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. A quantitative imaging software platform was used to extract radiomics. An extreme gradient boosting (XGBoost) machine learning algorithm was used to create mucinous classifiers using texture features only, or radiomic/radiologic and clinical combined models. Classifiers were compared using performance scoring metrics. Shapely additive explanation (SHAP) analyses were conducted to identify variables most important in model construction. RESULTS: Overall, 99 patients and 103 PCs were included in the analyses. Eighty (78%) patients had mucinous PCs on surgical pathology. Using multiple fivefold cross validations, the texture features only and combined XGBoost mucinous classifiers demonstrated an area under the curve of 0.72 ± 0.14 and 0.73 ± 0.14, respectively. By SHAP analysis, root mean square, mean attenuation, and kurtosis were the most predictive features in the texture features only model. Root mean square, cyst location, and mean attenuation were the most predictive features in the combined model. CONCLUSION: Machine learning principles can be applied to PC texture features to create a mucinous phenotype classifier. Model performance did not improve with the combined model. However, specific radiomic, radiologic, and clinical features most predictive in our models can be identified using SHAP analysis.

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). A similar analysis was performed in a sarcomatoid rich cohort (n = 43, 31M/12F, mean age 63.7 years) using size-matched non-sarcomatoid controls (n = 49) for identification of sarcomatoid change. RESULTS: The XG Boost Model performed best on the tested data. After manual and machine feature extraction, models consisted of 3, 7, 5, 10 radiomics features for NC sarc, PV sarc, NC NG and PV NG, respectively. The area under the receiver operating characteristic curve (AUC) for these models was 0.59, 0.65, 0.69 and 0.58 respectively. The multichannel NG model extracted 6 radiomic features using the feature selection strategy and showed an AUC of 0.67. CONCLUSIONS: Statistically significant but weak associations between aggressive tumor features (high nuclear grade, sarcomatoid features) in large RCC were identified using 3D radiomics and machine learning analysis.

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. In this paper, GBDL models were implemented in predicting flash point for the first time. We assessed the performance of two GBDL models, message-passing neural network (MPNN) and graph convolutional neural network (GCNN), by comparing against 12 previous QSPR studies using more traditional methods. Our result shows that MPNN both outperforms GCNN and yields slightly worse but comparable performance with previous QSPR studies. The average R2 and Mean Absolute Error (MAE) scores of MPNN are, respectively, 2.3 % lower and 2.0 K higher than previous comparable studies. To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules. The optimized MPNN gives a test data R2 of 0.803 and MAE of 17.8 K on the complete dataset. We also extracted 5 datasets from our integrated dataset based on molecular types (acids, organometallics, organogermaniums, organosilicons, and organotins) and explore the quality of the model in these classes.

N6-methyldeoxyadenosine directs nucleosome positioning in Tetrahymena DNA.

BACKGROUND: N6-methyldeoxyadenosine (6mA or m6dA) 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. RESULTS: We use Tetrahymena thermophila as a model organism to examine the effects of 6mA on nucleosome positioning. Independent methods reveal the enrichment of 6mA near and after transcription start sites with a periodic pattern and anti-correlation relationship with the positions of nucleosomes. The distribution pattern can be recapitulated by in vitro nucleosome assembly on native Tetrahymena genomic DNA but not on DNA without 6mA. Model DNA containing artificially installed 6mA resists nucleosome assembling compared to unmodified DNA in vitro. Computational simulation indicates that 6mA increases dsDNA rigidity, which disfavors nucleosome wrapping. Knockout of a potential 6mA methyltransferase leads to a transcriptome-wide change of gene expression. CONCLUSIONS: These findings uncover a mechanism by which DNA 6mA assists to shape the nucleosome positioning and potentially affects gene expression.

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. The flow velocity depends on the concentration drop across the channel and the structure and composition of the channel walls, but it is independent of the overall channel length. Our work thus presents new insight into the fluid transport in long microchannels commonly found in nature and is useful for designing novel micro- or nano-fluidic pumps.