bluuebunny/crossref_metadata_2025 huggingface dataset:
Body Composition as a Comorbidity-Independent Predictor of Survival following Nephroureterectomy for Urothelial Cancer of the Upper Urinary Tract
Authors: Pickl Christoph , Engelmann Simon , Girtner Florian , Gužvić Miodrag , van Rhijn Bas W. G. , Hartmann Valerie , Holbach Sonja , Kälble Sebastian , Haas Maximilian , Rosenhammer Bernd , Breyer Johannes , Burger Maximilian , Mayr Roman
Published: January 2023

Radical nephroureterectomy (NUE) is the gold standard treatment for high-risk urothelial cancer of the upper urinary tract (UTUC). Besides sarcopenia and frailty, fat distribution is moving increasingly into focus. Components of body composition were assessed in patients undergoing NUE due to UTUC. The study cohort included 142 patients. By using CT-based measurements, the skeletal muscle index (SMI), subcutaneous adipose tissue index (SATI), and visceral adipose tissue index (VATI) were measured at the height of the third lumbar vertebra. Overall survival (OS) and cancer-specific survival (CSS) were estimated using univariable und multivariable Cox regression models. The prevalence of sarcopenia in the study population (n = 142) was 37%. OS and CSS were significantly reduced in sarcopenic patients. In the multivariable cox regression analysis, including age, ACE-27, T-stage, R-stage, LVI and necrosis, sarcopenia remained a significant risk factor of OS (HR, 1.77; 95% CI 1.02–3.07; p = 0.042) and CSS (HR, 2.17; 95% CI 1.18–3.99; p = 0.012). High visceral adipose tissue seems to be protective, although not statistically significant. Sarcopenia is a comorbidity-independent risk factor in patients who underwent NUE due to UTUC. Visceral fat represents a potentially protective factor. These results suggest that specific factors of body composition can be used for better risk stratification.
Menopausal Status Contributes to Overall Survival in Endometrial Cancer Patients
Authors: Barczyński Bartłomiej , Frąszczak Karolina , Wnorowski Artur , Kotarski Jan
Published: January 2023

Endometrial cancer is the most common female genital tract malignancy in developed countries that occurs predominantly in postmenopausal women. The primary objective of our research was to investigate whether menopause status together with selected conventional prognostic indicators may contribute to overall (all-cause) survival in endometrial cancer patients. For this purpose, we applied the Cox proportional hazards regression model. Patients in advanced FIGO stage showed a relatively poor survival rate. The time since last menstruation and postoperative FSH concentration were identified as unfavorable prognostic factors in our model. Additionally, age at diagnosis, BMI value, adjuvant treatment (brachytherapy), and parity showed no impact on survival. To our knowledge, this is the first study to report a prognostic model for endometrial cancer including exact time from last menstruation as one of the prognostic variables. Due to the fact that there are no stratifying systems to reliably predict survival in patients with endometrial cancer, there is a strong need to revise and update existing models using complementary prognostic indicators. Collection of precise data on various risk factors may contribute to increased accuracy of artificial intelligence algorithms in order to personalize cancer care in the near future.
Identification and Characterization of Aptamers Targeting Ovarian Cancer Biomarker Human Epididymis Protein 4 for the Application in Urine
Authors: Hanžek Antonija , Ducongé Frédéric , Siatka Christian , Duc Anne-Cécile E.
Published: January 2023

Ovarian cancer is the deadliest gynecological cancer. With non-specific symptoms of the disease and the lack of effective diagnostic methods, late diagnosis remains the crucial hurdle of the poor prognosis. Therefore, development of novel diagnostic approaches are needed. The purpose of this study is to develop DNA-based aptamers as potential diagnostic probes to detect ovarian cancer biomarker Human epididymis protein 4 (HE4) in urine. HE4 is a protein overexpressed in ovarian cancer, but not in healthy or benign conditions. With high stability and diagnostic value for detection of ovarian cancer, urine HE4 appears as an attractive non-invasive biomarker. The high-affinity anti-HE4 DNA aptamers were selected through 10 cycles of High Fidelity Systematic Evolution of Ligands by EXponential enrichment (Hi-Fi SELEX), a method for aptamer selection based on digital droplet PCR. The anti-HE4 aptamers were identified using DNA sequencing and bioinformatics analysis. The candidate aptamer probes were characterized in urine for binding to HE4 protein using thermofluorimetry. Two anti-HE4 aptamers, AHE1 and AHE3, displayed binding to HE4 protein in urine, with a constant of dissociation in the nanomolar range, with Kd (AHE1) = 87 ± 9 nM and Kd (AHE3) aptamer of 127 ± 28 nM. Therefore, these aptamers could be promising tools for application in diagnostics and future development of urine tests or biosensors for ovarian cancer.
Tissue-Specific microRNA Expression Profiling to Derive Novel Biomarkers for the Diagnosis and Subtyping of Small B-Cell Lymphomas
Authors: Hue Susan Swee-Shan , Jin Yu , Cheng He , Bin Masroni Muhammad Sufyan , Tang Lloyd Wei Tat , Ho Yong Howe , Ong Diana Bee-Lan , Leong Sai Mun , Tan Soo Yong
Published: January 2023

