bluuebunny/crossref_metadata_2025 huggingface dataset:
Safety and oncological outcome of early intraoperative intravesicle mitomycin C vs. deferred instillation in patients receiving robot-assisted radical nephroureterectomy
Authors: Chou Sheng-Feng , Lin Wei-Ching , Chang Han , Huang Chi-Ping
Published: April 2024

Introduction

Radical nephroureterectomy with concurrent bladder cuff excision (RNUBCE) is the gold standard surgical approach for high-risk primary upper tract urothelial carcinoma (UTUC). Given the notably high incidence of bladder tumor recurrence following this procedure, this study aimed to evaluate the effect and safety of intraoperative mitomycin-C (MMC) instillation vs. deferred instillation on overall oncological outcomes following robot-assisted RNUBCE.

Methods

This is a retrospective chart review study. Patients with non-invasive (N0, not T3/T4) UTUC who underwent robotic RNUBCE combined an intraoperative MMC instillation or a deferred MMC instillation after surgery at a medical center in Taiwan between November 2013 and June 2020 were eligible for inclusion. Patients with prior bladder UC, carcinomas of other origins, received neoadjuvant chemotherapy, and had undergone kidney transplantation were excluded. All surgeries were executed by a single surgical team under the guidance of the same surgeon. The primary outcomes was the risk of bladder tumor recurrence between patients received intraoperative (IO) vs. deferred MMC instillation postoperatively (PO) during one-year follow-up. The secondary outcome was postoperative adverse events assessed by the Clavien–Dindo classification. Univariate and multivariable Cox regression analyses were performed to determine the associations between study variables and the outcomes.

Results

A total of 54 patients were included in the analysis. 12 (22.2%) patients experienced a bladder tumor recurrence during follow-up (IO: 7.7%, PO: 35.7%, p < 0.021). After adjustment in the multivariable, intraoperative MMC instillation was significantly associated with lower risk of bladder recurrence [adjusted hazard ratio (aHR) = 0.15, 95% CI: 0.03–0.81, p = 0.028]. No MMC-related Clavien–Dindo Grade III–IV adverse events were found in either group.

Conclusion

IIntraoperative MMC instillation is safe and associated with a lower bladder tumor recurrence risk in patients undergoing robotic RNUBCE for UTUC than deferred instillation. Future large, prospective studies are still warranted to confirm the findings.
Physical Modeling and Simulation of Reusable Rockets for GNC Verification and Validation
Authors: Farì Stefano , Sagliano Marco , Macés Hernández José Alfredo , Schneider Anton , Heidecker Ansgar , Schlotterer Markus , Woicke Svenja
Published: April 2024

Reusable rockets must rely on well-designed Guidance, Navigation and Control (GNC) algorithms. Because they are tested and verified in closed-loop, high-fidelity simulators, emphasizing the strategy to achieve such advanced models is of paramount importance. A wide spectrum of complex dynamic behaviors and their cross-couplings must be captured to achieve sufficiently representative simulations, hence a better assessment of the GNC performance and robustness. This paper focuses on of the main aspects related to the physical (acausal) modeling of reusable rockets, and the integration of these models into a suitable simulation framework oriented towards GNC Validation and Verification (V&V). Firstly, the modeling challenges and the need for physical multibody models are explained. Then, the Vertical Landing Vehicles Library (VLVLib), a Modelica-based library for the physical modeling and simulation of reusable rocket dynamics, is introduced. The VLVLib is built on specific principles that enable quick adaptations to vehicle changes and the introduction of new features during the design process, thereby enhancing project efficiency and reducing costs. Throughout the paper, we explain how these features allow for the rapid development of complex vehicle simulation models by adjusting the selected dynamic effects or changing their fidelity levels. Since the GNC algorithms are normally tested in Simulink®, we show how simulation models with a desired fidelity level can be developed, embedded and simulated within the Simulink® environment. Secondly, this work details the modeling aspects of four relevant vehicle dynamics: propellant sloshing, Thrust Vector Control (TVC), landing legs deployment and touchdown. The CALLISTO reusable rocket is taken as study case: representative simulation results are shown and analyzed to highlight the impact of the higher-fidelity models in comparison with a rigid-body model assumption.
Phenotypic and Functional Characterization of Bovine Adipose-Derived Mesenchymal Stromal Cells
Authors: Maldonado Vitali V. , Pokharel Sriya , Powell Jeremy G. , Samsonraj Rebekah M.
Published: April 2024

