bluuebunny/crossref_metadata_2025 huggingface dataset:
Predictive Factors of Adequate Bowel Cleansing for Colonoscopy in the Elderly: A Retrospective Analysis of a Prospective Cohort
Authors: Maida Marcello , Facciorusso Antonio , Sinagra Emanuele , Morreale Gaetano , Sferrazza Sandro , Scalisi Giuseppe , Pallio Socrate , Camilleri Salvatore
Published: November 2022

Factors affecting the quality of bowel preparation for colonoscopy in the elderly are not fully known, and current guidelines provide no specific recommendations. This study aimed to assess the difference in bowel cleansing in young and elderly patients and evaluate predictors of bowel cleansing in the elderly. We retrospectively reviewed a prospective cohort of 1289 patients performing colonoscopy after a 1-, 2-, or 4-L PEG-based preparation. All 1289 were included in the analysis. Overall, 44.6% of patients were aged ≥65 years. Cleansing success (CS) was achieved in 77.3% and 70.3% of patients aged <65 years and ≥65 years, respectively. At multivariable analysis, split regimen (OR = 2.43, 95% CI = 1.34–4.38; p = 0.003), adequate cleansing at previous colonoscopy (OR = 2.29, 95% CI = 1.14–4.73; p = 0.02), tolerability score (OR = 1.29, 95% CI = 1.16–1.44; p < 0.001), a low-fiber diet for at least 3 days (OR = 2.45, 95% CI = 1.42–4.24; p = 0.001), and colonoscopy within 5 h after the end of preparation (OR = 2.67, 95% CI = 1.28–5.56; p = 0.008) were independently associated with CS in the elderly. Combining a low-fiber diet for at least 3 days, split preparation, and colonoscopy within 5 h allowed a CS rate above 90% and should always be encouraged. A 1-L PEG-ASC preparation was also associated with greater high-quality cleansing of the right colon and may be preferred.
Investigation of SARS-CoV-2 Variants and Their Effect on SARS-CoV-2 Monoclonal Antibodies, Convalescent and Vaccine Plasma by a Novel Web Tool
Authors: Arikan Ayse , Sayan Murat
Published: November 2022

(1) Background: SARS-CoV-2 variants possess specific mutations throughout their genome; however, the effect of these mutations on pathogenesis is little known. The study aimed to identify SARS-CoV-2 variants and their susceptibility rate against monoclonal antibodies, convalescent, and vaccine plasma. (2) Methods: Strains isolated from COVID-19 cases in Turkey in April and September 2021 were involved. Illuma Nextera XT was processed for NGS, followed by virtual phenotyping (Coronavirus Antiviral and Resistance Database (CoV-RDB) by Stanford University). (3) Results: Among 211 strains, 79% were SARS-CoV-2 variants. B.1.1.7 (Alpha) was the most dominant, followed by B.1.617.2 (Delta), B.1.351 (Beta), and B.1.525 (Eta). Alpha and Delta were less susceptible to Etesevimab—Sotrovimab and Bamlanivimab—Etesevimab, respectively. Reduced efficacy was observed for convalescent plasma in Beta and Delta; AstraZeneca, Comirnaty plus AstraZeneca in Alpha; Comirnaty, Moderna, Novovax in Beta; Comirnaty in Delta. (4) Conclusion: CoV-RDB analysis is an efficient, rapid, and helpful web tool for SARS-CoV-2 variant detection and susceptibility analysis.
Bone-Metabolism-Related Serum microRNAs to Diagnose Osteoporosis in Middle-Aged and Elderly Women
Authors: Zhao Sheng-Li , Wen Zhen-Xing , Mo Xiao-Yi , Zhang Xiao-Yan , Li Hao-Nan , Cheung Wing-Hoi , Fu Dan , Zhang Shi-Hong , Wan Yong , Chen Bai-Ling
Published: November 2022

Objective: Postmenopausal osteoporosis (PMOP), a chronic systemic metabolic disease prevalent in middle-aged and elderly women, heavily relies on bone mineral density (BMD) measurement as the diagnostic indicator. In this study, we investigated serum microRNAs (miRNAs) as a possible screening tool for PMOP. Methods: This investigation recruited 83 eligible participants from 795 community-dwelling postmenopausal women between June 2020 and August 2021. The miRNA expression profiles in the serum of PMOP patients were evaluated via miRNA microarray (six PMOP patients and four postmenopausal women without osteoporosis (n-PMOP) as controls). Subsequently, results were verified in independent sample sets (47 PMOP patients and 26 n-PMOP controls) using quantitative real-time PCR. In addition, the target genes and main functions of the differentially expressed miRNAs were explored by bioinformatics analysis. Results: Four highly expressed miRNAs in the serum of patients (hsa-miR-144-5p, hsa-miR-506-3p, hsa-miR-8068, and hsa-miR-6851-3p) showed acceptable disease-independent discrimination performance (area under the curve range: 0.747–0.902) in the training set and verification set, outperforming traditional bone turnover markers. Among four key miRNAs, hsa-miR-144-5p is the only one that can simultaneously predict changes in BMD in lumbar spine 1–4, total hip, and femoral neck (β = −0.265, p = 0.022; β = −0.301, p = 0.005; and β = −0.324, p = 0.003, respectively). Bioinformatics analysis suggested that the differentially expressed miRNAs were targeted mainly to YY1, VIM, and YWHAE genes, which are extensively involved in bone metabolism processes. Conclusions: Bone-metabolism-related serum miRNAs, such as hsa-miR-144-5p, hsa-miR-506-3p, hsa-miR-8068, and hsa-miR-6851-3p, can be used as novel biomarkers for PMOP diagnosis independent of radiological findings and traditional bone turnover markers. Further study of these miRNAs and their target genes may provide new insights into the epigenetic regulatory mechanisms of the onset and progression of the disease.
Application of Machine Learning in Epileptic Seizure Detection
Authors: Tran Ly V. , Tran Hieu M. , Le Tuan M. , Huynh Tri T. M. , Tran Hung T. , Dao Son V. T.
Published: November 2022

