MorphGI: A Self-Propelling Soft Robotic Endoscope Through Morphing Shape.

Colonoscopy is currently the best method for detecting bowel cancer, but fundamental design and construction have not changed significantly in decades. Conventional colonoscope (CC) is difficult to maneuver and can lead to pain with a risk of damaging the bowel due to its rigidity. We present the MorphGI, a robotic endoscope system that is self-propelling and made of soft material, thus easy to operate and inherently safe to patient. After verifying kinematic control of the distal bending segment, the system was evaluated in: a benchtop colon simulator, using multiple colon configurations; a colon simulator with force sensors; and surgically removed pig colon tissue. In the colon simulator, the MorphGI completed a colonoscopy in an average of 10.84 min. The MorphGI showed an average of 77% and 62% reduction in peak forces compared to a CC in high- and low-stiffness modes, respectively. Self-propulsion was demonstrated in the excised tissue test but not in the live pig test, due to anatomical differences between pig and human colons. This work demonstrates the core features of MorphGI.

Positional multi-length and mutual-attention network for epileptic seizure classification.

The automatic classification of epilepsy electroencephalogram (EEG) signals plays a crucial role in diagnosing neurological diseases. Although promising results have been achieved by deep learning methods in this task, capturing the minute abnormal characteristics, contextual information, and long dependencies of EEG signals remains a challenge. To address this challenge, a positional multi-length and mutual-attention (PMM) network is proposed for the automatic classification of epilepsy EEG signals. The PMM network incorporates a positional feature encoding process that extracts minute abnormal characteristics from the EEG signal and utilizes a multi-length feature learning process with a hierarchy residual dilated LSTM (RDLSTM) to capture long contextual dependencies. Furthermore, a mutual-attention feature reinforcement process is employed to learn the global and relative feature dependencies and enhance the discriminative abilities of the network. To validate the effectiveness PMM network, we conduct extensive experiments on the public dataset and the experimental results demonstrate the superior performance of the PMM network compared to state-of-the-art methods.

Oxidation of selected fluoroquinolones by ferrate(VI) in water: Kinetics, mechanism, effects of constituents, and reaction pathways.

In this work, the oxidation of gatifloxacin (GAT), fleroxacin (FLE) and enoxacin (ENO) in aqueous solution by ferrate (Fe(VI)) was systemically investigated. Weak alkaline and high oxidant doses were favorable for the reaction. The pseudosecond-order rate constants were 0.18055, 0.29162, and 0.05476 L/(mg·min), and the activation energies were 25.13, 15.25, and 11.30 kJ/mol at pH = 8.00 and n(Fe(VI)):n(GAT) = 30:1, n(Fe(VI)):n(FLE) = 20:1, n(Fe(VI)):n(ENO) = 40:1 and a temperature of 25 °C. The maximum degradation rates of the GAT, FLE and ENO were 96.72%, 98.48% and 94.12%, respectively, well simulated by Response Surface Methodology. During the oxidation, the contribution of hydroxyl radicals (HO•) varied with time, whereas the final contribution was approximately 20% at 30 min. The removal efficiency was inhibited by anions by less than 10%, and cations by less than 25%, and significantly inhibited by high concentrations of humic acid. Moreover, two or three dominant reaction pathways were predicted, and the ring cleavages of quinolone and piperazine were mainly achieved through decarboxylation, demethlation and hydroxylation, and some pathways ended up with monocyclic chemicals, which were harmless to aquatic animals and plants. Theoretical calculations further proved that the reactions between FeO4- and neutral fluoroquinolone antibiotics were the major reactions. This work illustrates that Fe(VI) can efficiently remove fluoroquinolone antibiotics (FQs) in aqueous environments, and the results may contribute to the treatment of wastewater containing trace antibiotics and Fe(VI) chemistry.

Mechanisms of Gravitational Influence on Weld Pool Behavior and Weld Bead Performance in Variable Polarity Plasma Arc Welding across Different Welding Position.

