bluuebunny/crossref_metadata_2025_split huggingface dataset:
The applications of cold atmospheric plasma in dentistry
Authors: Silva Neusa , Marques Joana , da Cruz Mariana B. , Luís Henrique , Sério Susana , Mata António 
Published: July 2023

AbstractCold atmospheric plasma (CAP), as a noninvasive technology, has shown promise in dentistry as it might successfully treat various oral conditions. The antimicrobial capacity of CAP has been proven and it is effective in reducing the main microorganisms responsible for oral infections. Furthermore, CAP has also been explored in the field of tissue regeneration with a great response from both soft and hard tissue. The surface modification ability of CAP is another area of interest, revealing a potential improvement in the osseointegration of dental implants. Additionally, there are other areas within dentistry that have studied the use of CAP, such as surface disinfection, bleaching, and cavity preparation.
Improving protein structure prediction with extended sequence similarity searches and deep‐learning‐based refinement in CASP15
Authors: Oda Toshiyuki 
Published: July 2023

AbstractThe human predictor team PEZYFoldings got first place with the assessor's formulae (3rd place with Global Distance Test Total Score [GDT‐TS]) in the single‐domain category and 10th place in the multimer category in Critical Assessment of Structure Prediction 15. In this paper, I describe the exact method used by PEZYFoldings in the competition. As AlphaFold2 and AlphaFold‐Multimer, developed by DeepMind, were state‐of‐the‐art structure prediction tools, it was assumed that enhancing the input and output of the tools was an effective strategy to obtain the highest accuracy for structure prediction. Therefore, I used additional tools and databases to collect evolutionarily related sequences and introduced a deep‐learning‐based model in the refinement step. In addition to these modifications, manual interventions were performed to address various tasks. Detailed analyses were performed after the competition to identify the main contributors to performance. Comparing the number of evolutionarily related sequences I used with those of the other teams that provided AlphaFold2's baseline predictions revealed that an extensive sequence similarity search was one of the main contributors. Nonetheless, there were specific targets for which I could not identify any evolutionarily related sequences, resulting in my inability to construct accurate structures for these targets. Notably, I noticed that I had gained large Z‐scores with the subunits of H1137, for which I performed manual domain parsing considering the interfaces between the subunits. This finding implies that the manual intervention contributed to my performance. The influence of the refinement model on the accuracy of structure prediction was minimal. I could have predicted structures with a similar level of accuracy without employing the refinement model. However, from the perspective of accuracy self‐estimate, many structures demonstrated improvement after refinement. This improvement likely had a substantial influence on improving my position in the assessor's formulae rankings. These results highlight the opportunities for improvement in (1) multimer prediction, (2) building of larger and more diverse databases, and (3) developing tools to predict structures from primary sequences alone. In addition, transferring the manual intervention process to automation is a future concern.
Women's experience and long‐term perspectives: A qualitative substudy of a randomised controlled trial on weight reduction prior to in vitro fertilisation
Authors: Kluge Linda , Holter Herborg , Bergh Christina , Thurin‐Kjellberg Ann 
Published: July 2023