Accurate diagnosis of the most common histological subtypes of small B-cell lymphomas is challenging due to overlapping morphological features and limitations of ancillary testing, which involves a large number of immunostains and molecular investigations. In addition, a common diagnostic challenge is to distinguish reactive lymphoid hyperplasia that do not require additional stains from such lymphomas that need ancillary investigations. We investigated if tissue-specific microRNA (miRNA) expression may provide potential biomarkers to improve the pathology diagnostic workflow. This study seeks to distinguish reactive lymphoid proliferation (RL) from small B-cell lymphomas, and to further distinguish the four main subtypes of small B-cell lymphomas. Two datasets were included: a discovery cohort (n = 100) to screen for differentially expressed miRNAs and a validation cohort (n = 282) to develop classification models. The models were evaluated for accuracy in subtype prediction. MiRNA gene set enrichment was also performed to identify differentially regulated pathways. 306 miRNAs were detected and quantified, resulting in 90-miRNA classification models from which smaller panels of miRNAs biomarkers with good accuracy were derived. Bioinformatic analysis revealed the upregulation of known and other potentially relevant signaling pathways in such lymphomas. In conclusion, this study suggests that miRNA expression profiling may serve as a promising tool to aid the diagnosis of common lymphoid lesions.
Retroperitoneal Lymph Node Dissection in Colorectal Cancer with Lymph Node Metastasis: A Systematic Review
Authors: Fadel Michael G. , Ahmed Mosab , Pellino Gianluca , Rasheed Shahnawaz , Tekkis Paris , Nicol David , Kontovounisios Christos , Mayer Erik
Published: January 2023

The benefits and prognosis of RPLND in CRC have not yet been fully established. This systematic review aimed to evaluate the outcomes for CRC patients with RPLNM undergoing RPLND. A literature search of MEDLINE, EMBASE, EMCare, and CINAHL identified studies from between January 1990 and June 2022 that reported data on clinical outcomes for patients who underwent RPLND for RPLNM in CRC. The following primary outcome measures were derived: postoperative morbidity, disease free-survival (DFS), overall survival (OS), and re-recurrence. Nineteen studies with a total of 541 patients were included. Three hundred and sixty-three patients (67.1%) had synchronous RPLNM and 178 patients (32.9%) had metachronous RPLNM. Perioperative chemotherapy was administered in 496 (91.7%) patients. The median DFS was 8.6–38.0 months and 5-year DFS was 24.4% (10.0–60.5%). The median OS was 25.0–83.0 months and 5-year OS was 47.0% (15.0–87.5%). RPLND is a feasible treatment option with limited morbidity and possible oncological benefit for both synchronous and metachronous RPLNM in CRC. Further prospective clinical trials are required to establish a better evidence base for RPLND in the context of RPLNM in CRC and to understand the timing of RPLND in a multimodality pathway in order to optimise treatment outcomes for this group of patients.
Targeting Interleukin-6/Glycoprotein-130 Signaling by Raloxifene or SC144 Enhances Paclitaxel Efficacy in Pancreatic Cancer
Authors: Hering Nina A. , Günzler Emily , Arndt Marco , Zibell Miriam , Lauscher Johannes C. , Kreis Martin E. , Beyer Katharina , Seeliger Hendrik , Pozios Ioannis
Published: January 2023

Interleukine-6 plays a key role in the progression and poor survival in pancreatic ductal adenocarcinoma (PDAC). The present study aimed to clarify if targeting the interleukin-6/glycoprotein-130 signaling cascade using the small-molecule gp130 inhibitor SC144 or raloxifene, a non-steroidal selective estrogen receptor modulator, enhances paclitaxel efficacy. MTT/BrdU assays or TUNEL staining were performed to investigate cell viability, proliferation and apoptosis induction in L3.6pl and AsPC-1 human pancreatic cell lines. In vivo, effects were studied in an orthotopic PDAC mouse model. Tumor specimens were analyzed by qPCR, immunohistochemistry and ELISA. Combination of paclitaxel/raloxifene, but not paclitaxel/SC144, enhanced proliferation and viability inhibition and increased apoptosis compared to single treatment in vitro. Synergy score calculations confirmed an additive influence of raloxifene on paclitaxel. In the PDAC mouse model, both combinations of raloxifene/paclitaxel and SC144/paclitaxel reduced tumor weight and volume compared to single-agent therapy or control. Raloxifene/paclitaxel treatment decreased survivin mRNA expression and showed tendencies of increased caspase-3 staining in primary tumors. SC144/paclitaxel reduced interleukin-6 levels in mice’s tumors and plasma. In conclusion, raloxifene or SC144 can enhance the anti-tumorigenic effects of paclitaxel, suggesting that paclitaxel doses might also be reduced in combined chemotherapy to lessen paclitaxel side effects.
CircZBTB46 Protects Acute Myeloid Leukemia Cells from Ferroptotic Cell Death by Upregulating SCD
Authors: Long Fei , Lin Zhi , Long Qinpeng , Lu Zhixing , Zhu Kaiyu , Zhao Mingyi , Yang Minghua
Published: January 2023

