bluuebunny/crossref_metadata_2025 huggingface dataset:
An Optimization Numerical Spiking Neural Membrane System with Adaptive Multi-Mutation Operators for Brain Tumor Segmentation
Authors: Dong Jianping , Zhang Gexiang , Hu Yangheng , Wu Yijin , Rong Haina
Published: March 2024

Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ability to generate images without tissue damage or skull artifacts. Therefore, MRI images are widely used to achieve the segmentation of brain tumors. This paper is the first attempt to discuss the use of optimization spiking neural P systems to improve the threshold segmentation of brain tumor images. To be specific, a threshold segmentation approach based on optimization numerical spiking neural P systems with adaptive multi-mutation operators (ONSNPSamos) is proposed to segment brain tumor images. More specifically, an ONSNPSamo with a multi-mutation strategy is introduced to balance exploration and exploitation abilities. At the same time, an approach combining the ONSNPSamo and connectivity algorithms is proposed to address the brain tumor segmentation problem. Our experimental results from CEC 2017 benchmarks (basic, shifted and rotated, hybrid, and composition function optimization problems) demonstrate that the ONSNPSamo is better than or close to 12 optimization algorithms. Furthermore, case studies from BraTS 2019 show that the approach combining the ONSNPSamo and connectivity algorithms can more effectively segment brain tumor images than most algorithms involved.
The flavor puzzle: Textures and symmetries
Authors: Nilles Hans Peter , Ramos-Sánchez Saúl
Published: March 2024

We discuss aspects of a promising top-down origin of flavor symmetries in particle physics. Modular transformations originating from string theory dualities are shown to play a crucial role. We introduce the notion of an “eclectic” flavor scheme that unifies traditional flavor symmetries, modular symmetries and [Formula: see text]-transformations. It exhibits the phenomenon of “Local Flavor Unification” with enhanced flavor symmetries at fixed points or lines in moduli space. Successful fits of masses and mixing angles of quarks and leptons are found in the vicinity of these points and lines.
Modular invariance approach to the flavour problem (from bottom up)
Authors: Novichkov P. P. , Penedo J. T. , Petcov S. T.
Published: March 2024

We review aspects of the modular invariance approach to the flavour problem. Harald Fritzsch was among the first to realise the existence and the fundamental nature of the quark and lepton flavour problems in particle physics, that symmetries may be the key to the solution(s) of these problems and to propose in 1978 and 1979 a solution to the quark flavour problem in the form of the Fritzsch quark mass matrices with texture zeros. After introducing the general ingredients of the modular invariance approach, we describe the formalism that allows to construct models in which fermion (charged-lepton and quark) mass hierarchies follow solely from the properties of the modular forms, avoiding the fine-tuning of the constant parameters present in the fermion mass matrices and the need to introduce extra fields. Focusing on the lepton sector, we show how the indicated formalism can be used in lepton flavour models to obtain the charged lepton mass hierarchies without parameter fine-tuning. Only a very limited number of models satisfy the non-fine-tuning requirement in explaining the observed fermion mass hierarchies. We review next the general conditions under which also the peculiar pattern of Pontecorvo, Maki, Nakagawa and Sakata (PMNS) lepton mixing consisting of two large and one small angles can be reproduced without fine-tuning. We then give an example of a phenomenologically viable “minimal” modular lepton flavour model in which both the lepton mixing and charged lepton mass hierarchies are generated without fine-tuning. We conclude by listing some of the open questions and problems in the modular invariant approach to the flavour problem.
Probing flavor riddle á la Fritzsch
Authors: Gupta Manmohan , Ahuja Gulsheen , Rani Versha
Published: March 2024

An attempt has been made to discuss some of the developments, in a personalized historical perspective, regarding a viable answer to the ‘Flavor Riddle’ through a minor addition to the Fritzsch’s original proposal about texture zero mass matrices.
Using a dual network with PNN–LSTM nested structure to realize start-to-end accelerator surrogate model
Authors: Jia Yongzhi , Hu Yaxin , Sun Kunxiang , Chen Xiaolong , Wang Zhijun , Qi Xin , He Yuan
Published: March 2024

Neural network-based surrogate models can provide fast, reliable and realistic models of accelerators. However, training a surrogate model from start-to-end is still a challenge. For accelerators with dozens or even hundreds of elements, training surrogate models can lead to a dimensional disaster. In this paper, we use a dual network with polynomial neural networks (PNNs) and long short-term memory (LSTM) nested structure to realize a start-to-end surrogate model of China Accelerator Facility for Superheavy Element (CAFe2). Results show that the PNN–LSTM model can effectively realize the surrogate model of CAFe2. Compared to the running speed of TraceWin multi-particle simulation, the new surrogate model has improved by about three orders of magnitude while ensuring a sufficiently good level of accuracy. Moreover, our model can efficiently generate a comprehensive set of beam parameters along the entire beamline, significantly enhancing its practicality as a surrogate model.
Modeling convective transport in a reactive fluid near a vertical pervious plate influenced by intense magnetic forces, induced magnetic field, Hall current and thermo-diffusion
Authors: Das Sanatan , Tarafdar Bikarna , Jana Rabindra Nath
Published: March 2024

