bluuebunny/crossref_metadata_2025 huggingface dataset:
Surgical techniques to preserve continence after robot-assisted radical prostatectomy
Authors: Katsimperis Stamatios , Juliebø-Jones Patrick , Ta Anthony , Tandogdu Zafer , Al-Bermani Osama , Bellos Themistoklis , Esperto Francesco , Tonyali Senol , Mitsogiannis Iraklis , Skolarikos Andreas , Varkarakis Ioannis , Somani Bhaskar K. , Tzelves Lazaros
Published: November 2023

Radical prostatectomy significantly impacts the inherent anatomy of the male pelvis and the functional mechanisms of urinary continence. Incontinence has a considerable negative influence on the quality of life of patients, as well as their social and psychological wellbeing. Numerous surgical techniques have been demonstrated to support the preservation of continence during robot-assisted radical prostatectomy (RARP). In this in-depth analysis, we give a general summary of the surgical techniques used in RARP and their impact on incontinence rates.
Digital Maturity and Digital Transformation Strategy among Greek Small and Medium Enterprises
Authors: Kargas Antonios , Gialeris Emmanouil , Komisopoulos Faidon , Lymperiou Anastasios , Salmon Ioannis
Published: November 2023

The proposed study aims to investigate the digital transformation of Greek small and medium enterprises (SMEs), with a particular focus on their digital maturity and the strategic and organizational factors contributing to digital transformation. The research issue of digital transformation has been attracting considerable interest among academics and business practicians since COVID-19 accelerated the procedure of implementing Industry 4.0 principles all over global economies. Quantitative research on 147 Greek SMEs revealed the most important issues on how these businesses implement digital transformation, factors accelerating or decelerating the process, barriers and expected outcomes. The results indicate that digital maturity, digital transformation strategy and digital business models are strongly interconnected, while the main barriers of successfully implementing them is a lack of funding, cultural issues and the management’s engagement with the whole procedure. Greek SMEs are regarding digital transformation as a part of their customers’ satisfaction rather than as a holistic procedure of reshaping their operation. In almost 20% of Greek SMEs, there is no person who is responsible for the digital transformation procedure, while key drivers are suppliers and customers rather than competitors and the government. The results can be valuable for stakeholders who are enabled to the digital transformation process from both the business and academic points of view, while there exist aspects that can contribute to policy makers/motivation developers on the state’s level as well.
The Effect of Biochar and Straw Return on N2O Emissions and Crop Yield: A Three-Year Field Experiment
Authors: Gao Shangjie , Peng Qin , Liu Xingren , Xu Chunying
Published: November 2023

To evaluate the effects of application of biochar and straw return for consecutive years on N2O emissions and crop yields in North China, a three-year field experiment of applying biochar and straw following a ten-year application was conducted in a wheat–maize rotation system. Four treatments were set up, including F (NPK fertilizer only); FB (NPK fertilizer + 9.0 t·ha−1 biochar); FS (NPK fertilizer + straw); and FSB ((NPK fertilizer + 9.0 t·ha−1 biochar combined with straw). The results showed that compared with the F treatment, the FB treatment significantly reduced soil N2O emissions by 20.2%, while the FS and FSB treatments increased it by 23.7% and 41.4%, respectively. The FB treatment reduced soil N2O emissions by 15.1% in the wheat season and 23.2% in the maize season, respectively. The FS and FSB treatments increased the N2O emissions by 20.7% and 36.7% in the wheat season, respectively, and by 25.5% and 44.2% in the maize season, respectively. In the wheat season, the soil water content (SWC), NO3−-N content and pH were the main influencing factors of the soil N2O emissions. In the maize season, SWC and NO3−-N content were the main influencing factors. In addition, the FB, FS and FSB treatments increased the crop yield by 4.99%, 8.40% and 10.25% compared with the F treatment, respectively. In conclusion, consecutive application of biochar can significantly reduce N2O emissions and improve crop yield. Although FS and FSB treatments can also improve the crop yield, they are not beneficial to suppressing N2O emissions. Therefore, the successive application of biochar is an effective measure to reduce N2O emissions and maintain crop yield.
Pig Counting Algorithm Based on Improved YOLOv5n Model with Multiscene and Fewer Number of Parameters
Authors: Wang Yongsheng , Yang Duanli , Chen Hui , Wang Lianzeng , Gao Yuan
Published: November 2023

Pig counting is an important work in the breeding process of large-scale pig farms. In order to achieve high-precision pig identification in the conditions of pigs occluding each other, illumination difference, multiscenes, and differences in the number of pigs and the imaging size, and to also reduce the number of parameters of the model, a pig counting algorithm of improved YOLOv5n was proposed. Firstly, a multiscene dataset is created by selecting images from several different pig farms to enhance the generalization performance of the model; secondly, the Backbone of YOLOv5n was replaced by the FasterNet model to reduce the number of parameters and calculations to lay the foundation for the model to be applied to Android system; thirdly, the Neck of YOLOv5n was optimized by using the E-GFPN structure to enhance the feature fusion capability of the model; Finally, Focal EIoU loss function was used to replace the CIoU loss function of YOLOv5n to improve the model’s identification accuracy. The results showed that the AP of the improved model was 97.72%, the number of parameters, the amount of calculation, and the size of the model were reduced by 50.57%, 32.20%, and 47.21% compared with YOLOv5n, and the detection speed reached 75.87 f/s. The improved algorithm has better accuracy and robustness in multiscene and complex pig house environments, which not only ensured the accuracy of the model but also reduced the number of parameters as much as possible. Meanwhile, a pig counting application for the Android system was developed based on the optimized model, which truly realized the practical application of the technology. The improved algorithm and application could be easily extended and applied to the field of livestock and poultry counting, such as cattle, sheep, geese, etc., which has a widely practical value.
Behavioral and Physiological Differences in Female Rabbits at Different Stages of the Estrous Cycle
Authors: Chen Xin , Jin Rongshuai , Yang Anqi , Li Jiacheng , Song Ying , Zhao Bohao , Chen Yang , Wu Xinsheng
Published: November 2023

