Fuzzy Adaptive Shift Schedule of Tractor Subjected to Random Load

In this paper, the low frequency random load of a tractor is presented. Controlled by a theoretical three-parameter shift schedule, the random load would frequently trigger the random shift. Simultaneously, the driving force of the tractor should be consistent prior to and following this shift. Additionally, the higher traction efficiency and improved load utilization rate should be ensured by a choice of a transmission ratio of the tractor power shift transmission. The shift schedule was utilized for the aforementioned problems solution. An innovative method is presented for theoretical shift schedule modification by the fuzzy algorithm, based on the random load standard deviation and the alteration rate of both steady state values of the load and of the throttle position. The simulation results demonstrated that the modified shift schedule could discover the running state of the tractor. By shielding the random shift judgment caused by the random load, the stability of the tractor was ensured. When the shift was required, the schedule could rapidly respond, whereas the tractor driving force did not sustain a sudden alteration. The schedule could also automatically select and maintain the transmission ratio with higher traction efficiency

Selection of optimal oligonucleotide probes for microarrays using multiple criteria, global alignment and parameter estimation

The oligonucleotide specificity for microarray hybridization can be predicted by its sequence identity to non-targets, continuous stretch to non-targets, and/or binding free energy to non-targets. Most currently available programs only use one or two of these criteria, which may choose ‘false’ specific oligonucleotides or miss ‘true’ optimal probes in a considerable proportion. We have developed a software tool, called CommOligo using new algorithms and all three criteria for selection of optimal oligonucleotide probes. A series of filters, including sequence identity, free energy, continuous stretch, GC content, self-annealing, distance to the 3′-untranslated region (3′-UTR) and melting temperature (T(m)), are used to check each possible oligonucleotide. A sequence identity is calculated based on gapped global alignments. A traversal algorithm is used to generate alignments for free energy calculation. The optimal T(m) interval is determined based on probe candidates that have passed all other filters. Final probes are picked using a combination of user-configurable piece-wise linear functions and an iterative process. The thresholds for identity, stretch and free energy filters are automatically determined from experimental data by an accessory software tool, CommOligo_PE (CommOligo Parameter Estimator). The program was used to design probes for both whole-genome and highly homologous sequence data. CommOligo and CommOligo_PE are freely available to academic users upon request

Serine 58 of 14-3-3ζ Is a molecular switch regulating ASK1 and oxidant stress-induced cell death

Oxidant stress is a ubiquitous stressor with negative impacts on multiple cell types. ASK1 is a central mediator of oxidant injury, but while mechanisms of its inhibition, such as sequestration by 14-3-3 proteins and thioredoxin, have been identified, mechanisms of activation have remained obscure and the signaling pathways regulating this are not clear. Here, we report that phosphorylation of 14-3-3ζ at serine 58 (S58) is dynamically regulated in the cell and that the phosphorylation status of S58 is a critical factor regulating oxidant stress-induced cell death. Phosphorylation of S58 releases ASK1 from 14-3-3ζ, and ASK1 then activates stress-activated protein kinases, leading to cell death. While several members of the mammalian sterile 20 (Mst) family of kinases can phosphorylate S58 when overexpressed, we identify Ste20/oxidant stress response kinase 1 (SOK-1), an Mst family member known to be activated by oxidant stress, as a central endogenous regulator of S58 phosphorylation and thereby of ASK1-mediated cell death. Our findings identify a novel pathway that regulates ASK1 activation and oxidant stress-induced cell death

Analysis of Multi-Element Blended Course Teaching and Learning Mode Based on Student-Centered Concept under the Perspective of “Internet+”

The integration of Internet and education has changed students’ learning environment and affected their learning behavior, which poses a greater challenge to the traditional teaching mode. Through the SWOT analysis of the “student centered” multi-element blended teaching mode in the era of “Internet + education”, it is concluded that the adaptability of learners themselves and the mismatch between teachers’ educational ideas and this teaching model delay the development of education to a certain extent. Some suggestions are put forward, such as strengthening the supervision and guidance, implementing the teaching and learning model scientifically, improving teachers’ ideology and comprehensive quality, and making full use of the characteristics of Internet opening, sharing and collaboration to construct the public service system and platform of national educational resources

Search to Fine-tune Pre-trained Graph Neural Networks for Graph-level Tasks

Recently, graph neural networks (GNNs) have shown its unprecedented success in many graph-related tasks. However, GNNs face the label scarcity issue as other neural networks do. Thus, recent efforts try to pre-train GNNs on a large-scale unlabeled graph and adapt the knowledge from the unlabeled graph to the target downstream task. The adaptation is generally achieved by fine-tuning the pre-trained GNNs with a limited number of labeled data. Despite the importance of fine-tuning, current GNNs pre-training works often ignore designing a good fine-tuning strategy to better leverage transferred knowledge and improve the performance on downstream tasks. Only few works start to investigate a better fine-tuning strategy for pre-trained GNNs. But their designs either have strong assumptions or overlook the data-aware issue for various downstream datasets. Therefore, we aim to design a better fine-tuning strategy for pre-trained GNNs to improve the model performance in this paper. Given a pre-trained GNN, we propose to search to fine-tune pre-trained graph neural networks for graph-level tasks (S2PGNN), which adaptively design a suitable fine-tuning framework for the given labeled data on the downstream task. To ensure the improvement brought by searching fine-tuning strategy, we carefully summarize a proper search space of fine-tuning framework that is suitable for GNNs. The empirical studies show that S2PGNN can be implemented on the top of 10 famous pre-trained GNNs and consistently improve their performance. Besides, S2PGNN achieves better performance than existing fine-tuning strategies within and outside the GNN area. Our code is publicly available at \url{https://anonymous.4open.science/r/code_icde2024-A9CB/}

Dramatic Increases of Soil Microbial Functional Gene Diversity at the Treeline Ecotone of Changbai Mountain.

