An Anchored Band Bracing System for Deep Excavations in Clay

An anchored band bracing system was introduced for the bracing of a deep excavation in hard clay with low-level expansion. In this system, stress redistribution was considered to further deduce the thickness of the surfacing shotcrete, global stability was ensured by prestressed anchors and a concrete band for the distribution of the prestress. The bracing system was shown to be cost effective and reliable under certain conditions, it is the first project using prestressed anchors combined with shotcrete for deep excavation bracing in hard clay with low level expansion in P.R. China. In this paper, the design procedure and criteria for both surfacing and global stability were summarized

Investigating the Pavement Vibration Response for Roadway Service Condition Evaluation

Dynamic response of pavement provides service condition information and helps with damage prediction, while limited research is available with the simulation of pavement vibration response for evaluating roadway service condition. +is paper presents a numerical model for the analysis of the pavement vibration due to the dynamic load created by a passing vehicle. A quarter vehicle model was used for the determination of the vehicle moving load. Both random and spatial characteristics of the load were considered. The random nonuniform moving load was then introduced in a 3D finite element model for the determination of the traffic-induced pavement vibration. The validated numerical model was used to assess the effects of dynamic load, material properties, and pavement structures on pavement vibration response. Numerical analyses showed that the vibration modes changed considerably for the different roadway service conditions. The vibration signals reflect the level of road roughness, the stiffness of the pavement materials, and the integrity of pavement structure. The acceleration extrema, the time-domain signal waveform, the frequency distribution, and the sum of squares of Fourier amplitude can be potential indexes for evaluating roadway service condition. This provides recommendations for the application of pavement vibration response in early-warning and timely maintenance of road

Pavement Fatigue Crack Detection and Severity Classification Based on Convolutional Neural Network

Due to the varying intensity of pavement cracks, the complexity of topological structure, and the noise of texture background, image classification for asphalt pavement cracking has proven to be a challenging problem. Fatigue cracking, also known as alligator cracking, is one of the common distresses of asphalt pavement. It is thus important to detect and monitor the condition of alligator cracking on roadway pavements. Most research in this area has typically focused on pixel-level detection of cracking using limited datasets. A novel deep convolutional neural network that can achieve two objectives is proposed. The first objective of the proposed neural network is to classify presence of fatigue cracking based on pavement surface images. The second objective is to classify the fatigue cracking severity level based on the Distress Identification Manual (DIM) standard. In this paper, a databank of 4484 high-resolution pavement surface images is established in which images are taken locally in the Town of Blacksburg, Virginia, USA. In the data pre-preparation, over 4000 images are labeled into 4 categories manually according to DIM standards. A four-layer convolutional neural network model is then built to achieve the goal of classification of images by pavement crack severity category. The trained model reached the highest accuracy among all existing methods. After only 30 epochs of training, the model achieved a crack existence classification accuracy of 96.23% and a severity level classification accuracy of 96.74%. After 20 epochs of training, the model achieved a pavement marking presence classification accuracy of 97.64%.10 pages, 14 figures, 3 table

Research on performance evaluation of multiple recycling asphalt and multiple recycling asphalt mixtures

Whether reclaimed asphalt pavement can be reused multiple cycles has become a focal issue in the industry. This paper investigates the macroscopic physical property changes and microscopic four-component alteration mechanisms of multiple recycling asphalt, simulates the multiple recycling processes of asphalt mixtures through laboratory tests, determines the mix proportions of multiple recycling asphalt mixtures through Marshall test, and evaluates the pavement performance of the mixtures using high-temperature wheel tracking test, semi-circular bending test, and freeze-thaw splitting test. The results indicate that after multiple recycling, multiple recycling asphalt exhibits poorer rheological property; high-temperature performance of multiple recycling asphalt mixture is improved, and low-temperature performance of multiple recycling asphalt mixture is significantly degraded. The solubilization of rejuvenator in old asphalt intensifies with increasing rejuvenator dosage but decreases exponentially with the increasing number of recycling cycles. The high-temperature stability of recycled asphalt mixtures gradually increases with the number of recycling cycles, while water stability shows low sensitivity to the number of recycling cycles. After the third recycling, the Marshall volume indicators of the recycled asphalt mixtures can meet the regulatory requirement. After the second recycling, the low-temperature anti-cracking performance of the recycled mixtures deteriorates rapidly. In summary, when asphalt mixtures undergo multiple recycling, special attention should be paid to the restoration of their low-temperature property