This article comprehensively explores the cross-scale effects of gravity on macroscopic flow formation and weld bead formation in variable polarity plasma arc welding. Gravity-induced changes in welding direction were achieved through welding at different spatial positions. The properties of the weld bead were investigated at various spatial locations. Additionally, an elemental tracing technique was employed to study the internal flow behavior of molten metal. In the flat welding position, there is an observable trend of increasing grain size in the welded bead, accompanied by a significant expansion of the coarse grain zone. Consequently, the properties of the weld bead in the flat position are inferior to those achieved in the vertical welding position. This phenomenon can be attributed to the accumulation of molten metal at the exit side of the keyhole, resulting in temperature accumulation. Research indicates that the internal flow within the weld pool plays a critical role in causing this phenomenon. The study's findings reveal the presence of two distinct vortex flow patterns within the weld pool: one aligned with the welding direction and the other directed towards the interior of the weld pool. Particularly noteworthy is the substantial expansion of the flow channel area in the flat welding position, which significantly amplifies the impact of internal flow. This enhanced flow intensity inevitably leads to the increased buildup of molten metal at the keyhole exit side. These studies lay the groundwork for achieving high-quality and controllable spatial-position welding.

Therapeutic approach of natural products that treat osteoporosis by targeting epigenetic modulation.

Osteoporosis (OP) is a metabolic disease that affects bone, resulting in a progressive decrease in bone mass, quality, and micro-architectural degeneration. Natural products have become popular for managing OP in recent years due to their minimal adverse side effects and suitability for prolonged use compared to chemically synthesized products. These natural products are known to modulate multiple OP-related gene expressions, making epigenetics an important tool for optimal therapeutic development. In this study, we investigated the role of epigenetics in OP and reviewed existing research on using natural products for OP management. Our analysis identified around twenty natural products involved in epigenetics-based OP modulation, and we discussed potential mechanisms. These findings highlight the clinical significance of natural products and their potential as novel anti-OP therapeutics.

Smooth Tensor Qatar Riyal Decomposition for Dynamic MRI Reconstruction.

Dynamic magnetic resonance imaging (dMRI) speed and imaging quality have always been a crucial issue in medical imaging research. Most existing methods characterize the tensor rank-based minimization to reconstruct dMRI from sampling k- t space data. However, (1) these approaches that unfold the tensor along each dimension destroy the inherent structure of dMR images. (2) they focus on preserving global information only, while ignoring the local details reconstruction such as the spatial piece-wise smoothness and sharp boundaries. To overcome these obstacles, we suggest a novel low-rank tensor decomposition approach by integrating tensor Qatar Riyal (QR) decomposition, low-rank tensor nuclear norm, and asymmetric total variation to reconstruct dMRI, named TQRTV. Specifically, while preserving the tensor inherent structure by utilizing tensor nuclear norm minimization to approximate tensor rank, QR decomposition reduces the dimensions in the low-rank constraint term, thereby improving the reconstruction performance. TQRTV further exploits the asymmetric total variation regularizer to capture local details. Numerical experiments demonstrate that the proposed reconstruction approach is superior to the existing ones.

Domain-Aware Dual Attention for Generalized Medical Image Segmentation on Unseen Domains.

Recently, there has been significant progress in medical image segmentation utilizing deep learning techniques. However, these achievements largely rely on the supposition that the source and target domain data are identically distributed, and the direct application of related methods without addressing the distribution shift results in dramatic degradation in realistic clinical environments. Current approaches concerning the distribution shift either require the target domain data in advance for adaptation, or focus only on the distribution shift across domains while ignoring the intra-domain data variation. This paper proposes a domain-aware dual attention network for the generalized medical image segmentation task on unseen target domains. To alleviate the severe distribution shift between the source and target domains, an Extrinsic Attention (EA) module is designed to learn image features with knowledge originating from multi-source domains. Moreover, an Intrinsic Attention (IA) module is also proposed to handle the intra-domain variation by individually modeling the pixel-region relations derived from an image. The EA and IA modules complement each other well in terms of modeling the extrinsic and intrinsic domain relationships, respectively. To validate the model effectiveness, comprehensive experiments are conducted on various benchmark datasets, including the prostate segmentation in magnetic resonance imaging (MRI) scans and the optic cup/disc segmentation in fundus images. The experimental results demonstrate that our proposed model effectively generalizes to unseen domains and exceeds the existing advanced approaches.

Synthetic augmentation for semantic segmentation of class imbalanced biomedical images: A data pair generative adversarial network approach.

