Yield response of rice (Oryza sativa L.) genotypes to drought under rainfed lowlands 4. Vegetative stage screening in the dry season

Screening for drought resistance of rainfed lowland rice using drought score (leaf death) as a selection index has a long history of use in breeding programs. Genotypic variation for drought score during the vegetative stage in two dry season screens was examined among 128 recombinant inbred lines from four biparental crosses. The genotypic variation detected for drought score in the dry season was used to examine the reliability of the dry season screening method to estimate relative grain yield of genotypes under different types of drought stress in the wet season. Large genotypic variation for drought score existed in two experiments (A and B). However, there was no relationship between the drought scores of genotypes determined in these two experiments. Different patterns of development and severity of drought stress in these two experiments, i.e. slow development and mild plant water deficit in experiment A and fast development and severe plant water deficit in experiment B, were identified as the major factors contributing to the genotypes responding differently. Larger drought score in the dry season experiments was associated with lower grain yield under specific drought stress conditions in the wet season, but the association was weak to moderate and significant only in particular drought conditions. In most cases, a significant phenotypic and moderate genetic correlation between drought score in the dry season and grain yield in the wet season existed only when both drought score and grain yield of genotypes were affected by similar patterns and severity of drought stress in their respective experimental environments. The dry season environments used to measure genotypic variation for drought score should be managed to correspond to relevant types of drought environment that are frequent in the wet season. The efficiency of using the drought score as an indirect selection criterion for improving grain yield for drought conditions was lower than the direct selection for grain yield, and hence wet season screening with grain yield as a selection criterion would be more efficient. However, using drought score as a selection index, a larger number of genotypes can be evaluated than for wet season grain yield. Therefore, it is possible to apply higher selection intensities using the drought score system, and the selected lines can be further tested for grain yield in the wet season. (C) 2004 Elsevier B.V. All rights reserved.

An adaptive and dynamic dimensionality reduction method for high-dimensional indexing

The notorious "dimensionality curse" is a well-known phenomenon for any multi-dimensional indexes attempting to scale up to high dimensions. One well-known approach to overcome degradation in performance with respect to increasing dimensions is to reduce the dimensionality of the original dataset before constructing the index. However, identifying the correlation among the dimensions and effectively reducing them are challenging tasks. In this paper, we present an adaptive Multi-level Mahalanobis-based Dimensionality Reduction (MMDR) technique for high-dimensional indexing. Our MMDR technique has four notable features compared to existing methods. First, it discovers elliptical clusters for more effective dimensionality reduction by using only the low-dimensional subspaces. Second, data points in the different axis systems are indexed using a single B+-tree. Third, our technique is highly scalable in terms of data size and dimension. Finally, it is also dynamic and adaptive to insertions. An extensive performance study was conducted using both real and synthetic datasets, and the results show that our technique not only achieves higher precision, but also enables queries to be processed efficiently. Copyright Springer-Verlag 2005

Obsessive-compulsive disorder across developmental trajectory: Cognitive processing of threat in children, adolescents and adults

Background. While the cognitive theory of obsessive-compulsive disorder (OCD) is one of the most widely accepted accounts of the maintenance of the disorder in adults, no study to date has systematically evaluated the theory across children, adolescence and adults with OCD. Method. This paper investigated developmental differences in the cognitive processing of threat in a sample of children, adolescents and adults with OCD. Using an idiographic assessment approach, as well as self-report questionnaires, this study evaluated cognitive appraisals of responsibility, probability, severity, thought-action fusion (TAF), thought-suppression, self-doubt and cognitive control. It was hypothesised that there would be age related differences in reported responsibility for harm, probability of harm, severity of harm, thought suppression, TAR self-doubt and cognitive control. Results. Results of this study demonstrated that children with OCD reported experiencing fewer intrusive thoughts, which were less distressing and less uncontrollable than those experienced by adolescents and adults with OCD. Furthermore, responsibility attitudes, probability biases and thought suppression strategies were higher in adolescents and adults with OCD. Cognitive processes of TAF, perceived severity of harm, self-doubt and cognitive control were found to be comparable across age groups. Conclusions. These results suggest that the current cognitive theory of OCD needs to address developmental differences in the cognitive processing of threat. Furthermore, for a developmentally sensitive theory of OCD, further investigation is warranted into other possible age related maintenance factors. Implications of this investigation and directions for future research are discussed.

