bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of pomegranate consumption on cardiovascular risk factors in hemodialysis patients: A systematic review of clinical trials
Authors: Baghdadi Ghazal , Shidfar Farzad , Dehnad Afsaneh 
Published: July 2023

AbstractPatients undergoing maintenance dialysis have a significantly higher incidence of cardiovascular disease (CVD) compared with the general population. CVD is the most common cause of morbidity and mortality among hemodialysis patients, and has been attributed, among other causes, to increased oxidative stress, inflammation, hypertension and dyslipidemia. Pomegranate, a popular fruit worldwide, has demonstrated health benefits such as antioxidative, antidiabetic, antihypertensive, antihyperlipidemic and anti‐inflammatory effects. In this systematic review of clinical trials, we aim to summarize the effect of different parts of pomegranate and the effects of its use on CVD risk factors in hemodialysis patients. PubMed/MEDLINE, EMBASE, Scopus, and Web of Science were searched to identify eligible clinical trials up to December 2021. Ultimately, seven clinical trials were included in this study. Different parts of pomegranate used in these trials were pomegranate juice, pomegranate extract and pomegranate peel extract. The duration of the studies varied from one dialysis session to 1 year. Our results showed that different parts of pomegranate may have anti‐hypertensive, antioxidant, anti‐inflammatory effects and improve lipid profile by decreasing TG (triglycerides) and increasing HDL‐C (high‐density lipoprotein cholesterol) in hemodialysis patients. However, due to limited number of studies, more clinical trials need to be performed.
Parameterizing building effects on airflows within the urban canopy layer for high‐resolution models using a nudging approach
Authors: Cheng Ge , Schlünzen K. Heinke , Grawe David , Voss Vivien , Thatcher Marcus , Rayner Peter 
Published: July 2023

AbstractIn this study, a new multilayer urban canopy parameterization for high‐resolution (∼1 km) atmospheric models using the nudging approach to represent the impacts of urban canopies on airflow is presented. In our parameterization, a nudging term is added to the momentum equations and a source term to the turbulent kinetic energy equation to account for building effects. The challenge of this parameterization lies in defining appropriate values for the nudging coefficient and the weighting function used to reflect canopy effects. Values of both are derived and the parameterization developed is implemented and tested for idealized cases in the Mesoscale Transport and Stream model (METRAS). Comparison data are taken from obstacle‐resolving microscale model results. Results show that the parameterization using the nudging approach can simulate aerodynamic effects induced within the canopy by obstacles well, in terms of reduction of wind speeds and production of additional turbulent kinetic energy. Thus, models with existing nudging can use this approach as an efficient and effective method to parameterize dynamic urban canopy effects.
Use of water accounts based indicators for water management in India
Authors: Bassi Nitin 
Published: July 2023

AbstractIn India, water accounts on a river basin scale are not prepared regularly, mainly due to the unavailability of data on consumptive uses in various economic sectors at that scale. This research paper demonstrates an approach to prepare water accounts for the Mahanadi river basin in eastern India, which experiences climate‐induced hydrological extremes using the demographic, economic, hydrological, and geohydrological data available at different administrative and geographical scales. Further, selected water accounts derived indicators were computed to identify critical elements that need to be altered for improving water resources management at the river basin scale. The results show that the total inflow during the wet year exceeds 24%–26% in comparison to the normal and dry years. In turn, 23% and 24% of the inflow get drains to the sea in a dry and normal year, respectively, with agriculture accounting for the lion's share of the blue water consumed, about 57%–60% in all the years. The river has a huge dilution capacity, and, except for the biological oxygen demand in a dry year, the value of all other selected water quality parameters (nitrogen and electrical conductivity) is within the acceptable limits established by law. Reducing water consumption in irrigated crops during summer, controlling evaporation from the reservoirs, and considering nitrogen concentration and electrical conductivity in the existing approach to determine polluted river stretches in India are identified as crucial actions for improving water management in the basin. Further, assessment of water consumption and environmental flow requirement is identified as important decision factors to guide future water reallocation among riparian states.
Metal Ion Coordination Improves Graphite Nitride Carbon Microwave Therapy in Antibacterial and Osteomyelitis Treatment
Authors: Li Bo , Zhu Huiping , Lv Yuelin , Wang Chaofeng , Wu Shuilin , Zhu Shengli , Zheng Yufeng , Jiang Hui , Zhang Yu , Li Zhaoyang , Cui Zhenduo , Liu Xiangmei 
Published: July 2023

