Molecular mapping of movement-associated areas in the avian brain: a motor theory for vocal learning origin.

Vocal learning is a critical behavioral substrate for spoken human language. It is a rare trait found in three distantly related groups of birds-songbirds, hummingbirds, and parrots. These avian groups have remarkably similar systems of cerebral vocal nuclei for the control of learned vocalizations that are not found in their more closely related vocal non-learning relatives. These findings led to the hypothesis that brain pathways for vocal learning in different groups evolved independently from a common ancestor but under pre-existing constraints. Here, we suggest one constraint, a pre-existing system for movement control. Using behavioral molecular mapping, we discovered that in songbirds, parrots, and hummingbirds, all cerebral vocal learning nuclei are adjacent to discrete brain areas active during limb and body movements. Similar to the relationships between vocal nuclei activation and singing, activation in the adjacent areas correlated with the amount of movement performed and was independent of auditory and visual input. These same movement-associated brain areas were also present in female songbirds that do not learn vocalizations and have atrophied cerebral vocal nuclei, and in ring doves that are vocal non-learners and do not have cerebral vocal nuclei. A compilation of previous neural tracing experiments in songbirds suggests that the movement-associated areas are connected in a network that is in parallel with the adjacent vocal learning system. This study is the first global mapping that we are aware for movement-associated areas of the avian cerebrum and it indicates that brain systems that control vocal learning in distantly related birds are directly adjacent to brain systems involved in movement control. Based upon these findings, we propose a motor theory for the origin of vocal learning, this being that the brain areas specialized for vocal learning in vocal learners evolved as a specialization of a pre-existing motor pathway that controls movement.

Comparing the model-simulated global warming signal to observations using empirical estimates of unforced noise.

The comparison of observed global mean surface air temperature (GMT) change to the mean change simulated by climate models has received much public and scientific attention. For a given global warming signal produced by a climate model ensemble, there exists an envelope of GMT values representing the range of possible unforced states of the climate system (the Envelope of Unforced Noise; EUN). Typically, the EUN is derived from climate models themselves, but climate models might not accurately simulate the correct characteristics of unforced GMT variability. Here, we simulate a new, empirical, EUN that is based on instrumental and reconstructed surface temperature records. We compare the forced GMT signal produced by climate models to observations while noting the range of GMT values provided by the empirical EUN. We find that the empirical EUN is wide enough so that the interdecadal variability in the rate of global warming over the 20(th) century does not necessarily require corresponding variability in the rate-of-increase of the forced signal. The empirical EUN also indicates that the reduced GMT warming over the past decade or so is still consistent with a middle emission scenario's forced signal, but is likely inconsistent with the steepest emission scenario's forced signal.

A population model of folate-mediated one-carbon metabolism.

BACKGROUND: Previous mathematical models for hepatic and tissue one-carbon metabolism have been combined and extended to include a blood plasma compartment. We use this model to study how the concentrations of metabolites that can be measured in the plasma are related to their respective intracellular concentrations. METHODS: The model consists of a set of ordinary differential equations, one for each metabolite in each compartment, and kinetic equations for metabolism and for transport between compartments. The model was validated by comparison to a variety of experimental data such as the methionine load test and variation in folate intake. We further extended this model by introducing random and systematic variation in enzyme activity. OUTCOMES AND CONCLUSIONS: A database of 10,000 virtual individuals was generated, each with a quantitatively different one-carbon metabolism. Our population has distributions of folate and homocysteine in the plasma and tissues that are similar to those found in the NHANES data. The model reproduces many other sets of clinical data. We show that tissue and plasma folate is highly correlated, but liver and plasma folate much less so. Oxidative stress increases the plasma S-adenosylmethionine/S-adenosylhomocysteine (SAM/SAH) ratio. We show that many relationships among variables are nonlinear and in many cases we provide explanations. Sampling of subpopulations produces dramatically different apparent associations among variables. The model can be used to simulate populations with polymorphisms in genes for folate metabolism and variations in dietary input.

Characterizing the Switching Thresholds of Magnetophoretic Transistors.

The switching thresholds of magnetophoretic transistors for sorting cells in microfluidic environments are characterized. The transistor operating conditions require short 20-30 mA pulses of electrical current. By demonstrating both attractive and repulsive transistor modes, a single transistor architecture is used to implement the full write cycle for importing and exporting single cells in specified array sites.

The IGNITE network: a model for genomic medicine implementation and research.

