Rootstock selection, nitrogen and calcium influence postharvest disease in avocado

Field trials evaluating several parameters of growth, fruit yield and quality of 'Hass' avocado grafted to different rootstocks were established in 2004-2005 in four different growing regions of Australia. Fruit were harvested in three seasons from 2008, ripened and assessed for severity and incidence of anthracnose and stem end rot diseases. Peel samples were collected at harvest and analysed for concentrations of the cations (N, K, Ca, Mg). Rootstock significantly affected marketability of fruit (no stem end rot and less than 5% anthracnose) in 58% of the total number of trials evaluated, with better quality fruit harvested from 'Hass' grafted to Guatemalan or West Indian rootstocks such as 'A10' or 'Velvick'. Fruit quality was frequently poor from trees grafted to Mexican race rootstocks, regardless of growing location. Correlation analyses showed that fruit from rootstocks with superior fruit quality was often associated with lower skin N and higher Ca concentrations. There were significant positive correlations between anthracnose and skin N or N:Ca ratio in 75% of trials evaluated. There was a significant negative correlation between anthracnose and Ca in 42% of trials. The correlations between stem end rot and skin N (positive) or Ca (negative) were each significant in 42% of trials. Based on the results in this project, N:Ca ratios in the skin of unripe avocado fruit at harvest may provide one of the best indicators of potential postharvest disease in ripe fruit, and may have implications for fertiliser regimes.

Variation, correlation and heritability of interest characters for selection of African eggplant

The eggplant ( Solanum aethiopicum ) is the species of the Solanum genus, whose geographical distribution is broadest. It is grown throughout tropical Africa, and includes three groups of cultivars commonly called African or indigenous eggplant. Kumba group or “bitter eggplant” is an important Solanaceous vegetable crop in Burkina Faso. The objective of this study was to determine genetic variability, strength of association and level of heritability among agronomic interest traits. Phenotypic and genotypic variations and heritability of 14 traits were estimated in 61 accessions at Institut de Développement Rural (IDR), Gampela in Burkina Faso. High phenotypic and genotypic coefficients of variation were observed for fruit diameter, number of seeds per fruit, fruit weight, leaf blade length and width, and height at flowering. In addition, genetic and phenotypic variances were high for the number of seed, fruit weight, plant height at flowering and days to 50% flowering. High heritability estimates were recorded for all traits. Fruit weight showed a positive association with fruit diameter and thickness. The fifty percent flowering cycle registered positive correlations with plant height and fruit diameter. Fruit number showed a negative association with fruit weight and diameter, and 50% flowering cyle.

Neuroendocrine tumours of the female genital tract: a case-based imaging review with pathological correlation

Background Both primary and secondary gynaecological neuroendocrine (NE) tumours are uncommon, and the literature is scarce concerning their imaging features. Methods This article reviews the epidemiological, clinical and imaging features with pathological correlation of gynaecological NE tumours. Results The clinical features of gynaecological NE tumours are non-specific and depend on the organ of origin and on the extension and aggressiveness of the disease. The imaging approach to these tumours is similar to that for other histological types and the Revised International Federation of Gynecology and Obstetrics (FIGO) Staging System also applies to NE tumours. Neuroendocrine tumours were recently divided into two groups: poorly differentiated neuroendocrine carcinomas (NECs) and well-differentiated neuroendocrine tumours (NETs). NECs include small cell carcinoma and large cell neuroendocrine carcinoma, while NETs account for typical and atypical carcinoids. Cervical small cell carcinoma and ovarian carcinoid are the most common gynaecological NE tumours. The former typically behaves aggressively; the latter usually behaves in a benign fashion and tends to be confined to the organ. Conclusion While dealing with ovarian carcinoids, extraovarian extension, bilaterality and multinodularity raise the suspicion of metastatic disease. NE tumours of the endometrium and other gynaecological locations are very rare. Teaching Points • Primary or secondary neurondocrine (NE) tumours of the female genital tract are rare. • Cervical small cell carcinoma and ovarian carcinoids are the most common gynaecological NE tumours. • Cervical small cell carcinomas usually behave aggressively. • Ovarian carcinoids tend to behave in a benign fashion. • The imaging approach to gynaecological NE tumours and other histological types is similar.

