Can Self-Organizing Maps accurately predict photometric redshifts?

We present an unsupervised machine learning approach that can be employed for estimating photometric redshifts. The proposed method is based on a vector quantization approach called Self--Organizing Mapping (SOM). A variety of photometrically derived input values were utilized from the Sloan Digital Sky Survey's Main Galaxy Sample, Luminous Red Galaxy, and Quasar samples along with the PHAT0 data set from the PHoto-z Accuracy Testing project. Regression results obtained with this new approach were evaluated in terms of root mean square error (RMSE) to estimate the accuracy of the photometric redshift estimates. The results demonstrate competitive RMSE and outlier percentages when compared with several other popular approaches such as Artificial Neural Networks and Gaussian Process Regression. SOM RMSE--results (using $\Delta$z=z$_{phot}$--z$_{spec}$) for the Main Galaxy Sample are 0.023, for the Luminous Red Galaxy sample 0.027, Quasars are 0.418, and PHAT0 synthetic data are 0.022. The results demonstrate that there are non--unique solutions for estimating SOM RMSEs. Further research is needed in order to find more robust estimation techniques using SOMs, but the results herein are a positive indication of their capabilities when compared with other well-known methods.Comment: 5 pages, 3 figures, submitted to PAS

Release and Establishment of Megamelus scutellaris (Hemiptera: Delphacidae) on Waterhyacinth in Florida

More than 73,000 Megamelus scutellaris (Hemiptera: Delphacidae) were released in Florida over a 2 to 3 yr period at 10 sites in an attempt to establish sustainable populations on waterhyacinth, Eichhornia crassipes Mart. Solms (Commelinales: Pontederiaceae). Insect populations persisted at most sites including those furthest north and consecutive overwintering was confirmed in as many as three times at some sites. Establishment appeared to be promoted at sites with some cover or shading compared to open areas. Insects readily dispersed over short distances which made detection and monitoring difficultFil: Tipping, Philip W.. Invasive Plant Research Laboratory; Estados UnidosFil: Sosa, Alejandro Joaquín. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para el Estudio de Especies Invasivas; ArgentinaFil: Pokorny, Eileen N.. Invasive Plant Research Laboratory; Estados UnidosFil: Foley, Jeremiah. Invasive Plant Research Laboratory; Estados UnidosFil: Schmitz, Don C.. Florida Fish and Wildlife Conservation Commission; Estados UnidosFil: Lane, Jon S.. U.S. Army Corps of Engineers; Estados UnidosFil: Rodgers, Leroy. South Florida Water Management District; Estados UnidosFil: Mccloud, Lori. St. Johns River Water Management District; Estados UnidosFil: Livingston-Way, Pam. St. Johns River Water Management District; Estados UnidosFil: Cole, Matthew S.. St. Johns River Water Management District; Estados UnidosFil: Nichols, Gary. St. Johns River Water Management District; Estados Unido

Development and preliminary testing of the psychosocial adjustment to hereditary diseases scale

Background: The presence of Lynch syndrome (LS) can bring a lifetime of uncertainty to an entire family as members adjust to living with a high lifetime cancer risk. The research base on how individuals and families adjust to genetic-linked diseases following predictive genetic testing has increased our understanding of short-term impacts but gaps continue to exist in knowledge of important factors that facilitate or impede long-term adjustment. The failure of existing scales to detect psychosocial adjustment challenges in this population has led researchers to question the adequate sensitivity of these instruments. Furthermore, we have limited insight into the role of the family in promoting adjustment. Methods: The purpose of this study was to develop and initially validate the Psychosocial Adjustment to Hereditary Diseases (PAHD) scale. This scale consists of two subscales, the Burden of Knowing (BK) and Family Connectedness (FC). Items for the two subscales were generated from a qualitative data base and tested in a sample of 243 participants from families with LS. Results: The Multitrait/Multi-Item Analysis Program-Revised (MAP-R) was used to evaluate the psychometric properties of the PAHD. The findings support the convergent and discriminant validity of the subscales. Construct validity was confirmed by factor analysis and Cronbach’s alpha supported a strong internal consistency for BK (0.83) and FC (0.84). Conclusion: Preliminary testing suggests that the PAHD is a psychometrically sound scale capable of assessing psychosocial adjustment. We conclude that the PAHD may be a valuable monitoring tool to identify individuals and families who may require therapeutic interventions

