bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning to predict completion of treatment for pancreatic cancer
Authors: Pasha Shamsher A. , Khalid Abdullah , Levy Todd , Demyan Lyudmyla , Hartman Sarah , Newman Elliot , Weiss Matthew J. , King Daniel A. , Zanos Theodoros , Melis Marcovalerio 
Published: August 2024

AbstractBackgroundChemotherapy enhances survival rates for pancreatic cancer (PC) patients postsurgery, yet less than 60% complete adjuvant therapy, with a smaller fraction undergoing neoadjuvant treatment. Our study aimed to predict which patients would complete pre‐ or postoperative chemotherapy through machine learning (ML).MethodsPatients with resectable PC identified in our institutional pancreas database were grouped into two categories: those who completed all intended treatments (i.e., surgery plus either neoadjuvant or adjuvant chemotherapy), and those who did not. We applied logistic regression with lasso penalization and an extreme gradient boosting model for prediction, and further examined it through bootstrapping for sensitivity.ResultsAmong 208 patients, the median age was 69, with 49.5% female and 62% white participants. Most had an Eastern Cooperative Oncology Group (ECOG) performance status of ≤2. The PC predominantly affected the pancreatic head. Neoadjuvant and adjuvant chemotherapies were received by 26% and 47.1%, respectively, but only 49% completed all treatments. Incomplete therapy was correlated with older age and lower ECOG status. Negative prognostic factors included worsening diabetes, age, congestive heart failure, high body mass index, family history of PC, initial bilirubin levels, and tumor location in the pancreatic head. The models also flagged other factors, such as jaundice and specific cancer markers, impacting treatment completion. The predictive accuracy (area under the receiver operating characteristic curve) was 0.67 for both models, with performance expected to improve with larger datasets.ConclusionsOur findings underscore the potential of ML to forecast PC treatment completion, highlighting the importance of specific preoperative factors. Increasing data volumes may enhance predictive accuracy, offering valuable insights for personalized patient strategies.
Association of negative pressure wound therapy and surgical site infections in patients undergoing cytoreductive surgery: An entropy balanced analysis
Authors: Waheed Muhammad Talha , Malik Ibrahim , Ituarte Philip H. G. , Han Ernest , Lwin Thinzar , Paz Isaac B. , Woo Yanghee , Melstrom Laleh , Raoof Mustafa 
Published: August 2024

AbstractBackground and ObjectivesSurgical site infections (SSIs) after cytoreductive surgery (CRS) ± hyperthermic intraperitoneal chemotherapy (HIPEC) are a major cause of potentially avoidable morbidity. We explored the association of negative pressure wound therapy (NPWT) with SSI in patients undergoing CRS/HIPEC.MethodsRetrospective analysis of consecutive patients undergoing CRS/HIPEC for non‐gynecologic cancers. Exposure was the receipt of NPWT versus traditional skin closure. Primary outcome was SSI within 90 days of surgery. We performed multivariable logistic regression (before and after entropy balancing) to evaluate the association of exposure with outcomes.ResultsA total of 251 patients were included, of which 43 (17%) received NPWT and 26 (10.4%) developed SSIs. Baseline demographics and clinicopathologic characteristics were similar between the two groups with some exceptions: Patients who received NPWT had a higher Peritoneal Carcinomatosis Index (median 19 vs. 11, p = 0.002) and operative time (10 vs. 8.2 h, p = 0.003) but were less likely to undergo HIPEC (84% vs. 95%, p < 0.05). After entropy balancing, on multivariable logistic regression, NPWT was not associated with 90‐day SSI (odds ratio = 0.90; 95% confidence interval = 0.21–3.80; p = 0.89).ConclusionNPWT was not associated with a reduction in SSIs. These findings prompt a reevaluation of the routine use of NPWT in CRS/HIPEC.
Social support and travel: Enhancing relationships, communication, and understanding for travel companions
Authors: Zhuang Xueqiu , Wang Yahui 
Published: August 2024

