Does a Simple Lattice Protein Folding Model Exhibit Self-Organized Criticality?

Proteins are known to fold into tertiary structures that determine their functionality in living organisms. However, the way they consistently fold to the same structure is unknown. Our research sees if the folding process can be viewed computationally through the lens of self-organized criticality using a simple lattice-bound protein

Equipment Using a Predictive Health Model

Abstract—In this paper, a model-predictive control based framework is proposed for modeling and optimization of the health state of power system equipment. In the framework, a predictive health model is proposed that predicts the health state of the equipment based on its usage and maintenance actions. Based on the health state, the failure rate of the equipment can be estimated. We propose to use this predictive health model to predict the effects of different maintenance actions. The effects of maintenance actions over a future time window are evaluated by a cost function. The maintenance actions are optimized using this cost function. The proposed framework is applied in the optimization of the loading of transformers based on the thermal degradation of the paper insulation

Both Constitutive and Infection‐Responsive Secondary Metabolites Linked to Resistance against Austropuccinia psidii (Myrtle Rust) in Melaleuca quinquenervia

Austropuccinia psidii is a fungal plant pathogen that infects species within the Myrtaceae, causing the disease myrtle rust. Myrtle rust is causing declines in populations within natural and managed ecosystems and is expected to result in species extinctions. Despite this, variation in response to A. psidii exist within some species, from complete susceptibility to resistance that prevents or limits infection by the pathogen. Untargeted metabolomics using Ultra Performance Liquid Chromatography with Ion Mobility followed by analysis using MetaboAnalyst 3.0, was used to ex-plore the chemical defence profiles of resistant, hypersensitive and susceptible phenotypes within Melaleuca quinquenervia during the early stages of A. psidii infection. We were able to identify three separate pools of secondary metabolites: (i) metabolites classified structurally as flavonoids that were naturally higher in the leaves of resistant individuals prior to infection, (ii) organoheterocyclic and carbohydrate‐related metabolites that varied with the level of host resistance post‐infection, and (iii) metabolites from the terpenoid pathways that were responsive to disease progression re-gardless of resistance phenotype suggesting that these play a minimal role in disease resistance during the early stages of colonization of this species. Based on the classes of these secondary me-tabolites, our results provide an improved understanding of key pathways that could be linked more generally to rust resistance with particular application within Melaleuca. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Learning Latent Space Representations to Predict Patient Outcomes: Model Development and Validation

BACKGROUND: Scalable and accurate health outcome prediction using electronic health record (EHR) data has gained much attention in research recently. Previous machine learning models mostly ignore relations between different types of clinical data (ie, laboratory components, International Classification of Diseases codes, and medications). OBJECTIVE: This study aimed to model such relations and build predictive models using the EHR data from intensive care units. We developed innovative neural network models and compared them with the widely used logistic regression model and other state-of-the-art neural network models to predict the patient\u27s mortality using their longitudinal EHR data. METHODS: We built a set of neural network models that we collectively called as long short-term memory (LSTM) outcome prediction using comprehensive feature relations or in short, CLOUT. Our CLOUT models use a correlational neural network model to identify a latent space representation between different types of discrete clinical features during a patient\u27s encounter and integrate the latent representation into an LSTM-based predictive model framework. In addition, we designed an ablation experiment to identify risk factors from our CLOUT models. Using physicians\u27 input as the gold standard, we compared the risk factors identified by both CLOUT and logistic regression models. RESULTS: Experiments on the Medical Information Mart for Intensive Care-III dataset (selected patient population: 7537) show that CLOUT (area under the receiver operating characteristic curve=0.89) has surpassed logistic regression (0.82) and other baseline NN models ( \u3c 0.86). In addition, physicians\u27 agreement with the CLOUT-derived risk factor rankings was statistically significantly higher than the agreement with the logistic regression model. CONCLUSIONS: Our results support the applicability of CLOUT for real-world clinical use in identifying patients at high risk of mortality

A Large-scale Synthesis and Characterization of Quaternary CuInₓGa₁₋ₓS₂ Chalcopyrite Nanoparticles via Microwave Batch Reactions

Various quaternary CuInxGa1-xS2 (0≤x≤1) chalcopyrite nanoparticles have been prepared from molecular single-source precursors via microwave decomposition. We were able to control the nanoparticle size, phase, stoichiometry, and solubility. Depending on the choice of surface modifiers used, we were able to tune the solubility of the resulting nanoparticles. This method has been used to generate up to 5g of nanoparticles and up to 150g from multiple batch reactions with excellent reproducibility. Data from UV-Vis, photoluminescence, X-ray diffraction, TEM, DSC/TGA-MS, and ICP-OES analyses have shown high reproducibility in nanoparticle size, composition, and bandgap

A Large-Scale Synthesis and Characterization of Quaternary CuIn\u3csub\u3e\u3cem\u3ex\u3c/em\u3e\u3c/sub\u3eGa\u3csub\u3e1−\u3cem\u3ex\u3c/em\u3e\u3c/sub\u3eS\u3csub\u3e2\u3c/sub\u3e Chalcopyrite Nanoparticles via Microwave Batch Reactions

Various quaternary CuInxGa1−xS2 (0 ≤ x ≤ 1) chalcopyrite nanoparticles have been prepared from molecular single-source precursors via microwave decomposition. We were able to control the nanoparticle size, phase, stoichiometry, and solubility. Depending on the choice of surface modifiers used, we were able to tune the solubility of the resulting nanoparticles. This method has been used to generate up to 5 g of nanoparticles and up to 150 g from multiple batch reactions with excellent reproducibility. Data from UV-Vis, photoluminescence, X-ray diffraction, TEM, DSC/TGA-MS, and ICP-OES analyses have shown high reproducibility in nanoparticle size, composition, and bandgap

An Integrated Modeling System for Estimating Glacier and Snow Melt Driven Streamflow from Remote Sensing and Earth System Data Products in the Himalayas

Quantification of the contribution of the hydrologic components (snow, ice and rain) to river discharge in the Hindu Kush Himalayan (HKH) region is important for decision-making in water sensitive sectors, and for water resources management and flood risk reduction. In this area, access to and monitoring of the glaciers and their melt outflow is challenging due to difficult access, thus modeling based on remote sensing offers the potential for providing information to improve water resources management and decision making. This paper describes an integrated modeling system developed using downscaled NASA satellite based and earth system data products coupled with in-situ hydrologic data to assess the contribution of snow and glaciers to the flows of the rivers in the HKH region. Snow and glacier melt was estimated using the Utah Energy Balance (UEB) model, further enhanced to accommodate glacier ice melt over clean and debris-covered tongues, then meltwater was input into the USGS Geospatial Stream Flow Model (Geo- SFM). The two model components were integrated into Better Assessment Science Integrating point and Nonpoint Sources modeling framework (BASINS) as a user-friendly open source system and was made available to countries in high Asia. Here we present a case study from the Langtang Khola watershed in the monsoon-influenced Nepal Himalaya, used to validate our energy balance approach and to test the applicability of our modeling system. The snow and glacier melt model predicts that for the eight years used for model evaluation (October 2003-September 2010), the total surface water input over the basin was 9.43 m, originating as 62% from glacier melt, 30% from snowmelt and 8% from rainfall. Measured streamflow for those years were 5.02 m, reflecting a runoff coefficient of 0.53. GeoSFM simulated streamflow was 5.31 m indicating reasonable correspondence between measured and model confirming the capability of the integrated system to provide a quantification of water availability