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Coronary microvascular dysfunction (CMD) is one of the basic mechanisms of myocardial ischemia. Myocardial contrast echocardiography (MCE) is a bedside technique that utilises microbubbles which remain entirely within the intravascular space and denotes the status of microvascular perfusion within that region. Some pilot studies suggested that MCE may be used to diagnose CMD, but without further validation. This study is aimed to investigate the diagnostic performance of MCE for the evaluation of CMD. MCE was performed at rest and during adenosine triphosphate stress. ECG triggered real-time frames were acquired in the apical 4-chamber, 3-chamber, 2-chamber, and long-axis imaging planes. These images were imported into Narnar for further processing. Eighty-two participants with suspicion of coronary disease and absence of significant epicardial lesions were prospectively investigated. Thermodilution was used as the gold standard to diagnose CMD. CMD was present in 23 (28%) patients. Myocardial blood flow reserve (MBF) was assessed using MCE. CMD was defined as MBF reserve < 2. The MCE method had a high sensitivity (88.1%) and specificity (95.7%) in the diagnosis of CMD. There was strong agreement with thermodilution (Kappa coefficient was 0.727; 95% CI: 0.57–0.88, p < 0.001). However, the correlation coefficient (r = 0.376; p < 0.001) was not high.

Video-based person re-identification (Re-ID) aims at retrieving the person through the video sequences across non-overlapping cameras. Some characteristics of pedestrians are not consecutive across frames due to the variations of viewpoints, postures, and occlusions over time. However, existing methods ignore such data peculiarity and the networks tend to only learn those salient consecutive characteristics among frames in video sequences. As a result, the learned representations fail to cover all the characteristics of pedestrians, thus lacking integrity and discrimination. To tackle this problem, we present a novel deep architecture termed Hierarchical Mining Network (HMN), which mines as many pedestrians’ characteristics by referring to the temporal and intra-class knowledge. It consists of a novel Attentive Temporal Module (ATM) and a Dynamic Supervising Branch (DSB), with a Balancing Triplet Loss (BTL) assisting the training. The proposed ATM, with pedestrian perceiving capacity, is capable of evaluating each activation of features through temporal analysis, so that the temporally scattered characteristics of pedestrians can be better aggregated and the contaminated ones can be eliminated. Then, the DSB along with the BTL further enhances the integrity of representations by multiple supervision. Specifically, the DSB perceives the diversities of intra-class samples in each mini-batch and generates targeted supervising signals for them, in which process the BTL guarantees the signals with smaller intra-class variations and larger inter-class variations. Comprehensive experiments on two video-based datasets, i.e., MARS, and DukeMTMC-VideoReID, demonstrate the contribution of each component and the superiority of the proposed HMN over the state-of-the-arts. Benchmarking our model on three popular image-based datasets, i.e., Market1501, DukeMTMC-Reid, and MSMT17 additionally verifies the promising generalizability of the proposed DSB and BTL.

The ability to simulate vegetation dynamics and their feedback with nutrient cycling to affect ecosystem productivity underpins our prediction of the land carbon sink under climate change. Predictive models are now capable of simulating complex ecosystem processes, including the recent advancement in simulating vegetation dynamics and ecosystem phosphorus cycling, but there is a general lack of empirical evidence to form a systematic evaluation of the model predictions, especially how functional diversity affect ecosystem nutrient cycling and its consequence for productivity. Here, we developed a dataset based on 9 permanent plots (20 x 20 m) along an elevation gradient (300 – 1200m a.s.l.) in a subtropical forested mountain in eastern China. We measured vegetation growth, estimated forest structure and species composition, and compiled ecosystem-scale carbon (C), nitrogen (N) and phosphorus (P) budgets based on concentration, pool and flux data collected from dominant canopy trees, understorey herbaceous plants, and soil organic and inorganic components in these forested plots. Our aims are three-fold: 1) to understand how C, N and P are distributed along the plant-microbe-soil continuum; 2) to disentangle how different growth and nutrient use strategies of plant and soil microbes affect ecosystem productivity and regulate the rate nutrient cycling; and 3) to benchmark predictive models in simulating ecosystem vegetation dynamics and their interaction with C, N, and P cycle processes. Our research will contribute towards better understanding of the functional diversity and productivity relationship, and will contribute towards an improved predictive capacity to simulate vegetation dynamics and the land carbon sink under climate change.

