bluuebunny/crossref_metadata_2025 huggingface dataset:
Scalable Hierarchical Multilevel Sampling of Lognormal Fields Conditioned on Measured Data
Authors: Barker A. T. , Lee C. S. , Forouzanfar F. , Guion A. , Wu X.-H.
Published: October 2021

We explore the problem of drawing posterior samples from a lognormal permeability field conditioned by noisy measurements at discrete locations. The underlying unconditioned samples are based on a scalable PDE-sampling technique that shows better scalability for large problems than the traditional Karhunen-Loeve sampling, while still allowing for consistent samples to be drawn on a hierarchy of spatial scales. Lognormal random fields produced in this scalable and hierarchical way are then conditioned to measured data by a randomized maximum likelihood approach to draw from a Bayesian posterior distribution. The algorithm to draw from the posterior distribution can be shown to be equivalent to a PDE-constrained optimization problem, which allows for some efficient computational solution techniques. Numerical results demonstrate the efficiency of the proposed methods. In particular, we are able to match statistics for a simple flow problem on the fine grid with high accuracy and at much lower cost on a scale of coarser grids.
Coupling-Strength Criteria for Sequential Implicit Formulations
Authors: Franc Jacques , Møyner Olav , Tchelepi Hamdi A.
Published: October 2021

Sequential Fully Implicit (SFI) schemes have been proposed as an alternative to the Fully Implicit Method (FIM). A significant advantage of SFI is that one can employ scalable strategies to the flow and transport problems. However, the primary disadvantage of using SFI compared with FIM is the fact that the splitting errors induced by the decoupling operator, which separates the pressure from the saturation(s), can lead to serious convergence difficulties of the overall nonlinear problem. Thus, it is important to quantify the coupling strength in an adaptive manner in both space and time. We present criteria that localize the computational cells where the pressure and saturation solutions are tightly coupled. The approach is using terms in the FIM Jacobian matrix, we quantify the sensitivity of the mass and volume-balance equations to changes in the pressure and the saturations. We identify three criteria that provide a measure of the coupling strength across the equations and variables. The standard CFL stability criteria, which are based entirely on the saturation equations, are a subset of the new criteria. Here, the pressure equation is solved using Algebraic MultiGrid (AMG), or a multiscale solver, such as the Multiscale Restricted-Smooth Basis (MsRSB) approach. The transport equations are then solved using a fixed total-velocity. These ‘coupling strength’ criteria are used to identify the cells where the pressure-saturation coupling is strong. The applicability of the derived coupling-strength criteria is tested using several test cases. The first test is using a gravitational immiscible dead-oil lock-exchange under a unit mobility ratio and large differences in density. For this case, the SFI algorithm fails to converge to the fully coupled solution due to the large splitting errors. Introducing a fully coupled solution stage on the local subdomains as an additional correction step restores nonlinear convergence. Detailed analysis of the ‘coupling strength’ criteria indicates that the criteria related to the sensitivity of the mass balance to changes in the pressure and the sensitivity of the volume balance to changes in the saturations are the most important ones to satisfy. Other test cases include an alternate gas-water-gas injection in a top layer of the SPE 10 test case and an injection-production scenario in a three-dimensional reservoir with layered lognormally distributed permeability. We propose novel criteria to estimate the strength of coupling between pressure and saturation. These CFL-like numbers are used to identify the cells that require fully implicit treatment in the nonlinear solution strategy. These criteria can also be used to improve the nonlinear convergence rates of Adaptive Implicit Methods (AIM).
The Modeling for Coupled Elastoplastic Geomechanics and Two-Phase Flow With Capillary Hysteresis in Porous Media
Authors: Yoon H. C. , Kim J.
Published: October 2021

We study new constitutive relations employing the fundamental theory of elastoplasticity for two coupled irreversible processes: elastoplastic geomechanics and two-phase flow with capillary hysteresis. The fluid content is additively decomposed into elastic and plastic parts with infinitesimal transformation assumed. Specifically, the plastic fluid content, i.e., the total residual (or irrecoverable) saturation, is also additively decomposed into constituents due to the two irreversible processes: the geomechanical plasticity and the capillary hysteresis. The additive decomposition of the plastic fluid content facilitates combining the existing two individual simulators easily, for example, by using the fixed-stress sequential method. For pore pressure of the fluid in multi-phase which is coupled with the geomechanics, the equivalent pore pressure is employed, which yields the well-posedness of coupled multi-phase flow and geomechanics, regardless of the capillarity. We perform an energy analysis to show the well-posedness of the proposed model. And numerical examples demonstrate stable solutions for cyclic imbibition/drainage and loading/unloading processes. Employing the van Genuchten and the Drucker Prager models for capillary and the plasticity, respectively, we show the robustness of the model for capillary hysteresis in multiphase flow and elastoplastic geomechanics.
Numerical Simulation of Proppant Transport in Hydraulically Fractured Reservoirs
Authors: Gorucu Seyhan Emre , Shrivastava Vijay , Nghiem Long X.
Published: October 2021

An existing equation-of-state compositional simulator is extended to include proppant transport. The simulator determines the final location of the proppant after fracture closure, which allows the computation of the permeability along the hydraulic fracture. The simulation then continues until the end of the production.

