bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of Nanoscale Quantum Interconnects Aided by Conditional Generative Adversarial Networks
Authors: Preda Amanda Teodora , Pantis-Simut Calin-Andrei , Marciu Mihai , Anghel Dragos-Victor , Allosh Alaa , Ion Lucian , Manolescu Andrei , Nemnes George Alexandru
Published: January 2024

Interconnecting nanodevices with the aim of assembling quantum computing architectures is one of the current outstanding challenges. At the nanoscale, the quantum interconnects become comparable in complexity with the active devices and should be treated on equal footing. In addition, they can play an active role in the switching properties. Here, we investigate the charge localization in neuromorphic bi-dimensional systems, which serve as quantum interconnects (QIs) between quantum dot registers. We define a device structure where, by manipulating the charging of a floating gate array, one defines the QI potential map, which can host a few interacting electrons. The ground state charge density may be extracted by measuring the tunneling current perpendicular to the device surface, yielding a convoluted image of the electron distribution. Using image-to-image translation methods, we achieve the mapping of the charge density from the confinement potential, as well as by deconvoluting the tunneling current map, which can be obtained by a direct measurement. Thus, we provide a proof-of-concept for a reconfigurable device, which can be used to design quantum many-electron devices.
Quality Control of Cement Clinker through Operating Condition Classification and Free Calcium Oxide Content Prediction
Authors: Lyu Xukang , Chu Dongliang , Lu Xingran , Mu Jiahui , Zhang Zengji , Yun Daqing
Published: January 2024

Recent advances in artificial intelligence (AI) technologies such as deep learning open up new opportunities for various industries, such as cement manufacturing, to transition from traditional human-aided manually controlled production processes to the modern era of “intelligentization”. More and more practitioners have started to apply machine learning methods and deploy practical applications throughout the production process to automate manufacturing activities and optimize product quality. In this work, we employ machine learning methods to perform effective quality control for cement production through monitoring and predicting the density of free calcium oxide (f-CaO) in cement clinker. Based upon the control data measured and collected within the distributed control system (DCS) of cement production plants and the laboratory measurements of the density of free lime in cement clinker, we are able to train effective models to stabilize the cement production process and optimize the quality of cement clinker. We report the details of the methods used and illustrate the superiority and benefits of the adopted machine learning-based approaches.
A Two-Time-Scale Turbulence Model and Its Application in Free Shear Flows
Authors: Gul Mehmet Zafer , Yangaz Murat Umut , Sen Serhat
Published: January 2024

A novel three-equation turbulence model has been proposed as a potential solution to overcome some of the issues related to the k–ε models of turbulence. A number of turbulence models found in the literature designed for compressed turbulence within internal combustion engine cylinders tend to exhibit limitations when applied to turbulent shear flows, such as those occurring through intake or exhaust valves of the engine. In the event that the flow is out of equilibrium where Pk deviates from ε, the turbulence models require a separate turbulence time-scale determiner along with the dissipation, ε. In the current research, this is accomplished by resolving an additional equation that accounts for turbulence time scale, τ. After presenting the rationale behind the model, its application to three types of free shear flows were given. It has been shown that the three-equation k–ε–τ model outperforms the standard k–ε model as well as a number of two-equation models in these flows. Initially, the k–ε–τ model handles the issue of the plane jet/round jet anomaly in an effective manner. Secondly, it outperforms the two-equation models in predicting the flow behavior in the case of plane wake, one that is distinguished by its weak shear form.
Analysis of Sprite Activity in Middle Latitudes
Authors: Evtushenko Andrey , Svechnikova Ekaterina , Kudryavtsev Alexander
Published: January 2024

Sprite activity in the middle latitudes of the Northern Hemisphere is analyzed, with the example of the territory of Russia, aiming to facilitate the planning of observational campaigns in the region. The global model previously proposed by the authors is applied, using data from the WWLLN lightning detection network for 2015–2021. It is shown that the total number of sprites per year varies widely, from 394 in 2019 to 2354 in 2015. The most intense sprite activity almost always occurs in July, but in some years, there may be a shift to June (2015) and August (2021). The highest frequency of sprite initiation is observed in the Krasnodar Territory, Altai, and the Far East. Altai shows a high intensity of sprite activity every year, which is caused by the relief and underlying surface, while Krasnodar Territory and the Far East demonstrate sprites developed by incoming convective systems, which leads to high year-to-year variability.
Intramyocardial Injection of Hypoxia-Conditioned Extracellular Vesicles Modulates Response to Oxidative Stress in the Chronically Ischemic Myocardium
Authors: Harris Dwight D. , Sabe Sharif A. , Broadwin Mark , Xu Cynthia , Stone Christopher , Kanuparthy Meghamsh , Malhotra Akshay , Abid M. Ruhul , Sellke Frank W.
Published: January 2024

