bluuebunny/crossref_metadata_2025 huggingface dataset:
A Tris(terpyridine) Ligand-based Metal Coordination Nanosheet as Electrode Material with Good Supercapacitor Performance
Authors: Liu Qian , Xu Zhiwei , Guo Zengqi , Guo Su , Huang Mengru , Wong Wai-Yeung
Published: July 2024

In this work, through a facile liquid–liquid (L–L) interfacial-assisted synthesis at room temperature, a new two-dimensional (2D) metal coordination nanosheet Co-TPY-LB has been synthesized by the coordination between Co2+ ion and 1,3,5-tris(4′-tripyridinylphenyl)benzene (Tris-tpy). The flat and smooth sheet structure with a thickness of 83.3 nm and a hexagonal structure with a lattice spacing of about 0.206 nm for the as-fabricated nanosheet Co-TPY-LB has been identified by AFM and HRTEM respectively, providing a large number of redox sites and ion diffusion channels. The fabricated Co-TPY-LB have been used as electrodes and exhibit the highest capacity of 2741.5 F g−1 at the current density of 1 A g−1, an excellent cycling stability with 97.6% capacity retention after 1000 cycles at the current density of 10 A g−1. Meanwhile, the assembled asymmetric supercapacitor with Co-TPY-LB as the positive electrode shows a high energy density 73.6 Wh kg−1 at a power density of 4800 W kg−1 as well as an excellent cycle stability of 92.9% capacity retention after 5000 cycles. Our work provides a controllable synthesis for the construction of 2D metal coordination nanosheet to overcome some defects of 2D nanosheet by traditional synthesis, leading such bottom-up nanosheet to a promising application in energy storage.

Graphical Abstract
Gradient-Robust Hybrid DG Discretizations for the Compressible Stokes Equations
Authors: Lederer P. L. , Merdon C.
Published: July 2024

This paper studies two hybrid discontinuous Galerkin (HDG) discretizations for the velocity-density formulation of the compressible Stokes equations with respect to several desired structural properties, namely provable convergence, the preservation of non-negativity and mass constraints for the density, and gradient-robustness. The later property dramatically enhances the accuracy in well-balanced situations, such as the hydrostatic balance where the pressure gradient balances the gravity force. One of the studied schemes employs an $$H(\textrm{div})$$

H
(
div
)

-conforming velocity ansatz space which ensures all mentioned properties, while a fully discontinuous method is shown to satisfy all properties but the gradient-robustness. Also higher-order schemes for both variants are presented and compared in three numerical benchmark problems. The final example shows the importance also for non-hydrostatic well-balanced states for the compressible Navier–Stokes equations.
Resolution Enhancement by Variable Zoom Trajectory in X-Ray Computed Tomography
Authors: Blažek Pavel , Suppes Alexander , Wolfschläger Dominik , Zikmund Tomáš , Kaiser Jozef , Schmitt Robert H.
Published: July 2024

Flat objects like electronic boards are challenging samples for high-resolution X-ray computed tomography scanning because their largest dimension significantly limits the magnification using circular trajectory scans. One way to improve spatial resolution for such samples is to utilize variable zoom trajectory. During variable zoom trajectory scanning, the source-to-object distance changes during the 360° rotation to maximize the magnification in the projections. Here, we propose an automatic variable zoom trajectory generation algorithm for arbitrary object and region of interest (ROI). We analyze how such a trajectory can enhance resolution in different cases and how isotropic is the resolution in the reconstructed volume. We demonstrate that the resolution can be improved without destroying the sample. However, the improvement is manifested mainly in directions in which we achieved the highest magnification in the projection.
Observation of the Granular Josephson Mechanism and the Vortex-Glass Transition in the Polycrystalline GdBa2Cu3O7- δ Superconductor
Authors: Vargas-Pineda E. M. , Rivera-Contreras L. J. , Pineda-Peña G. , Téllez D. A. Landínez , Roa-Rojas J.
Published: July 2024

The resistive transition between the normal and superconducting states in high critical temperature superconducting materials is characterized by the occurrence of two well-defined stages: a transition in the higher temperature thermal region called pairing transition and another known as coherence transition. In the presence of low magnetic fields, in the first transition, it is possible to identify a genuinely critical fluctuation regime, with Gaussian regimes. The Aslamazov-Larkin model in the 3D regime allows to analyze the amplitude of the order parameter, leading to the determination of parameters such as coherence length and critical field. In the coherence transition, the phase of the order parameter is the relevant quantity that varies between the grains of the system, leading to Josephson-type effects in the intergranular barriers due to the polycrystalline feature of this family of materials. The coherence transition for the sample GdBa2Cu3O7-δ under the application of seven different electric transport currents was studied in this work. The sample was produced by the solid-state reaction and the resistive behavior was determined to perform the paraconductivity analysis and characterizes granularity effects on the superconducting order parameter. A genuinely critical region characterized by dynamic critical exponent z = 4.5 was identified, similar to the value reported for the transition vortex glass – fluid. This paper proposes to identify three-dimensional Gaussian fluctuations in the paracoherent to get a coherence length associated with Josephson effects in the granular barrier and to find a factor related with the Josephson current in function of the seven applied currents.
Investigation of bio-corrosion behavior, structural and thermal properties of Ni20Ti50Sn30 high-temperature shape memory alloy
Authors: Balci Esra
Published: July 2024