During hydraulic fracturing, proppant is injected in the reservoir along with water and additives like polymers. Hydraulic fracture gets created due to change in stress caused by the high injection pressure. Once the fracture opens, the bulk slurry moves along the hydraulic fracture. Proppant moves at a different speed than the bulk slurry and sinks down by gravity. While the proppant flows along the fracture, some of the slurry leaks off into the matrix. As the fracture closes after injection stops, the proppant becomes immobile. The immobilized proppant prevents the fracture from closing and thus keeps the permeability of the fracture high.

All the above phenomena are modelled effectively in this new implementation. Coupled geomechanics simulation is used to model opening and closure of the fracture following geomechanics criteria. Proppant retardation, gravitational settling and fluid leak-off are modeled with the appropriate equations. The propped fracture permeability is a function of the concentration of immobilized proppant. The developed proppant simulation feature is computationally stable and efficient. The time step size during the settling adapts to the settling velocity of the proppants. It is found that the final location of the proppants is highly dependent on its volumetric concentration and slurry viscosity due to retardation and settling effects. As the location and the concentration of the proppants determine the final fracture permeability, the additional feature is expected to correctly identify the stimulated region.

In this paper, the theory and the model formulation are presented along with a few key examples. The simulation can be used to design and optimize the amount of proppant and additives, injection timing, pressure, and well parameters required for successful hydraulic fracturing.
Physics Informed Deep Learning for Flow and Transport in Porous Media
Authors: Fraces Cedric G. , Tchelepi Hamdi
Published: October 2021

We present our progress on the application of physics informed deep learning to reservoir simulation problems. The model is a neural network that is jointly trained to respect governing physical laws and match boundary conditions. The methodology is hereby used to simulate a 2-phase immiscible transport problem (Buckley-Leverett). The model is able to produce an accurate physical solution both in terms of shock and rarefaction and honors the governing partial differential equation along with initial and boundary conditions. We test various hypothesis (uniform and non-uniform initial conditions) and show that with the proper implementation of physical constraints, a robust solution can be trained within a reasonable amount of time and iterations. We revisit some of the limitations presented in previous work [1] and further the applicability of this method in a forward, pure hyperbolic setup. We also share some practical findings on the application of physics informed neural networks (PINN). We review various network architectures presented in the literature and show tips that helped improve their convergence and accuracy. The proposed methodology is a simple and elegant way to instill physical knowledge to machine-learning algorithms. This alleviates the two most significant shortcomings of machine-learning algorithms: the requirement for large datasets and the reliability of extrapolation. The principles presented can be generalized in innumerable ways in the future and should lead to a new class of algorithms to solve both forward and inverse physical problems.
Bayesian Optimization for Field Scale Geological Carbon Sequestration
Authors: Lu Xueying , Jordan Kirk E. , Wheeler Mary F. , Pyzer-Knapp Edward O. , Benatan Matthew
Published: October 2021

We present a framework of the application of Bayesian Optimization (BO) to well management in geological carbon sequestration. The coupled compositional flow and poroelasticity simulator, IPARS, is utilized to accurately capture the underlying physical processes during CO2 sequestration. IPARS is coupled to IBM Bayesian Optimization (IBO) for parallel optimizations of CO2 injection strategies during field-scale CO2 sequestration. Bayesian optimization builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm, Gaussian process regression, and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample. IBO addresses the three weak points of the standard BO in that it supports parallel (batch) executions, scales better for high-dimensional problems, and is more robust to initializations. We demonstrate these algorithmic merits by an application to the optimization of the CO2 injection schedule in the Cranfield site using field data. The performance is benchmarked with genetic algorithm (GA) and covariance matrix adaptation evolution strategy (CMA-ES). Results show that IBO achieves competitive objective function value with over 60% less number of forward model evaluations. Furthermore, the Bayesian framework that BO builds upon allows uncertainty quantification and naturally extends to optimization under uncertainty.
Use of Horizontal Drift-Flux Models For Simulating Wellbore Flow in SAGD Operations
Authors: Heidari Mohammad , Istchenko Christopher , Bailey William , Stone Terry
Published: October 2021

The paper examines new horizontal drift-flux correlations for their ability to accurately model phase flow rates and pressure drops in horizontal and undulating wells that are part of a Steam-Assisted Gravity Drainage (SAGD) field operation. Pressure profiles within each well correlate to the overall performance of the pair. SAGD is a low-pressure process that is sensitive to reservoir heterogeneity and other factors, hence accurate simulation of in situ wellbore pressures is critical for both mitigating uneven steam chamber evolution and optimizing wellbore design and operation.

Recently published horizontal drift-flux correlations have been implemented in a commercial thermal reservoir simulator with a multi-segment well model. Valid for horizontally drilled wells with undulations, they complement previously reported drift-flux models developed for vertical and inclined wells down to approximately 5 degrees from horizontal. The formulation of these correlations has a high degree of nonlinearity. These models are tested in simulations of SAGD field operations.

