bluuebunny/crossref_metadata_2025 huggingface dataset:
A MULTIMOORA-Based Risk Evaluation Approach for CCUS Projects by Utilizing D Numbers Theory
Authors: Li Peilin , Che Lina , Wan Luhe , Fei Liguo
Published: April 2022

As the global climate warms, carbon emissions must be reduced in order to alleviate the human climate crisis. Carbon capture, utilization and storage (CCUS) is an emerging technology that can reduce carbon emissions. However, most of the CCUS projects have ended in failure. The reason can be attributed to insufficient risk assessment. To this end, the purpose of this study is to construct a comprehensive risk assessment model for CCUS projects. The main body of this research is divided into two parts. First, in order to evaluate the CCUS project, a risk indicator system is constructed. In what follows, a decision-making framework for risk assessment under the D numbers environment is proposed, including two stages of decision-making preparation and decision-making process. The main task of the preparation stage is to gather evaluation experts and collect decision-making information. In the decision-making stage, this paper takes the D numbers theory as the core (acting on the effective expression and fusion of subjective evaluation information), respectively, proposes the method of determining the weight of risk evaluators, the fusion method of decision-making information from different experts, and the comprehensive decision model based on the MULTIMOORA method. In order to verify the effectiveness of the constructed model, the case of CCUS project site selection in Shengli power plant is analyzed, and the results showed that the third site is the best option. This study finds the importance of a comprehensive and timely risk assessment for the successful implementation of CCUS projects, and suggests that stakeholders carry out a risk assessment of CCUS projects prior to implementation based on the method presented in this paper, so as to improve the success rate.
Automatic Programmable Bioreactor with pH Monitoring System for Tissue Engineering Application
Authors: Udomsom Suruk , Budwong Apiwat , Wongsa Chanyanut , Sangngam Pakorn , Baipaywad Phornsawat , Manaspon Chawan , Auephanwiriyakul Sansanee , Theera-Umpon Nipon , Paengnakorn Pathinan
Published: April 2022

Tissue engineering technology has been advanced and applied to various applications in the past few years. The presence of a bioreactor is one key factor to the successful development of advanced tissue engineering products. In this work, we developed a programmable bioreactor with a controlling program that allowed each component to be automatically operated. Moreover, we developed a new pH sensor for non-contact and real-time pH monitoring. We demonstrated that the prototype bioreactor could facilitate automatic cell culture of L929 cells. It showed that the cell viability was greater than 80% and cell proliferation was enhanced compared to that of the control obtained by a conventional cell culture procedure. This result suggests the possibility of a system that could be potentially useful for medical and industrial applications, including cultured meat, drug testing, etc.
Reduced Segmentation of Lesions Is Comparable to Whole-Body Segmentation for Response Assessment by PSMA PET/CT: Initial Experience with the Keyhole Approach
Authors: Hartrampf Philipp E. , Krebs Markus , Peter Lea , Heinrich Marieke , Ruffing Julia , Kalogirou Charis , Weinke Maximilian , Brumberg Joachim , Kübler Hubert , Buck Andreas K. , Werner Rudolf A. , Seitz Anna Katharina
Published: April 2022

(1) Background: Prostate-specific membrane antigen (PSMA) positron emission tomography (PET)-derived parameters, such as the commonly used standardized uptake value (SUV) and PSMA-positive tumor volume (PSMA-TV), have been proposed for response assessment in metastatic prostate cancer (PCa) patients. However, the calculation of whole-body PSMA-TV remains a time-consuming procedure. We hypothesized that it may be possible to quantify changes in PSMA-TV by considering only a limited number of representative lesions. (2) Methods: Sixty-five patients classified into different disease stages were assessed by PSMA PET/CT for staging and restaging after therapy. Whole-body PSMA-TV and whole-body SUVmax were calculated. We then repeated this calculation only including the five or ten hottest or largest lesions. The corresponding serum levels of prostate-specific antigen (PSA) were also determined. The derived delta between baseline and follow-up values provided the following parameters: ΔSUVmaxall, ΔSUVmax10, ΔSUVmax5, ΔPSMA-TVall, ΔPSMA-TV10, ΔPSMA-TV5, ΔPSA. Finally, we compared the findings from our whole-body segmentation with the results from our keyhole approach (focusing on a limited number of lesions) and correlated all values with the biochemical response (ΔPSA). (3) Results: Among patients with metastatic hormone-sensitive PCa (mHSPC), none showed a relevant deviation for ΔSUVmax10/ΔSUVmax5 or ΔPSMA-TV10/ΔPSMA-TV5 compared to ΔSUVmaxall and ΔPSMA-TVall. For patients treated with taxanes, up to 6/21 (28.6%) showed clinically relevant deviations between ΔSUVmaxall and ΔSUVmax10 or ΔSUVmax5, but only up to 2/21 (9.5%) patients showed clinically relevant deviations between ΔPSMA-TVall and ΔPSMA-TV10 or ΔPSMA-TV5. For patients treated with radioligand therapy (RLT), up to 5/28 (17.9%) showed clinically relevant deviations between ΔSUVmaxall and ΔSUVmax10 or ΔSUVmax5, but only 1/28 (3.6%) patients showed clinically relevant deviations between ΔPSMA-TVall and ΔPSMA-TV10 or ΔPSMA-TV5. The highest correlations with ΔPSA were found for ΔPSMA-TVall (r ≥ 0.59, p ≤ 0.01), followed by ΔPSMA-TV10 (r ≥ 0.57, p ≤ 0.01) and ΔPSMA-TV5 (r ≥ 0.53, p ≤ 0.02) in all cohorts. ΔPSA only correlated with ΔSUVmaxall (r = 0.60, p = 0.02) and with ΔSUVmax10 (r = 0.53, p = 0.03) in the mHSPC cohort, as well as with ΔSUVmaxall (r = 0.51, p = 0.01) in the RLT cohort. (4) Conclusion: Response assessment using PSMA-TV with a reduced number of lesions is feasible, and may allow for a simplified evaluation process for PSMA PET/CT.
Hybrid Deep Learning Approach for Automatic Detection in Musculoskeletal Radiographs
Authors: Singh Gurpreet , Anand Darpan , Cho Woong , Joshi Gyanendra Prasad , Son Kwang Chul
Published: April 2022

