Multispecies Ion Acceleration in 3D Magnetic Reconnection with Hybrid-Kinetic Simulations.

Magnetic reconnection drives multispecies particle acceleration broadly in space and astrophysics. We perform the first 3D hybrid simulations (fluid electrons, kinetic ions) that contain sufficient scale separation to produce nonthermal heavy-ion acceleration, with fragmented flux ropes critical for accelerating all species. We demonstrate the acceleration of all ion species (up to Fe) into power-law spectra with similar indices, by a common Fermi acceleration mechanism. The upstream ion velocities influence the first Fermi reflection for injection. The subsequent onsets of Fermi acceleration are delayed for ions with lower charge-mass ratios (Q/M), until growing flux ropes magnetize them. This leads to a species-dependent maximum energy/nucleon ∝(Q/M)^{α}. These findings are consistent with in situ observations in reconnection regions, suggesting Fermi acceleration as the dominant multispecies ion acceleration mechanism.

Nutritional Assessment of Plant-Based Meat Products Available on Hong Kong Market: A Cross-Sectional Survey.

BACKGROUND: Plant-based meat (PBM) takes up ever-increasing market shares and draws great attention from both customers and retailers these days. However, little is known about the nutritional quality of PBM products. OBJECTIVE: This study intended to profile and evaluate the overview nutrition of PBM with equivalent meat products on the Hong Kong market. METHODS: We conducted a cross-sectional survey of 274 PBM and 151 meat products from 27 different brands on the Hong Kong market in October 2022. The nutritional differences between PBM and meat products were assessed using analysis of covariance (ANCOVA) and two independent sample t-test. The nutritional quality of PBMs was evaluated according to nutrient reference value, front-of-package (FoP) criteria and nutritional score. RESULTS: PBM had relatively lower energy density, total fat, saturated fat, protein, and salt compared to meat. According to the FoP criteria, 91.36%, 17.88%, and 99.34% of PBMs were labeled as medium to high in fat, salt, and sugar, respectively. Through ingredient analysis of 81 PBM products, soy and canola were the main source of protein and fat. CONCLUSIONS: PBM products have a roughly better nutrient quality compared to muscle-based meat, though there is still potential for further refinement in terms of production, consumption, and regulation.

PDC-Net: parallel dilated convolutional network with channel attention mechanism for pituitary adenoma segmentation.

Accurate segmentation of the medical image is the basis and premise of intelligent diagnosis and treatment, which has a wide range of clinical application value. However, the robustness and effectiveness of medical image segmentation algorithms remains a challenging subject due to the unbalanced categories, blurred boundaries, highly variable anatomical structures and lack of training samples. For this reason, we present a parallel dilated convolutional network (PDC-Net) to address the pituitary adenoma segmentation in magnetic resonance imaging images. Firstly, the standard convolution block in U-Net is replaced by a basic convolution operation and a parallel dilated convolutional module (PDCM), to extract the multi-level feature information of different dilations. Furthermore, the channel attention mechanism (CAM) is integrated to enhance the ability of the network to distinguish between lesions and non-lesions in pituitary adenoma. Then, we introduce residual connections at each layer of the encoder-decoder, which can solve the problem of gradient disappearance and network performance degradation caused by network deepening. Finally, we employ the dice loss to deal with the class imbalance problem in samples. By testing on the self-established patient dataset from Quzhou People's Hospital, the experiment achieves 90.92% of Sensitivity, 99.68% of Specificity, 88.45% of Dice value and 79.43% of Intersection over Union (IoU).

BiDFDC-Net: a dense connection network based on bi-directional feedback for skin image segmentation.

