bluuebunny/crossref_metadata_2025 huggingface dataset:
COL7A1 Expression Improves Prognosis Prediction for Patients with Clear Cell Renal Cell Carcinoma Atop of Stage
Authors: Koca Dzenis , Séraudie Irinka , Jardillier Rémy , Cochet Claude , Filhol Odile , Guyon Laurent
Published: May 2023

Clear-cell renal cell carcinoma (ccRCC) accounts for 75% of kidney cancers. Due to the high recurrence rate and treatment options that come with high costs and potential side effects, a correct prognosis of patient survival is essential for the successful and effective treatment of patients. Novel biomarkers could play an important role in the assessment of the overall survival of patients. COL7A1 encodes for collagen type VII, a constituent of the basal membrane. COL7A1 is associated with survival in many cancers; however, the prognostic value of COL7A1 expression as a standalone biomarker in ccRCC has not been investigated. With five publicly available independent cohorts, we used Kaplan–Meier curves and the Cox proportional hazards model to investigate the prognostic value of COL7A1, as well as gene set enrichment analysis to investigate genes co-expressed with COL7A1. COL7A1 expression stratifies patients in terms of aggressiveness, where the 5-year survival probability of each of the four groups was 72.4%, 59.1%, 34.15%, and 8.6% in order of increasing expression. Additionally, COL7A1 expression was successfully used to further divide patients of each stage and histological grade into groups of high and low risk. Similar results were obtained in independent cohorts. In vitro knockdown of COL7A1 expression significantly affected ccRCC cells’ ability to migrate, leading to the hypothesis that COL7A1 may have a role in cancer aggressiveness. To conclude, we identified COL7A1 as a new prognosis marker that can stratify ccRCC patients.
The Effects of Peptide Receptor Radionuclide Therapy on the Neoplastic and Normal Pituitary
Authors: Marques Pedro
Published: May 2023

Pituitary neuroendocrine tumours (PitNETs) are usually benign and slow-growing; however, in some cases, they may behave aggressively and become resistant to conventional treatments. Therapeutic options for aggressive or metastatic PitNETs are limited, and currently mainly consist of temozolomide, with little experience of other emerging approaches, including peptide receptor radionuclide therapy (PRRT). Somatostatin receptor expression in PitNETs explains the effectiveness of somatostatin analogues for treating PitNETs, particularly those hypersecreting pituitary hormones, such as growth hormone or adrenocorticotropic hormone. The expression of such receptors in pituitary tumour cells has provided the rationale for using PRRT to treat patients with aggressive or metastatic PitNETs. However, the PRRT efficacy in this setting remains unestablished, as knowledge on this today is based only on few case reports and small series of cases, which are reviewed here. A total of 30 PRRT-treated patients have been thus far reported: 23 aggressive PitNETs, 5 carcinomas, and 2 of malignancy status unspecified. Of the 27 published cases with information regarding the response to PRRT, 5 (18%) showed a partial response, 8 (30%) had stable disease, and 14 (52%) had progressive disease. No major adverse effects have been reported, and there is also no increased risk of clinically relevant hypopituitarism in patients with pituitary or non-pituitary neuroendocrine tumours following PRRT. PRRT may be regarded as a safe option for patients with aggressive or metastatic PitNETs if other treatment approaches are not feasible or have failed in controlling the disease progression, with tumour shrinkage occurring in up to a fifth of cases, while about a third of aggressive pituitary tumours may achieve stable disease. Here, the data on PRRT in the management of patients with aggressive pituitary tumours are reviewed, as well as the effects of PRRT on the pituitary function in other PRRT-treated cancer patients.
TIGIT Expression on Activated NK Cells Correlates with Greater Anti-Tumor Activity but Promotes Functional Decline upon Lung Cancer Exposure: Implications for Adoptive Cell Therapy and TIGIT-Targeted Therapies
Authors: Hasan Md Faqrul , Croom-Perez Tayler J. , Oyer Jeremiah L. , Dieffenthaller Thomas A. , Robles-Carrillo Liza D. , Eloriaga Jonathan E. , Kumar Sanjana , Andersen Brendan W. , Copik Alicja J.
Published: May 2023

