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Abstract Understanding the complex growth and metabolic dynamics in microorganisms requires advanced kinetic models containing both metabolic reactions and enzymatic regulation to predict phenotypic behaviors under different conditions and perturbations. Most current kinetic models lack gene expression dynamics and are separately calibrated to distinct media, which consequently makes them unable to account for genetic perturbations or multiple substrates. This challenge limits our ability to gain a comprehensive understanding of microbial processes towards advanced metabolic optimizations that are desired for many biotechnology applications. Here, we present an integrated computational and experimental approach for the development and optimization of mechanistic kinetic models for microbial growth and metabolic and enzymatic dynamics. Our approach integrates growth dynamics, gene expression, protein secretion, and gene‐deletion phenotypes. We applied this methodology to build a dynamic model of the growth kinetics in batch culture of the bacteriumCellvibrio japonicusgrown using either cellobiose or glucose media. The model parameters were inferred from an experimental data set using an evolutionary computation method. The resulting model was able to explain the growth dynamics ofC. japonicususing either cellobiose or glucose media and was also able to accurately predict the metabolite concentrations in the wild‐type strain as well as in β‐glucosidase gene deletion mutant strains. We validated the model by correctly predicting the non‐diauxic growth and metabolite consumptions of the wild‐type strain in a mixed medium containing both cellobiose and glucose, made further predictions of mutant strains growth phenotypes when using cellobiose and glucose media, and demonstrated the utility of the model for designing industrially‐useful strains. Importantly, the model is able to explain the role of the different β‐glucosidases and their behavior under genetic perturbations. This integrated approach can be extended to other metabolic pathways to produce mechanistic models for the comprehensive understanding of enzymatic functions in multiple substrates. 

Proton beam therapy is a unique form of radiotherapy that utilizes protons to treat cancer by irradiating cancerous tumors, while avoiding unnecessary radiation exposure to surrounding healthy tissues. Real-time imaging of the proton beam can make this form of therapy more precise and safer for the patient during delivery. The use of Compton cameras is one proposed method for the real-time imaging of prompt gamma rays that are emitted by the proton beams as they travel through a patient’s body. Unfortunately, some of the Compton camera data is flawed and the reconstruction algorithm yields noisy and insufficiently detailed images to evaluate the proton delivery for the patient. Previous work used a deep residual fully connected neural network. The use of recurrent neural networks (RNNs) has been proposed, since they use recurrence relationships to make potentially better predictions. In this work, RNN architectures using two different recurrent layers are tested, the LSTM and the GRU. Although the deep residual fully connected neural network achieves over 75% testing accuracy and our models achieve only over 73% testing accuracy, the simplicity of our RNN models containing only 6 hidden layers as opposed to 512 is a significant advantage. Importantly in a clinical setting, the time to load the model from disk is significantly faster, potentially enabling the use of Compton camera image reconstruction in real-time during patient treatment. 

Atmospheric gravity waves are produced when gravity attempts to restore disturbances through stable layers in the atmosphere. They have a visible effect on many atmospheric phenomena such as global circulation and air turbulence. Despite their importance, however, little research has been conducted on how to detect gravity waves using machine learning algorithms. We faced two major challenges in our research: our raw data had a lot of noise and the labeled dataset was extremely small. In this study, we explored various methods of preprocessing and transfer learning in order to address those challenges. We pre-trained an autoencoder on unlabeled data before training it to classify labeled data. We also created a custom CNN by combining certain pre-trained layers from the InceptionV3 Model trained on ImageNet with custom layers and a custom learning rate scheduler. Experiments show that our best model outperformed the best performing baseline model by 6.36% in terms of test accuracy. 

There is a critical nationwide shortage of IT professionals as well as of scientists and engineers with high-performance computing (HPC) and big data related advanced computing skills. Simultaneously, the technology is growing in complexity and sophistication, which has led to the use of multi-disciplinary teams with members from a broad range of home domains everywhere in industry, government, and academia. Moreover, a lot of the vital team collaborations take will place virtually using a variety of software platforms now and in the future. We report here on experiences with preparing undergraduate and graduate students for these career opportunities in several contexts, from regular semester classes, an undergraduate summer research program, to an advanced graduate student CyberTraining program. All these programs are conducted fully online and leveraged concepts of flipped classrooms, recorded lectures, team-based and active learning, regular oral presentations, and more to ensure student engagement and lasting learning. 

During 2018, 2019, and 2020, the UMBC CyberTraining initiative “Big Data + HPC + Atmospheric Sciences” created an online team-based training program for advanced graduate students and junior researchers that trained a total of 58 participants. The year 2020 included 6 undergraduate students. Based on this experience, the authors created the summer undergraduate research program Online Interdisciplinary Big Data Analytics in Science and Engineering that will conduct 8-week online team-based undergraduate research programs (bigdatareu.umbc.edu) in the summers 2021, 2022, and 2023. Given the context of many institutions potentially expanding their online instruction, we share our experiences how the successful lessons from CyberTraining transfer to a high-intensity full-time online summer undergraduate research program. 

MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument onboard NASA’s Terra (launched in 1999) and Aqua (launched in 2002) satellite missions as part of the more extensive Earth Observation System (EOS). By measuring the reflection and emission by the Earth-Atmosphere system in 36 spectral bands from the visible to thermal infrared with near-daily global coverage and high-spatial-resolution (250 m ~ 1 km at nadir), MODIS is playing a vital role in developing validated, global, interactive Earth system models. MODIS products are processed into three levels, i.e., Level-1 (L1), Level-2 (L2) and Level-3 (L3). To shift the current static and “one-size-fits-all” data provision method of MODIS products, in this paper, we propose a service-oriented flexible and efficient MODIS aggregation framework. Using this framework, users only need to get aggregated MODIS L3 data based on their unique requirements and the aggregation can run in parallel to achieve a speedup. The experiments show that our aggregation results are almost identical to the current MODIS L3 products and our parallel execution with 8 computing nodes can work 88.63 times faster than a serial code execution on a single node.