bluuebunny/crossref_metadata_2025 huggingface dataset:
Experimental Study on Freezing Front Model of Alpine Tunnel under Wind Field
Authors: Li Zhiqiang , Zhao Jinpeng , Liu Lulu , Li Zhe
Published: January 2023

In order to study the freezing front characteristics of alpine tunnels under the condition of wind flow field and relying on the Osaka Mountain tunnel in Qinghai Province, the physical model test of an alpine tunnel was built. By using the Surfer software combined with the laboratory test data, the radial and longitudinal temperature variation trends of the tunnel were obtained, and the overall temperature vector graph of the tunnel was simulated; the radial and longitudinal evolutionary laws of a freezing front in an alpine tunnel under airflow conditions were systematically analyzed, and the radial and longitudinal governing equations of a freezing front in the tunnel model under airflow conditions were proposed. The results show that: With the decrease of the test air temperature, the thermonuclear area in the surrounding rock gradually shrinks, the frozen area of surrounding rock at the bottom of the arch gradually increases, and the frozen area of surrounding rock at the top of the arch gradually expands to the interior of the mountain. The influence degree of ventilation on the longitudinal and radial temperature distribution of the tunnel is obvious, and the greater the wind speed, the greater the influence degree. In particular, the fluctuation range of the longitudinal temperature distribution of the tunnel is more extensive under the influence of ventilation. The freezing front distance in the inverted arch area is the largest, and the expansion distance of the freezing front in the wall foot area is obviously higher than that in the vault; the variation of the longitudinal freezing front at different positions of the tunnel shows parabolic attenuation and with an increase in tunnel depth, the trend of freezing front gradually eases and becomes stable and disappears; the three radial regions of the freezing front and the longitudinal quadratic parabola governing equations can predict the specific distribution characteristics of the freezing front at different depths of the tunnel.
Synthesis of the Energy-Saving Dry Dual Clutch Control Mechanism
Authors: Sergienko Nikolay , Kalinin Pavel , Pavlenko Ivan , Ochowiak Marek , Ivanov Vitalii , Sergienko Anton , Pavlova Natalia , Basova Yevheniia , Titarenko Oksana , Nazarov Aleksandr , Krupińska Andżelika , Matuszak Magdalena , Włodarczak Sylwia
Published: January 2023

A promising direction in developing up-to-date transport vehicles is the use of transmissions, an essential element of which is a dual-clutch. Improving functional performance, energy efficiency, and environmental friendliness are relevant and require appropriate research. The object of study is a dry dual-clutch working with a manual transmission with high energy efficiency. In the proposed design scheme, a rotary lever and a movable carriage can change the structural diagram of the force interaction between the pressure spring and the clutch discs. The developed mathematical model of the clutches control mechanism allowed for analyzing the process of controlling the dual-clutch transmissions (DCT) and obtaining functional dependencies between pushing forces in friction pairs and the carriage position. As a result, a method for synthesizing DCT was proposed to ensure the transmission of a given torque. Furthermore, it was determined and numerically proven that the same radial movement of the carriage when switching-on the clutches does not guarantee equal loading for the frictional discs of each clutch. This fact increases the uneven dynamics of torque and disc wear. Overall, the synthesis of the energy-saving dry dual-clutch control mechanism providing the same clutch margin for each clutch was developed. The method can be generalized and applied to designing dry dual-clutch transmissions for machines of various purposes.
Photoplethysmograph Based Biofeedback for Stress Reduction under Real-Life Conditions in Healthcare Frontline
Authors: Rudics Emese , Nagy Ádám , Dombi József , Hompoth Emőke Adrienn , Szabó Zoltán , Horváth Rózsa , Balogh Mária , Lovas András , Bilicki Vilmos , Szendi István
Published: January 2023

Biofeedback (BF) therapy methods have evolved considerably in recent years. The best known is biofeedback training based on heart rate variability (HRV), which is used to treat asthma, depression, stress, and anxiety, among other conditions, by synchronizing the rhythm of breathing and heartbeat. The aim of our research was to develop a methodology and test its applicability using photoplethysmographs and smartphones to conduct biofeedback sessions for frontline healthcare workers under their everyday stressful conditions. Our hypothesis is that such a methodology is not only comparable to traditional training itself, but can make regular sessions increasingly effective in reducing real-life stress by providing appropriate feedback to the subject. The sample consisted 28 participants. Our proprietary method based on HRV biofeedback is able to determine the resonance frequency of the subjects, i.e., the number at which the pulse and respiration are in sync. Our research app then uses visual feedback to help the subject reach this frequency, which, if maintained, can significantly reduce stress. By comparing BF with Free relaxation, we conclude that BF does not lose effectiveness over time and repetitions, but increases it. This paper is our pilot study in which we discuss the method used to select participants, the development and operation of the protocol and algorithm, and present and analyze the results obtained. The showcased results demonstrate our hypothesis that purely IT-based relaxation techniques can effectively compete with spontaneous relaxation through biofeedback. This provides a basis for further investigation and development of the methodology and its widespread use to effectively reduce workplace stress.
Perceptual Similarities between Artificial Reverberation Algorithms and Real Reverberation
Authors: Mi Huan , Kearney Gavin , Daffern Helena
Published: January 2023

