bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bone tissue engineering for osteointegration: Where are we now?
Authors: Aykora Damla , Uzun Metehan 
Published: February 2024

AbstractBone fracture healing is a challenging process, due to insufficient and slow tissue repair. Sufferers from bone fractures struggle with one-third of nonunion, display graft rejection, high-costed implantation, or chronic pain. Novel advances in tissue engineering presented promising options for this strain. Biomaterials for bone repair allow accelerated regeneration, osteoblastic cell activation, and enhanced bone remodeling. There is a wide range of biomaterials that are biocompatible, bioresorbable, and biodegradable and used for bone tissue regeneration, promoting osteoconductive and osteoinductive properties. The main aim of bone tissue engineering is to generate rapid and optimal functional bone regeneration through a combination of biomaterials, growth factors, cells, and various agents. Recently bone tissue engineering has been attracted to the use of bioactive glass scaffolds incorporated with polymers and patient-specific fabrication of the bone healing material by 3D bioprinting. There are promising future outcomes that were reported by several research. The present review provides an outlook for recent most common biomaterials in bone tissue engineering suggesting bone tissue engineering practices should have been proceeded to clinical application.
Comparison of force measurement techniques in a short duration hypersonic facility
Authors: Hyslop Andrew , Doherty Luke J. , McGilvray Matthew 
Published: February 2024

AbstractForce measurement experiments have been conducted within the University of Oxford’s High Density Tunnel with a $$7^{\circ }$$

7
∘

half-angle cone. The purpose of the study was to provide a direct comparison between two independent force techniques in the same facility at the same free-stream conditions to provide a quantitative and qualitative discussion of the advantages and disadvantages of both techniques. The first force measurement technique used a conventional 4-axis sting-mounted force balance which was calibrated both statically and through the stress wave deconvolution method, whilst the second technique used the less established static free-flight methodology. Experiments were conducted at a Mach 5 test condition which provided sufficient dynamic pressure to generate aerodynamic forces suitable for the measurement range of the force balance. Results for lift, drag and pitching moment coefficients were obtained over a range of angles of attack and compared with predictions from a hypersonic panel method code. Agreement between the independent force techniques and numerical data sets was good over the range of angles of attack. Maximum uncertainties were shown to be 38.46 ± 0.56 N and 22.52 ± 0.44 N for free-flight in lift and drag, respectively, and 38.74 ± 1.59 N and 22.13 ± 1.27 N for the dynamically calibrated force balance which demonstrates the superiority of free-flight.
Improved subseasonal-to-seasonal precipitation prediction of climate models with nudging approach for better initialization of Tibetan Plateau-Rocky Mountain Circumglobal wave train and land surface conditions
Authors: Qin Yi , Tang Qi , Xue Yongkang , Liu Ye , Lin Yanluan 
Published: February 2024

AbstractReliable subseasonal-to-seasonal (S2S) precipitation prediction is highly desired due to the great socioeconomical implications, yet it remains one of the most challenging topics in the weather/climate prediction research area. As part of the Impact of Initialized Land Temperature and Snowpack on Sub-seasonal to Seasonal Prediction (LS4P) project of the Global Energy and Water Exchanges (GEWEX) program, twenty-one climate models follow the LS4P protocol to quantify the impact of the Tibetan Plateau (TP) land surface temperature/subsurface temperature (LST/SUBT) springtime anomalies on the global summertime precipitation. We find that nudging towards reanalysis winds is crucial for climate models to generate atmosphere and land surface initial conditions close to observations, which is necessary for meaningful S2S applications. Simulations with nudged initial conditions can better capture the summer precipitation responses to the imposed TP LST/SUBT spring anomalies at hotspot regions all over the world. Further analyses show that the enhanced S2S prediction skill is largely attributable to the substantially improved initialization of the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train pattern in the atmosphere. This study highlights the important role that initial condition plays in the S2S prediction and suggests that data assimilation technique (e.g., nudging) should be adopted to initialize climate models to improve their S2S prediction.
New insights into neuropathology and pathogenesis of autoimmune glial fibrillary acidic protein meningoencephalomyelitis
Authors: Guo Yong , Endmayr Verena , Zekeridou Anastasia , McKeon Andrew , Leypoldt Frank , Hess Katharina , Kalinowska-Lyszczarz Alicja , Klang Andrea , Pakozdy Akos , Höftberger Elisabeth , Hametner Simon , Haider Carmen , De Simoni Désirée , Peters Sönke , Gelpi Ellen , Röcken Christoph , Oberndorfer Stefan , Lassmann Hans , Lucchinetti Claudia F. , Höftberger Romana 
Published: February 2024

