Cassava is a major tropical food crop in the Euphorbiaceae family that has high carbohydrate production potential and adaptability to diverse environments. Here we present the draft genome sequences of a wild ancestor and a domesticated variety of cassava and comparative analyses with a partial inbred line. We identify 1,584 and 1,678 gene models specific to the wild and domesticated varieties, respectively, and discover high heterozygosity and millions of single-nucleotide variations. Our analyses reveal that genes involved in photosynthesis, starch accumulation and abiotic stresses have been positively selected, whereas those involved in cell wall biosynthesis and secondary metabolism, including cyanogenic glucoside formation, have been negatively selected in the cultivated varieties, reflecting the result of natural selection and domestication. Differences in microRNA genes and retrotransposon regulation could partly explain an increased carbon flux towards starch accumulation and reduced cyanogenic glucoside accumulation in domesticated cassava. These results may contribute to genetic improvement of cassava through better understanding of its biology.

BackgroundIt is a daunting task to discontinue pertussis completely in China owing to its growing increase in the incidence. While basic to any formulation of prevention and control measures is early response for future epidemic trends. Discrete wavelet transform(DWT) has been emerged as a powerful tool in decomposing time series into different constituents, which facilitates better improvement in prediction accuracy. Thus we aim to integrate modeling approaches as a decision-making supportive tool for formulating health resources.MethodsWe constructed a novel hybrid method based on the pertussis morbidity cases from January 2004 to May 2018 in China, where the approximations and details decomposed by DWT were forecasted by a seasonal autoregressive integrated moving average (SARIMA) and nonlinear autoregressive network (NAR), respectively. Then, the obtained values were aggregated as the final results predicted by the combined model. Finally, the performance was compared with the SARIMA, NAR and traditional SARIMA-NAR techniques.ResultsThe hybrid technique at level 2 of db2 wavelet including a SARIMA(0,1,3)(1,0,0)12modelfor the approximation-forecasting and NAR model with 12 hidden units and 4 delays for the detail d1-forecasting, along with another NAR model with 11 hidden units and 5 delays for the detail d2-forecasting notably outperformed other wavelets, SARIMA, NAR and traditional SARIMA-NAR techniques in terms of the mean square error, root mean square error, mean absolute error and mean absolute percentage error. Descriptive statistics exhibited that a substantial rise was observed in the notifications from 2013 to 2018, and there was an apparent seasonality with summer peak. Moreover, the trend was projected to continue upwards in the near future.ConclusionsThis hybrid approach has an outstanding ability to improve the prediction accuracy relative to the others, which can be of great help in the prevention of pertussis. Besides, under current trend of pertussis morbidity, it is required to urgently address strategically within the proper policy adopted.

Passion fruit (Passiflora edulis Sims) is an economically valuable fruit that is cultivated in tropical and subtropical regions of the world. Here, we report an ~1341.7 Mb chromosome-scale genome assembly of passion fruit, with 98.91% (~1327.18 Mb) of the assembly assigned to nine pseudochromosomes. The genome includes 23,171 protein-coding genes, and most of the assembled sequences are repetitive sequences, with long-terminal repeats (LTRs) being the most abundant. Phylogenetic analysis revealed that passion fruit diverged after Brassicaceae and before Euphorbiaceae. Ks analysis showed that two whole-genome duplication events occurred in passion fruit at 65 MYA and 12 MYA, which may have contributed to its large genome size. An integrated analysis of genomic, transcriptomic, and metabolomic data showed that ‘alpha-linolenic acid metabolism’, ‘metabolic pathways’, and ‘secondary metabolic pathways’ were the main pathways involved in the synthesis of important volatile organic compounds (VOCs) in passion fruit, and this analysis identified some candidate genes, including GDP-fucose Transporter 1-like, Tetratricopeptide repeat protein 33, protein NETWORKED 4B isoform X1, and Golgin Subfamily A member 6-like protein 22. In addition, we identified 13 important gene families in fatty acid pathways and eight important gene families in terpene pathways. Gene family analysis showed that the ACX, ADH, ALDH, and HPL gene families, especially ACX13/14/15/20, ADH13/26/33, ALDH1/4/21, and HPL4/6, were the key genes for ester synthesis, while the TPS gene family, especially PeTPS2/3/4/24, was the key gene family for terpene synthesis. This work provides insights into genome evolution and flavor trait biology and offers valuable resources for the improved cultivation of passion fruit.

We describe methods for the assessment of amplified-fragment single nucleotide polymorphism and methylation (AFSM) sites using a quick and simple molecular marker-assisted breeding strategy based on the use of two restriction enzyme pairs (EcoRI-MspI and EcoRI-HpaII) and a next-generation sequencing platform. Two sets of 85 adapter pairs were developed to concurrently identify SNPs, indels and methylation sites for 85 lines of cassava population in this study. In addition to SNPs and indels, the simplicity of the AFSM protocol makes it particularly suitable for high-throughput full methylation and hemi-methylation analyses. To further demonstrate the ease of this approach, a cassava genetic linkage map was constructed. This approach should be widely applicable for genetic mapping in a variety of organisms and will improve the application of crop genomics in assisted breeding.

