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Bacterioplankton communities are closely related to river alterations and biogeochemical processes in freshwater reservoirs. Affected by reservoir regulation and hydrological variation, regulated rivers can create distinctive patterns in bacterioplankton communities. In this study, we conducted 16S rRNA V4 amplicon high-throughput sequencing to investigate compositional changes in the bacterioplankton community and its environmental responses to damming effects in the Pengxi River, the largest tributary on the northern bank of the Three Gorges Reservoir (TGR). The bacterial community was dominated by the phyla Proteobacteria and Actinobacteria, accounting for 51.0% and 13.1% of all sequences, respectively. Distinct differences in the bacterioplankton community composition were observed between the backwater and riverine areas, and the dominant taxa showed clear shifts at the class level between the upper stream and backwater area sites along the Pengxi River. Conductivity, transparency and temperature were considered the major environmental factors driving the bacterioplankton composition variation. Additionally, the functional profiles of the bacterioplankton community corresponded with compositional changes in reservoir regulation by TGR. Therefore, these results provide insights into dam effects on the bacterioplankton community in the Pengxi River, and further research should focus on the potential ecological processes of river regulation by dams or reservoirs.

The effects of stocking both filter-feeding fish and piscivorous fish were compared to the effects of stocking only filter-feeding fish for suppressing algal blooms and improving water quality in the impoundment area of Xiaojiang River where catfish were dominant. Using only filter-feeding fish for algal suppression and water quality control was more effective in the short-term, but use of both filter-feeding fish and piscivorous fish was better in the long-term. Obvious suppression of phytoplankton biomass (PB) only occurred during the first 14 days regardless of the fish stocked. Adding fish to the enclosure clearly alters phytoplankton community structure and introducing piscivorous fish to an enclosure stocked with filter-feeding fish changed the relative densities of dominant algae species. While stocking filter-feeding fish decreased total nitrogen concentration by removing phytoplankton, it did not effectively decrease total phosphorus and Chlorophyll a concentrations. Introducing piscivorous fish to the enclosure weakened the relationship between nutrients and phytoplankton. Results indicate that stocking only filter-feeding fish to improve water quality and suppress phytoplankton in an impoundment area is insufficient and other technologies and means should be applied simultaneously.

Copper and cobalt, both strategic metals, play an indispensable role in many fields, such as national infrastructure and defense science and technology. Copper, one of the earliest nonferrous metals discovered, extracted, and applied by humans, teams up with cobalt—a silver-white metal—that serves as a crucial raw material for superalloys, permanent magnets, and chemical industries. The production and consumption of these metals significantly affect the development of national infrastructure, essentially controlling the lifeblood of the national economy. The wide-ranging application of copper and cobalt necessitates an efficient extraction process for these resources. For instance, Congo, Africa, hosts complex copper–cobalt co-associated deposits where the copper–cobalt ratio can reach 3∶1, making it a significant resource. Given the increasing global demand for copper and cobalt, extracting these valuable elements from complex copper–cobalt co-associated ores is of paramount importance. This paper reviews the mineralogical characteristics of these complex resources, including oxidized ores and sulfide ores. It compares and discusses the characteristics of extraction processes such as roasting and leaching. Ultimately, it evaluates the prospects and challenges of extracting copper and cobalt from complex co-associated copper–cobalt deposits. The review reveals that copper–cobalt oxide ores are more compatible with pure wet process treatment, while sulfide ores are better suited for an enhanced leaching process combined with roasting and leaching. The thermodynamic characteristics and kinetic limiting factors of metal extraction from copper–cobalt ores are also compared in this review. In the extraction of the copper–cobalt oxide ore, the mature extraction process is reduction leaching, and different reducing agents show different leaching effects. It is worth noting that there are significant differences in the degree of weathering of copper oxide cobalt ores. Considering Africa’s relatively underdeveloped power facilities and the shortage of chemical reagents, biological leaching and heap leaching present alternative methods for copper oxide cobalt ores. For copper and cobalt sulfide ores, the promising approach for industrial application is a method that combines enrichment, roasting, and leaching. As the copper–cobalt sulfide ore is nearly the only cobalt occurrence mineral, strengthening the phase transformation of copper–cobalt sulfide ore is vital for cobalt extraction. This can be optimized from both thermodynamic and kinetic perspectives. The aim of this paper is to provide some references for the efficient and reasonable collaborative extraction and utilization of copper–cobalt resources.

Abstract Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. The ‘CMRxRecon’ dataset contains raw k-space data and auto-calibration lines. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community.

Cavitation generated during injector jetting can significantly affect fuel atomization. Laser-induced cavitation bubble is an important phenomenon in laser induced plasma ignition technology. Limited by the difficulties in experimental measurements, numerical simulations have become an important tool in the study of laser-induced cavitation bubble, but most previous numerical models used to study the dynamics of laser-induced cavitation bubble usually ignore the effect of chemical reactions. In this study, the finite volume method is used to solve the compressible two-dimensional reynolds averaged Navier–Stokes equation by considering the heat and mass transfer as well as the chemical reactions within the cavitation bubble. The effects of overall reaction and elementary reactions on the cavitation bubble are evaluated, respectively. It is found that by additionally considering chemical reactions within the numerical model, lower maximum temperatures and higher maximum pressures are predicted within the bubble. And the generated non-condensable gases produced by the chemical reactions enhance the subsequent expansion process of the cavitation bubble. Besides, the effect of the one-sided wall boundary condition on cavitation bubble is compared with the infinite boundary condition. Influenced by the wall boundary, the cavitation bubble forms a localized high pressure on the side of the bubble away from the wall during the collapse process, which causes the bubble to be compressed into a ”crescent” shape. The maximum pressure and temperature inside the bubble are lower due to localized losses caused by the wall.

