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BackgroundIn ST-segment elevation myocardial infarction (STEMI) with the restoration of TIMI 3 flow by percutaneous coronary intervention (PCI), visually defined microvascular obstruction (MVO) was shown to be the predictor of poor prognosis, but not an ideal risk stratification method. We intend to introduce deep neural network (DNN) assisted myocardial contrast echocardiography (MCE) quantitative analysis and propose a better risk stratification model.Methods194 STEMI patients with successful primary PCI with at least 6 months follow-up were included. MCE was performed within 48 h after PCI. The major adverse cardiovascular events (MACE) were defined as cardiac death, congestive heart failure, reinfarction, stroke, and recurrent angina. The perfusion parameters were derived from a DNN-based myocardial segmentation framework. Three patterns of visual microvascular perfusion (MVP) qualitative analysis: normal, delay, and MVO. Clinical markers and imaging features, including global longitudinal strain (GLS) were analyzed. A calculator for risk was constructed and validated with bootstrap resampling.ResultsThe time-cost for processing 7,403 MCE frames is 773 s. The correlation coefficients of microvascular blood flow (MBF) were 0.99 to 0.97 for intra-observer and inter-observer variability. 38 patients met MACE in 6-month follow-up. We proposed A risk prediction model based on MBF [HR: 0.93 (0.91–0.95)] in culprit lesion areas and GLS [HR: 0.80 (0.73–0.88)]. At the best risk threshold of 40%, the AUC was 0.95 (sensitivity: 0.84, specificity: 0.94), better than visual MVP method (AUC: 0.70, Sensitivity: 0.89, Specificity: 0.40, IDI: −0.49). The Kaplan-Meier curves showed that the proposed risk prediction model allowed for better risk stratification.ConclusionThe MBF + GLS model allowed more accurate risk stratification of STEMI after PCI than visual qualitative analysis. The DNN-assisted MCE quantitative analysis is an objective, efficient and reproducible method to evaluate microvascular perfusion.

Lithium iron phosphate (LiFePO4) recovered from waste LiFePO4 batteries inevitably contains impurity aluminium, which may affect material electrochemical performance. Nearly all references believe that aluminium-doped LiFePO4 is a solid solution and that the material capacity increases firstly before decreasing with aluminium content. However, their reported performance of the aluminium-free LiFePO4 as a comparison is far lower than commercial LiFePO4 currently manufactured at large scales. Thus, conclusions drawn based on such a comparison are questionable. To better understand the effects of aluminium on LiFePO4 electrochemical performance, we first tracked aluminium behaviour in FePO4·2H2O, FePO4 precursors and LiFePO4 product. In FePO4·2H2O prepared from aqueous solution, aluminium exists as AlPO4·2H2O in two types (monoclinic and orthorhombic systems) of solid solutions (Fe1-xAlxPO4·2H2O). In Fe1-xAlxPO4 dehydrated from Fe1-xAlxPO4·2H2O, trigonal AlPO4 and FePO4 firstly form a solid solution (Fe1-xAlxPO4, berlinite). At higher aluminium content (x ≥ 0.026), additional AlPO4 forms an independent phase in monoclinic system. In Li1-xFe1-xAlxPO4/C (x = 0–0.048), no evidence of solid solution formation was observed. Secondly, the effects of aluminium on Li1-xFe1-xAlxPO4/C electrochemical performance were studied. It is found that, material specific discharge capacity only decreases monotonically with addition of aluminium at optimal calcination temperature (as 760 °C), unlike references, which “increases firstly before decreasing”. Finally, it is found that aluminium has no remarkable impact on material cycling stability under 100 cycles at 1C rate using coin test.

Coronary artery disease (CAD) is the most common cause of death globally, and its diagnosis is usually based on manual myocardial (MYO) segmentation of MRI sequences. As manual segmentation is tedious, time-consuming, and with low replicability, automatic MYO segmentation using machine learning techniques has been widely explored recently. However, almost all the existing methods treat the input MRI sequences independently, which fails to capture the temporal information between sequences, e.g., the shape and location information of the myocardium in sequences along time. In this article, we propose a MYO segmentation framework for sequence of cardiac MRI (CMR) scanning images of the left ventricular (LV) cavity, right ventricular (RV) cavity, and myocardium. Specifically, we propose to combine conventional neural networks and recurrent neural networks to incorporate temporal information between sequences to ensure temporal consistency. We evaluated our framework on the automated cardiac diagnosis challenge (ACDC) dataset. The experiment results demonstrate that our framework can improve the segmentation accuracy by up to 2% in the Dice coefficient.

