bluuebunny/crossref_metadata_2025 huggingface dataset:
Psychophysical State Aspect during UAV Operations
Authors: Maciejewska Marta , Galant-Gołębiewska Marta , Łodygowski Tomasz
Published: December 2023

The development of unmanned aerial vehicles (UAVs) and the increasing air traffic of these devices make it necessary to pay attention to the issue of the human factor in UAV operations. In this article, tests were conducted in real conditions on the unmanned aerial vehicle operator’s (UAVO) psychophysical state during training. The parameters of the human cardiovascular system, and more specifically the heart rate variability (HRV), were used to conduct research and analysis. The purpose of this research is to elaborate the typical HRV parameters for student operators during UAVO training. These reference values could be used during UAVO training to assess candidates’ psychophysical state objectively and could allow for the monitoring of operators’ state and management of their cognitive load. Monitoring operators’ state may have a positive impact on increasing training effectiveness. Research confirmed the thesis that HRV parameters are significantly different during performed tasks with cognitive load and can be used to assess candidates’ psychophysical state objectively. This can help flight instructors perform student assessment, meaning that they would not have to rely only on their subjective feelings.
Glass-Forming Ability and Magnetic Properties of Al82Fe16Ce2 and Al82Fe14Mn2Ce2 Alloys Prepared by Mechanical Alloying
Authors: Hai Nguyen Hong , Viet Nguyen Hoang , Oanh Nguyen Thi Hoang
Published: December 2023

Al82Fe16Ce2 and Al82Fe14Mn2Ce2 amorphous alloys were successfully synthesized by the mechanical alloying technique. The microstructural evolution of the milled powders was thoroughly investigated employing X-ray diffraction (XRD) and scanning electron microscopy (SEM). Additionally, their magnetic properties were quantitatively evaluated by a vibrating sample magnetometer (VSM). A full amorphous structure was obtained for both alloys after milling for 40 h. During the initial milling stage, extending from 5 to 20 h, an fcc solid solution phase was formed, coexisting with the residual Al phase. The partial substitution of 2 atomic percent (at.%) Mn for Fe in Al82Fe16Ce2 did not affect the alloy’s glass-forming ability. The amorphous Al82Fe16Ce2 and Al82Fe14Mn2Ce2 powders exhibited a nearly spherical shape, with diameters ranging from 1 to 3 µm and to 10 µm, respectively. Additionally, both the Al82Fe16Ce2 and Al82Fe14Mn2Ce2 alloys demonstrated characteristics of hard magnetism.
Single-Image Multi-Parametric Representation of Optical Properties through Encodings to the HSV Color Space
Authors: Mieites Verónica , Gutiérrez-Gutiérrez José A. , López-Higuera José M. , Conde Olga M.
Published: December 2023

The visualization of 2D clinical data often relies on color-coded images, but different colormaps can introduce cognitive biases, impacting result interpretation. Moreover, when using color for diagnosis with multiple biomarkers, the application of distinct colormaps for each parameter can hinder comparisons. Our aim was to introduce a visualization technique that utilizes the hue (H), saturation (S), and value (V) in a single image to convey multi-parametric data on various optical properties in an effective manner. To achieve this, we conducted a study involving two datasets, one comprising multi-modality measurements of the human aorta and the other featuring multiple parameters of dystrophic mice muscles. Through this analysis, we determined that H is best suited to emphasize differences related to pathology, while V highlights high-spatial-resolution data disparities, and color alterations effectively indicate changes in chemical component concentrations. Furthermore, encoding structural information as S and V within the same image assists in pinpointing the specific locations of these variations. In cases where all data are of a high resolution, H remains the optimal indicator of pathology, ensuring results’ interpretability. This approach simplifies the selection of an appropriate colormap and enhances the ability to grasp a sample’s characteristics at a single glance.
Fault Diagnosis Strategy Based on BOA-ResNet18 Method for Motor Bearing Signals with Simulated Hydrogen Refueling Station Operating Noise
Authors: Liu Shuyi , Chen Shengtao , Chen Zuzhi , Gong Yongjun
Published: December 2023

The harsh working environment of hydrogen refueling stations often causes equipment failure and is vulnerable to mechanical noise during monitoring. This limits the accuracy of equipment monitoring, ultimately decreasing efficiency. To address this issue, this paper presents a motor bearing vibration signal diagnosis method that employs a Bayesian optimization (BOA) residual neural network (ResNet). The industrial noise signal of the hydrogenation station is simulated and then combined with the motor bearing signal. The resulting one-dimensional bearing signal is processed and transformed into a two-dimensional signal using Fast Fourier Transform (FFT). Afterwards, the signal is segmented using the sliding window translation method to enhance the data volume. After comparing signal feature extraction and classification results from various convolutional neural network models, ResNet18 yields the best classification accuracy, achieving a training accuracy of 89.50% with the shortest computation time. Afterwards, the hyperparameters of ResNet18 such as InitialLearnRate, Momentum, and L2Regularization Parameter are optimized using the Bayesian optimization algorithm. The experiment findings demonstrate a diagnostic accuracy of 99.31% for the original signal model, while the accuracy for the bearing signal, with simulated industrial noise from the hydrogenation station, can reach over 92%.
MLRNet: A Meta-Loss Reweighting Network for Biased Data on Text Classification
Authors: Yu Hao , Li Xinfu
Published: December 2023

