Broad environmental adaptation of abundant microbial taxa in Robinia pseudoacacia forests during long-term vegetation restoration.

Vegetation restoration has significant impacts on ecosystems, and a comprehensive understanding of microbial environmental adaptability could facilitate coping with ecological challenges such as environmental change and biodiversity loss. Here, abundant and rare soil bacterial and fungal communities were characterized along a 15-45-year chronosequence of forest vegetation restoration in the Loess Plateau region. Phylogenetic-bin-based null model analysis (iCAMP), niche breadth index, and co-occurrence network analysis were used to assess microbial community assembly and environmental adaptation of a Robinia pseudoacacia plantation under long-term vegetation restoration. The drift process governed community assembly of abundant and rare soil fungi and bacteria. With increasing soil total phosphorus content, the relative importance of drift increased, while dispersal limitation and heterogeneous selection exhibited opposite trends for abundant and rare fungi. Rare soil fungal composition dissimilarities were dominated by species replacement processes. Abundant microbial taxa had higher ecological niche width and contribution to ecosystem multifunctionality than rare taxa. Node property values (e.g., degree and betweenness) of abundant microbial taxa were substantially higher than those of rare microbial taxa, indicating abundant species occupied a central position in the network. This study provides insights into the diversity and stability of microbial communities during vegetation restoration in Loess Plateau. The findings highlight that abundant soil fungi and bacteria have broad environmental adaptation and major implications for soil multifunctionality under long-term vegetation restoration.

Construction and effectiveness evaluation of a knowledge-based infectious disease monitoring and decision support system.

To improve the hospital's ability to proactively detect infectious diseases, a knowledge-based infectious disease monitoring and decision support system was established based on real medical records and knowledge rules. The effectiveness of the system was evaluated using interrupted time series analysis. In the system, a monitoring and alert rule library for infectious diseases was generated by combining infectious disease diagnosis guidelines with literature and a real medical record knowledge map. The system was integrated with the electronic medical record system, and doctors were provided with various types of real-time warning prompts when writing medical records. The effectiveness of the system's alerts was analyzed from the perspectives of false positive rates, rule accuracy, alert effectiveness, and missed case rates using interrupted time series analysis. Over a period of 12 months, the system analyzed 4,497,091 medical records, triggering a total of 12,027 monitoring alerts. Of these, 98.43% were clinically effective, while 1.56% were invalid alerts, mainly owing to the relatively rough rules generated by the guidelines leading to several false alarms. In addition, the effectiveness of the system's alerts, distribution of diagnosis times, and reporting efficiency of doctors were analyzed. 89.26% of infectious disease cases could be confirmed and reported by doctors within 5 min of receiving the alert, and 77.6% of doctors could complete the filling of 33 items of information within 2 min, which is a reduction in time compared to the past. The timely reminders from the system reduced the rate of missed cases by doctors; the analysis using interrupted time series method showed an average reduction of 4.4037% in the missed-case rate. This study proposed a knowledge-based infectious disease decision support system based on real medical records and knowledge rules, and its effectiveness was verified. The system improved the management of infectious diseases, increased the reliability of decision-making, and reduced the rate of underreporting.

Preparation of polyclonal antibodies against the Drosophila deacetylases SIRT 6 and SIRT 7.

SIRT6 and SIRT7, as members of the Sirtuins family, are indispensable for the growth and development of Drosophila. They play crucial roles in maintaining genome stability, regulating metabolic senescence, and controlling tumorigenesis. To investigate their involvement in the Drosophila life cycle, we focused on describing the expression and purification of recombinant Drosophila SIRT6 and SIRT7 proteins. Subsequently, these proteins were utilized for generating polyclonal antibodies against Drosophila SIRT6 and SIRT7. The recombinant expression plasmid was introduced into E. coli cells to enable the production of SIRT6 and SIRT7 proteins. Following immunizations of New Zealand white rabbits and guinea pigs with the recombinant proteins as antigens, specific polyclonal antisera against both proteins were obtained. After purification, the specificity of SIRT6 and SIRT7 was confirmed using ELISA and western blot analyses, demonstrating strong specificity. These antibodies hold promise for the development of detection assays required for further research.

Can ocean carbon sink trading achieve economic and environmental benefits? Simulation based on DICE-DSGE model.