The practice of Deep Convolution neural networks in the field of medicine has congregated immense success and significance in present situations. Previously, researchers have developed numerous models for detecting abnormalities in musculoskeletal radiographs of upper extremities, but did not succeed in achieving respectable accuracy in the case of finger radiographs. A novel deep neural network-based hybrid architecture named ComDNet-512 is proposed in this paper to efficiently detect the bone abnormalities in the musculoskeletal radiograph of a patient. ComDNet-512 comprises a three-phase pipeline structure: compression, training of the dense neural network, and progressive resizing. The ComDNet-512 hybrid model is trained with finger radiographs samples to make a binary prediction, i.e., normal or abnormal bones. The proposed model showed phenomenon outcomes when cross-validated on the testing samples of arthritis patients and gives many superior results when compared with state-of-the-art practices. The model is able to achieve an area under the ROC curve (AUC) equal to 0.894 (sensitivity = 0.941 and specificity = 0.847). The Precision, Recall, F1 Score, and Kappa values, recorded as 0.86, 0.94, 0.89, and 0.78, respectively, are better than any of the previous models’. With an increasing appearance of enormous cases of musculoskeletal conditions in people, deep learning-based computational solutions can play a big role in performing automated detections in the future.
Decoding the Role of Melatonin Structure on Plasmodium falciparum Human Malaria Parasites Synchronization Using 2-Sulfenylindoles Derivatives
Authors: Mallaupoma Lenna Rosanie Cordero , Dias Bárbara Karina de Menezes , Singh Maneesh Kumar , Honorio Rute Isabel , Nakabashi Myna , Kisukuri Camila de Menezes , Paixão Márcio Weber , Garcia Celia R. S.
Published: April 2022

Melatonin acts to synchronize the parasite’s intraerythrocytic cycle by triggering the phospholipase C-inositol 1,4,5-trisphosphate (PLC-IP3) signaling cascade. Compounds with an indole scaffold impair in vitro proliferation of blood-stage malaria parasites, indicating that this class of compounds is potentially emerging antiplasmodial drugs. Therefore, we aimed to study the role of the alkyl and aryl thiol moieties of 14 synthetic indole compounds against chloroquine-sensitive (3D7) and chloroquine-resistant (Dd2) strains of Plasmodium falciparum. Four compounds (3, 26, 18, 21) inhibited the growth of P. falciparum (3D7) by 50% at concentrations below 20 µM. A set of 2-sulfenylindoles also showed activity against Dd2 parasites. Our data suggest that Dd2 parasites are more susceptible to compounds 20 and 28 than 3D7 parasites. These data show that 2-sulfenylindoles are promising antimalarials against chloroquine-resistant parasite strains. We also evaluated the effects of the 14 compounds on the parasitemia of the 3D7 strain and their ability to interfere with the effect of 100 nM melatonin on the parasitemia of the 3D7 strain. Our results showed that compounds 3, 7, 8, 10, 14, 16, 17, and 20 slightly increased the effect of melatonin by increasing parasitemia by 8–20% compared with that of melatonin-only-treated 3D7 parasites. Moreover, we found that melatonin modulates the expression of kinase-related signaling components giving additional evidence to investigate inhibitors that can block melatonin signaling.
Fullerenes’ Interactions with Plasma Membranes: Insight from the MD Simulations
Authors: Nisoh Nililla , Jarerattanachat Viwan , Karttunen Mikko , Wong-ekkabut Jirasak
Published: April 2022

Understanding the interactions between carbon nanoparticles (CNPs) and biological membranes is critically important for applications of CNPs in biomedicine and toxicology. Due to the complexity and diversity of the systems, most molecular simulation studies have focused on the interactions of CNPs and single component bilayers. In this work, we performed coarse-grained molecular dynamic (CGMD) simulations to investigate the behaviors of fullerenes in the presence of multiple lipid components in the plasma membranes with varying fullerene concentrations. Our results reveal that fullerenes can spontaneously penetrate the plasma membrane. Interestingly, fullerenes prefer to locate themselves in the region of the highly unsaturated lipids that are enriched in the inner leaflet of the plasma membrane. This causes fullerene aggregation even at low concentrations. When increasing fullerene concentrations, the fullerene clusters grow, and budding may emerge at the inner leaflet of the plasma membrane. Our findings suggest by tuning the lipid composition, fullerenes can be loaded deeply inside the plasma membrane, which can be useful for designing drug carrier liposomes. Moreover, the mechanisms of how fullerenes perturb multicomponent cell membranes and how they directly enter the cell are proposed. These insights can help to determine fullerene toxicity in living cells.
Lung Cancer: Spectral and Numerical Differentiation among Benign and Malignant Pleural Effusions Based on the Surface-Enhanced Raman Spectroscopy
Authors: Kowalska Aneta Aniela , Czaplicka Marta , Nowicka Ariadna B. , Chmielewska Izabela , Kędra Karolina , Szymborski Tomasz , Kamińska Agnieszka
Published: April 2022

