De novo domestication of wild tomato using genome editing

Breeding of crops over millennia for yield and productivity1 has led to reduced genetic diversity. As a result, beneficial traits of wild species, such as disease resistance and stress tolerance, have been lost2. We devised a CRISPR–Cas9 genome engineering strategy to combine agronomically desirable traits with useful traits present in wild lines. We report that editing of six loci that are important for yield and productivity in present-day tomato crop lines enabled de novo domestication of wild Solanum pimpinellifolium. Engineered S. pimpinellifolium morphology was altered, together with the size, number and nutritional value of the fruits. Compared with the wild parent, our engineered lines have a threefold increase in fruit size and a tenfold increase in fruit number. Notably, fruit lycopene accumulation is improved by 500% compared with the widely cultivated S. lycopersicum. Our results pave the way for molecular breeding programs to exploit the genetic diversity present in wild plants

Ongoing Spillover of Hantaan and Gou Hantaviruses from Rodents Is Associated with Hemorrhagic Fever with Renal Syndrome (HFRS) in China

Peer reviewe

From Mendel’s discovery on pea to today’s plant genetics and breeding

In 2015, we celebrated the 150th anniversary of the presentation of the seminal work of Gregor Johann Mendel. While Darwin’s theory of evolution was based on differential survival and differential reproductive success, Mendel’s theory of heredity relies on equality and stability throughout all stages of the life cycle. Darwin’s concepts were continuous variation and “soft” heredity; Mendel espoused discontinuous variation and “hard” heredity. Thus, the combination of Mendelian genetics with Darwin’s theory of natural selection was the process that resulted in the modern synthesis of evolutionary biology. Although biology, genetics, and genomics have been revolutionized in recent years, modern genetics will forever rely on simple principles founded on pea breeding using seven single gene characters. Purposeful use of mutants to study gene function is one of the essential tools of modern genetics. Today, over 100 plant species genomes have been sequenced. Mapping populations and their use in segregation of molecular markers and marker–trait association to map and isolate genes, were developed on the basis of Mendel's work. Genome-wide or genomic selection is a recent approach for the development of improved breeding lines. The analysis of complex traits has been enhanced by high-throughput phenotyping and developments in statistical and modeling methods for the analysis of phenotypic data. Introgression of novel alleles from landraces and wild relatives widens genetic diversity and improves traits; transgenic methodologies allow for the introduction of novel genes from diverse sources, and gene editing approaches offer possibilities to manipulate gene in a precise manner

Reducing Quantitative Fluctuation of Laser-Induced Breakdown Spectroscopy by Kalman Filtering

Laser-induced breakdown spectroscopy (LIBS) is excellent for its potential of online compositional analysis. Large signal fluctuation is the major obstacle of LIBS for quantitative analysis application. A kalman filtering method is proposed to estimate the elemental concentration and smooth the quantitative results. The system state model and the measurement model are deduced. The relation matrix between the measured values and system state is estimated based on calibration curve built on some standard samples, and the measurement noise matrix is estimated by the variance of multiple measurements of the spectral intensity. In order to make Kalman filter follow the changes of elemental concentration, the initial value of the covariance matrix of estimation error is reset as a certain rule. The experimental results show that the Kalman filtering method can greatly reduce the fluctuation of quantitative results and improve the measurement accuracy

A method for improving wavelet threshold denoising in laser-induced breakdown spectroscopy

The wavelet threshold denoising method is an effective noise suppression approach for noisy laser-induced breakdown spectroscopy signal. In this paper, firstly, the noise sources of LIBS system are summarized. Secondly, wavelet multi-resolution analysis and wavelet threshold denoising method are introduced briefly. As one of the major factors influencing the denoising results in the process of wavelet threshold denoising, the optimal decomposition level selection is studied. Based on the entropy analysis of noisy LIBS signal and noise, a method of choosing optimal decomposition level is presented. Thirdly, the performance of the proposed method is verified by analyzing some synthetic signals. Not only the denoising results of the synthetic signals are analyzed, but also the ultimate denoising capacity of the wavelet threshold denoising method with the optimal decomposition level is explored. Finally, the experimental data analysis implies that the fluctuation of the noisy LIBS signals can be decreased and the weak LIBS signals can be recovered. The optimal decomposition level is able to improve the performance of the denoising results obtained by wavelet threshold denoising with non-optimal wavelet functions. The signal to noise ratios of the elements are improved and the limit of detection values are reduced by more than 50% by using the proposed method

Quantitative analysis of steels using PLS with three data reduction methods based on LIBS

Three methods, selecting characteristic lines of elements contained in the samples manually, selecting intensive spectral partitions manually and the whole spectra, were used to reduce dimensions of spectra of 27 steel samples acquired by Laser-Induced Breakdown Spectroscopy. The PLS models were built based on the data after dimension reduction to quantify the Mn concentration of samples. The results show that, PLS model built based on selecting intensive spectral partitions can achieve the best result with the least complexity and the highest generalization ability. Selecting intensive partitions is a promising solution to reduce dimensions for original spectra. © (2014) Trans Tech Publications, Switzerland

Selection of spectral data for classification of steels using laser-induced breakdown spectroscopy

Principal component analysis (PCA) combined with artificial neural networks was used to classify the spectra of 27 steel samples acquired using laser-induced breakdown spectroscopy. Three methods of spectral data selection, selecting all the peak lines of the spectra, selecting intensive spectral partitions and the whole spectra, were utilized to compare the influence of different inputs of PCA on the classification of steels. Three intensive partitions were selected based on experience and prior knowledge to compare the classification, as the partitions can obtain the best results compared to all peak lines and the whole spectra. We also used two test data sets, mean spectra after being averaged and raw spectra without any pretreatment, to verify the results of the classification. The results of this comprehensive comparison show that a back propagation network trained using the principal components of appropriate, carefully selected spectral partitions can obtain the best results. A perfect result with 100% classification accuracy can be achieved using the intensive spectral partitions ranging of 357-367 nm

A comparative study of two data reduction methods for steel classification based on LIBS

Spectra of 27 steel samples were acquired by Laser-Induced Breakdown Spectroscopy (LIBS) for steel classification. Two methods were used to reduce dimensions: the first is to select characteristic lines of elements contained in the samples manually and the second is to do principal component analysis (PCA) of original spectra. Then the data after reducing dimensions was used as the input of artificial neural networks (ANN) to classify steel samples. The results show that, the better result can be achieved by selecting peak lines manually, but this solution needs much priori knowledge and wastes much time. The principal components (PCs) of original spectra were utilized as the input of artificial neural networks can also attain a good result nevertheless and this method can be developed into an automatic solution without any priori knowledge

远距离冶金液态金属成分的原位、在线检测装置及方法

An in-situ on-line detection device and detection method for a long-distance metallurgical liquid metal component. The detection device comprises a front-end high-temperature resistant probe (18), a middle-end optical sensing device (19) and a back-end control platform (24), wherein the head of the front-end high-temperature resistant probe (18) is placed in a liquid metal (22), the tail thereof is coaxially connected to the middle-end optical sensing device (19), and an optical window (15) is arranged in the connection position; and the middle-end optical sensing device (19) is connected to the back-end control platform (24) through a signal line (25). The detection device and detection method can provide a timely and valid message for quality control and a melting end, so that the detection time is greatly shortened, the detection distance can be adjusted extensively, the measurement result is accurate, and it can be achieved to measure components that are difficult to measure, such as C, S, P, etc