Artificially generated datasets often exhibit biases, leading conventional deep neural networks to overfit. Typically, a weighted function adjusts sample impact during model updates using weighted loss. Meta-neural networks, trained with meta-learning principles, generalize well across tasks, acquiring generalized weights. This enables the self-generation of tailored weighted functions for data biases. However, datasets may simultaneously exhibit imbalanced classes and corrupted labels, posing a challenge for current meta-models. To address this, this paper presents Meta-Loss Reweighting Network (MLRNet) with fusion attention features. MLRNet continually evolves sample loss values, integrating them with sample features from self-attention layers in a semantic space. This enhances discriminative power for biased samples. By employing minimal unbiased meta-data for guidance, mutual optimization between the classifier and the meta-model is conducted, endowing biased samples with more reasonable weights. Experiments on English and Chinese benchmark datasets including artificial and real-world biased data show MLRNet’s superior performance under biased data conditions.
Novel Cooling Strategy for a Hybrid Photovoltaic/Parabolic Dish Concentrator
Authors: Saeed Farooq , Maatallah Taher , Houcine Ahlem , Jamal Arshad , Ali Sajid
Published: December 2023

In this paper, the thermo-optical performance using novel cooling strategy improvements for a hybrid photovoltaic/parabolic dish concentrator with a conical thermal receiver using a beam splitter filter (PV/PDC-CTR-BSF) is investigated. The study’s main goal is to improve the cooling effectiveness of the serpentine-shaped cooling duct by investigating the effect of the cross-section shape and positioning of the cooling duct under the PV panel. Typical cooling pipes have either a rectangular or circular cross-section and are usually attached to the back sheet of the PV panel using off-the-shelf adhesives that have very low thermal conductivity. With the advent of 3D printing technology, the back sheets could be 3D-printed with integral cooling ducts of different cross-sections at different locations and orientations within the back sheet that allow for increased heat transfer from the back sheet and thus improve PV/PDC-CTR-BSF’s thermos-optical performance. For this purpose, the study investigates and compares the thermal performance of four different cooling duct cross-sections that include: rectangular, semi-circular, semi-elliptical and triangular. For each of the cooling duct cross-sections, several positions and orientations, which include flush below the back sheet layer and embedded inside the back sheet but positioned at the bottom, middle and top of the back sheet, are examined. Numerical simulations using the commercial software ANSYS FLUENT(R2019) are performed to assess the performance of the cooling ducts and, in turn, the thermo-optical performance of the PV/PDC-CTR-BSF system. The semi-elliptical cross-section duct embedded in the middle of the back sheet was found to yield the best cooling performance since its rate of heat removal from the PV back sheet was found to be the highest.
Data-Driven Approach for Defining Demand Scenarios for Shared Autonomous Cargo Bike Fleets
Authors: Kania Malte , Mukku Vasu Dev , Kastner Karen , Assmann Tom
Published: December 2023

Bike sharing systems have become a sustainable alternative to motorized private transport in urban areas. However, users often face high costs and availability issues due to the operational effort required to redistribute bicycles between stations. For addressing those issues, the AuRa (Autonomes Rad, Eng. Autonomous Bicycle) project introduces a new mobility offer in terms of an on-demand, shared-use, self-driving cargo bikes service (OSABS) that enables automated redistribution. Within the project, we develop different order management and rebalancing strategies and validate them using simulation models. One prerequisite for this is sound demand scenarios. However, due to the novelty of OSABS, there is currently no information about its utilization. Consequently, the objective of this study was to develop an approach for defining OSABS demand scenarios in a temporally and spatially disaggregated manner as an input for simulation models. Therefore, we first derived city-wide usage potentials of OSABS from a survey on mobility needs. We then spatially and temporally disaggregated the determined usage likelihood using travel demand matrices and usage patterns from a conventional bike-sharing system, respectively. Finally, we performed cluster analyses on the resulting annual demand to summarize sections of the yearly profile into representative units and thus reduce the simulation effort. As we applied this approach as a case study to the city of Magdeburg, Germany, we could show that our methodology enables the determination of reasonable OSABS demand scenarios from scratch. Furthermore, we were able to show that annual usage patterns of (conventional) bike sharing systems can be modeled by using demand data for only eight representative weeks.
Gradient-Oriented Prioritization in Meta-Learning for Enhanced Few-Shot Fault Diagnosis in Industrial Systems
Authors: Sun Dexin , Fan Yunsheng , Wang Guofeng
Published: December 2023

In this paper, we propose the gradient-oriented prioritization meta-learning (GOPML) algorithm, a new approach for few-shot fault diagnosis in industrial systems. The GOPML algorithm utilizes gradient information to prioritize tasks, aiming to improve learning efficiency and diagnostic accuracy. This method contrasts with conventional techniques by considering both the magnitude and direction of gradients for task prioritization, which potentially enhances fault classification performance in scenarios with limited data. Our evaluation of GOPML’s performance across varied fault conditions and operational contexts includes extensive testing on the Tennessee Eastman Process (TEP) and Skoltech Anomaly Benchmark (SKAB) datasets. The results indicate a consistent level of performance across different dataset divisions, suggesting its utility in practical industrial settings. The adaptability of GOPML to specific task characteristics, particularly in environments with sparse data, represents a notable contribution to the field of meta-learning for industrial fault diagnosis. GOPML shows promise in addressing the challenges of few-shot fault diagnosis in industrial systems, contributing to the growing body of research in this area by offering an approach that balances accuracy and generalization with limited data.
Measuring the Risk of Vulnerabilities Exploitation
Authors: Brilhante Maria de Fátima , Pestana Dinis , Pestana Pedro , Rocha Maria Luísa
Published: December 2023

