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Hybrid energy storage system (HESS) can support integrated energy system (IES) under multiple time scales. To address the diversity of new energy sources and loads, a multi-objective configuration frame for HESS is proposed under comprehensive source-load conditions. First, the IES operation model for minimizing the electricity purchase rate of IES and the HESS planning model for minimizing the levelised cost and the average reserve idle rate of HESS are established. Then, under the full condition scenarios based on APC, a HESS planning scheme is optimised by the NSGA-III and TOPSIS method. Finally, compared with single scene, the rated power and capacity of BESS differ by 5.46 times and 6.1 times, respectively. Based on full-condition scenarios, the proposed frame can integrate various operating conditions and effectively avoid the limitations of single conditions. The capacity of HESS accounts for 9.06% of new energy sources and BESS plays a dominant role at 74.82%.

In our previous studies, ultrasonic washing was proven to induce phenolic biosynthesis in fresh-cut red cabbages; however, the related regulatory mechanisms remain unclear. In response, this study investigates the mechanisms of phenolic biosynthesis in fresh-cut red cabbages under ultrasound stress from the perspective of energy metabolism. The effects of exogenous adenosine triphosphate (ATP) and 2,4-dinitrophenol (DNP) on the phenolic biosynthesis and antioxidant enzymes of fresh-cut red cabbages were also studied. The results show that ultrasound treatment induces the activities of adenosine triphosphatases (ATPase), succinate dehydrogenase (SDH), and cytochrome oxidase (CCO), thus maintaining the high-energy status of fresh-cut red cabbages during storage, primarily characterized by an increase in ATP content and energy charge (EC). The high-energy status is beneficial for providing energy to the biosynthesis of phenolic compounds. In addition, the phenolic content, as well as the activities of phenylalanine ammonia-lyase (PAL), cinnamate-4-hydroxylase (C4H), 4-coumarate-CoA ligase (4CL), superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX), was increased in the fresh-cut red cabbages after ATP treatment, whereas DNP treatment showed the opposite effect. The results indicate that energy plays an essential role in regulating the antioxidant systems of fresh-cut red cabbages. The energy enhancement induced by ultrasound treatment is a key reason to promote the biosynthesis of non-enzymatic antioxidant–phenolic compounds and enhancing antioxidant enzyme activities. Therefore, the increase in energy levels induced by ultrasonic washing is closely related to the accumulation of phenolic compounds. Positive energy regulation could be a potential mechanism to improve the nutritive value of fruits and vegetables subjected to ultrasonic washing.

Motivation: The human microbiome plays an important role in human health and disease. The composition of the human microbiome is influenced by multiple factors and understanding these factors is critical to elucidate the role of the microbiome in health and disease and for development of new diagnostics or therapeutic targets based on the microbiome. 16S ribosomal RNA (rRNA) gene targeted amplicon sequencing is a commonly used approach to determine the taxonomic composition of the bacterial community. Operational taxonomic units (OTUs) are clustered based on generated sequence reads and used to determine whether and how the abundance of microbiome is correlated with some characteristics of the samples, such as health/disease status, smoking status, or dietary habit. However, OTU count data is not only overdispersed but also contains an excess number of zero counts due to undersampling. Efficient analytical tools are therefore needed for downstream statistical analysis which can simultaneously account for overdispersion and sparsity in microbiome data. Results: In this paper, we propose a Zero-inflated Negative Binomial (ZINB) regression for identifying differentially abundant taxa between two or more populations. The proposed method utilizes an Expectation Maximization (EM) algorithm, by incorporating a two-part mixture model consisting of (i) a negative binomial model to account for over-dispersion and (ii) a logistic regression model to account for excessive zero counts. Extensive simulation studies are conducted which indicate that ZINB demonstrates better performance as compared to several state-of-the-art approaches, as measured by the area under the curve (AUC). Application to two real datasets indicate that the proposed method is capable of detecting biologically meaningful taxa, consistent with previous studies. Availability: The software implementation of ZINB is available at: http://www.ssg.uab.edu/bhglm/. Supplementary information: Supplementary data are available at Journal of Bioinformatics and Genomics online.

Motivation: Many traditional clinical prognostic factors have been known for cancer for years, but usually provide poor survival prediction. Genomic information is more easily available now which offers opportunities to build more accurate prognostic models. The challenge is how to integrate them to improve survival prediction. The common approach of jointly analyzing all type of covariates directly in one single model may not improve the prediction due to increased model complexity and cannot be easily applied to different datasets. Results: We proposed a two-stage procedure to better combine different sources of information for survival prediction, and applied the two-stage procedure in two cancer datasets: myelodysplastic syndromes (MDS) and ovarian cancer. Our analysis suggests that the prediction performance of different data types are very different, and combining clinical, gene expression and mutation data using the two-stage procedure improves survival prediction in terms of improved concordance index and reduced prediction error. Availability and implementation: The two-stage procedure can be implemented in BhGLM package which is freely available at http://www.ssg.uab.edu/bhglm/. Keywords: gene expansion, mutation, clinical data, survival prediction, myelodysplastic syndromes, ovarian cancer.

