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Background: Pre-diagnostic stages of psychotic illnesses, including 'clinical high risk' (CHR), are marked by sleep disturbances. These sleep disturbances appear to represent a key aspect in the etiology and maintenance of psychotic disorders. We aimed to examine the relationship between self-reported sleep dysfunction and attenuated psychotic symptoms (APS) on a day-to-day basis. Methods: Seventy-six CHR young people completed the Experience Sampling Methodology (ESM) component of the European Union Gene-Environment Interaction Study, collected through PsyMate® devices, prompting sleep and symptom questionnaires 10 times daily for 6 days. Bayesian multilevel mixed linear regression analyses were performed on time-variant ESM data using the brms package in R. We investigated the day-to-day associations between sleep and psychotic experiences bidirectionally on an item level. Sleep items included sleep onset latency, fragmentation, and quality. Psychosis items assessed a range of perceptual, cognitive, and bizarre thought content common in the CHR population. Results: Two of the seven psychosis variables were unidirectionally predicted by previous night's number of awakenings: every unit increase in number of nightly awakenings predicted a 0.27 and 0.28 unit increase in feeling unreal or paranoid the next day, respectively. No other sleep variables credibly predicted next-day psychotic symptoms or vice-versa. Conclusion: In this study, the relationship between sleep disturbance and APS appears specific to the item in question. However, some APS, including perceptual disturbances, had low levels of endorsement amongst this sample. Nonetheless, these results provide evidence for a unidirectional relationship between sleep and some APS in this population. [ABSTRACT FROM AUTHOR]

The ability of plants to sense and orient their root growth towards gravity is studied in many laboratories. It is known that manual analysis of image data is subjected to human bias. Several semi-automated tools are available for analysing images from flatbed scanners, but there is no solution to automatically measure root bending angle over time for vertical-stage microscopy images. To address these problems, we developed ACORBA, which is an automated software that can measure root bending angle over time from vertical-stage microscope and flatbed scanner images. ACORBA also has a semi-automated mode for camera or stereomicroscope images. It represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. As the software is automated, it limits human interactions and is reproducible. ACORBA will support the plant biologist community by reducing labour and increasing reproducibility of image analysis of root gravitropism.

This editorial reflects on current methodological trends in translational research in mental health. It aims to build a bridge between two fields that are frequently siloed off from each other: interventional research and phenomenologically informed research. Recent years have witnessed a revival of phenomenological approaches in mental health, often - but not only - as a means of connecting the subjective character of experience with neurobiological explanatory accounts of illness. Rich phenomenological knowledge accrued in schizophrenia, and wider psychosis research, has opened up new opportunities for improving prediction, early detection, diagnosis, prognostic stratification, treatment and ethics of care. Novel qualitative studies of delusions and hallucinations have challenged longstanding assumptions about their nature and meaning, uncovering highly complex subjective dimensions that are not adequately captured by quantitative methodologies. Interdisciplinary and participatory research efforts, informed by phenomenological insights, have prompted revisions of pre-established narratives of mental disorder dominated by a dysfunction framework and by researcher-centric outcome measures. Despite these recent advances, there has been relatively little effort to integrate and translate phenomenological insights across applied clinical research, with the goal of producing more meaningful, patient-valued results. It is our contention that phenomenological psychopathology - as the basic science of psychiatry - represents an important methodology for advancing evidence-based practices in mental health, and ultimately improving real-world outcomes. Setting this project into motion requires a greater emphasis on subjectivity and the structures of experience, more attention to the quality and patient-centredness of outcome measures, and the identification of treatment targets that matter most to patients. [ABSTRACT FROM AUTHOR]

The ability of plants to sense and orient their root growth towards gravity is studied in many laboratories. It is known that manual analysis of image data is subjected to human bias. Several semi-automated tools are available for analysing images from flatbed scanners, but there is no solution to automatically measure root bending angle over time for vertical-stage microscopy images. To address these problems, we developed ACORBA, which is an automated software that can measure root bending angle over time from vertical-stage microscope and flatbed scanner images. ACORBA also has a semi-automated mode for camera or stereomicroscope images. It represents a flexible approach based on both traditional image processing and deep machine learning segmentation to measure root angle progression over time. As the software is automated, it limits human interactions and is reproducible. ACORBA will support the plant biologist community by reducing labour and increasing reproducibility of image analysis of root gravitropism. [ABSTRACT FROM AUTHOR]