Draft genome sequence of the mulberry tree Morus notabilis

Human utilization of the mulberry–silkworm interaction started at least 5,000 years ago and greatly influenced world history through the Silk Road. Complementing the silkworm genome sequence, here we describe the genome of a mulberry species Morus notabilis. In the 330-Mb genome assembly, we identify 128 Mb of repetitive sequences and 29,338 genes, 60.8% of which are supported by transcriptome sequencing. Mulberry gene sequences appear to evolve ~3 times faster than other Rosales, perhaps facilitating the species’ spread worldwide. The mulberry tree is among a few eudicots but several Rosales that have not preserved genome duplications in more than 100 million years; however, a neopolyploid series found in the mulberry tree and several others suggest that new duplications may confer benefits. Five predicted mulberry miRNAs are found in the haemolymph and silk glands of the silkworm, suggesting interactions at molecular levels in the plant–herbivore relationship. The identification and analyses of mulberry genes involved in diversifying selection, resistance and protease inhibitor expressed in the laticifers will accelerate the improvement of mulberry plants

Stretchable Filler/Solid Rubber Piezoresistive Thread Sensor for Gesture Recognition

Recently, the stretchable piezoresistive composites have become a focus in the fields of the biomechanical sensing and human posture recognition because they can be directly and conformally attached to bodies and clothes. Here, we present a stretchable piezoresistive thread sensor (SPTS) based on Ag plated glass microspheres (Ag@GMs)/solid rubber (SR) composite, which was prepared using new shear dispersion and extrusion vulcanization technology. The SPTS has the high gauge factors (7.8~11.1) over a large stretching range (0–50%) and approximate linear curves about the relative change of resistance versus the applied strain. Meanwhile, the SPTS demonstrates that the hysteresis is as low as 2.6% and has great stability during 1000 stretching/releasing cycles at 50% strain. Considering the excellent mechanical strain-driven characteristic, the SPTS was carried out to monitor posture recognitions and facial movements. Moreover, the novel SPTS can be successfully integrated with software and hardware information modules to realize an intelligent gesture recognition system, which can promptly and accurately reflect the produced electrical signals about digital gestures, and successfully be translated into text and voice. This work demonstrates great progress in stretchable piezoresistive sensors and provides a new strategy for achieving a real-time and effective-communication intelligent gesture recognition system

Activated Persulfate Oxidation of Perfluorooctanoic Acid (PFOA) in Groundwater under Acidic Conditions

Perfluorooctanoic acid (PFOA) is an emerging contaminant of concern due to its toxicity for human health and ecosystems. However, successful degradation of PFOA in aqueous solutions with a cost-effective method remains a challenge, especially for groundwater. In this study, the degradation of PFOA using activated persulfate under mild conditions was investigated. The impact of different factors on persulfate activity, including pH, temperature (25 °C–50 °C), persulfate dosage and reaction time, was evaluated under different experimental conditions. Contrary to the traditional alkaline-activated persulfate oxidation, it was found that PFOA can be effectively degraded using activated persulfate under acidic conditions, with the degradation kinetics following the pseudo-first-order decay model. Higher temperature, higher persulfate dosage and increased reaction time generally result in higher PFOA degradation efficiency. Experimental results show that a PFOA degradation efficiency of 89.9% can be achieved by activated persulfate at pH of 2.0, with the reaction temperature of 50 °C, molar ratio of PFOA to persulfate as 1:100, and a reaction time of 100 h. The corresponding defluorination ratio under these conditions was 23.9%, indicating that not all PFOA decomposed via fluorine removal. The electron paramagnetic resonance spectrometer analysis results indicate that both SO4−• and •OH contribute to the decomposition of PFOA. It is proposed that PFOA degradation occurs via a decarboxylation reaction triggered by SO4−•, followed by a HF elimination process aided by •OH, which produces one-CF2-unit-shortened perfluoroalkyl carboxylic acids (PFCAs, Cn−1F2n−1COOH). The decarboxylation and HF elimination processes would repeat and eventually lead to the complete mineralization all PFCAs

Molecular-scale descriptions and experimental characterizations of nitrocellulose soaked in pure liquid ethanol or diethyl ether respectively at room temperature