Tibetan Plateau is one of the most typical areas of biodiversity in the world because of its unique environmental and regional units, which breed unique biological communities and concentrate on many unique and rare wild animals and plants. Research on Chironomidae in the Tibetan Plateau is relatively weak. At present, the identification of Chironomidae species mainly depends on male adults, while identification of larvae and pupae is relatively difficult and there is less research on them.During the investigations of insect diversity in the Tibetan Plateau, larval and pupal stages of Orthocladius nitidoscutellatus Lundström, 1915 and Psectrocladius nevalis Akhrorov, 1977 were described and illustrated. Matching and identification of larval and pupal stages were based on DNA barcodes. Neighbour-joining trees were reconstructed, based on known Orthocladius and Psectrocladius COI DNA barcodes, respectively.

At present, the proton exchange membrane fuel cell (PEMFC) is one of the most promising new energy solutions. The structure and material properties of the gas diffusion layer (GDL) are important factors that restrict the efficient water management and structural stability of PEMFCs. The complex mesoscopic fibrous porous structure of the GDL results in complex multiphase flow dynamics problems that have highly nonlinear characteristics. It is difficult to describe the details of mesoscopic multiphase coupled transport dynamics and numerically solve problems in which there is a two-phase flow with a high-density ratio. To solve these problems, a mesoscopic multiphase coupled transport model based on a lattice Boltzmann method and volume-of-fluid (LBM-VOF) model is proposed. Moreover, we also discuss the mechanism of the interaction between the fibrous porous structure and internal multiphase flow. The results obtained illustrate that the proposed method can obtain dynamic details of the flow field for fibrous porous media. The surface wettability of fibres strongly influences both the distribution and stability of liquid water clusters within porous materials. Concurrently, the fibre diameter assumes a pivotal role in governing lateral water diffusion and exclusion efficiency. This work lays a foundation for efficient water and thermal management of PEMFCs.

The SICOMINES Cu–Co ore deposit is located in southwest Kolwezi, Congo (Kinshasa), and is a typical deposit in the Katanga Copper Belt in central Africa. Dozens of Cu and Co minerals exist in the deposit as a result of the superposition and transformation of three complex ore-forming stages, including the sediment-hosted, hydrothermal, and oxidation periods; some of these minerals include heterogenite, carrollite, chalcocite, malachite, Co-containing malachite, spherocobaltite, Cu/Co-containing psilomelane, and Co-containing limonite. The mineralogy and processability properties among Co minerals differ considerably. The variability in Co minerals poses substantial challenges in establishing a universal beneficiation or extraction process that can accommodate all geometallurgical variations. The current Co-recovery process integrates flotation and magnetic separation techniques. However, the lack of fundamental knowledge about the spatial distribution of Co minerals and the poor adaptability of current Co-recovery processes to adapt to variable ores contribute to considerable Co losses in mine tailings. The recovery efficiency for Co is generally low, and the operational stability of the process is unstable. To address the issues, this study devised a geometallurgical model of Co in an ore body using Datamine and Leapfrog software for the first time. Initially, historical exploration data were collected, strata and mineralized domain models were developed, and the spatial variation in Co grade was preliminarily obtained. Subsequently, a sampling design was implemented to collect samples for process mineralogical research, effectively representing the Co-grade distribution within the strata and ore bodies. Furthermore, quantitative data of the mineral content and Co-occurrence state for each sample were obtained using a process mineralogical method, and these data were incorporated into the model using interpolation methods such as single-domain assignment and the distance inverse power ratio. As a result, five spatial beneficiation zones were obtained based on the spatial distribution of Co minerals with varying processability properties. These zones were classified as suitable for flotation (TYPE1), suitable for magnetic separation (TYPE2), suitable for combined magnetic separation and flotation (TYPE3), suitable for leaching (TYPE4), and difficult to recover (TYPE5); this classification resulted in the formation of a preliminary geometallurgical model. Finally, comprehensive samples were collected from the five beneficiation zones for the beneficiation experiments. The results revealed that the integrated magnetic separation and flotation process employed in the mine achieved varying Co-recovery efficiencies across the five beneficiation zones. This process proves applicable solely to the spatial domains of TYPE1, TYPE2, and TYPE3. The results also indicated that the classification of beneficiation zones in the geometallurgical model was within reason. Reasonable ore blending, based on the occurrence state of Co and the effective Co grade in the model, contributes to stabilizing current production and enhancing Co recovery. The developed geometallurgy model can be continuously optimized by adding sampling points or mineralogy parameters such as Co mineral particle size, mineral liberation degree, and Co-associated relationship with other minerals. The developed geometallurgy model serves as a valuable guide for the realization of classified mining and separation of Co ores in the SICOMINES mining region and for appropriate management.

As medical imaging technologies advance, these tools are playing a more and more important role in assisting clinical disease diagnosis. The fusion of biomedical imaging and multi-modal information is profound, as it significantly enhances diagnostic precision and comprehensiveness. Integrating multi-organ imaging with genomic information can significantly enhance the accuracy of disease prediction because many diseases involve both environmental and genetic determinants. In the present study, we focused on the fusion of imaging-derived phenotypes (IDPs) and polygenic risk score (PRS) of diseases from different organs including the brain, heart, lung, liver, spleen, pancreas, and kidney for the prediction of the occurrence of nine common diseases, namely atrial fibrillation, heart failure (HF), hypertension, myocardial infarction, asthma, type 2 diabetes, chronic kidney disease, coronary artery disease (CAD), and chronic obstructive pulmonary disease, in the UK Biobank (UKBB) dataset. For each disease, three prediction models were developed utilizing imaging features, genomic data, and a fusion of both, respectively, and their performances were compared. The results indicated that for seven diseases, the model integrating both imaging and genomic data achieved superior predictive performance compared to models that used only imaging features or only genomic data. For instance, the Area Under Curve (AUC) of HF risk prediction was increased from 0.68 ± 0.15 to 0.79 ± 0.12, and the AUC of CAD diagnosis was increased from 0.76 ± 0.05 to 0.81 ± 0.06.