Accurate segmentation of skin lesions in dermoscopic images plays an important role in improving the survival rate of patients. However, due to the blurred boundaries of pigment regions, the diversity of lesion features, and the mutations and metastases of diseased cells, the effectiveness and robustness of skin image segmentation algorithms are still a challenging subject. For this reason, we proposed a bi-directional feedback dense connection network framework (called BiDFDC-Net), which can perform skin lesions accurately. Firstly, under the framework of U-Net, we integrated the edge modules into each layer of the encoder which can solve the problem of gradient vanishing and network information loss caused by network deepening. Then, each layer of our model takes input from the previous layer and passes its feature map to the densely connected network of subsequent layers to achieve information interaction and enhance feature propagation and reuse. Finally, in the decoder stage, a two-branch module was used to feed the dense feedback branch and the ordinary feedback branch back to the same layer of coding, to realize the fusion of multi-scale features and multi-level context information. By testing on the two datasets of ISIC-2018 and PH2, the accuracy on the two datasets was given by 93.51% and 94.58%, respectively.

Group theoretic particle swarm optimization for gray-level medical image enhancement.

As a principal category in the promising field of medical image processing, medical image enhancement has a powerful influence on the intermedia features and final results of the computer aided diagnosis (CAD) system by increasing the capacity to transfer the image information in the optimal form. The enhanced region of interest (ROI) would contribute to the early diagnosis and the survival rate of patients. Meanwhile, the enhancement schema can be treated as the optimization approach of image grayscale values, and metaheuristics are adopted popularly as the mainstream technologies for medical image enhancement. In this study, we propose an innovative metaheuristic algorithm named group theoretic particle swarm optimization (GT-PSO) to tackle the optimization problem of image enhancement. Based on the mathematical foundation of symmetric group theory, GT-PSO comprises particle encoding, solution landscape, neighborhood movement and swarm topology. The corresponding search paradigm takes place simultaneously under the guidance of hierarchical operations and random components, and it could optimize the hybrid fitness function of multiple measurements of medical images and improve the contrast of intensity distribution. The numerical results generated from the comparative experiments show that the proposed GT-PSO has outperformed most other methods on the real-world dataset. The implication also indicates that it would balance both global and local intensity transformations during the enhancement process.

Improved U-Net based on cross-layer connection for pituitary adenoma MRI image segmentation.

Pituitary adenoma is a common neuroendocrine neoplasm, and most of its MR images are characterized by blurred edges, high noise and similar to surrounding normal tissues. Therefore, it is extremely difficult to accurately locate and outline the lesion of pituitary adenoma. To sovle these limitations, we design a novel deep learning framework for pituitary adenoma MRI image segmentation. Under the framework of U-Net, a newly cross-layer connection is introduced to capture richer multi-scale features and contextual information. At the same time, full-scale skip structure can reasonably utilize the above information obtained by different layers. In addition, an improved inception-dense block is designed to replace the classical convolution layer, which can enlarge the effectiveness of the receiving field and increase the depth of our network. Finally, a novel loss function based on binary cross-entropy and Jaccard losses is utilized to eliminate the problem of small samples and unbalanced data. The sample data were collected from 30 patients in Quzhou People's Hospital, with a total of 500 lesion images. Experimental results show that although the amount of patient sample is small, the proposed method has better performance in pituitary adenoma image compared with existing algorithms, and its Dice, Intersection over Union (IoU), Matthews correlation coefficient (Mcc) and precision reach 88.87, 80.67, 88.91 and 97.63%, respectively.

Modified UNet++ with atrous spatial pyramid pooling for blood cell image segmentation.

Blood cell image segmentation is an important part of the field of computer-aided diagnosis. However, due to the low contrast, large differences in cell morphology and the scarcity of labeled images, the segmentation performance of cells cannot meet the requirements of an actual diagnosis. To address the above limitations, we present a deep learning-based approach to study cell segmentation on pathological images. Specifically, the algorithm selects UNet++ as the backbone network to extract multi-scale features. Then, the skip connection is redesigned to improve the degradation problem and reduce the computational complexity. In addition, the atrous spatial pyramid pooling (ASSP) is introduced to obtain cell image information features from each layer through different receptive domains. Finally, the multi-sided output fusion (MSOF) strategy is utilized to fuse the features of different semantic levels, so as to improve the accuracy of target segmentation. Experimental results on blood cell images for segmentation and classification (BCISC) dataset show that the proposed method has significant improvement in Matthew's correlation coefficient (Mcc), Dice and Jaccard values, which are better than the classical semantic segmentation network.

