Modeling Grace and Courtesy in a Montessori Classroom and its Influence on Children’s Social Behavior

It has long been known that teachers have a large influence on students, however, little is known about the effect that teachers may have on students’ ability to develop positive social behaviors. Accordingly, there is a need to collect data regarding the effect that a teacher modeling grace and courtesy may have on how students interact with their peers and teachers. Therefore, the purpose of this action research is to analyze the effects of intentional teacher role modeling of grace and courtesy on children’s social behaviors. A classroom of 24 lower elementary, mixed age children from five to eight years old were observed for six weeks in the mornings of every school day to see if demonstrating and modeling grace and courtesy would affect children’s interactions with peers and teachers. An observation log for frequency-count was used to record negative instances of behaviors related to grace and courtesy. Results indicated a positive relationship between children’s social behaviors and teachers modeling grace and courtesy in the classroom. The two conclusions are that children were not sensitive regarding improvements in their social behaviors, and children can be influenced to exhibit positive social behaviors by teachers. Implications of this action research paper show that for children to learn positive social behaviors, teachers should be patient and consistent when modeling, reinforcing, and encouraging children to behave with grace and courtesy

Revealing ecotype influences on Cistanche sinensis: from the perspective of endophytes to metabolites characteristics

IntroductionPlant microorganism is critical to plant health, adaptability, and productive forces. Intriguingly, the metabolites and microorganisms can act upon each other in a plant. The union of metabolomics and microbiome may uncover the crucial connections of the plant to its microbiome. It has important benefits for the agricultural industry and human being health, particularly for Chinese medical science investigation.MethodsIn this last 2 years study, on the strength of the UPLC–MS/MS detection platform, we accurately qualitatively, and quantitatively measured the Cistanche sinensis fleshy stems of two ecotypes. Thereafter, through high-throughput amplicon sequencing 16S/ITS sequences were procured.ResultsPhGs metabolites including echinacoside, isoacteoside, and cistanoside A were significantly downregulated at two ecotypes of C. sinensis. Add up to 876 metabolites were monitored and 231 differential metabolites were analyzed. Further analysis of 34 core differential metabolites showed that 15 compounds with up-regulated belonged to phenolic acids, flavonoids, and organic acids, while 19 compounds with down-regulated belonged to phenolic acids, flavonoids, alkaloids, amino acids, lipids, and nucleotides. There was no noteworthy discrepancy in the endophytic bacteria’s α and β diversity between sandy and loam ecotypes. By comparison, the α and β diversity of endophytic fungi was notably distinct. The fungal community of the loam ecotype is more abundant than the sandy ecotype. However, there were few such differences in bacteria. Most abundant genera included typical endophytes such as Phyllobacterium, Mycobacterium, Cistanche, Geosmithia, and Fusarium. LEfSe results revealed there were 11 and 20 biomarkers of endophytic bacteria and fungi in C. sinensis at two ecotypes, respectively. The combination parsing of microflora and metabolites indicated noteworthy relativity between the endophytic fungal communities and metabolite output. Key correlation results that Anseongella was positive relation with Syringin, Arsenicitalea is negative relation with 7-methylxanthine and Pseudogymnoascus is completely positively correlated with nepetin-7-O-alloside.DiscussionThe aim of this research is: (1) to explore firstly the influence of ecotype on C. sinensis from the perspective of endophytes and metabolites; (2) to investigate the relationship between endophytes and metabolites. This discovery advances our understanding of the interaction between endophytes and plants and provides a theoretical basis for cultivation of C. sinensis in future

Two Novel Tyrosinase Inhibitory Sesquiterpenes Induced by CuCl2 from a Marine-Derived Fungus Pestalotiopsis sp. Z233

Two new sesquiterpenes, 1β,5α,6α,14-tetraacetoxy-9α-benzoyloxy-7β H-eudesman-2β,11-diol (1) and 4α,5α-diacetoxy-9α-benzoyloxy-7βH-eudesman-1β,2β,11, 14-tetraol (2), were produced as stress metabolites in the cultured mycelia of Pestalotiopsis sp. Z233 isolated from the algae Sargassum horneri in response to abiotic stress elicitation by CuCl2. Their structures were established by spectroscopic means. New compounds 1 and 2 showed tyrosinase inhibitory activities with IC50 value of 14.8 µM and 22.3 µ