(1) Background: Mitochondrial genomes have been extensively employed as a crucial marker in numerous dipteran families for understanding phylogenetics and systematics relations, thereby playing a pivotal role in molecular biology studies. The phylogenetic relationship of the Harnischia generic complex remains contentious due to the paucity of taxonomic and molecular data. Specifically, the evolutionary relationships among Cryptochironomus, Demicryptochironomus, and Harnischia are still unclear. (2) Methods: In this study, Polypedilum and Endochironomus were used as outgroups to analyze phylogenetic relationships among Cryptochironomus, Demicryptochironomus, and Harnischia, mitogenomes of four Cryptochironomus, two Demicryptochironomus, two Harnischia, and two Cladopelma were newly sequenced. Subsequently, we conducted a thorough analysis of the nucleotide composition, sequence length, and evolutionary rate. (3) Results: All mitogenomes exhibited structural conservation, with all genes consistently arranged in the identical order as that of the ancestral mitogenome. Nucleotide composition varied significantly among different genes, and the control region displayed the highest A + T content. All protein-coding genes undergo rigorous purification selection, with the ATP8 gene exhibiting the most rapid evolutionary rate among them. Utilizing Bayesian Inference (BI) and Maximum Likelihood (ML) methods across various databases, we reconstructed the phylogenetic relationships among the genera within the Harnischia generic complex, drawing insights from an analysis of 14 mitochondrial genomes. (4) Conclusions: Our results showed that the monophyly of the genera Harnischia was well supported in all topologies; Cryptochironomus is sister to Demicryptochironomus.

Background: Bone metastasis is frequently common in advanced lung cancer with the major issue of a pathological fracture. Previous studies suggested that Astragalus membranaceus (Qi) and Ampelopsis japonica (Lian), which are used as folk medicine in China, have potential effects on inhibiting tumor growth and protecting bones, respectively. In this study, an experiment on the inhibitory effect of the Qilian formula (AAF) in vivo was designed to examine tumor growth in bone and osteoclast formation. Materials and Methods: The bone metastasis xenograft models were established by implanting NCI-H460-luc2 lung cancer cells into the right tibiae bones of mice. After confirming the model’s viability through optical imaging 7 days post-implantation, 2 groups, namely the AAF group and the control group, were administered 0.3 mL of AAF extract (9 g/kg/day) or normal saline via intragastric delivery for a duration of 4 weeks. Throughout the study, we longitudinally assessed tumor burden, bone destruction, and weight-bearing capacity in vivo using reporter gene bioluminescence imaging (BLI), micro-CT, and dynamic weight-bearing (DWB) tests. Mechanistic insights were gained through Hematoxylin-eosin (H&E) staining, immunohistochemical (IHC) analysis, western blotting, and flow cytometry. Results: Qilian formula produced significant inhibition to the progress of bone destruction and tumor burden in the right tibiae bone in the treatment group. It was further evidenced by molecular imaging in vivo via small animal micro-CT and BLI with parametric quantification, characterizing significantly lower uptake of BLI signal in the treated tumor lesions and improving the pathological changes in the microstructure of bone. Furthermore, DWB tests revealed that Qilian formula treatment significantly maintained the weight-bearing capacity. According to immunohistochemical analysis, the effect of the Qilian formula appeared to involve the suppression of osteoclast formation by lower expression of the tartrate-resistant acid phosphatase. Cell apoptosis and death induction were evidenced by a higher percentage of Bal2、BAX and caspase 3 expressions of Qilian formula-treated tumor tissues. Conclusions: Our study demonstrated a significant inhibitory effect of the Qilian formula on the progression of osteolytic invasion in vivo by suppressing osteoclastogenesis and promoting apoptotic cell death.

AbstractInhalable messenger RNA (mRNA) has demonstrated great potential in therapy and vaccine development to confront various lung diseases. However, few gene vectors could overcome the airway mucus and intracellular barriers for successful pulmonary mRNA delivery. Apart from the low pulmonary gene delivery efficiency, nonnegligible toxicity is another common problem that impedes the clinical application of many non-viral vectors. PEGylated cationic peptide-based mRNA delivery vector is a prospective approach to enhance the pulmonary delivery efficacy and safety of aerosolized mRNA by oral inhalation administration. In this study, different lengths of hydrophilic PEG chains were covalently linked to an amphiphilic, water-soluble pH-responsive peptide, and the peptide/mRNA nano self-assemblies were characterized by dynamic light scattering (DLS) and transmission electron microscopy (TEM). The in vitro mRNA binding and release, cellular uptake, transfection, and cytotoxicity were studied, and finally, a proper PEGylated peptide with enhanced pulmonary mRNA delivery efficiency and improved safety in mice was identified. These results showed that a proper N-terminus PEGylation strategy using 12-monomer linear monodisperse PEG could significantly improve the mRNA transfection efficiency and biocompatibility of the non-PEGylated cationic peptide carrier, while a longer PEG chain modification adversely decreased the cellular uptake and transfection on A549 and HepG2 cells, emphasizing the importance of a proper PEG chain length selection. Moreover, the optimized PEGylated peptide showed a significantly enhanced mRNA pulmonary delivery efficiency and ameliorated safety profiles over the non-PEGylated peptide and LipofectamineTM 2000 in mice. Our results reveal that the PEGylated peptide could be a promising mRNA delivery vector candidate for inhaled mRNA vaccines and therapeutic applications for the prevention and treatment of different respiratory diseases in the future.

IntroductionDiffusion-weighted imaging (DWI) with parallel reconstruction may suffer from a mismatch between the coil calibration scan and imaging scan due to motions, especially for abdominal imaging.MethodsThis study aimed to construct an iterative multichannel generative adversarial network (iMCGAN)-based framework for simultaneous sensitivity map estimation and calibration-free image reconstruction. The study included 106 healthy volunteers and 10 patients with tumors.ResultsThe performance of iMCGAN was evaluated in healthy participants and patients and compared with the SAKE, ALOHA-net, and DeepcomplexMRI reconstructions. The peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), root mean squared error (RMSE), and histograms of apparent diffusion coefficient (ADC) maps were calculated for assessing image qualities. The proposed iMCGAN outperformed the other methods in terms of the PSNR (iMCGAN: 41.82 ± 2.14; SAKE: 17.38 ± 1.78; ALOHA-net: 20.43 ± 2.11 and DeepcomplexMRI: 39.78 ± 2.78) for b = 800 DWI with an acceleration factor of 4. Besides, the ghosting artifacts in the SENSE due to the mismatch between the DW image and the sensitivity maps were avoided using the iMCGAN model.DiscussionThe current model iteratively refined the sensitivity maps and the reconstructed images without additional acquisitions. Thus, the quality of the reconstructed image was improved, and the aliasing artifact was alleviated when motions occurred during the imaging procedure.