Estrus involves a series of complex physiological signs and changes in behavior before ovulation, which play a crucial role in animal reproduction. However, there have been few studies that evaluate behaviors during the different stages of estrus cycle in female rabbits. Therefore, more detailed information is needed on distinguishing the various stages of the estrous cycle. This study explored the behavioral and physiological differences at various estrous cycle stages in female New Zealand White rabbits. The continuous recording method was employed to record the daily behaviors of twenty postpartum female rabbits during the estrous cycle. Compared with the diestrus stage, the duration of foraging and drinking behavior in estrus decreased significantly, and the frequency of grooming and biting behaviors increased (p < 0.05). Differences in reproductive hormone levels (FSH, LH, P4, and E2) and follicle development were measured at each stage via ELISA and HE staining. The FSH and LH levels showed an increasing trend and then decreased, with the lowest being in late estrus (p < 0.05). The P4 level was the lowest in estrus (p < 0.05), and E2 showed a gradually increasing trend. There was no significant difference in the number of primordial follicles at each stage, but the number of primary follicles in estrus was significantly higher than at the other stages (p < 0.05). To further understand the molecular regulation mechanism of the estrous cycle in female rabbits, we analyzed the ovarian transcription patterns of female rabbits in diestrus (D group) and estrus (E group) employing RNA-seq. A total of 967 differentially expressed genes (DEGs) were screened from the ovaries of female rabbits between the diestrus and estrus groups. A KEGG analysis of DEGs enriched in the estrogen signaling pathway, aldosterone synthesis, and secretion pathway, such as CYP19A1 and IGF1R, was performed. The rabbits’ behavior, related physiological hormones, and molecular regulation also differed at different estrous cycle stages. The results provide recommendations for the adequate management practices of postpartum re-estrus and breeding female rabbits.
Historic Horse Family Displaying Malformations of the Cervicothoracic Junction and Their Connection to Modern German Warmblood Horses
Authors: Zimmermann Elisa , Ros Katharina B. , Pfarrer Christiane , Distl Ottmar
Published: November 2023

Malformations of the equine cervicothoracic junction affect the C6 and C7 cervical vertebrae, the T1 thoracic vertebra and in variable extent the first and second sternal ribs. To date, the clinical impact of this malformation, its prevalence and mode of inheritance in equine populations are not yet determined. We examined five skeletons for signs of malformation of the cervicothoracic junction, including three skeletons from widely used Thoroughbred stallions affected with the malformation and two skeletons serving as a comparison. The three affected historical horses were the Thoroughbred stallions Der Loewe XX, Birkhahn XX and their common great grandsire Dark Ronald XX. Malformations of C6 and C7 showed a large variation between the three stallions, as Dark Ronald XX, Der Loewe XX and Birkhahn XX were affected uni-laterally at C6 and C7, uni-laterally at C6 and bi-laterally at C6 and C7, respectively, with varying grades. In order to evaluate whether or not these malformations are incidental, we took a random sample of 20 living German Warmblood horses, which are distant descendants of these stallions. This sample consisted of ten controls and ten horses with malformations of C6/C7. Blood proportions of the historical sires in the modern Warmblood horses ranged from 0.10 to 6.25%. The contribution to inbreeding in each individual horse of our selected horse group by those sires was expressed as a percentage of the total inbreeding coefficient and ranged from 0.01 to 17.96%, demonstrating their influence on the modern Warmblood. In the present study, we were able to describe the variability of the malformation of C6/C7 within a horse family including historic and modern horses. Additionally, we detected variations appearing in connection with malformations of the cervicothoracic junction that have not been described in the literature yet. This is the first time that the malformations of C6 and C7 have been described within a familial context, providing hints on inheritance in Thoroughbreds and Warmbloods. It is worthwhile to carry out further studies in a larger setting to gain more comprehensive insights into the inheritance of the malformation and the role of important ancestors.
BTDNet: A Multi-Modal Approach for Brain Tumor Radiogenomic Classification
Authors: Kollias Dimitrios , Vendal Karanjot , Gadhavi Priyankaben , Russom Solomon
Published: November 2023

