Search

This work aims to develop an experimental investigation into the effectiveness of the sliding-mode approach for hybrid vibration-based energy harvesting. A proposed sliding-mode triboelectric-electromagnetic-piezoelectric energy harvesting model involves a cantilever beam with a tip mass exposed to magnetic and frictional forces. The experimental findings indicate that the system can achieve its peak inter-well oscillation output within a low-frequency range of 4Hz–6Hz. Friction has a lesser impact on the open-circuit voltage output at an excitation acceleration of 1.5g compared with 1g. The distribution of tri-stability changes with the presence of friction. This model provides a deeper understanding of the influence of the dry friction coefficient (0.2–0.5) on the interactive behaviors of different generator units. [ABSTRACT FROM AUTHOR]

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings. [ABSTRACT FROM AUTHOR]

Low-field portable magnetic resonance imaging (MRI) scanners are more accessible, cost-effective, sustainable with lower carbon emissions than superconducting high-field MRI scanners. However, the images produced have relatively poor image quality, lower signal-to-noise ratio, and limited spatial resolution. This study develops and investigates an image-to-image translation deep learning model, LoHiResGAN, to enhance the quality of low-field (64mT) MRI scans and generate synthetic high-field (3T) MRI scans. We employed a paired dataset comprising T1- and T2-weighted MRI sequences from the 64mT and 3T and compared the performance of the LoHiResGAN model with other state-of-the-art models, including GANs, CycleGAN, U-Net, and cGAN. Our proposed method demonstrates superior performance in terms of image quality metrics, such as normalized root-mean-squared error, structural similarity index measure, peak signal-to-noise ratio, and perception-based image quality evaluator. Additionally, we evaluated the accuracy of brain morphometry measurements for 33 brain regions across the original 3T, 64mT, and synthetic 3T images. The results indicate that the synthetic 3T images created using our proposed LoHiResGAN model significantly improve the image quality of low-field MRI data compared to other methods (GANs, CycleGAN, U-Net, cGAN) and provide more consistent brain morphometry measurements across various brain regions in reference to 3T. Synthetic images generated by our method demonstrated high quality both quantitatively and qualitatively. However, additional research, involving diverse datasets and clinical validation, is necessary to fully understand its applicability for clinical diagnostics, especially in settings where high-field MRI scanners are less accessible. [ABSTRACT FROM AUTHOR]

Constrained multi-objective optimization problems (CMOPs) exist widely in the real world, which simultaneously contain multiple constraints to be satisfied and multiple conflicting objectives to be optimized. Therefore, the challage in addressing CMOPs is how to better balance constraints and objectives. To remedy this issue, this paper proposes a novel dual-population based constrained multi-objective evolutionary algorithm to solve CMOPs, in which two populations with different functions are employed. Specifically, the main population considers both objectives and constraints for solving the original CMOPs, while the auxiliary population is used only for optimization of objectives without considering constraints. In addition, a dynamic population size reducing mechanism is proposed, which is used to adjust the size of the auxiliary population, so as to reduce the consumption of computing resoruces in the later stage. Moreover, an independent external archive is set to store feasible solutions found by the auxiliary population, so as to provide high-quality feasible solutions for the main population. The experimental results on 55 benchmark functions show that the proposed algorithm exhibits superior or at least competitive performance compared to other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Hepatocytes function largely through the secretion of proteins that regulate cell proliferation, metabolism, and intercellular communications. During the progression of hepatocellular carcinoma (HCC), the hepatocyte secretome changes dynamically as both a consequence and a causative factor in tumorigenesis, although the full scope of secreted protein function in this process remains unclear. Here, we show that the secreted pseudo serine protease PRSS35 functions as a tumor suppressor in HCC. Mechanistically, we demonstrate that active PRSS35 is processed via cleavage by proprotein convertases. Active PRSS35 then suppresses protein levels of CXCL2 through targeted cleavage of tandem lysine (KK) recognition motif. Consequently, CXCL2 degradation attenuates neutrophil recruitment to tumors and formation of neutrophil extracellular traps, ultimately suppressing HCC progression. These findings expand our understanding of the hepatocyte secretome's role in cancer development while providing a basis for the clinical translation of PRRS35 as a therapeutic target or diagnostic biomarker. The secretome of hepatocytes and hepatocellular carcinoma (HCC) cells can contribute to cancer progression. Here the authors show that PRSS35 inhibits HCC progression through proteolytic depletion of CXCL2 and subsequently decreased neutrophil recruitment to tumours. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging. [ABSTRACT FROM AUTHOR]

