MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization.

MusiteDeep is an online resource providing a deep-learning framework for protein post-translational modification (PTM) site prediction and visualization. The predictor only uses protein sequences as input and no complex features are needed, which results in a real-time prediction for a large number of proteins. It takes less than three minutes to predict for 1000 sequences per PTM type. The output is presented at the amino acid level for the user-selected PTM types. The framework has been benchmarked and has demonstrated competitive performance in PTM site predictions by other researchers. In this webserver, we updated the previous framework by utilizing more advanced ensemble techniques, and providing prediction and visualization for multiple PTMs simultaneously for users to analyze potential PTM cross-talks directly. Besides prediction, users can interactively review the predicted PTM sites in the context of known PTM annotations and protein 3D structures through homology-based search. In addition, the server maintains a local database providing pre-processed PTM annotations from Uniport/Swiss-Prot for users to download. This database will be updated every three months. The MusiteDeep server is available at https://www.musite.net. The stand-alone tools for locally using MusiteDeep are available at https://github.com/duolinwang/MusiteDeep_web.

An Algorithm with Iteration Uncertainty Eliminate Based on Geomagnetic Fingerprint under Mobile Edge Computing for Indoor Localization.

Indoor localization problems are difficult due to that the information, such as WLAN and GPS, cannot achieve enough precision for indoor issues. This paper presents a novel indoor localization algorithm, GeoLoc, with uncertainty eliminate based on fusion of acceleration, angular rate, and magnetic field sensor data. The algorithm can be deployed in edge devices to overcome the problems of insufficient computing resources and long delay caused by high complexity of location calculation. Firstly, the magnetic map is built and magnetic values are matched. Secondly, orientation updating and position selection are iteratively executed using the fusion data, which gradually reduce uncertainty of orientation. Then, we filter the trajectory from a path set. By gradually reducing uncertainty, GeoLoc can bring a high positioning precision and a smooth trajectory. In addition, this method has an advantage in that it does not rely on any infrastructure such as base stations and beacons. It solves the common problems regarding the non-uniqueness of the geomagnetic fingerprint and the deviation of the sensor measurement. The experimental results show that our algorithm achieves an accuracy of less than 2.5 m in indoor environment, and the positioning results are relatively stable. It meets the basic requirements of indoor location-based services (LBSs).

Different responses of weather factors on hand, foot and mouth disease in three different climate areas of Gansu, China.

BACKGROUND: To determine the linear and non-linear interacting relationships between weather factors and hand, foot and mouth disease (HFMD) in children in Gansu, China, and gain further traction as an early warning signal based on weather variability for HFMD transmission. METHOD: Weekly HFMD cases aged less than 15 and meteorological information from 2010 to 2014 in Jiuquan, Lanzhou and Tianshu, Gansu, China were collected. Generalized linear regression models (GLM) with Poisson link and classification and regression trees (CART) were employed to determine the combined and interactive relationship of weather factors and HFMD in both linear and non-linear ways. RESULTS: GLM suggested an increase in weekly HFMD of 5.9% [95% confidence interval (CI): 5.4%, 6.5%] in Tianshui, 2.8% [2.5%, 3.1%] in Lanzhou and 1.8% [1.4%, 2.2%] in Jiuquan in association with a 1 °C increase in average temperature, respectively. And 1% increase of relative humidity could increase weekly HFMD of 2.47% [2.23%, 2.71%] in Lanzhou and 1.11% [0.72%, 1.51%] in Tianshui. CART revealed that average temperature and relative humidity were the first two important determinants, and their threshold values for average temperature deceased from 20 °C of Jiuquan to 16 °C in Tianshui; and for relative humidity, threshold values increased from 38% of Jiuquan to 65% of Tianshui. CONCLUSION: Average temperature was the primary weather factor in three areas, more sensitive in southeast Tianshui, compared with northwest Jiuquan; Relative humidity's effect on HFMD showed a non-linear interacting relationship with average temperature.