Modeling the vulnerabilities lifecycle and exploitation frequency are at the core of security of networks evaluation. Pareto, Weibull, and log-normal models have been widely used to model the exploit and patch availability dates, the time to compromise a system, the time between compromises, and the exploitation volumes. Random samples (systematic and simple random sampling) of the time from publication to update of cybervulnerabilities disclosed in 2021 and in 2022 are analyzed to evaluate the goodness-of-fit of the traditional Pareto and log-normal laws. As censoring and thinning almost surely occur, other heavy-tailed distributions in the domain of attraction of extreme value or geo-extreme value laws are investigated as suitable alternatives. Goodness-of-fit tests, the Akaike information criterion (AIC), and the Vuong test, support the statistical choice of log-logistic, a geo-max stable law in the domain of attraction of the Fréchet model of maxima, with hyperexponential and general extreme value fittings as runners-up. Evidence that the data come from a mixture of differently stretched populations affects vulnerabilities scoring systems, specifically the common vulnerabilities scoring system (CVSS).
Hans Namuth’s Photographs and Film Studies of Jackson Pollock: Transforming American Postwar Avant-Garde Labor into Popular Consumer Spectacle
Authors: Mohan Joseph
Published: December 2023

Abstract Expressionism is often regarded as the first purely American art movement and the first to gain mass cultural recognition. Prior to the 1940s, the consideration and appreciation of abstract art belonged to a certain intellectual elite, but the intimidating complexity of Abstract Expressionism, the daring allure of its artists, and the particularities of mid-century American culture converged to transform the avant-garde into consumer spectacle. This shift represented, and was symptomatic of, a larger societal rearrangement: information and commodity superseded industrialized labor as the core of American culture. Jackson Pollock, America’s first avant-garde superstar, stood at the center of this shift, at once representing both active creative labor and the commodification of the idea of that labor. Hans Namuth’s photographs and films of Pollock placed him and his art firmly in the realm of consumable popular spectacle, underlying further connections to Hollywood film and prominent print media. This article examines how Pollock became a paradigmatic figure in the avant-garde’s proliferation into mass culture and asserts that mass culture did not simply subsume the avant-garde. Rather, the two realms engaged in a mutual construction that pushed the avant-garde across numerous social boundaries. The artistic, critical, and popular receptions that grew out of this convergence erased distinctions between ‘high’ and ‘low’ culture.
Characterization of the Energy Balance of Wheat Grown under Irrigation in the Hot, Arid Environment of Sudan
Authors: Mohammed Almutaz Abdelkarim Abdelfattah , Tsubo Mitsuru , Kurosaki Yasunori , Ibaraki Yasuomi
Published: December 2023

An analysis of the crop microclimate is essential for assessing the climate’s appropriateness for cultivation. Here, the Bowen ratio (BR) was used to characterize the energy balance in an irrigated wheat field in a hot, arid environment in Sudan. The hourly BR was calculated using micrometeorological data, including net radiation (Rn) and soil heat flux (G), observed in the 2021–2022 and 2022–2023 growing seasons (December–March) and used to compute hourly daytime latent heat (LE) and sensible heat (H) fluxes during the days before and after irrigation. In both seasons, the observed significant evaporative cooling effect of irrigation led to a daily maximum temperature difference of 2.5–5.7 °C between the wheat field and a nearby meteorological station in a non-vegetated desert area. The energy balance calculation results showed that LE was dominant over H and G. Because BR tended to be negative, H was negative; thus, LE was larger than Rn because of sensible heat advection from the surrounding area. Further, a decrease in G after irrigation indicated an alteration in the soil’s thermal properties. These results provide insights into the micrometeorological response of irrigated wheat to a hot, arid environment and useful information for understanding soil–plant–atmosphere interactions in hot, dry environments.
Explainable Boosting Machine: A Contemporary Glass-Box Strategy for the Assessment of Wind Shear Severity in the Runway Vicinity Based on the Doppler Light Detection and Ranging Data
Authors: Khattak Afaq , Zhang Jianping , Chan Pak-Wai , Chen Feng , Almujibah Hamad
Published: December 2023

