Vowel Dispersion in English Diphthongs: Evidence from Adult Production

In this paper, I address the problem of including diphthong vowels into a Dispersion Theory (Flemming 2004) framework. First, I review the main aspects of Dispersion Theory in Flemming (2004), which gives an analysis of vowel inventories using a perception-based account of contrast, but noticeably omits diphthongs, which – while different from monophthongs – are highly productive, contrastive members of vowel inventories. Next, in order to correctly represent and incorporate diphthongs, I discuss acoustic properties of diphthongs and their presence in vowel inventories cross-linguistically. Diphthongs are compared to the monophthong inventory using production data to assess their relative positions in the vowel space. The English vowel production data should reflect the language-specific constraint ranking of *Effort with the maximum contrast and minimum distance constraints as predicted in Flemming's theory. To derive diphthongs, Flemming (2004)'s constraints as well as additional constraints from Minkova & Stockwell (2003) are used to account for the distance between the two offset targets. An additional constraint is proposed to account for the strong preference in the English production data to centralize the onset targets. Derivations for individual diphthong productions compared to possible surrounding candidates are provided in the analysis

The neuropathology of chromosome 17-linked dementia

We recently described a family with chromosome 17-linked dementia, characterized clinically by disinhibition-dementia parkinsonism-amyotrophy complex. We report now the neuropathology of 6 affected family members. This included semiquantitative scoring of neuronal loss, gliosis, and spongiosis and immunocytochemical and ultrastructural characterization of neuronal and glial inclusions. The changes consisted of circumscribed neuronal loss, gliosis, and spongiosis of limbic neocortical areas and frontal, temporal, and occipital association areas. Similar changes were present in subcortical nuclei, most severe in the substantia nigra, but also involved the ventral striatum and amygdala. The hippocampus was spared except for degeneration of the afferent perforant tract, secondary to entorhinal nerve cell loss. Hgyrophilic neuronal inclusions, with a characteristic immunocytochemical profile, were found in brainstem nuclei, hypothalamus, and basal ganglia. Ultrastructurally, in 3 patients these inclusions showed hitherto undescribed abnormally assembled filaments. Glial cytoplasmic inclusions were widespread in white matter structures. Immunocytochemistry failed to demonstrate the protease-resistant prion protein. The pathology appears to be unique, involving various cortical and subcortical structures, and is consistent with the clinical findings of Kliiver-Bucy-like syndrome, parkinsonism, and frontal lobe dementia. For this entity we suggest the term “chromosome 17- linked dementia”.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/50360/1/410390609_ftp.pd

Regulation of skeletal muscle oxidative capacity and insulin signaling by the Mitochondrial Rhomboid Protease PARL

Type 2 diabetes mellitus (T2DM) and aging are characterized by insulin resistance and impaired mitochondrial energetics. In lower organisms, remodeling by the protease pcp1 (PARL ortholog) maintains the function and lifecycle of mitochondria. We examined whether variation in PARL protein content is associated with mitochondrial abnormalities and insulin resistance. PARL mRNA and mitochondrial mass were both reduced in elderly subjects and in subjects with T2DM. Muscle knockdown of PARL in mice resulted in malformed mitochondrial cristae, lower mitochondrial content, decreased PGC1&alpha; protein levels, and impaired insulin signaling. Suppression of PARL protein in healthy myotubes lowered mitochondrial mass and insulin-stimulated glycogen synthesis and increased reactive oxygen species production. We propose that lower PARL expression may contribute to the mitochondrial abnormalities seen in aging and T2DM.<br /

Dental biofilm: ecological interactions in health and disease.

BACKGROUND: The oral microbiome is diverse and exists as multispecies microbial communities on oral surfaces in structurally and functionally organized biofilms. AIM: To describe the network of microbial interactions (both synergistic and antagonistic) occurring within these biofilms and assess their role in oral health and dental disease. METHODS: PubMed database was searched for studies on microbial ecological interactions in dental biofilms. The search results did not lend themselves to systematic review and have been summarized in a narrative review instead. RESULTS: Five hundred and forty-seven original research articles and 212 reviews were identified. The majority (86%) of research articles addressed bacterial-bacterial interactions, while inter-kingdom microbial interactions were the least studied. The interactions included physical and nutritional synergistic associations, antagonism, cell-to-cell communication and gene transfer. CONCLUSIONS: Oral microbial communities display emergent properties that cannot be inferred from studies of single species. Individual organisms grow in environments they would not tolerate in pure culture. The networks of multiple synergistic and antagonistic interactions generate microbial inter-dependencies and give biofilms a resilience to minor environmental perturbations, and this contributes to oral health. If key environmental pressures exceed thresholds associated with health, then the competitiveness among oral microorganisms is altered and dysbiosis can occur, increasing the risk of dental disease

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Common variants at ABCA7, MS4A6A/MS4A4E, EPHA1, CD33 and CD2AP are associated with Alzheimer's disease

We sought to identify new susceptibility loci for Alzheimer's disease through a staged association study (GERAD+) and by testing suggestive loci reported by the Alzheimer's Disease Genetic Consortium (ADGC) in a companion paper. We undertook a combined analysis of four genome-wide association datasets (stage 1) and identified ten newly associated variants with P ≤ 1 × 10−5. We tested these variants for association in an independent sample (stage 2). Three SNPs at two loci replicated and showed evidence for association in a further sample (stage 3). Meta-analyses of all data provided compelling evidence that ABCA7 (rs3764650, meta P = 4.5 × 10−17; including ADGC data, meta P = 5.0 × 10−21) and the MS4A gene cluster (rs610932, meta P = 1.8 × 10−14; including ADGC data, meta P = 1.2 × 10−16) are new Alzheimer's disease susceptibility loci. We also found independent evidence for association for three loci reported by the ADGC, which, when combined, showed genome-wide significance: CD2AP (GERAD+, P = 8.0 × 10−4; including ADGC data, meta P = 8.6 × 10−9), CD33 (GERAD+, P = 2.2 × 10−4; including ADGC data, meta P = 1.6 × 10−9) and EPHA1 (GERAD+, P = 3.4 × 10−4; including ADGC data, meta P = 6.0 × 10−10)