Texture as a Diagnostic Signal in Mammograms

Radiologists can discriminate between normal and abnormal breast tissue at a glance, suggesting that radiologists might be using some “global signal” of abnormality. Our study investigated whether texture descriptions can be used to characterize the global signal of abnormality and whether radiologists use this information during interpretation. Synthetic images were generated using a texture synthesis algorithm trained on texture descriptions extracted from sections of mammograms. Radiologists completed a task that required rating the abnormality of briefly presented tissue sections. When the abnormal tissue had no visible lesion, radiologists seemed to use texture descriptions; performance was similar across real and synthesized tissue sections. However, when the abnormal tissue had a visible lesion, radiologists seemed to rely on additional mechanisms beyond the texture descriptions; performance increased for the real tissue sections. These findings suggest that radiologists can use texture descriptions as global signals of abnormality in interpretation of breast tissue

Detecting the “gist” of breast cancer in mammograms three years before localized signs of cancer are visible

Objectives: After a 500 ms presentation, experts can distinguish abnormal mammograms at above chance levels even when only the breast contralateral to the lesion is shown. Here, we show that this signal of abnormality is detectable 3 years before localized signs of cancer become visible. Methods: In 4 prospective studies, 59 expert observers from 3 groups viewed 116–200 bilateral mammograms for 500 ms each. Half of the images were prior exams acquired 3 years prior to onset of visible, actionable cancer and half were normal. Exp. 1D included cases having visible abnormalities. Observers rated likelihood of abnormality on a 0–100 scale and categorized breast density. Performance was measured using receiver operating characteristic analysis. Results: In all three groups, observers could detect abnormal images at above chance levels 3 years prior to visible signs of breast cancer (p < 0.001). The results were not due to specific salient cases nor to breast density. Performance was correlated with expertise quantified by the number of mammographic cases read within a year. In Exp. 1D, with cases having visible actionable pathology included, the full group of readers failed to reliably detect abnormal priors; with the exception of a subgroup of the six most experienced observers. Conclusions: Imaging specialists can detect signals of abnormality in mammograms acquired years before lesions become visible. Detection may depend on expertise acquired by reading large numbers of cases. Advances in knowledge: Global gist signal can serve as imaging risk factor with the potential to identify patients with elevated risk for developing cancer, resulting in improved early cancer diagnosis rates and improved prognosis for females with breast cancer

Combining genotype, phenotype and environment to infer potential candidate genes: An example using the Loblolly pine (Pinus taeda)

Population genomic analyses can be an important tool in understanding local adaptation. Identification of potential adaptive loci in such analyses is usually based on the survey of a large genomic dataset in combination with environmental variables. Phenotypic data are less commonly incorporated into such studies, although combining a genome scan analysis with a phenotypic trait analysis can greatly improve the insights obtained from each analysis individually. Here, we aimed to identify loci potentially involved in adaptation to climate in 283 Loblolly pine (Pinus taeda) samples from throughout the species’ range in the southeastern United States. We analyzed associations between phenotypic, molecular and environmental variables from a published dataset of 3,082 SNP loci and published datasets containing three categories of phenotypic traits (gene expression, metabolites, and whole-plant traits). We found only six SNP loci that displayed potential signals of local adaptation. Five of the six identified SNPs are linked to gene expression traits for lignin development, and one is linked with whole-plant traits. We subsequently compared the six candidate genes with environmental variables and found a high correlation in only three of them (R2 > 0.2). Our study highlights the need for a combination of genotypes, phenotypes, and environmental variables, and for an appropriate sampling scheme and study design, to improve confidence in the identification of potential candidate genes