Pilots commonly undergo training to effectively manage instances of wind shear (WS) during both the landing and takeoff stages. Nevertheless, in exceptional circumstances, there may be instances of severe wind shear (SWS) surpassing a magnitude of 30 knots, leading to adverse effects on the operation of taking off and landing aircraft. This phenomenon can lead to the execution of aborted landing maneuvers and deviations from the intended glide path. This study utilized the explainable boosting machine (EBM), an advanced machine learning (ML) model known for its transparency, to predict the severity of WS occurrences and analyze the underlying factors. The dataset consisted of 21,392 data points from 2018 to 2022 acquired from two Doppler light detection and ranging (LiDAR) systems installed at Hong Kong International Airport (HKIA). Initially, the Doppler LiDAR data received data treatment in order to address the issue of data imbalance. Subsequently, utilizing the processed data, the hyperparameters of EBM were optimized using the Bayesian optimization technique. The EBM model underwent subsequent training and evaluation, wherein its performance metrics were computed and compared with those of an alternative glass-box model including decision tree (DT) and counterpart black-box models, namely, random forest (RF) and extreme gradient boosting (XGBoost). The EBM model trained on synthetic minority oversampling technique (SMOTE)-treated data demonstrated superior performance in comparison with the alternative models, as indicated by its higher geometric mean (0.77), balanced accuracy (0.78), and Matthews’ correlation coefficient (0.169). Furthermore, the EBM exhibited enhanced predictive performance and facilitated a comprehensive analysis of individual and pairwise factor interactions in the prediction of WS severity. This enabled the assessment of the factors that contributed to the instances of SWS in the proximity of airport runways.
Recent Research Progress on Black Carbon Emissions from Marine Diesel Engines
Authors: Wu Gang , Umar Jalloh Alpha , Li Tie , Zhou Xinyi , Chen Changsheng , Li Jiaqi , Chen Biwen
Published: December 2023

Black carbon (BC) emissions from shipboard diesel engines are the next potentially important issue of interest to the International Maritime Organization (IMO) and are considered to have a significant impact on the climate environment and human health. However, theories and technologies regarding the mechanisms of black carbon formation, oxidizing and influencing factors, emission detection methods, and abatement techniques are still missing in science and engineering. This paper provides a comprehensive overview of relevant advances in international maritime regulations, the frontier theory on formation mechanisms, comprehensive physical and chemical properties, and the potential reduction measures and control measures of emissions. These results suggest that BC is produced in the combustion flame of fuel and is related to the nucleation as well as the formation of PAHs. It helps to understand the initial generation process of black carbon and reduce its emission by studying it in detail and revealing some key factors, including micromorphology, nanostructural features, surface functional groups, oxidizing activity, size distribution, and elemental composition. Further, an in-depth understanding of the complex characteristics of BC can also help to identify viable BC measurement techniques and instrumentation for marine engines, thereby enhancing emission regulation. Overall, extensive technology can reduce BC emissions from marine diesel engines by approximately 50%. The information contained in this report can be used as a significant reference to further explore the BC formation mechanism and develop exclusive BC emission control strategies.
The Importance of Wind Simulations over Dried Lake Beds for Dust Emissions in the Middle East
Authors: Hamzeh Nasim Hossein , Abadi Abbas Ranjbar Saadat , Kaskaoutis Dimitris G. , Mirzaei Ebrahim , Shukurov Karim Abdukhakimovich , Sotiropoulou Rafaella-Eleni P. , Tagaris Efthimios
Published: December 2023

Dust storms are one of the major environmental hazards affecting the Middle East countries, and largely originate in vast deserts and narrow dried lake beds. This study analyzes the inter-annual variation in dust weather conditions from 2000 to 2020 using data obtained from ten meteorological stations located around dried (completely or partly) lakes in Northwest (Urmia Lake) and South (Bakhtegan Lake) Iran. Since the wind regime is one of the most important factors controlling dust emissions in the dust source areas, wind speed simulations from the Weather Research and Forecasting (WRF) Model for 134,113 grid points covering the Middle East area, with a resolution of 5 km, were analyzed and compared with wind measurements at the stations around Urmia and Bakhtegan Lakes from 2005 to 2015. The analysis shows that the annual number of dust days was highly variable, presenting a significant increase at the stations around Urmia Lake during 2008–2011 and at the stations around Bakhtegan Lake in 2007–2012. Eleven years of WRF simulations of the mean diurnal wind patterns revealed that the highest 10 m wind speed occurred mostly around the local noon (12 to 15 UTC), generally coinciding with the majority of the reported dust codes within this time frame, as a result of the association between wind speed and dust emissions (dust weather conditions) around these lake basins. Consequently, accurate wind simulation has high importance for unbiased numerical prediction and forecasting of dust conditions. The comparison between the measured mean monthly 10 m wind speed and WRF-simulated 10 m wind speed revealed that the model overestimated wind data in all the stations around the Bakhtegan Lake but performed better at reconstructing the wind speeds at stations around Urmia Lake. Furthermore, notable differences were observed between measured and simulated wind directions, thus leading to uncertainties in the simulations of the dust-plume transport.
A Mixture Quantitative Randomized Response Model That Improves Trust in RRT Methodology
Authors: Parker Michael , Gupta Sat , Khalil Sadia
Published: December 2023

The Quantitative Randomized Response Technique (RRT) can be used by researchers to obtain honest answers to questions that, due to their sensitive (socially undesirable, dangerous, or even illegal) nature, might otherwise invoke partially or completely falsified responses. Over the years, Quantitative RRT models, sometimes called Scrambling models, have been developed to incorporate such advancements as mixture, optionality and enhanced trust, each of which has important benefits. However, no single model incorporates all of these features. In this study, we propose just such a unified model, which we call the Mixture Optional Enhanced Trust (MOET) model. After developing methodologies to assess MOET based on standard approaches and using them to explore the key characteristics of the new model, we show that MOET has superior efficiency compared to the Quantitative Optional Enhanced Trust (OET) model. We also show that use of the model’s mixture capability allows practitioners to optimally balance the model’s efficiency with its privacy, making the model adaptable to a wide variety of research scenarios.
Further Geometric Properties of the Barnes–Mittag-Leffler Function
Authors: Alenazi Abdulaziz , Mehrez Khaled
Published: December 2023

