bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing the extended‐range forecast skills of the extreme heat events over South China based on three S2S models
Authors: Li Xiaoqi , Chen Ruidan , Qiao Yunting 
Published: June 2024

AbstractThis paper assesses the extended‐range forecast skills of the extreme heat events (EHEs) over South China based on three subseasonal‐to‐seasonal models (European Centre for Medium‐Range Weather Forecasts [ECMWF], National Centers for Environmental Prediction [NCEP], and China Meteorological Administration [CMA]). Overall, ECMWF has the best skill, NCEP the second and CMA the poorest. The predicting skills of EHEs depend on the predicting skills of relevant circulation. Cases studies (June 4–6, 1999, August 19–29, 2009, and August 3–5, 2010) show that the three models generally predict circulation anomalies weaker than observation, leading to the misses of some extreme heat days (EHDs). In these cases, ECMWF is able to well predict the influence of tropical circulation, capture the major characteristics of mid‐latitude circulation but with a slower propagating speed. NCEP could capture the main signals of tropical (mid‐latitude) circulation, but with slower propagating speed (slower propagating speed, deviated direction or more northward location). CMA might produce some EHDs but is derived from the circulation anomaly with the wrong origin or location. Therefore, ECMWF could predict the EHEs most accurately, NCEP could reasonably predict the formation of EHEs and tend to have more delayed predictions, while CMA has the poorest skill due to the false origins of anomalies. These results suggest potential ways to improve the current models' extended‐range forecast skills.
An effective LC–MS method for the simultaneous determination of a potential anti‐rheumatoid arthritis drug, carboxyamidotriazole, and its major metabolite in rat plasma
Authors: Lang Xuli , Tang Jia , Kou Yuhui , Xing Chengfeng , Mu Hongfei , Wang Yanan , Wang Baolian 
Published: June 2024

AbstractCarboxyamidotriazole (CAI) was previously recognized as a well‐tolerated anticancer drug. It has also demonstrated significant anti‐inflammatory effects in various cell and animal model experiments, prompting its investigation as a potential treatment for rheumatoid arthritis. In this study, the potential biotransformation metabolites of CAI were identified both in vitro and in vivo. A sensitive, specific, and accurate LC–MS method was developed for the quantitative analysis of CAI and its major metabolite, CAI‐OH, in rat plasma. CAI, CAI‐OH, and telmisartan (used as an internal standard) were separated using a Zorbax SB C18 column. The mobile phase consisted of water (phase A, containing 0.1% formic acid) and acetonitrile (phase B, containing 0.1% formic acid) at a flow rate of 0.2 mL/min. The analytes were examined using a high‐resolution mass spectrometer, with detected mass‐to‐charge ratios of m/z 424.01293 for CAI, m/z 440.00785 for CAI‐OH, and m/z 515.24415 for telmisartan. Good linearity was observed within the range of 10–5000 ng/mL. Both inter‐ and intra‐batch precision (relative standard deviation, %) were below 6%, and the accuracy ranged from 94.9% to 106.1%. The analytes remained stable throughout the entire experimental period. This method was successfully applied in a pharmacokinetic study of CAI following oral administration in rats.
Beyond success: unveiling the hidden potential of radiotherapy and immunotherapy in solid tumors
Authors: Wu Yuze , Yi Ming , Niu Mengke , Zhou Binghan , Mei Qi , Wu Kongming 
Published: June 2024