A home-based treadmill training reduced epicardial and abdominal fat in postmenopausal women with metabolic syndrome

Introduction: The current study was designed to determine the effect of home-based treadmill training on epicardial and abdominal adipose tissue in postmenopausal women with metabolic syndrome (MS). A secondary objective was to identify significant correlations between imaging and conventional anthropometric parameters. Material and methods: Sixty postmenopausal women with MS volunteered for the current trial. Thirty were randomly assigned to perform a supervised home-based 16-week treadmill training program, 3 sessions/week, consisting of a warm-up, 30-40 min treadmill exercise (increasing 5-minutes each 4-weeks) at a work intensity of 60-75% of peak heart rate (increasing 5% each 4-weeks) and cooling-down. Epicardial fat thickness (EFT) was assessed by echocardiography. Abdominal fat mass in the lumbar regions L1-L4 and L4-L5 was determined by dual X-ray absorptiometry. Results: Epicardial fat thickness and abdominal fat percentages were significantly improved after the completion of the training program. Another striking feature of the current study was the moderate correlation that was found between EFT and waist circumference (WC). Conclusion: Home-based treadmill training reduced epicardial and abdominal fat in postmenopausal women with MS. A secondary finding was that a moderate correlation was found between EFT and WC. While current investigations are promising, future studies are still required to consolidate this approach in clinical application.

Selection of optimal operating conditions based on minimum energy consumption for an acrylonitrile recovery unit

The selection of the optimal operating conditions for an industrial acrylonitrile recovery unit was conducted by the systematic application of the response surface methodology, based on the minimum energy consumption and products specifications as process constraints. Unit models and plant simulation were validated against operating data and information. A sensitivity analysis was carried out in order to identify the set of parameters that strongly affect the trajectories of the system while keeping products specifications. The results suggest that energy savings of up to 10% are possible by systematically adjusting operating conditions.

Habitat selection and phenotypic diversity in the anchovy of the Bay of Biscay

Diversity among individuals in a population is an important feature linking vital rates with behaviour and spatial occupation. We measured the growth increments in the otolith of individual fishes collected on the annual fisheries survey PELGAS from 2001 to 2015. Individuals who grew larger at juvenile stage occupied later in life more off-shore habitats. Further, we analysed the allozymes of 13 different loci from 2001 to 2006. Alleles of the enzyme IDH showed different frequencies in inshore and offshore habitats. The population spatially segregates along a coast to off-shore gradient with individuals showing different early growth and allele frequencies. Results show how individuals in a population segregate spatially in different habitats in relation with phenotypic diversity. This implies modelling the population with individual-based and physiological approaches to fully grasp its dynamics. It also implies developing management strategies to conserve infra-population diversity as a means to garantee the occupation of the full range of habitats.

Wave impedance selection for passivity-based bilateral teleoperation

When a task must be executed in a remote or dangerous environment, teleoperation systems may be employed to extend the influence of the human operator. In the case of manipulation tasks, haptic feedback of the forces experienced by the remote (slave) system is often highly useful in improving an operator's ability to perform effectively. In many of these cases (especially teleoperation over the internet and ground-to-space teleoperation), substantial communication latency exists in the control loop and has the strong tendency to cause instability of the system. The first viable solution to this problem in the literature was based on a scattering/wave transformation from transmission line theory. This wave transformation requires the designer to select a wave impedance parameter appropriate to the teleoperation system. It is widely recognized that a small value of wave impedance is well suited to free motion and a large value is preferable for contact tasks. Beyond this basic observation, however, very little guidance exists in the literature regarding the selection of an appropriate value. Moreover, prior research on impedance selection generally fails to account for the fact that in any realistic contact task there will simultaneously exist contact considerations (perpendicular to the surface of contact) and quasi-free-motion considerations (parallel to the surface of contact). The primary contribution of the present work is to introduce an approximate linearized optimum for the choice of wave impedance and to apply this quasi-optimal choice to the Cartesian reality of such a contact task, in which it cannot be expected that a given joint will be either perfectly normal to or perfectly parallel to the motion constraint. The proposed scheme selects a wave impedance matrix that is appropriate to the conditions encountered by the manipulator. This choice may be implemented as a static wave impedance value or as a time-varying choice updated according to the instantaneous conditions encountered. A Lyapunov-like analysis is presented demonstrating that time variation in wave impedance will not violate the passivity of the system. Experimental trials, both in simulation and on a haptic feedback device, are presented validating the technique. Consideration is also given to the case of an uncertain environment, in which an a priori impedance choice may not be possible.