In this paper, we find sufficient conditions to be imposed on the parameters of a class of functions related to the Barnes–Mittag-Leffler function that allow us to conclude that it possesses certain geometric properties (such as starlikeness, uniformly starlike (convex), strongly starlike (convex), convexity, and close-to-convexity) in the unit disk. The key tools in some of our proofs are the monotonicity properties of a certain class of functions related to the gamma function.
Surfactants as Performance-Enhancing Additives in Supercapacitor Electrolyte Solutions—An Overview
Authors: Chen Xuecheng , Holze Rudolf
Published: December 2023

Wetting the surface area of an electrode material as completely as possible is desirable to achieve optimum specific capacity of an electrode material. Keeping this surface area utilized even at high current densities and even when inside pores is required for high capacitance retention. The addition of surfactants at very small concentrations to aqueous supercapacitor electrolyte solutions has been suggested as a way to improve performance in terms of capacitance, capacitance retention at increased current density and stability. Effects are pronounced with carbon materials used in electrochemical double-layer capacitors; they are also observed with redox materials. The causes of the observed improvements and mode of operation of the added surfactants seem to need further investigation; they are inconclusive beyond the obvious statement of increased wetting. Reported examples and the current state of understanding are reviewed.
Batch Fabrication of Electrospun PAN/PU Composite Separators for Safe Lithium-Ion Batteries
Authors: Ding Wenfei , Xu Lan
Published: December 2023

As an important element of lithium-ion batteries (LIBs), the separator plays a critical role in the safety and comprehensive performance of the battery. Electrospun nanofiber separators have a high porosity and good electrolyte affinity, which are favorable to the transference of lithium ions. In this paper, the batch preparation of polyacrylonitrile (PAN)-based nanofiber separators are obtained via spherical section free surface electrospinning (SSFSE). Introducing an appropriate amount of polyester polyurethane (PU) can effectively enhance the mechanical property of PAN nanofiber separators and help the separators resist the external force extrusion. The results show that when PAN:PU = 8:2, the porosity and electrolyte uptake rate of the composite nanofiber separator (PAN-2) are 62.9% and 643.3%, respectively, exhibiting a high ionic conductivity (1.90 mS/cm). Additionally, the coin battery assembled with PAN-2 as a separator (LiFePO4/PAN-2/lithium metal) shows good cycling performance and good rate performance, with a capacity retention rate of 93.9% after 100 cycles at 0.5 C, indicating that the battery with PAN-2 has a good application potential in advanced energy storage.
Effect of 3-Dimensional Robotic Therapy Combined with Electromyography-Triggered Neuromuscular Electrical Stimulation on Upper Limb Function and Cerebral Cortex Activation in Stroke Patients: A Randomized Controlled Trial
Authors: Yang Seo-Won , Ma Sung-Ryong , Choi Jong-Bae
Published: December 2023

(1) Background: This study investigated the effect of 3-dimensional robotic therapy (RT) combined with electromyography-triggered neuromuscular electrical stimulation (RT–ENMES) on stroke patients’ upper-limb function and cerebral cortex activation. (2) Methods: Sixty-one stroke patients were assigned randomly to one of three groups. The stroke patients were in the subacute stage between 2 and 6 months after onset. The three groups received 20 min of RT and 20 min of electromyography-triggered neuromuscular electrical stimulation (ENMES) in the RT–ENMES group (n = 21), 40 min of RT in the RT group (n = 20), and 40 min of ENMES in the ENMES group (n = 20). The treatments were for 40 min, 5 days per week, and for 8 weeks. Upper-extremity function was evaluated using the Fugl–Meyer assessment for upper extremity (FMA-UE), Wolf motor function test, and action research arm test (ARAT); cerebral cortex activation and motor-evoked potential (MEP) amplitude were evaluated before and after the study. (3) Results: The analysis showed significant changes in all evaluation items for all three groups in the before-and-after comparisons. Significant changes were observed in the FMA-UE, ARAT, and MEP; in the posttest, the RT–ENMES group showed more significant changes in the FMA-UE, ARAT, and MEP than the other two groups. (4) Conclusions: The study analysis suggests that RT–ENMES effectively improves upper-limb function and cerebral cortex activation in patients with stroke.
A Systematic Comparison of Task Adaptation Techniques for Digital Histopathology
Authors: Sauter Daniel , Lodde Georg , Nensa Felix , Schadendorf Dirk , Livingstone Elisabeth , Kukuk Markus
Published: December 2023

