Influence of the test situation on pup retrieval behavior of normal and undernourished lactating rats

Undernutrition of dams and pups disrupts the retrieval efficiency of mothers. However, if the mothers are assessed in their home cages, they spend more time with their litters. In the present study the effect of test conditions on pup retrieval behavior of mothers receiving a 25% (well-nourished group) and 8% casein diet (undernourished group) was examined. In agreement with previous studies, undernourished mothers spent more time with their litters than well-nourished dams as lactation proceeded. Pup retrieval behavior varied with test conditions. In the first experiment, the maternal behavior of dams was assessed by the standard procedure (pups were separated from their mother and scattered over the floor of the home cage). The mother was then returned and the number of retrieved pups was recorded. From day 3 to 8, the retrieval efficiency of undernourished dams decreased, while the retrieval efficiency of well-nourished mothers did not vary. In the second experiment, mothers were subjected to a single retrieval test (on day 9 of lactation) using the procedure described for experiment 1. No difference between well-nourished and undernourished mothers was observed. In the third experiment, seven-day-old pups were separated from the mothers and returned individually to a clean home cage. Dietary treatment did not affect the retrieval efficiency. However, undernourished dams reconstructed the nest more slowly than did well-nourished dams. Taken together, these results suggest that pup retrieval behavior of the undernourished mother is not impaired by dietary restriction when the maternal environment is disturbed minimally.

Distance Matrix Approach to Content Image Retrieval

As the volume of image data and the need of using it in various applications is growing significantly in the last days it brings a necessity of retrieval efficiency and effectiveness. Unfortunately, existing indexing methods are not applicable to a wide range of problem-oriented fields due to their operating time limitations and strong dependency on the traditional descriptors extracted from the image. To meet higher requirements, a novel distance-based indexing method for region-based image retrieval has been proposed and investigated. The method creates premises for considering embedded partitions of images to carry out the search with different refinement or roughening level and so to seek the image meaningful content.

Improving cross language information retrieval using corpus based query suggestion approach

Users seeking information may not find relevant information pertaining to their information need in a specific language. But information may be available in a language different from their own, but users may not know that language. Thus users may experience difficulty in accessing the information present in different languages. Since the retrieval process depends on the translation of the user query, there are many issues in getting the right translation of the user query. For a pair of languages chosen by a user, resources, like incomplete dictionary, inaccurate machine translation system may exist. These resources may be insufficient to map the query terms in one language to its equivalent terms in another language. Also for a given query, there might exist multiple correct translations. The underlying corpus evidence may suggest a clue to select a probable set of translations that could eventually perform a better information retrieval. In this paper, we present a cross language information retrieval approach to effectively retrieve information present in a language other than the language of the user query using the corpus driven query suggestion approach. The idea is to utilize the corpus based evidence of one language to improve the retrieval and re-ranking of news documents in the other language. We use FIRE corpora - Tamil and English news collections in our experiments and illustrate the effectiveness of the proposed cross language information retrieval approach.

Learning sustainability by developing a solar dryer for microalgae retrieval

The development of nations depends on energy consumption, which is generally based on fossil fuels. This dependency produces irreversible and dramatic effects on the environment, e.g. large greenhouse gas emissions, which in turn cause global warming and climate changes, responsible for the rise of the sea level, floods, and other extreme weather events. Transportation is one of the main uses of energy, and its excessive fossil fuel dependency is driving the search for alternative and sustainable sources of energy such as microalgae, from which biodiesel, among other useful compounds, can be obtained. The process includes harvesting and drying, two energy consuming steps, which are, therefore, expensive and unsustainable. The goal of this EPS@ISEP Spring 2013 project was to develop a solar microalgae dryer for the microalgae laboratory of ISEP. A multinational team of five students from distinct fields of study was responsible for designing and building the solar microalgae dryer prototype. The prototype includes a control system to ensure that the microalgae are not destroyed during the drying process. The solar microalgae dryer works as a distiller, extracting the excess water from the microalgae suspension. This paper details the design steps, the building technologies, the ethical and sustainable concerns and compares the prototype with existing solutions. The proposed sustainable microalgae drying process is competitive as far as energy usage is concerned. Finally, the project contributed to increase the deontological ethics, social compromise skills and sustainable development awareness of the students.

