bluuebunny/crossref_metadata_2025 huggingface dataset:
Mortality risk for kidney transplant candidates with diabetes: a population cohort study
Authors: Rashid Raja , Chaudhry Daoud , Evison Felicity , Sharif Adnan
Published: August 2024

Aims/hypothesis

It is unclear whether kidney transplant candidates with diabetes have equitable transplantation opportunities or have divergent survival probabilities stratified by kidney replacement therapy. The aim of this study was to investigate these two issues using national transplant registry data in the UK.

Methods

A cohort study was undertaken of prospectively collected registry data of all wait-listed people with kidney failure receiving dialysis in the UK. All people listed for their first kidney-alone transplant between 2000 and 2019 were included. Stratification was done for cause of kidney failure. Primary outcome was all-cause mortality. Time-to-death from listing was analysed using adjusted non-proportional hazard Cox regression models, with transplantation handled as a time-dependent covariate.

Results

A total of 47,917 wait-listed people with kidney failure formed the total study cohort, of whom 6594 (13.8%) had diabetes classified as cause of kidney failure. People with kidney failure with diabetes comprised 27.6% of the cohort (n=3681/13,359) that did not proceed to transplantation vs only 8.4% (n=2913/34,558) of the cohort that received a transplant (p<0.001). Kidney transplant candidates with diabetes were more likely to be older, of male sex and of ethnic minority background compared with those without diabetes. In an adjusted analysis, compared with remaining on dialysis, any kidney transplant provided survival benefit for wait-listed kidney transplant candidates regardless of diabetes as cause of kidney failure (RR 0.26 [95% CI 0.25, 0.27], p<0.001).

Conclusions/interpretation

Kidney transplant candidates with diabetes have a lower chance of transplantation despite better survival after kidney transplantation vs remaining on dialysis. The reasons for this require further investigation to ensure equal transplantation opportunities.

Graphical Abstract
piR-823 tale as emerging cancer-hallmark molecular marker in different cancer types: a step-toward ncRNA-precision
Authors: Shaker Fatma H. , Sanad Eman F. , Elghazaly Hesham , Hsia Shih-Min , Hamdy Nadia M.
Published: August 2024

PIWI-interacting RNAs (piRNAs) have received a lot of attention for their functions in cancer research. This class of short non-coding RNAs (ncRNA) has roles in genomic stability, chromatin remodeling, messenger RNA (mRNA) integrity, and genome structure. We summarized the mechanisms underlying the biogenesis and regulatory molecular functions of piRNAs. Among all piRNAs studied in cancer, this review offers a comprehensive analysis of the emerging roles of piR-823 in various types of cancer, including colorectal, gastric, liver, breast, and renal cancers, as well as multiple myeloma. piR-823 has emerged as a crucial modulator of various cancer hallmarks through regulating multiple pathways. In the current review, we analyzed several databases and conducted an extensive literature search to explore the influence of piR-823 in carcinogenesis in addition to describing the potential application of piR-823 as prognostic and diagnostic markers as well as the therapeutic potential toward ncRNA precision.
A sharp square function estimate for the moment curve in $\mathbb{R}^{3}$
Authors: Maldague Dominique
Published: August 2024

We prove a sharp (up to $C_{\varepsilon }R^{\varepsilon }$

C
ε

R
ε

) $L^{7}$

L
7

square function estimate for the moment curve in $\mathbb{R}^{3}$

R
3

.
Integrated positron emission particle tracking (PEPT) and X-ray computed tomography (CT) imaging of flow phenomena in twisted tape swirl flow
Authors: Wiggins Cody S. , Cabral Arturo , Mafi Adam , Houston Jerel , Carasik Lane B.
Published: August 2024

A combined positron emission particle tracking (PEPT) and X-ray computed tomography (CT) technique is presented, and its utility is demonstrated through investigation of flow in a pipe with twisted tape swirl insert with varying flow conditions (diameter-based Reynolds numbers 16,300–63,300). A description of this technique is given, as well as data handling practices used to relate geometric information captured by CT to fluid flow data gathered via PEPT. It is found that the CT component is readily capable of capturing the stainless steel insert geometry in this present system, but the use of combined plastic and metal materials leads to artifacts in imaging of the plastic surface. Nonetheless, CT data are related to PEPT flow measurements, and average velocity fields are calculated via a pseudo-framing and interpolation scheme and used to visualize and interrogate key flow phenomena within the system. Radial velocity profiles of the mean flow characteristics are seen to collapse to a nearly common form across all flow conditions considered. Helical vortices are seen propagating through the flow field, generated by bypass flow around the gap between the insert and pipe wall, with additional coherent secondary flow structures seen in the higher Reynolds number cases. These findings enhance the understanding of the mixing mechanisms in these swirl flows and encourage the continued development of PEPT-CT methodologies for 3D flow measurements in optically inaccessible systems.
Enhanced microglial dynamics and a paucity of tau seeding in the amyloid plaque microenvironment contribute to cognitive resilience in Alzheimer’s disease
Authors: Jury-Garfe Nur , Redding-Ochoa Javier , You Yanwen , Martínez Pablo , Karahan Hande , Chimal-Juárez Enrique , Johnson Travis S. , Zhang Jie , Resnick Susan , Kim Jungsu , Troncoso Juan C. , Lasagna-Reeves Cristian A.
Published: August 2024