First, an overview of drift-flux models is discussed. This differentiates those based on vertical flow with gravity segregation to those that model horizontal flow with stratified and slug flow regimes. Second, the most recent and significant drift-flux correlation by Bailey et al. (2018, and hereafter referred to as Bailey-Tang-Stone) was robustly designed to be used in the well model of a reservoir simulator, can handle all inclination angles and was optimized to experimental data from the largest available databases to date. This and earlier drift-flux models are reviewed as to their strengths and weaknesses. Third, governing equations and implementation details are given of the Bailey-Tang-Stone model. Fourth, six case studies are presented that illustrate homogeneous and drift-flux flow model differences for various well scenarios.
A Cut-Cell Polyhedral Finite Element Model for Coupled Fluid Flow and Mechanics in Fractured Reservoirs
Authors: Shovkun Igor , Tchelepi Hamdi A.
Published: October 2021

Mechanical deformation induced by injection and withdrawal of fluids from the subsurface can significantly alter the flow paths in naturally fractured reservoirs. Modeling coupled fluid-flow and mechanical deformation in fractured reservoirs relies on either sophisticated gridding techniques, or enhancing the variables (degrees-of-freedom) that represent the physics in order to describe the behavior of fractured formation accurately. The objective of this study is to develop a spatial discretization scheme that cuts the "matrix" grid with fracture planes and utilizes traditional formulations for fluid flow and geomechanics.

The flow model uses the standard low-order finite-volume method with the Compartmental Embedded fracture Model (cEDFM). Due to the presence of non-standard polyhedra in the grid after cutting/splitting, we utilize numerical harmonic shape functions within a Polyhedral finite-element (PFE) formulation for mechanical deformation. In order to enforce fracture-contact constraints, we use a penalty approach.

We provide a series of comparisons between the approach that uses conforming Unstructured grids and a Discrete Fracture Model (Unstructured DFM) with the new cut-cell PFE formulation. The manuscript analyzes the convergence of both methods for linear elastic, single-fracture slip, and Mandel’s problems with tetrahedral, Cartesian, and PEBI-grids. Finally, the paper presents a fully-coupled 3D simulation with multiple inclined intersecting faults activated in shear by fluid injection, which caused an increase in effective reservoir permeability.

Our approach allows for great reduction in the complexity of the (gridded) model construction while retaining the solution accuracy together with great saving in the computational cost compared with UDFM. The flexibility of our model with respect to the types of grid polyhedra allows us to eliminate mesh artifacts in the solution of the transport equations typically observed when using tetrahedral grids and two-point flux approximation.
Reduced-Order Modeling for Multiphase Flow Using a Physics-Based Deep Learning
Authors: Alsulaimani Thamer , Wheeler Mary
Published: October 2021

Reservoir simulation is the most widely used tool for oil and gas production forecasting and reservoir management. Solving a large-scale system of nonlinear differential equations every timestep can be computationally expensive. In this work, we present a two-phase physics-constrained deep-learning reduced-order model as a surrogate model for subsurface flow production forecast. The implemented deep learning model is a physics-guided encoder-decoder, constructed based on the Embed-to-Control (E2C) framework. In our implementation, the E2C works in a way analogous to Proper Orthogonal Decomposition combined with Discrete Empirical Interpolation Method (POD-DEIM) or Trajectory Piece-Wise Linearization approach (POD-TPWL). The E2C-Reduced-order model (ROM) involves projecting the system from a high-dimensional space into a low-dimensional subspace using the encoder-decoder, approximating the progression of the system from one timestep to the next using a linear transition model, and finally projecting the system back to high-dimensional space using the encoder-decoder. To guarantee mass conservation, we adopt the Finite Elements Mixed Formulation in the neural network's loss function combined with the original data-based loss function. Training simulations are generated using a full-physics reservoir simulator (IPARS). High-fidelity pressure, velocity, and saturation solution snapshot at constant time intervals are taken as training input to the neural network. After training, the model is tested over large variations of well control settings. Accurate pressure and saturation solutions are predicted along with the injection and production well quantities using the proposed approach. Errors in the predicted quantities of interest are reduced with the increase in the number of training simulations used. Although it required a large number of training simulations for the offline (training) step, the model achieved a significant speedup in the online stage compared to the full physics model. Considering the overall computational cost, this ROM model is proper for cases when a large number of simulations are required like in the case of production optimization and uncertainty assessments. The proposed approach shows the capability of the deep-learning reduced-order model to accurately predict multiphase flow behavior such as well quantities, and global pressure and saturation fields. The model honors mass conservation and the underlying physics laws, which many existing approaches don't take into direct consideration.
Simplified Initialization of Reservoir Simulation Models with Continuously Varying Tilted Contacts and Complex Fluid Distributions
Authors: Kachuma Dick , Hild Jean-Claude , Belushko Irina
Published: October 2021

We tackle reservoir simulation model initialization in situations where the current free water level has risen significantly above the original paleo-contact. In such cases, traditional initialization based on primary drainage capillary-gravity equilibrium is insufficient because it does not consider the reimbibition that takes place after primary oil migration. Usage of traditional initialization would require a generation of several thousands of regions to have a satisfactory representation of such phenomenon in the model.