Treatments targeting TIGIT have gained a lot of attention due to strong preclinical and early clinical results, particularly with anti-PD-(L)1 therapeutics. However, this combination has failed to meet progression-free survival endpoints in phase III trials. Most of our understanding of TIGIT comes from studies of T cell function. Yet, this inhibitory receptor is often upregulated to the same, or higher, extent on NK cells in cancers. Studies in murine models have demonstrated that TIGIT inhibits NK cells and promotes exhaustion, with its effects on tumor control also being dependent on NK cells. However, there are limited studies assessing the role of TIGIT on the function of human NK cells (hNK), particularly in lung cancer. Most studies used NK cell lines or tested TIGIT blockade to reactivate exhausted cells obtained from cancer patients. For therapeutic advancement, a better understanding of TIGIT in the context of activated hNK cells is crucial, which is different than exhausted NK cells, and critical in the context of adoptive NK cell therapeutics that may be combined with TIGIT blockade. In this study, the effect of TIGIT blockade on the anti-tumor activities of human ex vivo-expanded NK cells was evaluated in vitro in the context of lung cancer. TIGIT expression was higher on activated and/or expanded NK cells compared to resting NK cells. More TIGIT+ NK cells expressed major activating receptors and exerted anti-tumor response as compared to TIGIT− cells, indicating that NK cells with greater anti-tumor function express more TIGIT. However, long-term TIGIT engagement upon exposure to PVR+ tumors downregulated the cytotoxic function of expanded NK cells while the inclusion of TIGIT blockade increased cytotoxicity, restored the effector functions against PVR-positive targets, and upregulated immune inflammation-related gene sets. These combined results indicate that TIGIT blockade can preserve the activation state of NK cells during exposure to PVR+ tumors. These results support the notion that a functional NK cell compartment is critical for anti-tumor response and anti-TIGIT/adoptive NK cell combinations have the potential to improve outcomes.
The Black Hole: CAR T Cell Therapy in AML
Authors: Atilla Erden , Benabdellah Karim
Published: May 2023

Despite exhaustive studies, researchers have made little progress in the field of adoptive cellular therapies for relapsed/refractory acute myeloid leukemia (AML), unlike the notable uptake for B cell malignancies. Various single antigen-targeting chimeric antigen receptor (CAR) T cell Phase I trials have been established worldwide and have recruited approximately 100 patients. The high heterogeneity at the genetic and molecular levels within and between AML patients resembles a black hole: a great gravitational field that sucks in everything. One must consider the fact that only around 30% of patients show a response; there are, however, consequential off-tumor effects. It is obvious that a new point of view is needed to achieve more promising results. This review first introduces the unique therapeutic challenges of not only CAR T cells but also other adoptive cellular therapies in AML. Next, recent single-cell sequencing data for AML to assess somatically acquired alterations at the DNA, epigenetic, RNA, and protein levels are discussed to give a perspective on cellular heterogeneity, intercellular hierarchies, and the cellular ecosystem. Finally, promising novel strategies are summarized, including more sophisticated next-generation CAR T, TCR-T, and CAR NK therapies; the approaches with which to tailor the microenvironment and target neoantigens; and allogeneic approaches.
Controllability and Robustness of Functional and Structural Connectomic Networks in Glioma Patients
Authors: Meyer-Baese Anke , Jütten Kerstin , Meyer-Baese Uwe , Amani Ali Moradi , Malberg Hagen , Stadlbauer Andreas , Kinfe Thomas , Na Chuh-Hyoun
Published: May 2023

Previous studies suggest that the topological properties of structural and functional neural networks in glioma patients are altered beyond the tumor location. These alterations are due to the dynamic interactions with large-scale neural circuits. Understanding and describing these interactions may be an important step towards deciphering glioma disease evolution. In this study, we analyze structural and functional brain networks in terms of determining the correlation between network robustness and topological features regarding the default-mode network (DMN), comparing prognostically differing patient groups to healthy controls. We determine the driver nodes of these networks, which are receptive to outside signals, and the critical nodes as the most important elements for controllability since their removal will dramatically affect network controllability. Our results suggest that network controllability and robustness of the DMN is decreased in glioma patients. We found losses of driver and critical nodes in patients, especially in the prognostically less favorable IDH wildtype (IDHwt) patients, which might reflect lesion-induced network disintegration. On the other hand, topological shifts of driver and critical nodes, and even increases in the number of critical nodes, were observed mainly in IDH mutated (IDHmut) patients, which might relate to varying degrees of network plasticity accompanying the chronic disease course in some of the patients, depending on tumor growth dynamics. We hereby implement a novel approach for further exploring disease evolution in brain cancer under the aspects of neural network controllability and robustness in glioma patients.
Cell Proliferation Indices in Regenerating Alitta virens (Annelida, Errantia)
Authors: Shalaeva Alexandra Y. , Kozin Vitaly V.
Published: May 2023

In recent years, interest in the possible molecular regulators of cell proliferation and differentiation in a wide range of regeneration models has grown significantly, but the cell kinetics of this process remain largely a mystery. Here we try to elucidate the cellular aspects of regeneration by EdU incorporation in intact and posteriorly amputated annelid Alitta virens using quantitative analysis. We found that the main mechanism of blastema formation in A. virens is local dedifferentiation; mitotically active cells of intact segments do not significantly contribute to the blastemal cellular sources. Amputation-induced proliferation occurred predominantly within the epidermal and intestinal epithelium, as well as wound-adjacent muscle fibers, where clusters of cells at the same stage of the cell cycle were found. The resulting regenerative bud had zones of high proliferative activity and consisted of a heterogeneous population of cells that differed in their anterior–posterior positions and in their cell cycle parameters. The data presented allowed for the quantification of cell proliferation in the context of annelid regeneration for the first time. Regenerative cells showed an unprecedentedly high cycle rate and an exceptionally large growth fraction, making this regeneration model especially valuable for studying coordinated cell cycle entry in vivo in response to injury.
Correction: Garza-Lopez et al. Protocols for Generating Surfaces and Measuring 3D Organelle Morphology Using Amira. Cells 2022, 11, 65
Authors: Garza-Lopez Edgar , Vue Zer , Katti Prasanna , Neikirk Kit , Biete Michelle , Lam Jacob , Beasley Heather K. , Marshall Andrea G. , Rodman Taylor A. , Christensen Trace A. , Salisbury Jeffrey L. , Vang Larry , Mungai Margaret , AshShareef Salma , Murray Sandra A. , Shao Jianqiang , Streeter Jennifer , Glancy Brian , Pereira Renata O. , Abel E. Dale , Hinton, Jr. Antentor
Published: May 2023