Due to an insufficient amount of image annotation, artificial intelligence in computational histopathology usually relies on fine-tuning pre-trained neural networks. While vanilla fine-tuning has shown to be effective, research on computer vision has recently proposed improved algorithms, promising better accuracy. While initial studies have demonstrated the benefits of these algorithms for medical AI, in particular for radiology, there is no empirical evidence for improved accuracy in histopathology. Therefore, based on the ConvNeXt architecture, our study performs a systematic comparison of nine task adaptation techniques, namely, DELTA, L2-SP, MARS-PGM, Bi-Tuning, BSS, MultiTune, SpotTune, Co-Tuning, and vanilla fine-tuning, on five histopathological classification tasks using eight datasets. The results are based on external testing and statistical validation and reveal a multifaceted picture: some techniques are better suited for histopathology than others, but depending on the classification task, a significant relative improvement in accuracy was observed for five advanced task adaptation techniques over the control method, i.e., vanilla fine-tuning (e.g., Co-Tuning: P(≫) = 0.942, d = 2.623). Furthermore, we studied the classification accuracy for three of the nine methods with respect to the training set size (e.g., Co-Tuning: P(≫) = 0.951, γ = 0.748). Overall, our results show that the performance of advanced task adaptation techniques in histopathology is affected by influencing factors such as the specific classification task or the size of the training dataset.
Usability and Feasibility Assessment of a Social Assistive Robot for the Older People: Results from the GUARDIAN Project
Authors: Amabili Giulio , Maranesi Elvira , Margaritini Arianna , Benadduci Marco , Barbarossa Federico , Casaccia Sara , Nap Henk Herman , Bevilacqua Roberta
Published: December 2023

In Italy, many people aged over 65 cannot live independently, causing an overall decrease in their quality of life and a need for social and health care. Due to the lack of both formal and informal caregivers, technological solutions become of paramount importance in this scenario. This article describes the user-centered development of the GUARDIAN ecosystem, consisting of a social robot integrated with two mobile applications which aim to monitor, coach, and keep the older user company in order to prolong his/her independence at home. In particular, the advancements from the alpha to the beta prototype of the ecosystem are described, achieved through the feedback collected from 41 end users—older people and their carers—that have tested the system for 6 weeks. By enhancing human–robot interaction, a positive improvement in terms of usability and acceptability of the system was retrieved. However, to increase the perceived usefulness and the impact on older users’ lives, it is necessary to make the entire system more customizable, and more capable in providing support for daily activities.
A Genetic Circuit Design for Targeted Viral RNA Degradation
Authors: Bello Adebayo J. , Popoola Abdulgafar , Okpuzor Joy , Ihekwaba-Ndibe Adaoha E. , Olorunniji Femi J.
Published: December 2023

Advances in synthetic biology have led to the design of biological parts that can be assembled in different ways to perform specific functions. For example, genetic circuits can be designed to execute specific therapeutic functions, including gene therapy or targeted detection and the destruction of invading viruses. Viral infections are difficult to manage through drug treatment. Due to their high mutation rates and their ability to hijack the host’s ribosomes to make viral proteins, very few therapeutic options are available. One approach to addressing this problem is to disrupt the process of converting viral RNA into proteins, thereby disrupting the mechanism for assembling new viral particles that could infect other cells. This can be done by ensuring precise control over the abundance of viral RNA (vRNA) inside host cells by designing biological circuits to target vRNA for degradation. RNA-binding proteins (RBPs) have become important biological devices in regulating RNA processing. Incorporating naturally upregulated RBPs into a gene circuit could be advantageous because such a circuit could mimic the natural pathway for RNA degradation. This review highlights the process of viral RNA degradation and different approaches to designing genetic circuits. We also provide a customizable template for designing genetic circuits that utilize RBPs as transcription activators for viral RNA degradation, with the overall goal of taking advantage of the natural functions of RBPs in host cells to activate targeted viral RNA degradation.
bioGWAS: A Simple and Flexible Tool for Simulating GWAS Datasets
Authors: Changalidis Anton I. , Alexeev Dmitry A. , Nasykhova Yulia A. , Glotov Andrey S. , Barbitoff Yury A.
Published: December 2023

Genome-wide association studies (GWAS) have proven to be a powerful tool for the identification of genetic susceptibility loci affecting human complex traits. In addition to pinpointing individual genes involved in a particular trait, GWAS results can be used to discover relevant biological processes for these traits. The development of new tools for extracting such information from GWAS results requires large-scale datasets with known biological ground truth. Simulation of GWAS results is a powerful method that may provide such datasets and facilitate the development of new methods. In this work, we developed bioGWAS, a simple and flexible pipeline for the simulation of genotypes, phenotypes, and GWAS summary statistics. Unlike existing methods, bioGWAS can be used to generate GWAS results for simulated quantitative and binary traits with a predefined set of causal genetic variants and/or molecular pathways. We demonstrate that the proposed method can recapitulate complete GWAS datasets using a set of reported genome-wide associations. We also used our method to benchmark several tools for gene set enrichment analysis for GWAS data. Taken together, our results suggest that bioGWAS provides an important set of functionalities that would aid the development of new methods for downstream processing of GWAS results.
Monitoring Immune Modulation in Season Population: Identifying Effects and Markers Related to Apis mellifera ligustica Honey Bee Health
Authors: Frunze Olga , Kim Hyunjee , Kim Byung-ju , Lee Jeong-Hyeon , Bilal Mustafa , Kwon Hyung-Wook
Published: December 2023

