No causal association between the volume of strenuous exercise and coronary atherosclerosis: a two-sample Mendelian randomization study.

Objective: Several observational studies have shown that high-volume and high-intensity exercise training increases the prevalence and severity of coronary atherosclerosis, but the causal effect still remains uncertain. This study aims to explore the causal relationship between the volume of strenuous exercise (SE) and coronary atherosclerosis (CA) using the Mendelian randomization (MR) method. Method: The exposure factors were two basic parameters of the volume of strenuous exercise (duration and frequency of strenuous exercise), the outcome factor was coronary atherosclerosis, and the relevant genetic loci were extracted from the summary data of the genome-wide association study (GWAS) as the instrumental variables, and MR analyses were performed using the inverse variance weighting (IVW) method, the weighted median method, and the MR-egger method. Sensitivity analyses were performed using heterogeneity analysis, pleiotropy analysis, and the "leave-one-out" method. The original results were tested using other coronary atherosclerosis data sets. Result: IVW results showed no causal association between duration of strenuous exercise (DOSE) [OR = 0.9937, 95% CI (0.9847, 1.0028), P = 0.1757] and frequency of strenuous exercise (FOSE) in the last 4 weeks [OR = 0.9930, 95% CI (0.9808, 1.0054), P = 0.2660] and coronary atherosclerosis. All of the above results were validated with other coronary atherosclerosis data sets. Conclusion: The present study supports that the causal association of duration and frequency of SE with CA was not found, and provides valuable insights into the choice of scientific and correct volume of SE to cardiac rehabilitation (CR).

Charge photogeneration dynamics in non-fullerene polymer solar cells with fluorinated and non-fluorinated acceptors.

In this work, charge photogeneration and recombination processes of PM6:IDIC-4F and PM6:IDIC blend films were investigated by the steady-state and time-resolved spectroscopies, as well as the time-dependent density functional theory calculations. The peaks in absorption and photoluminescence (PL) spectra of IDIC and IDIC-4F solutions were assigned by combining the experiment and the simulation of UV-vis absorption and PL spectra. For neat acceptor films, the exciton diffusion length of neat IDIC and IDIC-4F films was estimated as ∼28.9 and ∼19.9 nm, respectively. For PM6-based blend films, we find that the fluorine substitution engineering on the IDIC acceptor material can increase the phase separate size of acceptor material in blend films, resulting in the reduction of dissociation efficiencies of acceptor excitons. In addition, we find that the charge recombination in PM6:IDIC-4F is dominated by bimolecular recombination, in comparison to geminate type carrier recombination in PM6:IDIC blend films. In addition, we find that thermal annealing treatment has a weak influence on carrier recombination but slightly reduces the exciton dissociation efficiency of acceptor in PM6:IDIC blend films, leading to a slightly reduced power conversion efficiency of PM6:IDIC solar cells. These results may shed light on the design of high-performance semiconductor molecules for application in solar cells.

Machine Learning-Based Virtual Screening and Identification of the Fourth-Generation EGFR Inhibitors.

Epidermal growth factor receptor (EGFR) plays a pivotal regulatory role in treating patients with advanced nonsmall cell lung cancer (NSCLC). Following the emergence of the EGFR tertiary CIS C797S mutation, all types of inhibitors lose their inhibitory activity, necessitating the urgent development of new inhibitors. Computer systems employ machine learning methods to process substantial volumes of data and construct models that enable more accurate predictions of the outcomes of new inputs. The purpose of this article is to uncover innovative fourth-generation epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) with the aid of machine learning techniques. The paper's data set was high-dimensional and sparse, encompassing both structured and unstructured descriptors. To address this considerable challenge, we introduced a fusion framework to select critical molecule descriptors by integrating the full quadratic effect model and the Lasso model. Based on structural descriptors obtained from the full quadratic effect model, we conceived and synthesized a variety of small-molecule inhibitors. These inhibitors demonstrated potent inhibitory effects on the two mutated kinases L858R/T790M/C797S and Del19/T790M/C797S. Moreover, we applied our model to virtual screening, successfully identifying four hit compounds. We have evaluated these hit ADME characteristics and look forward to conducting activity evaluations on them in the future to discover a new generation of EGFR-TKI.

