bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of the Phytochemical and Pharmacological Potential of Taif’s Rose (Rosa damascena Mill var. trigintipetala) for Possible Recycling of Pruning Wastes
Authors: Galal Tarek M. , Al-Yasi Hatim M. , Fawzy Mustafa A. , Abdelkader Tharwat G. , Hamza Reham Z. , Eid Ebrahem M. , Ali Esmat F.
Published: February 2022

This study investigated the phytochemical contents of Taif’s rose pruning wastes and their potential application as phytomedicine, thereby practicing a waste-recycling perspective. In the Al-Shafa highland, four Taif rose farms of various ages were chosen for gathering the pruning wastes (leaves and stems) for phytochemical and pharmacological studies. The leaves and stems included significant amounts of carbohydrates, cardiac glycosides, alkaloids, flavonoids, and other phenolic compounds. The cardiac glycoside and flavonoid contents were higher in Taif rose stems, while the phenolic and alkaloid contents were higher in the plant leaves. Cardiovascular glycosides (2.98–5.69 mg g−1), phenolics (3.14–12.41 mg GAE g−1), flavonoids (5.09–9.33 mg RUE g −1), and alkaloids (3.22–10.96 mg AE g−1) were among the phytoconstituents found in rose tissues. According to the HPLC analysis of the phenolic compounds, Taif’s rose contains flavonoid components such as luteolin, apigenin, quercetin, rutin, kaempferol, and chrysoeriol; phenolics such as ellagic acid, catechol, resorcinol, gallic acid, and phloroglucinol; alkaloids such as berbamine, jatrorrhizine, palmatine, reticuline, isocorydine, and boldine. Warm water extract was highly effective against Bacillus subtilis, Escherichia coli, and Proteus vulgaris, whereas methanol and cold water extracts were moderately effective against Aspergillus fumigatus and Candida albicans. The study’s findings suggested that Taif’s rose wastes could be used for varied medical purposes.
Objective Perfusion Assessment in Gracilis Muscle Interposition—A Novel Software-Based Approach to Indocyanine Green Derived Near-Infrared Fluorescence in Reconstructive Surgery
Authors: Lobbes Leonard A. , Hoveling Richelle J. M. , Schmidt Leonard R. , Berns Susanne , Weixler Benjamin
Published: February 2022

Background: Gracilis muscle interposition (GMI) is an established treatment option for complex perineal fistulas and reconstruction. The outcome is limited by complications such as necrosis, impaired wound healing and fistula persistence or recurrence. Quantifiable methods of assessing muscle flap perfusion intraoperatively are lacking. This study evaluates a novel and objective software-based assessment of indocyanine green near-infrared fluorescence (ICG-NIRF) in GMI. Methods: Intraoperative ICG-NIRF visualization data of five patients with inflammatory bowel disease (IBD) undergoing GMI for perineal fistula and reconstruction were analyzed retrospectively. A new software was utilized to generate perfusion curves for the specific regions of interest (ROIs) of each GMI by depicting the fluorescence intensity over time. Additionally, a pixel-to-pixel and perfusion zone analysis were performed. The findings were correlated with the clinical outcome. Results: Four patients underwent GMI without postoperative complications within 3 months. The novel perfusion indicators identified here (shape of the perfusion curve, maximum slope value, distribution and range) indicated adequate perfusion. In one patient, GMI failed. In this case, the perfusion indicators suggested impaired perfusion. Conclusions: We present a novel, software-based approach for ICG-NIRF perfusion assessment, identifying previously unknown objective indicators of muscle flap perfusion. Ready for intraoperative real-time use, this method has considerable potential to optimize GMI surgery in the future.
Friction Issues over the Railway Wheels-Axis Assembly Motion
Authors: Derbiszewski Bogdan , Obraniak Andrzej , Wozniak Marek , Rylski Adam , Siczek Krzysztof , Kubiak Przemyslaw
Published: February 2022

The frictional issues during motion of the axis-wheels assembly occurring in contact wheel–rail and in bogie bearing were studied. The influence of greases upon friction therein was also considered. The lateral dynamic behavior of the four-axle freight wagon model with two-axle Y25 bogies equipped with swing bolster was analyzed. Simulation models of such a wagon with bogies with and without swing bolsters were elaborated for calculations considering the nonlinearities of wheel–rail contact geometry and nonlinear methods of bogie stability. In these two options, the cases of empty and fully loaded wagon bodies were considered. The lateral dynamic models with 22 and 24 degrees of freedom were considered to determine the nonlinear critical speeds of a freight wagon. It was found that the resistive torque in bearings of the assembly studied varied nonlinearly with wagon speed. During motion along the curve track, values of such a torque can be higher by 50% in case of the wheel under overloading and lower by 50% in case of the wheel under underloading, respectively, compared to those obtained during motion along straight track.
Theory of Antiferromagnet-Based Detector of Terahertz Frequency Signals
Authors: Safin Ansar , Nikitov Sergey , Kirilyuk Andrei , Tyberkevych Vasyl , Slavin Andrei
Published: February 2022

