The human immune system consists of a highly intelligent network of billions of independent, self-organized cells that interact with each other. Machine learning (ML) is an artificial intelligence (AI) tool that automatically processes huge amounts of image data. Immunotherapies have revolutionized the treatment of blood cancer. Specifically, one such therapy involves engineering immune cells to express chimeric antigen receptors (CAR), which combine tumor antigen specificity with immune cell activation in a single receptor. To improve their efficacy and expand their applicability to solid tumors, scientists optimize different CARs with different modifications. However, predicting and ranking the efficacy of different "off-the-shelf" immune products (e.g., CAR or Bispecific T-cell Engager [BiTE]) and selection of clinical responders are challenging in clinical practice. Meanwhile, identifying the optimal CAR construct for a researcher to further develop a potential clinical application is limited by the current, time-consuming, costly, and labor-intensive conventional tools used to evaluate efficacy. Particularly, more than 30 years of immunological synapse (IS) research data demonstrate that T cell efficacy is not only controlled by the specificity and avidity of the tumor antigen and T cell interaction, but also it depends on a collective process, involving multiple adhesion and regulatory molecules, as well as tumor microenvironment, spatially and temporally organized at the IS formed by cytotoxic T lymphocytes (CTL) and natural killer (NK) cells. The optimal function of cytotoxic lymphocytes (including CTL and NK) depends on IS quality. Recognizing the inadequacy of conventional tools and the importance of IS in immune cell functions, we investigate a new strategy for assessing CAR-T efficacy by quantifying CAR IS quality using the glass-support planar lipid bilayer system combined with ML-based data analysis. Previous studies in our group show that CAR-T IS quality correlates with antitumor activities in vitro and in vivo. However, current manually quantified IS quality data analysis is time-consuming and labor-intensive with low accuracy, reproducibility, and repeatability. In this study, we develop a novel ML-based method to quantify thousands of CAR cell IS images with enhanced accuracy and speed. Specifically, we used artificial neural networks (ANN) to incorporate object detection into segmentation. The proposed ANN model extracts the most useful information to differentiate different IS datasets. The network output is flexible and produces bounding boxes, instance segmentation, contour outlines (borders), intensities of the borders, and segmentations without borders. Based on requirements, one or a combination of this information is used in statistical analysis. The ML-based automated algorithm quantified CAR-T IS data correlates with the clinical responder and non-responder treated with Kappa-CAR-T cells directly from patients. The results suggest that CAR cell IS quality can be used as a potential composite biomarker and correlates with antitumor activities in patients, which is sufficiently discriminative to further test the CAR IS quality as a clinical biomarker to predict response to CAR immunotherapy in cancer. For translational research, the method developed here can also provide guidelines for designing and optimizing numerous CAR constructs for potential clinical development. Trial Registration: ClinicalTrials.gov NCT00881920.

There exists many powerful architectures for object detection and semantic segmentation of both biomedical and natural images. However, a difficulty arises in the ability to create training datasets that are large and well-varied. The importance of this subject is nested in the amount of training data that artificial neural networks need to accurately identify and segment objects in images and the infeasibility of acquiring a sufficient dataset within the biomedical field. This paper introduces a new data augmentation method that generates artificial cell nuclei microscopical images along with their correct semantic segmentation labels. Data augmentation provides a step toward accessing higher generalization capabilities of artificial neural networks. An initial set of segmentation objects is used with Greedy AutoAugment to find the strongest performing augmentation policies. The found policies and the initial set of segmentation objects are then used in the creation of the final artificial images. When comparing the state-of-the-art data augmentation methods with the proposed method, the proposed method is shown to consistently outperform current solutions in the generation of nuclei microscopical images.

In K-Means based clustering algorithms, different initial seeds can lead to different clustering results. Selecting the best result from different initial seeds is called the filtering process. The filtering process follows three steps, 1- performing several clustering trials, 2- scoring each trial, and 3- choosing the trial with the best score. A typical method to score the clustering results of K-Means is the within-cluster sum of squares (WCSS). There are more advanced methods that can be used to score the clustering trials. These methods usually provide a better score with the cost of being more computationally demanding. In this paper, we propose Condensed Silhouette, which is a very efficient version of the Silhouette algorithm. For this purpose, we replace the elements of Silhouette algorithm with similar elements of the K-Means algorithm. This helps us to maintain the accuracy of the Silhouette and at the same time, significantly reduce the computational requirements of the method. Our experiments on 14 real datasets show the effectiveness of the proposed method.

The idea behind data augmentation techniques is based on the fact that slight changes in the percept do not change the brain cognition. In classification, neural networks use this fact by applying transformations to the inputs to learn to predict the same label. However, in deep subspace clustering (DSC), the ground-truth labels are not available, and as a result, one cannot easily use data augmentation techniques. We propose a technique to exploit the benefits of data augmentation in DSC algorithms. We learn representations that have consistent subspaces for slightly transformed inputs. In particular, we introduce a temporal ensembling component to the objective function of DSC algorithms to enable the DSC networks to maintain consistent subspaces for random transformations in the input data. In addition, we provide a simple yet effective unsupervised procedure to find efficient data augmentation policies. An augmentation policy is defined as an image processing transformation with a certain magnitude and probability of being applied to each image in each epoch. We search through the policies in a search space of the most common augmentation policies to find the best policy such that the DSC network yields the highest mean Silhouette coefficient in its clustering results on a target dataset. Our method achieves state-of-the-art performance on four standard subspace clustering datasets.

Some unique attributes of P2P networks such as cost efficiency and scalability, contributed for the widespread adaptation of these networks. Since P2P applications are mostly used in file-sharing, preserving anonymity of users has become a very important subject for researchers. As a result, a lot of methods are suggested for P2P networks to preserve anonymity of users. Most of these methods, by relying on established anonymous solutions on client/server applications, are presented for unstructured P2P networks. But structured overlays, by using Distributed Hash Tables (DHT) for their routing, do not resemble traditional paradigms. Therefore, current anonymous methods can not be implemented for them easily. In this paper, we introduce a novel methodology to provide receiver's anonymity for circular P2P structures. With this method, we get help from inherited features of network infrastructure to establish a standard way for making tunnels. Our purpose is to introduce a flexible design which is able to manage different parts of the tunnels on current infrastructures. For this purpose, we implement our method on top of Chord to show how such design can be managed for real world applications. The results of applied method on a chord-like network shows that by managing critical features of our method, a trade-off can be made between stronger security and performance of the network.