Epigenetic regulation of human SOX3 gene expression during early phases of neural differentiation of NT2/D1 cells

Sox3/SOX3 is one of the earliest neural markers in vertebrates. Together with the Sox1/ SOX1 and Sox2/SOX2 genes it is implicated in the regulation of stem cell identity. In the present study, we performed the first analysis of epigenetic mechanisms (DNA methylation and histone marks) involved in the regulation of the human SOX3 gene expression during RA-induced neural differentiation of NT2/D1 cells. We show that the promoter of the human SOX3 gene is extremely hypomethylated both in undifferentiated NT2/D1 cells and during the early phases of RA-induced neural differentiation. By employing chromatin immunoprecipitation, we analyze several histone modifications across different regions of the SOX3 gene and their dynamics following initiation of differentiation. In the same timeframe we investigate profiles of selected histone marks on the promoters of human SOX1 and SOX2 genes. We demonstrate differences in histone signatures of SOX1, SOX2 and SOX3 genes. Considering the importance of SOXB1 genes in the process of neural differentiation, the present study contributes to a better understanding of epigenetic mechanisms implicated in the regulation of pluripotency maintenance and commitment towards the neural lineage

Challenges and opportunities in resuming spirometry services in England post-pandemic with potential to adopt Artificial Intelligence decision support software: a qualitative study

Background: Spirometry services to diagnose and monitor lung disease in primary care are restarting post-pandemic in England, identified as a priority in the NHS Long Term Plan, however evidence regarding best practice is limited.Aims: To explore perspectives on spirometry provision in primary care, and the potential for Artificial Intelligence (AI) decision support software to aid quality and interpretation.Design and Setting: Semi-structured interviews with stakeholders in spirometry services in primary care.Methods: Semi-structured interviews were conducted with key stakeholders in spirometry services across England. Participants were recruited by snowball sampling. Interviews explored the pre-pandemic delivery of spirometry, restarting of services and perceptions of the role of AI. Transcripts were analysed thematically.Results: 28 participants (mean [SD], 21.6 [9.4, range 3-40] years’ clinical experience) were interviewed between April and June 2022. Participants included clinicians (n=25) and commissioners (n=3); eight held regional and/or national respiratory network advisory roles. Four themes were identified: 1) Historical challenges in spirometry provision; 2) Inequity in post-pandemic spirometry provision and challenges to restarting spirometry in primary care; 3) Future delivery closer to patients’ homes by appropriately trained staff; 4) The potential for AI to have supportive roles in spirometry.Conclusion: Stakeholders highlighted historic challenges and the damaging effects of the pandemic contributing to inequity in provision of spirometry, which must be addressed. Overall stakeholders were positive about the potential of AI to support clinicians in quality assessment and interpretation of spirometry. However, it was evident that validation of the software must be sufficiently robust for clinicians and healthcare commissioners to have trust in the process

Collaboration between explainable artificial intelligence and pulmonologists improves the accuracy of pulmonary function test interpretation

Background Few studies have investigated the collaborative potential between artificial intelligence (AI) and pulmonologists for diagnosing pulmonary disease. We hypothesised that the collaboration between a pulmonologist and AI with explanations (explainable AI (XAI)) is superior in diagnostic interpretation of pulmonary function tests (PFTs) than the pulmonologist without support. Methods The study was conducted in two phases, a monocentre study (phase 1) and a multicentre intervention study (phase 2). Each phase utilised two different sets of 24 PFT reports of patients with a clinically validated gold standard diagnosis. Each PFT was interpreted without (control) and with XAI's suggestions (intervention). Pulmonologists provided a differential diagnosis consisting of a preferential diagnosis and optionally up to three additional diagnoses. The primary end-point compared accuracy of preferential and additional diagnoses between control and intervention. Secondary end-points were the number of diagnoses in differential diagnosis, diagnostic confidence and inter-rater agreement. We also analysed how XAI influenced pulmonologists’ decisions. Results In phase 1 (n=16 pulmonologists), mean preferential and differential diagnostic accuracy significantly increased by 10.4% and 9.4%, respectively, between control and intervention (p<0.001). Improvements were somewhat lower but highly significant (p<0.0001) in phase 2 (5.4% and 8.7%, respectively; n=62 pulmonologists). In both phases, the number of diagnoses in the differential diagnosis did not reduce, but diagnostic confidence and inter-rater agreement significantly increased during intervention. Pulmonologists updated their decisions with XAI's feedback and consistently improved their baseline performance if AI provided correct predictions. Conclusion A collaboration between a pulmonologist and XAI is better at interpreting PFTs than individual pulmonologists reading without XAI support or XAI alone

An evaluation tool kit of air quality micro-sensing units.

Recent developments in sensory and communication technologies have made the development of portable air-quality (AQ) micro-sensing units (MSUs) feasible. These MSUs allow AQ measurements in many new applications, such as ambulatory exposure analyses and citizen science. Typically, the performance of these devices is assessed using the mean error or correlation coefficients with respect to a laboratory equipment. However, these criteria do not represent how such sensors perform outside of laboratory conditions in large-scale field applications, and do not cover all aspects of possible differences in performance between the sensor-based and standardized equipment, or changes in performance over time. This paper presents a comprehensive Sensor Evaluation Toolbox (SET) for evaluating AQ MSUs by a range of criteria, to better assess their performance in varied applications and environments. Within the SET are included four new schemes for evaluating sensors' capability to: locate pollution sources; represent the pollution level on a coarse scale; capture the high temporal variability of the observed pollutant and their reliability. Each of the evaluation criteria allows for assessing sensors' performance in a different way, together constituting a holistic evaluation of the suitability and usability of the sensors in a wide range of applications. Application of the SET on measurements acquired by 25 MSUs deployed in eight cities across Europe showed that the suggested schemes facilitates a comprehensive cross platform analysis that can be used to determine and compare the sensors' performance. The SET was implemented in R and the code is available on the first author's website.CITI-SENSE, initiated in October 2012, is a four year Collaborative Project partly funded by the EU FP7-ENV-2012 under grant agreement 308524

Confined electron states in two-dimensional HgTe in magnetic field : quantum dot versus quantum ring behavior

We investigate the electron states and optical absorption in square- and hexagonal-shaped two-dimensional (2D) HgTe quantum dots and quantum rings in the presence of a perpendicular magnetic field. The electronic structure is modeled by means of the sp3d5s∗ tight-binding method within the nearest-neighbor approximation. Both bulklike and edge states appear in the energy spectrum. The bulklike states in quantum rings exhibit Aharonov-Bohm oscillations in magnetic field, whereas no such oscillations are found in quantum dots, which is ascribed to the different topology of the two systems. When magnetic field varies, all the edge states in square quantum dots appear as quasibands composed of almost fully flat levels, whereas some edge states in quantum rings are found to oscillate with magnetic field. However, the edge states in hexagonal quantum dots are localized like in rings. The absorption spectra of all the structures consist of numerous absorption lines, which substantially overlap even for small line broadening. The absorption lines in the infrared are found to originate from transitions between edge states. It is shown that the magnetic field can be used to efficiently tune the optical absorption of HgTe 2D quantum dot and quantum ring systems. © 2019 American Physical Society