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Abstract β-Branched aromatic α-amino acids are valuable building blocks in natural products and pharmaceutically active compounds. However, their chemical or enzymatic synthesis is challenging due to the presence of two stereocenters. We design phenylalanine ammonia lyases (PAL) variants for the direct asymmetric synthesis of β-branched aromatic α-amino acids. Based on extensive computational analyses, we unravel the enigma behind PAL’s inability to accept β-methyl cinnamic acid (β-MeCA) as substrate and achieve the synthesis of the corresponding amino acids of β-MeCA and analogs using a double (PcPAL-L256V-I460V) and a triple mutant (PcPAL-F137V-L256V-I460V). The reactions are scaled-up using an optimized E. coli based whole-cell biotransformation system to produce ten β-branched phenylalanine analogs with high diastereoselectivity (dr > 20:1) and enantioselectivity (ee > 99.5%) in yields ranging from 41-71%. Moreover, we decipher the mechanism of PcPAL-L256V-I460V for the acceptance of β-MeCA and converting it with excellent stereoselectivity by computational simulations. Thus, this study offers an efficient method for synthesizing β-branched aromatic α-amino acids.

Biocatalysis has been widely used to prepare drug leads and intermediates. Enzymatic synthesis has advantages, mainly in terms of strict chirality and regional selectivity compared with chemical methods. However, the enzymatic properties of wild-type enzymes may or may not meet the requirements for biopharmaceutical applications. Therefore, protein engineering is required to improve their catalytic activities. Thanks to advances in algorithmic models and the accumulation of immense biological data, artificial intelligence can provide novel approaches for the functional evolution of enzymes. Deep learning has the advantage of learning functions that can predict the properties of previously unknown protein sequences. Deep learning-based computational algorithms can intelligently navigate the sequence space and reduce the screening burden during evolution. Thus, intelligent computational design combined with laboratory evolution is a powerful and potentially versatile strategy for developing enzymes with novel functions. Herein, we introduce and summarize deep-learning-assisted enzyme functional adaptive evolution strategies based on recent studies on the application of deep learning in enzyme design and evolution. Altogether, with the developments of technology and the accumulation of data for the characterization of enzyme functions, artificial intelligence may become a powerful tool for the design and evolution of intelligent enzymes in the future.

To elucidate the variations in quality and volatile component changes in different forms of stored indica-japonica hybrid rice, a study was conducted with Yongyou 1540 rice as the test material. Under storage conditions of paddy, brown rice, and milled rice, compared to conventional warehousing, an investigation was conducted to assess the influence of low-temperature storage (15±1 ℃, RH 60%) on its quality (fatty acid value, sensory evaluation) and volatile components. Results showed that storing rice at low temperatures significantly enhanced sensory quality when comparing with conventional storage after six months, and the fatty acid value was notably superior after 12 months. Moreover, storing rice as paddy or brown rice significantly outperformed milled rice storage (at 12 months). Analysis using gas chromatography-ion mobility spectrometry (GC-IMS) to examine the volatile components of the rice under different treatments, aldehyde and ketone substances in rice stored for 6~12 months increased in comparison of fresh rice. Among these, the differences in volatile components were relatively minor between samples stored for 6 months and notably greater between samples stored for 12 months. Substances like 2-methyl-1-butanol, pentanal, hexanal, 3-methylbutanal were found in higher concentrations in room temperature samples compared to low-temperature storage, while cyclohexanone, 2-butanone, butanal, 3-hydroxy-2-butanone were lower in concentration. In conclusion, low-temperature storage of paddy or brown rice maintained the quality and volatile components of indica-japonica hybrid rice better than those of conventional storage. This research can provide essential data and theoretical foundations for post-harvest storage and production of indica-japonica hybrid rice.

