Dissolution of cellulose with NMMO by microwave heating

Environmentally friendly microwave heating process was applied to the dissolution of cellulose in N-methylmorpholine-N-oxide (NMMO) with 105–490 W and 2450 MHz microwave energy until the dissolution completed. Microwave heating caused the decrease in the dissolution time and energy consumption. Cellulose/NMMO/water solutions with different cellulose concentrations were converted to the membrane to measure the crystallinity and degree of polymerization. It was shown that microwave heating with the power of 210 W is an alternative heating system for dissolution of cellulose in NMMO. The membranes obtained with two different heating methods showed the same crystallinity and degree of polymerization. As a result, microwave heating has an advantage in shortening reaction times, compared to conventional heating.     

A novel approach for multi-domain and multi-gene family identification provides insights into evolutionary dynamics of disease resistance genes in core eudicot plants

Background

Recent advances in DNA sequencing techniques resulted in more than forty sequenced plant genomes representing a diverse set of taxa of agricultural, energy, medicinal and ecological importance. However, gene family curation is often only inferred from DNA sequence homology and lacks insights into evolutionary processes contributing to gene family dynamics. In a comparative genomics framework, we integrated multiple lines of evidence provided by gene synteny, sequence homology and protein-based Hidden Markov Modelling to extract homologous super-clusters composed of multi-domain resistance (R)-proteins of the NB-LRR type (for NUCLEOTIDE BINDING/LEUCINE-RICH REPEATS), that are involved in plant innate immunity.

Results

To assess the diversity of R-proteins within and between species, we screened twelve eudicot plant genomes including six major crops and found a total of 2,363 NB-LRR genes. Our curated R-proteins set shows a 50% average for tandem duplicates and a 22% fraction of gene copies retained from ancient polyploidy events (ohnologs). We provide evidence for strong positive selection and show significant differences in molecular evolution rates (Ka/Ks-ratio) among tandem- (mean = 1.59), ohnolog (mean = 1.36) and singleton (mean = 1.22) R-gene duplicates. To foster the process of gene-edited plant breeding, we report species-specific presence/absence of all 140 NB-LRR genes present in the model plant Arabidopsis and describe four distinct clusters of NB-LRR “gatekeeper” loci sharing syntenic orthologs across all analyzed genomes.

Conclusion

By curating a near-complete set of multi-domain R-protein clusters in an eudicot-wide scale, our analysis offers significant insight into evolutionary dynamics underlying diversification of the plant innate immune system. Furthermore, our methods provide a blueprint for future efforts to identify and more rapidly clone functional NB-LRR genes from any plant species.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-966) contains supplementary material, which is available to authorized users.     

The E3 ubiquitin ligase RNF121 is a positive regulator of NF-κB activation

Background

The nuclear factor κB (NF-κB) family members regulate several biological processes as cell proliferation and differentiation, inflammation, immunity and tumor progression. Ubiquitination plays a key role in NF-κB activation and the ubiquitylated transmitters of the NF-κB signaling cascade accumulate in close proximity to endomembranes.

Findings

We performed an unbiased siRNA library screen targeting the 46 E3 ubiquitin ligases bearing transmembrane domains to uncover new modulators of NF-κB activation, using tumor necrosis factor–α (TNF-α) receptor (TNFR) stimulation as a model. We report here the identification of a new Golgi Apparatus-resident protein, RNF121, as an enhancer of NF-κB promoter activity through the catalytic function of its RING domain. From a molecular standpoint, while knocking down RNF121 did not alter RIP1 ubiquitination and IKK activation, the proteasomal degradation of IκBα was impaired suggesting that this E3 ubiquitin ligase regulates this process. However, RNF121 did not directly ubiquitinate IκBα While they were found in the same complex. Finally, we discovered that RNF121 acts as a broad regulator of NF-κB signaling since its silencing also dampens NF-κB activation following stimulation of Toll-Like Receptors (TLRs), Nod-Like Receptors (NLRs), RIG-I-Like Receptors (RLRs) or after DNA damages.

Conclusions

These results unveil an unexpected role of Golgi Apparatus and reveal RNF121 as a new player involved in the signaling leading to NF-κB activation.

     

LEDPatNet19: Automated Emotion Recognition Model based on Nonlinear LED Pattern Feature Extraction Function using EEG Signals

Electroencephalography (EEG) signals collected from human brains have generally been used to diagnose diseases. Moreover, EEG signals can be used in several areas such as emotion recognition, driving fatigue detection. This work presents a new emotion recognition model by using EEG signals. The primary aim of this model is to present a highly accurate emotion recognition framework by using both a hand-crafted feature generation and a deep classifier. The presented framework uses a multilevel fused feature generation network. This network has three primary phases, which are tunable Q-factor wavelet transform (TQWT), statistical feature generation, and nonlinear textural feature generation phases. TQWT is applied to the EEG data for decomposing signals into different sub-bands and create a multilevel feature generation network. In the nonlinear feature generation, an S-box of the LED block cipher is utilized to create a pattern, which is named as Led-Pattern. Moreover, statistical feature extraction is processed using the widely used statistical moments. The proposed LED pattern and statistical feature extraction functions are applied to 18 TQWT sub-bands and an original EEG signal. Therefore, the proposed hand-crafted learning model is named LEDPatNet19. To select the most informative features, ReliefF and iterative Chi2 (RFIChi2) feature selector is deployed. The proposed model has been developed on the two EEG emotion datasets, which are GAMEEMO and DREAMER datasets. Our proposed hand-crafted learning network achieved 94.58%, 92.86%, and 94.44% classification accuracies for arousal, dominance, and valance cases of the DREAMER dataset. Furthermore, the best classification accuracy of the proposed model for the GAMEEMO dataset is equal to 99.29%. These results clearly illustrate the success of the proposed LEDPatNet19.