Circular RNAs (circRNAs) have been shown to be closely linked to the tumorigenesis and treatment response of hematological malignancies. However, the biological functions and clinical implications of circRNAs in acute myeloid leukemia (AML) remain largely unknown. CircRNA microarray datasets were analyzed to screen differentially expressed circRNAs in AML patients. It was found that circZBTB46 was significantly upregulated in AML patients and AML cells. Moreover, the expression of circZBTB46 was associated with the stages of AML patients and showed high sensitivity and specificity for diagnosing AML. Silencing of circZBTB46 inhibited AML cell proliferation and induced cell cycle arrest. Importantly, the depletion of circZBTB46 notably increased ferroptosis and enhanced RSL3-induced ferroptosis in AML cells. Mechanistically, circZBTB46 upregulated the expression of stearoyl-CoA desaturase 1 (SCD) possibly by acting as a miRNA sponge. Finally, the circZBTB46 knockdown repressed the tumor growth of AML in vivo. In conclusion, circZBTB46 protects AML cells from ferroptosis and promotes the proliferation by upregulating SCD, thus suggesting that circZBTB46 may be a potential therapeutic target for AML.
Rilotumumab Resistance Acquired by Intracrine Hepatocyte Growth Factor Signaling
Authors: Cecchi Fabiola , Rex Karen , Schmidt Joanna , Vocke Cathy D. , Lee Young H. , Burkett Sandra , Baker Daniel , Damore Michael A. , Coxon Angela , Burgess Teresa L. , Bottaro Donald P.
Published: January 2023

Drug resistance is a long-standing impediment to effective systemic cancer therapy and acquired drug resistance is a growing problem for molecularly-targeted therapeutics that otherwise have shown unprecedented successes in disease control. The hepatocyte growth factor (HGF)/Met receptor pathway signaling is frequently involved in cancer and has been a subject of targeted drug development for nearly 30 years. To anticipate and study specific resistance mechanisms associated with targeting this pathway, we engineered resistance to the HGF-neutralizing antibody rilotumumab in glioblastoma cells harboring autocrine HGF/Met signaling, a frequent abnormality of this brain cancer in humans. We found that rilotumumab resistance was acquired through an unusual mechanism comprising dramatic HGF overproduction and misfolding, endoplasmic reticulum (ER) stress-response signaling and redirected vesicular trafficking that effectively sequestered rilotumumab and misfolded HGF from native HGF and activated Met. Amplification of MET and HGF genes, with evidence of rapidly acquired intron-less, reverse-transcribed copies in DNA, was also observed. These changes enabled persistent Met pathway activation and improved cell survival under stress conditions. Point mutations in the HGF pathway or other complementary or downstream growth regulatory cascades that are frequently associated with targeted drug resistance in other prevalent cancer types were not observed. Although resistant cells were significantly more malignant, they retained sensitivity to Met kinase inhibition and acquired sensitivity to inhibition of ER stress signaling and cholesterol biosynthesis. Defining this mechanism reveals details of a rapidly acquired yet highly-orchestrated multisystem route of resistance to a selective molecularly-targeted agent and suggests strategies for early detection and effective intervention.
Protein-Carbonized Engineering to Construct Three-Dimensional Sponge-like Carbons for Oxygen Reduction Electrocatalysis
Authors: Luo Xinyi , Liu Yao , Liao Wenli , Li Zhongbin , Guo Chaozhong , Sun Wei , Luo Zhongli
Published: January 2023

The low specific surface area and insufficient exposure of active sites are usually the key reasons for the poor oxygen reduction reaction of catalysts. Here, we update a new method, using NaCl as a template, egg white as a carbon source and nitrogen source, adding FeCl3 as an iron source, and adopting a two-step pyrolysis method to synthesize a sponge-like porous Fe-N-C catalyst. This kind of three-dimensional sponge-like catalyst exhibits more defective structures, so it shows an excellent electrochemical performance with a half-wave potential of 0.73 V and onset potential of 0.88 V. Additionally, the catalyst has amazing stability, which proves that it is a promising candidate for green energy devices. Our research provides an innovative method to synthesize high-performance Fe-N-C catalysts using low-cost common biomaterials.
Sensing and Detection Capabilities of One-Dimensional Defective Photonic Crystal Suitable for Malaria Infection Diagnosis from Preliminary to Advanced Stage: Theoretical Study
Authors: Saini Sujit Kumar , Awasthi Suneet Kumar
Published: January 2023

In the present research work we have examined the biosensing capabilities of one-dimensional photonic crystals with defects for the detection and sensing of malaria infection in humans by investigating blood samples containing red blood cells. This theoretical scheme utilizes a transfer matrix formulation in addition to MATLAB software under normal incidence conditions. The purpose of considering normal incidence is to rule out the difficulties associated with oblique incidence. We have examined the performance of various structures of cavity layer thicknesses 1000 nm, 2200 nm, 3000 nm and 5000 nm. The comparison between the performances of various structures of different cavity thickness helps us to select the structure of particular cavity thicknesses giving optimum biosensing performance. Thus, the proper selection of cavity thickness is one of the most necessary requirements because it also decides how much volume of the blood sample has to be poured into the cavity to produce results of high accuracy. Moreover, the sensing and detection capabilities of the proposed design have been evaluated by examining the sensitivity, figure of merit and quality factor values of the design, corresponding to optimum cavity thickness.
tRNA-Derived RNA Fragments Are Novel Biomarkers for Diagnosis, Prognosis, and Tumor Subtypes in Prostate Cancer
Authors: Liu Weigang , Yu Mengqian , Cheng Sheng , Zhou Xiaoxu , Li Jia , Lu Yan , Liu Pengyuan , Ding Shiping
Published: January 2023

