STEAM GASIFICATION OF SOME MONGOLIAN COALS

The gasification tests for the Alagtolgoi and Ailbayan coal deposits were conducted in the temperature up to 850°C using bench scale reactor in order to evaluate product gas composition. Prior to the gasification experiments, the raw coal was pyrolysed in a stainless steel reactor under N2 atmosphere at a temperature of 500°C for 1 h. General behavior of the coal conversion was quite similar for both coals. The gasification tests show that an increase in temperature enhances the formation of hydrogen, carbon dioxide and carbon monoxide. The highest yield of hydrogen and carbon dioxide concentrations of the Ailbayan coal are achieved at temperature of 850°C, which were 2.859 mmol⋅g-1⋅min-1 and 1.054 mmol⋅g-1⋅min-1 respectively. However maximum rate of hydrogen for Alagtolgoi subbituminous coal reached around 800°C. Overall results show that the maximum gasification rate is reached earlier for subbituminous coal than for bituminous coal, but product gas evolution was higher for the investigated bituminous coal

Effects of Vegetation Belt Movement on Wildfire in the Mongolian Plateau over the Past 40 Years

The frequency and intensity of fires are increasing because of warmer temperatures and increased droughts, as well as climate-change induced fuel distribution changes. Vegetation in environments, such as those in the mid-to-high latitudes and high elevations, moves to higher latitudes or elevations in response to global warming. Over the past 40 years, the Mongolian Plateau has been arid and semi-arid, with a decrease in growing season vegetation in the southwest and an increase in growing season vegetation in the northeast. The northward movement of vegetation has brought fires, especially in the Dornod, Sukhbaatar, and Kent provinces near the Kent Mountains, and has become more obvious in the past 20 years. The occurrence of a dead fuel index (DFI) with high probability is distributed in northern Mongolia, the border area between China and Mongolia, and the forest-side meadow-steppe region of the Greater Khingan Mountains. These findings suggest that vegetation is moving northward because of climate change and this presents a challenge of future warming spreading fire northward, adding material to the study of the relationship between the northward movement of global vegetation and fires

Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches

A major challenge in the application of the CRISPR-Cas13d system is to accurately predict its guide-dependent on-target and off-target effect. Here, we perform CRISPR-Cas13d proliferation screens and design a deep learning model, named DeepCas13, to predict the on-target activity from guide sequences and secondary structures. DeepCas13 outperforms existing methods to predict the efficiency of guides targeting both protein-coding and non-coding RNAs. Guides targeting non-essential genes display off-target viability effects, which are closely related to their on-target efficiencies. Choosing proper negative control guides during normalization mitigates the associated false positives in proliferation screens. We apply DeepCas13 to the guides targeting lncRNAs, and identify lncRNAs that affect cell viability and proliferation in multiple cell lines. The higher prediction accuracy of DeepCas13 over existing methods is extensively confirmed via a secondary CRISPR-Cas13d screen and quantitative RT-PCR experiments. DeepCas13 is freely accessible via http://deepcas13.weililab.org