Showing papers by "University of Calgary published in 2022"

Lignin-enzyme interaction: A roadblock for efficient enzymatic hydrolysis of lignocellulosics

Abstract: Efficiently producing second-generation biofuels from biomass is of strategic significance and meets sustainability targets, but it remains a long-term challenge due to the existence of biomass recalcitrance. Lignin contributes significantly to biomass recalcitrance by physically limiting the access of enzymes to carbohydrates, and this could be partially overcome by applying a pretreatment step to directly target lignin. However, lignin typically cannot be completely removed, and its structure is also significantly altered during the pretreatment. As a result, lignin residue in the pretreated materials still significantly hindered a complete conversion of carbohydrate to its monosugars by interacting with cellulase enzymes. The non-productive adsorption driven by hydrophobic, electrostatic, and/or hydrogen bonding interactions is widely considered as the major mechanism of action governing the unfavored lignin-enzyme interaction. One could argue this type of interaction between lignin residue and the activated enzymes is the major roadblock for efficient enzymatic hydrolysis of pretreated lignocellulosics. To alleviate the negative effects of lignin on enzyme performance, a deep understanding of lignin structural transformation upon different types of pretreatments as well as how and where does lignin bind to enzymes are prerequisites. In the last decade, the progress toward a fundamental understanding of lignin-enzyme interaction, structural characterization of lignin during pretreatment and/or conformation change of enzyme during hydrolysis is resulting in advances in the development of methodologies to mitigate the negative effect of lignin. Here in this review, the lignin structural transformation upon different types of pretreatments and the inhibition mechanism of lignin in the bioconversion of lignocellulose to bioethanol are summarized. Some technologies to minimize the adverse impact of lignin on the enzymatic hydrolysis, including chemical modification of lignin, adding blocking additives, and post-treatment to remove lignin were also introduced. The production of liquid biofuels from lignocellulosic biomass has shown great environmental benefits such as reducing greenhouse gas emissions and mitigate climate change. By addressing the root causes of lignin-enzyme interaction and how to retard this interaction, it is our hope that this comprehensive review will pave the way for significantly reducing the high cost associated with the enzymatic hydrolysis process, and ultimately achieving a cost-effective and sustainable biorefinery system.

On the climate impacts of blue hydrogen production

Abstract: Natural gas based hydrogen production with carbon capture and storage is referred to as blue hydrogen. If substantial amounts of CO2 from natural gas reforming are captured and permanently stored, such hydrogen could be a low-carbon energy carrier. However, recent research raises questions about the effective climate impacts of blue hydrogen from a life cycle perspective. Our analysis sheds light on the relevant issues and provides a balanced perspective on the impacts on climate change associated with blue hydrogen. We show that such impacts may indeed vary over large ranges and depend on only a few key parameters: the methane emission rate of the natural gas supply chain, the CO2 removal rate at the hydrogen production plant, and the global warming metric applied. State-of-the-art reforming with high CO2 capture rates combined with natural gas supply featuring low methane emissions does indeed allow for substantial reduction of greenhouse gas emissions compared to both conventional natural gas reforming and direct combustion of natural gas. Under such conditions, blue hydrogen is compatible with low-carbon economies and exhibits climate change impacts at the upper end of the range of those caused by hydrogen production from renewable-based electricity. However, neither current blue nor green hydrogen production pathways render fully “net-zero” hydrogen without additional CO2 removal.

