bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multiple testing of interval composite null hypotheses using randomized p-values
Authors: Ochieng Daniel 
Published: July 2024

AbstractEquivalence tests are statistical hypothesis testing procedures that aim to establish practical equivalence rather than the usual statistical significant difference. These testing procedures are frequent in “bioequivalence studies," where one would wish to show that, for example, an existing drug and a new one under development have comparable therapeutic effects. In this article, we propose a two-stage randomized (RAND2) p-value that depends on a uniformly most powerful (UMP) p-value and an arbitrary tuning parameter $$c\in [0,1]$$

c
∈
[
0
,
1
]

for testing an interval composite null hypothesis. We investigate the behavior of the distribution function of the two p-values under the null hypothesis and alternative hypothesis for a fixed significance level $$t\in (0,1)$$

t
∈
(
0
,
1
)

and varying sample sizes. We evaluate the performance of the two p-values in estimating the proportion of true null hypotheses in multiple testing. We conduct a family-wise error rate control using an adaptive Bonferroni procedure with a plug-in estimator to account for the multiplicity that arises from our multiple hypotheses under consideration. The various claims in this research are verified using a simulation study and real-world data analysis.
A spectrophotometric analysis of extracted water-soluble phenolic metabolites of lichens
Authors: Furmanek Łukasz , Czarnota Paweł , Tekiela Agata , Kapusta Ireneusz , Seaward Mark R. D. 
Published: July 2024

Abstract
Main conclusion
Rainwater most probably constitutes a relatively effective solvent for lichen substances in nature which have the potential to provide for human and environmental needs in the future.

Abstract
The aims were (i) to test the hypothesis on the potential solubility of lichen phenolic compounds using rainwater under conditions that partly reflect the natural environment and (ii) to propose new and effective methods for the water extraction of lichen substances. The results of spectrophotometric analyses of total phenolic metabolites in rainwater-based extracts from epigeic and epiphytic lichens, employing the Folin–Ciocalteu (F.–C.) method, are presented. The water solvent was tested at three pH levels: natural, 3, and 9. Extraction methods were undertaken from two perspectives: the partial imitation of natural environmental conditions and the potential use of extraction for economic purposes. From an ecological perspective, room-temperature water extraction (‘cold’ method) was used for 10-, 60-, and 120-min extraction periods. A variant of water extraction at analogous time intervals was an ‘insolation’ with a 100W light bulb to simulate the heat energy of the sun. For economic purposes, the water extraction method used the Soxhlet apparatus and its modified version, the ‘tea-extraction’ method (‘hot’ ones). The results showed that those extractions without an external heat source were almost ineffective, but insolation over 60- and 120-min periods proved to be more effective. Both tested ‘hot’ methods also proved to be effective, especially the ‘tea-extraction’ one. Generally, an increase in the concentration of phenolic compounds in water extracts resulted from an increasing solvent pH. The results show the probable involvement of lichen substances in biogeochemical processes in nature and their promising use for a variety of human necessities.
Sequential therapy of refractory metastatic pancreatic cancer with 5-FU/LV/irinotecan (FOLFIRI) vs. 5-FU/LV/oxaliplatin (OFF). The PANTHEON trial (AIO PAK 0116)
Authors: Modest Dominik Paul , Heinemann Volker , Schütt Philipp , Angermeier Stefan , Haberkorn Mike , Waidmann Oliver , Graeven Ullrich , Wille Kai , Kunzmann Volker , Henze Larissa , Constantin Christian , de Wit Maike , Denzlinger Claudio , Ballhausen Alexej , Kurreck Annika , Jelas Ivan , Alig Annabel Helga Sophie , Stahler Arndt , Stintzing Sebastian , Oettle Helmut 
Published: July 2024

Abstract
Purpose
In patients with metastatic pancreatic cancer, after failure of gemcitabine/nab-paclitaxel, this trial compares the efficacy of second-line therapy with FOLFIRI vs. OFF (1:1 randomisation) with cross-over to the vice-versa regimen as third-line therapy.