Studies on nitrocellulose (NC) mixtures with little solubilities were neglected in many cases previously. This investigation was performed to provide supplemental characterizations of NC and its soaked state with pure liquid ethanol or diethyl ether by simulations and practical methods. Above all, a short-chained NC model (polymerisation degree: 8) and a dried NC specimen were characterized for their substitution of nitrate and microstructure. It was confirmed that both the numerical model and practical specimen belonged to low-nitrated NC. The bonding information of a glycosyl unit and nitrate ester were summarized via first-principle calculations. Then, ReaxFF potential based Molecular Dynamic (MD) simulations and soaking tests on binary organic mixtures demonstrated that both ethanol and diethyl ether had limited solubility for our specified NC. However, potential energies and diffusion coefficients of both computational models showed that the interactions from ethanol molecules were relatively stronger than diethyl ether molecules. The viscosities of saturated NC solutions also proved this consequence, as the difference between pure ether and its filtered NC solution was only 0.02 mm ^2 s ^−1 . Finally, the strong volatility of diethyl ether itself could keep the wetness of NC upper surface shortly, because this was an upward volatilization effect. Due to this effect, the penetration of NC-diethyl ether mixture was higher in the early period of penetration tests

Identification of the mulberry genes involved in ethylene biosynthesis and signaling pathways and the expression of MaERF-B2-1 and MaERF-B2-2 in the response to flooding stress

The phytohormone ethylene is essential to plant growth and development. It plays crucial roles in responses to biotic and abiotic stress. The mulberry tree is an important crop plant in countries in which people rear silkworms for silk production. The availability of the mulberry genome has made it possible to identify mulberry genes involved in ethylene biosynthesis and signal pathways. A total of 145 mulberry genes were identified by both homology-based and hidden Markov model (HMM) search, including 29 genes associated with ethylene biosynthesis and 116 genes in the AP2/ERF family. Studies on gene structure have provided a genetic basis for understanding the functions of these genes. The differences in gene expression were also observed in different tissues. The expression of two mulberry genes in the AP2/ERF family, MaERF-B2-1 and MaERF-B2-2, was found to be associated with the response to flooding stress. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s10142-014-0403-2) contains supplementary material, which is available to authorized users

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in Fan et al. [Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package gpumd. We increase the accuracy of NEP models both by improving the radial functions in the atomic-environment descriptor using a linear combination of Chebyshev basis functions and by extending the angular descriptor with some four-body and five-body contributions as in the atomic cluster expansion approach. We also detail our efficient implementation of the NEP approach in graphics processing units as well as our workflow for the construction of NEP models and demonstrate their application in large-scale atomistic simulations. By comparing to state-of-the-art MLPs, we show that the NEP approach not only achieves above-average accuracy but also is far more computationally efficient. These results demonstrate that the gpumd package is a promising tool for solving challenging problems requiring highly accurate, large-scale atomistic simulations. To enable the construction of MLPs using a minimal training set, we propose an active-learning scheme based on the latent space of a pre-trained NEP model. Finally, we introduce three separate Python packages, viz., gpyumd, calorine, and pynep, that enable the integration of gpumd into Python workflows

GPUMD: A package for constructing accurate machine-learned potentials and performing highly efficient atomistic simulations

We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in Fan et al. [Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package gpumd. We increase the accuracy of NEP models both by improving the radial functions in the atomic-environment descriptor using a linear combination of Chebyshev basis functions and by extending the angular descriptor with some four-body and five-body contributions as in the atomic cluster expansion approach. We also detail our efficient implementation of the NEP approach in graphics processing units as well as our workflow for the construction of NEP models and demonstrate their application in large-scale atomistic simulations. By comparing to state-of-the-art MLPs, we show that the NEP approach not only achieves above-average accuracy but also is far more computationally efficient. These results demonstrate that the gpumd package is a promising tool for solving challenging problems requiring highly accurate, large-scale atomistic simulations. To enable the construction of MLPs using a minimal training set, we propose an active-learning scheme based on the latent space of a pre-trained NEP model. Finally, we introduce three separate Python packages, viz., gpyumd, calorine, and pynep, that enable the integration of gpumd into Python workflows