Effects of Enzymatic Cottonseed Protein Concentrate as a Feed Protein Source on the Growth, Plasma Parameters, Liver Antioxidant Capacity and Immune Status of Largemouth Bass (Micropterus salmoides).

This study appraised the impact of enzymatic cottonseed protein concentrate (ECP) as a fish meal (FM) substitute on the growth and health of largemouth bass (Micropterus salmoides) (initial weight 14.99 ± 0.03 g). Five diets with equal nitrogen, fat, and energy were designed to replace 0%, 7.78%, 15.56%, 23.33%, and 31.11% FM by adding 0%, 3.6%, 7.2%, 10.8%, and 14.4% ECP, named ECP0, ECP3.6, ECP7.2, ECP10.8, and ECP14.4, respectively. We fed 300 fish with five experimental diets for 60 days. The results revealed that weight gain rate (WGR) and specific growth rate (SGR) did not notably reduce until the addition of ECP exceeded 7.2%. The proximate composition of fish was not affected by the amount of ECP added in diets. Plasma total protein (TP), albumin (ALB), and high-density lipoprotein (HDL) concentrations increased with the increase of ECP dosage, while the triglyceride (TG) and low-density lipoprotein (LDL) concentrations and alkaline phosphatase (ALP) activity showed an opposite trend. For hepatic antioxidant capacity, the hepatic total superoxide dismutase (T-SOD) and catalase (CAT) activities, glutathione (GSH) content, and the expression of nuclear factor erythroid 2-related factor 2 (Nrf2), superoxide dismutase (SOD), and CAT were increased by ECP, while the hepatic malondialdehyde (MDA) content and the expression of kelch-like-ECH-associated protein 1 (Keap1) were decreased. With regard to inflammation, the expression of nuclear factor-kappa B (NF-κB), interleukin-8 (IL-8), and tumor necrosis factor-α (TNF-α) were inhibited by ECP. In summary, the amount of ECP added to diet can reach 7.2% to replace 15.56% FM without hampering the growth of largemouth bass, and ECP can improve the antioxidant and immune capacity.

Application of Logistic Regression and Decision Tree Models in the Prediction of Activities of Daily Living in Patients with Stroke.

An improvement in the activities of daily living (ADLs) is significantly related to the quality of life and prognoses of patients with stroke. However, the factors predicting significant improvement in ADL (SI-ADL) have not yet been clarified. Therefore, we sought to identify the key factors affecting SI-ADL in patients with stroke after rehabilitation therapy using both logistic regression modeling and decision tree modeling. We retrospectively collected and analyzed the clinical data of 190 patients with stroke who underwent rehabilitation therapy at our hospital between January 2020 and July 2020. General and rehabilitation therapy data were extracted, and the Barthel index (BI) score was used for outcome assessment. We defined SI-ADL as an improvement in the BI score by 15 points or more during hospitalization. Logistic regression and decision tree models were established to explore the SI-ADL predictors. We then used receiver operating characteristic (ROC) curves to compare the logistic regression and decision tree models. Univariate analysis revealed that compared with the non-SI-ADL group, the SI-ADL group showed a significantly shorter course of stroke, longer hospital stay, and higher rate of receiving occupational and speech therapies (all P < 0.05). Binary logistic regression analysis revealed the course of stroke at admission (odds ratio (OR) = 0.986, 95%confidence interval (CI) = 0.979-0.993; P < 0.001) and the length of hospital stay (OR = 1.030, 95%CI = 1.013-1.047; P =0.001) as the independent predictors of SI-ADL. ROC comparisons revealed no significant differences in the areas under the curves for the logistic regression and decision tree models (0.808 vs. 0.831; z = 0.977, P = 0.329). Both models identified the course of disease at admission and the length of hospital stay as key factors affecting SI-ADL. Early initiation of rehabilitation therapy is of immense importance for improving the ADLs in patients with stroke.

Effects of replacing fishmeal with methanotroph (Methylococcus capsulatus, Bath) bacteria meal (FeedKind®) on growth and intestinal health status of juvenile largemouth bass (Micropterus salmoides).