Introduction: Patients with advanced coronary artery disease (CAD) who are not eligible for stenting or surgical bypass procedures have limited treatment options. Extracellular vesicles (EVs) have emerged as a potential therapeutic target for the treatment of advanced CAD. These EVs can be conditioned to modify their contents. In our previous research, we demonstrated increased perfusion, decreased inflammation, and reduced apoptosis with intramyocardial injection of hypoxia-conditioned EVs (HEVs). The goal of this study is to further understand the function of HEVs by examining their impact on oxidative stress using our clinically relevant and extensively validated swine model of chronic myocardial ischemia. Methods: Fourteen Yorkshire swine underwent a left thoracotomy for the placement of an ameroid constrictor on the left circumflex coronary artery to model chronic myocardial ischemia. After two weeks of recovery, the swine underwent a redo thoracotomy with injection of either HEVs (n = 7) or a saline control (CON, n = 7) into the ischemic myocardium. Five weeks after injection, the swine were subjected to terminal harvest. Protein expression was measured using immunoblotting. OxyBlot analysis and 3-nitrotyrosine staining were used to quantify total oxidative stress. Results: There was a significant increase in myocardial expression of the antioxidants SOD 2, GPX-1, HSF-1, UCP-2, catalase, and HO-1 (all p ≤ 0.05) in the HEV group when compared to control animals. The HEVs also exhibited a significant increase in pro-oxidant NADPH oxidase (NOX) 1, NOX 3, p47phox, and p67phox (all p ≤ 0.05). However, no change was observed in the expression of NFkB, KEAP 1, and PRDX1 (all p > 0.05) between the HEV and CON groups. There were no significant differences in total oxidative stress as determined by OxyBlot and 3-nitrotyrosine staining (p = 0.64, p = 0.32) between the groups. Conclusions: Administration of HEVs in ischemic myocardium induces a significant increase in pro- and antioxidant proteins without a net change in total oxidative stress. These findings suggest that HEV-induced changes in redox signaling pathways may play a role in increased perfusion, decreased inflammation, and reduced apoptosis in ischemic myocardium. Further studies are required to determine if HEVs alter the net oxidative stress in ischemic myocardium at an earlier time point of HEV administration.
Using AI Segmentation Models to Improve Foreign Body Detection and Triage from Ultrasound Images
Authors: Holland Lawrence , Hernandez Torres Sofia I. , Snider Eric J.
Published: January 2024

Medical imaging can be a critical tool for triaging casualties in trauma situations. In remote or military medicine scenarios, triage is essential for identifying how to use limited resources or prioritize evacuation for the most serious cases. Ultrasound imaging, while portable and often available near the point of injury, can only be used for triage if images are properly acquired, interpreted, and objectively triage scored. Here, we detail how AI segmentation models can be used for improving image interpretation and objective triage evaluation for a medical application focused on foreign bodies embedded in tissues at variable distances from critical neurovascular features. Ultrasound images previously collected in a tissue phantom with or without neurovascular features were labeled with ground truth masks. These image sets were used to train two different segmentation AI frameworks: YOLOv7 and U-Net segmentation models. Overall, both approaches were successful in identifying shrapnel in the image set, with U-Net outperforming YOLOv7 for single-class segmentation. Both segmentation models were also evaluated with a more complex image set containing shrapnel, artery, vein, and nerve features. YOLOv7 obtained higher precision scores across multiple classes whereas U-Net achieved higher recall scores. Using each AI model, a triage distance metric was adapted to measure the proximity of shrapnel to the nearest neurovascular feature, with U-Net more closely mirroring the triage distances measured from ground truth labels. Overall, the segmentation AI models were successful in detecting shrapnel in ultrasound images and could allow for improved injury triage in emergency medicine scenarios.
Deep Learning for Delineation of the Spinal Canal in Whole-Body Diffusion-Weighted Imaging: Normalising Inter- and Intra-Patient Intensity Signal in Multi-Centre Datasets
Authors: Candito Antonio , Holbrey Richard , Ribeiro Ana , Messiou Christina , Tunariu Nina , Koh Dow-Mu , Blackledge Matthew D.
Published: January 2024

