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Nickel iron bimetallic phosphide alloys are a promising electrocatalyst for oxygen evolution reaction (OER). In this work, a highly active Ni–Fe–P/Fe–Si heterojunction electrocatalyst for OER has been synthesized by one-step electrodeposition on iron plate. The synergism effect of Ni–Fe–P alloy and Fe–Si alloy particles after dealloying treatment promotes the construction of nanoflower-like heterogeneous structure with rich lattice defects and exposes a highly open multi-stage nanostructure surface. Furthermore, Si induced the localized electron redistribution optimizes catalytic performance by accelerating electron transfer. The optimal Ni–Fe–P/Fe–Si OER catalyst requires a much low overpotential of 209 mV at a current density of 10 mA cm−2, and 270 mV at a current density of 200 mA cm−2. The prepared Ni–Fe–P/Fe–Si electrode can maintain a current density of 10 mA cm−2 within 24 h, which shows significant electrochemical stability. This study provides a new prospect for the preparation of cost-effective heterogeneous catalysts. [Display omitted] • A new nanoflower shaped heterogeneous electrocatalyst: Ni–Fe–P/Fe–Si. • A excellent electrocatalytic activity of OER: 209 mV/10 mA cm−2 in 1 M KOH. • A new strategy design high activity and low-cost electrocatalysts. [ABSTRACT FROM AUTHOR]

The accurate and efficient prediction of chlorophyll-a (Chl-a) concentration is crucial for the early detection of algal blooms in reservoirs. Nevertheless, predicting Chl-a concentration in multivariate time series poses a significant challenge due to the complex interrelationships within the aquatic environment and the discrete and non-stationary nature of online monitoring of water quality data. To address the aforementioned issue, this paper proposes a novel prediction model named SGMD-KPCA-BiLSTM (SKB) for predicting Chl-a concentration. The model combines two-stage data processing and machine learning (ML). To capture nonlinear relationships in multivariate time series data, the optimal data subset is determined by combining symplectic geometry mode decomposition (SGMD) and kernel principal component analysis (KPCA). This subset is then input into a bidirectional long short-term memory (BiLSTM) model, and the model's hyperparameters are optimized using the sparrow search algorithm (SSA) to improve the accuracy of predictions. The performance of the model was evaluated at Qiaodian Reservoir in Shandong, China. To assess its superiority, the evaluation criteria included the root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE), coefficient of determination (R2), frequency histograms of the prediction error, and the Taylor diagram. The prediction performance of five single models, namely the back-propagation (BP) neural network, support vector regression (SVR), long short-term memory (LSTM), convolutional neural network with long short-term memory (CNN-LSTM), and BiLSTM, as well as three hybrid models, namely SGMD-LSTM, SGMD-KPCA-LSTM, and SGMD-BiLSTM, were compared against the SKB model. The results demonstrated that the SKB model performs best in predicting Chl-a concentration (R2 = 96.19%, RMSE = 1.05, MAE = 0.65, MAPE = 0.08). It significantly reduced the prediction error compared to other models for comparison. Furthermore, the multi-step predictive capabilities of the SKB model are also discussed. The analysis shows a decline in predictive performance with larger prediction time steps, and the SKB model exhibits slightly superior performance compared to the other model at corresponding prediction intervals. The model has significant advantages in terms of its ability to accurately predict the non-smooth and nonlinear Chl-a sequences observed by the online monitoring system. This study presents a potential solution for controlling and preventing reservoir eutrophication, as well as an innovative approach for predicting water quality. [ABSTRACT FROM AUTHOR]

In this study, an adapted bioleaching strain of Acidithiobacillus caldus UVS10 was successfully developed. Batch tests and tests in bioreactor were conducted to evaluate the metals bioleaching performance of A. caldus UVS10 to spent FCC catalyst (SFCCC). Results of batch experiments showed the bioleaching efficiency of Ni, V, La, and Ce in SFCCC reached 19.40%, 22.06%, 53.75%, and 59.56%, respectively. High SFCCC pulp density inhibited the leaching of metals. Sb leaching was inhibited in acidic environment caused by A. caldus UVS10. Contents of Ni, V, La, and Ce in extracellular polymeric substances (EPS) were significantly higher than those intracellular. Accumulation of metal in EPS and cytosol increased with the increase of SFCCC pulp density. V was less intercepted by EPS than Ni, La, and Ce, because of lower toxicity. Experimental results in bioreactor showed that Ni, V, La, and Ce could be effectively leached by A. caldus UVS10 under 10% pulp density. The aeration and stirring operating environment in bioreactor improved the leaching efficiency of metals in SFCCC. After bioleached in bioreactor, the available fraction content of four metals in SFCCC decreased significantly. Ecological risk analysis demonstrated the environmental risks of bioleached SFCCC were significantly lower than raw SFCCC. Different reaction kinetic models were used to represent metals leaching behavior under bioleaching of A. caldus UVS10, leaching of La and Ce showed good agreement with the product layer diffusion model, while Ni and V leaching kinetics fit well with the surface chemical reaction models. [ABSTRACT FROM AUTHOR]

