bluuebunny/crossref_metadata_2025_split huggingface dataset:
A systematic survey of fuzzy deep learning for uncertain medical data
Authors: Zheng Yuanhang , Xu Zeshui , Wu Tong , Yi Zhang 
Published: August 2024

AbstractIntelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. It is important and significant to review the contributions of fuzzy deep learning for uncertain medical data, because fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the presence of noise, artifact or high dimensional unstructured information in uncertain medical data. Therefore, taking focus on the intersection of both different fuzzy deep learning models and several types of uncertain medical data, the paper first constructs four types of frameworks of fuzzy deep learning models used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning models, uncertain medical data and application scenarios. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. This work has some original points: (1) four types of frameworks of applying fuzzy deep learning models for uncertain medical data are first proposed. (2) Seven fuzzy deep learning models, five types of uncertain medical data, and five application scenarios are reviewed in details, respectively. (3) The advantages, challenges, and future research directions of fuzzy deep learning for uncertain medical data are critically analyzed, providing valuable suggestions for further deep research.
Experimental validation of the neutron flux suppression in samples with high manganese content in instrumental neutron activation analysis
Authors: Schramm Lukas-Morris , Nemetz Markus , Renz Franz , Feng Bin , Steinhauser Georg 
Published: August 2024

AbstractInstrumental neutron activation analysis (INAA) relies on constant neutron flux densities throughout the activated samples. Although this concept is true for most typical samples, occasionally, the presence of highly neutron absorbing nuclides in the sample may cause a neutron flux density suppression which would ultimately lead to distorted results in the INAA. Here, we have investigated artificial samples with a high manganese (Mn) content. By adding aqueous gold solution, we introduced a liquid in-situ neutron flux monitor into the sample. An Mn content ≤ 50% shows little effect to the internal neutron flux density, however, the flux can be suppressed by ca. 20% when the Mn content reaches 63.2%.
Why some countries but not others? Urbanisation, GDP and endemic disease predict global SARS-CoV-2 excess mortality patterns
Authors: Fountain-Jones Nicholas M. , Charleston Michael , Flies Emily J. , Carver Scott , Yates Luke A. 
Published: August 2024

Abstract
Context
The global impact of the SARS-CoV-2 pandemic has been uneven, with some regions experiencing significant excess mortality while others have been relatively unaffected. Yet factors which predict this variation remain enigmatic, particularly at large spatial scales.

Objectives
We aimed to uncover the key drivers of excess mortality across countries and regions to help understand the factors contributing to the varied impacts of the pandemic worldwide.

Methods
We used spatially explicit Bayesian models that integrate environmental, socio-demographic and endemic disease data at the country level to provide robust global estimates of excess SARS-CoV-2 mortality (P-scores) for the years 2020 and 2021.

Results
We find that urbanization, gross domestic product (GDP) and spatial patterns are strong predictors of excess mortality, with countries characterized by low GDP but high urbanization experiencing the highest levels of excess mortality. Intriguingly, we also observed that the prevalence of malaria and human immunodeficiency virus (HIV) are associated with country-level SARS-CoV-2 excess mortality in Africa and the Western Pacific, whereby countries with low HIV prevalence but high malaria prevalence tend to have lower levels of excess mortality. While these associations are correlative in nature at the macro-scale, they emphasize that patterns of endemic disease and socio-demographic factors are needed to understand the global dynamics of SARS-CoV-2.

Conclusions
Our study identifies factors associated with variation in excess mortality across countries, providing insights into why some were more impacted by the pandemic than others. By understanding these predictors, we can better inform global outbreak management strategies, such as targeting medical resources to highly urban countries with low GDP and high HIV prevalence to reduce mortality during future outbreaks.
A generic approach for reproducible model distillation
Authors: Zhou Yunzhe , Xu Peiru , Hooker Giles 
Published: August 2024

AbstractModel distillation has been a popular method for producing interpretable machine learning. It uses an interpretable “student” model to mimic the predictions made by the black box “teacher” model. However, when the student model is sensitive to the variability of the data sets used for training even when keeping the teacher fixed, the corresponded interpretation is not reliable. Existing strategies stabilize model distillation by checking whether a large enough sample of pseudo-data is generated to reliably reproduce student models, but methods to do so have so far been developed separately for each specific class of student model. In this paper, we develop a generic approach for stable model distillation based on central limit theorem for the estimated fidelity of the student to the teacher. We start with a collection of candidate student models and search for candidates that reasonably agree with the teacher. Then we construct a multiple testing framework to select a sample size such that the consistent student model would be selected under different pseudo samples. We demonstrate the application of our proposed approach on three commonly used intelligible models: decision trees, falling rule lists and symbolic regression. Finally, we conduct simulation experiments on Mammographic Mass and Breast Cancer datasets and illustrate the testing procedure throughout a theoretical analysis with Markov process. The code is publicly available at https://github.com/yunzhe-zhou/GenericDistillation.
Impact of dietary regime on the metabolomic profile of bovine buttermilk and whole milk powder
Authors: Connolly Claire , Timlin Mark , Hogan Sean A. , Murphy Eoin G. , O’Callaghan Tom F. , Brodkorb André , Hennessy Deirdre , Fitzpartick Ellen , O’Donavan Michael , McCarthy Kieran , Murphy John P. , Yin Xiaofei , Brennan Lorraine 
Published: August 2024