BACKGROUND: Patients, clinicians, researchers and payers are seeking to understand the value of using genomic information (as reflected by genotyping, sequencing, family history or other data) to inform clinical decision-making. However, challenges exist to widespread clinical implementation of genomic medicine, a prerequisite for developing evidence of its real-world utility. METHODS: To address these challenges, the National Institutes of Health-funded IGNITE (Implementing GeNomics In pracTicE; www.ignite-genomics.org ) Network, comprised of six projects and a coordinating center, was established in 2013 to support the development, investigation and dissemination of genomic medicine practice models that seamlessly integrate genomic data into the electronic health record and that deploy tools for point of care decision making. IGNITE site projects are aligned in their purpose of testing these models, but individual projects vary in scope and design, including exploring genetic markers for disease risk prediction and prevention, developing tools for using family history data, incorporating pharmacogenomic data into clinical care, refining disease diagnosis using sequence-based mutation discovery, and creating novel educational approaches. RESULTS: This paper describes the IGNITE Network and member projects, including network structure, collaborative initiatives, clinical decision support strategies, methods for return of genomic test results, and educational initiatives for patients and providers. Clinical and outcomes data from individual sites and network-wide projects are anticipated to begin being published over the next few years. CONCLUSIONS: The IGNITE Network is an innovative series of projects and pilot demonstrations aiming to enhance translation of validated actionable genomic information into clinical settings and develop and use measures of outcome in response to genome-based clinical interventions using a pragmatic framework to provide early data and proofs of concept on the utility of these interventions. Through these efforts and collaboration with other stakeholders, IGNITE is poised to have a significant impact on the acceleration of genomic information into medical practice.

Future requirements for and supply of ophthalmologists for an aging population in Singapore.

BACKGROUND: Singapore's population, as that of many other countries, is aging; this is likely to lead to an increase in eye diseases and the demand for eye care. Since ophthalmologist training is long and expensive, early planning is essential. This paper forecasts workforce and training requirements for Singapore up to the year 2040 under several plausible future scenarios. METHODS: The Singapore Eye Care Workforce Model was created as a continuous time compartment model with explicit workforce stocks using system dynamics. The model has three modules: prevalence of eye disease, demand, and workforce requirements. The model is used to simulate the prevalence of eye diseases, patient visits, and workforce requirements for the public sector under different scenarios in order to determine training requirements. RESULTS: Four scenarios were constructed. Under the baseline business-as-usual scenario, the required number of ophthalmologists is projected to increase by 117% from 2015 to 2040. Under the current policy scenario (assuming an increase of service uptake due to increased awareness, availability, and accessibility of eye care services), the increase will be 175%, while under the new model of care scenario (considering the additional effect of providing some services by non-ophthalmologists) the increase will only be 150%. The moderated workload scenario (assuming in addition a reduction of the clinical workload) projects an increase in the required number of ophthalmologists of 192% by 2040. Considering the uncertainties in the projected demand for eye care services, under the business-as-usual scenario, a residency intake of 8-22 residents per year is required, 17-21 under the current policy scenario, 14-18 under the new model of care scenario, and, under the moderated workload scenario, an intake of 18-23 residents per year is required. CONCLUSIONS: The results show that under all scenarios considered, Singapore's aging and growing population will result in an almost doubling of the number of Singaporeans with eye conditions, a significant increase in public sector eye care demand and, consequently, a greater requirement for ophthalmologists.

Cumulative early life adversity predicts longevity in wild baboons.

In humans and other animals, harsh circumstances in early life predict morbidity and mortality in adulthood. Multiple adverse conditions are thought to be especially toxic, but this hypothesis has rarely been tested in a prospective, longitudinal framework, especially in long-lived mammals. Here we use prospective data on 196 wild female baboons to show that cumulative early adversity predicts natural adult lifespan. Females who experience ≥3 sources of early adversity die a median of 10 years earlier than females who experience ≤1 adverse circumstances (median lifespan is 18.5 years). Females who experience the most adversity are also socially isolated in adulthood, suggesting that social processes partially explain the link between early adversity and adult survival. Our results provide powerful evidence for the developmental origins of health and disease and indicate that close ties between early adversity and survival arise even in the absence of health habit and health care-related explanations.