AbstractImmunotherapy, particularly with immune checkpoint inhibitors, has significantly transformed cancer treatment. Despite its success, many patients struggle to respond adequately or sustain long‐lasting clinical improvement. A growing consensus has emerged that radiotherapy (RT) enhances the response rate and overall efficacy of immunotherapy. Although combining RT and immunotherapy has been extensively investigated in preclinical models and has shown promising results, establishing itself as a dynamic and thriving area of research, clinical evidence for this combination strategy over the past five years has shown both positive and disappointing results, suggesting the need for a more nuanced understanding. This review provides a balanced and updated analysis of the combination of immunotherapy and RT. We summarized the preclinical mechanisms through which RT boosts antitumor immune responses and mainly focused on the outcomes of recently updated clinical trials, including those that may not have met expectations. We investigated the optimization of the therapeutic potential of this combined strategy, including key challenges, such as fractionation and scheduling, lymph node irradiation, and toxicity. Finally, we offered insights into the prospects and challenges associated with the clinical translation of this combination therapy, providing a realistic perspective on the current state of research and potential future directions.
Bing–Neel syndrome, a rare manifestation of WM; a case report and review of literature
Authors: Rezvani Hamid , Salari Sina , Borhani Hamed , Mataji Maedeh , Azhdari Tehrani Hamed 
Published: June 2024

Key Clinical MessageBing–Neel syndrome (BNS) is a rare manifestation in individuals suffering from Waldenström macroglobulinemia (WM). Neurological signs and symptoms in this syndrome are almost difficult to be differentiated from other common neurological manifestations of hyper‐viscosity or Waldenström‐associated polyneuropathy. In this paper, we report a new case of WM with concurrent BNS, then review the clinical picture and treatment of this syndrome.
Invasive cribriform carcinoma of the breast presenting as an erythematous papule on the nipple: A case report
Authors: Chen Po‐Yu , Ho Yu‐Hsuan , Chen Chih‐Jung , Chiu Chien‐Shan 
Published: June 2024

Key Clinical MessageInvasive cribriform carcinoma (ICC) is a rare form of invasive breast carcinoma with good prognosis. To date, case reports considering skin manifestations of ICC are scarce. We herein report a case of pure ICC presenting as an erythematous papule on the nipple with mammary Paget's disease in the epidermis. We aim to bring awareness to skin manifestation of ICC.
Phthalocyanines Synthesis: A State‐of‐The‐Art Review of Sustainable Approaches Through Green Chemistry Metrics
Authors: Zanotti Gloria , Palmeri Federica , Raglione Venanzio 
Published: June 2024

AbstractDriven by escalating environmental concerns, synthetic chemistry faces an urgent need for a green revolution. Green chemistry, with its focus on low environmental impacting chemicals and minimized waste production, emerges as a powerful tool in addressing this challenge. Metrics such as the E‐factor guide the design of environmentally friendly strategies for chemical processes by quantifying the waste generated in obtaining target products, thus enabling interventions to minimize it. Phthalocyanines (Pcs), versatile molecules with exceptional physical and chemical properties, hold immense potential for technological applications. This review aims to bridge the gap between green chemistry and phthalocyanine synthesis by collecting the main examples of environmentally sustainable syntheses documented in the literature. The calculation of the E‐factor of a selection of them provides insights on how crucial it is to evaluate a synthetic process in its entirety. This approach allows for a better evaluation of the actual sustainability of the phthalocyanine synthetic process and indicates possible strategies to improve it.
Classification of pitting corrosion damage in process facilities using supervised machine learning
Authors: Patel Parth , Aryai Vahid , Arzaghi Ehsan , Kafian Hesam , Abbassi Rouzbeh , Garaniya Vikram 
Published: June 2024

AbstractCorrosion is widely known to be a major cause of the failures in process facilities. Prediction of corrosion damage is therefore essential for industries to manage the availability of their assets. This research aims to investigate the application of supervised machine learning methods for the classification of pitting corrosion damage. Several machine learning classifiers, namely ensemble methods, support vector machine (SVM), K‐nearest neighbours, and the decision tree are used to classify the extent of pitting corrosion damage in corroded steel samples. To simulate the corrosion of the steel samples, a series of laboratory experiments were conducted. After processing the results using appropriate statistical methods, the corrosion data was used to train the machine learning models. The trained models can predict the class of corrosion damage with acceptable accuracy using the material and environmental specifications of the samples. Additionally, a discussion on the selection of machine learning techniques which classify corrosion damage using a risk‐based approach is provided. With their optimal accuracy and lower risk of misclassification, the SVM and AdaBoost models perform better than the other studied models.
An investigation on hydrate prediction and inhibition: An industrial case study
Authors: Rahmanian Nejat , Söyler Nejmi , Wande Farai Munashe , Hashemi Hamed 
Published: June 2024

