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

Empirical Evaluation of a New Method for Calculating Signal to Noise Ratio (SNR) for Microarray Data Analysis

Signal-to-noise-ratio (SNR) thresholds for microarray data analysis were experimentally determined with an oligonucleotide array that contained perfect match (PM) and mismatch (MM) probes based upon four genes from Shewanella oneidensis MR-1. A new SNR calculation, called signal to both standard deviations ratio (SSDR) was developed, and evaluated along with other two methods, signal to standard deviation ratio (SSR), and signal to background ratio (SBR). At a low stringency, the thresholds of SSR, SBR, and SSDR were 2.5, 1.60 and 0.80 with oligonucleotide and PCR amplicon as target templates, and 2.0, 1.60 and 0.70 with genomic DNA as target templates. Slightly higher thresholds were obtained at the high stringency condition. The thresholds of SSR and SSDR decreased with an increase in the complexity of targets (e.g., target types), and the presence of background DNA, and a decrease in the composition of targets, while SBR remained unchanged under all situations. The lowest percentage of false positives (FP) and false negatives (FN) was observed with the SSDR calculation method, suggesting that it may be a better SNR calculation for more accurate determination of SNR thresholds. Positive spots identified by SNR thresholds were verified by the Student t-test, and consistent results were observed. This study provides general guidance for users to select appropriate SNR thresholds for different samples under different hybridization conditions