bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiple testing of interval composite null hypotheses using randomized p-values
Authors: Ochieng Daniel
Published: July 2024

Equivalence 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

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.

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

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

As 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

Chronic 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

Recent 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

Starting 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

Analysis 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

Informationen 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

Encephalopathy 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

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

Considerable 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

We 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

The 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.
Rapid expansion of Eurasian beavers in Hungary: thirty-year history of the species’ return
Authors: Czabán Dávid , Juhász Erika
Published: July 2024

The Eurasian beaver is an ecosystem engineer species capable of altering the riparian vegetation and the hydrology of freshwater habitats. The study of its distribution process is therefore important for both nature conservation and conflict management. Thanks to protection efforts and natural expansion, the beaver has already inhabited most of its former range, starting from the brink of extinction. The changes in the Eurasian distribution and population size have been continuously documented in the scientific literature. In these studies, however, Hungary, as a country with an important position along the beavers’ colonisation routes provided by the Danube drainage basin, has hitherto been underrepresented due to a severe lack of data. In this paper, we summarise all the available information about the thirty-year history, colonisation process, current distribution, as well as the management of the Hungarian population. Based on the newest available data, the beaver is present along all of the large rivers and along most permanent watercourses in the country. Despite the continuing growth trend of the population, a beaver management strategy has not yet been developed. Its establishment could be significantly supported by further monitoring surveys and scientific studies focusing on the region- and site-specific ecological effects of the species.
Heterogeneous wireless network selection using feed forward double hierarchy linguistic neural network
Authors: Abdullah Saleem , Ullah Ihsan , Ghani Fazal
Published: July 2024

Network selection in heterogeneous wireless networks (HWNs) is a complex issue that requires a thorough understanding of service features and user preferences. This is because the various wireless access technologies have varying capabilities and limitations, and the best network for a voice, video, and data service depends on a variety of factors. For selecting the optimal network in HWNs, varying factors such as the user’s position, accessible network resources, service quality requirements, and user preferences must be considered. The classical decision making procedure is very difficult and uncertain to select the desirable HWNs for voice, video, and data. Therefore, we develop a novel decision making model based on feed-forward neural networks under the double hierarchy linguistic information for the selection of the best HWNs for voice, video, and data. In this article, we introduce a novel feed-forward double hierarchy linguistic neural network using the Hamacher t-norm and t-conorm. Further, the feed-forward double hierarchy linguistic neural network applies to the decision making model for the selection of the best HWNs for voice, video, and data. In this novel decision making model, we first take the given data about HWNs and use the converting function to convert the given data into a double hierarchy linguistic term set. We calculate the hidden layer and output layer information by using Hamacher aggregation operations. Finally, we use the sigmoid activation function on the output layer information to decide on the best HWNs for voice, video, and data according to ranking. The proposed approach is compared with other existing models of decision making and the results of the comparison show that the proposed technique is applicable and reliable for the decision support model.
High Expression of miR-218-5p in the Peripheral Blood Stream and Tumor Tissues of Pediatric Patients with Sarcomas
Authors: Özdenoğlu Fazilet Yıldız , Ödemiş Demet Akdeniz , Erciyas Seda Kılıç , Tunçer Şeref Buğra , Gültaşlar Büşra Kurt , Salduz Ahmet , Büyükkapu Sema , Olgaç Necat Vakur , Kebudi Rejin , Yazıcı Hülya
Published: July 2024

Sarcomas are malignant tumors that may metastasize and the course of the disease is highly aggressive in children and young adults. Because of the rare incidence of sarcomas and the heterogeneity of tumors, there is a need for non-invasive diagnostic and prognostic biomarkers in sarcomas. The aim of the study was to investigate the level of miR-218-5p in peripheral blood and tumor tissue samples of Ewing’s sarcoma, osteosarcoma, spindle cell sarcoma patients, and healthy controls, and assessed whether the corresponding molecule was a diagnostic and prognostic biomarker. The study was performed patients (n = 22) diagnosed and treated with Ewing’s sarcoma and osteosarcoma and in a control group of 22 healthy children who were matched for age, gender, and ethnicity with the patient group. The expression level of miR-218-5p in RNA samples from peripheral blood and tissue samples were analyzed using the RT-PCR and the expression level of miR-218-5p was evaluated by comparison with the levels in patients and healthy controls. The expression level of miR-218-5p was found to be statistically higher (3.33-fold, p = 0.006) in pediatric patients with sarcomas and when the target genes of miR-218-5p were investigated using the bioinformatics tools, the miR-218-5p was found as an important miRNA in cancer. In this study, the miR-218-5p was shown for the first time to have been highly expressed in the peripheral blood and tumor tissue of sarcoma patients. The results suggest that miR-218-5p can be used as a diagnostic and prognostic biomarker in sarcomas and will be evaluated as an important therapeutic target.
Experience of Marginalization in Noncooperative Spaces: The Case of Undocumented Migrant Workers in Italy
Authors: Derakhshan Roya , Chowdhury Rashedur
Published: July 2024

