An assessment of archive stereo-aerial photographs for 3-dimensional reconstruction of damaged and destroyed archaeological earthworks

Archaeological earthworks are being damaged and destroyed at a rate and scale never before seen, which has resulted from increased mechanisation of human activity in the landscape since World War II. Along with natural degradation processes, recording earthwork metrics prior to their loss is increasingly difficult, which can subsequently hinder the interpretation of a site or landscape because of this missing evidence. A tool for regaining such data is vital to alleviate this problem and to fulfil the stipulation for metric information as required by national and international conservation charters. This research investigates whether it is possible to regain earthwork metrics from archive stereo-aerial photographs (SAPs) using digital photogrammetry to create digital surface models (DSMs) of archaeological sites within the UK dating from the 1940s to 2010. A literature search confirmed the utility of SAPs for reconstructing geomorphological events, such as landslides, whilst also verifying that such an approach had not been thoroughly investigated for archaeological adaptation. Via experimentation, a photogrammetric workflow has been designed and a number of variables identified that affect the quality of DSMs obtained from SAPs. The magnitude of these variables has been verified by quantitative assessment using independent survey data, namely Airborne Laser Scanning (ALS) gathered by the Environment Agency, and ground-based collection using Global Navigation Satellite Systems (GNSS) and Terrestrial Laser Scanning (TLS). Empirical differences between these independent data and the SAP DSMs were identified using global statistical measures such as Mean Error (ME), Standard Deviation (SD) and root mean square error (RMSE), and spatial autocorrelation techniques, namely Local Moran’s I. Two study sites were selected on which to ascertain whether variations occur in the empirical quality of SAP DSMs and archaeological content at different locations. Over six decades of photography were collected for Flowers Barrow Hillfort, situated near Lulworth in Dorset, UK, which has remained in good condition throughout this period, due to the protection afforded it by inclusion within Ministry of Defence land. Eggardon Hillfort and earthworks, near Bridport in Dorset, UK, were also selected due to the exceptional preservation state of some earthworks, versus the plough-damaged remains of others. These sites thus offered an opportunity to rigorously test the reconstruction capabilities of the SAPs. The results from both study sites confirmed that the metric quality of SAP DSMs improves as the age of the imagery decreases, although this is dependent on image quality, scanner properties (i.e. whether the scanner is photogrammetric or desktop) and the result of the block bundle adjustment in the photogrammetric software. This thesis concludes that SAPs can recreate earthwork metrics and provides a list of considerations for archaeologists to consult when planning the use of SAPs for creating DSMs. Recommendations for future work are provided that encourage the investigation of SAPs from other countries and the rigorous assessment of DSMs derived from structure-from-motion (SfM) software that is rapidly gaining popularity

a report from the Children's Oncology Group and the Utah Population Database

Relatively little is known about the epidemiology and factors underlying susceptibility to childhood rhabdomyosarcoma (RMS). To better characterize genetic susceptibility to childhood RMS, we evaluated the role of family history of cancer using data from the largest case–control study of RMS and the Utah Population Database (UPDB). RMS cases (n = 322) were obtained from the Children's Oncology Group (COG). Population-based controls (n = 322) were pair-matched to cases on race, sex, and age. Conditional logistic regression was used to evaluate the association between family history of cancer and childhood RMS. The results were validated using the UPDB, from which 130 RMS cases were identified and matched to controls (n = 1300) on sex and year of birth. The results were combined to generate summary odds ratios (ORs) and 95% confidence intervals (CI). Having a first-degree relative with a cancer history was more common in RMS cases than controls (ORs = 1.39, 95% CI: 0.97–1.98). Notably, this association was stronger among those with embryonal RMS (ORs = 2.44, 95% CI: 1.54–3.86). Moreover, having a first-degree relative who was younger at diagnosis of cancer (<30 years) was associated with a greater risk of RMS (ORs = 2.37, 95% CI: 1.34–4.18). In the largest analysis of its kind, we found that most children diagnosed with RMS did not have a family history of cancer. However, our results indicate an increased risk of RMS (particularly embryonal RMS) in children who have a first-degree relative with cancer, and among those whose relatives were diagnosed with cancer at <30 years of age

Assessing 3D metric data of digital surface models for extracting archaeological data from archive stereo-aerial photographs.