Exploring convective transport in conducting fluids under potent magnetic influences yields essential insights into numerous natural and designed systems. Such insights aid researchers and engineers in making enlightened progressions in their domains. This paper delves into the convective motion in a reactive fluid moving past a vertically perforated plate, governed by intense magnetic forces, the induced magnetic field (IMF) and Hall current. The model integrates factors like thermal radiation and thermo-diffusion (Soret effect). Formative equations for this model, which encapsulate the effects of distinct physical phenomena, are solved analytically. Graphical representations illuminate the influence of vital flow parameters on velocity, temperature, concentration fields, shear stresses and the rates of heat and mass transfer. From the graphs, it’s evident that Hall currents hinder the primary flow but enhance the secondary flow. A rise in radiation and suction parameters leads to a temperature drop. A heightened Soret number appears to magnify concentration distribution throughout the boundary layer. Intensifying suction at the plate diminishes the boundary layer’s thickness, which in turn elevates the heat and mass transfer rate. This physical model finds extensive applicability across sectors, encompassing metallurgy, magnetic fusion, plasma physics, materials fabrication, geothermal phenomena, geochemistry and ionospheric activities.
Thermal and mass transport investigation of magnetohydrodynamic reactive nanofluid flow utilizing Buongiorno’s model
Authors: Al-Harthi Turkiah M , Alqahtani Aisha M. , Ragab Islam , Alroobaea Roobaea , Rasheed Akhter , Abdou M. Modather M. , Ali Aatif
Published: March 2024

The paper’s primary goal is to investigate mass and heat transfer processes in reactive nanofluid particles. Within Buongiorno’s model, three chemical reactions are discussed. The main subject is on the nanoparticle fractions at the boundary. The characteristics of [Formula: see text] and [Formula: see text] with regard to the nanoparticle fraction have been found to be passively rather than actively controlled at the boundary. To put it another way, these qualities naturally develop and are controlled by the circumstances at the boundary or interface where the nanoparticles interact with the surrounding medium. They are not the result of active manipulation or outside forces. The system of partial differential equations was converted into ordinary differential equations using similarity transformations. To solve the system of ODEs, they combined the shooting method with a numerical technique known as RK-Fehlberg. The study examines various physical parameters and their effects using graphs. The paper also contains a table showing how different parameters affect the regional Nusselt and Sherwood numbers. This enables a deeper comprehension of the impact that these variables have on the heat and mass transfer within the reactive nanofluid particles. Core findings: Examining three chemical reactions involving nanofluids has led to the study’s key discoveries. Additionally, it looks into how specific physical variables may affect the Nusselt and Sherwood numbers.
Hybrid nanofluid flow and heat transfer in symmetric porous ducts with CuO nanoparticles and multi-walled carbon nanotubes under peristaltic motion
Authors: Akbar Noreen Sher , Akram Javaria , Fiaz Hussain M. , Maraj E. N. , Muhammad Taseer
Published: March 2024

This study focuses on the analysis of peristaltic transport of a hybrid nanofluid comprising deionized water as a base fluid and the multi-walled carbon nanotubes (MWCNTs) and copper oxide (CuO) as nanoparticles within a sinusoidal wavy porous duct, taking into consideration the influence of heat generation or absorption. The inaugural literature piece addresses the utilization of hybrid nanofluid in the context of peristaltic flow within ducts. To simplify the analysis, we have converted the non-dimensional equations into a two-dimensional (2D) coordinate system using the assumptions of a very long wavelength [Formula: see text] and low Reynolds number (Re). The non-dimensional equations governing the behavior of the hybrid nanofluid are then solved numerically using the finite volume method. Numerical solutions for temperature and the 2D peristaltic flow are obtained with the assistance of the Mathematics software MATLAB. These solutions are subsequently represented graphically using MATLAB software. The graphical results highlight several key findings for important parameters. First, it is observed that the pressure rise, temperature profile, and pressure gradient in the hybrid nanofluid (CuMWCNTs/H2O) flow increases as heat generation increases. Furthermore, an increase in the nanoparticle volume fraction of both nanoparticles leads to a decrease in the pressure rise and pressure gradient in the hybrid nanofluid flow. Additionally, the widening of the channel reduces the pressure gradient and pressure rise in the CuMWCNTs/H2O hybrid nanofluid. The analysis also includes the visualization of streamlines for peristaltic transport. These streamlines reveal that an increase in amplitude results in larger bolus sizes, while heightened heat generation has the opposite effect, decreasing bolus sizes. The results of this investigation can be found in various cooling devices as flows in the ducts are very frequently utilized for the cooling process of engines. A further topic is common in applications related to microfluidics, heat exchangers, and biomedical devices where peristaltic pumping is employed. Our results are in 100% agreement with the existing literature in special cases.
Study on soliton behaviors of the complex nonlinear Fokas–Lenells equation utilizing different methods
Authors: Aminakbari Najva , Gu Yongyi , Dang Guoqiang
Published: March 2024

In this paper, we study the Fokas–Lenells equation, which is a derivation form of the nonlinear Schrödinger equation and can be used to describe nonlinearity in the propagation of optical pulses. To seek solutions for this nonlinearity, the Logistic method has been proposed in order to drive acceptable and understandable results regarding physical meaning. This method is defined based on the summation of an ordinary differential equation. Moreover, for further study, the Bernoulli [Formula: see text]-expansion method is used by considering hypothetical solutions as a function of the Bernoulli equation. Subsequently, solutions of the Fokas–Lenells equation are successfully acquired, displaying efficient results in hyperbolic, trigonometric, and exponential equations. The importance of these results appears in the definition of the wave function under the consideration of the appropriate coefficients. Hence, computer simulation is used for better understanding of the generated results. The dynamic behaviors of these solutions are demonstrated in 3D graphs, contour maps, and line plots, under applying various parameters, solutions of the waves show different soliton behaviors, including bright-dark, bright, and breather solitons. The results indicate that the exerted methods are novel, reliable, and effective approaches which can be employed in a wide range of nonlinear differential equations. These methods and their begotten results are far from the complexities of mathematical structures and therefore go beyond previous efforts in the literature.
Impact of Predator-Driven Allee and Spatiotemporal Effect on a Simple Predator–Prey Model
Authors: Kayal Kaushik , Samanta Sudip , Rana Sourav , Karmakar Sagar , Chattopadhyay Joydev
Published: March 2024

