miR824-Regulated AGAMOUS-LIKE16 Contributes to Flowering Time Repression in Arabidopsis

The timing of flowering is pivotal for maximizing reproductive success under fluctuating environmental conditions. Flowering time is tightly controlled by complex genetic networks that integrate endogenous and exogenous cues, such as light, temperature, photoperiod, and hormones. Here, we show that AGAMOUS-LIKE16 (AGL16) and its negative regulator microRNA824 (miR824) control flowering time in Arabidopsis thaliana. Knockout of AGL16 effectively accelerates flowering in nonvernalized Col-FRI, in which the floral inhibitor FLOWERING LOCUS C (FLC) is strongly expressed, but shows no effect if plants are vernalized or grown in short days. Alteration of AGL16 expression levels by manipulating miR824 abundance influences the timing of flowering quantitatively, depending on the expression level and number of functional FLC alleles. The effect of AGL16 is fully dependent on the presence of FLOWERING LOCUS T (FT). Further experiments show that AGL16 can interact directly with SHORT VEGETATIVE PHASE and indirectly with FLC, two proteins that form a complex to repress expression of FT. Our data reveal that miR824 and AGL16 modulate the extent of flowering time repression in a long-day photoperiod

Plasma fibrinogen: now also an antidepressant response marker?

Major depressive disorder (MDD) is one of the leading causes of global disability. It is a risk factor for noncompliance with medical treatment, with about 40% of patients not responding to currently used antidepressant drugs. The identification and clinical implementation of biomarkers that can indicate the likelihood of treatment response are needed in order to predict which patients will benefit from an antidepressant drug. While analyzing the blood plasma proteome collected from MDD patients before the initiation of antidepressant medication, we observed different fibrinogen alpha (FGA) levels between drug responders and nonresponders. These results were replicated in a second set of patients. Our findings lend further support to a recently identified association between MDD and fibrinogen levels from a large-scale study

Improvements in data quality for decision support in intensive care

Nowadays, there is a plethora of technology in hospitals and, in particular, in intensive care units. The clinical data produced everyday can be integrated in a decision support system in real-time to improve quality of care of the critically ill patients. However, there are many sensitive aspects that must be taken into account, mainly the data quality and the integration of heterogeneous data sources. This paper presents INTCare, an Intelligent Decision Support System for Intensive Care in real-time and addresses the previous aspects, in particular, the development of an Electronic Nursing Record and the improvements in the quality of monitored data.Fundação para a Ciência e a Tecnologia (FCT

Scalable Cache Management for ISP-Operated Content Delivery Services

Content delivery networks (CDNs) have been the prevalent method for the efficient delivery of content across the Internet. Management operations performed by CDNs are usually applied only based on limited information about Internet Service Provider (ISP) networks, which can have a negative impact on the utilization of ISP resources. To overcome these issues, previous research efforts have been investigating ISP-operated content delivery services, by which an ISP can deploy its own in-network caching infrastructure and implement its own cache management strategies. In this paper, we extend our previous work on ISP-operated content distribution and develop a novel scalable and efficient distributed approach to control the placement of content in the available caching points. The proposed approach relies on parallelizing the decision-making process and the use of network partitioning to cluster the distributed decision-making points, which enables fast reconfiguration and limits the volume of information required to take reconfiguration decisions. We evaluate the performance of our approach based on a wide range of parameters. The results demonstrate that the proposed solution can outperform previous approaches in terms of management overhead and complexity while offering similar network and caching performance

Ambient-aware continuous care through semantic context dissemination

Background: The ultimate ambient-intelligent care room contains numerous sensors and devices to monitor the patient, sense and adjust the environment and support the staff. This sensor-based approach results in a large amount of data, which can be processed by current and future applications, e. g., task management and alerting systems. Today, nurses are responsible for coordinating all these applications and supplied information, which reduces the added value and slows down the adoption rate. The aim of the presented research is the design of a pervasive and scalable framework that is able to optimize continuous care processes by intelligently reasoning on the large amount of heterogeneous care data. Methods: The developed Ontology-based Care Platform (OCarePlatform) consists of modular components that perform a specific reasoning task. Consequently, they can easily be replicated and distributed. Complex reasoning is achieved by combining the results of different components. To ensure that the components only receive information, which is of interest to them at that time, they are able to dynamically generate and register filter rules with a Semantic Communication Bus (SCB). This SCB semantically filters all the heterogeneous care data according to the registered rules by using a continuous care ontology. The SCB can be distributed and a cache can be employed to ensure scalability. Results: A prototype implementation is presented consisting of a new-generation nurse call system supported by a localization and a home automation component. The amount of data that is filtered and the performance of the SCB are evaluated by testing the prototype in a living lab. The delay introduced by processing the filter rules is negligible when 10 or fewer rules are registered. Conclusions: The OCarePlatform allows disseminating relevant care data for the different applications and additionally supports composing complex applications from a set of smaller independent components. This way, the platform significantly reduces the amount of information that needs to be processed by the nurses. The delay resulting from processing the filter rules is linear in the amount of rules. Distributed deployment of the SCB and using a cache allows further improvement of these performance results