Machine learning principles applied to CT radiomics to predict mucinous pancreatic cysts.

Purpose: Current diagnostic and treatment modalities for pancreatic cysts (PCs) are invasive and are associated with patient morbidity. The purpose of this study is to develop and evaluate machine learning algorithms to delineate mucinous from non-mucinous PCs using non-invasive CT-based radiomics.

Methods: A retrospective, single-institution analysis of patients with non-pseudocystic PCs, contrast-enhanced computed tomography scans within 1 year of resection, and available surgical pathology were included.

Evaluation of radiomics and machine learning in identification of aggressive tumor features in renal cell carcinoma (RCC).

Purpose: The purpose of this study was to evaluate the use of CT radiomics features and machine learning analysis to identify aggressive tumor features, including high nuclear grade (NG) and sarcomatoid (sarc) features, in large renal cell carcinomas (RCCs).

Methods: CT-based volumetric radiomics analysis was performed on non-contrast (NC) and portal venous (PV) phase multidetector computed tomography images of large (> 7 cm) untreated RCCs in 141 patients (46W/95M, mean age 60 years). Machine learning analysis was applied to the extracted radiomics data to evaluate for association with high NG (grade 3-4), with multichannel analysis for NG performed in a subset of patients (n = 80).

Assessing Graph-based Deep Learning Models for Predicting Flash Point.

Flash points of organic molecules play an important role in preventing flammability hazards and large databases of measured values exist, although millions of compounds remain unmeasured. To rapidly extend existing data to new compounds many researchers have used quantitative structure-property relationship (QSPR) analysis to effectively predict flash points. In recent years graph-based deep learning (GBDL) has emerged as a powerful alternative method to traditional QSPR.

N-methyldeoxyadenosine directs nucleosome positioning in Tetrahymena DNA.

Background: N-methyldeoxyadenosine (6mA or mdA) was shown more than 40 years ago in simple eukaryotes. Recent studies revealed the presence of 6mA in more prevalent eukaryotes, even in vertebrates. However, functional characterizations have been limited.

Chemically driven fluid transport in long microchannels.

Chemical gradients maintained along surfaces can drive fluid flows by diffusio-osmosis, which become significant at micro- and nano-scales. Here, by means of mesoscopic simulations, we show that a concentration drop across microchannels with periodically inhomogeneous boundary walls can laterally transport fluids over arbitrarily long distances along the microchannel. The driving field is the secondary local chemical gradient parallel to the channel induced by the periodic inhomogeneity of the channel wall.