The sustainability of a Spanish Regional Virtual Library

Objectives: The Andalusian Health e-Library (BV-SSPA) is the National Health Library in the region of Andalusia (Spain). It is a corporate hospital library created in 2006. The year 2012 is a turning point for the Spanish economy, and the BV-SSPA has to demonstrate that it is cost-effective and sustainable. Methods: Andalusia is a wide Spanish region with more than 8 million inhabitants, more than 100,000 health professionals for 41 hospitals, 1,500 primary health care centers, and 28 centers for nonmedical attention purposes, and the BV-SSPA was created to cover all these health services. It was appointed the only intermediary for contracting electronic resources destined to the Andalusian Health System. Hospitals are not allowed to subscribe any resources, and the same services are offered for the whole system. Results: In 2011, the BV-SSPA reached the biggest electronic health sciences resource collection in Spain: a total amount of 2,431 subscribed titles, besides 8 databases and other scientific information resources. The following goals were also achieved: • Cost-effectiveness: In 2011, the BV-SSPA represented a saving percentage of 25.42% compared to the individual hospital subscription costs if they would have continued their contracting. • Efficiency: Central purchasing has meant for the Andalusian health professionals, the democracy of research resource access. Some services were also created: • integrated and safe remote access to all the library resources independent of the user’s location • citizenship website, where the resources for citizenship are grouped • Centralized Document Supply Service, focusing all the article orders from and for the Andalusian Health System • institutional repository, which contains the whole intellectual, scientific production generated by the Andalusian health professionals • computer application to study the Andalusian health system scientific production • Social media as instrument for communicating with users • science web, a defined space for researchers. Conclusions: Although Andalusia is facing a dreadful economic situation, the BV-SSPA has demonstrated its sustainability: • For 2012 renewals, it carried out a statistics study allowing obtaining enough data for deciding which titles were not being discharged by users. • Titles with no discharges or without impact factor were rejected after strong negotiation with suppliers, as the BV-SSPA after 6 years on, is considered a strong dealer by them. • This meant savings of 14% from the original budget for 2012, which allowed the continuity of the BV-SSPA without decreasing the quality offered to their users.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).

Computer-assisted cup placement techniques in total hip arthroplasty improve accuracy of placement.

Malposition of the acetabular component during hip arthroplasty increases the occurrence of impingement, reduces range of motion, and increases the risk of dislocation and long-term wear. To prevent malpositioned hip implants, an increasing number of computer-assisted orthopaedic systems have been described, but their accuracy is not well established. The purpose of this study was to determine the reproducibility and accuracy of conventional versus computer-assisted techniques for positioning the acetabular component in total hip arthroplasty. Using a lateral approach, 150 cups were placed by 10 surgeons in 10 identical plastic pelvis models (freehand, with a mechanical guide, using computer assistance). Conditions for cup implantations were made to mimic the operating room situation. Preoperative planning was done from a computed tomography scan. The accuracy of cup abduction and anteversion was assessed with an electromagnetic system. Freehand placement revealed a mean accuracy of cup anteversion and abduction of 10 degrees and 3.5 degrees, respectively (maximum error, 35 degrees). With the cup positioner, these angles measured 8 degrees and 4 degrees (maximum error, 29.8 degrees), respectively, and using computer assistance, 1.5 degrees and 2.5 degrees degrees (maximum error, 8 degrees), respectively. Computer-assisted cup placement was an accurate and reproducible technique for total hip arthroplasty. It was more accurate than traditional methods of cup positioning.

Object-oriented Bayesian networks for evaluating DIP-STR profiling results from unbalanced DNA mixtures.

