bluuebunny/crossref_metadata_2025_split huggingface dataset:
Neural processing of sad and happy autobiographical memories in women with depression and borderline personality disorder
Authors: Kulesza Maria , Rękawek Katarzyna , Holas Paweł , Żołnierczyk-Zreda Dorota , Sokół-Szawłowska Marlena , Poleszczyk Anna , Marchewka Artur , Wypych Marek 
Published: December 2024

AbstractPatients with major depressive disorder (MDD) and borderline personality disorder (BPD) are reported to have disrupted autobiographical memory (AM). Using functional magnetic resonance imaging we investigated behavioral and neural processing of the recall of emotional (sad and happy) memories in 30 MDD, 18 BPD, and 34 healthy control (HC) unmedicated women. The behavioral results showed that the MDD group experienced more sadness than the HC after the sad recall, while BPD participants experienced less happiness than HC after the happy recall. The fMRI results for sad AMs, compared to happy AMs, elicited greater activation in multiple brain regions (i.e., medial prefrontal cortex, posterior cingulate cortex, insula) linked to self-related information, emotional processing, and semantic recollection across all groups. Functional connectivity analysis revealed a significant main effect of group between the occipital cortex and precuneus and between occipital cortex and posterior cingulate cortex. The effect was driven by stronger connectivity between the occipital cortex and precuneus in the clinical groups taken together than in the HC. Our results suggest a need for stronger coordination between visual imagery and contextual recall for vivid memory retrieval in these clinical groups.
Comparative evaluation of feature reduction methods for drug response prediction
Authors: Firoozbakht Farzaneh , Yousefi Behnam , Tsoy Olga , Baumbach Jan , Schwikowski Benno 
Published: December 2024

AbstractPersonalized medicine aims to tailor medical treatments to individual patients, and predicting drug responses from molecular profiles using machine learning is crucial for this goal. However, the high dimensionality of the molecular profiles compared to the limited number of samples presents significant challenges. Knowledge-based feature selection methods are particularly suitable for drug response prediction, as they leverage biological insights to reduce dimensionality and improve model interpretability. This study presents the first comparative evaluation of nine different knowledge-based and data-driven feature reduction methods on cell line and tumor data. Our analysis employs six distinct machine learning models, with a total of more than 6,000 runs to ensure a robust evaluation. Our findings indicate that transcription factor activities outperform other methods in predicting drug responses, effectively distinguishing between sensitive and resistant tumors for seven of the 20 drugs evaluated.
The complex anatomy of the bronchial arteries: a meta-analysis with potential implications for thoracic surgery and hemoptysis treatment
Authors: Ostrowski Patryk , Bonczar Michał , Glądys Kinga , Klimeczek-Chrapusta Maria , Musiał Agata , Matuszyk Aleksandra , Balawender Krzysztof , Walocha Jerzy , Koziej Mateusz , Clarke Eduard , Polguj Michał , Smędra Anna , Żytkowski Andrzej , Wysiadecki Grzegorz 
Published: December 2024

AbstractThe present meta-analysis aimed to provide the most detailed and comprehensive anatomical description of bronchial arteries (BAs) using data available in the literature. Adequate knowledge of the normal anatomy and morphological variations of BAs can be clinically significant; for example, this approach can prevent potential risks while undertaking bronchial artery embolization (BAE) procedures and, ultimately, lead to better patient outcomes. Major medical databases such as PubMed, Scopus, Embase, Web of Science, Google Scholar, and the Cochrane Library were searched. The overall search process was conducted in three main stages. The number of BAs varied from one to six, and 16 arterial patterns were observed. The most common variation was in one right BA and one left BA, with a pooled prevalence of 19.54% (95% CI 6.69–36.44%). The pooled prevalence of BAs originating separately from the aorta was 41.42% (95% CI 37.42–45.48%). The number and location of BAs are highly inconsistent. However, the most prevalent pattern involved two BAs: one in the right BA and one in the left BA. Although BAs most frequently originate from the descending aorta, the cooccurrence of at least one ectopic BA is relatively high. The results of our meta-analysis can serve as a source of comprehensive information for thoracic surgeons and physicians performing endovascular procedures, especially BAE, a treatment for life-threatening hemoptysis.
A robust MPPT framework based on GWO-ANFIS controller for grid-tied EV charging stations
Authors: Mazumdar Debabrata , Biswas Pabitra Kumar , Sain Chiranjit , Ahmad Furkan , Al-Fagih Luluwah 
Published: December 2024