Background: Whole-Body Diffusion-Weighted Imaging (WBDWI) is an established technique for staging and evaluating treatment response in patients with multiple myeloma (MM) and advanced prostate cancer (APC). However, WBDWI scans show inter- and intra-patient intensity signal variability. This variability poses challenges in accurately quantifying bone disease, tracking changes over follow-up scans, and developing automated tools for bone lesion delineation. Here, we propose a novel automated pipeline for inter-station, inter-scan image signal standardisation on WBDWI that utilizes robust segmentation of the spinal canal through deep learning. Methods: We trained and validated a supervised 2D U-Net model to automatically delineate the spinal canal (both the spinal cord and surrounding cerebrospinal fluid, CSF) in an initial cohort of 40 patients who underwent WBDWI for treatment response evaluation (80 scans in total). Expert-validated contours were used as the target standard. The algorithm was further semi-quantitatively validated on four additional datasets (three internal, one external, 207 scans total) by comparing the distributions of average apparent diffusion coefficient (ADC) and volume of the spinal cord derived from a two-component Gaussian mixture model of segmented regions. Our pipeline subsequently standardises WBDWI signal intensity through two stages: (i) normalisation of signal between imaging stations within each patient through histogram equalisation of slices acquired on either side of the station gap, and (ii) inter-scan normalisation through histogram equalisation of the signal derived within segmented spinal canal regions. This approach was semi-quantitatively validated in all scans available to the study (N = 287). Results: The test dice score, precision, and recall of the spinal canal segmentation model were all above 0.87 when compared to manual delineation. The average ADC for the spinal cord (1.7 × 10−3 mm2/s) showed no significant difference from the manual contours. Furthermore, no significant differences were found between the average ADC values of the spinal cord across the additional four datasets. The signal-normalised, high-b-value images were visualised using a fixed contrast window level and demonstrated qualitatively better signal homogeneity across scans than scans that were not signal-normalised. Conclusion: Our proposed intensity signal WBDWI normalisation pipeline successfully harmonises intensity values across multi-centre cohorts. The computational time required is less than 10 s, preserving contrast-to-noise and signal-to-noise ratios in axial diffusion-weighted images. Importantly, no changes to the clinical MRI protocol are expected, and there is no need for additional reference MRI data or follow-up scans.
Multi-Echo Complex Quantitative Susceptibility Mapping and Quantitative Blood Oxygen Level-Dependent Magnitude (mcQSM + qBOLD or mcQQ) for Oxygen Extraction Fraction (OEF) Mapping
Authors: Cho Junghun , Zhang Jinwei , Spincemaille Pascal , Zhang Hang , Nguyen Thanh D. , Zhang Shun , Gupta Ajay , Wang Yi
Published: January 2024

Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an essential biomarker used to directly assess tissue viability and function in neurologic disorders. In ischemic stroke, for example, increased OEF can indicate the presence of penumbra—tissue with low perfusion yet intact cellular integrity—making it a primary therapeutic target. However, practical OEF mapping methods are not currently available in clinical settings, owing to the impractical data acquisitions in positron emission tomography (PET) and the limitations of existing MRI techniques. Recently, a novel MRI-based OEF mapping technique, termed QQ, was proposed. It shows high potential for clinical use by utilizing a routine sequence and removing the need for impractical multiple gas inhalations. However, QQ relies on the assumptions of Gaussian noise in susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low signal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic Gaussian noise in mGRE complex signals. We implemented mcQQ using a deep learning framework (mcQQ-NET) and compared it with the existing QQ-NET in simulations, ischemic stroke patients, and healthy subjects, using identical training and testing datasets and schemes. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the subacute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaffected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR, such as SNR ≤ 15 (dB). Therefore, mcQQ-NET improves OEF accuracy by more accurately reflecting realistic Gaussian noise in complex mGRE signals. Its enhanced sensitivity to OEF abnormalities, based on more realistic biophysics modeling, suggests that mcQQ-NET has potential for investigating tissue variability in neurologic disorders.
Simultaneous High-Frame-Rate Acoustic Plane-Wave and Optical Imaging of Intracranial Cavitation in Polyacrylamide Brain Phantoms during Blunt Force Impact
Authors: Galindo Eric J. , Flores Riley R. , Mejia-Alvarez Ricardo , Willis Adam M. , Tartis Michaelann S.
Published: January 2024

Blunt and blast impacts occur in civilian and military personnel, resulting in traumatic brain injuries necessitating a complete understanding of damage mechanisms and protective equipment design. However, the inability to monitor in vivo brain deformation and potential harmful cavitation events during collisions limits the investigation of injury mechanisms. To study the cavitation potential, we developed a full-scale human head phantom with features that allow a direct optical and acoustic observation at high frame rates during blunt impacts. The phantom consists of a transparent polyacrylamide material sealed with fluid in a 3D-printed skull where windows are integrated for data acquisition. The model has similar mechanical properties to brain tissue and includes simplified yet key anatomical features. Optical imaging indicated reproducible cavitation events above a threshold impact energy and localized cavitation to the fluid of the central sulcus, which appeared as high-intensity regions in acoustic images. An acoustic spectral analysis detected cavitation as harmonic and broadband signals that were mapped onto a reconstructed acoustic frame. Small bubbles trapped during phantom fabrication resulted in cavitation artifacts, which remain the largest challenge of the study. Ultimately, acoustic imaging demonstrated the potential to be a stand-alone tool, allowing observations at depth, where optical techniques are limited.
Neuroprotective Roles of the Biliverdin Reductase-A/Bilirubin Axis in the Brain
Authors: Paul Bindu D. , Pieper Andrew A.
Published: January 2024

Biliverdin reductase-A (BVRA) is a multi-functional enzyme with a multitude of important roles in physiologic redox homeostasis. Classically, BVRA is well known for converting the heme metabolite biliverdin to bilirubin, which is a potent antioxidant in both the periphery and the brain. However, BVRA additionally participates in many neuroprotective signaling cascades in the brain that preserve cognition. Here, we review the neuroprotective roles of BVRA and bilirubin in the brain, which together constitute a BVRA/bilirubin axis that influences healthy aging and cognitive function.
Methodological Pitfalls of Investigating Lipid Rafts in the Brain: What Are We Still Missing?
Authors: Mlinac-Jerkovic Kristina , Kalanj-Bognar Svjetlana , Heffer Marija , Blažetić Senka
Published: January 2024