The genetic characterization of unbalanced mixed stains remains an important area where improvement is imperative. In fact, with current methods for DNA analysis (Polymerase Chain Reaction with the SGM Plus™ multiplex kit), it is generally not possible to obtain a conventional autosomal DNA profile of the minor contributor if the ratio between the two contributors in a mixture is smaller than 1:10. This is a consequence of the fact that the major contributor's profile 'masks' that of the minor contributor. Besides known remedies to this problem, such as Y-STR analysis, a new compound genetic marker that consists of a Deletion/Insertion Polymorphism (DIP), linked to a Short Tandem Repeat (STR) polymorphism, has recently been developed and proposed elsewhere in literature [1]. The present paper reports on the derivation of an approach for the probabilistic evaluation of DIP-STR profiling results obtained from unbalanced DNA mixtures. The procedure is based on object-oriented Bayesian networks (OOBNs) and uses the likelihood ratio as an expression of the probative value. OOBNs are retained in this paper because they allow one to provide a clear description of the genotypic configuration observed for the mixed stain as well as for the various potential contributors (e.g., victim and suspect). These models also allow one to depict the assumed relevance relationships and perform the necessary probabilistic computations.

Traductor de pseudocodi a Java

L'objectiu del projecte és la realització d'una eina capaç de traduir el pseudocodi al llenguatge de programació Java, utilitzant la programació orientada a objectes. S'obté un programa en Java del qual es podrà comprovar el seu bon funcionament, tot compilant-lo amb qualsevol compilador estàndard de Java com el distribuït per Sun Microsystems. El projecte està basat en teories de llenguatges i creació d'autòmats reconeixedors de gramàtiques, ja que són els fonaments per tal de realitzar un compilador/traductor. En el traductor s'implementa tant l'anàlisi lèxica, com la sintàctica i la semàntica. Les etapes de generació de codi intermedi, optimització i generació de codi final són substituïdes per la generació de codi Java

Forest fires cluster detection with space-time scan statistics

Forest fires are defined as uncontrolled fires often occurring in wildland areas, but that can also affect houses or agricultural resources. Causes are both natural (e.g.,lightning phenomena) and anthropogenic (human negligence or arsons).Major environmental factors influencing the fire ignition and propagation are climate and vegetation. Wildfires are most common and severe during drought period and on windy days. Moreover, under water-stress conditions, which occur after a long hot and dry period, the vegetation is more vulnerable to fire. These conditions are common in the United State and Canada, where forest fires represent a big problem. We focused our analysis on the state of Florida, for which a big dataset on forest fires detection is readily available. USDA Forest Service Remote Sensing Application Center, in collaboration with NASA-Goddard Space Flight Center and the University of Maryland, has compiled daily MODIS Thermal Anomalies (fires and biomass burning images) produced by NASA using a contextual algorithm that exploits the strong emission of mid-infrared radiation from fires. Fire classes were converted in GIS format: daily MODIS fire detections are provided as the centroids of the 1 kilometer pixels and compiled into daily Arc/INFO point coverage.

Qualité des statistiques de causes de décès: problèmes actuels et perspectives. [Quality of statistics on causes of death: current problems and perspectives].

Statistics of causes of death remain an important source of epidemiological data for the evaluation of various medical and health problems. The improvement of analytical techniques and, above all, the transformation of demographic and morbid structures of populations have prompted researchers in the field to give more importance to the quality of death certificates. After describing the data collection system presently used in Switzerland, the paper discusses various indirect estimations of the quality of Swiss data and reviews the corresponding international literature.

The role of hypocretin in driving arousal and goal-oriented behaviors.

The hypocretins (Hcrts), also called orexins, are two neuropeptides secreted by a few thousand neurons restricted to the lateral hypothalamus. The Hcrt peptides bind to two receptors located in nuclei associated with diverse cognitive and physiological functions. Experimental evidence has demonstrated that the physiological roles of hypocretins extend far beyond its initial role in food consumption and has emerged as a key system in the fields of sleep disorders and drug addiction. Here, we discuss recent evidence demonstrating a key role of hypocretin in the motivation for reward seeking in general, and drug taking in particular, and we delineate a physiological framework for this peptidergic system in orchestrating the appropriate levels of alertness required for the elaboration and the execution of goal-oriented behaviors. We propose a general role for hypocretins in mediating arousal, especially when an organism must respond to unexpected stressors and environmental challenges, which serve to shape survival behaviors. We also discuss the limit of the current experimental paradigms to address the question of how a system normally involved in the regulation of vigilance states and hyperarousal may promote a pathological state that elicits compulsive craving and relapse to drug seeking.

