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ABSTRACT Reliable estimates of population abundance and demographics are essential for managing harvested species. Ice‐associated phocids, “ice seals,” are a vital resource for subsistence‐dependent coastal Native communities in western and northern Alaska, USA. In 2012, the Beringia distinct population segment of the bearded seal, Erignathus barbatus nauticus, was listed as “threatened” under the US Endangered Species Act requiring greater scrutiny for management assessments. We sought to estimate requisite population parameters from harvested seals by using close‐kin mark‐recapture (CKMR) methods, the first such application for marine mammals. Samples from 1758 bearded seals harvested by Bering, Chukchi, and Beaufort Sea communities during 1998–2020 were genotyped, genetically sexed, and aged by tooth annuli. After rigorous quality control, kin relationships were established for 1484 seals including two parent–offspring pairs (POPs) and 25 potential second‐order kin pairs. Most of the second‐order kin were half‐sibling pairs (HSPs), but four were potential grandparent‐grandchild pairs (GGPs). There were no full sibling pairs, suggesting a lack of mate fidelity. Mitochondrial DNA analysis identified 17 potential HSPs as paternally related, providing substantial evidence of persistent heterogeneity in reproductive success among adult males. The statistical CKMR model incorporates probabilities associated with POPs, HSPs, and GGPs and assumes known ages and a stable population. Our top model accommodates heterogeneity in adult male breeding success and yields an abundance estimate of ~409,000 with a coefficient of variation (CV) = 0.35, which is substantially greater than the “non‐heterogeneity” model estimate of ~232,000 (CV = 0.21), an important difference for managing a harvested species. Using CKMR methods with harvested species provides estimates of abundance with the added opportunity to acquire information about adult survival, fecundity, and breeding success that could be applied to other species of concern, marine and terrestrial.

The Anthropogenic Carbon Dioxide Monitoring (CO2M) mission is a constellation of satellites currently planned to be launched in 2026. CO2M is planned to be a core component of a Monitoring and Verification Support (MVS) service capacity under development as part of the Copernicus Atmosphere Monitoring Service (CAMS). The CO2M radiance measurements will be used to retrieve column-averaged dry-air mole fractions of atmospheric carbon dioxide (XCO2), methane (XCH4) and total columns of nitrogen dioxide (NO2). Using appropriate inverse modelling, the atmospheric greenhouse gas (GHG) observations will be used to derive United Nations Framework Convention on Climate Change (UNFCCC) COP 21 Paris Agreement relevant information on GHG sources and sinks. This challenging application requires highly accurate XCO2 and XCH4 retrievals. Three different retrieval algorithms to derive XCO2 and XCH4 are currently under development for the operational processing system at EUMETSAT. One of these algorithms uses the heritage of the FOCAL (Fast atmOspheric traCe gAs retrievaL) method, which has already successfully been applied to measurements from other satellites. Here, we show recent results generated using the CO2M version of FOCAL, called FOCAL-CO2M. To assess the quality of the FOCAL-CO2M retrievals, a large set of representative simulated radiance spectra has been generated using the radiative transfer model SCIATRAN. These simulations consider the planned viewing geometry of the CO2 instrument and corresponding geophysical scene data (including different types of aerosols and varying surface properties), which were taken from model data for the year 2015. We consider instrument noise and systematic errors caused by the retrieval method but have not considered additional error sources due to, for example, instrumental issues, spectroscopy or meteorology. On the other hand, we have also not taken advantage in this study of CO2M's MAP (multi-angle polarimeter) instrument, which will provide additional information on aerosols and cirrus clouds. By application of the FOCAL retrieval to these simulated data, confidence is gained that the FOCAL method is able to fulfil the challenging requirements for systematic errors for the CO2M mission (spatio-temporal bias ≤ 0.5 ppm for XCO2 and ≤ 5 ppb for XCH4).