Background. Calcific aortic valve disease (CAVD) is the most common native valve disease. Valvular interstitial cell (VIC) osteogenic differentiation and valvular endothelial cell (VEC) dysfunction are key steps in CAVD progression. Circular RNA (circRNAs) is involved in regulating osteogenic differentiation with mesenchymal cells and is associated with multiple disease progression, but the function of circRNAs in CAVD remains unknown. Here, we aimed to investigate the effect and potential significance of circRNA-miRNA-mRNA networks in CAVD. Methods. Two mRNA datasets, one miRNA dataset, and one circRNA dataset of CAVD downloaded from GEO were used to identify DE-circRNAs, DE-miRNAs, and DE-mRNAs. Based on the online website prediction function, the common mRNAs (FmRNAs) for constructing circRNA-miRNA-mRNA networks were identified. GO and KEGG enrichment analyses were performed on FmRNAs. In addition, hub genes were identified by PPI networks. Based on the expression of each data set, the circRNA-miRNA-hub gene network was constructed by Cytoscape (version 3.6.1). Results. 32 DE-circRNAs, 206 DE-miRNAs, and 2170 DE-mRNAs were identified. Fifty-nine FmRNAs were obtained by intersection. The KEGG pathway analysis of FmRNAs was enriched in pathways in cancer, JAK-STAT signaling pathway, cell cycle, and MAPK signaling pathway. Meanwhile, transcription, nucleolus, and protein homodimerization activity were significantly enriched in GO analysis. Eight hub genes were identified based on the PPI network. Three possible regulatory networks in CAVD disease were obtained based on the biological functions of circRNAs including: hsa_circ_0026817-hsa-miR-211-5p-CACNA1C, hsa_circ_0007215-hsa-miR-1252-5p-MECP2, and hsa_circ_0007215-hsa-miR-1343-3p- RBL1. Conclusion. The present bionformatics analysis suggests the functional effect for the circRNA-miRNA-mRNA network in CAVD pathogenesis and provides new targets for therapeutics.

Medical image-to-image translation is considered a new direction with many potential applications in the medical field. The medical image-to-image translation is dominated by two models, including supervised Pix2Pix and unsupervised cyclic-consistency generative adversarial network (GAN). However, existing methods still have two shortcomings: 1) the Pix2Pix requires paired and pixel-aligned images, which are difficult to acquire. Nevertheless, the optimum output of the cycle-consistency model may not be unique. 2) They are still deficient in capturing the global features and modeling long-distance interactions, which are critical for regions with complex anatomical structures. We propose a Swin Transformer-based GAN for Multi-Modal Medical Image Translation, named MMTrans. Specifically, MMTrans consists of a generator, a registration network, and a discriminator. The Swin Transformer-based generator enables to generate images with the same content as source modality images and similar style information of target modality images. The encoder part of the registration network, based on Swin Transformer, is utilized to predict deformable vector fields. The convolution-based discriminator determines whether the target modality images are similar to the generator or from the real images. Extensive experiments conducted using the public dataset and clinical datasets showed that our network outperformed other advanced medical image translation methods in both aligned and unpaired datasets and has great potential to be applied in clinical applications.

Liver tumor semantic segmentation is a crucial task in medical image analysis that requires multiple MRI modalities. This paper proposes a novel coarse-to-fine fusion segmentation approach to detect and segment small liver tumors of various sizes. To enhance the segmentation accuracy of small liver tumors, the method incorporates a detection module and a CSR (convolution-SE-residual) module, which includes a convolution block, an SE (squeeze and excitation) module, and a residual module for fine segmentation. The proposed method demonstrates superior performance compared to conventional single-stage end-to-end networks. A private liver MRI dataset comprising 218 patients with a total of 3605 tumors, including 3273 tumors smaller than 3.0 cm, were collected for the proposed method. There are five types of liver tumors identified in this dataset: hepatocellular carcinoma (HCC); metastases of the liver; cholangiocarcinoma (ICC); hepatic cyst; and liver hemangioma. The results indicate that the proposed method outperforms the single segmentation networks 3D UNet and nnU-Net as well as the fusion networks of 3D UNet and nnU-Net with nnDetection. The proposed architecture was evaluated on a test set of 44 images, with an average Dice similarity coefficient (DSC) and recall of 86.9% and 86.7%, respectively, which is a 1% improvement compared to the comparison method. More importantly, compared to existing methods, our proposed approach demonstrates state-of-the-art performance in segmenting small objects with sizes smaller than 10 mm, achieving a Dice score of 85.3% and a malignancy detection rate of 87.5%.

The moving pseudo-boundary method of fundamental solutions (MFS) was employed to solve the Laplace equation, which describes the potential flow in a two-dimensional (2D) numerical wave tank. The MFS is known for its ease of programming and the advantage of its high precision. The solution of the boundary value can be expressed by a linear combination of the fundamental solutions. The major issue with such an implementation is the optimal distribution of source nodes in the pseudo-boundary. Traditionally, the positions of the source nodes are assumed to be fixed to keep the set of equations closed. However, in the moving boundary problem, the distribution of source nodes may influence the stability of numerical calculations. Moreover, MFS is unstable in time iterations. Hence, it is necessary to constantly revise the weighting coefficients of fundamental solutions. In this study, the source nodes were free, and their locations were determined by solving a nonlinear least-squares problem using the Levenberg–Marquardt algorithm. To solve the above least-squares problem, the MATLAB© routine lsqnonlin was adopted. Additionally, the weighting coefficients of fundamental solutions were solved as a nonlinear least-squares problem using the aforementioned method. The numerical results indicated that the numerical simulation method adopted in this paper is accurate and reliable in solving the problem of 2D tank sloshing. The main contribution of this study is to expand the application of the MFS in engineering by integrating it with the optimal configuration problem of pseudo-boundaries to solve practical engineering problems.