AbstractAs electric vehicles gain popularity, there has been a lot of interest in supporting their continued development with the aim of enhancing their dependability, environmental advantages, and charging efficiency. The scheduling of navigation and charging for electric vehicles is among the most well-known research topics. For optimal navigation and charging scheduling, the coupled network state between the transportation and power networks must be met; moreover, the scheduling outcomes might significantly impact these networks. To address climate challenges, relying only on fossil fuel-based infrastructure for electric car charging is insufficient. Consequently, Multi-Energy Integrated EV charging stations have emerged as a workable solution that seamlessly integrates grid power, renewable energy sources—particularly solar energy—and EV charging needs. The enhanced grey wolf optimised (GWO) ANFIS controller for Maximum Power Point Tracking (MPPT), standby battery systems, solar power, neural network-integrated grids, and sophisticated control algorithms like PID controller are all proposed in this article as energy-efficient charging terminals for electric vehicles. Moreover, authors had considered four conditional case study and with the help of MATLAB/Simulink 2018a software, the design is thoroughly examined and assessed, providing a viable route for an efficient and sustainable EV charging infrastructure.
The short term impact of radiofrequency ablative techniques and peripheral nerve block on thermoregulation in mouse models
Authors: Yuba Tomoo , Koyama Yoshihisa , Kinishi Yuki , Fujino Yuji , Shimada Shoichi 
Published: December 2024

AbstractThis study investigated the impact of multiple nerve block methods (local anesthesia, conventional radiofrequency thermocoagulation [CRF], and pulsed radiofrequency [PRF]) on thermoregulation. Focusing on hypothalamic function, the effects of local anesthesia, CRF, and PRF on central and peripheral temperatures were analyzed and compared. Our findings revealed that all three nerve block groups cause a decrease in central temperature, with the CRF group exhibiting the most pronounced effect. Furthermore, immunostaining analysis showed decreased neural activity in the preoptic area, suggesting that nerve blocks may influence central thermoregulatory mechanisms. This study provides valuable insights into the effects of peripheral nerve blocks on thermoregulation and may contribute to the development of therapeutic strategies to managing perioperative hypothermia and enhancing pain management, especially in patients undergoing surgeries with high risks of thermoregulatory complications, such as on-pump surgery and laparoscopic surgery.
Proposal for a quantum mechanical test of gravity at millimeter scale
Authors: Cheng Yu , Lin Jiadu , Sheng Jie , Yanagida Tsutomu T. 
Published: December 2024

AbstractThe experimental verification of the Newton law of gravity at small scales has been a longstanding challenge. Recently, torsion balance experiments have successfully measured gravitational force at the millimeter scale. However, testing gravity force on quantum mechanical wave function at small scales remains difficult. In this paper, we propose a novel experiment that utilizes the Josephson effect to detect the different evolution of quantum phase induced from the potential difference caused by gravity. We demonstrate that this experiment can test gravity quantum mechanically at the millimeter scale, and also has a potential to investigate the parity invariance of gravity at small scales.
Towards a more objective and high-throughput spheroid invasion assay quantification method
Authors: Mungai Rozanne W. , Hartman Roger J. , Jolin Grace E. , Piskorowski Kevin W. , Billiar Kristen L. 
Published: December 2024