Asymptomatic Alzheimer’s disease (AsymAD) describes the status of individuals with preserved cognition but identifiable Alzheimer’s disease (AD) brain pathology (i.e., beta-amyloid (Aβ) deposits, neuritic plaques, and neurofibrillary tangles) at autopsy. In this study, we investigated the postmortem brains of a cohort of AsymAD subjects to gain insight into the mechanisms underlying resilience to AD pathology and cognitive decline. Our results showed that AsymAD cases exhibit enrichment in core plaques, decreased filamentous plaque accumulation, and increased plaque-surrounding microglia. Less pathological tau aggregation in dystrophic neurites was found in AsymAD brains than in AD brains, and tau seeding activity was comparable to that in healthy brains. We used spatial transcriptomics to characterize the plaque niche further and revealed autophagy, endocytosis, and phagocytosis as the pathways associated with the genes upregulated in the AsymAD plaque niche. Furthermore, the levels of ARP2 and CAP1, which are actin-based motility proteins that participate in the dynamics of actin filaments to allow cell motility, were increased in the microglia surrounding amyloid plaques in AsymAD cases. Our findings suggest that the amyloid-plaque microenvironment in AsymAD cases is characterized by the presence of microglia with highly efficient actin-based cell motility mechanisms and decreased tau seeding compared with that in AD brains. These two mechanisms can potentially protect against the toxic cascade initiated by Aβ, preserving brain health, and slowing AD pathology progression.
$$D_MD_RDF$$: diabetes mellitus and retinopathy detection framework using artificial intelligence and feature selection
Authors: Balaha Hossam Magdy , El-Gendy Eman M. , Saafan Mahmoud M.
Published: August 2024

Diabetes mellitus is one of the most common diseases affecting patients of different ages. Diabetes can be controlled if diagnosed as early as possible. One of the serious complications of diabetes affecting the retina is diabetic retinopathy. If not diagnosed early, it can lead to blindness. Our purpose is to propose a novel framework, named$$D_MD_RDF$$DMDRDF, for early and accurate diagnosis of diabetes and diabetic retinopathy. The framework consists of two phases, one for diabetes mellitus detection (DMD) and the other for diabetic retinopathy detection (DRD). The novelty of DMD phase is concerned in two contributions. Firstly, a novel feature selection approach called Advanced Aquila Optimizer Feature Selection ($$A^2OFS$$A2OFS) is introduced to choose the most promising features for diagnosing diabetes. This approach extracts the required features from the results of laboratory tests while ignoring the useless features. Secondly, a novel classification approach (CA) using five modified machine learning (ML) algorithms is used. This modification of the ML algorithms is proposed to automatically select the parameters of these algorithms using Grid Search (GS) algorithm. The novelty of DRD phase lies in the modification of 7 CNNs using Aquila Optimizer for the classification of diabetic retinopathy. The reported results concerning the DMD datasets shows that AO reports best performance metrics in the feature selection process with the help of modified ML classifiers. The best achieved accuracy is 98.65% with the GS-ERTC model and max-absolute scaling on the “Early Stage Diabetes Risk Prediction Dataset” dataset. Also, from the reported results concerning the DRD datasets, the AOMobileNet is considered a suitable model for this problem as it outperforms the other modified CNN models with accuracy of 95.80% on the “The SUSTech-SYSU dataset” dataset.
Virtual Prototyping of the RM Active Grid®: a DEM-MBD Study
Authors: Falkner Philipp , Fimbinger Eric , Flachberger Helmut
Published: August 2024

Numerical simulation is an established tool to improve knowledge of complex systems and to accelerate the development of new prototypes. In this contribution, the patented Active Grid® made by the company RM RUBBLE MASTER HMH GmbH is examined.

In an initial step, representative system parameters (spring properties) are found via a multibody dynamic simulation; at this point, without particles acting on the system components. After that, the virtually defined Active Grid® system is loaded with particles in order to include the interaction effects of various bulk materials acting on the system’s mechanical components. For this purpose, a DEM-MBD co-simulation, extending the discrete element method (DEM) towards multibody dynamics (MBD) simulation, is performed, accounting for the bulk materials via DEM and the interacting system components via MBD. This bi-directional co-simulation enables the consideration of interactions between the bulk materials (the particles) and the driven spring-damper system (the moving system components).