AbstractThis study explores the multifaceted relationship between travel patterns and mental health (MH) in China, offering a novel integrative approach that synthesizes various factors such as mode of transportation, cultural distance, financial implications, and trip planning. Utilizing a descriptive research design, 622 tourists were surveyed using a pen‐and‐paper questionnaire at designated tourist destinations in China. Findings reveal that travel positively influences MH by providing new experiences, socialization, and relaxation, leading to reduced stress and improved well‐being. Duration, frequency, and active travel modes are associated with better MH outcomes. Solo travel fosters personal growth, while group travel enhances social support. Natural environments offer greater MH benefits than urban settings, and leisure travel surpasses work‐related trips in promoting MH. Greater cultural distance, poor trip planning, and financial burdens negatively impact MH. This comprehensive framework offers insights into public health and tourism policies, advancing the understanding of how travel elements collectively influence MH.
Single‐Sided Peak Nasal Inspiratory Flow as an Adjunct Objective Measure in Functional Septorhinoplasty
Authors: Razavi Christopher R. , Ji Keven , Chen Patricia , Carlson Birgitta , Mace Jess C. , Krane Natalie A. , Wang Tom D. , Smith Timothy L. , Loyo Myriam 
Published: August 2024

ObjectiveNasal obstruction can negatively impact patient quality of life, which can be alleviated by functional nasal surgery. Quantification of improvement is most commonly evaluated with the use of validated survey instruments but lacks widely utilized objective measures. Herein, we evaluate the utility of single‐sided peak nasal inspiratory flow (PNIF) as an objective outcome measure in the evaluation and management of nasal obstruction.MethodsAdults presenting with nasal obstruction who were recommended septorhinoplasty were included in the study. Single‐sided and bilateral PNIF measures, nasal obstruction symptom evaluation (NOSE) scores, surgeon‐rated percent nasal obstruction, and nasal obstruction visual analog scale (VAS) scores were recorded preoperatively with Spearman's correlation coefficients (ρ) calculated. Correlation coefficients were also calculated between the change in the above variables from the pre‐ to postoperative state.ResultsOne Hundred Fifteen patients were enrolled in the study and underwent septorhinoplasty. Significant correlations between single‐sided PNIF and the associated VAS scores of the same laterality were found for the worse (ρ = −0.366; p < 0.001) and better (ρ = −0.313; p < 0.001) breathing sides. Correlations between postoperative improvement in single‐sided PNIF and improvement in VAS scores were also found (ρ = −0.330; p = 0.007, ρ = −0.354; p = 0.004). No correlation between NOSE scores and single‐sided PNIF was found except in the subgroup of patients presenting with bilateral asymmetric nasal obstruction, in who NOSE scores correlated with worse side PNIF (ρ = −0.369; p = 0.038).ConclusionsSingle‐sided PNIF has a better correlation to patients' symptoms as rated by VAS score than bilateral PNIF and may be a useful adjunct objective measure in the evaluation and quantification of improvement in patients undergoing septorhinoplasty.Level of Evidence3 Laryngoscope, 135:641–647, 2025
Impact of cultural tightness on consumers' preference for anthropomorphic AI services
Authors: Sui Jiarui , Shen Henry , Zhou Xinyue 
Published: August 2024

AbstractThe current research examines how cultural factors shape consumer preferences for anthropomorphic AI services. The authors investigate the impact of cultural tightness‐looseness, a measure of the strictness of social norms and the tolerance for nonconformity, on the acceptance of anthropomorphic AI services. Through analysis of more than 12,000 product reviews and four experiments, the findings indicate that cultural tightness reduces consumer preference for AI services with anthropomorphic characteristics. The results pinpoint social interaction anxiety as a mediator, whereby increased anxiety due to high cultural tightness subsequently lowers the preference for anthropomorphic AI services. Perceived human identity threat as an alternative explanation for this effect is ruled out. The results further show that the effect of cultural tightness is context‐dependent, affecting preferences in public settings but not private ones. This research bridges a critical gap in knowledge about consumer preferences for AI services, highlighting the significant impact of cultural factors. These findings are crucial for designing products, crafting marketing strategies, and shaping policies in a world increasingly influenced by AI.
Detection of Epstein‒Barr virus DNA methylation as tumor markers of nasopharyngeal carcinoma patients in saliva, oropharyngeal swab, oral swab, and mouthwash
Authors: Zheng Xiao‐Hui , Li Xi‐Zhao , Tang Cao‐Li , Zhang Yu‐Meng , Zhou Ting , Yang Xiao‐Jing , Liao Ying , He Yong‐Qiao , Wang Tong‐Min , Xue Wen‐Qiong , Jia Wei‐Hua 
Published: August 2024