This paper presents a study evaluating the perceptual similarity between artificial reverberation algorithms and acoustic measurements. An online headphone-based listening test was conducted and data were collected from 20 expert assessors. Seven reverberation algorithms were tested in the listening test, including the Dattorro, Directional Feedback Delay Network (DFDN), Feedback Delay Network (FDN), Gardner, Moorer, and Schroeder reverberation algorithms. A new Hybrid Moorer–Schroeder (HMS) reverberation algorithm was included as well. A solo cello piece, male speech, female singing, and a drumbeat were rendered with the seven reverberation algorithms in three different reverberation times (0.266 s, 0.95 s and 2.34 s) as the test conditions. The test was conducted online and based on the Multiple Stimuli with Hidden Reference and Anchor (MUSHRA) paradigm. The reference conditions consisted of the same audio samples convolved with measured binaural room impulse responses (BRIRs) with the same three reverberation times. The anchor was dual-mono 3.5 kHz low pass filtered audio. The similarity between the test audio and the reference audio was scored on a scale of zero to a hundred. Statistical analysis of the results shows that the Gardner and HMS reverberation algorithms are good candidates for exploration of artificial reverberation in Augmented Reality (AR) scenarios in future research.
Impact of Industrial Application of Fast Carbonation of Recycled Concrete Aggregates
Authors: Izoret Laurent , Pernin Thomas , Potier Jean-Marc , Torrenti Jean-Michel
Published: January 2023

The purpose of the national FastCarb project was to investigate whether the carbonation process of recycled concrete aggregates (RCAs) can be accelerated and benefit from both a carbon footprint point of view and the recycling of these aggregates in concrete. This article presents a part of the results obtained within the project. Two industrial demonstrators were carried out, which allowed for carbonating RCA and manufacturing concrete and concrete objects containing these aggregates. A life cycle analysis showed the importance of transport distances in the results concerning climate change. The project finally shows an interest in the technique for recycled concrete sands.
Cosmeceuticals and Thalassotherapy: Recovering the Skin and Well-Being after Cancer Therapies
Authors: Mourelle M. Lourdes , Gómez Carmen P. , Legido José L.
Published: January 2023

Cancer treatments have undergone significant advances in recent years, although they are not exempt from side effects, including skin toxicity. Different studies show that skin care for cancer patients can be effective in reducing sequelae such as inflammation, xerosis, skin rash, and radiodermatitis, among others. This is the reason why research is being carried out on the ingredients of cosmeceuticals for those indicated for oncological skin care. On the other hand, it is necessary to implement measures that improve the patient’s well-being and, therefore, thalassotherapy techniques and the marine environment could be an effective resource to achieve this goal. This article reviews the publications related to skin care after cancer treatment, including thalassotherapy techniques that can also contribute to well-being.
Force Analysis and Strength Determination of the Cemented Paste Backfill Roof in Underhand Drift Cut-and-Fill Stopping
Authors: Hu Yafei , Han Bin , Zhang Bo , Li Keqing
Published: January 2023

The stability of the cemented paste backfill roof (CPB roof) is critical to safe production in mines using the underhand drift cut-and-fill stopping. To investigate the scientific and reasonable design method of key parameters (size and strength) of the CPB roof and the stress state of the CPB roof during the mining process, field measurements were carried out with Jinchuan Group’s third mining area as the engineering background. Based on the measurement results, a mechanics model was constructed based on the thick plate theory. The field measurement results show that the overlying load on the CPB roof tends to increase first and then decrease with the gradual mining of the stope, and the maximum overlying load values of the two CPB roofs measured are 0.240 MPa and 0.244 MPa, respectively. With the gradual mining of the stope, the deformation of the CPB roof shows a trend of increasing first and then stabilizing. Based on the thick plate theory, the stress model of the CPB roof is constructed, and the error between the calculation results of the model and the field measurement results does not exceed 5%. Applying the research results to the three mines of Jinchuan Group, the span of the stope can be expanded from 5 m to 6 m under the existing strength standard of the filling body, which can increase its mining capacity by 20%. This study is the first to measure the overlying load and the tensile stress value on the CPB roof, which is an important guideline for related theoretical research.
HierTTS: Expressive End-to-End Text-to-Waveform Using a Multi-Scale Hierarchical Variational Auto-Encoder
Authors: Shang Zengqiang , Shi Peiyang , Zhang Pengyuan , Wang Li , Zhao Guangying
Published: January 2023