With this virtual prototype of the Active Grid®, performance benefits and characteristics in terms of screening efficiency for different material types and varying drive speeds are analysed. The overall aim is to achieve a scalable DEM-MBD model that allows virtual prototyping and, especially, optimisation for future system developments.
Peers and value preferences among adolescents in school classes: a social network and longitudinal approach
Authors: Cieciuch Jan , Kwiatkowska Maria , Kindschi Martin , Davidov Eldad , Algesheimer René
Published: August 2024

The aim of our study was twofold: (1) to explore the role of value preferences on peer relations in school classes (selection effect) and (2) to explore the role of peers’ values on adolescents’ values (influence or socialization effect) in three types of networks (friendship, advice, and trust). To answer these questions, we used a longitudinal social network approach in a study of N = 903 adolescents (57% girls) from 34 secondary school classes in Poland. Pupils began participating in the study when they joined their secondary school and were followed over two and a half years. Panel data were collected at six measurement time points during this period. Values were conceptualized according to the values theory proposed by Schwartz and measured by the Portrait Value Questionnaire. The collection of network data followed a roster design. Pupils were asked to evaluate the strength of their friendships, as well as the frequency with which they approached peers to ask for advice about school or homework or to talk about things that are important to them in the last 2 weeks. We found empirical support for both selection and socialization effects, especially for protection values (Conservation and Self-enhancement). The selection effect was most evident in advice and trust networks and the socialization effect was particularly prevalent in friendship and trust networks.
2022 Düzce, Türkiye earthquake: advances in the past 2 decades, lessons learned, and future projections
Authors: Demir Aydin , Günay Selim , Marinković Marko , Dilsiz Abdullah , Bektaş Nurullah , Khalil Zeyad , Arslan Mehmet Emin , Altunisik Ahmet Can , Caglar Naci , Mosalam Khalid , Sezen Halil
Published: August 2024

In the year 1999, two devastating earthquakes (Mw 7.4 Kocaeli earthquake in August and Mw 7.2 Düzce earthquake in November) occurred in Northwest Türkiye. These two earthquakes led to a very large number of casualties and building collapses. When the 1999 earthquakes occurred, most of the structures in the earthquake-impacted region were not designed according to modern seismic design codes. During the 25 years following those earthquakes, there have been significant advances in building construction in the light of earthquake engineering, including adequate seismic codes, new regulations, and effective code enforcement in the earthquake impacted region. These advances have been reflected in the construction of new structures in the region and the retrofitting of existing ones. As a result, 70–80% of the current building stock in Düzce was designed, constructed, or retrofitted after the 1999 earthquakes. Almost 23 years later, in 2022, an Mw 6.1 earthquake occurred in Düzce, with ground shaking close to the seismic design code life safety performance level. The 2022 earthquake provided a great opportunity to evaluate the effectiveness and consequences of the advances in earthquake engineering and the relevant policy-making and regulations. This paper provides a comparative overview of the 1999 and 2022 earthquakes that struck the city of Düzce in terms of hazard, vulnerability, and consequences. Furthermore, other key lessons learned from the 2022 Düzce earthquake are documented based on field reconnaissance and numerical simulations. The lessons learned are expected to provide useful guidance for the reconstruction efforts after the 2023 Kahramanmaraş Türkiye earthquake sequence or in similar efforts in other parts of the world.
Ways of “Not Hearing”: Corporate Denial in the Case of Aircraft Noise and Victimisation in the UK
Authors: Yıldırım Ayşegül
Published: August 2024

The elusive power dynamics behind the victimisation of aircraft noise pollution, a neglected type of invisible environmental harm, is the main concern of this study. I examine these dynamics through first-hand accounts of individuals’ aircraft noise complaint experiences with the airports in London, UK. An analysis of these experiences reveals specificities of corporate denial strategies in minimising or ignoring noise and the victims. I identify two different uses, namely reconstructive and obfuscation, of technical denial of cause, as the distinct types with which the individuals’ noise claims are confronted. As such, the paper contributes to our understanding of the experience of being an environmental victim. It also illustrates how the specific forms of corporate denial operate to the advantage of the powerful and contribute to victimisation.
Assessing the Asymmetric Effect of Global Climate Anomalies on Food Prices: Evidence from Local Prices
Authors: Emediegwu Lotanna E.
Published: August 2024

This paper uses time-varying smooth transition autoregressive model to investigate the asymmetric nature of El Niño Southern Oscillation (ENSO) —an exogenous climatic factor—with respect to the nonlinear dynamics of food prices in sub-Saharan Africa. Curating food price series from more than 1100 markets from 36 SSA countries, the study finds that ENSO (linearly or nonlinearly) affects roughly half of food prices considered, with most nonlinear models exhibiting strong asymmetric properties with shock-inflicted persistence. Moreover, in terms of the location of the burden of ENSO impact, I find a geographical and food product divide. Specifically, ENSO appears to be more efficacious on maize prices in Southern, Eastern, and some parts of Central Africa. Conversely, local rice, cassava, millet, and animal products are least affected. The policy implication of this dichotomy is that response to ENSO news should be subregion-specific rather than region-specific, depending on how the subregions absorb the shock.
An Overview of the Digital Competencies of Teachers in the Hungarian Secondary Agricultural Vocational Training System
Authors: Khademi-Vidra Anikó , Bakos Izabella Mária
Published: August 2024