How ß-Cyclodextrin-Functionalized Biochar Enhanced Biodenitrification in Low C/N Conditions via Regulating Substrate Metabolism and Electron Utilization.

Biodenitrification plays a vital role in the remediation of nitrogen-contaminated water. However, influent with a low C/N ratio limits the efficiency of denitrification and causes the accumulation/emission of noxious intermediates. In this study, ß-cyclodextrin-functionalized biochar (BC@ß-CD) was synthesized and applied to promote the denitrification performance of Paracoccus denitrificans when the C/N was only 4, accompanied by increased nitrate reduction efficiency and lower nitrite accumulation and nitrous oxide emission. Transcriptomic and enzymatic activity analyses showed BC@ß-CD enhanced glucose degradation by promoting the activities of glycolysis (EMP), the pentose phosphate pathway (PPP), and the tricarboxylic acid (TCA) cycle. Notably, BC@ß-CD drove a great generation of electron donors by stimulating the TCA cycle, causing a greater supply of substrate metabolism to denitrification. Meanwhile, the promotional effect of BC@ß-CD on oxidative phosphorylation accelerates electron transfer and ATP synthesis. Moreover, the presence of BC@ß-CD increased the intracellular iron level, causing further improved electron utilization in denitrification. BC@ß-CD helped to remove metabolites and induced positive feedback on the metabolism of P. denitrificans. Collectively, these effects elevated the glucose utilization for supporting denitrification from 36.37% to 51.19%. This study reveals the great potential of BC@ß-CD for enhancing denitrification under low C/N conditions and illustrates a potential application approach for ß-CD in wastewater bioremediation.

Photoelectric Conversion and Device Stability of PM6:PY-IT Solar Cells Based on a Water Solution-Processed MoO3 Hole Transport Layer.

To enhance the power conversion efficiency (PCE) and stability of all-polymer solar cells (all-PSCs), a new precursor solution based on an in situ chemical reaction of nanomolybdenum powder (Mo), hydrogen peroxide (H2O2), and ammonia (NH3·H2O) was developed for preparing a MoO3 hole transport layer (HTL) for all-PSCs. The results showed that the PCE and stability of PM6:PY-IT solar cells based on the MoO3 HTL were better than those based on a PEDOT:PSS layer. To further understand the relationship between the HTL and the device performance, ultrafast photophysical processes of all-PSCs based on different HTLs were contrastively analyzed. Our research indicated that the micromorphology of active layers could be influenced by the interfacial layer material, consequently determining the photoelectric conversion process of all-PSCs. The MoO3-based all-PSCs had longer charge lifetime, higher charge mobility, and lower charge recombination characteristics compared with the devices based on the PEDOT:PSS layer during the operation time. As a result, the MoO3-based PM6:PY-IT solar cells achieved an initial PCE of 15.2%, and they still maintained more than 80% of their initial efficiency after 1000 h.

Efficient aerobic denitrification without nitrite accumulation by Pseudomonas mendocina HITSZ-D1 isolated from sewage sludge.

A highly efficient aerobic denitrifying microbe was isolated from sewage sludge by using a denitrifier enrichment strategy based on decreasing carbon content. The microbe was identified as Pseudomonas mendocina HITSZ-D1 (hereafter, D1). Investigation of the conditions under which D1 grew and denitrified revealed that it performed good growth and nitrate removal performance under a wide range of conditions. In particular, D1 rapidly removed all types of inorganic nitrogen without accumulation of the intermediate products nitrite and nitrous oxide. Overall, D1 showed a total nitrogen removal efficiency >96% at a C/N ratio of 8. The biotransformation modes and fates of three typical types of inorganic nitrogen were also assessed. Moreover, D1 had significantly higher denitrification efficiency and enzyme activities than other aerobic denitrifying microbes (Paracoccus denitrificans, Pseudomonas aeruginosa, and Pseudomonas putida). These results suggest that D1 has great potential for treating wastewater containing high concentrations of nitrogen.

Density Functional Theory and Machine Learning-Based Quantitative Structure-Activity Relationship Models Enabling Prediction of Contaminant Degradation Performance with Heterogeneous Peroxymonosulfate Treatments.