MotivationMultiple instance learning (MIL) is a powerful technique to classify whole slide images (WSIs) for diagnostic pathology. The key challenge of MIL on WSI classification is to discover the critical instances that trigger the bag label. However, tumor heterogeneity significantly hinders the algorithm’s performance.ResultsHere, we propose a novel multiplex-detection-based multiple instance learning (MDMIL) which targets tumor heterogeneity by multiplex detection strategy and feature constraints among samples. Specifically, the internal query generated after the probability distribution analysis and the variational query optimized throughout the training process are utilized to detect potential instances in the form of internal and external assistance, respectively. The multiplex detection strategy significantly improves the instance-mining capacity of the deep neural network. Meanwhile, a memory-based contrastive loss is proposed to reach consistency on various phenotypes in the feature space. The novel network and loss function jointly achieve high robustness towards tumor heterogeneity. We conduct experiments on three computational pathology datasets, e.g. CAMELYON16, TCGA-NSCLC, and TCGA-RCC. Benchmarking experiments on the three datasets illustrate that our proposed MDMIL approach achieves superior performance over several existing state-of-the-art methods.Availability and implementationMDMIL is available for academic purposes at https://github.com/ZacharyWang-007/MDMIL.

Carbon nanomaterials, such as carbon nanoonions (CNOs), possess promising applications in various fields. There are urgent demands to synthesize carbon nanomaterials from a green and renewable carbon source. In this study, solid CNOs with relatively uniform size distribution (with diameters of about 30–50 nm), abundant structure defects and oxygen-containing surface functional groups (such as –OH and –COOH) are developed from co-pyrolysis of lignin (LG) and polyethylene (PE) in the presence of Ni-based catalysts. The type of catalyst, the concentration of catalyst and catalytic co-pyrolysis temperature play important roles in the morphologies and properties of CNOs as confirmed by TEM and SEM. Furthermore, the produced CNOs can act as a low-cost and highly-efficient adsorbent to remove Cu(II) from aqueous solution according to a homogeneous monolayer, chemical action-dominated, endothermic and spontaneous process. The theoretical maximum adsorption capacity of CNOs calculated from the Langmuir model is 100.00 mg g−1. Surface deposition, complexation, π electron–cation interaction and electrostatic interaction are responsible for the adsorption of Cu(II) using the prepared CNOs.

Arrhythmia is one of the common cardiovascular diseases. Nowadays, many methods identify arrhythmias from electrocardiograms (ECGs) by computer-aided systems. However, computer-aided systems could not identify arrhythmias effectively due to various the morphological change of abnormal ECG data. This paper proposes a deep method to classify ECG samples. Firstly, ECG features are extracted through continuous wavelet transform. Then, our method realizes the arrhythmia classification based on the new lightweight context transform blocks. The block is proposed by improving the linear content transform block by squeeze-and-excitation network and linear transformation. Finally, the proposed method is validated on the MIT-BIH arrhythmia database. The experimental results show that the proposed method can achieve a high accuracy on arrhythmia classification.

MotivationThe rapid accumulation of high-throughput sequence data demands the development of effective and efficient data-driven computational methods to functionally annotate proteins. However, most current approaches used for functional annotation simply focus on the use of protein-level information but ignore inter-relationships among annotations.ResultsHere, we established PFresGO, an attention-based deep-learning approach that incorporates hierarchical structures in Gene Ontology (GO) graphs and advances in natural language processing algorithms for the functional annotation of proteins. PFresGO employs a self-attention operation to capture the inter-relationships of GO terms, updates its embedding accordingly and uses a cross-attention operation to project protein representations and GO embedding into a common latent space to identify global protein sequence patterns and local functional residues. We demonstrate that PFresGO consistently achieves superior performance across GO categories when compared with ‘state-of-the-art’ methods. Importantly, we show that PFresGO can identify functionally important residues in protein sequences by assessing the distribution of attention weightings. PFresGO should serve as an effective tool for the accurate functional annotation of proteins and functional domains within proteins.Availability and implementationPFresGO is available for academic purposes at https://github.com/BioColLab/PFresGO.Supplementary informationSupplementary data are available at Bioinformatics online.

Inland surface water plays an important role in global carbon cycling, which responds to transformation between dissolved inorganic carbon (DIC) and dissolved organic carbon (DOC). Studies have shown that algae in karst lakes and reservoirs can convert DIC to organic matter (OM) and form stable carbon sinks via photosynthesis. However, the pathways of conversion of inorganic carbon to organic carbon during algal growth remain unclear and need further investigation. In this study, spectroscopic techniques were applied to investigate the variations in algal organic matter (AOM) composition in the growth metabolism of Chlorella vulgaris and Scenedesmus obliquus under simulated karst water condition. The results showed that algal extracellular organic matter (EOM) contained high DIC concentration during the adaptation phase, which formed the carbon source for algal photosynthesis. In addition, DOC in algae increased after entering the stationary phase, while more OM was released into water. As algal growth proceeded, the amino groups in EOM were consumed to produce more aromatic protein-like material, while more lipid material was produced in intracellular organic matter (IOM). The spectral characterization results could intuitively determine AOM dynamics in different growth stages of algae, which can be used for establishing effective approaches for detecting organic carbon variations and responding to regional carbon cycling in karst water.

