bluuebunny/crossref_metadata_2025_split huggingface dataset:
A method to assess the quality of GNSS satellite phase bias products
Authors: Duan Bingbing , Hugentobler Urs , Montenbruck Oliver 
Published: March 2024

AbstractAs part of the International GNSS Service (IGS), several analysis centers provide GPS and Galileo satellite phase bias products to support precise point positioning with ambiguity resolution (PPP-AR). Due to the high correlation with satellite orbits and clock offsets, it is difficult to assess directly the precision of satellite phase bias products. Once outliers exist in satellite phase biases, PPP-AR results are no longer reliable and the combination of satellite phase bias products from IGS analysis centers also gets difficult. In this contribution, we propose a method independent of ground measurements to detect outliers in satellite phase biases by computing the total Difference of satellite Orbits, Clock offsets and narrow-lane Biases at the midnight epoch between two consecutive days. Results over 180 days show that about 0.2, 1.1, 2.0 and 0.1% of the total DOCB values for GPS satellites exceed 0.15 narrow-lane cycles for CODE final, CODE rapid, CNES/CLS final and WUHN rapid satellite products, respectively, while the same outlier-ratios for Galileo satellites are 0.1, 0.9, 0.4 and 0.1%, respectively. As an important contribution to the orbit, clock and bias combination task, we check the consistency of satellite phase bias products between two analysis centers before and after removing these detected outliers from individual analysis centers. It is convincing that the number of large differences of satellite phase biases between two analysis centers is notably reduced.
Pervasiveness of health information in everyday life in Germany: insights from a photo-elicitation diary study on implicit health knowledge using the documentary method
Authors: Geldermann Anna , Thomaschewski Ricarda , Bertram Caren , Woopen Christiane , Jünger Saskia 
Published: March 2024

Abstract
Aim
In an information society, the complexity of health discourses and media–technological developments touch upon processes of finding, understanding, appraising, and using information. Encounters with health information in everyday life are omnipresent. Hence, applying perceived health information to lifeworlds becomes challenging. We were therefore particularly interested in understanding appraisals and practices regarding random encounters with health information.

Subject and methods
A photo-elicitation diary study was designed and conducted to collect data from encounters with health information in everyday life. Drawing on a sociology of knowledge approach and using the documentary method, appraisal patterns of health information were reconstructed.

Results
Four appraisal patterns were reconstructed: control, optimization, responsibility, and confidence.

Conclusion
The findings contribute to a critical reflection on the pervasiveness of health information in lifeworlds, also referred to as infodemic. The identified appraisal patterns and orientation frames offer insights into the impact of random encounters with health information in everyday life; they also provide a foundation for critically discussing quality criteria for health information in a diverse society, and can inform initiatives for strengthening individual and collective health literacy.
Five-year survival prognosis of young, middle-aged, and elderly adult female invasive breast cancer patients by clinical and lifestyle characteristics
Authors: Teng Yu-Tung , Wang Yong Alison , Dong Yaa-Hui , Liu Jason J. 
Published: March 2024

Abstract
Purpose
Early-onset breast cancer incidence has been increasing globally and in Taiwan. However, previous studies have not comprehensively examined how clinical and lifestyle characteristics influence the 5-year survival of breast cancer diagnosed at different stages of adulthood.

Methods
We analyzed the Taiwan National Cancer Registry and Cause of Death datasets to understand how clinical factors (including tumor and treatment characteristics) and lifestyle factors (including body mass index, cigarette smoking, and alcohol consumption) were associated with the 5-year survival of 8471 young, 57,695 middle-aged, and 14,074 elderly female adult invasive breast cancer patients respectively diagnosed at age 20–39, 40–64, and ≥ 65 years between 2002 and 2015, with mortality follow-up to 2020. Poisson regression was used for obtaining the crude and adjusted 5-year survival risk ratios.