Epileptic seizure is a neurological condition caused by short and unexpectedly occurring electrical disruptions in the brain. It is estimated that roughly 60 million individuals worldwide have had an epileptic seizure. Experiencing an epileptic seizure can have serious consequences for the patient. Automatic seizure detection on electroencephalogram (EEG) recordings is essential due to the irregular and unpredictable nature of seizures. By thoroughly analyzing EEG records, neurophysiologists can discover important information and patterns, and proper and timely treatments can be provided for the patients. This research presents a novel machine learning-based approach for detecting epileptic seizures in EEG signals. A public EEG dataset from the University of Bonn was used to validate the approach. Meaningful statistical features were extracted from the original data using discrete wavelet transform analysis, then the relevant features were selected using feature selection based on the binary particle swarm optimizer. This facilitated the reduction of 75% data dimensionality and 47% computational time, which eventually sped up the classification process. After having been selected, relevant features were used to train different machine learning models, then hyperparameter optimization was utilized to further enhance the models’ performance. The results achieved up to 98.4% accuracy and showed that the proposed method was very effective and practical in detecting seizure presence in EEG signals. In clinical applications, this method could help relieve the suffering of epilepsy patients and alleviate the workload of neurologists.
Entropy of Quantum Measurements
Authors: Gudder Stanley
Published: November 2022

If a is a quantum effect and ρ is a state, we define the ρ-entropy Sa(ρ) which gives the amount of uncertainty that a measurement of a provides about ρ. The smaller Sa(ρ) is, the more information a measurement of a gives about ρ. In Entropy for Effects, we provide bounds on Sa(ρ) and show that if a+b is an effect, then Sa+b(ρ)≥Sa(ρ)+Sb(ρ). We then prove a result concerning convex mixtures of effects. We also consider sequential products of effects and their ρ-entropies. In Entropy of Observables and Instruments, we employ Sa(ρ) to define the ρ-entropy SA(ρ) for an observable A. We show that SA(ρ) directly provides the ρ-entropy SI(ρ) for an instrument I. We establish bounds for SA(ρ) and prove characterizations for when these bounds are obtained. These give simplified proofs of results given in the literature. We also consider ρ-entropies for measurement models, sequential products of observables and coarse-graining of observables. Various examples that illustrate the theory are provided.
Quantum Coherence in Loopless Superconductive Networks
Authors: Lucci Massimiliano , Campanari Valerio , Cassi Davide , Merlo Vittorio , Romeo Francesco , Salina Gaetano , Cirillo Matteo
Published: November 2022

Measurements indicating that planar networks of superconductive islands connected by Josephson junctions display long-range quantum coherence are reported. The networks consist of superconducting islands connected by Josephson junctions and have a tree-like topological structure containing no loops. Enhancements of superconductive gaps over specific branches of the networks and sharp increases in pair currents are the main signatures of the coherent states. In order to unambiguously attribute the observed effects to branches being embedded in the networks, comparisons with geometrically equivalent—but isolated—counterparts are reported. Tuning the Josephson coupling energy by an external magnetic field generates increases in the Josephson currents, along the above-mentioned specific branches, which follow a functional dependence typical of phase transitions. Results are presented for double comb and star geometry networks, and in both cases, the observed effects provide positive quantitative evidence of the predictions of existing theoretical models.
Contrast-Controllable Image Enhancement Based on Limited Histogram
Authors: Fan Xin , Wang Junyan , Wang Haifeng , Xia Changgao
Published: November 2022

To address the technical shortcomings of conventional histogram equalization (HE), such as over-enhancement and artifacts, we propose a histogram-constrained and contrast-tunable HE technique for digital image enhancement. Firstly, the input image histogram is partitioned into two parts, the main histogram and the constrained histogram, by a cumulative probability density threshold; second, the main histogram is redistributed equally in the whole grayscale range; and finally, the nonlinearity of the constrained histogram is mapped to the main histogram. The experimental averages show that the values of the two metrics, information entropy and MS-SSIM, processed by the algorithms in this paper, are more accurate compared to the other six excellent algorithms.
An Experimental Study on a Wind Turbine Rotor Affected by Pitch Imbalance
Authors: Mazzeo Francesco , Micheletto Derek , Talamelli Alessandro , Segalini Antonio
Published: November 2022

An experimental and numerical investigation about the pitch imbalance effect on a wind turbine model is performed. The characterization of the power losses and loads generated on a small-scale model and the validation of an analytical framework for the performance of unbalanced rotors are proposed. Starting from the optimal collective pitch assessment (performed to identify the condition with the maximum power coefficient), the pitch of just one blade was systematically changed: it is seen that the presence of a pitch misalignment is associated with a degradation of the turbine performance, visible both from experiments and from Blade Element Momentum (BEM) calculations (modified to account for the load asymmetry). Up to 30% power losses and a 15% thrust increase are achievable when an imbalanced rotor operates at tip speed ratios around five, clearly highlighting the importance of avoiding this phenomenon when dealing with industrial applications. The numerical model predicts this result within 5% accuracy. Additional numerical simulations showed that, away from the optimal collective pitch, the blade imbalance can provide a power increase or a power decrease with respect to the balanced case, suggesting how an operator can maximise the production of an unbalanced rotor. An analysis of the axial and lateral forces showed a sensitivity of the loads’ standard deviation when imbalance is present. An increase of the lateral loads was observed in all unbalanced cases.
Solar Photovoltaic Power Estimation Using Meta-Optimized Neural Networks
Authors: Gumar Ali Kamil , Demir Funda
Published: November 2022