AbstractIntroductionObesity is associated with impaired results after in vitro fertilisation (IVF). Consequently, several fertility clinics have set limits of body mass index (BMI) and obese infertile women are thus obliged to lose weight before treatment. Randomised controlled trials (RCTs) have not been able to show an increase in live birth rate after weight reduction before fertility treatment. The purpose of this study was to investigate the patients' experiences and views of participating in a RCT on weight reduction for obese women before IVF, in a long‐term perspective.MethodsA qualitative follow‐up interview study including 17 women was performed between September and December 2020. The interviewed women had all been included in an RCT around 6 years earlier. Semistructured interviews were conducted, the interviews were audio recorded, transcribed verbatim, and analysed using thematic content analysis.ResultsDuring the analysis two main themes emerged; ‘Pros and cons related to trial participation’ and ‘Message to health care’. Several women were excited about the prospect of possibly receiving weight reduction before IVF. The women appreciated the support during the intervention but expressed that the diet was tough, and it was hard to maintain the weight loss. Their views regarding BMI limits differed. They advocated an individual assessment regardless of the woman's BMI and expressed that it could be of value to offer weight reduction treatment before IVF.ConclusionsEven though the RCT and a 2‐year follow‐up could not show an increase in live birth rate in the intervention group most women had a positive attitude to weight reduction treatment before IVF. The importance of this study is that the views of the women have been highlighted and their opinions can add valuable information for fertility clinics in their care of obese infertile women.
Suppressing Interfacial Side Reactions of Anode‐Free Lithium Batteries by an Organic Salt Monolayer
Authors: Wu Ningyu , Wang Min , Shadike Zulipiya , Hu Zhe , Hu Yanhong , Gao Yue 
Published: July 2023

AbstractAnode‐free lithium (Li) batteries are attractive owing to their high energy density. However, Li loss by forming solid‐electrolyte interphase (SEI) during cell activation leads to a ≈25% capacity decrease, and the capacity constantly fades upon cycling due to the side reactions on the copper (Cu) current collector. This paper reports high‐initial‐efficiency, long‐cycle‐life, and long‐calendar‐life anode‐free Li batteries by using an organic Li salt monolayer bonded on Cu. The functional salt, namely lithium ((4‐carbamoylphenyl)sulfonyl)(fluorosulfonyl)imide, electrochemically decomposes and passivates the Cu surface, which reduces Li sacrifice by SEI formation and suppresses galvanic Li corrosion and Li‐electrolyte reactions during cycling. This work records a LiF‐rich interphase on Cu and guided Li nucleation and growth. A 93.6% initial Li deposition efficiency is realized in a regular carbonate electrolyte, and the galvanic current is decreased to ≈40 nA cm−2, merely one‐tenth of bare Cu. After cell activation, 95.2% capacity is retained for a Cu|LiNi0.8Mn0.1Co0.1 pouch cell with a theoretical capacity of 200 mAh, and the cell is operated over 600 cycles. Calendar aging showed no damage to cell performance.
Phylogeny and revised classification of New World Ruellia
Authors: Manzitto‐Tripp Erin A. , Daniel Thomas F. 
Published: July 2023

AbstractWith ca. 375 species worldwide, Ruellia is a morphologically diverse and geographically widespread lineage of flowering plants. The majority of these species (ca. 275 of 375) occur in the New World (NW), with most of these occurring in the tropics and only about 65 occurring in temperate regions. In the NW, the genus spans some 77° of latitude, ranging from the northern United States (Wisconsin) to northeastern Argentina (Córdoba) and southern Uruguay (Canelones). In this study, we generated ddRAD molecular sequence data for 187 of 275 species of NW Ruellia (ca. 68% of NW species) to reconstruct phylogenetic relationships within this lineage. Coupled with morphological and ecological information, we used this well‐resolved and strongly supported molecular phylogeny to delimit 15 sections of NW Ruellia. Representing the first comprehensive attempt in ca. 125 years, we present a revised classification for NW Ruellia, which places a total of 205 species in the following sections: Ruellia sect. Aphragmia, sect. Blechum, sect. Boreosilva, sect. Brasilia, sect. Cerradicola, sect. Chiropterophilae, sect. Chromatoruellia, sect. Eurychanes, sect. Gymnacanthus, sect. Mexicanae, sect. Physiruellia, sect. Ruellia, sect. Siphonacanthus, sect. Stephanophysum, and sect. Strobiliformes. Nomenclatural innovations in this study include the description of several new sections, new combinations, and new names proposed. Our study complements an earlier reclassification of Old World species to provide a globally unique perspective on the taxonomy and phylogeny of a geographically widespread lineage of flowering plants.
Current status of nano‐vaccinology in veterinary medicine science
Authors: Sadr Soheil , Poorjafari Jafroodi Parian , Haratizadeh Mohammad Javad , Ghasemi Zahra , Borji Hassan , Hajjafari Ashkan 
Published: July 2023