Background: tRNA-derived RNA fragments (tRFs) are a novel class of small ncRNA that are derived from precursor or mature tRNAs. Recently, the general relevance of their roles and clinical values in tumorigenesis, metastasis, and recurrence have been increasingly highlighted. However, there has been no specific systematic study to elucidate any potential clinical significance for these tRFs in prostate adenocarcinoma (PRAD), one of the most common and malignant cancers that threatens male health worldwide. Here, we investigate the clinical value of 5′-tRFs in PRAD. Methods: Small RNA sequencing data were analyzed to discover new 5′-tRFs biomarkers for PRAD. Machine learning algorithms were used to identify 5′-tRF classifiers to distinguish PRAD tumors from normal tissues. LASSO and Cox regression analyses were used to construct 5′-tRF prognostic predictive models. NMF and consensus clustering analyses were performed on 5′-tRF profiles to identify molecular subtypes of PRAD. Results: The overall levels of 5′-tRFs were significantly upregulated in the PRAD tumor samples compared to their adjacent normal samples. tRF classifiers composed of 13 5′-tRFs achieved AUC values as high as 0.963, showing high sensitivity and specificity in distinguishing PRAD tumors from normal samples. Multiple 5′-tRFs were identified as being associated with the PRAD prognosis. The tRF score, defined by a set of eight 5′-tRFs, was highly predictive of survival in PRAD patients. The combination of tRF and Gleason scores showed a significantly better performance than the Gleason score alone, suggesting that 5′-tRFs can offer PRAD patients additional and improved prognostic information. Four molecular subtypes of the PRAD tumor were identified based on their 5′-tRF expression profiles. Genetically, these 5′-tRFs PRAD tumor subtypes exhibited distinct genomic landscapes in tumor cells. Clinically, they showed marked differences in survival and clinicopathological features. Conclusions: 5′-tRFs are potential clinical biomarkers for the diagnosis, prognosis, and classification of tumor subtypes on a molecular level. These can help clinicians formulate personalized treatment plans for PRAD patients and may have similar potential applications for other disease types.
Marine Noise Effects on Juvenile Sparid Fish Change among Species and Developmental Stages
Authors: Di Franco Eugenio , Rossi Francesca , Di Iorio Lucia , Sdiri Khalil , Calò Antonio , Di Franco Antonio , Harmelin-Vivien Mireille , Spennato Guillaume , Cottalorda Jean-Michel , Derijard Benoit , Bussotti Simona , Pierson Patricia , Guidetti Paolo
Published: January 2023

Marine noise is an emerging pollutant inducing a variety of negative impacts on many animal taxa, including fish. Fish population persistence and dynamics rely on the supply of early life stages, which are often very sensitive to disturbance. Impacts of marine noise pollution (MNP) on juvenile fish have rarely been investigated in temperate regions. This is particularly true for the Mediterranean Sea, which is considered as an MNP hotspot due to intensive maritime traffic. In this study, we investigate the relationship between MNP related to boat traffic and (i) assemblage structure and (ii) the density of juvenile fishes (post-settlers at different stages) belonging to the Sparidae family. We quantified MNP produced by boating at four coastal locations in the French Riviera (NW Mediterranean Sea) by linearly combining five variables into a ‘noise index’ (NI): (i) boat visitation, (ii) number of boat passages/hour, (iii) the instantaneous underwater noise levels of passing boats, (iv) continuous boat underwater noise levels and (v) duration of exposure to boat noise. Then, using the NI, we identified an MNP gradient. By using juvenile fish visual censuses (running a total of 1488 counts), we found that (i) the assemblage structure and (ii) the density patterns of three fish species (i.e., Diplodus sargus, D. puntazzo, D. vulgaris) changed along the MNP gradient. Specifically, the density of early D. sargus post-settlers was negatively related to MNP, while late post-settler densities of D. puntazzo and, less evidently, D. vulgaris tended to decrease more rapidly with decreasing MNP. Our findings suggest the following potential impacts of MNP on juvenile sparids related to coastal boat traffic: (i) idiosyncratic effects on density depending on the species and the developmental stage (early vs. late post-settlers); (ii) negative effects on recruitment, due to possible alteration of late post-settlement movement patterns.
Macro-moth (Lepidoptera) Diversity of a Newly Shaped Ecological Corridor and the Surrounding Forest Area in the Western Italian Alps
Authors: Piccini Irene , Depetris Marta , Paradiso Federica , Cochis Francesca , Audisio Michela , Artioli Patrick , Smargiassi Stefania , Bonifacino Marco , Giuliano Davide , La Cava Sara , Rijllo Giuseppe , Bonelli Simona , Scalercio Stefano
Published: January 2023