Reinforcement learning in urban network traffic signal control: A systematic literature review

Abstract: Improvement of traffic signal control (TSC) efficiency has been found to lead to improved urban transportation and enhanced quality of life. Recently, the use of reinforcement learning (RL) in various areas of TSC has gained significant traction; thus, we conducted a systematic literature review as a systematic, comprehensive, and reproducible review to dissect all the existing research that applied RL in the network-level TSC domain, called as RL in NTSC or RL-NTSC for brevity. The review only targeted the network-level articles that tested the proposed methods in networks with two or more intersections. This review covers 160 peer-reviewed articles from 30 countries published from 1994 to March 2020. The goal of this study is to provide the research community with statistical and conceptual knowledge, summarize existence evidence, characterize RL applications in NTSC domains, explore all applied methods and major first events in the defined scope, and identify areas for further research based on the explored research problems in current research. We analyzed the extracted data from the included articles in the following seven categories: (i) publication and authors’ data, (ii) method identification and analysis, (iii) environment attributes and traffic simulation, (iv) application domains of RL-NTSC, (v) major first events of RL-NTSC and authors’ key statements, (vi) code availability, and (vii) evaluation. This paper provides a comprehensive view of the past 26 years of research on applying RL to NTSC. It also reveals the role of advancing deep learning methods in the revival of the research area, the rise of using non-commercial microscopic traffic simulators, a lack of interaction between traffic and transportation engineering practitioners and researchers, and a lack of proposal and creation of testbeds which can likely bring different communities together around common goals.

Mechanistic understanding of cellulose β-1,4-glycosidic cleavage via photocatalysis

Abstract: Photoreforming of lignocellulosic biomass is an emerging and sustainable strategy for coproduction of high-value chemicals and fuels. Challenges remain to selectively convert biomass macromolecular via sunlight-driven photocatalysis due to limited mass diffusion, insufficient charge separation and lack of mechanistic understanding. Herein, inspired by natural photosynthesis, we demonstrate a hierarchically threedimensionally ordered macroporous (3DOM) TiO2-Au-CdS Z-scheme heterojunction photocatalyst to improve mass diffusion, charge separation and light absorption efficiency. We show the photocatalytic cleavage pathway of cellulose β-1,4-glycosidic linkage (the most abundant linkage within biomass) over 3DOM TiO2-Au-CdS heterojunction by using cellobiose as a model component. Similar to the oxidative enzymes in nature, the all-solid-state Z-scheme photocatalyst demonstrates oxygen insertion at C1 position followed by the elimination reaction, which oxidatively cleaves the β-1,4-glycosidic bond and results in gluconic acid and glucose generation. In presence of oxygen, glucose is further oxidized into gluconic acid which is subsequently oxidized or decarboxylated into glucaric acid or arabinose. The present study may serve as a framework to rationally design photocatalyst to reveal mechanistic understanding of biomass photoreforming towards high-value fuels and chemical feedstocks.

Mechanistic understanding of cellulose β-1,4-glycosidic cleavage via photocatalysis

Abstract: Photoreforming of lignocellulosic biomass is an emerging and sustainable strategy for coproduction of high-value chemicals and fuels. Challenges remain to selectively convert biomass macromolecular via sunlight-driven photocatalysis due to limited mass diffusion, insufficient charge separation and lack of mechanistic understanding. Herein, inspired by natural photosynthesis, we demonstrate a hierarchically threedimensionally ordered macroporous (3DOM) TiO2-Au-CdS Z-scheme heterojunction photocatalyst to improve mass diffusion, charge separation and light absorption efficiency. We show the photocatalytic cleavage pathway of cellulose β-1,4-glycosidic linkage (the most abundant linkage within biomass) over 3DOM TiO2-Au-CdS heterojunction by using cellobiose as a model component. Similar to the oxidative enzymes in nature, the all-solid-state Z-scheme photocatalyst demonstrates oxygen insertion at C1 position followed by the elimination reaction, which oxidatively cleaves the β-1,4-glycosidic bond and results in gluconic acid and glucose generation. In presence of oxygen, glucose is further oxidized into gluconic acid which is subsequently oxidized or decarboxylated into glucaric acid or arabinose. The present study may serve as a framework to rationally design photocatalyst to reveal mechanistic understanding of biomass photoreforming towards high-value fuels and chemical feedstocks.