In the original publication [...]
Implication of the Transmembrane Domain in the Interleukin 10 Receptor Platform Oligomerisation
Authors: Kuntzel Thomas , Spenlé Caroline , Pham-Van Lucas , Birmpili Dafni , Riou Aurélien , Loeuillet Aurore , Charmarke-Askar Imane , Bagnard Dominique
Published: May 2023

Interleukin 10 (IL-10) exerts anti-inflammatory and immune regulatory roles through its fixation to the IL-10 receptor (IL-10R). The two subunits (IL-10Rα and IL-10Rβ) organise themselves to form a hetero-tetramer to induce the activation of the transcription factor STAT3. We analysed the activation patterns of the IL-10R, especially the contribution of the transmembrane (TM) domain of the IL-10Rα and IL-10Rβ subunits, as evidence accumulates that this short domain has tremendous implications in receptor oligomerisation and activation. We also addressed whether targeting the TM domain of IL-10R with peptides mimicking the TM sequences of the subunits translates into biological consequences. The results illustrate the involvement of the TM domains from both subunits in receptor activation and feature a distinctive amino acid crucial for the interaction. The TM peptide targeting approach also appears to be suitable for modulating the activation of the receptor through its action on the dimerization capabilities of the TM domains and thereby constitutes a potential new strategy for the modulation of the inflammation in pathologic contexts.
Genome Editing and Cardiac Arrhythmias
Authors: Moore Oliver M. , Ho Kevin S. , Copeland Juwan S. , Parthasarathy Vaidya , Wehrens Xander H. T.
Published: May 2023

This article reviews progress in the field of cardiac genome editing, in particular, its potential utility in treating cardiac arrhythmias. First, we discuss genome editing methods by which DNA can be disrupted, inserted, deleted, or corrected in cardiomyocytes. Second, we provide an overview of in vivo genome editing in preclinical models of heritable and acquired arrhythmias. Third, we discuss recent advancements in cardiac gene transfer, including delivery methods, gene expression optimization, and potential adverse effects associated with therapeutic somatic genome editing. While genome editing for cardiac arrhythmias is still in its infancy, this approach holds great promise, especially for inherited arrhythmia syndromes with a defined genetic defect.
Severe Juvenile-Onset Systemic Lupus Erythematosus: A Case Series-Based Review and Update
Authors: Huerta-Calpe Sergi , Del Castillo-Velilla Ignacio , Felipe-Villalobos Aida , Jordan Iolanda , Hernández-Platero Lluïsa
Published: May 2023

Juvenile-onset systemic lupus erythematosus (jSLE) is a multisystemic disease diagnosed in young patients based on the clinical criteria of the European League Against Rheumatism (EULAR) and the American College of Rheumatology (ACR). The importance of this condition lies in its greater aggressiveness compared with lupus diagnosed during adulthood (aSLE). Management, which is based on supportive care and immunosuppressive drugs, aims to reduce the overall disease activity and to prevent exacerbation. Sometimes the onset is accompanied by life-threatening clinical conditions. In this paper, we introduce three recent cases of jSLE that required admission to the Pediatric Intensive Care Unit (PICU) of a Spanish pediatric hospital. This manuscript aims to review some of the main complications associated with jSLE, such as diffuse alveolar hemorrhage, cerebral vasculitis, or an antiphospholipid syndrome; these are life-threatening conditions but they have a chance of favorable prognosis if treated early and aggressively.
Diabetes Classification Using Machine Learning Techniques
Authors: Phongying Methaporn , Hiriote Sasiprapa
Published: May 2023

Machine learning techniques play an increasingly prominent role in medical diagnosis. With the use of these techniques, patients’ data can be analyzed to find patterns or facts that are difficult to explain, making diagnoses more reliable and convenient. The purpose of this research was to compare the efficiency of diabetic classification models using four machine learning techniques: decision trees, random forests, support vector machines, and K-nearest neighbors. In addition, new diabetic classification models are proposed that incorporate hyperparameter tuning and the addition of some interaction terms into the models. These models were evaluated based on accuracy, precision, recall, and the F1-score. The results of this study show that the proposed models with interaction terms have better classification performance than those without interaction terms for all four machine learning techniques. Among the proposed models with interaction terms, random forest classifiers had the best performance, with 97.5% accuracy, 97.4% precision, 96.6% recall, and a 97% F1-score. The findings from this study can be further developed into a program that can effectively screen potential diabetes patients.
The Cosmeceutical Potential of the Yellow-Green Alga Trachydiscus minutus Aqueous Extract: Preparation of a Natural-Based Dermal Formula as a Proof of Concept
Authors: Georgakis Nikolaos D. , Ioannou Elisavet , Chatzikonstantinou Marianna , Merino Marián , Chronopoulou Evangelia G. , Mullor José Luis , Madesis Panagiotis , Labrou Nikolaos E.
Published: May 2023