Brain tumors pose significant health challenges worldwide, with glioblastoma being one of the most aggressive forms. The accurate determination of the O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is crucial for personalized treatment strategies. However, traditional methods are labor-intensive and time-consuming. This paper proposes a novel multi-modal approach, BTDNet, that leverages multi-parametric MRI scans, including FLAIR, T1w, T1wCE, and T2 3D volumes, to predict the MGMT promoter methylation status. BTDNet’s main contribution involves addressing two main challenges: the variable volume lengths (i.e., each volume consists of a different number of slices) and the volume-level annotations (i.e., the whole 3D volume is annotated and not the independent slices that it consists of). BTDNet consists of four components: (i) data augmentation (which performs geometric transformations, convex combinations of data pairs, and test-time data augmentation); (ii) 3D analysis (which performs global analysis through a CNN-RNN); (iii) routing (which contains a mask layer that handles variable input feature lengths); and (iv) modality fusion (which effectively enhances data representation, reduces ambiguities, and mitigates data scarcity). The proposed method outperformed state-of-the-art methods in the RSNA-ASNR-MICCAI BraTS 2021 Challenge by at least 3.3% in terms of the F1 score, offering a promising avenue for enhancing brain tumor diagnosis and treatment.
Any-to-One Non-Parallel Voice Conversion System Using an Autoregressive Conversion Model and LPCNet Vocoder
Authors: Ezzine Kadria , Di Martino Joseph , Frikha Mondher
Published: November 2023

We present an any-to-one voice conversion (VC) system, using an autoregressive model and LPCNet vocoder, aimed at enhancing the converted speech in terms of naturalness, intelligibility, and speaker similarity. As the name implies, non-parallel any-to-one voice conversion does not require paired source and target speeches and can be employed for arbitrary speech conversion tasks. Recent advancements in neural-based vocoders, such as WaveNet, have improved the efficiency of speech synthesis. However, in practice, we find that the trajectory of some generated waveforms is not consistently smooth, leading to occasional voice errors. To address this issue, we propose to use an autoregressive (AR) conversion model along with the high-fidelity LPCNet vocoder. This combination not only solves the problems of waveform fluidity but also produces more natural and clear speech, with the added capability of real-time speech generation. To precisely represent the linguistic content of a given utterance, we use speaker-independent PPG features (SI-PPG) computed from an automatic speech recognition (ASR) model trained on a multi-speaker corpus. Next, a conversion model maps the SI-PPG to the acoustic representations used as input features for the LPCNet. The proposed autoregressive structure enables our system to produce the following prediction step outputs from the acoustic features predicted in the previous step. We evaluate the effectiveness of our system by performing any-to-one conversion pairs between native English speakers. Experimental results show that the proposed method outperforms state-of-the-art systems, producing higher speech quality and greater speaker similarity.
Multi-Sensor Data Fusion Method Based on Self-Attention Mechanism
Authors: Lin Xuezhu , Chao Shihan , Yan Dongming , Guo Lili , Liu Yue , Li Lijuan
Published: November 2023

In 3D reconstruction tasks, single-sensor data fusion based on deep learning is limited by the integrity and accuracy of the data, which reduces the accuracy and reliability of the fusion results. To address this issue, this study proposes a multi-sensor data fusion method based on a self-attention mechanism. A multi-sensor data fusion model for acquiring multi-source and multi-modal data is constructed, with the core component being a convolutional neural network with self-attention (CNN-SA), which employs CNNs to process multi-source and multi-modal data by extracting their features. Additionally, it introduces an SA mechanism to weigh and sum the features of different modalities, adaptively focusing on the importance of different modal data. This enables mutual support, complementarity, and correction among the multi-modal data. Experimental results demonstrate that the accuracy of the CNN-SA network is improved by 72.6%, surpassing the improvements of 29.9% for CNN-CBAM, 23.6% for CNN, and 11.4% for CNN-LSTM, exhibiting enhanced generalization capability, accuracy, and robustness. The proposed approach will contribute to the effectiveness of multi-sensor data fusion processing.
Concept to Reality: An Integrated Approach to Testing Software User Interfaces
Authors: Whaiduzzaman Md , Sakib Adnan , Khan Nisha Jaman , Chaki Sudipto , Shahrier Labiba , Ghosh Sudipto , Rahman Md. Saifur , Mahi Md. Julkar Nayeen , Barros Alistair , Fidge Colin , Thompson-Whiteside Scott , Jan Tony
Published: November 2023

This paper delves into the complex task of evaluating a website user interface (UI) and user experience (UX), a process complicated by gaps in research. To bridge this, we introduced an innovative human–computer interaction (HCI) framework that synergizes expert cognitive walkthroughs and user surveys for a comprehensive view. We transformed user responses into three key domains—control, engagement, and goal. Our work also generalized an extract of four context-level data metrics for a robust evaluation. The numerical evidence, such as a C1 score of 4.1, surpassing the expert usability benchmark, indicated our framework’s effectiveness. Our research not only addresses an essential gap by integrating assessments from both users and experts, but also offers actionable insights for UI/UX design. The findings extend beyond this specific context, providing a potent evaluation tool for website usability across various websites. Lastly, the research underscores the importance of prioritizing users’ needs and expert recommendations in design principles, significantly contributing to the broader domain of website usability and user experience.
Nozzles for Spraying Coal–Water Fuels
Authors: Gvozdyakov Dmitriy , Zenkov Andrey
Published: November 2023

