Deep Learning Models for Predicting Phenotypic Traits and Diseases from Omics Data

Computational analysis of high-throughput omics data, such as gene expressions, copy number alterations and DNA methylation (DNAm), has become popular in disease studies in recent decades because such analyses can be very helpful to predict whether a patient has certain disease or its subtypes. However, due to the high-dimensional nature of the data sets with hundreds of thousands of variables and very small number of samples, traditional machine learning approaches, such as support vector machines (SVMs) and random forests, have limitations to analyze these data efficiently. In this chapter, we reviewed the progress in applying deep learning algorithms to solve some biological questions. The focus is on potential software tools and public data sources for the tasks. Particularly, we show some case studies using deep neural network (DNN) models for classifying molecular subtypes of breast cancer and DNN-based regression models to account for interindividual variation in triglyceride concentrations measured at different visits of peripheral blood samples using DNAm profiles. We show that integration of multi-omics profiles into DNN-based learning methods could improve the prediction of the molecular subtypes of breast cancer. We also demonstrate the superiority of our proposed DNN models over the SVM model for predicting triglyceride concentrations

A Framework of Integrating Manufacturing Plants in Smart Grid Operation: Manufacturing Flexible Load Identification

In the deregulated electricity markets run by Independent System Operator (ISO), a two-settlement (day-ahead and real-time) process is typically used to determine the electricity price to the end-use customers at different buses. In the day-ahead settlement, the demand is predicted at each bus based on the previous consumption behavior of the consumers and thus, Locational Marginal Price (LMP) can be determined and shared to the consumers. A significant gap is usually observed between the planned and real-time demands due to the uncertainties of the weather (temperature, wind-speed etc.), the intensity of business, and everyday activities. Therefore, a large price variation may occur in the real-time market and the dispatching plan needs to be adjusted to respond to the variation. To reduce the gap between the day-ahead and real-time dispatching plans, a modified framework, i.e., a three-settlement process considering the integration of the manufacturing plants into the existing two-settlement process is proposed in this study. The manufacturing end-use customers report the flexibility of their loads to the ISO so that the ISO can update the day-ahead price through an updated dispatching plan that utilizes the feedback of the load flexibility from the manufacturers. A mathematical model is developed to identify the flexible and non-flexible loads of the manufacturers. Particle Swarm Optimization (PSO) is used to solve this mathematical model and a case study is conducted to illustrate the effectiveness of the model

Pyrene-based aggregation-induced emission luminogens and their applications

"Unity is force"-Aesop. It is a common phenomenon that traditional chromophores exhibit high fluorescence in dilute solutions, yet luminescence is quenched at high concentrations or in the aggregate state, i.e. "aggregation-caused quenching" (ACQ). Tang reported the unusual photophysical observation that luminogens can exhibit weak or no fluorescence in solution, yet they are highly emissive in the aggregate or solid state; this is defined as aggregation-induced emission (AIE). The discovery of AIE helped solve the ACQ effect in traditional luminophores. Pyrene is an important polycyclic aromatic hydrocarbon (PAH), which exhibits very different photophysical behavior in solution versus the aggregate state, and the ACQ effect has played a dominant role in pyrene chemistry. The ACQ effect is harmful for some practical applications and is a challenge in organic light-emitting diodes (OLEDs) and light-emitting electrochemical cells, for which the effect is more severe in the solid state. Thus, how to overcome the ACQ effect observed in pyrene chemistry still remains a challenge. In this review, we discuss how following basic AIE mechanisms such as the restriction of intramolecular motion (RIM), excited-state intramolecular proton transfer (ESIPT), and twisted intramolecular charge transfer (TICT), can transform pyrene-based ACQ luminogens to AIE luminogens with excellent optical properties. Furthermore, prospective applications of pyrene-based AIEgens are discussed, as is the potential for designing new organic functional materials

Superabsorbent polymers (SAP) enhance efficient and eco-friendly production of corn (Zea mays L.) in drought affected areas of northern China