Solar photovoltaic technology is spreading extremely rapidly and is becoming an aiding tool in grid networks. The power of solar photovoltaics is not static all the time; it changes due to many variables. This paper presents a full implementation and comparison between three optimization methods—genetic algorithm, particle swarm optimization, and artificial bee colony—to optimize artificial neural network weights for predicting solar power. The built artificial neural network was used to predict photovoltaic power depending on the measured features. The data were collected and stored as structured data (Excel file). The results from using the three methods have shown that the optimization is very effective. The results showed that particle swarm optimization outperformed the genetic algorithm and artificial bee colony.
Exergy and Thermoeconomic Analyses of a Carnot Battery System Comprising an Air Heat Pump and Steam Turbine
Authors: Lee Kum-Jung , Seo Seok-Ho , Cho Junhyun , Oh Si-Doek , Choi Sang-Ok , Kwak Ho-Young
Published: November 2022

In this study, exergy and thermoeconomic analyses were performed on a Carnot battery system (CBS) comprising an air heat pump and steam turbine. The Carnot battery technology employed in this study stored electricity as heat for 10 h during night-time, when electricity prices were low, and generated electricity for 10 h with power output of 500 MW during the day, when electricity prices were relatively high. The roundtrip efficiency and the energy density of the CBS were approximately 0.37, and 150.0 MJ/m3, respectively. When the unit cost of electricity supplied to the heat pump system was approximately 23.0 USD/GJ, that of electricity produced by the steam turbine was approximately 70.8 USD/GJ. The thermoeconomic results indicate that the changes in electricity cost flow rate relative to the reference value can be used as an indicator to make purchasing, replacement, and research and development decisions for specific components of the CBS.
Magnet Slotting Design to Reduce High Order Electromagnetic Force and Vibration of Permanent Magnet Motor
Authors: Wang Zifei , Tian Wei , Zhao Wenxiang
Published: November 2022

In this paper, a new method is proposed to suppress the vibration caused by the modulation effect of high-order electromagnetic forces in permanent magnet (PM) motors. Firstly, the modulation effect of the radial force was investigated, which indicated that the higher-order electromagnetic force could cause modulated vibrations through the modulation effect. Then, auxiliary slots on the magnet surface and their effect on vibration reduction were investigated. The optimal shape of the auxiliary slot was found to minimize the noise of motor vibration. Finally, the method was verified experimentally.
Effect of Landfill Arson to a “Lax” System in a Circular Economy under the Current EU Energy Policy: Perspective Review in Waste Management Law
Authors: Zębek Elżbieta M. , Zięty Jakub J.
Published: November 2022

The current EU energy policy aims to diversify energy sources to ensure energy security while decarbonising the economy and promoting low carbon and clean energy technologies. These tasks are carried out under the European Green Deal Program. Therefore, the overriding goal at present is to search for new sources of energy, including energy recovery from waste. In EU countries, the legal system for waste management is adapted to the circular economy. In Poland, due to the legal possibility of temporary storage and disposal of waste, a substantial volume of industrial waste is temporarily stored and landfilled (above 40%), compared to the importance of waste subjected to treatment. Moreover, energy recovery from waste accounts for a negligible share (below 5%). It may be due to the high costs of these processes, stringent emissions and environmental quality standards. Therefore, as in certain EU countries, the problem of landfill site arson attacks has been exacerbated in Poland (177 fires in 2019). The aim of this article is to determine the relationship between the application of the existing regulations concerning closed-loop waste management and the effectiveness of methods, ways and economic instruments preventing the illegal burning of landfill waste in Poland under the current EU energy policy. Therefore, it can be assumed that this system is not complete. Based on factor force analysis at a scale 1–5, it was found that technological (3.4), legal (3.16) and economic (3.0) factors have the greatest impact on this system. The waste management system should be oriented towards increased waste recovery and a more significant reduction in the volume of temporarily stored waste and landfill waste. It should be considered whether the current move away from the incineration of waste, according to the new EU energy policy, is a better solution in environmental and economic terms than incurring very high costs due to eliminating the effects of the incineration of landfill waste that causes environmental damage.
Improvement of Gas Compressibility Factor and Bottom-Hole Pressure Calculation Method for HTHP Reservoirs: A Field Case in Junggar Basin, China
Authors: Xia Yun , Bai Wenpeng , Xiang Zhipeng , Wang Wanbin , Guo Qiao , Wang Yang , Cheng Shiqing
Published: November 2022