The role of FoxO3a in the pathogenesis of osteoarthritis and its therapeutic applications

Osteoarthritis (OA) is a chronic degenerative joint disease predominantly observed in middle-aged and elderly individuals, with its complex pathological mechanisms significantly affecting patients’ quality of life. Due to the absence of effective treatment strategies, there has been a growing emphasis on molecular targeted therapies for OA. As a critical transcription factor, Forkhead box O3a (FoxO3a) plays a vital role in physiological processes such as cell differentiation, survival, and apoptosis. The activity of FoxO3a is modulated by post-translational modifications, including phosphorylation and acetylation, as well as by various signaling pathways. Recent studies have demonstrated that FoxO3a significantly influences the onset and progression of OA by regulating multiple processes in chondrocytes, including redox homeostasis, inflammatory response, cell survival, and matrix degradation. Its active expression presents potential value for the prevention and treatment of OA. This article reviews the research advancements regarding the role of FoxO3a in the pathogenesis of OA, emphasizing its effects on physiological activities such as oxidative stress and regulatory mechanisms in chondrocytes, with the aim of refining the understanding of OA pathogenesis and providing new insights for its prevention and treatment

FARCI: Fast and Robust Connectome Interference

The inference of neuronal connectome from large-scale neuronal activity recordings, such as two-photon Calcium imaging, represents an active area of research in computational neuroscience. In this work, we developed FARCI (Fast and Robust Connectome Inference), a MATLAB package for neuronal connectome inference from high-dimensional two-photon Calcium fluorescence data. We employed partial correlations as a measure of the functional association strength between pairs of neurons to reconstruct a neuronal connectome. We demonstrated using in silico datasets from the Neural Connectomics Challenge (NCC) and those generated using the state-of-the-art simulator of Neural Anatomy and Optimal Microscopy (NAOMi) that FARCI provides an accurate connectome and its performance is robust to network sizes, missing neurons, and noise levels. Moreover, FARCI is computationally efficient and highly scalable to large networks. In comparison with the best performing connectome inference algorithm in the NCC, Generalized Transfer Entropy (GTE), and Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP), FARCI produces more accurate networks over different network sizes, while providing significantly better computational speed and scaling

Data-driven analysis on the subbase strain prediction: a deep data augmentation-based study

The service quality of the subbase may affect the overall road performance during its service life. Thus, monitoring and prediction of subbase strain development are of great importance for civil engineers. In this paper, a method based on the time-series augmentation was employed to predict the subbase strain development. The time-series generative adversarial network (TimeGAN) model was implemented to perform the augmentation of time-series data based on the original monitored data. The augmented data was trained through deep learning network to learn the feature correlation of the subbase strain. The effectiveness of TimeGAN on the prediction accuracy was evaluated through the Attention-Sequence to Sequence (Attention-Seq2seq) model, and temporal convolution network-adaptively parametric rectifier linear units (TCN-APReLU) model. Results indicated that the TimeGAN network could capture sufficient information from the time-series monitored data of subbase strain development so that the corresponding augmented data matches well with the original data, which improves the prediction accuracy. It is also discovered that the combination of TimeGAN and TCN-APReLU appropriately predict the subbase strain development based on the original monitored data

Impact of Cordyceps sinensis on coronary computed tomography angiography image quality and renal function in a beagle model of renal impairment

