Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness - China, 2014-2019.

Introduction: This study investigated the lagged correlation between Baidu Index for influenza-related keywords and influenza-like illness percentage (ILI%) across regions in China. The aim is to establish a scientific foundation for utilizing Baidu Index as an early warning tool for influenza-like illness epidemics. Methods: In this study, data on ILI% and Baidu Index were collected from 30 provincial-level administrative divisions (PLADs) spanning April 2014 to March 2019. The Baidu Index was categorized into Overall Index, Ordinary Index, Prevention Index, Symptom Index, and Treatment Index based on search query themes. The lagged correlation between the Baidu Index and ILI% was examined through the cross-correlation function (CCF) method. Results: Correlating the Baidu Overall Index of 30 PLADs with ILI% revealed CCF values ranging from 0.46 to 0.86, with a median lag of 0.5 days. Subcategory analysis indicated that the Prevention Index and Symptom Index exhibited quicker responses to ILI%, with median lags of -9 and -0.5 days, respectively, compared to 0 and 3 days for the Ordinary and Treatment Indexes. The median lag days between the Baidu Index and the ILI% were earlier in the northern PLADs compared to the southern PLADs. Discussion: The Prevention and Symptom Indexes show promising predictive capabilities for influenza-like illness epidemics.

Comparison Between Threshold Method and Artificial Intelligence Approaches for Early Warning of Respiratory Infectious Diseases - Weifang City, Shandong Province, China, 2020-2023.

Introduction: Respiratory infectious diseases, such as influenza and coronavirus disease 2019 (COVID-19), present significant global public health challenges. The emergence of artificial intelligence (AI) and big data offers opportunities to improve traditional disease surveillance and early warning systems. Methods: The study analyzed data from January 2020 to May 2023, comprising influenza-like illness (ILI) statistics, Baidu index, and clinical data from Weifang. Three methodologies were evaluated: the adaptive dynamic threshold method (ADTM) for dynamic threshold adjustments, the machine learning supervised method (MLSM), and the machine learning unsupervised method (MLUM) utilizing anomaly detection. The comparison focused on sensitivity, specificity, timeliness, and warning consistency. Results: ADTM issued 37 warnings with a sensitivity of 71% and a specificity of 85%. MLSM generated 35 warnings, with a sensitivity of 82% and a specificity of 87%. MLUM produced 63 warnings with a sensitivity of 100% and specificity of 80%. The initial warnings from ADTM and MLUM preceded those from MLSM by five days. The Kappa coefficient indicated moderate agreement between the methods, with values ranging from 0.52 to 0.62 (P<0.05). Discussion: The study explores the comparison between traditional methods and two machine learning approaches for early warning systems. It emphasizes the validation of machine learning's reliability and underscores the unique advantages of each method. Furthermore, it stresses the significance of integrating machine learning models with various data sources to enhance public health preparedness and response, alongside acknowledging limitations and the need for broader validation.

Identification of suitable habitats and priority conservation areas under climate change scenarios for the Chinese alligator (Alligator sinensis).

Amphibians and reptiles, especially the critically endangered Chinese alligators, are vulnerable to climate change. Historically, the decline in suitable habitats and fragmentation has restricted the distribution of Chinese alligators to a small area in southeast Anhui Province in China. However, the effects of climate change on range-restricted Chinese alligator habitats are largely unknown. We aimed to predict current and future (2050s and 2070s) Chinese alligator distribution and identify priority conservation areas under climate change. We employed species distribution models, barycenter migration analyses, and the Marxian model to assess current and future Chinese alligator distribution and identify priority conservation areas under climate change. The results showed that the lowest temperature and rainfall seasonality in the coldest month were the two most important factors affecting the distribution of Chinese alligators. Future predictions indicate a reduction (3.39%-98.41%) in suitable habitats and a westward shift in their distribution. Further, the study emphasizes that suitable habitats for Chinese alligators are threatened by climate change. Despite the impact of the Anhui Chinese Alligator National Nature Reserve, protection gaps persist, with 78.27% of the area lacking priority protected area. Our study provides crucial data for Chinese alligator adaptation to climate change and underscores the need for improved conservation strategies. Future research should refine conservation efforts, consider individual plasticity, and address identified limitations to enhance the resilience of Chinese alligator populations in the face of ongoing climate change.