AbstractVaccination programmes provide a safe, effective and cost‐efficient strategy for maintaining population health. In veterinary medicine, vaccination not only reduces disease within animal populations but also serves to enhance public health by targeting zoonoses. Nevertheless, for many pathogens, an effective vaccine remains elusive. Recently, nanovaccines have proved to be successful for various infectious and non‐infectious diseases of animals. These novel technologies, such as virus‐like particles, self‐assembling proteins, polymeric nanoparticles, liposomes and virosomes, offer great potential for solving many of the vaccine production challenges. Their benefits include low immunotoxicity, antigen stability, enhanced immunogenicity, flexibility sustained release and the ability to evoke both humoral and cellular immune responses. Nanovaccines are more efficient than traditional vaccines due to ease of control and plasticity in their physio‐chemical properties. They use a highly targeted immunological approach which can provide strong and long‐lasting immunity. This article reviews the currently available nanovaccine technology and considers its utility for both infectious diseases and non‐infectious diseases such as auto‐immunity and cancer. Future research opportunities and application challenges from bench to clinical usage are also discussed.
Working with Omics Data: An Interdisciplinary Challenge at the Crossroads of Biology and Computer Science
Authors: Poinsignon Thibault , Poulain Pierre , Gallopin Mélina , Lelandais Gaëlle 
Published: July 2023

AbstractNowadays, generating omics data is a common activity for laboratories in biology. Experimental protocols to prepare biological samples are well described, and technical platforms to generate omics data from these samples are available in most research institutes. Furthermore, manufacturers constantly propose technical improvements, simultaneously decreasing the cost of experiments and increasing the amount of omics data obtained in a single experiment. In this context, biologists are facing the challenge of dealing with large omics datasets, also called “big data” or “data deluge.” Working with omics data raises issues usually handled by computer scientists, and thus cooperation between biologists and computer scientists has become essential to efficiently study cellular mechanisms in their entirety, as omics data promise. In this chapter, we define omics data, explain how they are produced, and, finally, present some of their applications in fundamental and medical research.
Medical Image Segmentation Using Deep Learning
Authors: Liu Han , Hu Dewei , Li Hao , Oguz Ipek 
Published: July 2023

AbstractImage segmentation plays an essential role in medical image analysis as it provides automated delineation of specific anatomical structures of interest and further enables many downstream tasks such as shape analysis and volume measurement. In particular, the rapid development of deep learning techniques in recent years has had a substantial impact in boosting the performance of segmentation algorithms by efficiently leveraging large amounts of labeled data to optimize complex models (supervised learning). However, the difficulty of obtaining manual labels for training can be a major obstacle for the implementation of learning-based methods for medical images. To address this problem, researchers have investigated many semi-supervised and unsupervised learning techniques to relax the labeling requirements. In this chapter, we present the basic ideas for deep learning-based segmentation as well as some current state-of-the-art approaches, organized by supervision type. Our goal is to provide the reader with some possible solutions for model selection, training strategies, and data manipulation given a specific segmentation task and dataset.
Data-Driven Disease Progression Modeling
Authors: Oxtoby Neil P. 
Published: July 2023

AbstractIntense debate in the neurology community before 2010 culminated in hypothetical models of Alzheimer’s disease progression: a pathophysiological cascade of biomarkers, each dynamic for only a segment of the full disease timeline. Inspired by this, data-driven disease progression modeling emerged from the computer science community with the aim to reconstruct neurodegenerative disease timelines using data from large cohorts of patients, healthy controls, and prodromal/at-risk individuals. This chapter describes selected highlights from the field, with a focus on utility for understanding and forecasting of disease progression.
Computational Pathology for Brain Disorders
Authors: Jiménez Gabriel , Racoceanu Daniel 
Published: July 2023