Numerous studies have focused on constructing multimodal machine learning models for estimating a person's cognitive load. However, a prevalent limitation is that these models are typically evaluated on data from the same scenario they were trained on. Little attention has been given to their robustness against data distribution shifts, which may occur during deployment. The aim of this paper is to investigate the performance of these models when confronted with a scenario different from the one on which they were trained. For this evaluation, we utilized a dataset encompassing two distinct scenarios: an n-Back test and a driving simulation. We selected a variety of classic machine learning and deep learning architectures, which were further complemented by various fusion techniques. The models were trained on the data from the n-Back task and tested on both scenarios to evaluate their predictive performance. However, the predictive performance alone may not lead to a trustworthy model. Therefore, we looked at the uncertainty estimates of these models. By leveraging these estimates, we can reduce misclassification by resorting to alternative measures in situations of high uncertainty. The findings indicate that late fusion produces stable classification results across the examined models for both scenarios, enhancing robustness compared to feature-based fusion methods. Although a simple logistic regression tends to provide the best predictive performance for n-Back, this is not always the case if the data distribution is shifted. Finally, the predictive performance of individual modalities differs significantly between the two scenarios. This research provides insights into the capabilities and limitations of multimodal machine learning models in handling distribution shifts and identifies which approaches may potentially be suitable for achieving robust results.

Introduction Ruptured aneurysmal SAH accounts up to 80% of nontraumatic SAH, with more than 80% located in the anterior circulation and a mortality rate upwards of 50%.1‐3 Endovascular treatment (EVT) of ruptured intracranial aneurysms (rIA) is increasingly being recognized as a standard treatment that may result in better early outcomes and independency when compared with open neurosurgical clipping in cases of appropriate equipoise.1,2,4–6 Despite this, EVT of intracranial aneurysms has its own risk of complications including intraoperative rupture and thromboembolic events.7 The complication rate is found to be higher in cases of rIA than that seen in cases of unruptured intracranial aneurysms.8 The use of somatosensory evoked potentials (SSEP) and electroencephalography (EEG) intraoperative neuromonitoring (IONM) have proven their efficacy in identifying iatrogenic neurological complications in vascular surgeries including carotid endarterectomy 9–11, EVT and microsurgical treatment of intracranial aneurysms. 11,12 rIA have their own unique challenges due to preoperative neurological deficits and perioperative vasospasm making it more difficult to identify new neurological deficits after the procedure. They are also associated with decreased vasoreactivity secondary to bleeding, and a higher rate of procedural complications that can alter the IONM signals.13,14 IONM in the form of SSEP and EEG can be utilized in anesthetized patients to indirectly monitor intraoperative cerebral perfusion as well as central and peripheral neuronal integrity.15,16 Changes in one or both modalities indicate a change in cerebral perfusion that can be related to EVT directly, for example, aneurysm perforation or coil herniation and embolism, or indirectly secondary to anesthesia and hemodynamic changes.17 (Figure1) Methods We reviewed the medical records of 323 patients who underwent EVT of ruptured aneurysms with IONM utilizing SSEP and EEG. We included all patients who had endovascular management of ruptured aneurysm and completed IONM records until the end of the procedure and excluded those with no IONM records or incomplete records. Patients were divided into 2 groups, one group with postprocedural neurological deficits (PPND) and one group without PPND. Results Total of 323 patients undergoing EVT, significant IONM changes were noted in 71 patients (21.98%) and 46 (14.24%) who experienced PPND. 22 out of 71 (30.98%) patients who had significant IONM changes experienced PPND. Univariable analysis demonstrated that persistent changes in SSEP and EEG were associated with PPND (p‐values:

In the paper differences and similarities comparing professional identity in educations for Nurse, Teacher and Bachelor of Nutrition and Health are discussed. Professional identity formation is conceptualized as a fluid and socially negotiated process. There are international studies about professional identity for nurses and teachers, but a lack of comparative studies. The empirical material are group interviews with students and educators analyzed by reflexive thematic analysis. Both groups are asked about the collective professional identity and experiences with external understandings (based on pictures), and the students also about their (pre)professional identity. The analyses show that all three are professions working with empathy in human relations. There are however different stereotypes related to the external understanding of the professions. The student teachers have experienced to a high degree to have to defense their educational choice and position themselves in opposition to the traditional picture of transmissive teaching. The educators and student nurses challenge the picture of a “nice girl”. They refer to the present political focus and refer to the identity as challenged. Bachelor of Nutrition and Health is a new and somewhat diffuse profession. Acting on evidence but also with respect for the individual is formulated as a core.