Heterogeneous peroxymonosulfate (PMS) treatment is recognized as an effective advanced oxidation process (AOP) for the treatment of organic contaminants. Quantitative structure-activity relationship (QSAR) models have been applied to predict the oxidation reaction rates of contaminants in homogeneous PMS treatment systems but are seldom applied in heterogeneous treatment systems. Herein, we established QSAR models updated with density functional theory (DFT) and machine learning approaches to predict the degradation performance for a series of contaminants in heterogeneous PMS systems. We imported the characteristics of organic molecules calculated using constrained DFT as input descriptors and predicted the apparent degradation rate constants of contaminants as the output. The genetic algorithm and deep neural networks were used to improve the predictive accuracy. The qualitative and quantitative results from the QSAR model for the degradation of contaminants can be used to select the most appropriate treatment system. A strategy for selection of the optimum catalyst for PMS treatment of specific contaminants was also established according to the QSAR models. This work not only increases our understanding of contaminant degradation in PMS treatment systems but also highlights a novel QSAR model to predict the degradation performance in complicated heterogeneous AOPs.

Charge Carrier Recombination Dynamics in MAPb(BrxCl1-x)3 Single Crystals.

Understanding carrier recombination processes in MAPb(BrxCl1-x)3 crystals is essential for their photoelectrical applications. In this work, carrier recombination dynamics in MAPb(BrxCl1-x)3 single crystals were studied by steady-state photoluminescence (PL), time-resolved photoluminescence (TRPL), and time-resolved microwave photoconductivity (TRMC). By comparing TRPL and TRMC, we find TRPL of MAPb(BrxCl1-x)3 (x < 0.98) single crystals is dominated by a hole trapping process while the long-lived component of TRMC is dominated by an electron trapping process. We also find both electron and hole trapping rates of MAPb(BrxCl1-x)3 (x < 0.98) crystals decrease with an increase in Br content. A temperature-dependent PL study shows there are shallow trap states besides the deep level trap states in the MAPb(Br0.82Cl0.18)3 crystal. The activation energy for holes in shallow trap states detrapped into the valence band is ∼0.1 eV, while the activation energy for free holes to be trapped into deep trap states is ∼0.4 eV. This work provides insight into carrier recombination processes in MAPb(BrxCl1-x)3 single crystals.

Biofilm inhibition based on controlling the transmembrane transport and extracellular accumulation of quorum sensing signals.

The regulation of bacterial quorum sensing (QS) has been used to inhibit biofouling in wastewater treatment plants and the formation of biofilms. In contrast to traditional QS regulation strategies, this study aimed to obstruct the transmembrane transport process of QS signals to decrease their extracellular accumulation. Three phytochemicals, astragaloside IV, eugenol, and baicalin were selected, their effects on biofilm formation by Pseudomonas aeruginosa PA14 were studied, and the mechanisms determined. The inhibition efficiency of biofilm formation by 50 mg/L astragaloside IV, 1 mg/L eugenol, and 1 mg/L baicalin were 37%, 26%, and 26%, respectively. Confocal laser scanning microscopy and analysis of extracellular polymeric substances indicated that the three inhibitors affected the structure and composition of the biofilms. Furthermore, bacterial motility and a variety of QS-related virulence factors were suppressed by the inhibitor treatment due to changes in bacterial QS. Notably, the three inhibitors all decreased the extracellular concentration of the QS signaling molecule 3-oxo-C12-homoseine lactone by affecting the function of efflux pump MexAB-OprM. This indirectly interfered with the bacterial QS system and thus inhibited biofilm formation. In conclusion, this study revealed the inhibitory effects and inhibition mechanism of three phytochemicals on efflux pump and QS of P. aeruginosa and realized the inhibition on biofilm formation. We update the efflux pump inhibitor library and provide a new way for biofilm contamination control.

Deep learning-based prediction of effluent quality of a constructed wetland.