AbstractThe ability to effectively treat deep bacterial infections while promoting osteogenesis is the biggest treatment demand for diseases such as osteomyelitis. Microwave therapy is widely studied due to its remarkable ability to penetrate deep tissue. This paper focuses on the development of a microwave‐responsive system, namely, a zinc ion (Zn2+) doped graphite carbon nitride (CN) system (BZCN), achieved through two high‐temperature burning processes. By subjecting composite materials to microwave irradiation, an impressive 99.81% eradication of Staphylococcus aureus is observed within 15 min. Moreover, this treatment enhances the growth of bone marrow stromal cells. The Zn2+ doping effectively alters the electronic structure of CN, resulting in the generation of a substantial number of free electrons on the material's surface. Under microwave stimulation, sodium ions collide and ionize with the free electrons generated by BZCN, generating a large amount of energy, which reacts with water and oxygen, producing reactive oxygen species. In addition, Zn2+ doping improves the conductivity of CN and increases the number of unsaturated electrons. Under microwave irradiation, polar molecules undergo movement and generate frictional heat. Finally, the released Zn2+ promotes macrophages to polarize toward the M2 phenotype, which is beneficial for tibial repair.
Excellent Energy‐Storage Performance in Lead‐Free Capacitors with Highly Dynamic Polarization Heterogeneous Nanoregions
Authors: Ma Qiansu , Chen Liang , Yu Huifen , Wu Jie , Zhu Lifeng , Yang Jikun , Qi He 
Published: July 2023

AbstractLead‐free dielectric ceramics with excellent energy‐storage performance are crucial to the development of the next‐generation advanced pulse power capacitors. However, low energy‐storage density limits the evolution of capacitors toward lightweight, miniaturization, and integration. Here, an effective strategy of constructing highly dynamic polarization heterogeneous nanoregions is proposed in lead‐free relaxors to realize an ultrahigh energy‐storage density of ≈8.0 J cm−3, making almost ten times the growth of energy‐storage density compared with pure Bi0.5Na0.5TiO3ceramic, accompanied by a higher energy efficiency of ≈80% as well as an ultrafast discharge rate of ≈20 ns. Ultrasmall polarization heterogeneous nanoregions with different orientations and ultrahigh flexibility, and significantly decreased grain size to submicron lead to reduced heat loss, improved breakdown electric field and polarization, enhanced relaxation, and delayed polarization saturation behaviors, contributing to the remarkable energy‐storage performance. Moreover, the breakdown path distribution or electrical tree evolution behaviors are systematically studied to reveal the origin of ultrahigh breakdown electric field through phase field simulations. This work demonstrates that constructing highly dynamic polarization heterogeneous nanoregions is a powerful approach to develop new lead‐free dielectric materials with high energy‐storage performance.
Manipulating Redox Kinetics using p‐n Heterojunction Biservice Matrix as both Cathode Sulfur Immobilizer and Anode Lithium Stabilizer for Practical Lithium–Sulfur Batteries
Authors: Du Xiaohang , Wen Chenxu , Luo Yuhong , Luo Dan , Yang Tingzhou , Wu Lanlan , Li Jingde , Liu Guihua , Chen Zhongwei 
Published: July 2023

AbstractAs an attractive high‐energy‐density technology, the practical application of lithium–sulfur (Li–S) batteries is severely limited by the notorious dissolution and shuttle effect of lithium polysulfides (LiPS), resulting in sluggish reaction kinetics and uncontrollable dendritic Li growth. Herein, a p‐n typed heterostructure consisting of n‐type MoS2 nanoflowers embedded with p‐type NiO nanoparticles is designed on carbon nanofibers (denoted as NiO‐MoS2@CNFs) as both cathode sulfur immobilizer and anode Li stabilizer for practical Li–S batteries. Such p‐n typed heterostructure is proposed to establish the built‐in electric field across the heterointerface for facilitated the positive charge to reach the surface of NiO‐MoS2, meanwhile inherits the excellent LiPS adsorption ability of p‐type NiO nanoparticles and catalytic ability of n‐type MoS2. As the anode matrix, the implementation of NiO–MoS2 heterostructure can prevent the growth of Li dendrites by enhancing the lithiophilicity and reducing local current density. The obtained Li–S full battery exhibits an ultra‐high areal capacity over 7.3 mAh cm−2, far exceeding that of current commercial Li‐ion batteries. Meanwhile, a stable cycling performance can be achieved under low electrolyte/sulfur ratio of 5.8 µL mg−1 and negative/positive capacity ratio of 1. The corresponding pouch cell maintains high energy density of 305 Wh kg−1 and stable cycling performance under various bending angles.
Reshaping Inner Helmholtz Layer and Electrolyte Structure via Multifunctional Organic Molecule Enabling Dendrite‐Free Zn Metal Anode
Authors: Liu Chang , Jiang Wen‐Bin , Xie Dan , Diao Wan‐Yue , Tao Fang‐Yu , Wang Xin‐Zhe , Sun Hai‐Zhu , Li Wen‐Liang , Wu Xing‐Long , Zhang Jing‐Ping 
Published: July 2023