In the present study, selected cosmeceutical properties of aqueous extracts from the microalgae strain Trachydiscus minutus were assessed and compared with those obtained using three widely used Chlorella strains (C. vulgaris, C. sorokiniana, and C. minutissima). Among all extracts, T. minutus extracts showed the highest total antioxidant capacity (TAC) and inhibitory potency towards elastase, suggesting potential activity in controlling skin aging. Furthermore, the cytotoxicity, anti-inflammatory activity and UVA protection of T. minutus extract were evaluated employing normal human dermal fibroblasts (NHDF) and human keratinocyte HaCaT cells. The results showed that the T. minutus extract was able to significantly inhibit the transcription of selected marker genes involved in inflammation [interleukin-6 (IL-6), interleukin-8 (IL-8) and tumor necrosis factor alpha (TNFα)]. In addition, treatment of NHDF and HaCaT cells with T. minutus extract ameliorate the UVA-induced cell damage by decreasing the accumulation of reactive oxygen species (ROS). Extracts from T. minutus were formulated into a skin care cream and an aqueous gel. Both formulas exhibited excellent compatibility and stability. Comprehensively, all these results suggest that T. minutus extract displays promising cosmeceutical properties by providing antioxidant, anti-aging, and anti-inflammatory activities, and therefore has potential for cosmeceutical use.
Endoscopic Ultrasound (EUS) Guided Elastography
Authors: Iglesias-Garcia Julio , de la Iglesia-Garcia Daniel , Lariño-Noia Jose , Dominguez-Muñoz Juan Enrique
Published: May 2023

Endoscopic ultrasound (EUS) is an essential technique for the management of several diseases. Over the years, new technologies have been developed to improve and overcome certain limitations related to EUS-guided tissue acquisition. Among these new methods, EUS-guided elastography, which is a real-time method for the evaluation of tissue stiffness, has arisen as one of the most widely recognized and available. At present, there are available two different systems to perform an elastographic evaluation: strain elastography and shear wave elastography. Strain elastography is based on the knowledge that certain diseases lead to a change in tissue hardness while shear wave elastography monitored shear-wave propagation and measures its velocity. EUS-guided elastography has shown in several studies high accuracy in differentiating benign from malignant lesions from many different locations, mostly in the pancreas and lymph nodes. Therefore, nowadays, there are well-established indications for this technology, mainly for supporting the management of pancreatic diseases (diagnosis of chronic pancreatitis and differential diagnosis of solid pancreatic tumors) and characterization of different diseases. However, there are more data on new potential indications for the near future. In this review, we will present the theoretical bases of this technology and we will discuss the scientific evidence to support its use.
A Deep Feature Fusion of Improved Suspected Keratoconus Detection with Deep Learning
Authors: Al-Timemy Ali H. , Alzubaidi Laith , Mosa Zahraa M. , Abdelmotaal Hazem , Ghaeb Nebras H. , Lavric Alexandru , Hazarbassanov Rossen M. , Takahashi Hidenori , Gu Yuantong , Yousefi Siamak
Published: May 2023

Detection of early clinical keratoconus (KCN) is a challenging task, even for expert clinicians. In this study, we propose a deep learning (DL) model to address this challenge. We first used Xception and InceptionResNetV2 DL architectures to extract features from three different corneal maps collected from 1371 eyes examined in an eye clinic in Egypt. We then fused features using Xception and InceptionResNetV2 to detect subclinical forms of KCN more accurately and robustly. We obtained an area under the receiver operating characteristic curves (AUC) of 0.99 and an accuracy range of 97–100% to distinguish normal eyes from eyes with subclinical and established KCN. We further validated the model based on an independent dataset with 213 eyes examined in Iraq and obtained AUCs of 0.91–0.92 and an accuracy range of 88–92%. The proposed model is a step toward improving the detection of clinical and subclinical forms of KCN.
Prediction of Cognitive Decline in Parkinson’s Disease Using Clinical and DAT SPECT Imaging Features, and Hybrid Machine Learning Systems
Authors: Hosseinzadeh Mahdi , Gorji Arman , Fathi Jouzdani Ali , Rezaeijo Seyed Masoud , Rahmim Arman , Salmanpour Mohammad R.
Published: May 2023