Abstract
Introduction
Bovine milk contains a rich matrix of nutrients such as carbohydrates, fat, protein and various vitamins and minerals, the composition of which is altered by factors including dietary regime.

Objectives
The objective of this research was to investigate the impact of dietary regime on the metabolite composition of bovine whole milk powder and buttermilk.

Methods
Bovine whole milk powder and buttermilk samples were obtained from spring-calving cows, consuming one of three diets. Group 1 grazed outdoors on perennial ryegrass which was supplemented with 5% concentrates; group 2 were maintained indoors and consumed a total mixed ration diet; and group 3 consumed a partial mixed ration diet consisting of perennial ryegrass during the day and total mixed ration maintained indoors at night.

Results
Metabolomic analysis of the whole milk powder (N = 27) and buttermilk (N = 29) samples was preformed using liquid chromatography-tandem mass spectrometry, with 504 and 134 metabolites identified in the samples respectively. In whole milk powder samples, a total of 174 metabolites from various compound classes were significantly different across dietary regimes (FDR adjusted p-value ≤ 0.05), including triglycerides, of which 66% had their highest levels in pasture-fed samples. Triglycerides with highest levels in pasture-fed samples were predominantly polyunsaturated with high total carbon number. Regarding buttermilk samples, metabolites significantly different across dietary regimes included phospholipids, sphingomyelins and an acylcarnitine.

Conclusion
In conclusion the results reveal a significant impact of a pasture-fed dietary regime on the metabolite composition of bovine dairy products, with a particular impact on lipid compound classes.
Relaxation and Noise-Driven Oscillations in a Model of Mitotic Spindle Dynamics
Authors: Hargreaves Dionn , Woolner Sarah , Jensen Oliver E. 
Published: August 2024

AbstractDuring cell division, the mitotic spindle moves dynamically through the cell to position the chromosomes and determine the ultimate spatial position of the two daughter cells. These movements have been attributed to the action of cortical force generators which pull on the astral microtubules to position the spindle, as well as pushing events by these same microtubules against the cell cortex and plasma membrane. Attachment and detachment of cortical force generators working antagonistically against centring forces of microtubules have been modelled previously (Grill et al. in Phys Rev Lett 94:108104, 2005) via stochastic simulations and mean-field Fokker–Planck equations (describing random motion of force generators) to predict oscillations of a spindle pole in one spatial dimension. Using systematic asymptotic methods, we reduce the Fokker–Planck system to a set of ordinary differential equations (ODEs), consistent with a set proposed by Grill et al., which can provide accurate predictions of the conditions for the Fokker–Planck system to exhibit oscillations. In the limit of small restoring forces, we derive an algebraic prediction of the amplitude of spindle-pole oscillations and demonstrate the relaxation structure of nonlinear oscillations. We also show how noise-induced oscillations can arise in stochastic simulations for conditions in which the mean-field Fokker–Planck system predicts stability, but for which the period can be estimated directly by the ODE model and the amplitude by a related stochastic differential equation that incorporates random binding kinetics.
Soil resilience assessment using soil profile descriptions and Analytic Hierarchy Process in Mediterranean mountains considering diverse fire occurrences
Authors: Rodrigo-Comino Jesús 
Published: August 2024