Compressed sensing (CS) can speed up the magnetic resonance imaging (MRI) process and reconstruct high-quality images from under-sampled $k$ -space data. However, traditional CS-MRI suffers from slow iterations and artifacts caused by noise when the acceleration factor is high. Currently, deep learning has been introduced to address these issues. Although some improvements have been achieved, the reconstruction problem under high under-sampling rates has not been solved. Thus, our study proposed a novel CS-MRI reconstruction method called RSCA-GAN. The generator of RSCA-GAN is a residual U-net consisting of both spatial and channel-wise attention. The proposed RSCA-GAN was compared to the zero-filling, DAGAN, RefineGAN, and RCA-GAN using both cartesian and non-cartesian under-sampling masks on brain and knee datasets. The sampling rates of cartesian masks are 25%, 16.7%, and 12.5% and the sampling rates of spiral and radial masks are 30%, 20%, and 10%. Peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and normalized mean square error (NMSE) were used to evaluate the reconstructed image quality, and the rank-sum test was adopted to evaluate the significant difference among different approaches. P < 0.05 indicated statistical significance. The results demonstrated that RSCA-GAN outperforms the other approaches for all the quantitative metrics. Thus, RSCA-GAN exhibits excellent reconstruction performance at high under-sampling rates and is suitable for clinical applications.

Magnetic resonance imaging (MRI) is an important medical imaging modality, but its acquisition speed is quite slow due to the physiological limitations. Recently, super-resolution methods have shown excellent performance in accelerating MRI. In some circumstances, it is difficult to obtain high-resolution images even with prolonged scan time. Therefore, we proposed a novel super-resolution method that uses a generative adversarial network with cyclic loss and attention mechanism to generate high-resolution MR images from low-resolution MR images by upsampling factors of $2\times $ and $4\times $ . We implemented our model on pelvic images from healthy subjects as training and validation data, while those data from patients were used for testing. The MR dataset was obtained using different imaging sequences, including T2, T2W SPAIR, and mDIXON-W. Four methods, i.e., BICUBIC, SRCNN, SRGAN, and EDSR were used for comparison. Structural similarity, peak signal to noise ratio, root mean square error, and variance inflation factor were used as calculation indicators to evaluate the performances of the proposed method. Various experimental results showed that our method can better restore the details of the high-resolution MR image as compared to the other methods. In addition, the reconstructed high-resolution MR image can provide better lesion textures in the tumor patients, which is promising to be used in clinical diagnosis.

The recent years have witnessed a fast-paced development of perovskite solar cells (PSCs). Unfortunately, the vast majority of PSCs relies on the use of highly polar aprotic solvents during the preparation process, such as dimethylformamide (DMF), which is toxic and detrimental to both humans and the environment. Here, we describe the preparation of PSCs under ambient conditions from an aqueous solution of lead nitrate, to which polyvinylpyrrolidone (PVP) was added in order to enhance the photoelectric performance of the PSCs. By a combination of SEM, EIS, PL and UV spectroscopy and other characterization approaches, we show that the PVP additive is effective in inhibiting carrier recombination, enhancing composite resistance and reducing film defects. Ultimately, we achieved an outstanding photoelectric performance of the PVP-doped PSCs shown by a power conversion efficiency (PCE) of 15.19% and an average steady-state PCE of 14.55% under AM 1.5G simulated solar irradiation with a shadow mask of 0.1 cm2. The PCE continued to be over 80% of the initial PCE after 60 days of storage. FInally, the introduced PVP-doped PSCs present a low-cost and low-toxicity way to commercialize perovskite solar cells.

Purpose: A phase correction method for high-resolution multi-shot (MSH) diffusion weighted imaging (DWI) is proposed. The efficacy and generalization capability of the method were validated on both healthy volunteers and patients. Theory and methods: Conventionally, inter-shot phase variations for MSH echo-planar imaging (EPI) DWI are corrected by model-based algorithms. However, many acquisition imperfections are hard to measure accurately for conventional model-based methods, making the phase estimation and artifacts suppression unreliable. We propose a deep learning multiplexed sensitivity-encoding (DL-MUSE) framework to improve the phase estimations based on convolutional neural network (CNN) reconstruction. Aliasing-free single-shot (SSH) DW images, which have been used routinely in clinical settings, were used for training before the aliasing correction of MSH-DWI images. A dual-channel U-net comprising multiple convolutional layers was used for the phase estimation of MSH-EPI. The network was trained on a dataset containing 30 healthy volunteers and tested on another dataset of 52 healthy subjects and 15 patients with lesions or tumors with different shot numbers (4, 6 and 8). To further validate the generalization capability of our network, we acquired a dataset with different numbers of shots, TEs, partial Fourier factors, resolutions, ETLs, FOVs, coil numbers, and image orientations from two sites. We also compared the reconstruction performance of our proposed method with that of the conventional MUSE and SSH-EPI qualitatively and quantitatively. Results: Our results show that DL-MUSE is capable of correcting inter-shot phase errors with high and robust performance. Compared to conventional model-based MUSE, our method, by applying deep learning-based phase corrections, showed reduced distortion, noise level, and signal loss in high b-value DWIs. The improvements of image quality become more evident as the shot number increases from 4 to 8, especially in those central regions of the images, where g-factor artifacts are severe. Furthermore, the proposed method could provide the information about the orientation of the white matter with better consistency and achieve finer fibers delineation compared to the SSH-EPI method. Besides, the experiments on volunteers and patients from two different sites demonstrated the generalizability of our proposed method preliminarily. Conclusion: A deep learning-based reconstruction algorithm for MSH-EPI images, which helps improve image quality greatly, was proposed. Results from healthy volunteers and tumor patients demonstrated the feasibility and generalization performances of our method for high-resolution MSH-EPI DWI, which can be used for routine clinical applications as well as neuroimaging research.