Background: Mesenchymal stem cells (MSCs) are increasingly trialed in cellular therapy applications in humans. They can also be applied to treat a range of diseases in animals, particularly in cattle to combat inflammatory conditions and aging-associated degenerative disorders. We sought to demonstrate the feasibility of obtaining MSCs from adipose tissue and characterizing them using established assays. Methods: Bovine adipose MSCs (BvAdMSCs) were isolated using in-house optimized tissue digestion protocols and characterized by performing a colony formation assay, cell growth assessments, cell surface marker analysis by immunocytochemistry and flow cytometry, osteogenic and adipogenic differentiation, and secretion of indoleamine 2,3-dioxygenease (IDO). Results: Our results demonstrate the feasibility of successful MSC isolation and culture expansion from bovine adipose tissues with characteristic features of colony formation, in vitro multilineage differentiation into osteogenic and adipogenic lineages, and cell surface marker expression of CD105, CD73, CD90, CD44, and CD166 with negative expression of CD45. BvAdMSCs secreted significant amounts of IDO with or without interferon–gamma stimulation, indicating ability for immunomodulation. Conclusions: We report a viable approach to obtaining autologous adipose-derived MSCs that can be applied as potential adjuvant cell therapy for tissue repair and regeneration in cattle. Our methodology can be utilized by veterinary cell therapy labs for preparing MSCs for disease management in cattle.
Assessment of the Development Performance of Additive Manufacturing VPP Parts Using Digital Light Processing (DLP) and Liquid Crystal Display (LCD) Technologies
Authors: Batista Moises , Mora-Jimenez Jairo , Salguero Jorge , Vazquez-Martinez Juan Manuel
Published: April 2024

Non-metallic additive manufacturing technology has seen a substantial improvement in the precision of the parts it produces. Its capability to achieve complex geometries and very small dimensions makes it suitable for integration into strategic industrial sectors, such as aeronautics and medicine. Among additive manufacturing technologies, resin development processes demonstrate enhanced precision when compared to other methods, like filament printing. This study conducts a comparative analysis between digital light processing (DLP) and liquid crystal display (LCD) photopolymerization processes to assess the performance of the technologies and how process parameters affect the accuracy of the resulting parts. The research evaluates the impact of the discretization process used during the digital model export, determining the optimal mesh size and then analyzing the geometric deviations that occur by altering various operating parameters of the process. Statistical methods will be employed to identify the most significant parameters in the manufacturing process. Among other aspects, the precision of manufacturing technologies regarding the movement axis has also been evaluated. Regarding the minimum size of the features that can be fabricated, DLP technology has surpassed LCD technology, successfully producing features as small as 200 µm, compared to 500 µm for LCD technology.
A Methodology for Estimating the Assembly Position of the Process Based on YOLO and Regression of Operator Hand Position and Time Information
Authors: Lim Byeongju , Jeong Seyun , Yoo Youngjun
Published: April 2024

These days, many assembly lines are becoming automated, leading to a trend of decreasing defect rates. However, in assembly lines that have opted for partial automation due to high cost of construction, defects still occur. The cause of defects are that the location of the work instructions and the work field are different, which is inefficient and some workers who are familiar with the process tend not to follow the work instructions. As a solution to establishing a system for object detection without disrupting the existing assembly lines, we decided to use wearable devices. As a result, it is possible to solve the problem of spatial constraints and save costs. We adopted the YOLO algorithm for object detection, an image recognition model that stands for “You Only Look Once”. Unlike R-CNN or Fast R-CNN, YOLO predicts images with a single network, making it up to 1000 times faster. The detection point was determined based on whether the pin was fastened after the worker’s hand appeared and disappeared. For the test, 1000 field data were used and the object-detection performance, mAP, was 35%. The trained model was analyzed using seven regression algorithms, among which Xgboost was the most excellent, with a result of 0.15. Distributing labeling and class-specific data equally is expected to enable the implementation of a better model. Based on this approach, the algorithm is considered to be an efficient algorithm that can be used in work fields.
Advanced Technologies in Food Processing—Development Perspective
Authors: Gazda Patrycja , Glibowski Paweł
Published: April 2024

Research into innovative techniques in food technology is developing dynamically. This is indicated by the significant increase in the number of scientific studies in this field. The aim of this work was to provide a comprehensive, in-depth analysis of the available scientific evidence on new techniques used in food that not only increase efficiency but also enable the creation of products with desirable sensory and nutritional characteristics. Research on techniques including cold plasma, high-pressure processing, ultrasound, pulsed electric fields, sous vide, and microwave heating aims to provide innovative methods of food processing, in the context of meeting growing consumer expectations and optimizing production processes in the food industry. Compared to traditional food processing methods, innovative techniques can provide more efficient solutions in the processing of products. Research on alternative non-thermal methods in food technology suggests their possible benefits, including enhancing sensory and nutritional quality, minimizing environmental impact, and increasing production efficiency, which are a significant challenge in the modern food industry. Despite the many benefits, it is worthwhile to continue research to further improve modern food technologies.
Design and Analysis of Micro Signal Detection Circuit for Magnetic Field Detection Utilizing Coil Sensors
Authors: Xu Qifan , Zhang Sichang , Li Siyu , Xu Zhe , Cao Shouqi , Wang Meiling
Published: April 2024