Community medical service construction: identifying factors that influence medical choice for patients with non-communicable chronic diseases in the Southwest China.

BACKGROUND: Community medical institutions play a vital role in China's healthcare system. While the number of these institutions has increased in recent years, their construction contents remain insufficient. The potential of community medical institutions in preventing, screening, diagnosing, and treating non-communicable chronic diseases (NCDs) has not been fully utilized. This study aims to assess the status of construction contents in community medical institutions in Southwest China and examine how these contents influence the medical choices of NCD patients. METHODS: Descriptive statistics were used to evaluate the construction content of community medical institutions. Multiple-sets of multinomial logistic regression were employed to analyze the associations and marginal impacts between construction content and medical choices. Shapley value analysis was applied to determine the contribution and ranking of these impacts. RESULTS: Descriptive statistics revealed satisfactory construction contents in community medical institutions. Notably, factors such as service attitude, nursing services, expert consultations, charging standards, medical equipment, medical examinations, privacy protection, and referrals significantly influenced medical choices. Among these, service attitude, charging standards, and privacy protection had the most significant marginal improvement effects on NCD patients' choices, with improvements of 12.7%, 10.2%, and 5.9%, respectively. The combined contribution of privacy protection, medical examinations, service attitude, charging standards, and nursing services to medical choices exceeded 80%. CONCLUSION: Optimizing the service contents of community institutions can encourage NCD patients to seek medical care at grassroots hospitals. This study addresses crucial gaps in existing literature and offers practical insights for implementing new medical reform policies, particularly in underdeveloped regions of Southwest China focusing on hierarchical diagnosis and treatment.

Predicting influenza-like illness trends based on sentinel surveillance data in China from 2011 to 2019: A modelling and comparative study1.

Background: Influenza is an acute respiratory infectious disease with a significant global disease burden. Additionally, the coronavirus disease 2019 pandemic and its related non-pharmaceutical interventions (NPIs) have introduced uncertainty to the spread of influenza. However, comparative studies on the performance of innovative models and approaches used for influenza prediction are limited. Therefore, this study aimed to predict the trend of influenza-like illness (ILI) in settings with diverse climate characteristics in China based on sentinel surveillance data using three approaches and evaluate and compare their predictive performance. Methods: The generalized additive model (GAM), deep learning hybrid model based on Gate Recurrent Unit (GRU), and autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) model were established to predict the trends of ILI 1-, 2-, 3-, and 4-week-ahead in Beijing, Tianjin, Shanxi, Hubei, Chongqing, Guangdong, Hainan, and the Hong Kong Special Administrative Region in China, based on sentinel surveillance data from 2011 to 2019. Three relevant metrics, namely, Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R squared, were calculated to evaluate and compare the goodness of fit and robustness of the three models. Results: Considering the MAPE, RMSE, and R squared values, the ARMA-GARCH model performed best, while the GRU-based deep learning hybrid model exhibited moderate performance and GAM made predictions with the least accuracy in the eight settings in China. Additionally, the models' predictive performance declined as the weeks ahead increased. Furthermore, blocked cross-validation indicated that all models were robust to changes in data and had low risks of overfitting. Conclusions: Our study suggested that the ARMA-GARCH model exhibited the best accuracy in predicting ILI trends in China compared to the GAM and GRU-based deep learning hybrid model. Therefore, in the future, the ARMA-GARCH model may be used to predict ILI trends in public health practice across diverse climatic zones, thereby contributing to influenza control and prevention efforts.

Enhancing infectious diseases early warning: A deep learning approach for influenza surveillance in China.