Modulatory effects of 5Hz rTMS over the primary somatosensory cortex in focal dystonia--an fMRI-TMS study.

Dystonia is associated with impaired somatosensory ability. The electrophysiological method of repetitive transcranial magnetic stimulation (rTMS) can be used for noninvasive stimulation of the human cortex and can alter cortical excitability and associated behavior. Among others, rTMS can alter/improve somatosensory discrimation abilities, as shown in healthy controls. We applied 5Hz-rTMS over the left primary somatosensory cortex (S1) in 5 patients with right-sided writer's dystonia and 5 controls. We studied rTMS effects on tactile discrimination accuracy and concomitant rTMS-induced changes in hemodynamic activity measured by functional magnetic resonance imaging (fMRI). Before rTMS, patients performed worse on the discrimination task than controls even though fMRI showed greater task-related activation bilaterally in the basal ganglia (BG). In controls, rTMS led to improved discrimination; fMRI revealed this was associated with increased activity of the stimulated S1, bilateral premotor cortex and BG. In dystonia patients, rTMS had no effect on discrimination; fMRI showed similar cortical effects to controls except for no effects in BG. Improved discrimination after rTMS in controls is linked to enhanced activation of S1 and BG. Failure of rTMS to increase BG activation in dystonia may be associated with the lack of effect on sensory discrimination in this group and may reflect impaired processing in BG-S1 connections. Alternatively, the increased BG activation seen in the baseline state without rTMS may reflect a compensatory strategy that saturates a BG contribution to this task.

A Clinical Decision Support using a Terminology Server to improve Patient Safety.

Clinical Decision Support Systems (CDSS) are software applications that support clinicians in making healthcare decisions providing relevant information for individual patients about their specific conditions. The lack of integration between CDSS and Electronic Health Record (EHR) has been identified as a significant barrier to CDSS development and adoption. Andalusia Healthcare Public System (AHPS) provides an interoperable health information infrastructure based on a Service Oriented Architecture (SOA) that eases CDSS implementation. This paper details the deployment of a CDSS jointly with the deployment of a Terminology Server (TS) within the AHPS infrastructure. It also explains a case study about the application of decision support to thromboembolism patients and its potential impact on improving patient safety. We will apply the inSPECt tool proposal to evaluate the appropriateness of alerts in this scenario.

Intérêt de la tomographie par émission de positons couplée à la scanographie (TEP/TDM) dans les cancers urologiques [Value of positron emission tomography and computer tomography (PET/CT) for urologic malignancies].

Positron emission tomography is a functional imaging technique that allows the detection of the regional metabolic rate, and is often coupled with other morphological imaging technique such as computed tomography. The rationale for its use is based on the clearly demonstrated fact that functional changes in tumor processes happen before morphological changes. Its introduction to the clinical practice added a new dimension in conventional imaging techniques. This review presents the current and proposed indications of the use of positron emission/computed tomography for prostate, bladder and testes, and the potential role of this exam in radiotherapy planning.

Searching patterns in painting images with computer vision techniques

This paper presents a pattern recognition method focused on paintings images. The purpose is construct a system able to recognize authors or art styles based on common elements of his work (here called patterns). The method is based on comparing images that contain the same or similar patterns. It uses different computer vision techniques, like SIFT and SURF, to describe the patterns in descriptors, K-Means to classify and simplify these descriptors, and RANSAC to determine and detect good results. The method are good to find patterns of known images but not so good if they are not.

Vital Statistics of Iowa, 2001

This publication is an historical recording of the most requested statistics on vital events and is a source of information that can be used in further analysis.