In this research paper, we consider a Leslie–Gower Reaction–Diffusion (RD) model with a predator-driven Allee term in the prey population. We derive conditions for the existence of nontrivial solutions, uniform boundedness, local stability at co-existing equilibrium points, and Hopf bifurcation criteria from the temporal system. We identify sufficient conditions for Turing instability with no-flux boundary condition for the spatial system. Our investigation delves into the analysis of diffusion-induced Turing instability, incorporating stability conditions for the constant steady-state in the spatial model. We also investigate the conditions for the existence and nonexistence of nonconstant steady states in the diffusion-induced model. During numerical simulations, we observe that the predator-driven Allee term is essential for the model to generate Turing structures. Our findings reveal intriguing properties within the RD system, demonstrating its ability to produce patterns within the Turing domain. The simulation confirms that cold–hot spots and stripes-like patterns (a mixture of spots and strips) arises for different strengths of the predation parameter and Allee parameter. In contrast, we observe that for the above threshold value of the Allee parameter, the above-mentioned patterns may disappear from the system. Interestingly, we also observe that the stationary system produces patterns for both large and small amplitudes of perturbation in the vicinity of the Turing boundary. Our research may contribute valuable insights into the Allee effect and enhance our understanding of predator–prey interactions in naturalistic environments.
COMPLEX NETWORKS GENERATED BY A SELF-SIMILAR PLANAR FRACTAL
Authors: WANG QIN , MA WENJIA , CUI KEQIN , ZENG QINGCHENG , XI LIFENG
Published: March 2024

Many complex networks have scale-free and small-world effects. In this paper, a family of evolving networks is constructed modeled by a non-symmetric self-similar planar fractal, using the encoding method in fractal geometry. Based on the self-similar structure, we study the degree distribution, clustering coefficient and average path length of our evolving network to verify their scale-free and small-world characteristics.
Influence of Entrepreneur’s Action Logics on His Intention to Adopt Crowdfunding: A Pecking Order Theory Perspective
Authors: Guy Omenguele René , Cédric Mbouolang Yimpi
Published: March 2024

Crowdfunding (CF), a new funding channel, is prompting more research on the non-financial determinants of its adoption. This paper is part of this emerging current and aims to explain using the logic of entrepreneurial action, the entrepreneur intention to adopt CF. The binomial logistic regression test on a simplified intention model based on the Unified Theory of Acceptance and Use of Technology model (UTAUT) reveals that the intention to adopt CF varies according to the entrepreneur’s commitment to entrepreneurial act logic and his management logic. These results reveal the validity of the pecking order theory and highlight the importance of incitation to risk-taking and entrepreneurial transparency, catalyst factors for participation in CF.
Investigation on the Dynamic Performance of High-Speed Trains Under Tunnel and Crosswind Environments Considering Car-Body Flexibility Based on a CFD-MBD Co-Simulation Method
Authors: Hu Yanlin , Chang Chao , Chen Qinghua , Ge Xin , Ling Liang , Wang Kaiyun
Published: March 2024

The dynamic performance of railway vehicles is significantly impacted by sudden-changed aerodynamic loads. To investigate the dynamic performance of high-speed trains (HSTs) under tunnel and crosswind environments considering the car-body flexibility, an intensive study is conducted. First, a train–track interaction dynamic model with a flexible car body is established in SIMPACK for dynamic response analysis. Concurrently, an aerodynamic model for calculating the distribution of aerodynamic loads is found in FLUENT to determine forces and moments applied to each part of the car body. Then, the two models are coupled utilizing a co-simulation method developed based on the User Data Protocol (UDP). Finally, a case study is carried out, involving a train passing through tunnels subjected to crosswinds. The results reveal that the distribution of aerodynamic loads on the car-body affected by crosswind is time-variant and non-even. Interestingly, the dynamic simulation results are almost unaffected by the method used to allocate the loads on the car body. Variations in the aerodynamic loads affected by crosswinds lead to flexible first-order diamond mode vibration of the car body at around 8.5[Formula: see text]Hz when exiting the tunnel. As the crosswind speed continues to increase, vibrations at frequencies of 18.2[Formula: see text]Hz and 24.2[Formula: see text]Hz will be enhanced, corresponding to the bending mode and combined mode of the car body. However, similar flexible vibrations are insignificant when the vehicle enters the tunnel. In addition, the vertical wheel–rail interaction obtained by the dynamic model with a rigid car body is slightly greater than that with a flexible car body.
Conjugacy classes of completely reducible cube-free solvable p′-subgroups of GL(2,q)
Authors: Kumar Prashun , Venkataraman Geetha
Published: March 2024

Let [Formula: see text] be a cube-free positive integer and let [Formula: see text] be a prime such that [Formula: see text]. In this paper, we find the number of conjugacy classes of completely reducible solvable cube-free subgroups in [Formula: see text] of order [Formula: see text], where [Formula: see text] is a power of [Formula: see text].
The Thermal-Mass-Redistributed Finite Element Method Combined with the Houbolt Method for 2D Transient Heat Conduction Problems
Authors: Shi Caixia , Niu Ruiping , Wu Chengtao
Published: March 2024