We apply this initialization to a reservoir with a complex initial fluid in which hydrocarbons come from multiple sources resulting in significant spatial variation. To capture this spatial variation, the scripting feature of a commercial simulator is used to generate on-the-fly PVT regions and fluid descriptions based on continuous fluid properties. Capillary-gravity equilibrium is then used to initialize the model with the primary drainage capillary pressure curves at the paleo-contact, which gives the minimum water saturation for each grid-block. From this, an imbibition scanning curve is generated for a second initialization based on the current oil-water contact thereby including reimbibition.

Our proposed approach was applied to a giant carbonate field for which we were able to generate on-the-fly PVT regions based on continuous property maps of fluid properties such as oil API, solution gas-oil-ratio and water salinity. This resulted in fewer regions being generated and thereby reducing the amount of memory required to initialize the model. We were also able to reduce the initialization time as compared to use of discretized contacts and regions. Our approach enabled us to initialize with both the paleo- and current contacts allowed to continuously vary spatially. This together with the use of a more appropriate capillary pressure hysteresis model, resulted in an initial reservoir state that gave a better match to the saturations obtained from logs.

This work illustrates the creation of PVT data and fluid regions on-the-fly based on continuous fluid properties. We also demonstrate the use of continuously varying paleo- and current oil-water contacts without the need to discretize these into regions. These aspects facilitate the propagation of an uncertainty workflow, starting with continuous fluid properties and structural modelling, directly to the simulation model with no need for intermediate discretization and without massive computational penalties.
Downregulation of the microbial protein biosynthesis machinery in response to weeks, years, and decades of soil warming
Authors: Söllinger Andrea , Séneca Joana , Dahl Mathilde Borg , Motleleng Liabo L. , Prommer Judith , Verbruggen Erik , Sigurdsson Bjarni , Janssens Ivan , Penuelas Josep , Urich Tim , Richter Andreas , Tveit Alexander
Published: October 2021

How soil microorganisms respond to global warming is key to infer future soil-climate feedbacks, yet poorly understood. Here we applied metatranscriptomics to investigate microbial physiological responses to medium- (8 years) and long-term (>50 years) subarctic grassland soil warming of +6 °C. Besides indications for a community-wide upregulation of central metabolisms and cell replication we observed a downregulation of the protein biosynthesis machinery in the warmed soils, coinciding with a lower microbial biomass, RNA, and soil substrate content. We conclude that permanently accelerated reaction rates at higher temperatures and reduced substrate concentrations results in a cellular reduction of ribosomes, the macromolecular complexes carrying out protein biosynthesis. Later efforts to test this, including a short-term warming experiment (6 weeks, +6 °C), further supported our conclusion. Downsizing the protein biosynthesis machinery facilitates liberation of energy and matter, allowing microorganisms to maintain high metabolic activities and cell division rates even after decades of warming.
Computed Tomographic Analysis of Aortic Arch Branching Patterns - Revisited
Authors: Kodikara Iroshani , Gamage Dhanusha , De Soyza Sampath , Ilayperuma Isurani
Published: October 2021

Background /ObjectivesBranching pattern of aortic arch (AA) has a direct impact on the outcome of thoracic surgical and angiographic procedures. Geographical variations in the branching pattern of AA has been described. Hence, this descriptive cross sectional study describes the AA variations in a Sri Lankan population compared to the available global statistics.MethodsContrast-enhanced computed tomographic studies (CTC) of thorax (n=219) performed in males (49.3%) and females (50.7%); aged 59±17 years (range: 4 to 96 years), were evaluated. Branching patterns of AA were categorized into seven types as described by Popieluszko et al.ResultsFour AA types were identified in the study population: Type 1 (90%; n=197), Type 2 (n=10, 4.6%), Type 3 (n=8, 3.7%) and Type 6 AA (n=4; 1.8%). The prevalence of AA variations was 10%. Type 1 was the most prevalent pattern in both genders: female-91%; males-88.9%. The most prevalent AA variant in females was Type 2 (n=6; 5.4%); males Type 3 (n=5; 4.6%). However, the branching pattern of AA has not demonstrated a significant gender influence (Odds: 0.792; 95% CI: 0.327 - 1.917; p=0.605). ConclusionVariations in branching pattern of AA is as high as 10% among Sri Lankans. Thus, an in-depth knowledge on population specific prevalence of AA variants would influence the modifications surgical approaches and the choice of angiographic catheters to be utilized, which in turn, would minimize inadvertent vascular injuries during thoracic surgical and angiographic interventions.
Gait Analysis of Patients With a Rotating Hinge Knee Prosthesis After Revision Total Knee Arthroplasty
Authors: Ohmi Takehiro , Yamada Takumi , Misaki Sadaya , Tazawa Tomohiro , Shimamura Ryota , Kato Junpei , Sugimoto Kazutaka
Published: October 2021

