Contrast agents have long helped researchers and physicians alike delineate boundaries, but new diagnostic information is always sought after. A new field of molecularly targeted CT agents hopes to fill this void and supply physicians with prognostic information to find better treatments for patients. Borrowing from drug delivery and design, nanoparticles and similar platforms are being explored to help visualize complex biologic processes with never before seen resolution and fidelity. We discuss the development of this field and feasibility of translating some of these prospects to the clinic. Advances in chemistry, molecular biology, and engineering have molded this field emphasizing the early detection and treatment of diseases at the molecular and cellular level. Myriads of nanomedicine platforms have been proposed and developed and tested in laboratories and in preclinical models. However, very few have been translated to clinical trials. It is therefore a critical issue to recognize the factors affecting their eventual application in human. Better understanding of biological and biophysical obstacles encountered by these agents is necessary. Toward this aim, we critically review our present understanding of the biological obstacles encountered by the nano-agents, which we hope will motivate more studies to tune these technologies for future translational and clinical applications.

In this study, we designed two deep neural networks to encode 16 features for early seizure detection in intracranial EEG and compared them and their frequency responses to 16 widely used engineered metrics to interpret their properties: epileptogenicity index (EI), phase locked high gamma (PLHG), time and frequency domain Cho Gaines distance (TDCG, FDCG), relative band powers, and log absolute band powers (from alpha, beta, theta, delta, low gamma, and high gamma bands). The deep learning models were pretrained for seizure identification on the time and frequency domains of 1 s, single-channel clips of 127 seizures (from 25 different subjects) using “leave-one-out” (LOO) cross validation. Each neural network extracted unique feature spaces that were interpreted using spectral power modulations before being used to train a Random Forest Classifier (RFC) for seizure identification. The Gini Importance of each feature was calculated from the pretrained RFC, enabling the most significant features (MSFs) for each task to be identified. The MSFs were extracted to train another RFC for UPenn and Mayo Clinic’s Seizure Detection Kaggle Challenge. They obtained an AUC score of 0.93, demonstrating a transferable method to identify and interpret biomarkers for seizure detection.

In this study we designed two deep neural networks to encode 16 feature latent spaces for early seizure detection in intracranial EEG and compared them to 16 widely used engineered metrics: Epileptogenicity Index (EI), Phase Locked High Gamma (PLHG), Time and Frequency Domain Cho Gaines Distance (TDCG, FDCG), relative band powers, and log absolute band powers (from alpha, beta, theta, delta, gamma, and high gamma bands. The deep learning models were pretrained for seizure identification on the time and frequency domains of one second single channel clips of 127 seizures (from 25 different subjects) using “leave-one-out” (LOO) cross validation. Each neural network extracted unique feature spaces that were used to train a Random Forest Classifier (RFC) for seizure identification and latency tasks. The Gini Importance of each feature was calculated from the pretrained RFC, enabling the most significant features (MSFs) for each task to be identified. The MSFs were extracted from the UPenn and Mayo Clinic's Seizure Detection Challenge to train another RFC for the contest. They obtained an AUC score of 0.93, demonstrating a transferable method to identify interpretable biomarkers for seizure detection.

The use of sexed semen at dairy farms has improved heifer replacement over the last decade by allowing greater control over the number of retained females and enabling the selection of dams with superior genetics. Alternatively, beef semen can be used in genetically inferior dairy cows to produce crossbred (beef x dairy) animals that can be sold at a higher price. Although crossbreeding became profitable for dairy farmers, meat cuts from beef x dairy crosses often lack quality and shape uniformity. Technologies for quickly predicting carcass traits for animal grouping before harvest may improve meat cut uniformity in crossbred cattle. Our objective was to develop a deep learning approach for predicting ribeye area and circularity of live animals through 3D body surface images using two neural networks: (1) nested Pyramid Scene Parsing Network (nPSPNet) for extracting features, and (2) Convolutional Neural Network (CNN) for estimating ribeye area and circularity from these features. A group of 56 calves were imaged using an Intel RealSense D435 camera. A total of 327 depth images were captured from 30 calves and labeled with masks outlining the calf body to train the nPSPNet for feature extraction. An additional 42,536 depth images were taken from the remaining 26 calves along with three ultrasound images collected for each calf from the 12/13 th ribs. The ultrasound images (three by calf) were manually segmented to calculate the average ribeye area and circularity and then paired with the depth images for CNN training. We implemented a nested cross-validation approach, in which all images for one calf were removed (leave-one-out, LOO), and the remaining calves were further divided into training (70%) and validation (30%) sets within each LOO iteration. The proposed model predicted ribeye area with an average coefficient of determination (R 2) of 0.74 and 7.3% mean absolute error of prediction (MAEP) and the ribeye circularity with an average R 2 of 0.87 and 2.4% MAEP. Our results indicate that computer vision systems could be used to predict ribeye area and circularity in live animals, allowing optimal management decisions toward smart animal grouping in beef x dairy crosses and purebred.