The paper proposes a finite element method with high precision and convergence for solving 2D transient heat conduction problems. The temperature gradient with respect to time is approximated using the Houbolt method, a third-order difference scheme, which improves the accuracy and convergence rate and permits the use of large time steps for long-term prediction. The proposed finite element method redistributes the thermal mass matrix to balance it with the thermal stiffness matrix, further enhancing the precision of solutions. Besides, the effective condition number is introduced to determine the position of optimal flexible integration points, ensuring that the error of the solutions is minimal. Finally, intensive transient heat conduction problems are analyzed using the present method to demonstrate good performance in precision and convergence. Compared to FEM using the backward finite difference method, the proposed method achieves more accurate and super convergent temperature solutions. In comparison to FEM with the forward finite difference method, it can use larger time steps while maintaining the same level of accuracy.
Cilia-Like Observer for Tumor Recurrence in Active Soft Matter
Authors: Stefanov S. Z. , Trifonova I.
Published: March 2024

Cilia-like sensitivity observer, at an adaptive treatment of a tumor recurrence in active soft matter, is built. Therefore, the activity of soft matter is shown as active colloidal chains with a cilia-like and flagella-like motility; the tumor recurrence, in active soft matter, is considered as run-away/quantum aging of soft matter; the adaptive treatment energy is defined as electromagnetic field energy, stabilizing or destabilizing life, and as energy of a force dipole. Information recovery at an adaptive treatment is fulfilled via two cilia-like quantum extremal islands. Information recovery is obtained for a mean maximum-tolerated dose of the adaptive treatment and cilia-like tumor growth in active soft matter. There are introduced four strategies for an adaptive treatment for run-away/quantum aging active soft matter with a mean maximum-tolerated dose at stabilized tumor/tumor diffusion. It is graphically determined, for each of these four strategies, the mean sensitivity of the adaptive treatment for three tumor types in 27 patients, according to a maximum-tolerated treatment dose and cilia-like tumor growth in active soft matter.
Electrospun photothermal tellurium nanosheet-loaded polycaprolactone/gelatin nanofibers to promote wound healing
Authors: Li Fangjia , Zhang Jing , Xiong Huan , Min Lihua , Gou Li
Published: March 2024

Low-cost and biodegradable photothermal wound dressings with remarkable therapeutic effects are highly desirable for next-generation wound healing. Herein, we report an efficient photothermal wound dressing mat made of tellurium nanosheet (TeNS)-loaded electrospun polycaprolactone/gelatin (PCL/GEL) nanofibers. The TeNS-loaded PCL/GEL nanofibrous architectures showed antibacterial efficacy against Escherichia coli and Staphylococcus aureus of 87.68% and 94.57%, respectively. Under near-infrared (NIR) light illumination, they can facilitate cell proliferation as revealed by in vitro scratch assay. The results from in vivo skin wounds combined with tissue staining experiments further showed that the TeNS-loaded dressing could substantially promote wound healing under photothermal conditions. Using immunohistochemical analysis, we found that the TeNS-loaded PCL/GEL nanofibers [Formula: see text] NIR group have a high expression of specific antigens in epidermal growth factor (EGF) ([Formula: see text]) and endothelial cell adhesion molecule-31 (CD31, [Formula: see text]), verifying that the nanofibrous mat can stimulate EGF generation and microvessel proliferation. Furthermore, the PCL/GEL/Te[Formula: see text]NIR group has the lowest expression in endothelial cell adhesion molecule-68 (CD68, [Formula: see text]), suggesting that the nanofibrous mats have a high anti-inflammatory efficiency. Our work sheds light on the development of novel nonanti-inflammatory wound dressings via photothermal sterilization and the promotion of cell growth using two-dimensional (2D) nanosheets.
Some Lp norm involving the polar derivative of a polynomial
Authors: Wani Irfan Ahmad , Mir Mohammad Ibrahim , Nazir Ishfaq
Published: March 2024

Let [Formula: see text], [Formula: see text], is a polynomial of degree [Formula: see text] having all zeros in [Formula: see text], [Formula: see text], then for [Formula: see text] with [Formula: see text], [Formula: see text] and for each [Formula: see text] with [Formula: see text]; it was proved by (N. A. Rather et al., Some [Formula: see text] inequalities involving the polar derivative of a polynomial, Appl. Math. E-Notes 14 (2014) 116–122) [Formula: see text] [Formula: see text] In this paper, we establish some refinements and generalizations of above and some known polynomial inequalities concerning the polar derivative of a polynomial.
Efficient 3D Representation Learning for Medical Image Analysis
Authors: Tang Yucheng , Liu Jie , Zhou Zongwei , Yu Xin , Huo Yuankai
Published: March 2024

Machine learning approaches have significantly advanced the 3D medical images analysis, such as the CT and MRI scans, which enables improved diagnosis and treatment evaluation. These image volumes provide rich spatial context for understanding the internal brain and body anatomies. Typical medical image analysis tasks, such as segmentation, reconstruction and registration, are essential for characterizing this context. Related to 3D data formats, meshes, point clouds and others are used to represent the anatomical structures, each with unique applications. To better capture the spatial information and address data scarcity, self- and semi-supervised learning methods have emerged. However, efficient 3D representation learning remains challenging. Recently, Transformers have shown promise, leveraging the self-attention mechanisms that perform well on transfer learning and self-supervised methods. These techniques are applied for medical domains without extensive manual labeling. This work explores data-efficient models, scalable deep learning, semantic context utilization and transferability in 3D medical image analysis. We also evaluated the foundational models, self-supervised pre- training, transfer learning and prompt tuning, thus advancing this critical field.
A Unified Framework for Jointly Compressing Visual and Semantic Data
Authors: Liu Shizhan , Lin Weiyao , Chen Yihang , Zhang Yufeng , Dai Wenrui , See John , Xiong Hong-Kai
Published: March 2024

