bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatial prediction of ground substrate thickness in shallow mountain area based on machine learning model
Authors: Zhu Xiaosong , Pei Xiaolong , Yang Siqi , Wang Wei , Dong Yue , Fang Mengyang , Liu Wenjie , Jiang Lingxiu
Published: September 2024

Introduction

The thickness of ground substrate in shallow mountainous areas is a crucial indicator for substrate investigations and a key factor in evaluating substrate quality and function. Reliable data acquisition methods are essential for effective investigation.

Methods

This study utilizes six machine learning algorithms—Gradient Boosting Machine (GB), Random Forest (RF), AdaBoost Regressor (AB), Neural Network (NN), Support Vector Machine (SVM), and k-Nearest Neighbors (kNN)—to predict ground substrate thickness. Grid search optimization was employed to fine-tune model parameters. The models’ performances were evaluated using four metrics: mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). The optimal parameter combinations for each model were then used to calculate the spatial distribution of ground substrate thickness in the study area.

Results

The results indicate that after parameter optimization, all models showed significant reductions in the MSE, RMSE, and MAE, while R2 values increased substantially. Under optimal parameters, the RF model achieved an MSE of 1,589, RMSE of 39.8, MAE of 26.5, and an R2 of 0.63, with a Pearson correlation coefficient of 0.80, outperforming the other models. Therefore, parameter tuning is a necessary step in using machine learning models to predict ground substrate thickness, and the performance of all six models improved significantly after tuning. Overall, ensemble learning models provided better predictive performance than other machine learning models, with the RF model demonstrating the best accuracy and robustness.

Discussion

Moreover, further attention is required on the characteristics of sample data and environmental variables in machine learning-based predictions.
Academic burnout and family communication as predictors of life satisfaction in Peruvian university students
Authors: Gutierrez Ticona Julia , Barahona Urbano Rocío Natalí , Barahona Urbano Diana Ruth , Turpo-Chaparro Josue Edison , Conde Rodríguez Isaac Alex
Published: September 2024

This study aimed to determine if academic burnout and family communication predict life satisfaction in Peruvian university students. The design was cross-sectional predictive, and 708 Peruvian university students participated. The Maslach Burnout Inventory (MBI), the Family Communication Scale (FCS), and the Satisfaction with Life Scale (SWLS) were used to obtain information on the variables. Descriptive and inferential analysis used structural equation modeling and the Robust Maximum Likelihood Estimator. The results present a negative relationship between academic burnout and Life Satisfaction with a significant estimate of −0.849 (p < 0.001), with a beta coefficient (β) of −0.469, and a positive relationship between Family Communication and Life Satisfaction, showing a significant positive estimate of 0.436 (p < 0.001), with a beta coefficient (β) of 0.420. These results confirm the need to implement programs that provide guidance to students on academic burnout and how to prevent it. Likewise, strategies to improve family communication must be incorporated in order to improve students’ life satisfaction. In conclusion, these findings confirm that AB and FC are predictive factors of LS, academic burnout decreases life satisfaction, and family communication increases life satisfaction in Peruvian university students.
A case study of actual versus desired inclusion of community-derived core concepts into neuroscience courses in different disciplines at a large university
Authors: Maita Isabella , Owens Melinda T. , Juavinett Ashley L.
Published: September 2024

Neuroscience is an inherently interdisciplinary and rapidly evolving field. While many universities have neuroscience or related majors, they are highly heterogeneous, and it is unclear how their content aligns with a recent proposal of what ideas make up the field of neuroscience. It is therefore important to document and assess the alignment of neuroscience curricula with core concepts in the field. Recently, a large effort by some members of the neuroscience education community described eight core concepts for undergraduate neuroscience curricula. In this paper, we focus primarily on courses in biology, cognitive science, and psychology at a large university, surveying the recent and current course instructors of these courses to ask them (1) to what extent these community-derived core concepts are incorporated into their classes and (2) to what extent these concepts should be incorporated into their classes. In addition, we map core concepts onto course syllabi. We found that core concepts are well-represented across disciplines, and identified differences between departments' inclusion of core concepts. We found that instructors cover fewer core concepts than they desire, and that two core concepts, “Evolution” and “Gene-environment interactions”, were less frequently addressed across disciplines. We consider barriers to instructors' ability to align course content with core concepts, both within and across disciplines. In this effort, we provide an example of how departments can evaluate their alignment of major requirements with the neuroscience core concepts.
DGDRP: drug-specific gene selection for drug response prediction via re-ranking through propagating and learning biological network
Authors: Pak Minwoo , Bang Dongmin , Sung Inyoung , Kim Sun , Lee Sunho
Published: September 2024