End-to-end text-to-speech (TTS) models that directly generate waveforms from text are gaining popularity. However, existing end-to-end models are still not natural enough in their prosodic expressiveness. Additionally, previous studies on improving the expressiveness of TTS have mainly focused on acoustic models. There is a lack of research on enhancing expressiveness in an end-to-end framework. Therefore, we propose HierTTS, a highly expressive end-to-end text-to-waveform generation model. It deeply couples the hierarchical properties of speech with hierarchical variational auto-encoders and models multi-scale latent variables, at the frame, phone, subword, word, and sentence levels. The hierarchical encoder encodes the speech signal from fine-grained features into coarse-grained latent variables. In contrast, the hierarchical decoder generates fine-grained features conditioned on the coarse-grained latent variables. We propose a staged KL-weighted annealing strategy to prevent hierarchical posterior collapse. Furthermore, we employ a hierarchical text encoder to extract linguistic information at different levels and act on both the encoder and the decoder. Experiments show that our model performs closer to natural speech in prosody expressiveness and has better generative diversity.
Large Field-Size Throughput/Area Accelerator for Elliptic-Curve Point Multiplication on FPGA
Authors: Alhomoud Ahmed , Jamal Sajjad Shaukat , Altowaijri Saleh M. , Ayari Mohamed , Alharbi Adel R. , Aljaedi Amer
Published: January 2023

This article presents a throughput/area accelerator for elliptic-curve point multiplication over GF(2571). To optimize the throughput, we proposed an efficient hardware accelerator architecture for a fully recursive Karatsuba multiplier to perform polynomial multiplications in one clock cycle. To minimize the hardware resources, we have utilized the proposed Karatsuba multiplier for modular square implementations. Moreover, the Itoh-Tsujii algorithm for modular inverse computation is operated using multiplier resources. These strategies permit us to reduce the hardware resources of our implemented accelerator over a large field size of 571 bits. A controller is implemented to provide control functionalities. Our throughput/area accelerator is implemented in Verilog HDL using the Vivado IDE tool. The results after the place-and-route are given on Xilinx Virtex-6 and Virtex-7 devices. The utilized slices on Virtex-6 and Virtex-7 devices are 6107 and 5683, respectively. For the same FPGA devices, our accelerator can operate at a maximum of 319 MHz and 361 MHz. The latency values for Virtex-6 and Virtex-7 devices are 28.73 μs and 25.38 μs. The comparison to the state-of-the-art shows that the proposed architecture outperforms in throughput/area values. Thus, our accelerator architecture is suitable for cryptographic applications that demand a throughput and area simultaneously.
FPGA Implementation of a Real-Time Edge Detection System Based on an Improved Canny Algorithm
Authors: Guo Laigong , Wu Sitong
Published: January 2023

Canny edge detection is one of the most widely used edge detection algorithms due to its superior performance. However, it is a complex, time-consuming process and has a high hardware cost. To overcome these issues, an improved Canny algorithm is proposed in this paper. It uses the Sobel operator and approximation methods to calculate the gradient magnitude and direction for replacing complex operations with reduced hardware costs. Otsu’s algorithm is introduced to adaptively determine the image threshold. However, Otsu’s algorithm has division operations, and the division operation is complex and has low efficiency and slow speed. We introduce a logarithmic unit to turn the division into a subtraction operation that is easy to implement by hardware but does not affect the selection of the threshold. Experimental results show that the system can detect the edge of the image well without adjusting the threshold value when the external environment changes and requires only 1.231 ms to detect the edges of the 512 × 512 image when clocked at 50 MHz. Compared with existing FPGA implementations, our implementation uses the least amount of logical resources. Thus, it is more suitable for platforms that have limited logical resources.
A Simple Solution for the General Fractional Ambartsumian Equation
Authors: Ortigueira Manuel Duarte , Bengochea Gabriel
Published: January 2023

Fractionalisation and solution of the Ambartsumian equation is considered. The general approach to fractional calculus suitable for applications in physics and engineering is described. It is shown that Liouville-type derivatives are the necessary ones, because they fully preserve backward compatibility with classical results. Such derivatives are used to define and solve the fractional Ambartsumian equation. First, a solution in terms of a slowly convergent fractional Taylor series is obtained. Then, a simple solution expressed in terms of an infinite linear combination of Mittag–Leffler functions is deduced. A fast algorithm, based on a bilinear transformation and using the fast Fourier transform, is described and demonstrated for its approximate numerical realisation.
Modelling the Structure and Anisotropy of London Clay Using the SA_BRICK Model
Authors: Jovičić Vojkan , Jurečič Nina , Vilhar Gregor
Published: January 2023

Several constitutive models had been developed by other researchers to cover the main features of mechanical behaviour of natural overconsolidated clays, such as the nonlinear stress–strain response at small and large strains, and the recent stress history effect. Kinematic hardening models include these features to facilitate realistic predictions of soil–structure interaction. This paper presents the further development of a kinematic hardening model BRICK that includes anisotropy and the influence of soil structure on the mechanical behaviour of a natural clay. High quality laboratory tests were used to calibrate the input parameters of the model in a single element configuration, and a documented boundary value problem of tunnel excavation was used to validate the model in finite element calculations. A comprehensive comparative study between the predictions of different kinematic hardening models, using two different software packages, was carried out. It was observed that the SA_BRICK model is in fair agreement with the observed data and gives improved predictions in comparison to other kinematic hardening models, particularly in terms of narrowness of the settlement trough above the tunnel. Advanced predictions of ground deformations caused by tunnel excavations can be effectively used to mitigate possible damage of existing structures affected by tunnelling in an urban environment.
Al-Foam Compression Tests in Parallel and Serial Concepts
Authors: Kertész József , File Máté , Nyikes Zoltán , Kovács Tünde Anna , Tóth László
Published: January 2023