In this study, Ni20Ti50Sn30 (atomic %) sample was prepared using the arc melting method. Some thermodynamic parameters (phase transformation temperatures, PTT; enthalpy, ΔH; and phase transformation hysteresis, H) obtained from the phase transformations of the alloy were determined using differential scanning calorimetry (DSC). The sample exhibited high-temperature shape memory alloy (HTSMA) properties. X-ray (XRD) analysis was performed to determine the crystal structure properties at the alloy. The presence of B2, B19′, Ti2Ni, β-Ti, Ni3Sn4, TiNiSn and Ti3Sn phases was identified through X-ray analysis of the alloy. Scanning electron microscopy (SEM) was used to examine the pre- and post-corrosion surface morphologies of the sample, with EDX analysis being performed to reveal chemical structure determinations (matrix phases, precipitates and weathering zones). The presence of dendritic arms was observed, while martensite plates were absent in all SEM images. Particularly in the SEM image taken after corrosion, it was noted that the TiO2 layer placed on the surface of the matrix rich in Ti elements altered the surface morphology of the alloy. The presence of Ti and O elements in the post-corrosion EDX analysis confirmed the existence of this layer. Biocompatibility studies of the alloy were conducted using potentiodynamic corrosion tests and electrochemical impedance spectroscopy (EIS) methods. It was determined that the NiTiSn sample was in the excellent stability class according to corrosion standards.
Leveraging systems’ non-linearity to tackle the scarcity of data in the design of intelligent fault diagnosis systems
Authors: Santamato Giancarlo , Garavagno Andrea Mattia , Solazzi Massimiliano , Frisoli Antonio
Published: July 2024

Deep transfer learning (DTL) allows for the efficient building of intelligent fault diagnosis systems (IFDS). On the other hand, DTL methods still heavily rely on large amounts of labelled data. Obtaining such an amount of data can be challenging when dealing with machines or structures faults. This document proposes a novel approach to the design of vibration-based IFDS using DTL in condition of strong data scarcity. A periodic multi-excitation level procedure leveraging intrinsic non-linearities of real-world systems is used to produce images that can be conveniently analysed by pre-trained Convolutional Neural Networks to diagnose faults. A new data visualization method and its augmentation technique are proposed in this paper to tackle the typical lack of data encountered during the design of IFDS. Experimental validation on a railway pantograph structure provides effective support for the proposed method.
Contrasting maize responses to soil phosphorus and potassium availability driven by biochar under reduced irrigation
Authors: Wan Heng , Hou Jingxiang , Wei Zhenhua , Liu Fulai
Published: July 2024

Background and aims

Biochar is increasingly recognized for its potential to enhance soil water retention and improve soil fertility in agricultural systems; however, few studies have evaluated the effects of biochar on nutrients from an integrated perspective. This study aims to investigate how biochar affects soil phosphorus (P) and potassium (K) availability and their effects on plant growth, especially under reduced irrigation conditions.

Methods

Maize plants were grown in split-root pots with full (FI), deficit (DI) and alternate partial root-zone drying (PRD) irrigation under 2% (w/w) softwood (SWB) and wheat-straw biochar (WSB) for three successive growth cycles.

Results

WSB amendment decreased soil P availability and resulted in a decrease in plant P content. However, WSB amendment significantly enhanced soil K availability inducing higher plant K content under reduced irrigation, thereby enhancing the biomass and harvest index of maize plants irrespective of growth cycles, especially with PRD. WSB amendment significantly enhanced P use efficiency due to lower plant P content and higher biomass in the initial growth cycles. Adding SWB decreased plant growth under reduced irrigation by inhibiting plant K content in the second growth cycle, which was mitigated by PRD by enhancing root growth.

Conclusion

Collectively, despite negative effects on soil P availability and plant P content, WSB combined with PRD could be a promising strategy in sustainable maize production under drought stress.
Pandemic data quality modelling: a Bayesian approach in the Italian case
Authors: Ferrari Luisa , Manzi Giancarlo , Micheletti Alessandra , Nicolussi Federica , Salini Silvia
Published: July 2024

When pandemics like COVID-19 spread around the world, the rapidly evolving situation compels officials and executives to take prompt decisions and adapt policies depending on the current state of the disease. In this context, it is crucial for policymakers to always have a firm grasp on what is the current state of the pandemic, and envision how the number of infections and possible deaths is going to evolve shortly. However, as in many other situations involving compulsory registration of sensitive data from multiple collectors, cases might be reported with errors, often with delays deferring an up-to-date view of the state of things. Errors in collecting new cases affect the overall mortality, resulting in excess deaths reported by official statistics only months later. In this paper, we provide tools for evaluating the quality of pandemic mortality data. We accomplish this through a Bayesian approach accounting for the excess mortality pandemics might bring with respect to the normal level of mortality in the population.
Adventitious Error and Its Implications for Testing Relations Between Variables and for Composite Measurement Outcomes
Authors: De Boeck Paul , DeKay Michael L. , Pek Jolynn
Published: July 2024