Gas reservoirs discovered in the southern margin of the Junggar Basin generally have high temperatures (up to 172.22 °C) and high pressures (up to 171.74 MPa). If using the PVT laboratory to get the gas compressibility factor, data from the laboratory are so little that it will not satisfy the demands of reservoir engineering calculations. There are many empirical correlations for calculating the Z-factor; however, these correlations give large errors at high gas reservoir pressures. The errors in estimating the Z-factor will lead to large errors in estimating all the other gas properties such as gas formation volume factor, gas compressibility, and gas in place. In this paper, a new accurate Z-factor correlation has been developed based on PVT data by correcting the high-pressure part of the most commonly used Dranchuk-Purvis-Robinson Correlation. Multivariate nonlinear regression is used to establish the independent variable function of pseudo-critical temperatures and pressures. By comparing it with the PVT data, the DPR correlation is continuously corrected to be suitable for ultra-deep gas reservoirs with HTHP. The new correlation can be used to determine the Z-factor at any pressure range, especially for high pressures, and the error is less than 1% compared to the PVT data. Then, based on the corrected Z-factor, the Cullender-Smith method is used to calculate the bottom hole pressure in the middle of the reservoir. Finally, the Z-factor under reservoir conditions of well H2 is predicted and the Z-factor chart at different temperatures is provided.
Numerical Study on Particulate Fouling Characteristics of Flue with a Particulate Fouling Model Considering Deposition and Removal Mechanisms
Authors: Liu Peng , Liu Wei , Gong Kexin , Han Chengjun , Zhang Hong , Sui Zhucheng , Hu Renguo
Published: November 2022

Due to a large amount of particulate matter in industrial flue gas, the formation of particulate deposits on the flue wall will increase the instability of equipment operation, which needs to be solved urgently. In this paper, a numerical investigation on the characteristics of particulate deposition and removal in the furnace flue was carried out for waste heat and energy recovery. This research adopted a comprehensive fouling model combined with the discrete phase model (DPM) which was performed by the CFD framework and extended by user-defined functions (UDFs). Firstly, the particulate deposition and removal algorithms were proposed to develop the judgment criterion of particle fouling based on the Grant and Tabakoff particle–wall rebound model and the Johnson–Kendall–Roberts (JKR) theory. This model not only considered the particles transport, sticking, rebound, and removal behaviors, but also analyzed the deposition occurring through the multiple impactions of particles with the flue wall. Then, the influence of furnace gas velocity, particle concentration, and inflection angle α of the tee section on the particulate fouling were predicted. The results show that the furnace gas velocity, particle concentration, and flue structure have significant effects on particle fouling and distribution, and the particle fouling mainly occurs in the blind elbow section and the tee sections of the flue. In addition, the fouling mass of particles decreases with an increasing furnace gas velocity and the decrease in particle concentration. Lastly, the fouling mass of particles decreases with the increase in the inflection angle α of the tee section, and the location of particle fouling gradually transfers from the blind elbow section to the tee section.
Research on the Mixing Mechanism and Parameter Optimization of Liquid Nitrogen Foam Generator
Authors: Liu Yulin , Wang Dongming , Xie Min , Xu Huanhuan , Ren Xiaohan
Published: November 2022

Nitrogen foam is expected to be an effective method to extinguish large-scale fires and suppress explosions. The key to its foaming process is that gas nitrogen (N2) and a foam solution are mixed uniformly in a foam generator. Moreover, liquid nitrogen (LN2) has been proposed as a source of gas nitrogen to generate nitrogen foam in previous experimental works. In this paper, the flow and heat transfer characteristics between liquid nitrogen and the foam solution are explored by numerical methods, which are then utilized to optimize the parameters of the foam generator. It is found that the flow pattern of the foam generator with a cone spoiler is a stratified flow by establishing the mixture and Lee’s evaporation-condensation model in ANSYS Fluent. Moreover, the spoiler in the foam generator plays a crucial role in breaking LN2 into droplets and increasing the contact area between phases, and a distance of 10 mm from the inlets to the spoiler is recommended. From previous results, an unreasonable flow rate ratio of LN2 to a foam solution may lead to icing or a low volume fraction of nitrogen, thus a flow rate ratio of 1:50 is determined in the foam generator. As for the shape of the spoiler, the spiral spoiler shows the best foaming performance compared to cone and semisphere spoilers, due to its fluid rotation instead of translation, which effectively increases the N2 volume fraction of foam from 0.616 to 0.717. Therefore, the mixing characteristics of the foam generator in this work lays a foundation for devising a practical nitrogen foam generator.
Taxonomic and Biochemical Characterization of Microalga Graesiella emersonii GEGS21 for Its Potential to Become Feedstock for Biofuels and Bioproducts
Authors: Kang Nam Seon , Cho Kichul , An Sung Min , Kim Eun Song , Ki Hyunji , Lee Chung Hyeon , Choi Grace , Hong Ji Won
Published: November 2022

Graesiella emersonii is a commercially exploitable source of bioactive compounds and biofuels with potential applications in microalgae-based industries. Despite this, little taxonomical information is available. Therefore, proper identification and characterization are needed for the sustainable utilization of isolated microalgae. In this study, an axenically isolated unicellular green alga from the Geumgang Estuary, Korea was investigated for its morphological, molecular, and biochemical characteristics. The morphological characteristics were typical of G. emersonii. Molecular phylogenetic analysis of the 18S rDNA sequence verified that the isolate belonged to G. emersonii and was subsequently named G. emersonii GEGS21. It was isolated from brackish water, and its optimal growth temperature, salinity, and light intensity were at 28–32 °C, 0 M NaCl, and 130–160 µmol m−2 s−1, respectively. The strain thrived over a range of temperatures (5–40 °C) and withstood up to 0.5 M NaCl. The isolate was rich in omega-6 linoleic acid (C18:2 n-6, 26.3%) and palmitic acid (C16:0, 27.5%). The fuel quality properties were determined, and biodiesel from GEGS21 could be used as a biodiesel blend. Value-added carotenoids lutein (1.5 mg g−1 dry cell weight, DCW) and neoxanthin (1.2 mg g−1 DCW) were biosynthesized as accessory pigments by this microalga. The biomass of this microalga may serve as feedstock for biodiesel production as well as producing valuable ω-6 and carotenoids.
Development of Broken Rotor Bar Fault Diagnosis Method with Sum of Weighted Fourier Series Coefficients Square
Authors: Gu Bon-Gwan
Published: November 2022