Background/Objective: The number of revision total knee arthroplasty (TKA) procedures performed worldwide is increasing. The rotating hinge knee type (RHK) is used for revision TKA. There has been no report on motion analysis of patients with RHK prosthesis. Therefore, this study aimed to clarify the kinetics and kinematics during gait in patients who underwent revision TKA with an RHK prosthesis.Methods: This study had a cross-sectional design. The patients who underwent revision TKA with an RHK prosthesis (R-RHK; 14 patients, 24 knees), those who underwent unilateral primary TKA with a cruciate-retaining (CR) type prosthesis (uniCR; five patients, 5 knees), and those who underwent bilateral primary TKA with a CR type prosthesis (bilCR; 10 patients, 20 knees) were included. Their comfortable gait was analyzed. Spatiotemporal parameters and knee joint angle and moment were calculated. The knee joint angle and tibia translation were compared among the three groups using analysis of variance and a post-hoc Tukey test. The knee adduction moment was compared between groups by analysis of covariance controlling for gait speed.Results: Gait speed of the R-RHK group was significantly less than that of the other groups. The knee joint angle, knee adduction moment, and tibia translation were not different between the R-RHK, uniCR, and bilCR groups during gait and at the beginning of the stance phase.Conclusions: The gait of patients undergoing revision surgery using an RHK prosthesis because of aseptic loosening may have the same biomechanics as that of patients undergoing primary TKA using a different type of prosthesis.
Mitigation of Cyanide From Coke Plant Wastewater Using Chemical Oxidation Process
Authors: Mondal Amit , Saha Priyanka , Sarkar Supriya , Nair Udayabhanu G
Published: October 2021

Biological treatment with a stable activated sludge process, followed by chemical treatment is one of the potential and accepted cyanide treatment process for coke plant waste water treatment. Biologically treated coke plant wastewater contains free cyanide above permissible limit. Presently chemical treatment with NaOCl is being used to attenuate free cyanide below permissible limit in biologically treated water. This process increases the TDS and colour content in discharge water. Ca(OCl)2 can be used as an alternative to NaOCl for cyanide remediation in biologically treated coke plant waste water without increasing TDS. In the present work, cyanide removal efficiency of NaOCl and Ca(OCl)2 for real coke plant waste water after biological treatment has been studied. Optimisation of chemical dosage, treatment time and pH has been done for Ca(OCl)2 and NaOCl treatment. It was found that up to 90% of free cyanide removal could be achieved through Ca(OCl)2 treatment without increasing the TDS value. In addition, more than 50% colour of the waste water has been removed. pH elevation step required in NaOCl treatment can be eliminated in Ca(OCl)2 treatment, thereby reducing caustic consumption. In conclusion, use of Ca(OCl)2 is economically more viable than that of NaOCl in cyanide treatment.
Highly-efficient Isolation of Microcrystalline Cellulose and Nanocellulose From Sun Flower Seeds Waste via Environmentally Benign “soft” Method
Authors: Akatan Kydyrmolla , Kabdrakhmanova Sana , Kuanyshbekov Tilek , Ibraeva Zhanar , Battalova Ainur , KS Joshy , Thomas Sabu
Published: October 2021

The main focus of this study was to introduce a natural microcrystalline cellulosic raw material source from the residue of sunflower seeds, after oil extraction. For this purpose, the conventional method of organosolvent oxidation was changed to a "soft" method where microcrystalline cellulose (MCC) was extracted from sunflower seed husk (SFH) and the characterization of MCC and cellulose nanocrystals (CNCs) were carefully carried out. In this method, the concentration of acetic acid and hydrogen peroxide required for the preparation of peroxyacetic acid (PAA) was reduced by two times. The physico-chemical characterization, particle size, optical properties, chemical and crystal structure, surface morphology, and thermal stability of MCC and CNC were studied. The FTIR analysis, revealed the structural similarity of all derivatives of cellulose. Surface morphology was monitored with SEM, and the surface of MCC fibers was rough, and the morphology of cellulose nanocrystal (CNC) was fine, smooth and rod-like (overlapping each other) appearance, CNC film surface was found to be brown was filled with nano fibrils. The XRD analysis and determination of average particle size revealed that the MCC had a CI of 72.9%, a coherent scattering length (CSL) of 2.9 nm, and an average particle size of 1971 nm along the fibers, and a width of 266 nm. The acid hydrolysis resulted in the reduction of length of CNCs by 4 times and the width was reduced about 5 times. At the same time there observed an increase in the crystallinity index (CI) value. It was found that cellulose materials were destroyed at temperatures between 200ₒС and 358ₒС. Overall, it was concluded that sunflower seed husk was considered as a potential source for CNCs and the method employed for the extraction of cellulose from agricultural biomass waste was cost effective and environmentally benign.
Association of Thiol–disulfide Homeostasis with Retinal Layer Thickness in Patients with Diabetes Mellitus
Authors: Bilginer Muhammet Cuneyt , Tam Abbas Ali , Ogmen Berna Evranos , Guler Bagdagul Yuksel , Ugurlu Nagihan , Yulek Fatma , Erel Ozcan , Topaloglu Oya , Ersoy Reyhan , Çakir Bekir
Published: October 2021