In addition to the compilation of biodiversity inventories, checklists, especially if combined with abundance data, are important tools to understand species distribution, habitat use, and community composition over time. Their importance is even higher when ecological indicator taxa are considered, as in the case of moths. In this work, we investigated macro-moth diversity in a forest area (30 ha) in the Western Italian Alps, recently subjected to intense management activities. Indeed, an ecological corridor, which includes 10 clearings, has been shaped thanks to forest compensation related to the construction site of the Turin–Lyon High-Speed Railway. Here, we identified 17 patches (9 clearings and 8 forests), and we conducted moth surveys using UV–LED light traps. A total of 15,614 individuals belonging to 442 species were collected in 2020 and 2021. Two and fifteen species are new records for Piedmont and for Susa Valley, respectively. In addition to the faunistic interest of the data, this study—using a standardized method—provides geo-referenced occurrences, species-richness, and abundance values useful to compile a baseline dataset for future comparisons. Indeed, the replicable and easy shareable method allows us to make comparisons with other research and thus assess the impact of environmental changes.
The Big Five: Species Distribution Models from Citizen Science Data as Tool for Preserving the Largest Protected Saproxylic Beetles in Italy
Authors: Redolfi De Zan Lara , Rossi de Gasperis Sarah , Andriani Vincenzo , Bardiani Marco , Campanaro Alessandro , Gisondi Silvia , Hardersen Sönke , Maurizi Emanuela , Mosconi Fabio , Nardi Gianluca , Zapponi Livia , Rombolà Pasquale , Romiti Federico
Published: January 2023

Background. Volunteers’ participation in scientific research has increased in recent decades. Citizen science (CS) data have been used in quantitative ecology to analyse species ranges by means of species distribution models. We investigated the Italian distribution of five large saproxylic beetles (big five), to describe their niche space, paramount areas for their conservation, and conservation gaps. Methods. CS data from two projects, climate and environmental variables were used to produce Habitat suitability (HS) maps for each species and averaged HS maps. The big five’s conservation status was assessed interpolating HS maps with the distribution of protected areas, concomitantly identifying conservation gaps. Results. The pre-alpine and Apennines arcs, north-eastern Sicily and eastern Sardinia, were identified as conservation’s hotspots. Ranking HS levels from minimum to optimal, the extent of conservation gaps decreases as environmental suitability for the big five increases. Conclusions. For the first time in Italy, CS data have been used to investigate niche space of the largest protected saproxylic beetles and analyse the distribution of their suitable habitat. The resulting HS raster maps and vector layers, reporting HS value in all Italian protected areas (n° 3771), were provided and discussed, reporting an application example for conservation purposes.
Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach
Authors: Akbulut Yaman
Published: January 2023

Many people have been affected by infectious lung diseases (ILD). With the outbreak of the COVID-19 disease in the last few years, many people have waited for weeks to recover in the intensive care wards of hospitals. Therefore, early diagnosis of ILD is of great importance to reduce the occupancy rates of health institutions and the treatment time of patients. Many artificial intelligence-based studies have been carried out in detecting and classifying diseases from medical images using imaging applications. The most important goal of these studies was to increase classification performance and model reliability. In this approach, a powerful algorithm based on a new customized deep learning model (ACL model), which trained synchronously with the attention and LSTM model with CNN models, was proposed to classify healthy, COVID-19 and Pneumonia. The important stains and traces in the chest X-ray (CX-R) image were emphasized with the marker-controlled watershed (MCW) segmentation algorithm. The ACL model was trained for different training-test ratios (90–10%, 80–20%, and 70–30%). For 90–10%, 80–20%, and 70–30% training-test ratios, accuracy scores were 100%, 96%, and 96%, respectively. The best performance results were obtained compared to the existing methods. In addition, the contribution of the strategies utilized in the proposed model to classification performance was analyzed in detail. Deep learning-based applications can be used as a useful decision support tool for physicians in the early diagnosis of ILD diseases. However, for the reliability of these applications, it is necessary to undertake verification with many datasets.
Preoperative Prediction of Microsatellite Instability in Rectal Cancer Using Five Machine Learning Algorithms Based on Multiparametric MRI Radiomics
Authors: Zhang Yang , Liu Jing , Wu Cuiyun , Peng Jiaxuan , Wei Yuguo , Cui Sijia
Published: January 2023

Objectives: To establish and verify radiomics models based on multiparametric MRI for preoperatively identifying the microsatellite instability (MSI) status of rectal cancer (RC) by comparing different machine learning algorithms. Methods: This retrospective study enrolled 383 (training set, 268; test set, 115) RC patients between January 2017 and June 2022. A total of 4148 radiomics features were extracted from multiparametric MRI, including T2-weighted imaging, T1-weighted imaging, apparent diffusion coefficient, and contrast-enhanced T1-weighted imaging. The analysis of variance, correlation test, univariate logistic analysis, and a gradient-boosting decision tree were used for the dimension reduction. Logistic regression, Bayes, support vector machine (SVM), K-nearest neighbor (KNN), and tree machine learning algorithms were used to build different radiomics models. The relative standard deviation (RSD) and bootstrap method were used to quantify the stability of these five algorithms. Then, predictive performances of different models were assessed using area under curves (AUCs). The performance of the best radiomics model was evaluated using calibration and discrimination. Results: Among these 383 patients, the prevalence of MSI was 14.62% (56/383). The RSD value of logistic regression algorithm was the lowest (4.64%), followed by Bayes (5.44%) and KNN (5.45%), which was significantly better than that of SVM (19.11%) and tree (11.94%) algorithms. The radiomics model based on logistic regression algorithm performed best, with AUCs of 0.827 and 0.739 in the training and test sets, respectively. Conclusions: We developed a radiomics model based on the logistic regression algorithm, which could potentially be used to facilitate the individualized prediction of MSI status in RC patients.
Modified Current Sensorless Incremental Conductance Algorithm for Photovoltaic Systems
Authors: Gruner Víctor Ferreira , Zanotti Jefferson William , Santos Walbermark Marques , Pereira Thiago Antonio , Schmitz Lenon , Martins Denizar Cruz , Coelho Roberto Francisco
Published: January 2023

