bluuebunny/crossref_metadata_2025 huggingface dataset:
CHIP/STUB1 Ubiquitin Ligase Functions as a Negative Regulator of ErbB2 by Promoting Its Early Post-Biosynthesis Degradation
Authors: Luan Haitao , Bailey Tameka A. , Clubb Robert J. , Mohapatra Bhopal C. , Bhat Aaqib M. , Chakraborty Sukanya , Islam Namista , Mushtaq Insha , Storck Matthew D. , Raja Srikumar M. , Band Vimla , Band Hamid
Published: August 2021

Overexpression of the epidermal growth factor receptor (EGFR) family member ErbB2 (HER2) drives oncogenesis in up to 25% of invasive breast cancers. ErbB2 expression at the cell surface is required for oncogenesis but mechanisms that ensure the optimal cell surface display of overexpressed ErbB2 following its biosynthesis in the endoplasmic reticulum are poorly understood. ErbB2 is dependent on continuous association with HSP90 molecular chaperone for its stability and function as an oncogenic driver. Here, we use knockdown and overexpression studies to show that the HSP90/HSC70-interacting negative co-chaperone CHIP (C-terminus of HSC70-Interacting protein)/STUB1 (STIP1-homologous U-Box containing protein 1) targets the newly synthesized, HSP90/HSC70-associated, ErbB2 for ubiquitin/proteasome-dependent degradation in the endoplasmic reticulum and Golgi, thus identifying a novel mechanism that negatively regulates cell surface ErbB2 levels in breast cancer cells, consistent with frequent loss of CHIP expression previously reported in ErbB2-overexpressing breast cancers. ErbB2-overexpressing breast cancer cells with low CHIP expression exhibited higher endoplasmic reticulum stress inducibility. Accordingly, the endoplasmic reticulum stress-inducing anticancer drug Bortezomib combined with ErbB2-targeted humanized antibody Trastuzumab showed synergistic inhibition of ErbB2-overexpressing breast cancer cell proliferation. Our findings reveal new insights into mechanisms that control the surface expression of overexpressed ErbB2 and suggest that reduced CHIP expression may specify ErbB2-overexpressing breast cancers suitable for combined treatment with Trastuzumab and ER stress inducing agents.
Effects of Pre-Operative Risk Factors on Intensive Care Unit Length of Stay (ICU-LOS) in Major Oral and Maxillofacial Cancer Surgery
Authors: Wallner Juergen , Schwaiger Michael , Edmondson Sarah-Jayne , Mischak Irene , Egger Jan , Feichtinger Matthias , Zemann Wolfgang , Pau Mauro
Published: August 2021

Objective: This study aimed to investigate the effect of certain pre-operative parameters directly on the post-operative intensive care unit (ICU)-length of stay (LOS), in order to identify at-risk patients that are expected to need prolonged intensive care management post-operatively. Material and Methods: Retrospectively, patients managed in an ICU after undergoing major oral and maxillofacial surgery were analyzed. Inclusion criteria entailed: age 18–90 years, major primary oral cancer surgery including tumor resection, neck dissection and microvascular free flap reconstruction, minimum operation time of 8 h. Exclusion criteria were: benign/borderline tumors, primary radiation, other defect reconstruction than microvascular, treatment at other centers. Separate parameters used within the clinical routine were set in correlation with ICU-LOS, by applying single testing calculations (t-tests, variance analysis, correlation coefficients, effect sizes) and a valid univariate linear regression model. The primary outcome of interest was ICU-LOS. Results: This study included a homogenous cohort of 122 patients. Mean surgery time was 11.4 (±2.2) h, mean ICU-LOS was 3.6 (±2.6) days. Patients with pre-operative renal dysfunction (p < 0.001), peripheral vascular disease-PVD (p = 0.01), increasing heart failure-NYHA stage categories (p = 0.009) and higher-grade categories of post-operative complications (p = 0.023) were identified as at-risk patients for a significantly prolonged post-operative ICU-LOS. Conclusions: At-risk patients are prone to need a significantly longer ICU-LOS than others. These patients are those with pre-operative severe renal dysfunction, PVD and/or high NYHA stage categories. Confounding parameters that contribute to a prolonged ICU-LOS in combination with other variables were identified as higher age, prolonged operative time, chronic obstructive pulmonary disease, and intra-operatively transfused blood.
Nuclear Pyruvate Kinase M2 (PKM2) Contributes to Phosphoserine Aminotransferase 1 (PSAT1)-Mediated Cell Migration in EGFR-Activated Lung Cancer Cells
Authors: Biyik-Sit Rumeysa , Kruer Traci , Dougherty Susan , Bradley James A. , Wilkey Daniel W. , Merchant Michael L. , Trent John O. , Clem Brian F.
Published: August 2021

