The space science data service: A study of its efficiencies and costs

Factors affecting the overall advantages and disadvantages of a centralized facility for both the data base and processing capability for NASA's Office of Space Science programs are examined in an effort to determine the best approach to data management in the light of the increasing number of data bits collected annually. Selected issues considered relate to software and storage savings, security precautions, and the phase-in plan. Information on the current mode of processing and on the potential impact of changes resulting from a conversion to a space science data base service was obtained from five user groups and is presented as an aid in determining the dollar benefits and advantages of a centralized system

Enhancing the Sensitivity of SMS Fiber Sensors by the Use of High Refractive Index Coatings

This paper presents a study on the behavior of single-mode/multimode/single-mode sensors with diamond-like carbon coating of high refractive index. Spectra and response for different values of the external medium refractive index are recorded and analyzed

Air quality and error quantity: pollution and performance in a high-skilled, quality-focused occupation

We provide the first evidence that short-term exposure to air pollution affects the work performance of a group of highly-skilled, quality-focused employees. We repeatedly observe the decision-making of individual professional baseball umpires, quasi-randomly assigned to varying air quality across time and space. Unique characteristics of this setting combined with high-frequency data disentangle effects of multiple pollutants and identify previously under-explored acute effects. We find a 1 ppm increase in 3-hour CO causes an 11.5% increase in the propensity of umpires to make incorrect calls and a 10 mg/m3 increase in 12-hour PM2.5 causes a 2.6% increase. We control carefully for a variety of potential confounders and results are supported by robustness and falsification checks

Three-Dimensional Deep Learning Normal Tissue Complication Probability Model to Predict Late Xerostomia in Patients With Head and Neck Cancer

PURPOSE: Conventional normal tissue complication probability (NTCP) models for patients with head and neck cancer are typically based on single-value variables, which, for radiation-induced xerostomia, are baseline xerostomia and mean salivary gland doses. This study aimed to improve the prediction of late xerostomia by using 3-dimensional information from radiation dose distributions, computed tomography imaging, organ-at-risk segmentations, and clinical variables with deep learning (DL).METHODS AND MATERIALS: An international cohort of 1208 patients with head and neck cancer from 2 institutes was used to train and twice validate DL models (deep convolutional neural network, EfficientNet-v2, and ResNet) with 3-dimensional dose distribution, computed tomography scan, organ-at-risk segmentations, baseline xerostomia score, sex, and age as input. The NTCP endpoint was moderate-to-severe xerostomia 12 months postradiation therapy. The DL models' prediction performance was compared with a reference model: a recently published xerostomia NTCP model that used baseline xerostomia score and mean salivary gland doses as input. Attention maps were created to visualize the focus regions of the DL predictions. Transfer learning was conducted to improve the DL model performance on the external validation set.RESULTS: All DL-based NTCP models showed better performance (area under the receiver operating characteristic curve [AUC] test, 0.78-0.79) than the reference NTCP model (AUC test, 0.74) in the independent test. Attention maps showed that the DL model focused on the major salivary glands, particularly the stem cell-rich region of the parotid glands. DL models obtained lower external validation performance (AUC external, 0.63) than the reference model (AUC external, 0.66). After transfer learning on a small external subset, the DL model (AUC tl, external, 0.66) performed better than the reference model (AUC tl, external, 0.64). CONCLUSION: DL-based NTCP models performed better than the reference model when validated in data from the same institute. Improved performance in the external data set was achieved with transfer learning, demonstrating the need for multicenter training data to realize generalizable DL-based NTCP models.</p

Evaluation of a comprehensive set of normal tissue complication probability models for patients with head and neck cancer in an international cohort