Introduction: Drug response prediction, especially in terms of cell viability prediction, is a well-studied research problem with significant implications for personalized medicine. It enables the identification of the most effective drugs based on individual genetic profiles, aids in selecting potential drug candidates, and helps identify biomarkers that predict drug efficacy and toxicity.A deeper investigation on drug response prediction reveals that drugs exert their effects by targeting specific proteins, which in turn perturb related genes in cascading ways. This perturbation affects cellular pathways and regulatory networks, ultimately influencing the cellular response to the drug. Identifying which genes are perturbed and how they interact can provide critical insights into the mechanisms of drug action. Hence, the problem of predicting drug response can be framed as a dual problem involving both the prediction of drug efficacy and the selection of drug-specific genes. Identifying these drug-specific genes (biomarkers) is crucial because they serve as indicators of how the drug will affect the biological system, thereby facilitating both drug response prediction and biomarker discovery.Methods: In this study, we propose DGDRP (Drug-specific Gene selection for Drug Response Prediction), a graph neural network (GNN)-based model that uses a novel rank-and-re-rank process for drug-specific gene selection. DGDRP first ranks genes using a pathway knowledge-enhanced network propagation algorithm based on drug target information, ensuring biological relevance. It then re-ranks genes based on the similarity between gene and drug target embeddings learned from the GNN, incorporating semantic relationships. Thus, our model adaptively learns to select drug mechanism-associated genes that contribute to drug response prediction. This integrated approach not only improves drug response predictions compared to other gene selection methods but also allows for effective biomarker discovery.Discussion: As a result, our approach demonstrates improved drug response predictions compared to other gene selection methods and demonstrates comparability with state-of-the-art deep learning models. Case studies further support our method by showing alignment of selected gene sets with the mechanisms of action of input drugs.Conclusion: Overall, DGDRP represents a deep learning based re-ranking strategy, offering a robust gene selection framework for more accurate drug response prediction. The source code for DGDRP can be found at: https://github.com/minwoopak/heteronet.
Potential contribution of GST-T1 and GST-M1 polymorphisms in the onset of hepatic steatosis: from radiological to molecular and medico-legal analyses
Authors: Cianci Vincenzo , Mondello Cristina , Baldino Gennaro , Spatari Giovanna , Alibrandi Angela , Cianci Alessio , Cracò Annalisa , Gualniera Patrizia , Asmundo Alessio , Gaeta Michele , Giorgianni Concetto , Sapienza Daniela
Published: September 2024

Introduction

Non-alcoholic fatty liver disease (NAFLD) is the most prevalent form of chronic liver disease in the world, and it is characterized by an excessive hepatic fat accumulation in more than 5% of hepatocytes documented by histology in the absence of alcohol consumption. It is a multifactorial pathology, where genetic component plays a fundamental role: the loss-of-function polymorphisms of genes coding for glutathione S-transferases would predispose to the pathology onset, also in the absence of other risk factors. The aim of the study was to evaluate the relation between the “NULL” GST-T1 and GST-M1 polymorphisms and the onset of NAFLD.

Methods

A group of 117 “apparently healthy” Caucasian volunteers, selected from a larger population through the analysis of previously administered short questionnaires, underwent both magnetic resonance imaging-proton density fat fraction (MRI-PDFF) and buccal swabs: the aim was to identify the possible presence of hepatic steatosis and of the aforementioned “NULL” polymorphisms of interest.

Results

A statistically significant association between the GST-T1 and GST-M1 “NULL” genotypes and the probability of developing NAFLD has been identified. In particular, the GST-T1 “NULL” genotype has been associated with a greater probability of developing steatosis in early age, while the GST-M1 “NULL” genotype seems to increase the risk of developing a higher grade of steatosis. No statistically significant correlations between the “NULL” genotype and sex have been detected.

Discussion

Among the numerous risk factors capable of predisposing to NAFLD onset and progression, the genetic factors seem to play an important role. In particular, GST-T1 and GST-M1 “NULL” polymorphisms would appear to acquire even greater importance, as their loss of function results in an increase of oxidative stress. At high concentrations, ROS can determine oxidative modifications of cellular macromolecules, such as lipids, determining their accumulation into hepatocytes. The study also highlighted the importance of MRI-PDFF for hepatic steatosis diagnosis: this method allows the acquisition of data comparable to those of conventional biopsy; however, it permits the entire liver parenchyma to be visualized.

Conclusion

A statistically significant correlation between the presence of GST-T1 and GST-M1 “NULL” genotypes and the presence of hepatic steatosis has been found.
The mechanism of small wear particles entrainment in friction under boundary lubrication
Authors: Tokoroyama Takayuki , Okashita Masahiro , Zhang Ruixi , Murashima Motoyuki , Tsuboi Ryo , Yoshida Takuji , Shiomi Hiroshi , Umehara Noritsugu
Published: September 2024

When the lubricating oil flowing between the contact surfaces contains simulated wear particles, it is considered that there is a high possibility for the particles to enter between the contact surfaces if the particles are present on the disk side in a friction test between a fixed ball and a rotating disk. For particles with a diameter of 0.8 μm, it has been previously shown that the entrainment frequency changes with the outside oil film thickness, but the effect of the disk material on the amount of particle deposition and the visualization of particle movement direction due to vortices generated between the contact surfaces had not been performed. In this study, by using SiO2 disks and sapphire disks with different Hamaker constants, the effect on the amount of particle deposition in front of the contact surfaces was experimentally clarified by changing the van der Waals forces acting between the particles and the disks. Additionally, assuming that vortices generated in the oil film between the contact surfaces move the particles to the disk surface side, the flow direction of the lubricating oil was visualized using Navier-Stokes simulation.
Deep learning-based Alzheimer's disease detection: reproducibility and the effect of modeling choices
Authors: Turrisi Rosanna , Verri Alessandro , Barla Annalisa
Published: September 2024

