bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Methodology for Estimating Surface Albedo in Heterogeneous Areas from Satellite Imagery
Authors: Andres-Anaya Paula , Sanchez-Aparicio Maria , Del Pozo Susana , Lagüela Susana , Hernández-López David , Gonzalez-Aguilera Diego
Published: December 2023

Precise mapping and continuous monitoring of fine-scale surface albedo are indispensable for assessing and optimizing renewable energy sources. Understanding the variations in surface reflectivity is crucial in capturing the full potential of renewable technologies, as it directly impacts the efficiency of harnessing solar energy for sustainable power generation. Satellite remote sensing stands out as the sole practical approach for estimating surface albedo at both regional and global scales. Although there are different methods to calculate albedo from satellite data, most satellite products result in low spatial resolution for large heterogeneous areas, such as urban and peri-urban areas. This paper evaluates and compares several methodologies to calculate surface albedo from Landsat 8 imagery. As a result, a new methodology for estimating surface albedo for heterogeneous areas has been proposed. The new methodology has been compared with direct and indirect albedo measurements, improving the original methodologies of Baldinelli and Silva with respect to the Arctic-Boreal Vulnerability Experiment (ABOVE) albedo images by reducing the RMSE by 85% and 52%, respectively.
Cluster-Based Approaches toward Developing a Customer Loyalty Program in a Private Security Company
Authors: de Sousa Arthur , Moro Sérgio , Pereira Renato
Published: December 2023

This study aimed to create a loyalty program for a private security company’s most valuable customers using clustering techniques on a dataset from the company. K-means was employed as an unsupervised machine learning algorithm to segment customers. Performance evaluation metrics, including the silhouette coefficient, were utilized to compare various algorithmic approaches. As a distinctive feature of this study, in addition to the evaluation metric, strategic questionnaires were administered to business decision-makers to facilitate the integrated development of a loyalty program with key stakeholders invested in customer retention and profitability. The results show the existence of three customer clusters with an optimal silhouette coefficient for loyalty program development. Interestingly, the customer group to be targeted for the loyalty program did not exhibit the highest silhouette coefficient metric. Business leaders selected the group they perceived as most efficient for program implementation. Consequently, the study concludes that customer segmentation not only entails statistical analyses of individual user groups but also requires a comprehensive understanding of the business and collaboration with stakeholders. Furthermore, this study aligns with findings from other authors, demonstrating that private security companies can benefit from implementing a loyalty program, although avenues for further investigation remain.
Recognition of Weak Microseismic Events Induced by Borehole Hydraulic Fracturing in Coal Seam Based on ResNet-10
Authors: Zhang Yunpeng , Li Nan , Sun Lihong , Qiu Jincheng , Huang Xiaokai , Li Yan
Published: December 2023

Borehole hydraulic fracturing in coal mines can effectively prevent coal rock dynamic disasters. Accurately recognizing weak microseismic events is an essential prerequisite for the micro-seismic monitoring of hydraulic fracturing in coal seams. This study proposes a recognition method for weak microseismic waveforms based on ResNet-10 to accurately recognize microseismic events generated by borehole hydraulic fracturing in coal mines. To begin with, the background noise and microseismic signals undergo pre-processing through noise reduction and filtering techniques. The preprocessed data are then fed into the ResNet-10 model, and the model parameters are continuously adjusted while the training and test data are updated. The training process stops when the model accuracy rate and loss function value are greater than 99.9% and less than 0.02 for five consecutive times. The model with the highest accuracy rate is then selected to detect the microseismic waveform. The recognition results of ResNet-10 are compared with the threshold value, STA/LTA, and expert recognition results. Finally, the study analyzes flow signal, blasting, and microseismic waveforms. The recognition accuracy rate and recall rate of ResNet-10 are much higher than those of threshold value and STA/LTA, and better than that of the experts. The results of the study show that ResNet-10 can accurately recognize weak microseismic events that are difficult for the threshold value, STA/LTA, and experts to recognize. When water flow signal occurs, it often corresponds to the penetration of hydraulic cracks and the seepage of water. The waveform recognition results demonstrate that the ResNet-10 method has great potential in recognizing weak microseismic waveforms generated by borehole hydraulic fracturing in coal seams.
Effects of Doped Hole-Transporting Layers on Perovskite Solar Cell Performances
Authors: Kwon Min Ji , Lee Hyesu , Jung Ji Yoon , Yu Jae-Woong
Published: December 2023

The influences of doped hole-transporting layers (HTLs) on the performances of perovskite solar cells were studied. The influences of electrostatic and surface roughness effects using solvent additive doping were compared. The electrostatic effect of solvent additive doping was compared through the addition of 1,8-diiodooctane and 1,8-dichlorooctane to the HTL. The surface roughness effect of solvent additive doping was examined using atomic force microscopy. The perovskite crystallinity of doped HTLs was studied using X-ray diffraction. The conductivity of HTLs was measured using four probe methods and was higher for doped devices. The enhanced conductivity of PEDOT: PSS film was also proved using fluorescence emission quenching. The long-term stability of the device was enhanced through doping HTLs. The device with the solvent additive exhibited much greater enhanced stability retained over 80% of the initial PCE for 400 h, while PCE of the device without the solvent additive decreased to less than 80% after 200 h. A very weak power microwave treatment was used for the post-annealing of the fully fabricated devices. Post-annealing enhanced the lifetime of the device. Comparing different sizes of cells, the device lifetime was decreased as the cell area increased and as the size of the cell increased; the extent of lifetime enhancement through doping was increased.
Fabrication of Smart Materials Using Laser Processing: Analysis and Prospects
Authors: Murzin Serguei P. , Stiglbrunner Christian
Published: December 2023