Wu and Browne (Psychometrika 80(3):571–600, 2015. https://doi.org/10.1007/s11336-015-9451-3; henceforth W & B) introduced the notion of adventitious error to explicitly take into account approximate goodness of fit of covariance structure models (CSMs). Adventitious error supposes that observed covariance matrices are not directly sampled from a theoretical population covariance matrix but from an operational population covariance matrix. This operational matrix is randomly distorted from the theoretical matrix due to differences in study implementations. W & B showed how adventitious error is linked to the root mean square error of approximation (RMSEA) and how the standard errors (SEs) of parameter estimates are augmented. Our contribution is to consider adventitious error as a general phenomenon and to illustrate its consequences. Using simulations, we illustrate that its impact on SEs can be generalized to pairwise relations between variables beyond the CSM context. Using derivations, we conjecture that heterogeneity of effect sizes across studies and overestimation of statistical power can both be interpreted as stemming from adventitious error. We also show that adventitious error, if it occurs, has an impact on the uncertainty of composite measurement outcomes such as factor scores and summed scores. The results of a simulation study show that the impact on measurement uncertainty is rather small although larger for factor scores than for summed scores. Adventitious error is an assumption about the data generating mechanism; the notion offers a statistical framework for understanding a broad range of phenomena, including approximate fit, varying research findings, heterogeneity of effects, and overestimates of power.
Landscape metric sensitivity to grain size in rural Japan
Authors: Kato Sadahisa , Motobe Akari
Published: July 2024

In this study, we investigated the sensitivity of landscape metrics to varying grain sizes in a rural, forested landscape in Japan, contributing to a broader understanding of landscape metric behavior across different scales. We analyzed six class-level and two landscape-level metrics on a land use map at grain sizes of 5, 10, 20, 30, 40, 50, 75, and 100 m. Our results indicate that the effect of increasing grain size on landscape metrics varies depending on the specific metric and land use type. Key metrics, such as the percentage of land use types in a landscape, and two landscape-level metrics showed minimal change across the range of grain sizes. Conversely, patch density and cohesion decreased, whereas Euclidean nearest neighbor distance increased. In addition, the patch area and radius of gyration showed variable responses across different land use types, influenced by their characteristic dimensions. This study highlights the limitations of using coarse-resolution data for detailed landscape analysis, as it may not fully capture landscape change or the relationship between landscape patterns and ecological processes. We propose an optimal grain size of 5–50 m for analyzing rural forested landscapes in Japan, which effectively captures fine-scale elements critical for biodiversity conservation. This range allows accurate comparisons between different regions and land use plans, especially in satoyama landscapes. This research highlights the importance of selecting appropriate grain size in landscape analysis and interpretation of landscape metrics as well as urges researchers and policymakers to ensure accurate ecological assessments and informed decision-making.
Scalable Bayesian p-generalized probit and logistic regression
Authors: Ding Zeyu , Omlor Simon , Ickstadt Katja , Munteanu Alexander
Published: July 2024

The logit and probit link functions are arguably the two most common choices for binary regression models. Many studies have extended the choice of link functions to avoid possible misspecification and to improve the model fit to the data. We introduce the p-generalized Gaussian distribution (p-GGD) to binary regression in a Bayesian framework. The p-GGD has received considerable attention due to its flexibility in modeling the tails, while generalizing, for instance, over the standard normal distribution where $$p=2$$

p
=
2

or the Laplace distribution where $$p=1$$

p
=
1

. Here, we extend from maximum likelihood estimation (MLE) to Bayesian posterior estimation using Markov Chain Monte Carlo (MCMC) sampling for the model parameters $$\beta$$
β
and the link function parameter p. We use simulated and real-world data to verify the effect of different parameters p on the estimation results, and how logistic regression and probit regression can be incorporated into a broader framework. To make our Bayesian methods scalable in the case of large data, we also incorporate coresets to reduce the data before running the complex and time-consuming MCMC analysis. This allows us to perform very efficient calculations while retaining the original posterior parameter distributions up to little distortions both, in practice, and with theoretical guarantees.
Patients with atrial fibrillation and common exclusion criteria from clinical trials are at high risk of clinical events: the Murcia AF Project II (MAFP-II) cohort study
Authors: Soler-Espejo Eva , Rivera-Caravaca José Miguel , Bru-Cánovas José Daniel , Esteve-Pastor María Asunción , Lip Gregory Yoke Hong , Marín Francisco , Roldán Vanessa
Published: July 2024

Background

Some clinical characteristics and comorbidities in atrial fibrillation (AF) patients are exclusion criteria in randomized clinical trials (RCTs) investigating oral anticoagulants (OAC). However, these conditions are present also in everyday clinical practice patients. We compared the risk of adverse clinical outcomes between patients with and without RCT exclusion criteria.

Methods

The Murcia AF Project II was an observational cohort study including AF outpatients starting vitamin K antagonists (VKAs) from July 2016 to June 2018. For the selection of the exclusion criteria, the four pivotal RCTs of direct-acting OAC (DOACs) were used as reference. During 2 years, all ischemic strokes/transient ischemic attacks, major adverse cardiovascular events (MACEs), major bleeds, and all-cause deaths were recorded.

Results

1050 patients (51.5% female, median age 77 years) were included, of whom 368 (35%) met at least one exclusion criterion for RCTs. During follow-up, the incidence rate ratios for major bleeding, MACE and all-cause mortality were higher among patients with exclusion criteria (all p < 0.001). Patients fulfilling at least one exclusion criterion had increased risks of major bleeding (aHR 1.48; 95% CI 1.22–1.81; p < 0.001), MACE (aHR 1.51, 95% CI 1.10–2.09, p = 0.012), and mortality (aHR 3.22, 95% CI 2.32–4.48, p < 0.001), as well as a lower event-free survival (all log-rank p < 0.001).