In contemporary times, possessing digital competence encompasses not only access to information and communication technologies (ICT) and their utilization, but also the acquisition of suitable knowledge and skills related to them. When it comes to the digital competencies of educators, studies in this field regularly report on the available professional development opportunities, but less attention is given to the educators’ personal attitudes. In our viewpoint, the success of the digital competence development process greatly hinges on realistic and effective teacher self-reflection. Within this framework, digital competence could become an essential component of teacher self-evaluation and emerge as a genuine demand within the predominantly supply-driven professional development market. Based on this observation, we determined to approach 400 instructors teaching at agricultural vocational schools and encourage them to thoughtfully contemplate their own skills and attitudes through our questionnaire. The objective of this exploratory research was to examine the current state of digital competence and future development possibilities among educators, specifically within the context of agricultural education. Moreover, it aimed to identify the actual needs of teachers. The research findings indicate that teachers engaged in Hungarian agricultural vocational education expressed a more positive view of their digital competence than anticipated. Concurrently, infrastructure gaps and developmental directions came to light. Among the novel findings of our research is the fact that real self-reflection is an essential attribute of the teaching career and plays an important role in the development of competences. In this context, a key finding of the research is that respondents strongly expressed their need for subject-specific and didactically-based, thematic and free workshops/courses to learn about the functioning of new ICT tools and platforms and their classroom applications.
Modelling vocational interests: application of the Personal Globe Inventory in Portugal
Authors: Morais M. , Silva J. M. T. , Paixão M. P. , Tracey T.
Published: August 2024

A translated version of the Personal Globe Inventory (PGI, Tracey, 2002) was administered to a Portuguese sample of college students (n = 312) and the data were examined with respect to its internal structure. Results of component analyses suggested, as expected, four substantive factors underlying PGI: General Interest, People/Things, Data/Ideas and Prestige. The randomized test of hypothesized order relations showed that spherical, eight and six (i.e., RIASEC) type models fit the Portuguese data equally well as data from the United States. No structure differences were found across gender. The results support the structural validity of the PGI to Portuguese respondents.
Synthesis, characterization, and azo dye degradation performance of mechanically alloyed Mg65Cu20Y13La2 nanocrystalline powders
Authors: Danacı Nilüfer , Göğebakan Musa , Yaykaşlı Hakan , Kurşun Celal , Uruş Serhan
Published: August 2024

This research describes the synthesis of the multicomponent Mg65Cu20Y13La2 alloy by mechanical alloying (MA) to investigate the influence of milling times on the microstructure of alloy and degradation performance of methyl orange. The structural evolution of this alloy was investigated by using X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive spectrometry (EDX) techniques. The thermal behavior of the alloys was investigated by differential scanning calorimetry (DSC). The crystallite size of the Mg65Cu20Y13La2 alloys was calculated using the Debye Scherrer equation with broadening of the XRD peaks. The methyl orange degradation efficiencies of the Mg65Cu20Y13La2 alloys were evaluated by using ultraviolet–visible (UV–Vis) spectroscopy, Fourier-transform infrared spectroscopy (FT-IR), and gas chromatography–mass spectrometry (GC–MS) techniques. The XRD and SEM results showed that the microstructure of the powders changed during MA. After 10 h milling time, three intermetallic phases were obtained as Mg2Cu2La, Mg24Y5, and Mg2Cu. The results also showed that a solid solution phase, α-Mg(Cu, Y, La), with an average crystallite size 21 nm was formed after 100 h milling time. DSC trace of the Mg65Cu20Y13La2 powders showed two exothermic peaks for the 10 h milling time, while it did not show any peaks for the 100 h milling time. Photocatalytic decomposition of the methyl orange solution by the Mg65Cu20Y13La2 alloy was evaluated by UV–Vis spectra with a decrease in absorbance at a wavelength of 465 nm. After a 20 min exposure, UV–Vis, FT-IR, and GC–MS analysis showed that the methyl orange samples were almost completely degradation by using the Mg65Cu20Y13La2 powders. The Mg65Cu20Y13La2 alloy exhibits a good reusability of 92% by the four cycle and a high efficiency was achieved in all the pH values in the range of 5–9. The results prove that the Mg65Cu20Y13La2 alloy is an efficient and promising material for dyeing wastewater treatment.
Work-Related Fear-Avoidance Beliefs and Risk of Low-Back Pain: Prospective Cohort Study Among Healthcare Workers
Authors: Jakobsen Markus Due , Vinstrup Jonas , Andersen Lars Louis
Published: August 2024

Purpose

Low-back pain (LBP) is a prevalent condition among healthcare workers, negatively affecting well-being and work ability. Research has identified fear-avoidance beliefs, i.e., the belief that physical activities worsen or prolong pain, as a key psychological factor in LBP. Given the physical demands of healthcare work, understanding the link between fear-avoidance and LBP is crucial for effective prevention and management strategies. This study investigated the prospective association between fear-avoidance beliefs and risk of increased LBP intensity and duration in hospital workers.

Methods

Fear-avoidance beliefs and LBP were assessed in 1933 healthcare workers from 389 departments at 19 hospitals at baseline and 1-year follow-up. Associations between baseline work-related fear-avoidance beliefs (FABW) and LBP intensity and duration at follow-up were analyzed using cumulative logistic regression, adjusting for various factors including age, sex, baseline LBP, education, seniority, patient transfers, psychosocial work environment, and lifestyle.