Patients and Methods
The primary endpoint was PFS (progression-free survival: time from randomization until progression or death) of second-line therapy. The trial aimed to demonstrate non-inferiority of FOLFIRI vs OFF (non-inferiority margin of a hazard ratio (HR) of 1.5, power of 80% and a significance level of 5%, 196 events needed). Secondary endpoints included overall survival (OS), progression-free survival of third-line therapy and safety. The trial is registered with EudraCT Nr. 2016–004640-11.

Results
The trial was terminated with 60 evaluable (37 with FOLFIRI, 23 with OFF) patients due to insufficient recruitment. PFS of second-line therapy was 2.4 (95% CI 2.3–2.6) months with FOLFIRI vs 2.4 (95% CI 2.2–2.7) months with OFF (HR: 0.80, 95% CI 0.45–1.42, P = 0.43). OS was comparable between the arms (HR: 0.95, 95% CI 0.54–1.66), P = 0.84). Only 4 out of 28 (14%) patients receiving third-line therapy achieved a disease control (partial remission or stable disease). Both second-line regimens were well tolerated without new or unexpected safety signals being observed.

Conclusion
The exploratory analysis of this early terminated trial suggests that FOLFIRI and OFF have similar efficacy ant toxicity as second-line therapy of PDAC after failure of gemcitabine/nab-paclitaxel. Third-line therapy regardless of regimen does not provide satisfactory efficacy in this sequential treatment algorithm.
A new paradigm for the efficient inclusion of stochasticity in engineering simulations: Time-separated stochastic mechanics
Authors: Geisler Hendrik , Erdogan Cem , Nagel Jan , Junker Philipp 
Published: July 2024

AbstractAs a physical fact, randomness is an inherent and ineliminable aspect in all physical measurements and engineering production. As a consequence, material parameters, serving as input data, are only known in a stochastic sense and thus, also output parameters, e.g., stresses, fluctuate. For the estimation of those fluctuations it is imperative to incoporate randomness into engineering simulations. Unfortunately, incorporating uncertain parameters into the modeling and simulation of inelastic materials is often computationally expensive, as many individual simulations may have to be performed. The promise of the proposed method is simple: using extended material models to include stochasticity reduces the number of needed simulations to one. This single computation is cheap, i.e., it has a comparable numerical effort as a single standard simulation. The extended material models are easily derived from standard deterministic material models and account for the effect of uncertainty by an extended set of deterministic material parameters. The time-dependent and stochastic aspects of the material behavior are separated, such that only the deterministic time-dependent behavior of the extended material model needs to be simulated. The effect of stochasticity is then included during post-processing. The feasibility of this approach is demonstrated for three different and highly non-linear material models: viscous damage, viscous phase transformations and elasto-viscoplasticity. A comparison to the Monte Carlo method showcases that the method is indeed able to provide reliable estimates of the expectation and variance of internal variables and stress at a minimal fraction of the computation cost.
Malnutrition management in children with chronic kidney disease
Authors: Corsello Antonio , Trovato Chiara Maria , Dipasquale Valeria , Proverbio Emanuele , Milani Gregorio Paolo , Diamanti Antonella , Agostoni Carlo , Romano Claudio 
Published: July 2024

AbstractChronic kidney disease (CKD) encompasses diverse conditions such as congenital anomalies, glomerulonephritis, and hereditary nephropathies, necessitating individualized nutritional interventions. Early detection is pivotal due to the heightened risk of adverse outcomes, including compromised growth and increased healthcare costs. The nutritional assessment in pediatric CKD employs a comprehensive, multidisciplinary approach, considering disease-specific factors, growth metrics, and dietary habits. The prevalence of malnutrition, as identified through diverse tools and guidelines, underscores the necessity for regular and vigilant monitoring. Nutritional management strategies seek equilibrium in calorie intake, protein requirements, and electrolyte considerations. Maintaining a well-balanced nutritional intake is crucial for preventing systemic complications and preserving the remaining kidney function. The nuanced landscape of enteral nutrition, inclusive of gastrostomy placement, warrants consideration in scenarios requiring prolonged support, with an emphasis on minimizing risks for optimized outcomes. In conclusion, the ongoing challenge of managing nutrition in pediatric CKD necessitates continuous assessment and adaptation. This review underscores the significance of tailored dietary approaches, not only to foster growth and prevent complications but also to enhance the overall quality of life for children grappling with CKD.
Graphical abstract
An updated LSU database and pipeline for environmental DNA identification of arbuscular mycorrhizal fungi
Authors: Delavaux Camille S. , Ramos Robert J. , Stürmer Sidney L. , Bever James D. 
Published: July 2024