Autologous Nanobody-Derived Fratricide-Resistant CD7-CAR T-cell Therapy for Patients with Relapsed and Refractory T-cell Acute Lymphoblastic Leukemia/Lymphoma

Abstract: Since CD7 may represent a potent target for T-lymphoblastic leukemia/lymphoma (T-ALL/LBL) immunotherapy, this study aimed to investigate safety and efficacy of autologous CD7-chimeric antigen receptor (CAR) T cells in patients with relapsed and refractory (R/R) T-ALL/LBL, as well as its manufacturing feasibility.Preclinical phase was conducted in NPG mice injected with Luc+ GFP+CCRF-CEM cells. Open-label phase I clinical trial (NCT04004637) enrolled patients with R/R CD7-positive T-ALL/LBL who received autologous CD7-CAR T-cell infusion. Primary endpoint was safety; secondary endpoints included efficacy and pharmacokinetic and pharmacodynamic parameters.CD7 blockade strategy was developed using tandem CD7 nanobody VHH6 coupled with an endoplasmic reticulum/Golgi-retention motif peptide to intracellularly fasten CD7 molecules. In preclinical phase CD7 blockade CAR T cells prevented fratricide and exerted potent cytolytic activity, significantly relieving leukemia progression and prolonged the median survival of mice. In clinical phase, the complete remission (CR) rate was 87.5% (7/8) 3 months after CAR T-cell infusion; 1 patient with leukemia achieved minimal residual disease-negative CR and 1 patient with lymphoma achieved CR for more than 12 months. Majority of patients (87.5%) only had grade 1 or 2 cytokine release syndrome with no T-cell hypoplasia or any neurologic toxicities observed. The median maximum concentration of CAR T cells was 857.2 cells/μL at approximately 12 days and remained detectable up to 270 days.Autologous nanobody-derived fratricide-resistant CD7-CAR T cells demonstrated a promising and durable antitumor response in R/R T-ALL/LBL with tolerable toxicity, warranting further studies in highly aggressive CD7-positive malignancies.

Polycystic Ovary Syndrome: An Evolutionary Adaptation to Lifestyle and the Environment

Abstract: Polycystic ovary syndrome (PCOS) is increasingly recognized as a complex metabolic disorder that manifests in genetically susceptible women following a range of negative exposures to nutritional and environmental factors related to contemporary lifestyle. The hypothesis that PCOS phenotypes are derived from a mismatch between ancient genetic survival mechanisms and modern lifestyle practices is supported by a diversity of research findings. The proposed evolutionary model of the pathogenesis of PCOS incorporates evidence related to evolutionary theory, genetic studies, in utero developmental epigenetic programming, transgenerational inheritance, metabolic features including insulin resistance, obesity and the apparent paradox of lean phenotypes, reproductive effects and subfertility, the impact of the microbiome and dysbiosis, endocrine-disrupting chemical exposure, and the influence of lifestyle factors such as poor-quality diet and physical inactivity. Based on these premises, the diverse lines of research are synthesized into a composite evolutionary model of the pathogenesis of PCOS. It is hoped that this model will assist clinicians and patients to understand the importance of lifestyle interventions in the prevention and management of PCOS and provide a conceptual framework for future research. It is appreciated that this theory represents a synthesis of the current evidence and that it is expected to evolve and change over time.