We present a theory of a detector of terahertz-frequency signals based on an antiferromagnetic (AFM) crystal. The conversion of a THz-frequency electromagnetic signal into the DC voltage is realized using the inverse spin Hall effect in an antiferromagnet/heavy metal bilayer. An additional bias DC magnetic field can be used to tune the antiferromagnetic resonance frequency. We show that if a uniaxial AFM is used, the detection of linearly polarized signals is possible only for a non-zero DC magnetic field, while circularly polarized signals can be detected in a zero DC magnetic field. In contrast, a detector based on a biaxial AFM can be used without a bias DC magnetic field for the rectification of both linearly and circularly polarized signals. The sensitivity of a proposed AFM detector can be increased by increasing the magnitude of the bias magnetic field, or by by decreasing the thickness of the AFM layer. We believe that the presented results will be useful for the practical development of tunable, sensitive and portable spintronic detectors of THz-frequency signals based of the antiferromagnetic resonance (AFMR).
Abnormality Detection and Failure Prediction Using Explainable Bayesian Deep Learning: Methodology and Case Study with Industrial Data
Authors: Nor Ahmad Kamal Mohd , Pedapati Srinivasa Rao , Muhammad Masdi , Leiva Víctor
Published: February 2022

Mistrust, amplified by numerous artificial intelligence (AI) related incidents, is an issue that has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and is fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle such an issue. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced into XAI. However, many deficiencies, particularly the lack of explanation assessment methods and uncertainty quantification, plague this young domain. In the present paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model and Shapley additive explanations (SHAP) to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as a marker for anomalies and expands the prognostic explanation scope to include the model’s confidence. In addition, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of studies on PHM-XAI. The quality of the explanation is examined employing local accuracy and consistency properties. The elaborated framework is tested on real-world gas turbine anomalies and synthetic turbofan failure prediction data. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed a 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.
Generalized q-Difference Equations for q-Hypergeometric Polynomials with Double q-Binomial Coefficients
Authors: Cao Jian , Srivastava Hari M. , Zhou Hong-Li , Arjika Sama
Published: February 2022

In this paper, we apply a general family of basic (or q-) polynomials with double q-binomial coefficients as well as some homogeneous q-operators in order to construct several q-difference equations involving seven variables. We derive the Rogers type and the extended Rogers type formulas as well as the Srivastava-Agarwal-type bilinear generating functions for the general q-polynomials, which generalize the generating functions for the Cigler polynomials. We also derive a class of mixed generating functions by means of the aforementioned q-difference equations. The various results, which we have derived in this paper, are new and sufficiently general in character. Moreover, the generating functions presented here are potentially applicable not only in the study of the general q-polynomials, which they have generated, but indeed also in finding solutions of the associated q-difference equations. Finally, we remark that it will be a rather trivial and inconsequential exercise to produce the so-called (p,q)-variations of the q-results, which we have investigated here, because the additional forced-in parameter p is obviously redundant.
New Sharp Double Inequality of Becker–Stark Type
Authors: Zhu Ling
Published: February 2022

In this paper, we establish new sharp double inequality of Becker–Stark type by using a role of the monotonicity criterion for the quotient of power series and the estimation of the ratio of two adjacent even-indexed Bernoulli numbers. The inequality results are better than those in the existing literature.
Permutation Variation and Alternative Hyper-Sphere Decomposition
Authors: Li Qingze , Pan Jianxin
Published: February 2022

Current covariance modeling methods work well in longitudinal data analysis. In the analysis of data with no nature order, a common covariance modeling method would be inadequate. In this paper, a study is implemented to investigate the effects of permutations of data on the estimation of covariance matrix Σ. Based on the Hyper-sphere decomposition method (HPC), this study suggests that the change of data’s permutation breaks the consistency of covariance estimation. An alternative Hyper-sphere decomposition method with permutation invariant is introduced later in this paper. The alternative method’s consistency and asymptotic normality are studied when the observations follow a normal distribution. These results are tested using some example studies. Furthermore, a real data analysis is conducted for illustration purposes.
Data-Driven Maintenance Priority and Resilience Evaluation of Performance Loss in a Main Coolant System
Authors: Dui Hongyan , Xu Zhe , Chen Liwei , Xing Liudong , Liu Bin
Published: February 2022

The main coolant system (MCS) plays a vital role in the stability and reliability of a nuclear power plant. However, human errors and natural disasters may cause some reactor coolant system components to fail, resulting in severe consequences such as nuclear leakage. Therefore, it is crucial to perform a resilience analysis of the MCS, to effectively reduce and prevent losses. In this paper, a resilience importance measure (RIM) for performance loss is proposed to evaluate the performance of the MCS. Specifically, a loss importance measure (LIM) is first proposed to indicate the component maintenance priority of the MCS under different failure conditions. Based on the LIM, RIMs for single component failure and multiple component failures were developed to measure the recovery efficiency of the system performance. Finally, a case study was conducted to demonstrate the proposed resilience measure for system reliability. Results provide a valuable reference for increasing the system security of the MCS and choosing the appropriate total maintenance cost.
Intelligent Optimization Based Multi-Factor Deep Learning Stock Selection Model and Quantitative Trading Strategy
Authors: Wang Jujie , Zhuang Zhenzhen , Feng Liu
Published: February 2022