Objective: This study aimed to develop a universally applicable, feedback-informed Self-Excitation Attention Residual Network (SEAR) model. This model dynamically adapts to evolving disease trends and surveillance system changes, accommodating various scenarios. Thereby enhancing the effectiveness of early warning systems. Methods: Surveillance data on influenza-like illness (ILI) was collected from various regions including Northern China, Southern China, Beijing, and Yunnan. The reproduction number (Rt) was estimated to determine the threshold for issuing warnings. The Self-Excitation Attention Residual Network (SEAR) was devised employing deep learning algorithms and was trained, validated, and tested. The SEAR model's efficacy was assessed based on five metrics: accuracy rate, recall rate, F1 score, confusion matrix, and the receiver operating characteristic curve. Results: With an advance warning set at three days, the SEAR model outperformed five primary models - logistic regression, support vector machine, random forest, Extreme Gradient Boosting, and Long Short-Term Memory model - in all five evaluation metrics. Notably, the model's warning performance declined with an increase in the early warning value and the number of warning days, albeit maintaining a ROC value over 0.7 in all scenarios. Conclusion: The SEAR model demonstrated robust early warning performance for influenza in diverse Chinese regions with high accuracy and specificity. This novel model, augmenting traditional systems, supports widespread application for respiratory disease outbreak monitoring. Future evaluations could incorporate alternative indicators, with the model continuously updating through data feedback, thus enhancing its universal applicability. Ongoing optimization, using iterative feedback and expert judgment, heralds a transformative approach to surveillance-based early warning strategies.

SAR model for accurate detection of multi-label arrhythmias from electrocardiograms.

Objective: Arrhythmias are prevalent symptoms of cardiovascular disease, necessitating accurate and timely detection to mitigate associated risks. Detecting arrhythmias from ECGs quickly and accurately holds great significance in preventing heart disease and reducing mortality. This research endeavors to outperform previous studies by developing a scientific neural network model capable of training and predicting ECG signals for 11 categories of arrhythmias, accounting for up to 5 co-existing labels. Methods: In this study, we initially address the issue of imbalanced datasets by employing Borderline SMOTE and Cluster Centroids techniques during preprocessing. Subsequently, we propose a novel SAR model that combines attention and resnet mechanisms. The dataset is subjected to a 10-fold validation process to train and evaluate the model. Finally, several metrics such as HammingLoss, RankingLoss, F1-score, AUC and Coverage are used to evaluate the model. Results: By evaluating the results of the tests, the average Hamming Loss is 1.12 %, the average Ranking Loss is 1.17 %, the average Micro F1-score is 98.46 %, the average Micro AUC is 98.76 %, and the average Coverage is 3.2762. The results show that the SAR model outperforms previous related studies on the task of classifying arrhythmia signals with multiple categories and labels. Conclusion: The SAR model demonstrated excellent performance in accurately classifying multi-category and multi-label arrhythmia signals, affirming its scientific validity. Compared with previous studies, the model achieves a certain improvement in performance, which can help cardiologists to achieve scientific and accurate diagnosis of arrhythmia diseases.

Association between ozone and influenza transmissibility in China.

BACKGROUND: Common air pollutants such as ozone (O3), sulfur dioxide (SO2), nitrogen dioxide (NO2), and particulate matter play significant roles as influential factors in influenza-like illness (ILI). However, evidence regarding the impact of O3 on influenza transmissibility in multi-subtropical regions is limited, and our understanding of the effects of O3 on influenza transmissibility in temperate regions remain unknown. METHODS: We studied the transmissibility of influenza in eight provinces across both temperate and subtropical regions in China based on 2013 to 2018 provincial-level surveillance data on influenza-like illness (ILI) incidence and viral activity. We estimated influenza transmissibility by using the instantaneous reproduction number ([Formula: see text]) and examined the relationships between transmissibility and daily O3 concentrations, air temperature, humidity, and school holidays. We developed a multivariable regression model for [Formula: see text] to quantify the contribution of O3 to variations in transmissibility. RESULTS: Our findings revealed a significant association between O3 and influenza transmissibility. In Beijing, Tianjin, Shanghai and Jiangsu, the association exhibited a U-shaped trend. In Liaoning, Gansu, Hunan, and Guangdong, the association was L-shaped. When aggregating data across all eight provinces, a U-shaped association was emerged. O3 was able to accounted for up to 13% of the variance in [Formula: see text]. O3 plus other environmental drivers including mean daily temperature, relative humidity, absolute humidity, and school holidays explained up to 20% of the variance in [Formula: see text]. CONCLUSIONS: O3 was a significant driver of influenza transmissibility, and the association between O3 and influenza transmissibility tended to display a U-shaped pattern.