AbstractMulticellular spheroids embedded in 3D hydrogels are prominent in vitro models for 3D cell invasion. Yet, quantification methods for spheroid cell invasion that are high‐throughput, objective and accessible are still lacking. Variations in spheroid sizes and the shapes of the cells within render it difficult to objectively assess invasion extent. The goal of this work is to develop a high-throughput quantification method of cell invasion into 3D matrices that minimizes sensitivity to initial spheroid size and cell spreading and provides precise integrative directionally-dependent metrics of invasion. By analyzing images of fluorescent cell nuclei, invasion metrics are automatically calculated at the pixel level. The initial spheroid boundary is segmented and automated calculations of the nuclear pixel distances from the initial boundary are used to compute common invasion metrics (i.e., the change in invasion area, mean distance) for the same spheroid at a later timepoint. We also introduce the area moment of inertia as an integrative metric of cell invasion that considers the invasion area as well as the pixel distances from the initial spheroid boundary. Further, we show that principal component analysis can be used to quantify the directional influence of a stimuli to invasion (e.g., due to a chemotactic gradient or contact guidance). To demonstrate the power of the analysis for cell types with different invasive potentials and the utility of this method for a variety of biological applications, the method is used to analyze the invasiveness of five different cell types. In all, implementation of this high‐throughput quantification method results in consistent and objective analysis of 3D multicellular spheroid invasion. We provide the analysis code in both MATLAB and Python languages as well as a GUI for ease of use for researchers with a range of computer programming skills and for applications in a variety of biological research areas such as wound healing and cancer metastasis.
Comparative evaluation of spatiotemporal methods for effective dengue cluster detection with a case study of national surveillance data in Thailand
Authors: Rotejanaprasert Chawarat , Chinpong Kawin , Lawson Andrew B. , Maude Richard J. 
Published: December 2024

AbstractDengue fever poses a significant public health burden in tropical regions, including Thailand, where periodic epidemics strain healthcare resources. Effective disease surveillance is essential for timely intervention and resource allocation. Various methods exist for spatiotemporal cluster detection, but their comparative performance remains unclear. This study compared spatiotemporal cluster detection methods using simulated and real dengue surveillance data from Thailand. A simulation study explored diverse disease scenarios, characterized by varying magnitudes and spatial-temporal patterns, while real data analysis utilized monthly national dengue surveillance data from 2018 to 2020. Evaluation metrics included accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. Bayesian models and FlexScan emerged as top performers, demonstrating superior accuracy and sensitivity. Traditional methods such as Getis Ord and Moran’s I showed poorer performance, while other scanning-based approaches like spatial SaTScan exhibited limitations in positive predictive value and tended to identify large clusters due to the inflexibility of its scanning window shape. Bayesian modeling with a space–time interaction term outperformed testing-based cluster detection methods, emphasizing the importance of incorporating spatiotemporal components. Our study highlights the superior performance of Bayesian models and FlexScan in spatiotemporal cluster detection for dengue surveillance. These findings offer valuable guidance for policymakers and public health authorities in refining disease surveillance strategies and resource allocation. Moreover, the insights gained from this research could be valuable for other diseases sharing similar characteristics and settings, broadening the applicability of our findings beyond dengue surveillance.
Hemoglobin decrease predicts untoward outcomes better than severity of anemia
Authors: Teutsch Brigitta , Tóth Zsolt Abonyi , Ferencz Orsolya , Vörhendi Nóra , Simon Orsolya Anna , Boros Eszter , Pálinkás Dániel , Frim Levente , Tari Edina , Kalló Patrícia , Gagyi Endre Botond , Hussein Tamás , Váncsa Szilárd , Vass Vivien , Szentesi Andrea , Vincze Áron , Izbéki Ferenc , Hegyi Péter , Hágendorn Roland , Szabó Imre , Erőss Bálint 
Published: December 2024

AbstractPatients with gastrointestinal bleeding (GIB) exhibit varying tolerances to acute blood loss. We aimed to investigate the effect of relative Hb decrease (ΔHb%) on GIB outcomes. Participants enrolled in the Hungarian GIB Registry between 2019 and 2022 were analyzed. The primary outcome, defined as a composite endpoint, included in-hospital bleeding-related mortality and the need for urgent intervention. Four groups were created based on the lowest Hb measured during hospitalization (nadirHb), along with four subgroups categorized by ΔHb%. Regardless of the nadirHb, participants with higher ΔHb% had a higher probability of reaching the composite endpoint. A 30–40% ΔHb% decrease to a nadirHb of 80–90 g/L resulted in a similar likelihood of reaching the primary endpoint as a 0–10% ΔHb% to 70–80 g/L or 60–70 g/L, respectively (10% vs. 12%, p = 1.00; 10% vs. 10%, p = 1.00). Our results showed that a higher Hb decrease in GIB is associated with an increased untoward outcome rate even when the lowest hemoglobin exceeds the recommended transfusion thresholds. New randomized controlled trials investigating transfusion thresholds should consider ΔHb% as a potential key variable and risk factor.
A hybrid encryption framework leveraging quantum and classical cryptography for secure transmission of medical images in IoT-based telemedicine networks
Authors: Shafique Arslan , Naqvi Syed Ali Atif , Raza Ali , Ghalaii Masoud , Papanastasiou Panagiotis , McCann Julie , Abbasi Qammer H. , Imran Muhammad Ali 
Published: December 2024