This paper examines pre-professional identity (PPI) in a young professional education. PPI is a matter of students’ dawning professional identity during education. The empirical material are group interviews with nutrition and health students and educators as well as participatory observation. Data is analysed using reflexive thematic analysis. The analysis shows that it is difficult for students and educators to identify the characteristics of the profession of nutrition and health. However, they do point to these unifying elements: A Bachelor of Nutrition and Health holds knowledge of nutrition and health, and works with foods, counselling and communication based on evidence and with respect and care of the individual. The analysis also shows that it impacts the understanding and development of PPI in young professional educations that students and educators find the profession broad. Therefore, it might be of relevance to facilitate students’ reflections on PPI during their education. This could be done by facilitating room for reflection across students, lecturers, head of education, and practitioners, and through a logbook for professional identity.

Different microscopy and scattering methods used in the literature to determine the dimensions of cellulose nanocrystals derived from cotton and bacterial cellulose were compared to investigate potential bias and discrepancies. Atomic force microscopy (AFM), small-angle X-ray scattering (SAXS), depolarized dynamic light scattering (DDLS), and static light scattering (SLS) were compared. The lengths, widths, and heights of the particles and their respective distributions were determined by AFM. In agreement with previous work, the CNCs were found to have a ribbon-like shape, regardless of the source of cellulose or the surface functional groups. Tip broadening and agglomeration of the particles during deposition cause AFM-derived lateral dimensions to be systematically larger those obtained from SAXS measurements. The radius of gyration determined by SLS showed a good correlation with the dimensions obtained by AFM. The hydrodynamic lateral dimensions determined by DDLS were found to have the same magnitude as either the width or height obtained from the other techniques; however, the precision of DDLS was limited due to the mismatch between the cylindrical model and the actual shape of the CNCs, and to constraints in the fitting procedure. Therefore, the combination of AFM and SAXS, or microscopy and small-angle scattering, is recommended for the most accurate determination of CNC dimensions.

Wnt signaling functions in tissue homeostasis and tumorigenesis. Here the authors show that ROR2, a Wnt receptor, plays roles not only in transducing Wnt signaling, but also in regulation of DNA damage response critical for stem cell maintenance.

Abstract Background Epilepsy, multiple sclerosis (MS) and depression are long term, central nervous system disorders which have a significant impact on everyday life. Evaluating symptoms of these conditions is problematic and typically involves repeated visits to a clinic. Remote measurement technology (RMT), consisting of smartphone apps and wearables, may offer a way to improve upon existing methods of managing these conditions. The present study aimed to establish the practical requirements that would enable clinical integration of data from patients’ RMT, according to healthcare professionals. Methods This paper reports findings from an online survey of 1006 healthcare professionals currently working in the care of people with epilepsy, MS or depression. The survey included questions on types of data considered useful, how often data should be collected, the value of RMT data, preferred methods of accessing the data, benefits and challenges to RMT implementation, impact of RMT data on clinical practice, and requirement for technical support. The survey was presented on the JISC online surveys platform. Results Among this sample of 1006 healthcare professionals, respondents were positive about the benefits of RMT, with 73.2% indicating their service would be likely or highly likely to benefit from the implementation of RMT in patient care plans. The data from patients’ RMT devices should be made available to all nursing and medical team members and could be reviewed between consultations where flagged by the system. However, results suggest it is also likely that RMT data would be reviewed in preparation for and during a consultation with a patient. Time to review information is likely to be one of the greatest barriers to successful implementation of RMT in clinical practice. Conclusions While further work would be required to quantify the benefits of RMT in clinical practice, the findings from this survey suggest that a wide array of clinical team members treating epilepsy, MS and depression would find benefit from RMT data in the care of their patients. Findings presented could inform the implementation of RMT and other digital interventions in the clinical management of a range of neurological and mental health conditions.