Results

Moderate and high FABW was associated with higher odds of increased pain intensity (OR: 1.37 [95% CI 1.09–1.73] and 1.85 [95% CI 1.18–2.88], respectively) and prolonged pain duration (OR: 1.37 [95% CI 1.05—1.78] and 2.27 [95% CI 1.50–3.44], respectively). A sensitivity analysis including only female nurses showed similar results, with the high FABW group having significantly higher odds of increased pain intensity (OR 2.95, 95% CI 1.84–4.72) and duration (OR 2.64, 95% CI 1.55–4.49).

Conclusions

Fear-avoidance beliefs increase the risk of LBP intensity and duration among healthcare workers, emphasizing the need for interventions dealing with psychological aspects of LBP.
Estimation of mode I fracture toughness of rocks exposed to different environmental conditions using simple and linear multiple regression
Authors: Özdemir Engin , Eren Sarici Didem
Published: August 2024

Mode I fracture toughness (Kıc) is a critical parameter in rock mechanics that is essential for understanding how rocks behave under tensile loading and crucial for applications ranging from safety assessments to structural design in geotechnical engineering. This study comprehensively investigates the influence of various environmental conditions (dry, saturated, frozen, thermal shock and thermal aging) on the physico-mechanical properties and Kıc of rocks. The primary novelty of this study lies in its comprehensive modeling approach under diverse environmental conditions, providing a nuanced understanding of factors influencing rock fracture toughness. By extending analysis to less-studied conditions such as freezing and thermal shock cycles, the study enhances the predictive capacity of fracture toughness models in practical geotechnical applications. Physico-mechanical properties, including uniaxial compressive strength, point load strength, Brazilian tensile strength (BT), Schmidt hardness, and ultrasonic wave velocity were evaluated across different environmental scenarios. Simple and linear multiple regression models were developed using these properties to predict Kıc. Notably, BT emerged as a significant predictor in the simple regression analyzes. Ten linear multiple regression models were formulated using SPSS 20, combining mechanical tests (UCS, BT, PL) with non-destructive testing methods (Vp, Vs, SH), demonstrating robust predictive capabilities with R2 values exceeding 0.95. Performance metrics (mean absolute error, mean absolute percentage error, root mean square error) were used to verify the accuracy of the model.
Pituitary Apoplexy: a re-appraisal of risk factors and best management strategies in the COVID-19 era
Authors: Boyke Andre E. , Michel Michelot , Mamelak Adam N.
Published: August 2024

Pituitary apoplexy (PA) is a clinical syndrome caused by acute hemorrhage and/or infarction of the pituitary gland, most commonly in the setting of a pituitary macroadenoma. PA generally presents with severe headache, nausea, vomiting, visual disturbance, and, in more severe cases, altered mental status. Many factors have been attributed to the risk of developing PA, including most recently, numerous reports showcasing an association with COVID-19 infection or vaccination. Initial management of PA includes evaluation and correction of deficient hormones and electrolytes and an assessment if surgical decompression to relieve pressure on optic nerves and other brain structures is needed. While prompt recognition and treatment are crucial to avoid morbidity and mortality, in the modern era, PA is less commonly considered a true neurosurgical emergency requiring immediate (< 24 h) surgical decompression. Traditionally, surgical decompression has been the standard of care for significant mass effects. However, several studies have shown similar outcomes in visual and hormonal recovery with either surgical decompression or conservative medical management. Unfortunately, most evidence on optimal management strategies is limited to retrospective case series, small prospective studies, and one multi-center observational study. This review aims to provide the most up-to-date evidence on the role of COVID-19 in PA and best management strategies.
Quantum computation with electrons trapped on liquid Helium by using the centimeter-wave manipulating techniques
Authors: Li Yufen , He Suirong , Zhang Miao , Wei Lianfu
Published: August 2024

Surface-state electrons floating on liquid Helium have been served as one of the great potential experimental platforms to implement quantum computation, wherein the qubits are usually encoded by either the lowest two levels of the vertical vibrations (i.e., Hydrogen-like atoms) or the electronic spins. Given the relevant operations require additional techniques, such as the corresponding millimeter-wave or magnetic field manipulations, here we investigate how to implement the scalable quantum computation with a trapped electron array by alternatively using the usual centimeter-wave manipulating techniques. This is because the eigenfrequency of the present qubit, encoded by the two lowest levels of the lateral vibration of the trapped electron, is limited in the centimeter-wave band. We show that, by biasing the electrodes properly and driving the coplanar waveguide transmission line resonator, the electrons can be individually trapped in a series of anharmonic potentials on liquid Helium. Therefore, the well-developed circuit quantum electrodynamics technique for the implementation of superconducting quantum computation can be conveniently utilized here in the present quantum computing platform (proposed firstly in Phys Rev Lett 105:040503, 2010, to implement the fundamental logic gates, typically such as the single-qubit rotations of the individually addressable trapped electrons, the switchable two-qubit manipulations between the electrons trapped in the distant traps, and also the high-fidelity readouts of the target qubits. The feasibility of the proposal is also discussed by numerical simulations.
Sleep quality in cancer patients: a common metric for several instruments measuring sleep quality
Authors: Friedrich Michael , Schulte Thomas , Malburg Merle , Hinz Andreas
Published: August 2024

Purpose

Sleep problems are frequently observed in cancer patients. Multiple questionnaires for assessing sleep quality have been developed. The aim of this study was to present transfer rules that allow the conversion of the patients’ scores from one questionnaire to another. In addition, we anchored this common metric to the general population.

