Plant organ phenotyping represents a powerful tool to investigate the effects of biotic and abiotic factors on plant growth and development. Phenotyping typically involves the initial capture of high-resolution images of the plant of interest and the subsequent measurement of different morphometric parameters of specific plant organs. This second step is typically very time consuming and difficult to automate. To deal with this bottleneck, we developed a multi-class segmentation model based on U-shape network to identify hypocotyls and roots of young seedlings of the reference plant Arabidopsis thaliana. We applied a balanced cross entropy loss function to learn an alternative optimal network structure for this multi-class segmentation task. We evaluated our segmentation machine using 66 images of the wild-type Arabidopsis strain Col-0, as well as 34 images of the highly agravitropic and morphologically distinct mutant strain, aux1-7. Our model achieved a mean BFscore of 0.81 for Col-0 seedlings and 0.75 for aux1-7 mutant seedlings on the test dataset. Our model was also able to maintain accuracy in these two morphologically different genotypes suggesting that our segmentation procedure could be successfully applied to Arabidopsis seedlings showing broad morphological differences due to their genotype or treatment conditions. Appropriate segmentation is the first step in identifying phenotypic changes under hormone-mediated stress response. The identified growth parameters will be useful to identify the response associated with both abiotic and biotic stresses, which include but not limited to drought stress, heat stress, and the presence of pests or pathogens. The quantification of these parameters will aid assessment of genetic factors that contribute to the stress response.

BACKGROUND:Plants respond to stress through highly tuned regulatory networks. While prior works identified master regulators of iron deficiency responses in A. thaliana from whole-root data, identifying regulators that act at the cellular level is critical to a more comprehensive understanding of iron homeostasis. Within the root epidermis complex molecular mechanisms that facilitate iron reduction and uptake from the rhizosphere are known to be regulated by bHLH transcriptional regulators. However, many questions remain about the regulatory mechanisms that control these responses, and how they may integrate with developmental processes within the epidermis. Here, we use transcriptional profiling to gain insight into root epidermis-specific regulatory processes.RESULTS:Set comparisons of differentially expressed genes (DEGs) between whole root and epidermis transcript measurements identified differences in magnitude and timing of organ-level vs. epidermis-specific responses. Utilizing a unique sampling method combined with a mutual information metric across time-lagged and non-time-lagged windows, we identified relationships between clusters of functionally relevant differentially expressed genes suggesting that developmental regulatory processes may act upstream of well-known Fe-specific responses. By integrating static data (DNA motif information) with time-series transcriptomic data and employing machine learning approaches, specifically logistic regression models with LASSO, we also identified putative motifs that served as crucial features for predicting differentially expressed genes. Twenty-eight transcription factors (TFs) known to bind to these motifs were not differentially expressed, indicating that these TFs may be regulated post-transcriptionally or post-translationally. Notably, many of these TFs also play a role in root development and general stress response.CONCLUSIONS:This work uncovered key differences in -Fe response identified using whole root data vs. cell-specific root epidermal data. Machine learning approaches combined with additional static data identified putative regulators of -Fe response that would not have been identified solely through transcriptomic profiles and reveal how developmental and general stress responses within the epidermis may act upstream of more specialized -Fe responses for Fe uptake.

Plants must tightly regulate iron (Fe) sensing, acquisition, transport, mobilization, and storage to ensure sufficient levels of this essential micronutrient. POPEYE (PYE) is an iron responsive transcription factor that positively regulates the iron deficiency response, while also repressing genes essential for maintaining iron homeostasis. However, little is known about how PYE plays such contradictory roles. Under iron-deficient conditions pPYE:GFP accumulates in the root pericycle while pPYE:PYE-GFP is localized to the nucleus in all Arabidopsis (Arabidopsis thaliana) root cells, suggesting that PYE may have cell-specific dynamics and functions. Using scanning fluorescence correlation spectroscopy (scanning FCS) and cell-specific promoters, we found that PYE-GFP moves between different cells and that the tendency for movement corresponds with transcript abundance. While localization to the cortex, endodermis, and vasculature is required to manage changes in iron availability, vasculature and endodermis localization of PYE-GFP protein exacerbated pye-1 defects and elicited a host of transcriptional changes that are detrimental to iron mobilization. Our findings indicate that PYE acts as a positive regulator of iron deficiency response by regulating iron bioavailability differentially across cells, which may trigger iron uptake from the surrounding rhizosphere and impact root energy metabolism.