With the rapid development of financial research theory and artificial intelligence technology, quantitative investment has gradually entered people’s attention. Compared with traditional investment, the advantage of quantitative investment lies in quantification and refinement. In quantitative investment technology, quantitative stock selection is the foundation. Without good stock selection ability, the effect of quantitative investment will be greatly reduced. Therefore, this paper builds an effective multi-factor stock selection model based on intelligent optimization algorithms and deep learning and proposes corresponding trading strategies based on this. First of all, this paper selects 26 effective factors of financial indicators, technical indicators and public opinion to construct the factor database. Secondly, a Gated Recurrent Unit (GRU) neural network based on the Cuckoo Search (CS) optimization algorithm is used to build a stock selection model. Finally, a quantitative investment strategy is designed, and the proposed multi-factor deep learning stock selection model based on intelligent optimization is applied to practice to test its effectiveness. The results show that the quantitative trading strategy based on this model achieved a Sharpe ratio of 127.08%, an annualized rate of return of 40.66%, an excess return of 13.13% and a maximum drawdown rate of −17.38% during the back test period. Compared with other benchmark models, the proposed stock selection model achieved better back test performance.
Robust Stability Analysis of Unstable Second Order Plus Time-Delay (SOPTD) Plant by Fractional-Order Proportional Integral (FOPI) Controllers
Authors: Asadi Marzieh , Farnam Arash , Nazifi Hamed , Roozbehani Sam , Crevecoeur Guillaume
Published: February 2022

This study investigates the robust stability analysis of an unstable second order plus time-delay (SOPTD) plant by using Fractional-Order Proportional Integral (FOPI) controllers. We assume that there are simultaneous uncertainties in gain, time-constants, and time-delay of the plant. At first, a graphical method is provided for a robust stability analysis of the closed-loop system. Then, a robust stability checking function is introduced to facilitate the robust stability analysis. Additionally, new bounds are presented to reduce the computational burden for the robust stability analysis. Finally, two examples are provided to show the correctness of the proposed method.
A Theoretical Analysis of a Fractional Multi-Dimensional System of Boundary Value Problems on the Methylpropane Graph via Fixed Point Technique
Authors: Rezapour Shahram , Deressa Chernet Tuge , Hussain Azhar , Etemad Sina , George Reny , Ahmad Bashir
Published: February 2022

Few studies have investigated the existence and uniqueness of solutions for fractional differential equations on star graphs until now. The published papers on the topic are based on the assumption of existence of one junction node and some boundary nodes as the origin on a star graph. These structures are special cases and do not cover more general non-star graph structures. In this paper, we state a labeling method for graph vertices, and then we prove the existence results for solutions to a new family of fractional boundary value problems (FBVPs) on the methylpropane graph. We design the chemical compound of the methylpropane graph with vertices specified by 0 or 1, and on every edge of the graph, we consider fractional differential equations. We prove the existence of solutions for the proposed FBVPs by means of the Krasnoselskii’s and Scheafer’s fixed point theorems, and further, we study the Ulam–Hyers type stability for the given multi-dimensional system. Finally, we provide an illustrative example to examine our results.
Weight Vector Generation in Multi-Criteria Decision-Making with Basic Uncertain Information
Authors: Xu Ya-Qiang , Jin Le-Sheng , Chen Zhen-Song , Yager Ronald R. , Špirková Jana , Kalina Martin , Borkotokey Surajit
Published: February 2022

This paper elaborates the different methods to generate normalized weight vector in multi-criteria decision-making where the given information of both criteria and inputs are uncertain and can be expressed by basic uncertain information. Some general weight allocation paradigms are proposed in view of their convenience in expression. In multi-criteria decision-making, the given importance for each considered criterion may have different extents of uncertainty. Accordingly, we propose some special induced weight-allocation methods. The inputs can be also associated with varying uncertainty extents, and then we develop several induced weight-generation methods for consideration. In addition, we present some suggested and prescriptive weight allocation rules and analyze their reasonability.
Novel Generalized Proportional Fractional Integral Inequalities on Probabilistic Random Variables and Their Applications
Authors: Sudsutad Weerawat , Jarasthitikulchai Nantapat , Thaiprayoon Chatthai , Kongson Jutarat , Alzabut Jehad
Published: February 2022

This study investigates a variety of novel estimations involving the expectation, variance, and moment functions of continuous random variables by applying a generalized proportional fractional integral operator. Additionally, a continuous random variable with a probability density function is presented in context of the proportional Riemann–Liouville fractional integral operator. We establish some interesting results of the proportional fractional expectation, variance, and moment functions. In addition, constructive examples are provided to support our conclusions. Meanwhile, we discuss a few specific examples that may be extrapolated from our primary results.
Kinematic Model and Redundant Space Analysis of 4-DOF Redundant Robot
Authors: Li Yu , Wang Liang
Published: February 2022