Eddy current inspection has been extensively employed in non-destructive testing of various conductive materials. The coil probe, as a mainstream sensor in the eddy current detection system, inevitably encounters interference from external signals while transmitting its own signal. Therefore, developing techniques to extract valuable signals from noisy ones is crucial for ensuring accurate detection. Carbon fiber composites not only possess significantly lower electrical conductivity compared to conventional metallic materials but also exhibit notable anisotropy. To address this issue, we designed an ‘8’ coil probe set where the excitation coil does not electromagnetically interfere with the detection coil. However, practical applications that require portability and miniaturization pose challenges when utilizing this coil probe set to identify carbon content or defects due to the typically weak output signal. To address this issue, this paper proposes a design that combines the ‘8’ structure of the planar coil probe with the principle of phase-locked amplification to create a dual-phase sensitive phase-locked amplification detection circuit. These specific design ideas were tested using a weak signal, which passed through the preamplifier, secondary amplifier, and band-pass filter comprising the target channel for signal amplification and noise filtering. The effective signal amplitude is proportional to the inverse phase difference between the direct current (DC) signal and inversely proportional to the amplitude of the signal. Finally, the DC signal was passed through an analog-to-digital converter (ADC). The analog-to-digital converter (A/D) is used to collect and calculate the DC signal, enabling the detection of weak electrical signals. Simulation experiments demonstrated that the signal detection circuit has an amplitude error below 0.2% and a phase error below 0.5%. The phase-locked amplification circuit designed in this paper can effectively extract the tiny impedance change signals of the planar coil sensor probe with high sensitivity and good robustness.
Enhancing Interpretability in Drill Bit Wear Analysis through Explainable Artificial Intelligence: A Grad-CAM Approach
Authors: Senjoba Lesego , Ikeda Hajime , Toriya Hisatoshi , Adachi Tsuyoshi , Kawamura Youhei
Published: April 2024

This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as fast Fourier transform (FFT) and wavelet transform (WT), require extensive knowledge of drilling equipment specifications, which limits their adaptability to different conditions. In contrast, our method leverages XAI algorithms applied to CNNs to directly identify fault signatures from vibration signals. The signals are transformed into their frequency components and then employed as inputs to a CNN model, which is trained to detect patterns indicative of drill bit failure. XAI algorithms are then employed to generate attention maps, highlighting regions of interest in the CNN. By scrutinizing these maps, engineers can identify critical frequencies associated with drill bit failure, providing valuable insights for maintenance and optimization. This method offers a transparent and interpretable framework for analyzing vibration data, enabling informed decision-making and proactive maintenance strategies to enhance drilling efficiency and minimize downtime. The integration of XAI with CNNs facilitates a deeper understanding of the root causes of drill bit failure and improves overall drilling performance.
Simple Ultrasonic-Based Localization System for Mobile Robots
Authors: Sukop Marek , Grytsiv Maksym , Jánoš Rudolf , Semjon Ján
Published: April 2024

This paper presents the development and validation of a cost-efficient and uncomplicated real-time localization system (RTLS) for use in mobile robotics, specifically within indoor and storage environments. By harnessing ultrasonic waves to measure distances from three beacons, the system provides stable and reliable localization. This method utilizes the time-of-flight (TOF) principle, allowing for accurate distance calculations with simple microcontrollers. The system is designed to update the robot’s position at a frequency of at least 10 times per second, ensuring smooth navigation. Our trilateration-based approach allows for the precise determination of the robot’s position with a notable standard deviation accuracy of up to 15 mm. The aim was to design a simple yet sufficiently accurate system and verify its precision through experimental measurements. The experimental results demonstrate the system’s efficacy and lay a solid foundation for advancing this technology. Furthermore, the cost for the components required to build this indoor localization system (ILS) with three beacons and one tag is remarkably low, under EUR 80.
The Effect of Situational Variables on Women’s Rink Hockey Match Outcomes
Authors: Arboix-Alió Jordi , Trabal Guillem , Moreno-Galcerán Dani , Buscà Bernat , Arboix Adrià , Vaz Vasco , Sarmento Hugo , Hileno Raúl
Published: April 2024

The main objective of the present study was to develop a concise predictive model to determine the likelihood of winning in female rink hockey based on various situational variables. Additionally, the study aimed to assess the individual impact of each predictor on match outcomes. The analysis encompassed a dataset of 840 matches during five consecutive seasons (from 2018–2019 to 2022–2023) in the Spanish first division (OkLiga). Employing the comprehensive method of all possible regressions, the most effective predictive logistic model for match outcomes was identified. This entire model featured five categorical predictor variables (match location, team level, opponent level, scoring first, and match status at halftime) and one binary outcome variable (match outcome). Subsequently, the final model, which exhibited a sensitivity and specificity surpassing 80% for a cut-off point of 0.439, emerged. This model was applied to predict winning a match in 18 frequent situations determined from a two-step cluster analysis. Within this predictive framework, match status at halftime emerged as the most influential predictor impacting the match outcome, followed by opponent level, team level, and match location. The implications of our findings extend to rink hockey coaches and practitioners. Recognizing the significant impact of situational variables on match outcomes empowers them to customize game plans and design more specific strategies, thereby enhancing game understanding and elevating the overall performance.
Improvement of Fresnel Diffraction Convolution Algorithm
Authors: Ge Cong , Song Qinghe , Caiyang Weinan , Gui Jinbin , Li Junchang , Qian Xiaofan , Li Qian , Dang Haining
Published: April 2024