This study proposes a broken rotor bar (BRB) fault diagnosis method for an induction motor using the sum of the weighted Fourier series coefficients squares of a complex current as a diagnosis signal. First, the sum of the squares of the Fourier series coefficients confirms the very narrow band-pass filter characteristics to derive a specific frequency component. This assists us in obtaining a BRB fault diagnosis signal that exists in a limited frequency range. Second, the magnitude of the Fourier series coefficients of the BRB fault signal is proportional to the slip frequency and load condition. A weighting factor is proposed to render the BRB fault signal irrelevant to the slip frequency and load condition. Consequently, the proposed fault diagnosis can be conducted without the slip frequency information or searching for the maximum coefficient component. Finally, the proposed fault diagnosis method is validated through experiments using a 55 kW induction motor with and without a BRB fault. It is implemented with a DSP controller at time intervals of 20, 10, 5, and 4 s for the Fourier series. The proposed diagnosis method performs well under various load conditions and shows that the derived fault signal exhibits a large difference between healthy and BRB faulty induction motors.
Multi-Controller Model for Improving the Performance of IoT Networks
Authors: Davanam Ganesh , Kallam Suresh , Singh Ninni , Gunjan Vinit Kumar , Roy Sudipta , Rahebi Javad , Farzamnia Ali , Saad Ismail
Published: November 2022

Internet of Things (IoT), a strong integration of radio frequency identifier (RFID), wireless devices, and sensors, has provided a difficult yet strong chance to shape existing systems into intelligent ones. Many new applications have been created in the last few years. As many as a million objects are anticipated to be linked together to form a network that can infer meaningful conclusions based on raw data. This means any IoT system is heterogeneous when it comes to the types of devices that are used in the system and how they communicate with each other. In most cases, an IoT network can be described as a layered network, with multiple tiers stacked on top of each other. IoT network performance improvement typically focuses on a single layer. As a result, effectiveness in one layer may rise while that of another may fall. Ultimately, the achievement issue must be addressed by considering improvements in all layers of an IoT network, or at the very least, by considering contiguous hierarchical levels. Using a parallel and clustered architecture in the device layer, this paper examines how to improve the performance of an IoT network’s controller layer. A particular clustered architecture at the device level has been shown to increase the performance of an IoT network by 16% percent. Using a clustered architecture at the device layer in conjunction with a parallel architecture at the controller layer boosts performance by 24% overall.
Access to Deposits as a Stage of Mining Works
Authors: Skrzypkowski Krzysztof , Zagórski Krzysztof , Zagórska Anna , Sengani Fhatuwani
Published: November 2022

An extremely important role in the underground method of mining deposits is the type of access, which is the initial stage of the mining operations. The main feature of mineral deposits is their exhaustiveness and the inability to restore resources. This necessitates the rational management of deposit resources, especially the minimization of resource losses during exploitation. This article presents information on the mining area and methods of access the deposits. In particular, attention was paid to the advantages and disadvantages of access by means of an adit, decline, and a vertical and inclined shaft. Given the relationships among the various stages of mining works, it was found that the number of active levels depends on the volume of production and the adopted mining methods. In addition, attention was drawn to the fact that the access to deposits at increasing depth is related to the intensification of natural hazards that affect the access structure.
Entropy Production Using Ecological and Physiological Models of Stand Growth Dynamics as an Example
Authors: Lisitsyn Victor Ivanovich , Matveev Nikolai Nikolatvich
Published: November 2022

According to the Prigogine–Glansdorff principle, in a thermodynamic system where non-equilibrium processes occur, the change in entropy production is negative or equal to zero. Forest plantations are an example of such thermodynamic systems. Based on the ecological–physiological models of the forest stand growth dynamics developed by the authors of this work, the specific entropy production in the growth of stands was calculated, which is known to be proportional to the entropy production. It is shown that at the age when the minimum value of the specific entropy production is reached, the biomass of the stand has a maximum value. This conclusion is an important predictive factor for practical forestry, since determining the time of the minimum entropy production allows us to determine the time interval at which the biomass of the stand reaches its maximum value.
Fire Severity in Reburns Depends on Vegetation Type in Arizona and New Mexico, U.S.A.
Authors: Yocom Larissa L. , Jenness Jeff , Fulé Peter Z. , Thode Andrea E.
Published: November 2022