An elevated expression of phosphoserine aminotransferase 1 (PSAT1) has been observed in multiple tumor types and is associated with poorer clinical outcomes. Although PSAT1 is postulated to promote tumor growth through its enzymatic function within the serine synthesis pathway (SSP), its role in cancer progression has not been fully characterized. Here, we explore a putative non-canonical function of PSAT1 that contributes to lung tumor progression. Biochemical studies found that PSAT1 selectively interacts with pyruvate kinase M2 (PKM2). Amino acid mutations within a PKM2-unique region significantly reduced this interaction. While PSAT1 loss had no effect on cellular pyruvate kinase activity and PKM2 expression in non-small-cell lung cancer (NSCLC) cells, fractionation studies demonstrated that the silencing of PSAT1 in epidermal growth factor receptor (EGFR)-mutant PC9 or EGF-stimulated A549 cells decreased PKM2 nuclear translocation. Further, PSAT1 suppression abrogated cell migration in these two cell types whereas PSAT1 restoration or overexpression induced cell migration along with an elevated nuclear PKM2 expression. Lastly, the nuclear re-expression of the acetyl-mimetic mutant of PKM2 (K433Q), but not the wild-type, partially restored cell migration in PSAT1-silenced cells. Therefore, we conclude that, in response to EGFR activation, PSAT1 contributes to lung cancer cell migration, in part, by promoting nuclear PKM2 translocation.
Identification of Immune Cell Infiltration in Murine Pheochromocytoma during Combined Mannan-BAM, TLR Ligand, and Anti-CD40 Antibody-Based Immunotherapy
Authors: Uher Ondrej , Huynh Thanh-Truc , Zhu Boqun , Horn Lucas A. , Caisova Veronika , Hadrava Vanova Katerina , Medina Rogelio , Wang Herui , Palena Claudia , Chmelar Jindrich , Zhuang Zhengping , Zenka Jan , Pacak Karel
Published: August 2021

Immunotherapy has become an essential component in cancer treatment. However, the majority of solid metastatic cancers, such as pheochromocytoma, are resistant to this approach. Therefore, understanding immune cell composition in primary and distant metastatic tumors is important for therapeutic intervention and diagnostics. Combined mannan-BAM, TLR ligand, and anti-CD40 antibody-based intratumoral immunotherapy (MBTA therapy) previously resulted in the complete eradication of murine subcutaneous pheochromocytoma and demonstrated a systemic antitumor immune response in a metastatic model. Here, we further evaluated this systemic effect using a bilateral pheochromocytoma model, performing MBTA therapy through injection into the primary tumor and using distant (non-injected) tumors to monitor size changes and detailed immune cell infiltration. MBTA therapy suppressed the growth of not only injected but also distal tumors and prolonged MBTA-treated mice survival. Our flow cytometry analysis showed that MBTA therapy led to increased recruitment of innate and adaptive immune cells in both tumors and the spleen. Moreover, adoptive CD4+ T cell transfer from successfully MBTA-treated mice (i.e., subcutaneous pheochromocytoma) demonstrates the importance of these cells in long-term immunological memory. In summary, this study unravels further details on the systemic effect of MBTA therapy and its use for tumor and metastasis reduction or even elimination.
CCL20/TNF/VEGFA Cytokine Secretory Phenotype of Tumor-Associated Macrophages Is a Negative Prognostic Factor in Cutaneous Melanoma
Authors: Gutiérrez-Seijo Alba , García-Martínez Elena , Barrio-Alonso Celia , Pareja-Malagón Miriam , Acosta-Ocampo Alejandra , Fernández-Santos María Eugenia , Puig-Kröger Amaya , Parra-Blanco Verónica , Mercader Enrique , Márquez-Rodas Iván , Avilés-Izquierdo José Antonio , Samaniego Rafael , Sánchez-Mateos Paloma
Published: August 2021

TAMs constitute a large fraction of infiltrating immune cells in melanoma tissues, but their significance for clinical outcomes remains unclear. We explored diverse TAM parameters in clinically relevant primary cutaneous melanoma samples, including density, location, size, and polarization marker expression; in addition, because cytokine production is a hallmark of macrophages function, we measured CCL20, TNF, and VEGFA intracellular cytokines by single-cell multiparametric confocal microscopy. The Kaplan–Meier method was used to analyze correlation with melanoma-specific disease-free survival and overall survival. No significant correlations with clinical parameters were observed for TAM density, morphology, or location. Significantly, higher contents of the intracellular cytokines CCL20, TNF, and VEGFA were quantified in TAMs infiltrating metastasizing compared to non-metastasizing skin primary melanomas (p < 0.001). To mechanistically explore cytokine up-regulation, we performed in vitro studies with melanoma-conditioned macrophages, using RNA-seq to explore involved pathways and specific inhibitors. We show that p53 and NF-κB coregulate CCL20, TNF, and VEGFA in melanoma-conditioned macrophages. These results delineate a clinically relevant pro-oncogenic cytokine profile of TAMs with prognostic significance in primary melanomas and point to the combined therapeutic targeting of NF-kB/p53 pathways to control the deviation of TAMs in melanoma.
Challenges in the Use of Artificial Intelligence for Prostate Cancer Diagnosis from Multiparametric Imaging Data
Authors: Corradini Daniele , Brizi Leonardo , Gaudiano Caterina , Bianchi Lorenzo , Marcelli Emanuela , Golfieri Rita , Schiavina Riccardo , Testa Claudia , Remondini Daniel
Published: August 2021