This paper proposes a novel maximum power point tracking algorithm applied to photovoltaic systems. The proposed method uses the derivative of power versus voltage to define the tracking path and has the advantage of requiring only a voltage sensor to be implemented. The absence of the current sensor and the auxiliary circuitry employed for conditioning the current signal imply cost reduction, configuring the main contribution of the proposed method, whose performance is kept close to the classical incremental conductance method, even with the reduced number of components. A DC-DC zeta converter is introduced in the content of this work as an interface between a photovoltaic array and a resistive load. The paper describes the operating principle and presents the mathematical formulation related to the proposed algorithm. Interesting simulation and experimental results are presented to validate the theory by comparing the proposed method with its traditional version under several scenarios of solar irradiance and temperature.
Dynamic Prediction of Natural Gas Calorific Value Based on Deep Learning
Authors: Hu Jingjing , Yang Zhaoming , Su Huai
Published: January 2023

The natural gas quality fluctuates in complex natural gas pipeline networks, because of the influence of the pipeline transmission process, changes in the gas source, and fluctuations in customer demand in the mixing process. Based on the dynamic characteristics of the system with large time lag and non−linearity, this article establishes a deep−learning−based dynamic prediction model for calorific value in natural gas pipeline networks, which is used to accurately and efficiently analyze the dynamic changes of calorific value in pipeline networks caused by non−stationary processes. Numerical experiment results show that the deep−learning model can effectively extract the effects of non−stationary and large time lag hydraulic characteristics on natural gas calorific value distribution. The method is able to rapidly predict the dynamic changes of gas calorific value in the pipeline network, based on real−time operational data such as pressure, flow rate, and gas quality parameters. It has a prediction accuracy of over 99% and a calculation time of only 1% of that of the physical simulation model (built and solved based on TGNET commercial software). Moreover, with noise and missing key parameters in the data samples, the method can still maintain an accuracy rate of over 97%, which can provide a new method for the dynamic assignment of calorific values to complex natural gas pipeline networks and on−site metering management.
Biomass-Derived Hard Carbon and Nitrogen-Sulfur Co-Doped Graphene for High-Performance Symmetric Sodium Ion Capacitor Devices
Authors: Thirumal Vediyappan , Sreekanth T. V. M. , Yoo Kisoo , Kim Jinho
Published: January 2023

An inexpensive bio-mass-derived hard carbon from tamarind pods was used as an anode, and nitrogen and nitrogen (N)/sulfur (S) co-doped graphene were used as a cathode for novel hybrid Na-ion supercapacitors. The structural and surface morphological analyses are investigated using a range of techniques. The 3D network of the heteroatom-doped graphene skeleton edges for N and NS-doping conformations were assigned as N-RGOs (N1s-5.09 at.%) and NS-RGOs (N1s-7.66 at.% and S1s-2.22 at.%) based on energy dispersive X-ray spectroscopy elemental mapping. The negative electrode (T-HC) hard carbon was pre-treated by pre-sodiation with a half-cell process by galvanostatic charge–discharge in a sodium-ion battery at 0.01–2.5 V vs. Na/Na+. The T-HC//NS-RGO, T-HC//N-RGO, and T-HC//RGO were used to construct the Na-ion supercapacitor device. In the CV experiments, the electrochemical galvanostatic charge–discharge was studied at 1.0–4.2 V. The specific capacitance was 352.18 F/g for the T.HC/NS-RGO device and 180.93 F/g for the T.HC/N-RGO device; both were symmetric devices. T.HC/NS-RGO device performance revealed excellent cycling stability, with T-HC//NS-RGO showing 89.26% capacitance retention over 5000 cycles. A carbon–carbon symmetric device, such as a Na-ion hybrid capacitor, can exhibit the characteristics of both batteries and supercapacitors for future electric vehicles.
Prognosis of Lithium-Ion Batteries’ Remaining Useful Life Based on a Sequence-to-Sequence Model with Variational Mode Decomposition
Authors: Zhu Chunxiang , He Zhiwei , Bao Zhengyi , Sun Changcheng , Gao Mingyu
Published: January 2023

The time-varying, dynamic, nonlinear, and other characteristics of lithium-ion batteries, as well as the capacity regeneration phenomenon, leads to the low accuracy of the traditional deep learning models in predicting the remaining useful life of lithium-ion batteries. This paper established a sequence-to-sequence model for remaining useful life prediction by combining the variational modal decomposition with bi-directional long short-term memory and Bayesian hyperparametric optimization. First, variational modal decomposition is used for noise reduction processing to maximize the retention of the original information of capacity degradation. Second, the capacity declining trend after noise reduction is modeled and predicted by the combination of bi-directional long-short term memory and temporal attention mechanism. In addition, a Bayesian optimizer is used to adaptively adjust the hyperparameters while training the model. Finally, the model was validated on NASA and CALCE data sets, and the results show that the model can accurately predict the future trend with only the initial 12% capacity data.
Optimizing the Transient Performance of Thermoelectric Generator with PCM by Taguchi Method
Authors: Shi Zhaochun , Wang Guohua , Liu Chunli , Lv Qiang , Gong Baoli , Zhang Yingchao , Yan Yuying
Published: January 2023