The kinematics and redundant space analysis of redundant robots constitutes important research content. Currently, methods such as geometric method and iterative optimization method are relatively complicated and inconvenient for programming and practical control applications. Moreover, little research has been conducted on redundant space analysis. This paper takes the 4-DOF PowerCube redundant robot as the research object. The forward and inverse kinematics equation of the robot are established based on the D-H matrix method, projection and cosine theorem method, and vector coordinate method. Then, the redundant space of the robot is obtained and the redundant space range of the robot is analyzed. Finally, the simulation of kinematic and redundant space research is conducted. It is shown that the change of each joint angle of the robot is smooth without mutation, so it can ensure the stable motion of the robot. Additionally, the different position and redundant variable have a big influence on robotic configuration. The calculation amount is relatively small and convenient for programming application, and the path calculation data of the joints are obtained, which are conducive to the motion control of modular robots. The method used in this paper can be extended to the multi-DOF redundant robot. Finally, the motion optimization, control, and obstacle-avoidance of redundant robots are briefly studied. The results of this paper provide an important basis for these aspects of research.
Network Representation Learning Algorithm Based on Complete Subgraph Folding
Authors: Chen Dongming , Nie Mingshuo , Yan Jiarui , Wang Dongqi , Gan Qianqian
Published: February 2022

Network representation learning is a machine learning method that maps network topology and node information into low-dimensional vector space. Network representation learning enables the reduction of temporal and spatial complexity in the downstream data mining of networks, such as node classification and graph clustering. Existing algorithms commonly ignore the global topological information of the network in network representation learning, leading to information loss. The complete subgraph in the network commonly has a community structure, or it is the component module of the community structure. We believe that the structure of the community serves as the revealed structure in the topology of the network and preserves global information. In this paper, we propose SF-NRL, a network representation learning algorithm based on complete subgraph folding. The algorithm preserves the global topological information of the original network completely, by finding complete subgraphs in the original network and folding them into the super nodes. We employ the network representation learning algorithm to study the node embeddings on the folded network, and then merge the embeddings of the folded network with those of the original network to obtain the final node embeddings. Experiments performed on four real-world networks prove the effectiveness of the SF-NRL algorithm. The proposed algorithm outperforms the baselines in evaluation metrics on community detection and multi-label classification tasks. The proposed algorithm can effectively generalize the global information of the network and provides excellent classification performance.
A Novel DE-CNN-BiLSTM Multi-Fusion Model for EEG Emotion Recognition
Authors: Cui Fachang , Wang Ruqing , Ding Weiwei , Chen Yao , Huang Liya
Published: February 2022

As a long-standing research topic in the field of brain–computer interface, emotion recognition still suffers from low recognition accuracy. In this research, we present a novel model named DE-CNN-BiLSTM deeply integrating the complexity of EEG signals, the spatial structure of brain and temporal contexts of emotion formation. Firstly, we extract the complexity properties of the EEG signal by calculating Differential Entropy in different time slices of different frequency bands to obtain 4D feature tensors according to brain location. Subsequently, the 4D tensors are input into the Convolutional Neural Network to learn brain structure and output time sequences; after that Bidirectional Long-Short Term Memory is used to learn past and future information of the time sequences. Compared with the existing emotion recognition models, the new model can decode the EEG signal deeply and extract key emotional features to improve accuracy. The simulation results show the algorithm achieves an average accuracy of 94% for DEAP dataset and 94.82% for SEED dataset, confirming its high accuracy and strong robustness.
A Surrogate Measure for Time-Varying Biomarkers in Randomized Clinical Trials
Authors: Zhuang Rui , Xia Fan , Wang Yixin , Chen Ying-Qing
Published: February 2022

Clinical trials with rare or distant outcomes are usually designed to be large in size and long term. The resource-demand and time-consuming characteristics limit the feasibility and efficiency of the studies. There are motivations to replace rare or distal clinical endpoints by reliable surrogate markers, which could be earlier and easier to collect. However, statistical challenges still exist to evaluate and rank potential surrogate markers. In this paper, we define a generalized proportion of treatment effect for survival settings. The measure’s definition and estimation do not rely on any model assumption. It is equipped with a consistent and asymptotically normal non-parametric estimator. Under proper conditions, the measure reflects the proportion of average treatment effect mediated by the surrogate marker among the group that would survive to mark the measurement time under both intervention and control arms.
Analysis of Deaths among HIV-Infected Patients Hospitalized in 2009–2018 in Main Centre of Infectious Disease in Region of Lower Silesia in Poland, Detailing Lesions in the Central Nervous System
Authors: Janocha-Litwin Justyna , Zińczuk Aleksander , Serafińska Sylwia , Szymanek-Pasternak Anna , Simon Krzysztof
Published: February 2022

Background and Objectives: Patients living with HIV (PLWH), especially those diagnosed too late or not receiving treatment with antiretroviral drugs in the stage of advanced immunodeficiency AIDS for various reasons, develop additional opportunistic infections or AIDS-defining diseases that may contribute directly to the death of these patients. Material and Methods: In this work, we focused on disorders of the central nervous system (CNS) by retrospectively analyzing the symptoms, clinical and autopsy diagnoses of patients diagnosed with HIV infection who died in the provincial specialist hospital in the Lower Silesia region in Poland. Results: The autopsy was performed in 27.4% cases. The cause of death was determined to be HIV-related/AIDS-associated in 78% patients. The most common AIDS-defining CNS diseases in our cohort were toxoplasmosis and cryptococcosis. Conslusions: The presented results of the most common causes of changes in the central nervous system among deceased HIV-infected patients are comparable to the results of studies by other scientists cited in the publication.
A Case of Stevens–Johnson Syndrome Complicated with Multimatrix System Mesalamine in Ulcerative Colitis
Authors: Kanazawa Mimari , Tominaga Keiichi , Kanamori Akira , Tanaka Takanao , Masuyama Satoshi , Watanabe Shoko , Abe Keiichiro , Yamamiya Akira , Goda Kenichi , Irisawa Atsushi
Published: February 2022