The purpose of this review is to succinctly examine the methodologies used in lipid raft research in the brain and to highlight the drawbacks of some investigative approaches. Lipid rafts are biochemically and biophysically different from the bulk membrane. A specific lipid environment within membrane domains provides a harbor for distinct raftophilic proteins, all of which in concert create a specialized platform orchestrating various cellular processes. Studying lipid rafts has proved to be arduous due to their elusive nature, mobility, and constant dynamic reorganization to meet the cellular needs. Studying neuronal lipid rafts is particularly cumbersome due to the immensely complex regional molecular architecture of the central nervous system. Biochemical fractionation, performed with or without detergents, is still the most widely used method to isolate lipid rafts. However, the differences in solubilization when various detergents are used has exposed a dire need to find more reliable methods to study particular rafts. Biochemical methods need to be complemented with other approaches such as live-cell microscopy, imaging mass spectrometry, and the development of specific non-invasive fluorescent probes to obtain a more complete image of raft dynamics and to study the spatio-temporal expression of rafts in live cells.
Circular RNAs in Breast Cancer: An Update
Authors: Bao Haolin , Li Jiehan , Zhao Qihang , Yang Qingling , Xu Yi
Published: January 2024

Breast cancer (BC), characterized by high heterogeneity, is the most commonly reported malignancy among females across the globe. Every year, many BC patients die owing to delayed diagnosis and treatment. Increasing researches have indicated that aberrantly expressed circular RNAs (circRNAs) are implicated in the tumorigenesis and progression of various tumors, including BC. Hence, this article provides a summary of the biogenesis and functions of circRNAs, as well as an examination of how circRNAs regulate the progression of BC. Moreover, circRNAs have aroused incremental attention as potential diagnostic and prognostic biomarkers for BC. Exosomes enriched with circRNAs can be secreted into the tumor microenvironment to mediate intercellular communication, affecting the progression of BC. Detecting the expression levels of exosomal circRNAs may provide reference for BC diagnosis and prognosis prediction. Illuminating insights into the earlier diagnosis and better treatment regimens of BC will be potentially available following elucidation of deeper regulatory mechanisms of circRNAs in this malignancy.
Unravelling the Complexity of the +33 C>G [HBB:c.-18C>G] Variant in Beta Thalassemia
Authors: Stephanou Coralea , Petrou Miranda , Kountouris Petros , Makariou Christiana , Christou Soteroula , Hadjigavriel Michael , Kleanthous Marina , Papasavva Thessalia
Published: January 2024

The +33 C>G variant [NM_000518.5(HBB):c.-18C>G] in the 5′ untranslated region (UTR) of the β-globin gene is described in the literature as both mild and silent, while it causes a phenotype of thalassemia intermedia in the presence of a severe β-thalassemia allele. Despite its potential clinical significance, the determination of its pathogenicity according to established standards requires a greater number of published cases and co-segregation evidence than what is currently available. The present study provides an extensive phenotypic characterization of +33 C>G using 26 heterozygous and 11 compound heterozygous novel cases detected in Cyprus and employs computational predictors (CADD, RegulomeDB) to better understand its impact on clinical severity. Genotype identification of globin gene variants, including α- and δ-thalassemia determinants, and rs7482144 (XmnI) was carried out using Sanger sequencing, gap-PCR, and restriction enzyme digestion methods. The heterozygous state of +33 C>G had a silent phenotype without apparent microcytosis or hypochromia, while compound heterozygosity with a β+ or β0 allele had a spectrum of clinical phenotypes. Awareness of the +33 C>G is required across Mediterranean populations where β-thalassemia is frequent, particularly in Cyprus, with significant relevance in population screening and fetal diagnostic applications.
Treatment with Cobra Venom Factor Decreases Ischemic Tissue Damage in Mice
Authors: Azubuike-Osu Sharon O. , Kuhs Amelie , Götz Philipp , Faro Anna , Preissner Klaus T. , Arnholdt Christoph , Deindl Elisabeth
Published: January 2024

Tissue ischemia, caused by the blockage of blood vessels, can result in substantial damage and impaired tissue performance. Information regarding the functional contribution of the complement system in the context of ischemia and angiogenesis is lacking. To investigate the influence of complement activation and depletion upon femoral artery ligation (FAL), Cobra venom factor (CVF) (that functionally resembles C3b, the activated form of complement component C3) was applied in mice in comparison to control mice. Seven days after induction of muscle ischemia through FAL, gastrocnemius muscles of mice were excised and subjected to (immuno-)histological analyses. H&E and apoptotic cell staining (TUNEL) staining revealed a significant reduction in ischemic tissue damage in CVF-treated mice compared to controls. The control mice, however, exhibited a significantly higher capillary-to-muscle fiber ratio and a higher number of proliferating endothelial cells (CD31+/CD45−/BrdU+). The total number of leukocytes (CD45+) substantially decreased in CVF-treated mice versus control mice. Moreover, the CVF-treated group displayed a shift towards the M2-like anti-inflammatory and regenerative macrophage phenotype (CD68+/MRC1+). In conclusion, our findings suggest that treatment with CVF leads to reduced ischemic tissue damage along with decreased leukocyte recruitment but increased numbers of M2-like polarized macrophages, thereby enhancing tissue regeneration, repair, and healing.
Correlation of Myeloid-Derived Suppressor Cell Expansion with Upregulated Transposable Elements in Severe COVID-19 Unveiled in Single-Cell RNA Sequencing Reanalysis
Authors: Farahmandnejad Mitra , Mosaddeghi Pouria , Dorvash Mohammadreza , Sakhteman Amirhossein , Negahdaripour Manica , Faridi Pouya
Published: January 2024

