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BackgroundIonic liquids (ILs) are promising pretreatment solvents for lignocellulosic biomass, but are largely prepared from petroleum precursors. Benzaldehydes from depolymerized lignin, such as vanillin, syringaldehyde, and 4-methoxy benzaldehyde, represent renewable feedstocks for the synthesis of ionic liquids. We herein report syntheses of novel lignin-derived ionic liquids, with extended N-alkyl chains, and examine their melting points, cellulose dissolution capacities, and toxicity profiles against Daphnia magna and E. coli strain 1A1. The latter organism has been engineered to produce isoprenol, a drop-in biofuel and precursor for commodity chemicals.ResultsThe new N,N-diethyl and N,N-dipropyl methyl benzylammonium ILs were liquids at room temperature, showing 75-100 °C decreased melting points as compared to their N,N,N-trimethyl benzylammonium analog. Extension of N-alkyl chains also increased antibacterial activity threefold, while ionic liquids prepared from vanillin showed 2- to 4-fold lower toxicity as compared to those prepared from syringaldehyde and 4-methoxybenzaldehyde. The trend of antibacterial activity for anions of lignin-derived ILs was found to be methanesulfonate

The legacy effects of invasive plant species can hinder the recovery of native communities, especially under nitrogen deposition conditions, where invasive species show growth advantages and trigger secondary invasions in controlled areas. Therefore, it is crucial to thoroughly investigate the effects of nitrogen deposition on the legacy effects of plant invasions and their mechanisms. The hypotheses of this study are as follows: (1) Nitrogen deposition amplifies the legacy effects of plant invasion. This phenomenon was investigated by analysing four potential mechanisms covering community system structure, nitrogen metabolism, geochemical cycles, and microbial mechanisms. The results suggest that microorganisms drive plant–soil feedback processes, even regulating or limiting other factors. (2) The impact of nitrogen deposition on the legacy effects of plant invasions may be intensified primarily through enhanced nitrogen metabolism via microbial anaerobes bacteria. Essential insights into invasion ecology and ecological management have been provided by analysing how nitrogen-fixing bacteria improve nitrogen metabolism and establish sustainable methods for controlling invasive plant species. This in-depth study contributes to our better understanding of the lasting effects of plant invasions on ecosystems and provides valuable guidance for future ecological management. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the usefulness of a novel computer-aided classification framework for the differential diagnosis of parkinsonian disorders (PDs) based on 11C-methyl-N-2β-carbomethoxy-3β-(4-fluorophenyl)-tropanel (11C-CFT) positron emission tomography (PET) imaging. Methods: Patients with different forms of PDs—including Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP)—underwent dopamine transporter (DAT) imaging with 11C-CFT PET. A novel multistep computer-aided classification framework—consisting of magnetic resonance imaging (MRI)-assisted PET segmentation, feature extraction and prediction, and automatic subject classification—was developed. A random forest method was used to assess the diagnostic relevance of different regions to the classification process. Finally, the performance of the computer-aided classification system was tested using various training strategies involving patients with early and advanced disease stages. Results: Accuracy values for identifying PD, MSA, and PSP were 85.0, 82.2, and 89.7%, respectively—with an overall accuracy of 80.4%. The caudate and putamen provided the highest diagnostic relevance to the proposed classification framework, whereas the contribution of midbrain was negligible. With the exception of sensitivity for diagnosing PSP, the strategy comprising both early and advanced disease stages performed better in terms of sensitivity, specificity, positive predictive value, and negative predictive value within each PDs subtype. Conclusions: The proposed computer-aided classification framework based on 11C-CFT PET imaging holds promise for improving the differential diagnosis of PDs. [ABSTRACT FROM AUTHOR]

Microalgae are one of the most promising feedstocks for biodiesel production due to their high lipid content and easy farming. However, the extraction of lipids from microalgae is energy intensive and costly and involves the use of toxic organic solvents. Compared with organic solvent extraction, supercritical CO2 (SCCO2) has demonstrated advantages through lower toxicity and no solvent-liquid separation. Due to the nonpolar nature of SCCO2, polar organic solvents such as methanol may need to be added as a modifier in order to increase the extraction ability of SCCO2. In this paper, pilot scale lipid extraction using SCCO2 was studied on two microalgae species: Spirulina sp. and Schizochytrium sp. For each species, SCCO2 extraction was conducted on 200 g of biomass for 6 h. Methanol was added as a cosolvent in the extraction process based on a volume ratio of 4%. The results showed that adding methanol in SCCO2 increased the lipid extraction yield significantly for both species. Under an operating pressure of 4000 psi, the lipid extraction yields for Spirulina sp. and Schizochytrium sp. were increased by 80% and 72%, respectively. It was also found that a stepwise addition of methanol was more effective than a one-time addition. In comparison with Soxhlet extraction using methylene chloride/methanol (2:1, v/v), the methanol-SCCO2 extraction demonstrated its high effectiveness for lipid extraction. In addition, the methanol-SCCO2 system showed a high lipid extraction yield after increasing biomass loading fivefold, indicating good potential for scaling up this method. Finally, a kinetic study of the SCCO2 extraction process was conducted, and the results showed that methanol concentration in SCCO2 has the strongest influence on the lipid extraction yield. [ABSTRACT FROM AUTHOR]