Comparative phylogeography reveals dissimilar genetic differentiation patterns in two sympatric amphibian species.

Global climate change is expected to have a profound effect on species distribution. Due to the temperature constraints, some narrow niche species could shift their narrow range to higher altitudes or latitudes. In this study, we explored the correlation between species traits, genetic structure, and geographical range size. More specifically, we analyzed how these variables are affected by differences in fundamental niche breadth or dispersal ability in the members of two sympatrically distributed stream-dwelling amphibian species (frog, Quasipaa yei; salamander, Pachyhynobius shangchengensis), in Dabie Mountains, East China. Both species showed relatively high genetic diversity in most geographical populations and similar genetic diversity patterns (JTX, low; BYM, high) correlation with habitat changes and population demography. Multiple clustering analyses were used to disclose differentiation among the geographical populations of these two amphibian species. Q. yei disclosed the relatively shallow genetic differentiation, while P. shangchengensis showed an opposite pattern. Under different historical climatic conditions, all ecological niche modeling disclosed a larger suitable habitat area for Q. yei than for P. shangchengensis; these results indicated a wider environment tolerance or wider niche width of Q. yei than P. shangchengensis. Our findings suggest that the synergistic effects of environmental niche variation and dispersal ability may help shape genetic structure across geographical topology, particularly for species with extremely narrow distribution.

Structural and biological insights into outer membrane protein lipotoxin F of Pseudomonas aeruginosa: Implications for vaccine application.

Due to the increasing antibiotic resistance of Pseudomonas aeruginosa (PA), an effective vaccine is urgently needed. However, no PA vaccine has been approved to date, and new protective antigens are needed to improve their efficacy. In this study, Luminex beads were used to identify new candidate antigens, after which their crystal structure was determined, and their potential contribution to bacterial pathogenesis was assessed in vitro and in vivo. Notably, a significant antibody response against the outer membrane protein LptF (lipotoxin F) was detected in sera from 409 volunteers. Moreover, vaccination with recombinant LptF conferred effective protection in an acute PA pneumonia model. The crystal structure showed that LptF comprises a 3-stranded ß-sheet (ß1-ß3) and three α-helices (α1-α3) that are organized in an α/ß/α/ß/α/ß pattern, which is structurally homologous to OmpA and related outer membrane proteins. In addition, LptF binds to peptidoglycan in an atypical manner, contributing to the pathogenesis and survival of PA under stress. Our data indicate that LptF is an important virulence factor and thus a promising candidate antigen for PA vaccines.

Candidate Regulatory Genes for Hindlimb Development in the Embryos of the Chinese Alligator (Alligator sinensis).

Crocodilians, which are a kind of animal secondary adaptation to an aquatic environment, their hindlimb can provide the power needed to engage in various life activities, even in low-oxygen water environments. The development of limbs is an important aspect of animal growth and development, as it is closely linked to body movement, support, heat production, and other critical functions. For the Chinese alligator, the hindlimb is one of the main sources of power, and its development and differentiation will directly influence the survival ability in the wild. Furthermore, a better understanding of the hindlimb developmental process will provide data support for the comparative evolutionary and functional genomics of crocodilians. In this study, the expression levels of genes related to hindlimb development in the Chinese alligator embryos during fetal development (on days 29, 35, 41, and 46) were investigated through transcriptome analysis. A total of 1675 differentially expressed genes (DEGs) at different stages were identified by using limma software. These DEGs were then analyzed using weighted correlation network analysis (WGCNA), and 4 gene expression modules and 20 hub genes were identified that were associated with the development of hindlimbs in the Chinese alligator at different periods. The results of GO enrichment and hub gene expression showed that the hindlimb development of the Chinese alligator embryos involves the development of the embryonic structure, nervous system, and hindlimb muscle in the early stage (H29) and the development of metabolic capacity occurs in the later stage (H46). Additionally, the enrichment results showed that the AMPK signaling pathway, calcium signaling pathway, HIF-1 signaling pathway, and neuroactive ligand-receptor interaction are involved in the development of the hindlimb of the Chinese alligator. Among these, the HIF-1 signaling pathway and neuroactive ligand-receptor interaction may be related to the adaptation of Chinese alligators to low-oxygen environments. Additionally, five DEGs (CAV1, IRS2, LDHA, LDB3, and MYL3) were randomly selected for qRT-PCR to verify the transcriptome results. It is expected that further research on these genes will help us to better understand the process of embryonic hindlimb development in the Chinese alligator.

Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study.

BACKGROUND: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. OBJECTIVE: This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. METHODS: We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. RESULTS: This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. CONCLUSIONS: Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models.

A lightweight neural network for lung nodule detection based on improved ghost module.

Background: Computer tomography images are the preferred method of preoperative evaluation for lung disease. However, it remains difficult to detect and recognize nodules accurately and efficiently due to poor data imaging quality, heavy reliance on physician experience and the need for more human-computer interaction for diagnosis. Currently, image nodule detection based on deep convolutional neural networks has gained much momentum. Methods: To alleviate doctors' tremendous labor in the diagnosis procedure, and improve the accuracy of intelligent detection of lung nodules, we improved GhostNet and proposed a lightweight neural network for object detection for lung nodule image detection. Firstly, the bneck structure in the backbone feature extraction network is adopted and improved from the structure of MobileNetV3. The weights are adjusted by changing the initial channel attention mechanism and introducing a spatial-temporal attention mechanism. Then, in the enhanced feature extraction part, we mainly use depth-separable convolution blocks to replace the 3×3 convolution of the original network for the purpose of reducing the model parameters, and make more improvements based on the network structure to enhance the applicability of the network. Diagnostic precision, recall, F1-score, mAP and parameter count were calculated. Results: According to our lightweight neural network, F1-score, precision, and recall were 0.87, 86.34%, and 86.69%, respectively. Based on our dataset, the Yolov4-GNet network proposed in this research outperforms the current neural networks on both precision and recall as well as F1. Conclusions: The lung nodule detection method proposed in this research not only simplifies the processing of images, but also outperforms comparable methods in nodule detection rate and positioning accuracy, providing a new way for lung nodule detection.

Molecular characteristics of clinical IMP-producing Klebsiella pneumoniae isolates: novel IMP-90 and integron In2147.

BACKGROUND: Since the first report of carbapenem-resistant Klebsiella pneumoniae isolates in China in 2007, the prevalence of CRKP and CRE has increased significantly. However, the molecular characteristics of IMP-producing Klebsiella pneumoniae (IMPKp) are rarely reported. METHODS: A total of 29 IMPKp isolates were collected from a Chinese tertiary hospital from 2011 to 2017. Clinical IMPKp were identified by VITEK®MS, and further analyzed by whole-genome DNA sequencing with HiSeq and PacBio RSII sequencer. Sequencing data were analyzed using CSI Phylogeny 1.4, Resfinder, PlasmidFinder and the MLST tool provided by the Centre for Genomic Epidemiology. The analysis results were visualized using iTOL editor v1_1. The open reading frames and pseudogenes were predicted using RAST 2.0 combined with BLASTP/BLASTN searches against the RefSeq database. The databases CARD, ResFinder, ISfinder, and INTEGRALL were performed for annotation of the resistance genes, mobile elements, and other features. The types of blaIMP in clinical isolates were determined by BIGSdb-Pasteur. Integrons were drawn by Snapgene, and the gene organization diagrams were drawn by Inkscape 0.48.1. RESULTS: Four novel ST type, including ST5422, ST5423, ST5426 and ST5427 were identified. The IMP-4 and IMP-1 were the dominant IMP type. The majority of blaIMP-carrying plasmids belonged to IncN and IncHI5. Two novel blaIMP-carrying integrons (In2146 and In2147) were uncovered. A novel variant blaIMP-90 presented in novel integron In2147 has been identified. CONCLUSIONS: IMPKp showed low prevalence in China. Novel molecular characteristics of IMPKp have been identified. Continuous monitoring of IMPKp shall also be carried out in the future.

Assessing the impact of COVID-19 interventions on influenza-like illness in Beijing and Hong Kong: an observational and modeling study.