In arid and semiarid regions of northern China, there is an increasing interest in using reduced rate of chemical fertilizer along with water-saving superabsorbent polymer (SAP) for field crop production. The objective was to evaluate the effectiveness of different rates of SAP (low, 0.75; medium, 11.3 and high, 15.0 kg ha-1) against half amount of conventional standard rate of chemical fertilizer for summer corn (Zea mays L.) production in a drought-affected field of northern China. Corn yield increased following SAP application by 11.2% under low 18.8% under medium and 29.2% under high rate with only half amount (150 kg ha-1) of fertilizer compared with control plants, which received conventional standard fertilizer rate (300 kg ha-1). At the same time plant height, stem diameter, leaf area, biomass accumulation and relative water content as well as protein and sugar contents in the grain also increased significantly following SAP treatments. The optimum application of SAP in the study area would be 15 kg ha-1 as it brings progressive increase in corn growth and also maintain proper nutrients balance in the soil after harvest. Other rates are not sufficient to maintain proper plant growth or soil nutrient balance against half fertilizer. We suggest that, the application of SAP at 15 kg ha-1 plus only half the amount of conventional fertilizer rate (150 kg ha-1) would be a more appropriate practice for sustainable corn production under arid and semiarid conditions of northern China or the areas with similar ecologies.Key words: Corn, drought stress, fertilizer use efficiency, northern China, superabsorbent polymer

Antioxidant enzyme activities and lipid peroxidation in corn (Zea mays L.) following soil application of superabsorbent polymer at different fertilizer regimes

In arid and semiarid regions of northern China, there is an increasing interest in using reduced rate of inorganic fertilizer together with water-saving superabsorbent polymer (SAP) for field crop production. Thus, an efficient management of fertilizer and study of metabolic changes in response to SAP application is important for improved production of corn. 24 undisturbed soil lysimeters (35 cm in diameter and 150 cm in depth) were installed in a field lysimeter facility during 2010, to study yield and physiological mechanisms in corn (Zea mays L.) subjected to application (30 kg ha-1) or without application of SAP at different fertilization levels (standard, medium or 75% and low or 50% of conventional fertilization rate). The results show that the corn yield fell by 19.7% under medium and 37.7% under low fertilization; the application of SAP increased it significantly by 80.3%. Although SAP had marginal effect under standard fertilization, plants treated with SAP under reduced fertilization showed a significant decrease in superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), ascorbate peroxidase (APX) and glutathione reductase (GR) activities in leaves when compared with control plants. Our results suggest that drought stress as well as fertilizer reduction leads to production of oxygen radicals, which results to oxidative stress in the plant and the application of superabsorbent polymer could conserve soil water and nutrients, making same available for plants to reduce oxidative stress and increase biomass accumulation, especially under reduced fertilization level.Key words: Antioxidant enzymes, lysimeter, corn, drought stress, superabsorbent polymer

CuInS2/ZnS nanocrystals as sensitisers for NiO photocathodes

Nickel oxide (NiO) is the most universally studied photocathode to date, however, its poor fill factor (FF) makes its efficiency much lower than its counterpart, n-type photoanodes. Its significance in photovoltaics is based on the potential to fabricate tandem photoelectrodes in order to enhance the overall efficiency of the existing devices. Furthermore, limited work on the sensitisation of NiO with semiconducting nanocrystals (NCs) exists. For the first time, we have fabricated NiO photocathodes sensitised with aqueous CuInS2/ZnS NCs. The NCs were chemically bound to the NiO films with the aid of carboxyl and thiol groups. This was achieved without modifying the bulk surface properties of NiO. Binding of the NCs was investigated using TEM, SEM, XPS, XANES, EXAFS modelling and ToF-SIMS. NiO films were assembled into CuInS2/ZnS NC sensitised photocathodes and their photovoltaic properties were compared to those of unsensitised and dye-sensitised NiO solar cells. We demonstrate that nontoxic NCs can be used to sensitise NiO photocathodes to achieve an (almost) all-inorganic system

Techno-environmental analysis of battery storage for grid level energy services

With more and more renewable energy sources (RES) going into power grids, the balancing of supply and demand during peak times will be a growing challenge due to the inherent intermittency and unpredictable nature of RES. Grid level batteries can store energy when there is excess generation from wind and solar and discharge it to meet variable peak demand that is currently supplied by combined cycle gas turbine (CCGT) plants in the UK. This paper assesses the potential of battery storage to replace CCGT in responding to variable peak demand for current and future energy scenarios (FES) in the UK from technical and environmental perspectives. Results from technical analysis show that batteries, assuming size is optimised for different supply and demand scenarios proposed by the National Grid, are able to supply 6.04%, 13.5% and 29.1% of the total variable peak demand in 2016, 2020 and 2035, respectively while CCGT plants supply the rest of the demand. Particularly, to phase out CCGT variable generation from the UK grid in 2035, electricity supply from wind and solar needs to increase by 1.33 times their predicted supply in National Grid’s FES. The environmental implications of replacing CCGT by batteries are studied and compared through a simplified life cycle assessment (LCA). Results from LCA studies show that if batteries are used in place of CCGT, it can reduce up to 87% of greenhouse gas emissions and that is an estimated 1.98 MtCO2 eq. for an optimal supply, 29.1%, of variable peak demand in 203