With the development of digital holography, the accuracy requirements for the reconstruction phase are becoming increasingly high. The transfer function of the double fast transform (D-FFT) algorithm is distorted when the diffraction distance is larger than the criterion distance dt, which reduces the accuracy of solving the phase. In this paper, the Fresnel diffraction integration algorithm is improved by using the low-pass Tukey window to obtain more accurate reconstructed phases. The improved algorithm is called the D-FFT (Tukey) algorithm. The D-FFT (Tukey) algorithm adjusts the degree of edge smoothing of the Tukey window, using the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) to remove the ringing effect and obtain a more accurate reconstructed phase. In a simulation of USAF1951, the longitudinal resolution of the reconstructed phase obtained by D-FFT (Tukey) reached 1.5 μm, which was lower than the 3 μm obtained by the T-FFT algorithm. The results of Fresnel holography experiments on lung cancer cell slices also demonstrated that the phase quality obtained by the D-FFT (Tukey) algorithm was superior to that of the T-FFT algorithm. D-FFT (Tukey) algorithm has potential applications in phase correction, structured illumination digital holographic microscopy, and microscopic digital holography.
Non-Conscious Affective Processing in Asset Managers during Financial Decisions: A Neurobiological Perspective
Authors: Walla Peter , Patschka Maximilian
Published: April 2024

In the world of finance, considerable attention is given to improving machine learning techniques to predict the future of stock markets. However, for obvious reasons, this turns out to be an unsolvable mission, most likely because the real world is not driven by algorithms but by human beings. In response to this, the present study has its focus on raw affective responses in actual asset managers during their decision making regarding controlled financial scenarios. Nineteen asset managers were invited and asked to make sell/buy decisions related to visual presentations of three different price developments of different assets. The three scenarios were “crash”, “stable” and “gain”. Parallel to their decision making, startle reflex modulation (SRM) was used to measure non-conscious affective responses without demanding any respective explicit responses (no conscious language processing involved). Interestingly, two further factors were introduced. First, all participants had to make their decisions once while being informed that 0% prior investments (low exposure) have been made into the presented assets, and once being informed that a large investment consisting of 25% of ones’ overall portfolio has been made prior to making the decision (high exposure). Second, the factor experience was included dividing all participants into two groups, one with low experience and the other with high experience. First, across both these extra factors, it was found that “crash” scenarios resulted in the most negative affective responses. The most positive affective responses were found for “gain” scenarios, while the “stable” condition was in between. Interestingly, the factor of prior investment (i.e., exposure) had an effect. Non-conscious affective responses during decision making related to the “stable” condition varied as a function of “exposure”. In the low exposure condition, affective responses to decision making during the “stable” scenario were most negative, even more negative than in “crash” scenarios. The factor experience also had an effect, but due to the small sample size, no significant interaction occurred. However, t-tests revealed the same significant effects in the experienced group as found in the 0% prior investment condition. To our knowledge, this is the first empirical investigation measuring non-conscious affective responses during decision making in the context of asset management. Thus, this study might form an interesting basis for new strategies to explore non-conscious human brain functions instead of inventing new algorithms to make asset management more successful.
Human Activity Recognition Based on Deep Learning Regardless of Sensor Orientation
Authors: He Zhenyu , Sun Yulin , Zhang Zhen
Published: April 2024

In recent years, the continuous progress of wireless communication and sensor technology has enabled sensors to be better integrated into mobile devices. Therefore, sensor-based Human Activity Recognition (HAR) has attracted widespread attention among researchers, especially in the fields of wearable technology and ubiquitous computing. In these applications, mobile devices’ built-in accelerometers and gyroscopes have been typically used for human activity recognition. However, devices such as smartphones were placed in users’ pockets and not fixed to their bodies, and the resulting changes in the orientation of the sensors due to users’ habits or external forces can lead to a decrease in the accuracy of activity recognition. Unfortunately, there is currently a lack of publicly available datasets specifically designed to address the issue of device angle change. The contributions of this study are as follows. First, we constructed a dataset with eight different sensor placement angles using accelerometers and gyroscopes as a prerequisite for the subsequent research. Second, we introduced the Madgwick algorithm to extract quaternion mode features and alleviate the impact of angle changes on recognition performance by fusing raw accelerometer data and quaternion mode features. The resulting study provides a comprehensive analysis. On the one hand, we fine-tuned ResNet and tested its stability on our dataset, achieving a recognition accuracy of 97.13%. We included two independent experiments, one for user-related scenarios and the other for user-independent scenarios. In addition, we validated our research results on two publicly available datasets, demonstrating that our method has good generalization performance.
Finite-Time Attitude Control of Quadrotor Unmanned Aerial Vehicle with Disturbance and Actuator Saturation
Authors: Zhang Zheng , Li Xingwei , Zhang Lilian
Published: April 2024