AbstractWildfires are complex natural phenomena that exert significant impacts on landscapes, societies, and economies. Understanding the concept of resilience is crucial in mitigating its possible negative impacts, as it involves preparing for, responding to, and recovering from wildfires. This research aims to demonstrate the utility of in situ soil profile description in assessing land use resilience using an Analytic Hierarchy Process (AHP) through an expert panel survey. The study examines a catchment located in the Balearic Islands, considering two fire occurrences (once and twice), comparing abandoned agricultural terraces and natural hillslopes. The results demonstrated that the priority ranking of variables to assess soil profile resilience against wildfires, determined by a panel of 10 experts, identified horizon depth (25.1%), slope inclination (21.5%), and hydrological connectivity (16.6%) as the most crucial factors. Other variables, such as number and size of roots, structure of pedal soil material, size class structure, and rock fragments, also contributed to resilience but to a lesser extent, with scores ranging from 5.7% to 9.6%. Analyzing the priorities established by the experts using AHP, the results showed that the least resilient soil horizon was H1 of the control hillslope, especially under high and low connectivity processes, which aligned with the loss of superficial soil horizons after one and two wildfires. Hillslopes showed greater changes in resilience after occurring wildfires compared to terraces, with the most significant alterations occurring after the second wildfire event. This study addresses a significant knowledge gap in the field by highlighting the interconnectedness of wildfires, resilience, and land use, providing insights into land management strategies for wildfire-prone regions.
Innovative utilization of olive mill wastewater phenolics extracted by lecithin: spray-dried powders in cake formulations
Authors: Karadag Ayse , Cakmakoglu Selma Kayacan , Bekiroglu Hatice , Karasu Salih , Ozer Hayrettin , Sagdic Osman , Yildirim Rusen Metin 
Published: August 2024

AbstractIn the present study, the olive mill wastewater (OMW) phenolics were extracted with soy lecithin using the cloud point extraction method, and this enriched lecithin (OMW-L) was converted into spray-dried powders using maltodextrin (MD) and whey protein concentrate to be utilized as a dry food ingredient in a cake premix. The inlet temperature of 170 °C and a 3 mL/min feed flow rate yielded the highest powder yield (70.88 ± 2.12%) with a moisture content of 3.78 ± 0.03% when the mass ratio of lecithin to MD was 1:3 (w:w). The hydroxytyrosol and tyrosol contents of the powder were 42.60 ± 4.51 mg/100 g and 15.48 ± 2.50 mg/100 g, respectively. Vanillic acid, caffeic acid, 3-hydroxybenzoic acid, catechin, and rutin were also identified in the powders. The spray-dried OMW-L powder with a higher loading of polyphenols was then used in a cake premix, replacing 1% and 3% of wheat flour. This substitution significantly reduced the K value of the cake batter, as determined by rheological analyses. The addition of spray-dried OMW-L powder to the cake samples, particularly at higher concentrations (3%), influenced both crust and crumb color, causing changes in L*, a*, and b* values. The hardness values of the cake samples did not alter when blank or OMW-enriched lecithin powders were added; rather, the hardness value was influenced by the powder content. Overall, this research offers a different perspective on the use of OMW phenolics in food applications, especially in ready-to-use blends, and demonstrates the effects of the obtained spray-dried lecithin powders on batter rheology and cake characteristics.
Graphical abstract
Bridging human expertise and machine learning in production management: a case study on ML-based decision support systems to prevent missing parts at assembly
Authors: Sauer Carl René , Burggräf Peter , Steinberg Fabian 
Published: August 2024

AbstractIn the field of production management, decision support systems (DSS) equipped with machine learning (ML) have significantly advanced production planning and control within manufacturing companies. These systems are crucial, particularly in the machinery industry, for predicting shortages such as missing parts at the start of assembly. However, current ML-based DSS typically focus solely on predicting occurring problems or suggesting options for simplified scenarios, often missing the critical integration of human operators in the decision-making loop. This study introduces an advanced DSS that integrates ML to predict a missing part for the assembly start and to automatically provide a clear indication of the causes behind predicted shortages. This is achieved by employing shapley additive explanations (SHAP) to the respective ML-based prediction model. Thus, this analysis enables production controllers to initiate both proactive and/or reactive actions by detailed insights into the system’s predictions, and fostering a more dynamic interaction providing between human decision-makers and automated systems. This integration reduces the reliance on time-consuming manual analyses and enhances transparency in decision-making processes. The efficacy of the integrated approach is demonstrated by a case study conducted at a German machinery manufacturer, specialized in low-volume, high-variety production. The findings of this case study confirm that the DSS is efficacious in supporting complex decision-making processes, making it a valuable tool for modern production environments.
Advances in Digital Health for Gastrointestinal Nutrition
Authors: Kim Sowon , Zhang Yi , Mauldin Kasuen , Limketkai Berkeley N. 
Published: August 2024

Abstract
Purpose of Review
Advances in computing have led to an explosion in digital health. This review discusses potential applications of digital technologies, their implications for patient care, and future directions at the intersection between nutrition and gastrointestinal (GI) conditions.

Recent Findings
We present an array of digital technologies, including mobile applications, wearable devices, biosensors, artificial intelligence (AI), and telehealth, that have uses for GI nutrition. While having practical value, many are still limited to the early phases of development or have not yet been proven to provide clinical benefit.