We present here that the surface-enhanced Raman spectroscopy (SERS) technique in conjunction with the partial least squares analysis is as a potential tool for the differentiation of pleural effusion in the course of the cancerous disease and a tool for faster diagnosis of lung cancer. Pleural effusion occurs mainly in cancer patients due to the spread of the tumor, usually caused by lung cancer. Furthermore, it can also be initiated by non-neoplastic diseases, such as chronic inflammatory infection (the most common reason for histopathological examination of the exudate). The correlation between pleural effusion induced by tumor and non-cancerous diseases were found using surface-enhanced Raman spectroscopy combined with principal component regression (PCR) and partial least squares (PLS) multivariate analysis method. The PCR predicts 96% variance for the division of neoplastic and non-neoplastic samples in 13 principal components while PLS 95% in only 10 factors. Similarly, when analyzing the SERS data to differentiate the type of tumor (squamous cell vs. adenocarcinoma), PLS gives more satisfactory results. This is evidenced by the calculated values of the root mean square errors of calibration and prediction but also the coefficients of calibration determination and prediction (R2C = 0.9570 and R2C = 0.7968), which are more robust and rugged compared to those calculated for PCR. In addition, the relationship between cancerous and non-cancerous samples in the dependence on the gender of the studied patients is presented.
Resilience of Spike-Specific Immunity Induced by COVID-19 Vaccines against SARS-CoV-2 Variants
Authors: Ballesteros-Sanabria Laura , Pelaez-Prestel Hector F. , Ras-Carmona Alvaro , Reche Pedro A.
Published: April 2022

The outbreak of SARS-CoV-2 leading to the declaration of the COVID-19 global pandemic has led to the urgent development and deployment of several COVID-19 vaccines. Many of these new vaccines, including those based on mRNA and adenoviruses, are aimed to generate neutralizing antibodies against the spike glycoprotein, which is known to bind to the receptor angiotensin converting enzyme 2 (ACE2) in host cells via the receptor-binding domain (RBD). Antibodies binding to this domain can block the interaction with the receptor and prevent viral entry into the cells. Additionally, these vaccines can also induce spike-specific T cells which could contribute to providing protection against the virus. However, the emergence of new SARS-CoV-2 variants can impair the immunity generated by COVID-19 vaccines if mutations occur in cognate epitopes, precluding immune recognition. Here, we evaluated the chance of five SARS-CoV-2 variants of concern (VOCs), Alpha, Beta, Gamma, Delta and Omicron, to escape spike-specific immunity induced by vaccines. To that end, we examined the impact of the SARS-CoV-2 variant mutations on residues located on experimentally verified spike-specific epitopes, deposited at the Immune Epitope Database, that are targeted by neutralizing antibodies or recognized by T cells. We found about 300 of such B cell epitopes, which were largely overlapping, and could be grouped into 54 B cell epitope clusters sharing ≥ 7 residues. Most of the B cell epitope clusters map in the RBD domain (39 out of 54) and 20%, 50%, 37%, 44% and 57% of the total are mutated in SARS-CoV-2 Alpha, Beta, Gamma, Delta and Omicron variants, respectively. We also found 234 experimentally verified CD8 and CD4 T cell epitopes that were distributed evenly throughout the spike protein. Interestingly, in each SARS-CoV-2 VOC, over 87% and 79% of CD8 and CD4 T cell epitopes, respectively, are not mutated. These observations suggest that SARS-CoV-2 VOCs—particularly the Omicron variant—may be prone to escape spike-specific antibody immunity, but not cellular immunity, elicited by COVID-19 vaccines.
Therapeutic Properties of Ayahuasca Components in Ischemia/Reperfusion Injury of the Eye
Authors: Szilágyi Anna , Takács Barbara , Szekeres Réka , Tarjányi Vera , Bombicz Mariann , Priksz Dániel , Kovács Attila , Juhász Béla , Frecska Ede , Szilvássy Zoltán , Varga Balázs
Published: April 2022

Ischemic eye diseases are major causes of vision impairment. Thus, potential retinoprotective effects of N’N-dimethyltryptamine (DMT) were investigated. To inhibit its rapid breakdown by monoamine-oxidase A (MAO-A) enzyme, DMT was co-administered with harmaline, a β-carboline in the Amazonian Ayahuasca brew. Using ligation, 60 min of ischemia was provoked in eyes of rats, followed by 7 days of reperfusion whilst animals received harmaline alone, DMT + harmaline, or vehicle treatment. After 1 week of reperfusion, electroretinographical (ERG) measurements, histological analysis, and Western blot were performed. Harmaline alone exhibited retinoprotection in ischemia–reperfusion (I/R) which was, surprisingly, counterbalanced by DMT in case of co-administration. As both MAO-A inhibition and DMT increase serotoninergic tone synergistically, communicated to be anti-ischemic, thus, involvement of other pathways was investigated. Based on our experiments, DMT and harmaline exert opposite effects on important ocular proteins such as PARP1, NFκB, MMP9, or HSP70, each having a critical role in a different mechanism of eye-ischemia-related pathologies, e.g., cell death, inflammation, tissue destruction, and oxidative stress. Since DMT is proclaimed to be a promising drug candidate, its potentially undesirable effect on eye-ischemia should be further investigated. Meanwhile, this experiment revealed the potential therapeutic effect of MAO-A inhibitor harmaline in I/R-related eye diseases.
Cerebrospinal Fluid in Classical Trigeminal Neuralgia: An Exploratory Study on Candidate Biomarkers
Authors: Svedung Wettervik Teodor , Folkvaljon Dick , Gordh Torsten , Freyhult Eva , Kultima Kim , Ericson Hans , Abu Hamdeh Sami
Published: April 2022