The multicore fiber amplifier, as a key component in spatial division multiplexing (SDM) communication systems, presents higher technical difficulty compared to traditional multi-channel single core fiber amplifiers, which has sparked widespread attention. To achieve balance, efficiency, miniaturization, and cost-effectiveness in the performance of multi-core optical fiber amplifiers, we propose an innovative triple cladding 13-core Er/Yb co-doped microstructured fiber (13CEYDMOF). The proposed fiber features an outer cladding with peanut-shaped air holes, which enables uniform excitation of the 13 cores using a single multimode laser pump source within the inner cladding. This approach also prevents damage or aging of the fiber’s outer coating due to the pump laser. Furthermore, the design of Peanut-Shaped Air Holes effectively increases the numerical aperture (NA) of the inner cladding while reducing the outer diameter of the fiber, enhancing the fiber’s mechanical flexibility. To address the coupling difficulties caused by air holes, we bi-directionally pump the 13CEYDMOFA by utilizing a combined technique of the side winding and end pumping. The experimental results show that the 13CEYDMOFA can achieve an average gain of 23.8 dB, a noise figure (NF) of ∼4.6 dB, and an inter-core gain difference of less than 2 dB in the wavelength range of 1529–1565 nm. The in-line amplified transmission experiment demonstrates that the 13CEYDMOFA is well suited for the 13 spatial channels transmission. To the best of our knowledge, this is the first time to realize high performance telecommunication band amplification in a multicore microstructure fiber.

Abstract Background Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research. Results The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575. Conclusions We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.

Abstract Background The provincial-level sero-survey was launched to learn the updated seroprevalence of hepatitis B virus (HBV) infection in the general population aged 1–69 years in Chongqing and to assess the risk factors for HBV infection to effectively screen persons with chronic hepatitis B (CHB). Methods A total of 1828 individuals aged 1–69 years were investigated, and hepatitis B surface antigen (HBsAg), antibody to HBsAg (HBsAb), and antibody to B core antigen (HBcAb) were detected. Logistic regression and three machine learning (ML) algorithms, including random forest (RF), support vector machine (SVM), and stochastic gradient boosting (SGB), were developed for analysis. Results The HBsAg prevalence of the total population was 3.83%, and among persons aged 1–14 years and 15–69 years, it was 0.24% and 4.89%, respectively. A large figure of 95.18% (770/809) of adults was unaware of their occult HBV infection. Age, region, and immunization history were found to be statistically associated with HBcAb prevalence with a logistic regression model. The prediction accuracies were 0.717, 0.727, and 0.725 for the proposed RF, SVM, and SGB models, respectively. Conclusions The logistic regression integrated with ML models could helpfully screen the risk factors for HBV infection and identify high-risk populations with CHB.

The Chinese Society of Clinical Oncology (CSCO) issued the new version of the guidelines on diagnosis and treatment of NSCLC in April 2023.The new version updated the diagnostic and therapeutic strategy of rare oncogenic mutations, including ROS1 fusion, BRAF V600E mutation, NTRK fusion, MET exon 14 skipping mutation, RET fusion, and EGFR exon 20 insertion mutation, in NSCLC.This review will interpret the most important updates in the guidelines 2023 regarding the diagnosis as well as first-line and post-line therapies of these rare oncogenic mutations.

Agricultural products usually contain a large number of spoilage bacteria and endogenous enzymes, which can adversely affect the quality of agricultural products. Radio frequency is a kind of dielectric heating technology, which has fast heating speed and high efficiency, can effectively inactivate microorganisms and endogenous enzymes in agricultural products and has little influence on the quality of agricultural products， such as color, texture and nutritional composition, etc. This review summarized the mechanism of action of radiofrequency sterilization blunt enzyme technology and its application in agricultural products processing in recent years, and the shortcomings and solutions of radiofrequency sterilization blunt enzyme technology was pointed out. Moreover, the future application of radiofrequency sterilization blunt enzyme technology in agricultural products and food was also prospected.

Under the "dual carbon" background, the development of high-performance energy and environmental catalysis materials is of great significance for promoting energy clean transformation and environmental pollution control. The traditional research and development mode of catalysts mainly relies on experimental and trial-and-error methods, which to a large extent cannot meet the research and development needs of efficient catalysts in emerging energy and environmental fields. The rapid development of data science technologies such as machine learning is expected to bring about a paradigm shift in catalyst research and development. By using machine learning methods to quickly screen high-performance energy and environmental catalysis materials using experimental or computational data, the limitations of traditional trial-and-error methods could be overcome, and the problem of low efficiency and high cost in catalyst research and development could be solved. This article reviewed the main processes and research progress of machine learning methods in the development of energy and environmental catalysis materials from the perspective of active-sites prediction,catalysts screening, morphology design and reaction mechanism revelation, and the ML methods construction corresponding to various training data acquisition and their applications in the catalytic research. We also discussed the future direction of this method in the catalysis field, in order to provide perspective and promote its application in the energy and environmental fields.