This work presents a review of modern devices for spraying coal–water fuels. The efficiency of their operation was analyzed according to several criteria: the simplicity of their design (A), the fuel channel’s predisposition to clogging (B), durability in terms of the erosive wear of the nozzle channel (C), and dispersion of the jet—the average size of droplets in the jet (D). It was established that, from the point of view of operational efficiency, the most preferred devices for spraying coal–water fuels are those with a quasi-internal mixing of fuel and spraying agents. In combination with refractory or ceramic materials and the large diameter of the fuel channel, its rapid erosion wear and the likelihood of clogging with large particles of coal or agglomerates of viscous coal–water fuel are excluded. At the same time, the possibility of forming a fine-dispersed jet remains. The cooling of the nozzle with an external mixing of fuel and spraying agents during its operation also reduces the rate of erosion wear. Narrowing the fuel channel was proven to be an undesirable solution when creating a coal–water fuel nozzle. This review will be useful for both researchers studying the processes of spraying coal–water fuels and designers involved in the creation of coal–water fuel injectors or the transfer of existing boilers to coal–water fuel.
An Alternative Formulation of the Harrison Model
Authors: Chwastek Krzysztof , Gębara Piotr , Przybył Anna , Gozdur Roman , Baghel Ajay P. S. , Ram Boggavarapu Sai
Published: November 2023

The approach advanced by Harrison puts in the spotlight the fundamental role of bistability in hysteresis modeling. The description is based on physical premises concerning irreversible thermodynamics. In the original model, the upscaling of irreversible phenomena acting on the micromagnetic level is carried out by the introduction of a phenomenological parameter β. In the present paper, an alternative approach is proposed. The outputs of individual outputs of elementary hysteresis units (hysterons) are considered like in the stop model. A verification of the proposed model is carried out using measurement data for a praseodymium–dysprosium ribbon sample and a cylinder core made of cobalt-based amorphous material.
Comparison of the Effectiveness of Various Classifiers for Breast Cancer Detection Using Data Mining Methods
Authors: Al-Qazzaz Noor Kamal , Mohammed Iyden Kamil , Al-Qazzaz Halah Kamal , Ali Sawal Hamid Bin Mohd , Ahmad Siti Anom
Published: November 2023

Countless women and men worldwide have lost their lives to breast cancer (BC). Although researchers from around the world have proposed various diagnostic methods for detecting this disease, there is still room for improvement in the accuracy and efficiency with which they can be used. A novel approach has been proposed for the early detection of BC by applying data mining techniques to the levels of prolactin (P), testosterone (T), cortisol (C), and human chorionic gonadotropin (HCG) in the blood and saliva of 20 women with histologically confirmed BC, 20 benign subjects, and 20 age-matched control women. In the proposed method, blood and saliva were used to categorize the severity of the BC into normal, benign, and malignant cases. Ten statistical features were collected to identify the severity of the BC using three different classification schemes—a decision tree (DT), a support vector machine (SVM), and k-nearest neighbors (KNN) were evaluated. Moreover, dimensionality reduction techniques using factor analysis (FA) and t-stochastic neighbor embedding (t-SNE) have been computed to obtain the best hyperparameters. The model has been validated using the k-fold cross-validation method in the proposed approach. Metrics for gauging a model’s effectiveness were applied. Dimensionality reduction approaches for salivary biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 66.67% to 93.3% and 90%, respectively, by utilizing t-SNE and FA. Furthermore, dimensionality reduction strategies for blood biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 60% to 80% and 93.3%, respectively, by utilizing FA and t-SNE. These findings point to t-SNE as a potentially useful feature selection for aiding in the identification of patients with BC, as it consistently improves the discrimination of benign, malignant, and control healthy subjects, thereby promising to aid in the improvement of breast tumour early detection.
Experimental Investigation on the Anchorage Performance of a Tension–Compression-Dispersed Composite Anti-Floating Anchor
Authors: Liu Yuguo , Xia Kai , Wang Botong , Le Ji , Ma Yanqing , Zhang Mingli
Published: November 2023

Rapid advancements in construction technologies have accelerated the development of complex and deep underground structures, raising concerns about the impact of groundwater on structures, particularly anti-floating measures. Traditional tensioned anchors, commonly used for preventing flotation, suffer from limitations like low pull-out bearing capacity, shallow critical anchoring depth, and localized stress concentration. To overcome these limitations, this paper introduces a tension–compression dispersed composite anchor, which combines casing, load-bearing plates, and tensioned anchors. Comparative tests were conducted between these composite anchors and traditional tensioned anchors to analyze their anchoring behavior. Our results show that tensioned anchors exhibit a stable axial force distribution as anchoring length increases. By identifying abrupt changes in the axial force curve, optimal anchoring lengths for load-dispersed anchors can be determined, thereby enhancing rock and soil strength utilization. The tension–compression-dispersed composite anchor outperforms tensioned anchors, with 1.44 times the ultimate bearing capacity for equivalent anchoring lengths and 1.1 times the capacity for an additional 1 m length. It also displays superior deformation adaptability and structural ductility under high-bearing loads compared to tensioned anchors with extended anchoring lengths. Effectively mobilizing the strength of the lower anchoring segment within the rock and soil results in a lower critical anchoring depth and a more uniform distribution of lateral friction resistance. In conclusion, the tension–compression-dispersed composite anchor offers significant advantages, making it a promising engineering solution for anti-floating anchor systems in complex underground environments.
An Ensemble Forecast Wind Field Correction Model with Multiple Factors and Spatio-Temporal Features
Authors: Chen Min , Yang Hao , Mao Bo , Xie Kaiwen , Chen Chaoping , Dong Yuanchang
Published: November 2023