Many efforts have been carried out for the standardization of multiparametric Magnetic Resonance (mp-MR) images evaluation to detect Prostate Cancer (PCa), and specifically to differentiate levels of aggressiveness, a crucial aspect for clinical decision-making. Prostate Imaging—Reporting and Data System (PI-RADS) has contributed noteworthily to this aim. Nevertheless, as pointed out by the European Association of Urology (EAU 2020), the PI-RADS still has limitations mainly due to the moderate inter-reader reproducibility of mp-MRI. In recent years, many aspects in the diagnosis of cancer have taken advantage of the use of Artificial Intelligence (AI) such as detection, segmentation of organs and/or lesions, and characterization. Here a focus on AI as a potentially important tool for the aim of standardization and reproducibility in the characterization of PCa by mp-MRI is reported. AI includes methods such as Machine Learning and Deep learning techniques that have shown to be successful in classifying mp-MR images, with similar performances obtained by radiologists. Nevertheless, they perform differently depending on the acquisition system and protocol used. Besides, these methods need a large number of samples that cover most of the variability of the lesion aspect and zone to avoid overfitting. The use of publicly available datasets could improve AI performance to achieve a higher level of generalizability, exploiting large numbers of cases and a big range of variability in the images. Here we explore the promise and the advantages, as well as emphasizing the pitfall and the warnings, outlined in some recent studies that attempted to classify clinically significant PCa and indolent lesions using AI methods. Specifically, we focus on the overfitting issue due to the scarcity of data and the lack of standardization and reproducibility in every step of the mp-MR image acquisition and the classifier implementation. In the end, we point out that a solution can be found in the use of publicly available datasets, whose usage has already been promoted by some important initiatives. Our future perspective is that AI models may become reliable tools for clinicians in PCa diagnosis, reducing inter-observer variability and evaluation time.
Survival and Death Causes in Thyroid Cancer in Taiwan: A Nationwide Case–Control Cohort Study
Authors: Lu Yu-Ling , Lin Shu-Fu , Wu Ming-Hsien , Lee Yi-Yin , Lee Pai-Wei , Chang Shang-Hung , Huang Yu-Tung
Published: August 2021

The incidence of thyroid cancer has increased substantially worldwide. However, the overall mortality risk and actual causes of death in thyroid cancer patients have not been extensively evaluated. In this study, patients with thyroid cancer diagnosed between 2001 and 2017 were analyzed from Taiwan’s National Health Insurance Research Database. We compared these patients with control subjects matched for age, gender, history of cardiovascular disease (CVD), hyperlipidemia, diabetes mellitus, hypertension, and occupation to assess the risk of overall mortality and cause-specific mortality. Finally, our cohort comprised 30,778 patients with thyroid cancer. Three hundred and ninety-eight deaths (1.29%) occurred during a median follow-up of 60.0 months (range: 30.3 to 117.6 months). The primary cause of death was thyroid cancer mortality (31.2%), followed by other malignancy-related mortality (29.9%) and CVD mortality (12.3%). The overall mortality risk was similar between the thyroid cancer and control groups (unadjusted hazard ratio (HR): 0.98; 95% confidence interval (CI): 0.88–1.10); the adjusted HR was 1.07 (95% CI: 0.95–1.20) after multivariate adjustment for age, gender, history of CVD, hyperlipidemia, diabetes mellitus, hypertension, and occupation. The risk of other malignancy-related mortality was comparable between two groups. CVD mortality risk was lower in the thyroid cancer group, with an unadjusted HR of 0.51 (95% CI: 0.38–0.69) and adjusted HR of 0.56 (95% CI: 0.42–0.76). In conclusion, patients with thyroid cancer had excellent overall survival. Thyroid cancer-specific mortality was the leading cause of death, highlighting the importance of thyroid cancer management. Thyroid cancer patients had lower CVD mortality risk than the general population.
Early Tumor Response and Safety of Atezolizumab Plus Bevacizumab for Patients with Unresectable Hepatocellular Carcinoma in Real-World Practice
Authors: Ando Yuwa , Kawaoka Tomokazu , Kosaka Masanari , Shirane Yuki , Johira Yusuke , Miura Ryoichi , Murakami Serami , Yano Shigeki , Amioka Kei , Naruto Kensuke , Kosaka Yumi , Uchikawa Shinsuke , Kodama Kenichiro , Fujino Hatsue , Nakahara Takashi , Ono Atsushi , Murakami Eisuke , Yamauchi Masami , Okamoto Wataru , Takahashi Shoichi , Imamura Michio , Chayama Kazuaki , Aikata Hiroshi
Published: August 2021

The aim of this study was to investigate the early tumor response and safety of atezolizumab plus bevacizumab for patients with unresectable hepatocellular carcinoma in real-world practice. Forty patients with Child-Pugh class A liver function and eastern cooperative oncology group performance status 0 or 1 were enrolled. The objective response rate (ORR) at six weeks after the start of treatment, changes in α-fetoprotein (AFP) and des-γ-carboxyprothrombin, incidence of adverse events (AEs), and changes in albumin-bilirubin (ALBI) score and serum ammonia level, were evaluated. Among 40 patients, 24 had histories of prior molecular targeted agents (MTAs). The ORR was 22.5% based on mRECIST. Multivariate analysis showed that an AFP ratio <1.0 at three weeks (odds ratio 39.2, 95% confidence interval CI 2.37–649.0, p = 0.0103) was the only significant factor for predicting early response. There was no significant difference in the frequency of AEs between patients receiving first-line treatments and others. Fatigue, proteinuria, and ascites were more frequent in patients who experienced prior treatment. No decrease in ALBI score or increase in serum ammonia level was observed. Our study demonstrated that AFP may be useful in assessing early response and that this treatment is safe, including in patients with prior MTA treatments.
High-Density EEG in a Charles Bonnet Syndrome Patient during and without Visual Hallucinations: A Case-Report Study
Authors: Piarulli Andrea , Annen Jitka , Kupers Ron , Laureys Steven , Martial Charlotte
Published: August 2021

