Agrobacterium-mediated transformation of indica rice cultivars using binary and superbinary vectors

The Agrobacterium-mediated transformation system was extended to two indica cultivars: a widely cultivated breeding line IR-64 and an elite basmati cultivar Karnal Local. Root tips and shoot tips of seedlings, and scutellar-calli derived from mature seeds showed high-efficiency Agrobacterium tumefaciens infection and stable transformation. In addition to the superbinary vector pTOK233 in Agrobacterium strain LBA4404, almost equally high levels of transformation were achieved with a relatively much smaller (13.1 kb) binary vector (pCAMBIA1301) in a supervirulent host strain AGL1. In both cases, as well as in both cultivars, while 60–90% of the infected explants produced calli resistant to the selectable agent hygromycin, 59–75% of such calli tested positive for GUS. A high level (400 μM) of acetosyringone in the preinduction medium for Agrobacterium and a higher level (500 μM) in the cocultivation medium was necessary for an enhancement in transformation frequency of the binary vector to levels comparable to a superbinary. Hygromycin-resistant calli could be produced from all the explants used. Transformants could be regenerated for both cultivars using the superbinary and binary vector, but only for calli of scutellar origin. In addition to the molecular confirmation of hpt and gus gene transfer and transcription, absence of gene sequences outside the transferred DNA (T-DNA) region confirmed absence of any long T-DNA transfer.

Histogram of weighted local directions for gait recognition

In this paper, we explore the effectiveness of patch-based gradient feature extraction methods when applied to appearance-based gait recognition. Extending existing popular feature extraction methods such as HOG and LDP, we propose a novel technique which we term the Histogram of Weighted Local Directions (HWLD). These 3 methods are applied to gait recognition using the GEI feature, with classification performed using SRC. Evaluations on the CASIA and OULP datasets show significant improvements using these patch-based methods over existing implementations, with the proposed method achieving the highest recognition rate for the respective datasets. In addition, the HWLD can easily be extended to 3D, which we demonstrate using the GEV feature on the DGD dataset, observing improvements in performance.

Local stress-strain distribution and load transfer across cartilage matrix at micro-scale using combined microscopy-based finite element method

Articular cartilage is the load-bearing tissue that consists of proteoglycan macromolecules entrapped between collagen fibrils in a three-dimensional architecture. To date, the drudgery of searching for mathematical models to represent the biomechanics of such a system continues without providing a fitting description of its functional response to load at micro-scale level. We believe that the major complication arose when cartilage was first envisaged as a multiphasic model with distinguishable components and that quantifying those and searching for the laws that govern their interaction is inadequate. To the thesis of this paper, cartilage as a bulk is as much continuum as is the response of its components to the external stimuli. For this reason, we framed the fundamental question as to what would be the mechano-structural functionality of such a system in the total absence of one of its key constituents-proteoglycans. To answer this, hydrated normal and proteoglycan depleted samples were tested under confined compression while finite element models were reproduced, for the first time, based on the structural microarchitecture of the cross-sectional profile of the matrices. These micro-porous in silico models served as virtual transducers to produce an internal noninvasive probing mechanism beyond experimental capabilities to render the matrices micromechanics and several others properties like permeability, orientation etc. The results demonstrated that load transfer was closely related to the microarchitecture of the hyperelastic models that represent solid skeleton stress and fluid response based on the state of the collagen network with and without the swollen proteoglycans. In other words, the stress gradient during deformation was a function of the structural pattern of the network and acted in concert with the position-dependent compositional state of the matrix. This reveals that the interaction between indistinguishable components in real cartilage is superimposed by its microarchitectural state which directly influences macromechanical behavior.

