bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prevalence and coexistence of type 2 inflammatory diseases
Authors: Mora Toni , Sánchez‐Collado Irene , Muñoz‐Cano Rosa , Ribó Paula , Palomo‐Jiménez Paloma I. , Mullol Joaquim , Valero Antonio 
Published: June 2024

AbstractBackgroundType 2 inflammation has been described as a pathophysiological basis common to some diseases, such as atopic dermatitis (AD), chronic rhinosinusitis with nasal polyps, and asthma (CRSwNP).ObjectiveThe present study used population‐based prevalence in Catalonia to analyse the coexistence of type 2 inflammatory diseases in patients primarily diagnosed with the above mentioned conditions.ResultsWe found a high degree of coexistence of type 2 inflammatory diseases among these patients, with the prevalence being higher in the severe forms, except for AD. For the severe forms of primary diseases, the proportion of patients with coexisting type 2 inflammatory diseases (severe or non‐severe) was 16.2% for AD, 19.8% for asthma, and a striking 62.4% for CRSwNP. This patient population has the highest proportion of coexisting type 2 inflammatory diseases, both severe (48.9%) and non‐severe (13.5%).ConclusionOur findings have significant implications for the management of patients with AD, asthma, and CRSwNP.
Impact of Food on the Oral Absorption of N‐Acetyl‐D‐Mannosamine in Healthy Men and Women
Authors: Evans Allan M. , Fornasini Gianfranco , Meola Tahlia R. , Gahl William A. , Huizing Marjan , Polasek Thomas M. , Reuter Stephanie E. 
Published: June 2024

AbstractN‐Acetyl‐D‐mannosamine (ManNAc) is an endogenous monosaccharide and precursor of N‐acetylneuraminic acid (Neu5Ac), a critical sialic acid. ManNAc is currently under clinical development to treat GNE myopathy, a rare muscle‐wasting disease. In this randomized, open‐label, 2‐sequence, crossover study, 16 healthy women and men were administered a single oral dose of ManNAc under fasting and fed conditions. Blood samples were collected for 48 hours after dosing for quantification of plasma ManNAc and Neu5Ac concentrations. Noncompartmental pharmacokinetic and deconvolution analyses were performed using baseline‐corrected plasma concentration data. Administration of ManNAc in the fed state resulted in a 1.6‐fold increase in ManNAc exposure, compared to fasting conditions. A concurrent increase in Neu5Ac exposure was observed in the presence of food. Deconvolution analysis indicated that the findings were attributed to prolonged absorption rather than an enhanced rate of absorption. The impact of food on ManNAc pharmacokinetics was greater in women than men (fed/fasted area under the concentration‐time curve from time 0 to infinity mean ratio: 198% compared to 121%). It is hypothesized that the presence of food slows gastric emptying, allowing a gradual release of ManNAc into the small intestine, translating into improved ManNAc absorption. The results suggest that taking ManNAc with food may enhance its therapeutic activity and/or reduce the daily dosage requirement.
Laser‐Induced Nucleation of Acetaminophen through the Addition of Insoluble Impurities and Acidic Polymers
Authors: Xie Xiongfei , Li Shuai , Liu Yao 
Published: June 2024

AbstractThis study investigates the crystallization of acetaminophen (ACET) in ultrapure water and a 10 wt.% aqueous polyacrylic acid (PAA) solution using non‐photochemical laser‐induced nucleation (NPLIN) for the first time. Using a 532 nm nanosecond laser, two distinct crystal morphologies—rhombic and tetragonal plate‐like—are formed in both solvents after adding impurities. Notably, the PAA solution showed a reduced number of crystals and slower growth rates compared to ultrapure water, suggesting that the acidic polymer modulates crystal growth. Interestingly, crystals are not induced by the laser without impurities. However, impurities like copper phthalocyanine (CuPc) or boron carbide (CB4) enabled successful NPLIN, with CB4 showing higher nucleation efficiency than CuPc. The study also explores how laser power affects nucleation probability and identifies potential laser energy thresholds. Experimental data on ACET crystal sizes over time are fitted to derived equations, which accurately represented trends and predicted results. The nanoparticle heating mechanism and the role of acidic polymers in affecting nucleation probability and growth rate are discussed, along with potential mechanisms for changes in crystal morphology.
Negotiating distinctiveness and building resilience: Arab and Jewish women's acts of micro‐emancipation in the context of Israeli municipal councils
Authors: Syna Helena Desivilya 
Published: June 2024

