Effect of 5-aminolevulinic acid on yield and quality of lettuce in sunlit greenhouse

The role of 5-aminolevulinic acid (ALA) as a precursor of chlorophyll and heme is well documented. Low concentration of exogenous ALA has been found to regulate plant growth and increase crop yield, but there is little information on how ALA influences the yield and quality of lettuce in sunlit greenhouse. Here, we report the effects of ALA on photosynthetic rate, yield and quality of lettuce in sunlit greenhouse.5-aminolevulinic acid and 5-aminolevulinic acid with nitrogen fertilizer (ALA+N) were applied by foliage and soil. The results showed that application of ALA improved the photosynthetic rate of lettuce leaves by 23.9 to 34.7% and by 35.3 to 41.6%. Moreover, exogenous ALA increased vitamin C and soluble sugar content, reduced nitrate and crude fiber content and lead to better quality and taste of lettuce.Keywords: 5-aminolevulinic acid, lettuce, plant growth, promotive effects, yield, vegetable qualit

Deep LSTM with Guided Filter for Hyperspectral Image Classification

Hyperspectral image (HSI) classification has been a hot topic in the remote sensing community. A large number of methods have been proposed for HSI classification. However, most of them are based on the extraction of spectral feature, which leads to information loss. Moreover, they rarely consider the correlation among the spectrums. In this paper, we see spectral information as a sequential data which should be relevant to each other. We introduce long short-term memory (LSTM) model, which is a typical recurrent neural network (RNN), to deal with HSI classification. To tackle the problem of overfitting caused by limited labeled samples, regularization strategy is introduced. For unbalance in different classes, we improve LSTM by weighted cost function. Also, we employ guided filter to smooth the HSI that can greatly improve the classification accuracy. And we proposed a method for modeling hyperspectral sequential data, which is very useful for future research work. Finally, the experimental results show that our proposed method can improve the classification performance as compared to other methods in three popular hyperspectral datasets

Translational signatures and mRNA levels are highly correlated in human stably expressed genes

BACKGROUND: Gene expression is one of the most relevant biological processes of living cells. Due to the relative small population sizes, it is predicted that human gene sequences are not strongly influenced by selection towards expression efficiency. One of the major problems in estimating to what extent gene characteristics can be selected to maximize expression efficiency is the wide variation that exists in RNA and protein levels among physiological states and different tissues. Analyses of datasets of stably expressed genes (i.e. with consistent expression between physiological states and tissues) would provide more accurate and reliable measurements of associations between variations of a specific gene characteristic and expression, and how distinct gene features work to optimize gene expression. RESULTS: Using a dataset of human genes with consistent expression between physiological states we selected gene sequence signatures related to translation that can predict about 42% of mRNA variation. The prediction can be increased to 51% when selecting genes that are stably expressed in more than 1 tissue. These genes are enriched for translation and ribosome biosynthesis processes and have higher translation efficiency scores, smaller coding sequences and 3(′) UTR sizes and lower folding energies when compared to other datasets. Additionally, the amino acid frequencies weighted by expression showed higher correlations with isoacceptor tRNA gene copy number, and smaller absolute correlation values with biosynthetic costs. CONCLUSION: Our results indicate that human gene sequence characteristics related to transcription and translation processes can co-evolve in an integrated manner in order to optimize gene expression

Label Denoising through Cross-Model Agreement

Learning from corrupted labels is very common in real-world machine-learning applications. Memorizing such noisy labels could affect the learning of the model, leading to sub-optimal performances. In this work, we propose a novel framework to learn robust machine-learning models from noisy labels. Through an empirical study, we find that different models make relatively similar predictions on clean examples, while the predictions on noisy examples vary much more across different models. Motivated by this observation, we propose \em denoising with cross-model agreement \em (DeCA) which aims to minimize the KL-divergence between the true label distributions parameterized by two machine learning models while maximizing the likelihood of data observation. We employ the proposed DeCA on both the binary label scenario and the multiple label scenario. For the binary label scenario, we select implicit feedback recommendation as the downstream task and conduct experiments with four state-of-the-art recommendation models on four datasets. For the multiple-label scenario, the downstream application is image classification on two benchmark datasets. Experimental results demonstrate that the proposed methods significantly improve the model performance compared with normal training and other denoising methods on both binary and multiple-label scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:2105.0960