A 41-year-old man was treated with prednisolone (PSL) and multimatrix (MMX) mesalamine for remission induction therapy of ulcerative colitis. PSL was tapered due to successful remission induction treatment. During the treatment course, ocular foreign body sensation, eyelid swelling, ocular conjunctiva hyperemia, facial redness and swelling, watery nasal discharge, stomatitis, anal pain, and reddish puffiness on the bilateral dorsum of the hands appeared, and he was diagnosed with Stevens–Johnson syndrome (SJS). SJS was improved by PSL treatment and intravenous immunoglobulin. MMX mesalamine was the causative agent by drug-induced lymphocyte stimulation test. This is the first reported case of SJS with MMX mesalamine.
Chia Seed Mucilage Edible Films with Origanum vulgare and Satureja montana Essential Oils: Characterization and Antifungal Properties
Authors: Muñoz-Tébar Nuria , Carmona Manuel , Ortiz de Elguea-Culebras Gonzalo , Molina Ana , Berruga María Isabel
Published: February 2022

Films made with mucilage obtained from defatted chia seeds and incorporated with oregano (Origanum vulgare) and savory (Satureja montana) essential oils (0.1,1.0 and 1.5% v/v) were prepared to evaluate their physical, optical, mechanical and antifungal properties as well as their microstructure. The use of different types of essential oils (oregano or savory) only had a significant effect on the light transmittance, total color difference (∆E) and antifungal activity of the films. However, the kind of essential oil was not significant for the physical, optical and mechanical properties of the films. Increasing concentrations of essential oils up to 1.5% v/v led to a decrease in tensile strength (TS) and elongation at break (EB). Antifungal properties significantly increased with the incorporation of essential oils (p < 0.05). The antifungal activity of the chia mucilage films incorporated with O. vulgare and S. montana essential oil was screened by agar disc-diffusion assay against five mold strains commonly found in foods. Films containing 0.1% v/v of essential oils were not active, whereas films containing 1.0 and 1.5% v/v were very effective at inhibiting the growth of the tested mold strains (38.01–77.66%). Scanning electron microscopy showed that incorporation of essential oils caused some heterogeneity in the films and the surface displayed no pores or cracks as well as a better integration of oregano EO in the polymeric network. The results pointed out that the incorporation of oregano and savory essential oils as a natural antimicrobial agent has appreciable potential for the development of films as active packaging to control mold contamination and increase food safety.
Extraction of Magnesium and Nickel from Nickel-Rich Serpentine with Sulfation Roasting and Water Leaching
Authors: Yang Xiyun , Gao Lingyu , Wu Yulou , Chen Yingli , Tong Lirong
Published: February 2022

Magnesium and nickel were recovered from nickel-rich serpentine through sulfation roasting and water leaching. The factors affecting the extraction percentages of Mg and Ni were discussed. Under the conditions of the ratio of acid to ore of 0.8:1 and roasting temperature of 650 °C for 120 min, 91.6% of Mg and 88.7% of Ni but only 4.8% of Fe were extracted. The roasting kinetics of Mg and Ni were investigated. The results showed that the roasting stage was governed by internal diffusion in the temperature range of 350–650 °C, and the activation energy of nickel and magnesium were different in the time ranges of 0–30 min and 60–120 min, with 17.45 kJ⋅mol−1 (0–30 min) and 14.12 kJ⋅mol−1 (60–120 min) for magnesium and 15.48 kJ⋅mol−1 (0–30 min) and 12.46 kJ⋅mol−1 (60–120 min) for nickel. The kinetic equations were obtained.
Magnetron Sputtering High-Entropy Alloy Coatings: A Mini-Review
Authors: Padamata Sai Krishna , Yasinskiy Andrey , Yanov Valentin , Saevarsdottir Gudrun
Published: February 2022

Surface coatings can enhance the substrate material’s properties and increase its lifetime. HEA-based materials have been extensively investigated as coating materials due to their superior hardness, excellent oxidation and corrosion resistance, effective diffusion barrier properties and wear resistance. Magnetron sputtering has been regarded as one of the most efficient methods for the deposition of HEA-based thin films. Metallic- and nitride-based HEA coatings can be easily deposited by introducing N2 gas along with the Ar in the reaction chamber. The parameters such as target composition, bias voltage, sputtering power and notably, gas flow ratio, influence the thin film’s morphology and mechanical properties.
Numerical Analysis of Metal Transfer Process in Plasma MIG Welding
Authors: Tashiro Shinichi , Mamat Sarizam Bin , Murphy Anthony B , Yuji Toshifumi , Tanaka Manabu
Published: February 2022