Fatigue driving will seriously threaten public safety and health, so monitoring the brain's cognitive state accurately and exploring the fatigue process is essential. This paper proposes a 5-D Brain Cognitive Dynamic Recognition Network (E-5-D-BCDRNet) that synchronizes the brain's cognitive changes with global features, local features, and sequence changes. The global feature refers to the 3-D brain global cognitive power maps (3-D-BGCM) constructed based on the new fatigue cognitive indicators. Local features refer to the 1-D brain local cognitive array (1-D-BLCA) created based on fatigue recognition contribution of different rhythms in each brain region. This paper uses a non-invasive wearable device to collect electroencephalogram (EEG) data from 14 subjects to evaluate the model's performance. Compared with the existing advanced methods, the proposed method achieves the best detection effect (88.5%). In addition, this study finds that the features learned based on different indicators are different and distributed in various brain regions by presenting the learned characteristics. Moreover, by comparing the detection results based on the local features of a single rhythm, this study finds that the frontal and occipital regions of the driver are significantly different before and after. More importantly, only a single region can also achieve significant detection effects, which shows that our method can achieve satisfactory results based on small data. In a word, the research in this paper can lay a theoretical foundation for real-time brain fatigue detection and promote the development of intelligent transportation systems. • This paper designs new fatigue cognitive indicators for brain detection. • This paper constructs global and local features to reflect fatigue comprehensively. • We proposes an E-5-D-BCDRNet to learn global and local features simultaneously. [ABSTRACT FROM AUTHOR]

This research established a set of methods suitable for China to assess the informatisation leveI in rural areas and analysed the status of rural informatisation in different parts of China. The study established a synthetic index method, and the process includes establishment of an indicator system, data collection, data standardisation and indexation. The average level of national rural informatisation was found to be 99.69. The top six areas are municipalities of Shanghai and Beijing, and provinces of Guangdong, Hebei, Zhejiang and Jiangsu. The lnformatisation Index of Shanghai is 259.15, which is 160% higher than that of the average level in the whole country and 971% of the level in Tibet. The building of infrastructures for rural informatisation and the application of agricultural information technology (IT) have played an evident role in the process of informatisation construction in rural areas. However, the gap of rural informatisation development between eastern and western China is still wide. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the usefulness of a novel computer-aided classification framework for the differential diagnosis of parkinsonian disorders (PDs) based on 11 C-methyl-N-2β-carbomethoxy-3β-(4-fluorophenyl)-tropanel ( 11 C-CFT) positron emission tomography (PET) imaging., Methods: Patients with different forms of PDs-including Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP)-underwent dopamine transporter (DAT) imaging with 11 C-CFT PET. A novel multistep computer-aided classification framework-consisting of magnetic resonance imaging (MRI)-assisted PET segmentation, feature extraction and prediction, and automatic subject classification-was developed. A random forest method was used to assess the diagnostic relevance of different regions to the classification process. Finally, the performance of the computer-aided classification system was tested using various training strategies involving patients with early and advanced disease stages., Results: Accuracy values for identifying PD, MSA, and PSP were 85.0, 82.2, and 89.7%, respectively-with an overall accuracy of 80.4%. The caudate and putamen provided the highest diagnostic relevance to the proposed classification framework, whereas the contribution of midbrain was negligible. With the exception of sensitivity for diagnosing PSP, the strategy comprising both early and advanced disease stages performed better in terms of sensitivity, specificity, positive predictive value, and negative predictive value within each PDs subtype., Conclusions: The proposed computer-aided classification framework based on 11 C-CFT PET imaging holds promise for improving the differential diagnosis of PDs., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xu, Xu, Liu, Li, Li, Yen, Wu, Wang, Zuo, Wu and Zhuang.)