AbstractAnti-glial fibrillary acidic protein (GFAP) meningoencephalomyelitis (autoimmune GFAP astrocytopathy) is a new autoimmune central nervous system (CNS) disease diagnosable by the presence of anti-GFAP autoantibodies in the cerebrospinal fluid and presents as meningoencephalomyelitis in the majority of patients. Only few neuropathological reports are available and little is known about the pathogenic mechanisms. We performed a histopathological study of two autopsies and nine CNS biopsies of patients with anti-GFAP autoantibodies and found predominantly a lymphocytic and in one autopsy case a granulomatous inflammatory phenotype. Inflammatory infiltrates were composed of B and T cells, including tissue-resident memory T cells. Although obvious astrocytic damage was absent in the GFAP-staining, we found cytotoxic T cell-mediated reactions reflected by the presence of CD8+/perforin+/granzyme A/B+ cells, polarized towards astrocytes. MHC-class-I was upregulated in reactive astrocytes of all biopsies and two autopsies but not in healthy controls. Importantly, we observed a prominent immunoreactivity of astrocytes with the complement factor C4d. Finally, we provided insight into an early phase of GFAP autoimmunity in an autopsy of a pug dog encephalitis that was characterized by marked meningoencephalitis with selective astrocytic damage with loss of GFAP and AQP4 in the lesions.Our histopathological findings indicate that a cytotoxic T cell-mediated immune reaction is present in GFAP autoimmunity. Complement C4d deposition on astrocytes could either represent the cause or consequence of astrocytic reactivity. Selective astrocytic damage is prominent in the early phase of GFAP autoimmunity in a canine autopsy case, but mild or absent in subacute and chronic stages in human disease, probably due to the high regeneration potential of astrocytes. The lymphocytic and granulomatous phenotypes might reflect different stages of lesion development or patient-specific modifications of the immune response. Future studies will be necessary to investigate possible implications of pathological subtypes for clinical disease course and therapeutic strategies.
Hair cortisol concentrations in pregnant women with bipolar, depressive, or schizophrenic spectrum disorders
Authors: Nyström-Hansen Maja , Andersen Marianne Skovsager , Davidsen Kirstine Agnete , Roehder Katrine , Trier Christopher , Nayberg Emilie , Lyons-Ruth Karlen , Harder Susanne 
Published: February 2024

Abstract
Purpose
Maternal cortisol levels in pregnancy may support the growth of or adversely affect fetal organs, including the brain. While moderate cortisol levels are essential for fetal development, excessive or prolonged elevations may have negative health consequences for both the mother and the offspring. Little is known about predictors of altered hypothalamic–pituitary–adrenal (HPA) axis activity during pregnancy. This study examined maternal hair cortisol concentration (HCC) in the 3rd trimester of pregnancy in relation to severe psychopathology.

Methods
Hair samples were collected from 69 women, 32 with a lifetime diagnosis of severe mental disorders (bipolar I or II disorder, moderate or severe depressive disorder, schizophrenic spectrum disorder), and 37 non-clinical controls. Hair samples were collected during the 3rd trimester, and liquid chromatography tandem mass spectrometry was used for cortisol assessment. Psychiatric diagnosis and current level of symptomatic functioning were assessed using the structured clinical interview from the DSM-5 and the global assessment of functioning scale.

Results
Women with a lifetime diagnosis of severe mental illness had significantly elevated HCC compared to controls. Poorer current symptomatic functioning was also significantly associated with elevated HCC in pregnancy.