Background: Early diagnosis of acute kidney injury (AKI) is essential in clinical settings. None of the current biomarkers are widely applied. The combination of pulse-shifting multi-echo asymmetric spin-echo sequence (psMASE) and a modified hemodynamic response imaging (HRI) technique is promising. The purpose of this study was to evaluate the feasibility of psMASE combined with HRI in detecting early ischemic AKI in animal models of different severities.Methods: Twenty rabbits were divided into four groups (mild, moderate, and severe AKI and control groups). Transarterial embolization with different doses of microspheres was performed to establish AKI animal models of different severities. The 3T psMASE and HRI scans of kidneys were conducted. The R2*, R2, and R2' during room air and gas stimulation were acquired and the difference of R2' (dR2') was evaluated in different AKI groups.Results: The values were not different in R2* and R2 during room air and in R2* and R2, and R2' during gas stimulation. The value of R2' was significantly different during room air (P = 0.014), but the difference was only found between control and moderate/severe AKI groups (P = 0.032 and 0.022). The values of dR2' were different among groups (P < 0.0001) and differences between every two groups except comparison of moderate and severe AKI groups were significant (P < 0.01).Conclusion: The dR2' imaging acquired by a combination of renal psMASE and HRI technique can serve as a potential quantitative biomarker for early detection and staging of AKI.

Currently, oil spill pollution is one of the major environmental concerns for sea cucumber (Apostichopus japonicus) aquaculture. During oil spills, spraying chemical dispersants is generally considered an efficient oil spill response. However, the impact of chemical dispersant deployment during oil spills on sea cucumbers is still less known. In this study, we treated sea cucumbers with physically and chemically (by GM-2 chemical dispersant) dispersed Oman crude oil for 24 h. For antioxidant defense capacities, our results showed that physically dispersed crude oil caused a significant elevation on superoxide dismutase (SOD) and catalase (CAT) activities, and glutathione (GSH) content, while chemically dispersed crude oil caused a significant decrease in SOD activity and GSH content with no apparent change in CAT activity. As for non-specific immune responses, our results indicated that physically dispersed crude oil up-regulated acid phosphatase (ACP) and lysozyme (LZM) activities but had no obvious impact on alkaline phosphatase (ALP) activity. Differently, chemically dispersed crude oil down-regulated ACP and LZM activities while up-regulating ALP activity. Based on the integrated biomarker response analysis, the overall impact of chemically dispersed crude oil on antioxidant defense capacities and non-specific immune responses of sea cucumbers was more severe than physically dispersed crude oil.

Neurite orientation dispersion and density imaging (NODDI) is a diffusion model specifically designed for brain magnetic resonance imaging. Despite recent studies suggesting that NODDI modeling might be more sensitive to brain development than diffusion tensor imaging (DTI), these studies were limited to a relatively small age range and mainly based on the manually operated region of interest analysis. Therefore, this study applied NODDI to investigate brain development in a large sample size of 214 subjects ranging in ages from 0 to 14. The whole brain was automatically segmented into 122 regions. The maturation trajectory of each region was characterized by the time course of diffusion metrics and further quantified using nonlinear regression. The NODDI-derived metrics, neurite density index (NDI) and orientation dispersion index (ODI), increased with age. And these two metrics were superior to the DTI-derived metrics in SVM regression models of age. The NDI in white matter exhibited a more rapid growth than that in gray matter (including the cortex and deep nucleus). These diffusion indicators experienced conspicuous increases during early childhood and the growth speed slowed down in adolescence. Region-specific maturation patterns were described throughout the brain, including white matter, cortical and deep gray matter. These development patterns were evaluated and discussed on the basis of NODDI’s model assumptions. To summarize, this study verified the high sensitivity of NODDI to age over a crucial developmental period from newborn to adolescence. Moreover, the existing knowledge of brain development has been complemented, suggesting that NODDI has a potential capability in the investigation of brain development.

PurposeTo evaluate the effect of resolution on iron content using quantitative susceptibility mapping (QSM); to verify the consistency of QSM across field strengths and manufacturers in evaluating the iron content of deep gray matter (DGM) of the human brain using subjects from multiple sites; and to establish a susceptibility baseline as a function of age for each DGM structure using both a global and regional iron analysis.MethodsData from 623 healthy adults, ranging from 20 to 90 years old, were collected across 3 sites using gradient echo imaging on one 1.5 Tesla and two 3.0 Tesla MR scanners. Eight subcortical gray matter nuclei were semi-automatically segmented using a full-width half maximum threshold-based analysis of the QSM data. Mean susceptibility, volume and total iron content with age correlations were evaluated for each measured structure for both the whole-region and RII (high iron content regions) analysis. For the purpose of studying the effect of resolution on QSM, a digitized model of the brain was applied.ResultsThe mean susceptibilities of the caudate nucleus (CN), globus pallidus (GP) and putamen (PUT) were not significantly affected by changing the slice thickness from 0.5 to 3 mm. But for small structures, the susceptibility was reduced by 10% for 2 mm thick slices. For global analysis, the mean susceptibility correlated positively with age for the CN, PUT, red nucleus (RN), substantia nigra (SN), and dentate nucleus (DN). There was a negative correlation with age in the thalamus (THA). The volumes of most nuclei were negatively correlated with age. Apart from the GP, THA, and pulvinar thalamus (PT), all the other structures showed an increasing total iron content despite the reductions in volume with age. For the RII regional high iron content analysis, mean susceptibility in most of the structures was moderately to strongly correlated with age. Similar to the global analysis, apart from the GP, THA, and PT, all structures showed an increasing total iron content.ConclusionA reasonable estimate for age-related iron behavior can be obtained from a large cross site, cross manufacturer set of data when high enough resolutions are used. These estimates can be used for correcting for age related iron changes when studying diseases like Parkinson’s disease, Alzheimer’s disease, and other iron related neurodegenerative diseases.