Conclusions

In this AF cohort taking VKAs, more than one-third had at least one RCT exclusion criteria, which translates into higher risk of major bleeding, MACE, and death. These observations should be considered when translating RCTs results to AF patients for a proper and a more patient-centered management.
On the Expansion of Resolvents and the Integrated Density of States for Poisson Distributed Schrödinger Operators
Authors: Hasler David , Koberstein Jannis
Published: July 2024

We consider a Schrödinger operator with random potential distributed according to a Poisson process. We show that under a uniform moment bound expectations of matrix elements of the resolvent as well as the integrated density of states can be approximated to arbitrary precision in powers of the coupling constant. The expansion coefficients are given in terms of expectations obtained by Neumann expanding the potential around the free Laplacian. Our results are valid for arbitrary strength of the disorder parameter, including the small disorder regime.
Task potential in relation to teaching quality and teacher competence in secondary mathematics classrooms
Authors: Glegola Ann-Kristin , Jentsch Armin , Ross Natalie , König Johannes , Kaiser Gabriele
Published: July 2024

The potential of tasks to foster mathematical learning and understanding is an important aspect of instruction and their implementation in teaching is thus often viewed to be positively related to the quality of instruction. Both the selection of tasks as well as their implementation in the classroom depend on many factors, with teachers’ knowledge and skills as one of the most important ones. The present study aims to analyze the relations between different aspects of task potential, the quality of instruction, and teachers’ competence in order to investigate whether task potential can be seen as an indicator for teaching quality, for teacher competence, or as an independent construct in models of educational effectiveness. To this end, we draw on data from the TEDS-Validate study, namely tests of mathematics teachers’ competence (n = 31) observations in their classrooms (n = 60), and an in-depth analysis of all tasks used in the respective lessons (n = 2490). Multiple regression analysis suggests that while some facets of task potential are related to either teaching quality or teacher competence, the potential of tasks emerges as an independent construct with some characteristics predicting the teaching quality of the respective lessons. Implications of these results for the role of tasks in educational effectiveness research are discussed.
Epistemological justification
Authors: Harel Guershon
Published: July 2024

Epistemological justification is a way of thinking that manifests itself through perturbation-resolution cycles revolving around the question why and how was a piece of mathematical knowledge conceived? The paper offers a conceptual framework for constituent elements of epistemological justification. The framework provides: (a) a theoretical basis for epistemological justification, (b) criteria for its occurrence, and (c) analysis of its relation to mathematical explanation. The criteria are illustrated by a series of learning-teaching events taken from teaching experiments aimed at investigating the learning and teaching in linear algebra. The contribution of the proposed framework is three-fold: (a) it addresses a critical aspect of proof understanding not explicitly addressed in the literature; (b) it goes beyond the traditional treatment of mathematical understanding and production into questions about learners’ conceptualization of the origins of mathematical knowledge; and (c) it theorizes instructional approaches that can advance this conceptualization among students.
The Anti-candidal and Absorbtion Performance of PVA/PVP-Based Jania rubens Hydrogel on Candida tropicalis and Some Physicochemical Properties of the Hydrogel
Authors: Boran Meltem , Eliuz Elif Erdogan , Ayas Deniz
Published: July 2024

This study was aimed to create a bioactive hydrogel form with PVA/PVP (polyvinyl alcohol/poly(N-vinylpyrrolidone) polymer using acetone and ethanol extractions of Janiarubens red algae and investigate some pharmaceutical properties. The anti-candidal activity and some inhibition performance of J. rubens/PVA/PVP hydrogel were investigated on Candida tropicalis which is one of the important causes of bloodstream infections. The physicochemical properties of J. rubens/PVA/PVP hydrogel were revealed using FTIR and swelling-absorption tests. The volatile compounds of J. rubens extracts were examined by GCMS. By mixing the extracts in equal proportions, PVA/PVP-based hydrogel was prepared. According to the results, Cumulative Drug Release was stable at 25 °C for the first 5 h. The IZ (inhibition zone) and MIC (minimum inhibitory concentration) of J. rubens/PVA/PVP hydrogel were 9.01 mm and 80.20 mg/mL, respectively. It was found that logarithmic reduction and percent reduction were seen as 1.5 CFU/mL and 97.5%, respectively, on C. tropicalis exposed to J. rubens/PVA/PVP hydrogel in the first 5 min of the incubation. After exposure of C. tropicalis to J. rubens/PVA/PVP, the number of viable cells transferred from the gel to water was between 76.1 and 73.1% in high glucose medium, while it was between 92.2 and 80.8% for the PVA/PVP hydrogel under the same conditions. As a result, PVA/PVP hydrogel was made bioactive with J. rubens extracts for the first time in this study, and its potential for use as a functional anticandidal hydrogel on C.tropicalis has been demonstrated.
Evaluation of Boric Acid Treatment on microRNA‐127‐5p and Metastasis Genes Orchestration of Breast Cancer Stem Cells
Authors: Semerci Sevimli Tuğba , Ghorbani Aynaz , Gakhiyeva Fidan , Ebrahimi Aliakbar , Ghorbanpoor Hamed , Altuğ Burcugül , Ergen Fulya Buge , Ahmadova Zarifa , Soykan Merve Nur , Tufekcioglu Emre
Published: July 2024