Background: This study aimed to investigate the relationship between early changes in retinal layer thickness and thiol–disulfide homeostasis in patients with type II diabetes mellitus (T2DM).Materials-Methods: There were 69 patients with T2DM (61 patients without retinopathy, 8 patients with retinopathy) and 21 healthy controls. In patients without retinopathy, 31 of the patients had a disease duration under 10 years, 30 of the patients had a disease duration over 10 years. Retinal layer thickness of the right eye was measured using Spectral Domain Optical Coherence Tomography. Results: Patients with T2DM and healthy controls had mean ages of 48.40 ± 8.25 years and 45.94 ± 7.32 years, respectively. The ganglion cell layer and retinal pigment epithelium thicknesses were significantly lesser in patients without diabetic retinopathy than those in the control group. In patients without diabetic retinopathy and with a disease duration of under 10 years, there was a negative correlation between the retinal nerve fiber layer thickness (µm) and disulphide/total thiol ratio, between the inner nuclear layer thickness (µm) and disulphide/native thiol ratio as well as disulphide/total thiol ratio (r= −0.376, p= 0.037; r= −0.356, p= 0.050; r= −0.380, p= 0.035, respectively) and positive correlation between the INL thickness (µm) and native thiol/total thiol ratio (r= 0.359, p= 0.047).Conclusion: Early changes in retinal layers in patients with DM were associated with thiol–disulfide homeostasis. Administration of therapeutic supplements may aid in the management of low thiol concentrations; this increases the importance of the study findings.
Design of a Novel Optical Sensor for the Detection of Waterborne Bacteria Based on a Photonic Crystal with an Ultra-High Sensitivity
Authors: Daher Malek G. , Taya Sofyan A. , Colak Ilhami , Ramahi Omar M.
Published: October 2021

Today, detecting processes of waterborne bacteria in drinking water is a global challenge because these bacteria can lead to dangerous diseases to the human body. In this paper, we have developed a new sensor for the detection of waterborne bacteria based on a one-dimensional (1D) defective binary photonic crystal. The defect layer is taken as a water sample located in the middle of the photonic crystal structure. A resonant peak is then created within the photonic bandgap. The sensing mechanism of the proposed detector is based on the refractive index difference between pure water and waterborne bacteria samples. This index change leads to a shift in the resonant peak position in the transmission spectrum. The effects of many parameters, such as incident angle, defect layer thickness, thicknesses of periodic layers and the number of periods on the sensitivity are investigated. At the optimum conditions, the proposed sensor exhibits a sensitivity of 3639.53 nm/RIU which is ultra-high compared with recently published biosensor papers. The proposed design could distinguish between the different types of waterborne bacteria although the minute difference between their refractive indices. In addition to that, it has a simple design so that it can be easily fabricated.
Accuracy of the Breast Cancer Surveillance Consortium model among women with LCIS
Authors: Eroglu Idil , Sevilimedu Varadan , Park Anna , King Tari A , Pilewskie Melissa L
Published: October 2021

PurposeThe Breast Cancer Surveillance Consortium (BCSC) model predicts risk of invasive breast cancer risk based on age, race, family history, breast density, and history of benign breast disease, including lobular carcinoma in situ (LCIS). However, validation studies for this model included few women with LCIS. We sought to evaluate the accuracy of the BCSC model among this cohort. MethodsWomen with LCIS diagnosed between 1983 and 2017 were identified from a prospectively maintained database. The BCSC score was calculated; those with prior breast cancer, unknown breast density, aged <35 or >74 or history of chemoprevention use were excluded. The Kaplan-Meier method was used to estimate incidence rates. Time-dependent receiver operating characteristic (ROC) analysis was used to analyze the discriminative capacity of the model. Results1302 women with LCIS were included. At a median follow-up of 7 years, 152 women (12%) developed invasive cancer (6 with bilateral disease). Cumulative incidences of invasive breast cancer were 7.1% (95% CI 5.6, 8.7) and 13.3% (95% CI 10.9,15.6), respectively, and the median BCSC risk scores were 4.9 and 10.4, respectively, at 5 and 10 years. The median 10year BCSC score was significantly lower than the 10-year Tyrer-Cuzick score (10.4 vs 20.8, p<0.001). The ROC curve scores (AUC) for BCSC at 5 and 10 years were 0.59 (95% CI 0.52, 0.66) and 0.58 (95% CI 0.52, 0.64), respectively. ConclusionThe BCSC model has moderate accuracy in predicting invasive breast cancer risk among women with LCIS with fair discrimination for risk prediction between individuals.
Glycerol Esterification for Triacetin Production using Fe3O4@SiO2@PO43- as Heterogeneous Magnetic Catalyst
Authors: Ali Amjad , Abida Km , Jindal Ritika
Published: October 2021

To facilitate the magnetic separation, phosphate group is embedded onto silica-coated Fe3O4 magnetic nanoparticles to prepare Fe3O4@SiO2@PO43− solid catalyst for the glycerol esterification with acetic acid. The catalyst was characterized by powder X-ray diffraction (XRD), transmission electron microscopy (TEM), vibrating magnetic spectroscopy (VSM) and Fourier Transform Infrared (FTIR) spectroscopy. The Fe3O4@SiO2@PO43− magnetic catalyst during the glycerol esterification with acetic acid was found to demonstrate excellent glycerol conversion levels (97 %) while retaining 92 % triacetin selectivity. The plausible mechanism of glycerol esterification suggests the initiation of the reaction by the protonation of the acetic acid. The catalyst was recovered from the reaction mixture under the influence of external magnetic field and reused during 4 consecutive reaction cycles.
Health Professionals’ Preferences With the Use of Pegfilgrastim On-Body Injector At Oncology Centers in 8 Cities in Colombia.
Authors: Larrarte-Gonzalez Maria Alejandra , Pineda-Posada Mariana , Gaitan Alvaro , Amaya-Amaya Jenny , Ojeda Kelman
Published: October 2021