BACKGROUND: The impact of coronavirus diseases 2019 (COVID-19) related non-pharmaceutical interventions (NPIs) on influenza activity in the presence of other known seasonal driving factors is unclear, especially at the municipal scale. This study aimed to assess the impact of NPIs on outpatient influenza-like illness (ILI) consultations in Beijing and the Hong Kong Special Administrative Region (SAR) of China. METHODS: We descriptively analyzed the temporal characteristics of the weekly ILI counts, nine NPI indicators, mean temperature, relative humidity, and absolute humidity from 2011 to 2021. Generalized additive models (GAM) using data in 2011-2019 were established to predict the weekly ILI counts under a counterfactual scenario of no COVID-19 interventions in Beijing and the Hong Kong SAR in 2020-2021, respectively. GAM models were further built to evaluate the potential impact of each individual or combined NPIs on weekly ILI counts in the presence of other seasonal driving factors in the above settings in 2020-2021. RESULTS: The weekly ILI counts in Beijing and the Hong Kong SAR fluctuated across years and months in 2011-2019, with an obvious winter-spring seasonality in Beijing. During the 2020-2021 season, the observed weekly ILI counts in both Beijing and the Hong Kong SAR were much lower than those of the past 9 flu seasons, with a 47.5% [95% confidence interval (CI): 42.3%, 52.2%) and 60.0% (95% CI: 58.6%, 61.1%) reduction, respectively. The observed numbers for these two cities also accounted for only 40.2% (95% CI: 35.4%, 45.3%) and 58.0% (95% CI: 54.1%, 61.5%) of the GAM model estimates in the absence of COVID-19 NPIs, respectively. Our study revealed that, "Cancelling public events" and "Restrictions on internal travel" measures played an important role in the reduction of ILI in Beijing, while the "restrictions on international travel" was statistically most associated with ILI reductions in the Hong Kong SAR. CONCLUSIONS: Our study suggests that COVID-19 NPIs had been reducing outpatient ILI consultations in the presence of other seasonal driving factors in Beijing and the Hong Kong SAR from 2020 to 2021. In cities with varying local circumstances, some NPIs with appropriate stringency may be tailored to reduce the burden of ILI caused by severe influenza strains or other respiratory infections in future.

Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation.

BACKGROUND: In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement. OBJECTIVE: The aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity. METHODS: We collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI%) cases and the product of ILI% and the influenza-positive rate (ILI%×positive%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R2 value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models. RESULTS: The highest correlation coefficients were found for the Baidu search data for ILI% and for air quality for ILI%×positive%. We first used the MAL model to calculate the ILI%, and then combined ILI% with climate, demographic, and Baidu data in different forms. The ILI%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R2 reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI%×positive% and combined this prediction with different data forms. The ILI%×positive%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R2 reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. CONCLUSIONS: The newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population.

Water deficit aggravated the inhibition of photosynthetic performance of maize under mercury stress but is alleviated by brassinosteroids.

Mercury (Hg) significantly inhibits maize (Zea mays L.) production, which could be aggravated by water deficit (WD) due to climate change. However, there is no report on the maize in response to combined their stresses. This work was conducted for assessing the response and adaptive mechanism of maize to combined Hg and WD stress using two maize cultivars, Xianyu (XY) 335 and Yudan (YD) 132. The analysis was based on plant growth, physiological function, and transcriptomic data. Compared with the single Hg stress, Hg accumulation in whole plant and translocation factor (TF) under Hg+WD were increased by 64.51 % (1.44 mg kg-1) and 260.00 %, respectively, for XY 335; and 50.32 % (0.62 mg kg-1) and 220.02 %, respectively, for YD 132. Combined Hg and WD stress further increased the reactive oxygen species accumulation, aggravated the damage of the thylakoid membrane, and decreased chlorophyll content compared with single stress. For example, Chl a and Chl b contents of XY 335 were significantly decreased by 48.67 % and 28.08 %, respectively at 48 h after Hg+WD treatment compared with Hg stress. Furthermore, transcriptome analysis revealed that most of down-regulated genes were enriched in photosynthetic-antenna proteins, photosynthesis, chlorophyll and porphyrin metabolism pathways (PsbS1, PSBQ1 and FDX1 etc.) under combined stress, reducing light energy capture and electron transport. However, most genes related to the brassinosteroids (BRs) signaling pathway were up-regulated under Hg+WD stress. Correspondingly, exogenous BRs significantly enhanced the maize tolerance to stress by decreasing Hg accumulation and TF, and raising activities of antioxidant enzyme, the content of chlorophyll and photosynthetic performance. The PI, Fv/Fm and Fv/Fo of Hg+WD+BR treatment were increased by 29.88 %, 32.06 %, and 14.56 %, respectively, for XY 335 compared to Hg+WD. Overall, combined Hg and WD stress decreased photosynthetic efficiency by adversely affecting light absorption and electron transport, especially in stress-sensitive variety, but BRs could alleviate the inhibition of photosynthesis, providing a novel strategy for enhancing crop Hg and WD tolerance and food safety.

