Barriers to fieldwork in undergraduate geoscience degrees

Fieldwork is an integral part of geoscience subjects, but changing career pathways and student demographics have major implications for the future of compulsory fieldwork. The ways in which fieldwork is taught and the learning outcomes it fulfils urgently need updating.<br/

New first trimester crown-rump length's equations optimized by structured data collection from a French general population

--- Objectives --- Prior to foetal karyotyping, the likelihood of Down's syndrome is often determined combining maternal age, serum free beta-HCG, PAPP-A levels and embryonic measurements of crown-rump length and nuchal translucency for gestational ages between 11 and 13 weeks. It appeared important to get a precise knowledge of these scan parameters' normal values during the first trimester. This paper focused on crown-rump length. --- METHODS --- 402 pregnancies from in-vitro fertilization allowing a precise estimation of foetal ages (FA) were used to determine the best model that describes crown-rump length (CRL) as a function of FA. Scan measures by a single operator from 3846 spontaneous pregnancies representative of the general population from Northern France were used to build a mathematical model linking FA and CRL in a context as close as possible to normal scan screening used in Down's syndrome likelihood determination. We modeled both CRL as a function of FA and FA as a function of CRL. For this, we used a clear methodology and performed regressions with heteroskedastic corrections and robust regressions. The results were compared by cross-validation to retain the equations with the best predictive power. We also studied the errors between observed and predicted values. --- Results --- Data from 513 spontaneous pregnancies allowed to model CRL as a function of age of foetal age. The best model was a polynomial of degree 2. Datation with our equation that models spontaneous pregnancies from a general population was in quite agreement with objective datations obtained from 402 IVF pregnancies and thus support the validity of our model. The most precise measure of CRL was when the SD was minimal (1.83mm), for a CRL of 23.6 mm where our model predicted a 49.4 days of foetal age. Our study allowed to model the SD from 30 to 90 days of foetal age and offers the opportunity of using Zscores in the future to detect growth abnormalities. --- Conclusion --- With powerful statistical tools we report a good modeling of the first trimester embryonic growth in the general population allowing a better knowledge of the date of fertilization useful in the ultrasound screening of Down's syndrome. The optimal period to measure CRL and predict foetal age was 49.4 days (9 weeks of gestational age). Our results open the way to the detection of foetal growth abnormalities using CRL Zscores throughout the first trimester

Association of Sodium-Glucose Cotransporter 2 Inhibitor vs Dipeptidyl Peptidase-4 Inhibitor Use With Risk of Incident Obstructive Airway Disease and Exacerbation Events Among Patients With Type 2 Diabetes in Hong Kong

Importance Patients with diabetes are at higher risk for obstructive airway disease (OAD). In recent meta-analyses of post hoc analyses of cardiorenal trials, sodium-glucose cotransporter 2 inhibitors (SGLT2Is) were suggested to reduce the risk of OAD adverse events. However, a clinical investigation of this association is warranted. Objective This study aimed to investigate the association of SGLT2I use vs dipeptidyl peptidase-4 inhibitor (DPP4I) use with OAD incidence and exacerbation events in patients with type 2 diabetes. Design, Setting, and Participants This retrospective population-based cohort study used electronic health data from a territory-wide electronic medical database in Hong Kong. Data were collected for patients with type 2 diabetes who were prescribed SGLT2Is or DPP4Is between January 1, 2015, and December 31, 2018. Patients were followed for a median of 2.2 years between January 1, 2015, and December 31, 2020. A prevalent new-user design was adopted to match patients based on previous exposure to the study drugs. Propensity score matching was used to balance baseline characteristics. Exposures Patients with type 2 diabetes using SGLT2Is (exposure of interest) or DPP4Is (active comparator). Main Outcomes and Measures The main outcomes were the first incidence of OAD and the count of OAD exacerbations. The risk of incident OAD was estimated using a Cox proportional hazards regression model. The rate of exacerbations was estimated using zero-inflated Poisson regression. Statistical analysis was performed on November 13, 2022. Results This study included 30 385 patients. The propensity score–matched non-OAD cohort (incidence analysis) consisted of 5696 SGLT2I users and 22 784 DPP4I users, while the matched OAD cohort (exacerbations analysis) comprised 381 SGLT2I users and 1524 DPP4I users. At baseline, 56% of patients in the non-OAD cohort were men and the mean (SD) age was 61.2 (9.9) years; 51% of patients in the OAD cohort were men and the mean age was 62.2 (10.8) years. Compared with DPP4I use, SGLT2I use was associated with a lower risk of incident OAD (hazard ratio, 0.65 [95% CI, 0.54-0.79]; P &amp;amp;lt; .001) and a lower rate of exacerbations (rate ratio, 0.54 [95% CI, 0.36-0.83]; P = .01). The associations were consistent in sex subgroup analysis. Conclusions and Relevance The findings of this retrospective cohort study of patients with type 2 diabetes in Hong Kong suggest that SGLT2I use was associated with a reduced risk of incident OAD and a lower rate of exacerbations in a clinical setting compared with DPP4I use. These findings further suggest that SGLT2Is may provide additional protective effects against OAD for patients with type 2 diabetes and that further investigation is warranted

