Overlapping Coalition Formation for Efficient Data Fusion in Multi-Sensor Networks

This paper develops new algorithms for coalition formation within multi-sensor networks tasked with performing wide-area surveillance. Specifically, we cast this application as an instance of coalition formation, with overlapping coalitions. We show that within this application area sub-additive coalition valuations are typical, and we thus use this structural property of the problem to we derive two novel algorithms (an approximate greedy one that operates in polynomial time and has a calculated bound to the optimum, and an optimal branch-and-bound one) to find the optimal coalition structure in this instance. We empirically evaluate the performance of these algorithms within a generic model of a multi-sensor network performing wide area surveillance. These results show that the polynomial algorithm typically generated solutions much closer the optimal than the theoretical bound, and prove the effectiveness of our pruning procedure

Integration of a Cognitive Assessment Task into Exergame Gameplay Elements

Lack of exercise is related to a variety of health issues, including cognitive decline in older adults. Tools for encouraging regular exercise such as exergames are a useful preventative measure, but regular screening for impairment is still important. However, standard cognitive screening methods can be both time-consuming and tedious. Integrating these screening methods into a frequently played exergame is one way to enable regular screening, but requires that the integration is not obtrusive and does not interfere with the gameplay. We present an exergame in which a standard cognitive screening tool, the AX-Continuous Performance Task, is integrated into the game in a non-obstrusive fashion. As an starting step in assessing this approach, we validate the comparability of the measurement capacity of this integrated tool by assessing user performance in the test with non-impaired adults. Our results indicate that the test is comparable to the traditional form of the test when conducted within the context of a game, and is not clearly perceived as a test rather than a gameplay element by the users. However, increasing task complexity through additional gameplay elements does interfere with task performance

Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation

Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules near other lung structures, and similar visual aspects. This study proposes a new model named Lung_PAYNet, a pyramidal attention-based architecture, for improved lung nodule segmentation in low-dose CT images. In this architecture, the encoder and decoder are designed using an inverted residual block and swish activation function. It also employs a feature pyramid attention network between the encoder and decoder to extract exact dense features for pixel classification. The proposed architecture was compared to the existing UNet architecture, and the proposed methodology yielded significant results. The proposed model was comprehensively trained and validated using the LIDC-IDRI dataset available in the public domain. The experimental results revealed that the Lung_PAYNet delivered remarkable segmentation with a Dice similarity coefficient of 95.7%, mIOU of 91.75%, sensitivity of 92.57%, and precision of 96.75%

Injectable Nano-Network for Glucose-Mediated Insulin Delivery

Diabetes mellitus, a disorder of glucose regulation, is a global burden affecting 366 million people across the world. An artificial “closed-loop” system able to mimic pancreas activity and release insulin in response to glucose level changes has the potential to improve patient compliance and health. Herein we develop a glucose-mediated release strategy for the self-regulated delivery of insulin using an injectable and acid-degradable polymeric network. Formed by electrostatic interaction between oppositely charged dextran nanoparticles loaded with insulin and glucose-specific enzymes, the nanocomposite-based porous architecture can be dissociated and subsequently release insulin in a hyperglycemic state through the catalytic conversion of glucose into gluconic acid. In vitro insulin release can be modulated in a pulsatile profile in response to glucose concentrations. In vivo studies validated that these formulations provided improved glucose control in type 1 diabetic mice subcutaneously administered with a degradable nano-network. A single injection of the developed nano-network facilitated stabilization of the blood glucose levels in the normoglycemic state (\u3c200 mg/dL) for up to 10 days

Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions.

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies

Mixed Chamber Ensembles

Kennesaw State University School of Music presents Mixed Chamber Ensembles, 4:00 performance.https://digitalcommons.kennesaw.edu/musicprograms/1428/thumbnail.jp