AbstractThe dendrite growth and parasitic reactions that occur on Zn metal anode (ZMA)/electrolyte interface hinder the development of aqueous zinc ion batteries (AZIBs) in next‐generation renewable energy storage systems. Fortunately, reconstructing the inner Helmholtz layer (IHL) by introducing an electrolyte additive, is viewed as one of the most promising strategies to harvest the stable ZMA. Herein, (4‐chloro‐3‐nitrophenyl) (pyridin‐4‐yl) methanone (CNPM) with quadruple functional groups is introduced into the ZnSO4 electrolyte to reshape the interface between ZMA and electrolyte and change the solvation structure of Zn2+. Density functional theory (DFT) calculations manifest that the ─C═O, ─Cl, ─C═N─, and ─NO2 functional groups of CNPM interact with metallic Zn simultaneously and adsorb on the ZMA surface in a parallel arrangement manner, thus forming a water‐poor IHL and creating well‐arranged ion transportation channels. Furthermore, theoretical calculations and experimental results demonstrate that CNPM absorbed on the Zn anode surface can serve as zincophilic sites for inducing uniform Zn deposition along the (002) plane. Benefiting from the synergistic effect of these functions, the dendrite growth and parasitic reactions are suppressed significantly. As a result, ZMA exhibits a long cycle life (2900 h) and high coulombic efficiency (CE) (500 cycles) in the ZnSO4+CNPM electrolyte.
Management of severe hypotension in a dog undergoing transvenous pacemaker implantation following acepromazine and subsequent terbutaline administration
Authors: Arenas Alvarez Almudena , Bruniges Natalie 
Published: July 2023

AbstractThis case report describes events that occurred under general anaesthesia of a dog undergoing transvenous pacemaker implantation for third‐degree atrioventricular block. Following premedication with acepromazine and buprenorphine, general anaesthesia was induced with alfaxalone, and maintained with isoflurane in oxygen. The dog developed severe hypotension during the procedure with concurrent bradycardia before commencement of pacing. In order to increase the heart rate, terbutaline was administered intravenously. This was followed by a worsening of the hypotension, with only a small increase in heart rate. Blood pressure improved following noradrenaline infusion. The animal recovered uneventfully in intensive care, and was sedated due to its anxious temperament to avoid dislodgement of the pacemaker device in the postoperative period.
Subtyping Brain Diseases from Imaging Data
Authors: Wen Junhao , Varol Erdem , Yang Zhijian , Hwang Gyujoon , Dwyer Dominique , Kazerooni Anahita Fathi , Lalousis Paris Alexandros , Davatzikos Christos 
Published: July 2023

AbstractThe imaging community has increasingly adopted machine learning (ML) methods to provide individualized imaging signatures related to disease diagnosis, prognosis, and response to treatment. Clinical neuroscience and cancer imaging have been two areas in which ML has offered particular promise. However, many neurologic and neuropsychiatric diseases, as well as cancer, are often heterogeneous in terms of their clinical manifestations, neuroanatomical patterns, or genetic underpinnings. Therefore, in such cases, seeking a single disease signature might be ineffectual in delivering individualized precision diagnostics. The current chapter focuses on ML methods, especially semi-supervised clustering, that seek disease subtypes using imaging data. Work from Alzheimer’s disease and its prodromal stages, psychosis, depression, autism, and brain cancer are discussed. Our goal is to provide the readers with a broad overview in terms of methodology and clinical applications.
Interpretability of Machine Learning Methods Applied to Neuroimaging
Authors: Thibeau-Sutre Elina , Collin Sasha , Burgos Ninon , Colliot Olivier 
Published: July 2023