Scale invariance and universality of force networks in static granular matter

Force networks form the skeleton of static granular matter. They are the key ingredient to mechanical properties, such as stability, elasticity and sound transmission, which are of utmost importance for civil engineering and industrial processing. Previous studies have focused on the global structure of external forces (the boundary condition), and on the probability distribution of individual contact forces. The disordered spatial structure of the force network, however, has remained elusive so far. Here we report evidence for scale invariance of clusters of particles that interact via relatively strong forces. We analyzed granular packings generated by molecular dynamics simulations mimicking real granular matter; despite the visual variation, force networks for various values of the confining pressure and other parameters have identical scaling exponents and scaling function, and thus determine a universality class. Remarkably, the flat ensemble of force configurations--a simple generalization of equilibrium statistical mechanics--belongs to the same universality class, while some widely studied simplified models do not.Comment: 15 pages, 4 figures; to appear in Natur

Outcome of ATP-based tumor chemosensitivity assay directed chemotherapy in heavily pre-treated recurrent ovarian carcinoma

BACKGROUND: We wished to evaluate the clinical response following ATP-Tumor Chemosensitivity Assay (ATP-TCA) directed salvage chemotherapy in a series of UK patients with advanced ovarian cancer. The results are compared with that of a similar assay used in a different country in terms of evaluability and clinical endpoints. METHODS: From November 1998 to November 2001, 46 patients with pre-treated, advanced ovarian cancer were given a total of 56 courses of chemotherapy based on in-vitro ATP-TCA responses obtained from fresh tumor samples or ascites. Forty-four patients were evaluable for results. Of these, 18 patients had clinically platinum resistant disease (relapse < 6 months after first course of chemotherapy). There was evidence of cisplatin resistance in 31 patients from their first ATP-TCA. Response to treatment was assessed by radiology, clinical assessment and tumor marker level (CA 125). RESULTS: The overall response rate was 59% (33/56) per course of chemotherapy, including 12 complete responses, 21 partial responses, 6 with stable disease, and 15 with progressive disease. Two patients were not evaluable for response having received just one cycle of chemotherapy: if these were excluded the response rate is 61%. Fifteen patients are still alive. Median progression free survival (PFS) was 6.6 months per course of chemotherapy; median overall survival (OAS) for each patient following the start of TCA-directed therapy was 10.4 months (95% confidence interval 7.9-12.8 months). CONCLUSION: The results show similar response rates to previous studies using ATP-TCA directed therapy in recurrent ovarian cancer. The assay shows high evaluability and this study adds weight to the reproducibility of results from different centre

Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation

Cine cardiac magnetic resonance (CMR) has become the gold standard for the non-invasive evaluation of cardiac function. In particular, it allows the accurate quantification of functional parameters including the chamber volumes and ejection fraction. Deep learning has shown the potential to automate the requisite cardiac structure segmentation. However, the lack of robustness of deep learning models has hindered their widespread clinical adoption. Due to differences in the data characteristics, neural networks trained on data from a specific scanner are not guaranteed to generalise well to data acquired at a different centre or with a different scanner. In this work, we propose a principled solution to the problem of this domain shift. Domain-adversarial learning is used to train a domain-invariant 2D U-Net using labelled and unlabelled data. This approach is evaluated on both seen and unseen domains from the M\&Ms challenge dataset and the domain-adversarial approach shows improved performance as compared to standard training. Additionally, we show that the domain information cannot be recovered from the learned features.Comment: Accepted at the STACOM workshop at MICCAI 202

EPP0051 Empower: Design of a digital intervention for workplace stress and mental health. A European study

INTRODUCTION: Work stress, anxiety and depression have an enormous impact on the well-being of employees, their employers, and society. Due to the loss of productivity, common mental disorders have a substantial economic impact. Major depression alone has been attributed to 50% of long-term absences from work, and depressive symptoms are related to lowered productivity while at work. Anxiety also contributes to loss of productivity and sickness absence. Treatment of common mental disorders in a work setting may improve symptoms, however, that does not automatically lead to improved work productivity. Addressing mental well-being at the workplace might improve work functioning, and digital interventions have been introduced with that objective. However, their evaluation in research has been limited. The European Intervention to Promote Wellbeing and Health in the Workplace (EMPOWER) digital intervention is designed to provide and evaluate an integrative user programme that meets the needs of employees and employers in addressing work stress. This work was supported by the European Union Horizon 2020 Research and Innovation Programme Health (grant number APP1195937, 848180). The EMPOWER project started 1.1.2020 and is currently ongoing. OBJECTIVES: We aim to 1) describe the design and development of the digital intervention. 2) culturally validate the intervention in three countries 3) test the prototype and beta version for its usability in the RCT to evaluate its effect in four countries that is currently ongoing. METHODS: A user-centred design process was followed from January 2020 until November 2021 to create a beta version for usability testing. A tailored algorithm was developed to provide support at the individual employee level and the company level. Each element of the digital intervention was translated and culturally validated in four languages in Spain, the United Kingdom, Poland, and Finland. Usability testing was conducted in each country (n=31) to explore validity, usability, and user experience. RESULTS: The digital intervention consists of a website and a mobile application (app). The website has a public section and an employer portal that provides recommendations to reduce psychosocial risks in their company based upon clustered input from employees. The app provides algorithm-based personalised content after assessing a user’s physical and psychological symptoms, work functioning, and psychosocial risk factors for work stress. The usability testing improved the flow through the app and high ease of use and completion of tasks by participants. CONCLUSIONS: The EMPOWER digital intervention is a tailored multimodal intervention addressing wellbeing, work stress, mental and physical health problems, and work productivity. Usability testing provided validation of the app as version to be evaluated in the EMPOWER RCT

Interactive Visual Labelling versus Active Learning: An Experimental Comparison

Methods from supervised machine learning allow the classification of new data automatically and are tremendously helpful for data analysis. The quality of supervised maching learning depends not only on the type of algorithm used, but also on the quality of the labelled dataset used to train the classifier. Labelling instances in a training dataset is often done manually relying on selections and annotations by expert analysts, and is often a tedious and time-consuming process. Active learning algorithms can automatically determine a subset of data instances for which labels would provide useful input to the learning process. Interactive visual labelling techniques are a promising alternative, providing effective visual overviews from which an analyst can simultaneously explore data records and select items to a label. By putting the analyst in the loop, higher accuracy can be achieved in the resulting classifier. While initial results of interactive visual labelling techniques are promising in the sense that user labelling can improve supervised learning, many aspects of these techniques are still largely unexplored. This paper presents a study conducted using the mVis tool to compare three interactive visualisations, similarity map, scatterplot matrix (SPLOM), and parallel coordinates, with each other and with active learning for the purpose of labelling a multivariate dataset. The results show that all three interactive visual labelling techniques surpass active learning algorithms in terms of classifier accuracy, and that users subjectively prefer the similarity map over SPLOM and parallel coordinates for labelling. Users also employ different labelling strategies depending on the visualisation used