Conclusions
The implications of alterations in HCC on both maternal and infant health need further study.
Advances in automated support for requirements engineering: a systematic literature review
Authors: Umar Muhammad Aminu , Lano Kevin 
Published: February 2024

AbstractRequirements Engineering (RE) has undergone several transitions over the years, from traditional methods to agile approaches emphasising increased automation. In many software development projects, requirements are expressed in natural language and embedded within large volumes of text documents. At the same time, RE activities aim to define software systems' functionalities and constraints. However, manually executing these tasks is time-consuming and prone to errors. Numerous research efforts have proposed tools and technologies for automating RE activities to address this challenge, which are documented in published works. This review aims to examine empirical evidence on automated RE and analyse its impact on the RE sub-domain and software development. To achieve our goal, we conducted a Systematic Literature Review (SLR) following established guidelines for conducting SLRs. We aimed to identify, aggregate, and analyse papers on automated RE published between 1996 and 2022. We outlined the output of the support tool, the RE phase covered, levels of automation, development approach, and evaluation approaches. We identified 85 papers that discussed automated RE from various perspectives and methodologies. The results of this review demonstrate the significance of automated RE for the software development community, which has the potential to shorten development cycles and reduce associated costs. The support tools primarily assist in generating UML models (44.7%) and other activities such as omission of steps, consistency checking, and requirement validation. The analysis phase of RE is the most widely automated phase, with 49.53% of automated tools developed for this purpose. Natural language processing technologies, particularly POS tagging and Parser, are widely employed in developing these support tools. Controlled experimental methods are the most frequently used (48.2%) for evaluating automated RE tools, while user studies are the least employed evaluation method (8.2%). This paper contributes to the existing body of knowledge by providing an updated overview of the research literature, enabling a better understanding of trends and state-of-the-art practices in automated RE for researchers and practitioners. It also paves the way for future research directions in automated requirements engineering.
Ablative fractional laser treatment reduces hedgehog pathway gene expression in murine basal cell carcinomas
Authors: Pedersen Kristian Kåber , Granborg Jonatan Riber , Lerche Catharina Margrethe , Litman Thomas , Olesen Uffe Høgh , Hædersdal Merete 
Published: February 2024

Abstract
This study aimed to investigate the impact of ablative fractional laser (AFL) on hedgehog pathway gene expression in murine microscopic basal cell carcinomas (BCCs) and compare these results to the effect of topical treatment with vismodegib, an FDA-approved hedgehog inhibitor. In 25 mice, 1 cm2 skin test sites (n = 44) containing microscopic BCCs were exposed to one of three interventions: a single CO2 AFL treatment (1 pulse, 40 mJ/microbeam, wavelength 10.6 μm, 5% density, pulse rate 250 Hz, n = 12), eight topical vismodegib treatments (3.8 mg/mL, n = 8), or combination of AFL and vismodegib treatments (n = 9). Untreated controls were included for comparison (n = 15). After 4 days, skin samples were analyzed for hedgehog gene expression (Gli1, Gli2, and Ptch1) by qPCR and vismodegib concentrations by liquid chromatography mass spectrometry (data analyzed with two-tailed t-tests and linear regression). A single treatment with AFL monotherapy significantly reduced hedgehog gene expression compared to untreated controls (Gli1 72.4% reduction, p = 0.003; Gli2 55.2%, p = 0.010; Ptch1 70.9%, p < 0.001). Vismodegib treatment also reduced hedgehog gene expression (Gli1 91.6%; Gli2 83.3%; Ptch1 83.0%), significantly surpassing AFL monotherapy for two out of three genes (Gli1, p = 0.017; Gli2, p = 0.007; Ptch1, p = 0.15). AFL and vismodegib combination mirrored the effects of vismodegib monotherapy (Gli1, p = 0.424; Gli2, p = 0.289; Ptch1, p = 0.593), possibly due to comparable cutaneous vismodegib concentrations (mean ± SD, vismodegib monotherapy 850 ± 475 µmol/L; combination 1036 ± 824 µmol/L; p = 0.573). In conclusion, a single AFL treatment significantly reduced hedgehog gene expression in murine BCCs mimicking the effects of eight topical applications of vismodegib. Further studies are needed to assess whether AFL can be utilized for BCC treatment, either as monotherapy or in combination with other drugs.
Comparing heuristic valuation processes between health state valuation from child and adult perspectives
Authors: Lipman Stefan A. , Reckers-Droog Vivian T. 
Published: February 2024