Nonlinear model predictive control for Type 1 diabetes

Diabetes is known as Diabetes Mellitus in the medical terminology and characterized by insufficient production of insulin from the pancreas. Diabetes can be grouped as type 1 and type 2 diabetes mellitus. Diabetic patient population is %7.2 of the total population in Turkey and this disease is included in the most five killer in the world. Therefore we face to face serious problem because all of the patients aren?t treated by doctors. In order to overcome this problem, automatic control algorithms have been developed in the last decade. Previously, classical control algorithms such as proportional control and proportional integral control etc were suggested for controlling blood glucose. Afterwards a lot of mathematical models have been developed about glucose-insulin dynamics. Especially in the last decade improved mathematical model triggered model based control algorithms. Due to the deal with constraints model based control algorithms are defined as the most appropriate control strategy. In this study we used a model developed by Hovorka and this model is known as the most popular model in the literature. This model is included the most important term which is gut absorption rate for blood glucose concentration. In order to decrease clinical intensity, and increase patient comfort automatic blood glucose algorithm must be developed. For this reason we developed several control strategies by using Hovorka model.This work aimed at controlling blood glucose concentration of type 1 diabetes, by manipulating the insulin infusion rate. The algorithm is based on Hovorka model, which was firstly simulated under open loop disturbances introduced as meal digestions. After satisfactory results in compliance with general clinical observations were obtained in open loop runs, the model was linearized around the operating point. Then the model predictive control algorithm was implemented in MATLAB by using the Model Predictive Control (MPC) toolbox. Besides other control staregies such as NARMA-L2 (Nonlinear Auto Regressive Moving Average), NNMPC (Neural Network Model Predictive Control), PID (Proportional Integral Control) and SQP&NLMPC (Successive or Sequential Quadratic Programming& Nonlinear Model Predictive Control) were developed in this study. In the controller design we asuume that the patient was connected to an insulin infusion pump whose rate is adjusted in order to keep the blood glucose level within desired limits. The performance of the controller was tested through simulation runs under the disturbing effects of meal intake, and also set point change. The most suitable control strategy was defined as SQP for controlling blood glucose concentrationIn addition this we carried out model validation with clinical data. For this purpose we used healthy subject data. Because of the applicability of the Hovorka model for diabetic patients, simulation results and clinical data weren?t consistent. For this reason we will study model validation with type 1 diabetic patient data in the future.The results indicated that, the controller was capable of maintaining the glucose level in the blood within the normal glycemic range, with relatively high overshoots occurring in cases of excessive amounts of meal intake in accordance with clinical practice.Key Words: Diabetes, Hovorka model, blood glucose control, control algorithm, model predictive control, Nonlinear Auto Regressive Moving Average, Neural Network Model Predictive Control, Proportional Integral Control, Successive or Sequential Quadratic Programming& Nonlinear Model Predictive ControlTıp terminolojisinde Diabetes Mellitus olarak bilinen Diyabet pankreastan yeterli miktarda insulin üretilememesi olarak tanımlanmaktadır. Bilinen iki türü tip 1 ve tip 2 diyabettir. Türkiye'de toplam nüfusun % 7.2 si diyabetik hasta popülasyonunu oluşturmakta ve bu hastalık dünyadaki ilk beş öldürücü arasında yer almaktadır. Bütün hastaların doktor desteği alamaması bakımından oldukça ciddi bir problemle karşı karşıya kalınmaktadır. Bu problemin üstesinden gelebilmek için son on yılda otomatik kontrol algoritmaları geliştirilmiştir. Başlangıçta kan şekeri kontrolü için klasik kontrol algoritmaları olarak tanımlanan oransal, oransal-integral kontrol ediciler önerilmiştir. Daha sonraları glikoz-insülin dinamiği ile ilgili pek çok matematiksel model geliştirilmiştir. Özellikle son on yılda matematiksel modellerin gelişimi model temelli kontrol edicilerin gelişimini tetiklemiştir. Bu tür kontrol algoritmalarının sistemlerinde kısıtlamaların dahil edilmesi sebebiyle bu yöntemler en uygun kontrol stratejisi olarak görülmektedir.Bu çalışmada süreli yayınlarda oldukça popüler olarak tanımlanan Hovorka modeli kullanılmıştır. Bu model kan şekeri derişimi için önemli olan bağırsak absorbsiyon hızı terimini içermektedir. Klinik yoğunluğu azaltmak ve hasta koşullarının rahatlığını attırmak adına otomatik kontrol algoritmaları geliştirilmesi yadsınamaz bir gerçektir. Bu sebeple Hovorka modeli kullanılarak birçok kontrol stratejisi geliştirilmiştir.Bu çalışma ile insülin infüzyon hızı ayarlanarak kan şekeri derişiminin kontrolü amaçlanmıştır. Algoritmanın temel aldığı Hovorka modelinin ilk olarak yemek sindirim miktarı olarak tanımlanan düzensizlik etkisi varlığında açık döngü benzetimi yapılmıştır. Açık döngü sonuçlarının klinik gözlemlerle uyumu kontrol edildikten sonra, model çalışma koşulları etrafında doğrusallaştırılmıştır. MATLAB ortamında model öngörmeli kontrol araç kutusu (MPC toolbox) kullanılarak model öngörmeli kontrol algoritması geliştirilmiştir. Bunların yanı sıra NARMA-L2, yapay sinir ağı temelli model öngörmeli kontrol tekniği (NNMPC), oransal-integral kontrol edici (PID), SQP ve doğrusal olmayan model öngörmeli kontrol tekniği (NLMPC) gibi diğer kontrol teknikleri de geliştirilmiştir. Kontrol edici tasarımında kan şekeri derişimini kontrol etmek için hastaların insülin infüzyon pompasına bağlı oldukları kabul edilmektedir. Kontrol edicilerin performansı yemek etkisinin düzensizliği varlığında test edilmiş ve aynı zamanda set noktası takibi yapılmıştır. Kan şekeri kontrolü çalışmasında SQP kontrol algoritması en uygun teknik olarak belirlenmiştir.Bunlara ek olarak klinik veri kullanılarak modelin test edilmesi çalışmaları da yapılmıştır. Bu amaçla sağlıklı bireylerden alınan veriler kullanılmıştır. Hovorka modelinin sadece diyabetik hastalara uygulanabilirliliği sebebiyle benzetim sonuçları ile klinik veriler arasında uyumsuzluk gözlenmiştir. Bu sebeple gelecekte modelin test edilmesi tip1 diyabetik hasta verisi ile gerçekleştirilecektir.Sonuçlar, kontrol stratejilerinin normal glisemik değerleri yakaladığını ve klinik verilerle uygun olarak aşırı yemek alımında bağıl yüksek kan şekeri değerine ulaşıldığını göstermiştir.Anahtar Kelimeler: Diyabet, Hovorka model, kan şekeri kontrolü, control algoritması , model öngörmeli kontrol, Nonlinear Auto Regressive Moving Average, Yapay sinir ağı temelli control tekniği, oransal integral control edici , SQP&doğrusal olmayan model öngörmeli kontro