Laser processing is a versatile tool that enhances smart materials for diverse industries, allowing precise changes in material properties and customization of surface characteristics. It drives the development of smart materials with adaptive properties through laser modification, utilizing photothermal reactions and functional additives for meticulous control. These laser-processed smart materials form the foundation of 4D printing that enables dynamic shape changes depending on external influences, with significant potential in the aerospace, robotics, health care, electronics, and automotive sectors, thus fostering innovation. Laser processing also advances photonics and optoelectronics, facilitating precise control over optical properties and promoting responsive device development for various applications. The application of computer-generated diffractive optical elements (DOEs) enhances laser precision, allowing for predetermined temperature distribution and showcasing substantial promise in enhancing smart material properties. This comprehensive overview explores the applications of laser technology and nanotechnology involving DOEs, underscoring their transformative potential in the realms of photonics and optoelectronics. The growing potential for further research and practical applications in this field suggests promising prospects in the near future.
Numerical Study of Aircraft Wake Vortex Evolution under the Influence of Vertical Winds
Authors: Yuan Jianhui , Liu Jixin , Li Changcheng , Zhao Zheng
Published: December 2023

Separating wake vortices is crucial for aircraft landing safety and essential to airport operational efficiency. Vertical wind, as a typical atmospheric condition, plays a significant role, and studying the evolution characteristics of wake vortices under this condition is of paramount importance for developing dynamic wake separation systems. In this study, we employed the SST k-ω turbulence model based on an O-Block structured grid to numerically simulate the simplified wing model. We analyzed the variations in the wake vortex structure and parameters of the Airbus A320 during the near-field phase under different vertical wind directions and speeds. The results indicate that favorable vertical winds cause a “flattening” deformation in the wake vortex. Vertical winds reduce the initial vortex strength, accelerate the rate of vortex decay, and influence the trajectory of the vortex core. Notably, under wind speeds of 1~3 m/s, the decay rate is more significant than under 4 m/s. When vertical wind speeds are substantial, it can lead to irregular motion and interactions within the vortex core, forming secondary vortices.
Mean Reattachment Length of Roof Separation Bubbles Using Proper Orthogonal Decomposition
Authors: Ham Hee Jung , Lee Sungsu , Choi Seung Hun , Kim Ho-Jeong
Published: December 2023

Investigating flow separation regions on the surfaces of three-dimensional bluff bodies in turbulent flows is important because these regions can induce significant aerodynamic loads. Separation bubbles can generate extreme pressures, making the roof components of low-rise buildings vulnerable. In this study, proper orthogonal decomposition (POD) was applied to wind-induced roof pressures to elucidate the physical significance of the dominant modes. Based on the interpretation of the first mode from the POD, the mean reattachment length of the roof separation bubbles on a low-rise building model in turbulent flow was determined. The mean reattachment length derived from the POD was then compared with the length obtained from an aerodynamic database. For the centerline of the roof, the mean reattachment length based on the POD aligned well with that from the aerodynamic database, showing a difference of less than 5%. This study highlights the efficacy of POD as a powerful tool for estimating the reattachment length of separation bubbles on bluff bodies.
Microgravity Decoupling in Torsion Pendulum for Enhanced Micro-Newton Thrust Measurement
Authors: Cong Linxiao , Wang Jiabin , Long Jianfei , Mu Jianchao , Deng Haoye , Qiao Congfeng
Published: December 2023

To enhance the accuracy of micro-Newton thrust measurements via a torsion pendulum, addressing microgravity coupling effects caused by platform tilt and pendulum mass eccentricity is crucial. This study focuses on analyzing and minimizing these effects by alleviating reference surface tilt and calibrating the center of mass during thrust measurements. The study introduced analysis techniques and compensation measures. It first examined the impact of reference tilt and center of mass eccentricity on the stiffness and compliance of the torsion pendulum by reconstructing its dynamic model. Simscape Multibody was initially employed for numerical analysis to assess the dynamic coupling effects of the tilted pendulum. The results showed the influence of reference tilt on the stiffness and compliance of the torsion pendulum through simulation. An inverted pendulum was developed to amplify the platform’s tilt angle for microgravity drag-free control. Center of mass calibration can identify the gravity coupling caused by the center of mass position. Based on the displacement signal from the capacitive sensor located at the end of the inverted pendulum, which represents the platform’s tilt angle, the pendulum’s vibration at 0.1 mHz was reduced from 5.7 μm/Hz1/2 to 0.28 μm/Hz1/2 by adjusting the voltage of piezoelectric actuator. Finally, a new two-stage torsion pendulum structure was proposed to decouple the tilt coupling buried in both pitch and roll angle. The study utilized theoretical models, numerical analysis, and experimental testing to validate the analysis methods and compensation measures for microgravity coupling effects in torsion pendulums. This led to a reduction in low-frequency noise caused by ground vibrations and thermal strains, ultimately improving the micro-Newton thrust measurement accuracy of the torsion pendulum through the platform’s drag-free control.
Multi-Label Weighted Contrastive Cross-Modal Hashing
Authors: Yi Zeqian , Zhu Xinghui , Wu Runbing , Zou Zhuoyang , Liu Yi , Zhu Lei
Published: December 2023