Coregulation of microRNAs (miRNAs) and cancer stem cells (CSCs) is very important in carcinogenesis. miR-127-5p is known to be downregulated in breast cancer. In this study, we aimed to investigate how boric acid (BA), known for its previously unstudied anti-cancer properties, would affect the expression of miR127-5p and genes responsible for breast cancer stem cells (BC-SCs) metastasis. BC-SCs were isolated from human breast cancer cells (MCF-7) by immunomagnetic cell separation and characterized with flow cytometry and sphere formation. The viability of BC-SCs and the determination of its IC50 value in response to boric acid (BA) were assessed via the MTT assay. Boric acid exhibited dose- and time-dependent inhibition of cell viability in cells. The IC50 doses of boric acid in MCF-7 cells and BC-SCs were 45.69 mM and 41.27 mM, respectively. The impact of BA on the expression of metastatic genes and miR127-5p was elucidated through RT-qPCR analysis. While the expression of the COL1A1 (p < 0.05) and VIM (p < 0.01) was downregulated, the expression of the miR-127-5p, ZEB1 (p < 0.01), CDH1 (p < 0.05), ITGB1 (p < 0.05), ITGA5 (p < 0.05), LAMA5 (p < 0.01), and SNAIL (p < 0.05), was up-regulated in dose-treated BC-SCs (p < 0.001) to the RT-qPCR results. Our findings suggest that boric acid could induce miR-127-5p expression. However, it cannot be said that it improves the metastasis properties of breast cancer stem cells.

Graphical Abstract
C-Peptide and BMi predict anti-hyperglycemic treatment lines in breast cancer patients treated with Alpelisib
Authors: Carrillo-Lopez Elena , Sebastian-Valles Fernando , Sager La Ganga Carolina , Ballesteros Anabel , Navas-Moreno Victor , Bañón Dulce , López Martí María Pilar , Marazuela Mónica , Arranz Martín José Alfonso
Published: July 2024

Purpose

Alpelisib is a PI3K (Phosphoinositide 3-kinases) inhibitor used for breast cancer which develops hyperglycemia based on its action on glucose metabolism regulation. This study aims to identify potential risk factors predicting hyperglycemia development and the need for multiple treatments for hyperglycemia in patients receiving Alpelisib.

Methods

Fourteen women diagnosed with metastatic hormone receptor-positive breast cancer carrying PI3K mutations who initiated treatment with Alpelisib were monitored through consultations in the Oncology and Endocrinology departments. Non-parametric ROC curves were generated to assess the need for three or more antidiabetic medications to achieve glycemic control.

Results

The study population had a median age of 64 years (range:48–69) with a median body mass index (BMI) of 26.6 kg/m2 (range: 22.9–29.4). Overweight was observed in 35.7% of the participants and obesity in 21.4%. Fifty percent of the participants had prediabetes, and 85.7% developed hyperglycemia requiring pharmacological treatment, although none of them needed to discontinue treatment for this reason. Baseline C-peptide levels and BMI were associated with the number of antidiabetic drugs used (Spearman’s Rho 0.553, p = 0.040; Spearman’s Rho 0.581, p = 0.030, respectively). ROC curve analysis showed and area under the curve (AUC) of 0.819 for the variable risk profile (defined as baseline C-peptide >10.5 ng/ml and BMI > 27 kg/m2), whereas AUC values were 0.556 and 0.514 for HbA1c and baseline glucose, respectively, (p = 0.012).

Conclusion

A joint follow-up by an Oncology department and a Diabetes Unit can prevent treatment discontinuation in patients under Alpelisib therapy. Baseline BMI and plasma C-peptide levels can predict an increased need for anti-hyperglycemic treatment.
Contribution of methamphetamine and insulin to the death of a woman suffering from type I diabetes – which played the greater role?
Authors: Handlosová Klára , Šištík Pavel , Uvíra Matěj , Andelova Kateřina , Handlos Petr , Stejskal David
Published: July 2024

This report presents a fatal case of a young female Type I diabetic patient who developed convulsions and loss of consciousness after taking methamphetamine and spending some time in a dance club. During the convulsions, she was given sugar and when no response occurred, her boyfriend who was not experienced in the use of insulin administered a dose of insulin to her. The woman lost consciousness and died despite the efforts of the emergency service. A biochemical analysis revealed a high level of insulin (196.67 mU/L) and low levels of glucose (2.96 mmol/L) and C-peptide (26 pmol/L). Toxicological analysis revealed a methamphetamine concentration of 389 ng/mL and an amphetamine concentration of 19 ng/mL. The forensic perspective of the difficult determination of the contribution of each of the factors to the death, i.e., the pre-existing medical condition (Type I diabetes), the use of methamphetamine, the physical exertion at the dance club, and, finally, the non-indicated administration of insulin, is discussed. The ruling of the court is also reported.
Stability and numerical solutions for second-order ordinary differential equations with application in mechanical systems
Authors: Turab Ali , Montoyo Andrés , Nescolarde-Selva Josué-Antonio
Published: July 2024