Charles Bonnet syndrome (CBS) is a rare clinical condition characterized by complex visual hallucinations in people with loss of vision. So far, the neurobiological mechanisms underlying the hallucinations remain elusive. This case-report study aims at investigating electrical activity changes in a CBS patient during visual hallucinations, as compared to a resting-state period (without hallucinations). Prior to the EEG, the patient underwent neuropsychological, ophthalmologic, and neurological examinations. Spectral and connectivity, graph analyses and signal diversity were applied to high-density EEG data. Visual hallucinations (as compared to resting-state) were characterized by a significant reduction of power in the frontal areas, paralleled by an increase in the midline posterior regions in delta and theta bands and by an increase of alpha power in the occipital and midline posterior regions. We next observed a reduction of theta connectivity in the frontal and right posterior areas, which at a network level was complemented by a disruption of small-worldness (lower local and global efficiency) and by an increase of network modularity. Finally, we found a higher signal complexity especially when considering the frontal areas in the alpha band. The emergence of hallucinations may stem from these changes in the visual cortex and in core cortical regions encompassing both the default mode and the fronto-parietal attentional networks.
MOS Sensors Array for the Discrimination of Lung Cancer and At-Risk Subjects with Exhaled Breath Analysis
Authors: Marzorati Davide , Mainardi Luca , Sedda Giulia , Gasparri Roberto , Spaggiari Lorenzo , Cerveri Pietro
Published: August 2021

Lung cancer is characterized by a tremendously high mortality rate and a low 5-year survival rate when diagnosed at a late stage. Early diagnosis of lung cancer drastically reduces its mortality rate and improves survival. Exhaled breath analysis could offer a tool to clinicians to improve the ability to detect lung cancer at an early stage, thus leading to a reduction in the associated survival rate. In this paper, we present an electronic nose for the automatic analysis of exhaled breath. A total of five a-specific gas sensors were embedded in the electronic nose, making it sensitive to different volatile organic compounds (VOCs) contained in exhaled breath. Nine features were extracted from each gas sensor response to exhaled breath, identifying the subject breathprint. We tested the electronic nose on a cohort of 80 subjects, equally split between lung cancer and at-risk control subjects. Including gas sensor features and clinical features in a classification model, recall, precision, and accuracy of 78%, 80%, and 77% were reached using a fourfold cross-validation approach. The addition of other a-specific gas sensors, or of sensors specific to certain compounds, could improve the classification accuracy, therefore allowing for the development of a clinical tool to be integrated in the clinical pipeline for exhaled breath analysis and lung cancer early diagnosis.
Effect of Temperature on the Microwave-Absorbing Properties of an Al2O3–MoSi2 Coating Mixed with Copper
Authors: Gao Cheng , Jiang Yangsheng , Cai Dayong , Xu Jinyong , Xiao Weiyao
Published: August 2021

To obtain a high temperature-resistant microwave absorbing coating, an Al2O3–MoSi2/Cu composite coating was prepared by atmospheric plasma spraying. Compared with a normal temperature environment, there are a few reports on Cu as an absorbent for high temperature microwave absorbing coatings. Therefore, in this regard, wave absorbing property can be improved by using a Cu absorbent. The microstructure of a Al2O3–MoSi2/Cu coating was observed, and the dielectric properties of the composite coating in the high-temperature environment of the X-band were tested. The experimental results show that with the increase in temperature, Cu transforms Cu2O in the high-temperature environment and improves the coating’s wave absorption performance with MoSi2. In addition, a 1.4 mm-thick coating showed best microwave absorbing performance at 700 °C. The reflection loss was −19.09 dB, and the effective microwave absorbing bandwidth was 2.83 GHz (Reflection Loss < −10 dB). It was found that the Al2O3–MoSi2/Cu composite coating has good wave-absorbing performance in a 700 °C high-temperature environment.
A Comparative Analysis of Semi-Supervised Learning in Detecting Burst Header Packet Flooding Attack in Optical Burst Switching Network
Authors: Hossain Md. Kamrul , Haque Md. Mokammel , Dewan M. Ali Akber
Published: August 2021

This paper presents a comparative analysis of four semi-supervised machine learning (SSML) algorithms for detecting malicious nodes in an optical burst switching (OBS) network. The SSML approaches include a modified version of K-means clustering, a Gaussian mixture model (GMM), a classical self-training (ST) model, and a modified version of self-training (MST) model. All the four approaches work in semi-supervised fashion, while the MST uses an ensemble of classifiers for the final decision making. SSML approaches are particularly useful when a limited number of labeled data is available for training and validation of the classification model. Manual labeling of a large dataset is complex and time consuming. It is even worse for the OBS network data. SSML can be used to leverage the unlabeled data for making a better prediction than using a smaller set of labelled data. We evaluated the performance of four SSML approaches for two (Behaving, Not-behaving), three (Behaving, Not-behaving, and Potentially Not-behaving), and four (No-Block, Block, NB- wait and NB-No-Block) class classifications using precision, recall, and F1 score. In case of the two-class classification, the K-means and GMM-based approaches performed better than the others. In case of the three-class classification, the K-means and the classical ST approaches performed better than the others. In case of the four-class classification, the MST showed the best performance. Finally, the SSML approaches were compared with two supervised learning (SL) based approaches. The comparison results showed that the SSML based approaches outperform when a smaller sized labeled data is available to train the classification models.
The Ethanol Extract of Musa sapientum Linn. Peel Inhibits Melanogenesis through AKT Signaling Pathway
Authors: Phacharapiyangkul Naphichaya , Thirapanmethee Krit , Sa-ngiamsuntorn Khanit , Panich Uraiwan , Lee Che-Hsin , Chomnawang Mullika Traidej
Published: August 2021