Undocumented migrant workers are among a group of marginalized stakeholders who are severely exploited at their workplace and across broader society. Despite recent scholarly discussions in marginalized stakeholder theory and migration studies, our understanding of how undocumented workers experience marginalization in noncooperative spaces remains very limited. In noncooperative spaces, uncooperative powerful actors deliberately thwart cooperation with local marginalized stakeholders and fail to develop supportive institutional frameworks, such as regulative and transparent governance principles. To address these issues, we conducted interviews with 47 undocumented workers and civil society workers in Italy. Our findings reveal that the marginalization experienced by undocumented workers encompasses socio-economic immobility, systemic incapability, and a sense of meaninglessness. Further, our research challenges the principles of stakeholder capitalism inherent in traditional stakeholder theory, revealing the inadequacy of conventional notions in noncooperative spaces where marginalized stakeholders deal with disempowerment and immobility. We delve into the silent and tacit collusion among uncooperative firms in these spaces, shedding light on the ways in which this problematic cooperation leads to the creation of normative harm. Moreover, we introduce the experience of meaninglessness as an internal barrier hindering migrant inclusion, underscoring the imperative need for widespread immigration reforms and normative changes to foster an environment conducive to meaningful transformations for migrants.
Genetically predicted immune cells mediate the association between gut microbiota and neuropathy pain
Authors: Lan Zhixuan , Wei Yi , Yue Kan , He Ruilin , Jiang Zongbin
Published: July 2024

Background

Previous observational studies have indicated a complex association between gut microbiota (GM) and neuropathic pain (NP). Nonetheless, the precise biological mechanisms underlying this association remain unclear. Therefore, we adopted a Mendelian randomization (MR) approach to investigate the causal relationship between GM and neuropathic pain including post-herpetic neuralgia (PHN), painful diabetic peripheral neuropathy (PDPN), and trigeminal neuralgia (TN), as well as to explore the potential mediation effects of immune cells.

Methods

We performed a two-step, two-sample Mendelian randomization study with an inverse variance-weighted (IVW) approach to investigate the causal role of GM on three major kinds of NP and the mediation effect of immune cells between the association of GM and NP. In addition, we determine the strongest causal associations using Bayesian weighted Mendelian randomization (BWMR) analysis. Furthermore, we will investigate the mediating role of immune cells through a two-step Mendelian randomization design.

Results

We identified 53 taxonomies and pathways of gut microbiota that had significant causal associations with NP. In addition, we also discovered 120 immune cells that exhibited significant causal associations with NP. According to the BWMR and two-step Mendelian randomization analysis, we identified the following results CD4 on CM CD4 + (maturation stages of T cell) mediated 6.7% of the risk reduction for PHN through the pathway of fucose degradation (FUCCAT.PWY). CD28 + DN (CD4-CD8-) AC (Treg) mediated 12.5% of the risk reduction for PHN through the influence on Roseburia inulinivorans. CD45 on lymphocyte (Myeloid cell) mediated 11.9% of the risk increase for TN through the superpathway of acetyl-CoA biosynthesis (PWY.5173). HLA DR + CD8br %T cell (TBNK) mediated 3.2% of the risk reduction for TN through the superpathway of GDP-mannose-derived O-antigen building blocks biosynthesis (PWY.7323). IgD-CD38-AC (B cell) mediated 7.5% of the risk reduction for DPN through the pathway of thiazole biosynthesis I in E. coli (PWY.6892).

