The idea of this article is to determine the sense of the Logos in the Prologue of John's gospel by making use of the subsequent Christian doctrinal tradition. As an introduction, the general ...influence of Hellenistic Judaism on early Christian speculative theology and exegesis is illustrated by examples from Philo and Justin. Justin's exegesis is evaluated in accordance with the principle of Wilhelm Bousset, that learned scriptural demonstration (Schriftgelehrsamkeit) is not the source of doctrine but a post-rationalisation of existing doctrines. Then, Justin's argument from Scripture for Logos-Christology (Dial. 61-62), which is based on Genesis 1:26 and Wisdom 8:22-30, is taken as the point of departure. This argument informs us about the philosophical ideas behind Justin's Logos-Christology, which according to Bousset's principle preceded it. Further, it is argued that Justin's scriptual argument shows that the traditional derivation of the Logos of the Prologue from the word of creation of Genesis 1 did not exist at that early stage, since if it did, that derivation ought to have appeared in Justin. Since no other derivation of a Logos in the cosmological sense from the Bible is possible, the presence of this idea in John can only be explained as the result of influence from the eclectic philosophy of Jewish Hellenism (Philo). This conclusion is confirmed by the demonstration that the idea of universal innate knowledge, familiar from Justin's doctrine of the Logos, also appears in the Prologue of John. The argument for this is that it cannot be fortuitous that the traditional translation of John 1:9 lends itself to this interpretation. As the idea of universal innate knowledge is an idea unique to Greek philosophy, this observation settles the matter definitively. The origin of the traditional interpretation of the Logos goes back to Tertullian's interest in producing an exegesis that complies with the Latin translation of John 1. Reprinted by permission of Brill Academic Publishers
Old, hearing-impaired listeners generally benefit little from lateral separation of multiple talkers when listening to one of them. This study aimed to determine how spatial release from masking ...(SRM) in such listeners is affected when the interaural time differences (ITDs) in the temporal fine structure (TFS) are manipulated by tone-vocoding (TVC) at the ears by a master hearing aid system. Word recall was compared, with and without TVC, when target and masker sentences from a closed set were played simultaneously from the front loudspeaker (co-located) and when the maskers were played 45° to the left and right of the listener (separated). For 20 hearing-impaired listeners aged 64 to 86, SRM was 3.7 dB smaller with TVC than without TVC. This difference in SRM correlated with mean audiometric thresholds below 1.5 kHz, even when monaural TFS sensitivity (discrimination of frequency-shifts in identically filtered complexes) was partialed out, suggesting that low-frequency audiometric thresholds may be a good indicator of candidacy for hearing aids that preserve ITDs. The TVC difference in SRM was not correlated with age, pure-tone ITD thresholds, nor fundamental frequency difference limens, and only with monaural TFS sensitivity before control for low-frequency audiometric thresholds.
The scarcity of health care resources calls for their rational allocation, including within hearing health care. Policies define the course of action to reach specific goals such as optimal hearing ...health. The process of policy making can be divided into 4 steps: (a) problem identification and issue recognition, (b) policy formulation, (c) policy implementation, and (d) policy evaluation. Data and evidence, especially Big Data, can inform each of the steps of this process. Big Data can inform the macrolevel (policies that determine the general goals and actions), mesolevel (specific services and guidelines in organizations), and microlevel (clinical care) of hearing health care services. The research project EVOTION applies Big Data collection and analysis to form an evidence base for future hearing health care policies.
The EVOTION research project collects heterogeneous data both from retrospective and prospective cohorts (clinical validation) of people with hearing impairment. Retrospective data from clinical repositories in the United Kingdom and Denmark will be combined. As part of a clinical validation, over 1,000 people with hearing impairment will receive smart EVOTION hearing aids and a mobile phone application from clinics located in the United Kingdom and Greece. These clients will also complete a battery of assessments, and a subsample will also receive a smartwatch including biosensors. Big Data analytics will identify associations between client characteristics, context, and hearing aid outcomes.
The evidence EVOTION will generate is relevant especially for the first 2 steps of the policy-making process, namely, problem identification and issue recognition, as well as policy formulation. EVOTION will inform microlevel, mesolevel, and macrolevel of hearing health care services through evidence-informed policies, clinical guidelines, and clinical care.