Hyperpigmentation caused by melanin overproduction can be induced by UV radiation. The quest for effective depigmenting agents continues because many anti-melanin agents have restricted use and/or produce side-effects. The present study was aimed to investigate the inhibitory activity of Musa sapientum Linn. (AA group) peel ethanol extracts (MPE) on α-melanocyte stimulating hormone (α-MSH)-induced melanin production. In addition, the molecular mechanism related to this process was examined in B16F10 mouse melanoma cells. The results indicated that MPE remarkably inhibited melanogenesis in α-MSH-stimulated B16F10 cells. Microphthalmia-associated transcription factor (MITF) and tyrosinase expressions were suppressed by MPE in a concentration-dependent manner. In addition, MPE significantly decreased the expression of melanosome transfer protein markers (Rab27a and Pmel17) in a dose-dependent manner. This study found that the elevated phosphorylation of AKT in the B16F10 cells was diminished by MPE treatment. Furthermore, microtubule-associated protein 1 light chain 3 (LC3)-II and p62 (autophagy markers) were affected after the B16F10 cells were treated with MPE. This study demonstrated that MPE might be an effective agent for anti-melanogenesis through the AKT pathway, subsequently diminishing MITF expression and tyrosinase enzyme family production. The findings indicated that MPE could potentially serve as a depigmenting agent in cosmeceuticals.
Carcinoembryonic Antigen Increase in a Patient with Colon Cancer Who Have Achieved Complete Remission and Negative 18F-FDG PET/CT: Don’t Forget the Thyroid!
Authors: Lugat Alexandre , Hulo Pauline , Ansquer Catherine , Touchefeu Yann , Mirallié Eric , Bennouna Jaafar , Drui Delphine
Published: August 2021

Serum carcinoembryonic antigen (CEA) is a tumor marker especially used to follow a patient with colorectal cancer. However, it is non-specific and could be increased in several cancers and some benign conditions. We report the case of a 70-year-old man followed since 2014 for a left colon adenocarcinoma with the persistence of an increased CEA. There was no evidence of recurrence, but a right lobar thyroid nodule without a significantly increased uptake was incidentally discovered on the CT scan of 18F-fluorodeoxyglucose (18F-FDG) PET/CT. We suspected a medullary thyroid carcinoma (MTC) explaining the persistent elevation of CEA. Plasma calcitonin levels were 47 ng/L (N < 10). Fine needle aspiration cytology found atypia of undetermined significance and the patient was reluctant to undergo surgery without any further exploration. We performed a 18F-fluorodihydroxyphenylalanine (18F-FDOPA) PET/CT preoperatively which revealed a punctiform focus of the right thyroid lobe corresponding to a pT1aN1aMxR0 medullary thyroid carcinoma, histopathologically confirmed. This case highlights that despite the potential usefulness of 18F-FDG PET/CT in case of an unknown source of elevated CEA this imaging may be falsely negative as in the case of MTC and should lead to further explorations.
Biodiversity of Russian Local Sheep Breeds Based on Pattern of Runs of Homozygosity
Authors: Deniskova Tatiana , Dotsev Arsen , Selionova Marina , Brem Gottfried , Zinovieva Natalia
Published: August 2021

Russian sheep breeds traditionally raised in specific environments are valuable parts of sociocultural heritage and economic component of the regions. However, the import of commercial breeds negatively influences the population sizes of local sheep populations and might lead to biodiversity loss. Estimation of the runs of homozygosity (ROH) in local sheep genomes is an informative tool to address their current genetic state. In this work, we aimed to address the ROH distribution and to estimate genome inbreeding based on SNP data to evaluate genetic diversity in Russian local sheep breeds. Materials for this study included SNP-genotypes from twenty-seven Russian local sheep breeds which were generated using the Illumina OvineSNP50 BeadChip (n = 391) or the Illumina Ovine Infinium HD BeadChip (n = 315). A consecutive runs method was used to calculate ROH which were estimated for each animal and then categorized in the ROH length classes. The ROH were found in all breeds. The mean ROH length varied from 86 to 280 Mb, while the ROH number ranged from 37 to 123. The genomic inbreeding coefficient varied from 0.033 to 0.106. Our findings provide evidence of low to moderate genomic inbreeding in major local sheep populations.
Bacterial Diversity and Community in Regional Water Microbiota between Different Towns in World’s Longevity Township Jiaoling, China
Authors: Wu Lei , Xie Xinqiang , Zhang Jumei , Ding Yu , Wu Qingping
Published: August 2021