AbstractSaliva biopsy of nasopharyngeal carcinoma (NPC) has been developed in our latest study, indicating the application of oral sampling in NPC detection. Further exploration of the potential for self‐sampling from the oral cavity is necessary. A total of 907 various samples from oral cavity, including saliva (n = 262), oropharyngeal swabs (n = 250), oral swabs (n = 210), and mouthwash (n = 185), were collected. Epstein‒Barr virus (EBV) DNA methylation at the 12,420 bp CpG site in EBV genome from the repeat‐copy W promoter (Wp) region and at the 11,029 bp CpG site in the single‐copy C promoter (Cp) region were simultaneously detected in these samples. A significant increase in EBV methylation, no matter at Wp or Cp region, was found in all types of samples from NPC patients. However, EBV DNA methylation in saliva and oropharyngeal swab showed a better diagnostic performance in detecting NPC. The combination of these two sample types and two markers could help to improve the detection of NPC. Our study further explored the optimal self‐sampling methods and detection target in the detection of NPC and may facilitate the application of EBV DNA methylation detection in a home‐based large‐scale screening of NPC.
Simultaneous frequency and phase corrections of single‐shot MRS data using cross‐correlation
Authors: Deelchand Dinesh K. 
Published: August 2024

AbstractPurposeThe objective of this study was to propose a novel preprocessing approach to simultaneously correct for the frequency and phase drifts in MRS data using cross‐correlation technique.MethodsThe performance of the proposed method was first investigated at different SNR levels using simulation. Random frequency and phase offsets were added to a previously acquired STEAM human data at 7 T, simulating two different noise levels with and without baseline artifacts. Alongside the proposed spectral cross‐correlation (SC) method, three other simultaneous alignment approaches were evaluated. Validation was performed on human brain data at 3 T and mouse brain data at 16.4 T.ResultsThe results showed that the SC technique effectively corrects for both small and large frequency and phase drifts, even at low SNR levels. Furthermore, the mean square measurement error of the SC algorithm was comparable to the other three methods used, with much faster processing time. The efficacy of the proposed technique was successfully demonstrated in both human brain MRS data and in a noisy MRS dataset acquired from a small volume‐of‐interest in the mouse brain.ConclusionThe study demonstrated the availability of a fast and robust technique that accurately corrects for both small and large frequency and phase shifts in MRS.
Dixon‐based B0 self‐navigation in radial stack‐of‐stars multi‐echo gradient echo imaging
Authors: Stelter Jonathan , Weiss Kilian , Wu Mingming , Raspe Johannes , Braun Philipp , Zöllner Christoph , Karampinos Dimitrios C. 
Published: August 2024

AbstractPurposeTo develop a Dixon‐based self‐navigation approach to estimate and correct temporal variations in radial stack‐of‐stars gradient echo imaging for quantitative body MRI.MethodsThe proposed method estimates temporal variations using a self‐navigator estimated by a graph‐cut‐based water‐fat separation algorithm on the oversampled k‐space center. The self‐navigator was employed to correct for phase differences between radial spokes (one‐dimensional [1D] correction) and to perform a motion‐resolved reconstruction to correct spatiotemporal pseudo‐periodic variations (three‐dimensional [3D] correction). Numerical simulations, phantom experiments and in vivo neck scans were performed to evaluate the effects of temporal variations on the field‐map, proton density fat fraction (PDFF) and map, and to validate the proposed method.ResultsTemporal variations were found to cause signal loss and phase shifts on the multi‐echo images that lead to an underestimation of , while PDFF mapping was less affected. The self‐navigator captured slowly varying temporal drifts and temporal variations caused by respiratory motion. While the 1D correction effectively corrected drifts in phantom studies, it was insufficient in vivo due to 3D spatially varying temporal variations with amplitudes of up to 25 Hz at 3 T near the lungs. The proposed 3D correction locally improved the correction of field‐map and and reduced image artifacts.ConclusionTemporal variations particularly affect mapping in radial stack‐of‐stars imaging. The self‐navigation approach can be applied without modifying the MR acquisition to correct for drift and physiological motion‐induced variations, especially in the presence of fat.
Data‐driven models for optimizing second‐hand ship trading strategies under contextual information
Authors: Wang Haoqing , Sun Qinghe , Wang Shuaian 
Published: August 2024