Background: We aimed to predict Montreal Cognitive Assessment (MoCA) scores in Parkinson’s disease patients at year 4 using handcrafted radiomics (RF), deep (DF), and clinical (CF) features at year 0 (baseline) applied to hybrid machine learning systems (HMLSs). Methods: 297 patients were selected from the Parkinson’s Progressive Marker Initiative (PPMI) database. The standardized SERA radiomics software and a 3D encoder were employed to extract RFs and DFs from single-photon emission computed tomography (DAT-SPECT) images, respectively. The patients with MoCA scores over 26 were indicated as normal; otherwise, scores under 26 were indicated as abnormal. Moreover, we applied different combinations of feature sets to HMLSs, including the Analysis of Variance (ANOVA) feature selection, which was linked with eight classifiers, including Multi-Layer Perceptron (MLP), K-Neighbors Classifier (KNN), Extra Trees Classifier (ETC), and others. We employed 80% of the patients to select the best model in a 5-fold cross-validation process, and the remaining 20% were employed for hold-out testing. Results: For the sole usage of RFs and DFs, ANOVA and MLP resulted in averaged accuracies of 59 ± 3% and 65 ± 4% for 5-fold cross-validation, respectively, with hold-out testing accuracies of 59 ± 1% and 56 ± 2%, respectively. For sole CFs, a higher performance of 77 ± 8% for 5-fold cross-validation and a hold-out testing performance of 82 + 2% were obtained from ANOVA and ETC. RF+DF obtained a performance of 64 ± 7%, with a hold-out testing performance of 59 ± 2% through ANOVA and XGBC. Usage of CF+RF, CF+DF, and RF+DF+CF enabled the highest averaged accuracies of 78 ± 7%, 78 ± 9%, and 76 ± 8% for 5-fold cross-validation, and hold-out testing accuracies of 81 ± 2%, 82 ± 2%, and 83 ± 4%, respectively. Conclusions: We demonstrated that CFs vitally contribute to predictive performance, and combining them with appropriate imaging features and HMLSs can result in the best prediction performance.
Physical Activity, Body Image, and Emotional Intelligence Differences in Adults with Overweight and Obesity
Authors: Gilyana Marilyn , Batrakoulis Alexios , Zisi Vasiliki
Published: May 2023

Physical activity (PA) and emotional intelligence (EI) are integral parts of human nature. Body image (BI) and body mass index (BMI) may be indications of the psycho-emotional and physical health of human beings. The purpose of this study was to investigate the relationship between PA and EI of Greek adults living with overweight and obesity, as well as to identify the BI and EI differences in this population. A cross-sectional study design was used, involving 216 participants (65% females) of whom 51.4% were young adults (20–40 years), 48.6% were middle-aged adults (41–60 years), while 51.4% of participants were living with overweight or obesity. According to the results, all indicators of PA had very low correlations with EI factors, while statistically significant correlations were observed only for PA at work and the total score of the International Physical Activity Questionnaire with the use of emotions (r = 0.16 and r = 0.17, respectively, p < 0.05). Women had significantly higher EI scores than men regarding the care and empathy factor, while the individuals with obesity had lower scores in the use of emotions factor. Regarding BI, young adults who were satisfied with their BI had better control of feelings than the middle-aged adult counterparts. In conclusion, BI satisfaction and EI may differ between individuals living with overweight and obesity in both genders. Younger individuals with obesity may compensate better for their BI and better control their emotions. On the other side, PA does not seem to have an important role in these associations.
Towards Understanding the Key Signature Pathways Associated from Differentially Expressed Gene Analysis in an Indian Prostate Cancer Cohort
Authors: Shukla Nidhi , Kour Bhumandeep , Sharma Devendra , Vijayvargiya Maneesh , Sadasukhi T. C. , Medicherla Krishna Mohan , Malik Babita , Bissa Bhawana , Vuree Sugunakar , Lohiya Nirmal Kumar , Suravajhala Prashanth
Published: May 2023

Prostate cancer (PCa) is one of the most prevalent cancers among men in India. Although studies on PCa have dealt with genetics, genomics, and the environmental influence in the causality of PCa, not many studies employing the Next Generation Sequencing (NGS) approaches of PCa have been carried out. In our previous study, we identified some causal genes and mutations specific to Indian PCa using Whole Exome Sequencing (WES). In the recent past, with the help of different cancer consortiums such as The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC), along with differentially expressed genes (DEGs), many cancer-associated novel non-coding RNAs have been identified as biomarkers. In this work, we attempt to identify differentially expressed genes (DEGs) including long non-coding RNAs (lncRNAs) associated with signature pathways from an Indian PCa cohort using the RNA-sequencing (RNA-seq) approach. From a cohort of 60, we screened six patients who underwent prostatectomy; we performed whole transcriptome shotgun sequencing (WTSS)/RNA-sequencing to decipher the DEGs. We further normalized the read counts using fragments per kilobase of transcript per million mapped reads (FPKM) and analyzed the DEGs using a cohort of downstream regulatory tools, viz., GeneMANIA, Stringdb, Cytoscape-Cytohubba, and cbioportal, to map the inherent signatures associated with PCa. By comparing the RNA-seq data obtained from the pairs of normal and PCa tissue samples using our benchmarked in-house cuffdiff pipeline, we observed some important genes specific to PCa, such as STEAP2, APP, PMEPA1, PABPC1, NFE2L2, and HN1L, and some other important genes known to be involved in different cancer pathways, such as COL6A1, DOK5, STX6, BCAS1, BACE1, BACE2, LMOD1, SNX9, CTNND1, etc. We also identified a few novel lncRNAs such as LINC01440, SOX2OT, ENSG00000232855, ENSG00000287903, and ENST00000647843.1 that need to be characterized further. In comparison with publicly available datasets, we have identified characteristic DEGs and novel lncRNAs implicated in signature PCa pathways in an Indian PCa cohort which perhaps have not been reported. This has set a precedent for us to validate candidates further experimentally, and we firmly believe this will pave a way toward the discovery of biomarkers and the development of novel therapies.
Analysis of Success Factors, Benefits, and Challenges of Issuing Green Bonds in Lithuania
Authors: Bužinskė Julija , Stankevičienė Jelena
Published: May 2023