Discussion

These findings provided evidence supporting the causal effect of GM with NP, with immune cells playing a mediating role. These findings may inform prevention strategies and interventions directed toward NP. Future studies should explore other plausible biological mechanisms.
Optimizing seaweed biomass production - a two kelp solution
Authors: Stekoll Michael , Pryor Alf , Meyer Alexandra , Kite-Powell Hauke L. , Bailey David , Barbery Kendall , Goudey Clifford A. , Lindell Scott , Roberson Loretta , Yarish Charles
Published: July 2024

Interest in farming kelps has grown beyond using kelp for food, feed or biofuels. There is considerable interest in generating biomass from seaweed for use in bioplastics and other products that would substitute for petroleum-derived products. For these uses to be viable, large amounts of biomass are needed. Very large kelp farms can be expensive to build and maintain, leading to the need to optimize the biomass per unit area. Although close spacing of growlines can lead to poor growth, a viable approach may be to grow two species of kelps together: one that hangs down and one that is buoyant, growing up. This system would increase the spacing in three dimensions. In Alaska, Saccharina latissima is commonly grown hanging down from longlines. One of the buoyant Alaskan kelps is Nereocystis luetkeana. Because there are commercial uses for wild-harvested Nereocystis in Alaska, we undertook a preliminary trial in Kodiak, Alaska, that grew both Saccharina and Nereocystis in the same longline array. Closely spaced lines were seeded the first week of February 2023 and set at 3 m below the surface. The arrays were harvested in late June 2023. Total yields were greatest on the combined arrays, followed by the Nereocystis only and Saccharina only arrays. Despite having 45% fewer grow-lines, the total yield of the Nereocystis on the combined arrays was statistically similar to the Nereocystis only arrays. These results may have significance for large scale macroalgal production.
Development of prospective elementary teachers’ knowledge to teach early algebra through case discussions
Authors: Öztürk-Tavşan Nejla , İşler-Baykal Işıl
Published: July 2024

This study investigated the development of prospective elementary teachers (PETs)’ subject matter and pedagogical content knowledge for teaching early algebra through participation in case discussions. PETs attended five weeks of intervention as part of a method course, which included case discussions. During the intervention, the participants were presented with big ideas of equivalence and equations, generalized arithmetic, and functional thinking as the content of early algebra through text-based classroom cases. Data were collected through one-hour individual interviews before and after the early algebra lessons. The analysis of the pre-interviews indicated that prospective elementary teachers may not be ready to foster algebraic thinking in elementary grades in terms of the required knowledge for teaching. We found that PETs needed more development of required subject matter knowledge to guide algebraic thinking in elementary grades, such as the relational meaning of the equal sign, generalizing, representing, or justifying arithmetic or functional relationships, and reasoning with them. Likewise, PETs were found to lack in pedagogical content knowledge related to students’ conceptions and misconceptions and effective teaching strategies to promote early algebraic thinking. However, we found that specifically designed method courses could help develop PETs’ knowledge of teaching early algebra. Following early algebra lessons centered around case discussions, PETs showed progress in multiple aspects of knowledge across all big ideas, though their improvement in generalized arithmetic was somewhat less pronounced. This progress was observed in their subject matter and pedagogical content knowledge.
Improving Amplification Bandwidth by Combining Josephson Parametric Amplifiers for Active Axion Search Experiments at IBS/CAPP
Authors: Uchaikin Sergey V. , Kim Jinmyeong , Ivanov Boris I. , van Loo Arjan F. , Nakamura Yasunobu , Ahn Saebyeok , Oh Seonjeong , Park Seongtae , Matlashov Andrei , Chung Woohyun , Semertzidis Yannis K.
Published: July 2024

The Center for Axion and Precision Physics Research at the Institute for Basic Science in Republic of Korea is home to multiple active axion search experiments using cavity haloscopes that operate within the frequency range of 1–6 GHz. The haloscopes convert axions to photons, resulting in an output power of about 10$${^{-24}}$$

-
24

–10$${^{-22}}$$

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22

W. To detect such a small signal amidst noise, quantum-limited noise amplifiers and ultra-low-temperature environment (a few tenths of mK) are required for all critical readout components to minimize noise from all active and passive lossy components. Our primary objective is to achieve the highest possible scanning-frequency speed, which includes the time for maintenance and system calibration. This paper presents the development and operation of low-noise amplifiers for haloscope experiments targeting different frequency ranges and provides design, operational, and performance details of the amplifiers.
Volatile matter characterization of birch biochar produced under pressurized conditions
Authors: Babinszki Bence , Czirok István Sándor , Johnson Robert , Sebestyén Zoltán , Jakab Emma , Wang Liang , Turn Scott , Skreiberg Øyvind , Czégény Zsuzsanna
Published: July 2024