Trigeminal neuralgia (TN) is a severe type of facial pain. A neurovascular conflict between cranial nerve V and a nearby vessel is the main pathophysiological mechanism, but additional factors are likely necessary to elicit TN. In this study, the primary aim was to explore differences in protein expression in the cerebrospinal fluid (CSF) of TN patients in relation to controls. Methods: Sixteen TN patients treated with microvascular decompression and 16 control patients undergoing spinal anesthesia for urological conditions were included. Lumbar CSF was collected preoperatively for the TN patients and before spinal anesthesia for the controls. A multiplexed proximity extension analysis of 91 CSF proteins was conducted using Proseek Multiplex Development 96, including biomarkers of cell communication, cell death, neurogenesis, and inflammation Results: The TN patients and the controls were of similar age, sex, and burden of co-morbidities. The TN patients exhibited higher concentrations of Clec11a, LGMN, MFG-E8, and ANGPTL-4 in CSF than the controls (q < 0.05). Conclusions: TN patients exhibited increased CSF biomarkers indicative of peripheral demyelinating injury (Clec11a), immune tolerance and destruction of myelin (LGMN), neuronal cell death (MFG-E8), and disturbances in myelin clearance (ANGPTL-8). Our findings are hypothesis-generating for candidate biomarkers and pathophysiological processes in classical TN.
Shear Strength Prediction of Slender Steel Fiber Reinforced Concrete Beams Using a Gradient Boosting Regression Tree Method
Authors: Shatnawi Amjed , Alkassar Hana Mahmood , Al-Abdaly Nadia Moneem , Al-Hamdany Emadaldeen A. , Bernardo Luís Filipe Almeida , Imran Hamza
Published: April 2022

For the design or assessment of concrete structures that incorporate steel fiber in their elements, the accurate prediction of the shear strength of steel fiber reinforced concrete (SFRC) beams is critical. Unfortunately, traditional empirical methods are based on a small and limited dataset, and their abilities to accurately estimate the shear strength of SFRC beams are arguable. This drawback can be reduced by developing an accurate machine learning based model. The problem with using a high accuracy machine learning (ML) model is its interpretation since it works as a black-box model that is highly sophisticated for humans to comprehend directly. For this reason, Shapley additive explanations (SHAP), one of the methods used to open a black-box machine learning model, is combined with highly accurate machine learning techniques to build an explainable ML model to predict the shear strength of SFRC slender beams. For this, a database of 330 beams with varying design attributes and geometries was developed. The new gradient boosting regression tree (GBRT) machine learning model was compared statistically to experimental data and current shear design models to evaluate its performance. The proposed GBRT model gives predictions that are very similar to the experimentally observed shear strength and has a better and unbiased predictive performance in comparison to other existing developed models. The SHAP approach shows that the beam width and effective depth are the most important factors, followed by the concrete strength and the longitudinal reinforcement ratio. In addition, the outputs are also affected by the steel fiber factor and the shear-span to effective depth ratio. The fiber tensile strength and the aggregate size have the lowest effect, with only about 1% on average to change the predicted value of the shear strength. By building an accurate ML model and by opening its black-box, future researchers can focus on some attributes rather than others.
Pt/Pd Decorate MOFs Derived Co-N-C Materials as High-Performance Catalysts for Oxygen Reduction Reaction
Authors: Jiang Yuhang , Zhu Dejing , Zhao Xiangchuan , Chu Zhaoyun , Zhang Liping , Cao Yue , Si Weimeng
Published: April 2022

We report here, a strategy to prepare Pt/Pd nanoparticles decorated with Co-N-C materials, where Co-N-C was obtained via pyrolysis of ZIF-67 directly. As-prepared Pt/Pd/Co-N-C catalysts showed excellent ORR performance, offered with a higher limit current density (6.6 mA cm−2) and similar half-wave potential positive (E1/2 = 0.84 V) compared with commercial Pt/C. In addition to an ORR activity, it also exhibits robust durability. The current density of Pt/Pd/Co-N-C decreased by only 9% after adding methanol, and a 10% current density loss was obtained after continuous testing at 36,000 s.
Coupled Multiphysics Modelling of Sensors for Chemical, Biomedical, and Environmental Applications with Focus on Smart Materials and Low-Dimensional Nanostructures
Authors: Singh Sundeep , Melnik Roderick
Published: April 2022