Results
Clinical and lifestyle characteristics were distributed differently but had mostly similar direction of association with 5-year survival for the three age groups. Receiving any treatment was associated with better survival, especially for elderly patients. Being underweight at initial cancer treatment was associated with worse survival than having normal weight, especially for elderly patients. Current smokers had worse survival than never smokers for middle-aged and elderly patients. The 5-year breast cancer-specific survival was not significantly higher for those of age 45–49 years than 40–44 years, despite the recommended starting screening age is 45 years in Taiwan.

Conclusion
Our findings contribute to the understanding of early-onset and later-onset female breast cancer characteristics and prognosis, which may inform surveillance and treatment strategies to achieve better breast cancer prognosis.
Can Turbidity Data from Remote Sensing Explain Modelled Spatial and Temporal Sediment Loading Patterns? An Application in the Lake Tana Basin
Authors: Nkwasa Albert , Getachew Rediet Esayas , Lekarkar Katoria , Yimer Estifanos Addisu , Martínez Analy Baltodano , Tang Ting , van Griensven Ann 
Published: March 2024

AbstractUnderstanding the spatial and temporal patterns of sediment loading in water bodies is crucial for effective water quality management. Remote sensing (RS) has emerged as a valuable and reliable tool for monitoring turbidity, which can provide insights into sediment dynamics in water bodies. In this study, we investigate the potential of turbidity data derived from RS to explain simulated spatial and temporal sediment loading patterns in the Lake Tana basin, Ethiopia. Utilizing existing RS lake turbidity data from Copernicus Global Land Service (CGLS) and simulated seasonal and multiyear trends of river sediment loadings into Lake Tana from the Soil and Water Assessment Tool (SWAT + model), we estimate correlations at different river inlets into Lake Tana. The results reveal a strong positive correlation (R2 > 0.66) between the multiyear monthly average sediment load from inflow rivers and RS lake turbidity at most river inlets. This indicates that the simulated river sediment loads and lake turbidity at river inlets exhibit similar seasonal patterns. Notably, higher turbidity levels are observed at the river inlet with the highest sediment load export. These findings highlight the potential of RS turbidity products in characterizing temporal and spatial patterns of sediment loadings, particularly in data-scarce regions, contributing to a better understanding of water quality dynamics in such areas.
Evidence-Based Guidelines for Low-Risk Ethics Applicants: A Qualitative Analysis of the Most Frequent Feedback Made by Human Research Ethics Proposal Reviewers
Authors: McLinton Sarven S. , Menz Sarah N. , Guerin Bernard , McInnes Elspeth 
Published: March 2024

AbstractHuman Research Ethics Committee (HREC) reviewers often provide similar feedback across applications, which suggests that the problem lies in researcher awareness of key issues rather than novel, unsolvable challenges. If common problems can be addressed before lodgement by applicants referencing clear evidence-based supports (e.g., FAQs on common application shortcomings), it would improve efficiency for HREC members and expedite approvals. We aim to inform such supports by analysing the patterns in the most frequent feedback made by HREC members during review processes. We collected every instance (N = 4,195) of feedback made on N = 197 ‘low-risk’ protocols by all HREC staff (N = 16) at one institution over the course of a full year (2019). Reflexive thematic analysis to identify themes (and content analysis to determine relative frequency) revealed that the top three themes are consistent with existing literature: Consent, Administrative, and Methodological concerns. However, we identified important new themes that are not captured in previous research, including ‘Risk to Researchers’, ‘Commercial benefit, scope and scale’, ‘Diversity’ (covering issues of cultural sensitivity, language and accessibility), as well as fair right to a complaints process. Our thorough exploration of information-rich primary data marks an important methodological improvement over previous studies and offers a theoretical contribution to understanding themes that have heretofore been overlooked in the ethics review process. By identifying the common challenges experienced in HREC review we can better inform tailored supports to applicants (by extension reducing workload burdens on HREC systems) and reduce their perceived barriers to engaging in challenging but meaningful research.
Bright soliton of the third-order nonlinear Schrödinger equation with power law of self-phase modulation in the absence of chromatic dispersion
Authors: Durmus Selvi Altun , Ozdemir Neslihan , Secer Aydin , Ozisik Muslum , Bayram Mustafa 
Published: March 2024