Semi-binary based video features for activity representation

Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

Science of winning soccer : emergent pattern-forming dynamics in association football

Quantitative analysis is increasingly being used in team sports to better understand performance in these stylized, delineated, complex social systems. Here we provide a first step toward understanding the pattern-forming dynamics that emerge from collective offensive and defensive behavior in team sports. We propose a novel method of analysis that captures how teams occupy sub-areas of the field as the ball changes location. We used the method to analyze a game of association football (soccer) based upon a hypothesis that local player numerical dominance is key to defensive stability and offensive opportunity. We found that the teams consistently allocated more players than their opponents in sub-areas of play closer to their own goal. This is consistent with a predominantly defensive strategy intended to prevent yielding even a single goal. We also find differences between the two teams' strategies: while both adopted the same distribution of defensive, midfield, and attacking players (a 4:3:3 system of play), one team was significantly more effective both in maintaining defensive and offensive numerical dominance for defensive stability and offensive opportunity. That team indeed won the match with an advantage of one goal (2 to 1) but the analysis shows the advantage in play was more pervasive than the single goal victory would indicate. Our focus on the local dynamics of team collective behavior is distinct from the traditional focus on individual player capability. It supports a broader view in which specific player abilities contribute within the context of the dynamics of multiplayer team coordination and coaching strategy. By applying this complex system analysis to association football, we can understand how players' and teams' strategies result in successful and unsuccessful relationships between teammates and opponents in the area of play.

Identifying steganographic payload location in binary image

In this paper, we propose a steganalysis method that is able to identify the locations of stego bearing pixels in the binary image. In order to do that, our proposed method will calculate the residual between a given stego image and its estimated cover image. After that, we will compute the local entropy difference between these two versions of images as well. Finally, we will compute the mean of residual and mean of local entropy difference across multiple stego images. From these two means, the locations of stego bearing pixels can be identified. The presented empirical results demonstrate that our proposed method can identify the stego bearing locations of near perfect accuracy when sufficient stego images are supplied. Hence, our proposed method can be used to reveal which pixels in the binary image have been used to carry the secret message.

Mixed data types and the use of pattern analysis on the Australian groundnut germplasm data

Data in germplasm collections contain a mixture of data types; binary, multistate and quantitative. Given the multivariate nature of these data, the pattern analysis methods of classification and ordination have been identified as suitable techniques for statistically evaluating the available diversity. The proximity (or resemblance) measure, which is in part the basis of the complementary nature of classification and ordination techniques, is often specific to particular data types. The use of a combined resemblance matrix has an advantage over data type specific proximity measures. This measure accommodates the different data types without manipulating them to be of a specific type. Descriptors are partitioned into their data types and an appropriate proximity measure is used on each. The separate proximity matrices, after range standardisation, are added as a weighted average and the combined resemblance matrix is then used for classification and ordination. Germplasm evaluation data for 831 accessions of groundnut (Arachis hypogaea L.) from the Australian Tropical Field Crops Genetic Resource Centre, Biloela, Queensland were examined. Data for four binary, five ordered multistate and seven quantitative descriptors have been documented. The interpretative value of different weightings - equal and unequal weighting of data types to obtain a combined resemblance matrix - was investigated by using principal co-ordinate analysis (ordination) and hierarchical cluster analysis. Equal weighting of data types was found to be more valuable for these data as the results provided a greater insight into the patterns of variability available in the Australian groundnut germplasm collection. The complementary nature of pattern analysis techniques enables plant breeders to identify relevant accessions in relation to the descriptors which distinguish amongst them. This additional information may provide plant breeders with a more defined entry point into the germplasm collection for identifying sources of variability for their plant improvement program, thus improving the utilisation of germplasm resources.

Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.

Using multi-label classification for acoustic pattern detection and assisting bird species surveys

Acoustics is a rich source of environmental information that can reflect the ecological dynamics. To deal with the escalating acoustic data, a variety of automated classification techniques have been used for acoustic patterns or scene recognition, including urban soundscapes such as streets and restaurants; and natural soundscapes such as raining and thundering. It is common to classify acoustic patterns under the assumption that a single type of soundscapes present in an audio clip. This assumption is reasonable for some carefully selected audios. However, only few experiments have been focused on classifying simultaneous acoustic patterns in long-duration recordings. This paper proposes a binary relevance based multi-label classification approach to recognise simultaneous acoustic patterns in one-minute audio clips. By utilising acoustic indices as global features and multilayer perceptron as a base classifier, we achieve good classification performance on in-the-field data. Compared with single-label classification, multi-label classification approach provides more detailed information about the distributions of various acoustic patterns in long-duration recordings. These results will merit further biodiversity investigations, such as bird species surveys.