For many horticultural crops, variation in quality (e.g., shape and size) contribute significantly to the crop’s market value. Metrics characterizing less subjective harvest quantities (e.g., yield and total biomass) are routinely monitored. In contrast, metrics quantifying more subjective crop quality characteristics such as ideal size and shape remain difficult to characterize objectively at the production-scale due to the lack of modular technologies for high-throughput sensing and computation. Several horticultural crops are sent to packing facilities after having been harvested, where they are sorted into boxes and containers using high-throughput scanners. These scanners capture images of each fruit or vegetable being sorted and packed, but the images are typically used solely for sorting purposes and promptly discarded. With further analysis, these images could offer unparalleled insight on how crop quality metrics vary at the industrial production-scale and provide further insight into how these characteristics translate to overall market value. At present, methods for extracting and quantifying quality characteristics of crops using images generated by existing industrial infrastructure have not been developed. Furthermore, prior studies that investigated horticultural crop quality metrics, specifically of size and shape, used a limited number of samples, did not incorporate deformed or non-marketable samples, and did not use images captured from high-throughput systems. In this work, using sweetpotato (SP) as a use case, we introduce a computer vision algorithm for quantifying shape and size characteristics in a high-throughput manner. This approach generates 3D model of SPs from two 2D images captured by an industrial sorter 90 degrees apart and extracts 3D shape features in a few hundred milliseconds. We applied the 3D reconstruction and feature extraction method to thousands of image samples to demonstrate how variations in shape features across sweetptoato cultivars can be quantified. We created a sweetpotato shape dataset containing sweetpotato images, extracted shape features, and qualitative shape types (U.S. No. 1 or Cull). We used this dataset to develop a neural network-based shape classifier that was able to predict Cull vs. U.S. No. 1 sweetpotato with 84.59% accuracy. In addition, using univariate Chi-squared tests and random forest, we identified the most important features for determining qualitative shape (U.S. No. 1 or Cull) of the sweetpotatoes. Our study serves as the first step towards enabling big data analytics for sweetpotato agriculture. The methodological framework is readily transferable to other horticultural crops, particularly those that are sorted using commercial imaging equipment.

Depth as well as intensity of a pixel plays a significant role in labeling objects in 3D environments. This paper presents a novel approach of labeling objects from multi-view video sequences by incorporating rich depth information. The depth map of a scene is estimated from focus-cues using the Gaussian–Hermite moments (GHMs) of local neighboring pixels. It is expected that the depth map obtained from GHMs provides robust features as compared to that provided by other popular depth maps such as those obtained from Kinect and defocus cue. We use the rich depth and intensity values of a pixel to score every point of a video frame for generating labeled probability maps in a 3D environment. These maps are then used to create a 3D scene wherein available objects are labeled distinctively. Experimental results reveal that our proposed approach yields excellent performance of object labeling for different multi-view scenes taken from RGB-D object dataset, in particular showing significant improvements in precision–recall characteristics and F1-score.

The iron deficiency response in plants is a complex biological process with a host of influencing factors. The ability to precisely modulate this process at the transcriptome level would enable genetic manipulations allowing plants to survive in nutritionally poor soils and accumulate increased iron content in edible tissues. Despite the collected experimental data describing different aspects of the iron deficiency response in plants, no attempts have been made towards aggregating this information into a descriptive and predictive model of gene expression changes over time. We formulated and trained a dynamic model of the iron deficiency induced transcriptional response in Arabidopsis thaliana. Gene activity dynamics were modelled with a set of ordinary differential equations that contain biologically tractable parameters. The trained model was able to capture and account for a significant difference in mRNA decay rates under iron sufficient and iron deficient conditions, approximate the expression behaviour of currently unknown gene regulators, unveil potential synergistic effects between the modulating transcription factors and predict the effect of double regulator mutants. The presented modelling approach illustrates a framework for experimental design, data analysis and information aggregation in an effort to gain a deeper understanding of various aspects of a biological process of interest.

