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Learning Semi-Supervised Medical Image Segmentation from Spatial Registration
Get PDFSemi-supervised medical image segmentation has shown promise in training models with limited labeled data and abundant unlabeled data. However, state-of-the-art methods ignore a potentially valuable source of unsupervised semantic information-spatial registration transforms between image volumes. To address this, we propose CCT-R, a contrastive cross-teaching framework incorporating registration information. To leverage the semantic information available in registrations between volume pairs, CCT-R incorporates two proposed modules: Registration Supervision Loss (RSL) and Registration-Enhanced Positive Sampling (REPS). The RSL leverages segmentation knowledge derived from transforms between labeled and unlabeled volume pairs, providing an additional source of pseudo-labels. REPS enhances contrastive learning by identifying anatomically-corresponding positives across volumes using registration transforms. Experimental results on two challenging medical segmentation benchmarks demonstrate the effectiveness and superiority of CCT-R across various semi-supervised settings, with as few as one labeled case. Our code is available at https://github.com/kathyliu579/ContrastiveCross-teachingWithRegistration
Association of non-cardiac comorbidities and sex with long-term re-hospitalization for heart failure
Get PDFHeart failure (HF) often coexists with non-cardiac comorbidities (NCC), but their association with long-term HF re-hospitalizations is not defined.
Using the Lombardy Regional Health Database, that includes >10 million residents, we assessed the risk of re-hospitalization for HF after first HF discharge as a function of NCC, employing age- and sex-adjusted Cox proportional-hazard models. Kaplan Meier curves for HF re-hospitalizations were stratified for number of NCC. End of follow-up was June 30th 2021.
Between January 1st 2015 to December 31st 2019, 88,528 consecutive patients were discharged from hospital with a primary diagnosis of HF; over 42.8 ± 18.3 months follow-up, 79,533 HF re-hospitalizations occurred (32.94/100 patient/year). Number of NCC, age, and male sex were significantly associated with re-hospitalization risk. Compared to those without NCC, females and males with >4 NCC had a 3.08 (CI 2.73–3.47) and a 2.62 (CI 2.39–2.87) fold higher risk, respectively. Risk of all-cause death increased with number of NCC (hazard ratio (HR): 1.42 (1.38–1.46) for HF patients with 1–2 NCC, HR: 1.90 (1.82–1.98) for patients with 3–4 NCC, HR: 2.20 (2.01–2.40) for those with HF and >4 NCC), as it did the number of days spent in hospital because of HF (from 19.91±19.25 for patients without NCC to 45.35±33.00 days for those with >4 NCC, p < 0.0001).
In conclusion, this study shows that in patients hospitalized with HF, HF re-hospitalizations, all-cause mortality, and time spent in hospital increased with number of NCC. NCC associates with a worse clinical trajectory in patients with HF
Regional anaesthesia research priorities: a Regional Anaesthesia UK (RA-UK) priority setting partnership involving patients, carers and healthcare professionals
No full textSummary.
Introduction:
Regional anaesthesia provides important clinical benefits to patients but is underutilised. A barrier to widespread adoption may be the focus of regional anaesthesia research on novel techniques rather than evaluating and optimising existing approaches. Research priorities in regional anaesthesia identified by anaesthetists have been published, but the views of patients, carers and other healthcare professionals have not been considered previously. Therefore, we launched a multidisciplinary research priority setting partnership that aimed to establish key regional anaesthesia research priorities for the UK.
Methods:
Research suggestions from key stakeholders (defined by their interaction with regional anaesthesia) were gathered using an online survey. These suggestions were analysed to identify common themes and then combined to formulate indicative research questions. After an extensive literature review, unanswered and partially answered questions were prioritised via an interim online survey and then ranked as a top 10 list during a final live virtual multidisciplinary prioritisation workshop.
Results:
In total, 210 individuals completed the initial survey and suggested 518 research questions. Fifty-seven indicative questions were formed, of which three were considered fully answered after literature review and one not feasible. The interim online survey received 335 responses, which identified the 24 highest priority questions from the 53 presented. At the final live prioritisation workshop, through a nominal group process, we identified the top 10 regional anaesthesia research priorities. These aligned with three broad thematic areas: pain management (two questions); patient safety (six questions); and recovery from surgery (two questions).
Discussion:
This initiative has resulted in a list of research questions prioritised by patients, carers and a multidisciplinary group of healthcare professionals that should be used to inform and support future regional anaesthesia research in the UK
Recent advances in UV-B signalling: Interaction of proteins with the UVR8 photoreceptor
Get PDFThe UV RESISTANCE LOCUS 8 (UVR8) photoreceptor mediates many plant responses to UV-B and short wavelength UV-A light. UVR8 functions through interactions with other proteins which lead to extensive changes in gene expression. Interactions with particular proteins determine the nature of the response to UV-B. It is therefore important to understand the molecular basis of these interactions: how are different proteins able to bind to UVR8 and how is differential binding regulated? This concise review highlights recent developments in addressing these questions. Key advances are discussed with regard to: identification of proteins that interact with UVR8; the mechanism of UVR8 accumulation in the nucleus; the photoactivation of UVR8 monomer; the structural basis of interaction between UVR8 and CONSTITUTIVELY PHOTOMORPHOGENIC 1 (COP1) and REPRESSOR OF UV-B PHOTOMORPHOGENESIS (RUP) proteins; and the role of UVR8 phosphorylation in modulating interactions and responses to UV-B. Nevertheless, much remains to be understood, and the need to extend future research to the growing list of interactors is emphasized
Cross-national trends in adolescents psychological and somatic complaints before and after the onset of COVID-19 pandemic
Get PDFPurpose:
Building on research suggesting that the COVID-19 pandemic may have led to an exacerbation of deteriorating trends in mental health among adolescents, this paper examined trends in adolescents' psychological and somatic complaints across 35 countries from 2010 to 2022, and tested trends in sociodemographic inequalities in these outcomes between 2018 and 2022.