This study undertakes a comprehensive analysis of second-order Ordinary Differential Equations (ODEs) to examine animal avoidance behaviors, specifically emphasizing analytical and computational aspects. By using the Picard–Lindelöf and fixed-point theorems, we prove the existence of unique solutions and examine their stability according to the Ulam-Hyers criterion. We also investigate the effect of external forces and the system’s sensitivity to initial conditions. This investigation applies Euler and Runge–Kutta fourth-order (RK4) methods to a mass-spring-damper system for numerical approximation. A detailed analysis of the numerical approaches, including a rigorous evaluation of both absolute and relative errors, demonstrates the efficacy of these techniques compared to the exact solutions. This robust examination enhances the theoretical foundations and practical use of such ODEs in understanding complex behavioral patterns, showcasing the connection between theoretical understanding and real-world applications.
Cellular Traction Force Holds the Potential as a Drug Testing Readout for In Vitro Cancer Metastasis
Authors: Liew Hui Yan , Liew Xiao Hui , Lin Wei Xuan , Lee Yee Zhen , Ong Yong Sze , Ogawa Satoshi , Chong Lor Huai
Published: July 2024

Introduction

Metastasis is responsible for 90% of cancer-related deaths worldwide. However, the potential inhibitory effects of metastasis by various anticancer drugs have been left largely unexplored. Existing preclinical models primarily focus on antiproliferative agents on the primary tumor to halt the cancer growth but not in metastasis. Unlike primary tumors, metastasis requires cancer cells to exert sufficient cellular traction force through the actomyosin machinery to migrate away from the primary tumor site. Therefore, we seek to explore the potential of cellular traction force as a novel readout for screening drugs that target cancer metastasis.

Methods

In vitro models of invasive and non-invasive breast cancer were first established using MDA-MB-231 and MCF-7 cell lines, respectively. Cellular morphology was characterized, revealing spindle-like morphology in MDA-MB-231 and spherical morphology in MCF-7 cells. The baseline cellular traction force was quantified using the Traction force Microscopy technique. Cisplatin, a paradigm antimetastatic drug, and 5-Fluorouracil (5FU), a non-antimetastatic drug, were selected to evaluate the potential of cellular traction force as a drug testing readout for the in vitro cancer metastasis.

Results

MDA-MB-231 cells exhibited significantly higher baseline cellular traction force compared to MCF-7 cells. Treatment with Cisplatin, an antimetastatic drug, and 5-Fluorouracil (5FU), a non-antimetastatic drug, demonstrated distinct effects on cellular traction force in MDA-MB-231 but not in MCF-7 cells. These findings correlate with the invasive potential observed in the two models.

Conclusion

Cellular traction force emerges as a promising metric for evaluating drug efficacy in inhibiting cancer metastasis using in vitro models. This approach could enhance the screening and development of novel anti-metastatic therapies, addressing a critical gap in current anticancer drug research.
Investigating the Effectiveness of Problem Based Learning on Academic Achievement in EFL Classroom: A Meta-Analysis
Authors: Orhan Ali
Published: July 2024

Problem based learning (PBL) has great potential to enhance learning outcomes and this potential has been investigated and proved in different disciplines by many meta-analysis studies. However, there are not any meta-analysis studies aiming to investigate the effectiveness of problem based learning in English as a Foreign Language (EFL) classrooms which is an important gap that needs to be filled. Therefore, this meta-analysis study aimed to investigate the overall effect size of PBL on achievement in EFL classrooms and to examine the possible moderator variables that might be effective on this overall effect size. Along with this aim, the studies investigating the effect of PBL on academic achievement in EFL classrooms are included and analyses were carried out with 41 data (extracted from 36 unique studies). Investigation of publication bias using various methods showed that there is no publication bias. This study showed that the effect size of PBL is 1.067 indicating a large effect which means that PBL is highly effective to enhance the language achievement of students in EFL classrooms. Moderator analyses showed that language skill is a real moderator on the effect size of PBL on EFL success while the study group, treatment duration, location of study, learning environment, and document type are not real moderators.
Inflammation-based scores in a large cohort of adrenocortical carcinoma and adrenocortical adenoma: role of the hormonal secretion pattern
Authors: Mangone A. , Favero V. , Prete A. , Elhassan Y. S. , Asia M. , Hardy R. , Mantovani G. , Chiodini I. , Ronchi C. L.
Published: July 2024

Background

Serum inflammation-based scores can predict clinical outcome in several cancer types, including adrenocortical carcinoma (ACC). It is unclear whether the extent of inflammation-based scores alterations in ACC reflects malignancy, steroid excess, or both.

Methods

We investigated a large retrospective cohort of adrenocortical adenomas (ACA, n = 429) and ACC (n = 61) with available baseline full blood count and hormonal evaluation. We examined the relationship between different inflammation-based scores [neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune-inflammation index (SII), and prognostic nutrition index (PNI)] and both malignancy and steroid secretion patterns.

Results

All inflammation-based scores differed between ACC and ACA: patients with ACC had higher NLR, PLR, SII and lower LMR and PNI levels compared to ACA (all p values < 0.001). NLR showed a positive correlation with cortisol levels after overnight 1 mg-dexamethasone suppression test (1 mg-DST), both in ACC and ACA (p < 0.01). The ROC curve analysis determined NLR > 2.6 as the best cut-off to discriminate ACC from ACA [AUC = 0.846, p < 0.01]. At multivariable analysis, NLR > 2.6 was independently associated with ACC, 1 mg-DST cortisol levels and age, but not with tumour size. Considering the ACC, NLR and SII were higher and PNI was lower in patients with cortisol excess compared to those without cortisol excess (p = 0.002, p = 0.007, and p = 0.044 respectively). Finally, LMR and NLR differed between inactive-ACC (n = 10) and inactive-ACA (n = 215) (p = 0.040 and p = 0.031, respectively).