In recent years, deep learning (DL) has been recognized very useful in the semantic segmentation of biomedical images. Such an application, however, is significantly hindered by the lack of pixel-wise annotations. In this work, we propose a data pair generative adversarial network (DPGAN) for the purpose of synthesizing concurrently the diverse biomedical images and the segmentation labels from random latent vectors. First, a hierarchical structure is constructed consisting of three variational auto-encoder generative adversarial networks (VAEGANs) with an extra discriminator. Subsequently, to alleviate the influence from the imbalance between lesions and non-lesions areas in biomedical segmentation data sets, we divide the DPGAN into three stages, namely, background stage, mask stage and advanced stage, with each stage deploying a VAEGAN. In such a way, a large number of new segmentation data pairs are generated from random latent vectors and then used to augment the original data sets. Finally, to validate the effectiveness of the proposed DPGAN, experiments are carried out on a vestibular schwannoma data set, a kidney tumor data set and a skin cancer data set. The results indicate that, in comparison to other state-of-the-art GAN-based methods, the proposed DPGAN shows better performance in the generative quality, and meanwhile, gains an effective boost on semantic segmentation of class imbalanced biomedical images.

Deep Scale-Variant Network for Femur Trochanteric Fracture Classification with HP Loss.

Achieving automatic classification of femur trochanteric fracture from the edge computing device is of great importance and value for remote diagnosis and treatment. Nevertheless, designing a highly accurate classification model on 31A1/31A2/31A3 fractures from the X-ray is still limited due to the failure of capturing the scale-variant and contextual information. As a result, this paper proposes a deep scale-variant (DSV) network with a hybrid and progressive (HP) loss function to aggregate more influential representations of the fracture regions. More specifically, the DSV network is based on the ResNet and integrated with the designed scale-variant (SV) layer and HP loss, where the SV layer aims to enhance the representation ability to extract the scale-variant features, and HP loss is intended to force the network to condense more contextual clues. Furthermore, to evaluate the effect of the proposed DSV network, we carry out a series of experiments on the real X-ray images for comparison and evaluation, and the experimental results demonstrate that the proposed DSV network could outperform other classification methods on this classification task.

Cross-Modal Prostate Cancer Segmentation via Self-Attention Distillation.

The automatic and accurate segmentation of the prostate cancer from the multi-modal magnetic resonance images is of prime importance for the disease assessment and follow-up treatment plan. However, how to use the multi-modal image features more efficiently is still a challenging problem in the field of medical image segmentation. In this paper, we develop a cross-modal self-attention distillation network by fully exploiting the encoded information of the intermediate layers from different modalities, and the generated attention maps of different modalities enable the model to transfer significant and discriminative information that contains more details. Moreover, a novel spatial correlated feature fusion module is further employed for learning more complementary correlation and non-linear information of different modality images. We evaluate our model in five-fold cross-validation on 358 MRI images with biopsy confirmed. Without bells and whistles, our proposed network achieves state-of-the-art performance on extensive experiments.

A Deep Segmentation Network of Multi-Scale Feature Fusion Based on Attention Mechanism for IVOCT Lumen Contour.

Recently, coronary heart disease has attracted more and more attention, where segmentation and analysis for vascular lumen contour are helpful for treatment. And intravascular optical coherence tomography (IVOCT) images are used to display lumen shapes in clinic. Thus, an automatic segmentation method for IVOCT lumen contour is necessary to reduce the doctors' workload while ensuring diagnostic accuracy. In this paper, we proposed a deep residual segmentation network of multi-scale feature fusion based on attention mechanism (RSM-Network, Residual Squeezed Multi-Scale Network) to segment the lumen contour in IVOCT images. Firstly, three different data augmentation methods including mirror level turnover, rotation and vertical flip are considered to expand the training set. Then in the proposed RSM-Network, U-Net is contained as the main body, considering its characteristic of accepting input images with any sizes. Meanwhile, the combination of residual network and attention mechanism is applied to improve the ability of global feature extraction and solve the vanishing gradient problem. Moreover, the pyramid feature extraction structure is introduced to enhance the learning ability for multi-scale features. Finally, in order to increase the matching degree between the actual output and expected output, the cross entropy loss function is also used. A series of metrics are presented to evaluate the performance of our proposed network and the experimental results demonstrate that the proposed RSM-Network can learn the contour details better, contributing to strong robustness and accuracy for IVOCT lumen contour segmentation.

MNL-Network: A Multi-Scale Non-local Network for Epilepsy Detection From EEG Signals.