Low-carbon development requires joint efforts in terms of "carbon reduction" and "carbon sink increase." This study thus proposes a DICE-DSGE model for exploring the environmental and economic benefits of ocean carbon sinks and provides policy suggestions for marine economic development and carbon emission policy choices. The results are as follows: (1) while the economic benefits of heterogeneous technological shocks are apparent, the environmental benefits of carbon tax and carbon quota shocks are significant; (2) increasing the efficiency of ocean carbon sinks improves the environmental benefits of technological shocks as well as the output benefits of emission reduction tools, while increasing the share of marine output can improve both the economic benefits of technological shocks and the environmental benefits of emission reduction tools; and (3) ocean output proportion has the most considerable positive effect on social welfare, followed by marine total factor productivity (TFP). The correlation effect of ocean carbon sink efficiency is negative.

A neuroprotective role of Ufmylation through Atg9 in the aging brain of Drosophila.

Ufmylation is a recently identified small ubiquitin-like modification, whose biological function and relevant cellular targets are poorly understood. Here we present evidence of a neuroprotective role for Ufmylation involving Autophagy-related gene 9 (Atg9) during Drosophila aging. The Ufm1 system ensures the health of aged neurons via Atg9 by coordinating autophagy and mTORC1, and maintaining mitochondrial homeostasis and JNK (c-Jun N-terminal kinase) activity. Neuron-specific expression of Atg9 suppresses the age-associated movement defect and lethality caused by loss of Ufmylation. Furthermore, Atg9 is identified as a conserved target of Ufm1 conjugation mediated by Ddrgk1, a critical regulator of Ufmylation. Mammalian Ddrgk1 was shown to be indispensable for the stability of endogenous Atg9A protein in mouse embryonic fibroblast (MEF) cells. Taken together, our findings might have important implications for neurodegenerative diseases in mammals.

Clinical assistant decision-making model of tuberculosis based on electronic health records.

BACKGROUND: Tuberculosis is a dangerous infectious disease with the largest number of reported cases in China every year. Preventing missed diagnosis has an important impact on the prevention, treatment, and recovery of tuberculosis. The earliest pulmonary tuberculosis prediction models mainly used traditional image data combined with neural network models. However, a single data source tends to miss important information, such as primary symptoms and laboratory test results, that is available in multi-source data like medical records and tests. In this study, we propose a multi-stream integrated pulmonary tuberculosis diagnosis model based on structured and unstructured multi-source data from electronic health records. With the limited number of lung specialists and the high prevalence of tuberculosis, the application of this auxiliary diagnosis model can make substantial contributions to clinical settings. METHODS: The subjects were patients at the respiratory department and infectious cases department of a large comprehensive hospital in China between 2015 to 2020. A total of 95,294 medical records were selected through a quality control process. Each record contains structured and unstructured data. First, numerical expressions of features for structured data were created. Then, feature engineering was performed through decision tree model, random forest, and GBDT. Features were included in the feature exclusion set as per their weights in descending order. When the importance of the set was higher than 0.7, this process was concluded. Finally, the contained features were used for model training. In addition, the unstructured free-text data was segmented at the character level and input into the model after indexing. Tuberculosis prediction was conducted through a multi-stream integration tuberculosis diagnosis model (MSI-PTDM), and the evaluation indices of accuracy, AUC, sensitivity, and specificity were compared against the prediction results of XGBoost, Text-CNN, Random Forest, SVM, and so on. RESULTS: Through a variety of characteristic engineering methods, 20 characteristic factors, such as main complaint hemoptysis, cough, and test erythrocyte sedimentation rate, were selected, and the influencing factors were analyzed using the Chinese diagnostic standard of pulmonary tuberculosis. The area under the curve values for MSI-PTDM, XGBoost, Text-CNN, RF, and SVM were 0.9858, 0.9571, 0.9486, 0.9428, and 0.9429, respectively. The sensitivity, specificity, and accuracy of MSI-PTDM were 93.18%, 96.96%, and 96.96%, respectively. The MSI-PTDM prediction model was installed at a doctor workstation and operated in a real clinic environment for 4 months. A total of 692,949 patients were monitored, including 484 patients with confirmed pulmonary tuberculosis. The model predicted 440 cases of pulmonary tuberculosis. The positive sample recognition rate was 90.91%, the false-positive rate was 9.09%, the negative sample recognition rate was 96.17%, and the false-negative rate was 3.83%. CONCLUSIONS: MSI-PTDM can process sparse data, dense data, and unstructured text data concurrently. The model adds a feature domain vector embedding the medical sparse features, and the single-valued sparse vectors are represented by multi-dimensional dense hidden vectors, which not only enhances the feature expression but also alleviates the side effects of sparsity on the model training. However, there may be information loss when features are extracted from text, and adding the processing of original unstructured text makes up for the error within the above process to a certain extent, so that the model can learn data more comprehensively and effectively. In addition, MSI-PTDM also allows interaction between features, considers the combination effect between patient features, adds more complex nonlinear calculation considerations, and improves the learning ability of the model. It has been verified using a test set and via deployment within an actual outpatient environment.