A ten-week feeding trial evaluated the feasibility of methanotroph (Methylococcus capsulatus) bacteria meal (FeedKind®, FK) as a fishmeal substitute in largemouth bass (Micropterus salmoides) diets. Six isonitrogenous and isoenergetic diets with different inclusion levels of FK (0 (fishmeal group), 43, 86, 129, 172 and 215 g/kg) were formulated to replace 0, 50, 100, 150, 200 and 250 g/kg fishmeal, respectively. The results showed that FK inclusion level could reach 129 g/kg without significantly affecting growth or feed coefficient rate (P > 0.05), while growth performance was decreased and feed coefficient rate increased when FK inclusion levels exceeded 129 g/kg (P < 0.05). Increase in FK inclusion levels tended to reduce plasma total cholesterol and total triglyceride whilst plasma total protein, albumin, alanine aminotransferase and aspartate aminotransferase in FK treatment groups were unchanged compared with fishmeal group (P > 0.05). FK inclusion levels at 43 g/kg and 86 g/kg were not detrimental to intestinal morphology whilst it was unfavourable when FK inclusion levels exceeded 86 g/kg as the total length of intestinal wall thickness and villus height, villus height were obviously decreased compared with fishmeal group (P < 0.05). As regards to inflammatory cytokine genes, FK instead of fishmeal increased the expression levels of TLR2, RelA, TNF-α, IL-1ß, IL-10 and TGF-ß, 43 g/kg and 86 g/kg FK decreased the expression level of Caspase-3 (P < 0.05). In conclusion, 129 g/kg FK can replace 150 g/kg fishmeal without negative effects on the growth performance, and replacing 100 g/kg fishmeal with 86 g/kg FK is more beneficial to intestinal health.

Properties and Acceleration Mechanisms of Electrons Up To 200 keV Associated With a Flux Rope Pair and Reconnection X-Lines Around It in Earth's Plasma Sheet.

The properties and acceleration mechanisms of electrons (<200 keV) associated with a pair of tailward traveling flux ropes and accompanied reconnection X-lines in Earth's plasma sheet are investigated with MMS measurements. Energetic electrons are enhanced on both boundaries and core of the flux ropes. The power-law spectra of energetic electrons near the X-lines and in flux ropes are harder than those on flux rope boundaries. Theoretical calculations show that the highest energy of adiabatic electrons is a few keV around the X-lines, tens of keV immediately downstream of the X-lines, hundreds of keV on the flux rope boundaries, and a few MeV in the flux rope cores. The X-lines cause strong energy dissipation, which may generate the energetic electron beams around them. The enhanced electron parallel temperature can be caused by the curvature-driven Fermi acceleration and the parallel electric potential. Betatron acceleration due to the magnetic field compression is strong on flux rope boundaries, which enhances energetic electrons in the perpendicular direction. Electrons can be trapped between the flux rope pair due to mirror force and parallel electric potential. Electrostatic structures in the flux rope cores correspond to potential drops up to half of the electron temperature. The energetic electrons and the electron distribution functions in the flux rope cores are suggested to be transported from other dawn-dusk directions, which is a 3-dimensional effect. The acceleration and deceleration of the Betatron and Fermi processes appear alternately indicating that the magnetic field and plasma are turbulent around the flux ropes.

Efficient Nonthermal Ion and Electron Acceleration Enabled by the Flux-Rope Kink Instability in 3D Nonrelativistic Magnetic Reconnection.

The relaxation of field-line tension during magnetic reconnection gives rise to a universal Fermi acceleration process involving the curvature drift of particles. However, the efficiency of this mechanism is limited by the trapping of energetic particles within flux ropes. Using 3D fully kinetic simulations, we demonstrate that the flux-rope kink instability leads to strong field-line chaos in weak-guide-field regimes where the Fermi mechanism is most efficient, thus allowing particles to transport out of flux ropes and undergo further acceleration. As a consequence, both ions and electrons develop clear power-law energy spectra that contain a significant fraction of the released energy. The low-energy bounds are determined by the injection physics, while the high-energy cutoffs are limited only by the system size. These results have strong relevance to observations of nonthermal particle acceleration in space and astrophysics.