WormBase: A modern Model Organism Information Resource

WormBase (https://wormbase.org/) is a mature Model Organism Information Resource supporting researchers using the nematode Caenorhabditis elegans as a model system for studies across a broad range of basic biological processes. Toward this mission, WormBase efforts are arranged in three primary facets: curation, user interface and architecture. In this update, we describe progress in each of these three areas. In particular, we discuss the status of literature curation and recently added data, detail new features of the web interface and options for users wishing to conduct data mining workflows, and discuss our efforts to build a robust and scalable architecture by leveraging commercial cloud offerings. We conclude with a description of WormBase\u27s role as a founding member of the nascent Alliance of Genome Resources

Resource Aware Sensor Nodes in Wireless Sensor Networks

Wireless sensor networks are continuing to receive considerable research interest due, in part, to the range of possible applications. One of the greatest challenges facing researchers is in overcoming the limited network lifetime inherent in the small locally powered sensor nodes. In this paper, we propose IDEALS, a system to manage a wireless sensor network using a combination of information management, energy harvesting and energy monitoring, which we label resource awareness. Through this, IDEALS is able to extend the network lifetime for important messages, by controlling the degradation of the network to maximise information throughput

Energy Harvesting and Management for Wireless Autonomous Sensors

Wireless autonomous sensors that harvest ambient energy are attractive solutions, due to their convenience and economic benefits. A number of wireless autonomous sensor platforms which consume less than 100?W under duty-cycled operation are available. Energy harvesting technology (including photovoltaics, vibration harvesters, and thermoelectrics) can be used to power autonomous sensors. A developed system is presented that uses a photovoltaic module to efficiently charge a supercapacitor, which in turn provides energy to a microcontroller-based autonomous sensing platform. The embedded software on the node is structured around a framework in which equal precedent is given to each aspect of the sensor node through the inclusion of distinct software stacks for energy management and sensor processing. This promotes structured and modular design, allowing for efficient code reuse and encourages the standardisation of interchangeable protocols

Rule Managed Reporting in Energy Controlled Wireless Sensor Networks

This paper proposes a technique to extend the network lifetime of a wireless sensor network, whereby each sensor node decides its network involvement, based on energy resources and the information in each message (ascertained through a system of rules). Results obtained from the simulation of an industrial monitoring scenario have shown that a considerable increase in the lifetime and connectivity can be obtained

Energy managed reporting for wireless sensor networks

In this paper, we propose a technique to extend the network lifetime of a wireless sensor network, whereby each sensor node decides its individual network involvement based on its own energy resources and the information contained in each packet. The information content is ascertained through a system of rules describing prospective events in the sensed environment, and how important such events are. While the packets deemed most important are propagated by all sensor nodes, low importance packets are handled by only the nodes with high energy reserves. Results obtained from simulations depicting a wireless sensor network used to monitor pump temperature in an industrial environment have shown that a considerable increase in the network lifetime and network connectivity can be obtained. The results also show that when coupled with a form of energy harvesting, our technique can enable perpetual network operatio

Constructing Emotion Categorization: Insights From Developmental Psychology Applied to a Young Adult Sample

Previous research has found that the categorization of emotional facial expressions is influenced by a variety of factors, such as processing time, facial mimicry, emotion labels, and perceptual cues. However, past research has frequently confounded these factors, making it impossible to ascertain how adults use this varied information to categorize emotions. The current study is the first to explore the magnitude of impact for each of these factors on emotion categorization in the same paradigm. Participants (N = 102) categorized anger and disgust emotional facial expressions in a novel computerized task, modeled on similar tasks in the developmental literature with preverbal infants. Experimental conditions manipulated (a) whether the task was time-restricted, and (b) whether the labels "anger" and "disgust" were used in the instructions. Participants were significantly more accurate when provided with unlimited response time and emotion labels. Participants who were given restricted sorting time (2s) and no emotion labels tended to focus on perceptual features of the faces when categorizing the emotions, which led to low sorting accuracy. In addition, facial mimicry related to greater sorting accuracy. These results suggest that when high-level (labeling) categorization strategies are unavailable, adults use low-level (perceptual) strategies to categorize facial expressions. Methodological implications for the study of emotion are discussed

Developmental Changes in Infants' Categorization of Anger and Disgust Facial Expressions

For decades, scholars have examined how children first recognize emotional facial expressions. This research has found that infants younger than 10 months can discriminate negative, within-valence facial expressions in looking time tasks, and children older than 24 months struggle to categorize these expressions in labeling and free-sort tasks. Specifically, these older children, and even adults, consistently misidentify disgust expressions as anger. Although some scholars have hypothesized that young infants would also be unable to categorize anger and disgust expressions, this question has not been empirically tested. In addition, very little research has examined developmental changes in infants' perceptual categorization abilities with high arousal, within-valence emotions. For this reason, the current study tested 10- and 18-month-olds in a looking time task and found that both age groups could perceptually categorize anger and disgust facial expressions. Furthermore, 18-month-olds showed a heightened sensitivity to novel anger expressions, suggesting that, over the second year of life, infants' emotion categorization skills undergo developmental change. These findings are the first to demonstrate that young infants can categorize anger and disgust facial expressions and to document how this skill develops and changes over time

Cn-AMP2 from green coconut water is an anionic anticancer peptide

Globally, death due to cancers is likely to rise to over 20 million by 2030,which has created an urgent need for novel approaches to anticancer therapies such as the development of host defence peptides. Cn-AMP2 (TESYFVFSVGM), an anionic host defence peptide from green coconut water of the plant Cocos nucifera, showed anti-proliferative activity against the 1321N1 and =U87MG human glioma cell lines with IC50 values of 1.25 and 1.85mM, respectively. The membrane interactive formof the peptide was found to be an extended conformation, which primarily included β-type structures (levels>45%) and random coil architecture (levels>45%). On the basis of these and other data, it is suggested that the short anionic N-terminal sequence(TES) of Cn-AMP2 interacts with positively charged moieties in the cancer cell membrane. Concomitantly, the long hydrophobic C-terminal sequence (YFVFSVGM) of the peptide penetrates the membrane core region, thereby driving the translocation of Cn-AMP2 across the cancer cell membrane to attack intracellular targets and induce anti-proliferative mechanisms. This work is the first to demonstrate that anionic host defence peptides have activity against human glioblastoma, which potentially provides an untapped source of lead compounds for development as novel agents in the treatment of these and other cancers. Copyright © 2014 European Peptide Society and John Wiley & Sons, Ltd

Efficient Irrigation for Water Conservation in the Rio Grande Basin

The Rio Grande Basin Initiative began in 2001 aimed at improving irrigation and water use efficiencies, and meeting present and future water demands throughout the Basin in Texas and New Mexico