Introduction

Machine Learning (ML) has emerged as a promising approach in healthcare, outperforming traditional statistical techniques. However, to establish ML as a reliable tool in clinical practice, adherence to best practices in data handling, and modeling design and assessment is crucial. In this work, we summarize and strictly adhere to such practices to ensure reproducible and reliable ML. Specifically, we focus on Alzheimer's Disease (AD) detection, a challenging problem in healthcare. Additionally, we investigate the impact of modeling choices, including different data augmentation techniques and model complexity, on overall performance.

Methods

We utilize Magnetic Resonance Imaging (MRI) data from the ADNI corpus to address a binary classification problem using 3D Convolutional Neural Networks (CNNs). Data processing and modeling are specifically tailored to address data scarcity and minimize computational overhead. Within this framework, we train 15 predictive models, considering three different data augmentation strategies and five distinct 3D CNN architectures with varying convolutional layers counts. The augmentation strategies involve affine transformations, such as zoom, shift, and rotation, applied either concurrently or separately.

Results

The combined effect of data augmentation and model complexity results in up to 10% variation in prediction accuracy. Notably, when affine transformation are applied separately, the model achieves higher accuracy, regardless the chosen architecture. Across all strategies, the model accuracy exhibits a concave behavior as the number of convolutional layers increases, peaking at an intermediate value. The best model reaches excellent performance both on the internal and additional external testing set.

Discussions

Our work underscores the critical importance of adhering to rigorous experimental practices in the field of ML applied to healthcare. The results clearly demonstrate how data augmentation and model depth—often overlooked factors– can dramatically impact final performance if not thoroughly investigated. This highlights both the necessity of exploring neglected modeling aspects and the need to comprehensively report all modeling choices to ensure reproducibility and facilitate meaningful comparisons across studies.
Development of language-specific stress discrimination in European Portuguese: an electrophysiological study
Authors: Lu Shuang , Severino Cátia , Vigário Marina , Frota Sónia
Published: September 2024

European Portuguese (EP) is a language with unpredictable stress. Previous behavioral studies have shown that without vowel reduction EP adult speakers displayed a stress deafness effect akin to that observed in speakers of fixed-stress languages, suggesting that vowel quality may be the primary cue for stress discrimination in EP. However, an event-related potentials (ERPs) study reported that EP adults were able to discriminate stress contrasts pre-attentively in the absence of vowel quality cues. These results seemed to indicate that EP adult speakers may attend to different cues in the attentive and pre-attentive stress perception. Moreover, both the behavioral and ERPs studies have revealed a processing advantage for iambic stress, which could not be predicted by the rhythmic properties of EP, the language-specific weighting of stress correlates, or the frequency distributions of trochaic and iambic stresses in EP. A recent eye-tracking study has found that EP-learning infants at 5–6 months already exhibited an iambic preference in the absence of vowel reduction, manifested by longer looking time at the iambic stress. The present study used a passive oddball paradigm to examine pre-attentive stress perception without vowel quality cues by 5-to-7-month-old EP-learning infants. Results from twenty-two participants showed that both the trochaic and iambic conditions yielded a positive discrimination response (p-MMR). In addition, the iambic condition elicited a prominent late discriminative negativity (LDN) as well as a P3a component. Our findings present the first evidence for reciprocal discrimination of stress patterns in EP-learning infants, showing that, as in adult speakers, stress processing might also differ at the pre-attentive and attentive stages in infants. Importantly, the stress perception ability in EP-learning infants seems to develop asymmetrically, with an advantage for the iambic stress pattern. The present study highlighted the role of language-specific factors that may affect developing stress perception.
Spiking representation learning for associative memories
Authors: Ravichandran Naresh , Lansner Anders , Herman Pawel
Published: September 2024

Networks of interconnected neurons communicating through spiking signals offer the bedrock of neural computations. Our brain’s spiking neural networks have the computational capacity to achieve complex pattern recognition and cognitive functions effortlessly. However, solving real-world problems with artificial spiking neural networks (SNNs) has proved to be difficult for a variety of reasons. Crucially, scaling SNNs to large networks and processing large-scale real-world datasets have been challenging, especially when compared to their non-spiking deep learning counterparts. The critical operation that is needed of SNNs is the ability to learn distributed representations from data and use these representations for perceptual, cognitive and memory operations. In this work, we introduce a novel SNN that performs unsupervised representation learning and associative memory operations leveraging Hebbian synaptic and activity-dependent structural plasticity coupled with neuron-units modelled as Poisson spike generators with sparse firing (~1 Hz mean and ~100 Hz maximum firing rate). Crucially, the architecture of our model derives from the neocortical columnar organization and combines feedforward projections for learning hidden representations and recurrent projections for forming associative memories. We evaluated the model on properties relevant for attractor-based associative memories such as pattern completion, perceptual rivalry, distortion resistance, and prototype extraction.
Neurodegenerative diseases reflect the reciprocal roles played by retroelements in regulating memory and immunity
Authors: Herbert Alan
Published: September 2024