Honey bees play a significant role in ecology, producing biologically active substances used to promote human health. However, unlike humans, the molecular markers indicating honey bee health remain unknown. Unfortunately, numerous reports of honey bee collapse have been documented. To identify health markers, we analyzed ten defense system genes in Apis mellifera ligustica honey bees from winter (Owb) and spring (Fb for foragers and Nb for newly emerged) populations sampled in February and late April 2023, respectively. We focused on colonies free from SBV and DWV viruses. Molecular profiling revealed five molecular markers of honey bee health. Of these, two seasonal molecular markers—domeless and spz genes—were significantly downregulated in Owb compared to Nb and Fb honey bees. One task-related marker gene, apid-1, was identified as being downregulated in Owb and Nb compared to Fb honey bees. Two recommended general health markers, SOD and defensin-2, were upregulated in honey bees. These markers require further testing across various honey bee subspecies in different climatic regions. They can diagnose bee health without colony intervention, especially during low-temperature months like winter. Beekeepers can use this information to make timely adjustments to nutrients or heating to prevent seasonal losses.
Uncoupling Protein 3 Catalyzes the Exchange of C4 Metabolites Similar to UCP2
Authors: Kreiter Jürgen , Tyschuk Tatyana , Pohl Elena E.
Published: December 2023

Uncoupling protein 3 (UCP3) belongs to the mitochondrial carrier protein superfamily SLC25 and is abundant in brown adipose tissue (BAT), the heart, and muscles. The expression of UCP3 in tissues mainly dependent on fatty acid oxidation suggests its involvement in cellular metabolism and has drawn attention to its possible transport function beyond the transport of protons in the presence of fatty acids. Based on the high homology between UCP2 and UCP3, we hypothesized that UCP3 transports C4 metabolites similar to UCP2. To test this, we measured the transport of substrates against phosphate (32Pi) in proteoliposomes reconstituted with recombinant murine UCP3 (mUCP3). We found that mUCP3 mainly transports aspartate and sulfate but also malate, malonate, oxaloacetate, and succinate. The transport rates calculated from the exchange of 32Pi against extraliposomal aspartate and sulfate were 23.9 ± 5.8 and 17.5 ± 5.1 µmol/min/mg, respectively. Using site-directed mutagenesis, we revealed that mutation of R84 resulted in impaired aspartate/phosphate exchange, demonstrating its critical role in substrate transport. The difference in substrate preference between mUCP2 and mUCP3 may be explained by their different tissue expression patterns and biological functions in these tissues.
Gross Chromosomal Rearrangement at Centromeres
Authors: Xu Ran , Pan Ziyi , Nakagawa Takuro
Published: December 2023

Centromeres play essential roles in the faithful segregation of chromosomes. CENP-A, the centromere-specific histone H3 variant, and heterochromatin characterized by di- or tri-methylation of histone H3 9th lysine (H3K9) are the hallmarks of centromere chromatin. Contrary to the epigenetic marks, DNA sequences underlying the centromere region of chromosomes are not well conserved through evolution. However, centromeres consist of repetitive sequences in many eukaryotes, including animals, plants, and a subset of fungi, including fission yeast. Advances in long-read sequencing techniques have uncovered the complete sequence of human centromeres containing more than thousands of alpha satellite repeats and other types of repetitive sequences. Not only tandem but also inverted repeats are present at a centromere. DNA recombination between centromere repeats can result in gross chromosomal rearrangement (GCR), such as translocation and isochromosome formation. CENP-A chromatin and heterochromatin suppress the centromeric GCR. The key player of homologous recombination, Rad51, safeguards centromere integrity through conservative noncrossover recombination between centromere repeats. In contrast to Rad51-dependent recombination, Rad52-mediated single-strand annealing (SSA) and microhomology-mediated end-joining (MMEJ) lead to centromeric GCR. This review summarizes recent findings on the role of centromere and recombination proteins in maintaining centromere integrity and discusses how GCR occurs at centromeres.
Comparison of Transcriptomic Analysis of the Conjunctiva in Glaucoma-Treated Eyes with Dry Eyes and Healthy Controls
Authors: Carnero Elena , Irigoyen-Bañegil Cristina , Gutiérrez Itziar , Extramiana Leire , Sabater Alfonso L. , Moreno-Montañes Javier
Published: December 2023

Ocular surface disease (OSD) associated with topical glaucoma drugs is a common issue impacting treatment adherence. We aimed to identify conjunctival transcriptomic changes in glaucoma and dry eye patients, comparing them to healthy controls. Bulbar conjunctival specimens were collected via impression cytology from 33 patients treated for glaucoma, 9 patients with dry eye, and 14 healthy controls. RNA extraction and bulk RNA sequencing were performed, followed by bioinformatics analysis to detect gene dysregulation. Ingenuity pathways analysis (IPA) identified pathways and biological processes associated with these transcriptomic changes. Sequencing analysis revealed 200 modified genes in glaucoma patients compared to healthy individuals, 233 differentially expressed genes in dry eye patients versus controls, and 650 genes in treated versus dry eye samples. In glaucoma patients, 79% of altered pathways were related to host defense, while dry eye patients showed a 39% involvement of host response, 15% in cellular proliferation and integrity, and 16% of mitochondrial dysfunction. These findings were validated through qRT-PCR. Glaucoma patients showed an intensified conjunctival immune response as a potential cause of OSD, whereas in dry eye patients, in addition to the immune response, other mechanisms such as mitochondrial dysfunction or reduced cellular proliferation were observed.
Retinal Functional Impairment in Diabetic Retinopathy
Authors: Tănasie Cornelia Andreea , Dan Alexandra Oltea , Ică Oana Maria , Mercuț Maria Filoftea , Mitroi George , Taisescu Citto-Iulian , Sfredel Veronica , Corbeanu Ramona Ingrid , Mocanu Carmen Luminița , Danielescu Ciprian
Published: December 2023