Some studies have investigated the potential role of transposable elements (TEs) in COVID-19 pathogenesis and complications. However, to the best of our knowledge, there is no study to examine the possible association of TE expression in cell functions and its potential role in COVID-19 immune response at the single-cell level. In this study, we reanalyzed single-cell RNA seq data of bronchoalveolar lavage (BAL) samples obtained from six severe COVID-19 patients and three healthy donors to assess the probable correlation of TE expression with the immune responses induced by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in COVID-19 patients. Our findings indicate that the expansion of myeloid-derived suppressor cells (MDSCs) may be a characteristic feature of COVID-19. Additionally, a significant increase in TE expression in MDSCs was observed. This upregulation of TEs in COVID-19 may be linked to the adaptability of these cells in response to their microenvironments. Furthermore, it appears that the identification of overexpressed TEs by pattern recognition receptors (PRRs) in MDSCs may enhance the suppressive capacity of these cells. Thus, this study emphasizes the crucial role of TEs in the functionality of MDSCs during COVID-19.
Overview of the Current Knowledge and Conventional MRI Characteristics of Peri- and Para-Vascular Spaces
Authors: Parillo Marco , Vaccarino Federica , Di Gennaro Gianfranco , Kumar Sumeet , Van Goethem Johan , Beomonte Zobel Bruno , Quattrocchi Carlo Cosimo , Parizel Paul M. , Mallio Carlo Augusto
Published: January 2024

Brain spaces around (perivascular spaces) and alongside (paravascular or Virchow–Robin spaces) vessels have gained significant attention in recent years due to the advancements of in vivo imaging tools and to their crucial role in maintaining brain health, contributing to the anatomic foundation of the glymphatic system. In fact, it is widely accepted that peri- and para-vascular spaces function as waste clearance pathways for the brain for materials such as ß-amyloid by allowing exchange between cerebrospinal fluid and interstitial fluid. Visible brain spaces on magnetic resonance imaging are often a normal finding, but they have also been associated with a wide range of neurological and systemic conditions, suggesting their potential as early indicators of intracranial pressure and neurofluid imbalance. Nonetheless, several aspects of these spaces are still controversial. This article offers an overview of the current knowledge and magnetic resonance imaging characteristics of peri- and para-vascular spaces, which can help in daily clinical practice image description and interpretation. This paper is organized into different sections, including the microscopic anatomy of peri- and para-vascular spaces, their associations with pathological and physiological events, and their differential diagnosis.
Developmental Dyslexia: Insights from EEG-Based Findings and Molecular Signatures—A Pilot Study
Authors: Theodoridou Daniela , Tsiantis Christos-Orestis , Vlaikou Angeliki-Maria , Chondrou Vasiliki , Zakopoulou Victoria , Christodoulides Pavlos , Oikonomou Emmanouil D. , Tzimourta Katerina D. , Kostoulas Charilaos , Tzallas Alexandros T. , Tsamis Konstantinos I. , Peschos Dimitrios , Sgourou Argyro , Filiou Michaela D. , Syrrou Maria
Published: January 2024

Developmental dyslexia (DD) is a learning disorder. Although risk genes have been identified, environmental factors, and particularly stress arising from constant difficulties, have been associated with the occurrence of DD by affecting brain plasticity and function, especially during critical neurodevelopmental stages. In this work, electroencephalogram (EEG) findings were coupled with the genetic and epigenetic molecular signatures of individuals with DD and matched controls. Specifically, we investigated the genetic and epigenetic correlates of key stress-associated genes (NR3C1, NR3C2, FKBP5, GILZ, SLC6A4) with psychological characteristics (depression, anxiety, and stress) often included in DD diagnostic criteria, as well as with brain EEG findings. We paired the observed brain rhythms with the expression levels of stress-related genes, investigated the epigenetic profile of the stress regulator glucocorticoid receptor (GR) and correlated such indices with demographic findings. This study presents a new interdisciplinary approach and findings that support the idea that stress, attributed to the demands of the school environment, may act as a contributing factor in the occurrence of the DD phenotype.
High-Temperature Behavior of Carbon Reinforced Concrete
Authors: Ehlig Daniel , Schumann Alexander , Nietner Lutz
Published: January 2024