After more than a century of low fire activity in the western United States, wildfires are now becoming more common. Reburns, which are areas burned in two or more fires, are also increasing. How fires interact over time is of interest ecologically as well as for management. Wildfires may act as fuel treatments, reducing subsequent fire severity, or they may increase subsequent fire severity by leaving high fuel loads behind. Our goal was to assess whether previous wildfire severity influenced subsequent fire severity across vegetation types and over time in the Southwest U.S. using remotely sensed fire severity data in 2275 fires that burned between 1984 and 2019. Points that reburned tended to be those that burned with lower severity initially. Shrublands burned predominantly at moderate to high severity in initial fires and in reburns. Pinyon-juniper-oak systems burned with mixed severity, and fire severity was consistent from fire to fire. In ponderosa pine and aspen-mixed conifer, fire severity tended to decrease with each fire. Initial and subsequent fire severity was lower in points that reburned after a short interval. These remotely sensed observations of reburn severity need verification through field work to understand specific effects caused by reburns in different ecosystems. However, in ponderosa pine and aspen-mixed conifer forests, it may be beneficial to consider wildfires as fuel treatments and work to maintain the fuel reduction effects they have on forested ecosystems.
Simultaneous Production and Immobilization of Lipase Using Pomegranate-Seed Residue: A New Biocatalyst for Hydrolysis Reactions and Structured Lipids Synthesis
Authors: Diniz Marianne M. , Pereira Adejanildo da S. , Albagli Gabriel , Amaral Priscilla F. F.
Published: November 2022

Pomegranate-seed residue (PSR) was used in a new strategy for the simultaneous production of Yarrowia lipolytica lipase by submerged fermentation and its immobilization by adsorption. This biocatalyst—the fermented solid residue containing the adsorbed lipase (fermPSR)—was evaluated in hydrolysis reactions and in structured lipid synthesis. In shake flasks, yeast extract and urea were the best nitrogen sources for lipase production with PSR and their simultaneous use increased the lipase production even further. This result was confirmed in a 3.5-liter bioreactor, with lipase activity in an extracellular medium of 40 U/mL. A maximum reaction rate (Vmax) of 49.5 µmol/min/g, a Michaelis–Menten constant (Km) of 207 µmol/L, and a turnover number (Kcat) of 130 s−1 were determined for the new biocatalyst, fermPSR, for the hydrolysis of p-nitrophenyl laurate (p-NPL) into p-nitrophenol. The conversion of p-NPL into p-nitrophenol in subsequent reactions confirmed fermPSR’s potential for industrial hydrolytic reactions. The production of structured lipids from vegetable oil and free fatty acids by fermPSR evidences the versatility of this new biocatalyst.
Internal Energy Relaxation Processes and Bulk Viscosities in Fluids
Authors: Bruno Domenico , Giovangigli Vincent
Published: November 2022

Internal energy relaxation processes in fluid models derived from the kinetic theory are revisited, as are related bulk viscosity coefficients. The apparition of bulk viscosity coefficients in relaxation regimes and the links with equilibrium one-temperature bulk viscosity coefficients are discussed. First, a two-temperature model with a single internal energy mode is investigated, then a two-temperature model with two internal energy modes and finally a state-to-state model for mixtures of gases. All these models lead to a unique physical interpretation of the apparition of bulk viscosity effects when relaxation characteristic times are smaller than fluid times. Monte Carlo numerical simulations of internal energy relaxation processes in model gases are then performed, and power spectrums of density fluctuations are computed. When the energy relaxation time is smaller than the fluid time, both the two temperature and the single-temperature model including bulk viscosity yield a satisfactory description. When the energy relaxation time is larger than the fluid time, however, only the two-temperature model is in agreement with Boltzmann equation. The quantum population of a He-H2 mixture is also simulated with detailed He-H2 cross sections, and the resulting bulk viscosity evaluated from the Green–Kubo formula is in agreement with the theory. The impact of bulk viscosity in fluid mechanics is also addressed, as well as various mathematical aspects of internal energy relaxation and Chapman–Enskog asymptotic expansion for a two-temperature fluid model.
Computational Screening of Plant-Derived Natural Products against SARS-CoV-2 Variants
Authors: Ansari Waseem Ahmad , Khan Mohd Aamish , Rizvi Fahmina , Ali Kajim , Hussain Mohd Kamil , Saquib Mohammad , Khan Mohammad Faheem
Published: November 2022

The present study explores the efficacy of plant-derived natural products (PDNPs) against spike glycoproteins (S-glycoprotein) of SARS-CoV-2 variants using molecular docking, ADMET, molecular dynamics (MD) simulation and density-functional theory (DFT) analysis. In all, 100 PDNPs were screened against spike glycoprotein of SARS-CoV-2 variants, namely alpha (B.1.1.17), beta (B.1.351), delta (B.1.617), gamma (P.1) and omicron (B.1.1.529). Results showed that rutin, EGCG, hesperidin, withanolide G, rosmarinic acid, diosmetin, myricetin, epicatechin and quercetin were the top hit compounds against each of the SARS-CoV-2 variants. The most active compounds, rutin, hesperidin, EGCG and rosmarinic acid gave binding scores of −10.2, −8.1, −8.9, −8.3 and −9.2 kcal/mol, against omicron, delta, alpha, beta and gamma variants, respectively. Further, the stability of docked complexes was confirmed by the analysis of molecular descriptors (RMSD, RMSF, SASA, Rg and H-bonds) in molecular dynamic simulation analysis. Moreover, the physiochemical properties and drug-likeness of the tested compounds showed that they have no toxicity or carcinogenicity and may be used as druggable targets. In addition, the DFT study revealed the higher activity of the tested compounds against the target proteins. This led us to conclude that rutin, hesperidin, EGCG and rosmarinic acid are good candidates to target the S-glycoproteins of SARS-CoV-2 variants. Further, in vivo and clinical studies needed to develop them as drug leads against existing or new SARS-CoV-2 variants are currently underway in our laboratory.
miR-9 and miR-181a Target Gab2 to Inhibit the Proliferation and Migration of Hepatocellular Carcinoma HepG2 Cells
Authors: Huang Lantang , Liu Ruimin , Zhou Peiyi , Tian Yingpu , Lu Zhongxian
Published: November 2022