AbstractNoninvasive brain imaging techniques allow understanding the behavior and macro changes in the brain to determine the progress of a disease. However, computational pathology provides a deeper understanding of brain disorders at cellular level, able to consolidate a diagnosis and make the bridge between the medical image and the omics analysis. In traditional histopathology, histology slides are visually inspected, under the microscope, by trained pathologists. This process is time-consuming and labor-intensive; therefore, the emergence of computational pathology has triggered great hope to ease this tedious task and make it more robust. This chapter focuses on understanding the state-of-the-art machine learning techniques used to analyze whole slide images within the context of brain disorders. We present a selective set of remarkable machine learning algorithms providing discriminative approaches and quality results on brain disorders. These methodologies are applied to different tasks, such as monitoring mechanisms contributing to disease progression and patient survival rates, analyzing morphological phenotypes for classification and quantitative assessment of disease, improving clinical care, diagnosing tumor specimens, and intraoperative interpretation. Thanks to the recent progress in machine learning algorithms for high-content image processing, computational pathology marks the rise of a new generation of medical discoveries and clinical protocols, including in brain disorders.
Evaluating Machine Learning Models and Their Diagnostic Value
Authors: Varoquaux Gael , Colliot Olivier 
Published: July 2023

AbstractThis chapter describes model validation, a crucial part of machine learning whether it is to select the best model or to assess performance of a given model. We start by detailing the main performance metrics for different tasks (classification, regression), and how they may be interpreted, including in the face of class imbalance, varying prevalence, or asymmetric cost–benefit trade-offs. We then explain how to estimate these metrics in an unbiased manner using training, validation, and test sets. We describe cross-validation procedures—to use a larger part of the data for both training and testing—and the dangers of data leakage—optimism bias due to training data contaminating the test set. Finally, we discuss how to obtain confidence intervals of performance metrics, distinguishing two situations: internal validation or evaluation of learning algorithms and external validation or evaluation of resulting prediction models.
Reproducibility in Machine Learning for Medical Imaging
Authors: Colliot Olivier , Thibeau-Sutre Elina , Burgos Ninon 
Published: July 2023

AbstractReproducibility is a cornerstone of science, as the replication of findings is the process through which they become knowledge. It is widely considered that many fields of science are undergoing a reproducibility crisis. This has led to the publications of various guidelines in order to improve research reproducibility.This didactic chapter intends at being an introduction to reproducibility for researchers in the field of machine learning for medical imaging. We first distinguish between different types of reproducibility. For each of them, we aim at defining it, at describing the requirements to achieve it, and at discussing its utility. The chapter ends with a discussion on the benefits of reproducibility and with a plea for a nondogmatic approach to this concept and its implementation in research practice.
Machine Learning for Alzheimer’s Disease and Related Dementias
Authors: Modat Marc , Cash David M. , Dos Santos Canas Liane , Bocchetta Martina , Ourselin Sébastien 
Published: July 2023

AbstractDementia denotes the condition that affects people suffering from cognitive and behavioral impairments due to brain damage. Common causes of dementia include Alzheimer’s disease, vascular dementia, or frontotemporal dementia, among others. The onset of these pathologies often occurs at least a decade before any clinical symptoms are perceived. Several biomarkers have been developed to gain a better insight into disease progression, both in the prodromal and the symptomatic phases. Those markers are commonly derived from genetic information, biofluid, medical images, or clinical and cognitive assessments. Information is nowadays also captured using smart devices to further understand how patients are affected. In the last two to three decades, the research community has made a great effort to capture and share for research a large amount of data from many sources. As a result, many approaches using machine learning have been proposed in the scientific literature. Those include dedicated tools for data harmonization, extraction of biomarkers that act as disease progression proxy, classification tools, or creation of focused modeling tools that mimic and help predict disease progression. To date, however, very few methods have been translated to clinical care, and many challenges still need addressing.
Machine Learning for Parkinson’s Disease and Related Disorders
Authors: Faouzi Johann , Colliot Olivier , Corvol Jean-Christophe 
Published: July 2023

