Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A Hybrid Framework for Multivariate Time Series Forecasting of Daily Urban Water Demand Using Attention-Based Convolutional Neural Network and Long Short-Term Memory Network

Urban water demand forecasting is beneficial for reducing the waste of water resources and enhancing environmental protection in sustainable water management. However, it is a challenging task to accurately predict water demand affected by a range of factors with nonlinear and uncertainty temporal patterns. This paper proposes a new hybrid framework for urban daily water demand with multiple variables, called the attention-based CNN-LSTM model, which combines convolutional neural network (CNN), long short-term memory (LSTM), attention mechanism (AM), and encoder-decoder network. CNN layers are used to learn the representation and correlation between multivariate variables. LSTM layers are utilized as the building blocks of the encoder-decoder network to capture temporal characteristics from the input sequence, while AM is introduced to the encoder-decoder network to assign corresponding attention according to the importance of water demand multivariable time series at different times. The new hybrid framework considers correlation between multiple variables and neglects irrelevant data points, which helps to improve the prediction accuracy of multivariable time series. The proposed model is contrasted with the LSTM model, the CNN-LSTM model, and the attention-based LSTM to predict the daily water demand time series in Suzhou, China. The results show that the hybrid model achieves higher prediction performance with the smallest mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), and largest correlation coefficient (R2)

The Fuzzy DEA-Based Manufacturing Service Efficiency Evaluation and Ranking Approach for a Parallel Two-Stage Structure of a Complex Product System on the Example of Solid Waste Recycling

Accurate production efficiency evaluation can assist enterprises in adjusting production strategies, improving production efficiency, and, thereby, weakening environmental impacts. However, the current studies on production efficiency evaluation do not accurately consider interactions inside the production system in parallel production processes. Based on the concept of the manufacturing service, this paper describes the production process of a complex product system (CoPS) with a manufacturing service chain. An efficiency calculation model based on the triangular intuitionistic fuzzy number–solid waste recycling–super-efficiency data envelopment analysis (TIFN-SWR-SDEA) is proposed under the consideration of the internal parallel structure of the production system on the example of solid waste recycling. Additionally, the technique for order preference by similarity to ideal solution (TOPSIS) method and the entropy weight method were combined to determine the proportion of solid waste recycling, and an improved proposed index rank (PIR) method was employed to rank the efficiency interval results. Finally, the effectiveness and superiority of the method were verified by comparative analysis. The results show that the overall efficiency of the CoPS production system can be improved by using green manufacturing technology, increasing the recycling of renewable resources, using clean energy, and improving the utilization rate of materials in the production process

Multivariate Analysis of Trace Element Concentrations in Atmospheric Deposition in the Yangtze River Delta, East China

The Yangtze River Delta (YRD), one of the fastest developing regions in China, was investigated for its trace element concentrations. Forty-three samples of atmospheric deposition were analyzed for their concentrations of thirteen elements, As, Cd, Cr, Cu, Fe, Hg, Mn, Mo, Ni, Pb, Se, S and Zn. The results show that, in comparison with Chinese soil, the atmospheric deposition in the YRD generally has elevated trace element concentrations, except for Fe and Mn. The current atmospheric deposition of Cd, Cr, Cu, Pb and Zn in the YRD is significantly higher than the results from previous studies in other regions around the world. Four main sources of the trace elements were identified using statistical techniques including descriptive, correlation, and multivariate analyses, such as principal component analysis (PCA) and cluster analysis (CA). The four sources and associated cluster elements are: (1) road traffic emissions contributing As, Hg, Cu, Cd, Mo, S and Zn; (2) pyrometallurgical processes associated with Cr and Ni; (3) resuspension of soil particles contributing Fe and Mn; (4) coal combustion associated with Pb and Se. The four major sources were further verified by enrichment factor (EF) calculation and spatial analysis. Spatial distributions of four factor scores and EFs of elements show that high scores and EFs of trace metals (As, Hg, Cu, Cd, Mo, S and Zn) are mostly concentrated in the sites with high traffic conditions, and high scores of Fe and Mn are found at rural sites associated with high impact of soil particles resuspension, while Cr and Ni are higher in the area with long history of alloy machining

Bioavailability of Cd in Agricultural Soils Evaluated by DGT Measurements and the DIFS Model in Relation to Uptake by Rice and Tea Plants

The elevated accumulation of cadmium (Cd) in rice (Oryza sativa L.) and tea (Camellia sinensis L.) grown in agricultural soils may lead to a variety of adverse health effects. This study collected and analyzed crop samples along with paired rhizosphere soil samples from 61 sites in Cd-contaminated regions in Anhui Province, China. The findings revealed that both the diffusive gradients in thin-films (DGT) and soil solution were capable of effectively predicting Cd contents in crops. Conventional chemical extraction methods were inappropriate to evaluate the bioavailability of Cd. However, the effective concentrations (CE) corrected by the DGT-induced fluxes in soils (DIFS) model exhibited the strongest correlation with crop Cd contents. Except for CE, various measurement methods yielded better results for predicting Cd bioavailability in tea compared to rice. Pearson’s correlation analysis and the random forest (RF) model identified the key influencing factors controlling Cd uptake by rice and tea, including pH, soil texture, and contents of zinc (Zn) and selenium (Se) in soils, which antagonize Cd. To reduce the potential health risk from rice and tea, the application of soil liming and/or Se-oxidizing bacteria was expected to be an effective management strategy