AbstractDeep learning methods have become very popular for the processing of natural images and were then successfully adapted to the neuroimaging field. As these methods are non-transparent, interpretability methods are needed to validate them and ensure their reliability. Indeed, it has been shown that deep learning models may obtain high performance even when using irrelevant features, by exploiting biases in the training set. Such undesirable situations can potentially be detected by using interpretability methods. Recently, many methods have been proposed to interpret neural networks. However, this domain is not mature yet. Machine learning users face two major issues when aiming to interpret their models: which method to choose and how to assess its reliability. Here, we aim at providing answers to these questions by presenting the most common interpretability methods and metrics developed to assess their reliability, as well as their applications and benchmarks in the neuroimaging context. Note that this is not an exhaustive survey: we aimed to focus on the studies which we found to be the most representative and relevant.
Machine Learning in Multiple Sclerosis
Authors: Jasperse Bas , Barkhof Frederik 
Published: July 2023

AbstractMultiple sclerosis (MS) is characterized by inflammatory activity and neurodegeneration, leading to the accumulation of damage to the central nervous system resulting in the accumulation of disability. MRI depicts an important part of the pathology of this disease and therefore plays a key part in diagnosis and disease monitoring. Still, major challenges exist with regard to the differential diagnosis, adequate monitoring of disease progression, quantification of CNS damage, and prediction of disease progression. Machine learning techniques have been employed in an attempt to overcome these challenges. This chapter aims to give an overview of how machine learning techniques are employed in MS with applications for diagnostic classification, lesion segmentation, improved visualization of relevant brain pathology, characterization of neurodegeneration, and prognostic subtyping.
Deep Learning: Basics and Convolutional Neural Networks (CNNs)
Authors: Vakalopoulou Maria , Christodoulidis Stergios , Burgos Ninon , Colliot Olivier , Lepetit Vincent 
Published: July 2023

AbstractDeep learning belongs to the broader family of machine learning methods and currently provides state-of-the-art performance in a variety of fields, including medical applications. Deep learning architectures can be categorized into different groups depending on their components. However, most of them share similar modules and mathematical formulations. In this chapter, the basic concepts of deep learning will be presented to provide a better understanding of these powerful and broadly used algorithms. The analysis is structured around the main components of deep learning architectures, focusing on convolutional neural networks and autoencoders.
Machine Learning for Neurodevelopmental Disorders
Authors: Moreau Clara , Deruelle Christine , Auzias Guillaume 
Published: July 2023

AbstractNeurodevelopmental disorders (NDDs) constitute a major health issue with >10% of the general worldwide population affected by at least one of these conditions—such as autism spectrum disorders (ASD) and attention deficit hyperactivity disorders (ADHD). Each NDD is particularly complex to dissect for several reasons, including a high prevalence of comorbidities and a substantial heterogeneity of the clinical presentation. At the genetic level, several thousands of genes have been identified (polygenicity), while a part of them was already involved in other psychiatric conditions (pleiotropy). Given these multiple sources of variance, gathering sufficient data for the proper application and evaluation of machine learning (ML) techniques is essential but challenging. In this chapter, we offer an overview of the ML methods most widely used to tackle NDDs’ complexity—from stratification techniques to diagnosis prediction. We point out challenges specific to NDDs, such as early diagnosis, that can benefit from the recent advances in the ML field. These techniques also have the potential to delineate homogeneous subgroups of patients that would enable a refined understanding of underlying physiopathology. We finally survey a selection of recent papers that we consider as particularly representative of the opportunities offered by contemporary ML techniques applied to large open datasets or that illustrate the challenges faced by current approaches to be addressed in the near future.
Recurrent Neural Networks (RNNs): Architectures, Training Tricks, and Introduction to Influential Research
Authors: Das Susmita , Tariq Amara , Santos Thiago , Kantareddy Sai Sandeep , Banerjee Imon 
Published: July 2023