AbstractThe paper narrows the research gaps on women's performances enacted to ascertain their voice and influence in perplexing environments and how these attempts evolve in the particular social, political fields of local government. It shows how women use negotiation to build resilience, cope with persistent gender inequalities and exert influence in strategic management forums of local governments. The findings are based on content analysis of semistructured interviews with 20 Arab and Jewish women members of municipal councils. Three main themes emerged from the analysis of the interview data: “noisy silences”—subtle counter‐moves, disrupting the field and planting new trees and the mysteries of success. These themes encapsulate marks of micro‐emancipation, illustrating women council members' acts of resistance—transforming the imperfections attributed to them by their male counterparts, into opportunities—, allowing to sound their voices and exert influence at the unhospitable and chilly political field of local governments. Women are capable of adapting their negotiation repertoire to the emerging circumstances. The task of changing the playing field of municipal councils is markedly more arduous for women in Arab towns, nonetheless some of them engage it.
Impacts of genotype, edaphic factors, and plant nutrients on yield and fruit quality for a perennial specialty crop (Vaccinium macrocarpon Ait.)
Authors: Millar David , Kennedy Casey , Zalapa Juan , Jeranyama Peter , Mupambi Giverson , Wiegman Adrian , Buda Anthony 
Published: June 2024

AbstractCompared to conventional crops, less is known about how genetic and environmental variability affect the yield and quality of specialty crops like cranberry (Vaccinium macrocarpon Ait.). Herein, we performed a multifaceted analysis of six commercial cranberry beds planted to the Stevens cultivar. The six beds included three with above‐average multiyear yields and three that were lower than average. We considered genotype, edaphic factors, and plant nutrient content as driving variables of yield and fruit quality. We found that genetic purity within beds raised the odds of obtaining above‐average yields over an 8‐year period. The highest levels of genetic contamination (38%–75%) were found at the low‐yield beds, where significant differences in yield and fruit quality were observed between genotypes, within beds. Across all beds, focusing only on plots genetically confirmed to be Stevens cultivar, we also found that plot‐scale yield in 2020 was significantly higher for two of three high‐yield beds, suggesting other factors besides genetic contamination influenced differences in bed‐scale yield. A factor analysis of mixed data that jointly included genotype, edaphic variables, and plant tissue nutrient content revealed complex relations among these variables that were tied to grouping plots based on long‐term yield. Findings highlight the need for further research into the complex genetic and environmental factors that control cranberry yield and fruit quality.
Decadal‐scale time series highlight the role of chronic disturbances in driving ecosystem collapse in the Anthropocene
Authors: Edmunds Peter J. 
Published: June 2024

AbstractBiome degradation characterizes the Anthropocene Epoch, and modern ecology is deeply involved with describing the changes underway. Most research has focused on the role of acute disturbances in causing conspicuous changes in ecosystem structure, which leads to an underappreciation of the chronic effects causing large changes through the cumulative effects of small perturbations over decades. Coral reefs epitomize this trend, because the changes in community structure are profound, yet the data to quantify these effects are usually insufficient to evaluate the relative roles of different disturbance types. Here, four decades of surveys from two coral reefs (9 and 14 m depth) off St. John, US Virgin Islands, are used to quantify the associations of acute and chronic events with the changes in benthic community structure. These reefs profoundly changed over 36 years, with coral death altering species assemblages to depress abundances of the ecologically important coral Orbicella spp. and elevating the coverage of macroalgae and crustose coralline algae/turf/bare space (CTB). Linear mixed models revealed the prominent role of chronic variation in temperature in accounting for changes in coverage of corals, macroalgae, and CTB, with rising temperature associated with increases in coral cover on the deep reef, and declines on the shallow reef. Hurricanes were also associated with declines in coral cover on the shallow reef, and increases on the deep reef. Multivariate analyses revealed strong associations between community structure and temperature, but weaker associations with hurricanes, bleaching, and diseases. These results highlight the overwhelming importance of chronically increasing temperature in altering the benthic community structure of Caribbean reefs.
Cognitive blockchain and its application to optimize performance in blockchain systems
Authors: Ameri Reyhaneh , Meybodi Mohammad Reza 
Published: June 2024