Miniaturized fiber optic ultrasound sensor with multiplexing for photoacoustic imaging.

A miniaturized ultrasound sensor based on optical fiber is designed and realized for multichannel parallel ultrasound detection and photoacoustic imaging. The fiber optic sensor is composed of a polymer coating, a reflective mirror and a single-mode optical fiber, with only 125 µm in diameter. By integrating the coherent demodulation technology and multiplexing technology, which using a relatively cheap fixed wavelength laser, hundreds of sensors could work simultaneously. Meanwhile, highly sensitive ultrasound detection has been demonstrated with the noise equivalent pressure as low as 0.46 kPa and the sensor exhibits a nearly omnidirectional directivity. Furthermore, a photoacoustic imaging system based on three sensors working in parallel is demonstrated. High lateral resolutions of 165-217 µm and axial resolutions of 112-131 µm over a depth range of larger than 5 mm are obtained. A three-dimensional phantom imaging experiment is also demonstrated. Benefited from parallel detection, the imaging speed is three times faster than that of a single sensor. The miniaturized fiber optic ultrasound sensor probe provides a competitive alternative for mechanically scanning-free endoscopic imaging, which is beneficial from small size, omnidirectional directivity and parallel detection capability.

Heavy metal and metalloid emissions during co-processing of waste in a sintering kiln: Migration characteristics in the kiln and long-term leaching from bricks.

Heavy metals (HMs) in mixed hazardous waste can be volatilized in the kiln for preparing sintered bricks, which greatly increases the environmental risk. In this study, the volatilization, transformation, and leaching of HMs from bricks were evaluated. Field tests and laboratory leaching experiments were carried out. HM-contaminated soil was used to prepare sintered bricks at high-temperature in a tunnel kiln. Release of HMs from brick under rainfall conditions was investigated in laboratory simulation experiments. The field tests showed that the total amount of Pb, Zn, Cd distributed to the gas phase were all less than 2%, but the amount of Hg entering the gas phase 40.1%-60.5% in the particulate forms. The As leaching rate increased after sintering of bricks in the kiln, which was attributed to the increased formation of soluble arsenate and the reduced availability of sorption sites. The tank leaching test indicated that the release mechanism of trance elements (Cr, As, Zn, Cd, Pb and Ni) was mainly controlled by diffusion. This study provides useful knowledge for decreasing the volatilization and leaching of HMs from sintered bricks prepared using hazardous waste.

Multi-channel parallel ultrasound detection based on a photothermal tunable fiber optic sensor array.

A multi-channel parallel ultrasound detection system based on a photothermal tunable fiber optic sensor array is proposed. The resonant wavelength of the ultrasound sensor has a quadratic relationship with the power of a 980-nm heating laser. The maximum tuning range is larger than 15 nm. Through photothermal tuning, the inconsistent operating wavelengths of the Fabry-Perot (FP) sensor array can be solved, and then a multiplexing capacity of up to 53 can be theoretically realized, which could greatly reduce the time required for data acquisition. Then, a fixed wavelength laser with ultra-narrow linewidth is used to interrogate the sensor array. The interrogation system demonstrates a noise equivalent pressure (NEP) as low as 0.12 kPa, which is 5.5-times lower than the commercial hydrophone. Furthermore, a prototype of a four-channel ultrasound detection system is built to demonstrate the parallel detection capability. Compared with the independent detection, the SNR of parallel detection does not deteriorate, proving that the parallel detection system and the sensor array own very low cross talk characteristics. The parallel detection technique paves a way for real-time photoacoustic/ultrasound imaging.