Archaeological remains are under increasing threat of attrition from natural processes and the continued mechanisation of anthropogenic activities. This research analyses the ability of digital photogrammetry software to reconstruct extant, damaged, and destroyed archaeological earthworks from archive stereo-aerial photographs. Case studies of Flower's Barrow and Eggardon hillforts, both situated in Dorset, UK, are examined using a range of imagery dating from the 1940s to 2010. Specialist photogrammetric software SocetGXP® is used to extract digital surface models, and the results compared with airborne and terrestrial laser scanning data to assess their accuracy. Global summary statistics and spatial autocorrelation techniques are used to examine error scales and distributions. Extracted earthwork profiles are compared to both current and historical surveys of each study site. The results demonstrate that metric information relating to earthwork form can be successfully obtained from archival photography. In some instances, these data out-perform airborne laser scanning in the provision of digital surface models with minimal error. The role of archival photography in regaining metric data from upstanding archaeology and the consequent place for this approach to impact heritage management strategies is demonstrated

MODEL PENGELOLAAN PASCA TANGKAP SEBAGAI UPAYA PENGENTASAN KEMISKINAN MASYARAKAT KAMPUNG NELAYAN DI PULAU ENGGANO

Relatively little is known about the epidemiology and factors underlying susceptibility to childhood rhabdomyosarcoma (RMS). To better characterize genetic susceptibility to childhood RMS, we evaluated the role of family history of cancer using data from the largest case-control study of RMS and the Utah Population Database (UPDB). RMS cases (n=322) were obtained from the Children's Oncology Group (COG). Population-based controls (n=322) were pair-matched to cases on race, sex, and age. Conditional logistic regression was used to evaluate the association between family history of cancer and childhood RMS. The results were validated using the UPDB, from which 130 RMS cases were identified and matched to controls (n=1300) on sex and year of birth. The results were combined to generate summary odds ratios (ORs) and 95% confidence intervals (CI). Having a first-degree relative with a cancer history was more common in RMS cases than controls (ORs=1.39, 95% CI: 0.97-1.98). Notably, this association was stronger among those with embryonal RMS (ORs=2.44, 95% CI: 1.54-3.86). Moreover, having a first-degree relative who was younger at diagnosis of cancer (&lt;30years) was associated with a greater risk of RMS (ORs=2.37, 95% CI: 1.34-4.18). In the largest analysis of its kind, we found that most children diagnosed with RMS did not have a family history of cancer. However, our results indicate an increased risk of RMS (particularly embryonal RMS) in children who have a first-degree relative with cancer, and among those whose relatives were diagnosed with cancer at &lt;30years of age

Family history of cancer and childhood rhabdomyosarcoma : a report from the Children's Oncology Group and the Utah Population Database

Relatively little is known about the epidemiology and factors underlying susceptibility to childhood rhabdomyosarcoma (RMS). To better characterize genetic susceptibility to childhood RMS, we evaluated the role of family history of cancer using data from the largest case-control study of RMS and the Utah Population Database (UPDB). RMS cases (n=322) were obtained from the Children's Oncology Group (COG). Population-based controls (n=322) were pair-matched to cases on race, sex, and age. Conditional logistic regression was used to evaluate the association between family history of cancer and childhood RMS. The results were validated using the UPDB, from which 130 RMS cases were identified and matched to controls (n=1300) on sex and year of birth. The results were combined to generate summary odds ratios (ORs) and 95% confidence intervals (CI). Having a first-degree relative with a cancer history was more common in RMS cases than controls (ORs=1.39, 95% CI: 0.97-1.98). Notably, this association was stronger among those with embryonal RMS (ORs=2.44, 95% CI: 1.54-3.86). Moreover, having a first-degree relative who was younger at diagnosis of cancer (&lt;30years) was associated with a greater risk of RMS (ORs=2.37, 95% CI: 1.34-4.18). In the largest analysis of its kind, we found that most children diagnosed with RMS did not have a family history of cancer. However, our results indicate an increased risk of RMS (particularly embryonal RMS) in children who have a first-degree relative with cancer, and among those whose relatives were diagnosed with cancer at &lt;30years of age