A conceptual model and key variables for guiding supportive interventions for family caregivers of people receiving palliative care

According to the World Health Organization, the patient and family should be viewed as the "unit of care" when palliative care is required. Therefore family caregivers should receive optimal supportive care from health professionals. However, the impact of supporting a dying relative is frequently described as having negative physical and psychosocial sequalae. Furthermore, family caregivers consistently report unmet needs and there has been a dearth of rigorous supportive interventions published. In addition, comprehensive conceptual frameworks to navigate the family caregiver experience and guide intervention development are lacking. This article draws on Lazarus and Folkman's seminal work on the transactional stress and coping framework to present a conceptual model specific to family caregivers of patients receiving palliative care. A comprehensive account of key variables to aid understanding of the family caregiver experience and intervention design is provided.

Development of novel oral formulations prepared via hot melt extrusion for targeted delivery of photosensitizer to the colon

Colon-residing bacteria, such as vancomycin-resistant Enterococcus faecalis and Bacteroides fragilis, can cause a range of serious clinical infections. Photodynamic antimicrobial chemotherapy (PACT) may be a novel treatment option for these multidrug resistant organisms. The aim of this study was to formulate a Eudragit®-based drug delivery system, via hot melt extrusion (HME), for targeting colonic release of photosensitizer. The susceptibility of E. faecalis and B. fragilis to PACT mediated by methylene blue (MB), meso-tetra(N-methyl-4-pyridyl)porphine tetra-tosylate (TMP), or 5-aminolevulinic acid hexyl-ester (h-ALA) was determined, with tetrachlorodecaoxide (TCDO), an oxygen-releasing compound, added in some studies. Results show that, for MB, an average of 30% of the total drug load was released over a 6-h period. For TMP and h-ALA, these values were 50% and 16% respectively. No drug was released in the acidic media. Levels of E. faecalis and B. fragilis were reduced by up to 4.67 and 7.73 logs, respectively, on PACT exposure under anaerobic conditions, with increased kill associated with TCDO. With these formulations, photosensitizer release could potentially be targeted to the colon, and colon-residing pathogens killed by PACT. TCDO could be used in vivo to generate oxygen, which could significantly impact on the success of PACT in the clinic.

Emotions and identity in the figured world of becoming a doctor

CONTEXT: There is little room in clerkship curricula for students to express emotions, particularly those associated with the development of a caring identity. Yet it is recognised that competence, alone, does not make a good doctor. We therefore set out to explore the relationship between emotions and identity in clerkship education. Our exploration was conceptually oriented towards Figured Worlds theory, which is linked to Bakhtin's theory of dialogism.

METHODS: Nine female and one male member of a mixed student cohort kept audio-diaries and participated in both semi-structured and cognitive individual interviews. The researchers identified 43 emotionally salient utterances in the dataset and subjected them to critical discourse analysis. They applied Figured Worlds constructs to within-case and cross-case analyses, supporting one another's reflexivity and openness to different interpretations, and constantly comparing their evolving interpretation against the complete set of transcripts.

RESULTS: Students' emotions were closely related to their identity development in the world of medicine. Patients were disempowered by their illnesses. Doctors were powerful because they could treat those illnesses. Students expressed positive emotions when they were granted positions in the world of medicine and were able to identify with the figures of doctors or other health professionals. They identified with doctors who behaved in caring and professionally appropriate ways towards patients and supportively towards students. Students expressed negative emotions when they were unable to develop their identities.

CONCLUSIONS: Critical discourse analysis has uncovered a link between students' emotions and their identity development in the powerful world of becoming and being a doctor. At present, identity development, emotions and power are mostly tacit in undergraduate clinical curricula. We speculate that helping students to express emotions and exercise power in the most effective ways might help them to develop caring identities.

A model for information retrieval driven by conceptual spaces

A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model.

Algorithmes d’apprentissage profonds supervisés et non-supervisés: applications et résultats théoriques