The existing Beijing Pingguoyuan Subway Station was extended through a extension project. The excavation for the extension was located directly above the existing station. Complex interactions exist between the existing structure and the retaining pile wall of the excavation. Based on this project, three-dimensional finite element models were established to investigate the mechanical characteristics of the embedded and non-embedded retaining pile walls. A parametric analysis was performed for both types of pile walls. The stress and deformation characteristics of the retaining pile walls and existing structures were analyzed. The results show that when the bottom of the non-embedded retaining pile walls are connected to the existing structure, the uplift of the existing structure is essentially constant; however, the maximum displacement of the pile is increased by approximately 2.7 times, and the bending moment of the pile is reduced to 57.1% of the connection condition. As the distance between the embedded retaining pile wall and the existing station increases, the uplift of the existing station increases linearly, whereas the soil between the pile and the station exhibits a non-linear increasing trend. The displacement of the embedded retaining pile wall increases as the inner force decreases. When the distance is greater than 4.7 m, the displacement and force of the pile remains essentially unchanged. The effect of the pile embedded depth on the force and deformation of the pile is mainly observed in the lower part of the pile. As the embedded depth increases, the maximum displacement decreases by approximately 16.9%, the maximum bending moment decreases, and the maximum negative bending moment increases. The key contribution of this research is to provide a prediction method for the mechanical behaviors of a expansion project. The findings from the study also provide industry practitioners with a comprehensive guide regarding the specific applications of the construction technology of a deep excavation structure overlying an existing subway station.

Correct and continuous weight measurement of a burn patient is important for fluid resuscitation. Generally, a body weight bed uses four sensors to obtain signals. However, owing to signals dynamic characteristics, the air fluidized therapy bed has a large error in this mode, which is difficult to reduce. In this study, we optimized the sensor configuration scheme and developed a set of signal processing and methods to evaluate noise reduction. Additionally, the wavelet neural network was used to improve the weighing accuracy. Furthermore, medical tools are located on different body parts of a patient to acquire weight characteristics to assist with the rehydration of patient. The average error of 183.33g is acceptable from a medical perspective. An average error of 250.21g in dynamic weight after using wavelet de-noising and wavelet neural network methods was obtained.

Soil organic carbon (SOC) in deeper soils may be more protected by mineral particles than SOC in shallower soils; thus, the vertical SOC distribution is closely related to the overall SOC stability. This study aimed to verify the relationship between the solubility temperature dependence of water extractable SOC fractions and their vertical distribution characteristics. Soil samples collected from each soil horizon in natural dragon spruce (Picea asperata) and red birch (Betula albosinensis) forests were analyzed. Cold water-extracted organic carbon (CWEOC) and hot water-extracted organic carbon (HWEOC) were extracted at 20 °C and 80 °C, respectively. The sum of CWEOC and HWEOC was considered the water-extracted organic carbon (WEOC) content. The carbohydrate-C (Car-C) and phenolic-C (Phe-C) contents extracted by hot water were also determined. The CWEOC, HWEOC, Car-C, and Phe-C contents varied significantly (P

Iron is an important element and its biogeochemical processes are vital to the matter and energy cycles of wetland ecosystems. Hydrology greatly controls characteristics of soil property and plant community in wetlands, which can regulate the behavior of iron and its oxides. However, it remains unclear how the spatial distribution of iron and its forms in estuarine wetlands responses to hydrological conditions. Five typical plant communities along a naturally hydrological gradient in the Yellow River Estuary wetland, including Phragmites australis in freshwater marsh (FPA), Phragmites australis in salt marsh (SPA), Tamarix chinensis in salt marsh (TC), Suaeda salsa in salt marsh (SS) and Spartina alterniflora in salt marsh (SA), as sites to collect soil samples. The total iron (FeT) and three iron oxides (complexed iron, Fep; amorphous iron, Feo; free iron, Fed) in samples were determined to clarify the spatial distribution of iron and explore its impact factors. The mean contents of FeT, Fep, Feo and Fed were 28079.4, 152.0, 617.2 and 8285.3 mg⋅kg–1 of soil at 0–40 cm depth in the different sites, respectively. The means were significantly different across communities along the hydrological gradient, with the higher values for SA on the upper intertidal zone and for SPA on the lower intertidal zone, respectively. Iron and its forms were positively correlated with the total organic carbon (TOC), dissolved organic carbon (DOC), total nitrogen (TN) and clay, and negatively correlated with electrical conductivity (EC). The indexes of iron oxides (Fep/Fed, Feo/Fed and Fed/FeT) were also different across communities, with a higher value for SA, which were positively correlated with soil water content (WC) and TOC. The results indicate that a variety of plant community and soil property derived from the difference of hydrology might result in a spatial heterogeneity of iron in estuarine wetlands.