Due to the low storage cost and high computation efficiency of hashing, cross-modal hashing has been attracting widespread attention in recent years. In this paper, we investigate how supervised cross-modal hashing (CMH) benefits from multi-label and contrastive learning (CL) by overcoming the following two challenges: (i) how to combine multi-label and supervised contrastive learning to consider diverse relationships among cross-modal instances, and (ii) how to reduce the sparsity of multi-label representation so as to improve the similarity measurement accuracy. To this end, we propose a novel cross-modal hashing framework, dubbed Multi-Label Weighted Contrastive Hashing (MLWCH). This framework involves compact consistent similarity representation, a new designed multi-label similarity calculation method that efficiently reduces the sparsity of multi-label by reducing redundant zero elements. Furthermore, a novel multi-label weighted contrastive learning strategy is developed to significantly improve hashing learning by assigning similarity weight to positive samples under both linear and non-linear similarities. Extensive experiments and ablation analysis over three benchmark datasets validate the superiority of our MLWCH method, especially over several outstanding baselines.
Uniaxial Reloading Damage Characteristics of Critically Damaged Coal Samples at Different Confining Pressure Unloading Spans
Authors: Wang Ping , Mei Chengcheng , Zhu Yongjian
Published: December 2023

To investigate the damage characteristics of the lower critical damage coal samples under different perimeter pressure unloading spans, a 13-group plus unloading perimeter pressure and then uniaxial loading test scheme was designed. Firstly, the coal samples were subjected to triaxial lowering and raising of the circumferential pressure to determine the critical damage range of the coal samples. Finally, acoustic emission signal, fractal theory, and energy theory were used to study the mechanical characteristics of the coal samples during damage. The results show that the surface cracks of the critically damaged coal samples develop from tension cracks to coexistence of tension and shear cracks to shear cracks as the unloading span of the surrounding pressure of the critically damaged coal samples decreases; through the triaxial unloading–uniaxial reloading experimental scheme, the critical damage range of the coal samples is determined as 60–70% of the triaxial compressive strength during axial pressure loading in the process of surrounding rock stress adjustment. The smaller the unloading span of the critically damaged coal sample, the smaller the peak strength of the sample, the more severe the damage, the weaker the energy storage capacity, the weaker the ability to deform elastically, the easier it is for the specimen to crack and deform plastically, and the larger the weak surface formed in the specimen, resulting in the larger the size of the fragment produced by the damage, the smaller the fractal dimension.
Intelligent Gangue Sorting System Based on Dual-Energy X-ray and Improved YOLOv5 Algorithm
Authors: Qin Yuchen , Kou Ziming , Han Cong , Wang Yutong
Published: December 2023

Intelligent gangue sorting with high precision is of vital importance for improving coal quality. To tackle the challenges associated with coal gangue target detection, including algorithm performance imbalance and hardware deployment difficulties, in this paper, an intelligent gangue separation system that adopts the elevated YOLO-v5 algorithm and dual-energy X-rays is proposed. Firstly, images of dual-energy X-ray transmission coal gangue mixture under the actual operation of a coal mine were collected, and datasets for training and validation were self-constructed. Then, in the YOLOv5 backbone network, the EfficientNetv2 was used to replace the original cross stage partial darknet (CSPDarknet) to achieve the lightweight of the backbone network; in the neck, a light path aggregation network (LPAN) was designed based on PAN, and a convolutional block attention module (CBAM) was integrated into the BottleneckCSP of the feature fusion block to raise the feature acquisition capability of the network and maximize the learning effect. Subsequently, to accelerate the rate of convergence, an efficient intersection over union (EIOU) was used instead of the complete intersection over union (CIOU) loss function. Finally, to address the problem of low resolution of small targets leading to missed detection, an L2 detection head was introduced to the head section to improve the multi-scale target detection performance of the algorithm. The experimental results indicate that in comparison with YOLOv5-S, the same version of the algorithm proposed in this paper increases by 19.2% and 32.4% on mAP @.5 and mAP @.5:.95, respectively. The number of parameters decline by 51.5%, and the calculation complexity declines by 14.7%. The algorithm suggested in this article offers new ideas for the design of identification algorithms for coal gangue sorting systems, which is expected to save energy and reduce consumption, reduce labor, improve efficiency, and be more friendly to the embedded platform.
Chaotic Characteristic Analysis of Spillway Radial Gate Vibration under Discharge Excitation
Authors: Lu Yangliang , Liu Yakun , Zhang Di , Cao Ze , Fu Xuemin
Published: December 2023