Accurate wind speed prediction is significantly important for the full utilization of wind energy resources and the improvement in the economic benefits of wind farms. Because the ensemble forecast takes into account the uncertainty of information about the atmospheric motion, domestic and foreign weather service forecast centers often choose to use the ensemble numerical forecast to achieve the fine forecast of wind speed. However, due to the unavoidable systematic errors of the ensemble numerical forecast model, it is necessary to correct the deviation in the ensemble numerical forecast wind speed. Considering the typical spatio-temporal characteristics of the grid prediction data of the wind field, based on Convolutional Long–Short Term Memory (ConvLSTM) units and attention mechanism, this paper takes the complex and representative North China region as the research area, aiming to reveal the shortcomings of existing deep learning integrated prediction correction models in extracting temporal features of grid prediction data. We propose a new ensemble prediction wind field correction model integrating multi-factor and spatio-temporal characteristics. This model uses reanalyzed land data provided by the European Center for Medium-Range Weather Forecasts as the real data to correct the deviation in the near-surface 10 m wind field data predicted by the regional ensemble numerical prediction model of the China Meteorological Administration. We used the reanalyzed land data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) as the live data to correct the deviation in the near-surface 10 m wind field data predicted by the regional ensemble numerical forecast model of the China Meteorological Administration (CMA). At the same time, Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) were used as the scoring indicators, and the results of the China Meteorological Administration–Regional Ensemble Prediction System (CMA–REPS) ensemble average, multiple linear regression method correction, Long–Short Term Memory (LSTM) method correction, and U-net (UNET) method correction were compared. Compared with the UNET model method, the experimental results show that when processing the 10 m zonal wind data, 10 m meridional wind data, and 10 m average wind speed data of CMA–REPS 24 h forecasts, the correction results of our model can reduce the RMSE score index by 9.15%, 4.83%, and 7.79%. At the same time, when processing the 48 h and 72 h near-surface 10 m wind field data of the CMA–REPS forecast, our model can improve the prediction accuracy of CMA–REPS near-surface wind forecast data. Therefore, the correction effect of the proposed model in a complex terrain area is evidently better compared to other methods.
Solution of High-Order Nonlinear Integrable Systems Using Darboux Transformation
Authors: Wu Xinhui , Hu Jiawei , Zhang Ning
Published: November 2023

The 4×4 trace-free complex matrix set is introduced in this study. By using it, we are able to create a novel soliton hierarchy that is reduced to demonstrate its bi-Hamiltonian structure. Additionally, we give the Darboux matrix T, whose elements are connected to the spectral parameter in accordance with the various positions and numbers of the spectral parameter λ. The Darboux transformation approach has also been successfully applicated to superintegrable systems.
Continuous Physiological Monitoring of the Combined Exposure to Hypoxia and High Cognitive Load in Military Personnel
Authors: Temme Leonard A. , Wittels Harrison L. , Wishon Michael J. , St. Onge Paul , McDonald Samantha M. , Hecocks Dustin , Wittels S. Howard
Published: November 2023

Military aviators endure high cognitive loads and hypoxic environments during flight operations, impacting the autonomic nervous system (ANS). The synergistic effects of these exposures on the ANS, however, are less clear. This study investigated the simultaneous effects of mild hypoxia and high cognitive load on the ANS in military personnel. This study employed a two-factor experimental design. Twenty-four healthy participants aged between 19 and 45 years were exposed to mild hypoxia (14.0% O2), normoxia (21.0% O2), and hyperoxia (33.0% O2). During each epoch (n = 5), participants continuously performed one 15 min and one 10 min series of simulated, in-flight tasks separated by 1 min of rest. Exposure sequences (hypoxia–normoxia and normoxia–hyperoxia) were separated by a 60 min break. Heart rate (HR), heart rate variability (HRV), and O2 saturation (SpO2) were continuously measured via an armband monitor (Warfighter MonitorTM, Tiger Tech Solutions, Inc., Miami, FL, USA). Paired and independent t-tests were used to evaluate differences in HR, HRV, and SpO2 within and between exposure sequences. Survival analyses were performed to assess the timing and magnitude of the ANS responses. Sympathetic nervous system (SNS) activity during hypoxia was highest in epoch 1 (HR: +6.9 bpm, p = 0.002; rMSSD: −9.7 ms, p = 0.003; SDNN: −11.3 ms, p = 0.003; SpO2: −8.4%, p < 0.0000) and appeared to slightly decline with non-significant increases in HRV. During normoxia, SNS activity was heightened, albeit non-significantly, in epoch 1, with higher HR (68.5 bpm vs. 73.0 bpm, p = 0.06), lower HRV (rMSSD: 45.1 ms vs. 38.7 ms, p = 0.09 and SDNN: 52.5 ms vs. 45.1 ms, p = 0.08), and lower SpO2 (−0.7% p = 0.05). In epochs 2–4, HR, HRV, and SpO2 trended towards baseline values. Significant between-group differences in HR, HRV, and O2 saturation were observed. Hypoxia elicited significantly greater HRs (+5.0, p = 0.03), lower rMSSD (−7.1, p = 0.03), lower SDNN (−8.2, p = 0.03), and lower SpO2 (−1.4%, p = 0.002) compared to normoxia. Hyperoxia appeared to augment the parasympathetic reactivation reflected by significantly lower HR, in addition to higher HRV and O2 relative to normoxia. Hypoxia induced a greater ANS response in military personnel during the simultaneous exposure to high cognitive load. The significant and differential ANS responses to varying O2 levels and high cognitive load observed highlight the importance of continuously monitoring multiple physiological parameters during flight operations.
Glutamate Receptor Dysregulation and Platelet Glutamate Dynamics in Alzheimer’s and Parkinson’s Diseases: Insights into Current Medications
Authors: Gautam Deepa , Naik Ulhas P. , Naik Meghna U. , Yadav Santosh K. , Chaurasia Rameshwar Nath , Dash Debabrata
Published: November 2023