Healthy longevity is associated with many factors, however, the potential correlation between longevity and microbiota remains elusive. To address this, we explored environmental microbiota from one of the world’s longevity townships in China. We used 16S rRNA gene high-throughput sequencing to analyze the composition and function of water microbiota. The composition and diversity of water microbiota significantly differed between the towns. Lactobacillus, Streptococcus, Bacteroides, Faecalibacterium, and Stenotrophomonas were only dominant in Xinpu, a town with an exceptionally high centenarian population. Several biomarkers were identified, including Flavobacterium, Acinetobacter, Paracoccus, Lactobacillales, Psychrobacter, Bacteroides, Ruminococcaceae, and Faecalibacterium, and these shown to be responsible for the significant differences between towns. The main species contributing to the differences between towns were Cyanobacteria, Cupriavidus and Ralstonia. Based on KEGG pathways showed that the predicted metabolic characteristics of the water microbiota in Xinpu towns were significantly different to those of the other towns. The results revealed significant differences in the composition and diversity of water microbiota in the longevity township. These findings provide a foundation for further research on the role of water microbiota in healthy longevity.
VISEMURE: A Visual Analytics System for Making Sense of Multimorbidity Using Electronic Medical Record Data
Authors: Nouri Maede , Lizotte Daniel , Sedig Kamran , Abdullah Sheikh
Published: August 2021

Multimorbidity is a growing healthcare problem, especially for aging populations. Traditional single disease-centric approaches are not suitable for multimorbidity, and a holistic framework is required for health research and for enhancing patient care. Patterns of multimorbidity within populations are complex and difficult to communicate with static visualization techniques such as tables and charts. We designed a visual analytics system called VISEMURE that facilitates making sense of data collected from patients with multimorbidity. With VISEMURE, users can interactively create different subsets of electronic medical record data to investigate multimorbidity within different subsets of patients with pre-existing chronic diseases. It also allows the creation of groups of patients based on age, gender, and socioeconomic status for investigation. VISEMURE can use a range of statistical and machine learning techniques and can integrate them seamlessly to compute prevalence and correlation estimates for selected diseases. It presents results using interactive visualizations to help healthcare researchers in making sense of multimorbidity. Using a case study, we demonstrate how VISEMURE can be used to explore the high-dimensional joint distribution of random variables that describes the multimorbidity present in a patient population.
Combining Deep Learning and Graph-Theoretic Brain Features to Detect Posttraumatic Stress Disorder at the Individual Level
Authors: Zhu Ziyu , Lei Du , Qin Kun , Suo Xueling , Li Wenbin , Li Lingjiang , DelBello Melissa P. , Sweeney John A. , Gong Qiyong
Published: August 2021

Previous studies using resting-state functional MRI (rs-fMRI) have revealed alterations in graphical metrics in groups of individuals with posttraumatic stress disorder (PTSD). To explore the ability of graph measures to diagnose PTSD and capture its essential features in individual patients, we used a deep learning (DL) model based on a graph-theoretic approach to discriminate PTSD from trauma-exposed non-PTSD at the individual level and to identify its most discriminant features. Our study was performed on rs-fMRI data from 91 individuals with PTSD and 126 trauma-exposed non-PTSD patients. To evaluate our DL method, we used the traditional support vector machine (SVM) classifier as a reference. Our results showed that the proposed DL model allowed single-subject discrimination of PTSD and trauma-exposed non-PTSD individuals with higher accuracy (average: 80%) than the traditional SVM (average: 57.7%). The top 10 DL features were identified within the default mode, central executive, and salience networks; the first two of these networks were also identified in the SVM classification. We also found that nodal efficiency in the left fusiform gyrus was negatively correlated with the Clinician Administered PTSD Scale score. These findings demonstrate that DL based on graphical features is a promising method for assisting in the diagnosis of PTSD.
The Investigation of Associations between TP53 rs1042522, BBC3 rs2032809, CCND1 rs9344, EGFR rs2227983 Polymorphisms and Breast Cancer Phenotype and Prognosis
Authors: Bekampytė Justina , Bartnykaitė Agnė , Savukaitytė Aistė , Ugenskienė Rasa , Korobeinikova Erika , Gudaitienė Jurgita , Juozaitytė Elona
Published: August 2021

Breast cancer is one of the most common oncological diseases among women worldwide. Cell cycle and apoptosis—related genes TP53, BBC3, CCND1 and EGFR play an important role in the pathogenesis of breast cancer. However, the roles of single nucleotide polymorphisms (SNPs) in these genes have not been fully defined. Therefore, this study aimed to analyze the association between TP53 rs1042522, BBC3 rs2032809, CCND1 rs9344 and EGFR rs2227983 polymorphisms and breast cancer phenotype and prognosis. For the purpose of the analysis, 171 Lithuanian women were enrolled. Genomic DNA was extracted from peripheral blood; PCR-RFLP was used for SNPs analysis. The results showed that BBC3 rs2032809 was associated with age at the time of diagnosis, disease progression, metastasis and death. CCND1 rs9344 was associated with tumor size, however an association resulted in loss of significance after Bonferroni correction. In survival analysis, significant associations were observed between BBC3 rs2032809 and OS, PFS and MFS. EGFR rs2227983 also showed some associations with OS and PFS (univariate Cox regression analysis). However, the results were in loss of significance (multivariate Cox regression analysis). In conclusion, BBC3 rs2032809 polymorphism was associated with breast cancer phenotype and prognosis. Therefore, it could be applied as potential markers for breast cancer prognosis.
Digital Competence Assessment Methods in Higher Education: A Systematic Literature Review
Authors: Sillat Linda Helene , Tammets Kairit , Laanpere Mart
Published: August 2021