Background: Diabetic retinopathy (DR) is a neurodegenerative disease of the retina. The aim of our study was to analyze latency changes in a full-field electroretinogram (ERG) in patients with type 2 diabetes. Material: This prospective study included 15 diabetic patients without DR, 16 diabetic patients with non-proliferative DR, 14 patients with pre-proliferative DR, 15 patients with proliferative DR, and 14 age-matched controls. All the participants underwent ophthalmologic examination and full-field ERGs. The ERGs were recorded with the Metrovision MonPackOne system. The latencies were analyzed for “a”- and “b”-waves in the dark-adapted (DA) 0.01 ERG, DA 3.0 ERG, DA oscillatory potentials, light-adapted (LA) 3.0 ERG, and 30 Hz flicker ERG. Results: The delayed responses of healthy subjects compared to diabetic patients without DR were the DA oscillatory potentials (25.45 ± 1.04 ms vs. 26.15 ± 0.96 ms, p = 0.027). When comparing diabetic patients without DR and with non-proliferative DR, we did not obtain statistically significant delays. Significant delays in the DA 0.01 “b”-wave (61.91 ± 5.52 ms vs. 66.36 ± 8.12 ms, p = 0.029), DA 3.0 “b”-wave (41.01 ± 2.50 ms vs. 44.16 ± 3.78 ms, p = 0.035), and LA 3.0 “a”-wave (16.21 ± 0.91 ms vs. 16.99 ± 1.16 ms, p = 0.045) were found between non-proliferative DR and pre-proliferative DR. When comparing the groups of patients with pre-proliferative DR and proliferative DR, the LA 3.0 ERG “b”-wave (32. 63 ± 2.53 ms vs. 36.19 ± 3.21 ms, p < 0.0001), LA 30 Hz flicker ERG “a”-wave (19.56 ± 3.59 vs. 21.75 ± 4.74 ms, p= 0.025), and “b”-wave (32.23 ± 4.02 vs. 36.68 ± 3.48 ms, p = 0.017) were delayed. Conclusions: the electrophysiological findings from our study indicate that there is a substantial dysfunction of the neural retina in all stages of DR.
Signal Transducer and Activator of Transcription Proteins at the Nexus of Immunodeficiency, Autoimmunity and Cancer
Authors: Liongue Clifford , Sobah Mohamed Luban , Ward Alister C.
Published: December 2023

The signal transducer and activator of transcription (STAT) family of proteins has been demonstrated to perform pivotal roles downstream of a myriad of cytokines, particularly those that control immune cell production and function. This is highlighted by both gain-of-function (GOF) and loss-of-function (LOF) mutations being implicated in various diseases impacting cells of the immune system. These mutations are typically inherited, although somatic GOF mutations are commonly observed in certain immune cell malignancies. This review details the growing appreciation of STAT proteins as a key node linking immunodeficiency, autoimmunity and cancer.
Adiponectin C1q/Tumor Necrosis Factor-Related Protein 13 (CTRP13) Protects against Renal Inflammation and Fibrosis in Obstructive Nephropathy
Authors: Li Yongxia , Wang Wenzhe , Liu Changxuan , Zeng Min , Xu Li , Du Rong , Wang Cheng
Published: December 2023

Renal inflammation and fibrosis are the important pathological phenomena associated with obstructive nephropathy. However, the underlying mechanism associated with this disease has yet to be fully elucidated. The present study, therefore, aimed to investigate the effects mediated by C1q/tumor necrosis factor-related protein 13 (CTRP13) on renal inflammation and fibrosis in addition to elucidating the underlying mechanism. To meet this aim, a mouse unilateral ureteral obstruction (UUO)-mediated renal dysfunction model was established. In addition, hematoxylin–eosin staining (H&E) staining and immunofluorescence experiments as well as Western blotting and reverse transcription quantitative (RT q) PCR analyses were performed. Recombinant CTRP13 was used to investigate the role of CTRP13 in chronic renal inflammation and fibrosis. A decreased expression level of CTRP13 was identified in the plasma of patients with renal fibrosis and in UUO-model mice. The renal histopathological and functional analyses revealed that CTRP13 could both reverse UUO mediated renal dysfunction and ameliorate the conditions of tubulointerstitial fibrosis and tubular injury. Additionally, CTRP13 was found to inhibit the expression levels of extracellular matrix proteins and proinflammatory mediators. In terms of the underlying mechanism, the protective effects on inflammation and fibrosis of the kidneys of CTRP13-treated mice undergoing UUO were found to be associated with the inactivation of the TGF β/Smad and NF κB p65 signaling pathways. Taken together, these findings have suggested that CTRP13 fulfills a vital role in the progression of obstructive nephropathy, thereby uncovering brand new insights into possible leads for the therapeutic treatment of chronic kidney disease (CKD).