Two of the most prevalent neurodegenerative disorders (NDDs), Alzheimer’s disease (AD) and Parkinson’s disease (PD), present significant challenges to healthcare systems worldwide. While the etiologies of AD and PD differ, both diseases share commonalities in synaptic dysfunction, thereby focusing attention on the role of neurotransmitters. The possible functions that platelets may play in neurodegenerative illnesses including PD and AD are becoming more acknowledged. In AD, platelets have been investigated for their ability to generate amyloid-ß (Aß) peptides, contributing to the formation of neurotoxic plaques. Moreover, platelets are considered biomarkers for early AD diagnosis. In PD, platelets have been studied for their involvement in oxidative stress and mitochondrial dysfunction, which are key factors in the disease’s pathogenesis. Emerging research shows that platelets, which release glutamate upon activation, also play a role in these disorders. Decreased glutamate uptake in platelets has been observed in Alzheimer’s and Parkinson’s patients, pointing to a systemic dysfunction in glutamate handling. This paper aims to elucidate the critical role that glutamate receptors play in the pathophysiology of both AD and PD. Utilizing data from clinical trials, animal models, and cellular studies, we reviewed how glutamate receptors dysfunction contributes to neurodegenerative (ND) processes such as excitotoxicity, synaptic loss, and cognitive impairment. The paper also reviews all current medications including glutamate receptor antagonists for AD and PD, highlighting their mode of action and limitations. A deeper understanding of glutamate receptor involvement including its systemic regulation by platelets could open new avenues for more effective treatments, potentially slowing disease progression and improving patient outcomes.
Pyrazolo-triazolo-pyrimidine Scaffold as a Molecular Passepartout for the Pan-Recognition of Human Adenosine Receptors
Authors: Salmaso Veronica , Persico Margherita , Da Ros Tatiana , Spalluto Giampiero , Kachler Sonja , Klotz Karl-Norbert , Moro Stefano , Federico Stephanie
Published: November 2023

Adenosine receptors are largely distributed in our organism and are promising therapeutic targets for the treatment of many pathologies. In this perspective, investigating the structural features of the ligands leading to affinity and/or selectivity is of great interest. In this work, we have focused on a small series of pyrazolo-triazolo-pyrimidine antagonists substituted in positions 2, 5, and N8, where bulky acyl moieties at the N5 position and small alkyl groups at the N8 position are associated with affinity and selectivity at the A3 adenosine receptor even if a good affinity toward the A2B adenosine receptor has also been observed. Conversely, a free amino function at the 5 position induces high affinity at the A2A and A1 receptors with selectivity vs. the A3 subtype. A molecular modeling study suggests that differences in affinity toward A1, A2A, and A3 receptors could be ascribed to two residues: one in the EL2, E168 in human A2A/E172 in human A1, that is occupied by the hydrophobic residue V169 in the human A3 receptor; and the other in TM6, occupied by H250/H251 in human A2A and A1 receptors and by a less bulky S247 in the A3 receptor. In the end, these findings could help to design new subtype-selective adenosine receptor ligands.
A Pictorial Exploration of Mammary Paget Disease: Insights and Perspectives
Authors: Mariano Luciano , Nicosia Luca , Pupo Davide , Olivieri Antonia Maria , Scolari Sofia , Pesapane Filippo , Latronico Antuono , Bozzini Anna Carla , Fusco Nicola , Blanco Marta Cruz , Mazzarol Giovanni , Corso Giovanni , Galimberti Viviana Enrica , Venturini Massimo , Pizzamiglio Maria , Cassano Enrico
Published: November 2023