Taking into consideration the additional weight of a vehicle, today’s requirements can only be met using new materials and designs. The application of metal foam is one of the most promising methods of enhancing the impact energy absorption ability of the crumple zone. The energy-absorbing capacity of thin-walled structures filled with metal foams during compression can be notably improved, which results in lower loading on the passengers. The main goal of our research is to develop a new design that is suited to absorb more impact energy while taking into consideration weight optimization. The authors wanted to unveil the effect of the inhomogeneous filler material in these thin-walled structures. Therefore, the present study investigates the compression test of two metal foams of different densities, in different ways. In the first section, the foams were compressed independently from each other by a recording of a stress–strain diagram. After the single compression, the foams were loaded together, first in parallel, and subsequently in a serial scheme. The study aimed to reveal the effect of the parallel and serial compression scheme focusing on the sum of impact energy absorption.
Partial Correlation Analysis and Neural-Network-Based Prediction Model for Biochemical Recurrence of Prostate Cancer after Radical Prostatectomy
Authors: Kim Jae-Kwon , Hong Sung-Hoo , Choi In-Young
Published: January 2023

Biochemical recurrence (BCR) of prostate cancer occurs when the PSA level increases after treatment. BCR prediction is necessary for successful prostate cancer treatment. We propose a model to predict the BCR of prostate cancer using a partial correlation neural network (PCNN). Our study used data from 1021 patients with prostate cancer who underwent radical prostatectomy at a tertiary hospital. There were nine input variables with BCR as the outcome variable. Feature-sensitive and partial correlation analyses were performed to develop the PCNN. The PCNN provides an NN architecture that is optimized for BCR prediction. The proposed PCNN achieved higher performance in BCR prediction than other machine learning methodologies, with accuracy, sensitivity, and specificity values of 87.16%, 90.80%, and 85.62%, respectively. The enhanced performance of the PCNN is owing to the reduction in unnecessary predictive factors through the correlation between the variables that are used. The PCNN can be used in the clinical treatment stage following prostate treatment. It is expected to be used as a clinical decision-making system in clinical follow-ups for prostate cancer.
Comparison of Physical Activity Training Using Augmented Reality and Conventional Therapy on Physical Performance following a Total Knee Replacement: A Randomized Controlled Trial
Authors: Yu Jae-Ho , Nekar Daekook M. , Kang Hye-Yun , Lee Jae-Won , Oh Sung-Yeon
Published: January 2023

There is growing interest in using augmented reality (AR)-based training for rehabilitation programs, while it remains unclear whether physical exercises using AR can be more effective than conventional therapy for patients with total knee replacement (TKR). This study, therefore, aimed to compare the effects of AR-based training to conventional therapist-based training on the physical performance of early-stage rehabilitation in patients after a TKR. It was a double-blind randomized controlled trial with repeated measures (pre-surgery, post-surgery, and post-intervention). Twenty-four participants with TKR were allocated to either AR-based training or therapist-based training. Both groups received a training program for 30 min per session, three sessions per week, for four weeks. The outcome measures included the range of motion (ROM), muscle strength, balance, and perceived pain. The results showed significant improvements in all measured outcomes in both groups (p < 0.05). However, despite our hypothesis that ART would be more effective than the TKR, no significant differences in all the outcomes were found between the two groups. While there was some evidence showing that performing physical exercises using AR could improve physical performance in patients with TKR after surgery, a comparison with conventional therapy did not show superior effectiveness. However, AR could be used to provide real-time feedback and motivation appropriate for home-training programs.
Acceleration of Nuclear Reactor Simulation and Uncertainty Quantification Using Low-Precision Arithmetic
Authors: Cherezov Alexey , Vasiliev Alexander , Ferroukhi Hakim
Published: January 2023

In recent years, interest in approximate computing has been increasing significantly in many disciplines in the context of saving energy and computation cost by trading off on the quality of numerical simulation. The hardware acceleration based on the low-precision floating-point arithmetic is anticipated by the upcoming generation of microprocessors and code compilers and has already proven to be beneficial for weather and climate modelling and neural network training. The present work illustrates the application of low-precision arithmetic for the nuclear reactor core uncertainty analysis. We studied the performance of an elementary transient reactor core model for the arbitrary precision of the floating-point multiplication in a direct linear system solver. Using this model, we calculated the reactor core transients initiated by the control rod ejection taking into account the uncertainty of the model input parameters. Then, we evaluated the round-off errors of the model outputs for different precision levels. The comparison of the round-off errors and the model uncertainty showed the model could be run using a 15-bit floating-point number precision with an acceptable degradation of the result’s accuracy. This precision corresponds to a gain of about 6× in the bit complexity of the linear system solution algorithm, which can be actualized in terms of reduced energy costs on low-precision hardware.
RETRACTED: Express Data Processing on FPGA: Network Interface Cards for Streamlined Software Inspection for Packet Processing
Authors: Pradeep Sunkari , Sharma Yogesh , Verma Chaman , Sreeram Gutha , Hanumantha Rao Panugati
Published: January 2023