This paper introduces a nonlinear dynamic inversion control algorithm designed to address unknown disturbances and actuator saturation issues in unmanned aerial vehicle (UAV) attitude control. The algorithm is based on a combination of finite-time disturbance observer and anti-saturation auxiliary system, which ensures the rapid convergence of attitude tracking error. Firstly, based on the Newton–Euler equations, this paper establishes a model of the attitude system for quadrotor UAVs, and this paper eliminates the small-angle flight assumption. Secondly, considering the actuator saturation problem, an anti-saturation auxiliary control system is designed to shorten the time when the control volume is in the saturation interval and achieve finite-time convergence of the attitude error. And then, to improve the robustness of the controller, this paper proposes a disturbance observer based on the finite-time stability theory, which achieves a continuous smooth output of the observation results by introducing a hyperbolic tangent function in the observer, so that the observation error can be converged to zero in a finite time. Finally, it is demonstrated by Simulink simulation that the attitude error and the observation error converge quickly to zero.
Land-Use Transitions Impact the Ecosystem Services Value in a Coastal Region by Coupling the Geo-Informatic Tupu and Benefit-Transfer Method: The Case of Ningde City, China
Authors: Peng Qingxia , Shen Lingzhi , Lin Wenxiong , Fan Shuisheng , Su Kai
Published: April 2024

Exploring the mechanisms and processes of land-use transitions (LUTs) and their impact on ecosystem services can effectively elucidate the intricate interactions between human and natural systems, which is pivotal for advancing the sustainable development of regional economies and enhancing ecological environments. However, the existing literature lacks comprehensive analysis regarding the spatial and temporal evolution of LUTs, with insufficient integration of the “spatial pattern” and “time process”. Moreover, traditional assessments of the ecosystem services value (ESV) often overlook their negative costs. To address these gaps, this study first utilized the Google Earth Engine (GEE) cloud platform and employed the random forest algorithm to conduct supervised classification on Landsat remote-sensing images from the years 2000, 2010, and 2020 within the research area, thereby obtaining land-use data for three distinct periods. And then, we investigated the geographic features of LUTs and their ecological effects in the Ningde City of China from 2000 to 2020. The geo-informatic Tupu model and a newly revised method of benefit transfer were primarily employed for this purpose. The findings indicate the following: (1) Over the study period, the land-use structure of Ningde City predominantly comprised cultivated land and forest land, with continuous decreases in both types and a concurrent increase in built-up land. (2) Significant disparities exist in the spatial distribution of Tupu units, notably with “forest land → cultivated land” and “cultivated land → built-up land” as crucial units influencing ESV changes. (3) The ESV in Ningde City decreased from CNY 1105.54 × 108 to CNY 1020.47 × 108 over 2000–2020, while the ecosystem dis-services value exhibited an opposing trend, rising from CNY 12.68 × 108 to CNY 20.39 × 108. (4) The net ESV in Ningde City showed a decline over the same period, indicating a certain vulnerability in the city’s ecological system structure. This study aims to enhance our understanding of the influence of land-use patterns on ESV, offering valuable insights for regional ecological–environment management and land-use policy formulation, thereby fostering sustainable development in ecological, environmental, and socio-economic dimensions. Furthermore, the results serve as a reference for evaluating net ecosystem services value in other countries/regions.
Hyperholomorphicity by Proposing the Corresponding Cauchy–Riemann Equation in the Extended Quaternion Field
Authors: Kim Ji-Eun
Published: April 2024

In algebra, the sedenions, an extension of the octonion system, form a 16-dimensional noncommutative and nonassociative algebra over the real numbers. It can be expressed as two octonions, and a function and differential operator can be defined to treat the sedenion, expressed as two octonions, as a variable. By configuring elements using the structure of complex numbers, the characteristics of octonions, the stage before expansion, can be utilized. The basis of a sedenion can be simplified and used for calculations. We propose a corresponding Cauchy–Riemann equation by defining a regular function for two octonions with a complex structure. Based on this, the integration theorem of regular functions with a sedenion of the complex structure is given. The relationship between regular functions and holomorphy is presented, presenting the basis of function theory for a sedenion of the complex structure.
Deep-Learning-Based Automated Anomaly Detection of EEGs in Intensive Care Units
Authors: Wu Jacky Chung-Hao , Liao Nien-Chen , Yang Ta-Hsin , Hsieh Chen-Cheng , Huang Jin-An , Pai Yen-Wei , Huang Yi-Jhen , Wu Chieh-Liang , Lu Henry Horng-Shing
Published: April 2024

An intensive care unit (ICU) is a special ward in the hospital for patients who require intensive care. It is equipped with many instruments monitoring patients’ vital signs and supported by the medical staff. However, continuous monitoring demands a massive workload of medical care. To ease the burden, we aim to develop an automatic detection model to monitor when brain anomalies occur. In this study, we focus on electroencephalography (EEG), which monitors the brain electroactivity of patients continuously. It is mainly for the diagnosis of brain malfunction. We propose the gated-recurrent-unit-based (GRU-based) model for detecting brain anomalies; it predicts whether the spike or sharp wave happens within a short time window. Based on the banana montage setting, the proposed model exploits characteristics of multiple channels simultaneously to detect anomalies. It is trained, validated, and tested on separated EEG data and achieves more than 90% testing performance on sensitivity, specificity, and balanced accuracy. The proposed anomaly detection model detects the existence of a spike or sharp wave precisely; it will notify the ICU medical staff, who can provide immediate follow-up treatment. Consequently, it can reduce the medical workload in the ICU significantly.
Impact of Polyethylene Terephthalate Microplastics on Drosophila melanogaster Biological Profiles and Heat Shock Protein Levels
Authors: Kauts Simran , Mishra Yachana , Singh Mahendra P.
Published: April 2024