The incidence of liver cancer ranks seventh globally, with nearly half of all cases occurring in East Asia, but currently, there are very few drugs to treat it. Our previous studies demonstrated that the signal integration protein Gab2 is a potential drug target for the prevention and therapy of liver cancer. Here, we screened for and identified two miRNAs that target Gab2 to suppress the proliferation and migration of hepatocellular carcinoma (HCC) cells. First, we predicted Gab2-targeting miRNAs through biological websites, and we selected nine miRNAs that were reported in the literature as being abnormally expressed in liver cancer and fatty liver tissue. Then, we measured the expression of these miRNAs in the hepatic epithelial cell line HL-7702 and the HCC cell line HepG2. The expression levels of miR-9, miR-181a, miR-181c, miR-34a, and miR-134 were high in HL-7702 cells but low in HepG2 cells, and their expression patterns were the opposite of Gab2 in these cells. Furthermore, we transfected miR-9, miR-34a, miR-181a, and miR-181c mimics into HepG2 cells and found that only miR-9 and miR-181a reduced the level of Gab2 proteins. miR-9 also reduced the Gab2 mRNA level, but miR-181a did not affect the Gab2 mRNA levels. Using a miRNA-Gab2 3′UTR binding reporter, we confirmed that miR-9 and miR-181a bind to the Gab2 3′UTR region. Finally, we introduced miR-9 and miR-181a mimics into HepG2 cells and found that cell proliferation and migration were significantly inhibited. In conclusion, we identified two novel miRNAs targeting Gab2 and provided potential drug targets for the prevention and treatment of liver cancer.
Novel Homozygous ADAMTS2 Variants and Associated Disease Phenotypes in Dogs with Dermatosparactic Ehlers–Danlos Syndrome
Authors: Jaffey Jared A. , Bullock Garrett , Guo Juyuan , Mhlanga-Mutangadura Tendai , O’Brien Dennis P. , Coates Joan R. , Morrissey Rochelle , Hutchison Robert , Donnelly Kevin S. , Cohn Leah A. , Katz Martin L. , Johnson Gary S.
Published: November 2022

Tissue fragility, skin hyperextensibility and joint hypermobility are defining characteristics of Ehlers–Danlos syndrome (EDS). Human EDS is subclassified into fourteen types including dermatosparactic EDS, characterized by extreme skin fragility and caused by biallelic ADAMTS2 mutations. We report two novel, ADAMTS2 variants in DNA from EDS-affected dogs. Separate whole-genome sequences from a Pit Bull Terrier and an Alapaha Blue Blood Bulldog each contained a rare, homozygous variant (11:2280117delC, CanFam3.1), predicted to produce a frameshift in the transcript from the first coding ADAMTS2 exon (c.10delC) and a severely truncated protein product, p.(Pro4ArgfsTer175). The clinical features of these dogs and 4 others with the same homozygous deletion included multifocal wounds, atrophic scars, joint hypermobility, narrowed palpebral fissures, skin hyperextensibility, and joint-associated swellings. Due to severe skin fragility, the owners of all 6 dogs elected euthanasia before the dogs reached 13 weeks of age. Cross sections of collagen fibrils in post-mortem dermal tissues from 2 of these dogs showed hieroglyphic-like figures similar to those from cases of severe dermatosparaxis in other species. The whole-genome sequence from an adult Catahoula Leopard Dog contained a homozygous ADAMTS2 missense mutation, [11:2491238G>A; p.(Arg966His)]. This dog exhibited multifocal wounds, atrophic scars, and joint hypermobility, but has survived for at least 9 years. This report expands the spectrum of clinical features of the canine dermatosparactic subtype of EDS and illustrates the potential utility of subclassifying canine EDS by the identity of gene harboring the causal variant.
Comparative Pan-Genomic Analysis Revealed an Improved Multi-Locus Sequence Typing Scheme for Staphylococcus aureus
Authors: Jalil Maira , Quddos Fatima , Anwer Farha , Nasir Samavi , Rahman Abdur , Alharbi Metab , Alshammari Abdulrahman , Alshammari Huda Kamel , Ali Amjad
Published: November 2022

The growing prevalence of antibiotic-resistant Staphylococcus aureus strains mandates selective susceptibility testing and epidemiological investigations. It also draws attention to an efficient typing strategy. Whole genome sequencing helps in genetic comparison, strain differentiation, and typing; however, it is not that cost-effective. In comparison, Multi-Locus Sequence Typing (MLST) is an efficient typing method employed for bacterial strain typing and characterizations. In this paper, a comprehensive pangenome and phylogenetic analysis of 502/1279 S. aureus genomes is carried out to understand the species divergence. Additionally, the current Multi-Locus Sequence Typing (MLST) scheme was evaluated, and genes were excluded or substituted by alternative genes based on reported shortcomings, genomic data, and statistical scores calculated. The data generated were helpful in devising a new Multi-Locus Sequence Typing (MLST) scheme for the efficient typing of S. aureus strains. The revised scheme is now a blend of previously used genes and new candidate genes. The genes yQil, aroE, and gmk are replaced with better gene candidates, opuCC, aspS, and rpiB, based on their genome localization, representation, and statistical scores. Therefore, the proposed Multi-Locus Sequence Typing (MLST) method offers a greater resolution with 58 sequence types (STs) in comparison to the prior scheme’s 42 STs.
Assessment of the Genetic Relationship and Population Structure in Oil-Tea Camellia Species Using Simple Sequence Repeat (SSR) Markers
Authors: Yan Heqin , Qi Huasha , Li Yang , Wu Yougen , Wang Yong , Chen Jianmiao , Yu Jing
Published: November 2022