Mammary Paget disease (MPD) is a rare condition primarily affecting adult women, characterized by unilateral skin changes in the nipple–areolar complex (NAC) and frequently associated with underlying breast carcinoma. Histologically, MPD is identified by large intraepidermal epithelial cells (Paget cells) with distinct characteristics. Immunohistochemical profiles aid in distinguishing MPD from other skin conditions. Clinical evaluation and imaging techniques, including magnetic resonance imaging (MRI), are recommended if MPD is suspected, although definitive diagnosis always requires histological examination. This review delves into the historical context, epidemiology, pathogenesis, clinical manifestations, and diagnosis of MPD, emphasizing the need for early detection. The classification of MPD based on pathogenesis is explored, shedding light on its varied presentations. Treatment options, including mastectomy and breast-conserving surgery, are discussed with clear guidelines for different scenarios. Adjuvant therapies are considered, particularly in cases with underlying breast cancer. Prognostic factors are outlined, underlining the importance of early intervention. Looking to the future, emerging techniques, like liquid biopsy, new immunohistochemical and molecular markers, and artificial intelligence-based image analysis, hold the potential to transform MPD diagnosis and treatment. These innovations offer hope for early detection and improved patient care, though validation through large-scale clinical trials is needed.
RNA Editing in Cancer Progression
Authors: Frezza Valentina , Chellini Lidia , Del Verme Arianna , Paronetto Maria Paola
Published: November 2023

Coding and noncoding RNA molecules play their roles in ensuring cell function and tissue homeostasis in an ordered and systematic fashion. RNA chemical modifications can occur both at bases and ribose sugar, and, similarly to DNA and histone modifications, can be written, erased, and recognized by the corresponding enzymes, thus modulating RNA activities and fine-tuning gene expression programs. RNA editing is one of the most prevalent and abundant forms of post-transcriptional RNA modification in normal physiological processes. By altering the sequences of mRNAs, it makes them different from the corresponding genomic template. Hence, edited mRNAs can produce protein isoforms that are functionally different from the corresponding genome-encoded variants. Abnormalities in regulatory enzymes and changes in RNA-modification patterns are closely associated with the occurrence and development of various human diseases, including cancer. To date, the roles played by RNA modifications in cancer are gathering increasing interest. In this review, we focus on the role of RNA editing in cancer transformation and provide a new perspective on its impact on tumorigenesis, by regulating cell proliferation, differentiation, invasion, migration, stemness, metabolism, and drug resistance.
Enhancing the Accuracy of Lymph-Node-Metastasis Prediction in Gynecologic Malignancies Using Multimodal Federated Learning: Integrating CT, MRI, and PET/CT
Authors: Hu Zhijun , Ma Ling , Ding Yue , Zhao Xuanxuan , Shi Xiaohua , Lu Hongtao , Liu Kaijiang
Published: November 2023

Gynecological malignancies, particularly lymph node metastasis, have presented a diagnostic challenge, even with traditional imaging techniques such as CT, MRI, and PET/CT. This study was conceived to explore and, subsequently, to bridge this diagnostic gap through a more holistic and innovative approach. By developing a comprehensive framework that integrates both non-image data and detailed MRI image analyses, this study harnessed the capabilities of a multimodal federated-learning model. Employing a composite neural network within a federated-learning environment, this study adeptly merged diverse data sources to enhance prediction accuracy. This was further complemented by a sophisticated deep convolutional neural network with an enhanced U-NET architecture for meticulous MRI image processing. Traditional imaging yielded sensitivities ranging from 32.63% to 57.69%. In contrast, the federated-learning model, without incorporating image data, achieved an impressive sensitivity of approximately 0.9231, which soared to 0.9412 with the integration of MRI data. Such advancements underscore the significant potential of this approach, suggesting that federated learning, especially when combined with MRI assessment data, can revolutionize lymph-node-metastasis detection in gynecological malignancies. This paves the way for more precise patient care, potentially transforming the current diagnostic paradigm and resulting in improved patient outcomes.
Smart Poly(N-Isopropylacrylamide)-Based Microgels Supplemented with Nanomaterials for Catalytic Reduction Reactions—A Review
Authors: Alam Mir Waqas
Published: November 2023

The continuous and irresponsible addition of environmental pollutants into aqueous reservoirs due to excessive industrialization is a significant contemporary challenge. Nanomaterial-based catalytic reduction provides an effective way to convert these materials into environmentally useful products. Responsive polymeric assemblies, complemented with nanomaterials, represent advanced nanocatalysts that are gaining interest within the scientific community. These assemblies exhibit reversible morphological transitions in response to variations induced by external factors such as temperature, pH, or electromagnetic irradiation treatment. The term hybrid microgels has been coined for assemblies that contain both nanomaterial and smart polymeric components. This review presents recent advancements in the field of hybrid microgels as nanocatalysts for conducting reduction reactions on pollutants present in aqueous media. Apart from placing detailed emphasis on the advancements documented for these assemblies, the fundamentals associated with hybrid microgels, as well as the typical catalytic reduction, are also emphasized to develop an understanding for new academicians looking to explore this field. The author hopes that this critical review of the most recent academic literature, including the years spanning 2020 to 2023, will serve as a tutorial for the identification of research gaps in this field, along with its prospective solutions.
Prediction of Surface Residual Stresses after Laser Shock Processing on TC4 Titanium Alloy Using Different Neural Network Agent Models
Authors: Ding Xiangyu , Li Hongliang , Jiang Zonghong , Zhang Junlong , Ma Sijie , Zhong Jida , Wang Shengchao , Wang Cheng
Published: November 2023