Microplastics and nanoplastics are abundant in the environment. Further research is necessary to examine the consequences of microplastic contamination on living species, given its widespread presence. In our research, we determined the toxic effects of PET microplastics on Drosophila melanogaster at the cellular and genetic levels. Our study revealed severe cytotoxicity in the midgut of larvae and the induction of oxidative stress after 24 and 48 h of treatment, as indicated by the total protein, Cu-Zn SOD, CAT, and MDA contents. For the first time, cell damage in the reproductive parts of the ovaries of female flies, as well as in the accessory glands and testes of male flies, has been observed. Furthermore, a decline in reproductive health was noted, resulting in decreased fertility among the flies. By analyzing stress-related genes such as hsp83, hsp70, hsp60, and hsp26, we detected elevated expression of hsp83 and hsp70. Our study identified hsp83 as a specific biomarker for detecting early redox changes in cells caused by PET microplastics in all the treated groups, helping to elucidate the primary defense mechanism against PET microplastic toxicity. This study offers foundational insights into the emerging environmental threats posed by microplastics, revealing discernible alterations at the genetic level.
Protective Effects of Long-Term Escitalopram Administration on Memory and Hippocampal BDNF and BCL-2 Gene Expressions in Rats Exposed to Predictable and Unpredictable Chronic Mild Stress
Authors: Saedi Marghmaleki Vajihe , Radahmadi Maryam , Alaei Hojjatallah , Khanahmad Hossein
Published: April 2024

Stress and escitalopram (an anti-stress medication) can affect brain functions and related gene expression. This study investigated the protective effects of long-term escitalopram administration on memory, as well as on hippocampal BDNF and BCL-2 gene expressions in rats exposed to predictable and unpredictable chronic mild stress (PCMS and UCMS, respectively). Male rats were randomly assigned to different groups: control (Co), sham (Sh), predictable and unpredictable stress (PSt and USt, respectively; 2 h/day for 21 consecutive days), escitalopram (Esc; 10 mg/kg for 21 days), and predictable and unpredictable stress with escitalopram (PSt-Esc and USt-Esc, respectively). The passive avoidance test was used to assess behavioral variables. The expressions of the BDNF and BCL-2 genes were assessed using real-time quantitative PCR. Latency significantly decreased in the PSt and USt groups. Additionally, latency showed significant improvement in the PSt-Esc group compared to the PSt group. The expression of the BDNF gene significantly decreased only in the USt group. BDNF gene expression significantly increased in the PSt-Esc and USt-Esc groups compared to their respective stress-related groups, whereas the expression of the BCL-2 gene did not change significantly in both PSt-Esc and USt-Esc groups. PCMS and UCMS had devastating effects on memory. Escitalopram improved memory only under PCMS conditions. PCMS and UCMS exhibited fundamental differences in hippocampal BDNF and BCL-2 gene expressions. Furthermore, escitalopram increased hippocampal BDNF gene expression in the PCMS and UCMS subjects. Hence, neurogenesis occurred more significantly than anti-apoptosis under both PCMS and UCMS conditions with escitalopram.
Attention-Modulated Cortical Responses as a Biomarker for Tinnitus
Authors: Richardson Matthew L. , Luo Jiaxin , Zeng Fan-Gang
Published: April 2024

Attention plays an important role in not only the awareness and perception of tinnitus but also its interactions with external sounds. Recent evidence suggests that attention is heightened in the tinnitus brain, likely as a result of relatively local cortical changes specific to deafferentation sites or global changes that help maintain normal cognitive capabilities in individuals with hearing loss. However, most electrophysiological studies have used passive listening paradigms to probe the tinnitus brain and produced mixed results in terms of finding a distinctive biomarker for tinnitus. Here, we designed a selective attention task, in which human adults attended to one of two interleaved tonal (500 Hz and 5 kHz) sequences. In total, 16 tinnitus (5 females) and 13 age- and hearing-matched control (8 females) subjects participated in the study, with the tinnitus subjects matching the tinnitus pitch to 5.4 kHz (range = 1.9–10.8 kHz). Cortical responses were recorded in both passive and attentive listening conditions, producing no differences in P1, N1, and P2 between the tinnitus and control subjects under any conditions. However, a different pattern of results emerged when the difference was examined between the attended and unattended responses. This attention-modulated cortical response was significantly greater in the tinnitus than control subjects: 3.9-times greater for N1 at 5 kHz (95% CI: 2.9 to 5.0, p = 0.007, ηp2 = 0.24) and 3.0 for P2 at 500 Hz (95% CI: 1.9 to 4.5, p = 0.026, ηp2 = 0.17). We interpreted the greater N1 modulation as local neural changes specific to the tinnitus frequency and the greater P2 as global changes to hearing loss. These two cortical measures were used to differentiate between the tinnitus and control subjects, producing 83.3% sensitivity and 76.9% specificity (AUC = 0.81, p = 0.006). These results suggest that the tinnitus brain is more plastic than that of the matched non-tinnitus controls and that the attention-modulated cortical response can be developed as a clinically meaningful biomarker for tinnitus.
A Comparison of Brain-State Representations of Binary Neuroimaging Connectivity Data. Comment on Samantaray et al. Unique Brain Network Identification Number for Parkinson’s and Healthy Individuals Using Structural MRI. Brain Sci. 2023, 13, 1297
Authors: Kelly Josephine Noah , Fu Bowen , Li Zhenyu , Kelly Robert Emmett
Published: April 2024

