Evaluation of regional and distant metastases in head and neck squamous cell carcinoma patients with special reference to the assessment of regional lymph nodes in oral cancer

For optimal treatment planning, a thorough assessment of the metastatic status of mucosal squamous cell carcinoma of the head and neck (HNSCC) is required. Current imaging methods do not allow the recognition of all patients with metastatic disease. Therefore, elective treatment of the cervical lymph nodes is usually given to patients in whom the risk of subclinical metastasis is estimated to exceed 15-20%. The objective of this study was to improve the pre-treatment evaluation of patients diagnosed with HNSCC. Particularly, we aimed at improving the identification of patients who will benefit from elective neck treatment. Computed tomography (CT) of the chest and abdomen was performed prospectively for 100 patients diagnosed with HNSCC. The findings were analysed to clarify the indications for this examination in this patient group. CT of the chest influenced the treatment approach in 3% of patients, while CT of the abdomen did not reveal any significant findings. Our results suggest that CT of the chest and abdomen is not indicated routinely for patients with newly diagnosed HNSCC but can be considered in selected cases. Retrospective analysis of 80 patients treated for early stage squamous cell carcinoma of the oral tongue was performed to investigate the potential benefits of elective neck treatment and to examine whether histopathological features of the primary tumour could be used in the prediction of occult metastases, local recurrence, or/and poor survival. Patients who had received elective neck treatment had significantly fewer cervical recurrences during the follow-up when compared to those who only had close observation of the cervical lymph nodes. Elective neck treatment did not result in survival benefit, however. Of the histopathological parameters examined, depth of infiltration and pT-category (representing tumour diameter) predicted occult cervical metastasis, but only the pT-category predicted local recurrence. Depth of infiltration can be used in the identification of at risk patients but no clear cut-off value separating high-risk and low-risk patients was found. None of the histopathological parameters examined predicted survival. Sentinel lymph node (SLN) biopsy was studied as a means of diagnosing patients with subclinical cervical metastases. SLN biopsy was applied to 46 patients who underwent elective neck dissection for oral squamous cell carcinoma. In addition, SLN biopsy was applied to 13 patients with small oral cavity tumours who were not intended to undergo elective neck dissection because of low risk of occult metastasis. The sensitivity of SLN biopsy for finding subclinical cervical metastases was found to be 67%, when SLN status was compared to the metastatic status of the rest of the neck dissection specimen. Of the patients not planned to have elective neck dissection, SLN biopsy revealed cervical metastasis in 15% of the patients. Our results suggest that SLN biopsy can not yet entirely replace elective neck dissection in the treatment of oral cancer, but it seems beneficial for patients with low risk of metastasis who are not intended for elective neck treatment according to current treatment protocols.

Social Living Labs for Digital Participation: Designing with Regional and Rural Communities [workshop proceedings]

“Fostering digital participation through Living Labs in regional and rural Australian communities,” is a three year research project funded by the Australian Research Council. The project aims to identify the specific digital needs and practices of regional and rural residents in the context of the implementation of high speed internet. It seeks to identify new ways for enabling residents to develop their digital confidence and skills both at home and in the community. This two-day symposium will bring together researchers and practitioners from diverse backgrounds to discuss design practices in social living labs that aim to foster digital inclusion and participation. Day one will consist of practitioner and research reports, while day two will provide an opportunity for participants to imagine and design future digital participation strategies. Academic participants will also have an opportunity to contribute to a refereed edited volume by Chandos Publishing (an imprint of Elsevier).

Acidification of lakes in Finland : regional estimates of lake chemistry and critical loads

Yhteenveto: Järvien happamoituminen Suomessa: Alueellinen vedenlaatu ja kriittinen kuormitus

Constraining uncertainty in regional hydrologic impacts of climate change: Nonstationarity in downscaling

Regional impacts of climate change remain subject to large uncertainties accumulating from various sources, including those due to choice of general circulation models (GCMs), scenarios, and downscaling methods. Objective constraints to reduce the uncertainty in regional predictions have proven elusive. In most studies to date the nature of the downscaling relationship (DSR) used for such regional predictions has been assumed to remain unchanged in a future climate. However,studies have shown that climate change may manifest in terms of changes in frequencies of occurrence of the leading modes of variability, and hence, stationarity of DSRs is not really a valid assumption in regional climate impact assessment. This work presents an uncertainty modeling framework where, in addition to GCM and scenario uncertainty, uncertainty in the nature of the DSR is explored by linking downscaling with changes in frequencies of such modes of natural variability. Future projections of the regional hydrologic variable obtained by training a conditional random field (CRF) model on each natural cluster are combined using the weighted Dempster-Shafer (D-S) theory of evidence combination. Each projection is weighted with the future projected frequency of occurrence of that cluster (''cluster linking'') and scaled by the GCM performance with respect to the associated cluster for the present period (''frequency scaling''). The D-S theory was chosen for its ability to express beliefs in some hypotheses, describe uncertainty and ignorance in the system, and give a quantitative measurement of belief and plausibility in results. The methodology is tested for predicting monsoon streamflow of the Mahanadi River at Hirakud Reservoir in Orissa, India. The results show an increasing probability of extreme, severe, and moderate droughts due to limate change. Significantly improved agreement between GCM predictions owing to cluster linking and frequency scaling is seen, suggesting that by linking regional impacts to natural regime frequencies, uncertainty in regional predictions can be realistically quantified. Additionally, by using a measure of GCM performance in simulating natural regimes, this uncertainty can be effectively constrained.