In worldwide, lung cancer has a major socio-economic impact and is one of the most common causes of cancer-related deaths. Current therapies for lung cancer are still quite unsatisfactory, urging for alternative new treatments. Traditional Chinese Medicine (TCM) is currently increasingly popular and exhibits a complicated intervention in cancers therapy. In this study, we evaluated the anti-tumor effect and explored the mechanisms of a TCM formula Yangyinwenyang (YYWY) in non-small cell lung cancer (NSCLC) models. YYWY induced the apoptosis of lung cancer cells in vitro. In Lewis NSCLC-bearing mice model, YYWY significantly inhibited the tumor growth. Further, RNA-seq analysis and immunostaining of the tumor tissue implied the critical role of YYWY in the regulation of immune response, especially the dendritic cells (DCs) in the effect of YYWY. Therefore, we focused on DCs, which were the initiator and modulator of the immune response. YYWY facilitated the maturation of DCs through MAPK and NF-κB signaling pathways and promoted the release of the cytokines IFN-γ, interleukin (IL)-1β, IL-2, IL-12, and tumor necrosis factor (TNF)-α by DCs. Moreover, the YYWY-matured DCs enhanced the proliferation of T cells and promoted the differentiation of T cells into T helper Th1 and cytotoxic T cell (CTL). In addition, YYWY increased the ratio of Th1/Th2 (IFN-γ/IL-4 radio). Collectively, our findings clearly suggested that YYWY exerted an anti-tumor effect on NSCLC, at least partially through facilitating the mature DCs to activate the proliferation and differentiation of T cells.

Deep learning has demonstrated superior performance in image reconstruction compared to most conventional iterative algorithms. However, their effectiveness and generalization capability are highly dependent on the sample size and diversity of the training data. Deep learning-based reconstruction requires multi-coil raw k-space data, which are not collected by routine scans. On the other hand, large amounts of magnitude images are readily available in hospitals. Hence, we proposed the MAGnitude Images to Complex K-space (MAGIC-K) Net to generate multi-coil k-space data from existing magnitude images and a limited number of required raw k-space data to facilitate the reconstruction. Compared to some basic data augmentation methods applying global intensity and displacement transformations to the source images, the MAGIC-K Net can generate more realistic intensity variations and displacements from pairs of anatomical Digital Imaging and Communications in Medicine (DICOM) images. The reconstruction performance was validated in 30 healthy volunteers and 6 patients with different types of tumors. The experimental results demonstrated that the high-resolution Diffusion Weighted Image (DWI) reconstruction benefited from the proposed augmentation method. The MAGIC-K Net enabled the deep learning network to reconstruct images with superior performance in both healthy and tumor patients, qualitatively and quantitatively.

With the rising of environmental level of copper, the use of algae to assess copper contamination has drawn wide attention. The effects of copper at concentrations be-tween 0.36-1.43 mg.l-1 was investigated on growth, photo-synthetic pigments and chlorophyll fluorescence parame-ters of three green algae. And the sensitivity of major chlo-rophyll fluorescence parameters to copper exposure was especially evaluated. Based on median effective concen-tration (EC50) values, Scenedesmus obliquus was the most sensitive species to copper and relative electron transport rate (rETR) was the most sensitive parameter in Scenedes-mus obliquus. Chlorella pyrenoidosa was the least sensi-tive to copper toxicity. Chlamydomonas reinhardii was also not suitable for monitoring copper contamination due to its fast recovery and irregularly fluctuating EC50s. Chlo-rophyll fluorescence of Scenedesmus obliquus can be used for online monitoring of copper contaminations in fresh-waters of China. [ABSTRACT FROM AUTHOR]

Accurate grading of liver fibrosis can effectively assess the severity of liver disease and help doctors make an appropriate diagnosis. This study aimed to perform the automatic staging of hepatic fibrosis on patients with hepatitis B, who underwent gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced magnetic resonance imaging with dynamic radiomics analysis. The proposed dynamic radiomics model combined imaging features from multi-phase dynamic contrast-enhanced (DCE) images and time-domain information. Imaging features were extracted from the deep learning-based segmented liver volume, and time-domain features were further explored to analyze the variation in features during contrast enhancement. Model construction and evaluation were based on a 132-case data set. The proposed model achieved remarkable performance in significant fibrosis (fibrosis stage S1 vs. S2–S4; accuracy (ACC) = 0.875, area under the curve (AUC) = 0.867), advanced fibrosis (S1–S2 vs. S3–S4; ACC = 0.825, AUC = 0.874), and cirrhosis (S1–S3 vs. S4; ACC = 0.850, AUC = 0.900) classifications in the test set. It was more dominant compared with the conventional single-phase or multi-phase DCE-based radiomics models, normalized liver enhancement, and some serological indicators. Time-domain features were found to play an important role in the classification models. The dynamic radiomics model can be applied for highly accurate automatic hepatic fibrosis staging.

In this study, we proposed a model combing parallel imaging (PI) with generative adversarial network (GAN) architecture (PIC-GAN) for accelerated multi-channel magnetic resonance imaging (MRI) reconstruction. This model integrated data fidelity and regularization terms into the generator to benefit from multi-coils information and provide an “end-to-end” reconstruction. Besides, to better preserve image details during reconstruction, we combined the adversarial loss with pixel-wise loss in both image and frequency domains. The proposed PIC-GAN framework was evaluated on abdominal and knee MRI images using 2, 4 and 6-fold accelerations with different undersampling patterns. The performance of the PIC-GAN was compared to the sparsity-based parallel imaging (L1-ESPIRiT), the variational network (VN), and conventional GAN with single-channel images as input (zero-filled (ZF)-GAN). Experimental results show that our PIC-GAN can effectively reconstruct multi-channel MR images at a low noise level and improved structure similarity of the reconstructed images. PIC-GAN has yielded the lowest Normalized Mean Square Error (in ×10−5) (PIC-GAN: 0.58 ± 0.37, ZF-GAN: 1.93 ± 1.41, VN: 1.87 ± 1.28, L1-ESPIRiT: 2.49 ± 1.04 for abdominal MRI data and PIC-GAN: 0.80 ± 0.26, ZF-GAN: 0.93 ± 0.29, VN:1.18 ± 0.31, L1-ESPIRiT: 1.28 ± 0.24 for knee MRI data) and the highest Peak Signal to Noise Ratio (PIC-GAN: 34.43 ± 1.92, ZF-GAN: 31.45 ± 4.0, VN: 29.26 ± 2.98, L1-ESPIRiT: 25.40 ± 1.88 for abdominal MRI data and PIC-GAN: 34.10 ± 1.09, ZF-GAN: 31.47 ± 1.05, VN: 30.01 ± 1.01, L1-ESPIRiT: 28.01 ± 0.98 for knee MRI data) compared to ZF-GAN, VN and L1-ESPIRiT with an under-sampling factor of 6. The proposed PIC-GAN framework has shown superior reconstruction performance in terms of reducing aliasing artifacts and restoring tissue structures as compared to other conventional and state-of-the-art reconstruction methods.