AbstractIn the era of the Internet of Things (IoT), the transmission of medical reports in the form of scan images for collaborative diagnosis is vital for any telemedicine network. In this context, ensuring secure transmission and communication is necessary to protect medical data to maintain privacy. To address such privacy concerns and secure medical images against cyberattacks, this research presents a robust hybrid encryption framework that integrates quantum, and classical cryptographic methods. The proposed framework not only secure medical data against cyber threats but also protects the secret security keys. Initially, a Quantum Key Distribution (QKD) is employed to generate a shared key, which is then used to secure the symmetric keys via One-Time Pad (OTP) encryption. Next, bit-planes are extracted from each color component. The rows and columns of the extracted bit-planes are scrambled using random sequences which are generated by a 6D hyperchaotic Chen system and the Ikeda map. To further increase confusion in the original data, multiple-step pixel scrambling operations such as pixel shuffling, pixel value shuffling, and rotational and flipping operations are implemented. After the confusion phase, a combination of affine transformations with non-linear functions, Discrete Cosine Transform (DCT) with complex modulation, Discrete Wavelet Transform (DWT) with random phase modulation, bilinear transformation, and nonlinear polynomial mapping are employed to create diffusion in the scrambled components. These multiple encryption operations aim to maximize randomness in the final ciphertext image. Additionally, to reduce computational complexity, only the Most Significant Bit-Planes (MSBs) are encrypted, as they contain more than 94% of the plaintext information. Several experimental results and analyses are conducted to assess the proposed encryption framework, including entropy analysis, key sensitivity analysis, correlation analysis lossless analysis, and histogram analysis. Furthermore, the framework is tested against various cyberattacks such as brute-force attacks, clipping attacks, and noise attacks on the ciphertext images, to demonstrate its resilience against such threats.
Spinally targeted paired associative stimulation with high-frequency peripheral component induces spinal level plasticity in healthy subjects
Authors: Nätkynmäki Anna , Lauronen Leena , Haakana Piia , Kirveskari Erika , Avela Janne , Shulga Anastasia 
Published: December 2024

Abstract
A novel variant of paired-associative stimulation (PAS) consisting of high-frequency peripheral nerve stimulation (PNS) and high-intensity transcranial magnetic stimulation (TMS) above the motor cortex, called high-PAS, can lead to improved motor function in patients with incomplete spinal cord injury. In PAS, the interstimulus interval (ISI) between the PNS and TMS pulses plays a significant role in the location of the intended effect of the induced plastic changes. While conventional PAS protocols (single TMS pulse often applied with intensity close to resting motor threshold, and single PNS pulse) usually require precisely defined ISIs, high-PAS can induce plasticity at a wide range of ISIs and also in spite of small ISI errors, which is helpful in clinical settings where precise ISI determination can be challenging. However, this also makes the determination of high-PAS level of plasticity induction more challenging and calls for more research on the mechanism of action of high-PAS. We sought to determine if the TMS-induced orthodromic activation in upper motor neurons and PNS-induced antidromic activation in lower motor neurons arriving simultaneously to the intervening synapses at the spinal cord level can be shown to induce acute changes at the targeted location, unlike an otherwise identical but cortically targeted equivalent. Ten healthy subjects participated in two separate sessions, where high-PAS induced activation was set to target spinal (SPINAL) or cortical (CORTICAL) levels with ISI manipulation between otherwise identically applied TMS and PNS pulses. The outcomes were assessed with motor-evoked potentials (MEPs) and Hoffmann (H)-reflex before (PRE), immediately after, and 30 and 60 min after (POST, POST30, POST60) the intervention. MEPs were significantly enhanced in both interventions. In the SPINAL but not in the CORTICAL session, maximal H-reflex amplitudes significantly increased at two timepoints, indicating an increase in spinal excitability. The H/M ratio (maximal H-reflex normalized to maximal M-wave) also showed a significant increase from PRE to POST30 timepoint in the SPINAL session when compared with the CORTICAL equivalent. These results confirm that spinally targeted high-PAS with individualized ISIs indeed has an effect at the spinal level in the sensorimotor system. High-PAS is a novel PAS variant that has shown promising results in motor rehabilitation of individuals with SCI and these new findings contribute to the understanding of its mechanism of action. This provides further evidence for high-PAS as an option for clinical settings to target plasticity at different levels of the corticospinal tract.
Multi-sensor fusion and segmentation for autonomous vehicle multi-object tracking using deep Q networks
Authors: Vinoth K. , Sasikumar P. 
Published: December 2024

