γ-Aminobutyric Acid (GABA) Priming Improves Seed Germination and Seedling Stress Tolerance Associated With Enhanced Antioxidant Metabolism, DREB Expression, and Dehydrin Accumulation in White Clover Under Water Stress

As an important plant growth regulator, the role of γ-aminobutyric acid (GABA) in regulating seeds germination was less well elucidated under water stress. The present study was conducted to investigate the impact of GABA pretreatment on seeds germination of white clover (Trifolium repens) under water deficient condition. Results demonstrated that seeds pretreated with 2μmol/l GABA significantly alleviated decreases in endogenous GABA content, germination percentage, germination potential, germination index, root length, and fresh weight along with marked reduction in mean germination time after 7days of germination under drought stress. In addition, seeds priming with GABA significantly increased the accumulation of soluble sugars, non-enzymatic antioxidants [reduced ascorbate, dehydroascorbic acid, oxidized glutathione (GSSG), and reduced glutathione (GSH)], and enzymes [superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), ascorbate peroxidase (APX), dehydroascorbate reductase (DHAR), glutathioe reductase, and monodehydroasorbate reductase (MDHR)] activities involved in antioxidant metabolism, which could be associated with significant reduction in osmotic potential and the accumulation of superoxide anion, hydrogen peroxide, electrical leakage, and malondialdehyde in seeds under drought stress. The GABA-pretreated seeds exhibited significantly higher abundance of dehydrin (DHN, 56 KDa) and expression levels of DHNs encoding genes (SK2, Y2K, Y2SK, and Dehydrin b) and transcription factors (DREB2, DREB3, DREB4, and DREB5) than the untreated seeds during germination under water-limited condition. These results indicated that the GABA regulated improvement in seeds germination associated with enhancement in osmotic adjustment, antioxidant metabolism, and DREB-related DHNs expression. Current study will provide a better insight about the GABA-regulated defense mechanism during seeds germination under water-limited condition.

Rapid and Accurate Varieties Classification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

Rapid varieties classification of crop seeds is significant for breeders to screen out seeds with specific traits and market regulators to detect seed purity. However, collecting high-quality, large-scale samples takes high costs in some cases, making it difficult to build an accurate classification model. This study aimed to explore a rapid and accurate method for varieties classification of different crop seeds under the sample-limited condition based on hyperspectral imaging (HSI) and deep transfer learning. Three deep neural networks with typical structures were designed based on a sample-rich Pea dataset. Obtained the highest accuracy of 99.57%, VGG-MODEL was transferred to classify four target datasets (rice, oat, wheat, and cotton) with limited samples. Accuracies of the deep transferred model achieved 95, 99, 80.8, and 83.86% on the four datasets, respectively. Using training sets with different sizes, the deep transferred model could always obtain higher performance than other traditional methods. The visualization of the deep features and classification results confirmed the portability of the shared features of seed spectra, providing an interpreted method for rapid and accurate varieties classification of crop seeds. The overall results showed great superiority of HSI combined with deep transfer learning for seed detection under sample-limited condition. This study provided a new idea for facilitating a crop germplasm screening process under the scenario of sample scarcity and the detection of other qualities of crop seeds under sample-limited condition based on HSI.

OPTIMALISASI PEMBELAJARAN AKIDAH BERBASIS TEKNOLOGI DI MASA PANDEMI

Departing from the covid-19 pandemic that is hitting humans today, all sectors of life seem to be experiencing shocks, especially education. The learning process that should be carried out directly between teachers and students, has now been transferred to an online system. The existence of major changes in the current education system has forced teachers to mobilize all their potential as educators by teaching using technology. Especially faith education, teachers are required to be able to provide faith education to their students as in face-to-face learning in the past. This is of course a new responsibility for teachers. However, in this limited condition, the teacher's work will be greatly helped by the support from the parents who are always by their children's side. It is a joint task between teachers and parents in optimizing the process of creed education for students.Keywords: Creed, Technology, and Pandemic

Acute onset supraclavicular lymphadenopathy coinciding with intramuscular mRNA vaccination against COVID-19 may be related to vaccine injection technique, Spain, January and February 2021

Monitoring adverse reactions following immunisation is essential, particularly for new vaccines such as those against COVID-19. We describe 20 cases of acute onset of a single supraclavicular lymphadenopathy manifesting between 24 h and 9 days after ipsilateral intramuscular administration of an mRNA-based COVID-19 vaccine, referred to our WHO Collaborating Centre for Vaccine Safety. Our results indicate that the swelling of supraclavicular lymph nodes following immunisation may constitute a benign and self-limited condition, related to a higher than recommended injection site.

Research on Identification of Flow Units Based on FZI

The FZI expression is obtained by derivation, and the geological significance of FZI is proposed for the first time - it is a parameter reflecting the micro-pore structure of the rock, determined by the microstructure of the rock. Further analysis of FZI indicates that there are misconceptions in it, and it is feasible to identify flow units based on FZI. It points out the advantages and disadvantages of FZI. With the introduction of limited condition, better division of flow units can be realized based on FZI.

Success of Knowledge Management Strategies in Manufacturing Companies- A Study with Reference to Chennai, Kanchipuram and Thiruvallur Districts

Knowledge management strategy deals with careful approach and implementation of knowledge to achieve the best results within the available limited condition of employees, organisational employment and capabilities of employer. This study is to analyse about the components of knowledge management strategies and to measure the influence of organisational variables in knowledge management strategies. The samples was collected from 7 top manufacturing companies distributed over 3 districts, Chennai, Kanchipuram Tiruvallur and especially in greater Chennai. With t-test the analysis is done and we can analyse that knowledge management strategies depend upon employees’s interest in implementing the knowledge with in the premises of the organisation

Rapid and Accurate Varieties Identification of Different Crop Seeds Under Sample-Limited Condition Based on Hyperspectral Imaging and Deep Transfer Learning

Background: Varieties identification of crop seeds is significant for breeders to screen out seeds with specific traits and for market regulators to detect seeds purity. Hyperspectral imaging technology provides a fast and non-destructive means for varieties identification. And deep learning algorithm is suitable for effective analysis of redundant spectral data. However, deep learning algorithms have serious big data dependency, while collecting high-quality large-scale samples was high-cost in many cases. This made it difficult to build an accurate identification model. This study aimed to explore a rapid and accurate method for varieties identification of different crop seeds under sample-limited condition based on hyperspectral imaging and deep transfer learning.Results: Three deep neural networks with typical structures were designed based on a samples-rich Pea dataset. Obtained the highest accuracy of 99.57 %, VGG-MODEL was transferred to classify four target datasets (Rice, Oat, Wheat, Cotton) with limited samples. The accuracies of deep transferred model achieved 95 %, 99 %, 80.8 %, and 83.86 % on the four datasets, respectively. Using training sets with different sizes, deep transferred model could always obtain higher performance than other traditional methods. Visualization of training process and classification results confirmed the portability of common features of seed spectra and provided an interpreted method for rapid and accurate varieties identification of crop seeds.Conclusions: This study combined hyperspectral imaging and deep transfer learning to identify varieties of different crop seeds, which was proved to be efficient under sample-limited condition. This facilitated crop variety screening process under the scenario of sample scarcity. It also provided a new idea for the detection of other qualities of crop seeds based on hyperspectral imaging under sample-limited condition.