Tetrapod endogenous retroelements (ERE) encode proteins that have been exapted to perform many roles in development and also in innate immunity, including GAG (group specific antigen) proteins from the ERE long terminal repeat (LTR) family, some of which can assemble into viral-like capsids (VLCs) and transmit mRNA across synapses. The best characterized member of this family is ARC (activity-regulated cytoskeletal gene), that is involved in memory formation. Other types of EREs, such as LINES and SINES (long and short interspersed elements), have instead been exapted for immune defenses against infectious agents. These immune EREs identify host transcripts by forming the unusual left-handed Z-DNA and Z-RNA conformations to enable self/nonself discrimination. Elevated levels of immune EREs in the brain are associated with neurodegenerative disease. Here I address the question of how pathways based on immune EREs are relate to the memory EREs that mediate neural plasticity. I propose that during infection and in other inflammatory states, ERE encoded GAG capsids deliver interferon-induced immune EREs that rapidly inhibit translation of viral RNAs in the dendritic splines by activation of protein kinase R (PKR). The response limits transmission of viruses and autonomously replicating elements, while protecting bystander cells from stress-induced cell death. Further, the PKR-dependent phosphorylation of proteins, like tau, disrupts the endocytic pathways exploited by viruses to spread to other cells. The responses come at a cost. They impair memory formation and can contribute to pathology by increasing the deposition of amyloid beta.
On the same space? Measuring the ideology of voters and political elites
Authors: Walder Maxime
Published: September 2024

Ideology is a key concept in political science. In studies related to political representation, policy responsiveness, and the quality of decision-making in elections, ideology is a central concept that needs precise measurement to test related hypotheses. Despite its prominence in the political science literature, ideology is often measured differently for elites and voters with indicators that do not correlate among themselves. As a result, it is not possible to assess whether the measures of ideology and the latent dimensionality behind measures of ideology in the population and the political elites align or to what extent they differ. In this paper, I build on existing research and propose a way to measure the ideology of voters in population survey on the same space as the political elites by the same policy questions answered by political elites to voters. Furthermore, I show that researchers only need a rather limited number of questions to reach an accurate measure of ideology. Using Monte-Carlo simulations as well as real-world election data from a voting advice application (VAA) and a post-election survey in a Bayesian Item-Response Theory framework, I show that, using only a limited number of policy questions, it is possible to position voters and political elites in a similar ideological space. In sum, this paper shows that taking a handful of policy questions from elites surveys enables the operationalization of a common ideological space for political elites and citizens. This has large implications for the study of the relationship between citizens, voters, parties, and politicians.
Psychometric properties of the Sinhala perceived stress questionnaire (PSQ8-11) in Sri Lankan primary school children
Authors: Peiris D. L. I. H. K. , Duan Yanping , Vandelanotte Corneel , Liang Wei
Published: September 2024

Background

Stress influences examination performance among Sri Lankan students. Validated tests are required to evaluate stress levels among elementary students in Sri Lanka. Therefore, the Perceived Stress Questionnaire 8–11 (PSQ8-11) was translated into a Sinhala version. The aim of this study was to examine the psychometric properties of the translated and adapted scale among elementary level school children in Sri Lanka and examine invariance across male and female children.

Methods

The participants were 1021 students from seven schools. After removing missing values, responses from 693 students (mean age = 9.65 ± 0.478 years, 51.8% male) were analysed for participant characteristics. Cronbach’s alpha, Spearman’s correlation, and confirmatory factor analysis with measurement invariance models were conducted after adding one item to the original PSQ8-11 version.

Results

The Cronbach’s alpha value for the 20-item modified PSQ8-11 Sinhala version was.788. The two subscales, psychological stress (Cronbach’s alpha = 0.615) and physiological stress (Cronbach’s alpha = 0.711), indicated a satisfactory level of internal consistency. Furthermore, a statistically significant correlation (p < 0.01; 2-tailed) was reported among each of the subscales. Confirmatory factor analyses demonstrated a satisfactory goodness-of-fit across the two models by confirming the theoretical constructs of the PSQ8-11 translated version with its two subscales. The two-factor model has better model fit indices compared to the unidimensional model (χ2/df = 1.447, CFI = 0.947, TLI = 0.938, WRMR = 0.028, RMSEA = 0.026, SRMSR = 0.0341, and PCLOSE = 1 of the two-factor model). Measurement variance across gender was supported by the establishment of configural and metric invariances.