Low-dimensional nanostructures have many advantages when used in sensors compared to the traditional bulk materials, in particular in their sensitivity and specificity. In such nanostructures, the motion of carriers can be confined from one, two, or all three spatial dimensions, leading to their unique properties. New advancements in nanosensors, based on low-dimensional nanostructures, permit their functioning at scales comparable with biological processes and natural systems, allowing their efficient functionalization with chemical and biological molecules. In this article, we provide details of such sensors, focusing on their several important classes, as well as the issues of their designs based on mathematical and computational models covering a range of scales. Such multiscale models require state-of-the-art techniques for their solutions, and we provide an overview of the associated numerical methodologies and approaches in this context. We emphasize the importance of accounting for coupling between different physical fields such as thermal, electromechanical, and magnetic, as well as of additional nonlinear and nonlocal effects which can be salient features of new applications and sensor designs. Our special attention is given to nanowires and nanotubes which are well suited for nanosensor designs and applications, being able to carry a double functionality, as transducers and the media to transmit the signal. One of the key properties of these nanostructures is an enhancement in sensitivity resulting from their high surface-to-volume ratio, which leads to their geometry-dependant properties. This dependency requires careful consideration at the modelling stage, and we provide further details on this issue. Another important class of sensors analyzed here is pertinent to sensor and actuator technologies based on smart materials. The modelling of such materials in their dynamics-enabled applications represents a significant challenge as we have to deal with strongly nonlinear coupled problems, accounting for dynamic interactions between different physical fields and microstructure evolution. Among other classes, important in novel sensor applications, we have given our special attention to heterostructures and nucleic acid based nanostructures. In terms of the application areas, we have focused on chemical and biomedical fields, as well as on green energy and environmentally-friendly technologies where the efficient designs and opportune deployments of sensors are both urgent and compelling.
Use of Meropenem and Other Antimicrobial Lock Therapy in the Treatment of Catheter-Related Blood Stream Infections in Neonates: A Retrospective Study
Authors: Piersigilli Fiammetta , Auriti Cinzia , Dotta Andrea , Goffredo Bianca Maria , Cairoli Sara , Savarese Immacolata , Campi Francesca , Corsetti Tiziana , Bersani Iliana
Published: April 2022

(1) Background: Newborns admitted to Neonatal Intensive Care Units (NICUs) often require the placement of central vascular catheters (CVC), which are a major risk factor for hospital infection. Numerous strategies exist to prevent central line-associated blood stream infections (CLABSIs) and catheter-related bloodstream infections (CRBSIs), with only a few offering options to save the catheter when it is impossible to replace. CRBSIs continue to be a major problem for neonates in NICUs. Most CRBSIs are resistant to systemic antibiotics due to the presence of intraluminal bacterial biofilm. Therefore, catheter removal is frequently necessary when a CRBSI occurs. The so-called Antibiotic Lock Therapy (ALT) is an antimicrobial therapeutic strategy which seems to be promising in neonates when catheter removal is difficult due to critical conditions. To date, evidence about the use of ALT in the neonatal period is still fragmentary, since only poor and heterogeneous data exist. (2) Methods: We report our successful experience with ALT in seriously ill neonates with CRBSI for whom the replacement of the catheter could have been life threatening. (3) Results: ALT repetitively performed for at least 12 h was effective in 11 out of 13 infants (84.6%). It was not effective in two infants in whom ALT was performed for only 6 h. Moreover, we present new data about the stability testing of meropenem for its use during ALT in neonates. (4) Conclusions: When CRBSI occurs—bearing in mind that the optimal management is catheter removal if antibiotic therapy is not effective within 48 h—ALT seems to be a valid alternative therapy when removal is impractical due to critical conditions.
Therapy Strategies for Children Suffering from Inflammatory Bowel Disease (IBD)—A Narrative Review
Authors: Légeret Corinne , Furlano Raoul , Köhler Henrik
Published: April 2022

The incidence of inflammatory bowel disease (IBD) is increasing, and more children at a younger age are affected. The pathogenesis seems to be an interaction of microbial factors, a sensitivity of the immune system, and the intestinal barrier, leading to an inappropriate immune response. Not only has the role of biological agents become more important in the last decade in the treatment of children and adolescents, but also new insights into the composition of the gastrointestinal microbiome and personal diet implications have increased our understanding of the disease and opened up potential therapeutic pathways. This narrative review provides an overview of current recommendations, therapeutic options, drug monitoring, and practical guidelines for paediatricians involved with paediatric IBD patients. Furthermore, the off-label use of potential helpful drugs in the treatment of these patients is discussed.
Cognitive Profiles of Children with Low Motor Performance: A Contribution to the Validation of the WPPSI-IV
Authors: Jascenoka Julia , Walter Franziska
Published: April 2022

(1) Background: Developmental Coordination Disorder (DCD) is a common developmental disorder of preschool age. Children often show cognitive deficits in addition to motor problems. Various studies point in particular to problems in visual perception, working memory and processing speed. In this context, it is investigated whether the Wechsler Preschool and Primary Scale-IV (WPPSI-IV) is a suitable instrument for mapping these deficits in a valid and economical way. (2) Methods: The WPPSI-IV profiles of children with DCD (n = 12), below-average motor performance (n = 22) and a control group (n = 32) were compared. (3) Results: Children with DCD achieved significantly poorer test performance in the primary indices Verbal Comprehension, Visual Spatial, Processing Speed and Full Scale compared to a control group. Children with below-average motor skills, on the other hand, do not differ from the children in the control group. (4) Conclusions: The WPPSI-IV is a suitable instrument for diagnosing cognitive deficits in the context of DCD. The Fluid Reasoning and Verbal Comprehension indices should be used as a supplement to assess cognitive performance levels.
Patterned Printing of Fragrant Microcapsules to Cotton Fabric
Authors: Golja Barbara , Forte Tavčer Petra
Published: April 2022