This paper aims to assess the nonlinear vibration of a radial gate induced by flood discharge; the measured acceleration response data of a spillway radial gate are analyzed using the chaos theory. The results show that the vibration responses of the gate at three opening heights present clear chaotic characteristics, and the chaotic characteristics of the lower main beam point are greater than other points. Moreover, the y-direction (vertical) correlation dimensions of the three measuring points on the supporting arm are larger than those of the x-direction (axial) and z-direction (lateral). The vertical vibration of the supporting arm is more complex and presents more uncertainties, which should be paid attention to in the literature. Under three different gate opening heights, the maximum Lyapunov exponent of each measuring point ranges from 0.0246 to 0.0681. In addition, the flow fluctuation load is the main excitation source of the gate vibration chaotic characteristics.
The Added Value of Bronchoalveolar Lavage for Pulmonary Tuberculosis Diagnosis in High-Risk Hospitalized Patients with Negative Sputum Samples
Authors: Freund Ophir , Hadad Yitzhac , Lagziel Tomer , Friedman Regev Inbal , Kleinhendler Eyal , Unterman Avraham , Bar-Shai Amir , Perluk Tal Moshe
Published: December 2023

Hospitalized patients with a high suspicion of pulmonary tuberculosis (HS-PTB) are isolated until a definite diagnosis can be determined. If doubt remains after negative sputum samples, bronchoscopy with bronchoalveolar lavage (BAL) is often sought. Still, evidence of the added value of BAL in this patient population is scarce. To address this issue, we included consecutive HS-PTB patients with negative sputum samples who underwent BAL between 2017 and 2018. Chest X-rays (CXR) and CT scans were evaluated by a chest radiologist blind to the final diagnosis. Independent predictors for PTB were assessed by multivariate regression, using all positive PTB patients between 2017 and 2019 (by sputum or BAL) as a control group (n = 41). Overall, 42 HS-PTB patients were included (mean age 51 ± 9, 36% female). BAL was a viable diagnostic for PTB in three (7%) cases and for other clinically relevant pathogens in six (14%). Independent predictors for PTB were ≥2 sub-acute symptoms (adjusted OR 3.18, 95% CI 1.04–9.8), CXR upper-lobe consolidation (AOR 8.70, 95% CI 2.5–29), and centrilobular nodules in chest CT (AOR 3.96, 95% CI 1.20–13.0, p = 0.02). In conclusion, bronchoscopy with BAL in hospitalized patients with HS-PTB had a 7% added diagnostic value after negative sputum samples. Our findings highlight specific predictors for PTB diagnosis that could be used in future controlled studies to personalize the diagnostic evaluation.
Enhanced Wind-Field Detection Using an Adaptive Noise-Reduction Peak-Retrieval (ANRPR) Algorithm for Coherent Doppler Lidar
Authors: Li Qingsong , Zhang Xiaojie , Feng Zhihao , Chen Jiahong , Zhou Xue , Luo Jiankang , Sun Jingqi , Zhao Yuefeng
Published: December 2023

Wind fields provide direct power for exchanging energy and matter in the atmosphere. All-fiber coherent Doppler lidar is a powerful tool for detecting boundary-layer wind fields. According to the characteristics of the lidar echo signal, an adaptive noise-reduction peak retrieval (ANRPR) algorithm is proposed in this study. Firstly, the power spectrum data are divided into several continuous range gates according to the time series. Then, the adaptive iterative reweighted penalized least-squares (airPLS) method is used to reduce the background noise. Secondly, the continuity between spectra is enhanced by 2D Gaussian low-pass filtering. Finally, an adaptive peak-retrieval algorithm is employed to extract the Doppler shift, facilitating the synthesis of a spatial atmospheric 3D wind field through the vector synthesis method. When comparing data from different heights of the meteorological gradient tower, both the horizontal wind-speed correlation and the horizontal wind-direction correlation exceed 0.90. Experimental results show that the proposed algorithm has better robustness and a longer detection distance than the traditional algorithm.
Assessment of the Emission Characteristics of Major States in the United States using Satellite Observations of CO2, CO, and NO2
Authors: Xu Anqi , Xiang Chengzhi
Published: December 2023

By using space-based measurements of the column-averaged dry air mole fraction of carbon dioxide (XCO2) from the Orbiting Carbon Observatory-2 (OCO-2) and CO and NO2 from the Tropospheric Monitoring Instrument (TROPOMI), this study investigates the seasonal variation in the characteristics of CO2, CO, and NO2 across major states in the United States. Beyond correlating these trends with natural factors, significant emphasis is placed on human activities, including heating demands, energy usage, and the impacts of the COVID-19 pandemic. Concentration enhancements in observations influenced by anthropogenic emissions from urban regions relative to background values are calculated to estimate gas emissions. Our investigation reveals a strong correlation between NO2 and CO2 emissions, as evidenced by a correlation coefficient (r) of 0.75. Furthermore, we observe a correlation of 0.48 between CO2 and CO emissions and a weaker correlation of 0.37 between CO and NO2 emissions. Notably, we identify the NO2 concentration as a reliable indicator of CO2 emission levels, in which a 1% increase in NO2 concentration corresponds to a 0.8194% (±0.0942%) rise in annual mean CO2 emissions. Enhancement ratios among NO2, CO, and XCO2 are also calculated, uncovering that high ΔNO2: ΔXCO2 ratios often signify outdated industrial structures and production technologies, while low ΔCO: ΔXCO2 ratios are linked to states that utilize clean energy sources. This approach offers a deeper understanding of the effect of human activities on atmospheric gas concentrations, paving the way for more effective environmental monitoring and policy-making.
Unsupervised Feature Selection with Latent Relationship Penalty Term
Authors: Ma Ziping , Huang Yulei , Li Huirong , Wang Jingyu
Published: December 2023