Subcellular localization of messenger RNAs (mRNAs) plays a key role in the spatial regulation of gene activity. The functions of mRNAs have been shown to be closely linked with their localizations. As such, understanding of the subcellular localizations of mRNAs can help elucidate gene regulatory networks. Despite several computational methods that have been developed to predict mRNA localizations within cells, there is still much room for improvement in predictive performance, especially for the multiple-location prediction. In this study, we proposed a novel multi-label multi-class predictor, termed Clarion, for mRNA subcellular localization prediction. Clarion was developed based on a manually curated benchmark dataset and leveraged the weighted series method for multi-label transformation. Extensive benchmarking tests demonstrated Clarion achieved competitive predictive performance and the weighted series method plays a crucial role in securing superior performance of Clarion. In addition, the independent test results indicate that Clarion outperformed the state-of-the-art methods and can secure accuracy of 81.47, 91.29, 79.77, 92.10, 89.15, 83.74, 80.74, 79.23 and 84.74% for chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus and ribosome, respectively. The webserver and local stand-alone tool of Clarion is freely available at http://monash.bioweb.cloud.edu.au/Clarion/.

To retrospectively evaluate whether repositioning the bone window leads to a better outcome of three-dimensional sinus augmentation in lateral sinus floor elevation (LSFE) with simultaneous implant placement.34 patients with a total of 40 implants (14: test group, 26: control group) receiving LSFE with simultaneous implant placement were included in this retrospective research. CBCT images were taken before surgery, immediately and 6 months after surgery. The two-dimensional augmentation parameters, including apical bone height (ABH), endo-sinus bone gain (ESBG), and palatal/buccal bone height (PBH/BBH), and three-dimensional parameters, including augmentation volume (AV) and palatal/buccal augmentation volume (PAV/BAV), were measured. The lateral defect length (LDL) and lateral window length (LWL) were also measured to evaluate the lateral antrostomy recovery.At the 6-month follow-up, the reduction rates at ABH, ESBG, and BBH of the test group (ABH: 10.41% ± 30.30%, ESBG: 2.55% ± 8.91%, BBH: 2.50% ± 8.65%) were significantly lower than those of the control group (ABH: 25.10% ± 22.02%, ESBG: 11.47% ± 9.79%, BBH: 7.10% ± 5.37%; p .05). In addition, the test group showed better three-dimensional augmentation stability on the buccal side (BAV reduction: 15.51% ± 10.86% vs. 27.15% ± 12.61%; p .05). Moreover, the LDL/LWL ratio of the test group was significantly lower than that of the control group (p .05).Within the limitations of this study, repositioning of the bone window in LSFE with simultaneous implant placement could contribute to endo-sinus augmentation stability on the buccal side at the 6-month follow-up. Moreover, it would also facilitate recovery of the lateral antrostomy defect.

Dendrobium officinale flos (DOF) is the flower of Dendrobium officinale Kimura et Migo, which is usually regarded as a by-product of Dendrobii Offcinalis Caulis. Based on its use as an alternative medicine, we evaluated the antidepressant-like effect of DOF extracts on chronic, unpredictable, mild stress-induced, depression-like behaviour in mice and tested the effects of DOF on the regulation of neurotrophic factors in mouse astrocyte primary cultures and PC12 cell lines. Oral treatment with DOF ethanol extract (DOF-E) could alleviate depression-like behaviours in stress-exposed mice, as evidenced by increased sucrose consumption and decreased immobile time in a forced swim test. In the hippocampus, DOF extracts increased the expression of NGF and BDNF, both at the transcriptional and protein levels. In astrocytes, DOF-E increased the expression of NGF and BDNF via a cAMP-dependent mechanism and regulated plasminogen and matrix metallopeptidase 9 (MMP-9), which are related to the metabolic regulation of neurotrophic factors. In PC12 cells, DOF-E induced the expression of neurofilaments and potentiated the induction of neurite outgrowth upon treatment with a low dose of NGF. Based on these findings, DOF might be used as a supplement for antidepressant therapy in patients with depression.

