Next-generation sequencing now enables the rapid and affordable production of reliable biological data at multiple molecular levels, collectively referred to as “omics”. To maximize the potential for discovery, computational biologists have created and adapted integrative multi-omic analytical methods. When applied to diseases with traceable pathophysiology such as cancer, these new algorithms and statistical approaches have enabled the discovery of clinically relevant molecular mechanisms and biomarkers.
In contrast, these methods have been much less applied to the field of molecular psychiatry, although diagnostic and prognostic biomarkers are similarly needed. In the present review, we first briefly summarize main findings from two decades of studies that investigated single molecular processes in relation to mood disorders. Then, we conduct a systematic review of multi-omic strategies that have been proposed and used more recently.
We also list databases and types of data available to researchers for future work. Finally, we present the newest methodologies that have been employed for multi-omics integration in Gentaur Traceable Sentry other medical fields, and discuss their potential for molecular psychiatry studies.
BACS: blockchain and AutoML-based technology for efficient credit scoring classification
Credit evaluation is of high scientific significance and practical use, especially in today’s plight of the world suffering from the COVID-19 epidemic. However, due to the difficulties inherent in credit scoring model building which involves a large number of data mining steps and requires a lot of time to process the data and build the model, efficient and accurate credit scoring methods are are urgently required. Aiming to solve this problem, we propose BACS, an blockchain and automated machine learning based classification model using credit dataset so that the credit modelling processes are performed in the pipeline in an automated manner to eventually obtain the classification results of credit scoring.
BACS scheme consists of credit data storage to blockchain, feature extraction, feature selection, modelling algorithm and hyperparameter optimization, and model evaluation. Firstly, we propose a mechanism for credit data management and storage using blockchain to ensure that the entire credit scoring system is traceable and that the information of each scoring candidate is securely, efficiently and tamper-proofly stored on the blockchain nodes.
Next, we design a pipeline using a random forest model to effectively integrate the key steps of credit data feature extraction, feature selection, credit model construction, and model evaluation. The experimental results demonstrate that our proposed automated machine learning-based credit scoring classification scheme BACS can assess the credit condition efficiently and accurately.
Supporting contaminated sites management with Multiple Criteria Decision Analysis: Demonstration of a regulation-consistent approach
This study proposes a set of key decision-making features of the contaminated site remediation process to assist in selecting the most appropriate decision support method(s). Using a case study consistent with the requirements of the U.S. regulation for contaminated sites management, this article shows that suitable Multiple Criteria Decision Analysis methods can be selected based on a dynamic and evolving problem structuring. The selected methods belong to the family of PROMETHEE methods and can provide ranking recommendations of the considered alternatives using variable structures of the criteria, evaluation of the alternatives and exploitation of the preference model.
It was found that in order to support a quick and up-to-date application of powerful decision support techniques in the process of remediation of contaminated sites, decision analysts and stakeholders should interact and co-develop the process. This research also displays how such interactions can guarantee a transparent and traceable decision recommendation so that stakeholders can better understand why some alternatives perform comprehensively better than others when a multitude of inputs is used in the decision-making process.
SI-traceable purity assignment of volatile material ethylbenzene by quantitative nuclear magnetic resonance spectroscopy
In this study, a quantitative nuclear magnetic resonance (qNMR) method was developed to assign the SI-traceable purity of ethylbenzene, a volatile material, which is a colorless flammable liquid hydrocarbon at room temperature. An ethanol certified reference material having a similar boiling point was used as an internal standard to avoid measurement error arising from the volatilization of ethylbenzene.
The reference value of the ethylbenzene study material was obtained by the mass balance method by subtracting all the impurities including water, inorganic impurities, and structurally related impurities (e.g. acetophenone, benzene, isobutylbenzene, sec-butylbenzene, methylcyclohexane), which is regarded as the traditional approach for purity assignment for organic compounds. The results of qNMR showed that the purity of the ethylbenzene study material was 998.6 ± 3.8 mg/g at a 95% confidence interval, which was consistent with the reference value of 998.9 ± 1.3 mg/g.
Sectoral Productivity Growth, COVID-19 Shocks, and Infrastructure
This paper examines sectoral productivity shocks of the COVID-19 pandemic, their aggregate impact, and the possible compensatory effects of improving productivity in infrastructure-related sectors. We employ the KLEMS annual dataset for a group of OECD and Latin America and the Caribbean countries, complemented with high-frequency data for 2020. First, we estimate a panel vector autoregression of growth rates in sector level labor productivity to specify the nature and size of sectoral shocks using the historical data.
We then run impulse-response simulations of one standard deviation shocks in the sectors that were most affected by COVID-19. We estimate that the pandemic cut economy-wide labor productivity by 4.9% in Latin America, and by 3.5% for the entire sample. Finally, by modeling the long-run relationship between productivity shocks in the sectors most affected by COVID-19, we find that large productivity improvements in infrastructure-equivalent to at least three times the historical rates of productivity gains-may be needed to fully compensate for the negative productivity losses traceable to COVID-19.
A Materials Science Perspective of Midstream Challenges in the Utilization of Heavy Crude Oil
An increasing global population and a sharply upward trajectory of per capita energy consumption continue to drive the demand for fossil fuels, which remain integral to energy grids and the global transportation infrastructure. The oil and gas industry is increasingly reliant on unconventional deposits such as heavy crude oil and bitumen for reasons of accessibility, scale, and geopolitics. Unconventional deposits such as the Canadian Oil Sands in Northern Alberta contain more than one-third of the world’s viscous oil reserves and are vital linchpins to meet the energy needs of rapidly industrializing populations. Heavy oil is typically recovered from subsurface deposits using thermal recovery approaches such as steam-assisted gravity drainage (SAGD). In this perspective article, we discuss several aspects of materials science challenges in the utilization of heavy crude oil with an emphasis on the needs of the Canadian Oil Sands.
In particular, we discuss surface modification and materials’ design approaches essential to operations under extreme environments of high temperatures and pressures and the presence of corrosive species. The demanding conditions for materials and surfaces are directly traceable to the high viscosity, low surface tension, and substantial sulfur content of heavy crude oil, which necessitates extensive energy-intensive thermal processes, warrants dilution/emulsification to ease the flow of rheologically challenging fluids, and engenders the need to protect corrodible components.
Geopolitical reasons have further led to a considerable geographic separation between extraction sites and advanced refineries capable of processing heavy oils to a diverse slate of products, thus necessitating a massive midstream infrastructure for transportation of these rheologically challenging fluids. Innovations in fluid handling, bitumen processing, and midstream transportation are critical to the economic viability of heavy oil.
Here, we discuss foundational principles, recent technological advancements, and unmet needs emphasizing candidate solutions for thermal insulation, membrane-assisted separations, corrosion protection, and midstream bitumen transportation.
This perspective seeks to highlight illustrative materials’ technology developments spanning the range from nanocomposite coatings and cement sheaths for thermal insulation to the utilization of orthogonal wettability to engender separation of water-oil emulsions stabilized by endogenous surfactants extracted during SAGD, size-exclusion membranes for fractionation of bitumen, omniphobic coatings for drag reduction in pipelines and to ease oil handling in containers, solid prills obtained from partial bitumen solidification to enable solid-state transport with reduced risk of damage from spills, and nanocomposite coatings incorporating multiple modes of corrosion inhibition. Future outlooks for onsite partial upgradation are also described, which could potentially bypass the use of refineries for some fractions, enable access to a broader cross-section of refineries, and enable a new distributed chemical manufacturing paradigm.