Conclusion

Acceptable psychometric properties for the PSQ8-11 Sinhala version were observed in elementary schoolers in Sri Lanka.
A global model-agnostic rule-based XAI method based on Parameterized Event Primitives for time series classifiers
Authors: Mekonnen Ephrem Tibebe , Longo Luca , Dondio Pierpaolo
Published: September 2024

Time series classification is a challenging research area where machine learning and deep learning techniques have shown remarkable performance. However, often, these are seen as black boxes due to their minimal interpretability. On the one hand, there is a plethora of eXplainable AI (XAI) methods designed to elucidate the functioning of models trained on image and tabular data. On the other hand, adapting these methods to explain deep learning-based time series classifiers may not be straightforward due to the temporal nature of time series data. This research proposes a novel global post-hoc explainable method for unearthing the key time steps behind the inferences made by deep learning-based time series classifiers. This novel approach generates a decision tree graph, a specific set of rules, that can be seen as explanations, potentially enhancing interpretability. The methodology involves two major phases: (1) training and evaluating deep-learning-based time series classification models, and (2) extracting parameterized primitive events, such as increasing, decreasing, local max and local min, from each instance of the evaluation set and clustering such events to extract prototypical ones. These prototypical primitive events are then used as input to a decision-tree classifier trained to fit the model predictions of the test set rather than the ground truth data. Experiments were conducted on diverse real-world datasets sourced from the UCR archive, employing metrics such as accuracy, fidelity, robustness, number of nodes, and depth of the extracted rules. The findings indicate that this global post-hoc method can improve the global interpretability of complex time series classification models.
Autologous fat grafting for cosmetic temporal augmentation: a systematic review
Authors: Nasim Sahra , Nasim Henna , Kauke Martin , Safi Ali-Farid
Published: September 2024

Background

Autologous fat grafting for temporal augmentation is increasingly popular in aesthetic surgery. However, its high absorption rate, unpredictable volume retention rate, and potential safety risks are significant drawbacks. Evaluation methods for the fat graft survival rate, especially volume retention in the temporal area, vary widely and tend to be more subjective than objective. Therefore, this systematic review aims to analyze the unpredictable volume retention rate, associated safety concerns, and the various assessment strategies following autologous fat grafting for cosmetic temporal augmentation.

Methods

We conducted a systematic review of manuscripts listed in the MEDLINE/PubMed database on autologous fat grafting for cosmetic temporal augmentation. Articles had to be available in full text and written in English. Studies not presenting human data or not discussing cosmetic indications were excluded. We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Results

Eight articles were included. The average fat volume injected into each temporal region was 10.69 ml (range 6–17.5) on the right and 10.64 ml (range 5.9–17.4) on the left side. All included articles utilized photographic documentation before and after treatment, along with various questionnaires and scales (37.5% Likert Scale, 12.5% Hollowness Severity Rating Scale, 12.5% Visual Analogue Scale, 12.5% Allergan Temple Hollowing Scale). For objective assessment, one article (12.5%) used computed tomography, and another (12.5%) employed a three-dimensional scanning system to objectively evaluate fat graft survival.

Conclusion

Autologous fat grafting effectively addresses temporal hollowness, with high patient satisfaction and a favorable safety profile. However, the variability in fat retention rates highlights the need for more controlled studies to establish reliable, validated methods for evaluating fat graft survival in the temporal area, and to further assess the safety of this procedure.
Repeat Faecal Immunochemical Testing for Colorectal Cancer Detection in Symptomatic and Screening Patients: A Systematic Review and Meta-Analysis
Authors: Gerrard Adam D. , Garau Roberta , Xu Wei , Maeda Yasuko , Dunlop Malcolm G. , Theodoratou Evropi , Din Farhat V. N.
Published: September 2024

Background: Faecal immunochemical testing (FIT) is widely used in bowel screening programmes and assessing symptomatic patients for suspected colorectal cancer (CRC). The evidence for single test performance of FIT in both settings is considerable; however, the use of a repeat test to increase sensitivity remains uncertain. We aimed to review what increase in test positivity would be generated by additional FITs, whether a repeated FIT detects previously missed CRC and advanced colorectal neoplasia (ACRN), and to estimate the sensitivity of double-FIT strategies to diagnose CRC and ACRN. Methods: A systematic search of MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials (CENTRAL) was performed using key search terms. Studies reporting the use of more than one FIT in the same screening round or planned assessment of a single symptomatic patient episode were included. Studies were categorised by the reported study population into asymptomatic, mixed (cohorts of combined asymptomatic, symptomatic, or high-risk surveillance), or symptomatic cohorts. Results: A total of 68 studies were included for analysis (39 asymptomatic, 21 mixed, 7 symptomatic, and 1 study with discrete asymptomatic and symptomatic data). At a threshold of 10 µg Hb/g, the two-test positivity ranged between 8.1 and 34.5%, with an increase from the second test of 3–9.2 percentage points. Four out of five studies comparing one versus two tests for diagnosing CRC at 10 µg Hb/g identified additional cases with the second test, with a minimum of 50% reduction in missed CRC. At a threshold of 20 µg Hb/g, the second test increased the positivity by 1.3–6.7 percentage points, with a two-test positivity of between 5.1 and 25.0%. Using a threshold of 20 µg Hb/g, five out of seven studies had a 25% reduction in missed CRC. A meta-analysis estimated the double-FIT sensitivity at 10 µg Hb/g for CRC in mixed-risk and symptomatic cohorts to be 94% and 98%, respectively. Conclusions: Repeated use of FIT helps to diagnose more cases of CRC with a moderate increase in positivity. A double-FIT strategy at 10 µg Hb/g in mixed and symptomatic cohorts has a very high sensitivity for CRC.
Sentinel Lymph Node Assessment in Endometrial Cancer: A Review
Authors: Clark Christopher , Loizzi Vera , Cormio Gennaro , Lopez Salvatore
Published: September 2024