AbstractRecent work established a backbone reference tree and phylogenetic placement pipeline for identification of arbuscular mycorrhizal fungal (AMF) large subunit (LSU) rDNA environmental sequences. Our previously published pipeline allowed any environmental sequence to be identified as putative AMF or within one of the major families. Despite this contribution, difficulties in implementation of the pipeline remain. Here, we present an updated database and pipeline with (1) an expanded backbone tree to include four newly described genera and (2) several changes to improve ease and consistency of implementation. In particular, packages required for the pipeline are now installed as a single folder (conda environment) and the pipeline has been tested across three university computing clusters. This updated backbone tree and pipeline will enable broadened adoption by the community, advancing our understanding of these ubiquitous and ecologically important fungi.
The Transpolar Drift current: an ocean-ice-wind complex in rotating, spherical coordinates
Authors: Johnson R. S. 
Published: July 2024

AbstractStarting from the governing equations for a viscous, incompressible fluid, written in a rotating, spherical coordinate system that is valid at the North Pole, the thin-shell approximation is invoked. No further approximations are needed in the derivation of the system of asymptotic equations used here. Suitable stress conditions on the upper and lower surfaces of the ice are described, leading to the construction of a solution for the Transpolar Drift current. This involves the specification of a suitable geostrophic flow, combined with an Ekman component. Then, via the stress conditions across the ice at the surface, a solution for the motion of the ice, and for the associated wind blowing over it, are obtained. In addition, the model adopted here provides a prediction for the reduction in ice thickness along the Transpolar Drift current as it passes through the Fram Strait. The formulation that we present allows considerable freedom in the choices of the various elements of the flow; the model chosen for the physical properties of the ice is particularly significant. All these aspects are discussed critically, and it is shown that many avenues for future investigation have been opened.
Non-stationary low flow frequency analysis under climate change
Authors: Yılmaz Muhammet , Tosunoğlu Fatih 
Published: July 2024

AbstractAnalysis of low river flows provides important information for effective management of water resources in a region. Despite the critical importance of understanding low flow dynamics, there is a gap in the literature regarding the use of non-stationary models to analyze low flow data under climate change in Turkey. In this research, low flow series from 80 measuring stations in Turkey are investigated by employing both stationary and non-stationary models based on the Generalized Additive Models for Location, Scale and Shape (GAMLSS). For constructing non-stationary models, 31 explanatory variables consisting of time, precipitation, temperature and atmospheric oscillation indices were used to model the parameters of the chosen distributions. The results show that stationary models are more successful at 7 stations, while non-stationary models are more successful at 73 stations. Comparisons between non-stationary models showed that for most stations, the best performing models were non-stationary models with annual precipitation as covariates. In addition, successful results were obtained when Western Mediterranean Oscillation and North Atlantic Oscillation indices were used as explanatory variables. Additionally, this study investigated 20 and 50-year return levels by fitting the non-stationary frequency distribution models for low flows over historical and projection periods under SSP2-4.5 and SSP5-8.5 climate scenarios. GAMLSS incorporated annual total precipitation, which is the most effective explanatory variable for low flows, as a covariate, and thus changes in low flows were analyzed. The results show that decreases are expected in low flows, except for the stations in the upper Euphrates basin compared to the historical period.
Prognose der oberflächennahen Grundwassertemperatur in Sachsen-Anhalt
Authors: Noethen Maximilian , Hemmerle Hannes , Meyer Laura , Bayer Peter 
Published: July 2024