Transplantation of oligodendrocyte progenitor cell (OPC) at the injury site is being developed as a potential therapeutic strategy for promoting remyelination and locomotor function recovery after spinal cord injury (SCI). To this end, the development of expandable and functional human OPCs is crucial for testing their efficacy in SCI. In mice and rats, the endocannabinoid signaling system is crucial for the survival, differentiation, and maturation of OPCs. Similar studies in humans are lacking currently. Endocannabinoids and exogenous cannabinoids exert their effects mainly via cannabinoid receptors (CB1R and CB2R). We demonstrated that these receptors were differentially expressed in iPSC-derived human NSCs and OPCs, and they could be activated by WIN55212-2 (WIN), a potent CB1R/CB2R agonist to upregulate the endocannabinoid signaling during glial induction. WIN primed NSCs generated more OLIG2 + glial progenitors and migratory PDGFRα + OPC in a CB1/CB2 dependent manner compared to unprimed NSCs. Furthermore, WIN-induced OPCs (WIN-OPCs) robustly differentiated into functional oligodendrocytes and myelinate in vitro and in vivo in a mouse spinal cord injury model. RNA-Seq revealed that WIN upregulated the biological process of positive regulation of oligodendrocyte differentiation. Mechanistically, WIN could act as a partial smoothed (SMO) inhibitor or activate CB1/CB2 to form heteromeric complexes with SMO leading to the inhibition of GLI1 in the Sonic hedgehog pathway. The partial and temporal inhibition of GLI1 during glial induction is shown to promote OPCs that differentiate faster than control's. Thus, CB1R/CB2R activation results in more efficient generation of OPCs that can mature and efficiently myelinate. [ABSTRACT FROM AUTHOR]

Parcellation of whole brain tractograms is a critical step to study brain white matter structures and connectivity patterns. The existing methods based on supervised classification of streamlines into predefined streamline bundle types are not designed to explore sub-bundle structures, and methods with manually designed features are expensive to compute streamline-wise similarities. To resolve these issues, we propose a novel atlas-free method that learns a latent space using a deep recurrent auto-encoder trained in an unsupervised manner. The method efficiently embeds any length of streamlines to fixed-size feature vectors, named streamline embedding, for tractogram parcellation using non-parametric clustering in the latent space. The method was evaluated on the ISMRM 2015 tractography challenge dataset with discrimination of major bundles using clustering algorithms and streamline querying based on similarity, as well as real tractograms of 102 subjects Human Connectome Project. The learnt latent streamline and bundle representations open the possibility of quantitative studies of arbitrary granularity of sub-bundle structures using generic data mining techniques. [ABSTRACT FROM AUTHOR]

Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement. A brief introduction to conventional image processing techniques in PET is firstly presented. We then review methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularisation or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also reviewed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are discussed and future research directions to address these challenges are presented. [ABSTRACT FROM AUTHOR]

Copyright of Acta Mechanica Sinica is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Understanding how the living human brain functions requires sophisticated in vivo neuroimaging technologies to characterise the complexity of neuroanatomy, neural function, and brain metabolism. Fluorodeoxyglucose positron emission tomography (FDG-PET) studies of human brain function have historically been limited in their capacity to measure dynamic neural activity. Simultaneous [18 F]-FDG-PET and functional magnetic resonance imaging (fMRI) with FDG infusion protocols enable examination of dynamic changes in cerebral glucose metabolism simultaneously with dynamic changes in blood oxygenation. The Monash vis-fPET-fMRI dataset is a simultaneously acquired FDG-fPET/BOLD-fMRI dataset acquired from n = 10 healthy adults (18–49 yrs) whilst they viewed a flickering checkerboard task. The dataset contains both raw (unprocessed) images and source data organized according to the BIDS specification. The source data includes PET listmode, normalization, sinogram and physiology data. Here, the technical feasibility of using opensource frameworks to reconstruct the PET listmode data is demonstrated. The dataset has significant re-use value for the development of new processing pipelines, signal optimisation methods, and to formulate new hypotheses concerning the relationship between neuronal glucose uptake and cerebral haemodynamics. Measurement(s) brain activity Technology Type(s) functional magnetic resonance imaging • FDG-Positron Emission Tomography Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.14977851 [ABSTRACT FROM AUTHOR]