BackgroundShopping data can be analyzed using machine learning techniques to study population health. It is unknown if the use of such methods can successfully investigate prediagnosis purchases linked to self-medication of symptoms of ovarian cancer. ObjectiveThe aims of this study were to gain new domain knowledge from women’s experiences, understand how women’s shopping behavior relates to their pathway to the diagnosis of ovarian cancer, and inform research on computational analysis of shopping data for population health. MethodsA web-based survey on individuals’ shopping patterns prior to an ovarian cancer diagnosis was analyzed to identify key knowledge about health care purchases. Logistic regression and random forest models were employed to statistically examine how products linked to potential symptoms related to presentation to health care and timing of diagnosis. ResultsOf the 101 women surveyed with ovarian cancer, 58.4% (59/101) bought nonprescription health care products for up to more than a year prior to diagnosis, including pain relief and abdominal products. General practitioner advice was the primary reason for the purchases (23/59, 39%), with 51% (30/59) occurring due to a participant’s doctor believing their health problems were due to a condition other than ovarian cancer. Associations were shown between purchases made because a participant’s doctor believing their health problems were due to a condition other than ovarian cancer and the following variables: health problems for longer than a year prior to diagnosis (odds ratio [OR] 7.33, 95% CI 1.58-33.97), buying health care products for more than 6 months to a year (OR 3.82, 95% CI 1.04-13.98) or for more than a year (OR 7.64, 95% CI 1.38-42.33), and the number of health care product types purchased (OR 1.54, 95% CI 1.13-2.11). Purchasing patterns are shown to be potentially predictive of a participant’s doctor thinking their health problems were due to some condition other than ovarian cancer, with nested cross-validation of random forest classification models achieving an overall in-sample accuracy score of 89.1% and an out-of-sample score of 70.1%. ConclusionsWomen in the survey were 7 times more likely to have had a duration of more than a year of health problems prior to a diagnosis of ovarian cancer if they were self-medicating based on advice from a doctor rather than having made the decision to self-medicate independently. Predictive modelling indicates that women in such situations, who are self-medicating because their doctor believes their health problems may be due to a condition other than ovarian cancer, exhibit distinct shopping behaviors that may be identifiable within purchasing data. Through exploratory research combining women sharing their behaviors prior to diagnosis and computational analysis of these data, this study demonstrates that women’s shopping data could potentially be useful for early ovarian cancer detection.

Abstract Background A variety of smartphone apps and wearables are available both to help patients monitor their health and to support health care professionals (HCPs) in providing clinical care. As part of the RADAR-CNS consortium, we have conducted research into the application of wearables and smartphone apps in the care of people with multiple sclerosis, epilepsy, or depression. Methods We conducted a large online survey study to explore the experiences of HCPs working with patients who have one or more of these conditions. The survey covered smartphone apps and wearables used by clinicians and their patients, and how data from these technologies impacted on the respondents' clinical practice. The survey was conducted between February 2019 and March 2020 via a web-based platform. Detailed statistical analysis was performed on the answers. Results Of 1009 survey responses from HCPs, 1006 were included in the analysis after data cleaning. Smartphone apps are used by more than half of responding HCPs and more than three quarters report that their patients use smartphone apps or wearable devices for health-related purposes. HCPs widely believe the data that patients collect using these devices impacts their clinical practice. Subgroup analyses show that views on the impact of this data on different aspects of clinical work varies according to whether respondents use apps themselves, and, to a lesser extent, according to their clinical setting and job role. Conclusions Use of smartphone apps is widespread among HCPs participating in this large European survey and caring for people with epilepsy, multiple sclerosis and depression. The majority of respondents indicate that they treat patients who use wearables and other devices for health-related purposes and that data from these devices has an impact on clinical practice.