BACKGROUND/PURPOSE: Normal tissue complication probability (NTCP) models can be used to guide radiation therapy (RT) decisions by estimating side-effect risks pretreatment to minimize (late) side-effects. Recently, a comprehensive individual toxicity risk (CITOR) profile of NTCP models addressing common side-effects in head and neck cancer (HNC) patients was developed. This study investigates the generalizability of these models in an international setting, with different treatment approaches and side-effect assessments, promoting their integration into more widespread clinical practice.MATERIALS/METHODS: From a prospective registry study, 407 HNC patients were included who were treated with definitive RT with or without systemic therapy between 2015 and 2022. NTCP models predicting dysphagia, aspiration, xerostomia, sticky saliva, taste loss, speech problems, oral pain, and fatigue at 6 and 12 months after RT were evaluated. All side-effects were patient-rated using the MDASI-HN, except dysphagia which was reported by clinicians using the PSS-HN diet normalcy score. Model performance was appraised by discrimination (area under the curve [AUC]) and calibration.RESULTS: CITOR models showed moderate-to-high performance in this cohort (mean AUC = 0.67[range = 0.55-0.80], moderate-to-good calibration). NTCP models for dysphagia, xerostomia, sticky saliva, and fatigue were the top performing models. Models for aspiration, taste loss and speech problems performed moderately well, which was partly explained by lower incidences.CONCLUSION: Despite differences between the CITOR development and this evaluation cohort, including use of different side-effect scoring systems, most models exhibited moderate-to-high performance. This demonstrated that the dose-effect relations were generalizable. Therefore, this study supports further integration of these NTCP models in clinical practice.</p

Effectiveness of the nurse-led Activate intervention in patients at risk of cardiovascular disease in primary care: a cluster-randomised controlled trial

BACKGROUND: To understand better the success of self-management interventions and to enable tailoring of such interventions at specific subgroups of patients, the nurse-led Activate intervention is developed targeting one component of self-management (physical activity) in a heterogeneous subgroup (patients at risk of cardiovascular disease) in Dutch primary care. AIM: The aim of this study was to evaluate the effectiveness of the Activate intervention and identifying which patient-related characteristics modify the effect. METHODS: A two-armed cluster-randomised controlled trial was conducted comparing the intervention with care as usual. The intervention consisted of four nurse-led behaviour change consultations within a 3-month period. Data were collected at baseline, 3 months and 6 months. Primary outcome was the daily amount of moderate to vigorous physical activity at 6 months. Secondary outcomes included sedentary behaviour, self-efficacy for physical activity, patient activation for self-management and health status. Prespecified effect modifiers were age, body mass index, level of education, social support, depression, patient provider relationship and baseline physical activity. RESULTS: Thirty-one general practices (n = 195 patients) were included (intervention group n = 93; control group n = 102). No significant between-group difference was found for physical activity (mean difference 2.49 minutes; 95% confidence interval -2.1; 7.1; P = 0.28) and secondary outcomes. Patients with low perceived social support (P = 0.01) and patients with a low baseline activity level (P = 0.02) benefitted more from the intervention. CONCLUSION: The Activate intervention did not improve patients' physical activity and secondary outcomes in primary care patients at risk of cardiovascular disease. To understand the results, the intervention fidelity and active components for effective self-management require further investigation. Trial registration: ClinicalTrials.gov NCT02725203

Healthcare Recommendations from the Personalised ICT Supported Service for Independent Living and Active Ageing (PERSSILAA) Study

In the face of demographic ageing European healthcare providers and policy makers are recognising an increasing prevalence of frail, community-dwelling older adults, prone to adverse healthcare outcomes. Prefrailty, before onset of functional decline, is suggested to be reversible but interventions targeting this risk syndrome are limited. No consensus on the definition, diagnosis or management of pre-frailty exists. The PERsonalised ICT Supported Service for Independent Living and Active Ageing (PERSSILAA) project (2013-2016 under Framework Programme 7, grant #610359) developed a comprehensive Information and Communication Technologies (ICT) supported platform to screen, assess, manage and monitor pre-frail community-dwelling older adults in order to address pre-frailty and promote active and healthy ageing. PERSSILAA, a multi-domain ICT service, targets three pre-frailty: nutrition, cognition and physical function. The project produced 42 recommendations across clinical (screening, monitoring and managing of pre-frail older adults) technical (ICT-based innovations) and societal (health literacy in older adults, guidance to healthcare professional, patients, caregivers and policy makers) areas. This paper describes the 25 healthcare related recommendations of PERSSILAA, exploring how they could be used in the development of future European guidelines on the screening and prevention of frailty