Purpose: Estimation of accurate attenuation maps for whole-body positron emission tomography (PET) imaging in simultaneous PET-MRI systems is a challenging problem as it affects the quantitative nature of the modality. In this study, we aimed to improve the accuracy of estimated attenuation maps from MRI Dixon contrast images by training an augmented generative adversarial network (GANs) in a supervised manner. We augmented the GANs by perturbing the non-linear deformation field during image registration between MRI and the ground truth CT images. Methods: We acquired the CT and the corresponding PET-MR images for a cohort of 28 prostate cancer patients. Data from 18 patients (2160 slices and later augmented to 270,000 slices) was used for training the GANs and others for validation. We calculated the error in bone and soft tissue regions for the AC μ-maps and the reconstructed PET images. Results: For quantitative analysis, we use the average relative absolute errors and validate the proposed technique on 10 patients. The DL-based MR methods generated the pseudo-CT AC μ-maps with an accuracy of 4.5% more than standard MR-based techniques. Particularly, the proposed method demonstrates improved accuracy in the pelvic regions without affecting the uptake values. The lowest error of the AC μ-map in the pelvic region was 1.9% for μ-mapGAN + aug compared with 6.4% for μ-mapdixon, 5.9% for μ-mapdixon + bone, 2.1% for μ-mapU-Net and 2.0% for μ-mapU-Net + aug. For the reconstructed PET images, the lowest error was 2.2% for PETGAN + aug compared with 10.3% for PETdixon, 8.7% for PETdixon + bone, 2.6% for PETU-Net and 2.4% for PETU-Net + aug.. Conclusion: The proposed technique to augment the training datasets for training of the GAN results in improved accuracy of the estimated μ-map and consequently the PET quantification compared to the state of the art. [ABSTRACT FROM AUTHOR]

Mastering the "arcana of neuroimaging analysis", the obscure knowledge required to apply an appropriate combination of software tools and parameters to analyse a given neuroimaging dataset, is a time consuming process. Therefore, it is not typically feasible to invest the additional effort required generalise workflow implementations to accommodate for the various acquisition parameters, data storage conventions and computing environments in use at different research sites, limiting the reusability of published workflows. We present a novel software framework, Abstraction of Repository-Centric ANAlysis (Arcana), which enables the development of complex, "end-to-end" workflows that are adaptable to new analyses and portable to a wide range of computing infrastructures. Analysis templates for specific image types (e.g. MRI contrast) are implemented as Python classes, which define a range of potential derivatives and analysis methods. Arcana retrieves data from imaging repositories, which can be BIDS datasets, XNAT instances or plain directories, and stores selected derivatives and associated provenance back into a repository for reuse by subsequent analyses. Workflows are constructed using Nipype and can be executed on local workstations or in high performance computing environments. Generic analysis methods can be consolidated within common base classes to facilitate code-reuse and collaborative development, which can be specialised for study-specific requirements via class inheritance. Arcana provides a framework in which to develop unified neuroimaging workflows that can be reused across a wide range of research studies and sites. [ABSTRACT FROM AUTHOR]

Objective: To aim of this study was to determine the clinical and biological prognostic factors for locoregional recurrence (LRR) in patients with thoracic esophageal squamous cell carcinoma (ESCC) undergoing radical two-field lymph node dissection (2FLD). Methods: A total of 462 patients diagnosed with thoracic ESCC underwent radical esophagectomy between March 2001 and May 2010 at Sun Yat-Sen University Cancer Center. Clinical characteristics, CD44 expression, and tumor-infiltrating lymphocyte (TIL) levels were evaluated in 198 patients who underwent R0 dissection with long-term follow-up. Partial Cox regression analysis with leave-one-out cross-validation was performed to validate the selected risk factors. Results: With a median follow-up of 54 months, the 5-year local failure-free survival (LFFS) rate of 198 patients was 62.5%. Multivariate analysis revealed that T stage ( p = 0.043), pathological positive tumor above the carina ( p = 0.000), CD44 expression level ( p = 0.045) and TIL level ( p = 0.007) were prognostic factors for LFFS, while the Cox model with risk scores had an area under the curve value of 83.6% for the prediction of 5-year LFFS. The best cut-off value (sum score = 11.19) was used to determine the high- and low-risk groups, with patients at high risk having a significantly shorter 5-year LFFS than patients at low risk ( p = 0.000). The LRR pattern revealed significantly high incidences of recurrent disease at the supraclavicular and cervical sites, mediastinum (above the carina), and anastomosis. Conclusions: Our predictive model was able to distinguish between patients at high risk for LRR and patients at low risk for LRR. LRR primarily involved the upper thorax and this area must be considered in future study designs for radical trimodality treatment. [ABSTRACT FROM AUTHOR]