Samantaray et al [...]
The Role of Neutrophils in Multiple Sclerosis and Ischemic Stroke
Authors: Nowaczewska-Kuchta Anna , Ksiazek-Winiarek Dominika , Szpakowski Piotr , Glabinski Andrzej
Published: April 2024

Inflammation plays an important role in numerous central nervous system (CNS) disorders. Its role is ambiguous—it can induce detrimental effects, as well as repair and recovery. In response to injury or infection, resident CNS cells secrete numerous factors that alter blood–brain barrier (BBB) function and recruit immune cells into the brain, like neutrophils. Their role in the pathophysiology of CNS diseases, like multiple sclerosis (MS) and stroke, is highly recognized. Neutrophils alter BBB permeability and attract other immune cells into the CNS. Previously, neutrophils were considered a homogenous population. Nowadays, it is known that various subtypes of these cells exist, which reveal proinflammatory or immunosuppressive functions. The primary goal of this review was to discuss the current knowledge regarding the important role of neutrophils in MS and stroke development and progression. As the pathogenesis of these two disorders is completely different, it gives the opportunity to get insight into diverse mechanisms of neutrophil involvement in brain pathology. Our understanding of the role of neutrophils in CNS diseases is still evolving as new aspects of their activity are being unraveled. Neutrophil plasticity adds another level to their functional complexity and their importance for CNS pathophysiology.
Edge AI-Enabled Road Fixture Monitoring System
Authors: Younesi Heravi Moein , Dola Israt Sharmin , Jang Youjin , Jeong Inbae
Published: April 2024

Effective monitoring of road fixtures is essential for urban safety and functionality. However, traditional inspections are time-consuming, costly, and error prone, while current automated solutions struggle with high initial setup costs, limited flexibility preventing wide adaptation, and reliance on centralized processing that can delay response times. This study introduces an edge AI-based remote road fixture monitoring system which automatically and continuously updates the information of the road digital twin (DT). The main component is a small-sized edge device consisting of a camera, GPS, and IMU sensors designed to be installed in typical cars. The device captures images, detects the fixture, and estimates their location by employing deep learning and feature matching. This information is transmitted to a dedicated cloud server and represented on a user-friendly user interface. Experiments were conducted to test the system’s performance. The results showed that the device could successfully detect the fixture and estimate their global coordinates. Outputs were marked and shown on the road DT, proving the integrated and smooth operation of the whole system. The proposed Edge AI device demonstrated that it could significantly reduce the data size by 80–84% compared to traditional methods. With a satisfactory object detection accuracy of 65%, the system effectively identifies traffic poles, stop signs, and streetlights, integrating these findings into a digital twin for real-time monitoring. The proposed system improves road monitoring by cutting down on maintenance and emergency response times, increasing the ease of data use, and offering a foundation for an overview of urban road fixtures’ current state. However, the system’s reliance on the quality of data collected under varying environmental conditions suggests potential improvements for consistent performance across diverse scenarios.
A Numerical Simulation Study on the Out-of-Plane Performance of Timber Framework–Brick Wall Systems in Traditional Residential Buildings of Northern China
Authors: Dai Ning , Cui Lanhao , Li Yingpei , Fan Liwei , Chen Jiakun
Published: April 2024

To improve the out-of-plane collaborative performance of timber frames and walls, a metal connector is proposed and designed. A finite element model of the wall is established, and the composite block damage criteria and surface contact behavior are validated. Additionally, one group without metal connectors and three groups with different numbers of metal connectors placed at various positions in traditional residential wall models are established. Using static loading simulation, the influence of different numbers of metal connectors on the out-of-plane damage patterns, deformation characteristics, and shear force distribution is analyzed. The study reveals that top metal connectors significantly reduce the out-of-plane displacement of the top wall by up to 84.6%. Metal connectors have a significant impact on the deformation capacity of brick walls, with a maximum enhancement of 65.3%. The metal connectors in the middle and lower parts transfer the wall loads to the columns, increasing the horizontal shear at the column head by approximately 7%. The connectors in the middle and lower parts effectively improve the collaborative performance of brick walls and wooden frames.
Assessing the Effects of Curcumin and 450 nm Photodynamic Therapy on Oxidative Metabolism and Cell Cycle in Head and Neck Squamous Cell Carcinoma: An In Vitro Study
Authors: Ravera Silvia , Pasquale Claudio , Panfoli Isabella , Bozzo Matteo , Agas Dimitrios , Bruno Silvia , Hamblin Michael R. , Amaroli Andrea
Published: April 2024

