon cancer between February 2017 and June 2022. Participants reported GI symptoms through the EORTC QLQ-C30 and QLQ-CR29 at enrollment and as frequently as every 6 months for 5 years. Main outcome measures were visits, telephone encounters, and secure messages with a medical provider within specialty oncology clinics within 6 months after each survey completion. Generalized linear mixed regression model for repeated measurements with random trajectory for each participant was performed to estimate the associations between symptoms and healthcare use. Models were adjusted for demographics, clinical and surgical factors, and timing in relation to onset of the COVID-19 pandemic.

Results
In the 6 months after each survey time point, patients averaged 1.2 visits, 0.5 telephone encounters, and 3.2 patient-initiated messages. In adjusted models, those with any abdominal pain (RR 1.45; p = 0.002), buttock pain (RR 1.30; p = 0.050), or increased stool frequency (RR 1.26; p = 0.046) had more clinic visits in the following 6 months than those without these symptoms. Including these three symptoms in one model revealed that only abdominal pain was statistically significantly associated with increased clinic visits (RR 1.36; p = 0.016). Patients with any blood or mucus in stool (RR 2.46; p = 0.009) had significantly more telephone encounters, and those with any abdominal pain (RR 1.65; p = 0.002) had significantly more patient-initiated messages than those without these symptoms.

Conclusions
Our findings identify GI symptoms associated with increased use of oncologic specialty care among colon cancer survivors, with abdominal pain as an important predictor of utilization.

Implications for cancer survivors
Early identification and anticipatory management of colon cancer survivors experiencing abdominal pain may decrease healthcare utilization.
Enrollment and completion rates of a nationwide guided digital parenting program for children with disruptive behavior before and during COVID-19
Authors: Lintula Sakari , Sourander Andre , Hinkka-Yli-Salomäki Susanna , Ristkari Terja , Kinnunen Malin , Kurki Marjo , Marjamäki Altti , Gyllenberg David , Kim Hyoun , Baumel Amit 
Published: August 2024

Abstract
Our aim was to study enrollment and completion levels for the internet-based and telephone-assisted Finnish Strongest Families Smart Website (SFSW) parent training intervention, for parents of young children with disruptive behavior before and after the COVID-19 lockdown period. Population-based screening was carried out on 39,251 children during routine check- ups at 4 years of age. The parents of children scoring at least 5 on the Strengths and Difficulties Questionnaire were assessed against inclusion and exclusion criteria. Associations with enrollment or completion were analyzed using logistic regression models. The effects of COVID-19 restrictions on these were estimated using interrupted timeseries analysis. Of 39,251 families, 4894 screened positive and met the eligibility criteria. Of those, 3068 (62.6%) decided to enroll in the SFSW program and 2672 (87.1%) of those families completed it. The highest level of disruptive behavior (OR 1.33, 95% CI 1.12–1.57, p < 0.001) and overall severity of difficulties (OR 2.22, 95% CI 1.91–2.57, p < 0.001) were independently associated with enrollment. Higher parental education was associated with enrollment and completion. Higher paternal age was associated with enrollment, and parent depressive symptoms with non-completion. The SFSW enrollment did not significantly change following the COVID-19 restrictions, while the completion rate increased (COVID-19 completion OR 1.75, 95% CI 1.22–2.50, p = 0.002). Guided digital parenting interventions increase the sustainability of services, by addressing the child mental health treatment gap and ensuring service consistency during crisis situations.
A distributed and cooperative signature-based intrusion detection system framework for multi-channel man-in-the-middle attacks against protected Wi-Fi networks
Authors: Thankappan Manesh , Rifà-Pous Helena , Garrigues Carles 
Published: August 2024

AbstractA Multi-Channel Man-in-the-Middle (MC-MitM) attack is an advanced form of MitM attack, characterized by its ability to manipulate encrypted wireless communications between the Access Point (AP) and clients within a WiFi network. MC-MitM attacks can target any Wi-Fi client, regardless of the authentication method used with the AP. Notable examples of such attacks include Key Reinstallation Attacks and FragAttacks, which have impacted millions of WiFi systems worldwide, especially those involving Internet of Things devices. Current defense mechanisms are inadequate against these attacks due to interoperability challenges and the need for modifications to devices or protocols within the targeted Wi-Fi networks. This paper introduces a distributed and cooperative signature-based wireless intrusion detection mechanism designed for online passive monitoring to detect malicious traffic patterns during MC-MitM attacks in any environment, from apartments and houses to large areas like hotels, offices or industrial sites. We implemented the proposed framework on Raspberry Pis and evaluated it in real-world settings. Our evaluation demonstrates that this framework can effectively identify MC-MitM attacks with an average accuracy of 98% when deployed across different locations within our experimental testbed.
Differentiating broadcast from viral: a causal inference approach for information diffusion analysis
Authors: Riazi Amin , Wang Yingfeng 
Published: August 2024