In the present study, the coked catalysts derived from catalytic reforming of the pyrolysis volatiles of polyethylene (PE), lignin (LG) and their mixture were developed as low-cost and environmentally-friendly carbon materials-containing composites to remove heavy metal ions from aqueous solution. The composites were thoroughly characterized by SEM, TEM, XRD, TGA and FT-IR and then their adsorption capability towards Pb(ii) was investigated. It is found that curved cone-shape carbon nanotubes (CNTs) with abundant structural defects and O-containing surface functional groups, such as C-O, CO and -OH, can be obtained from the catalytic reforming of the mixture of PE and LG. The CNT-containing catalyst composite presents a superior adsorption capability towards Pb(ii) when it is employed in Pb(ii) removal. Adsorption isotherm and adsorption kinetics studies show that the adsorption process can be well simulated by the Langmuir isotherm and pseudo-second-order model, demonstrating that the adsorption is subjected to a homogeneous and chemical process. The calculated maximum adsorption capacity is as high as 146.08 mg g-1, which is much higher than most of the adsorbents reported. Moreover, thermodynamic analysis reveals that the adsorption is spontaneous and endothermic. Accordingly, the used catalyst from the catalytic reforming can be developed as a low-cost and highly-efficient adsorbent.

Lithium–sulfur batteries are regarded as the most promising electrochemical energy storage device as a result of their satisfactory high specific capacity and high energy density values. However, the inferior conversion efficiency of lithium polysulfides (LiPSs) in essence leads to fast capacity decay, especially at a large current density during the charge–discharge process. Herein, we have successfully demonstrated that the conversion efficiency of LiPSs was remarkably promoted by employing nickel nanoparticles incorporated with nitrogen-doped multi-channel carbon nanofibers (Ni-NMCCF) as a multifunctional modification layer of the separator. The high polarity of the nitrogen atoms and the Lewis acid characteristics of the nickel atoms collectively create strong anchoring activity towards LiPSs, therefore leading to the severe inhibition of the occurrence of the shuttle effect. The electrocatalytic activity of the nickel nanoparticles greatly accelerates the reversible conversion efficiency of LiPSs, ensuring high capacity retention during long-term cycling. Thanks to these advantages, Ni-NMCCF delivered a superb rate performance of 1020 mA h g−1 at 1C and maintained 671 mA h g−1 even after 500 cycles, which is equivalent to a capacity decay of only 0.07% per cycle.

Purpose Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is a promising technique to accelerate dynamic cardiac MR imaging (DCMRI). For DCMRI, the CS-MRI usually exploits image signal sparsity and low-rank property to reconstruct dynamic images from the undersampled k-space data. In this paper, a novel CS algorithm is investigated to improve dynamic cardiac MR image reconstruction quality under the condition of minimizing the k-space recording. Methods The sparse representation of 3D cardiac magnetic resonance data is implemented by synergistically integrating 3D total generalized variation (3D-TGV) algorithm and high order singular value decomposition (HOSVD) based Tensor Decomposition, termed k-t TGV-TD method. In the proposed method, the low rank structure of the 3D dynamic cardiac MR data is performed with the HOSVD method, and the localized image sparsity is achieved by the 3D-TGV method. Moreover, the Fast Composite Splitting Algorithm (FCSA) method, combining the variable splitting with operator splitting techniques, is employed to solve the low-rank and sparse problem. Two different cardiac MR datasets (cardiac perfusion and cine MR datasets) are used to evaluate the performance of the proposed method. Results Compared with the state-of-art methods, such as k-t SLR, 3D-TGV, HOSVD based tensor decomposition and low-rank plus sparse method, the proposed k-t TGV-TD method can offer improved reconstruction accuracy in terms of higher peak SNR (PSNR) and structural similarity index (SSIM). The proposed k-t TGV-TD method can achieve significantly better and stable reconstruction results than state-of-the-art methods in terms of both PSNR and SSIM, especially for cardiac perfusion MR dataset. Conclusions This work proved that the k-t TGV-TD method was an effective sparse representation way for DCMRI, which was capable of significantly improving the reconstruction accuracy with different acceleration factors.