Microcapsules with fragrance in the core were used to produce a scented textile. The wall of microcapsules was based on a melamine-formaldehyde polymer while an essential oil was in the core. They were applied to cotton fabric using screen printing in two ways: over the entire surface and by pattern. The properties of the differently printed samples were analyzed. The fragrance evaluation was performed, mechanical properties were studied, antibacterial activity against Staphylococcus aureus and Escherihia coli was evaluated, and resistance of the samples to soil microorganisms was determined. The amount of formaldehyde on the samples was measured. The results showed that all samples kept the fragrance even after 10 washes. The mechanical properties of the fully printed fabric were different from the properties of the patterned fabric. None of the prints of scented microcapsules provided antibacterial activity. All samples were biodegradable. Less formaldehyde was measured on patterned samples than on fully printed samples. The amount decreased after washing.
Radiation Modification of Optical Characteristics of LiNbO3:Zn and LiNbO3:Mg Crystals
Authors: Palatnikov Mikhail , Sidorov Nikolay , Panasjuk Sergey , Teplyakova Natalya , Makarova Olga
Published: April 2022

The modification of the optical characteristics of LiNbO3:Zn and LiNbO3:Mg crystals grown by the Czochralski method was investigated using β and γ radiation. The photorefractive effect was found to be inhibited by ionizing radiation in the LiNbO3:Zn ([ZnO] ≈ 2.1 mol%) crystal, which belonged to a below-threshold concentration range. The inhibition was attributed to a stepwise radiation annealing of charged defects. Ionizing radiation increased the general optical uniformity of above-threshold crystals LiNbO3:Zn ([ZnO] ≈ 5.9 mol%) and LiNbO3:Mg([MgO] ≈ 5.6 mol%). In addition, we determined that radiation annealing substantially influenced photorefraction dynamics in lightly doped LiNbO3:Zn ([ZnO] ≈ 0.1 mol%) crystals, which widens their application areas.
Study of Thermal Compression Constitutive Relation for 5182-Sc-Zr Alloy Based on Arrhenius-Type and ANN Model
Authors: Li Jingxiao , Yang Xiaofang , Zhu Yulong , Zhang Yongfa , Qiu Youcai , Sanders Robert Edward
Published: April 2022

Hot compression experiments were performed on alloy 5182 with small additions of Sc and Zr. The 5182 alloy containing Sc and Zr is critical for expanding the 5182 alloy’s range of applications, and a thorough understanding of its thermal processing behavior is of great importance to avoid processing defects. Alloy microstructure, including grain structures and Al3(ScxZr1−x) dispersoids were analyzed by EBSD and TEM. Stable flow stresses were observed below a strain rate of 1 s−1 for the Sc-Zr containing alloy. The results of constitutive models, with and without strain−compensation, and artificial neural network (ANN) were used to compare to the experimental results. The Al3(ScxZr1−x) dispersoid data was introduced into the ANN model as a nonlinear influence factor. Addition of the Al3(ScxZr1−x) dispersoid information as input data improved the accuracy and practicality of the artificial neural network in predicting the deformation behavior of the alloy. The squared correlation coefficients of ANN prediction data reached 0.99.
Conceptual Evolution and Current Approach to Spitz Tumors
Authors: Urso Carmelo , De Giorgi Vincenzo , Massi Daniela
Published: April 2022

Over the past several decades, the study of Spitz neoplasms has flourished, with expanded conceptualization and refined terminology, providing a framework for the assessment and classification of Spitz nevi, atypical Spitz Tumors, and Spitz melanoma. Cancer genomics have generated concepts such as driver and passenger genes and clonal evolution, which can be applied to Spitz tumors. Herein, we provide a historical perspective, followed by a summary of current knowledge and clinical approaches for these challenging tumors.
A Single-Center Experience on HLA Typing with 11 Loci Next Generation Sequencing in Korean Patients with Hematologic Disease
Authors: Kim Namsoo , Kim Sinyoung , Choi Jong Rak , Park Younhee
Published: April 2022

The human leukocyte antigen (HLA) system comprises the most polymorphic genes of the human genome and is famous for its potential pathological roles. To accurately type HLA genes and find HLA-matched donors, which are critical for effective hematopoietic transplantation, HLA typing using next-generation sequencing (NGS) was implemented. We aimed to share the experience of HLA typing using NGS in patients with hematologic malignancies and evaluate its association with hematologic diseases. Data from 211 Korean, non-familial patients diagnosed with a hematologic disease were reviewed, and NGS was performed for 11 HLA loci. Three-field HLA typing with G code was successfully achieved for all loci and the known linkage between HLA-DRB3/4/5 and HLA-DRB1 was fully matched. Therefore, NGS-based HLA typing enables a detailed, high-resolution analysis of the HLA system that can help with the selection of suitable donors. Notably, HLA-DRB1*08:02:01G was significantly associated with myelodysplastic syndrome. Although this result confirms the tendency of some alleles to be associated with hematological disorders, this may not be the case in hematologic malignancies. Nonetheless, NGS-based HLA typing data for HLA-DP, HLA-DQ, and HLA-DRB3/4/5 are still warranted for a better understanding of the corresponding locus.
MRI-Based Radiomic Features Help Identify Lesions and Predict Histopathological Grade of Hepatocellular Carcinoma
Authors: Brancato Valentina , Garbino Nunzia , Salvatore Marco , Cavaliere Carlo
Published: April 2022