AbstractSecond‐hand ship online trading platforms (SOTPs) are reshaping the traditional broker‐reliant second‐hand ship transactions. This study investigates the decision‐making process within the context of SOTP from a shipowner's perspective. We introduce a comprehensive framework tailored to guide shipowners in strategically navigating pivotal decisions, including the adoption of SOTP and the specification of optimal minimum starting prices while leveraging the value of online transaction data. Our approach is rooted in data‐driven decision‐making under uncertainty, employing quantile regression forests (QRF), and weighted sample average approximation (wSAA). The latter encompasses a predictive wSAA model, a local wSAA model, and a residual‐based wSAA model. Each of these models provides a unique perspective on weight determination within the wSAA paradigm. To validate our proposed approach, we draw upon extensive real‐world data sourced from a Chinese SOTP between January 2017 and May 2023. Within this context, our numerical experiments pursue three primary objectives: (i) identifying performance disparities among the models, (ii) assessing the value of contextual information, and (iii) evaluating the optimal strategy for shipowners. Our findings not only underscore the efficacy of our approaches but also provide invaluable insights into the adoption of SOTPs, establishing a robust foundation for informed decision‐making in the continually evolving SOTP landscape.
Significant age‐related differences between lower leg muscles of older and younger female subjects detected by ultrashort echo time magnetization transfer modeling
Authors: Jerban Saeed , Shaterian Mohammadi Hamidreza , Athertya Jiyo S. , Afsahi Amir Masoud , Shojaeiadib Niloofar , Moazamian Dina , Ward Samuel R. , Woods Gina , Chung Christine B. , Du Jiang , Chang Eric Y. 
Published: August 2024

AbstractMagnetization transfer (MT) magnetic resonance imaging (MRI) can be used to estimate the fraction of water and macromolecular proton pools in tissues. MT modeling paired with ultrashort echo time acquisition (UTE‐MT modeling) has been proposed to improve the evaluation of the myotendinous junction and fibrosis in muscle tissues, which the latter increases with aging. This study aimed to determine if the UTE‐MT modeling technique is sensitive to age‐related changes in the skeletal muscles of the lower leg. Institutional review board approval was obtained, and all recruited subjects provided written informed consent. The legs of 31 healthy younger (28.1 ± 6.1 years old, BMI = 22.3 ± 3.5) and 20 older (74.7 ± 5.5 years old, BMI = 26.7 ± 5.9) female subjects were imaged using UTE sequences on a 3 T MRI scanner. MT ratio (MTR), macromolecular fraction (MMF), macromolecular T2 (T2‐MM), and water T2 (T2‐W) were calculated using UTE‐MT modeling for the anterior tibialis (ATM), posterior tibialis (PTM), soleus (SM), and combined lateral muscles. Results were compared between groups using the Wilcoxon rank sum test. Three independent observers selected regions of interest (ROIs) and processed UTE‐MRI images separately, and the intraclass correlation coefficient (ICC) was calculated for a reproducibility study. Significantly lower mean MTR and MMF values were present in the older compared with the younger group in all studied lower leg muscles. T2‐MM showed significantly lower values in the older group only for PTM and SM muscles. In contrast, T2‐W showed significantly higher values in the older group. The age‐related differences were more pronounced for MMF (−17 to −19%) and T2‐W (+20 to 47%) measurements in all muscle groups compared with other investigated MR measures. ICCs were higher than 0.93, indicating excellent consistency between the ROI selection and MRI measurements of independent readers. As demonstrated by significant differences between younger and older groups, this research emphasizes the potential of UTE‐MT MRI techniques in evaluating age‐related skeletal muscle changes.
Intrusion Detection for Blockchain‐Based Internet of Things Using Gaussian Mixture–Fully Convolutional Variational Autoencoder Model
Authors: Om Kumar C. U. , Marappan Suguna , Murugeshan Bhavadharini , Beaulah P. Mercy Rajaselvi 
Published: August 2024

