Accurate and reliable predictions of infectious disease can be valuable to public health organizations that plan interventions to decrease or prevent disease transmission. A great variety of models ...have been developed for this task. However, for different data series, the performance of these models varies. Hepatitis E, as an acute liver disease, has been a major public health problem. Which model is more appropriate for predicting the incidence of hepatitis E? In this paper, three different methods are used and the performance of the three methods is compared.
Autoregressive integrated moving average(ARIMA), support vector machine(SVM) and long short-term memory(LSTM) recurrent neural network were adopted and compared. ARIMA was implemented by python with the help of statsmodels. SVM was accomplished by matlab with libSVM library. LSTM was designed by ourselves with Keras, a deep learning library. To tackle the problem of overfitting caused by limited training samples, we adopted dropout and regularization strategies in our LSTM model. Experimental data were obtained from the monthly incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE).
By analyzing data, we took ARIMA(1, 1, 1), ARIMA(3, 1, 2) as monthly incidence prediction model and cases number prediction model, respectively. Cross-validation and grid search were used to optimize parameters of SVM. Penalty coefficient C and kernel function parameter g were set 8, 0.125 for incidence prediction, and 22, 0.01 for cases number prediction. LSTM has 4 nodes. Dropout and L2 regularization parameters were set 0.15, 0.001, respectively. By the metrics of RMSE, we obtained 0.022, 0.0204, 0.01 for incidence prediction, using ARIMA, SVM and LSTM. And we obtained 22.25, 20.0368, 11.75 for cases number prediction, using three models. For MAPE metrics, the results were 23.5%, 21.7%, 15.08%, and 23.6%, 21.44%, 13.6%, for incidence prediction and cases number prediction, respectively. For MAE metrics, the results were 0.018, 0.0167, 0.011 and 18.003, 16.5815, 9.984, for incidence prediction and cases number prediction, respectively.
Comparing ARIMA, SVM and LSTM, we found that nonlinear models(SVM, LSTM) outperform linear models(ARIMA). LSTM obtained the best performance in all three metrics of RSME, MAPE, MAE. Hence, LSTM is the most suitable for predicting hepatitis E monthly incidence and cases number.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Infectious diseases are a major threat to public health, causing serious medical consumption and casualties. Accurate prediction of infectious diseases incidence is of great significance for public ...health organizations to prevent the spread of diseases. However, only using historical incidence data for prediction can not get good results. This study analyzes the influence of meteorological factors on the incidence of hepatitis E, which are used to improve the accuracy of incidence prediction.
We extracted the monthly meteorological data, incidence and cases number of hepatitis E from January 2005 to December 2017 in Shandong province, China. We employ GRA method to analyze the correlation between the incidence and meteorological factors. With these meteorological factors, we achieve a variety of methods for incidence of hepatitis E by LSTM and attention-based LSTM. We selected data from July 2015 to December 2017 to validate the models, and the rest was taken as training set. Three metrics were applied to compare the performance of models, including root mean square error(RMSE), mean absolute percentage error(MAPE) and mean absolute error(MAE).
Duration of sunshine and rainfall-related factors(total rainfall, maximum daily rainfall) are more relevant to the incidence of hepatitis E than other factors. Without meteorological factors, we obtained 20.74%, 19.50% for incidence in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we obtained 14.74%, 12.91%, 13.21%, 16.83% for incidence, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.83%. Without meteorological factors, we achieved 20.41%, 19.39% for cases in term of MAPE, by LSTM and A-LSTM, respectively. With meteorological factors, we achieved 14.20%, 12.49%, 12.72%, 15.73% for cases, in term of MAPE, by LSTM-All, MA-LSTM-All, TA-LSTM-All, BiA-LSTM-All, respectively. The prediction accuracy increased by 7.92%. More detailed results are shown in results section of this paper.
The experiments show that attention-based LSTM is superior to other comparative models. Multivariate attention and temporal attention can greatly improve the prediction performance of the models. Among them, when all meteorological factors are used, multivariate attention performance is better. This study can provide reference for the prediction of other infectious diseases.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Objective
To improve the production of A40926, a combined strategy of constructing the engineered strain and optimizing the medium was implemented.
Results
The engineered strain lcu1 with the genetic ...features of
dbv23
deletion and
dbv3
-
dbv20
coexpression increased by 30.6% in the production of A40926, compared to the original strain. In addition, a combined medium called M9 was designed to be further optimized by the central composite design method. The optimized M9 medium was verified to significantly improve the A40926 yield from 257 to 332 mg l
−1
.
Conclusions
The engineered strain lcu1 could significantly promote A40926 production in the optimized M9 medium, which indicated that the polygenic genetic manipulation and the media optimization played an equally important role in increasing the A40926 yield.