AbstractIn this article, we are interested in two principal topics. First, the bright optical soliton solutions of the third-order (1+1)-nonlinear Schrödinger equation including power law nonlinearity with inter-modal and spatio-temporal dispersions are perused by taking advantage of the new Kudryashov method. Second, the impacts of power law nonlinearity parameters on soliton attitude are investigated for acquired bright soliton form. With the proposed technique, the bright optical soliton solution is acquired, and 3D, contour, and 2D plots are depicted. Then, the impact of power law nonlinearity parameters on the soliton attitude has been successfully demonstrated. As is clear from this perusal power law parameters have an important impact on the soliton attitude, and this impact alters based on the soliton form. As regards our investigation, this form of the equation has not been studied with the power law nonlinearity in the absence of the chromatic dispersion for nonlinear models and the proposed method has not been applied the introduced equation before. It is expected that the consequences which are acquired in this study will shed light on the studies in this field.
Machine learning-aided search for ligands of P2Y6 and other P2Y receptors
Authors: Puhl Ana C. , Lewicki Sarah A. , Gao Zhan-Guo , Pramanik Asmita , Makarov Vadim , Ekins Sean , Jacobson Kenneth A. 
Published: March 2024

AbstractThe P2Y6 receptor, activated by uridine diphosphate (UDP), is a target for antagonists in inflammatory, neurodegenerative, and metabolic disorders, yet few potent and selective antagonists are known to date. This prompted us to use machine learning as a novel approach to aid ligand discovery, with pharmacological evaluation at three P2YR subtypes: initially P2Y6 and subsequently P2Y1 and P2Y14. Relying on extensive published data for P2Y6R agonists, we generated and validated an array of classification machine learning model using the algorithms deep learning (DL), adaboost classifier (ada), Bernoulli NB (bnb), k-nearest neighbors (kNN) classifier, logistic regression (lreg), random forest classifier (rf), support vector classification (SVC), and XGBoost (XGB) classifier models, and the common consensus was applied to molecular selection of 21 diverse structures. Compounds were screened using human P2Y6R-induced functional calcium transients in transfected 1321N1 astrocytoma cells and fluorescent binding inhibition at closely related hP2Y14R expressed in CHO cells. The hit compound ABBV-744, an experimental anticancer drug with a 6-methyl-7-oxo-6,7-dihydro-1H-pyrrolo[2,3-c]pyridine scaffold, had multifaceted interactions with the P2YR family: hP2Y6R inhibition in a non-surmountable fashion, suggesting that noncompetitive antagonism, and hP2Y1R enhancement, but not hP2Y14R binding inhibition. Other machine learning-selected compounds were either weak (experimental anti-asthmatic drug AZD5423 with a phenyl-1H-indazole scaffold) or inactive in inhibiting the hP2Y6R. Experimental drugs TAK-593 and GSK1070916 (100 µM) inhibited P2Y14R fluorescent binding by 50% and 38%, respectively, and all other compounds by < 20%. Thus, machine learning has led the way toward revealing previously unknown modulators of several P2YR subtypes that have varied effects.
Binary effects of fluoxetine and zinc on the biomarker responses of the non-target model organism Daphnia magna
Authors: Atli Gülüzar , Sevgiler Yusuf 
Published: March 2024