AbstractAutonomous vehicles, often known as self-driving cars, have emerged as a disruptive technology with the promise of safer, more efficient, and convenient transportation. The existing works provide achievable results but lack effective solutions, as accumulation on roads can obscure lane markings and traffic signs, making it difficult for the self-driving car to navigate safely. Heavy rain, snow, fog, or dust storms can severely limit the car’s sensors’ ability to detect obstacles, pedestrians, and other vehicles, which pose potential safety risks. So, we have presented a multi-sensor fusion and segmentation for multi-object tracking using DQN in self-driving cars. Our proposed scheme incorporates the handling of pipelines for camera and LiDAR data and the development of an autonomous solution for object detection by handling sensor images. An Improved Adaptive Extended Kalman Filter (IAEKF) was used for noise reduction. The Contrast enhancement was done using a Normalised Gamma Transformation based CLAHE (NGT-CLAHE), and the adaptive thresholding was implemented using an Improved Adaptive Weighted Mean Filter (IAWMF) which was used for preprocessing. The multi-segmentation based on orientation employs various segmentation techniques and degrees. The dense net-based multi-image fusion gives more efficiency and a high memory in terms of fast processing time. The Energy Valley Optimizer (EVO) approach is used to select grid map-based paths and lanes. This strategy solves complicated tasks in a simple manner, which leads to ease of flexibility, resilience, and scalability. In addition, the YOLO V7 model is used for detection and categorization. The proposed work is evaluated using metrics such as velocity, accuracy rate, success rate, success ratio, mean squared error, loss rate, and accumulated reward.
The comparison of the antioxidant, antibacterial and antiviral potential of Polish fir honeydew and Manuka honeys
Authors: Grabek-Lejko Dorota , Miłek Michał , Dżugan Małgorzata 
Published: December 2024

AbstractThe aim of the present study was to compare the antioxidant, antibacterial and antiviral activities of Podkarpackie coniferous honeydew honey and Manuka honey. The quality of tested honey samples (honeydew-12 and Manuka-4) regarding honey standard was evaluated as well as additional indicators (methylglyoxal, total phenolics and HPTLC phenolic profile, antioxidant potential, glucose oxidase activity, and hydrogen peroxide) were compared. Antibacterial potential was analyzed against Gram-positive (S. aureus and B. cereus) and Gram-negative (E. coli and S. enterica) bacteria. Antiviral activity against different RNA (phi6, MS2) and DNA (T7, phiX174) bacteriophages considered as “viral surrogates” was determined. Based on the determined physicochemical parameters the good quality of tested honeys was confirmed, excluding two samples. The content of polyphenolic compounds in honeydew honey ranged from 583.87 to 1102.42 mg of gallic acid/kg and was strongly correlated with the antioxidant properties. Moreover, for samples with the strongest activity these parameters were comparable to Manuka honey. However, the obtained HPTLC polyphenolic profiles were completely different for honeydew than for Manuka honey which exhibited additional bands (Rf = 0.74 and 0.52). Honeydew honeys were characterized by a strong antiviral and antibacterial properties most of all against Gram-positive bacteria. The MICs (minimal inhibitory concentrations) for S. aureus and B. cereus ranged 15–35% and 8–15% for honeydew and Manuka honeys, respectively. The strongest antiviral properties of honeydew honey were demonstrated mainly against RNA bacteriophages (phi6, MS2) which was even higher than for Manuka honey, especially against MS2 virus. The obtained results suggest that Podkarpackie honeydew honey with the controlled glucose oxidase activity may be a natural substance used to combat viral and bacterial diseases.