Epstein-Barr virus (EBV) is a complex oncogenic symbiont. The molecular mechanisms governing EBV carcinogenesis remain elusive and the functional interactions between virus and host cells are ...incompletely defined. Here we present a comprehensive map of the host cell-pathogen interactome in EBV-associated cancers. We systematically analyzed RNA sequencing from >1,000 patients with 15 different cancer types, comparing virus and host factors of EBV
to EBV
tissues. EBV preferentially integrated at highly accessible regions of the cancer genome, with significant enrichment in super-enhancer architecture. Twelve EBV transcripts, including LMP1 and LMP2, correlated inversely with EBV reactivation signature. Overexpression of these genes significantly suppressed viral reactivation, consistent with a "virostatic" function. In cancer samples, hundreds of novel frequent missense and nonsense variations in virostatic genes were identified, and variant genes failed to regulate their viral and cellular targets in cancer. For example, one-third of patients with EBV
NK/T-cell lymphoma carried two novel nonsense variants (Q322X, G342X) of
and both variant proteins failed to restrict viral reactivation, confirming loss of virostatic function. Host cell transcriptional changes in response to EBV infection classified tumors into two molecular subtypes based on patterns of IFN signature genes and immune checkpoint markers, such as PD-L1 and IDO1. Overall, these findings uncover novel points of interaction between a common oncovirus and the human genome and identify novel regulatory nodes and druggable targets for individualized EBV and cancer-specific therapies. SIGNIFICANCE: This study provides a comprehensive map of the host cell-pathogen interactome in EBV
malignancies.
.
Limited information is available about the temporal trend in the prevalence and evolution of hepatitis B virus (HBV) S-gene mutations in the post-immunization era in China. From 2005 to 2013, 1077 ...hepatitis B cases under 15 years of age reported through Chinese National Notifiable Disease Reporting System (NNDRS) were successfully sequenced of S-gene in Shandong province, China. A total of 97 (9.01%) cases had amino acid (aa) substitution in the "α" determinant of HBsAg. The yearly prevalence from 2005 to 2013 maintained at a relatively stable level, and showed no significant change (P > 0.05). Multivariate logistic regression analysis demonstrated that the prevalence of "α" mutations was independently associated with the maternal HBsAg status (P < 0.05), and not with surveillance year and hepatitis B vaccination (P > 0.05). The hottest mutation position was aa126 (I126S/N and T126A, 29.63%), and aa 145 (G145R/A, 25.93%). Mutated residue 126 tended to occur less frequent, while that of residue 145 was more frequent with increasing year. Our data showed that there was no increase in the frequency of HBV "α" mutations over time during the post-immunization period. However, long-term vaccination might enhance the change of HBV mutational pattern, and G145 mutation was becoming dominant.
The glycopeptide A40926 biosynthesized by
Nonomuraea gerenzanensis
is a precursor of the second generation glycopeptide antibiotic dalbavancin. The skeleton of this glycopeptide consists of seven ...amino acids and is biosynthesized by the NRPS gene module. L-valine, a branched amino acid, is also a significant precursor for A40926 production. This study details the use of pH-responsive alginate–chitosan microspheres loaded with L-valine prepared by internal emulsification gelation. The effects of process and formulation variables on microsphere size, loading capacity, and encapsulation efficiency were investigated. Then, effects on A40926 production by the pH-responsive microspheres were evaluated in a 10-L fermenter. Results demonstrated that use of the pH-responsive microspheres could improve A40926 yield from 465 to 602 mg L
−1
in a 10-L scale fermenter.
Tox is a member of the high mobility group (HMG)-Box transcription factors and plays important roles in thymic T cell development. Outside of the thymus, however, Tox is also highly expressed by CD8 ...and CD4 T cells in various states of activation and in settings of cancer and autoimmune disease. In CD4 T cells, Tox has been primarily studied in T follicular helper (TFH) cells where it, along with Tox2, promotes TFH differentiation by regulating key TFH-associated genes and suppressing CD4 cytotoxic T cell differentiation. However, the role of Tox in other T helper (Th) cell subtypes is less clear. Here, we show that Tox is expressed in several physiologically-activated Th subtypes and its ectopic expression enhances the
differentiation of Th2 and T regulatory (Treg) cells. Tox overexpression in unpolarized Th cells also induced the expression of several genes involved in cell activation (
), cellular trafficking (
) and suppressing inflammation (
) across multiple Th subtypes. We found that Tox binds the regulatory regions of these genes along with the transcription factors BATF, IRF4, and JunB and that Tox-induced expression of IL-10, but not PD-1, is BATF-dependent. Based on these data, we propose a model where Tox regulates Th cell chemotactic genes involved in facilitating dendritic cell-T cell interactions and aids in the resolution or prevention of inflammation through the production of IL-10.
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