The rapid increase in recent years in the number of different digital competency frameworks, models, and strategies has prompted an increasing popularity for making the argument in favor of the need to evaluate and assess digital competence. To support the process of digital competence assessment, it is consequently necessary to understand the different approaches and methods. This paper carries out a systematic literature review and includes an analysis of the existing proposals and conceptions of digital competence assessment processes and methods in higher education, with the aim of better understanding the field of research. The review follows three objectives: (i) describe the characteristics of digital competence assessment processes and methods in higher education; (ii) provide an overview of current trends; and, finally, (iii) identify challenges and issues in digital competence assessment in higher education with a focus on the reliability and validity of the proposed methods. On the basis of the findings, and as a result of the COVID-19 pandemic, digital competence assessment in higher education requires more attention, with a specific focus on instrument validity and reliability. Furthermore, it will be of great importance to further investigate the use of assessment tools to support systematic digital competence assessment processes. The analysis includes possible opportunities and ideas for future lines of work in digital competence evaluation in higher education.
Higher Education Students’ Perceptions of Online Learning during COVID-19—A Comparative Study
Authors: Cranfield Desireé J. , Tick Andrea , Venter Isabella M. , Blignaut Renette J. , Renaud Karen
Published: August 2021

The pandemic and subsequent ‘lockdowns’ dramatically changed the educational landscape of higher education institutions. Before-COVID-19, traditional universities had choices in pedagogical practice, which included a variety of teaching delivery modes. Overnight, a single mode of delivery became the only option for traditional higher education institutions. All services migrated to digital platforms, leading to a period of “emergency eLearning”. The full impact of this sudden shift to digital platforms on all cohorts of students is still unclear. A measure of disruption to the normal student learning experience, especially for those attending traditional universities, was inevitable. Moreover, this disruption was varied depending on the University’s country and the country’s lockdown logistics. This international, comparative, quantitative research project investigated and explored higher education students’ perceptions of emergency eLearning during the COVID-19 pandemic. Experiences of students at universities in three countries were evaluated in terms of four dimensions: (1) home learning environment, (2) engagement, (3) participation preference, and (4) impact on learning skills. The research revealed significant differences between the participating universities students’ experiences. The most important differences were in the ‘home learning environment’, followed by ‘engagement’ and the perception of ‘impact on learning skills’. The differences in the ‘home learning environment’ can be attributed to the differing economic and digital development of the surveyed countries: South Africa, Wales, and Hungary. Finally, different cultural backgrounds suggest a noticeable difference in student engagement, participation, and learning skills.
Quality Requirements for Implementing Augmented Reality in Heritage Spaces: Teachers’ Perspective
Authors: Aso Borja , Navarro-Neri Iñaki , García-Ceballos Silvia , Rivero Pilar
Published: August 2021

As a consequence of the scarcity of studies of augmented reality (AR) in Spain, this study developed a questionnaire to evaluate teachers’ perceptions on the use of AR for heritage teaching (n = 347 teachers: n = 131 in-service/n = 216 in-training). The objectives were to: (1) identify teachers’ existing knowledge about AR; (2) evaluate educational strategies teachers value most in AR apps for teaching; (3) evaluate necessary AR functions; (4) determine desired technical and functional characteristics; and (5) compare any significant differences between the two groups. The results provide a contribution to the increasing implementation of AR apps in heritage education, which promotes the understanding, enjoyment, experience and knowledge of heritage. Heritage education is increasingly present in Spanish classrooms due to awareness of the country’s rich heritage, and AR is a good tool for understanding and linking society with its heritage. From the results, it is clear that, despite their desire for implementation, there is a lack of teacher training in applying AR; both subsamples pointed out the importance of humanising the explanations in AR apps with experts or actors who mediate heritage and value the ease of use of these apps, multifunctionality, low battery consumption, and use in the background. Significant differences suggest potentially greater educational commitment among active teachers who seek deep and meaningful learning, above the superficiality and enjoyment reported by teachers in training and, on the contrary, the value placed on technical and visual aspects, related to the high use of mobile devices.
Experimental Investigation into the Optimum Position of a Ring Reflector for an Axial Virtual Cathode Oscillator
Authors: Kim Se-Hoon , Lee Chang-Jin , Kim Wan-Il , Ko Kwang-Cheol
Published: August 2021

A ring reflector was experimentally investigated using an axial virtual cathode oscillator (vircator). The ring reflector was installed behind the mesh anode of the axial vircator to enhance the microwave power output by forming a resonant cavity and increasing the electron beam to microwave energy conversion efficiency. The optimum position of the ring reflector is analyzed through simulations and experiments by varying the anode to reflector distance from 6 mm to 24 mm in 3 mm steps. PIC simulations show that the ring reflector enhances the microwave power of the axial vircator up to 220%. Experiments show that the microwave power from the axial vircator without the ring reflector is 11.22 MW. The maximum average peak microwave power of the axial vircator with the ring reflector is 25.82 MW when the anode to ring reflector distance is 18 mm. From the simulations and experiments, it can be seen that the ring reflector yields decaying enhancement that is inversely proportional to the anode to ring reflector distance and there is no noticeable microwave enhancement after 24 mm. The frequency range attained from the simulations and experiments is 5.8 to 6.7 GHz and 5.16 to 5.8 GHz, respectively. The difference between the simulation and experimental results is due to the error in the anode to cathode gap distance. Although the frequency is slightly changed, the ring reflector seems to have no influence on the frequency of the generated microwave.
Progressive Convolutional Neural Network for Incremental Learning
Authors: Siddiqui Zahid Ali , Park Unsang
Published: August 2021