AbstractA distributed ledger called a blockchain is used for logging authenticated cryptographic transactions. The global ledger is updated with transactions using consensus techniques. Consensus algorithms are developed for networks with untrusted nodes to achieve reliability. Academics are paying attention to this technology because it has essential features like decentralization, stability, anonymity, and transparency. Even though the blockchain has some unique features, it has to deal with many challenges and restrictions, such as scalability, security, hidden centrality, and high cost. Artificial intelligence and blockchain are technologies that have been much discussed in the last decade and are developing rapidly. Combining the two to meet the existing challenges can have fascinating results. In this paper, we introduce the novel idea of cognitive blockchain by incorporating intelligent thought into the blockchain. With cognitive capabilities, blockchain technology can perceive the state of the network, analyze the data it has collected, make good decisions, and take appropriate action to improve network performance. We provide an operational framework for cognitive blockchain, which primarily refers to the connections between fundamental cognitive processes, including the perception‐action cycle, data analytics, knowledge discovery, intelligent decision‐making, and service provision. Then, using learning automata, we provide methods for creating cognitive engines for performance optimization by intelligently adjusting the block size, time interval, and validators in blockchain systems with BFT‐based consensus algorithms. Several experiments have been conducted to assess the suggested approaches' effectiveness. The findings demonstrate that the proposed performance optimization approach improves blockchain performance criteria.
Identical code‐data cyclic shift transmission for time‐dispersive channel
Authors: Vasudevan S. , Stuwart S. Lenty 
Published: June 2024

AbstractMultiple access techniques working on time‐dispersive channels must employ spreading codes with good correlation properties to minimize multipath interference and multiple access interference. Popular spreading codes, such as the Walsh‐Hadamard code (orthogonal) and maximal length sequence (m‐sequence) (semi‐orthogonal), suffer from intersymbol interference (ISI). ISI increases owing to the poor autocorrelation properties of the spreading codes, resulting in a deteriorated bit‐error‐rate (BER) performance. Recently, a cyclic interleaving transmission has been proposed to enhance multipath diversity in the presence of ISI. In this study, an identical code‐data cyclic shift (ICDCS) transmission scheme is introduced to achieve improved multipath diversity in time‐dispersive channels. The proposed ICDCS transmission is designed based on cyclically shifting identical codes and data. The code and data involved in the first set of spreading are cyclically shifted for the next set of spreading, and the two sets are blended to perform cyclic interleaving. The data are despread from each multipath component at the receiver using different codes derived from a single m‐sequence through identical code cyclic shifting (ICCS). Consequently, multipath diversity increases, resulting in a significant reduction in the BER. ICDCS transmission effectively utilizes all multipath components and the codes derived from the ICCS to mitigate ISI. The simulation results confirm that the proposed ICDCS transmission significantly enhances the multipath diversity compared to the conventional cyclic interleaving transmission scheme.
Fast refacing of MR images with a generative neural network lowers re‐identification risk and preserves volumetric consistency
Authors: Molchanova Nataliia , Maréchal Bénédicte , Thiran Jean‐Philippe , Kober Tobias , Huelnhagen Till , Richiardi Jonas ,   the Alzheimer's Disease Neuroimaging Initiative
Published: June 2024