Acoustic and device feature fusion for load recognition

Appliance-specific Load Monitoring (LM) provides a possible solution to the problem of energy conservation which is becoming increasingly challenging, due to growing energy demands within offices and residential spaces. It is essential to perform automatic appliance recognition and monitoring for optimal resource utilization. In this paper, we study the use of non-intrusive LM methods that rely on steady-state appliance signatures for classifying most commonly used office appliances, while demonstrating their limitation in terms of accurately discerning the low-power devices due to overlapping load signatures. We propose a multi-layer decision architecture that makes use of audio features derived from device sounds and fuse it with load signatures acquired from energy meter. For the recognition of device sounds, we perform feature set selection by evaluating the combination of time-domain and FFT-based audio features on the state of the art machine learning algorithms. Further, we demonstrate that our proposed feature set which is a concatenation of device audio feature and load signature significantly improves the device recognition accuracy in comparison to the use of steady-state load signatures only.

Multiple time series anomaly detection based on compression and correlation analysis: a medical surveillance case study

In this paper, we present a novel anomaly detection framework for multiple heterogeneous yet correlated time series, such as the medical surveillance series data. In our framework, we propose an anomaly detection algorithm from the viewpoint of trend and correlation analysis. Moreover, to efficiently process huge amount of observed time series, a new clustering-based compression method is proposed. Experimental results indicate that our framework is more effective and efficient than its peers. © 2012 Springer-Verlag Berlin Heidelberg.

Clouds selection for network appliances based on trust credibility

With the development of Cloud services and virtual appliances, more and more clients are willing to use these services to host their applications across different Cloud platforms. However, it becomes harder and harder for clients to select the most trustable Cloud service provider to host their applications. In this paper, we propose a Cloud provider selection model to choose the most reliable platform to deploy network appliances. This selection model is based on the trust credibility to select reliable and cost-effective Cloud providers. A preliminary evaluation is presented to show the effectiveness of our proposed trust model and selection approach.

Multi-layer attribute selection and classification algorithm for the diagnosis of cardiac autonomic neuropathy based on HRV attributes

Cardiac autonomic neuropathy (CAN) poses an important clinical problem, which often remains undetected due difficulty of conducting the current tests and their lack of sensitivity. CAN has been associated with growth in the risk of unexpected death in cardiac patients with diabetes mellitus. Heart rate variability (HRV) attributes have been actively investigated, since they are important for diagnostics in diabetes, Parkinson's disease, cardiac and renal disease. Due to the adverse effects of CAN it is important to obtain a robust and highly accurate diagnostic tool for identification of early CAN, when treatment has the best outcome. Use of HRV attributes to enhance the effectiveness of diagnosis of CAN progression may provide such a tool. In the present paper we propose a new machine learning algorithm, the Multi-Layer Attribute Selection and Classification (MLASC), for the diagnosis of CAN progression based on HRV attributes. It incorporates our new automated attribute selection procedure, Double Wrapper Subset Evaluator with Particle Swarm Optimization (DWSE-PSO). We present the results of experiments, which compare MLASC with other simpler versions and counterpart methods. The experiments used our large and well-known diabetes complications database. The results of experiments demonstrate that MLASC has significantly outperformed other simpler techniques.

Identifying diffusion sources in large networks: a community structure based approach

The global diffusion of epidemics, rumors and computer viruses causes great damage to our society. It is critical to identify the diffusion sources and promptly quarantine them. However, most methods proposed so far are unsuitable for large networks because of their computational cost and the complex spatiotemporal diffusion processes. In this paper, we develop a community structure based approach to efficiently identify diffusion sources in large networks. We first detect the community structure of a network and assign sensors on community bridge nodes to record diffusion dynamics. From the infection time of bridge sensors, we can determine the very first infected community from which the diffusion started and spread out to other communities. This, therefore, overcomes the scalability issue in source identification problems by narrowing the set of suspects down to the first infected community. Then, to accurately locate the diffusion source from suspects, we utilize an intrinsic feature of diffusion sources that the relative infection time of any node is linear with its effective distance from the diffusion source. Thus, for each suspect, we compute the correlation coefficient to measure the degree of linear dependence between sensors' relative infection times and their effective distances from the suspect, and consider the one with the greatest correlation coefficient as the source. We evaluate our approach in two large networks containing more than 300,000 nodes, which are collected from Twitter. The experiment results show that our method can identify diffusion sources with very high degree of accuracy. Especially when the average community size shrinks, the accuracy of our approach increases dramatically.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.