As the number of patients diagnosed with endometrial cancer rises, so does the number of patients who undergo surgical treatment, consisting of radical hysterectomy, bilateral salpingo-oophorectomy, and bilateral pelvic lymphadenectomy or lymph node sampling. The latter entail intra- and post-surgical complications, such as lymphedema and increased intra-operative bleeding, which often outweigh their benefits. Sentinel Lymph Node (SLN) sampling is now common practice in surgical management of breast cancer, as it provides important information about the disease without jeopardizing surgical radicality and patient outcomes. While this technique has also been shown to be feasible in patients with endometrial cancer, there is little consensus on several aspects, such as tracer injection volume and site, pathological ultrastaging, and result interpretation. The aim of this review is to analyze the current literature on SLN assessment in order to help standardize the procedure.
A Novel Approach for Predicting the Survival of Colorectal Cancer Patients Using Machine Learning Techniques and Advanced Parameter Optimization Methods
Authors: Woźniacki Andrzej , Książek Wojciech , Mrowczyk Patrycja
Published: September 2024

Background: Colorectal cancer is one of the most prevalent forms of cancer and is associated with a high mortality rate. Additionally, an increasing number of adults under 50 are being diagnosed with the disease. This underscores the importance of leveraging modern technologies, such as artificial intelligence, for early diagnosis and treatment support. Methods: Eight classifiers were utilized in this research: Random Forest, XGBoost, CatBoost, LightGBM, Gradient Boosting, Extra Trees, the k-nearest neighbor algorithm (KNN), and decision trees. These algorithms were optimized using the frameworks Optuna, RayTune, and HyperOpt. This study was conducted on a public dataset from Brazil, containing information on tens of thousands of patients. Results: The models developed in this study demonstrated high classification accuracy in predicting one-, three-, and five-year survival, as well as overall mortality and cancer-specific mortality. The CatBoost, LightGBM, Gradient Boosting, and Random Forest classifiers delivered the best performance, achieving an accuracy of approximately 80% across all the evaluated tasks. Conclusions: This research enabled the development of effective classification models that can be applied in clinical practice.
Ex Vivo Confocal Microscopy Speeds up Surgical Margin Control of Re-Excised Skin Tumors and Greatly Shortens In-Hospital Stay
Authors: Gellrich Frank Friedrich , Laske Jörg , Steininger Julian , Eberl Nadia , Meier Friedegund , Beissert Stefan , Hobelsberger Sarah
Published: September 2024

Background/Objectives: To ensure that non-melanoma skin cancer (NMSC) is completely removed in healthy tissue, micrographically controlled surgery (3D histology) is often performed, which can prolong the inpatient stay. This study examined ex vivo reflectance confocal microscopy (evRCM) for perioperative assessment of surgical margins, specifically in cases where re-excision was necessary due to incomplete removal of cutaneous tumor tissue. Methods: NMSC re-excisions were evaluated using evRCM by a cutaneous surgeon, with retrospective review by an independent pathologist when results differed from histology. Results: evRCM demonstrated high specificity (0.96; 95% CI, 0.90–0.99) but low sensitivity (0.20; 95% CI, 0.06–0.51). Unlike pathology, which discards outer surgical margins, evRCM examined the true surgical margins. Retrospective pathology analysis of the misdiagnosed cases confirmed that 25% (n = 2/8) were false negative and 75% (n = 6/8) were potentially false positive, resulting in a sensitivity of 0.2–0.8. Notably, evRCM led to a 113-day reduction in in-hospital stays, probably resulting in increased patient satisfaction and cost-effectiveness. Conclusions: evRCM was valuable for speeding up the assessment of surgical margins in patients with re-excised NMSC. Proper tissue preparation and assessment require interdisciplinary collaboration between cutaneous surgeons, pathologists, and physician assistants, emphasizing the need for standardized operating procedures.
Types of Breast Cancer Surgery and Breast Reconstruction
Authors: Golara Anna , Kozłowski Mateusz , Lubikowski Jerzy , Cymbaluk-Płoska Aneta
Published: September 2024