The increasing penetration of distributed energy resources in next-generation distribution networks has resulted in an explosion of the Internet of Things to upgrade their control and monitoring systems. This poses new challenges for the efficient energy management and reliable decision-making of these systems. This is due to the potentially large amount of data that cannot be handled by the traditional architecture of control and data acquisition systems, which have limited storage and computation capabilities. In order to adapt to the new energy management requirements of next-generation distribution networks, a state-of-the-art energy management method called cloud-fog hierarchical architecture is proposed in this work. Based on this architecture, we established a utility and revenue model for various stakeholders, including normal customers, prosumers, and distribution system operators. Furthermore, by embedding an artificial intelligence module in the proposed architecture, energy management could be implemented automatically. Neural networks were used at fog computing layers to achieve regression prediction of energy usage behavior and power source output. Moreover, based on the maximizing utility objective function, the amount of energy consumption of customers and prosumers in the distribution network was optimized with a genetic algorithm at cloud layer. The proposed methods were tested with a set of normal customers and prosumers in a general distribution network, and the results, including the captured usage patterns of the customers and revenues of various stakeholders, verify the effectiveness of the proposed method. This work provides an effective reference for the development of real-time energy management systems for the next-generation distribution network.

Inflamm-aging is a challenging and promising new branch of aging-related research fields that includes areas such as immunosenescence. Increasing evidence indicates that inflamm-aging is intensively associated with many aging diseases, such as Alzheimer’s disease, atherosclerosis, heart disease, type II diabetes, and cancer. Mounting studies have focused on the role of inflamm-aging in disease progression and many advances have been made in the last decade. However, the underlying mechanisms by which inflamm-aging affects pathological changes and disease development are still unclear. Here, we review studies of inflamm-aging that explore the concept, pathological features, mechanisms, intervention, and the therapeutic strategies of inflamm-aging in disease progression.

Exfoliative cytology has been widely used for early diagnosis of oral squamous cell carcinoma (OSCC). Test outcome is reported as "negative", "atypical" (defined as abnormal epithelial changes of uncertain diagnostic significance), and "positive" (defined as definitive cellular evidence of epithelial dysplasia or carcinoma). The major challenge is how to properly manage the "atypical" patients in order to diagnose OSCC early and prevent OSCC. In this study, we collected exfoliative cytology data, histopathology data, and clinical data of normal subjects (n=102), oral leukoplakia (OLK) patients (n=82), and OSCC patients (n=93), and developed a data analysis procedure for quantitative risk stratification of OLK patients. This procedure involving a step called expert-guided data transformation and reconstruction (EdTAR) which allows automatic data processing and reconstruction and reveals informative signals for subsequent risk stratification. Modern machine learning techniques were utilized to build statistical prediction models on the reconstructed data. Among the several models tested using resampling methods for parameter pruning and performance evaluation, Support Vector Machine (SVM) was found to be optimal with a high sensitivity (median>0.98) and specificity (median>0.99). With the SVM model, we constructed an oral cancer risk index (OCRI) which may potentially guide clinical follow-up of OLK patients. One OLK patient with an initial OCRI of 0.88 developed OSCC after 40 months of follow-up. In conclusion, we have developed a statistical method for qualitative risk stratification of OLK patients. This method may potentially improve cost-effectiveness of clinical follow-up of OLK patients, and help design clinical chemoprevention trial for high-risk populations.

High charge and energy (HZE) particles are a main hazard of the space radiation environment. Uncertainty regarding their health effects is a limiting factor in the design of human exploration-class space missions, that is, missions beyond low earth orbit. Previous work has shown that HZE exposure increases cancer risk and elicits other aging-like phenomena in animal models. Here, we investigate how a single exposure to HZE particle radiation, early in life, influences the subsequent age-dependent evolution of oxidative stress and appearance of degenerative tissue changes. Embryos of the laboratory model organism, Oryzias latipes (Japanese medaka fish), were exposed to HZE particle radiation at doses overlapping the range of anticipated human exposure. A separate cohort was exposed to reference γ-radiation. Survival was monitored for 750 days, well beyond the median lifespan. The population was also sampled at intervals and liver tissue was subjected to histological and molecular analysis. HZE particle radiation dose and aging contributed synergistically to accumulation of lipid peroxidation products, which are a marker of chronic oxidative stress. This was mirrored by a decline in PPARGC1A mRNA, which encodes a transcriptional co-activator required for expression of oxidative stress defense genes and for mitochondrial maintenance. Consistent with chronic oxidative stress, mitochondria had an elongated and enlarged ultrastructure. Livers also had distinctive, cystic lesions. Depending on the endpoint, effects of γ-rays in the same dose range were either lesser or not detected. Results provide a quantitative and qualitative framework for understanding relative contributions of HZE particle radiation exposure and aging to chronic oxidative stress and tissue degeneration.