ZusammenfassungInformationen zu Grundwassertemperaturen (GWT) sind häufig spärlich und nur als Punktdaten aus Grundwassermessstellen verfügbar. Für räumlich aufgelöste Vorhersagen der GWT bieten sich Satellitendaten als Alternative zur herkömmlichen Interpolation an. Dabei wird sich die thermische Kopplung der Landoberfläche und des oberflächennahen Untergrundes zunutze gemacht. In dieser Studie wird die Verteilung der GWT in Sachsen-Anhalt erstmals charakterisiert. Basierend auf Fernerkundungsdaten und Messdaten aus 436 Grundwassermessstellen wird eine Vorhersage mit einer flächigen Auflösung von 500 m getroffen. Zudem werden neben der GWT und der Landoberflächentemperatur als Prognoseparameter weitere Variablen genutzt, welche die GWT beeinflussen und so die Genauigkeit der Vorhersage erhöhen. Die Vorhersage zeigt im Vergleich zu vorangegangenen Studien die bisher höchste Auflösung und Genauigkeit (RMSE = 0,74 K). Die Methodik liefert für alle Landnutzungsklassen gute Ergebnisse (RMSE = 0,55 − 1 K). Ausreißer zeigen jedoch auch, dass eine höhere Auflösung nötig ist, um lokale Hotspots zuverlässiger zu detektieren.
The neurocognitive and neuropsychiatric manifestations of Susac syndrome: a brief review of the literature and future directions
Authors: Koncz Rebecca , Say Miranda J. , Gleason Andrew , Hardy Todd A. 
Published: July 2024

AbstractEncephalopathy is part of the clinical triad of Susac syndrome, but a detailed understanding of the neurocognitive and neuropsychiatric profile of this condition is lacking. Existing literature indicates that cognitive deficits range in severity from subtle to profound. Executive function and short-term recall are affected frequently. Psychiatric manifestations may be absent or may include anxiety, mood disorders or psychosis. If psychiatric phenomena develop during the disease course, it can be hard to disentangle whether symptoms directly relate to the pathology of Susac syndrome or are secondary to treatment-related side effects. In this article, we review what is known about the cognitive and psychiatric morbidity of Susac syndrome and identify areas where knowledge is deficient. Importantly, we also provide a framework for future research, arguing that better phenotyping, understanding of pathophysiology, evaluation of treatments on cognitive and psychiatric outcomes, and longitudinal data capture are vital to improving patient outcomes.
An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video)
Authors: Lee Seunghan , Jeon Jiwoon , Park Jinbae , Chang Young Hoon , Shin Cheol Min , Oh Mi Jin , Kim Su Hyun , Kang Seungkyung , Park Su Hee , Kim Sang Gyun , Lee Hyuk-Joon , Yang Han-Kwang , Lee Hey Seung , Cho Soo-Jeong 
Published: July 2024

Abstract
Background
Accurate prediction of pathologic results for early gastric cancer (EGC) based on endoscopic findings is essential in deciding between endoscopic and surgical resection. This study aimed to develop an artificial intelligence (AI) model to assess comprehensive pathologic characteristics of EGC using white-light endoscopic images and videos.

Methods
To train the model, we retrospectively collected 4,336 images and prospectively included 153 videos from patients with EGC who underwent endoscopic or surgical resection. The performance of the model was tested and compared to that of 16 endoscopists (nine experts and seven novices) using a mutually exclusive set of 260 images and 10 videos. Finally, we conducted external validation using 436 images and 89 videos from another institution.

Results
After training, the model achieved predictive accuracies of 89.7% for undifferentiated histology, 88.0% for submucosal invasion, 87.9% for lymphovascular invasion (LVI), and 92.7% for lymph node metastasis (LNM), using endoscopic videos. The area under the curve values of the model were 0.992 for undifferentiated histology, 0.902 for submucosal invasion, 0.706 for LVI, and 0.680 for LNM in the test. In addition, the model showed significantly higher accuracy than the experts in predicting undifferentiated histology (92.7% vs. 71.6%), submucosal invasion (87.3% vs. 72.6%), and LNM (87.7% vs. 72.3%). The external validation showed accuracies of 75.6% and 71.9% for undifferentiated histology and submucosal invasion, respectively.