Objective. With the improvement of living standards, heart disease has become one of the common diseases that threaten human health. Electrocardiography (ECG) is an effective way of diagnosing cardiovascular diseases. With the rapid growth of ECG examinations and the shortage of cardiologists, accurate and automatic arrhythmias classification has become a research hotspot. The main purpose of this paper is to improve accuracy in detecting abnormal ECG patterns. Approach. A hybrid 1D Resnet-GRU method, consisting of the Resnet and gated recurrent unit (GRU) modules, is proposed to implement classification of arrhythmias from 12-lead ECG recordings. In addition, the focal Loss function is used to solve the problem of unbalanced datasets. Based on the proposed 1D Resnet-GRU model, we use class-discriminative visualization to improve interpretability and transparency as an additional step. In this paper, the Grad-CAM++ mechanism has been employed to the trained network model and generate thermal images superimposed on raw signals to explore underlying explanations of various ECG segments. Main results. The experimental results show that the proposed method can achieve a high score of 0.821 (F1-score) in classifying 9 kinds of arrythmias, and Grad-CAM++ not only provides insight into the predictive power of the model, but is also consistent with the diagnostic approach of the arrhythmia classification. Significance. The proposed method can effectively select and integrate ECG features to achieve the goal of end-to-end arrhythmia classification by using 12-lead ECG signals, which can serve a promising and useful way for automatic arrhythmia classification, and can provide an explainable deep leaning model for clinical diagnosis.

Sandy soils in tropical areas are more vulnerable to potential toxic elements as a result of their low nutrition. The composite addition of biochar and phosphate material is considered a promising method of immobilizing toxic metals in sandy soils, but the synergistic effects of this process still need to be further explored, especially in typical tropical vegetables. In this study, a pot experiment was conducted to evaluate the agronomic and toxic metal-immobilization effects of single amendments (phosphate rock, activated phosphate rock, and biochar) and combined amendments, including biochar mixed with phosphate rock (BCPR) and biochar mixed with activated phosphate rock (BCAPR), on vegetables grown in tropical sandy soil. Among these amendments, the composite amendment BCAPR was the most effective for increasing Ca, Mg, and P uptake based on water spinach (Ipomoea aquatica L.) and pepper (Capsicum annuum L.), showing increased ratios of 22.5%, 146.0%, and 136.0%, respectively. The SEM-EDS and FTIR analysis verified that the activation process induced by humic acid resulted in the complexation and chelation of the elements P, Ca, and Mg into bioavailable forms. Furthermore, the retention of available nutrition elements was enhanced due to the strong adsorption capacity of the biochar. In terms of cadmium (Cd) and lead (Pb) passivation, the formation of insoluble mineral precipitates reduced the mobility of these metals within the BCAPR treatments, with the maximum level of extractable Cd (86.6%) and Pb (39.2%) reduction being observed in the tropical sandy soil. These results explore the use of sustainable novel cost-effective and highly efficient bi-functional mineral-based soil amendments for metal passivation and plant protection.

Li–S batteries have been attracting increasing interest owing to their remarkable advantages of low cost, high theoretical capacity and high theoretical energy density. Nevertheless, the severe “shuttle effects” of lithium polysulfides have markedly limited the performance of the cells and further hindered their commercial applications. Herein, a novel scheme combining a transformation strategy with ammonia treatment was developed to fabricate ZIF-8-derived nitrogen-doped hierarchically porous carbon (NHPC/NH3). When NHPC/NH3 was used as a host of sulfur, the obtained S@NHPC/NH3 cathode for Li–S cells presented an initial specific capacity of 1654 mA h g−1 and an outstanding cycling stability with only 0.27% attenuation per cycle from the 30th cycle to 130th cycle. Together with the theoretical calculation, it was concluded that such excellent electrochemical performances should be attributed to the suppressed “shuttle effect” via both physical and chemical adsorption of lithium polysulfides in the optimized microporous structures with effective nitrogen doping sites as well as the improved kinetics owing to the abundant meso/macroporous structures.

Algal blooms have posed great threats to livestocks and human health. Although flocculation is effective, its efficiency may hinder the direct application for algal blooms. In this study, critical (optimal) conditions and mechanisms for AlCl

In this study, disinfection by-products (DBP) formation from dissolved organic matter (DOM) and its fractions, including both hydrophilic and hydrophobic components, were investigated at a typical karst surface water. The subsequent DBP formation potential was evaluated by deducing chemical characteristics of DOM fractions and representative algal organic matter (Chlorella sp. AOM) under the influence of divalent ions (Ca

Osseointegration is the direct and intimate contact between mineralized tissue and titanium implant at the bone-implant interface. Early establishment and stable maintenance of osseointegration is the key to long-term implant success. However, in patients with compromised conditions such as osteoporosis and patients beginning early load-bearing activities such as walking, lower osseointegration around titanium implants is often observed, which might result in implant early failure. Gene-activated implants show an exciting prospect of combining gene delivery and biomedical implants to solve the problems of poor osseointegration formation, overcoming the shortcomings of protein therapy, including rapid degradation and overdose adverse effects. The conception of gene-activated titanium implants is based on "gene-activated matrix" (GAM), which means scaffolds using non-viral vectors for in situ gene delivery to achieve a long-term and efficient transfection of target cells. Current preclinical studies in animal models have shown that plasmid DNA (pDNA), microRNA (miRNA), and small interference RNA (siRNA) functionalized titanium implants can enhance osseointegration with safety and efficiency, leading to the expectation of applying this technique in dental and orthopedic clinical scenarios. This review aims to comprehensively summarize fabrication strategies, current applications, and futural outlooks of gene-activated implants, emphasizing nucleic acid targets, non-viral vectors, implant surface modification techniques, nucleic acid/vector complexes loading strategies.