Three-dimensional gas property geological modeling and simulation

Abstract: 3D visualization of geological and geophysical data has been continuously developed in recent decades for better prediction and simulation of subsurface systems. 3D modeling has gained attention from interpreters as well as decisions makers in the energy sector. The integration of 3D structural, facies, petrophysical, and geomechanical models can be used to obtain more reliable estimates of gas in place in the conventional and unconventional gas reservoirs. As well, 3D geomechanical model can be used to expose the root cause of drilling difficulties in gas reservoir and development of gas reservoirs. This chapter seeks to introduce the workflow and 3D modeling procedures in gas reservoirs. It provides a case study of 3D structural, facies, petrophysical, and geomechanical modeling in a gas reservoir from the southern central, Gulf of Suez, Egypt. Banner headline Three-dimensional modeling and visualization of subsurface gas reservoirs is a critical industry practice, and its significance originates from its theoretical and application value in exploration, development, and production. When modeling the subsurface system in both conventional and unconventional resources, it is critical to take into account a large number of datasets. The more data entered into the model, the better the model's accuracy. The heterogeneity of the data, the time required for data handling, numerical storage and accessibility, and the reliability of geological information assessments could all be sources of uncertainty that impact the 3D modeling process, posing modeling challenges. Such 3D modeling challenges can be overcome with more integrated data, precise calibration, and core measurements, resulting in more precise models.

Three-dimensional gas property geological modeling and simulation

Abstract: 3D visualization of geological and geophysical data has been continuously developed in recent decades for better prediction and simulation of subsurface systems. 3D modeling has gained attention from interpreters as well as decisions makers in the energy sector. The integration of 3D structural, facies, petrophysical, and geomechanical models can be used to obtain more reliable estimates of gas in place in the conventional and unconventional gas reservoirs. As well, 3D geomechanical model can be used to expose the root cause of drilling difficulties in gas reservoir and development of gas reservoirs. This chapter seeks to introduce the workflow and 3D modeling procedures in gas reservoirs. It provides a case study of 3D structural, facies, petrophysical, and geomechanical modeling in a gas reservoir from the southern central, Gulf of Suez, Egypt. Banner headline Three-dimensional modeling and visualization of subsurface gas reservoirs is a critical industry practice, and its significance originates from its theoretical and application value in exploration, development, and production. When modeling the subsurface system in both conventional and unconventional resources, it is critical to take into account a large number of datasets. The more data entered into the model, the better the model's accuracy. The heterogeneity of the data, the time required for data handling, numerical storage and accessibility, and the reliability of geological information assessments could all be sources of uncertainty that impact the 3D modeling process, posing modeling challenges. Such 3D modeling challenges can be overcome with more integrated data, precise calibration, and core measurements, resulting in more precise models.

Performance evaluation of outlier detection techniques in production timeseries: A systematic review and meta-analysis

Abstract: Time-series data have been extensively collected and analyzed in many disciplines, such as stock market, medical diagnosis, meteorology, and oil and gas industry. Numerous data in these disciplines are sequence of observations measured as functions of time, which can be further used for different applications via analytical or data analytics techniques (e.g., to forecast future price, climate change, etc.). However, presence of outliers can cause significant uncertainties to interpretation results; hence, it is essential to remove the outliers accurately and efficiently before conducting any further analysis. A total of 17 techniques that belong to statistical, regression-based, and machine learning (ML) based categories for outlier detection in timeseries are applied to the oil and gas production data analysis. 15 of these methods are utilized for production data analysis for the first time. Two state-of-the-art and high-performance techniques are then selected for data cleaning which require minimum control and time complexity. Moreover, performances of these techniques are evaluated based on several metrics including the accuracy, precision, recall, F1 score, and Cohen’s Kappa to rank the techniques. Results show that eight unsupervised algorithms outperform the rest of the methods based on the synthetic case study with known outliers. For example, accuracies of the eight shortlisted methods are in the range of 0.83–0.99 with a precision between 0.83 and 0.98, compared to 0.65–0.82 and 0.07–0.77 for the others. In addition, ML-based techniques perform better than statistical techniques. Our experimental results on real field data further indicate that the k-nearest neighbor (KNN) and Fulford-Blasingame methods are superior to other outlier detection frameworks for outlier detection in production data, followed by four others including density-based spatial clustering of applications with noise (DBSCAN), and angle-based outlier detection (ABOD). Even though the techniques are examined with oil and gas production data, but the same data cleaning workflow can be used to detect timeseries’ outliers in other disciplines.