The Effect of Osmo and Hormone Priming on Germination and Seed Reserve Utilization of Millet Seeds under Drought Stress

The objective of this research was to evaluate the effect of seed priming with osmo and hormone priming on growth and seed reserve utilization of millet seeds under drought stress. Treatments were combinations of 4 levels of drought stress (0, -4, -8 and -12 bar) and 3 levels of seed priming and control with 3 replications. Results showed that with increase in drought stress, germination components such as germination percentage, germination index, mean time to germination, normal seedling percentage, seedling length, seedling dry weight, weight of utilized (mobilized) seed and seed reserve utilization efficiency decreased, but seed priming showed lower reduction. The highest germination characteristics and seed reserve utilization was obtained by priming in control conditions. It is concluded that priming results in improvement in germination components of millet in drought stress conditions

Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions

In recent years, deep learning-based networks have achieved state-of-the-art performance in medical image segmentation. Among the existing networks, U-Net has been successfully applied on medical image segmentation. In this paper, we propose an extension of U-Net, Bi-directional ConvLSTM U-Net with Densely connected convolutions (BCDU-Net), for medical image segmentation, in which we take full advantages of U-Net, bi-directional ConvLSTM (BConvLSTM) and the mechanism of dense convolutions. Instead of a simple concatenation in the skip connection of U-Net, we employ BConvLSTM to combine the feature maps extracted from the corresponding encoding path and the previous decoding up-convolutional layer in a non-linear way. To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path. Finally, we can accelerate the convergence speed of the proposed network by employing batch normalization (BN). The proposed model is evaluated on three datasets of: retinal blood vessel segmentation, skin lesion segmentation, and lung nodule segmentation, achieving state-of-the-art performance

Germination and the Biochemical Response of Pumpkin Seeds to Different Concentrations of Humic Acid under Cadmium Stress

The poisoning of heavy metals and their accumulation in food chains are major environmental and health risks. There have been several reports that determined that pumpkins tend to collect small amounts of nitrate or heavy metals. Therefore, the aim of the present study is to investigate the effect of organic matter (humic acid) on the germination and activity of antioxidant enzymes, glycosylate cycle enzymes, and utilization of lipid and protein reserves of pumpkin seeds under cadmium stress conditions. An experiment was conducted to quantify the germination response and biochemical change of pumpkin seeds to the use of humic acid under cadmium stress conditions. The treatments were cadmium at three levels (0 (control), 100, and 200 mg.L-1) and humic acid at five levels (0 (control), 100, 200, 300, and 400 mg.L-1). Linear and sigmoidal models were used to investigate the trend of trait changes. The results show that changes in the germination percentage and seed vigor were affected by applying humic acid and cadmium stress. The highest germination percentage for pumpkins was observed without stress and cadmium stress at a concentration of 200 mg.L-1. The results of quantification for the germination and seed vigor also showed that the model of germination changes by the use of humic acid was sigmoidal in non-stress and cadmium stress conditions of 100 mg.L-1, but it was linear for seed vigor in the stress conditions of 200 mg.L-1. The activity of superoxide dismutase, catalase, peroxidase, isocitrate lyase, and malate synthase was also affected by the simultaneous use of humic acid and cadmium stress, and the trend of their changes was linear

Effects of ligands on (de-)enhancement of plasmonic excitations of silver, gold and bimetallic nanoclusters: TD-DFT+TB calculations

Metal nanoclusters can be synthesized in various sizes and shapes and are typically protected with ligands to stabilize them. These ligands can also be used to tune the plasmonic properties of the clusters as the absorption spectrum of a protected cluster can be significantly altered compared to the bare cluster. In this paper, we computationally investigate the influence of thiolate ligands on the plasmonic intensity for silver, gold and alloy clusters. Using time-dependent density functional theory with tight-binding approximations, TD-DFT+TB, we show that this level of theory can reproduce the broad experimental spectra of Au144(SR)60 and Ag53Au91(SR)60 (R = CH3) compounds with satisfactory agreement. As TD-DFT+TB does not depend on atom-type parameters we were able to apply this approach on large ligand-protected clusters with various compositions. With these calculations we predict that the effect of ligands on the absorption can be a quenching as well as an enhancement. We furthermore show that it is possible to unambiguously identify the plasmonic peaks by the scaled Coulomb kernel technique and explain the influence of ligands on the intensity (de-)enhancement by analyzing the plasmonic excitations in terms of the dominant orbital contributions