The rapid advancement of multimedia and imaging technologies has resulted in increasingly diverse visual and semantic data. A large range of applications such as remote-assisted driving requires the amalgamated storage and transmission of various visual and semantic data. However, existing works suffer from the limitation of insufficiently exploiting the redundancy between different types of data. In this article, we propose a unified framework to jointly compress a diverse spectrum of visual and semantic data, including images, point clouds, segmentation maps, object attributes, and relations. We develop a unifying process that embeds the representations of these data into a joint embedding graph according to their categories, which enables flexible handling of joint compression tasks for various visual and semantic data. To fully leverage the redundancy between different data types, we further introduce an embedding-based adaptive joint encoding process and a Semantic Adaptation Module to efficiently encode diverse data based on the learned embeddings in the joint embedding graph. Experiments on the Cityscapes, MSCOCO, and KITTI datasets demonstrate the superiority of our framework, highlighting promising steps toward scalable multimedia processing.
Relationship of emotional intelligence and capability of answering higher-order knowledge questions in physiology among first-year medical students
Authors: Mondal Himel , Mondal Shaikat , Singh Amita , Kumari Amita , Pinjar Mohammed Jaffer , Juhi Ayesha , Nath Santanu , Dhanvijay Anup Kumar D. , Kumari Anita , Gupta Pratima
Published: March 2024

This study assessed the correlation between emotional intelligence (EI) and the capability of medical students to answer higher-order knowledge questions (HOQs) in the specific context of physiology. The finding reveals one of the multifaceted dimensions of the relationship between EI and academic performance. This novel perspective opens the door to further investigations to explore the relationship in other subjects and other dimensions to understand why students with higher EI have higher academic performance.
The calcium-sensing receptor has only a parathyroid hormone-dependent role in the acute response of renal phosphate transporters to phosphate intake
Authors: Daryadel Arezoo , Küng Catharina J. , Haykir Betül , Sabrautzki Sibylle , de Angelis Martin Hrabĕ , Hernando Nati , Rubio-Aliaga Isabel , Wagner Carsten A.
Published: March 2024

Consumption of phosphate-rich diets causes an adaptive response of the body leading to the urinary excretion of phosphate. The underlying mechanisms are still poorly understood. Here, we examined the role of the calcium-sensing receptor (CaSR) that senses both calcium and phosphate. We confirmed that the receptor increases the secretion of parathyroid hormone involved in stimulating urinary phosphate excretion. However, we did not find any evidence for a role of the receptor beyond this function.
Associations of Combined Exposure to Metabolic and Inflammatory Indicators with Thyroid Nodules in Adults: A Nested Case-Control Study
Authors: Zhu Xin-Yi , Meng Xing-Chen , Cheng Bei-Jing , Wang Chun , Wang Jia , Li Tian-Lin , Li Hui , Meng Ke , Liu Ran
Published: March 2024

Objective. To explore associations of combined exposure to metabolic/inflammatory indicators with thyroid nodules. Methods. We reviewed personal data for health screenings from 2020 to 2021. A propensity score matching method was used to match 931 adults recently diagnosed with thyroid nodules in a 1 : 4 ratio based on age and gender. Conditional logistic regression and Bayesian kernel machine regression (BKMR) were used to explore the associations of single metabolic/inflammatory indicators and the mixture with thyroid nodules, respectively. Results. In the adjusted models, five indicators (ORQ4 vs. Q1: 1.30, 95% CI: 1.07–1.58 for fasting blood glucose; ORQ4 vs. Q1: 1.30, 95% CI: 1.08–1.57 for systolic blood pressure; ORQ4 vs. Q1: 1.26, 95% CI: 1.04–1.53 for diastolic blood pressure; ORQ4 vs. Q1: 1.23, 95% CI: 1.02–1.48 for white blood cell; ORQ4 vs. Q1: 1.28, 95% CI: 1.07–1.55 for neutrophil) were positively associated with the risk of thyroid nodules, while high-density lipoproteins (ORQ3 vs. Q1: 0.75, 95% CI: 0.61–0.91) were negatively associated with the risk of thyroid nodules. Univariate exposure-response functions from BKMR models showed similar results. Moreover, the metabolic and inflammatory mixture exhibited a significant positive association with thyroid nodules in a dose-response pattern, with systolic blood pressure being the greatest contributor within the mixture (conditional posterior inclusion probability of 0.82). No interaction effects were found among the five indicators. These associations were more prominent in males, participants with higher age (≥40 years old), and individuals with abnormal body mass index status. Conclusions. Levels of the metabolic and inflammatory mixture have a linear dose-response relationship with the risk of developing thyroid nodules, with systolic blood pressure levels being the most important contributor.
The Causal Relationship between Plasma Myeloperoxidase Levels and Respiratory Tract Infections: A Bidirectional Mendelian Randomization Study
Authors: Liu Xiu , Zhang Chuchu , Ren Jiajia , Deng Guorong , Xu Xi , Liu Jueheng , Gao Xiaoming , Li Ruohan , Li Jiamei , Wang Gang
Published: March 2024