Purpose: Compressed Sensing Magnetic Resonance Imaging (CS-MRI) is a promising technique to accelerate dynamic cardiac MR imaging (DCMRI). For DCMRI, the CS-MRI usually exploits image signal sparsity and low-rank property to reconstruct dynamic images from the undersampled k-space data. In this paper, a novel CS algorithm is investigated to improve dynamic cardiac MR image reconstruction quality under the condition of minimizing the k-space recording.Methods: The sparse representation of 3D cardiac magnetic resonance data is implemented by synergistically integrating 3D TGV algorithm and high order singular value decomposition (HOSVD) based Tensor Decomposition, termed as k-t TGV-TD method. In the proposed method, the low rank structure of the 3D dynamic cardiac MR data is performed by the HOSVD method, and the localized image sparsity is achieved by the 3D TGV method. Moreover, the Fast Composite Splitting Algorithm (FCSA) method, combining the variable splitting with operator splitting techniques, is employed to solve the low-rank and sparse problem. Two different cardiac MR datasets (cardiac cine and cardiac perfusion MR data) are used to evaluate the performance of the proposed method.Results: Compared with the state-of-art methods, such as the k-t SLR method, 3D TGV method and HOSVD based tensor decomposition method, the proposed method can offer improved reconstruction accuracy in terms of higher signal-to-error ratio (SER).Conclusions: This work proved that the k-t TGV-TD method was an effective sparse representation way for DC-MRI, which was capable of significantly improving the reconstruction accuracy with different reduction factor.

Carbon nanomaterials, such as carbon nanoonions (CNOs), possess promising applications in various fields. There are urgent demands to synthesize carbon nanomaterials from a green and renewable carbon source. In this study, solid CNOs with relatively uniform size distribution (with diameters of about 30-50 nm), abundant structure defects and oxygen-containing surface functional groups (such as -OH and -COOH) are developed from co-pyrolysis of lignin (LG) and polyethylene (PE) in the presence of Ni-based catalysts. The type of catalyst, the concentration of catalyst and catalytic co-pyrolysis temperature play important roles in the morphologies and properties of CNOs as confirmed by TEM and SEM. Furthermore, the produced CNOs can act as a low-cost and highly-efficient adsorbent to remove Cu(ii) from aqueous solution according to a homogeneous monolayer, chemical action-dominated, endothermic and spontaneous process. The theoretical maximum adsorption capacity of CNOs calculated from the Langmuir model is 100.00 mg g

In the present study, the coked catalysts derived from catalytic reforming of the pyrolysis volatiles of polyethylene (PE), lignin (LG) and their mixture were developed as low-cost and environmentally-friendly carbon materials-containing composites to remove heavy metal ions from aqueous solution. The composites were thoroughly characterized by SEM, TEM, XRD, TGA and FT-IR and then their adsorption capability towards Pb(ii) was investigated. It is found that curved cone-shape carbon nanotubes (CNTs) with abundant structural defects and O-containing surface functional groups, such as C-O, C[double bond, length as m-dash]O and -OH, can be obtained from the catalytic reforming of the mixture of PE and LG. The CNT-containing catalyst composite presents a superior adsorption capability towards Pb(ii) when it is employed in Pb(ii) removal. Adsorption isotherm and adsorption kinetics studies show that the adsorption process can be well simulated by the Langmuir isotherm and pseudo-second-order model, demonstrating that the adsorption is subjected to a homogeneous and chemical process. The calculated maximum adsorption capacity is as high as 146.08 mg g