Carbon reinforced concrete is perceived by industry as a promising alternative to the currently established construction products. Previous building authority approvals and approvals for this construction method largely exclude questions of preventive fire protection with regard to load-bearing behavior under fire because there are hardly any reliable research results available in this field. This article shows the results of experimental investigations including thermogravimetric analyses of carbon reinforcement and tensile tests on the composite material carbon reinforced concrete. The thermogravimetric analyses show the loss of mass of the carbon reinforcement under a temperature load. A decomposition of the coating system of the carbon fibers and, with increasing temperature load, also of the carbon was observed. By varying various boundary conditions, such as the heating rate and the oxygen content present, their influences can be assessed. Stationary and non-stationary tensile tests on strip-shaped carbon reinforced concrete specimens were used to determine the load-bearing and deformation behavior in the high-temperature range up to 700 °C. The investigations were carried out under constant heating rates of 2 K/min and 10 K/min. This made it possible to obtain stress-strain curves and information on the various temperature-dependent deformation components from mechanical strains and load-independent strains. The time- and temperature-dependent decomposition of the carbon resulted in a reduction in the tensile load-bearing capacity of the reinforcement in the high-temperature range. This effect can be taken into account by considering the cross-sectional loss of the carbon reinforcement in a hot design.
A Novel Model for Calculating Human-Body Angle Factor in Radiant Heat Transfer: Balancing Computation Accuracy and Speed
Authors: Chen Yuyan , He Yingdong , Li Nianping
Published: January 2024

With the growing interest in radiant heating and cooling systems, driven by their improved efficiency and enhanced thermal comfort compared to air systems, there is an increasing need to develop a more accessible method for designers to understand the implications of radiation heat exchange between the human body and radiant panels. To address this, a novel angle factor calculation model, named the HNU Angle Factor Model, was developed, taking into account the spatial arrangement and geometric relationship between the human body and radiant panels. The angle factors obtained using the HNU Angle Factor Model exhibited good agreement with the results obtained with Fanger diagrams and the contour integral method, with average relative differences of 8.1% and 10.0% for 140 cases, respectively. Furthermore, placing a radiant panel on the floor while maintaining its fixed size can contribute to the creation of an even and efficient thermal environment for individuals in both seated and standing positions. By implementing the HNU Angle Factor Model in practical engineering applications, more effective utilization of radiant systems can be achieved, as it provides an evaluation of the heat transfer between the human body and radiant panels.
Estimation of the Bond Strength of Fiber-Reinforced Polymer Bars in Concrete Using Artificial Intelligence Systems
Authors: Shbeeb Nadim I. , Katash Alma A. , Oguzmert Metin , Barham Wasim S.
Published: January 2024

Fiber-reinforced polymer (FRP) bars have recently been introduced to the market as an alternative to steel for internal reinforcement for concrete construction exposed to situations that could cause corrosion. The bond behavior of FRP bars varies from that of steel bars, mostly due to variations in material properties and surface textures. Because of the unexpected nature of the crucial FRP–concrete interfacial (FCI) bond strength, the bond strength between FRP bars and concrete cannot be exactly determined. Numerous experimental investigations have been conducted with related empirical models established in an attempt to resolve this problem. These models were found to have a restricted capacity for generalization due to the small sample sizes of the experiments. Therefore, a more powerful numerical technique capable of processing large data sets with all possible parameters that may affect the relationship and considering the nonlinearity of data tendency is needed. In this study, the artificial neural networks technique and adaptive neuro-fuzzy inference system were utilized to predict the FRP–concrete bond behavior based on 238 data points collected from different studies in the literature. The performance of the ANN and ANFIS models in predicting the bonding strength was compared to other models published in the literature and codes. The results showed that the ANN and ANFIS models gave higher prediction performance than other models, with a slight advantage for the ANN model. For instance, the R-squared values of the proposed ANN and ANFIS were 0.94 and 0.92, respectively, for 20 data points that were not used to develop the ANN and ANFIS models. Based on the sensitivity analysis, the FRP diameter and compressive strength of concrete were found to be the most effective parameters on the bond strength in both the ANN and ANFIS models. In contrast, the bar position and surface texture had a lower importance index.
The Immunopathology of Pulmonary Rejection after Murine Lung Transplantation
Authors: Kaes Janne , Pollenus Emilie , Hooft Charlotte , Liu Hengshuo , Aelbrecht Celine , Cambier Seppe , Jin Xin , Van Slambrouck Jan , Beeckmans Hanne , Kerckhof Pieterjan , Velde Greetje Vande , Van Raemdonck Dirk , Yildirim Ali Önder , Van den Steen Philippe E. , Vos Robin , Ceulemans Laurens J. , Vanaudenaerde Bart M.
Published: January 2024