Background. Observational researches reported the underlying correlation of plasma myeloperoxidase (MPO) concentration with respiratory tract infections (RTIs), but their causality remained unclear. Here, we examined the cause–effect relation between plasma MPO levels and RTIs. Materials and Methods. Datasets of plasma MPO levels were from the Folkersen et al. study (n = 21,758) and INTERVAL study (n = 3,301). Summarized data for upper respiratory tract infection (URTI) (2,795 cases and 483,689 controls) and lower respiratory tract infection (LRTI) in the intensive care unit (ICU) (585 cases and 430,780 controls) were from the UK Biobank database. The primary method for Mendelian randomization (MR) analysis was the inverse variance weighted approach, with MR-Egger and weighted median methods as supplements. Cochrane’s Q test, MR-Egger intercept test, MR pleiotropy residual sum and outliers global test, funnel plots, and leave-one-out analysis were used for sensitivity analysis. Results. We found that plasma MPO levels were positively associated with URTI (odds ratio (OR) = 1.135; 95% confidence interval (CI) = 1.011–1.274; P=0.032) and LRTI (ICU) (OR = 1.323; 95% CI = 1.006–1.739; P=0.045). The consistent impact direction is shown when additional plasma MPO level genome-wide association study datasets are used (URTI: OR = 1.158; 95% CI = 1.072–1.251; P<0.001; LRTI (ICU): OR = 1.216; 95% CI = 1.020–1.450; P=0.030). There was no evidence of a causal effect of URTI and LRTI (ICU) on plasma MPO concentration in the reverse analysis (P>0.050). The sensitivity analysis revealed no violations of MR presumptions. Conclusions. Plasma MPO levels may causally affect the risks of URTI and LRTI (ICU). In contrast, the causal role of URTI and LRTI (ICU) on plasma MPO concentration was not supported in our MR analysis. Further studies are needed to identify the relationship between RTIs and plasma MPO levels.
Acute and Subchronic Toxicological Study of the Cocktail Extract from Curcuma xanthorrhiza Roxb, Phyllanthus niruri L. and Morinda citrifolia L.
Authors: Rosidah Idah , Renggani Tiya Novlita , Firdausi Nisrina , Ningsih Sri , Yunianto Prasetyawan , Permatasari Devi , Pongtuluran Olivia Bunga , Bahua Hismiaty , Efendi Julham , Kusumastuti Siska Andrina , Nuralih  , El Muttaqien Sjaikhurrizal , Nizar  , Kusumaningrum Susi , Agustini Kurnia
Published: March 2024

Curcuma xanthorrhiza Roxb, Phyllanthus niruri L., and Morinda citrifolia L. are Indonesian medicinal herbs used empirically as traditional therapeutics for maintaining health. The cocktail extract of these three plants (CECPM) had been developed and demonstrated immunostimulant activity in rats. This study aimed to evaluate the acute and subchronic toxicity of CECPM in vivo. The acute toxicity assay was conducted by orally administering a range dose of CECPM (313, 625, 1250, 2500, or 5000 mg/kg body weight (bw) on female mice once and then evaluating the toxic symptom every day for 14 days later. The chronic toxicity test was carried out by giving various doses of CECPM (600, 800, and 1000 mg/kg·bw) to female and male rats orally continuously for 90 consecutive days. The signs of toxicities were evaluated at the 90- and 28 days postadministration. The acute oral toxicity assays showed that there was no toxic syndrome and mortality found during the period of the experiment. The lethal dose level (LD50) of CECPM was more than 5000 g/kg, which was categorized as practically non-toxic. Meanwhile, in the sub-chronic toxicity study, some parameters tested at 90 days postadministration and after 28 days of withdrawal, such as the body weight, relative organ weight, food intake, hematological and biochemical blood parameters, and also histopathological examination of five primary tissues (heart, liver, kidney, spleen, and lung) revealed no abnormalities. There was no-observed adverse effect level (NOAEL) for the present study of CECPM 1000 mg/kg·bw of the rat. Therefore, it is concluded that the orally administered CECPM was relatively nontoxic during acute and subchronic toxicology studies.
Hepatitis B Virus–Mediated m6A Demethylation Increases Hepatocellular Carcinoma Stemness and Immune Escape
Authors: Meng Yuting , Shu Zheyue , Wang Xueyao , Hong Liang , Wang Baohua , Jiang Jingjing , He Kangxin , Cao Qingyi , Shi Fan , Wang Hai , Gong Lan , Diao Hongyan
Published: March 2024

Hepatitis B viral (HBV) persistent infection plays a significant role in hepatocellular carcinoma (HCC) tumorigenesis. Many studies have revealed the pivotal roles of N6-methyladenosine (m6A) in multiple cancers, while the regulatory mechanism in stemness maintenance of HBV persistent infection-related HCC remains elusive. Here, we demonstrated that the level of m6A modification was downregulated by HBV in HBV-positive HCC, through enhanced stability of ALKBH5 mRNA. More specifically, we also identified that ALKBH5 mRNA was functionally required for the stemness maintenance and self-renewal in the HBV-positive HCC, but dispensable in HBV-negative HCC. Mechanistically, ALKBH5 demethylated the m6A modification in the 3′ untranslated region of the oncogenic gene SNAI2 to prevent the recognition of YTHDF2 therewith stabilize SNAI2 transcripts, contributing to cancer stem cell traits in HBV-positive HCC. Moreover, the expression of SNAI2 reversed the suppression of stemness properties by knocking down ALKBH5. In addition, ALKBH5/SNAI2 axis accelerates tumor immune evasion through activated ligand of immune checkpoint CD155. Our study unveiled that the ALKBH5 induces m6A demethylation of the SNAI2 as a key regulator in HBV-related HCC, and identifies the function of ALKBH5/SNAI2/YTHDF2 axis in promoting the stem-like cells phenotype and immune escape during HBV infection.