Socio-ecological factors and hand, foot and mouth disease in dry climate regions: a Bayesian spatial approach in Gansu, China.

The influence of socio-ecological factors on hand, foot and mouth disease (HFMD) were explored in this study using Bayesian spatial modeling and spatial patterns identified in dry regions of Gansu, China. Notified HFMD cases and socio-ecological data were obtained from the China Information System for Disease Control and Prevention, Gansu Yearbook and Gansu Meteorological Bureau. A Bayesian spatial conditional autoregressive model was used to quantify the effects of socio-ecological factors on the HFMD and explore spatial patterns, with the consideration of its socio-ecological effects. Our non-spatial model suggests temperature (relative risk (RR) 1.15, 95 % CI 1.01-1.31), GDP per capita (RR 1.19, 95 % CI 1.01-1.39) and population density (RR 1.98, 95 % CI 1.19-3.17) to have a significant effect on HFMD transmission. However, after controlling for spatial random effects, only temperature (RR 1.25, 95 % CI 1.04-1.53) showed significant association with HFMD. The spatial model demonstrates temperature to play a major role in the transmission of HFMD in dry regions. Estimated residual variation after taking into account the socio-ecological variables indicated that high incidences of HFMD were mainly clustered in the northwest of Gansu. And, spatial structure showed a unique distribution after taking account of socio-ecological effects.

Atmospheric Chemistry of 1H-Heptafluorocyclopentene (cyc-CF2CF2CF2CF═CH-): Rate Constant, Products, and Mechanism of Gas-Phase Reactions with OH Radicals, IR Absorption Spectrum, Photochemical Ozone Creation Potential, and Global Warming Potential.

The rate constant for gas-phase reactions of OH radicals with 1H-heptafluorocyclopentene (cyc-CF2CF2CF2CF═CH-) was measured using a relative rate method at 298 K: (5.20 ± 0.09) × 10-14 cm3 molecule-1 s-1. The quoted uncertainty includes two standard deviations from the least-squares regression, the systematic error from the GC analysis, and the uncertainties of the rate constants of the reference compounds. The OH-radical-initiated oxidation of cyc-CF2CF2CF2CF═CH- gives the main products COF2, CO, and CO2, leading to negligible environmental impact. For consumptions of cyc-CF2CF2CF2CF═CH- of less than 54%, the yield of the formation of ([COF2] + [CO] + [CO2])/5 (based on the conservation of carbon) was 0.99 ± 0.02, which is very close to 100%. A possible degradation mechanism was proposed. The radiative efficiency (RE) of cyc-CF2CF2CF2CF═CH- measured at room temperature was 0.215 W m-2 ppb-1. The atmospheric lifetime of cyc-CF2CF2CF2CF═CH- was calculated as 0.61 year, and the photochemical ozone creation potential (POCP) was negligible. The 20-, 100-, and 500-year time horizon global warming potentials (GWPs) were estimated as 153, 42, and 12, respectively.

Influence and prediction of meteorological factors on brucellosis in a northwest region of China.

This paper aims to study the cumulative lag effect of meteorological factors on brucellosis incidence and the prediction performance based on Random Forest model. The monthly number of brucellosis cases and meteorological data from 2015 to 2019 in Yongchang of Gansu Province, northwest China, were used to build distributed lag nonlinear model (DLNM). The number of brucellosis cases of lag 1 month and meteorological data from 2015 to 2018 were used to build RF model to predict the brucellosis incidence in 2019. Meanwhile, SARIMA model was established to compare the prediction performance with RF model according to R2 and RMSE. The results indicated that the population had a high incidence risk at temperature between 5 and 13 °C and lag between 0 and 18 days, sunshine duration between 225 and 260 h and lag between 0 and 1 month, and atmosphere pressure between 789 and 793.5 hPa and lag between 0 and 18 days. The R2 and RMSE of train set and test set in RF model were 0.903, 1.609, 0.824, and 2.657, respectively, and the R2 and RMSE in SARIMA model were 0.530 and 7.008. This study found significant nonlinear and lag associations between meteorological factors and brucellosis incidence. The prediction performance of RF model was more accurate and practical compared with SARIMA model.