ABSTRACTThe Internet of Things (IoT) is an evolving paradigm that has dramatically transformed the traditional style of living into a smart lifestyle. IoT devices have recently attained great attention due to their wide range of applications in various sectors, such as healthcare, smart home devices, smart industries, smart cities, and so forth. However, security is still a challenging issue in the IoT environment. Because of the disparate nature of IoT devices, it is hard to detect the different kinds of attacks available in IoT. Various existing works aim to provide a reliable intrusion detection system (IDS) technique. But they failed to work because of several security issues. Thus, the proposed study presents a blockchain‐based deep learning model for IDS. Initially, the input data are preprocessed using min‐max normalization, converting the raw input data into improved quality. In order to detect the presented attacks in the provided dataset, the proposed work introduced Gaussian mixture–fully convolutional variational autoencoder (GM‐FCVAE) model. The implementation is performed in Python, and the performance of the proposed GM‐FCVAE model is analyzed by evaluating several metrics. The proposed GM‐FCVAE model is tested on three datasets and attained superior accuracy of 99.18%, 98.81%, and 98.4% with UNSW‐NB15, CICIDS 2019, and N_BaIoT datasets, respectively. The comparison reveals that the proposed GM‐FCVAE model obtained higher results than the other deep learning techniques. The outperformance shows the efficacy of the proposed study in identifying security attacks.
The influence of crocodile density on the prevalence of human attacks
Authors: Baker Cameron J. , Campbell Mariana A. , Udyawer Vinay , Kopf R. Keller , Campbell Hamish A. 
Published: August 2024

Abstract

Large predator attacks on humans often provoke calls for animal population reduction, assuming it will reduce such incidents. Whilst this seems logical, there is currently little evidence supporting a consistent link between large predator density and attacks on humans.

Here, we assessed whether large predator density is linked to the frequency of attacks on humans using estuarine crocodile (Crocodylus porosus) data in the Northern Territory (NT), Australia. Over the past 50 years, the estuarine crocodile population in the NT has grown from a few thousand to over 100,000 non‐hatchling individuals. Crocodile and human population densities have been closely monitored throughout this period, allowing the frequency of attacks on humans to be assessed over a wide range of densities for both populations.

Our analysis showed an increase in the frequency of attacks on humans as the crocodile population recovered from very low levels in the 1970s. However, the attack rates stabilised around 2009, despite crocodile density and the human population continuing to increase. Based on the relationship between crocodile density and human‐attack frequency, scenario modelling suggested that the crocodile population would need to be culled to a critically endangered level (e.g. 90% population reduction) to reduce attacks on humans from 2.16 to 1.16 attacks per year.

We conclude that whilst crocodile density significantly influences crocodile attack rates at low crocodile population sizes, this relationship becomes weaker as the density increases. For estuarine crocodiles in the NT, a plateauing of attack risk occurred once crocodile density attained ~2 crocodiles per km of river, and we argue that this was because high crocodile densities instigated management (e.g. removal of bold animals, exclusion zones) and education initiates by the government (e.g. ‘Be crocwise’ campaign) that subsequently evoked a change in human behaviour around waterways and stabilised the attack rate.

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Physical properties of isolated cellulose fiber from jute and banana fiber through kraft pulping: Potential applications in packaging and regenerated fibers
Authors: Rahman Md. Mahfuzur , Payel Md. Turab Haque , Asaduzzaman Md. , Hossain Sajid , Ali Mohammad 
Published: August 2024

AbstractCellulose, a naturally abundant biopolymer, holds great potential as a sustainable alternative to synthetic fibers. However, the limited understanding and awareness surrounding cellulose utilization, particularly from agricultural origins, have impeded the complete harnessing of this highly biodegradable resource. This study aimed to extract and characterize cellulose from jute and banana fibers. The extracted cellulose exhibits a light yellow to white color, and microscopic analysis of the fibers showed micro‐fibrils. X‐ray diffraction (XRD) characterization indicated that the extracted cellulose from biomass primarily consists of cellulose II structures, except for the treated banana fiber (M:L = 1:8), which contains both cellulose I and II. Moreover, increasing the M:L ratio of alkali treatment enhanced the percentage of cellulose‐II, as observed from the XRD data. The findings of this study carry significant implications for the efficient production of cellulose fibers, with diverse applications spanning from high‐volume products like regenerated fibers, automotive parts, packaging, absorbent products (diapers), textiles, and precast concrete, drug delivery mediums, electronics, additive manufacturing, bone and tissue scaffolding, and so on. This research opens the door to harnessing the potential of cellulose derived from jute and banana fibers in various industries.Highlights
Extraction cellulose using the kraft process.
Isolated cellulose shows a micron‐sized structure.
Optimal extraction achieved with M:L ratio of 1:4.
Applications of isolated cellulose: regenerated fibers, packaging, absorbent products (diapers), textiles, and so on.