AbstractThis investigation reports the first study to predict natural gas hydrate formation using both Aspen HYSYS® and HydraFlash software for various gas compositions and thermodynamic inhibitors (monoethylene glycol [MEG] concentrations at 10, 20, 30, and 40 wt.% and methanol concentrations at 10 and 20 wt.%). The simulated predictions are compared with the results of the experimental data in the literature. It has been shown that HydraFlash software can accurately predict hydrate formation conditions for a given industrial case, without having to carry out costly experimental work. This work also evaluated the effect of inhibitors and it appears that inhibitor type and concentration are determined according to condition of gas composition. MEG is consequently selected as the most ideal hydrate inhibitor for the industrial case. This also was confirmed through COSMO‐RS studies in which the sigma profile and sigma potential of the considered inhibitors were obtained and presented using density functional (DFT) calculations to verify the hydrogen bonding affinities of the inhibitors to water molecules. HydraFlash was utilized to predict the dissociation conditions of hydrates under the influence of a high concentration of MEG inhibition, reaching up to 40 wt.% at 313 K and a pressure of 311.1 bar. Finally, it is shown that both software packages are quite accurate and useful tools for the prediction of hydrate for simple systems. However, HydraFlash can simulate more complex systems, including different types of salts at higher pressures. Investigation results indicate insightful guidance for accurately predicting hydrate dissociation under simulated conditions.
Distribution of rupture sites and blebs on intracranial aneurysm walls suggests distinct rupture patterns in ACom and MCA aneurysms
Authors: Karnam Yogesh , Mut Fernando , Yu Alexander K. , Cheng Boyle , Amin‐Hanjani Sepideh , Charbel Fady T. , Woo Henry H. , Niemelä Mika , Tulamo Riikka , Jahromi Behnam Rezai , Frösen Juhana , Tobe Yasutaka , Robertson Anne M. , Cebral Juan R. 
Published: June 2024

AbstractThe mechanisms behind intracranial aneurysm formation and rupture are not fully understood, with factors such as location, patient demographics, and hemodynamics playing a role. Additionally, the significance of anatomical features like blebs in ruptures is debated. This highlights the necessity for comprehensive research that combines patient‐specific risk factors with a detailed analysis of local hemodynamic characteristics at bleb and rupture sites. Our study analyzed 359 intracranial aneurysms from 268 patients, reconstructing patient‐specific models for hemodynamic simulations based on 3D rotational angiographic images and intraoperative videos. We identified aneurysm subregions and delineated rupture sites, characterizing blebs and their regional overlap, employing statistical comparisons across demographics, and other risk factors. This work identifies patterns in aneurysm rupture sites, predominantly at the dome, with variations across patient demographics. Hypertensive and anterior communicating artery (ACom) aneurysms showed specific rupture patterns and bleb associations, indicating two pathways: high‐flow in ACom with thin blebs at impingement sites and low‐flow, oscillatory conditions in middle cerebral artery (MCA) aneurysms fostering thick blebs. Bleb characteristics varied with gender, age, and smoking, linking rupture risks to hemodynamic factors and patient profiles. These insights enhance understanding of the hemodynamic mechanisms leading to rupture events. This analysis elucidates the role of localized hemodynamics in intracranial aneurysm rupture, challenging the emphasis on location by revealing how flow variations influence stability and risk. We identify two pathways to wall failure—high‐flow and low‐flow conditions—highlighting the complexity of aneurysm behavior. Additionally, this research advances our knowledge of how inherent patient‐specific characteristics impact these processes, which need further investigation.
Data Harmonization Guidelines to Combine Multi‐platform Genomic Data from Admixed Populations and Boost Power in Genome‐Wide Association Studies
Authors: Croock Dayna , Swart Yolandi , Schurz Haiko , Petersen Desiree C. , Möller Marlo , Uren Caitlin 
Published: June 2024