AI-Aided Design of Novel Targeted Covalent Inhibitors against SARS-CoV-2.

The drug repurposing of known approved drugs (e.g., lopinavir/ritonavir) has failed to treat SARS-CoV-2-infected patients. Therefore, it is important to generate new chemical entities against this virus. As a critical enzyme in the lifecycle of the coronavirus, the 3C-like main protease (3CLpro or Mpro) is the most attractive target for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with a fragment-based drug design (ADQN-FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CLpro. We obtained a series of derivatives from the lead compounds based on our structure-based optimization policy (SBOP). All of the 47 lead compounds obtained directly with our AI model and related derivatives based on the SBOP are accessible in our molecular library. These compounds can be used as potential candidates by researchers to develop drugs against SARS-CoV-2.

AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2.

The focused drug repurposing of known approved drugs (such as lopinavir/ritonavir) has been reported failed for curing SARS-CoV-2 infected patients. It is urgent to generate new chemical entities against this virus. As a key enzyme in the life-cycle of coronavirus, the 3C-like main protease (3CL pro or M pro ) is the most attractive for antiviral drug design. Based on a recently solved structure (PDB ID: 6LU7), we developed a novel advanced deep Q-learning network with the fragment-based drug design (ADQN-FBDD) for generating potential lead compounds targeting SARS-CoV-2 3CL pro . We obtained a series of derivatives from those lead compounds by our structure-based optimization policy (SBOP). All the 47 lead compounds directly from our AI-model and related derivatives based on SBOP are accessible in our molecular library at https://github.com/tbwxmu/2019-nCov . These compounds can be used as potential candidates for researchers in their development of drugs against SARS-CoV-2.

Comparing the similarity and difference of three influenza surveillance systems in China.

Three main surveillance systems (laboratory-confirmed, influenza-like illness (ILI) and nationwide Notifiable Infectious Diseases Reporting Information System (NIDRIS)) have been used for influenza surveillance in China. However, it is unclear which surveillance system is more reliable in developing influenza early warning system based on surveillance data. This study aims to evaluate the similarity and difference of the three surveillance systems and provide practical knowledge for improving the effectiveness of influenza surveillance. Weekly influenza data for the three systems were obtained from March 2010 to February 2015. Spearman correlation and time series seasonal decomposition were used to assess the relationship between the three surveillance systems and to explore seasonal patterns and characteristics of influenza epidemics in Gansu, China. Our results showed influenza epidemics appeared a single-peak around January in all three surveillance systems. Time series seasonal decomposition analysis demonstrated a similar seasonal pattern in the three systems, while long-term trends were observed to be different. Our research suggested that a combination of the NIDRIS together with ILI and laboratory-confirmed surveillance is an informative, comprehensive way to monitor influenza transmission in Gansu, China. These results will provide a useful information for developing influenza early warning systems based on influenza surveillance data.

Investigation and source-tracing of an anthrax outbreak in Gansu Province, China.