Oral cancer is the 16th most common malignant tumor worldwide. The risk of recurrence and mortality is high, and the survival rate is low over the following five years. Recent studies have shown that curcumin causes apoptosis in tumor cells by affecting FoF1-ATP synthase (ATP synthase) activity, which, in turn, hinders cell energy production, leading to a loss of cell viability. Additionally, irradiation of curcumin within cells can intensify its detrimental effects on cancer cell viability and proliferation (photodynamic therapy). We treated the OHSU-974 cell line, a model for human head and neck squamous cell carcinoma (HNSCC), and primary human fibroblasts. The treatment involved a 1 h exposure of cells to 0.1, 1.0, and 10 μM curcumin, followed or not by irradiation or the addition of the same concentration of pre-irradiated curcumin. Both instances involved a diode laser with a wavelength of 450 nm (0.25 W, 15 J, 60 s, 1 cm2, continuous wave mode). The treatment with non-irradiated 1 and 10 µM curcumin caused ATP synthase inhibition and a consequent reduction in the oxygen consumption rate (OCR) and the ATP/AMP ratio, which was associated with a decrement in lipid peroxidation accumulation and a slight increase in glutathione reductase and catalase activity. By contrast, 60 s curcumin irradiation with 0.25 W—450 nm caused a further oxidative phosphorylation (OxPhos) metabolism impairment that induced an uncoupling between respiration and energy production, leading to increased oxidative damage, a cellular growth and viability reduction, and a cell cycle block in the G1 phase. These effects appeared to be more evident when the curcumin was irradiated after cell incubation. Since cells belonging to the HNSCC microenvironment support tumor development, curcumin’s effects have been analyzed on primary human fibroblasts, and a decrease in cell energy status has been observed with both irradiated and non-irradiated curcumin and an increase in oxidative lipid damage and a slowing of cell growth were observed when the curcumin was irradiated before or after cellular administration. Thus, although curcumin displays an anti-cancer role on OHSU-974 in its native form, photoactivation seems to enhance its effects, making it effective even at low dosages.
Computer-Guided Surgery Can Avoid Prophylactic Endodontic Treatment in Autologous Tooth Transplantation: A 5-Year Follow-Up Case Report
Authors: Boschini Luca , Giudice Amerigo , Melillo Michele , Bennardo Francesco , Abella Sans Francesc , Arcari Matteo , Tagliatesta Luigi
Published: April 2024

Autotransplantation is a successful technique to replace compromised teeth. This study presents a computer-guided surgical approach for preparing the receiving socket for a mature mandibular third molar donor tooth with a wait-and-see approach instead of prophylactic endodontic treatment. A 42-year-old woman developed root resorption of tooth 3.7. Extraction of 3.7 and autotransplantation of 3.8 was planned, following a 6-week orthodontic phase for periodontal ligament activation and teeth mobilization. Due to the different root morphology between the compromised and donor teeth and the high mandibular bone density, the receiving socket preparation was performed using guided surgery templates. Two surgical splints were designed with a surgical planning software. Tooth 3.7 was extracted, the recipient site was guided-milled, and tooth 3.8 was transplanted into the new socket in approximately one second of extra-alveolar time. The rapidity of the extra-alveolar time facilitated complete healing without resorting to root canal treatment. Five-year radiological control does not show any periapical lesion or root resorption. The surgical procedure for tooth autotransplantation is fundamental: it must be as atraumatic as possible to preserve the periodontal ligament of the tooth and the receiving socket, and the dentist must minimize the extra-alveolar time. Guided surgery is a reliable solution to combine all these aspects.
Identity Trajectories of Faculty Members through Interdisciplinary STEAM Collaboration Paired with Public Communication
Authors: Desing Renee M. , Pelan Renee , Kajfez Rachel L. , Wallwey Cassie , Clark Abigail M. , Gopalakrishnan Sathya
Published: April 2024

Faculty members in science, technology, engineering, and mathematics (STEM) fields are accustomed to presenting their research findings through journal publications, conference presentations, textbooks, and other academic mediums. However, the audience for these traditional forms of communication are other researchers, which raises concerns about how science research and knowledge are communicated to audiences who have less expertise on these topics. We sought to understand how faculty members develop their identities through collaborative professional development opportunities aimed at growing communication skills to communicate with audiences less familiar with research through interdisciplinary science, technology, engineering, arts, and mathematics (STEAM) activities. We conducted a qualitative, longitudinal study with sixteen STEAM faculty members to explore their identity trajectories as their interdisciplinary cohorts participated in various collaborations to engage with public audiences about their research. Through our analysis, we found that each faculty member’s dominant identity played a significant role in their identity trajectory through their professional development. We observed a significant growth in faculty members’ communication skills, such as learning new presentation techniques to engage others in their research areas of expertise and in their understanding of interdisciplinary STEAM collaboration. Our results provide insights into the identity trajectories of faculty members and how their identity development through these interdisciplinary STEAM collaborations will impact their formal education roles as researchers and teachers moving forward.