Modern computers’ network interface cards (NICs) are undergoing changes in order to handle greater data rates and assist with scaling problems caused by general-purpose CPU technology. The inclusion of programmable accelerators to the NIC’s data channel is one of the ongoing improvements that is particularly intriguing since it gives the accelerator the chance to take on a portion of the CPU’s network packet processing duties. Accelerators are frequently developed using platforms like field-programmable gate arrays because packet processing operations have severe latency requirements (FPGAs). When implementing packet processing activities, FPGAs’ gain for through put is the number of data packets being successfully sent per second and latency is the actual time those packets take. However, due to their restricted resources, programming may need to be shared throughout a variety of applications. We provide hXDP, a software solution for FPGAs that targets the Linux eXpress Data Path and performs packet processing functions outlined with the eBPF technology. While maintaining performance on par with top-tier CPUs, hXDP only uses a tiny portion from the field programmable gate arrays, which are semiconductor devices that are based around a matrix of configuration logic blocks (CLB) connected over programmable interconnects. However, we demonstrate that when aiming towards a purpose-built FPGA architecture, many extended Berkeley packet filters (eBPF) allow programmers to use Berkeley packet filter byte code that makes use of certain kernel resources and instruction set architecture, to collocate and even eliminate, with considerably productivity and effectiveness. On an FPGA NIC, we implement hXDP and test its effectiveness using authentic eBPF programmes from the real world. Our version consumes 15% of the FPGA resources and operates at 156.25 MHz. This can constantly change and lead to the act of identification, inspection, extraction, and manipulation so that a network may make more intelligent management decisions.
Diurnal Variation Characteristics of the Surface Sensible Heat Flux over the Tibetan Plateau
Authors: Zhu Zhu , Wang Meirong , Wang Jun , Ma Xulin , Luo Jingjia , Yao Xiuping
Published: January 2023

The characteristics of diurnal variation of the surface sensible heat flux (SH) over the Tibetan Plateau (TP) are comprehensively investigated by using the long-term dataset of integrated land–atmosphere interaction observations (2006–2016) on the TP. Results show that the diurnal variation of SH shows obvious seasonal variabilities in terms of amplitude, duration, and peak time. At the Muztagh Ata Westerly Observation and Research Station (MAWORS), the Ngari Desert Observation and Research Station (NADORS), and the Qomolangma Atmospheric and Environmental Observation and Research Station (QOMS), the SH diurnal amplitude is consistently the largest in spring, followed by summer and autumn, and the smallest in winter, with a peak at 15:00. However, for the Southeast Tibet Observation and Research Station (SETORS), the amplitude in winter is rather violent with the peak at 12:00. We find that positive SH at most stations has the longest duration from May to August. Moreover, the peak time fluctuates from month to month, even showing a shift at the QOMS before and after 2015, and the double-peak phenomenon of SH mainly occurs in spring and autumn. Additionally, magnitudes of calculated SH with the conventional heat transfer coefficient (CDH) of 0.004 are about 64–100% larger than those of directly observed SH at the QOMS and the Nam Co Monitoring and Research Station (NAMORS). We here additionally recommend a new CDH values of about 2.24 × 10−3 in spring and 2.78 × 10−3 in summer, respectively, to more accurately calculate the TP SH.
Special Issue: Air Pollution at the Urban and Regional Level: Sources, Sinks, and Transportation
Authors: Hristova Elena Svilenova , Manousakas Manousos Ioannis
Published: January 2023

Air pollution poses a significant risk to human health [...]
Efferent Control in Musicians: A Review
Authors: Acuña Francisca , Jeria Rodrigo , Pavez Elisabeth , Aguilar-Vidal Enzo
Published: January 2023

It is widely established that musicians possess a higher level in certain auditory perceptual abilities when compared to non-musicians. This improvement may be mediated, at least in part, by changes in the cochlear response induced by reflex activation of the olivocochlear efferent system. In this review, we describe and analyze the scientific evidence regarding possible differences in the efferent response in musicians and non-musicians. The main evidence observed is that musicians present a greater robustness of the efferent olivocochlear reflex when measured by suppression of otoacoustic emissions and compared to non-musicians. Analyzing the articles presented in this review, it is possible to point out that the differential role of the efferent effect in musicians is not yet established. There is not enough evidence to support the idea that the olivocochlear system favors comparative changes in the properties of musicians’ auditory filters. New studies with psychoacoustic techniques, among others, are needed to measure the effect of the olivocochlear reflex on tuning, gain, compression, or temporal resolution in musicians and non-musicians.
Pancreatic Mass Characterization Using IVIM-DKI MRI and Machine Learning-Based Multi-Parametric Texture Analysis
Authors: Malagi Archana Vadiraj , Shivaji Sivachander , Kandasamy Devasenathipathy , Sharma Raju , Garg Pramod , Gupta Siddhartha Datta , Gamanagatti Shivanand , Mehndiratta Amit
Published: January 2023