Evaluating the regional and distributional impacts of forestry cost-share payments.

XVIII IUFRO World Congress, Ljubljana 1986.

Contrained linear spectral unmixing technique for regional land cover mapping using MODIS data

Over the last few decades, there has been a significant land cover (LC) change across the globe due to the increasing demand of the burgeoning population and urban sprawl. In order to take account of the change, there is a need for accurate and up- to-date LC maps. Mapping and monitoring of LC in India is being carried out at national level using multi-temporal IRS AWiFS data. Multispectral data such as IKONOS, Landsat- TM/ETM+, IRS-1C/D LISS-III/IV, AWiFS and SPOT-5, etc. have adequate spatial resolution (~ 1m to 56m) for LC mapping to generate 1:50,000 maps. However, for developing countries and those with large geographical extent, seasonal LC mapping is prohibitive with data from commercial sensors of limited spatial coverage. Superspectral data from the MODIS sensor are freely available, have better temporal (8 day composites) and spectral information. MODIS pixels typically contain a mixture of various LC types (due to coarse spatial resolution of 250, 500 and 1000 m), especially in more fragmented landscapes. In this context, linear spectral unmixing would be useful for mapping patchy land covers, such as those that characterise much of the Indian subcontinent. This work evaluates the existing unmixing technique for LC mapping using MODIS data, using end- members that are extracted through Pixel Purity Index (PPI), Scatter plot and N-dimensional visualisation. The abundance maps were generated for agriculture, built up, forest, plantations, waste land/others and water bodies. The assessment of the results using ground truth and a LISS-III classified map shows 86% overall accuracy, suggesting the potential for broad-scale applicability of the technique with superspectral data for natural resource planning and inventory applications.

On Selection of Features for Regional Hydrologic Studies

Feature selection is an important first step in regional hydrologic studies (RHYS). Over the past few decades, advances in data collection facilities have resulted in development of data archives on a variety of hydro-meteorological variables that may be used as features in RHYS. Currently there are no established procedures for selecting features from such archives. Therefore, hydrologists often use subjective methods to arrive at a set of features. This may lead to misleading results. To alleviate this problem, a probabilistic clustering method for regionalization is presented to determine appropriate features from the available dataset. The effectiveness of the method is demonstrated by application to regionalization of watersheds in conterminous United States for low flow frequency analysis. Plausible homogeneous regions that are formed by using the proposed clustering method are compared with those from conventional methods of regionalization using L-moment based homogeneity tests. Results show that the proposed methodology is promising for RHYS.

Geographical Information System Approach for Regional Biogas Potential Assessment

The economic prosperity and quality of life in a region are closely linked to the level of its per capita energy consumption. In India more than 70% of the total population inhabits rural areas and 85-90% of energy requirement is being met by bioresources. With dwindling resources, attention of planners is diverted to viable energy alternatives to meet the rural energy demand. Biogas as fuel is one such alternative, which can be obtained by anaerobic digestion of animal residues and domestic and farm wastes, abundantly available in the countryside. Study presents the techniques to assess biogas potential spatially using GIS in Kolar district, Karnataka State, India. This would help decision makers in selecting villages for implementing biogas programmes based on resource availability. Analyses reveal that the domestic energy requirement of more than 60% population can be met by biogas option. This is based on the estimation of the per capita requirement of gas for domestic purposes and availability of livestock residues.

Regional Flood Frequency Analysis using Two-level Clustering Approach

Clustering techniques are used in regional flood frequency analysis (RFFA) to partition watersheds into natural groups or regions with similar hydrologic responses. The linear Kohonen's self‐organizing feature map (SOFM) has been applied as a clustering technique for RFFA in several recent studies. However, it is seldom possible to interpret clusters from the output of an SOFM, irrespective of its size and dimensionality. In this study, we demonstrate that SOFMs may, however, serve as a useful precursor to clustering algorithms. We present a two‐level. SOFM‐based clustering approach to form regions for FFA. In the first level, the SOFM is used to form a two‐dimensional feature map. In the second level, the output nodes of SOFM are clustered using Fuzzy c‐means algorithm to form regions. The optimal number of regions is based on fuzzy cluster validation measures. Effectiveness of the proposed approach in forming homogeneous regions for FFA is illustrated through application to data from watersheds in Indiana, USA. Results show that the performance of the proposed approach to form regions is better than that based on classical SOFM.