The objective of this study is to establish understanding of the success factors in issuing green bonds including the corresponding benefits and challenges of their issuance in Lithuania. The research methodology is comprised of the three parts: (1) a literature review to examine the success factors, benefits, and challenges of issuing green bonds as identified by researchers in different countries; (2) use of the methodological potential of the analysed field to formulate an expert survey via the analytical hierarchy process method; (3) summary of the results of the survey and proposals for its further development. Findings suggest that the reputation, good credit rating, and the environmental, social, and governance score of the issuer are the key considerations in the success of green bonds issuance. On the benefits side, green bonds bear low investment risks with the ability to raise large investment amounts while providing quantifiable and measurable benefits. The challenges related to the issuance of green bonds include greenwashing, the questionable role of the green bond market in environmental protection, and insufficient financial and economic benefits of issuance. Practical implications of the study are based on the notion that findings can be applied as a reference point by potential issuers willing to issue green bonds, investors willing to invest in green bonds, and policy-makers willing to promote sustainable and green finance. An original aspect of this paper is its study focus on peculiarities of the issuance of green bonds in the region, in a smaller country which can serve as a reference point for considering potential issuance of green bonds.
Empirical Linkages between Branching, Lending, and Competition: A Study of Pakistani Banks
Authors: Ahmed Jaleel , Farooq Umar , Al-Naimi Ahmad A. , Tabash Mosab I. , Drachal Krzysztof
Published: May 2023

This study examines the relationship between branching, lending, and competition in Pakistani banks. Due to denationalization, Pakistani banks started to increase their branch networks and change loan and deposit policies. To check the effect of geographic diversification and distance on the performance of banks, the market power of loans and deposits, and the effect of large and medium banks on the performance of small banks, a sample of commercial banks is selected. The study finds that geographic diversification and distance between bank branches and headquarters do not affect the performance of the banks, but geographic diversification of banks in different areas affects the market power of loans and deposits. The results show that medium and large banks do not affect the performance of the small banks because small banks are better performing in the local market. Medium and large banks are affected by the market power of the loans and deposits of small banks. The study recommends an important policy regarding branch management and its effect on bank performance.
Chances of Early School Leaving—With Special Regard to the Impact of Roma Identity
Authors: Bocsi Veronika , Varga Aranka , Fehérvári Anikó
Published: May 2023

Early school leaving rates among Roma are higher than in the majority population, as confirmed by several studies, but the descriptions are often two-dimensional. Intersectionality is described as a persistent situation formed by several social dimensions, and this specific position can be advantageous or disadvantageous from the aspect of educational inequalities. This study aims to explore what type of intersectional position can raise the chance of early school leaving and what is the role of the Roma ethnic identity in this. Earlier empirical analyses are rather two-dimensional, and these intersectional situations cannot be identified with them. The Hungarian Youth Survey databases provided an opportunity to conduct this type of multiple approaches. With the help of our results, the intersectional position can be described in which the chance of early school leaving is higher. The elements of this situation are embedded in economic, educational, geographical and ethnic categories at the same time. The effect of Roma ethnic identity is significant but not the strongest in our model. With the help of a comparison of the two waves of the research project, we can state that the patterns of this intersectional and disadvantageous situation are stable but not unchanged.
Implicit Emotion Analysis Based on Improved Supervised Contrastive Learning
Authors: Kong Linghui , Yang Wenzhong , Wei Fuyuan
Published: May 2023

Implicit sentiment analysis (ISA) can be distinguished from traditional text sentiment analysis by the fact that it does not rely on emotional words as emotional clues, and the expression is usually vaguer. Identifying implicit emotions is more difficult, as it requires a deeper understanding of the context, even when emotional words are absent. Researchers have focused on context feature modeling and developing sophisticated feature extraction mechanisms instead of starting from the emotional perspective. Enhancing the difference in emotional features of text samples is an intuitive method to address this challenge. We proposed a supervised contrastive learning (SCL) method during training that enables the model to conduct contrastive learning based on emotion labels even while training on weak emotion features. SCL training can strengthen the average embedding distance between texts with different emotion labels and enhance implicit emotion discrimination. Moreover, research indicates that contextual information can improve implicit emotion classification ability. Therefore, we applied a straightforward context feature fusion method (bi-affine) over a more complicated context feature modeling approach. To evaluate the effectiveness of our proposed method, we conducted experiments on the SMP2019-ECISA (Chinese implicit sentiment analysis) dataset. The results show a 2.13% enhancement in the F1 value compared to the BERT baseline, proving the effectiveness of our methods.
Long-Term Power Load Forecasting Using LSTM-Informer with Ensemble Learning
Authors: Wang Kun , Zhang Junlong , Li Xiwang , Zhang Yaxin
Published: May 2023