Abstract Objective Population ageing, as a hot issue in global development, increases the burden of medical resources in society. This study aims to assess the current spatiotemporal evolution and interaction between population ageing and medical resources in mainland China; evaluate the matching level of medical resources to population ageing; and forecast future trends of ageing, medical resources, and the indicator of ageing-resources (IAR). Methods Data on ageing (EPR) and medical resources (NHI, NBHI, and NHTP) were obtained from China Health Statistics Yearbook and China Statistical Yearbook (2011–2020). We employed spatial autocorrelation to examine the spatial–temporal distribution trends and analyzed the spatio-temporal interaction using a Bayesian spatio-temporal effect model. The IAR, an improved evaluation indicator, was used to measure the matching level of medical resources to population ageing with kernel density analysis for visualization. Finally, an ETS-DNN model was used to forecast the trends in population ageing, medical resources, and their matching level over the next decade. Results The study found that China's ageing population and medical resources are growing annually, yet distribution is uneven across districts. There is a spatio-temporal interaction effect between ageing and medical resources, with higher levels of both in Eastern China and lower levels in Western China. The IAR is relatively high in Northwest, North China, and the Yangtze River Delta, but showed a declining trend in North China and the Yangtze River Delta. The hybrid model (ETS-DNN) gained an R2 of 0.9719, and the predicted median IAR for 2030 (0.99) across 31 regions was higher than the median IAR for 2020 (0.93). Conclusion This study analyzes the relationship between population ageing and medical resources, revealing a spatio-temporal interaction between them. The IAR evaluation indicator highlights the need to address ageing population challenges and cultivate a competent health workforce. The ETS-DNN forecasts indicate higher concentrations of both medical resources and ageing populations in eastern China, emphasizing the need for region-specific ageing security systems and health service industries. The findings provide valuable policy insights for addressing a hyper-aged society in the future.

To solve the problem of collaborative search mission planning (CSMP) for a heterogeneous unmanned aerial vehicle (UAV) swarm for dynamic targets, this paper proposes a new CSMP scheme for a heterogeneous UAV swarm. First, a new polar coordinate system motion method is used to establish the UAV motion model and decision input solution model, which effectively solves the path-unified coding problem of a heterogeneous UAV swarm. Then, in the mission area, a dynamically updated multisearch situation map model is established. Finally, to improve the global searching capability of a UAV, an improved path optimization algorithm PSO-Rolling Horizon Control (PSO-RHC) is designed. Multiple Monte Carlo simulations are performed for three time-sensitive moving target types and three constraint types. The simulation results show that the task execution efficiency indexes of the proposed scheme for the decision input solution model, pheromone update mechanism, and optimization algorithm are improved by 188%, 72%, and 102%, respectively, and the overall performance is improved by 227%.

The specific emitter identification is widely used in electronic countermeasures, spectrum control, wireless network security and other civil and military fields. In response to the problems that the traditional specific emitter identification algorithm relies on a priori knowledge and has poor generalizability, and the existing specific emitter identification algorithm based on deep learning has poor feature selection and the adopted feature extraction network is not targeted, etc., the specific emitter identification algorithm based on multi-sequence feature learning is proposed. Firstly, multiple sequence features of the emitted signal of the communication radiation source are extracted, and these features are combined into multiple sequence features. Secondly, the multiple sequence fusion convolutional network is constructed to fuse and deeply extract the multiple sequence features and complete the classification of individual communication radiation sources through the classifier of neural network. The selected sequence features of this algorithm contain more and more essential RFF information, while the targeted design of the multi-sequence feature fusion learning network can effectively extract the essential RFF information. The results show that the algorithm can significantly improve the performance of SEI compared with the benchmark algorithm, with a recognition rate gain of about 17%.