Methods

A sample of 1,733 cancer patients completed the following questionnaires: Pittsburgh Sleep Quality Index, Insomnia Sleep Index, Jenkins Sleep Scale, EORTC QLQ-C30, and the sleep scale of the EORTC QLQ-SURV100. The methods for establishing a common metric were based on Item Response Theory.

Results

The main result of the study is a figure that allows the conversion from one of the above-mentioned sleep scales into another. Furthermore, the scores of the questionnaires can be transferred to theta scores that indicate the position within the group of cancer patients and also to T scores that indicate the position in relation to the general population. The correlations between the sleep scales ranged between 0.70 and 0.85.

Conclusions

The conversion rules presented in the study enable researchers and clinicians to directly compare single scores or mean scores across studies using different sleep scales, to assess the degree of sleep problems with regard to the general population, and to relate cutoff scores from one questionnaire to another.
The mutual effect of nutrients on plant–herbivore interactions
Authors: Zeng Ming
Published: August 2024

Nutrients contained in plant tissues serve as the driving force behind plant and herbivore interactions. The nutrient level, including different kinds of elements and amount of fertilizer, can vary owing to variations in soil composition and this can directly impact plant defense response against herbivores. Plants take up nutrients via two pathways: (i) directly through the root hairs and epidermis and (ii) indirectly through the beneficial microbes that interact with plant roots, such as mycorrhizal fungi and rhizobacteria. The associated microbes, modifying plant nutrient composition, can indirectly affect plant and herbivore interactions. These direct and indirect nutrient uptake pathways adjust plant resistance or tolerance to herbivore attack, as evidenced by factors such as herbivore performance, plant nutrient status, biomass allocation, and compensatory growth. In turn, aboveground and belowground herbivory can exert an effect on nutrient exchange between plants and their associated microbes, primarily on the key resources such as carbon (C), nitrogen (N) and phosphorus (P). Furthermore, it shows that herbivory, in the presence of plant associated beneficial microbes, has no negative effect on plant N and C content. The molecular mechanisms underlying these ecological interactions are being systematically uncovered. In order to outline the research progress in this field, this review synthesizes the current scientific literature regarding the mutual effect of nutrients on plant–herbivore interactions. It aims to support efforts in maintaining the sustainability of ecosystem by optimizing plant defense strategies via managing nutrient variability.
An autonomous centreless approach to chunk-wise data partitioning
Authors: Gu Xiaowei
Published: August 2024

In this paper, a novel autonomous centreless algorithm is proposed for data partitioning. The proposed algorithm firstly constructs the nearest neighbour affinity graph and identifies the local peaks of data density to build micro-clusters. Unlike the vast majority of partitional clustering algorithms, the proposed algorithm does not rely on singleton prototypes, namely, centres or medoids of the micro-clusters to partition the data space. Instead, these micro-clusters are directly utilised to attract nearby data samples to form shape-free Voronoi tessellations, hence, being centreless and robust to noisy data. A fusion scheme is further implemented to fuse these data clouds with higher intra-cluster similarity together to attain a more compact partitioning of data. The proposed algorithm is able to perform data partitioning on a chunk-wise basis and is highly computationally efficient with the default distance measure. Therefore, it is suitable for both static data partitioning in offline scenarios and streaming data partitioning in online scenarios. Numerical examples on a variety of benchmark datasets demonstrate the efficacy of the proposed algorithm.
An explainable approach to deep learning from CT-scans for Covid identification
Authors: Soares Eduardo , Angelov Plamen , Zhang Ziyang
Published: August 2024

The Covid-19 disease has spread widely over the whole world since the beginning of 2020. Following the epidemic which started in Wuhan, China on January 30, 2020 the World Health Organization (WHO) declared a global health emergency and a pandemic. Researchers of different disciplines work along with public health officials to understand the SARS-CoV-2 pathogenesis and jointly with the policymakers urgently develop strategies to control the spread of this new disease. Recent findings have observed specific image patterns from computed tomography (CT) for patients infected by SARS-CoV-2 which are distinct from the other pulmonary diseases. In this paper, we propose an explainable-by-design that has an integrated image segmentation mechanism based on SLIC that improves the algorithm performance and the interpretability of the resulting model. In order to evaluate the proposed approach, we used the SARS-CoV-2 CT scan dataset that we published recently and has been widely used in the literature. The proposed Super-xDNN could obtain statistically better results than traditional deep learning approaches as DenseNet-201 and Resnet-152. Furthermore, it also improved the explainability and interpretability of its decision mechanism when compared with the xDNN basis approach that uses the whole image as prototype. The segmentation mechanism of Super-xDNN favored a decision structure that is more close to the human logic. Moreover, it also allowed the provision of new insights as a heat-map which highlights the areas with highest similarities with Covid-19 prototypes, and an estimation of the area affected by the disease.
Maize-grain zinc and iron concentrations as influenced by agronomic management and biophysical factors: a meta-analysis
Authors: Kihara Job , Sileshi Gudeta W , Bolo Peter , Mutambu Dominic , Senthilkumar Kalimuthu , Sila Andrew , Devkota Mina , Saito Kazuki
Published: August 2024