Content Based Video Retrieval using SURF Descriptor

This paper presents a Robust Content Based Video Retrieval (CBVR) system. This system retrieves similar videos based on a local feature descriptor called SURF (Speeded Up Robust Feature). The higher dimensionality of SURF like feature descriptors causes huge storage consumption during indexing of video information. To achieve a dimensionality reduction on the SURF feature descriptor, this system employs a stochastic dimensionality reduction method and thus provides a model data for the videos. On retrieval, the model data of the test clip is classified to its similar videos using a minimum distance classifier. The performance of this system is evaluated using two different minimum distance classifiers during the retrieval stage. The experimental analyses performed on the system shows that the system has a retrieval performance of 78%. This system also analyses the performance efficiency of the low dimensional SURF descriptor.

A scalable re-ranking method for content-based image retrieval

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mechanical thrombectomy for acute ischemic stroke: thrombus-device interaction, efficiency, and complications in vivo

BACKGROUND AND PURPOSE: Mechanical thrombectomy is a promising new modality of interventional stroke treatment. The various devices differ with regard to where they apply force on the thrombus, taking a proximal approach such as aspiration devices or a distal approach such as basket-like devices. The study compares the in vivo effectiveness and thrombus-device interaction of these 2 approaches. METHODS: Angiography and embolization with a radioopaque whole blood thrombus was performed in 10 swine. Mechanical thrombectomy was performed in 20 cranial vessels using a proximal aspiration device (Vasco35) and a distal basket-like device (Catch) with and without proximal balloon occlusion. Fifty-six retrieval attempts were made. RESULTS: The proximal device allowed fast repeated application with a low risk of thromboembolic events (3%) and vasospasm, but it had a significantly lower success rate (39.4%) in retrieving thrombotic material than the distal device (DD) (82.6%; odds ratio, 7.3; 95% CI, 2.0 to 26.4). The compaction of the thrombus during retrieval with DD increased the risk of vessel wall irritation significantly (P<0.01) and complicated retrieval into the guiding catheter. The number of embolic events was significantly higher with DD (26%; odds ratio, 11.3; 95% CI, 1.35 to 101.6) unless proximal balloon occlusion was used. CONCLUSIONS: The proximal and the distal approaches to mechanical thrombectomy proved to be effective at achieving recanalization of cranial vessels. The proximal device is faster in application and allowed repeated attempts with a low complication rate. The DD is more successful at removing thrombotic material, but its method of application and attendant thrombus compaction increase the risk of thromboembolic events and vasospasms.

Occlusion length is a crucial determinant of efficiency and complication rate in thrombectomy for acute ischemic stroke

BACKGROUND AND PURPOSE: Although mechanical thrombectomy (MT) has an encouragingly high recanalization rate in treating stroke, it is associated with severe complications of which the underlying factors have yet to be identified. Because MT is a mechanical approach, the mechanical properties of the thrombus might be crucial for its success. The present study assesses the effect of thrombus length on the in vivo effectiveness and complication rate of MT. MATERIALS AND METHODS: Angiography and embolization of 21 cranial vessels with radiopaque whole-blood thrombi 10, 20, and 40 mm in length (7 occlusions each) were performed in 7 swine. MT was carried out using a distal snarelike device (BCR Roadsaver) with proximal balloon occlusion. A total of 61 retrievals were attempted. RESULTS: In the group of 10-mm occlusions, 77.8% of the attempts achieved complete recanalisation. For longer occlusions, the success rates decreased significantly to 20% of attempts for 20-mm occlusions (odds ratio [OR], 14; 95% confidence interval [CI], 2.2-89.2) and 11.1% for 40-mm occlusions (OR, 28; 95% CI, 3.9-202.2; P < .005). The low success rates were largely due to complications associated with thrombus compaction during retrieval. Similarly, the rate of thromboembolic events increased from 0% in 10-mm occlusions to 14.8% in 40-mm occlusions. CONCLUSIONS: MT using a distal device proved to be a fast, effective, and safe procedure for recanalizing short (10-mm) occlusions in the animal model. However, occlusion length emerged as a crucial determinant for MT with a significant decrease in recanalization success per attempt and increased complication rates. These findings suggest limitations of MT in the clinical application.

Better Memory and Neural Efficiency in Young Apolipoprotein E 4 Carriers

The apolipoprotein E (APOE) epsilon4 allele is the major genetic risk factor for Alzheimer's disease, but an APOE effect on memory performance and memory-related neurophysiology in young, healthy subjects is unknown. We found an association of APOE epsilon4 with better episodic memory compared with APOE epsilon2 and epsilon3 in 340 young, healthy persons. Neuroimaging was performed in a subset of 34 memory-matched individuals to study genetic effects on memory-related brain activity independently of differential performance. E4 carriers decreased brain activity over 3 learning runs, whereas epsilon2 and epsilon3 carriers increased activity. This smaller neural investment of epsilon4 carriers into learning reappeared during retrieval: epsilon4 carriers exhibited reduced retrieval-related activity with equal retrieval performance. APOE isoforms had no differential effects on cognitive measures other than memory, brain volumes, and brain activity related to working memory. We suggest that APOE epsilon4 is associated with good episodic memory and an economic use of memory-related neural resources in young, healthy humans.