The economics of inclusion body processing

Many recombinant proteins are often over-expressed in host cells, such as Escherichia coli, and are found as insoluble and inactive protein aggregates known as inclusion bodies (IBs). Recently, a novel process for IB extraction and solubilisation, based on chemical extraction, has been reported. While this method has the potential to radically intensify traditional IB processing, the process economics of the new technique have yet to be reported. This study focuses on the evaluation of process economics for several IB processing schemes based on chemical extraction and/or traditional techniques. Simulations and economic analysis were conducted at various processing conditions using granulocyte macrophage-colony stimulating factor, expressed as IBs in E. coli, as a model protein. In most cases, IB processing schemes based on chemical extraction having a shorter downstream cascade demonstrated a competitive economic edge over the conventional route, validating the new process as an economically more viable alternative for IB processing.

Structure repair of a compacted vertisol with wet-dry cycles and crops

We hypothesized that the four rotation crops: wheat (Triticum aestivum L.), sorghum [Sorghum bicolor (L.) Merr.], lablab [Lablab purpureus (L.) Sweet] and mung bean [ Vigna radiata (L.) R. Wilczek] differ in their ability to repair soil structure. The study was conducted on a Typic Haplustert, Queensland, Australia, locally termed a Black Earth and considered a prime cropping soil. Large (0.5-m depth by 0.3-m diam.) soil cores, collected from compacted wheel furrows in an irrigated cotton (Gossypium hirsutum L.) field, were subjected to three, six, or nine wet-dry cycles that simulated local flood irrigation practices. After each cycle, soil profiles were sampled for clod bulk density, image analysis of soil structure, and evapotranspiration. Generally, all crops improved soil structure over the initial field condition but lablab and mung bean gave improvements to greater depths and more rapidly than wheat and sorghum. Mung bean and lablab caused up to a threefold increase in clod porosity in the 0.1- to 0.4-m soil layer after only three wet-dry cycles, whereas sorghum required nine wet-dry cycles to increase clod porosity in only the 0.2- to 0.3-m layer, and wheat gave no improvement even after nine wet-dry cycles. Image analysis of soil structure showed that lablab and mung bean rapidly (by three wet-dry cycles) produced smaller peds with more interconnected pore space than wheat and sorghum. By nine wet-dry cycles, sorghum achieved deep cracking of the soil but the material between the cracks remained large and dense. Evapotranspiration was double under lablab and mung bean compared with wheat and sorghum. Our results indicate greater cycles of wetting and drying under lablab and mung bean than wheat and sorghum that have led to rapid repair of soil compaction.

Biologically inspired visual landmark processing for simultaneous localization and mapping

This paper illustrates a method for finding useful visual landmarks for performing simultaneous localization and mapping (SLAM). The method is based loosely on biological principles, using layers of filtering and pooling to create learned templates that correspond to different views of the environment. Rather than using a set of landmarks and reporting range and bearing to the landmark, this system maps views to poses. The challenge is to produce a system that produces the same view for small changes in robot pose, but provides different views for larger changes in pose. The method has been developed to interface with the RatSLAM system, a biologically inspired method of SLAM. The paper describes the method of learning and recalling visual landmarks in detail, and shows the performance of the visual system in real robot tests.

A new indexing method for high dimensional dataset

Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering curse of dimensionality problems, Pyramid-tree type of index structures, which are based on the B-tree, have been proposed to break the curse of dimensionality. However, for high dimensional data, the number of pyramids is often insufficient to discriminate data points when the number of dimensions is high. Its effectiveness degrades dramatically with the increase of dimensionality. In this paper, we focus on one particular issue of curse of dimensionality; that is, the surface of a hypercube in a high dimensional space approaches 100% of the total hypercube volume when the number of dimensions approaches infinite. We propose a new indexing method based on the surface of dimensionality. We prove that the Pyramid tree technology is a special case of our method. The results of our experiments demonstrate clear priority of our novel method.