Data-driven approaches that make timely predictions about pollutant concentrations in the effluent of constructed wetlands are essential for improving the treatment performance of constructed wetlands. However, the effect of the meteorological condition and flow changes in a real scenario are generally neglected in water quality prediction. To address this problem, in this study, we propose an approach based on multi-source data fusion that considers the following indicators: water quality indicators, water quantity indicators, and meteorological indicators. In this study, we establish four representative methods to simultaneously predict the concentrations of three representative pollutants in the effluent of a practical large-scale constructed wetland: (1) multiple linear regression; (2) backpropagation neural network (BPNN); (3) genetic algorithm combined with the BPNN to solve the local minima problem; and (4) long short-term memory (LSTM) neural network to consider the influence of past results on the present. The results suggest that the LSTM-predicting model performed considerably better than the other deep neural network-based model or linear method, with a satisfactory R2. Additionally, given the huge fluctuation of different pollutant concentrations in the effluent, we used a moving average method to smooth the original data, which successfully improved the accuracy of traditional neural networks and hybrid neural networks. The results of this study indicate that the hybrid modeling concept that combines intelligent and scientific data preprocessing methods with deep learning algorithms is a feasible approach for forecasting water quality in the effluent of actual engineering.

Promoting Energy Transfer Between Quasi-2D Perovskite Layers Toward Highly Efficient Red Light-Emitting Diodes.

Although tremendous progress has recently been made in quasi-2D perovskite light-emitting diodes (PeLEDs), the performance of red PeLEDs emitting at ≈650-660 nm, which have wide prospects for application in photodynamic therapy, is still limited by an inefficient energy transfer process between the quasi-2D perovskite layers. Herein, a symmetric molecule of 3,3'-(9H-fluorene-9,9-diyl)dipropanamide (FDPA) is designed and developed with two functional acylamino groups and incorporated into the quasi-2D perovskites as the additive for achieving high-performance red PeLEDs. It is demonstrated that the agent can simultaneously diminish the van der Waals gaps between individual perovskite layers and passivate uncoordinated Pb2+ related defects at the surface and grain boundaries of the quasi-2D perovskites, which truly results in an efficient energy transfer in the quasi-2D perovskite films. Consequently, the red PeLEDs emitting at 653 nm with a peak external quantum efficiency of 18.5% and a maximum luminance of 2545 cd m-2 are achieved, which is among the best performing red quasi-2D PeLEDs emitting at ≈650-660 nm. This work opens a way to further improve the electroluminescence performance of red PeLEDs.

How Nitrogen and Sulfur Doping Modified Material Structure, Transformed Oxidation Pathways, and Improved Degradation Performance in Peroxymonosulfate Activation.

Current research has widely applied heteroatom doping for the promotion of catalyst activity in peroxymonosulfate (PMS) systems; however, the relationship between heteroatom doping and stimulated activation mechanism transformation is not fully understood. Herein, we introduce nitrogen and sulfur doping into a Co@rGO material for PMS activation to degrade tetracycline (TC) and systematically investigate how heteroatom doping transformed the activation mechanism of the original Co@rGO/PMS system. N was homogeneously inserted into the reduced graphene oxide (rGO) matrix of Co@rGO, inducing a significant increase in the degradation efficiency without affecting the activation mechanism transformation. Additionally, S doping converted Co3O4 to Co4S3 in Co@rGO and transformed the cooperative oxidation pathway into a single non-radical pathway with stronger intensity, which led to a higher stability against environmental interferences. Notably, based on density functional theory (DFT) calculations, we demonstrated that Co4S3 had a higher energy barrier for PMS adsorption and cleavage than Co3O4, and therefore, the radical pathway was not easily stimulated by Co4S3. Overall, this study not only illustrated the improvement due to the heteroatom doping of Co@rGO for TC degradation in a PMS system but also bridged the knowledge gap between the catalyst structure and degradation performance through activation mechanism transformation drawn from theoretical and experimental analyses.

A MALDI-TOF mass spectrometry-based haemoglobin chain quantification method for rapid screen of thalassaemia.