Epilepsy is a prevalent neurological disorder that threatens human health in the world. The most commonly used method to detect epilepsy is using the electroencephalogram (EEG). However, epilepsy detection from the EEG is time-consuming and error-prone work because of the varying levels of experience we find in physicians. To tackle this challenge, in this paper, we propose a multi-scale non-local (MNL) network to achieve automatic EEG signal detection. Our MNL-Network is based on 1D convolution neural network involving two specific layers to improve the classification performance. One layer is named the signal pooling layer which incorporates three different sizes of 1D max-pooling layers to learn the multi-scale features from the EEG signal. The other one is called a multi-scale non-local layer, which calculates the correlation of different multi-scale extracted features and outputs the correlative encoded features to further enhance the classification performance. To evaluate the effectiveness of our model, we conduct experiments on the Bonn dataset. The experimental results demonstrate that our MNL-Network could achieve competitive results in the EEG classification task.

Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts.

Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal motion of electronic components, which reduces the quality and reliability of the images. This paper puts forward a hybrid denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all the denoised subimages to obtain the denoised MR image. The experimental results show that the proposed method has significantly better performance in terms of mean square error and peak signal-to-noise ratio than each method alone.

A Novel Deep Neural Network for Robust Detection of Seizures Using EEG Signals.

The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures. Manual recognition is a time-consuming and laborious process that places a heavy burden on neurologists, and hence, the automatic identification of epilepsy has become an important issue. Traditional EEG recognition models largely depend on artificial experience and are of weak generalization ability. To break these limitations, we propose a novel one-dimensional deep neural network for robust detection of seizures, which composes of three convolutional blocks and three fully connected layers. Thereinto, each convolutional block consists of five types of layers: convolutional layer, batch normalization layer, nonlinear activation layer, dropout layer, and max-pooling layer. Model performance is evaluated on the University of Bonn dataset, which achieves the accuracy of 97.63%∼99.52% in the two-class classification problem, 96.73%∼98.06% in the three-class EEG classification problem, and 93.55% in classifying the complicated five-class problem.

A Dynamic Multi-Scale Network for EEG Signal Classification.

Accurate and automatic classification of the speech imagery electroencephalography (EEG) signals from a Brain-Computer Interface (BCI) system is highly demanded in clinical diagnosis. The key factor in designing an automatic classification system is to extract essential features from the original input; though many methods have achieved great success in this domain, they may fail to process the multi-scale representations from different receptive fields and thus hinder the model from achieving a higher performance. To address this challenge, in this paper, we propose a novel dynamic multi-scale network to achieve the EEG signal classification. The whole classification network is based on ResNet, and the input signal first encodes the features by the Short-time Fourier Transform (STFT); then, to further improve the multi-scale feature extraction ability, we incorporate a dynamic multi-scale (DMS) layer, which allows the network to learn multi-scale features from different receptive fields at a more granular level. To validate the effectiveness of our designed network, we conduct extensive experiments on public dataset III of BCI competition II, and the experimental results demonstrate that our proposed dynamic multi-scale network could achieve promising classification performance in this task.

High Uric Acid Inhibits Cardiomyocyte Viability Through the ERK/P38 Pathway via Oxidative Stress.

BACKGROUND/AIMS: Clinical studies have shown that hyperuricaemia is strongly associated with cardiovascular disease. However, the molecular mechanisms of high uric acid (HUA) associated with cardiovascular disease remain poorly understood. In this study, we investigated the effect of HUA on cardiomyocytes. METHODS: We exposed H9c2 cardiomyocytes to HUA, then cell viability was determined by MTT assay, and reactive oxygen species' (ROS) production was detected by a fluorescence assay. Western blot analysis was used to examine phosphorylation of extracellular signal-regulated kinase (ERK), p38, phosphatidylinositol 3-kinase (PI3K) and Akt. We monitored the impact of HUA on phospho-ERK and phospho-p38 levels in myocardial tissue from an acute hyperuricaemia mouse model established by potassium oxonate treatment. RESULTS: HUA decreased cardiomyocyte viability and increased ROS production in cardiomyocytes; pre-treatment with N-acetyl-L-cysteine, a ROS scavenger, and PD98059, an ERK inhibitor, reversed HUA-inhibited viability of cardiomyocytes. Further examination of signal transduction pathways revealed HUA-induced ROS involved in activating ERK/P38 and inhibiting PI3K/Akt in cardiomyocytes. Furthermore, the acute hyperuricaemic mouse model showed an increased phospho-ERK/p38 level in myocardial tissues. CONCLUSION: HUA induced oxidative damage and inhibited the viability of cardiomyocytes by activating ERK/p38 signalling, for a novel potential mechanism of hyperuricaemic-related cardiovascular disease.