BM+-tree: A hyperplane-based index method for high-dimensional metric spaces

In this paper, we propose a novel high-dimensional index method, the BM+-tree, to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a rotary binary hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the M+-tree. Compared with the key dimension concept in the M+-tree, the binary hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Experimental results using two types of real data sets illustrate a significantly improved filtering efficiency.

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.

Monitoring Infants By Automatic Video Processing

This work has, as its objective, the development of non-invasive and low-cost systems for monitoring and automatic diagnosing specific neonatal diseases by means of the analysis of suitable video signals. We focus on monitoring infants potentially at risk of diseases characterized by the presence or absence of rhythmic movements of one or more body parts. Seizures and respiratory diseases are specifically considered, but the approach is general. Seizures are defined as sudden neurological and behavioural alterations. They are age-dependent phenomena and the most common sign of central nervous system dysfunction. Neonatal seizures have onset within the 28th day of life in newborns at term and within the 44th week of conceptional age in preterm infants. Their main causes are hypoxic-ischaemic encephalopathy, intracranial haemorrhage, and sepsis. Studies indicate an incidence rate of neonatal seizures of 0.2% live births, 1.1% for preterm neonates, and 1.3% for infants weighing less than 2500 g at birth. Neonatal seizures can be classified into four main categories: clonic, tonic, myoclonic, and subtle. Seizures in newborns have to be promptly and accurately recognized in order to establish timely treatments that could avoid an increase of the underlying brain damage. Respiratory diseases related to the occurrence of apnoea episodes may be caused by cerebrovascular events. Among the wide range of causes of apnoea, besides seizures, a relevant one is Congenital Central Hypoventilation Syndrome (CCHS) \cite{Healy}. With a reported prevalence of 1 in 200,000 live births, CCHS, formerly known as Ondine's curse, is a rare life-threatening disorder characterized by a failure of the automatic control of breathing, caused by mutations in a gene classified as PHOX2B. CCHS manifests itself, in the neonatal period, with episodes of cyanosis or apnoea, especially during quiet sleep. The reported mortality rates range from 8% to 38% of newborn with genetically confirmed CCHS. Nowadays, CCHS is considered a disorder of autonomic regulation, with related risk of sudden infant death syndrome (SIDS). Currently, the standard method of diagnosis, for both diseases, is based on polysomnography, a set of sensors such as ElectroEncephaloGram (EEG) sensors, ElectroMyoGraphy (EMG) sensors, ElectroCardioGraphy (ECG) sensors, elastic belt sensors, pulse-oximeter and nasal flow-meters. This monitoring system is very expensive, time-consuming, moderately invasive and requires particularly skilled medical personnel, not always available in a Neonatal Intensive Care Unit (NICU). Therefore, automatic, real-time and non-invasive monitoring equipments able to reliably recognize these diseases would be of significant value in the NICU. A very appealing monitoring tool to automatically detect neonatal seizures or breathing disorders may be based on acquiring, through a network of sensors, e.g., a set of video cameras, the movements of the newborn's body (e.g., limbs, chest) and properly processing the relevant signals. An automatic multi-sensor system could be used to permanently monitor every patient in the NICU or specific patients at home. Furthermore, a wire-free technique may be more user-friendly and highly desirable when used with infants, in particular with newborns. This work has focused on a reliable method to estimate the periodicity in pathological movements based on the use of the Maximum Likelihood (ML) criterion. In particular, average differential luminance signals from multiple Red, Green and Blue (RGB) cameras or depth-sensor devices are extracted and the presence or absence of a significant periodicity is analysed in order to detect possible pathological conditions. The efficacy of this monitoring system has been measured on the basis of video recordings provided by the Department of Neurosciences of the University of Parma. Concerning clonic seizures, a kinematic analysis was performed to establish a relationship between neonatal seizures and human inborn pattern of quadrupedal locomotion. Moreover, we have decided to realize simulators able to replicate the symptomatic movements characteristic of the diseases under consideration. The reasons is, essentially, the opportunity to have, at any time, a 'subject' on which to test the continuously evolving detection algorithms. Finally, we have developed a smartphone App, called 'Smartphone based contactless epilepsy detector' (SmartCED), able to detect neonatal clonic seizures and warn the user about the occurrence in real-time.