ObjectiveThis study aims to investigate the protective effects of Cordyceps sinensis against renal injury induced by low-dose contrast medium (CM) in coronary computed tomography angiography (CCTA) imaging, and to evaluate its efficacy using functional magnetic resonance imaging (fMRI).MethodsTwenty Beagle dogs with induced renal insufficiency were enrolled in the study and randomly assigned to one of four groups (n = 5 per group). Group A received Cordyceps sinensis for 1 week prior to undergoing heart rate-dependent personalized CM CCTA scanning; Group B received Cordyceps sinensis for 1 week followed by conventional dose CM CCTA scanning; Group C did not receive Cordyceps sinensis but underwent HR-dependent CM CCTA scanning; and Group D did not receive Cordyceps sinensis but underwent conventional dose CM CCTA scanning. Renal function was assessed using MRI before and after the intervention, with IVIM (Intravoxel Incoherent Motion) and BOLD (Blood Oxygen Level Dependent) imaging of the kidneys. Key parameters, including the pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f), and R2*values, were quantified. Laboratory renal function markers were measured multiple times before and after the intervention, and their correlation with fMRI parameters was analyzed.ResultsCCTA imaging revealed that the CT values of the major coronary artery branches in all groups met the international diagnostic criteria for coronary arteries. No statistically significant differences in image quality were observed among the four groups (P > 0.05). In Groups A and D, significant changes were observed in renal function parameters, as well as in D, D*, f, and R2* values, both pre- and post-CCTA (P < 0.05). However, Groups B and C exhibited no significant changes pre- and post-CCTA (P > 0.05). A significant correlation was found between MRI parameters and laboratory renal function markers, with excellent inter- and intra-observer reproducibility, and high repeatability in the measurements.ConclusionHR-dependent personalized CM CCTA imaging did not compromise image quality. Administration of Cordyceps sinensis demonstrated a potential protective effect on renal function. The combination of IVIM and BOLD functional MRI offers a reliable, non-invasive approach to assess the protective effects of Cordyceps sinensis on renal injury induced by low-dose CCTA in Beagle dogs

An allelic atlas of immunoglobulin heavy chain variable regions reveals antibody binding epitope preference resilient to SARS-CoV-2 mutation escape

BackgroundAlthough immunoglobulin (Ig) alleles play a pivotal role in the antibody response to pathogens, research to understand their role in the humoral immune response is still limited.MethodsWe retrieved the germline sequences for the IGHV from the IMGT database to illustrate the amino acid polymorphism present within germline sequences of IGHV genes. We aassembled the sequences of IgM and IgD repertoire from 130 people to investigate the genetic variations in the population. A dataset comprising 10,643 SARS-CoV-2 spike-specific antibodies, obtained from COV-AbDab, was compiled to assess the impact of SARS-CoV-2 infection on allelic gene utilization. Binding affinity and neutralizing activity were determined using bio-layer interferometry and pseudovirus neutralization assays. Primary docking was performed using ZDOCK (3.0.2) to generate the initial conformation of the antigen-antibody complex, followed by simulations of the complete conformations using Rosetta SnugDock software. The original and simulated structural conformations were visualized and presented using ChimeraX (v1.5).ResultsWe present an allelic atlas of immunoglobulin heavy chain (IgH) variable regions, illustrating the diversity of allelic variants across 33 IGHV family germline sequences by sequencing the IgH repertoire of in the population. Our comprehensive analysis of SARS-CoV-2 spike-specific antibodies revealed the preferential use of specific Ig alleles among these antibodies. We observed an association between Ig alleles and antibody binding epitopes. Different allelic genotypes binding to the same RBD epitope on the spike show different neutralizing potency and breadth. We found that antibodies carrying the IGHV1-69*02 allele tended to bind to the RBD E2.2 epitope. The antibodies carrying G50 and L55 amino acid residues exhibit potential enhancements in binding affinity and neutralizing potency to SARS-CoV-2 variants containing the L452R mutation on RBD, whereas R50 and F55 amino acid residues tend to have reduced binding affinity and neutralizing potency. IGHV2-5*02 antibodies using the D56 allele bind to the RBD D2 epitope with greater binding and neutralizing potency due to the interaction between D56 on HCDR2 and K444 on RBD of most Omicron subvariants. In contrast, IGHV2-5*01 antibodies using the N56 allele show increased binding resistance to the K444T mutation on RBD.DiscussionThis study provides valuable insights into humoral immune responses from the perspective of Ig alleles and population genetics. These findings underscore the importance of Ig alleles in vaccine design and therapeutic antibody development