Summary
Digital health technologies offer unprecedented insight into the individual’s health and lifestyle. AI technologies, coupled with the wealth of data collected, offer a path toward precision nutrition. Balanced against the incredible potential and promise of digital health, we should also recognize its limitations, particularly risks involving patient privacy and AI bias, when implementing these tools. Much more research is needed for creating, refining, and validating these technologies for GI nutrition before they can be more widely adopted in clinical practice.
Victim’s identification and social categorization: first- and second-order effects on altruistic behavior
Authors: Regasa Wubeshet , Galmarini Umberto , Porro Giuseppe 
Published: August 2024

AbstractWe explore in laboratory how donations to a charity can be influenced by the identifiability and the social categorization of the recipients. We find that donors give more, on average, to unidentified than to identified beneficiaries, since the latter are more likely to receive small donations than the former. Average donations are the same for in- and out-group beneficiaries; however, an in-group recipient is more likely to receive a top donation than an out-group one, whereas the latter is more likely than the former to receive an intermediate donation. Both first- and second-order effects are associated to the Dynamic Identity Fusion Index elicited from participants toward the ‘Multicultural World’.
Mobility and the use of littoral resources in the Late Mesolithic of Northern Spain: the case of La Chora cave (Voto, Cantabria, N Spain)
Authors: León-Cristóbal Alejandro , García-Escárzaga Asier , Fano Miguel Ángel , Arniz-Mateos Rosa , Quesada José Manuel , Abril-Orzaiz Jon , Gutiérrez-Zugasti Igor 
Published: August 2024

AbstractLittoral resources have been consumed by humans since at least the Middle Palaeolithic. Examples of the use of molluscs have been documented along the shores of Europe during that period but it was not until many millennia later that European hunter-fisher-gatherer societies exploited those resources intensively—see the case of Nerja cave during the Younger Dryas. This economic activity caused the accumulation of shells at archaeological sites during the Mesolithic, resulting in the formation of the so-called shell middens, a very common type of deposit along the Atlantic seaboard of Europe. Despite the large number of research projects that have studied the exploitation of coastal environments and the way of life of Mesolithic populations, questions such as the relationship between human mobility and mollusc exploitation patterns still remain. The archaeomalacological study of the shell midden in La Chora cave (Cantabria, Spain) confirms that people foraged for shellfish at several places along the coast, mainly in the estuary of the River Asón. The main difference between La Chora and other Mesolithic sites is its longer shellfish collection radius as the inhabitants travelled over 10 km to the open coast to collect shellfish. This study has expanded the available data about the subsistence strategies of Mesolithic groups in a little-studied area and improved our knowledge of mobility patterns among Mesolithic societies in the northern Iberian Peninsula.
Development of 3D-iNET ORION: a novel, pre-clinical, three-dimensional in vitro cell model for modeling human metastatic neuroendocrine tumor of the pancreas
Authors: Strnadel Jan , Valasek Mark A. , Lin Grace Y. , Lin Huahui , Tipps Ann M. Ponsford , Woo Sang Myung , Fujimura Ken , Wang Huawei , Choi Sunkyu , Bui Jack , Hermosillo Christopher , Jepsen Kristen , Navarro Michael R. , Kelber Jonathan A. , Klemke Richard L. , Bouvet Michael 
Published: August 2024

AbstractNeuroendocrine tumors (NETs) of the pancreas are rare neoplasms that present complex challenges to diagnosis and treatment due to their indolent course. The incidence of pancreatic neuroendocrine tumors has increased significantly over the past two decades. A limited number of pancreatic neuroendocrine cell lines are currently available for the research. Here, we present 3D-iNET ORION, a novel 3-dimensional (spheroid) cell line, isolated from human pancreatic neuroendocrine tumor liver metastasis. Three-dimensionally grown (3D) cancer cell lines have gained interest over the past years as 3D cancer cell lines better recapitulate the in vivo structure of tumors, and are more suitable for in vitro and in vivo experiments. 3D-iNET ORION cancer cell line showed high potential to form tumorspheres when embedded in Matrigel matrix and expresses synaptophysin and EpCAM. Electron microscopy analysis of cancer cell line proved the presence of dense neurosecretory granules. When xenografted into athymic mice, 3D-iNET ORION cells produce slow-growing tumors, positive for chromogranin and synaptophysin. Human Core Exome Panel Analysis has shown that 3DiNET ORION cell line retains the genetic aberration profile detected in the original tumor. In conclusion, our newly developed neuroendocrine cancer cell line can be considered as a new research tool for in vitro and in vivo experiments.
Galois representations in arithmetic geometry
Authors: Lombardo Davide 
Published: August 2024

AbstractWe survey some recent recent results whose proofs depend in an essential way on the study of Galois representations. We discuss in particular the scarcity of rational points on ramified covers of abelian varieties, the problem of algorithmically computing endomorphism rings of abelian varieties over number fields, and a version of Kummer theory for commutative algebraic groups.