Conclusions
AI may assist endoscopists with high predictive performance for differentiation status and invasion depth of EGC. Further research is needed to improve the detection of LVI and LNM.
Artwork pricing model integrating the popularity and ability of artists
Authors: Park Jinsu , Lee Yoonjin , Yang Daewon , Park Jongho , Jung Hohyun 
Published: July 2024

AbstractConsiderable research has been devoted to understanding the popularity effect on the art market dynamics, meaning that artworks by popular artists tend to have high prices. The hedonic pricing model has employed artists’ reputation attributes, such as survey results, to understand the popularity effect, but the reputation attributes are constant and not properly defined at the point of artwork sales. Moreover, the artist’s ability has been measured via random effect in the hedonic model, which fails to reflect ability changes. To remedy these problems, we present a method to define the popularity measure using the artwork sales dataset without relying on the artist’s reputation attributes. Also, we propose a novel pricing model to appropriately infer the time-dependent artist’s abilities using the presented popularity measure. An inference algorithm is presented using the EM algorithm and Gibbs sampling to estimate model parameters and artist abilities. We use the Artnet dataset to investigate the size of the rich-get-richer effect and the variables affecting artwork prices in real-world art market dynamics. We further conduct inferences about artists’ abilities under the popularity effect and examine how ability changes over time for various artists with remarkable interpretations.
Evaluation of the sub-annual sea level anomalies in the continental shelf of the Southwestern Atlantic and their relation to wind variability
Authors: Juhl Marie-Christin , Passaro Marcello , Dettmering Denise , Saraceno Martin 
Published: July 2024

AbstractWe have analyzed the relationship between wind variability and sea level anomalies (SLA) on the Southwestern Atlantic Continental Shelf, focusing on sub-annual temporal scales. For this, we tested the capability of gridded altimetry to represent wind-driven SLA and compared results using an oceanographic model and tide gauge data. The present study used coherence analysis to analyze frequencies for which SLA and wind stress are coherent. The altimetry-SLA were found to have less energy below the three-month period compared to the model SLA. The coherence of along-shore wind stress and altimetry SLA was only significant for > 50 days (d), while the model SLA showed significant agreement in all periods considered, 20 d to annual. We further showed that geostrophic velocities on the continental shelf agreed significantly with SLA for > 50 d. As a result of an Empirical Orthogonal Function (EOF) analysis, we found that the second mode is highly coherent with the along-shore wind stress and accounts for 18.1% and 10.7% of variability in the model and altimetry sea level anomalies, respectively.
Using machine learning algorithms to cluster and classify stone pine (Pinus pinea L.) populations based on seed and seedling characteristics
Authors: Caliskan Servet , Kartal Elif , Balekoglu Safa , Çalışkan Fatma 
Published: July 2024

AbstractThe phenotype of a woody plant represents its unique morphological properties. Population discrimination and individual classification are crucial for breeding populations and conserving genetic diversity. Machine Learning (ML) algorithms are gaining traction as powerful tools for predicting phenotypes. The present study is focused on classifying and clustering the seeds and seedlings in terms of morphological characteristics using ML algorithms. In addition, the k-means algorithm is used to determine the ideal number of clusters. The results obtained from the k-means algorithm were then compared with reality. The best classification performance achieved by the Random Forest algorithm was an accuracy of 0.648 and an F1-Score of 0.658 for the seed traits. Also, the best classification performance for stone pine seedlings was observed for the k-Nearest Neighbors algorithm (k = 18), for which the accuracy and F1-Score were 0.571 and 0.582, respectively. The best clustering performance was achieved with k = 2 for the seed (average Silhouette index = 0.48) and seedling (average Silhouette Index = 0.51) traits. According to the principal component analysis, two dimensions accounted for 97% and 63% of the traits of seeds and seedlings, respectively. The most important features between the seed and seedling traits were cone weight and bud set, respectively. This study will provide a foundation and motivation for future efforts in forest management practices, particularly regarding reforestation, yield optimization, and breeding programs.