Oil-tea camellia trees, the collective term for a class of economically valuable woody oil crops in China, have attracted extensive attention because of their rich nutritional and pharmaceutical value. This study aimed to analyze the genetic relationship and genetic diversity of oil-tea camellia species using polymorphic SSR markers. One-hundred and forty samples of five species were tested for genetic diversity using twenty-four SSR markers. In this study, a total of 385 alleles were identified using 24 SSR markers, and the average number of alleles per locus was 16.0417. The average Shannon’s information index (I) was 0.1890, and the percentages of polymorphic loci (P) of oil-tea camellia trees were 7.79−79.48%, indicating that oil-tea camellia trees have low diversity. Analysis of molecular variance (AMOVA) showed that the majority of genetic variation (77%) was within populations, and a small fraction (23%) occurred among populations. Principal coordinate analysis (PCoA) results indicated that the first two principal axes explained 7.30% (PC1) and 6.68% (PC2) of the total variance, respectively. Both UPGMA and PCoA divided the 140 accessions into three groups. Camellia oleifera clustered into one class, Camellia vietnamensis and Camellia gauchowensis clustered into one class, and Camellia crapnelliana and Camellia chekiangoleosa clustered into another class. It could be speculated that the genetic relationship of C. vietnamensis and C. gauchowensis is quite close. SSR markers could reflect the genetic relationship among oil-tea camellia germplasm resources, and the results of this study could provide comprehensive information on the conservation, collection, and breeding of oil-tea camellia germplasms.
Microsatellite Characterization and Panel Selection for Brown Bear (Ursus arctos) Population Assessment
Authors: Buono Vincenzo , Burgio Salvatore , Macrì Nicole , Catania Giovanni , Hauffe Heidi C. , Mucci Nadia , Davoli Francesca
Published: November 2022

An assessment of the genetic diversity and structure of a population is essential for designing recovery plans for threatened species. Italy hosts two brown bear populations, Ursus arctos marsicanus (Uam), endemic to the Apennines of central Italy, and Ursus arctos arctos (Uaa), in the Italian Alps. Both populations are endangered and occasionally involved in human–wildlife conflict; thus, detailed management plans have been in place for several decades, including genetic monitoring. Here, we propose a simple cost-effective microsatellite-based protocol for the management of populations with low genetic variation. We sampled 22 Uam and 22 Uaa individuals and analyzed a total of 32 microsatellite loci in order to evaluate their applicability in individual identification. Based on genetic variability estimates, we compared data from four different STR marker sets, to evaluate the optimal settings in long-term monitoring projects. Allelic richness and gene diversity were the highest for the Uaa population, whereas depleted genetic variability was noted for the Uam population, which should be regarded as a conservation priority. Our results identified the most effective STR sets for the estimation of genetic diversity and individual discrimination in Uam (9 loci, PIC 0.45; PID 2.0 × 10−5), and Uaa (12 loci, PIC 0.64; PID 6.9 × 10−11) populations, which can easily be utilized by smaller laboratories to support local governments in regular population monitoring. The method we proposed to select the most variable markers could be adopted for the genetic characterization of other small and isolated populations.
Identification and Validation of UPF1 as a Novel Prognostic Biomarker in Renal Clear Cell Carcinoma
Authors: Wu Chun , Li Hongmu , Chang Wuguang , Zhong Leqi , Zhang Lin , Wen Zhesheng , Mai Shijuan
Published: November 2022

Background: Up frameshift protein 1 (UPF1) is a key component of nonsense-mediated mRNA decay (NMD) of mRNA containing premature termination codons (PTCs). The dysregulation of UPF1 has been reported in various cancers. However, the expression profile of UPF1 and its clinical significance in clear cell renal cell carcinoma (ccRCC) remains unclear. Methods: In order to detect UPF1 expression in ccRCC and its relationship with the clinical features of ccRCC, bulk RNA sequencing data were analyzed from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and ArrayExpress databases. The impact of UPF1 on the immune microenvironment of ccRCC was evaluated by multiple immune scoring algorithms to identify the cell groups that typically express UPF1 using ccRCC single cell sequencing (scRNA) data. In addition, genes co-expressed with UPF1 were identified by the weighted gene correlation network analysis (WGCNA), followed by KEGG and Reactome enrichment analysis. A series of functional experiments were performed to assess the roles of UPF1 in renal cancer cells. Finally, pan-cancer analysis of UPF1 was also performed. Results: Compared with normal tissues, the expression levels of UPF1 mRNA and protein in tumor tissues of ccRCC patients decreased significantly. In addition, patients with low expression of UPF1 had a worse prognosis. Analysis of the immune microenvironment indicated that UPF1 immune cell infiltration was closely related and the ccRCC scRNA-seq data identified that UPF1 was mainly expressed in macrophages. WGCNA analysis suggested that the functions of co-expressed genes are mainly enriched in cell proliferation and cellular processes. Experimental tests showed that knockdown of UPF1 can promote the invasion, migration and proliferation of ccRCC cells. Lastly, pan-cancer analysis revealed that UPF1 disorders were closely associated with various cancer outcomes. Conclusions: UPF1 may play a tumor suppressive role in ccRCC and modulate the immune microenvironment. The loss of UPF1 can predict the prognosis of ccRCC, making it a promising biomarker and providing a new reference for prevention and treatment.