In plasma MIG welding, inert gas introduced from the torch nozzle is ionized in the upstream region of the MIG arc, which is termed “plasma”. This study aims to clarify the effect of the plasma on the metal-transfer process in the plasma MIG welding through numerical analysis. As a result, the plasma with a temperature of approximately 10,000 K was found to be formed around the wire tip. The MIG arc temperature around the wire tip was 11,000 K at the maximum, which was lower than that of the conventional MIG welding by approximately 1000 K. This difference was caused by the decreased current density around the wire tip due to the influence of the plasma. The droplet temperature was also decreased by 400 K due to this lower current density. The amount of the metal vapor evaporated from the droplet was decreased compared to that of the conventional MIG welding due to the lower droplet temperature. This might lead to a decrease in fume formation generally known in the plasma MIG welding. In the conventional MIG welding, the arc attachment was concentrated around the wire tip, leading to a higher current density. However, in the plasma MIG welding, the plasma transported to the surrounding of the wire tip increases the electric conductivity in that region, due to the influence of the metal vapor mixture. This leads to the dispersion of the arc attachment toward the wire root. Consequently, the current density in the plasma MIG welding was found to decrease compared with that of the conventional MIG welding. The lower current density in plasma MIG welding decreases the Lorenz force acting on the wire neck, thus delaying droplet detachment to make the droplet diameter larger and the metal transfer frequency smaller. The latter was about 20% of that in the conventional MIG welding.
Use of Porous Titanium Trabecular as a Bone Defect Regenerator: In Vivo Study
Authors: Torres Pérez Ana Isabel , Fernández Fairén Mariano , Torres Pérez Ángel Antonio , Gil Mur Javier
Published: February 2022

The application of porous materials is increasingly being used in orthopaedic surgery due to its good results. Bone growth within the pores results in excellent mechanical fixation with the bone, as well as good bone regeneration. The pores, in addition to being colonised by bone, produce a decrease in the modulus of elasticity that favours the transfer of loads to the bone. This research shows the results of an experimental study where we have created critical osteoperiosteal defects of 10 mm on rabbit’s radius diaphysis. In one group of 10 rabbits (experimental group) we have implanted a bioactive porous titanium cylinder, and in another group we have allowed spontaneous regeneration (control group). Mechanical tests were performed to assess the material. Image diagnostic techniques (X-ray, scanner and 3D scan: there are no references on the literature with the use of CT-scan in bone defects) and histological and histomorphometric studies post-op and after 3, 6 and 12 months after the surgery were performed. All the control cases went through a pseudoarthrosis. In 9 of the 10 cases of the experimental group complete regeneration was observed, with a normal cortical-marrow structure established at 6 months, similar to normal bone. Titanium trabecular reached a bone percentage of bone inside the implant of 49.3% on its surface 3 months post-op, 75.6% at 6 months and 81.3% at 12 months. This porous titanium biomaterial has appropriate characteristics to allow bone ingrowth, and it can be proposed as a bone graft substitute to regenerate bone defects, as a scaffold, or as a coating to achieve implant osteointegration.
Relationship between Microstructure and Properties of 1380 MPa Grade Bainitic Rail Steel Treated by Online Bainite-Based Quenching and Partitioning Concept
Authors: Liu Miao , Fan Yusong , Gui Xiaolu , Hu Jie , Wang Xi , Gao Guhui
Published: February 2022

According to the concept of the bainite-based quenching and partitioning (BQ&P) process, we designed the online heat treatment routes of bainitic rail steel for heavy haul railway. The new heat treatment process reduced the fraction and size of the blocky martensite/austenite (M/A) islands formed during the conventional air-cooling process. The M/A islands are coarse and undesirable for mechanical properties. A new kind of 1380 MPa grade bainitic rail steel with more uniform microstructure and better mechanical properties was produced by the online BQ&P process. We characterized the multiphase microstructures containing bainite, martensite, and retained austenite of 1380 MPa grade bainitic rail steels via optical microscope, scanning electron microscopy, transmission electron microscopy, and X-ray diffractometer. We investigated in-depth the relationship between the microstructure, retained austenite stability, and mechanical properties, particularly the resistance to wear and rolling contact fatigue, of the new 1380 MPa grade bainitic rail steels. Meanwhile, the conventional air-cooling bainitic rail steel was studied as a comparison.
Native Microbiome Members of C. elegans Act Synergistically in Biosynthesis of Pyridoxal 5′-Phosphate
Authors: Haçariz Orçun , Viau Charles , Gu Xue , Xia Jianguo
Published: February 2022