Background: Breast cancer continues to be a significant diagnostic and therapeutic problem. Mastectomy is still a frequently used treatment method, but its form is changing with progress in medicine. Methods: We have described important types of surgical treatments for breast cancer, such as modified radical mastectomy, breast-conserving surgery, contralateral prophylactic mastectomy, and robotic mastectomy. Breast reconstruction is also a very important element of treatment because it directly affects the mental state of patients after the procedure. We have also described types of breast reconstruction, such as implants, acellular dermal matrices, autologous reconstruction, robotic breast reconstruction, and fat grafting. Results: The aim of our study was to compare available types of surgical treatment for breast cancer and breast reconstruction to help tailor personalized treatment to patients.
Estimating the Proportion of Overdiagnosis among Prostate, Breast, and Thyroid Cancers in China: Findings from the Global Burden of Disease 2019
Authors: Wang Shuting , Ji Yanlai , Ren Mingxue , Li Jun , Yang Zuyao
Published: September 2024

The incidence of prostate, breast, and thyroid cancers has increased in China over the past few decades. Whether and how much these increases can be attributed to overdiagnosis are less understood. This study aimed to estimate the proportion of overdiagnosis among these three cancers in China during 2004–2019. The age-specific cancer incidence, cancer mortality, and all-cause mortality in China were extracted from the Global Burden of Diseases 2019. The lifetime risk of developing and that of dying from each cancer were calculated using the life table method. The proportion of overdiagnosis of a cancer was estimated as the difference between the lifetime risk of developing the cancer and that of suffering from the cancer (including death, metastasis, and symptoms caused by the cancer), further divided by the lifetime risk of developing the cancer. The highest possible values of these parameters were adopted in the estimation so as to obtain the lower bounds of the proportions of overdiagnosis. Sensitivity analyses assuming different lag periods between the diagnosis of a cancer and death from the cancer were performed. The results showed that the lifetime risk of developing prostate, breast, and thyroid cancer increased dramatically from 2004 to 2019 in China, while the increase in the lifetime risk of dying from these cancers was less pronounced. The proportions of overdiagnosis among prostate, breast, and thyroid cancers were estimated to be 7.88%, 18.99%, and 24.92%, respectively, in 2004, and increased to 18.20%, 26.25%, and 29.24%, respectively, in 2019. The increasing trends were statistically significant for all three cancers (all p < 0.001). In sensitivity analyses, the proportions of overdiagnosis decreased, but upward trends over time remained for all three cancers. In conclusion, the overdiagnosis of prostate, breast, and thyroid cancers in China increased from 2004 to 2019, with the highest proportion seen in thyroid cancer and the most rapid increase seen in prostate cancer. Multifaceted efforts by policy makers, guideline developers, and clinicians are needed to tackle this problem.
Extinct or Not? Confirming the “Extinct” Status of Hieracium tolstoii (Asteraceae) with Integrated Taxonomic Investigation
Authors: Fainelli Federico , Baldesi Giacomo , Pallanza Mattia , Orsenigo Simone
Published: September 2024

Hieracium is one of the largest flowering plant genera that has recently experienced a rapid evolutionary radiation. Due to the massive morphological variation among species, frequent hybridization, and polyploidization events, combined with apomictic reproduction in polyploids, the concept of species in Hieracium is widely debated, and species identification is incredibly challenging. Taxonomic investigation can lead to changes in conservation status or, in some cases, to de-extinctions of species. Taxa with doubtful classification are particularly sensitive to this kind of problem. Hieracium tolstoii was a narrow Italian endemic species that is nowadays considered extinct. Described as a morphological intermediate form between two other species, no further investigations were conducted after its first description. To clarify H. tolstoii’s origin and ensure the validity of its current conservation status, we conducted a taxonomic investigation on herbaria specimens of the early XX century. Specimens were studied using a morphometric analysis that compared H. tolstoii with other closely related taxa. Moreover, we performed phylogenetic investigations using three plastid intergenic spacers to evaluate the relationship between species. Plastid markers revealed the presence of indels and SNPs in H. tolstoii sequences that differ from sequences of the supposed parental species. Morphometry revealed differences among species that led us to confirm the validity of H. tolstoii as an independent apomictic species and, therefore, unfortunately, its extinction.
Some Results for Double Cyclic Codes over Fq+vFq+v2Fq
Authors: Deng Tenghui , Yang Jing
Published: September 2024

Let Fq be a finite field with an odd characteristic. In this paper, we present a new result about double cyclic codes over a finite non-chain ring. Specifically, we study the double cyclic code over Fq+vFq+v2Fq with v3=v, which is isomorphic to Fq×Fq×Fq. This study mainly involves generator polynomials and generator matrices. The generating polynomial of the dual code is also obtained. We show the relationship between the generating polynomials of the double cyclic codes and those of their dual codes. Finally, as an application of these results, we construct some optimal codes over F3.
Fluid Flow Behavior in Nanometer-Scale Pores and Its Impact on Shale Oil Recovery Efficiency
Authors: Dou Xiangji , Qian Menxing , Zhao Xinli , Wang An , Lei Zhengdong , Guo Erpeng , Chen Yufei
Published: September 2024

