Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State Vowel Identification

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. Such a transformation enables speech to be understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitchindependent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Speaker Normalization Using Cortical Strip Maps: A Neural Model for Steady State vowel Categorization

Auditory signals of speech are speaker-dependent, but representations of language meaning are speaker-independent. The transformation from speaker-dependent to speaker-independent language representations enables speech to be learned and understood from different speakers. A neural model is presented that performs speaker normalization to generate a pitch-independent representation of speech sounds, while also preserving information about speaker identity. This speaker-invariant representation is categorized into unitized speech items, which input to sequential working memories whose distributed patterns can be categorized, or chunked, into syllable and word representations. The proposed model fits into an emerging model of auditory streaming and speech categorization. The auditory streaming and speaker normalization parts of the model both use multiple strip representations and asymmetric competitive circuits, thereby suggesting that these two circuits arose from similar neural designs. The normalized speech items are rapidly categorized and stably remembered by Adaptive Resonance Theory circuits. Simulations use synthesized steady-state vowels from the Peterson and Barney [J. Acoust. Soc. Am. 24, 175-184 (1952)] vowel database and achieve accuracy rates similar to those achieved by human listeners. These results are compared to behavioral data and other speaker normalization models.National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.Center for Excellence for Learning Education, Science, and Technology (SBE-0354378); Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624

Assessing qualitative data richness and thickness: Development of an evidence-based tool for use in qualitative evidence synthesis

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Communication of children's weight status: what is effective and what are the children's and parents' experiences and preferences? A mixed methods systematic review

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Using the COMMVAC taxonomy to map vaccination communication interventions in Mozambique

Improved communication about childhood vaccination is fundamental to increasing vaccine uptake in low-income countries. Mozambique, with 64% of children fully vaccinated, uses a range of communication interventions to promote uptake of childhood immunisation.; Using a taxonomy developed by the 'Communicate to Vaccinate' (COMMVAC) project, the study aims to identify and classify the existing communication interventions for vaccination in Mozambique and to find the gaps.; We used a qualitative research approach to identify the range of communication interventions used in Mozambique. In-depth semi-structured interviews were carried out with key purposively selected personnel at national level and relevant documents were collected and analysed. These data were complemented with observations of communication during routine vaccination and campaigns in Nampula province. We used the COMMVAC taxonomy, which organises vaccination communication intervention according to its intended purpose and the population targeted, to map both routine and campaign interventions.; We identified interventions used in campaign and routine vaccination, or in both, fitting five of the seven taxonomy purposes, with informing or educating community members predominating. We did not identify any interventions that aimed to provide support or facilitate decision-making. There were interventions for all main target groups, although fewer for health providers. Overlap occurred: for example, interventions often targeted both parents and community members.; We consider that the predominant focus on informing and educating community members is appropriate in the Mozambican context, where there is a high level of illiteracy and poor knowledge of the reasons for vaccination. We recommend increasing interventions for health providers, in particular training them in better communication for vaccination. The taxonomy was useful for identifying gaps, but needs to be more user-friendly if it is to be employed as a tool by health service managers

Acceptability, values, and preferences of older people for chronic low back pain management; a qualitative evidence synthesis

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Building and strengthening climate resilient health systems in low- and middle-income country contexts

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Kommunikasjon om barns vektstatus til foreldre og barn: Hva er effektivt og hva er barns og foreldres erfaringer og preferanser? En «mixed methods» systematisk oversikt

Key message Early intervention and conversation about a child’s weight may offer a greater chance of success in reducing weight and implementing a healthier lifestyle. This review explores the most effective way to notify parents and children about their weight as well as their preferences for and experiences with weight notification. Studies of effect found that the format of feedback made little or no difference in parents attending further treatment, recognising their child as overweight or obese, reactions to the way the weight notification is given, motivation for lifestyle change, understanding how to reduce the risk of overweight, or taking any action. However, parents receiving feedback with motivational interviewing have somewhat greater satisfaction with the way the healthcare worker supports them. Qualitative studies found that parents had clear preferences for the format, timing, content and amount of information they wanted to receive in relation to both the weighing process and weight notification. They also had clear preferences for how they wanted health care providers to interact and communicate with them and their children. Both parents and children often felt that they were not receiving enough information and worried about how their results would be kept private. Many parents experienced an emotional response when told about their child’s weight ranging from positive, disbelief and negative feelings. Those who reacted with disbelief or negatively were less likely to accept their child’s weight status and/or act upon the notification letter. These qualitative results show that it is important that those working with weight assessment and notification programs take parents’ preferences into account when developing feedback formats, consider the mode of feedback they use and provide parents and children with tailored feedback and personalized follow up once a child is identified as underweight, overweight or obese.publishedVersio

Implementering av maskinlæring i en kunnskapsoppsummeringsgruppe: Anbefalinger basert på en treårig implementeringsprosess

The evidence synthesis process is a labour- and resource intensive process but using machine learning (ML) is one way to expedite the evidence synthesis process without compromising quality. Therefore, in 2020, the Cluster for Reviews and Health Technology Assessments (HTV) at the Norwegian Institute of Public Health (NIPH) established a dedicated ML team to implement ML in evidence synthesis processes in HTV. The main aim was to enhance evidence synthesis practices by combining human intelligence with ML to optimize workflow changes throughout the evidence synthesis process. This report provides recommendations on how to carry out implementation of ML functions in ML-naïve evidence synthesis groups, based on the experiences implementing ML at HTV. It offers "best practice" suggestions, rooted in our reflections on implementation, aiming to assist other ML naïve groups or institutions in implementing ML functions in the evidence synthesis process. The guide is adaptable to different organizational goals and objectives, while providing insights applicable to implementation of various ML tools and functions. The report is structured into three main sections corresponding to different phases of implementation: pre-implementation, implementation, and sustainment/evaluation. We use the EPIS framework throughout the document as a tool to explain the different implementation phases and important aspects to consider in each phase. Each section concludes with a "Take home message" based on our implementation experiences, summarized as practical tips on important aspects that we believe are important to consider in the implementation process.publishedVersio