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The use of PCR is widespread in biological fields. Some fields, such as forensic biology, push PCR to its limits as DNA profiling may be required in short timeframes, may be produced from minute amounts of starting material, and may be required to perform in the presence of inhibitory compounds. Due to the extreme high-throughput of samples using PCR in forensic science, any small improvement in the ability of PCR to address these challenges can have dramatic effects for the community. At least part of the improvement in PCR performance could potentially come by altering PCR cycling conditions. These alterations could be general, in that they are applied to all samples, or they could be tailored to individual samples for maximum targeted effect. Further to this, there may be the ability to respond in real time to the conditions of PCR for a sample and make cycling parameters change on the fly. Such a goal would require both a means to track the conditions of the PCR in real time, and the knowledge of how cycling parameters should be altered, given the current conditions. In Part 1 of our work, we carry out the theoretical groundwork for the ambitious goal of creating a smart PCR system that can respond appropriately to features within individual samples in real time. We approach this task using an open qPCR instrument to provide real-time feedback and machine learning to identify what a successful PCR 'looks like' at different stages of the process. We describe the fundamental steps to set up a real-time feedback system, devise a method of controlling PCR cycling conditions from cycle to cycle, and to develop a system of defining PCR goals, scoring the performance of the system towards achieving those goals. We then present three proof-of-concept studies that prove the feasibility of this overall method. In a later Part 2 of our work, we demonstrate the performance of the theory outlined in this paper on a large-scale PCR cycling condition alteration experiment. The aim is to utilise machine learning so that throughout the process of PCR automatic adjustments can be made to best alter cycling conditions towards a user-defined goal. The realisation of smart PCR systems will have large-scale ramifications for biological fields that utilise PCR. [ABSTRACT FROM AUTHOR]

Background: The health and wellbeing consequences of social determinants of health and health behaviours are well established. This has led to a growing interest in social prescribing, which involves linking people to services and supports in the community and voluntary sectors to address non-medical needs. However, there is considerable variability in approaches to social prescribing with little guidance on how social prescribing could be developed to reflect local health systems and needs. The purpose of this scoping review was to describe the types of social prescribing models used to address non-medical needs to inform co-design and decision-making for social prescribing program developers. Methods: We searched Ovid MEDLINE(R), CINAHL, Web of Science, Scopus, National Institute for Health Research Clinical Research Network, Cochrane Central Register of Controlled Trials, WHO International Clinical Trial Registry Platform, and ProQuest – Dissertations and Theses for articles and grey literature describing social prescribing programs. Reference lists of literature reviews were also searched. The searches were conducted on 2 August 2021 and yielded 5383 results following removal of duplicates. Results: 148 documents describing 159 social prescribing programs were included in the review. We describe the contexts in which the programs were delivered, the program target groups and services/supports to which participants were referred, the staff involved in the programs, program funding, and the use of digital systems. Conclusions: There is significant variability in social prescribing approaches internationally. Social prescribing programs can be summarised as including six planning stages and six program processes. We provide guidance for decision-makers regarding what to consider when designing social prescribing programs. [ABSTRACT FROM AUTHOR]

Social determinants of health have a significant effect on health and wellbeing. There is increasing recognition of the need for health professionals to measure and address the social determinants affecting the health and wellbeing of their clients, yet efforts to do so are hampered by the lack of a validated, brief self-report measure that covers a range of determinants in a single measure. This study aimed to develop and conduct initial validation of a measure of social determinants of health, the Steps to Better Health Questionnaire (STBH-Q). Construct validity was assessed via factorial validity using exploratory factor analysis and second order confirmatory factor analysis (CFA), followed by convergent validity against the EQ-5D-5L quality of life measure. Reliability was assessed by exploring internal consistency. A convenience sampling approach was used, and 330 Australian adults aged 18 and over completed the survey. This initial validation suggests an underlying structure of STBH-Q consisting of Access; Employment, Finances & Education; Safety; Physical & Mental Health; and Family & Childhood, confirmed by second-order CFA. Results of convergent validity testing with the EQ-5D-5L demonstrated significant correlations of modest to moderate strength with the instrument as a whole and with the sub-scales in the expected direction. Cronbach's alpha for the five scales ranged from 0.561 to 0.827. Further development and validation of the STBH-Q is needed to explore alternative questions regarding social connection and habits/addictions and add additional items to factors with only two items. [ABSTRACT FROM AUTHOR]

In an empirical user study, we assessed two approaches to ranking the results from a keyword search using semantic contextual match based on Latent Semantic Analysis. These techniques involved searches initiated from words found in a seed document within a corpus. The first approach used the sentence around the search query in the document as context while the second used the entire document. With a corpus of 20,000 documents and a small proportion of relevant documents (<0.1%), both techniques outperformed a conventional keyword search on a recall-based information retrieval (IR) task. These context-based techniques were associated with a reduction in the number of searches conducted, an increase in users' precision and, to a lesser extent, an increase in recall. This improvement was strongest when the ranking was based on document, rather than sentence, context. Individuals were more effective on the IR task when the lists returned by the techniques were ranked better. User performance on the task also correlated with achievement on a generalized IQ test but not on a linguistic ability test. [ABSTRACT FROM AUTHOR]