AbstractWith the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have been developed to de‐identify imaging data using blurring, defacing or refacing. Completely removing facial structures provides the best re‐identification protection but can significantly impact post‐processing steps, like brain morphometry. As an alternative, refacing methods that replace individual facial structures with generic templates have a lower effect on the geometry and intensity distribution of original scans, and are able to provide more consistent post‐processing results by the price of higher re‐identification risk and computational complexity. In the current study, we propose a novel method for anonymized face generation for defaced 3D T1‐weighted scans based on a 3D conditional generative adversarial network. To evaluate the performance of the proposed de‐identification tool, a comparative study was conducted between several existing defacing and refacing tools, with two different segmentation algorithms (FAST and Morphobox). The aim was to evaluate (i) impact on brain morphometry reproducibility, (ii) re‐identification risk, (iii) balance between (i) and (ii), and (iv) the processing time. The proposed method takes 9 s for face generation and is suitable for recovering consistent post‐processing results after defacing.
Confounding association between plasma HDL‐C levels and increased fracture risk: A correspondence
Authors: Abbasi Arooba Noor , Qaiser Syed Faiq , Hoda Fatimah , Memon Aaima , Lakho Arooba 
Published: June 2024

AbstractBackgroundThis article explores the association between fractures, particularly in the elderly, and elevated plasma high‐density lipoprotein cholesterol (HDL‐C) levels. The study challenges the conventional idea of HDL‐C as “good cholesterol” by revealing its potential role as a risk factor for fractures. Factors contributing to fractures in the elderly, such as diminishing bone strength due to aging‐related tissue breakdown, are discussed. Sedentary lifestyles, low bone mineral density (BMD), and habits like smoking and alcohol consumption compound fracture susceptibility.Materials and MethodsThe study delves into mechanisms linking elevated HDL‐C to fractures, using data from the ASPREE‐Fracturesub‐study of the ASPREE trial involving Australian and American participants aged 65 and above.ResultsThe study showed that over a 4‐year period, elevated HDL‐C levels in healthy older people were linked to a 14% higher fracture risk. This revelation expands the understanding of fracture risk factors beyond the established norms.ConclusionThe article emphasizes the need to reconsider HDL‐C's traditional role as an indicator of cardiovascular health, particularly in light of medications like Statins and Anacetrapib that raise HDL‐C levels. It calls for further exploration into the relationship between HDL‐C, fractures at varying sites, and different age groups. Practical implications involve incorporating fracture risk associated with high HDL‐C into clinical considerations, alongside advocating lifestyle changes for optimal HDL‐C levels. In summary, this study prompts a reevaluation of HDL‐C's implications in clinical practice, demanding further investigation into the intricacies of this relationship.
Urethral catheter entrapped in vesicourethral anastomotic sutures after laparoscopic radical prostatectomy successfully removed by transurethral approach
Authors: Yoshihara Kentaro , Tashiro Kojiro , Kimura Shoji , Yasue Keiji , Urabe Fumihiko , Yamada Hiroki , Kimura Takahiro 
Published: June 2024

IntroductionUrethral catheter entrapped in vesicourethral anastomotic sutures after radical prostatectomy is a relatively common complication. We herein present a novel and safe technique to remove urethral catheter.Case presentationA 64‐year‐old man was diagnosed with prostate cancer. Subsequently he underwent laparoscopic radical prostatectomy. On postoperative Day 7, the patient experienced difficulty in removing the catheter, and entanglement of the suture with the urinary catheter was suspected. After conservative follow‐up, a rigid endoscope was inserted into the urethra beside urethral catheter, identifying suture entanglement with the catheter. Finally, the suture was cut with scissor forceps.ConclusionTo the best of our knowledge, this is the first reported case in which scissor forceps were used to cut the entangled thread in such a complication. This case highlights a novel but simple method for difficult removal of an entrapped catheter.
Ensemble of Deep CNN Models for Human Skin Disease Classification
Authors: Askale Getnet Tigabie , Assress Demeke Ayele , Salau Ayodeji Olalekan , Yibel Achenef Behulu 
Published: June 2024