To improve outcomes following lung transplantation, it is essential to understand the immunological mechanisms that result in chronic graft failure. The associated clinical syndrome is termed chronic lung allograft dysfunction (CLAD), which is known to be induced by alloimmune-dependent (i.e., rejection) and alloimmune-independent factors (e.g., infections, reflux and environmental factors). We aimed to explore the alloimmune-related mechanism, i.e., pulmonary rejection. In this study, we use a murine orthotopic left lung transplant model using isografts and allografts (C57BL/6 or BALB/c as donors to C57BL/6 recipients), with daily immunosuppression (10 mg/kg cyclosporin A and 1.6 mg/kg methylprednisolone). Serial sacrifice was performed at days 1, 7 and 35 post-transplantation (n = 6 at each time point for each group). Left transplanted lungs were harvested, a single-cell suspension was made and absolute numbers of immune cells were quantified using multicolor flow cytometry. The rejection process followed the principles of a classic immune response, including innate but mainly adaptive immune cells. At day 7 following transplantation, the numbers of interstitial macrophages, monocytes, dendritic cells, NK cells, NKT cells, CD4+ T cells and CD8+ T and B cells were increased in allografts compared with isografts. Only dendritic cells and CD4+ T cells remained elevated at day 35 in allografts. Our study provides insights into the immunological mechanisms of true pulmonary rejection after murine lung transplantation. These results might be important in further research on diagnostic evaluation and treatment for CLAD.
Dual Targeting of Glioblastoma Cells with Bispecific Killer Cell Engagers Directed to EGFR and ErbB2 (HER2) Facilitates Effective Elimination by NKG2D-CAR-Engineered NK Cells
Authors: Kiefer Anne , Prüfer Maren , Röder Jasmin , Pfeifer Serrahima Jordi , Bodden Malena , Kühnel Ines , Oberoi Pranav , Wels Winfried S.
Published: January 2024

NKG2D is an activating receptor of natural killer cells that recognizes stress-induced ligands (NKG2DL) expressed by many tumor cells. Nevertheless, NKG2DL downregulation or shedding can still allow cancer cells to evade immune surveillance. Here, we used lentiviral gene transfer to engineer clinically usable NK-92 cells with a chimeric antigen receptor (NKAR) which contains the extracellular domain of NKG2D for target recognition, or an NKAR, together with the IL-15 superagonist RD-IL15, and combined these effector cells with recombinant NKG2D-interacting bispecific engagers that simultaneously recognize the tumor-associated antigens epidermal growth factor receptor (EGFR) or ErbB2 (HER2). Applied individually, in in vitro cell-killing assays, these NKAB-EGFR and NKAB-ErbB2 antibodies specifically redirected NKAR-NK-92 and NKAR_RD-IL15-NK-92 cells to glioblastoma and other cancer cells with elevated EGFR or ErbB2 levels. However, in mixed glioblastoma cell cultures, used as a model for heterogeneous target antigen expression, NKAR-NK cells only lysed the EGFR- or ErbB2-expressing subpopulations in the presence of one of the NKAB molecules. This was circumvented by applying NKAB-EGFR and NKAB-ErbB2 together, resulting in effective antitumor activity similar to that against glioblastoma cells expressing both target antigens. Our results demonstrate that combining NK cells carrying an activating NKAR receptor with bispecific NKAB antibodies allows for flexible targeting, which can enhance tumor-antigen-specific cytotoxicity and prevent immune escape.
Molecular Mechanisms of Cachexia: A Review
Authors: Neshan Mahdi , Tsilimigras Diamantis I. , Han Xu , Zhu Hua , Pawlik Timothy M.
Published: January 2024

Cachexia is a condition characterized by substantial loss of body weight resulting from the depletion of skeletal muscle and adipose tissue. A considerable fraction of patients with advanced cancer, particularly those who have been diagnosed with pancreatic or gastric cancer, lung cancer, prostate cancer, colon cancer, breast cancer, or leukemias, are impacted by this condition. This syndrome manifests at all stages of cancer and is associated with an unfavorable prognosis. It heightens the susceptibility to surgical complications, chemotherapy toxicity, functional impairments, breathing difficulties, and fatigue. The early detection of patients with cancer cachexia has the potential to enhance both their quality of life and overall survival rates. Regarding this matter, blood biomarkers, although helpful, possess certain limitations and do not exhibit universal application. Additionally, the available treatment options for cachexia are currently limited, and there is a lack of comprehensive understanding of the underlying molecular pathways associated with this condition. Thus, this review aims to provide an overview of molecular mechanisms associated with cachexia and potential therapeutic targets for the development of effective treatments for this devastating condition.
Circulating Mesenchymal Stromal Cells in Patients with Infantile Hemangioma: Evaluation of Their Functional Capacity and Gene Expression Profile
Authors: Abbà Carlotta , Croce Stefania , Valsecchi Chiara , Lenta Elisa , Campanelli Rita , Codazzi Alessia C. , Brazzelli Valeria , Carolei Adriana , Catarsi Paolo , Acquafredda Gloria , Apicella Antonia , Caliogna Laura , Berni Micaela , Mannarino Savina , Avanzini Maria A. , Rosti Vittorio , Massa Margherita
Published: January 2024