With the exponential growth of high dimensional unlabeled data, unsupervised feature selection (UFS) has attracted considerable attention due to its excellent performance in machine learning. Existing UFS methods implicitly assigned the same attribute score to each sample, which disregarded the distinctiveness of features and weakened the clustering performance of UFS methods to some extent. To alleviate these issues, a novel UFS method is proposed, named unsupervised feature selection with latent relationship penalty term (LRPFS). Firstly, latent learning is innovatively designed by assigning explicitly an attribute score to each sample according to its unique importance in clustering results. With this strategy, the inevitable noise interference can be removed effectively while retaining the intrinsic structure of data samples. Secondly, an appropriate sparse model is incorporated into the penalty term to further optimize its roles as follows: (1) It imposes potential constraints on the feature matrix to guarantee the uniqueness of the solution. (2) The interconnection between data instances is established by a pairwise relationship situation. Extensive experiments on benchmark datasets demonstrate that the proposed method is superior to relevant state-of-the-art algorithms with an average improvement of 10.17% in terms of accuracy.
From HX-Groups to HX-Polygroups
Authors: Mousavi Seyed Sh. , Jafarpour Morteza , Cristea Irina
Published: December 2023

HX-groups are a natural generalization of groups that are similar in construction to hypergroups. However, they do not have to be considered as hypercompositional structures like hypergroups; instead, they are classical groups. After clarifying this difference between the two algebraic structures, we review the main properties of HX-groups, focusing on the regularity property. An HX-group G on a group G with the identity e is called regular whenever the identity E of G contains e. Any regular HX-group may be characterized as a group of cosets, and equivalent conditions for describing this property are established. New properties of HX-groups are discussed and illustrated by examples. These properties are uniformity and essentiality. In the second part of the paper, we introduce a new algebraic structure, that of HX-polygroups on a polygroup. Similarly to HX-groups, we propose some characterizations of HX-polygroups as polygroups of cosets or double cosets. We conclude the paper by proposing several lines of research related to HX-groups.
REHABS: An Innovative and User-Friendly Device for Rehabilitation
Authors: Vizza Patrizia , Marotta Nicola , Ammendolia Antonio , Guzzi Pietro Hiram , Veltri Pierangelo , Tradigo Giuseppe
Published: December 2023

Rehabilitation is a complex set of interventions involving the assessment, management, and treatment of injuries. It aims to support and facilitate an individual’s recovery process by restoring a physiological function, e.g., limb movement, compromised by physical impairments, injuries or diseases to a condition as close to normal as possible. Innovative devices and solutions make the rehabilitation process of patients easier during their daily activities. Devices support physicians and physiotherapists in monitoring and measuring patients’ physical improvements during rehabilitation. In this context, we report the design and implementation of a low-cost rehabilitation system, which is a programmable device designed to support tele-rehabilitation of the upper limbs. The proposed system includes a mechanism to acquire and analyze data and signals related to rehabilitation processes.
Development of a Highly Differentiated Human Primary Proximal Tubule MPS Model (aProximate MPS Flow)
Authors: Pisapia Francesca , O’Brien Donovan , Tasinato Elena , Garner Kathryn L. , Brown Colin D. A.
Published: December 2023

The kidney proximal tubule (PT) mediates renal drug elimination in vivo and is a major site of drug-induced toxicity. To reliably assess drug efficacy, it is crucial to construct a model in which PT functions are replicated. Current animal studies have proven poorly predictive of human outcome. To address this, we developed a physiologically relevant micro-physiological system (MPS) model of the human PT, the aProximate MPS Flow platform (Patent No: G001336.GB). In this model, primary human PT cells (hPTCs) are subjected to fluidic media flow and a shear stress of 0.01–0.2 Pa. We observe that these cells replicate the polarity of hPTCs and exhibit a higher expression of all the key transporters of SLC22A6 (OAT1), SLC22A8 (OAT3), SLC22A2 (OCT2), SLC47A1 (MATE1), SLC22A12 (URAT1), SLC2A9 (GLUT9), ABCB1 (MDR1), ABCC2 (MRP2), LRP2 (megalin), CUBN (cubilin), compared with cells grown under static conditions. Immunofluorescence microscopy confirmed an increase in OAT1, OAT3, and cilia protein expression. Increased sensitivity to nephrotoxic protein cisplatin was observed; creatinine and FITC-albumin uptake was significantly increased under fluidic shear stress conditions. Taken together, these data suggest that growing human PT cells under media flow significantly improves the phenotype and function of hPTC monolayers and has benefits to the utility and near-physiology of the model.
Phytochrome-Interacting Proteins
Authors: Kaeser Gero , Krauß Norbert , Roughan Clare , Sauthof Luisa , Scheerer Patrick , Lamparter Tilman
Published: December 2023