A mechanistic target of rapamycin complex 1/2 (mTORC1)/V-Akt murine thymoma viral oncogene homolog 1 (AKT1)/cathepsin H axis controls filaggrin expression and processing in skin, a novel mechanism for skin barrier disruption in patients with atopic dermatitis

BACKGROUND: Filaggrin, encoded by the FLG gene, is an important component of the skin’s barrier to the external environment and genetic defects in FLG strongly associate with Atopic Dermatitis (AD). However, not all AD patients have FLG mutations. OBJECTIVE: We hypothesised that these patients may possess other defects in filaggrin expression and processing, contributing to barrier disruption and AD, and therefore present novel therapeutic targets for this disease. RESULTS: We describe the relationship between the mTORC1 protein subunit RAPTOR, the serine/threonine kinase AKT1 and the protease cathepsin H, for which we establish a role in filaggrin expression and processing. Increased RAPTOR levels correlated with decreased filaggrin expression in AD. In keratinocyte cell culture, RAPTOR up-regulation or AKT1 shRNA knockdown reduced the expression of the protease cathepsin H. Skin of cathepsin H-deficient mice and CTSH shRNA knockdown keratinocytes showed reduced filaggrin processing and the mouse showed both impaired skin barrier function and a mild proinflammatory phenotype. CONCLUSION: Our findings highlight a novel, potentially treatable, signalling axis controlling filaggrin expression and processing which is defective in AD

Noninvasive Instrument-based Tests for Detecting and Measuring Vitreous Inflammation in Uveitis: A Systematic Review

PURPOSE: This systematic review aims to identify instrument-based tests for quantifying vitreous inflammation in uveitis, report the test reliability and the level of correlation with clinician grading. METHODS: Studies describing instrument-based tests for detecting vitreous inflammation were identified by searching bibliographic databases and trials registers. Test reliability measures and level of correlation with clinician vitreous haze grading are extracted. RESULTS: Twelve studies describing ultrasound, optical coherence tomography (OCT), and retinal photography for detecting vitreous inflammation were included: Ultrasound was used for detection of disease features, whereas OCT and retinal photography provided quantifiable measurements. Correlation with clinician grading for OCT was 0.53-0.60 (three studies) and for retinal photography was 0.51 (1 study). Both instruments showed high inter- and intra-observer reliability (>0.70 intraclass correlation and Cohen's kappa), where reported in four studies. CONCLUSION: Retinal photography and OCT are able to detect and measure vitreous inflammation. Both techniques are reliable, automatable, and warrant further evaluation

Two novel approaches for photometric redshift estimation based on SDSS and 2MASS databases

We investigate two training-set methods: support vector machines (SVMs) and Kernel Regression (KR) for photometric redshift estimation with the data from the Sloan Digital Sky Survey Data Release 5 and Two Micron All Sky Survey databases. We probe the performances of SVMs and KR for different input patterns. Our experiments show that the more parameters considered, the accuracy doesn't always increase, and only when appropriate parameters chosen, the accuracy can improve. Moreover for different approaches, the best input pattern is different. With different parameters as input, the optimal bandwidth is dissimilar for KR. The rms errors of photometric redshifts based on SVM and KR methods are less than 0.03 and 0.02, respectively. Finally the strengths and weaknesses of the two approaches are summarized. Compared to other methods of estimating photometric redshifts, they show their superiorities, especially KR, in terms of accuracy.Comment: accepted for publication in ChJA