Conclusion

Inflammation-based scores are related to steroid secretion both in ACC and ACA. ACCs present a higher grade of inflammation regardless of their hormonal secretion, likely as a feature of malignancy itself.
Peer Mentoring by Medical Students for Medical Students: A Scoping Review
Authors: Preovolos Christos , Grant Abby , Rayner Morgan , Fitzgerald Kylie , Ng Louisa
Published: July 2024

Medical school transitions pose challenges for students. Mentoring programs may aid students, but evidence supporting peer/near-peer mentoring in medical school is unclear. Our review explores peer mentoring’s benefits, elements for success and challenges. Searches in major databases yielded 1676 records, resulting in 20 eligible studies involving 4591 participants. Longitudinal (n = 15) and shorter, focused programs were examined. Mentors and mentees reported psychosocial, professional and academic benefits. Essential elements included matching, orientation and clear goals, with training crucial yet balanced to avoid mentor overload. Social congruence underpinned successful peer mentoring, particularly benefiting under-represented groups. Challenges include balancing mentor load and logistics.
The State-of-the-Art Overview to Application of Deep Learning in Accurate Protein Design and Structure Prediction
Authors: Saharkhiz Saber , Mostafavi Mehrnaz , Birashk Amin , Karimian Shiva , Khalilollah Shayan , Jaferian Sohrab , Yazdani Yalda , Alipourfard Iraj , Huh Yun Suk , Farani Marzieh Ramezani , Akhavan-Sigari Reza
Published: July 2024

In recent years, there has been a notable increase in the scientific community's interest in rational protein design. The prospect of designing an amino acid sequence that can reliably fold into a desired three-dimensional structure and exhibit the intended function is captivating. However, a major challenge in this endeavor lies in accurately predicting the resulting protein structure. The exponential growth of protein databases has fueled the advancement of the field, while newly developed algorithms have pushed the boundaries of what was previously achievable in structure prediction. In particular, using deep learning methods instead of brute force approaches has emerged as a faster and more accurate strategy. These deep-learning techniques leverage the vast amount of data available in protein databases to extract meaningful patterns and predict protein structures with improved precision. In this article, we explore the recent developments in the field of protein structure prediction. We delve into the newly developed methods that leverage deep learning approaches, highlighting their significance and potential for advancing our understanding of protein design.
Carbon-based nanomaterials cause toxicity by oxidative stress to the liver and brain in Sprague–Dawley rats
Authors: Xu Ying-Ying , Jin Chan , Wu Meng , Zhou Jian-Ye , Wei Hui-Ling
Published: July 2024

Carbon-based nanomaterials have important research significance in various disciplines, such as composite materials, nanoelectronic devices, biosensors, biological imaging, and drug delivery. Recently, the human and ecological risks associated with carbon-based nanomaterials have received increasing attention. However, the biological safety of carbon based nanomaterials has not been systematically studied. In this study, we used different types of carbon materials, namely, graphene oxide (GO), single-walled carbon nanotubes (SWCNTs), and multiwalled carbon nanotubes (MWCNTs), as models to observe their distribution and oxidative damage in vivo. The results of Histopathological and ultrastructural examinations indicated that the liver and lungs were the main accumulation targets of these nanomaterials. SR-$$\upmu$$
μ
-XRF analysis revealed that SWCNTs and MWCNTs might be present in the brain. This shows that the three types of carbon-based nanomaterials could cross the gas–blood barrier and eventually reach the liver tissue. In addition, SWCNTs and MWCNTs could cross the blood–brain barrier and accumulate in the cerebral cortex. The increase in ROS and MDA levels and the decrease in GSH, SOD, and CAT levels indicated that the three types of nanomaterials might cause oxidative stress in the liver. This suggests that direct instillation of these carbon-based nanomaterials into rats could induce ROS generation. In addition, iron (Fe) contaminants in these nanomaterials were a definite source of free radicals. However, these nanomaterials did not cause obvious damage to the rat brain tissue. The deposition of selenoprotein in the rat brain was found to be related to oxidative stress and Fe deficiency. This information may support the development of secure and reasonable applications of the studied carbon-based nanomaterials.
Groundwater Recharge Potential Index and artificial groundwater recharge in the alluvial soils of the middle Ganga Basin
Authors: Arya Vinay , Rao Mavidnam Someshwar
Published: July 2024