In the future, Big Data can inform all steps of the hearing health policy-making process and all levels of hearing health care services.
IntroductionThe holistic management of hearing loss (HL) requires an understanding of factors that predict hearing aid (HA) use and benefit beyond the acoustics of listening environments. Although ...several predictors have been identified, no study has explored the role of audiological, cognitive, behavioural and physiological data nor has any study collected real-time HA data. This study will collect ‘big data’, including retrospective HA logging data, prospective clinical data and real-time data via smart HAs, a mobile application and biosensors. The main objective is to enable the validation of the EVOTION platform as a public health policy-making tool for HL.Methods and analysisThis will be a big data international multicentre study consisting of retrospective and prospective data collection. Existing data from approximately 35 000 HA users will be extracted from clinical repositories in the UK and Denmark. For the prospective data collection, 1260 HA candidates will be recruited across four clinics in the UK and Greece. Participants will complete a battery of audiological and other assessments (measures of patient-reported HA benefit, mood, cognition, quality of life). Patients will be offered smart HAs and a mobile phone application and a subset will also be given wearable biosensors, to enable the collection of dynamic real-life HA usage data. Big data analytics will be used to detect correlations between contextualised HA usage and effectiveness, and different factors and comorbidities affecting HL, with a view to informing public health decision-making.Ethics and disseminationEthical approval was received from the London South East Research Ethics Committee (17/LO/0789), the Hippokrateion Hospital Ethics Committee (1847) and the Athens Medical Center’s Ethics Committee (KM140670). Results will be disseminated through national and international events in Greece and the UK, scientific journals, newsletters, magazines and social media. Target audiences include HA users, clinicians, policy-makers and the general public.Trial registration number NCT03316287; Pre-results.
Purpose: Frequency fluctuations in human voices can usually be described as coherent frequency modulation (FM). As listeners with hearing impairment (HI listeners) are typically less sensitive to FM ...than listeners with normal hearing (NH listeners), this study investigated whether hearing loss affects the perception of a sung vowel based on FM cues. Method: Vibrato maps were obtained in 14 NH and 12 HI listeners with different degrees of musical experience. The FM rate and FM excursion of a synthesized vowel, to which coherent FM was applied, were adjusted until a singing voice emerged. Results: In NH listeners, adding FM to the steady vowel components produced perception of a singing voice for FM rates between 4.1 and 7.5 Hz and FM excursions between 17 and 83 cents on average. In contrast, HI listeners showed substantially broader vibrato maps. Individual differences in map boundaries were, overall, not correlated with audibility or frequency selectivity at the vowel fundamental frequency, with no clear effect of musical experience. Conclusion: Overall, it was shown that hearing loss affects the perception of a sung vowel based on FM-rate and FM-excursion cues, possibly due to deficits in FM detection or discrimination or to a degraded ability to follow the rate of frequency changes.
The current paper summarises the research investigating associations between physiological data and hearing performance. An overview of state-of-the-art research and literature is given as well as ...promising directions for associations between physiological data and data regarding hearing loss and hearing performance. The physiological parameters included in this paper are: electrodermal activity, heart rate variability, blood pressure, blood oxygenation and respiratory rate. Furthermore, the environmental and behavioural measurements of physical activity and body mass index, alcohol consumption and smoking have been included. So far, only electrodermal activity and heart rate variability are physiological signals simultaneously associated with hearing loss or hearing performance. Initial findings suggest blood pressure and respiratory rate to be the most promising physiological measures that relate to hearing loss and hearing performance.
We discuss condition monitoring based on mean field independent components analysis of acoustic emission energy signals. Within this framework, it is possible to formulate a generative model that ...explains the sources, their mixing and the noise statistics of the observed signals. Using a novelty detection approach based on normal-condition examples only, we detect faulty examples with high precision. The detection is done by evaluating the likelihood that the model, trained with normal examples, generated the signals, compared to a threshold obtained with normal examples. Acoustic emission energy signals from a large diesel engine are used to demonstrate this approach. The experiment show that mean field independent components analysis detects the induced fault with higher accuracy than principal components analysis, while at the same time selecting a more compact model.
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