Methods:
Using data from 792,606 adolescents from 35 countries (51% girls; mean age = 13.5; standard deviation 1.6) across four Health Behaviour in School-aged Children surveys (2010, 2014, 2018, 2022), hierarchical multilevel models estimated cross-national trends in adolescent psychological and somatic complaints. We tested whether observed values in 2022 were in line with predicted values based on 2010–2018 linear trends. Finally, moderation effects of age, family affluence, and family structures on the outcomes were tested (2018–2022).
Results:
Both girls and boys showed substantially higher levels of psychological complaints in 2022 compared with the predicted values. For somatic complaints, higher levels than predicted in 2022 were observed only in girls. Moderation analyses revealed an increase from 2018 to 2022 in age gaps and a narrowing in the socioeconomic gap for both outcomes. Also, there was a widening gap between adolescents living with 2 parents and those living in a single parent household in 2022 compared to 2018.
Discussion:
Cross-national increases in adolescent psychological and somatic complaints were higher than expected in 2022, based on previous trends. Magnitudes of change varied across different sociodemographics groups, with implications for pre-existing mental health inequalities
Fluorescence‐based detector design principles for low vapor pressure analytes
Get PDFFluorescence-based sensing is a promising method for detecting trace quantities (vapors) of chemical threats. However, direct detection at standard temperature and pressure of chemicals with low volatilities, such as the salts of illegal drugs, is difficult to achieve. Herein, the development of a testing platform designed to maximize the response from fluorescent material detection of low volatility analytes, using the salts of illicit drugs as exemplars, is described. The challenges encountered in detecting low-volatility analytes are highlighted, and the hardware solutions employed to overcome them are detailed. The testing platform is composed of a swab heating unit, a sensing chamber, and optical components that enable detection of illicit drugs via a fluorescence quenching mechanism. The swab heating unit facilitates volatilization of the analytes, with the shape of the sensing chamber and its fabrication material optimized to maximize the interaction of the analyte with the sensing element, increasing sensitivity. The detection platform is able to detect trace amounts (down to 30 ng) of (±)-3,4-methylenedioxyamphetamine hydrochloride (MDA•HCl), along with other common illicit drug salts such as cocaine hydrochloride (cocaine•HCl), fentanyl•HCl, and methamphetamine•HCl (MA•HCl)
Sustainable portfolio construction via machine learning: ESG, SDG, and sentiment
Get PDFThis study proposes portfolio construction strategies based on novel sentiment, ESG and SDG scores. We utilize natural language processing to establish a novel daily score system that mitigates concerns of different rating standards. The portfolios constructed are optimized via machine learning algorithms on a monthly basis using daily historical returns. Utilizing the equal-weighted portfolios as benchmarks, we empirically show that our optimized portfolios exhibit better trading performance in both the SPX500 and STOXX600 indices. The findings demonstrate that nonlinear models such as random forests, neural networks, and genetic algorithms can perform better than other machine learning models in portfolio management
Evaluation of On-Wafer Noise Parameter Measurement Techniques at Cryogenic Temperatures
No full textIn this paper, we highlight the current techniques for on-wafer noise parameter measurements under cryogenic conditions, and demonstrate their benefits and limitations. We first compared two noise parameter measurement systems at room temperature: one using the internal tuner of the network analyzer; and the other using an external tuner. We then used the internal tuner of the network analyzer for characterizing a GaN high electron mobility transistor at temperatures down to 78 K. The aim of this process was to identify which factors need addressing to improve the accuracy and precision of on-wafer noise parameter measurements at cryogenic temperatures. Stable noise data is reported up to 16 GHz; the minimum noise figure increased with both temperature and frequency, as expected. Predictions are also made for problems that may occur at lower temperatures, such as 4 K and mK
Using cluster-based permutation tests to estimate MEG/EEG onsets: how bad is it?
Get PDFLocalising effects in space, time and other dimensions is a fundamental goal of magneto- and electroencephalography (EEG) research. A popular exploratory approach applies mass-univariate statistics followed by cluster-sum inferences, an effective way to correct for multiple comparisons while preserving high statistical power by pooling together neighbouring effects. Yet, these cluster-based methods have an important limitation: each cluster is associated with a unique p-value, such that there is no error control at individual timepoints, and one must be cautious about interpreting when and where effects start and end. Sassenhagen and Draschkow (2019) provided an important reminder of this limitation. They also reported results from a simulation, suggesting that onsets estimated from EEG data are both positively biased and very variable. However, the simulation lacked comparisons to other methods. Here, I report such comparisons in a new simulation, replicating the positive bias of the cluster-sum method, but also demonstrating that it performs relatively well, in terms of bias and variability, compared to other methods that provide pointwise p-values: two methods that control the false discovery rate and two methods that control the familywise error rate (cluster-depth and maximum statistic methods). I also present several strategies to reduce estimation bias, including group calibration, group comparison and using binary segmentation, a simple change point detection algorithm that outperformed mass-univariate methods in simulations. Finally, I demonstrate how to generate onset hierarchical bootstrap confidence intervals that integrate variability over trials and participants, a substantial improvement over standard group approaches that ignore measurement uncertainty
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