Plants integrate a wide range of cellular, developmental, and environmental signals to regulate complex patterns of gene expression. Recent advances in genomic technologies enable differential gene expression analysis at a systems level, allowing for improved inference of the network of regulatory interactions between genes. These gene regulatory networks, or GRNs, are used to visualize the causal regulatory relationships between regulators and their downstream target genes. Accordingly, these GRNs can represent spatial, temporal, and/or environmental regulations and can identify functional genes. This review summarizes recent computational approaches applied to different types of gene expression data to infer GRNs in the context of plant growth and development. Three stages of GRN inference are described: first, data collection and analysis based on the dataset type; second, network inference application based on data availability and proposed hypotheses; and third, validation based on in silico, in vivo, and in planta methods. In addition, this review relates data collection strategies to biological questions, organizes inference algorithms based on statistical methods and data types, discusses experimental design considerations, and provides guidelines for GRN inference with an emphasis on the benefits of integrative approaches, especially when a priori information is limited. Finally, this review concludes that computational frameworks integrating large-scale heterogeneous datasets are needed for a more accurate (e.g. fewer false interactions), detailed (e.g. discrimination between direct versus indirect interactions), and comprehensive (e.g. genetic regulation under various conditions and spatial locations) inference of GRNs.

We implemented a computationally efficient model for a corner-supported, thin, rectangular, orthotropic poly-vinylidene fluoride (PVDF) laminate membrane, actuated by a two-dimensional array of segmented electrodes. The laminate can be used as shape-controlled electromagnetic reflector and the model estimates the reflector's shape given an array of control voltages. In this paper, we describe a model to determine the shape of the laminate for a given distribution of control voltages. Then, we investigate the surface shape error and its sensitivity to the model parameters. Subsequently, we analyze the simulated deflection of the actuated bimorph using a Zernike polynomial decomposition. Finally, we provide a probabilistic description of reflector performance using statistical methods to quantify uncertainty. We make design recommendations for nominal parameter values and their tolerances based on optimization under uncertainty using multiple methods.

The aim of the current research work was to analyze the control potentials of plant extracts against stored product pests. The research work was carried out in the Crop Protection & Toxicology Lab, University of Rajshahi, Bangladesh, during April 2016 to November 2016. Pet. ether, CHCl3 and CH3OH extracts of Saraca indica L. were subjected to repellent activity and dose-mortality tests against Callosobruchus chinensis (L.), Sitophilus oryzae (L.) and Tribolium castaneum (Hbst.). Pet. ether extracts of root and stem bark; CHCl3 extracts of leaves, root and stem bark didn’t show mortality at all. However, other parts of the test plant extractives provided mortality to the test insects by yielding different LD50 values in different time exposure. In repellency test the extracts were found moderately repellent (P<0.01) and mild repellent (P<0.05). However, CH3OH extracts of leaves, CHCl3 and CH3OH extracts of stem bark didn’t show repellent activity at all.

Depth information of objects plays a significant role in image-based rendering. Traditional depth estimation techniques use different visual cues including the disparity, motion, geometry, and defocus of objects. This paper presents a novel approach of focus cue-based depth estimation for still images using the Gaussian-Hermite moments (GHMs) of local neighboring pixels. The GHMs are chosen due to their superior reconstruction ability and invariance properties to intensity and geometric distortions of objects as compared to other moments. Since depths of local neighboring pixels are significantly correlated, the Laplacian matting is employed to obtain final depth map from the moment-based focus map. Experiments are conducted on images of indoor and outdoor scenes having objects with varying natures of resolution, edge, occlusion, and blur contents. Experimental results reveal that the depth estimated from GHMs can provide anaglyph images with stereo quality better than that provided by existing methods using traditional visual cues. (C) 2016 Elsevier Inc. All rights reserved.

DLNA based media sharing is very popular nowadays. In current DLNA specification, a DLNA device advertises its presence to everyone in the network. Any control point application receiving the advertisement can access/control the device. However, with increasing popularity and availability of public Wi-Fi hotspots, it is necessary for devices to have some sort of access control. DLNA specification has no mandatory authentication procedure. So a device receiving a request from any unwanted control point cannot block/verify its access. The UPnP recommended authentication procedure is computationally expensive and complex for most personal devices. So, in this paper we propose a simple User-Agent based access control system that is effective to protect devices from unwanted control point applications.

We propose a novel type of photonic crystal cavity with a flattened and elongated central hole. The confinement capability of the cavity for the TE mode is studied using the 2D-FDTD method. The holes immediately around the cavity are shifted in a space modulation scheme in order to optimize the confinement capability of the proposed cavity. The change in the spatial confinement as well as quality factor of the cavity with respect to modulation depth are studied and reported. The ferroelectric Barium Titanate (BaTiO3), which has a large electrooptic coefficient, is used as the base material of the proposed cavity. The effect of applied bias on the properties of the cavity is also studied. To make the calculations as realistic as possible, the dispersive and absorptive nature of the material has been taken into account. Despite the absorptive nature of BaTiO3 as well as the finite extent of the photonic crystal, the calculated quality factor compares favorably with previously reported values in literature.