Purpose

Febrile neutropenia associated with some chemotherapy regimens can lead to potentially fatal complications and high health care costs. Administration of Pegfilgrastim 27 hours after chemotherapy, using an On-Body Injector (OBI), may be more convenient for cancer patients and physicians. This study aims to describe physician and nurse preferences regarding options for administration of Pegfilgrastim at cancer centers, the demographics of the study population and characteristics of participating cancer centers. We aim to determine the conditions and chemotherapy schemes for which Pegfilgrastim is most frequently prescribed.
Methods

Observational, descriptive, cross-sectional study and survey, conducted between 2019 and 2020. It included 60 participants who were contacted and surveyed via telephone.
Results

Information from 60 participants was analyzed. It was found that 35% of the respondents are haemato-oncologists, oncologists or hematologists, 30% general practitioners, and 35% are other healthcare professionals (i.e., nurse, oncology nurse and head nurse). 48.02% of professionals give priority to the use of OBI to treat patients at home; among the reasons for this choice the patient's transportation limitations and the release of the nursing staff to attend to another patient in the patient's absence stand out.
Conclusions

The present study is the first one in Colombia that sought the preferences of healthcare professionals with the use of Neulastim (OBI) compared to pre-filled syringes for the administration of Pegfilgrastim. Our results suggest OBI is the preferred alternative by most HCPs and a good resource optimization strategy in the context of cancer patients’ health care in Colombia.
Effects of Pilot-Scale Co-Composting of Gentamicin Mycelial Residue with Rice Chaff on Gentamicin Degradation, Compost Maturity and Microbial Community Dynamics
Authors: Bu Wenjing , Wan Junfeng , Zhang Huimin , Liu Nan , Wang Ke , Wang Yan
Published: October 2021

Purpose It is challenging to treat and manage gentamicin mycelial residue (GMR) due to the high residual antibiotic content in GMR. The objective of this study is aimed to investigate the viability of recycling GMR by co-composting with rice chaff, describe the dynamics of the physicochemical and biology parameters and microbial community, and evaluate the maturity of the compost products. Methods The co-composting process applied was a pilot-scale composting process under the conditions found outside the laboratory. Three 1-tonne piles of fresh GMR were composted in each treatment; test treatment contained rich chaff and the control did not. Dried GMR was made by drying fresh GMR. Three compost treatments were as follows: (1) no rice chaff, 8:1 weight/weight fresh GMR-to-dried GMR (CK); (2) 8:1 weight/weight fresh GMR-to-rice chaff (T1); and (3) 4:1 weight/weight fresh GMR-to-rice chaff (T2). The raw materials were made into three compost cones of 2 m diameter and 1.5 m height.Results The optimal fresh GMR:rice chaff ratio (w/w) was 4:1. Over 99% of gentamicin was degraded after 73 days of co-composting. The key parameters of the final products, such as the pH, C/N ratio, germination index and crop growth indexes, all met the national standards for compost maturity indicators. Compared with those of fungi, the abundance and diversity of bacteria obviously increased during co-composting. Canonical correlation analysis (CCA) revealed that the bacterial community dynamics were closely correlated with the amount of residual gentamicin. Micromonospora and Enterococcus may have been the key microorganisms degrading the gentamicin.Conclusion The addition of rice chaff improved the decomposition of gentamicin residue in the GMR and made the GMR usable in fertilizer; this result could help antibiotic production factories recycle more of their waste products. The results provide new insight into the potential for co-composting with rice chaff to achieve sustainable GMR management.
Investigation of The Influence of Halogens, Cycloalkanes, Carbonyls and the Hydroxyl Groups on The Reactivity of 1, 3-Butadienes With Imine Dienophile: Insight From DFT, MEDT, and QTAIM
Authors: Asogwa Fredrick , Louis Hitler , Kenthurky Dollars I. , Enudi Obieze C.
Published: October 2021