Aberrant expression of viral-like repeat elements is a common feature of epithelial cancers, and the substantial diversity of repeat species provides a distinct view of the cancer transcriptome. Repeatome profiling across ovarian, pancreatic, and colorectal cell lines identifies distinct clustering independent of tissue origin that is seen with coding gene analysis. Deeper analysis of ovarian cancer cell lines demonstrated that human satellite II (HSATII) satellite repeat expression was highly associated with epithelial-mesenchymal transition (EMT) and anticorrelated with IFN-response genes indicative of a more aggressive phenotype. SATII expression — and its correlation with EMT and anticorrelation with IFN-response genes — was also found in ovarian cancer RNA-Seq data and was associated with significantly shorter survival in a second independent cohort of patients with ovarian cancer. Repeat RNAs were enriched in tumor-derived extracellular vesicles capable of stimulating monocyte-derived macrophages, demonstrating a mechanism that alters the tumor microenvironment with these viral-like sequences. Targeting of HSATII with antisense locked nucleic acids stimulated IFN response and induced MHC I expression in ovarian cancer cell lines, highlighting a potential strategy of modulating the repeatome to reestablish antitumor cell immune surveillance.

**Objectives:** Elevated serum troponin values, regardless of cause, are associated with increased short-term and long-term mortality. The objective of this study was to describe and quantify use of troponin-related terms in hospital discharge summaries. **Study Design:** Retrospective chart review. **Methods:** We included patients admitted to the medical service of three Rhode Island hospitals in 2018. Patients had to have two or more troponin measurements, including at least one elevated value, as well as a rise-and-fall pattern. We reviewed text from three sections in the discharge summary—the problem list, hospital course, and attending attestation—to identify terms referring to troponin. We double-coded 10% of the sample to assess inter-rater reliability. Troponin terms were grouped by whether they referenced an abnormal level, as well as by the etiology of the elevation. Descriptive statistics were used. **Results:** 257 patients were included; 66% of their discharge summaries had a troponin descriptor. We identified 19 unique troponin terms: three of these terms implied a normal troponin level (e.g., “flat troponin”), seven described an abnormal elevation without specifically referencing myocardial injury as the etiology (e.g., “troponin bump”), and the rest referenced ischemia or infarction (e.g., “NSTEMI”). Over one-third (34%) of the records had no troponin terms in the discharge summary, despite those patients having at least one abnormal troponin level during their hospitalization. **Conclusions:** We found that clinicians used a wide variety of terms to describe troponin elevations in their discharge summaries, including minimizing language and potentially conflicting terms; a third of discharge summaries omitted any mention of the abnormal troponin value. More precise language may facilitate appropriate evaluation and management of these higher risk patients after hospital discharge.

Solar-induced fluorescence (SIF) data from satellites are increasingly used as a proxy for photosynthetic activity by vegetation and as a constraint on gross primary production. Here we report on improvements in the algorithm to retrieve mid-morning (09:30 LT) SIF estimates on the global scale from the GOME-2 sensor on the MetOp-A satellite (GOME-2A) for the period 2007–2019. Our new SIFTER (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval) v2 algorithm improves over a previous version by using a narrower spectral window that avoids strong oxygen absorption and being less sensitive to water vapour absorption, by constructing stable reference spectra from a 6-year period (2007–2012) of atmospheric spectra over the Sahara and by applying a latitude-dependent zero-level adjustment that accounts for biases in the data product. We generated stable, good-quality SIF retrievals between January 2007 and June 2013, when GOME-2A degradation in the near infrared was still limited. After the narrowing of the GOME-2A swath in July 2013, we characterised the throughput degradation of the level-1 data in order to derive reflectance corrections and apply these for the SIF retrievals between July 2013 and December 2018. SIFTER v2 data compare well with the independent NASA v2.8 data product. Especially in the evergreen tropics, SIFTER v2 no longer shows the underestimates against other satellite products that were seen in SIFTER v1. The new data product includes uncertainty estimates for individual observations and is best used for mostly clear-sky scenes and when spectral residuals remain below a certain spectral autocorrelation threshold. Our results support the use of SIFTER v2 data being used as an independent constraint on photosynthetic activity on regional to global scales.