AbstractClassifying information diffusion patterns is critical to many information analysis areas, e.g., misleading information detection. However, diffusion pattern classification remains challenging when multiple users are involved. To address this challenge, this study aims to classify how information diffuses, distinguishing between broadcast and viral spreading, solely through the analysis of observational data from retweet networks on X (formerly known as Twitter). In broadcasting, most users directly receive information. However, viral spreading allows users the opportunity to receive information from a variety of sources. Therefore, viral spreading increases the likelihood of identifying misleading information. Existing methods classify diffusion types mainly through structural virality, which relies on the average distance between the users. However, when dealing with diffusion networks involving two or more information sources, these approaches can potentially lead to confusion regarding causality. To tackle this problem, we develop a deterministic causal inference method for categorizing information diffusion types. To the best of our knowledge, this is the first study investigating information diffusion types based on causality. This approach can be used to assess source credibility and assist in detecting misleading information. It can also be extended to other social media platforms.
Graphical Abstract
Atheroprotective role of vinpocetine: an old drug with new indication
Authors: Abu-Alghayth Mohammed H. , Al-kuraishy Hayder M. , Al-Gareeb Ali I. , Alexiou Athanasios , Papadakis Marios , Bahaa Mostafa M. , Afifi Mohammed , AL-Farga Ammar , Wahsh Eman , Batiha Gaber El-Saber 
Published: August 2024

AbstractEndothelial dysfunction is considered one of the main causes of atherosclerosis and elevated blood pressure. Atherosclerosis (AS) formation is enhanced by different mechanisms including cytokine generation, vascular smooth muscle cell proliferation, and migration. One of the recent treatment toward endothelial dysfunction is vinpocetine (VPN). VPN is an ethyl apovincaminate used in the management of different cerebrovascular disorders and endothelial dysfunction through inhibition of atherosclerosis formation. VPN is a potent inhibitor of phosphodiesterase enzyme 1 (PDE1) as well it has anti-inflammatory and antioxidant effects through inhibition of the expression of nuclear factor kappa B (NF-κB). VPN has been shown to be effective against development and progression of AS. However, the underlying molecular mechanism was not fully clarified. Consequently, objective of the present narrative review was to clarify the mechanistic role of VPN in AS. Most of pro-inflammatory cytokines released from macrophages are inhibited by the action of VPN via NF-κB-dependent mechanism. VPN blocks monocyte adhesion and migration by inhibiting the expression of pro-inflammatory cytokines. As well, VPN is effective in reducing oxidative stress, a cornerstone in the pathogenesis of AS, through inhibition of NF-κB and PDE1. VPN promotes plaque stability and prevent erosion and rupture of atherosclerotic plaque. In conclusion, VPN through mitigation of inflammatory and oxidative stress with plaque stability effects could be effective agent in the management of endothelial dysfunction through inhibition of atherosclerosis mediators.
Modelling Low-Frequency Vibration and Defect Detection in Homogeneous Plate-Like Solids
Authors: Aigbotsua Joshua O. , Smith Robert A. , Marshall Tom , Drinkwater Bruce W. 
Published: August 2024

AbstractThe inspection of thick-section sandwich structures with skins around core materials such as honeycomb, balsa, and foam relies on low-frequency vibration techniques to identify defects through changes in amplitude or phase response. However, current industrial methods are often limited to detecting specific types of defects, potentially overlooking others. Moreover, these methods do not gather detailed information about the defect type or depth, as they only analyse a small portion of the available data instead of the full relevant response spectrum. This paper explores the scientific basis of using low-frequency vibration in the pitch-catch variant for defect detection in homogeneous solids, through analysis of the full relevant frequency spectrum (5–50 kHz). Defects in structures lead to reduced local stiffness and mass in the affected area, causing resonance in the layer above, resulting in amplified vibrations known as local defect resonance (LDR). In this work, an aluminium plate with a 40 mm diameter circular flat-bottomed hole (FBH) at a depth of 1 mm (representing a skin defect) is excited with a chirp signal of 5–50 kHz, and the response is monitored 17 mm away from the excitation point. Finite-element analysis (FEA) is used for the numerical model, addressing challenges in creating an accurate model. The process to optimise the numerical model and the reduce model-experiment error is outlined, including challenges such as the lack of knowledge of material damping. The study emphasizes the importance of modelling the probe’s stiffness and damping effects for achieving agreement between the model and experiment. After incorporating these effects, the maximum LDR frequency error decreased from approximately 3 kHz to less than 1 kHz. In addition, this study presents a method with the potential for defect classification through comparison to modelled responses. The minimum difference error was used to quantify the resonance frequencies’ error between the model and the experiment. Since the resonant frequencies are a function of the defect’s shape, size, and depth, a relatively low root mean squared (RMS) error across the resonance frequency error spectrum indicates the defect’s characteristics. Finally, defect detection and sizing using the pitch-catch probe are explored with a wide-band excitation signal and a line scan through the mid-plane of the defect. A method for defect sizing using a pitch-catch probe is presented and experimentally validated. Accurate defect sizing is achieved with the pitch-catch probe when the defect width is at least $$\ge $$
≥
twice the 17 mm pin-spacing of the probe.