The roles of the healthy microbiome on the host and the relationships between members of the microbiome remain to be fully characterized. Due to the complexity of the interactions between the mammalian microbiome and its host, the use of model organisms such as the nematode worm Caenorhabditis elegans is a promising strategy to study host-microbiome interactions in vivo, as well as bacterial crosstalk within the host. Previously it was found that native bacterial isolates of the worm, Chryseobacterium sp. CHNTR56 MYb120 and Comamonas sp. 12022 MYb131, possess genomic diversity in the biosynthesis of the active form of vitamin B6, pyridoxal 5′-phosphate (PLP), and contribute to host fitness and lifespan extension. However, the relative contribution of PLP from each isolate, as well as the existence of interbacterial relationships within the worm gut remain to be characterized. In the present work, we investigated the presence and measured the abundance of PLP in the isolates and in the worms grown with the isolates using ultraperformance liquid chromatography tandem-mass spectrometry (UPLC-MS/MS). Our analyses confirmed the presence of PLP in vitro and in vivo. The elevated abundance of PLP in the isolates (which reached statistically significant levels when the two isolates were combined), and within worms grown with the combination of bacterial isolates, compared to control, indicated synergism between the isolates in the production of PLP. Isotope labeling revealed that Comamonas sp. 12022 MYb131 was the main provider of PLP in worms grown with the combination of bacterial isolates. The dominance of this isolate inside the worm was further confirmed by a colonization assay. An untargeted metabolomics analysis of the bacteria showed that the pathways related to cell growth, protein synthesis and lipid synthesis/energy production were regulated in the combination group in comparison with Comamonas sp. 12022 MYb131 alone. Furthermore, glutamine, involved in the de novo synthesis of purine and pyrimidines, was specifically abundant in this group, indicating the potential role of this metabolite in initiating and sustaining bacterial growth. This bacterial crosstalk is suggested to promote the growth of Comamonas sp. 12022 MYb131 in vivo, and synthesis of bacterial metabolites such as PLP in the worm gut.
New Kids on the Block: Bile Salt Conjugates of Microbial Origin
Authors: Ay Ümran , Leníček Martin , Classen Arno , Olde Damink Steven W. M. , Bolm Carsten , Schaap Frank G.
Published: February 2022

Biotransformation of host bile salts by gut microbes results in generation of secondary bile salt species that have biological and physicochemical properties that are distinct from the parent compounds. There is increased awareness that a bile salt–gut microbiome axis modulates various processes in the host, including innate and adaptive immunity, by interaction of microbial bile salt metabolites with host receptors. Omics and targeted approaches have vastly expanded the number and repertoire of secondary bile salt species. A new class of microbial bile salt metabolites was reported in 2020 and comprises bile salts that are conjugated by microbial enzymes. Amino acids other than those employed by host enzymes (glycine and taurine) are used as substrates in the formation of these microbial bile salt conjugates (MBSCs). Leucocholic acid, phenylalanocholic acid and tyrosocholic acid were the first MBSCs identified in mice and humans. The number of distinct MBSCs is now approaching 50, with variation both at the level of bile salt and amino acid employed for conjugation. Evidence is emerging that MBSC generation is a common feature of human gut bacteria, and initial links with disease states have been reported. In this review, we discuss this intriguing new class of secondary bile salts, with yet enigmatic function.
Induction of Broad β-lactam Resistance in Achromobacter ruhlandii by Exposure to Ticarcillin Is Primarily Linked to Substitutions in Murein Peptide Ligase Mpl
Authors: Andersen Camilla , Gabrielaite Migle , Nørskov-Lauritsen Niels
Published: February 2022

Achromobacter species are emerging pathogens in cystic fibrosis with inherent resistance to several classes of antimicrobial agents. We exposed strains with wild-type antimicrobial susceptibility to ticarcillin and generated mutants with broad β-lactam resistance. Within the detection limit of the assay, the capability to develop mutational resistance was strain-specific and reproducible. Mutational resistance was observed for all three tested strains of Achromobacter ruhlandii, for one of seven strains of Achromobacter xylosoxidans, and for none of five strains of Achromobacter insuavis. All mutants were resistant to piperacillin-tazobactam, while minimal inhibitory concentration of several other β-lactams increased 4–32-fold. Whole genome sequencing identified 1–4 non-synonymous mutations in known genes per mutant. All mutants encoded amino acid substitutions in cell wall recycling proteins, primarily Mpl, and the observed resistance is probably caused by hyperproduction of OXA-114-like β-lactamases. Related, but not identical substitutions were detected in clinical strains expressing acquired antimicrobial resistance.
Evaluation of BCG Vaccination and Plasma Amyloid: A Prospective, Pilot Study with Implications for Alzheimer’s Disease
Authors: Dow Coad Thomas , Greenblatt Charles L. , Chan Edward D. , Dow Jordan F.
Published: February 2022

BCG vaccine has been used for 100 years to prevent tuberculosis. Not all countries, including the United States, adopted the initial World Health Organization recommendation to use BCG. Moreover, many Western countries that had routinely used BCG have discontinued its use. Recent population studies demonstrate lower prevalence of Alzheimer’s disease (AD) in countries with high BCG coverage. Intravesicular instillation of BCG is also used to treat bladder cancer that has not invaded the bladder muscle wall and has been shown to reduce recurrence. Several retrospective studies of bladder cancer patients demonstrated that BCG treatment was associated with a significantly reduced risk of developing AD. Plasma amyloid β assessment has become a fertile area of study for an AD biomarker that is predictive of a positive amyloid PET scan. Mass spectrometry-based plasma amyloid 42/40 ratio has proven to be accurate and robust, and when combined with age and ApoE, is shown to accurately predict current and future brain amyloid status. These parameters, amyloid 42/40 ratio, age and ApoE genotype are incorporated into an Amyloid Probability Score (APS)–a score that identifies low, intermediate or high risk of having a PET scan positive for cerebral amyloid. Community recruitment was used for this open-label pilot study. Forty-nine BCG-naïve, immunocompetent individuals completed our study: prior to BCG prime and boost, as determined by the APS, 34 had low risk (APS 0–35), 5 had intermediate risk (APS 36–57) and 10 had high risk (APS 58–100). The APS range for the participant group was 0 to 94. Follow-up plasma amyloid testing 9 months after vaccination revealed a reduction in the APS in all the risk groups: low risk group (p = 0. 37), intermediate risk group (p = 0.13) and the high-risk group (statistically significant, p = 0.016). Greater benefit was seen in younger participants and those with the highest risk. The small number of participants and the nascent status of plasma amyloid testing will rightfully temper embracement of these results. However, both the favorable direction of change after BCG as well as the utility of the APS—a valuable surrogate AD biomarker—may prompt a definitive large-scale multicenter investigation of BCG and AD risk as determined by plasma amyloid peptide ratios and APS.
Assessment of Diagnostic Specificity of Anti-SARS-CoV-2 Antibody Tests and Their Application for Monitoring of Seroconversion and Stability of Antiviral Antibody Response in Healthcare Workers in Moscow
Authors: Kichatova Vera S. , Asadi Mobarkhan Fedor A. , Potemkin Ilya A. , Zlobin Sergey P. , Perfilieva Oksana M. , Valuev-Elliston Vladimir T. , Ivanov Alexander V. , Solonin Sergey A. , Godkov Mikhail A. , Belikova Maria G. , Mikhailov Mikhail I. , Kyuregyan Karen K.
Published: February 2022