Phytochromes are photoreceptors of plants, fungi, slime molds bacteria and heterokonts. These biliproteins sense red and far-red light and undergo light-induced changes between the two spectral forms, Pr and Pfr. Photoconversion triggered by light induces conformational changes in the bilin chromophore around the ring C-D-connecting methine bridge and is followed by conformational changes in the protein. For plant phytochromes, multiple phytochrome interacting proteins that mediate signal transduction, nuclear translocation or protein degradation have been identified. Few interacting proteins are known as bacterial or fungal phytochromes. Here, we describe how the interacting partners were identified, what is known about the different interactions and in which context of signal transduction these interactions are to be seen. The three-dimensional arrangement of these interacting partners is not known. Using an artificial intelligence system-based modeling software, a few predicted and modulated examples of interactions of bacterial phytochromes with their interaction partners are interpreted.
Evaluation of Myocilin Variant Protein Structures Modeled by AlphaFold2
Authors: Ng Tsz Kin , Ji Jie , Liu Qingping , Yao Yao , Wang Wen-Ying , Cao Yingjie , Chen Chong-Bo , Lin Jian-Wei , Dong Geng , Cen Ling-Ping , Huang Chukai , Zhang Mingzhi
Published: December 2023

Deep neural network-based programs can be applied to protein structure modeling by inputting amino acid sequences. Here, we aimed to evaluate the AlphaFold2-modeled myocilin wild-type and variant protein structures and compare to the experimentally determined protein structures. Molecular dynamic and ligand binding properties of the experimentally determined and AlphaFold2-modeled protein structures were also analyzed. AlphaFold2-modeled myocilin variant protein structures showed high similarities in overall structure to the experimentally determined mutant protein structures, but the orientations and geometries of amino acid side chains were slightly different. The olfactomedin-like domain of the modeled missense variant protein structures showed fewer folding changes than the nonsense variant when compared to the predicted wild-type protein structure. Differences were also observed in molecular dynamics and ligand binding sites between the AlphaFold2-modeled and experimentally determined structures as well as between the wild-type and variant structures. In summary, the folding of the AlphaFold2-modeled MYOC variant protein structures could be similar to that determined by the experiments but with differences in amino acid side chain orientations and geometries. Careful comparisons with experimentally determined structures are needed before the applications of the in silico modeled variant protein structures.
From Genes to Therapy: Pituitary Adenomas in the Era of Precision Medicine
Authors: Toader Corneliu , Dobrin Nicolaie , Tataru Catalina-Ioana , Covache-Busuioc Razvan-Adrian , Bratu Bogdan-Gabriel , Glavan Luca Andrei , Costin Horia Petre , Corlatescu Antonio Daniel , Dumitrascu David-Ioan , Ciurea Alexandru Vlad
Published: December 2023

This review presents a comprehensive analysis of pituitary adenomas, a type of brain tumor with diverse behaviors and complexities. We cover various treatment approaches, including surgery, radiotherapy, chemotherapy, and their integration with newer treatments. Key to the discussion is the role of biomarkers in oncology for risk assessment, diagnosis, prognosis, and the monitoring of pituitary adenomas. We highlight advances in genomic, epigenomic, and transcriptomic analyses and their contributions to understanding the pathogenesis and molecular pathology of these tumors. Special attention is given to the molecular mechanisms, including the impact of epigenetic factors like histone modifications, DNA methylation, and transcriptomic changes on different subtypes of pituitary adenomas. The importance of the tumor immune microenvironment in tumor behavior and treatment response is thoroughly analyzed. We highlight potential breakthroughs and innovations for a more effective management and treatment of pituitary adenomas, while shedding light on the ongoing need for research and development in this field to translate scientific knowledge into clinical advancements, aiming to improve patient outcomes.
Induction of Collagenolytic MMP-8 and -9 Tissue Destruction Cascade in Mouth by Head and Neck Cancer Radiotherapy: A Cohort Study
Authors: Brandt Ella , Keskin Mutlu , Räisänen Ismo T. , Tervahartiala Taina , Mäkitie Antti , Harmankaya İlknur , Karaçetin Didem , Hagström Jaana , Rautava Jaana , Sorsa Timo
Published: December 2023

The effect of head and neck cancer (HNC) radiotherapy (RT) on biomarkers is not known but there is a lot of potential for gaining more precise cancer treatments and less side effects. This cohort study investigated the levels and molecular forms of the matrix metalloproteinase (MMP) -8 and -9, tissue inhibitor of metalloproteinase (TIMP)-1, myeloperoxidase (MPO) and interleukin (IL)-6 in mouth-rinse samples as well as the clinical periodontal status in HNC patients (n = 21) receiving RT. Complete periodontal examinations were performed pre-RT and one month after RT. Mouth-rinse samples (pre-RT, after six weeks of RT and one month after RT) were assayed using a point-of-care-kit (PerioSafe®/ORALyzer® (Dentognostics GmbH, Jena, Germany)) for active MMP-8 and ELISA analysis for total MMP-8 and -9, MPO, TIMP-1, and IL-6 levels. Molecular forms of MMP-9 were assessed by gelatinolytic zymography and MMP-8 by western immunoblot. Significant changes were observed between the three time points in the mean levels of active and total MMP-8, active MMP-9, and IL-6. Their levels increased during the RT and decreased after the RT period. The aMMP-8 levels stayed elevated even one month after RT compared to the pre-RT. Clinical attachment loss, probing depths, and bleeding on probing were increased between pre- and post-calculations in periodontal status. Elevated inflammatory biomarker levels together with clinical recordings strongly suggest that RT eventually increases the risk to the periodontal tissue destruction by inducing the active proteolytical MMP-cascade, and especially by prolonged activity of collagenolytic aMMP-8. Eventually, the aMMP-8 point-of-care mouth-rinse test could be an easy, early detection tool for estimating the risk for periodontal damage by the destructive MMP-cascade in HNC patients with RT treatment.
Optimization of Density Peak Clustering Algorithm Based on Improved Black Widow Algorithm
Authors: Huang Huajuan , Wu Hao , Wei Xiuxi , Zhou Yongquan
Published: December 2023