Accurate power load forecasting can facilitate effective distribution of power and avoid wasting power so as to reduce costs. Power load is affected by many factors, so accurate forecasting is more difficult, and the current methods are mostly aimed at short-term power load forecasting problems. There is no good method for long-term power load forecasting problems. Aiming at this problem, this paper proposes an LSTM-Informer model based on ensemble learning to solve the long-term load forecasting problem. The bottom layer of the model uses the long short-term memory network (LSTM) model as a learner to capture the short-term time correlation of power load, and the top layer uses the Informer model to solve the long-term dependence problem of power load forecasting. In this way, the LSTM-Informer model can not only capture short-term time correlation but can also accurately predict long-term power load. In this paper, a one-year dataset of the distribution network in the city of Tetouan in northern Morocco was used for experiments, and the mean square error (MSE) and mean absolute error (MAE) were used as evaluation criteria. The long-term prediction of this model is 0.58 and 0.38 higher than that of the lstm model based on MSE and MAE. The experimental results show that the LSTM-Informer model based on ensemble learning has more advantages in long-term power load forecasting than the advanced baseline method.
Design of Enhanced Document HTML and the Reliable Electronic Document Distribution Service
Authors: Hwang Hyun-Cheon , Kim Woo-Je
Published: May 2023

Electronic documents are becoming increasingly popular in various industries and sectors as they provide greater convenience and cost-efficiency than physical documents. PDF is a widely used format for creating and sharing electronic documents, while HTML is commonly used in mobile environments as the foundation for creating web pages displayed on mobile devices, such as smartphones and tablets. HTML is becoming a more critical document format as mobile environments have been raised as the primary communication channel nowadays. However, HTML does not have the standard content integrity feature, and an electronic document based on HTML consists of a set of related files. Therefore, it has a vulnerability in terms of reliable electronic documents. We have proposed Document HTML, a single independent file with extended meta tags, to be a reliable electronic document and Chained Document, a single independent file with a blockchain network to secure content integrity and delivery assurance. In this paper, we improved the definition of Document HTML and researched certified electronic document intermediaries. Additionally, we designed and validated the electronic document distribution service using Enhanced Document HTML for real usability. Moreover, we conducted experimental verification using a tax notification electronic document, which has one of the top distribution volumes in Korea, to confirm how Document HTML provides a content integrity verification feature. Document HTML can be used in an enterprise that must send a reliable electronic document to a customer with an electronic document delivery service provider.
Research on SLAM and Path Planning Method of Inspection Robot in Complex Scenarios
Authors: Wang Xiaohui , Ma Xi , Li Zhaowei
Published: May 2023

Factory safety inspections are crucial for maintaining a secure production environment. Currently, inspections are predominantly performed manually on a regular basis, leading to low efficiency and a high workload. Utilizing inspection robots can significantly improve the reliability and efficiency of these tasks. The development of robot localization and path planning technologies ensures that factory inspection robots can autonomously complete their missions in complex environments. In response to the application requirements of factory inspections, this paper investigates mapping, localization, and path planning methods for robots. Considering the limitations of cameras and laser sensors due to their inherent characteristics, as well as their varying applicability in different environments, this paper proposes SLAM application systems based on multi-line laser radar and visual perception for diverse scenarios. To address the issue of low efficiency in inspection tasks, a hybrid path planning algorithm that combines the A-star algorithm and time elastic band method is introduced. This approach effectively resolves the problem of path planning becoming trapped in local optima in complex environments, subsequently enhancing the inspection efficiency of robots. Experimental results demonstrate that the designed SLAM and path planning methods can satisfy the inspection requirements of robots in complex scenarios, exhibiting excellent reliability and stability.
Single-Image Defogging Algorithm Based on Improved Cycle-Consistent Adversarial Network
Authors: Zhang Junkai , Sun Xiaoming , Chen Yan , Duan Yan , Wang Yongliang
Published: May 2023

With the wave of artificial intelligence and deep learning sweeping the world, there are many algorithms based on deep learning for image defog research. However, there is still serious color distortion, contrast reduction, incomplete fog removal, and other problems. To solve these problems, this paper proposes an improved image defogging network based on the traditional cycle-consistent adversarial network. We add the self-attention module and atrous convolution multi-scale feature fusion module on the basis of the traditional CycleGAN network to enhance the feature extraction capability of the network. The perceptual loss function is introduced into the loss function of the model to enhance the texture sense of the generated image. Finally, by comparing several typical defogging algorithms, the superiority of the defogging model proposed in this paper is proved qualitatively and quantitatively. Among them, on the indoor synthetic data set, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measurement (SSIM) of the network designed by us can reach 23.22 and 0.8809, respectively. On the outdoor synthetic data set, the PSNR and SSIM of our designed network can be as high as 25.72 and 0.8859, respectively. On the real data set, the PSNR and SSIM of our designed network can reach 21.02 and 0.8166, respectively. It is proved that the defogging network in this paper has good practicability and universality.
Blockchain-Based Secure Firmware Update Using an UAV
Authors: Seo Jong Wan , Islam Anik , Masuduzzaman Md , Shin Soo Young
Published: May 2023