The high incidence, seasonal pattern and frequent outbreaks of hand, foot, and mouth disease (HFMD) represent a threat for millions of children in mainland China. And advanced response is being used to address this. Here, we aimed to model time series with a long short-term memory (LSTM) based on the HFMD notified data from June 2008 to June 2018 and the ultimate performance was compared with the autoregressive integrated moving average (ARIMA) and nonlinear auto-regressive neural network (NAR). The results indicated that the identified best-fitting LSTM with the better superiority, be it in modeling dataset or two robustness tests dataset, than the best-conducting NAR and seasonal ARIMA (SARIMA) methods in forecasting performances, including the minimum indices of root mean square error, mean absolute error and mean absolute percentage error. The epidemic trends of HFMD remained stable during the study period, but the reported cases were even at significantly high levels with a notable high-risk seasonality in summer, and the incident cases projected by the LSTM would still be fairly high with a slightly upward trend in the future. In this regard, the LSTM approach should be highlighted in forecasting the epidemics of HFMD, and therefore assisting decision makers in making efficient decisions derived from the early detection of the disease incidents.

With the re-emergence of brucellosis in mainland China since the mid-1990s, an increasing threat to public health tends to become even more violent, advanced warning plays a pivotal role in the control of brucellosis. However, a model integrating the autoregressive integrated moving average (ARIMA) with Error-Trend-Seasonal (ETS) methods remains unexplored in the epidemiological prediction. The hybrid ARIMA-ETS model based on discrete wavelet transform was hence constructed to assess the epidemics of human brucellosis from January 2004 to February 2018 in mainland China. The preferred hybrid model including the best-performing ARIMA method for approximation-forecasting and the best-fitting ETS approach for detail-forecasting is evidently superior to the standard ARIMA and ETS techniques in both three in-sample simulating and out-of-sample forecasting horizons in terms of the minimum performance indices of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error. Whereafter, an ahead prediction from March to December in 2018 displays a dropping trend compared to the preceding years. But being still present, in various trends, in the present or future. This hybrid model can be highlighted in predicting the temporal trends of human brucellosis, which may act as the potential for far-reaching implications for prevention and control of this disease.

Cassava (Manihot esculenta Crantz) is a major tuberous crop produced worldwide. In this study, we sequenced 158 diverse cassava varieties and identified 349,827 single-nucleotide polymorphisms (SNPs) and indels. In each chromosome, the number of SNPs and the physical length of the respective chromosome were in agreement. Population structure analysis indicated that this panel can be divided into three subgroups. Genetic diversity analysis indicated that the average nucleotide diversity of the panel was 1.21 × 10-4 for all sampled landraces. This average nucleotide diversity was 1.97 × 10-4, 1.01 × 10-4, and 1.89 × 10-4 for subgroups 1, 2, and 3, respectively. Genome-wide linkage disequilibrium (LD) analysis demonstrated that the average LD was about ∼8 kb. We evaluated 158 cassava varieties under 11 different environments. Finally, we identified 36 loci that were related to 11 agronomic traits by genome-wide association analyses. Four loci were associated with two traits, and 62 candidate genes were identified in the peak SNP sites. We found that 40 of these genes showed different expression profiles in different tissues. Of the candidate genes related to storage roots, Manes.13G023300, Manes.16G000800, Manes.02G154700, Manes.02G192500, and Manes.09G099100 had higher expression levels in storage roots than in leaf and stem; on the other hand, of the candidate genes related to leaves, Manes.05G164500, Manes.05G164600, Manes.04G057300, Manes.01G202000, and Manes.03G186500 had higher expression levels in leaves than in storage roots and stem. This study provides basis for research on genetics and the genetic improvement of cassava.

ObjectivesIn a 24/7 society, the negative metabolic effects of rotating night shift work have been increasingly explored. This study aimed to examine the association between rotating night shift work and non-alcoholic fatty liver disease (NAFLD) in steelworkers.MethodsA total of 6881 subjects was included in this study. Different exposure metrics of night shift work including current shift status, duration of night shifts (years), cumulative number of night shifts (nights), cumulative length of night shifts (hours), average frequency of night shifts (nights/month) and average length of night shifts (hours/night) were used to examine the relationship between night shift work and NAFLD.ResultsCurrent night shift workers had elevated odds of NAFLD (OR, 1.23, 95% CI 1.02 to 1.48) compared with those who never worked night shifts after adjustment for potential confounders. Duration of night shifts, cumulative number of night shifts and cumulative length of night shifts were positively associated with NAFLD. Both the average frequency of night shifts (>7 nights/month vs ≤7 nights/month: OR, 1.24, 95% CI 1.06 to 1.45) and average length of night shifts (>8 hours/night vs ≤8 hours/night: OR, 1.27, 95% CI 1.08 to 1.51) were independently associated with overall NAFLD after mutually adjusting for the duration of night shifts and other potential confounders among night shift workers. No significant association was found in female workers between different exposure metrics of night shift work and NAFLD.ConclusionsRotating night shift work is associated with elevated odds of NAFLD in male steelworkers.