Implications:

HBV promotes HCC stemness maintenance through elevate m6A modification of SNAI2 in an ALKBH5-YTHDF2–dependent manner and increases the expression of the ligand of immune checkpoint CD155.
An Improvement of Exertional Dyspnea by the Reintroduction of Anti-EGFR Antibody in Patients with Metastatic Rectal Cancer Who Developed Cancerous Lymphangiopathy: A Case Report
Authors: Shibutani Masatsune , Tanda Hideki , Fukuoka Tatsunari , Kasashima Hiroaki , Kashiwagi Shinichiro , Maeda Kiyoshi
Published: March 2024

Introduction: Reexposure to anti-EGFR antibodies, such as a reintroduction or rechallenge with anti-EGFR antibodies, has attracted much attention in the field of metastatic colorectal cancer. A reintroduction of anti-EGFR antibodies often shows good therapeutic outcomes, as most patients eligible for such reintroduction discontinued treatment due to adverse events despite a good treatment response during front-line treatment. We herein report a case demonstrating an improvement in exertional dyspnea after the reintroduction of anti-EGFR antibody in a patient with metastatic rectal cancer who developed cancerous lymphangiopathy. Case Presentation: A 68-year-old man who had undergone curative surgery for stage IIIB rectal cancer was diagnosed with multiple lung metastases. During the late-line treatment, respiratory failure developed because of multiple lung metastases and cancerous lymphangiopathy. Two months after the initiation of irinotecan + cetuximab, which had been discontinued due to acneiform eruptions despite a good treatment response as a first-line treatment, his dyspnea and performance status dramatically improved. Conclusion: This case indicates that the reintroduction of anti-EGFR antibody to patients who have discontinued anti-EGFR antibody due to skin toxicity despite a good treatment response is a very useful treatment option for metastatic colorectal cancer.
Gut Microbiota and Blood Metabolites Related to Fiber Intake and Type 2 Diabetes
Authors: Wang Zheng , Peters Brandilyn A. , Yu Bing , Grove Megan L. , Wang Tao , Xue Xiaonan , Thyagarajan Bharat , Daviglus Martha L. , Boerwinkle Eric , Hu Gang , Mossavar-Rahmani Yasmin , Isasi Carmen R. , Knight Rob , Burk Robert D. , Kaplan Robert C. , Qi Qibin
Published: March 2024

BACKGROUND:

Consistent evidence suggests diabetes-protective effects of dietary fiber intake. However, the underlying mechanisms, particularly the role of gut microbiota and host circulating metabolites, are not fully understood. We aimed to investigate gut microbiota and circulating metabolites associated with dietary fiber intake and their relationships with type 2 diabetes (T2D).

METHODS:

This study included up to 11 394 participants from the HCHS/SOL (Hispanic Community Health Study/Study of Latinos). Diet was assessed with two 24-hour dietary recalls at baseline. We examined associations of dietary fiber intake with gut microbiome measured by shotgun metagenomics (350 species/85 genera and 1958 enzymes; n=2992 at visit 2), serum metabolome measured by untargeted metabolomics (624 metabolites; n=6198 at baseline), and associations between fiber-related gut bacteria and metabolites (n=804 at visit 2). We examined prospective associations of serum microbial-associated metabolites (n=3579 at baseline) with incident T2D over 6 years.

RESULTS:

We identified multiple bacterial genera, species, and related enzymes associated with fiber intake. Several bacteria (eg,
Butyrivibrio
,
Faecalibacterium
) and enzymes involved in fiber degradation (eg, xylanase EC3.2.1.156) were positively associated with fiber intake, inversely associated with prevalent T2D, and favorably associated with T2D-related metabolic traits. We identified 159 metabolites associated with fiber intake, 47 of which were associated with incident T2D. We identified 18 of these 47 metabolites associated with the identified fiber-related bacteria, including several microbial metabolites (eg, indolepropionate and 3-phenylpropionate) inversely associated with the risk of T2D. Both
Butyrivibrio
and
Faecalibacterium
were associated with these favorable metabolites. The associations of fiber-related bacteria, especially
Faecalibacterium
and
Butyrivibrio
, with T2D were attenuated after further adjustment for these microbial metabolites.

CONCLUSIONS:

Among United States Hispanics/Latinos, dietary fiber intake was associated with favorable profiles of gut microbiota and circulating metabolites for T2D. These findings advance our understanding of the role of gut microbiota and microbial metabolites in the relationship between diet and T2D.
PRERISK: A Personalized, Artificial Intelligence–Based and Statistically–Based Stroke Recurrence Predictor for Recurrent Stroke
Authors: Colangelo Giorgio , Ribo Marc , Montiel Estefanía , Dominguez Didier , Olivé-Gadea Marta , Muchada Marian , Garcia-Tornel Álvaro , Requena Manuel , Pagola Jorge , Juega Jesús , Rodriguez-Luna David , Rodriguez-Villatoro Noelia , Rizzo Federica , Taborda Belén , Molina Carlos A. , Rubiera Marta
Published: March 2024

BACKGROUND:

Predicting stroke recurrence for individual patients is difficult, but individualized prediction may improve stroke survivors’ engagement in self-care. We developed PRERISK: a statistical and machine learning classifier to predict individual risk of stroke recurrence.