Driver monitoring systems play an important role in lower to mid-level autonomous vehicles. Our work focuses on the detection of cognitive load as a component of driver-state estimation to improve traffic safety. By inducing single and dual-task workloads of increasing intensity on 51 subjects, while continuously measuring signals from multiple modalities, based on physiological measurements such as ECG, EDA, EMG, PPG, respiration rate, skin temperature and eye tracker data, as well as behavioral measurements such as action units extracted from facial videos, performance metrics like reaction time and subjective feedback using questionnaires, we create ADABase (Autonomous Driving Cognitive Load Assessment Database) As a reference method to induce cognitive load onto subjects, we use the well-established n-back test, in addition to our novel simulator-based k-drive test, motivated by real-world semi-autonomously vehicles. We extract expert features of all measurements and find significant changes in multiple modalities. Ultimately we train and evaluate machine learning algorithms using single and multimodal inputs to distinguish cognitive load levels. We carefully evaluate model behavior and study feature importance. In summary, we introduce a novel cognitive load test, create a cognitive load database, validate changes using statistical tests, introduce novel classification and regression tasks for machine learning and train and evaluate machine learning models.

Abstract Herbivory and disturbance are major drivers of biological invasions, but it is unclear how they interact to determine exotic vs. native seedling recruitment and what consequences arise for biodiversity and ecosystem functioning. Previous studies neglected the roles of different, potentially interacting, guilds of generalist herbivores such as rodents and gastropods. We therefore set up a full-factorial rodent exclusion x gastropod exclusion x disturbance x seed-addition experiment in a grassland community in Central Germany and measured early seedling recruitment, as well as species richness, species composition and aboveground biomass. Gastropod herbivory reduced the positive effect of disturbance on seedling recruitment, particularly for exotic species. Rodent herbivory had weak positive effects on seedling recruitment at undisturbed sites, irrespective of species origin. This effect was likely driven by their strong negative effect on productivity. Interactive effects between both herbivore guilds became only evident for species richness and composition. How many species established themselves depended on disturbance, but was independent of species origin. The fewer exotic species that established themselves increased productivity to a stronger extent compared to native species. Our study highlights that joint effects of disturbance, herbivory and species origin shape early recruitment, while they only weakly affect biodiversity and ecosystem functioning.

Abstract Background The changing healthcare landscape requires physicians to develop new knowledge and skills such as high-value care, systems improvement, population health, and team-based care, which together may be referred to as the Science of Health Care Delivery (SHCD). To engender public trust and confidence, educators must be able to meaningfully assess physicians’ abilities in SHCD. We aimed to develop a novel set of SHCD milestones based on published Accreditation Council for Graduate Medical Education (ACGME) milestones that can be used by medical schools to assess medical students’ competence in SHCD. Methods We reviewed all ACGME milestones for 25 specialties available in September 2013. We used an iterative, qualitative process to group the ACGME milestones into SHCD content domains, from which SHCD milestones were derived. The SHCD milestones were categorized within the current ACGME core competencies and were also mapped to Association of American Medical Colleges’ Entrustable Professional Activities (AAMC EPAs). Results Fifteen SHCD sub-competencies and corresponding milestones are provided, grouped within ACGME core competencies and mapped to multiple AAMC EPAs. Conclusions This novel set of milestones, grounded within the existing ACGME competencies, defines fundamental expectations within SHCD that can be used and adapted by medical schools in the assessment of medical students in this emerging curricular area. These milestones provide a blueprint for SHCD content and assessment as ongoing revisions to milestones and curricula occur.

