Recipe for Banda Sea water

Water from the western Pacific flows through the Indonesian Seas following different pathways and is modified by various processes to form the uniquely characterized isohaline Banda Sea Water. The processes contributing to the isohaline structure are studied using data from three ARLINDO cruises in 1993, 1994, and 1996. An inverse-model analysis using salinity and CFC-11 data is applied to a vertical section along the main path of flow, from the Makassar Strait to the Flores Sea and Banda Sea. The model reproduces the seasonal and interannual variability of the throughflow and shows reversals of flow in the vertical structure. The model solutions suggest strong baroclinic flows during the southeast monsoon of 1993 and 1996 and a small, more barotropic flow during the northwest monsoon of 1994. The isohaline structure can be accounted for by isopycnal mixing of different source waters and by vertical exchanges, which are significant in this region. A downward flux equivalent to a downwelling velocity of 5 × 10-7 m/s is estimated for the section. The total balance also suggests that seasonally and possibly interannually variable backflushing of water from the Banda Sea into the Straits contribute to the isohaline structure of Banda Sea water

Developing Prognostic Systems of Cancer Patients by Ensemble Clustering

Accurate prediction of survival rates of cancer patients is often key to stratify patients for prognosis and treatment. Survival prediction is often accomplished by the TNM system that involves only three factors: tumor extent, lymph node involvement, and metastasis. This prediction from the TNM has been limited, because other potential prognostic factors are not used in the system. Based on availability of large cancer datasets, it is possible to establish powerful prediction systems by using machine learning procedures and statistical methods. In this paper, we present an ensemble clustering-based approach to develop prognostic systems of cancer patients. Our method starts with grouping combinations that are formed using levels of factors recorded in the data. The dissimilarity measure between combinations is obtained through a sequence of data partitions produced by multiple use of PAM algorithm. This dissimilarity measure is then used with a hierarchical clustering method in order to find clusters of combinations. Prediction of survival is made simply by using the survival function derived from each cluster. Our approach admits multiple factors and provides a practical and useful tool in outcome prediction of cancer patients. A demonstration of use of the proposed method is given for lung cancer patients

Mucosal Melanomas of the Head and Neck: The Role of Postoperative Radiation Therapy

Objectives. Mucosal melanomas are rarer than their cutaneous counterparts and are associated with a poorer prognosis. We report the clinical outcomes of patients with mucosal melanomas of the head and neck region generally treated with definitive surgery followed by postoperative radiation therapy (RT). Methods. We reviewed the records of 17 patients treated at the University of Miami in 1990–2007. Patients generally received conventionally fractionated RT regimens to the postoperative bed. Elective nodal RT was not routinely delivered. Eight patients received adjuvant chemotherapy or immunotherapy. Results. Median followup was 35.2 months (range 5–225). As the first site of failure: 3 patients recurred locally, 2 regionally and 2 distantly. All 3 patients who recurred locally had not received RT. Of the 5 locoregional recurrences, 4 were salvaged successfully with multimodality therapy with no evidence of disease at last followup. Overall survival was 64.7% at 2 years and 51.5% at 5 years. Conclusions. Patients with mucosal melanoma of the head and neck are best treated with surgery to achieve negative margins, followed by postoperative RT to optimize local control. Elective nodal irradiation may not be indicated in all cases, as regional failures were not predominant. Distant metastases were fewer when compared to historical data, potentially due to advancements in adjuvant therapies as well as aggressive multi-modality salvage at time of failure

α8β1 integrin regulates nutrient absorption through an Mfge8-PTEN dependent mechanism.

Coordinated gastrointestinal smooth muscle contraction is critical for proper nutrient absorption and is altered in a number of medical disorders. In this work, we demonstrate a critical role for the RGD-binding integrin α8β1 in promoting nutrient absorption through regulation of gastrointestinal motility. Smooth muscle-specific deletion and antibody blockade of α8 in mice result in enhanced gastric antral smooth muscle contraction, more rapid gastric emptying, and more rapid transit of food through the small intestine leading to malabsorption of dietary fats and carbohydrates as well as protection from weight gain in a diet-induced model of obesity. Mechanistically, ligation of α8β1 by the milk protein Mfge8 reduces antral smooth muscle contractile force by preventing RhoA activation through a PTEN-dependent mechanism. Collectively, our results identify a role for α8β1 in regulating gastrointestinal motility and identify α8 as a potential target for disorders characterized by hypo- or hyper-motility

On an ensemble algorithm for clustering cancer patient data

Background The TNM staging system is based on three anatomic prognostic factors: Tumor, Lymph Node and Metastasis. However, cancer is no longer considered an anatomic disease. Therefore, the TNM should be expanded to accommodate new prognostic factors in order to increase the accuracy of estimating cancer patient outcome. The ensemble algorithm for clustering cancer data (EACCD) by Chen et al. reflects an effort to expand the TNM without changing its basic definitions. Though results on using EACCD have been reported, there has been no study on the analysis of the algorithm. In this report, we examine various aspects of EACCD using a large breast cancer patient dataset. We compared the output of EACCD with the corresponding survival curves, investigated the effect of different settings in EACCD, and compared EACCD with alternative clustering approaches. Results Using the basic T and N definitions, EACCD generated a dendrogram that shows a graphic relationship among the survival curves of the breast cancer patients. The dendrograms from EACCD are robust for large values of m (the number of runs in the learning step). When m is large, the dendrograms depend on the linkage functions. The statistical tests, however, employed in the learning step have minimal effect on the dendrogram for large m. In addition, if omitting the step for learning dissimilarity in EACCD, the resulting approaches can have a degraded performance. Furthermore, clustering only based on prognostic factors could generate misleading dendrograms, and direct use of partitioning techniques could lead to misleading assignments to clusters. Conclusions When only the Partitioning Around Medoids (PAM) algorithm is involved in the step of learning dissimilarity, large values of m are required to obtain robust dendrograms, and for a large mEACCD can effectively cluster cancer patient data