The presence of nitrogen atom either on the diene or the dienophile structure gives rise to aza or imino-Diels-Alder reactions (DARs). Among hetero-DARs, imino-dienophiles yields numerous functionalized compounds with numerous biological activities including but not limited to antifungal, antibacterial and enzymatic properties. Density functional theory (DFT) using the B3LYP functional at the 6-31+G (d, p) basis set along with topological studies (QTAIM) were used for the investigation of 10 different (SD1-SD10) DARs which differ in the nature of substituents groups attached to the diene molecule. The study indicates higher electron density and stronger interaction for substituted dienes with the cycloalkanes, furan, carbonyl, and -OH groups. It was observed in the HOMO-LUMO energy differences that the bulky groups; SD3, SD4, and SD6 had destabilization energy of -7.86 and 0.09, -7.88 and 0.10 and -7.50 and -0.014 eV respectively in their HOMO and LUMO levels while the halogen substituted dienes SD1, SD2, and SD5 had -8.20 and -0.32, -8.31 and -0.34 and -8.19 and -0.20 eV respectively. The study showed that synthesis of hetero-nuclear aza-cyclohexene is achieved faster with furan substituent of energy gap 7.534 eV and molecular hardness of 3.677 compared to 7.799 (SD7) -8.100 eV (SD8) and 3.899 (SD7) - 4.050 (SD8) respectively scored by other substituents noting that smaller energy gap leads to higher reactivity. HCOCH3 (SD8) retarded the rate of the reaction by about 58% (unspontaneous) following the calculated Gibb’s free energy of activation while SD6 in the ELF analysis showed complete covalent character against other cycloalkanes that showed dual characteristics of a double and single bond between N-C at their transition states.
Hypoconnectivity networks in schizophrenia patients: a voxelwise meta-analysis of rs-fMRI
Authors: Ruiz-Torras Silvia , Fernández-Vazquez Oscar , Cañete-Massé Cristina , Peró-Cebollero Maribel , Guàrdia-Olmos Joan
Published: October 2021

In the last few years, the field of brain connectivity has focused on identifying biomarkers to describe different health states and to discriminate between patients and healthy controls through the characterization of brain networks. A particularly interesting case, because of the symptoms' severity, is the work done with samples of patients diagnosed with schizophrenia. This meta-analysis aims to identify connectivity networks with different activation patterns between people diagnosed with schizophrenia and healthy controls. Therefore, we collected primary studies exploring whole brain connectivity by functional magnetic resonance imaging at rest in patients with schizophrenia compared to healthy people. Thus, we identified 25 high-quality studies that included a total of 1285 people with schizophrenia and 1279 healthy controls. The results indicate hypoactivation in the right precentral gyrus and in the left superior temporal gyrus of people with schizophrenia compared with the control group. These regions have been linked to deficits in gesticulation and the experience of auditory hallucinations in people with schizophrenia. A study of heterogeneity demonstrated that the effect size was influenced by the sample size and type of analysis. These results imply new contributions to the knowledge, diagnosis, and treatment of schizophrenia both clinically and in research.
Effects of Intraspecific Density on Garlic Mustard (Alliaria Petiolata) Sinigrin Concentration
Authors: Harris Mercedes , Cousins Elsa , Stinson Kristina
Published: October 2021

The production of secondary defense chemicals in plants represents a trade-off between defense and the primary functions of growth and reproduction, but the relative allocation to growth versus defense varies across species, types of defenses, ontogeny, and environment. Alliaria petiolata (garlic mustard) is a brassica that produces glucosinolates, a class of constituent secondary metabolites that defend against herbivores and pathogens. Sinigrin, a hydrolyzed product of glucosinolate present in garlic mustard, may aid in its success as an invasive species by disrupting native plant–mycorrhizae mutualisms and decreasing forest species diversity in North America. Here, we measured sinigrin concentration in garlic mustard populations of different field densities and in greenhouse experiments to evaluate the relationship between sinigrin concentration and growth in response to density and varying environmental conditions. We found clear evidence for growth vs. defense tradeoffs in both experimental and field settings, as well as higher levels of defense in more densely growing, smaller individual plants. However, sinigrin levels and tradeoffs were not explained by soil fertility or light, allowing us to conclude that sinigrin expression is not controlled by limitations in the measured abiotic factors. Our findings suggest sinigrin leaf concentration increases at high densities despite the pressures of intraspecific competition that demand allocation to growth.
Interfacial Effects of Plasticizers On The Properties of Cellulose Diacetate Materials
Authors: Hu Shuaishuai , Zhang Mengting , Wei Yufan , Qi Rui , Zhu Yujia , Chen Shuangjun
Published: October 2021

Cellulose diacetate (CDA) is a biodegradable biobased polymer, which is not easily melt-processable with a narrow window between flowing and decomposition temperature. Selecting the appropriate plasticizer and evaluating its effect on the properties are important for the application developments of CDA materials. In this paper, eco-friendly plasticizers, such as glycerin (GL), diacetin (DA), triacetin (TA) and polyethylene glycol (PEG) are necessarily introduced. The plasticizing effect is evaluated by harmonic interface energy between CDA and plasticizer from contact angle test. Compared with solubility parameter method, the interfacial method is more effective to estimate PEG with different molecular weight in CDA. Thermal behaviors verify the interfacial method works. Lower interface energy of CDA-DA shows lower glass transition temperature (Tg) determined by differential scanning calorimetry (DSC). Meanwhile, mechanical property, transparency and moisture permeability are all related to the interfacial effects of plasticizers. For instance, the elongation at break of CDA-DA and CDA-TA, which have lower interfacial energy, increase from 14% to 26% and 29%. The CDA-DA and CDA-TA films also have high visible light transmittance about 90%. The moisture permeability of plasticized CDA film increases with the decreased interface energy between water and plasticizer. Interfacial method provides a new route for preparing high-performance CDA materials.