Objective·To modify previous clinically relevant post-operative pancreatic fistula (CR-POPF) risk evaluation models with quantitative evaluation of pancreatic tissue stiffness by ultrasound shear wave elastography (SWE).Methods·In this prospective study, the patients who were diagnosed as having pancreatic tumors and scheduled to undergo pancreatectomy at Zhongshan Hospital, Fudan University were initially enrolled, whose clinical information was collected. Virtual touch tissue imaging and quantification technology (VTIQ) assessment was applied to the patients within one week before the surgery to measure the shear wave velocity (SWV) of pancreatic lesions and the normal parenchyma of pancreatic body in the superficial layer of the portal vein. During the surgery, the surgeons qualitatively evaluated the stiffness of pancreases via direct palpation and divided them into soft pancreases and medium-hard pancreases. During the 3-week follow-up period after pancreatectomy, CR-POPF was diagnosed according to 2016 International Study Group of Pancreatic Fistula (ISGPF) standard. Peri-operative risk factors of CR-POPF were analyzed by univariate and multivariate Logistic regression to build the prediction model. Evaluation and comparison of diagnostic efficacy and clinical benefits among different models were then performed via receiver operating characteristic (ROC) curve and decision curve analysis (DCA).Results·From September 2021 to March 2022, 100 patients were enrolled in this study, including 33 patients (33.0%) who received pancreaticoduodenectomy (PD) and 67 patients (67.0%) who received distal pancreatectomy. CR-POPF was diagnosed in 35 patients (35.0%) during the 3-week post-pancreatectomy follow-up. Multivariate Logistic regression analysis revealed that the SWV value of the body part of pancreatic parenchyma in the superficial layer of the portal vein [lgOR=-2.934 (95%CI -4.387‒-1.479), P=0.000] and the presence of a non-dilated main pancreatic duct (≤3 mm) [lgOR=0.805 (95%CI 0.274‒1.335), P=0.003] were independent risk factors that significantly correlated with the occurrence of CR-POPF after pancreatectomy. The modified model based on the SWE parameter achieved the area under the ROC curve of 0.842, with the sensitivity, the specificity, the positive predictive value, the negative predictive value and the likelihood ratio of 85.7%, 64.6%, 70.5%, 81.8% and 2.422 in predicting CR-POPF. DCA revealed a better clinical benefit of the modified model compared to the previous prediction models [fistula risk score (FRS) and alternative fistula risk score (a-FRS)].Conclusion·The modified model based on the SWE parameter and identified clinical risk factors can make non-invasive, quantitative and objective evaluation of CR-POPF risk before pancreatectomy, and provide sufficient diagnostic efficacy and clinical benefits.

Magnetic remote-reference is the most practical noise suppression method in magnetotelluric data processing.In actual audio-frequency magnetotelluric (AMT) sounding observation, usually the "one with three" or "one with four" acquisition method is used, i.e., acquiring magnetic field of only one site and simultaneously acquiring electric field of multiple sites, without magnetic field data for remote-reference processing.The natural electro-magnetic field in the 5~1 kHz "dead band" range of signal strength is weak, resulting in the impedance in this band to have "outlies".In this paper, we found that the impedance estimated by using the local magnetic field and the local electric field as the reference in the "dead band" will be "upward" and "downward" biased respectively, while the electric field as the remote-reference channel can significantly improve the impedance quality in the "dead band".After processing the measured data of different reference distances, it is found that the effect of the electric field remote-reference processing is better than that of the local magnetic field and the local electric field.And the effect is comparable to that of magnetic field remote-reference.This has practical implications for AMT data processing.

Background: We evaluated the predictive factors associated with survival for gastric cancer patients with large tumor size (≥10 cm).Method: In this retrospective study, we investigated 279 gastric cancer patients with large-sized tumors, treated at the Chonnam National University Hospital, Gwangju, Korea. We analyzed the clinicopathological features and predictive factors associated with survival in these gastric cancer patients with large tumors. Survival was calculated using the Kaplan-Meier method and the relative prognostic importance of the parameters was investigated through Cox's proportional hazards model.Results: The recurrence rates were significantly different between the large and small tumor groups (76.3% and 36.6%, respectively; P < 0.001). Multivariate analysis showed that the presence of serosal invasion, nodal involvement, the extent of lymph node dissection, the presence of peritoneal dissemination, and curability had prognostic effects in gastric cancer patients with large-sized tumors. The 5-year survival rate was lower in patients with tumors ≥10 cm compared with those with smaller tumors (23.6% versus 57.0%, respectively; P < 0.0001). The 5-year survival rate was higher in patients with large tumor size who underwent curative resection (39.1%) than those who did not (10.0%) (P < 0.0001).Conclusion: The long-term survival of gastric cancer patients with large-sized tumors is related to curative resection. Thus, curative resection is of significance for predicting survival.