Clustering is an unsupervised learning method. Density Peak Clustering (DPC), a density-based algorithm, intuitively determines the number of clusters and identifies clusters of arbitrary shapes. However, it cannot function effectively without the correct parameter, referred to as the cutoff distance (dc). The traditional DPC algorithm exhibits noticeable shortcomings in the initial setting of dc when confronted with different datasets, necessitating manual readjustment. To solve this defect, we propose a new algorithm where we integrate DPC with the Black Widow Optimization Algorithm (BWOA), named Black Widow Density Peaks Clustering (BWDPC), to automatically optimize dc for maximizing accuracy, achieving automatic determination of dc. In the experiment, BWDPC is used to compare with three other algorithms on six synthetic data and six University of California Irvine (UCI) datasets. The results demonstrate that the proposed BWDPC algorithm more accurately identifies density peak points (cluster centers). Moreover, BWDPC achieves superior clustering results. Therefore, BWDPC represents an effective improvement over DPC.
An Integrated ddPCR Lab-on-a-Disc Device for Rapid Screening of Infectious Diseases
Authors: Zhang Wanyi , Cui Lili , Wang Yuye , Xie Zhenming , Wei Yuanyuan , Zhu Shaodi , Nawaz Mehmood , Mak Wing-Cheung , Ho Ho-Pui , Gu Dayong , Zeng Shuwen
Published: December 2023

Digital droplet PCR (ddPCR) is a powerful amplification technique for absolute quantification of viral nucleic acids. Although commercial ddPCR devices are effective in the lab bench tests, they cannot meet current urgent requirements for on-site and rapid screening for patients. Here, we have developed a portable and fully integrated lab-on-a-disc (LOAD) device for quantitively screening infectious disease agents. Our designed LOAD device has integrated (i) microfluidics chips, (ii) a transparent circulating oil-based heat exchanger, and (iii) an on-disc transmitted-light fluorescent imaging system into one compact and portable box. Thus, droplet generation, PCR thermocycling, and analysis can be achieved in a single LOAD device. This feature is a significant attribute for the current clinical application of disease screening. For this custom-built ddPCR setup, we have first demonstrated the loading and ddPCR amplification ability by using influenza A virus-specific DNA fragments with different concentrations (diluted from the original concentration to 107 times), followed by analyzing the droplets with an external fluorescence microscope as a standard calibration test. The measured DNA concentration is linearly related to the gradient–dilution factor, which validated the precise quantification for the samples. In addition to the calibration tests using DNA fragments, we also employed this ddPCR-LOAD device for clinical samples with different viruses. Infectious samples containing five different viruses, including influenza A virus (IAV), respiratory syncytial virus (RSV), varicella zoster virus (VZV), Zika virus (ZIKV), and adenovirus (ADV), were injected into the device, followed by analyzing the droplets with an external fluorescence microscope with the lowest detected concentration of 20.24 copies/µL. Finally, we demonstrated the proof-of-concept detection of clinical samples of IAV using the on-disc fluorescence imaging system in our fully integrated device, which proves the capability of this device in clinical sample detection. We anticipate that this integrated ddPCR-LOAD device will become a flexible tool for on-site disease detection.
Terahertz Metamaterials for Biosensing Applications: A Review
Authors: Zhang Wu , Lin Jiahan , Yuan Zhengxin , Lin Yanxiao , Shang Wenli , Chin Lip Ket , Zhang Meng
Published: December 2023

In recent decades, THz metamaterials have emerged as a promising technology for biosensing by extracting useful information (composition, structure and dynamics) of biological samples from the interaction between the THz wave and the biological samples. Advantages of biosensing with THz metamaterials include label-free and non-invasive detection with high sensitivity. In this review, we first summarize different THz sensing principles modulated by the metamaterial for bio-analyte detection. Then, we compare various resonance modes induced in the THz range for biosensing enhancement. In addition, non-conventional materials used in the THz metamaterial to improve the biosensing performance are evaluated. We categorize and review different types of bio-analyte detection using THz metamaterials. Finally, we discuss the future perspective of THz metamaterial in biosensing.
Investigating Aphasia Recovery: Demographic and Clinical Factors
Authors: Papageorgiou Georgios , Kasselimis Dimitrios , Angelopoulou Georgia , Laskaris Nikolaos , Tsolakopoulos Dimitrios , Velonakis Georgios , Tountopoulou Argyro , Vassilopoulou Sophia , Potagas Constantin
Published: December 2023