Non-invasive characterization of pancreatic masses aids in the management of pancreatic lesions. Intravoxel incoherent motion-diffusion kurtosis imaging (IVIM-DKI) and machine learning-based texture analysis was used to differentiate pancreatic masses such as pancreatic ductal adenocarcinoma (PDAC), pancreatic neuroendocrine tumor (pNET), solid pseudopapillary epithelial neoplasm (SPEN), and mass-forming chronic pancreatitis (MFCP). A total of forty-eight biopsy-proven patients with pancreatic masses were recruited and classified into pNET (n = 13), MFCP (n = 6), SPEN (n = 4), and PDAC (n = 25) groups. All patients were scanned for IVIM-DKI sequences acquired with 14 b-values (0 to 2500 s/mm2) on a 1.5T MRI. An IVIM-DKI model with a 3D total variation (TV) penalty function was implemented to estimate the precise IVIM-DKI parametric maps. Texture analysis (TA) of the apparent diffusion coefficient (ADC) and IVIM-DKI parametric map was performed and reduced using the chi-square test. These features were fed to an artificial neural network (ANN) for characterization of pancreatic mass subtypes and validated by 5-fold cross-validation. Receiver operator characteristics (ROC) analyses were used to compute the area under curve (AUC). Perfusion fraction (f) was significantly higher (p < 0.05) in pNET than PDAC. The f showed better diagnostic performance for PDAC vs. MFCP with AUC:0.77. Both pseudo-diffusion coefficient (D*) and f for PDAC vs. pNET showed an AUC of 0.73. ADC and diffusion coefficient (D) showed good diagnostic performance for pNET vs. MFCP with AUC: 0.79 and 0.76, respectively. In the TA of PDAC vs. non-PDAC, f and combined IVIM-DKI parameters showed high accuracy ≥ 84.3% and AUC ≥ 0.84. Mean f and combined IVIM-DKI parameters estimated that the IVIM-DKI model with TV texture features has the potential to be helpful in characterizing pancreatic masses.
Artificial Hummingbird Algorithm with Transfer-Learning-Based Mitotic Nuclei Classification on Histopathologic Breast Cancer Images
Authors: Malibari Areej A. , Obayya Marwa , Gaddah Abdulbaset , Mehanna Amal S. , Hamza Manar Ahmed , Ibrahim Alsaid Mohamed , Yaseen Ishfaq , Abdelmageed Amgad Atta
Published: January 2023

Recently, artificial intelligence (AI) is an extremely revolutionized domain of medical image processing. Specifically, image segmentation is a task that generally aids in such an improvement. This boost performs great developments in the conversion of AI approaches in the research lab to real medical applications, particularly for computer-aided diagnosis (CAD) and image-guided operation. Mitotic nuclei estimates in breast cancer instances have a prognostic impact on diagnosis of cancer aggressiveness and grading methods. The automated analysis of mitotic nuclei is difficult due to its high similarity with nonmitotic nuclei and heteromorphic form. This study designs an artificial hummingbird algorithm with transfer-learning-based mitotic nuclei classification (AHBATL-MNC) on histopathologic breast cancer images. The goal of the AHBATL-MNC technique lies in the identification of mitotic and nonmitotic nuclei on histopathology images (HIs). For HI segmentation process, the PSPNet model is utilized to identify the candidate mitotic patches. Next, the residual network (ResNet) model is employed as feature extractor, and extreme gradient boosting (XGBoost) model is applied as a classifier. To enhance the classification performance, the parameter tuning of the XGBoost model takes place by making use of the AHBA approach. The simulation values of the AHBATL-MNC system are tested on medical imaging datasets and the outcomes are investigated in distinct measures. The simulation values demonstrate the enhanced outcomes of the AHBATL-MNC method compared to other current approaches.
Portable Iontophoresis Device for Efficient Drug Delivery
Authors: Bok Moonjeong , Kwon Young Il , Huang Zheng Min , Lim Eunju
Published: January 2023

The timely delivery of drugs to specific locations in the body is imperative to ensure the efficacy of treatment. This study introduces a portable facial device that can deliver drugs efficiently using iontophoresis. Two types of power supplies—direct current and pulse ionization supplies—were manufactured by injection molding. Electrical stimulation elements, which contained Ag metal wires, were woven into facial mask packs. The diffusion phenomenon in the skin and iontophoresis were numerically modeled. Injection molding was simulated before the device was manufactured. Analysis using rhodamine B demonstrated a remarkable increase in the moisture content of the skin and effective absorption of the drug under an applied electric field upon the application of iontophoresis. The proposed concept and design constitute a new method of achieving effective drug absorption with wearable devices.
Parkinson’s Disease, It Takes Guts: The Correlation between Intestinal Microbiome and Cytokine Network with Neurodegeneration
Authors: Xiromerisiou Georgia , Marogianni Chrysoula , Androutsopoulou Anastasia , Ntavaroukas Panagiotis , Mysiris Dimitrios , Papoutsopoulou Stamatia
Published: January 2023