Abstract With the recent prevalence of COVID-19, cryptic transmission is worthy of attention and research. Early perception of the occurrence and development risk of cryptic transmission is an important part of controlling the spread of COVID-19. Previous relevant studies have limited data sources, and no effective analysis has been carried out on the occurrence and development of cryptic transmission. Hence, we collect Internet multisource big data (including retrieval, migration, and media data) and propose comprehensive and relative application strategies to eliminate the impact of national and media data. We use statistical classification and regression to construct an early warning model for occurrence and development. Under the guidance of the improved coronavirus herd immunity optimizer (ICHIO), we construct a “sampling-feature-hyperparameter-weight” synchronous optimization strategy. In occurrence warning, we propose an undersampling synchronous evolutionary ensemble (USEE); in development warning, we propose a bootstrap-sampling synchronous evolutionary ensemble (BSEE). Regarding the internal training data (Heilongjiang Province), the ROC-AUC of USEE3 incorporating multisource data is 0.9553, the PR-AUC is 0.8327, and the R 2 of BSEE2 fused by the “nonlinear + linear” method is 0.8698. Regarding the external validation data (Shaanxi Province), the ROC-AUC and PR-AUC values of USEE3 were 0.9680 and 0.9548, respectively, and the R 2 of BSEE2 was 0.8255. Our method has good accuracy and generalization and can be flexibly used in the prediction of cryptic transmission in various regions. We propose strategy research that integrates multiple early warning tasks based on multisource Internet big data and combines multiple ensemble models. It is an extension of the research in the field of traditional infectious disease monitoring and has important practical significance and innovative theoretical value.

ObjectiveTo explore the differential expression of messenger RNA （mRNA） in myocardial tissues of rats with sudden coronary death （SCD）, and to provide ideas for the forensic identification of SCD.MethodsThe rat SCD model was established, and the transcriptome sequencing was performed by next-generation sequencing technology. Differentially expressed genes （DEGs） in myocardial tissues of SCD rats were screened by using the R package limma. A protein-protein interaction （PPI） network was constructed by using the STRING database and Cytoscape 3.8.2 on DEG, and hub genes were screened based on cytoHubba plug-in. Finally, the R package clusterProfiler was used to analyze the biological function and signal pathway enrichment of the selected DEG.ResultsA total of 177 DEGs were associated with SCD and were mainly involved in the renin-angiotensin system and PI3K-Akt signaling pathway. The genes including angiotensinogen （AGT）, complement component 4a （C4a）, Fos proto-oncogene （FOS） and others played key roles in the development of SCD.ConclusionGenes such as AGT, C4a, FOS and other genes are expected to be potential biomarkers for forensic identification of SCD. The study based on mRNA expression profile can provide a reference for forensic identification of SCD.

BackgroundEarly stroke prognosis assessments are critical for decision-making regarding therapeutic intervention. We introduced the concepts of data combination, method integration, and algorithm parallelization, aiming to build an integrated deep learning model based on a combination of clinical and radiomics features and analyze its application value in prognosis prediction.MethodsThe research steps in this study include data source and feature extraction, data processing and feature fusion, model building and optimization, model training, and so on. Using data from 441 stroke patients, clinical and radiomics features were extracted, and feature selection was performed. Clinical, radiomics, and combined features were included to construct predictive models. We applied the concept of deep integration to the joint analysis of multiple deep learning methods, used a metaheuristic algorithm to improve the parameter search efficiency, and finally, developed an acute ischemic stroke (AIS) prognosis prediction method, namely, the optimized ensemble of deep learning (OEDL) method.ResultsAmong the clinical features, 17 features passed the correlation check. Among the radiomics features, 19 features were selected. In the comparison of the prediction performance of each method, the OEDL method based on the concept of ensemble optimization had the best classification performance. In the comparison to the predictive performance of each feature, the inclusion of the combined features resulted in better classification performance than that of the clinical and radiomics features. In the comparison to the prediction performance of each balanced method, SMOTEENN, which is based on a hybrid sampling method, achieved the best classification performance than that of the unbalanced, oversampled, and undersampled methods. The OEDL method with combined features and mixed sampling achieved the best classification performance, with 97.89, 95.74, 94.75, 94.03, and 94.35% for Macro-AUC, ACC, Macro-R, Macro-P, and Macro-F1, respectively, and achieved advanced performance in comparison with that of methods in previous studies.ConclusionThe OEDL approach proposed herein could effectively achieve improved stroke prognosis prediction performance, the effect of using combined data modeling was significantly better than that of single clinical or radiomics feature models, and the proposed method had a better intervention guidance value. Our approach is beneficial for optimizing the early clinical intervention process and providing the necessary clinical decision support for personalized treatment.

To investigate the diagnostic value of resistance index (RI) in differentiating focal liver lesions.