Shale oil reservoirs, as an unconventional hydrocarbon resource, have the potential to substitute conventional hydrocarbon resources and alleviate energy shortages, making their exploration and development critically significant. However, due to the low permeability and the development of nanopores in shale reservoirs, shale oil production is challenging and recovery efficiency is low. During the imbibition stage, fracturing fluid displaces the oil in the pores primarily under capillary forces, but the complex pore structure of shale reservoirs makes the imbibition mechanism unclear. This research studies the imbibition flow mechanism in nanopores based on the capillary force model and two-phase flow theory, coupled with numerical simulation methods. The results indicated that within a nanopore diameter range of 10–20 nm, increasing the pore diameter leads to a higher imbibition displacement volume. Increased pressure can enhance the imbibition displacement, but the effect diminishes gradually. Under the water-wet conditions, the imbibition displacement volume increases as the contact angle decreases. When the oil phase viscosity decreases from 10 mPa·s to 1 mPa·s, the imbibition displacement rate can increase by 72%. Moreover, merely increasing the water phase viscosity results in only a 5% increase in the imbibition displacement rate. The results provide new insights into the imbibition flow mechanism in nanopores within shale oil reservoirs and offer a theoretical foundation and technical support for efficient shale oil development.
Heat Transfer of Crude Waxy Oil with Yield Stress in a Pipe
Authors: Zhapbasbayev Uzak , Bekibayev Timur , Pakhomov Maksim , Ramazanova Gaukhar
Published: September 2024

This article is devoted to the study of heat exchange of a heated flow of waxy oil in a pipe. Heat exchange between the waxy oil flow and the surrounding environment decreases the oil temperature and sharply increases the rheological properties. The appearance of a solid-like region within the yield-stress fluid flow is a non-trivial problem. This flow property greatly complicates the numerical solution of the system of equations governing the flow and heat transfer of viscoplastic fluids. The Bingham–Papanastasiou model allows one to solve the problem by regularizing the formula for effective molecular viscosity. The novelty of this work lies in establishing the dependence of the Nusselt number on the Reynolds and Bingham numbers for the flow of viscoplastic fluid in a pipe. Via calculations, velocity, temperature, and pressure distributions in the flow were obtained for Bingham numbers ranging from 1.7 to 118.29 and Reynolds numbers ranging from 104 to 2615. The Nusselt number dependence increases with the increase in the Reynolds number and decreases with the decrease in the Bingham number along the pipe length.
A Simplified Guide to Control Algorithms for Grid Converters in Renewable Energy Systems
Authors: Dmitruk Krzysztof
Published: September 2024

This article provides an overview of control algorithms for grid-connected converters in renewable energy systems, demonstrating their relevance and potential for further research. A common issue among authors of scientific works is the lack of a wide range of conducted research. Often, the results showing the operation of the developed algorithms in transient states or under abnormal supply conditions (such as asymmetries and distortions) are missing. This significantly reduces the ability to compare developed solutions, making it difficult to choose the appropriate control method for future research. The article suggests which control algorithm for power converters is best suited for research considering various factors.
Modeling the Operating Conditions of Electric Power Systems Feeding DC and AC Traction Substations
Authors: Iliev Iliya K. , Kryukov Andrey V. , Suslov Konstantin V. , Cherepanov Aleksandr V. , Hieu Nguyen Quoc , Beloev Ivan H. , Valeeva Yuliya S.
Published: September 2024

This paper presents the findings of the research aimed at developing computer models to determine the operating conditions in electric power systems (EPSs) feeding DC and AC railway substations. The object of the research is an EPS with a predominant traction load whose high-voltage power lines are connected to transformer and converter substations with 3 kV and 27.5 kV traction networks. The supply network includes 110 kV and 220 kV power lines. The EPS operating parameters are calculated based on the decomposition of the system into alternating and direct current segments. Calculations are performed for the fundamental frequency and high harmonic frequencies. The modeling technique is universal and can be used to determine the operating parameters and power quality indices for any configuration of an EPS and various designs of traction networks. With this technique, one can solve numerous additional problems, such as calculating the processes of ice melting in traction networks and power lines, determining electromagnetic field strengths, and assessing the heating of power line wires and catenary suspensions. The results obtained show that the voltages on the current collectors are within acceptable limits for all AC and DC electric locomotives. The levels of asymmetry on the 110 and 220 kV tires of traction substations (TP) do not exceed the normally permissible values. The values of the asymmetry coefficients for DC TP are tenths of a percent. With an increase in the size of traffic and in post-emergency conditions caused by the disconnection of communication between one of the support substations and the EPS, the asymmetry indicators on the 220 kV buses of AC substations may exceed the permissible limits. Phase-controlled reactive power sources can be used to reduce them. The analysis of the results of the determination of non-sinusoidal modes allows us to formulate the conclusion that the values of harmonic distortion go beyond the normative limits. Passive and active filters of higher harmonics can be used to normalize them. Calculations of thermal modes of traction transformers show that the temperatures of the most heated points do not exceed acceptable values.