Phase change material (PCM) is an effective thermal management method to improve the thermoelectric conversion performance of a system. PCM can not only absorb excessive thermal energy at high temperature to protect the thermoelectric module (TEM) and increase the maximum available temperature range, but also compensate for intermittent energy to extend the working time of the TEM. In the paper, the transient performance is improved by adding PCM to a traditional thermoelectric generator (TEG) system. Due to the low thermal conductivity of PCM, metal fins are used to improve the thermal conductivity of PCM. To achieve maximum efficiency of the TEG system, the Taguchi method is employed. Four factors are heat source thermal power, PCM type, height of the PCM box, and filling ratio of the PCM, respectively. The results show that heat source thermal power has the greatest effect, and PCM has the least effect on the conversion efficiency of the TEG system. Conversion efficiency from thermal to electricity is about 1.472% during 2300 s of the heating and cooling stages.
Control Methodologies to Mitigate and Regulate Second-Order Ripples in DC–AC Conversions and Microgrids: A Brief Review
Authors: Chaturvedi Shivam , Wang Mengqi , Fan Yaoyu , Fulwani Deepak , Hollweg Guilherme , Khan Shahid , Su Wencong
Published: January 2023

Second-order ripples occur in the voltage and current during any DC–AC power conversion. These conversions occur in the voltage source inverters (VSIs), current source inverters (CSIs), and various single-stage inverters (SSIs) topologies. The second-order ripples lead to oscillating source node currents and DC bus voltages when there is an interconnection between the AC and DC microgrids or when an AC load is connected to the DC bus of the microgrid. Second-order ripples have various detrimental effects on the sources and the battery storage. In the storage battery, they lead to the depletion of electrodes. They also lead to stress in the converter or inverter components. This may lead to the failure of a component and hence affect the reliability of the system. Furthermore, the second-order ripple currents (SRCs) lead to ripple torque in wind turbines and lead to mechanical stress. SRCs cause a rise in the temperature of photovoltaic panels. An increase in the temperature of PV panels leads to a reduction in the power generated. Furthermore, the second-order voltage and current oscillations lead to a varying maximum power point in PV panels. Hence, the maximum power may not be extracted from it. To mitigate SRCs, oversizing of the components is needed. To improve the lifespan of the sources, storage, and converter components, the SRCs must be mitigated or kept within the desired limits. In the literature, different methodologies have been proposed to mitigate and regulate these second-order ripple components. This manuscript presents a comprehensive review of different effects of second-order ripples on different sources and the methodologies adopted to mitigate the ripples. Different active power decoupling methodologies, virtual impedance-based methodologies, pulse width modulation-based signal injection methodologies, and control methods adopted in distributed power generation methods for DC microgrids have been presented. The application of ripple control methods spans from single converters such as SSIs and VSIs to a network of interconnected converters. Furthermore, different challenges in the field of virtual impedance control and ripple mitigation in distributed power generation environments are discussed. This paper brings a review regarding control methodologies to mitigate and regulate second-order ripples in DC–AC conversions and microgrids.
A Novel Analytical Formulation of SiC-MOSFET Losses to Size High-Efficiency Three-Phase Inverters
Authors: Costa Pedro , Pinto Sónia , Silva José Fernando
Published: January 2023

This paper presents a novel analytical loss formulation to predict the efficiency of three-phase inverters using silicon carbide (SiC) metal—oxide—semiconductor field-effect transistors (MOSFETs). The proposed analytical formulation accounts for the influence of the output current harmonic distortion on the conduction losses as well as the impact of the output parasitic capacitances and the deadtime on the switching losses. The losses are formulated in balanced conditions to select suitable SiC MOFETs for the desired target efficiency. To validate the proposed methodology, a 3-phase inverter is designed to present full load efficiency in excess of 99% when built using SiC MOSFETs antiparalleled with SiC Schottky diodes selected for the specified full load efficiency. Experimental assessment of the designed inverter efficiency is compared with the expected values from the proposed analytical formulation and shown to match or exceed the predicted results for loads ranging from 40% to 100% of full load.
Incorporation of Blockchain Technology for Different Smart Grid Applications: Architecture, Prospects, and Challenges
Authors: Waseem Muhammad , Adnan Khan Muhammad , Goudarzi Arman , Fahad Shah , Sajjad Intisar , Siano Pierluigi
Published: January 2023

Smart grid integrates computer, communication, and sensing technologies into existing power grid networks to achieve significant informatization-related advantages. It will provide communication between neighbors, localized management, bidirectional power transfer, and effective demand response. Smart grids (SG) replace conventional grids by integrating various operational measures, including smart automation appliances, smart meters, and renewable energy sources. Regarding energy management and resolving energy issues, SG is one of the most cutting-edge and potentially game-changing innovations. Even still, its complexity suggests that decentralization may provide significant gains. Because of its increasing digitization and interconnectedness, it is also vulnerable to cyber threats. Blockchain, in this sense, is a potential SG paradigm solution that provides several great benefits. Even though blockchains have been widely discussed to decentralize and strengthen smart grid cybersecurity, they have not yet been researched in depth from an application and architectural standpoint. Blockchain-enabled SG applications are the subject of an in-depth research investigation. Electric vehicles (EVs), home automation, energy management systems, etc., are only a few of the many examples that have prompted the proposal of blockchain designs for their respective use cases. Information communication network security is of paramount importance. However, this evolving system raises cybersecurity issues. This paper aims to guide researchers in the right manner so they may build blockchain-based, secure, distributed SG applications in the future. This article also summarizes cybersecurity threats pertaining to smart grids. It starts with a description of a blockchain followed by the blockchain infrastructure, challenges, and solutions for different smart grid applications. A look back at the tried-and-true methods of securing a power grid is offered, and then it discusses the newer and more complex cybersecurity threats to the smart grid. In addition, models of common cyberattacks are presented, and the methods of defense against them are examined.
Efficiency of Polish Energy Companies in the Context of EU Climate Policy
Authors: Nawrocki Tomasz L. , Jonek-Kowalska Izabela
Published: January 2023