Anti-SARS-CoV-2 antibody testing is an efficient tool to assess the proportion of seropositive population due to infection and/or vaccination. Numerous test systems utilizing various antigen composition(s) are routinely used for detection and quantitation of anti-SARS-CoV-2 antibodies. We determined their diagnostic specificity using archived true-negative samples collected before the onset of the COVID-19 pandemic. Using test systems demonstrating 98.5–100% specificity, we assessed the dynamics of SARS-CoV-2 seroconversion and durability of anti-spike (S) antibodies in healthcare professionals (n = 100) working in Moscow during the first two cycles of the pandemic (May 2020 to June 2021) outside of the “red zone”. Analysis revealed a rapid increase in anti-SARS-CoV-2 seropositivity from 19 to 80% (19/100 and 80/100, respectively) due to virus exposition/infection; only 16.3% of seroconversion cases (13/80) were due to vaccination, but not the virus exposure, although massive COVID-19 vaccination of healthcare workers was performed beginning in December 2020. In total, 12.7% (8/63) remained positive for anti-SARS-CoV-2 IgM for >6 months, indicating unsuitability of IgM for identification of newly infected individuals. All except one remained seropositive for anti-S antibodies for >9 months on average. Significant (>15%) declines in anti-SARS-CoV-2 antibody concentrations were observed in only 18% of individuals (9/50). Our data on the high seropositivity rate and stability of anti-SARS-CoV-2 antibody levels in healthcare personnel working outside of the “red zone” indicate their regular exposition to SARS-CoV-2/an increased risk of infection, while a low frequency of vaccine-induced antibody response acquired after the start of vaccination points to vaccine hesitancy.
Biological Nitrogen Removal Database: A Manually Curated Data Resource
Authors: Ngara Tanyaradzwa R. , Zeng Peiji , Zhang Houjin
Published: February 2022

Biological nitrogen removal (BNR) technologies are the most effective approaches for the remediation of environmental nitrogen pollutants from wastewater treatment plants (WWTPs). Presently, research is going on to elucidate the structure and function of BNR microbial communities and optimizing BNR treatment systems to enhance nitrogen removal efficiency. The literature on BNR microbial communities and experimental datasets is not unified across various repositories, while a uniform resource for the collection, annotation, and structuring of these BNR datasets is still unavailable. Herein, we present the Biological Nitrogen Removal Database (BNRdb), an integrated resource containing various manually curated BNR-related data. At present, BNRdb contains 23,308 microbial strains, 46 gene families, 24 enzymes, 18 reactions, 301 BNR treatment datasets, 860 BNR-associated next-generation sequencing datasets, and 6 common BNR bioreactor systems. BNRdb provides a user-friendly interface enabling interactive data browsing. To our knowledge, BNRdb is the first BNR data resource that systematically integrates BNR data from archaeal, bacterial, and fungal communities. We believe that BNRdb will contribute to a better understanding of BNR process and nitrogen bioremediation research.
Introduction to Bacterial Anhydrobiosis: A General Perspective and the Mechanisms of Desiccation-Associated Damage
Authors: Grzyb Tomasz , Skłodowska Aleksandra
Published: February 2022

Anhydrobiosis is the ability of selected organisms to lose almost all water and enter a state of reversible ametabolism. Such an organism dries up to a state of equilibrium with dry air. Unless special protective mechanisms exist, desiccation leads to damage, mainly to proteins, nucleic acids, and membrane lipids. A short historical outline of research on extreme dehydration of living organisms and the current state of research are presented. Terminological issues are outlined. The role of water in the cell and the mechanisms of damage occurring in the cell under the desiccation stress are briefly discussed. Particular attention was paid to damage to proteins, nucleic acids, and membrane lipids. Understanding the nature of the changes and damage associated with desiccation is essential for the study of desiccation-tolerance mechanisms and application research. Difficulties related to the definition of life and the limits of life in the scientific discussion, caused by the phenomenon of anhydrobiosis, were also indicated.