Stipagrostis pennata (Trin.) De Winter Artificial Seed Production and Seedlings Multiplication in Temporary Immersion Bioreactors

This study was conducted to develop the protocol for artificial seed production of Stipagrostis pennata (Trin.) De Winter via somatic embryo encapsulation as well as test a temporary bioreactor system for germination and seedling growth. Embryogenic calli were encapsulated using sodium alginate and calcium chloride and then sowed in the Murashige and Skoog (MS) germination medium in in vitro cultures. The experiments were conducted as a factorial based on a completely randomized design with three replications. The treatments include three concentrations of sodium alginate (1.5%, 2.5%, and 3.5%), two ion exchange times (20 and 30 min), and two artificial seed germination media (hormone-free MS and MS supplemented with zeatin riboside and L-proline). Germination percentage and number of days needed until the beginning of germination were studied. The highest percentage of artificial seed germination was obtained when 2.5% sodium alginate was used for 30 min (ion exchange time) and when the seeds were placed on the MS germination medium supplemented with zeatin riboside and L-proline. The results of the analysis of variance in the temporary immersion bioreactor system showed that the main effects observed on the seedling growth were associated with different growth hormones in culture media and the number of feeding cycles. Experimental results also indicated that the total protein analyses of zygotic seedlings and seedlings originating from the synthetic seeds showed no statistically significant differences between these samples

Two Stream Auto-encoder Decoder Network for Kidney and Tumor Segmentation

In this competition, we apply two-streams auto-encoder decoder structure for learning kidney and kidneys tumor segmentation. To do so, first, we extract axial layers of the tissues along with their segmentation mask from the 3D volume. These axial layers are then clipped using Hanford distance between +512 to -512 to eliminate non-object of interest. These axial layers are then normalized to form the 2D grayscale images. For each of these normalized images, we generate kidney and kidney tumor masks to train two-stream deep networks. The two-streams deep model learns kidney and tumor masks separately and they generate final mask by concatenating the generated masks. We utilize BCDU-net (extended version of U-Net model) as a deep auto-encoder decoder model for segmentation. We utilize 70% of the Kits19 as the training set and the rest of data as the validation set. Experimental results demonstrate that the proposed structure achieves state-of-the-art performance in the segmentation of kidney and tumor region

Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey

Interest in automatic action and gesture recognition has grown considerably in the last few years. This is due in part to the large number of application domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gesture recognition in sequences of images. The survey reviews both fundamental and cutting edge methodologies reported in the last few years. We introduce a taxonomy that summarizes important aspects of deep learning for approaching both tasks. Details of the proposed architectures, fusion strategies, main datasets, and competitions are reviewed. Also, we summarize and discuss the main works proposed so far with particular interest on how they treat the temporal dimension of data, their highlighting features, and opportunities and challenges for future research. To the best of our knowledge this is the first survey in the topic. We foresee this survey will become a reference in this ever dynamic field of research

Touché: Data-Driven Interactive Sword Fighting in Virtual Reality

VR games offer new freedom for players to interact naturally using motion. This makes it harder to design games that react to player motions convincingly. We present a framework for VR sword fighting experiences against a virtual character that simplifies the necessary technical work to achieve a convincing simulation. The framework facilitates VR design by abstracting from difficult details on the lower “physical” level of interaction, using data-driven models to automate both the identification of user actions and the synthesis of character animations. Designers are able to specify the character's behaviour on a higher “semantic” level using parameterised building blocks, which allow for control over the experience while minimising manual development work. We conducted a technical evaluation, a questionnaire study and an interactive user study. Our results suggest that the framework produces more realistic and engaging interactions than simple hand-crafted interaction logic, while supporting a controllable and understandable behaviour design