AbstractData harmonization involves combining data from multiple independent sources and processing the data to produce one uniform dataset. Merging separate genotypes or whole‐genome sequencing datasets has been proposed as a strategy to increase the statistical power of association tests by increasing the effective sample size. However, data harmonization is not a widely adopted strategy due to the difficulties with merging data (including confounding produced by batch effects and population stratification). Detailed data harmonization protocols are scarce and are often conflicting. Moreover, data harmonization protocols that accommodate samples of admixed ancestry are practically non‐existent. Existing data harmonization procedures must be modified to ensure the heterogeneous ancestry of admixed individuals is incorporated into additional downstream analyses without confounding results. Here, we propose a set of guidelines for merging multi‐platform genetic data from admixed samples that can be adopted by any investigator with elementary bioinformatics experience. We have applied these guidelines to aggregate 1544 tuberculosis (TB) case‐control samples from six separate in‐house datasets and conducted a genome‐wide association study (GWAS) of TB susceptibility. The GWAS performed on the merged dataset had improved power over analyzing the datasets individually and produced summary statistics free from bias introduced by batch effects and population stratification. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Processing separate datasets comprising array genotype dataAlternate Protocol 1: Processing separate datasets comprising array genotype and whole‐genome sequencing dataAlternate Protocol 2: Performing imputation using a local reference panelBasic Protocol 2: Merging separate datasetsBasic Protocol 3: Ancestry inference using ADMIXTURE and RFMixBasic Protocol 4: Batch effect correction using pseudo‐case‐control comparisons
Un “boxing” the reality: Knowledge, attitude, and perception of orthodontists, dental practitioners, and orthodontic patients toward mouthguards' wear during contact sports—A cross‐sectional study
Authors: Raghavan Shailaja , Abu Alhaija Elham S. , Nasrawi Yousef , Al‐ Khateeb Susan , Sunna Samer 
Published: June 2024

AbstractIntroductionMouthguards (MGs) have the potential to prevent contact sport‐related dental injuries. However, varying perceptions of their effectiveness persist, influencing recommendations by dental professionals.AimTo assess the attitudes, knowledge, and perceptions of orthodontists, other dental practitioners (general dentists and other dental specialists), and orthodontic patients involved in contact sports regarding the use of MGs.MethodologyA cross‐sectional survey was designed to collect information from dental clinicians (orthodontists and other dental practitioners) and their orthodontic patients about using MGs during sports participation. A convenience sampling technique was used to recruit the participants for an online survey. A total of 107 (32 males/75 females) dental clinicians and 147 (75 males/72 females) orthodontic patients (mean age 17.5 ± 5.84 years) participated in the study. Pre‐validated questionnaires, specifically designed for dental clinicians and orthodontic patients, were used. Data were analyzed using SPSS software (version 28.0; SPSS).ResultsNearly 50% of dental clinicians have recently recommended MGs to their patients in their clinical practice out of which 59% preferred the stock type; 33% of dental clinicians enquired their patients about involvement in contact sports during initial visits. The majority of orthodontic patients acquired knowledge about MGs through the Internet. More than half of orthodontic patients expressed unwillingness to pay for MGs, and 89% of orthodontic patients found using MGs during contact sports uncomfortable.ConclusionThe findings provide valuable insights into the practices and attitudes of dental clinicians and orthodontic patients regarding MGs, their recommendations, and the comfort levels associated with using them during contact sports.
Age is not just a number: How incorrect ageing impacts close‐kin mark‐recapture estimates of population size
Authors: Petersma Felix T. , Thomas Len , Harris Danielle , Bradley Darcy , Papastamatiou Yannis P. 
Published: June 2024