Hereditary cardiac disease accounts for a large proportion of sudden cardiac death （SCD） in young adults. Hereditary cardiac disease can be divided into hereditary structural heart disease and channelopathies. Hereditary structural heart disease mainly includes hereditary cardiomyopathy, which results in arhythmia, heart failure and SCD. The autopsy and histopathological examinations of SCD caused by channelopathies lack characteristic morphological manifestations. Therefore, how to determine the cause of death in the process of examination has become one of the urgent problems to be solved in forensic identification. Based on the review of recent domestic and foreign research results on channelopathies and hereditary cardiomyopathy, this paper systematically reviews the pathogenesis and molecular genetics of channelopathies and hereditary cardiomyopathy, and discusses the application of postmortem genetic testing in forensic identification, to provide reference for forensic pathology research and identification of SCD.

China's convenience food industry is developing rapidly. In recent years, the development of new product standards for convenience food has been continuously strengthened. Besides the approved industry standards such as “convenience dishes”, there are a number of industry standards currently being approved for development plan, which will effectively guide and standardize the industry's high-quality and healthy development. But on the whole, the product standard in this field needs to be revised and the product standard system needs to be further improved. By analyzing and interpreting the current standards and the standard system and the main contents of the newly formulated standards, this paper sorts out the main problems in the formulation and implementation of the standards, and puts forward the direction and suggestions for the construction of the standard system and its future revision, to facilitate the food industry’s product research and development, production and supervision to provide a technical basis.

Carbon sequestration refers to the technology of capturing carbon and storing it safely instead of directly discharging CO2 into the atmosphere. Rock salt is a promising CO2 reservoir, and the underground rock salt structure formed by salt cavern reservoirs in geological history has a natural sealing function. Storing carbon dioxide in salt caves can ensure that carbon dioxide is stably stored underground for a long time. However, at present, there are few studies on carbon sequestration in abandoned salt caves. This study analyses the characteristics of carbonate-evaporite strata in the high fold zone of eastern Sichuan and qualitatively evaluates Triassic carbonate-evaporite layers formed in the depth range of 800-3000 m in various sedimentary basins in Chongqing. A large set of carbonates-evaporites are developed in Chongqing, and the carbonate rocks near evaporites have low porosity and permeability, which can be used as CO2 storage caprock in salt cavities. Thick evaporated strata can also provide a good cover for CO2 storage in abandoned salt caves, which has great potential space resources.

Both the anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA) and unilateral absence of the pulmonary artery (UAPA) are rare congenital malformations, ALCAPA accompanied with UAPA is extremely rare. Here, we reported a middle-aged man admitted to our department for evaluation of chest pain during exercise. Physical examination and lab tests did not unveil obvious abnormality; however, transthoracic echocardiogram (TTE) revealed multivessel myocardial collateral blood flow signals in the left ventricular wall and ventricular septum, a shunting flow from the left coronary artery into the pulmonary artery and dilated right coronary artery (RCA), which supported but did not confirm the diagnosis of ALCAPA. Coronary angiography (CAG) showed an absent left coronary ostium and a dilated RCA, with extensive collaterals supplying the left coronary system. Multidetector computed tomography angiography (MDCTA) was then performed and revealed the anomalous origin of the left main coronary artery (LMCA) arising from the pulmonary artery, and it incidentally unveiled another rare congenital malformation of UAPA. The patient underwent surgical correction of ALCAPA by reimplantation of the LMCA to the aorta, without surgical treatment of UAPA. The patient had been in good clinical condition and remained angina free with good exercise tolerance during follow-up (∼6 months so far). In this case, we discussed the diagnostic value of TTE, CAG, and MDCTA on rare abnormalities as ALCAPA and UAPA. We highlighted the role of multiple non-invasive imaging modalities in diagnosing rare causes of angina in adult patients, and the importance of careful examination in avoiding misdiagnosis. To our best knowledge, this is the first report of ALCAPA accompanied with UAPA in an adult patient.

Atrial fibrillation (AF) is one of the most common arrhythmias encountered in clinical practice. The pathophysiological mechanisms responsible for its development are complex, vary amongst individuals, and associated with predisposing factors. Here, we report a case of AF caused by annular constrictive pericarditis (ACP), which is extremely rare due to its unusual anatomical form. In our patient, AF was refractory to multiple antiarrhythmic medications; however, spontaneous conversion to sinus rhythm occurred when the ring encircling the right and left ventricular (RV and LV) cavities along the atrioventricular (AV) groove was severed. This suggests that atrial stretch due to atrial enlargement and increased left atrial (LA) pressure may contribute to the initiation and maintenance of AF. This report highlights the importance of the careful investigation of rare predisposing factors for AF using non-invasive diagnostic approaches and mechanical-electric feedback (MEF) as a pathophysiological mechanism for AF initiation and maintenance.