Abstract
Objectives
Health state valuation assumes that respondents trade off between all aspects of choice tasks and maximize their utility. Yet, respondents may use heuristic valuation processes, i.e., strategies to simplify or avoid the trade-offs that are core to health state valuation. The objective of this study is to explore if heuristic valuation processes are more prevalent for valuation from a 10-year-old child’s perspective compared to the use of an adult perspective.

Methods
We reused existing data in which EQ-5D health states were valued from adult and child perspectives with composite time trade-off (cTTO) and discrete choice experiment (DCE) tasks. Our analyses focused on comparing completion time and responding patterns across both perspectives. We also explored how reflective of a set of heuristic strategies respondents’ choices were in both perspectives.

Results
We found no evidence for systematic differences in completion time across perspectives. Generally, we find different responding patterns in child perspectives, e.g., more speeding, dominance violations, and clustering of utilities at 1.0, 0.8, and 0. Very few heuristic strategies provide a coherent explanation for the observed DCE responses.

Conclusion
Our results provide some, albeit indirect, evidence for differences in heuristic valuation processes between perspectives, although not across all data sources. Potential effects of heuristic valuation processes, such as transfer of responsibility, may be identified through studying responding patterns in cTTO and DCE responses.
Cerebral activation caused by dental sounds: a functional magnetic resonance imaging study
Authors: Karibe Hiroyuki , Koeda Michihiko , Kato Yuichi , Hama Tomoko , Tanaka Satoshi , Tateno Amane , Suzuki Hidenori , Okubo Yoshiro 
Published: February 2024

AbstractDental drilling sounds can induce anxiety in some patients. This study aimed to use functional magnetic resonance imaging (fMRI) to assess the relationship between dental fear and auditory stimuli. Thirty-four right-handed individuals (21 women and 13 men; average age, 31.2 years) were selected. The level of dental fear was assessed using the dental fear survey (DFS). Based on a threshold DFS score > 52, participants were categorized into two groups: dental fear (DF) group (n = 12) and control group (n = 22). Two types of stimuli were presented in a single session: dental and neutral sounds. Cerebral activation during the presentation of these sounds was evaluated using contrast-enhanced blood oxygenation level-dependent fMRI. In the DF group, dental sounds induced significantly stronger activation in the left inferior frontal gyrus and left caudate nucleus (one-sample t test, P < 0.001). In contrast, in the control group, significantly stronger activation was observed in the bilateral Heschl’s gyri and left middle frontal gyrus (one-sample t test, P < 0.001). Additionally, a two-sample t test revealed that dental sounds induced a significantly stronger activation in the left caudate nucleus in the DF group than in the control group (P < 0.005). These findings suggest that the cerebral activation pattern in individuals with DF differs from that in controls. Increased activation of subcortical regions may be associated with sound memory during dental treatment.
Toward an ontology for EA modeling and EA model quality
Authors: Schoonderbeek Jan A. H. , Proper Henderik A. 
Published: February 2024