In this paper, we present a novel incremental learning technique to solve the catastrophic forgetting problem observed in the CNN architectures. We used a progressive deep neural network to incrementally learn new classes while keeping the performance of the network unchanged on old classes. The incremental training requires us to train the network only for new classes and fine-tune the final fully connected layer, without needing to train the entire network again, which significantly reduces the training time. We evaluate the proposed architecture extensively on image classification task using Fashion MNIST, CIFAR-100 and ImageNet-1000 datasets. Experimental results show that the proposed network architecture not only alleviates catastrophic forgetting but can also leverages prior knowledge via lateral connections to previously learned classes and their features. In addition, the proposed scheme is easily scalable and does not require structural changes on the network trained on the old task, which are highly required properties in embedded systems.
A Survey on Vulnerabilities and Countermeasures in the Communications of the Smart Grid
Authors: Lázaro Jesús , Astarloa Armando , Rodríguez Mikel , Bidarte Unai , Jiménez Jaime
Published: August 2021

Since the 1990s, the digitalization process has transformed the communication infrastructure within the electrical grid: proprietary infrastructures and protocols have been replaced by the IEC 61850 approach, which realizes interoperability among vendors. Furthermore, the latest networking solutions merge operational technologies (OTs) and informational technology (IT) traffics in the same media, such as time-sensitive networking (TSN)—standard, interoperable, deterministic, and Ethernet-based. It merges OT and IT worlds by defining three basic traffic types: scheduled, best-effort, and reserved traffic. However, TSN demands security against potential new cyberattacks, primarily, to protect real-time critical messages. Consequently, security in the smart grid has turned into a hot topic under regulation, standardization, and business. This survey collects vulnerabilities of the communication in the smart grid and reveals security mechanisms introduced by international electrotechnical commission (IEC) 62351-6 and how to apply them to time-sensitive networking.
Filter Extracted Sliding Mode Approach for DC Microgrids
Authors: Yasin Abdul Rehman , Yasin Amina , Riaz Mudassar , Ehab Muhammad , Raza Ali
Published: August 2021

The advantages offered by DC microgrids, such as elimination of skin effect losses, no requirement of frequency synchronization and high efficiency for power transmission are the major reasons that microgrids have attracted the attention of researchers in the last decade. Moreover, the DC friendly nature of renewable energy resources makes them a perfect choice for integration with DC microgrids, resulting in increased reliability and improved stability. However, in order to integrate renewable energy resources with the DC microgrids, challenges like equal load sharing and voltage regulation of the busbar under diverse varying load conditions are to be addressed. Conventionally, droop control with PI compensation is used to serve this purpose. However, this cascaded scheme results in poor regulation to large load variations and steady state errors. To address this issue, this paper presents a sliding mode control-based approach. Key features of SMC are its ease of implementation, robustness to load variations, and fast dynamic response. The system model is derived and simulated to analyze the stability and performance of the proposed controller. An experimental test bench is developed to demonstrate the effectiveness of SMC against modeled dynamics and is compared with the droop controller. The results show an improvement of 26% and 27.4% in the rise time and settling time, respectively. Robustness of the proposed scheme is also tested by switching it with a step load and an improvement of 40% has been observed.
Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting
Authors: Lateko Andi A. H. , Yang Hong-Tzer , Huang Chao-Ming , Aprillia Happy , Hsu Che-Yuan , Zhong Jie-Lun , Phương Nguyễn H.
Published: August 2021

Photovoltaic (PV) power forecasting urges in economic and secure operations of power systems. To avoid an inaccurate individual forecasting model, we propose an approach for a one-day to three-day ahead PV power hourly forecasting based on the stacking ensemble model with a recurrent neural network (RNN) as a meta-learner. The proposed approach is built by using real weather data and forecasted weather data in the training and testing stages, respectively. To accommodate uncertain weather, a daily clustering method based on statistical features, e.g., daily average, maximum, and standard deviation of PV power is applied in the data sets. Historical PV power output and weather data are used to train and test the model. The single learner considered in this research are artificial neural network, deep neural network, support vector regressions, long short-term memory, and convolutional neural network. Then, RNN is used to combine the forecasting results of each single learner. It is also important to observe the best combination of the single learners in this paper. Furthermore, to compare the performance of the proposed method, a random forest ensemble instead of RNN is used as a benchmark for comparison. Mean relative error (MRE) and mean absolute error (MAE) are used as criteria to validate the accuracy of different forecasting models. The MRE of the proposed RNN ensemble learner model is 4.29%, which has significant improvements by about 7–40%, 7–30%, and 8% compared to the single models, the combinations of fewer single learners, and the benchmark method, respectively. The results show that the proposed method is promising for use in real PV power forecasting systems.