Human Zn and Fe deficiencies can be reduced through agronomic biofortification, but information on factors influencing maize grain-Zn and -Fe levels remain scanty. This analysis: (1) Establishes the global distribution of Zn and Fe concentrations in maize grain; (2) assess the contribution of different agronomic practices to the effectiveness of Zn fertilizers for increasing grain yields, and Zn and Fe levels in maize grain; and (3) identify key biophysical factors and metrics to more effectively guide agronomic biofortification of Zn. Using 5874 data points in 138 published papers from 34 countries, we estimated a 7.5% probability of grain-Zn concentrations exceeding the benchmark target of 38 mg kg−1. Using 3187 data points from 65 studies across 27 countries we estimated a 8.5% probability of grain-Fe concentrations exceeding the target of 60 mg kg−1. Our 70-paper meta-analysis revealed that applying Zn and/or Fe in combination with inorganic NPK fertilizer can increase maize-grain-Zn and-Fe concentrations by 31% (p < 0.01) relative to the control (NPK only). In 52% and 37.5% of the studies respectively, grain-Zn and -Fe levels showed significant and concomitant increase with grain-yield increases. Soil organic matter, pH, soil-available Zn, organic input applications, and N, Zn and Fe application rates and methods were among the key factors influencing grain Zn and Fe. We conclude there is substantial room for increasing maize-grain Zn and Fe concentrations, and applying Zn, especially in combined soil and foliar applications, gives substantial increases in grain-Zn and -Fe concentrations. This global review reveals large data gaps on maize-grain nutrient levels, and we call for routine collection of such information in future research.
Performance Evaluation of Different Clustering Techniques and Parameters of Hybrid PSO- and GA-ANFIS on Optimization and Prediction of Biomethane Yield of Alkali-Pretreated Groundnut Shells
Authors: Olatunji K. O. , Oladipo S. O. , Madyira D. M. , Sun Y.
Published: August 2024

The study focuses on optimizing biomethane yield in the anaerobic digestion of alkali-pretreated groundnut shells, involving varied input parameters. Biomethane optimization will improve the economy of the technology, which will assist in managing the environmental challenges of fossil fuel combustion. Traditional methods prove challenging, inaccurate, and uneconomical, necessitating efficient optimization models. This research hybridizes particle swarm optimization (PSO) and genetic algorithms (GA) with adaptive neuro-fuzzy inference system (ANFIS) models, assessing input parameters’ influence on biomethane yield through renowned performance metrics. Comparing the best model in the hybrid analysis, encompassing pretreatments A-E, the PSO-ANFIS (RMSE = 1.1719, MADE = 0.6525, MAE = 0.9314, Theil’s U = 0.1844, and SD = 0.7737) outperformed the GA-ANFIS (RMSE = 1.9338, MADE = 0.9318, MAE = 1.6557, Theil’s U = 0.2734, SD = 1.0598), using the same cluster radius of 0.50. Furthermore, compared to the GA-ANFIS model, the PSO-ANFIS model demonstrated significant improvements across various metrics: RMSE by 39.40%, MADE by 29.97%, MAE by 43.75%, Theil’s U by 32.56%, and SD by 27.00%. Results indicate that the PSO-ANFIS model outperforms the GA-ANFIS model, emphasizing the importance of suitable clustering algorithms and precise parameter adjustment for optimal performance in predicting biomethane yield from pretreated lignocellulose feedstocks.

Graphical Abstract
mint: Integrating scientific visualizations into virtual reality
Authors: Geringer Sergej , Geiselhart Florian , Bäuerle Alex , Dec Dominik , Odenthal Olivia , Reina Guido , Ropinski Timo , Weiskopf Daniel
Published: August 2024

We present an image-based approach to integrate state-of-the-art scientific visualization into virtual reality (VR) environments: the mint visualization/VR inter-operation system. We enable the integration of visualization algorithms from within their software frameworks directly into VR without the need to explicitly port visualization implementations to the underlying VR framework—thus retaining their capabilities, specializations, and optimizations. Consequently, our approach also facilitates enriching VR-based scientific data exploration with established or novel VR immersion and interaction techniques available in VR authoring tools. The separation of concerns enables researchers and users in different domains, like virtual immersive environments, immersive analytics, and scientific visualization, to independently work with existing software suitable for their domain while being able to interface with one another easily. We present our system architecture and inter-operation protocol (mint), an example of a collaborative VR environment implemented in the Unity engine (VRAUKE), as well as the integration of the protocol for the visualization frameworks Inviwo, MegaMol, and ParaView. Our implementation is publicly available as open-source software.