AbstractThe antidepressant effect of zinc on mammals has been documented in recent decades, and the concentration of the antidepressant fluoxetine (FLX) in aquatic environments has been rising constantly. The aim of the present study is to evaluate the combined toxicity of a serotonin reuptake inhibitor (FLX) and Zn2+ on a non-target aquatic model organism Daphnia magna. Animals were exposed to single and binary combinations of FLX (20.5 and 41 µg/L for subchronic and 41 and 82 µg/L for acute exposures) and Zn2+ (40 µg/L for subchronic and 80 µg/L for acute exposures). In vivo experiments were done for 7 days subchronic and 48 h acute exposure, while subcellular supernatants of whole Daphnia lysate (WDL) were directly treated with the same concentrations used in the acute experiments. Morphological characteristics, Ca2+-ATPase, antioxidant enzyme activities, and lipid peroxidation were examined. There was antioxidant system suppression and Ca2+-ATPase inhibition despite the diverse response patterns due to duration, concentration, and toxicant type. After acute exposure, biomarkers showed a diminishing trend compared to subchronic exposure. According to integrated biomarker response index (IBR) analysis, in vivo Zn2+ exposure was reasonably effective on the health of D. magna, whereas exposure of WDL to Zn2+ had a lesser impact. FLX toxicity increased in a concentration-dependent manner, reversed by the combined exposure. We concluded that potential pro-oxidative and adverse Ca2+-ATPase effects of FLX and Zn2+ in D. magna may also have harmful impact on ecosystem levels. Pharmaceutical exposure (FLX) should be considered along with their potential to interact with other toxicants in aquatic biota.
Graphical Abstract
Synthesis of an innovative SF/NZVI catalyst and investigation of its effectiveness on bio-oil production in liquefaction process alongside other parameters
Authors: Ersöz Kübra , Bayrak Bahar , Gündüz Figen , Karaca Hüseyin 
Published: March 2024

AbstractToday, new energy sources alternative to fossil fuels are needed to meet the increasing energy demand. It is becoming increasingly important to constitute new energy sources from waste biomass through the liquefaction process. In this study, walnut shells (WS) were liquefied catalytically and non-catalytically under different parameters using the liquefaction method. In this process, the effect of silica fume/nano zero-valent iron (SF/NZVI) catalysts on the conversion rates was investigated. The catalyst was synthesized by reducing NZVI using a liquid phase chemical reduction method on SF. The SF/NZVI catalyst was characterized by scanning electron microscopy- energy dispersive X-ray (SEM–EDX), transmission electron microscope (TEM), Brunauer–Emmett–Teller (BET), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) analysis. The effect of various process parameters on the liquefaction process was investigated. In this context, the reaction temperature ranged from 300 to 400 °C, the solid/solvent ratio ranged from 1/1 to 1/3, the reaction time ranged from 30 to 90 min, and the catalyst concentration ranged from 1 to 6%. According to the results obtained, the most suitable operating conditions for non-catalytic experiments in liquefaction of WS were found to be temperature of 400 °C, reaction time of 60 min, and solid/solvent of 1/3. In catalytic conditions, the optimum values were obtained as temperature of 375 °C, reaction time of 60 min, solid/solvent ratio of 1/3, and catalyst concentration of 6%. The highest total conversion and (oil + gas) % conversion were 90.4% and 46.7% under non-catalytic conditions and 90.7% and 62.3% under catalytic conditions, respectively. Gas chromatography/mass spectrometry (GC/MS) analysis revealed the bio-oil was mainly composed of aromatic compounds (benzene, butyl-, indane and their derivatives,) and polyaromatic compounds (naphthalene, decahydro-, cis-, naphthalene, 1-methyl-.). The aim of increasing the quantity and quality of the light liquid product in the study has been achieved.
Characteristics and Risk of Adverse Mental Health Events Amongst Users of the National Overdose Response Service (NORS) Telephone Hotline
Authors: Viste Dylan , Rioux Will , Rider Nathan , Orr Taylor , Cristall Nora , Seitz Dallas , Ghosh S. Monty 
Published: March 2024