Post-stroke language recovery remains one of the main unresolved topics in the field of aphasia. In recent years, there have been efforts to identify specific factors that could potentially lead to improved language recovery. However, the exact relationship between the recovery of particular language functions and possible predictors, such as demographic or lesion variables, is yet to be fully understood. In the present study, we attempted to investigate such relationships in 42 patients with aphasia after left hemisphere stroke, focusing on three language domains: auditory comprehension, naming and speech fluency. Structural imaging data were also obtained for the identification of the lesion sites. According to our findings, patients demonstrated an overall improvement in all three language domains, while no demographic factor significantly contributed to aphasia recovery. Interestingly, specific lesion loci seemed to have a differential effect on language performance, depending on the time of testing (i.e., acute/subacute vs. chronic phase). We argue that this variability concerning lesion–deficit associations reflects the dynamic nature of aphasia and further discuss possible explanations in the framework of neuroplastic changes during aphasia recovery.
Transcranial Direct Current Stimulation (tDCS) Effects on Quantitative Sensory Testing (QST) and Nociceptive Processing in Healthy Subjects: A Systematic Review and Meta-Analysis
Authors: Gurdiel-Álvarez Francisco , González-Zamorano Yeray , Lerma-Lara Sergio , Gómez-Soriano Julio , Sánchez-González Juan Luis , Fernández-Carnero Josué , Navarro-López Víctor
Published: December 2023

Background: The aim of this study is to determine the effect that different tDCS protocols have on pain processing in healthy people, assessed using quantitative sensory tests (QST) and evoked pain intensity. Methods: We systematically searched in EMBASE, CINAHL, PubMed, PEDro, PsycInfo, and Web of Science. Articles on tDCS on a healthy population and regarding QST, such as pressure pain thresholds (PPT), heat pain thresholds (HPT), cold pain threshold (CPT), or evoked pain intensity were selected. Quality was analyzed using the Cochrane Risk of Bias Tool and PEDro scale. Results: Twenty-six RCTs were included in the qualitative analysis and sixteen in the meta-analysis. There were no significant differences in PPTs between tDCS and sham, but differences were observed when applying tDCS over S1 in PPTs compared to sham. Significant differences in CPTs were observed between tDCS and sham over DLPFC and differences in pain intensity were observed between tDCS and sham over M1. Non-significant effects were found for the effects of tDCS on HPTs. Conclusion: tDCS anodic over S1 stimulation increases PPTs, while a-tDCS over DLPFC affects CPTs. The HPTs with tDCS are worse. Finally, M1 a-tDCS seems to reduce evoked pain intensity in healthy subjects.
Well-Being, Mental Health, and Study Characteristics of Medical Students before and during the Pandemic
Authors: Huber Alexandra , Rabl Luna , Höge-Raisig Thomas , Höfer Stefan
Published: December 2023

Medical students report high demands, stressors, pressure to perform, and a lack of resources, and are therefore at a higher risk for mental strain and burnout. Due to the COVID-19 pandemic, study conditions have changed, and new challenges have emerged. Thus, this study aimed to examine medical students’ well-being, mental health, and study characteristics before and during the pandemic. Data from 988 Austrian medical students were included into the cross-sectional comparisons, and 63 students were included into the longitudinal analyses (variance analyses/t-tests or appropriate non-parametric tests). Well-being before and during the pandemic did not differ significantly but the peri-pandemic cohort reported higher study satisfaction, more social support from lecturers, and less emotional exhaustion, cognitive demands, and stressors (information problems, organizational stressors, work overload). Longitudinally, work overload was also perceived to be higher before the pandemic; however, study satisfaction was lower. During the pandemic, approximately every seventh student exceeded the cut-off value for generalized anxiety disorder, and approximately every tenth student exceeded the cut-off value for major depression. These unexpected peri-pandemic results concerning constant high well-being, study satisfaction, and the perception of conditions may be based on response shift effects that require further exploration. The scores exceeding the reasonable cut-point for identifying probable cases of generalized anxiety disorder and depression may reflect medical students’ needs, calling for an in-depth analysis if further health promotion is necessary.
Analysis Based on Ecotect Software of the Energy Consumption and Economic Viability of Solar Collector Pig Houses with Different Roof Forms and Translucent Materials
Authors: Zhou Lina , Zhou Leijinyu , Guo Xinhe , Wu Hongbo , Li Jinsheng , Zhu Fengwu , Chen Limei
Published: December 2023

This study investigated improvements in solar energy efficiency in pig houses with different roof types and translucent materials using Ecotect simulation software v2011. An experimental pig house in Qian Guo County, Songyuan City, was selected as the research object. First, the optimal building orientation for pig houses was explored by analysing the solar irradiance received as a primary indicator. Next, the energy consumption of pig houses was simulated using various translucent materials in the roof. Various roof types were designed based on the optimal translucent material. The energy consumption of these designs was compared with that of the experimental pig house. Finally, the economic viability of the different pig houses was analysed. The results showed that the optimal building orientation for a pig house was south by west 25°. Among the pig houses with equal-slope roofs using FRP daylighting boards, double-layer polycarbonate (PC) sheets, flat glass, and organic glass as translucent materials, the pig house using double-layer PC sheets required the least amount of additional heat during the heating season (62,109 kWh). For pig houses with double-layer PC sheets, four roof designs were assessed (equal-slope roof, front and rear roofs with unequal slopes, upper and lower staggered unequal-slope roof, and four-equal-slope roof), and it was found that the pig house with a four-equal-slope roof required the least amount of additional heat during the heating season (48,138 kWh). The economic analysis indicates that the combination of a four-equal-slope roof and double-layer PC sheets is the most cost-effective option, with the lowest total life cycle cost. This design saves 9521 USD compared with the experimental pig housing, effectively improving solar energy utilisation efficiency. These analyses provide a reference for exploring the thermal performance of pig houses.