Graphical abstract
Changes in Neuroimmunological Synapses During Cerebral Ischemia
Authors: Bitar Lynn , Puig Berta , Oertner Thomas G. , Dénes Ádám , Magnus Tim
Published: August 2024

The direct interplay between the immune and nervous systems is now well established. Within the brain, these interactions take place between neurons and resident glial cells, i.e., microglia and astrocytes, or infiltrating immune cells, influenced by systemic factors. A special form of physical cell–cell interactions is the so-called “neuroimmunological (NI) synapse.” There is compelling evidence that the same signaling pathways that regulate inflammatory responses to injury or ischemia also play potent roles in brain development, plasticity, and function. Proper synaptic wiring is as important during development as it is during disease states, as it is necessary for activity-dependent refinement of neuronal circuits. Since the process of forming synaptic connections in the brain is highly dynamic, with constant changes in strength and connectivity, the immune component is perfectly suited for the regulatory task as it is in constant turnover. Many cellular and molecular players in this interaction remain to be uncovered, especially in pathological states. In this review, we discuss and propose possible communication hubs between components of the adaptive and innate immune systems and the synaptic element in ischemic stroke pathology.
Collaborative Inhibition: A Phenomenological Perspective
Authors: Gyollai Daniel
Published: August 2024

The tendency for people to remember less as members of a group than they would be capable of were they to remember alone is a phenomenon known as collaborative inhibition. The article offers a phenomenological account of this highly counterintuitive effect of group remembering. It argues that the mutual failure to live up to one’s potential does not warrant the standard, strongly negative views about the role of others in recall. Rather, the phenomenon may imply that sharedness itself becomes constitutive of the process in the sense that interaction partners co-determine what and how to remember. Drawing on phenomenological approaches to remembering and second-person engagement, the article argues that individuals participating in shared remembering co-construct their memories by reciprocally and dynamically incorporating each other’s perspectives, attitudes, and emotions about their shared past.
Gender differences in online education
Authors: Erdemli Candan , Vall Castelló Judit
Published: August 2024

Online learning resources have become extremely popular, particularly after the restrictions caused by the Covid-19 outbreak. In this paper, we use data for Spain at the individual level from an online math learning platform which is used by children from over 100 countries, to document the gender differences in the context of online learning. We quantify the gender gaps in effort and relative performance outcomes and analyze whether the gaps differ by the gender of the parent who mainly supervises the children. Our main results point toward significant gender gaps in the relative performance outcomes in favor of boys, while the evidence for the effort gender gaps is only significant when we compare the siblings of the opposite gender (controlling for parent fixed effects). Further, we find that living in municipalities with more egalitarian gender norms is associated with narrower or positive gender gaps in effort outcomes, while we do not find such differences in the relative performance outcomes. Taking into account the increase in the use of online learning tools and their progressive integration into the regular educational system, our results provide important information to minimize gender biases in these new settings.
AI, Radical Ignorance, and the Institutional Approach to Consent
Authors: Steinberg Etye
Published: August 2024

More and more, we face AI-based products and services. Using these services often requires our explicit consent, e.g., by agreeing to the services’ Terms and Conditions clause. Current advances introduce the ability of AI to evolve and change its own modus operandi over time in such a way that we cannot know, at the moment of consent, what it is in the future to which we are now agreeing. Therefore, informed consent is impossible regarding certain kinds of AI. Call this the problem of radical ignorance. Interestingly, radical ignorance exists in consent contexts other than AI, where it seems that individuals can provide informed consent. The article argues that radical ignorance can undermine informed consent in some contexts but not others because, under certain institutional, autonomy-protecting conditions, consent can be valid without being (perfectly) informed. By understanding these institutional conditions, we can formulate practical solutions to foster valid, albeit imperfectly informed consent across various decision contexts and within different institutions.
Multi-objective Optimization of Process Parameters for Surface Quality and Geometric Tolerances of AlSi10Mg Samples Produced by Additive Manufacturing Method Using Taguchi-Based Gray Relational Analysis
Authors: Işik Uğur , Demir Halil , Özlü Barış
Published: August 2024

In this study, it has been focused on examining the effects of production parameters on quality parameters such as surface roughness and geometric tolerances in the production of AlSi10Mg samples by the additive manufacturing method. The experimental design has been prepared according to the Taguchi L27 orthogonal array. As a result, in the production of samples, increasing laser power (P) contributed positively to surface roughness and diameter change, and increasing scanning distance (SD) negatively contributed to circularity change and concentricity. Further, it has been determined that increasing the scanning speed (SS) negatively affects the concentricity change of the produced samples. The optimum production parameters for surface roughness and diameter variation has been determined as A1B1C3. The optimum production parameters for circularity variation and concentricity have been determined as A3B3C1 and A3B1C1, respectively. According to the ANOVA analysis results, the most effective parameters for surface roughness, diameter change, circularity change and concentricity have been 53.22% P, 62.45% SD, 37.23% SS and 40.41% SD, respectively. Furthermore, as a result of the gray relationship analysis (GRA) performed for the output parameters, the optimum production parameter has been determined as A2B1C3.