BackgroundThe Shanghai COVID-19 epidemic is an important example of a local outbreak and of the implementation of normalized prevention and disease control strategies. The precise impact of public health interventions on epidemic prevention and control is unknown.MethodsWe collected information on COVID-19 patients reported in Shanghai, China, from January 30 to May 31, 2022. These newly added cases were classified as local confirmed cases, local asymptomatic infections, imported confirmed cases and imported asymptomatic infections. We used polynomial fitting correlation analysis and illustrated the time lag plot in the correlation analysis of local and imported cases. Analyzing the conversion of asymptomatic infections to confirmed cases, we proposed a new measure of the conversion rate (Cr). In the evolution of epidemic transmission and the analysis of intervention effects, we calculated the effective reproduction number (Rt). Additionally, we used simulated predictions of public health interventions in transmission, correlation, and conversion analyses.Results(1) The overall level of Rt in the first three stages was higher than the epidemic threshold. After the implementation of public health intervention measures in the third stage, Rt decreased rapidly, and the overall Rt level in the last three stages was lower than the epidemic threshold. The longer the public health interventions were delayed, the more cases that were expected and the later the epidemic was expected to end. (2) In the correlation analysis, the outbreak in Shanghai was characterized by double peaks. (3) In the conversion analysis, when the incubation period was short (3 or 7 days), the conversion rate fluctuated smoothly and did not reflect the effect of the intervention. When the incubation period was extended (10 and 14 days), the conversion rate fluctuated in each period, being higher in the first five stages and lower in the sixth stage.ConclusionEffective public health interventions helped slow the spread of COVID-19 in Shanghai, shorten the outbreak duration, and protect the healthcare system from stress. Our research can serve as a positive guideline for addressing infectious disease prevention and control in China and other countries and regions.

Aim. This study evaluated the survival of gastric cancer patients with metastasis to the hepatoduodenal, retropancreatic, mesenteric, and para-aortic lymph nodes. Materials and methods. We analyzed the survival rate of 435 gastric cancer patients who underwent operation from 2001 to 2010 at the Department of Surgery, Chonnam National University Hospital. There were 43, 25, 16, and 55 patients with metastasis to the hepatoduodenal, retropancreatic, mesenteric, and para-aortic nodes, respectively. Results. Based on tumor location, metastasis to the para-aortic lymph nodes was more common in upper-third cancer, and that to the hepatoduodenal lymph nodes was more common in lower-third cancer. The survival rate of patients with non-regional lymph node metastasis was better than that of patients with hepatic metastasis or peritoneal dissemination (p

A metal- and aldehyde-free visible-light-driven photoredox-neutral alkene acylarylation with readily available cyanoarenes is described. A variety of 3-(arylmethyl)chroman-4-ones (i.e., homoisoflavonoids) and analogs are efficiently synthesized with good functional group tolerance. This mild protocol relies on a phosphoranyl radical-mediated acyl radical-initiated cyclization and selective radical-radical coupling sequence, and is also further highlighted by subsequent derivatization to chromone and 2H-chromene as well as its application in the three-component alkene acylarylation.

A chemodivergent photocatalytic approach to 1-pyrrolines and 1-tetralones from alkyl bromides and vinyl azides has been developed through chemoselectively controllable intermolecular [3 + 2] and [4 + 2] cyclization. This photoredox-neutral two-component protocol involves intermolecular radical addition and switchable distal C(sp3)–H functionalization enabled by iminyl radical-mediated 1,5-hydrogen atom transfer. Meanwhile, chemoselectivity between C(sp3)–N bond formation and C(sp3)–C(sp2) bond formation is precisely switched by photocatalysts (Ru(bpy)3(PF6)2 vs. fac-Ir(ppy)3) and additives (base vs. acid).