The volatile matter (VM) content and composition of birch biochars produced at 320 °C under elevated pressure (0.1–11 MPa) and constant pressure or constant volume reactor conditions were characterized by thermogravimetry/mass spectrometry (TG/MS) and pyrolysis–gas chromatography/mass spectrometry (Py–GC/MS). Some of the thermal properties of the biochars and the composition of the VMs varied as a function of the maximal pressure applied during carbonization. The samples prepared at higher pressures released more volatiles up to 320 °C, while the maximal rate of thermal decomposition at around 440 °C showed decreasing tendency with the carbonization pressure. In terms of VM composition, the most apparent effect was the significant increase of the amounts of apoallobetulins from biochars prepared at elevated pressures, which were formed by dehydration, ring closure and rearrangement from the betulin content of birch. The change in the ratio of the evolved guaiacol and 4-methylguaiacol as well as that of syringol and 4-methylsyringol as a function of the maximal pressure of carbonization indicated a modification of the lignin decomposition mechanism.
Brain magnetic resonance image (MRI) segmentation using multimodal optimization
Authors: Akan Taymaz , Oskouei Amin Golzari , Alp Sait , Bhuiyan Mohammad Alfrad Nobel
Published: July 2024

One of the highly focused areas in the medical science community is segmenting tumors from brain magnetic resonance imaging (MRI). The diagnosis of malignant tumors at an early stage is necessary to provide treatment for patients. The patient’s prognosis will improve if it is detected early. Medical experts use a manual method of segmentation when making a diagnosis of brain tumors. This study proposes a new approach to simplify and automate this process. In recent research, multi-level segmentation has been widely used in medical image analysis, and the effectiveness and precision of the segmentation method are directly tied to the number of segments used. However, choosing the appropriate number of segments is often left up to the user and is challenging for many segmentation algorithms. The proposed method is a modified version of the 3D Histogram-based segmentation method, which can automatically determine an appropriate number of segments. The general algorithm contains three main steps: The first step is to use a Gaussian filter to smooth the 3D RGB histogram of an image. This eliminates unreliable and non-dominating histogram peaks that are too close together. Next, a multimodal particle swarm optimization method identifies the histogram’s peaks. In the end, pixels are placed in the cluster that best fits their characteristics based on the non-Euclidean distance. The proposed algorithm has been applied to a Cancer Imaging Archive (TCIA) and brain MRI Images for brain Tumor detection dataset. The results of the proposed method are compared with those of three clustering methods: FCM, FCM_FWCW, and FCM_FW. In the comparative analysis of the three algorithms across various MRI slices. Our algorithm consistently demonstrates superior performance. It achieves the top mean rank in all three metrics, indicating its robustness and effectiveness in clustering. The proposed method is effective in experiments, proving its capacity to find the proper clusters.
Likelihood-of-harm/help of microsurgery compared to radiosurgery in large vestibular schwannoma
Authors: Wang Sophie Shih-Yüng , Horstmann Gerhard , van Eck Albertus , Tatagiba Marcos , Naros Georgios
Published: July 2024

Purpose

It has been shown that in large vestibular schwannomas (VS), radiosurgery (SRS) is inferior with respect to tumor control compared to microsurgical resection (SURGERY). However, SURGERY poses a significantly higher risk of facial-function deterioration (FFD). The aim of this study was to illustrate the effectiveness in terms of number-needed-to-treat/operate (NNO), number-needed-to-harm (NNH), and likelihood-of-harm/help (LHH) by comparing both treatment modalities in large VS.

Methods

This was a retrospective, dual-center cohort study. Tumor size was classified by Hannover Classification. Absolute risk reduction and risk increase were used to derive additional estimates of treatment effectiveness, namely NNO and NNH. LHH was then calculated by a quotient of NNH/NNO to illustrate the risk–benefit-ratio of SURGERY.

Results

Four hundred and forty–nine patients treated met the inclusion criteria. The incidence of tumor recurrence was significantly higher in SRS (14%), compared to SURGERY (3%) resulting in ARR of 11% and NNO of 10. At the same time, SURGERY was related to a significant risk of FFD resulting in an NNH of 12. Overall, the LHH calculated at 1.20 was favored SURGERY, especially in patients under the age of 40 years (LHH = 2.40), cystic VS (LHH = 4.33), and Hannover T3a (LHH = 1.83) and T3b (LHH = 1.80).