BACKGROUND: Thalassaemia is one of the most common inherited monogenic diseases worldwide with a heavy global health burden. Considering its high prevalence in low and middle-income countries, a cheap, accurate and high-throughput screening test of thalassaemia prior to a more expensive confirmatory diagnostic test is urgently needed. METHODS: In this study, we constructed a machine learning model based on MALDI-TOF mass spectrometry quantification of haemoglobin chains in blood, and for the first time, evaluated its diagnostic efficacy in 674 thalassaemia (including both asymptomatic carriers and symptomatic patients) and control samples collected in three hospitals. Parameters related to haemoglobin imbalance (α-globin, ß-globin, γ-globin, α/ß and α-ß) were used for feature selection before classification model construction with 8 machine learning methods in cohort 1 and further model efficiency validation in cohort 2. RESULTS: The logistic regression model with 5 haemoglobin peak features achieved good classification performance in validation cohort 2 (AUC 0.99, 95% CI 0.98-1, sensitivity 98.7%, specificity 95.5%). Furthermore, the logistic regression model with 6 haemoglobin peak features was also constructed to specifically identify ß-thalassaemia (AUC 0.94, 95% CI 0.91-0.97, sensitivity 96.5%, specificity 87.8% in validation cohort 2). CONCLUSIONS: For the first time, we constructed an inexpensive, accurate and high-throughput classification model based on MALDI-TOF mass spectrometry quantification of haemoglobin chains and demonstrated its great potential in rapid screening of thalassaemia in large populations.Key messagesThalassaemia is one of the most common inherited monogenic diseases worldwide with a heavy global health burden.We constructed a machine learning model based on MALDI-TOF mass spectrometry quantification of haemoglobin chains to screen for thalassaemia.

Why the cooperation of radical and non-radical pathways in PMS system leads to a higher efficiency than a single pathway in tetracycline degradation.

Current research focused on developing multiple active species in peroxymonosulfate (PMS) system to degrade contaminants, but deepening concern lacks over why cooperation of those active species facilitated a faster degradation. Here, we employed Co3O4, rGO and Co3O4@rGO composite to activate PMS for tetracycline (TC) degradation, and detected crucial factors toward highest performance of Co3O4@rGO/PMS system. Batch experiments exhibited a satisfactory TC degradation efficiency under Co3O4@rGO/PMS, complete degraded 50 mg/L TC within 20 min. Analytical tests discovered that radical active species generated by Co3O4/PMS and non-radical species by rGO/PMS were successfully co-existed in Co3O4@rGO/PMS system, significantly improving the performance of TC removal. Subsequently, a combination of density functional theory (DFT) calculation and intermediates analysis revealed that, in Co3O4@rGO/PMS system, the cooperation rather than independent effect of radical and non-radical active species expanded TC degradation pathways, enhancing the degradation performance. Furthermore, decent adaptability, stability, and recyclability toward affecting factors variation of Co3O4@rGO/PMS demonstrated it as a potent and economical system to degrade TC. Overall, this study developed a novel Co3O4@rGO/PMS system with a cooperative oxidation pathway for highly efficient TC removal, and managed to clarify why this oxidation pathway achieved high efficiency through a combination of theoretical and experimental method.

Cloning, characterization and expression analysis of glutathione S-transferase from the Antarctic yeast Rhodotorula mucilaginosa AN5.

The gene for glutathione S-transferase (GST) in Antarctic sea-ice yeast Rhodotorula mucilaginosa AN5 was cloned and expressed in Escherichia coli and named RmGST. Sequence analysis showed that the RmGST gene contained a 843 bp open reading frame, which encoded 280 amino acid residues with a calculated molecular mass of 30.4 kDa and isoelectric point of 5.40. RmGST has the typical C- and N-terminal double domains of glutathione S-transferase. Recombinant RmGST (rRmGST) was expressed in E. coli to produce heterologous protein that had a high specific activity of 60.2 U/mg after purification. The apparent Km values of rRmGST for glutathione and 1-chloro-2,4-dinitrobenzene were 0.35 mM and 0.40 mM, respectively. Optimum enzyme activity was measured at 35 °C and at pH 7.0 and complete inactivation was observed after incubation at 55 °C for 60 min rRmGST tolerated high salt concentrations (1.0 M NaCl) and was stable at pH 3.0. Additionally, the recombinant protein nearly kept whole activity in Hg2+ and Mn2+, and could tolerate Ca2+, Cu2+, Mg2+, Cd2+, EDTA, thiourea, urea, Tween-80, H2O2 and Triton X-100. Real-time quantitative PCR showed that relative expression of the GST gene was significantly increased under Cu2+ and low temperature stress. These results indicate that rRmGST is a typical low thermostable enzyme, while its other characteristics, heavy metal and low temperature tolerance, might be related to its Antarctic home environment.