This paper is part of a series of studies on the development of a multi-temperature thermodynamically consistent model for salt lake brine systems. The objective of this study is to extend the sulfate binary models published in our previous study to multi-component systems, i.e. Li+-Na+-K+-Mg2+-Ca2+-SO42−-H2O and its sub- ternary, quaternary and quinary systems. The model is based on our revised Pitzer–Simonson–Clegg equations and the CALPHAD-type thermodynamic framework. Ternary PSC mixing parameters of 10 systems and standard Gibbs energy of double salts occurred in these ternary systems are determined by fitting ternary solubility data at various temperatures. The standard Gibbs energy of triple salts 2Li2SO4·Na2SO4·K2SO4(cr) and polyhalite are determined by fitting their solubility data in corresponding quaternary systems (i.e. Li2SO4+Na2SO4+K2SO4+H2O and K2SO4+MgSO4+CaSO4+H2O). The ternary model reproduces these fitted data well for most systems. With the help of these ternary models, complete phase diagrams of these ternary systems are critically constructed from the lowest eutectic temperature to approximately 373.15 K. In addition, phase diagrams of 10 quaternary systems and invariant points in a quinary system are predicted using the PSC parameters determined from binary and ternary systems. These results are consistent with most available experimental data, indicting a very good predictive ability of the model for multi-component systems. In the following study of our series work, we will combine the multi-component sulfate part and our previously published multi-component chloride part to achieve a complete model for the core salt brine system Li+-Na+-K+-Mg2+-Ca2+-SO42−-Cl−-H2O.

Heat stress (HS) has been considered as a severe threat to crop yields in recent years. Sucrose, as a major product of photosynthesis, plays an important role in plant growth and stress response. Sucrose phosphate synthase (SPS) is a key rate-limiting enzyme in the sucrose synthesis pathway in plants. However, its molecular mechanism and signaling pathway remain unclear. In this study, we identified a novel SPS gene (SlSPS) in tomato and generated over-expression and knock-out of SlSPS gene transgenic tomato plants to investigate its biological functions related to the growth and thermotolerance of tomato. Over-expression of SlSPS gene increased the growth and biomass of transgenic tomato plants, such as fresh weight, dry weight, plant height, fruit weight and root length. In contrast, knock-out of SlSPS gene decreased the growth and biomass of transgenic tomato plants. Under heat stress, the survival rates were positively correlated with the expression level of SlSPS gene in different tomato varieties. Furthermore, SlSPS-overexpressing tomato plants showed higher SPS activity and sucrose content and heat stress resistant phenotypes. By comparison, knock-out tomato plants showed lower SPS activity and sucrose content and susceptible to heat stress. The determination of several reference values of oxidative stress parameters were also consistent with their heat resistance of these transgenic plants. In summary, SlSPS gene could positively mediate the growth and thermotolerance in tomato plants.

Knowledge of the structure and speciation of aqueous Ni(II)-chloride complexes is important for understanding Ni behavior in hydrometallurgical extraction. The effect of concentration on the first-shell structure of Ni(II) in aqueous NiCl2 and NiCl2-MgCl2 solutions was investigated by Ni K edge X-ray absorption (XAS) and UV-Vis spectroscopy at ambient conditions. Both techniques show that no large structural change (e.g., transition from octahedral to tetrahedral-like configuration) occurs. Both methods confirm that the Ni(II) aqua ion (with six coordinated water molecules at RNi-O = 2.07(2) Å) is the dominant species over the whole NiCl2 concentration range. However, XANES, EXAFS and UV-Vis data show subtle changes at high salinity (> 2 mol∙kg(-1) NiCl2), which are consistent with the formation of small amounts of the NiCl+ complex (up to 0.44(23) Cl at a Ni-Cl distance of 2.35(2) Å in 5.05 mol∙kg(-1) NiCl2) in the pure NiCl2 solutions. At high Cl:Ni ratio in the NiCl2-MgCl2-H2O solutions, small amounts of [NiCl2]0 are also present. We developed a speciation-based mixed-solvent electrolyte (MSE) model to describe activity-composition relationships in NiCl2-MgCl2-H2O solutions, and at the same time predict Ni(II) speciation that is consistent with our XAS and UV-Vis data and with existing literature data up to the solubility limit, resolving a long-standing uncertainty about the role of chloride complexing in this system.