Hepatocellular carcinoma (HCC) is the most common form of liver cancer. Radiomics is a promising tool that may increase the value of magnetic resonance imaging (MRI) in the management of HCC. The purpose of our study is to develop an MRI-based radiomics approach to preoperatively detect HCC and predict its histological grade. Thirty-eight HCC patients at staging who underwent axial T2-weighted and dynamic contrast-enhanced MRI (DCE-MRI) were considered. Three-dimensional volumes of interest (VOIs) were manually placed on HCC lesions and normal hepatic tissue (HT) on arterial phase post-contrast images. Radiomic features from T2 images and arterial, portal and tardive post-contrast images from DCE-MRI were extracted by using Pyradiomics. Feature selection was performed using correlation filter, Wilcoxon-rank sum test and mutual information. Predictive models were constructed for HCC differentiation with respect to HT and HCC histopathologic grading used at each step an imbalance-adjusted bootstrap resampling (IABR) on 1000 samples. Promising results were obtained from radiomic prediction models, with best AUCs ranging from 71% to 96%. Radiomics MRI based on T2 and DCE-MRI revealed promising results concerning both HCC detection and grading. It may be a suitable tool for personalized treatment of HCC patients and could also be used to develop new prognostic biomarkers useful for HCC assessment without the need for invasive procedures.
MHSA-EC: An Indoor Localization Algorithm Fusing the Multi-Head Self-Attention Mechanism and Effective CSI
Authors: Liu Wen , Jia Mingjie , Deng Zhongliang , Qin Changyan
Published: April 2022

Channel state information (CSI) provides a fine-grained description of the signal propagation process, which has attracted extensive attention in the field of indoor positioning. The CSI signals collected by different fingerprint points have a high degree of discrimination due to the influence of multi-path effects. This multi-path effect is reflected in the correlation between subcarriers and antennas. However, in mining such correlations, previous methods are difficult to aggregate non-adjacent features, resulting in insufficient multi-path information extraction. In addition, the existence of the multi-path effect makes the relationship between the original CSI signal and the distance not obvious, and it is easy to cause mismatching of long-distance points. Therefore, this paper proposes an indoor localization algorithm that combines the multi-head self-attention mechanism and effective CSI (MHSA-EC). This algorithm is used to solve the problem where it is difficult for traditional algorithms to effectively aggregate long-distance CSI features and mismatches of long-distance points. This paper verifies the stability and accuracy of MHSA-EC positioning through a large number of experiments. The average positioning error of MHSA-EC is 0.71 m in the comprehensive office and 0.64 m in the laboratory.
Metacognition as a Consequence of Competing Evolutionary Time Scales
Authors: Kuchling Franz , Fields Chris , Levin Michael
Published: April 2022

Evolution is full of coevolving systems characterized by complex spatio-temporal interactions that lead to intertwined processes of adaptation. Yet, how adaptation across multiple levels of temporal scales and biological complexity is achieved remains unclear. Here, we formalize how evolutionary multi-scale processing underlying adaptation constitutes a form of metacognition flowing from definitions of metaprocessing in machine learning. We show (1) how the evolution of metacognitive systems can be expected when fitness landscapes vary on multiple time scales, and (2) how multiple time scales emerge during coevolutionary processes of sufficiently complex interactions. After defining a metaprocessor as a regulator with local memory, we prove that metacognition is more energetically efficient than purely object-level cognition when selection operates at multiple timescales in evolution. Furthermore, we show that existing modeling approaches to coadaptation and coevolution—here active inference networks, predator–prey interactions, coupled genetic algorithms, and generative adversarial networks—lead to multiple emergent timescales underlying forms of metacognition. Lastly, we show how coarse-grained structures emerge naturally in any resource-limited system, providing sufficient evidence for metacognitive systems to be a prevalent and vital component of (co-)evolution. Therefore, multi-scale processing is a necessary requirement for many evolutionary scenarios, leading to de facto metacognitive evolutionary outcomes.
State-of-the-Art in Satellite Communication Networks
Authors: Tropea Mauro
Published: April 2022

Satellite networks provide a wide range of coverage throughout the globe and play a very important role due to their broadcasting capacity and bandwidth flexibility [...]
A Survey of High-Performance Interconnection Networks in High-Performance Computer Systems
Authors: Lu Ping-Jing , Lai Ming-Che , Chang Jun-Sheng
Published: April 2022

High-performance interconnection network is the key to realizing high-speed, collaborative, parallel computing at each node in a high-performance computer system. Its performance and scalability directly affect the performance and scalability of the whole system. With continuous improvements in the performance of high-performance computer systems, the trend in the development of high-performance interconnection networks is mainly reflected in network sizes and network bandwidths. With the slowdown of Moore’s Law, it is necessary to adopt new packaging design technologies to implement high-performance interconnection networks for high-performance computing. This article analyzes the main interconnection networks used by high-performance computer systems in the Top500 list of November 2021, and it elaborates the design of representative, state-of-the-art, high-performance interconnection networks, including NVIDIA InfiniBand, Intel Omni-Path, Cray Slingshot/Aries, and custom or proprietary networks, including Fugaku Tofu, Bull BXI, TH Express, and so forth. This article also comprehensively discusses the latest technologies and trends in this field. In addition, based on the analysis of the challenges faced by high-performance interconnection network design in the post-Moore era and the exascale computing era, this article presents a perspective on high-performance interconnection networks.
Broadband and Wide-Angle Performance of a Perfect Absorber Using a MIM Structure with 2D MXene
Authors: Jia Yue , Song Chunmei , Liao Yunlong , Cai Houzhi
Published: April 2022