We previously published that in patients with infantile hemangioma (IH) at the onset (T0) colony forming unit-fibroblasts (CFU-Fs) are present in in vitro cultures from PB. Herein, we characterize these CFU-Fs and investigate their potential role in IH pathogenesis, before and after propranolol therapy. The CFU-F phenotype (by flow cytometry), their differentiation capacity and ability to support angiogenesis (by in vitro cultures) and their gene expression (by RT-PCR) were evaluated. We found that CFU-Fs are actual circulating MSCs (cMSCs). In patients at T0, cMSCs had reduced adipogenic potential, supported the formation of tube-like structures in vitro and showed either inflammatory (IL1β and ESM1) or angiogenic (F3) gene expression higher than that of cMSCs from CTRLs. In patients receiving one-year propranolol therapy, the cMSC differentiation in adipocytes improved, while their support in in vitro tube-like formation was lost; no difference was found between patient and CTRL cMSC gene expressions. In conclusion, in patients with IH at T0 the cMSC reduced adipogenic potential, their support in angiogenic activity and the inflammatory/angiogenic gene expression may fuel the tumor growth. One-year propranolol therapy modifies this picture, suggesting cMSCs as one of the drug targets.
Temperature Coefficient of Electronic Polarizability in Thin Polymer Films Deposited on Si and SiO2 Substrates Determined via Spectroscopic Ellipsometry
Authors: Bednarski Henryk , Hajduk Barbara , Jarka Paweł , Kumari Pallavi
Published: January 2024

Ellipsometry is widely used to determine the thermo-optical properties of thin polymer films. However, if the thermo-optic coefficient (TOC) and the linear thermal expansion coefficient (LTEC) are to be used to determine the temperature coefficient of electronic polarizability (TCEP) in thin polymer films, their values must be determined with the greatest possible accuracy, as both have the opposite effect. In this article, we analyze changes in ellipsometric parameters resulting from changes in the thin film temperature in order to develop a data analysis method for temperature-dependent spectroscopic ellipsometry that will facilitate the accurate determination of thermo-optical parameters, including the TCEP, in polymer thin films. As practical application examples, we identified optimal spectral windows to accurately determine the thermo-optical parameters of 50 to 150 nm-thick PMMA thin films deposited on Si and SiO2 substrates. The influence of thin-film thickness on the accuracy of TOC and LTEC determination is discussed.
Improved Recognition of Kurdish Sign Language Using Modified CNN
Authors: Hama Rawf Karwan Mahdi Hama , Abdulrahman Ayub Othman , Mohammed Aree Ali
Published: January 2024

The deaf society supports Sign Language Recognition (SLR) since it is used to educate individuals in communication, education, and socialization. In this study, the results of using the modified Convolutional Neural Network (CNN) technique to develop a model for real-time Kurdish sign recognition are presented. Recognizing the Kurdish alphabet is the primary focus of this investigation. Using a variety of activation functions over several iterations, the model was trained and then used to make predictions on the KuSL2023 dataset. There are a total of 71,400 pictures in the dataset, drawn from two separate sources, representing the 34 sign languages and alphabets used by the Kurds. A large collection of real user images is used to evaluate the accuracy of the suggested strategy. A novel Kurdish Sign Language (KuSL) model for classification is presented in this research. Furthermore, the hand region must be identified in a picture with a complex backdrop, including lighting, ambience, and image color changes of varying intensities. Using a genuine public dataset, real-time classification, and personal independence while maintaining high classification accuracy, the proposed technique is an improvement over previous research on KuSL detection. The collected findings demonstrate that the performance of the proposed system offers improvements, with an average training accuracy of 99.05% for both classification and prediction models. Compared to earlier research on KuSL, these outcomes indicate very strong performance.
Curating, Collecting, and Cataloguing Global COVID-19 Datasets for the Aim of Predicting Personalized Risk
Authors: Khatami Sepehr Golriz , Sargsyan Astghik , Russo Maria Francesca , Domingo-Fernández Daniel , Zaliani Andrea , Kaladharan Abish , Sethumadhavan Priya , Mubeen Sarah , Gadiya Yojana , Karki Reagon , Gebel Stephan , Ruppa Surulinathan Ram Kumar , Lage-Rupprecht Vanessa , Archipovas Saulius , Mingrone Geltrude , Jacobs Marc , Claussen Carsten , Hofmann-Apitius Martin , Kodamullil Alpha Tom
Published: January 2024

Although hundreds of datasets have been published since the beginning of the coronavirus pandemic, there is a lack of centralized resources where these datasets are listed and harmonized to facilitate their applicability and uptake by predictive modeling approaches. Firstly, such a centralized resource provides information about data owners to researchers who are searching datasets to develop their predictive models. Secondly, the harmonization of the datasets supports simultaneously taking advantage of several similar datasets. This, in turn, does not only ease the imperative external validation of data-driven models but can also be used for virtual cohort generation, which helps to overcome data sharing impediments. Here, we present that the COVID-19 data catalogue is a repository that provides a landscape view of COVID-19 studies and datasets as a putative source to enable researchers to develop personalized COVID-19 predictive risk models. The COVID-19 data catalogue currently contains over 400 studies and their relevant information collected from a wide range of global sources such as global initiatives, clinical trial repositories, publications, and data repositories. Further, the curated content stored in this data catalogue is complemented by a web application, providing visualizations of these studies, including their references, relevant information such as measured variables, and the geographical locations of where these studies were performed. This resource is one of the first to capture, organize, and store studies, datasets, and metadata related to COVID-19 in a comprehensive repository. We believe that our work will facilitate future research and development of personalized predictive risk models for COVID-19.