A solvent-free matrix application method for matrix-assisted laser desorption/ionization imaging of small molecules

Matrix application continues to be a critical step in sample preparation for matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI). Imaging of small molecules such as drugs and metabolites is particularly problematic because the commonly used washing steps to remove salts are usually omitted as they may also remove the analyte, and analyte spreading is more likely with conventional wet matrix application methods. We have developed a method which uses the application of matrix as a dry, finely divided powder, here referred to as dry matrix application, for the imaging of drug compounds. This appears to offer a complementary method to wet matrix application for the MALDI-MSI of small molecules, with the alternative matrix application techniques producing different ion profiles, and allows the visualization of compounds not observed using wet matrix application methods. We demonstrate its value in imaging clozapine from rat kidney and 4-bromophenyl-1,4-diazabicyclo(3.2.2)nonane-4-carboxylic acid from rat brain. In addition, exposure of the dry matrix coated sample to a saturated moist atmosphere appears to enhance the visualization of a different set of molecules.

Numerical implementation of the coarse step method with a varying differential group delay

The effect of having a fixed differential group delay term in the coarse step method results in a periodic pattern in the inserting a varying DGD term at each integration step, according to a Gaussian distribution. Simulation results are given to illustrate the phenomenon and provide some evidence about its statistical nature.

Variable time-scale information processing

With the extensive use of pulse modulation methods in telecommunications, much work has been done in the search for a better utilisation of the transmission channel.The present research is an extension of these investigations. A new modulation method, 'Variable Time-Scale Information Processing', (VTSIP), is proposed.The basic principles of this system have been established, and the main advantages and disadvantages investigated. With the proposed system, comparison circuits detect the instants at which the input signal voltage crosses predetermined amplitude levels.The time intervals between these occurrences are measured digitally and the results are temporarily stored, before being transmitted.After reception, an inverse process enables the original signal to be reconstituted.The advantage of this system is that the irregularities in the rate of information contained in the input signal are smoothed out before transmission, allowing the use of a smaller transmission bandwidth. A disadvantage of the system is the time delay necessarily introduced by the storage process.Another disadvantage is a type of distortion caused by the finite store capacity.A simulation of the system has been made using a standard speech signal, to make some assessment of this distortion. It is concluded that the new system should be an improvement on existing pulse transmission systems, allowing the use of a smaller transmission bandwidth, but introducing a time delay.

Use of a solvent-free dry matrix coating for quantitative matrix-assisted laser desorption ionization imaging of 4-bromophenyl-1,4-diazabicyclo(3.2.2)nonane-4-carboxylate in rat brain and quantitative analysis of the drug from laser microdissected tissue regions

A dry matrix application for matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) was used to profile the distribution of 4-bromophenyl-1,4-diazabicyclo(3.2.2)nonane-4-carboxylate, monohydrochloride (BDNC, SSR180711) in rat brain tissue sections. Matrix application involved applying layers of finely ground dry alpha-cyano-4-hydroxycinnamic acid (CHCA) to the surface of tissue sections thaw mounted onto MALDI targets. It was not possible to detect the drug when applying matrix in a standard aqueous-organic solvent solution. The drug was detected at higher concentrations in specific regions of the brain, particularly the white matter of the cerebellum. Pseudomultiple reaction monitoring imaging was used to validate that the observed distribution was the target compound. The semiquantitative data obtained from signal intensities in the imaging was confirmed by laser microdissection of specific regions of the brain directed by the imaging, followed by hydrophilic interaction chromatography in combination with a quantitative high-resolution mass spectrometry method. This study illustrates that a dry matrix coating is a valuable and complementary matrix application method for analysis of small polar drugs and metabolites that can be used for semiquantitative analysis.