La liste des domaines touchés par l’apprentissage machine s’allonge rapidement. Au fur et à mesure que la quantité de données disponibles augmente, le développement d’algorithmes d’apprentissage de plus en plus puissants est crucial. Ce mémoire est constitué de trois parties: d’abord un survol des concepts de bases de l’apprentissage automatique et les détails nécessaires pour l’entraînement de réseaux de neurones, modèles qui se livrent bien à des architectures profondes. Ensuite, le premier article présente une application de l’apprentissage machine aux jeux vidéos, puis une méthode de mesure performance pour ceux-ci en tant que politique de décision. Finalement, le deuxième article présente des résultats théoriques concernant l’entraînement d’architectures profondes nonsupervisées. Les jeux vidéos sont un domaine particulièrement fertile pour l’apprentissage automatique: il estf facile d’accumuler d’importantes quantités de données, et les applications ne manquent pas. La formation d’équipes selon un critère donné est une tˆache commune pour les jeux en lignes. Le premier article compare différents algorithmes d’apprentissage à des réseaux de neurones profonds appliqués à la prédiction de la balance d’un match. Ensuite nous présentons une méthode par simulation pour évaluer les modèles ainsi obtenus utilisés dans le cadre d’une politique de décision en ligne. Dans un deuxième temps nous présentons une nouvelleméthode pour entraîner des modèles génératifs. Des résultats théoriques nous indiquent qu’il est possible d’entraîner par rétropropagation des modèles non-supervisés pouvant générer des échantillons qui suivent la distribution des données. Ceci est un résultat pertinent dans le cadre de la récente littérature scientifique investiguant les propriétés des autoencodeurs comme modèles génératifs. Ces résultats sont supportés avec des expériences qualitatives préliminaires ainsi que quelques résultats quantitatifs.

A stochastic parameterization for deep convection based on equilibrium statistics

A stochastic parameterization scheme for deep convection is described, suitable for use in both climate and NWP models. Theoretical arguments and the results of cloud-resolving models, are discussed in order to motivate the form of the scheme. In the deterministic limit, it tends to a spectrum of entraining/detraining plumes and is similar to other current parameterizations. The stochastic variability describes the local fluctuations about a large-scale equilibrium state. Plumes are drawn at random from a probability distribution function (pdf) that defines the chance of finding a plume of given cloud-base mass flux within each model grid box. The normalization of the pdf is given by the ensemble-mean mass flux, and this is computed with a CAPE closure method. The characteristics of each plume produced are determined using an adaptation of the plume model from the Kain-Fritsch parameterization. Initial tests in the single column version of the Unified Model verify that the scheme is effective in producing the desired distributions of convective variability without adversely affecting the mean state.

A historical review of the nurse practitioner role in Australia

Internationally, the nurse practitioner role has been shown to be cost-effective, safe, and instrumental in improving patient outcomes. The nurse practitioner role in Australia is in its infancy. Major stakeholders such as the nurses' boards and state departments of health throughout Australia were contacted to identify major policies and discussion papers. Database searches were conducted in CINAHL and EBSCOhost. Disparity between states exists in all facets of the nurse practitioner role, especially in definition of the role, scope of practice, educational qualifications, and specialized functions. Access to Medicare funding is unobtainable, resulting in inequity of access to health services for disadvantaged communities. The State Nurse Practitioner Taskforce Reports highlight the disparity between the role of nurse practitioner in each State of Australia and has led to fragmentation of the role at a national level. There is a need for consistency, which could be achieved if it were coordinated by a national nursing body with a voice in national health policy development and implementation

How to find natural reservoir hosts from endemic prevalence in a multi-host population : a case study of influenza in waterfowl

The transmission dynamics of infectious diseases critically depend on reservoir hosts, which can sustain the pathogen (or maintain the transmission) in the population even in the absence of other hosts. Although a theoretical foundation of the transmission dynamics in a multi-host population has been established, no quantitative methods exist for the identification of natural reservoir hosts. For a host to maintain the transmission alone, the host-specific reproduction number (U), interpreted as the average number of secondary transmissions caused by a single primary case in the host(s) of interest in the absence of all other hosts, must be greater than unity. If the host-excluded reproduction number (Q), representing the average number of secondary transmissions per single primary case in other hosts in the absence of the host(s) of interest, is below unity, transmission cannot be maintained in the multi-host population in the absence of the focal host(s).

The present study proposes a simple method for the identification of reservoir host(s) from observed endemic prevalence data across a range of host species. As an example, we analyze an aggregated surveillance dataset of influenza A virus in wild birds among which dabbling ducks exhibit higher prevalence compared to other bird species. Since the heterogeneous contact patterns between different host species are not directly observable, we test four different contact structures to account for the uncertainty. Meeting the requirements of U > 1 and Q < 1 for all four different contact structures, mallards and other dabbling ducks most likely constitute the reservoir community which plays a predominant role in maintaining the transmission of influenza A virus in the water bird population. We further discuss epidemiological issues which are concerned with the interpretation of influenza prevalence data, identifying key features to be fully clarified in the future.