Pancreatic enzyme replacement therapy (PERT) has been recommended as the preferred method for pancreatic exocrine insufficiency caused by chronic pancreatitis (CP). However, at present, the patient-related factors for the poor PERT management are not clear, and there are no studies on the adherence to PERT in patients with CP in East China. This was a mixed-method study following the principle of sequential explanatory design and included two parts: a quantitative and qualitative study. A cross-sectional survey of medication adherence (MA) was first carried out, followed by a semi-structured interview to further explore and explain the influencing factors of adherence to PERT. Of the 148 patients included in this study, 48.0% had poor MA and only 12.8% had good MA. Multivariate logistic regression showed that lower levels of education and income were contributing factors for non-adherence to PERT. Semi-structured interviews with 24 patients revealed that the reasons for non-adherence also included lack of knowledge, self-adjustment of PERT, lifetime of medication, side effects of PERT, forgetfulness, financial burdens, and accessibility issues. The adherence to PERT was poor among patients with CP in East China. Healthcare providers should personalize medication strategies to improve patients' MA. [ABSTRACT FROM AUTHOR]

Abstract Constipation, mainly manifesting as abdominal discomfort and painful defecation, is considered as a chronic disorder. Due to a lack of effective therapy, it imposes a significant economic burden and greatly impacts patients’ quality of life which prompt searches for new, original approaches. Based on the research of vibrating capsule (VC) carried out by Ron et al., we investigated the safety and efficacy of an innovative, multi-mode VC in terms of its effect on defecation in animal studies. The parameters associated with different operation modes of VCs can be detected and adjusted by smartphone controlled external configuration device (ECD). The results of blood tests, physiological parameters, CT scan and pathological examination showed no significant abnormality, which undoubtedly confirmed the safety of VCs. For efficacy studies, defecation frequency of beagles increased after administration of these capsules without influence on stool characters. Meanwhile, the mean time of capsule evacuation tended to be reduced while showing no significant difference between different modes. In summary, this study elucidates the safety and effectiveness of VC in prompting the passage of gastrointestinal walls thus greatly increasing the defecation frequency. This study innovatively displays the promising application of VC in the treatment of constipation.

Abstract Background SPINK1 (serine protease inhibitor, kazal-type, 1), which encodes human pancreatic secretory trypsin inhibitor, is one of the most extensively studied genes underlying chronic pancreatitis. Recently, based upon data from qualitative reverse transcription-PCR (RT-PCR) analyses of transfected HEK293T cells, we concluded that 24 studied SPINK1 intronic variants were not of pathological significance, the sole exceptions being two canonical splice site variants (i.e., c.87 + 1G > A and c.194 + 2T > C). Herein, we employed the splicing prediction tools included within the Alamut software suite to prioritize the ‘non-pathological’ SPINK1 intronic variants for further quantitative RT-PCR analysis. Results Although our results demonstrated the utility of in silico prediction in classifying and prioritizing intronic variants, we made two observations worth noting. First, we established that most of the prediction tools employed ignored the general rule that GC is a weaker donor splice site than the canonical GT site. This finding is potentially important because for a given disease gene, a GC variant donor splice site may be associated with a milder clinical manifestation. Second, the non-pathological c.194 + 13T > G variant was consistently predicted by different programs to generate a new and viable donor splice site, the prediction scores being comparable to those for the physiological c.194 + 2T donor splice site and even higher than those for the physiological c.87 + 1G donor splice site. We do however provide convincing in vitro evidence that the predicted donor splice site was not entirely spurious. Conclusions Our findings, taken together, serve to emphasize the importance of functional analysis in helping to establish or refute the pathogenicity of specific intronic variants.