We have designed and studied the confinement capability of a defect cavity on a curvilinear lattice photonic crystal using finite domain time difference (FDTD) method. Gallium arsenide has been used as the base material, where the nonlinear and dispersive nature of the material have been taken into account. The resonant transverse magnetic (TM) and transverse electric (TE) mode frequencies are calculated from frequency analysis and the appropriate field profiles at the resonant frequencies are calculated. It is found that the magnitude of the electric field of the TM and TE mode photons decay exponentially away from the cavity. The temporal decay of the energy in the cavity is also found to be exponential. From the decay of the energy in the cavity, the quality factor of the cavity resonator is calculated.

Jute is one of the most important fibre crop of the Indian subcontinent second only to cotton, in providing an environment-friendly, biodegradable and renewable ligno-cel lulose fibre. The jute fibre is used, as a raw material, for several products like hessians, sacs, and carpet backings. The two species of the genus Corchorus , which are cultivated as jute crop include C. capsularis (white jute) and C. olitorius (tossa jute), each with 2n = 14, although 50--60 species are widely known and 170 names are described under the genus Corchorus in Index Kewensis (Palve et al. 2003: Edmonds 1990). As many as 27 varieties of jute with higher productivity, improved fibre quality and resistance to biotic and abiotic stresses were released through AINP on Jute and Allied Fibres. Though the potential yield of some of the recently released jute varieties like JR0-8432, JR0-66, JR0-128, S-19 and JRC-698 is 35 to 40 q/ha. the actual realization in the farmers' field is little more than 50% of the potential yield (Bis was, 2009). Therefore, research on genetic divergence in this crop is very important in formulating a successful breeding programme for evolving cultivars superior in both yield and quality to cater to the increasing demand of value added jute products in the domestic and international markets. In the present study an attempt was made to identify suitable genotypes for a breeding programme, from a collection of 52 tossa jute germplasm accessions. Fifty two genotypes of Corchorus olitorius having diverse origin were sown in Randomized block design replicated thrice, at the Instructional Farm, UBKV, Pundibari, Cooch Behar, during the pre-kharif season of 2007. The recommended agronomic practices were followed to obtain an optimum fibre yield. Observations were recorded from ten plants selected randomly from each replication for the six fibre yield related traits namely plant height (cm), basal diameter (mm), green weight (g planr\ fibre yield (g planr ), fibre percentage, and stick weight (g planf). For the two fibre quality traits viz. fibre tenacity (g tex) and fibre fineness (tex), data were recorded at NIRJAFT, Kolkata. Divergence was studied by multivariate analysis (Sasmal. 1978) using Mahalanobis D statistics and the genotypes were grouped into different clusters by employing Euclidean method as described by Rao (1952).

Chitosans are naturally occurring biologically safe and non-toxic polymer of polysaccharides. In the present study, Chitosan [(1-4) 2-amino-2-deoxy-β-D-glucan] was extracted from the exoskeleton of Black Tiger (Penaeus monodon) shrimp shells by alkaline deacetylation of chitin. Three different chitosan extracts, coded as Chito A (3.33% w/w), Chito B (4.01% w/w) and Chito C (3.45% w/w) were extracted using 3%, 4% and 5% w/v concentrations of HCl as decalcifiers respectively. Physicochemical properties such as appearance, odor, insolubility and pH of all the extracts were found to comply with the compendial specifications of pharmaceutical grade chitosan. Qualitative identification of the samples was carried out using Infrared (IR) spectroscopy. Major peaks of the extracts matched with reference IR spectrum of standard chitosan. Extracted chitosans were found lacking cytotoxicity in brine shrimp lethality bioassay. All the extracts showed strong antioxidant activity in DPPH free radical scavenging assay and the IC50 values were found to be 37 ± 2 μg/mL (Chito A), 35 ± 1 μg/mL (Chito B) and 30 ± 2 μg/mL (Chito C) while the standard antioxidant Quercetin showed an IC50 value of 15 ± 2 μg/mL. Key words: Chitosan; Black Tiger (Penaeus monodon) shrimp shells; deacetylation of chitin; IR spectroscopy; cytotoxicity; DPPH; free radical scavenging.DOI: 10.3329/sjps.v2i2.5821Stamford Journal of Pharmaceutical Sciences Vol.2(2) 2009: 27-30