AbstractRecurrent neural networks (RNNs) are neural network architectures with hidden state and which use feedback loops to process a sequence of data that ultimately informs the final output. Therefore, RNN models can recognize sequential characteristics in the data and help to predict the next likely data point in the data sequence. Leveraging the power of sequential data processing, RNN use cases tend to be connected to either language models or time-series data analysis. However, multiple popular RNN architectures have been introduced in the field, starting from SimpleRNN and LSTM to deep RNN, and applied in different experimental settings. In this chapter, we will present six distinct RNN architectures and will highlight the pros and cons of each model. Afterward, we will discuss real-life tips and tricks for training the RNN models. Finally, we will present four popular language modeling applications of the RNN models –text classification, summarization, machine translation, and image-to-text translation– thereby demonstrating influential research in the field.
Grundlagen
Authors: Künnemann Andreas 
Published: July 2023

ZusammenfassungDieses Kapitel wird genutzt, um wichtige Begriffe und Zusammenhänge elementarer Natur zu sammeln. Dabei soll keine umfangreiche Aufarbeitung grundlegender Theorien erfolgen, sondern vorrangig ein Nachschlagewerk wiederkehrender und fundamentaler Inhalte für dieses Buch entstehen. Besonders soll ein Verständnis für vektorwertige Lebesgue- und Sobolev-Räume sowie für verschiedene Stetigkeitsbegriffe geschaffen werden.
Regularitätstheorie zur Stetigkeit von Minimierern
Authors: Künnemann Andreas 
Published: July 2023

ZusammenfassungDieses Kapitel behandelt Fragen nach der Stetigkeit von Minimierern für Variationsprobleme. Im Rahmen der Regularitätstheorie wird das Dirichletsche Wachstumstheorem von Morrey gezeigt, welches ein hinreichendes Kriterium für die Hölder- Stetigkeit einer Funktion $$X \in W^{1,2} \left( G \right)$$

X
∈

W

1
,
2

G

liefert. Zum Nachweis der Anwendbarkeit des DirichletschenWachstumstheorems auf Minimierer von Variationsproblemen wird ein Wachstumslemma verwendet. Dabei werden eine Verknüpfung des Dirichlet-Integrals mit Fourierreihen sowie das Dirichlet-Integral der harmonischen Ersetzung einer Funktion mit L2 -Randwerten genutzt. Infolgedessen ergibt sich die Hölder-Stetigkeit eines Minimierers im Inneren. Zudem wird die Stetigkeit des Minimierers bis zum Rand gezeigt, sodass die Existenz eines stetigen Minimierers für eine breite Klasse von Variationsproblemen gesichert ist.
Designing Waterborne Protective Coatings Through Manipulating the Nanostructure of Acrylic-Based Nanocomposites
Authors: Ji S. , Gui H. , Guan G. , Zhou M. , Guo Q. , Tan M. Y. J. 
Published: July 2023

AbstractWaterborne coatings with intended functionalities have been designed by manipulating acrylic-based nanocomposites with different nanostructures. Taking advantage of the favorable structure of acrylic copolymers, three waterborne coatings with various desired properties were created through molecular engineering either by copolymerizing with other components or through nanocomposite formation. This approach was demonstrated by synthesizing acrylic-based waterborne coatings with three different nanostructures, namely homogeneous, worm-like, and spherical-like nanostructures. The properties of coating samples prepared by this new approach and by traditional physical blending were compared experimentally, which revealed that the incorporation of 3-methacryloxypropyltrimethoxysilane (MPS)-modified nanoparticle TiO2 in an acrylic base enabled the formation of a nanocomposite with nanoparticles uniformly distributed in the acrylic base. The coating film with this acrylic-TiO2 nanocomposite showed significantly better UV absorption performance than the coating made by physical blending. The copolymerization of acrylic copolymers with an organic polymer (alkyd) created a worm-like nanostructure of acrylic–alkyd composite that allowed uniform distribution of the acrylic–alkyd nanocomposite in a more closely packed dense coating film, leading to enhanced barrier property and significantly improved corrosion resistance as confirmed by electrochemical impedance spectroscopy and salt spray tests. The copolymerization of acrylic monomers with an inorganic polymer (polydimethylsiloxane [PDMS]) led to a spherical-like nanostructure of acrylic–PDMS composite film. The formation of this nanostructure arose from the migration of PDMS segments, and a PDMS-rich phase formed on the film’s surface, which resulted in a coating film with PDMS functionalities such as low dirt-picking behavior. Overall, these three cases demonstrated that acrylic copolymer are an excellent base for developing various nanocomposite waterborne coatings with different functionalities through copolymerization and that the nanocomposites with different nanostructures have a significant influence on the coatings’ performance.