AbstractThe National Overdose Response Service (NORS) is a Canadian mobile or virtual overdose response hotline intended to prevent drug overdose deaths but has unexpectedly received mental health related calls, including adverse mental health events. Our study aimed to examine these occurrences and caller characteristics predictive of adverse mental health outcomes. Using the NORS call dataset, we conducted a descriptive representation of mental health occurrences and mental health emergencies along with correlative statistics. We found that NORS had received 2518 mental health calls, with 28 (1.1%) being adverse events. Men, rural callers, polyroute substance consumption and history of overdosing were found to have increased odds of having an adverse mental health event, while being from Quebec, using non-standard consumption routes and using the line between 50 and 99 times were found to decrease odds. This supports the utility of overdose prevention hotlines to also support people experiencing adverse mental health situations and reduce harm for individuals with mental health and/or substance use disorders.
Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer’s Disease Using Biophysical Modeling and Deep Learning
Authors: Saghafi Soheil , Rumbell Timothy , Gurev Viatcheslav , Kozloski James , Tamagnini Francesco , Wedgwood Kyle C. A. , Diekman Casey O. 
Published: March 2024

AbstractAlzheimer’s disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We used deep hybrid modeling (DeepHM), a recently developed parameter inference technique that combines deep learning with biophysical modeling, to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched wildtype littermate controls to the parameter space of a conductance-based CA1 model. Although mechanistic modeling and machine learning methods are by themselves powerful tools for approximating biological systems and making accurate predictions from data, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. DeepHM addresses these shortcomings by using conditional generative adversarial networks to provide an inverse mapping of data to mechanistic models that identifies the distributions of mechanistic modeling parameters coherent to the data. Here, we demonstrated that DeepHM accurately infers parameter distributions of the conductance-based model on several test cases using synthetic data generated with complex underlying parameter structures. We then used DeepHM to estimate parameter distributions corresponding to the experimental data and infer which ion channels are altered in the Alzheimer’s mouse models compared to their wildtype controls at 12 and 24 months. We found that the conductances most disrupted by tauopathy, amyloidopathy, and aging are delayed rectifier potassium, transient sodium, and hyperpolarization-activated potassium, respectively.
AIxSuture: vision-based assessment of open suturing skills
Authors: Hoffmann Hanna , Funke Isabel , Peters Philipp , Venkatesh Danush Kumar , Egger Jan , Rivoir Dominik , Röhrig Rainer , Hölzle Frank , Bodenstedt Sebastian , Willemer Marie-Christin , Speidel Stefanie , Puladi Behrus 
Published: March 2024

Abstract
Purpose
Efficient and precise surgical skills are essential in ensuring positive patient outcomes. By continuously providing real-time, data driven, and objective evaluation of surgical performance, automated skill assessment has the potential to greatly improve surgical skill training. Whereas machine learning-based surgical skill assessment is gaining traction for minimally invasive techniques, this cannot be said for open surgery skills. Open surgery generally has more degrees of freedom when compared to minimally invasive surgery, making it more difficult to interpret. In this paper, we present novel approaches for skill assessment for open surgery skills.

Methods
We analyzed a novel video dataset for open suturing training. We provide a detailed analysis of the dataset and define evaluation guidelines, using state of the art deep learning models. Furthermore, we present novel benchmarking results for surgical skill assessment in open suturing. The models are trained to classify a video into three skill levels based on the global rating score. To obtain initial results for video-based surgical skill classification, we benchmarked a temporal segment network with both an I3D and a Video Swin backbone on this dataset.

Results
The dataset is composed of 314 videos of approximately five minutes each. Model benchmarking results are an accuracy and F1 score of up to 75 and 72%, respectively. This is similar to the performance achieved by the individual raters, regarding inter-rater agreement and rater variability. We present the first end-to-end trained approach for skill assessment for open surgery training.

Conclusion
We provide a thorough analysis of a new dataset as well as novel benchmarking results for surgical skill assessment. This opens the doors to new advances in skill assessment by enabling video-based skill assessment for classic surgical techniques with the potential to improve the surgical outcome of patients.