Background: MTBH, a novel hesperetin derivative, possesses in vivo hepatoprotective effects against carbon tetrachloride (CCl)-induced acute liver injury in Institute of Cancer Research (ICR) mice. Objectives: This study investigated the pharmacokinetics and tissue distribution of MTBH and its conjugated metabolites in rats after a single dose of MTBH. Methods: Male Sprague-Dawley (SD) rats were orally administered (25, 50, 100 mg/kg) or intravenously administered (25 mg/kg) MTBH and blood samples were withdrawn at specific times. Moreover, after a single oral dose of MTBH (200 mg/kg), tissues (heart, liver, spleen, lung, kidney, stomach, intestine, brain and muscle) were collected at scheduled time points. Results: The concentration of MTBH in plasma and tissues was assayed by HPLC before and after hydrolysis with β-glucuronidase or sulfatase. The glucuronides/sulfates were extensively present in the plasma, moreover, the free form was detectable in the plasma, but in a small amount equivalent to nearly 0.85-1.46 % of the amount of glucuronides/sulfates, the absolute bioavailability of MTBH was approximately 31.27 %. In tissues, the free form appeared in all tissues examined, with trace amount in brain and muscle, and considerable concentration in stomach and lung. Glucuronides/sulfates were the major forms in intestine, kidney and liver, whereas not detectable in heart, brain and muscle. The liver and intestine were found likely to accumulate MTBH at a high concentration among all tissues. Conclusions: The free form of MTBH was present in the circulation and all assayed organs, whereas its glucuronides/sulfates were the major forms in plasma and intestine, kidney and liver after a single dose. [ABSTRACT FROM AUTHOR]

Simultaneous [18 F]-fluorodeoxyglucose positron emission tomography and functional magnetic resonance imaging (FDG-PET/fMRI) provides the capability to image two sources of energetic dynamics in the brain – cerebral glucose uptake and the cerebrovascular haemodynamic response. Resting-state fMRI connectivity has been enormously useful for characterising interactions between distributed brain regions in humans. Metabolic connectivity has recently emerged as a complementary measure to investigate brain network dynamics. Functional PET (fPET) is a new approach for measuring FDG uptake with high temporal resolution and has recently shown promise for assessing the dynamics of neural metabolism. Simultaneous fMRI/fPET is a relatively new hybrid imaging modality, with only a few biomedical imaging research facilities able to acquire FDG PET and BOLD fMRI data simultaneously. We present data for n = 27 healthy young adults (18–20 yrs) who underwent a 95-min simultaneous fMRI/fPET scan while resting with their eyes open. This dataset provides significant re-use value to understand the neural dynamics of glucose metabolism and the haemodynamic response, the synchrony, and interaction between these measures, and the development of new single- and multi-modality image preparation and analysis procedures. Measurement(s) glucose import • haemodynamic response • functional brain measurement Technology Type(s) FDG-Positron Emission Tomography • Blood Oxygen Level-Dependent Functional MRI Factor Type(s) resting human brain Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12994508 [ABSTRACT FROM AUTHOR]

The article explores the therapeutic use of macrophage migration inhibitory factor (MIF) in rheumatoid arthritis (RA). It reports that MIF regulates inflammation of joints and acts as upstream mediator of innate immune response. It also describes the signal transduction pathways of inflammatory cells in RA synovial fluid activated by MIF.

Increased expression of microRNA-323-3p (miR-323-3p) has been demonstrated in rheumatoid arthritis synovial fibroblast. The gene encoding miR-323-3p, which is a biomarker in immune and inflammatory responses, occurs in an miRNA cluster in chromosomal region 14q32.31. [ABSTRACT FROM AUTHOR]