METHODS:

We analyzed clinical and socioeconomic data from a prospectively collected public health care–based data set of 41 975 patients admitted with stroke diagnosis in 88 public health centers over 6 years (2014–2020) in Catalonia-Spain. A new stroke diagnosis at least 24 hours after the index event was considered as a recurrent stroke, which was considered as our outcome of interest. We trained several supervised machine learning models to provide individualized risk over time and compared them with a Cox regression model. Models were trained to predict early, late, and long-term recurrence risk, within 90, 91 to 365, and >365 days, respectively. C statistics and area under the receiver operating characteristic curve were used to assess the accuracy of the models.

RESULTS:

Overall, 16.21% (5932 of 36 114) of patients had stroke recurrence during a median follow-up of 2.69 years. The most powerful predictors of stroke recurrence were time from previous stroke, Barthel Index, atrial fibrillation, dyslipidemia, age, diabetes, and sex, which were used to create a simplified model with similar performance, together with modifiable vascular risk factors (glycemia, body mass index, high blood pressure, cholesterol, tobacco dependence, and alcohol abuse). The areas under the receiver operating characteristic curve were 0.76 (95% CI, 0.74–0.77), 0.60 (95% CI, 0.58–0.61), and 0.71 (95% CI, 0.69–0.72) for early, late, and long-term recurrence risk, respectively. The areas under the receiver operating characteristic curve of the Cox risk class probability were 0.73 (95% CI, 0.72–0.75), 0.59 (95% CI, 0.57–0.61), and 0.67 (95% CI, 0.66–0.70); machine learning approaches (random forest and AdaBoost) showed statistically significant improvement (
P
<0.05) over the Cox model for the 3 recurrence time periods. Stroke recurrence curves can be simulated for each patient under different degrees of control of modifiable factors.

CONCLUSIONS:

PRERISK is a novel approach that provides a personalized and fairly accurate risk prediction of stroke recurrence over time. The model has the potential to incorporate dynamic control of risk factors.
Agile Leadership Characteristic of Hakkari University Managers in Perspective of VUCA-RR
Authors: Yaşbay Kobal Hava
Published: March 2024

The aim of the study is to investigate the agile leadership characteristics of managers working at Hakkari University. Data was collected from 22 managers working at Hakkari University. Data was collected from 22 managers and analyzed in the Nvivo12 qualitative analysis program. Analysis results show that managers act result-oriented by motivating employees and by example. Managers stated that they attach importance to teamwork and also that they act in the direction of making joint decisions in the organization. It has been determined that the managers support the activities that will help the development of the employees such as training and seminars, and make an effort to acquire and use technological competence. Finally, it has been determined that the managers exhibit agile leadership characteristics by acting flexible and fast.
Film Pazarlama Stratejilerinde Transmedya Etkisi: Barbie Örneği
Authors: Akdemir Dilek Buket , Çelimli Akgül Azime Ayşenur
Published: March 2024

Transmedya araçlarından biri olan sinema, reklam faaliyetlerini tutundurma amacıyla aktif olarak kullanılırken, son yıllarda sosyal medyayı kullanması dikkat çekmektedir. Bu çalışmanın konusu olan Temmuz 2023’te vizyona giren Barbie filmi yapımcıları, geleneksel reklam mecralarının ötesinde dijital dünyada bir Barbie ekosistemi oluşturmayı hedefleyerek dünyanın dört bir yanında Instagram Influencerları ile anlaşma yapmıştır. Pazarlama bütçesinin filmin yapım maliyetinden yüksek olması sinemada reklam faaliyetlerinin önemini kanıtlar niteliktedir. Araştırmada Barbie’nin resmi Instagram ve Youtube kanalı ile Türkiye’de en çok izlenen Barbie içerikli Reels’ların karşılaştırması yapılmıştır. Farklı kategorilerde Türkçe içerik üreten amaçlı örneklemle seçilen 5 influencerın reelsları içerik çözümlemesi yöntemiyle incelenmiştir. Transmedya hikâye anlatıcılığında kullanılan 10 maddeye göre videoların içerikleri tablolaştırılarak yorumlanmıştır. Barbie filmi örneğiyle, transmedya bünyesinde ürünün de ötesine geçen dijital içerik üreticilerinin (influencerların) hazırladığı içerikler, yeni bir pazarlama stratejisi olarak değerlendirilmesi söz konusu tartışmalardandır.
Analysis of the Relationship Between Cross Capital Flows and Stock Exchange Index with Machine Learning
Authors: Akusta Ahmet
Published: March 2024

This paper investigates forecasting the BIST100 stock index using cross-capital flow analysis. It employs feature engineering and the Orthogonal Matching Pursuit (OMP) model to navigate the intricacies of financial time series prediction. The study meticulously selects features such as lagged values, moving averages, and volatility metrics, normalized to ensure unbiased model impact. The OMP model is carefully optimized to handle the dimensionality of financial data, avoiding overfitting through a sparsity constraint. This approach yields an R-squared score of 0.88, indicating a solid capability to capture index variance. Visual comparisons between actual and predicted values further validate the model's accuracy. The paper highlights the importance of methodological precision in developing models capable of discerning complex patterns, offering valuable insights for investment strategies. Implications of the study show that cross-capital movements and macroeconomic variables are a good fit with ML to predict the Stock Market despite the complexity of financial markets.