Due to the extraordinary optoelectronic properties and surface-rich functional groups, MXene has shown great promise in many applications, such as electromagnetic shielding, catalysis, sensors, ultrafast photons, etc. In this work, we propose a wide-angle absorber based on a metal-insulator-metal (MIM) metamaterial consisting of MXene. By optimizing the design, the absorption efficiency can be further improved throughout the entire wavelength range. More importantly, the absorber exhibits high-efficiency broadband and wide-angle (20–80°) absorption in the near-infrared range (NIR: 1.1–1.7 μm) by numerical calculation. It is foreseeable that the excellent absorption characteristics and easy-to-manufacture structure of the designed absorber will bring some inspiration to the absorption device in the NIR and its practical application.
Methods for Continuous Blood Pressure Estimation Using Temporal Convolutional Neural Networks and Ensemble Empirical Mode Decomposition
Authors: Zhou Kai , Yin Zhixiang , Peng Yu , Zeng Zhiliang
Published: April 2022

Arterial blood pressure is not only an important index that must be measured in routine physical examination but also a key monitoring parameter of the cardiovascular system in cardiac surgery, drug testing, and intensive care. To improve the measurement accuracy of continuous blood pressure, this paper uses photoplethysmography (PPG) signals to estimate diastolic blood pressure and systolic blood pressure based on ensemble empirical mode decomposition (EEMD) and temporal convolutional network (TCN). In this method, the clean PPG signal is decomposed by EEMD to obtain n-order intrinsic mode functions (IMF), and then the IMF and the original PPG are input into the constructed TCN neural network model, and the results are output. The results show that TCN has better performance than CNN, CNN-LSTM, and CNN-GRU. Using the data added with IMF, the results of the above neural network model are better than those of the model with only PPG as input, in which the systolic blood pressure (SBP) and diastolic blood pressure (DBP) results of EEMD-TCN are −1.55 ± 9.92 mmHg and 0.41 ± 4.86 mmHg. According to the estimation results, DBP meets the requirements of the AAMI standard, BHS evaluates it as Grade A, SD of SBP is close to the standard AAMI, and BHS evaluates it as Grade B.
Design and Demonstration of a 540 V/28 V SiC-Based Resonant DC–DC Converter for Auxiliary Power Supply in More Electric Aircraft
Authors: Bhattacharya Sumantra , Willich Caroline , Kallo Josef
Published: April 2022

Efficient and robust power electronic converters are vital to the success of the electrification of aircraft. Especially, low voltage auxiliary converters, which usually supply high current and low voltage loads, are not readily available industrially and need special attention. In terms of energy density and efficiency, LLC converters are among the most commonly used and efficient topologies for automotive and aerospace applications. In the case of aerospace applications, a fault-tolerant topology is highly desirable to reduce the need for redundant components and weight by removing backup systems. To solve this issue, this study introduces a new 2.0 kW LLC-based converter with a reconfigurable fault-tolerant architecture. With the help of a specially designed secondary side, the proposed converter can reconfigure itself so that even if one of the semiconductor switches fails permanently, the converter can still maintain power at nominal voltage levels, ensuring that the aircraft’s vital functionality is preserved. This paper also describes the basic operation principle, component-design aspects, conduction loss reduction techniques, and control system algorithm. Finally, a 2.0 kW experimental prototype is built to verify and demonstrate the operation of the proposed reconfigurable LLC converter.
The Refinement of Petri Net with Inhibitor Arcs Based Representation for Embedded Systems
Authors: Xia Chuanliang , Wang Zhuangzhuang , Wang Zhong
Published: April 2022

Embedded systems are widely used in various devices. PRES+ (Petri net- based Representation for Embedded Systems) has been used to model and analyze embedded systems. However, it cannot characterize the priority of events, and cannot fully express the complex data flow and control flow. To solve this problem, inhibitor arcs are added to PRES+ and PIRES+ (PRES+ with Inhibitor arcs) is obtained. However, PIRES+’s state space explosion problem is a handicap when modeling, verifying, and controlling complex, large embedded systems. To mitigate the state space explosion problem of PIRES+ and analyze complex embedded systems, we propose the place refinement approach and the place set refinement approach for PIRES+. Under specific conditions, several important properties of PIRES+, such as timing, functionality, reachability, liveness, and boundedness, are preserved by using these refinement approaches. In order to illustrate the effectiveness of these refinement methods, as an example, the modeling and analysis of a network communication system is proposed. The refinement methods proposed have certain feasibility and practicability and provide a more practical theoretical basis for the modeling of some embedded systems.
An Explicable Neighboring-Pixel Reconstruction Algorithm for Temperature Distribution by Acoustic Tomography
Authors: Qiu Qirong , Zhou Wanting , Zhao Qing , Liu Shi
Published: April 2022

Acoustic process tomography is a powerful tool for monitoring multiphase flow and combustion. However, its capability of revealing details of the interrogation zone is restricted by the ill-posed and rank deficiency problems. In each projection, a probing sound beam only passes the pixels along its propagation path, resulting in a large number of zero-valued elements in the measurement matrix. This is more pronounced as the resolution of the imaging zone becomes gradually finer, which is detrimental to image reconstruction. In this study, a mathematically explicable reconstruction algorithm of regularization is proposed by assigning each zero-valued pixel with a combination of the values of the neighboring pixels, ruled by the appropriate regularization factors. The formula to determine the regularization factors is also derived. Simulations are carried out to verify this new approach, and some representative cases are presented. As a result, the ambiguity of the inverse process is removed, and the accuracy of the image reconstruction is significantly improved. The results show the robustness of the algorithm and certain advantages over the standard Tikhonov regularization formula.
Advanced Wireless Power Transfer Technologies
Authors: Lu Fei , Zhu Chong
Published: April 2022

Wireless power transfer technology is technology in which power is delivered without any metal-to-metal contact, which can make practical applications very convenient [...]