Design of Virtual Fixtures for Robotic Cholecystectomy.

BACKGROUND AND OBJECTIVE: With the ongoing developments in robotic surgery, the associated adverse events need to be carefully evaluated. Virtual fixtures (VFs), a safety design feature against unintended motion during robotic surgery, have been proposed, but the methodology for designing VFs remains experimental. In this study, we propose a novel methodology for designing VFs for robotic cholecystectomy. MATERIALS AND METHODS: Laparoscopic cholecystectomy (LC) was performed in 24 patients with cholecystitis. Active working space (AWS), the distance between instrument heads (DBIH), motion speed of bilateral hands, and instrument heads were calculated and analyzed. RESULTS: DBIH was 14.78 ± 6.94 cm. Diameter of right and left AWS was 15.81 ± 3.69 cm and 15.33 ± 1.52 cm, respectively. DBIH was found to significantly correlate with the surgeon's experience. Bilateral AWS was found to be significantly associated with body circumference at Murphy's point level. However, no association was observed between bilateral AWS and surgeon's experience. CONCLUSIONS: A novel methodology to build VFs for designing VFs for robotic cholecystectomy is established. Surgeon's experience appears to play an important role in determining the DBIH during robotic laparoscopic cholecystectomy, but does not affect bilateral AWS.

Conceptual design of a novel multi-DoF manual instrument for laparoscopic surgery.

BACKGROUND: Laparoscopic surgery is becoming increasingly popular throughout the world. But the conventional instruments used in many surgeries are not flexible enough to be operated. Challenging tasks, such as suturing and knot-tying tasks are difficult to complete using conventional instruments with limited degrees of freedom (DoFs). In the paper, a novel cable-driven multi-DoF manual instrument is presented with a simple structure but strong functionality. METHODS: The proposed instrument has been developed with a wristlike operation end (OE), a wristlike end effector (EE), and the transmission system. It can be operated intuitively. The orientation and the position of the EE are directly controlled by surgeons due to the one-to-one motion mapping structure. The clamp structure and tension device are reasonably designed. The pitch, yaw, and the open and close motion are actuated by cables. Based on the optimization index Global Condition Index (GCI), four cables are used to actuate the pitch and yaw motions, while other two are used for the open and close motion. The layout of the cables is also determined by the GCI. RESULTS: Experiments carried out with a prototype show that tasks such as suturing and knot-tying can be completed comfortably. Due to the intuitive control and multi-DoFs, surgeons can use the prototype to finish the tasks with ease. CONCLUSIONS: The instrument developed herein with intuitive control and dexterity can be used alone or together with a robotic system to accomplish some challenging tasks that are difficult for conventional instruments.

[Comparison of thermogenic character of liver and muscle in Chinese bulbul Pycnonotus sinensis between summer and winter].

Acclimatization to winter conditions is an essential prerequisite for survival of small passerines. Seasonal changes in a bird's physiology and behavior are considered to be part of an adaptive strategy for survival and reproductive success. Changes in photoperiod, ambient temperature and food availability trigger seasonal acclimatization in physiology and behavior of many birds. In the present study, seasonal adjustments in several physiological, hormonal, and biochemical markers were examined in wild-captured Chinese bulbuls (Pycnonotus sinensis) from the Zhejiang Province in China. Oxygen consumption was measured using the closed-circuit respirometer containing 3.6 L animal chambers. State-4 respiration in liver and muscle mitochondria was measured at 30 degree with a Clark electrode. The activities of cytochrome C oxidase (COX) in liver and muscle were measured polarographically at 30 degree using a Clark electrode. The protein content of mitochondria was determined by the Folin phenol method, with bovine serum albumin as standard. In winter sparrows had higher body mass and basal metabolic rate (BMR). The contents of mitochondrial protein in liver, and state-4 respiration and COX activity in liver and muscle increased significantly in winter. Circulating level of serum triiodothyronine (T3) was significantly higher in winter than in summer. Together, these data suggest that Chinese bulbuls mainly coped with cold by enhancing thermogenic capacities through increased activity of respiratory enzymes activities. The results support the view that prominent winter increases in BMR are manifestations of winter acclimatization in Chinese bulbuls and that seasonal variation in metabolism in bulbuls is similar to that in other small wintering birds.