Aim: The detection and segmentation of cerebral microbleeds (CMBs) images are the focus of clinical diagnosis and treatment. However, segmentation is difficult in clinical practice, and missed diagnosis may occur. Few related studies on the automated segmentation of CMB images have been performed, and we provide the most effective CMB segmentation to date using an automated segmentation system.Materials and Methods: From a research perspective, we focused on the automated segmentation of CMB targets in susceptibility weighted imaging (SWI) for the first time and then constructed a deep learning network focused on the segmentation of micro-objects. We collected and marked clinical datasets and proposed a new medical micro-object cascade network (MMOC-Net). In the first stage, U-Net was utilized to select the region of interest (ROI). In the second stage, we utilized a full-resolution network (FRN) to complete fine segmentation. We also incorporated residual atrous spatial pyramid pooling (R-ASPP) and a new joint loss function.Results: The most suitable segmentation result was achieved with a ROI size of 32 × 32. To verify the validity of each part of the method, ablation studies were performed, which showed that the best segmentation results were obtained when FRN, R-ASPP and the combined loss function were used simultaneously. Under these conditions, the obtained Dice similarity coefficient (DSC) value was 87.93% and the F2-score (F2) value was 90.69%. We also innovatively developed a visual clinical diagnosis system that can provide effective support for clinical diagnosis and treatment decisions.Conclusions: We created the MMOC-Net method to perform the automated segmentation task of CMBs in an SWI and obtained better segmentation performance; hence, this pioneering method has research significance.

Introduction: The impact of age on the prognosis of patients with gastric cancer is controversial. This study aimed to investigate the clinicopathologic features and prognosis of elderly, advanced gastric cancer patients without serosal invasion compared to their younger counterparts. Materials and Methods: We retrospectively evaluated 43 elderly patients with advanced gastric cancer without serosal invasion. The clinicopathologic findings were compared between the elderly (age >70 years) and young (age

Summary Coronavirus‐related disease 2019 (COVID‐19) became a pandemic in February 2020, and worldwide researchers try to tackle the disease with approved drugs of all kinds, or to develop novel compounds inhibiting viral spreading. Flavonoids, already investigated as antivirals in general, also might bear activities specific for the viral agent causing COVID‐19, SARS‐CoV‐2. Microbial biotechnology and especially synthetic biology may help to produce flavonoids, which are exclusive plant secondary metabolites, at a larger scale or indeed to find novel pharmaceutically active flavonoids. Here, we review the state of the art in (i) antiviral activity of flavonoids specific for coronaviruses and (ii) results derived from computational studies, mostly docking studies mainly inhibiting specific coronaviral proteins such as the 3CL (main) protease, the spike protein or the RNA‐dependent RNA polymerase. In the end, we strive towards a synthetic biology pipeline making the fast and tailored production of valuable antiviral flavonoids possible by applying the last concepts of division of labour through co‐cultivation/microbial community approaches to the DBTL (Design, Build, Test, Learn) principle.

Adequate evidence has shown that miRNA-disease interactions are strongly involved in the pathological processes of complex human diseases. However, it is commonly time-consuming and labor-intensive to utilize laboratory biological experiments to reveal unknown miRNA-disease pairs. Since the previously proposed calculation model has more or fewer deficiencies, we developed the semi-supervised method called Hessian Regularized Non-negative Matrix Factorization Method for miRNA-disease Association prediction (HRNMFMDA). This model introduced Hessian regularization into the NMF framework to preserve the local manifold information and increased an $l_{2,1}$ -norm penalty term to ensure the feature selection of the coding matrix and an approximate orthogonal constraint to obtain discriminative information. In the model performance evaluation, HRNMFMDA outperformed eight existing models, achieving AUC values of 0.9074, 0.8618, and 0.9044+/-0.0080 in the global leave-one-out cross-validation (LOOCV), local LOOCV and 5-fold cross-validation, respectively. In addition, we applied HRNMFMDA to several high-incidence human carcinomas via three kinds of case studies. Almost all predicted miRNAs were confirmed by external databases derived from experimental literature. Therefore, the conclusion can be drawn that HRNMFMDA is reliable for revealing uncovered miRNA-disease pairs.

Small defects on the rails develop fast under the continuous load of passing trains, and this may lead to train derailment and other disasters. In recent years, many types of wireless sensor systems have been developed for rail defect detection. However, there has been a lack of comprehensive reviews on the working principles, functions, and trade-offs of these wireless sensor systems. Therefore, we provide in this paper a systematic review of recent studies on wireless sensor-based rail defect detection systems from three different perspectives: sensing principles, wireless networks, and power supply. We analyzed and compared six sensing methods to discuss their detection accuracy, detectable types of defects, and their detection efficiency. For wireless networks, we analyzed and compared their application scenarios, the advantages and disadvantages of different network topologies, and the capabilities of different transmission media. From the perspective of power supply, we analyzed and compared different power supply modules in terms of installation and energy harvesting methods, and the amount of energy they can supply. Finally, we offered three suggestions that may inspire the future development of wireless sensor-based rail defect detection systems.