This paper proposes a blockchain-based firmware update method using unmanned aerial vehicles (UAVs) to solve one of the security issues arising in the Internet of Things (IoT) environment, which is the firmware security problem. It has high scalability and transaction speed using private blockchains and solves the limitations of internet connections by updating the firmware using an UAV. The proposed firmware update system safely manages the IoT device and firmware information through four processes: participant registration, firmware registration/update, firmware update request, and firmware update. The verification of IoT devices and UAVs is performed using the IoT device’s public key and Bloom filter, and firmware updates can be safely performed using public-key encryption communication. To prove the security of the proposed method, a security analysis based on the STRIDE model was conducted, and the performance of the blockchain network was analyzed by simulation on the Hyperledger.
Stochastic Maximum Likelihood Direction Finding in the Presence of Nonuniform Noise Fields
Authors: Gong Ming-Yan , Lyu Bin
Published: May 2023

The maximum likelihood (ML) technique plays an important role in direction-of-arrival (DOA) estimation. In this paper, we employ and design the expectation–conditional maximization either (ECME) algorithm, a generalization of the expectation–maximization algorithm, for solving the ML direction finding problem of stochastic sources, which may be correlated, in unknown nonuniform noise. Unlike alternating maximization, the ECME algorithm updates both the source and noise covariance matrix estimates by explicit formulas, and can guarantee that both estimates are positive semi-definite and definite, respectively. Thus, the ECME algorithm is computationally efficient and operationally stable. Simulation results confirm that the ECME algorithm can efficiently obtain the ML based DOA estimate of each stochastic source.
Fine-Grained Image Retrieval via Object Localization
Authors: Wang Rong , Zou Wei , Wang Jiajun
Published: May 2023

In this paper, a network consisting of an object localization module and a discriminative feature extraction module is designed for fine-grained image retrieval (FGIR). In order to reduce the interference of complex backgrounds, the object localization module is introduced into the network before feature extraction. By selecting the convolutional feature descriptors, the main object is separated from the background, and thus, most of the interference is filtered out. Further, in order to improve the overall performance of the network, a discriminative filter bank is introduced into the network as the local feature detector. Hence, the local discriminative features can be extracted directly from the original feature map. The experimental results based on the CUB-200-2011 and Cars-196 datasets demonstrate that the proposed method can improve the performance of FGIR.
Effects of Gamma Irradiation on Switching Characteristics of SiC MOSFET Power Devices of Different Structures
Authors: Shu Lei , Liao Huai-Lin , Wu Zi-Yuan , Fang Xing-Yu , Liang Shi-Wei , Li Tong-De , Wang Liang , Wang Jun , Zhao Yuan-Fu
Published: May 2023

The switching characteristics of silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) power devices of different structures were experimented after exposure to a gamma irradiation environment. The experimental results for on-state were studied. The comparisons are shown for SiC MOSFET power devices with planar, trench and double trench structures tested for total ionizing dose (TID). A higher degradation of the switching characteristics was observed for the double trench structure. The physical mechanisms for these switching characteristics variations were analyzed. In addition, they were confirmed by technology computer-aided design (TCAD) simulation.
Application of Selenocysteine Increased Soil Nitrogen Content, Enzyme Activity, and Microbial Quantity in Camellia oleifera Abel. Forests
Authors: Li Jian , Tang Wei , Lu Sheng , Wang Ye , Kuang Zuoying , Yuan Jun
Published: May 2023

The effect mechanism of inorganic selenium on soil fertility has been effectively explained, but the effect of selenocysteine as organic selenium on the soil of Camellia oleifera Abel. forests has not been reported. In this study, the soil of a C. oleifera forests under natural conditions was taken as the control, and four treatments, namely selenocysteine (SeCys), cysteine + sodium selenite (Cys + Se), urea + sodium selenite (Ur + Se), and cysteine (Cys), were set up through a pot experiment to analyze the effects of different treatments on the physicochemical properties and biological characteristics of soil in C. oleifera forests. The results showed that SeCys significantly increased the soil total nitrogen content, nitrate nitrogen, and ammonium nitrogen contents compared with the treatment with inorganic selenium. In addition, the application of SeCys improved the activities of soil urease, soil acid phosphatase, soil nitrate reductase, and soil nitrite reductase on day 24 of culture, while under Cys + Se treatment, the activities of these four enzymes showed significant effects on day 32. The effect of SeCys on increasing the number of soil bacteria and fungi was significantly higher than that of other treatments and increased by 800% and 217%, respectively, compared with the control. SeCys also had significant effects on selenium and nitrogen content of Camellia oleifera seedlings. Correlation analysis showed that soil microbial biomass carbon and nitrogen were significantly correlated with soil enzyme activity, suggesting that SeCys could promote enzyme activity in C. oleifera forests by increasing the microbial number and improving microbial metabolism. The results indicated that SeCys could be used as an ingredient in new high-efficiency fertilizers.