Recent studies prove the faster depletion of groundwater is a cause of concern in the middle Ganga basin. Most of the groundwater-related studies in the middle Ganga basin deal with groundwater levels and quality aspects. The past studies on groundwater potential are confined to arid and humid zones of India, which leaves ample space for semi-humid to semi-arid, frost-prone, and alluvial region. The basin has alluvial soil due to sediments deposited by the river Ganga. In this study, eight thematic maps were prepared for the assessment of the groundwater recharge potential. These maps are; rainfall, geomorphology, slope, drainage density, lineament density, lithology, Land Use/Land Cover (LULC), and Groundwater Fluctuation (GWF). The weighted overlay of these thematic maps gave the Groundwater Recharge Potential Zone (GWRPZ) map representing the spatial variation of the Groundwater Recharge Potential Index (GWRPI) in the basin, whose value varies from 0.071 to 0.309. A total of 92 artificial groundwater recharge sites have been identified using specialized GIS processes taking into account stream network. The Analytic Hierarchy Process (AHP) based weights classification offers a robust framework for prioritizing and understanding the relative significance of factors affecting groundwater recharge. Alluvial soils are capable of holding large amount of water, and are overlaid by large aquifers. A large number of wells and tube wells have led to the over-extraction of groundwater in the region. This is the reason of the study being carried out despite being a region infested with large aquifers.
Water-saving control system based on multiple intelligent algorithms
Authors: Liu Fengnian , Yu Xiang , Tang Junya
Published: July 2024

Water conservation has become a global problem as the population increases. In many densely populated cities in China, leaks from century-old pipe works have been widespread. However, entirely eradicating the issues involves replacing all water networks, which is costly and time-consuming. This paper proposed an AI-enabled water-saving control system with three control modes: time division control, flow regulation, and critical point control according to actual flow. Firstly, based on the current leaking situation of water supply networks in China and the capability level of China’s water management, a water-saving technology integrating PID control and a series of deep learning algorithms was proposed. Secondly, a multi-jet control valve was designed to control pressure and reduce water distribution network cavitation. This technology has been successfully applied in industrial settings in China and has achieved gratifying water-saving results.
Halide solid-state electrolyte achieving high ionic conductivity by engineering nanocrystals
Authors: Bashir Sajid , Liu Jingbo Louise
Published: July 2024

Lithium-ion battery (LIB) technologies utilize liquid electrolytes, which can cause safety issues due to electrolyte leakage, uncontrolled side reactions between the liquid electrolyte and electrode, dendrite formation, and flammability of the liquid components with air. These problems can be minimized using solid-state electrolytes (SSEs) containing the functionality of an electrolyte. Our research discovery meets the urgent requirement of developing rapid ionic conductive solid-state electrolytes for lithium metal battery applications, emphasizing safe operation and high energy density. The breakthrough lies in the functionalization and tunability of monoclinic doped Li3InCl6-based solid electrolytes to achieve desirable structural and high ionic conductivity (> 0.15 S cm−1). We report four formulations of solid-state electrolytes obtained using modified sol–gel synthesis and used to assemble symmetrical half cells for electrochemical impedance spectroscopic (EIS) analyses in the frequency ranging from 10–2 to 106 Hz under five different temperatures (15–55 °C). The EIS data of non-doped, F-, Ce-, and Mo-doped electrolytes showed R1 (solid-electrolyte) ranging from 0.05 to 0.10 Ohm and R2 (interfacial) resistance varying from 0.05 to 1.25 Ohm, resulting in superionic conductivity (0.15–0.45 S cm−1), equivalent to the commercially available liquid electrolyte and evidenced two magnitudes increase compared to the published data.

Graphical abstract
PANoptosis-based molecular subtype and prognostic model predict survival and immune landscape in esophageal cancer
Authors: Liu Zheming , Liu Jiahui , Liao Fuben , Li Wei , Wang Jing , Zhang Chi
Published: July 2024

Purpose

To establish a prognostic model to predict the survival of patients with esophageal cancer (EC).

Methods

We extracted the expression profiles of prognostic-related genes and clinicopathological data from TCGA and GEO databases. Subsequently, a comprehensive bioinformatics analysis was conducted to construct a prognostic model utilizing LASSO and multivariate Cox regression. The stability of the risk signature was validated through Kaplan-Meier and ROC curve analyses on the training, internal testing, and external testing sets. Furthermore, we developed a nomogram that incorporates the risk score and clinical features to predict the suvival. Additionally, a nomogram incorporating the risk score and relevant clinical parameters was developed to enhance survivorship prediction. Furthermore, we delved into exploring the correlation between the risk score and immune cell abundance, expression of cancer checkpoints, as well as responses to immunotherapy and chemotherapeutic agents.

Results

In this study, we successfully identified 19 prognosis-related genes out of a pool of 65 PANoptosis-related genes (PRGs) sourced from existing literature. Through consensus clustering analysis, we classified patients into two distinct groups as PANcluster A and B. Furthermore, the risk score derived from the five PANoptosis-related signatures emerged as an independent prognostic factor among patients with EC. To enhance the prognostic accuracy, we devised a nomogram integrating the risk score with clinical risk characteristics, enabling the prediction of 1-year, 2-year, and 3-year overall survival (OS) rates. Notably, individuals classified in the high-risk group demonstrated poorer prognoses compared to their low-risk counterparts. Furthermore, the risk score displayed substantial correlations with immune cell abundance, expression levels of cancer checkpoints, and responses to immunotherapy and chemotherapeutic agents. These pivotal findings underscore the significance of considering PANoptosis-related patterns in improving prognostic assessment and predicting treatment responses in patients diagnosed with esophageal cancer.

Conclusion

We constructed a reliable prognostic risk model for EC utilizing five PRGs. The developed nomogram serves as a valuable tool in predicting patient outcomes, offering crucial insights that can inform and guide treatment decisions for individuals diagnosed with EC.