SHAPFUZZ: Efficient Fuzzing via Shapley-Guided Byte Selection

Mutation-based fuzzing is popular and effective in discovering unseen code and exposing bugs. However, only a few studies have concentrated on quantifying the importance of input bytes, which refers to the degree to which a byte contributes to the discovery of new code. They often focus on obtaining the relationship between input bytes and path constraints, ignoring the fact that not all constraint-related bytes can discover new code. In this paper, we conduct Shapely analysis to understand the effect of byte positions on fuzzing performance, and find that some byte positions contribute more than others and this property often holds across seeds. Based on this observation, we propose a novel fuzzing solution, ShapFuzz, to guide byte selection and mutation. Specifically, ShapFuzz updates Shapley values (importance) of bytes when each input is tested during fuzzing with a low overhead, and utilizes contextual multi-armed bandit to trade off between mutating high Shapley value bytes and low-frequently chosen bytes. We implement a prototype of this solution based on AFL++, i.e., ShapFuzz. We evaluate ShapFuzz against ten state-of-the-art fuzzers, including five byte schedule-reinforced fuzzers and five commonly used fuzzers. Compared with byte schedule-reinforced fuzzers, ShapFuzz discovers more edges and exposes more bugs than the best baseline on three different sets of initial seeds. Compared with commonly used fuzzers, ShapFuzz exposes 20 more bugs than the best comparison fuzzer, and discovers 6 more CVEs than the best baseline on MAGMA. Furthermore, ShapFuzz discovers 11 new bugs on the latest versions of programs, and 3 of them are confirmed by vendors

Development of a sensitive nested-polymerase chain reaction (PCR) assay for the detection of Ustilago scitaminea

A species-specific polymerase chain reaction (PCR) assay was developed for rapid and accurate detection of Ustilago scitaminea, the causal agent of sugarcane smut disease. Based on nucleotide differences in the internal transcribed spacer (ITS) sequences of U. scitaminea, a pair of species-specific primers, SL1 (5`-CAGTGCACGAAAGTACCTGTGG-3`) and SR2 (5`-CTAGGGCGGTGTTCAGAAGCAC-3`) was designed by using a panel of fungal and bacterial species as controls. The primers SL1/SR2 specifically amplified a unique PCR product about 530 bp in length from U. scitaminea strains with a detecting sensitivity at 200 fg of the fungal genomic DNA in a 25 μl reaction solution. To increase sensitivity, a nested-PCR protocol was further established, which used ITS4/ITS5 as the first-round primers followed by the primer pair SL1/SR2. This protocol increased the detection sensitivity by 10,000-fold compared to the PCR method and could detect the fungal DNA as low as 20 ag. The nested-PCR detected U. scitaminea from young sugarcane leaves with no visible smut disease symptoms. The findings from this study provide a sensitive and reliable technique for the early detection of U. scitaminea, which would be useful for sugarcane quarantine and production of germ-free seedcanes.Keywords: Sugarcane, Ustilago scitaminea, nested-polymerase chain reaction (PCR), molecular detectio

Towards Strengthening Deep Learning-based Side Channel Attacks with Mixup

In recent years, various deep learning techniques have been exploited in side channel attacks, with the anticipation of obtaining more appreciable attack results. Most of them concentrate on improving network architectures or putting forward novel algorithms, assuming that there are adequate profiling traces available to train an appropriate neural network. However, in practical scenarios, profiling traces are probably insufficient, which makes the network learn deficiently and compromises attack performance. In this paper, we investigate a kind of data augmentation technique, called mixup, and first propose to exploit it in deep-learning based side channel attacks, for the purpose of expanding the profiling set and facilitating the chances of mounting a successful attack. We perform Correlation Power Analysis for generated traces and original traces, and discover that there exists consistency between them regarding leakage information. Our experiments show that mixup is truly capable of enhancing attack performance especially for insufficient profiling traces. Specifically, when the size of the training set is decreased to 30% of the original set, mixup can significantly reduce acquired attacking traces. We test three mixup parameter values and conclude that generally all of them can bring about improvements. Besides, we compare three leakage models and unexpectedly find that least significant bit model, which is less frequently used in previous works, actually surpasses prevalent identity model and hamming weight model in terms of attack results