Opioid addiction is a chronic brain disease with a high heritability. However, the genetic underpinnings remain uncertain. DNA methylation is involved in the adaptive changes in neuroplasticity after prolonged drug use. The dopamine receptor D4 (DRD4) has an essential role in the reward processes associated with addictive drugs. To further elucidate the potential role and mechanism of the DRD4 gene variants in heroin addiction, we detected the methylation level of 46 CpG sites in the promoter region and the genotypes of three SNPs in the DRD4 gene. Correlations between the SNPs and methylation levels of the CpG sites, i.e., the analysis of methylation quantitative trait loci (mQTLs) was conducted. Following the identification of mQTLs that are unique in the heroin addiction group, we performed an association study between the mQTLs and traits of heroin addiction. Our results revealed that there were several correlations of SNPs rs3758653 and rs11246226 with the methylation levels of some CpG sites in the DRD4 gene. Among these SNP-CpG pairs, rs3758653-DRD4_04, rs3758653-DRD4_05, rs3758653-DRD4_13 and rs3758653-DRD4_03 were unique in the heroin addiction group. Moreover, we found that mQTL rs3758653 was associated with duration from first heroin exposure to addiction, and the expression level of the DRD4 gene in human brain regions of the frontal cortex and hippocampus. Our findings suggested that some mQTLs in the genome may be associated with traits of opioid addiction through implicating the processes of DNA methylation and gene expression.

In recent years, with the development of artificial intelligence, deep learning model has achieved initial success in ECG data analysis, especially the detection of atrial fibrillation. In order to solve the problems of ignoring the correlation between contexts and gradient dispersion in traditional deep convolution neural network model, the hybrid attention-based deep learning network (HADLN) method is proposed to implement arrhythmia classification. The HADLN can make full use of the advantages of residual network (ResNet) and bidirectional long–short-term memory (Bi-LSTM) architecture to obtain fusion features containing local and global information and improve the interpretability of the model through the attention mechanism. The method is trained and verified by using the PhysioNet 2017 challenge dataset. Without loss of generality, the ECG signal is classified into four categories, including atrial fibrillation, noise, other, and normal signals. By combining the fusion features and the attention mechanism, the learned model has a great improvement in classification performance and certain interpretability. The experimental results show that the proposed HADLN method can achieve precision of 0.866, recall of 0.859, accuracy of 0.867, and F1-score of 0.880 on 10-fold cross-validation.

Here we report two Ba(II) coordination compounds, 2D [Ba(btzphda)(H2O)4] (1) and 3D [Ba2(pytzipa)4(H2O)3]·2H2O (2) derived from 1,3-bis(tetrazol-5-yl)benzene-N2,N2′-diacetic acid (H2btzphda) and 5-(4-pyridyl)tetrazole-2-isopropionic acid (Hpytzipa). Furthermore, differential scanning calorimetry (DSC) and thermogravimetric-differential thermogravimetric (TG-DTG) analyses were applied to evaluate the thermal decomposition behavior of such compounds. The relevant thermodynamic parameters (ΔH, ΔS, and ΔG) of the decomposition process of compounds 1 and 2 were calculated, as well.

China produces the most cement product worldwide, and cement plants (CPs) have been regarded as the largest anthropogenic sources of atmospheric mercury (Hg) emissions in China since 2009. Onsite studies of this source are scarce compare to the huge numbers of CPs in China. Hence, quantifying Hg emissions from more CPs is needed in reducing the large uncertainties existed in the current Hg emission inventories and for assessing subsequent impacts of Hg on human and ecosystem health. In this study, two pre-calciner CPs in Guizhou province of Southwest China were selected for quantifying the emission factor and mass balance of Hg. Results showed that Hg emission levels in the two CPs were obviously different due to the differences in Hg input and circulation in the production system. In cement plant #1 (CP #1), the input and output of Hg reached a dynamic equilibrium condition, the emission factor was 76.1 mg Hg·t−1 clinker, and Hg concentration in the stack flue gas was in the range of 14.46–16.64 μg m−3. In cement plant #2 (CP #2), Hg was in an enriching status because it was a new plant with operation of several months and most input Hg was retained inside the production system, hence with a much lower emission factor of 1.8 mg Hg·t−1 clinker and Hg concentration of 0.15–0.49 μg m−3 in the stack gas. Kiln tail stack was the main output pathway of Hg in the clinker production process. With removal efficiency of 73.06% and 99.95% at kiln tail by ESP in CP #1 and humidifier + ESP-FF in CP #2, respectively, Hg emitted into the atmosphere was mainly in the forms of gaseous oxidized mercury (Hg2+) and gaseous elemental mercury (Hg0). Besides, the operation mode (on or off) of raw mill had great impact on the concentration and speciation of Hg in flue gas and flue gas temperature at kiln tail. In the clinker production system, limestone is the main source of Hg input (41.4–56.4%), followed by the fueled coal (15.3–32.5%). While, in the clinker to cement production process, the additives (mainly gypsum from coal-fired power plants, 83.2–94.4%) was the main source of Hg in the cement because Hg concentration in the clinker was very low.