AbstractModels have long since been used, in different shapes and forms, to understand, communicate about, and (re)shape, the world around us; including many different social, economic, biological, chemical, physical, and digital aspects. This is also the case in the context of enterprise architecture (EA), where we see a wide range of models in many different shapes and forms being used as well. Researchers in EA modeling usually introduce their own lexicon, and perspective of what a model actually is, while accepting (often implicitly) the accompanying ontological commitments. Similarly, practitioners of EA modeling implicitly also commit to (different) ontologies, resulting in models that have an uncertain ontological standing. This is because, for the subject domain of enterprise architecture models (as opposed to the content of such models), no single ontology has gained major traction. As a result, studies into aspects of enterprise architecture models, such as “model quality” and “return on modeling effort”, are fragmented, and cannot readily be compared or combined. This paper proposes a comprehensive applied ontology, specifically geared to enterprise architecture modeling. Ontologies represent structured knowledge about a particular subject domain. It allows for study into, and reasoning about, that subject domain. Our ontology is derived from a theory of modeling, while clarifying concepts such as “enterprise architecture model”, and introduces novel concepts such as “model audience” and “model objective”. Furthermore, the relevant interrelations between these different concepts are identified and defined. The resulting ontology for enterprise architecture models is represented in OntoUML, and shown to be consistent with the foundational ontology for modeling, Unified Foundational Ontology.
Deep learning based computer aided diagnosis of Alzheimer’s disease: a snapshot of last 5 years, gaps, and future directions
Authors: Bhandarkar Anish , Naik Pratham , Vakkund Kavita , Junjappanavar Srasthi , Bakare Savita , Pattar Santosh 
Published: February 2024

AbstractAlzheimer’s disease affects around one in every nine persons among the elderly population. Being a neurodegenerative disease, its cure has not been established till date and is managed through supportive care by the health care providers. Thus, early diagnosis of this disease is a crucial step towards its treatment plan. There exist several diagnostic procedures viz., clinical, scans, biomedical, psychological, and others for the disease’s detection. Computer-aided diagnostic techniques aid in the early detection of this disease and in the past, several such mechanisms have been proposed. These techniques utilize machine learning models to develop a disease classification system. However, the focus of these systems has now gradually shifted to the newer deep learning models. In this regards, this article aims in providing a comprehensive review of the present state-of-the-art techniques as a snapshot of the last 5 years. It also summarizes various tools and datasets available for the development of the early diagnostic systems that provide fundamentals of this field to a novice researcher. Finally, we discussed the need for exploring biomarkers, identification and extraction of relevant features, trade-off between traditional machine learning and deep learning models and the essence of multimodal datasets. This enables both medical, engineering researchers and developers to address the identified gaps and develop an effective diagnostic system for the Alzheimer’s disease.
Autoencoders and their applications in machine learning: a survey
Authors: Berahmand Kamal , Daneshfar Fatemeh , Salehi Elaheh Sadat , Li Yuefeng , Xu Yue 
Published: February 2024

AbstractAutoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction method. With rapid evolution of autoencoder methods, there has yet to be a complete study that provides a full autoencoders roadmap for both stimulating technical improvements and orienting research newbies to autoencoders. In this paper, we present a comprehensive survey of autoencoders, starting with an explanation of the principle of conventional autoencoder and their primary development process. We then provide a taxonomy of autoencoders based on their structures and principles and thoroughly analyze and discuss the related models. Furthermore, we review the applications of autoencoders in various fields, including machine vision, natural language processing, complex network, recommender system, speech process, anomaly detection, and others. Lastly, we summarize the limitations of current autoencoder algorithms and discuss the future directions of the field.
UoloNet: based on multi-tasking enhanced small target medical segmentation model
Authors: Zhang Kejia , Zhang Lan , Pan Haiwei 
Published: February 2024

AbstractIn recent years, UNET (Ronneberger et al. 2015) and its derivative models have been widely used in medical image segmentation with more superficial structures and excellent segmentation results. Due to the lack of modeling for the overall characteristics of the target, the division tasks of minor marks will produce some discrete noise points, resulting in a decline in model accuracy and application effects. We propose a multi-tasking medical image analysis model UoloNet, a YOLO-based (Redmon et al. 2016; Shafiee et al. 2017) object detection branch is added based on UNET. The shared learning of the two tasks through semantic segmentation and object detection has promoted the model’s mastery of the overall characteristics of the target. In the reasoning stage, merging the two functions of object detection and semantic segmentation can effectively remove discrete noise points in the division and enhance the accuracy of semantic segmentation. In the future, the object detection task will be the problem of excessive convergence of semantic segmentation tasks. The model uses CIOU (Zheng et al. 2020) losses instead of IOU losses in YOLO, which further improves the model’s overall accuracy. The effectiveness of the proposed model is verified both in the MRI dataset SEHPI, which we posted and in the public dataset LITS (Christ 2017).