Sea cucumber (Apostichopus japonicus) is mainly cultured in the coastal zone, where it is easily threatened by accidental oil spills. Chemical dispersant is one of the efficient oil spill responses for mitigating the overall environmental damage of oil spills. However, the impact of crude oil and chemical dispersants on sea cucumber is less well known. Hence, the present study focused on exploring the antioxidant response and oxidative stress in the respiratory tree of sea cucumber following exposure to GM-2 chemical dispersant (DISP), water-accommodated fractions (WAF), and chemically enhanced WAF (CEWAF) of Oman crude oil for 24 h. Results manifested that WAF exposure caused a significant increase in the reactive oxygen species (ROS) level (5.29 ± 0.30 AU·mgprot−1), and the effect was much more obvious in CEWAF treatment (5.73 ± 0.16 AU·mgprot−1). Total antioxidant capacity (T-AOC), as an important biomarker of the antioxidant defense capacity, showed an increasing trend following WAF exposure (0.95 ± 0.12 U·mgprot−1) while a significant reduction in T-AOC was observed following CEWAF exposure (0.23 ± 0.13 U·mgprot−1). Moreover, we also evaluated the oxidative damage of the macromolecules (DNA, protein, and lipid), and our results revealed that the presence of chemical dispersant enhanced oxidative damage caused by crude oil to sea cucumber.

Objectives. The presence of motilin receptor in the GI tract of different animal species has been verified. However, the quantitation of motilin receptor expression in different regions of the GI tract remains unclear. The aim of this study was to investigate the expression of motilin receptor in the GI tract and semiquantitatively compare the expression difference in different GI regions in dogs. Methods. Antrum, duodenum, jejunum, ileum, proximal colon, middle colon, and distal colon were obtained from various parts of the GI tract of six sacrificed dogs. The distribution of motilin receptor was determined by immunohistochemistry. The expression levels of motilin receptor mRNA in different regions were measured by RT-PCR. Results. Motilin receptor was expressed throughout the GI tract in dogs. Multiple comparisons of the mean motilin receptor mRNA expression among various regions were significant (P

Invasion and metastasis are the major causes of death in patients with esophageal squamous cell carcinoma (ESCC). Epithelial-mesenchymal transition (EMT) is a critical step in tumor progression and transforming growth factor-β1 (TGF-β1) signaling has been shown to play an important role in EMT. In this study, we investigated how TGF-β1 signaling pathways contributed to EMT in three ESCC cell lines as well as 100 patients of nomadic ethnic Kazakhs residing in northwest Xinjiang Province of China. In vitro analyses included Western blotting to detect the expression of TGF-β1/Smad and EMT-associated proteins in Eca109, EC9706 and KYSE150 cell lines following stimulation with recombinant TGF-β1 and SB431542, a potent inhibitor of ALK5 that also inhibits TGF-β type II receptor. TGF-β-activated Smad2/3 signaling in EMT was significantly upregulated as indicated by mesenchymal markers of N-cadherin and Vimentin, and in the meantime, epithelial marker, E-cadherin, was markedly downregulated. In contrast, SB431542 addition downregulated the expression of N-cadherin and Vimentin, but upregulated the expression of E-cadherin. Moreover, the TGF-β1-induced EMT promoted invasion capability of Eca109 cells. Tumor cells undergoing EMT acquire fibroblastoid-like phenotype. Expressed levels of TGF-β1/Smad signaling molecules and EMT-associated proteins were examined using immunohistochemical analyses in 100 ESCC tissues of Kazakh patients and 58 matched noncancerous adjacent tissues. The results showed that ESCC tissues exhibited upregulated expression of TGF-β1/Smad. We also analyzed the relationship between the above proteins and the patients' clinicopathological characteristics. The TGF-β1/Smad signaling pathway in human Eca109 ESCC cells may carry similar features as in Kazakh ESCC patients, suggesting that TGF-β1/Smad signaling pathway may be involved in the regulation of EMT in ethnic Kazakh patients with ESCC from Xinjiang, China.

Some reservoirs in China have recently become polluted and eutrophic. Few studies have focused on the relationships between chlorophyll a (Chl) and the nutrients or nutrient limitation in reservoirs. We analyzed the relationships of nitrogen and phosphorus with Chl in 139 reservoirs in China using data from the China Knowledge Resource Integrated Database to reveal the response regulation of Chl to the nutrients in reservoirs and to identify the effects of phosphorus and nitrogen on Chl at varying total nitrogen (TN): total phosphorus (TP) ratios. Results of linear least square regression and stepwise multiple regression predicted lower Chl concentrations from nutrition than in lakes, and Chl was found to be significantly related to TP or TN whether the reservoirs were divided into three categories based on their sizes (large, medium, small) or based on their regions (South, Middle, North). TN was found to be a better predictor of Chl than TP in small reservoirs and in reservoirs in southern China, which differs from the nutrient limitation assessment criterion by Sakamoto (10-17). The difference can also be found in reservoirs in the middle categories (17 < TN:TP 25) based on the TN:TP ratio. We can therefore argue that the TN:TP ratio is not always an appropriate index for determining nutrient limitation in some reservoirs in China. [ABSTRACT FROM AUTHOR]