Self‐Sacrificial Template‐Directed Synthesis of Metal–Organic Framework‐Derived Porous Carbon for Energy‐Storage Devices

Metal–organic framework (MOF)‐derived carbon materials exhibit large surface areas, but dominant micropore characteristics and uncontrollable dimensions. Herein, we propose a self‐sacrificial template‐directed synthesis method to engineer the porous structure and dimensions of MOF‐derived carbon materials. A porous zinc oxide (ZnO) nanosheet solid is selected as the self‐sacrificial template and two‐dimensional (2D) nanostructure‐directing agent to prepare 2D ZIF‐8‐derived carbon nanosheets (ZCNs). The as‐prepared ZCN materials exhibit a large surface area with hierarchical porosity. These intriguing features render ZCN materials advanced electrode materials for electrochemical energy‐storage devices, demonstrating large ion‐accessible surface area and high ion‐/electron‐transport rates. This self‐sacrificial template‐directed synthesis method offers new avenues for rational engineering of the porous structure and dimensions of MOF‐derived porous carbon materials, thus exploiting their full potential for electrochemical energy‐storage devices.On the surface: A self‐sacrificial template‐directed synthesis method is proposed to engineer the porosity and dimensions of MOF‐derived carbon materials. By using a porous nanosheet solid as the self‐sacrificial template and two‐dimensional (2D) nanostructure‐directing agent, 2D ZIF‐8‐derived carbon nanosheets are prepared, which exhibit a large ion‐accessible surface area and rapid ion transport as the electrode materials for electrochemical energy‐storage devices.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/137193/1/celc201500536-sup-0001-misc_information.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137193/2/celc201500536.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/137193/3/celc201500536_am.pd

Development of a COVID-19 early risk assessment system based on multiple machine learning algorithms and routine blood tests: a real-world study

BackgroundsDuring the Coronavirus Disease 2019 (COVID-19) epidemic, the massive spread of the disease has placed an enormous burden on the world’s healthcare and economy. The early risk assessment system based on a variety of machine learning (ML) algorithms may be able to provide more accurate advice on the classification of COVID-19 patients, offering predictive, preventive, and personalized medicine (PPPM) solutions in the future.MethodsIn this retrospective study, we divided a portion of the data into training and validation cohorts in a 7:3 ratio and established a model based on a combination of two ML algorithms first. Then, we used another portion of the data as an independent testing cohort to determine the most accurate and stable model and compared it with other scoring systems. Finally, patients were categorized according to risk scores and then the correlation between their clinical data and risk scores was studied.ResultsThe elderly accounted for the majority of hospitalized patients with COVID-19. The C-index of the model constructed by combining the stepcox[both] and survivalSVM algorithms was 0.840 in the training cohort and 0.815 in the validation cohort, which was calculated to have the highest C-index in the testing cohort compared to the other 119 ML model combinations. Compared with current scoring systems, including the CURB-65 and several reported prognosis models previously, our model had the highest AUC value of 0.778, representing an even higher predictive performance. In addition, the model’s AUC values for specific time intervals, including days 7,14 and 28, demonstrate excellent predictive performance. Most importantly, we stratified patients according to the model’s risk score and demonstrated a difference in survival status between the high-risk, median-risk, and low-risk groups, which means a new and stable risk assessment system was built. Finally, we found that COVID-19 patients with a history of cerebral infarction had a significantly higher risk of death.ConclusionThis novel risk assessment system is highly accurate in predicting the prognosis of patients with COVID-19, especially elderly patients with COVID-19, and can be well applied within the PPPM framework. Our ML model facilitates stratified patient management, meanwhile promoting the optimal use of healthcare resources

Genome sequence of the cultivated cotton <i>Gossypium arboreum</i>

The complex allotetraploid nature of the cotton genome (AADD; 2n = 52) makes genetic, genomic and functional analyses extremely challenging. Here we sequenced and assembled the Gossypium arboreum (AA; 2n = 26) genome, a putative contributor of the A subgenome. A total of 193.6 Gb of clean sequence covering the genome by 112.6-fold was obtained by paired-end sequencing. We further anchored and oriented 90.4% of the assembly on 13 pseudochromosomes and found that 68.5% of the genome is occupied by repetitive DNA sequences. We predicted 41,330 protein-coding genes in G. arboreum. Two whole-genome duplications were shared by G. arboreum and Gossypium raimondii before speciation. Insertions of long terminal repeats in the past 5 million years are responsible for the twofold difference in the sizes of these genomes. Comparative transcriptome studies showed the key role of the nucleotide binding site (NBS)-encoding gene family in resistance to Verticillium dahliae and the involvement of ethylene in the development of cotton fiber cells.Genetics &amp; HereditySCI(E)[email protected]; [email protected]; [email protected]