The purpose of this article is to assess the impact of carbon allowances on the financial performance and strategic behavior of Polish energy companies listed on the Warsaw Stock Exchange, with a particular focus on the period when the price of these allowances increased. The eight largest Polish energy companies were surveyed, and the research period covered the period of 2010–2021. The research process used an analysis of financial condition and its determinants in the current and long-term perspective. In the current approach, the following were used: sales margin, operating margin, and cost and revenue structure. In the long-term approach, an assessment of the regularity of the capital structure and debt ratios was used. In both research perspectives, the results were confronted with the structure of power generation sources and the segmentation of the core business, including production, distribution, and trading. The results allow us to conclude that the increase in the price of emission allowances has adversely and most strongly affected companies focused on energy generation from high-carbon sources.
Effects of Oak Processionary Moth (Thaumetopoea processionea L.) Outbreaks on the Leaf Performance and Health of Urban and Forest Oak Trees (Quercus robur L.) in Brandenburg, Germany
Authors: Arnold Anne L. M. , McGrath Conor , Reinhardt Annett
Published: January 2023

Forest insects are among the most important factors of disturbance in European forests. The increase in and duration of drought stress events due to climate change not only increase the vulnerability of rural and urban forests but also predispose weakened stands to insect pest calamities. In this context, many German and European forest research institutes and environmental institutions report an increase in the densities and calamity developments of the oak processionary moth (Thaumetopoea processionea L.) not only in oak and mixed-oak forests but also in smaller areas where oak trees regularly occur, e.g., parklands, urban areas, copses, avenues, recreational forests, etc. It is expected that the oak processionary moth (OPM) will benefit from the overall weakened vitality of both individual oaks and oak stands in the future and that mass outbreaks will occur at an increased frequency. This paper reports on the effects that the OPM can have on tree performance for rural forest and urban oak trees by applying the chlorophyll fluorescence non-destructive diagnostic method for the identification and quantification of damage to oak leaves. The aim of the study was to investigate the effects of OPM frass activity on tree leaf health using chlorophyll fluorescence measurements, comparing infested host oaks with non-infested oaks in urban and forest environs. The study assessed: 1. the quantum efficiency of photosystem II (PS II), which counts as an indicator for leaf conditions, 2. the performance index, which indicates the efficiency of the photosynthetic light reaction, 3. the energy loss of the photosynthetic reaction, which is an indicator for cell damage, and 4. the degree of open reaction centers in PS II, which indicates how well light energy can be absorbed for photosynthesis. Infested urban and rural oaks showed a significantly reduced quantum yield of PS II by up to 10% compared to non-infested oak leaves. The leaf performance was significantly reduced by up to 35% for infested urban oaks and by up to 60% for infested forest oaks, respectively. The energy losses were two times higher for infested urban and forest oaks. However, OPM infestation led to a higher reduction in the photosynthetic performance in the leaves of forest oaks compared to that of urban oaks. In order to avoid permanent damage, suitable countermeasures must be taken quickly, as, immediately after pest infestation, the performance decreases significantly. A lower performance means a significant loss in biomass production as well as in tree vitality.
A Preliminary Validation and Assessment of a GIS Approach Related to Precision Forest Harvesting in Central Italy
Authors: Stefanoni Walter , Tocci Damiano , Latterini Francesco , Venanzi Rachele , Gaglioppa Pierluca , Pari Luigi , Picchio Rodolfo
Published: January 2023

There has been a growing awareness of the need for sustainable forest management among forest engineers during the last decades. The selection of the best harvesting system for logging operations plays a fundamental role in this effort. Nowadays, in the decision-making process, forest engineers rely on their own experience, and it is to be expected that their evaluation of the level of appropriateness may be biased. There may be a potential solution to this problem in the implementation of AHP (Analytic Hierarchy Process) in the GIS (Geographic Information System) environment. The application of AHP gives objective information and thus reduces the level of bias in deciding which logging harvest system is best to use. The aim of this study was to evaluate the reliability of a GIS-AHP approach applied to the development of the harvesting plan of a public forest estate in Central Italy. Three extraction methods were considered: forwarder, cable skidder, and all-terrain cable yarder. Thus, the GIS-AHP model was run by relying on the basic data attainable from the local forest management plan and then on detailed information provided by field data collected from 10 harvested blocks. Among these, in three blocks, the logging harvest method was changed from forwarder to cable skidder. Statistically significant differences were found between the suitability values obtained from the forest management plan and those retrieved from the field surveys for the forwarder and cable skidders but not for all-terrain cable yarders.