Parkinson’s disease is a progressive neurodegenerative disorder with motor, physical and behavioral symptoms that can have a profound impact on the patient’s quality of life. Most cases are idiopathic, and the exact mechanism of the disease’s cause is unknown. The current hypothesis focuses on the gut-brain axis and states that gut microbiota dysbiosis can trigger inflammation and advances the development of Parkinson’s disease. This systematic review presents the current knowledge of gut microbiota analysis and inflammation based on selected studies on Parkinson’s patients and experimental animal models. Changes in gut microbiota correlate with Parkinson’s disease, but only a few studies have considered inflammatory modulators as important triggers of the disease. Nevertheless, it is evident that proinflammatory cytokines and chemokines are induced in the gut, the circulation, and the brain before the development of the disease’s neurological symptoms and exacerbate the disease. Increased levels of tumor necrosis factor, interleukin-1β, interleukin-6, interleukin-17A and interferon-γ can correlate with altered gut microbiota. Instead, treatment of gut dysbiosis is accompanied by reduced levels of inflammatory mediators in specific tissues, such as the colon, brain and serum and/or cerebrospinal fluid. Deciphering the role of the immune responses and the mechanisms of the PD-associated gut microbiota will assist the interpretation of the pathogenesis of Parkinson’s and will elucidate appropriate therapeutic strategies.
Silencing miR-146a-5p Protects against Injury-Induced Osteoarthritis in Mice
Authors: Qin Haocheng , Wang Cuicui , He Yonghua , Lu Aiwu , Li Tiandao , Zhang Bo , Shen Jie
Published: January 2023

Osteoarthritis (OA), the most prevalent joint disease and the leading cause of disability, remains an incurable disease largely because the etiology and pathogenesis underlying this degenerative process are poorly understood. Low-grade inflammation within joints is a well-established factor that disturbs joint homeostasis and leads to an imbalance between anabolic and catabolic processes in articular cartilage; however, the complexity of the network between inflammatory factors that often involves positive and negative feedback loops makes current anti-cytokine therapy ineffective. MicroRNAs (miRNAs) have emerged as key regulators to control inflammation, and aberrant miRNAs expression has recently been linked to OA pathophysiology. In the present study, we characterized transcriptomic profiles of miRNAs in primary murine articular chondrocytes in response to a proinflammatory cytokine, IL-1β, and identified miR-146a-5p as the most responsive miRNA to IL-1β. miR-146a-5p was also found to be upregulated in human OA cartilage. We further demonstrated that knockdown of miR-146a-5p antagonized IL-1β-mediated inflammatory responses and IL-1β-induced catabolism in vitro, and silencing of miR-146a in chondrocytes ameliorated articular cartilage destruction and reduced OA-evoked pain in an injury-induced murine OA model. Moreover, parallel RNA sequencing revealed that differentially expressed genes in response to IL-1β were enriched in pathways related to inflammatory processes, cartilage matrix homeostasis, and cell metabolism. Bioinformatic analyses of putative miR-146a-5p gene targets and following prediction of protein–protein interactions suggest a functional role of miR-146a-5p in mediating inflammatory processes and regulation of cartilage homeostasis. Our genetic and transcriptomic data define a crucial role of miR-146a-5p in OA pathogenesis and implicate modulation of miR-146a-5p in articular chondrocytes as a potential therapeutic strategy to alleviate OA.
Mapping DNA Conformations Using Single-Molecule Conductance Measurements
Authors: Alangari Mashari , Demir Busra , Gultakti Caglanaz Akin , Oren Ersin Emre , Hihath Joshua
Published: January 2023

DNA is an attractive material for a range of applications in nanoscience and nanotechnology, and it has recently been demonstrated that the electronic properties of DNA are uniquely sensitive to its sequence and structure, opening new opportunities for the development of electronic DNA biosensors. In this report, we examine the origin of multiple conductance peaks that can occur during single-molecule break-junction (SMBJ)-based conductance measurements on DNA. We demonstrate that these peaks originate from the presence of multiple DNA conformations within the solutions, in particular, double-stranded B-form DNA (dsDNA) and G-quadruplex structures. Using a combination of circular dichroism (CD) spectroscopy, computational approaches, sequence and environmental controls, and single-molecule conductance measurements, we disentangle the conductance information and demonstrate that specific conductance values come from specific conformations of the DNA and that the occurrence of these peaks can be controlled by controlling the local environment. In addition, we demonstrate that conductance measurements are uniquely sensitive to identifying these conformations in solutions and that multiple configurations can be detected in solutions over an extremely large concentration range, opening new possibilities for examining low-probability DNA conformations in solutions.
Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge
Authors: Giri Nabin , Cheng Jianlin
Published: January 2023

Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information.
Genetic Predisposition to Neurological Complications in Patients with COVID-19
Authors: Sahajpal Nikhil Shri , Hastie Alex R. , Schieck Maximilian , Mondal Ashis K. , Felde Marc , van der Made Caspar I. , Chou Janet S. , Randolph Adrienne G. , Illig Thomas , Zody Michael C. , Brownstein Catherine A. , Beggs Alan H. , Hoischen Alexander , Chaubey Alka , Kolhe Ravindra
Published: January 2023

Several studies have identified rare and common genetic variants associated with severe COVID-19, but no study has reported genetic determinants as predisposition factors for neurological complications. In this report, we identified rare/unique structural variants (SVs) implicated in neurological functions in two individuals with neurological manifestations of COVID-19. This report highlights the possible genetic link to the neurological symptoms with COVID-19 and calls for a collective effort to study these cohorts for a possible genetic linkage.
The Missing Pieces: The Role of Secretion Systems in Campylobacter jejuni Virulence
Authors: Gabbert Amber D. , Mydosh Jennifer L. , Talukdar Prabhat K. , Gloss Lisa M. , McDermott Jason E. , Cooper Kerry K. , Clair Geremy C. , Konkel Michael E.
Published: January 2023

Campylobacter jejuni is likely the most common bacterial cause of gastroenteritis worldwide, responsible for millions of cases of inflammatory diarrhea characterized by severe abdominal cramps and blood in the stool. Further, C. jejuni infections are associated with post-infection sequelae in developed countries and malnutrition and growth-stunting in low- and middle-income countries. Despite the increasing prevalence of the disease, campylobacteriosis, and the recognition that this pathogen is a serious health threat, our understanding of C. jejuni pathogenesis remains incomplete. In this review, we focus on the Campylobacter secretion systems proposed to contribute to host-cell interactions and survival in the host. Moreover, we have applied a genomics approach to defining the structural and mechanistic features of C. jejuni type III, IV, and VI secretion systems. Special attention is focused on the flagellar type III secretion system and the prediction of putative effectors, given that the proteins exported via this system are essential for host cell invasion and the inflammatory response. We conclude that C. jejuni does not possess a type IV secretion system and relies on the type III and type VI secretion systems to establish a niche and potentiate disease.
Impact of Hypermannosylation on the Structure and Functionality of the ER and the Golgi Complex
Authors: Franzka Patricia , Schüler Svenja Caren , Kentache Takfarinas , Storm Robert , Bock Andrea , Katona Istvan , Weis Joachim , Buder Katrin , Kaether Christoph , Hübner Christian A.
Published: January 2023

Proteins of the secretory pathway undergo glycosylation in the endoplasmic reticulum (ER) and the Golgi apparatus. Altered protein glycosylation can manifest in serious, sometimes fatal malfunctions. We recently showed that mutations in GDP-mannose pyrophosphorylase A (GMPPA) can cause a syndrome characterized by alacrima, achalasia, mental retardation, and myopathic alterations (AAMR syndrome). GMPPA acts as a feedback inhibitor of GDP-mannose pyrophosphorylase B (GMPPB), which provides GDP-mannose as a substrate for protein glycosylation. Loss of GMPPA thus enhances the incorporation of mannose into glycochains of various proteins, including α-dystroglycan (α-DG), a protein that links the extracellular matrix with the cytoskeleton. Here, we further characterized the consequences of loss of GMPPA for the secretory pathway. This includes a fragmentation of the Golgi apparatus, which comes along with a regulation of the abundance of several ER- and Golgi-resident proteins. We further show that the activity of the Golgi-associated endoprotease furin is reduced. Moreover, the fraction of α-DG, which is retained in the ER, is increased. Notably, WT cells cultured at a high mannose concentration display similar changes with increased retention of α-DG, altered structure of the Golgi apparatus, and a decrease in furin activity. In summary, our data underline the importance of a balanced mannose homeostasis for the secretory pathway.
The Many Faces of Immune Activation in HIV-1 Infection: A Multifactorial Interconnection
Authors: Mazzuti Laura , Turriziani Ombretta , Mezzaroma Ivano
Published: January 2023

Chronic immune activation has a significant role in HIV-1 disease pathogenesis and CD4+ T-cell depletion. The causes of chronic inflammation and immune activation are incompletely understood, but they are likely multifactorial in nature, involving both direct and indirect stimuli. Possible explanations include microbial translocation, coinfection, and continued presence of competent replicating virus. In fact, long-term viral suppression treatments are unable to normalize elevated markers of systemic immune activation. Furthermore, high levels of pro-inflammatory cytokines increase susceptibility to premature aging of the immune system. The phenomenon of “inflammaging” has begun to be evident in the last decades, as a consequence of increased life expectancy due to the introduction of cART. Quality of life and survival have improved substantially; however, PLWH are predisposed to chronic inflammatory conditions leading to age-associated diseases, such as inflammatory bowel disease, neurocognitive disorders, cardiovascular diseases, metabolic syndrome, bone abnormalities, and non-HIV-associated cancers. Several approaches have been studied in numerous uncontrolled and/or randomized clinical trials with the aim of reducing immune activation/inflammatory status in PLWH, none of which have achieved consistent results.