Conclusions

Due to a poorer response of large VS to SRS, SURGERY is superior with respect to tumor control. One tumor recurrence can be prevented, when 10 patients are treated by SURGERY instead of SRS. Thus, LHH portrays the benefit of SURGERY in large VS even when taking raised FFD into account.
Fine Representation of Hessian of Convex Functions and Ricci Tensor on RCD Spaces
Authors: Brena Camillo , Gigli Nicola
Published: July 2024

It is known that on RCD spaces one can define a distributional Ricci tensor $$\textbf{Ric}$$
Ric
. Here we give a fine description of this object by showing that it admits the polar decomposition $$\begin{aligned} \textbf{Ric}=\omega \,|\textbf{Ric}| \end{aligned}$$

Ric
=
ω

|
Ric
|

for a suitable non-negative measure $$|\textbf{Ric}|$$

|
Ric
|

and unitary tensor field $$\omega $$
ω
. The regularity of both the mass measure and of the polar vector are also described. The representation provided here allows to answer some open problems about the structure of the Ricci tensor in such singular setting. Our discussion also covers the case of Hessians of convex functions and, under suitable assumptions on the base space, of the Sectional curvature operator.
The effect of prepaid incentives on panelists’ response across survey modes in a sequential mixed-mode design
Authors: Becker Rolf
Published: July 2024

While the use of prepaid incentives and data collection in a sequential mixed-mode survey design is standard in cross-sectional and longitudinal studies, little is known about the interaction of these strategies and how this affects target persons’ survey participation. Therefore, based on a panel study, this study investigates whether such unconditional prepayments—monetary or non-monetary incentives—increase the efficiency of a sequential mixed-mode survey design in regard to boosted response rates and speed of return after receipt of an invitation to participate in a survey. While the survey design is always identical, the different incentives vary across the waves considered. Different prepayments, such as cash or in-kind incentives, have different effects on invitees’ participation in different survey modes. In particular, non-monetary incentives seem to work only in a sequential mixed-mode design, while cash, as a universal medium, always works in the expected way, across different survey modes. In sum, the study finds that when a single sequential mixed-mode design is used across panel waves the overall response rates are rather similar, even when different incentives are provided.
Investor emotions and market bubbles
Authors: Agarwal Vineet , Taffler Richard J. , Wang Chenyang
Published: July 2024

Asset pricing bubbles are highly emotional market episodes. Despite this, investor emotions are not part of traditional bubble models. We measure the powerful affects influencing investor decisions during speculative market bubbles directly employing textual analysis of media narratives and domain-specific emotion keyword dictionaries and show how understanding investor emotional dynamics helps explain market behavior. Specifically, we focus on the two Chinese stock market bubbles of 2005–2008 and 2014–2016; there is no evidence of investor learning from experience. Despite Chinese media being censored we show it still has strong explanatory power although the independent English language media can provide an additional perspective. Deeper emotions dominate more superficial feelings in information content.
Impact as equalizer: the demise of gender-related differences in anti-doping research
Authors: Kiss Anna , Soós Sándor , Petróczi Andrea
Published: July 2024

In general, the presence and performance of women in science have increased significantly in recent decades. However, gender-related differences persist and remain a global phenomenon. Women make a greater contribution to multidisciplinary research, which renders anti-doping research a compelling area for investigating the gendered aspects of academic research. The research design was based on the overall research aim to investigate whether gender in a specific field (ADS) has an effect on different aspects of research impact, including (1) the size of citation impact obtained by the research output, (2) the impact on the development of the knowledge base of ADS, expressed as the capacity of integrating knowledge from different research areas, and (3) the (expected) type of research impact targeting either societal or scientific developments (or both). We used a previously compiled dataset of 1341 scientific outputs. Using regression analysis, we explored the role of authors’ gender in citations and the effect of authorship features on scientific impact. We employed network analysis and developed a novel indicator (LinkScore) to quantify gendered authors’ knowledge integration capacity. We carried out a content analysis on a subsample of 210 outputs to explore gender differences in research goal orientation as related to gender patterns. Women’s representation has been considerably extended in the domain of ADS throughout the last two decades. On average, outputs with female corresponding authors yield a higher average citation score. Regarding women's knowledge integration roles, we can infer that no substantial gender differences can be detected. Dominantly female papers were overrepresented among publications classified as aimed at scientific progress, while the share of male-authored papers was higher in publications classified as aimed at societal progress. Although no significant gender difference was observed in knowledge integration roles, in anti-doping women appear to be more interdisciplinary than men.