Background: Environmental enteropathy (EE), the adverse impact of frequent and numerous enteric infections on the gut resulting in a state of persistent immune activation and altered permeability, has been proposed as a key determinant of growth failure in children in low- and middle-income populations. A theory-driven systems model to critically evaluate pathways through which enteropathogens, gut permeability, and intestinal and systemic inflammation affect child growth was conducted within the framework of the Etiology, Risk Factors and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) birth cohort study that included children from eight countries. Methods: Non-diarrheal stool samples (N = 22,846) from 1253 children from multiple sites were evaluated for a panel of 40 enteropathogens and fecal concentrations of myeloperoxidase, alpha-1-antitrypsin, and neopterin. Among these same children, urinary lactulose:mannitol (L:M) (N = 6363) and plasma alpha-1-acid glycoprotein (AGP) (N = 2797) were also measured. The temporal sampling design was used to create a directed acyclic graph of proposed mechanistic pathways between enteropathogen detection in non-diarrheal stools, biomarkers of intestinal permeability and inflammation, systemic inflammation and change in length- and weight- for age in children 0–2 years of age. Findings: Children in these populations had frequent enteric infections and high levels of both intestinal and systemic inflammation. Higher burdens of enteropathogens, especially those categorized as being enteroinvasive or causing mucosal disruption, were associated with elevated biomarker concentrations of gut and systemic inflammation and, via these associations, indirectly associated with both reduced linear and ponderal growth. Evidence for the association with reduced linear growth was stronger for systemic inflammation than for gut inflammation; the opposite was true of reduced ponderal growth. Although Giardia was associated with reduced growth, the association was not mediated by any of the biomarkers evaluated. Interpretation: The large quantity of empirical evidence contributing to this analysis supports the conceptual model of EE. The effects of EE on growth faltering in young children were small, but multiple mechanistic pathways underlying the attribution of growth failure to asymptomatic enteric infections had statistical support in the analysis. The strongest evidence for EE was the association between enteropathogens and linear growth mediated through systemic inflammation. Funding: Bill & Melinda Gates Foundation.

Knowledge of the field of view (FOV) of a remote sensing instrument is particularly important when interpreting their data and merging them with other spatially referenced data. Especially for instruments in space, information on the actual FOV, which may change during operation, may be difficult to obtain. Also, the FOV of ground-based devices may change during transportation to the field site, where appropriate equipment for the FOV determination may be unavailable. This paper presents an independent, simple and robust method to retrieve the FOV of an instrument during operation, i.e. the two-dimensional sensitivity distribution, sampled on a discrete grid. The method relies on correlated measurements featuring a significantly higher spatial resolution, e.g. by an imaging instrument accompanying a spectrometer. The method was applied to two satellite instruments, GOME-2 and OMI, and a ground-based differential optical absorption spectroscopy (DOAS) instrument integrated in an SO2 camera. For GOME-2, quadrangular FOVs could be retrieved, which almost perfectly match the provided FOV edges after applying a correction for spatial aliasing inherent to GOME-type instruments. More complex sensitivity distributions were found at certain scanner angles, which are probably caused by degradation of the moving parts within the instrument. For OMI, which does not feature any moving parts, retrieved sensitivity distributions were much smoother compared to GOME-2. A 2-D super-Gaussian with six parameters was found to be an appropriate model to describe the retrieved OMI FOV. The comparison with operationally provided FOV dimensions revealed small differences, which could be mostly explained by the limitations of our IFR implementation. For the ground-based DOAS instrument, the FOV retrieved using SO2-camera data was slightly smaller than the flat-disc distribution, which is assumed by the state-of-the-art correlation technique. Differences between both methods may be attributed to spatial inhomogeneities. In general, our results confirm the already deduced FOV distributions of OMI, GOME-2, and the ground-based DOAS. It is certainly applicable for degradation monitoring and verification exercises. For satellite instruments, the gained information is expected to increase the accuracy of combined products, where measurements of different instruments are integrated, e.g. mapping of high-resolution cloud information, incorporation of surface climatologies. For the SO2-camera community, the method presents a new and efficient tool to monitor the DOAS FOV in the field.

The combination of time-resolved (TR) and power-dependent relative (PDR) photoluminescence (PL) measurements reveals the possibility of separating the radiative and non-radiative minority carrier lifetimes and measuring the sample-dependent effective radiative recombination coefficient in direct bandgap semiconductors. To demonstrate the method, measurements on 2 μm thick p-type GaAs double-hetero structures were conducted for various doping concentrations in the range of 5x1016 and 1x1018 cm-3. With a photon recycling factor of 0.76 ± 0.04 the radiative recombination coefficient was determined to be (3.3±0.6)×10-10 cm3s-1 for the structures with a doping concentration below 1*1018 cm-3, whereas the effective radiative recombination parameter for an absorber thickness of 2 μm was directly measured to be (0.78±0.07) ×10-10 cm3s-1. For a doping concentration of 1×1018 cm-3, the radiative recombination coefficient decreases significantly probably due to the degeneracy of the semiconductor.