Anthrax is an endemic disease in China. Cases are reported every year, especially in the northwestern areas. In August 2016, an outbreak of 21 cutaneous anthrax cases was reported in Min County, Gansu Province, China. In this study, the general characteristics of the anthrax outbreak are described. Two molecular typing methods, canonical single-nucleotide polymorphism (canSNP) and multiple-locus variable-number tandem repeat analysis with 15 markers (MLVA15), were used to investigate the possible source of transmission and to identify the genetic relationship among the strains/samples isolated in this outbreak as well as previous isolates. In this outbreak, all patients were infected through contact with diseased livestock or contaminated animal products. Livestock had been introduced into the local area shortly before the outbreak from Gannan Prefecture (in Gansu Province), Sichuan and Qinghai Provinces. In the molecular typing analysis, there were two canSNP subgroups found in Gansu, A.Br.001/002 and A.Br.Ames, and five MLVA15 genotypes were observed. The strains collected from the anthrax outbreak in Min County in 2016 belonged to the A.Br.001/002 canSNP subgroup and the MLVA15-28 and MLVA15-30 genotype. Strains previously isolated from Sichuan, Inner Mongolia and Maqu County (in Gannan Prefecture, Gansu Province) were clustered with these outbreak-related strains/samples according to the MLVA15-30 genotype. The MLVA15-28 genotype was found in strains isolated from Gansu and Xinjiang in previous studies. Combining the epidemiological investigation and molecular typing results, we conclude that the patients in this outbreak were infected by a local pathogen present in the adjoining area of Gansu, Sichuan and Qinghai Provinces.

[Study on early warning method for influenza epidemic in Gansu province].

OBJECTIVE: To explore the appropriate early warning method for influenza epidemic in Gansu province. METHODS: By using simple control chart, moving percentile method, exponential smoothing method and cumulative sum control chart method, the annual incidence data of influenza-like illness in Gansu province during 2014-2015 were analyzed, and the sensitivities, specificities, positive predictive values, Jorden indexes and Kappa values of the 4 methods were evaluated and compared. RESULTS: The 2014-2015 seasonal influenza epidemic occurred in the fiftieth week of 2014 in Gansu, and the epidemic peak lasted for 6 weeks. Cumulative sum control chart method had the best early warning effect with the sensitivity of 66.67% and specificity of 93.48%. CONCLUSION: It is feasible to use cumulative sum control chart method to give early warning of influenza epidemic in Gansu.

[Spatial-temporal distribution of hepatitis B in Gansu province, 2009-2014].

OBJECTIVE: To understand the hot/cold spots and the spatial-temporal clustering of hepatitis B in Gansu province during 2009-2014 by using spatial statistics, and provide scientific evidence for the prevention and control of hepatitis B. METHODS: The spatial hot/cold spots and its trend, and the time frame and areas of its spatial-temporal clustering of hepatitis B in Gansu were analyzed by using the county specific incidence of hepatitis B from 2009 to 2014 and spatial statistical software GeoDa and SatScan. RESULTS: The incidences of hepatitis B from 2009 to 2014 in Gansu were spatial autocorrelated respectively. Local G scan statistics indicated that the number of hot spots was in decline in Hexi area, while the hot spots was in increase in Linxia Hui autonomous prefecture and Gannan Tibetan autonomous prefecture. There was no obvious pattern in cold spots. Temporal-spatial scan statistics showed that the areas with high hepatitis B incidence most likely clustered in Hexi area during 2009-2011, and the areas with low hepatitis B incidence most likely clustered in eastern Gansu during 2012-2014. CONCLUSIONS: The spatial and temporal clustering of hepatitis B was observed in Gansu from 2009 to 2014. The number of hot spots in Hexi area was in decline, while the numbers of hot spots in Linxia and Gannan were in increase, suggesting that the hepatitis B control and prevention in these areas should be strengthened.

[Spatial temporal distribution of mumps in Gansu, 2009-2013].

OBJECTIVE: To detect the spatial temporal distribution of mumps in Gansu by means of spatial statistics. METHODS: The county-based incidence of mumps from 2009 to 2013 was used to calculate the global Moran's I and local G statistics, and analyze its spatial temporal distribution characteristics. RESULTS: The incidence of mumps in Gansu were spatial auto-correlated from 2009 to 2013 respectively (P<0.001), and the hot spots were mainly distributed in Hexi area, while the cold spots were distributed in Tianshui, Longnan and Qingyang. Spatial temporal analysis showed that the high incidence of mumps was most likely to be detect in Hexi area (RR=3.05, LLR=4 670.995, P<0.001), and the low incidence was most likely to be detect in Longdong area (RR=0.36, LLR=1 980.686, P<0.001). CONCLUSION: The spatial and spatial temporal clustering of mumps existed in Gansu from 2009 to 2013, the results can be used in the development of mumps prevention and control measure in Gansu.