AbstractParkinson’s disease is a complex heterogeneous neurodegenerative disorder characterized by the loss of dopamine neurons in the basal ganglia, resulting in many motor and non-motor symptoms. Although there is no cure to date, the dopamine replacement therapy can improve motor symptoms and the quality of life of the patients. The cardinal symptoms of this disorder are tremor, bradykinesia, and rigidity, referred to as parkinsonism. Other related disorders, such as dementia with Lewy bodies, multiple system atrophy, and progressive supranuclear palsy, share similar motor symptoms although they have different pathophysiology and are less responsive to the dopamine replacement therapy. Machine learning can be of great utility to better understand Parkinson’s disease and related disorders and to improve patient care. Many challenges are still open, including early accurate diagnosis, differential diagnosis, better understanding of the pathologies, symptom detection and quantification, individual disease progression prediction, and personalized therapies. In this chapter, we review research works on Parkinson’s disease and related disorders using machine learning.
Machine Learning for Cerebrovascular Disorders
Authors: Yu Yannan , Chen David Yen-Ting 
Published: July 2023

AbstractCerebrovascular disease refers to a group of conditions that affect blood flow and the blood vessels in the brain. It is one of the leading causes of mortality and disability worldwide, imposing a significant socioeconomic burden to society. Research on cerebrovascular diseases has been rapidly progressing leading to improvement in the diagnosis and management of patients nowadays. Machine learning holds many promises for further improving clinical care of these disorders. In this chapter, we will briefly introduce general information regarding cerebrovascular disorders and summarize some of the most promising fields in which machine learning shall be valuable to improve research and patient care. More specifically, we will cover the following cerebrovascular disorders: stroke (both ischemic and hemorrhagic), cerebral microbleeds, cerebral vascular malformations, intracranial aneurysms, and cerebral small vessel disease (white matter hyperintensities, lacunes, perivascular spaces).
The Role of Artificial Intelligence in Neuro-oncology Imaging
Authors: Soun Jennifer , Masudathaya Lu-Aung Yosuke , Biswas Arabdha , Chow Daniel S. 
Published: July 2023

AbstractDiagnostic imaging is widely used to assess, characterize, and monitor brain tumors. However, there remain several challenges in each of these categories due to the heterogeneous nature of these tumors. This may include variations in tumor biology that relate to variable degrees of cellular proliferation, invasion, and necrosis that in turn have different imaging manifestations. These variations have created challenges for tumor assessment, including segmentation, surveillance, and molecular characterizations. Although several rule-based approaches have been implemented that relates to tumor size and appearance, these methods inherently distill the rich amount of tumor imaging data into a limited number of variables. Approaches in artificial intelligence, machine learning, and deep learning have been increasingly leveraged to computer vision tasks, including tumor imaging, given their effectiveness for solving image-based challenges. This objective of this chapter is to summarize some of these advances in the field of tumor imaging.
Generative Adversarial Networks and Other Generative Models
Authors: Wenzel Markus 
Published: July 2023

AbstractGenerative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially been meant not to be an image analysis tool but to produce naturally looking images. The adversarial training paradigm has been proposed to stabilize generative methods and has proven to be highly successful—though by no means from the first attempt.This chapter gives a basic introduction into the motivation for generative adversarial networks (GANs) and traces the path of their success by abstracting the basic task and working mechanism and deriving the difficulty of early practical approaches. Methods for a more stable training will be shown, as well as typical signs for poor convergence and their reasons.Though this chapter focuses on GANs that are meant for image generation and image analysis, the adversarial training paradigm itself is not specific to images and also generalizes to tasks in image analysis. Examples of architectures for image semantic segmentation and abnormality detection will be acclaimed, before contrasting GANs with further generative modeling approaches lately entering the scene. This will allow a contextualized view on the limits but also benefits of GANs.