Nowadays, it has become a trend to use finite element simulation instead of experimental processes, and this is widely used in the fields of structural mechanics, fluid mechanics, fracture mechanics, and so on. By replacing the experimental process with finite element simulation, we can reduce time and costs; however, when using finite element simulation, we need to define a series of settings, such as modeling, material assignment, environment settings, and many other operations. For laser shock processing intensification, the simulation experiment process is cumbersome and time-consuming. It involves performing neural network agent modeling, replacing finite element simulation with the learning and prediction capabilities of neural networks, learning by using some of the simulation results as a training sets for the neural network, and then learning by using the remaining simulation results as testing sets to test the predictive ability of the neural network agent model. TC4 titanium alloy was selected as the experimental material. Three kinds of neural network agent models, a genetic algorithm-optimized BP network, a strong classifier design based on BP_Adaboost, and an extreme learning machine, instead of finite element simulation experiments, were used to predict the residual stresses generated on the surfaces of the material under different laser shock parameters. Comparing the prediction performances of different neural network agent models, the genetic algorithm-optimized BP network shows the best prediction performance, and its prediction value matches well with the experimental value. The R2, RMSE, and MAE of the testing sets of the BP network optimized using the genetic algorithm were 0.9985, 44.4518, and 30.6285, respectively. The BP network agent model optimized using the genetic algorithm for laser shock parameters other than the 208 sets of data also had good prediction performance, and the predicted values were similar to the actual experimental results. The prediction results show that the BP network optimized using the genetic algorithm can predict the residual stresses on the surface of TC4 titanium alloy material under strengthening via laser shock processing; the genetic algorithm-optimized BP neural network agent model is more convenient and quicker compared to the finite element simulation, and the predicted value is also similar to the actual value. It can thus be used to replace finite element simulation by establishing a more convenient and quicker neural network agent model.
The Effect of Isoniazid–Maltitol Solid Dispersions on Aqueous Solubility and Permeability
Authors: Poka Madan Sai , Milne Marnus , Wessels Anita , Aucamp Marique
Published: November 2023

Maltitol (MAL) is a well-known polyol with potential pharmaceutical applications. Unlike other polyols, its utilization as a carrier for solid dispersions (SDs) has not been adequately investigated. This research studied the feasibility of MAL as an SD carrier to enhance the biopharmaceutical properties of a BCS class I/III drug, isoniazid (INH). SDs of INH–MAL were prepared by the fusion method, and physicochemical characteristics were investigated to determine the solid-state habit, solubility and permeation enhancement of INH. Fourier-transform infrared (FT-IR) spectroscopy demonstrated significant peak broadening for the SDs consisting of a higher MAL concentration. Powder X-ray diffraction indicated a decrease in degree of crystallinity with increasing MAL concentration. Hot-stage microscopy (HSM) and scanning electron microscopy (SEM) revealed that INH–MAL molar ratios affect the type of SD prepared via the fusion method. Results from the equilibrium solubility studies indicated significant INH solubility improvement (p < 0.05) with SDs in comparison with the pure drug and physical mixtures. The artificial membrane permeation assay (PAMPA) of INH was positively affected by the presence of MAL. The results of the study indicated the potential for MAL as a carrier in the preparation of SDs for the solubility and/or permeability enhancement of drugs.
Testing the Minimum System Entropy and the Quantum of Entropy
Authors: Hohm Uwe , Schiller Christoph
Published: November 2023

Experimental and theoretical results about entropy limits for macroscopic and single-particle systems are reviewed. All experiments confirm the minimum system entropy S⩾kln2. We clarify in which cases it is possible to speak about a minimum system entropykln2 and in which cases about a quantum of entropy. Conceptual tensions with the third law of thermodynamics, with the additivity of entropy, with statistical calculations, and with entropy production are resolved. Black hole entropy is surveyed. Claims for smaller system entropy values are shown to contradict the requirement of observability, which, as possibly argued for the first time here, also implies the minimum system entropy kln2. The uncertainty relations involving the Boltzmann constant and the possibility of deriving thermodynamics from the existence of minimum system entropy enable one to speak about a general principle that is valid across nature.
A Novel Compute-and-Forward Relaying Method for Multi-Antenna Wireless Relay Networks
Authors: Yang Xuan , Yan Jiaqi , Xu Yonggang , Wang Desheng , Hua Gang
Published: November 2023

Compute-and-Forward (CoF) is an innovative physical layer network coding strategy, designed to enable receivers in wireless communications to effectively utilize interference. The key idea of CoF is to implement integer combinations based on the codewords from multiple transmitters, rather than decoding individual source codewords. Although CoF is widely used in wireless relay networks, there are still some problems to be solved, such as rank failure, single antenna reception, and the shortest vector problem. In this paper, we introduce a successive extended CoF (SECoF) as a pioneering solution tailored for multi-source, multi-relay, and multi-antenna wireless relay networks. First, we analyze the traditional CoF, and design a SECoF method combining the concepts of matrix projection and successive interference cancellation, which overcomes the problem of CoF rate tending to zero and rank failure and improves the network performance. Secondly, we obtain an approximate solution to the integer-value coefficient vectors by using the LLL lattice-based resolution algorithm. In addition, we deduce the corresponding concise formulas of SECoF. Simulation results show that the SECoF has strong robustness and the approaches outperform the state-of-the-art methods in terms of computation rate, rank failure probability, and outage probability.