[Time-space study on viral hepatitis C in Gansu province, from 2009 to 2013].

OBJECTIVE: To study the time-space distribution of viral hepatitis C in Gansu province during 2009-2013, using the time-space statistics. METHODS: Using Geoda to analysis the univariate Moran's I and univariate local Moran's I while using SaTScan to detect the time-space gathering areas. RESULTS: There was spatial autocorrelation on incidence of hepatitis C noticed in Gansu during 2009-2013. The hot spots areas were counties as Jinchang, Wuwei, Zhangye and Lanzhou. Cold spot areas would include counties as Dingxi, Longnan, Pingliang, Gannan, Jiuquan, Qingyang, Baiyin and Tianshui. There were time-space gathering areas nitoced, during 2009-2010. Qinzhou and Maiji counties belonged to high incidence gathering areas. Lintao and Linxia were of low incidence gathering areas. In 2011-2013, high incidence gathering area would include counties as Zhangye, Jinchang, Wuwei Lanzhou and Baiyin while low incidence gathering areas would include counties as Dingxi, Tianshui, Pingliang, Longnan and Qingyang. CONCLUSION: There appeared time-space gathering of hepatitis C in Gansu province during 2009-2013. High and low gathering areas varied with time and high incidence gathering area mainly distributed in the western and central areas of Gansu province.

[Spatial clustering of hand-foot-mouth disease in Gansu, 2012].

OBJECTIVE: The purpose of this study was to explore the spatial distribution and spatial clustering of hand-foot-mouth disease (HFMD) in Gansu, 2012. METHODS: Spatial autocorrelation and Spatial scanning analysis were used to conduct spatial statistical analyses for the HFMD at the county/district level. RESULTS: HFMD cases did not show a random distribution but with significant spatial aggregation. When Local Autocorrelation analysis was applied at the county/district level, with nine hot spot areas as Jiayuguan, Yumen, Dunhuang, Jinta, Suzhou, Chengguan, Anning, Xigu and Gaolan, were discovered. Four statistically significant HFMD clusters were identified by spatial scan statistics. CONCLUSION: HFMD was noticed geographically clustered in Gansu in 2012. Results from this study indicated that the spatial autocorrelation and spatial scanning analysis could effectively detect the areas which presenting significant clusters. Cluster Detection System (CDS) could provide evidence for the development of an effective measure concerning the prevention and control of HFMD.

[Spatial-temporal specific incidence of pulmonary tuberculosis in Gansu, 2009-2013].

OBJECTIVE: To understand the spatial-temporal specific incidence of pulmonary tuberculosis (TB) in Gansu. METHODS: The county-based incidence of pulmonary TB in Gansu from 2009 to 2013 was used to calculate Moran's I and G statistics, and analyze the spatial-temporal distribution of areas with different pulmonary TB incidences. RESULTS: The spatial correlation in incidence of pulmonary TB was found in Gansu from 2009 to 2013 (P<0.001), and the hot spot areas were mainly in Hexi area, Linxia, part of Dingxi, while the cold spot areas were in Lanzhou, part of Dingxi, Tianshui, Pingliang and Qingyang. Spatial-temporal analysis showed that the clustering of high pulmonary TB incidence areas were most likely in the Hexi area during 2009-2010 (LLR=3,031.10, RR=2.27, P<0.001), and the clustering of low pulmonary TB incidence areas were most likely in Lanzhou during 2011-2013 (LLR=1,545.52, RR=0.37, P<0.001). CONCLUSION: The analysis on spatial and spatial-temporal specific incidences of pulmonary TB in Gansu from 2009 to 2013 indicated that Hexi area is the key area in pulmonary TB prevention and control in Gansu.