Can environmental regulation promote the green output bias in China's mariculture?

The green output bias of technological progress is the key factor driving the green transformation of mariculture. This study considers whether environmental regulation can promote the green output bias. Utilizing data envelopment analysis (DEA), we decomposed the green output biased technological progress index (OBTC) and constructed the index to measure the output bias between output value and pollutant emissions. The results show spatial and temporal differences in the green output bias, with different bias values for three major Chinese coastal regions during the study period. As the bias value mainly fluctuates around the zero value, the degree of green output bias must be enhanced. Considering the heterogeneity of environmental regulation, we empirically study the relation that exists between environmental regulation and green output bias. Our findings indicate a U-shaped relation between two categories of environmental regulation and the green output bias. Green technology partially intermediates the mechanisms by which these environmental regulations influence green output bias. Our results have implications for policymakers and other stakeholders for strengthening environmental regulation, promoting green technological innovation, and the scientific planning of the species and scale of mariculture to promote sustainable development.

Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records.

PURPOSE: Predictively diagnosing infectious diseases helps in providing better treatment and enhances the prevention and control of such diseases. This study uses actual data from a hospital. A multiple infectious disease diagnostic model (MIDDM) is designed for conducting multi-classification of infectious diseases so as to assist in clinical infectious-disease decision-making. METHODS: Based on actual hospital medical records of infectious diseases from December 2012 to December 2020, a deep learning model for multi-classification research on infectious diseases is constructed. The data includes 20,620 cases covering seven types of infectious diseases, including outpatients and inpatients, of which training data accounted for 80%, i.e., 16,496 cases, and test data accounted for 20%, i.e., 4124 cases. Through the auto-encoder, data normalization and sparse data densification processing are carried out to improve the model training effect. A residual network and attention mechanism are introduced into the MIDDM model to improve the performance of the model. RESULT: MIDDM achieved improved prediction results in diagnosing seven kinds of infectious diseases. In the case of similar disease diagnosis characteristics and similar interference factors, the prediction accuracy of disease classification with more sample data is significantly higher than the prediction accuracy of disease classification with fewer sample data. For instance, the training data for viral hepatitis, influenza, and hand foot and mouth disease were 2954, 3924, and 3015 respectively and the corresponding test accuracy rates were 99.86%, 98.47%, and 97.31%. There is less training data for syphilis, infectious diarrhea, and measles, i.e., 1208, 575, and 190 respectively and the corresponding test accuracy rates were noticeably lower, i.e., 83.03%, 87.30%, and42.11%. We also compared the MIDDM model with the models used in other studies. Using the same input data, taking viral hepatitis as an example, the accuracy of MIDDM is 99.44%, which is significantly higher than that of XGBoost (96.19%), Decision tree (90.13%), Bayesian method (85.19%), and logistic regression (91.26%). Other diseases were also significantly better predicted by MIDDM than by these three models. CONCLUSION: The application of the MIDDM model to multi-class diagnosis and prediction of infectious diseases can improve the accuracy of infectious-disease diagnosis. However, these results need to be further confirmed via clinical randomized controlled trials.

The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method.

Recently, combining feature extraction and classification method of electroencephalogram (EEG) signals has been widely used in identifying mild cognitive impairment. However, it remains unclear which feature of EEG signals is best effective in assessing amnestic mild cognitive impairment (aMCI) with type 2 diabetes mellitus (T2DM) when combining one classifier. This study proposed a novel feature extraction method of EEG signals named feature-fusion multispectral image method (FMIM) for diagnosis of aMCI with T2DM. The FMIM was integrated with convolutional neural network (CNN) to classify the processed multispectral image data. The results showed that FMIM could effectively identify aMCI with T2DM from the control group compared to existing multispectral image method (MIM), with improvements including the type and quantity of feature extraction. Meanwhile, part of the invalid calculation could be avoided during the classification process. In addition, the classification evaluation indexes were best under the combination of Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-1, and were also best under the combination of the Theta-Alpha1-Alpha2-Beta1-Beta2 frequency bands in data set based on FMIM-2. Therefore, FMIM can be used as an effective feature extraction method of aMCI with T2DM, and as a valuable biomarker in clinical applications.