AbstractPopulation size is a key parameter for the conservation of animal species. Close‐kin mark‐recapture (CKMR) relies on the observed frequency and type of kinship among individuals sampled from the population to estimate population size. Knowledge of the age of the individuals, or a surrogate thereof, is essential for inference with acceptable precision. One common approach, particularly in fish studies, is to measure animal length and infer age using an assumed age‐length relationship (a ‘growth curve’). We used simulation to test the effect of misspecifying the length measurement error and the growth curve on population size estimation. Simulated populations represented two fictional shark species, one with a relatively simple life history and the other with a more complex life history based on the grey reef shark (Carcharhinus amblyrhynchos). We estimated sex‐specific adult abundance, which we assumed to be constant in time. We observed small median biases in these estimates ranging from 1.35% to 2.79% when specifying the correct measurement error standard deviation and growth curve. CI coverage was adequate whenever the growth curve was correctly specified. Introducing error via misspecified growth curves resulted in changes in the magnitude of the estimated adult population, where underestimating age negatively biased the abundance estimates. Over‐ and underestimating the standard deviation of length measurement error did not introduce a bias and had negligible effect on the variance in the estimates. Our findings show that assuming an incorrect standard deviation of length measurement error has little effect on estimation, but having an accurate growth curve is crucial for CKMR whenever ageing is based on length measurements. If ageing could be biased, researchers should be cautious when interpreting CKMR results and consider the potential biases arising from inaccurate age inference.
Evolutionary adaptations generally reverse phenotypic plasticity to restore ancestral phenotypes during new environment adaptation in cattle
Authors: Jiang Qiang , Zhu Li , Zeng Hao , Basang Zhuzha , Suolang Quji , Huang Jinming , Cai Yafei 
Published: June 2024

AbstractPhenotype plasticity and evolution adaptations are the two main ways in which allow populations to deal with environmental changes, but the potential relationship between them remains controversial. Using a reciprocal transplant approach with cattle adapted to the Tibetan Plateau and adjacent lowlands, we aim to investigate the relative contributions of evolutionary processes and phenotypic plasticity in driving both phenotypic and transcriptomic changes under natural conditions. We observed that while numerous genetic transcriptomic changes were evident during the forward adaptation to highland environments, plastic changes predominantly facilitate the transformation of transcriptomes into a preferred state when Tibetan cattle are reintroduced to lowland habitats. Genes with ancestral plasticity are generally reversed by evolutionary adaptations and show a closer expression level to the ancestral stage in evolved Tibetan cattle. A similar trend was also observed at the phenotypes level, with a majority of biochemical and hemorheology phenotypes showing a tendency to revert to their ancestral patterns, suggesting the restoration of ancestral expression levels is a widespread evolutionary trend during adaptation. The findings of our study contribute to the debate regarding the relative contributions of plasticity and genetic changes in mammal environment adaptation. Furthermore, we highlight that the restoration of ancestral phenotypes represents a general pattern in cattle new environment adaptation.
Forecasting interval‐valued returns of crude oil: A novel kernel‐based approach
Authors: Yang Kun , Xu Xueqing , Wei Yunjie , Wang Shouyang 
Published: June 2024

AbstractThis paper proposes a novel kernel‐based generalized random interval multilayer perceptron (KG‐iMLP) method for predicting high‐volatility interval‐valued returns of crude oil. The KG‐iMLP model is constructed by utilizing the
distance based on a kernel function, which outperforms the conventional Euclidean distance. Additionally, the optimal kernel function is estimated using the variance–covariance matrix of the prediction error, contributing to a better understanding of the overall characteristics of interval‐valued data. The introduction of the kernel function renders the algorithms used for estimating machine learning parameters ineffective. Therefore, this paper further proposes a backward
distance of accumulative error propagation algorithm to estimate both the kernel function and model parameters, which provides a feasible approach for utilizing kernel function in interval neural networks. In the empirical analysis of weekly and daily returns of WTI crude oil, the superior predictive performance of the proposed method is demonstrated, enabling stable and accurate predictions for both point values and interval values. The model exhibits consistent outstanding performance across different network structures, showcasing the potential of KG‐iMLP for crude oil price forecasting.