Meta-model for calculating the mean energy demand of automated storage and retrieval systems

Energy efficiency has become an important research topic in intralogistics. Especially in this field the focus is placed on automated storage and retrieval systems (AS/RS) utilizing stacker cranes as these systems are widespread and consume a significant portion of the total energy demand of intralogistical systems. Numerical simulation models were developed to calculate the energy demand rather precisely for discrete single and dual command cycles. Unfortunately these simulation models are not suitable to perform fast calculations to determine a mean energy demand value of a complete storage aisle. For this purpose analytical approaches would be more convenient but until now analytical approaches only deliver results for certain configurations. In particular, for commonly used stacker cranes equipped with an intermediate circuit connection within their drive configuration there is no analytical approach available to calculate the mean energy demand. This article should address this research gap and present a calculation approach which enables planners to quickly calculate the energy demand of these systems.

Scp160p is required for translational efficiency of codon-optimized mRNAs in yeast

The budding yeast multi-K homology domain RNA-binding protein Scp160p binds to > 1000 messenger RNAs (mRNAs) and polyribosomes, and its mammalian homolog vigilin binds transfer RNAs (tRNAs) and translation elongation factor EF1alpha. Despite its implication in translation, studies on Scp160p's molecular function are lacking to date. We applied translational profiling approaches and demonstrate that the association of a specific subset of mRNAs with ribosomes or heavy polysomes depends on Scp160p. Interaction of Scp160p with these mRNAs requires the conserved K homology domains 13 and 14. Transfer RNA pairing index analysis of Scp160p target mRNAs indicates a high degree of consecutive use of iso-decoding codons. As shown for one target mRNA encoding the glycoprotein Pry3p, Scp160p depletion results in translational downregulation but increased association with polysomes, suggesting that it is required for efficient translation elongation. Depletion of Scp160p also decreased the relative abundance of ribosome-associated tRNAs whose codons show low potential for autocorrelation on mRNAs. Conversely, tRNAs with highly autocorrelated codons in mRNAs are less impaired. Our data indicate that Scp160p might increase the efficiency of tRNA recharge, or prevent diffusion of discharged tRNAs, both of which were also proposed to be the likely basis for the translational fitness effect of tRNA pairing.

A cyber-physical approach to combined HW-SW monitoring for improving energy efficiency in data centers

High-Performance Computing, Cloud computing and next-generation applications such e-Health or Smart Cities have dramatically increased the computational demand of Data Centers. The huge energy consumption, increasing levels of CO2 and the economic costs of these facilities represent a challenge for industry and researchers alike. Recent research trends propose the usage of holistic optimization techniques to jointly minimize Data Center computational and cooling costs from a multilevel perspective. This paper presents an analysis on the parameters needed to integrate the Data Center in a holistic optimization framework and leverages the usage of Cyber-Physical systems to gather workload, server and environmental data via software techniques and by deploying a non-intrusive Wireless Sensor Net- work (WSN). This solution tackles data sampling, retrieval and storage from a reconfigurable perspective, reducing the amount of data generated for optimization by a 68% without information loss, doubling the lifetime of the WSN nodes and allowing runtime energy minimization techniques in a real scenario.

Task-based annotation and retrieval for image information management

Continuing advances in digital image capture and storage are resulting in a proliferation of imagery and associated problems of information overload in image domains. In this work we present a framework that supports image management using an interactive approach that captures and reuses task-based contextual information. Our framework models the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. During image analysis, interactions are captured and a task context is dynamically constructed so that human expertise, proficiency and knowledge can be leveraged to support other users in carrying out similar domain tasks using case-based reasoning techniques. In this article we present our framework for capturing task context and describe how we have implemented the framework as two image retrieval applications in the geo-spatial and medical domains. We present an evaluation that tests the efficiency of our algorithms for retrieving image context information and the effectiveness of the framework for carrying out goal-directed image tasks. © 2010 Springer Science+Business Media, LLC.

Fast Information Retrieval in the Open Grid Service Architecture

This is an extended version of an article presented at the Second International Conference on Software, Services and Semantic Technologies, Sofia, Bulgaria, 11–12 September 2010.

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.