The elevational and latitudinal diversity patterns of microbial taxa have attracted great attention in the past decade. Recently, the distribution of functional attributes has been in the spotlight. Here, we report a study profiling soil microbial communities along an elevation gradient (500-2200 m) on Changbai Mountain. Using a comprehensive functional gene microarray (GeoChip 5.0), we found that microbial functional gene richness exhibited a dramatic increase at the treeline ecotone, but the bacterial taxonomic and phylogenetic diversity based on 16S rRNA gene sequencing did not exhibit such a similar trend. However, the β-diversity (compositional dissimilarity among sites) pattern for both bacterial taxa and functional genes was similar, showing significant elevational distance-decay patterns which presented increased dissimilarity with elevation. The bacterial taxonomic diversity/structure was strongly influenced by soil pH, while the functional gene diversity/structure was significantly correlated with soil dissolved organic carbon (DOC). This finding highlights that soil DOC may be a good predictor in determining the elevational distribution of microbial functional genes. The finding of significant shifts in functional gene diversity at the treeline ecotone could also provide valuable information for predicting the responses of microbial functions to climate change

The Shewanella Federation: Functional Genomic Investigations of Dissimilatory Metal-Reducing Shewanella

Generation and validation of a Shewanella oneidensis MR-1 clone set for protein expression and phage display. An ORF clone set for S. oneidensis was created using the lambda recombinase system. ORFs within entry vectors in this system can be readily transferred into multiple destination vectors, making the clone set a useful resource for research groups studying this microorganism. To establish that the S. oneidensis clone set could be used for protein expression and functional studies, three sets of ORFs were examined for expression of His-tag proteins, expression of His/GST-tag proteins, or for effective display on phage. A total of 21 out of 30 (70%) predicted two-component transcriptional regulators from S. oneidensis were successfully expressed in the His-tag format. The use of the S. oneidensis clone set for functional studies was tested using a phage display system. The method involves the fusion of peptides or proteins to a coat protein of a bacteriophage. This results in display of the fused protein on the exterior of the phage, while the DNA encoding the fusion resides within the virion. The physical linkage between the displayed protein and the DNA encoding it allows screening of vast numbers of proteins for ligand-binding properties. With this technology, a phage clone encoding thioredoxin TrxA was isolated from a sub-library consisting of 80 clones. It is evident that the S. oneidensis clone set can be used for expression of functional S. oneidensis proteins in E. coli using the appropriate destination vectors. Characterization of ArcA. In Escherichia coli, metabolic transitions between aerobic and anaerobic growth states occur when cells enter an oxygen-limited condition. Many of these metabolic transitions are controlled at the transcriptional level by the activities of the global regulatory proteins ArcA (aerobic respiration control) and Fnr (fumarate nitrate regulator). A homolog of ArcA (81% amino acid sequence identity) was identified in S. oneidensis MR-1, and arcA mutants with MR-1 as the parental strain were generated. Phenotype characterization showed the arcA deletion mutant grew slower than the wild-type and was hypersensitive to H2O2 stress. Microarray analysis indicated that S. oneidensis ArcA regulates a large number of different genes from that in E. coli although they do have overlapping regulatory functions on a small set of genes. The S. oneidensis arcA gene was also cloned and expressed in E. coli. The ArcA proteins from the wild-type and a point mutant strains (D54N) were purified and their DNA binding properties were analyzed by electrophoretic motility shift (EMS) and DNase I footprinting assays. The results indicate that phosphorylated ArcA proteins bind to a DNA site similar in sequence to the E. coli ArcA binding site. The common feature of the binding sites is the presence of a conserved 15 base pair motif that contains 2-3 mismatches when compared to the E. coli ArcA-P consensus binding motif. Genome scale computational predictions of binding sites were also performed and 331 putative ArcA regulatory targets were identified. Therefore, the regulation of aerobic/anaerobic respiration may be more complex than it was expected in S. oneidensis. A high-throughput percentage-of-binding strategy to measure binding energies in DNA–protein interactions. Based on results of studies on ArcA of S. oneidensis, we developed a high-throughput approach to measure binding energies in DNA-protein interactions, which enables a more precise prediction for DNA-binding sites in genomes. With this approach, the importance of each position within the ArcA-P binding site was quantitatively established by characterizing the interaction between Shewanella ArcA-P and a series of mutant promoter DNAs, whereby each position in the binding site was systematically mutated to all possible single nucleotide changes. The results of the fine mapping were used to create a position-specific energy matrix (PEM) that was used for a genome-scale prediction of 45 ArcA-P sites in Shewanella. A further examination suggests that this prediction is >81% consistency with in vivo gene regulation according to microarray studies and >92% (13/14) accuracy in comparison with published in vitro gel shift validation binding assays. In addition, this study predicted 27 ArcA-P sites for 15 published E. coli ArcA-P footprinted DNAs, and 24 of them were found exactly within the footprinting protected regions and the other three sites fall into the regions that were not examined by footprinting assays. This is the first report showing that footprinting protected regions can be effectively predicted by starting from a single known transcription factor binding site. Finally, the predicted H. influenzae ArcA-P sites correlate well with in vivo regulation determined by a microarray analysis in that the eight predicted binding sites with the most favorable ∆∆G scores all exhibit ArcA dependent gene regulation