ABSTRACTSkin diseases are among the leading causes of disability worldwide and are a significant cause of morbidity in sub‐Saharan Africa. It can be cured if identified early. Only an expert dermatologist can classify skin disease by examining clinical signs. Sometimes, it can happen that dermatologists do not correctly classify the Skin disease, and therefore prescribe inappropriate drugs to the patient. Various research has been done to automate skin disease classification. Almost all the studies were concentrated on classifying three to four types of skin diseases. Developing a model that can be used in real‐world practical AI applications is important. In this study, we present an ensemble model based on the hard‐voting scheme of three deep CNN architectures: SKDCNET, FVGG16, and InceptionV3 for automatic classification of the top eight skin diseases. The proposed model utilizes three architectural diversities: training from scratch, fine‐tuning, and transfer learning. We used median filter noise removal and data augmentation technique to increase the number of training datasets. The proposed ensemble model produces 98% of accuracy. As an outcome of this study, the proposed model has the potential to be used as a decision support method for dermatologists. It can also contribute to the early identification (treatment) of skin diseases to reduce their further spread.
Surface chemistry of electrode materials toward improving electrolyte‐wettability: A method review
Authors: Zhao Lei , Peng Yuanyou , Dou Peiyao , Li Yuan , He Tianqi , Ran Fen 
Published: June 2024

AbstractThe electrolyte‐wettability at electrode material/electrolyte interface is a critical factor that governs the fundamental mechanisms of electrochemical reaction efficiency and kinetics of electrode materials in practical electrochemical energy storage. Therefore, the design and construction of electrode material surfaces with improved electrolyte‐wettability has been demonstrated to be important to optimize electrochemical energy storage performance of electrode material. Here, we comprehensively summarize advanced strategies and key progresses in surface chemical modification for enhancing electrolyte‐wettability of electrode materials, including polar atom doping by post treatment, introducing functional groups, grafting molecular brushes, and surface coating by in situ reaction. Specifically, the basic principles, characteristics, and challenges of these surface chemical strategies for improving electrolyte‐wettability of electrode materials are discussed in detail. Finally, the potential research directions regarding the surface chemical strategies and advanced characterization techniques for electrolyte‐wettability in the future are provided. This review not only insights into the surface chemical strategies for improving electrolyte‐wettability of electrode materials, but also provides strategic guidance for the electrolyte‐wettability modification and optimization of electrode materials in pursuing high‐performance electrochemical energy storage devices.image
SAnDReS 2.0: Development of machine‐learning models to explore the scoring function space
Authors: de Azevedo Walter Filgueira , Quiroga Rodrigo , Villarreal Marcos Ariel , da Silveira Nelson José Freitas , Bitencourt‐Ferreira Gabriela , da Silva Amauri Duarte , Veit‐Acosta Martina , Oliveira Patricia Rufino , Tutone Marco , Biziukova Nadezhda , Poroikov Vladimir , Tarasova Olga , Baud Stéphaine 
Published: June 2024

AbstractClassical scoring functions may exhibit low accuracy in determining ligand binding affinity for proteins. The availability of both protein–ligand structures and affinity data make it possible to develop machine‐learning models focused on specific protein systems with superior predictive performance. Here, we report a new methodology named SAnDReS that combines AutoDock Vina 1.2 with 54 regression methods available in Scikit‐Learn to calculate binding affinity based on protein–ligand structures. This approach allows exploration of the scoring function space. SAnDReS generates machine‐learning models based on crystal, docked, and AlphaFold‐generated structures. As a proof of concept, we examine the performance of SAnDReS‐generated models in three case studies. For all three cases, our models outperformed classical scoring functions. Also, SAnDReS‐generated models showed predictive performance close to or better than other machine‐learning models such as KDEEP, CSM‐lig, and ΔVinaRF20. SAnDReS 2.0 is available to download at https://github.com/azevedolab/sandres.