bluuebunny/crossref_metadata_2025 huggingface dataset:
Columns Occurrences Graph to Improve Column Prediction in Deep Learning Nlidb
Authors: Abbas Shanza , Khan Muhammad Umair , Lee Scott Uk-Jin , Abbas Asad
Published: December 2021

Natural language interfaces to databases (NLIDB) has been a research topic for a decade. Significant data collections are available in the form of databases. To utilize them for research purposes, a system that can translate a natural language query into a structured one can make a huge difference. Efforts toward such systems have been made with pipelining methods for more than a decade. Natural language processing techniques integrated with data science methods are researched as pipelining NLIDB systems. With significant advancements in machine learning and natural language processing, NLIDB with deep learning has emerged as a new research trend in this area. Deep learning has shown potential for rapid growth and improvement in text-to-SQL tasks. In deep learning NLIDB, closing the semantic gap in predicting users’ intended columns has arisen as one of the critical and fundamental problems in this research field. Contributions toward this issue have consisted of preprocessed feature inputs and encoding schema elements afore of and more impactful to the targeted model. Various significant work contributed towards this problem notwithstanding, this has been shown to be one of the critical issues for the task of developing NLIDB. Working towards closing the semantic gap between user intention and predicted columns, we present an approach for deep learning text-to-SQL tasks that includes previous columns’ occurrences scores as an additional input feature. Overall exact match accuracy can also be improved by emphasizing the improvement of columns’ prediction accuracy, which depends significantly on column prediction itself. For this purpose, we extract the query fragments from previous queries’ data and obtain the columns’ occurrences and co-occurrences scores. Column occurrences and co-occurrences scores are processed as input features for the encoder–decoder-based text to the SQL model. These scores contribute, as a factor, the probability of having already used columns and tables together in the query history. We experimented with our approach on the currently popular text-to-SQL dataset Spider. Spider is a complex data set containing multiple databases. This dataset includes query–question pairs along with schema information. We compared our exact match accuracy performance with a base model using their test and training data splits. It outperformed the base model’s accuracy, and accuracy was further boosted in experiments with the pretrained language model BERT.
An Intelligent Diagnosis Method for Machine Fault Based on Federated Learning
Authors: Li Zhinong , Li Zedong , Li Yunlong , Tao Junyong , Mao Qinghua , Zhang Xuhui
Published: December 2021

In engineering, the fault data unevenly distribute and difficultly share, which causes that the existing fault diagnosis methods cannot recognize the newly added fault types. An intelligent diagnosis method for machine fault is proposed based on federated learning. Firstly, the local fault diagnosis models diagnosing the existing fault data and the newly added fault data are established by deep convolutional neural network. Then, the weight parameters of local models are fused into global model parameters by federated learning. Finally, the global model parameters are transmitted to each local model. Therefore, each local model update into a global shared model which can recognize the newly added fault types. The proposed method is verified by bearing data. Compared with the traditional model, which can only diagnose the existing fault data but cannot recognize newly added fault types, the federated fault diagnosis model fusing weight parameters can diagnose newly added faults without exchanging the data, and the accuracy is 100%. The proposed method provides an effective method to solve the poor sharing of fault data and poor generalization of fault diagnosis model for mechanical equipment.
Settlement and Bearing Capacity of Rectangular Footing in Reliance on the Pre-Overburden Pressure of Soil Foundation
Authors: Ter-Martirosyan Zaven G. , Ter-Martirosyan Armen Z. , Dam Huu H.
Published: December 2021

This article presents a solution for the quantitative evaluation of the stress–strain state (SSS) and the bearing capacity of rectangular foundations, factoring in the unit weight of the soil mass and different values of pre-overburden pressure (POP). In order to assess the SSS of the soil subgrade below a rigid rectangular footing under a uniformly distributed load, the authors applied the Boussinesq basic solution for an elastic half-space subjected to a vertical point load on its surface. As a result, the formulas for vertical stress, mean stress, shear strain, and volumetric strain for any point in Cartesian coordinates (x, y, z) and foundation settlement were determined. Additionally, the application of Hencky’s system of physical equations, with non-linear dependencies between mean stress and volumetric strain as well as deviator stress and shear strain, along with the experimental curves, depicts the relationships between bulk modulus and volume stress, and shear modulus and shear stress. The authors point out the non-linear behavior of the subgrade soil and propose a method for estimating the bearing capacity of a rigid rectangular foundation.
Acyclic Nucleic Acids with Phosphodiester Linkages—Synthesis, Properties and Potential Applications
Authors: Tomaszewska-Antczak Agnieszka , Guga Piotr
Published: December 2021

This review summarizes the synthetic efforts on acyclic analogs of nucleic acids and provides information on the most interesting features of selected classes of such compounds. The selection includes the following types of analogs: Flexible (FNA), Unlocked (UNA), Glycol (GNA), Butyl (BuNA), Threoninol (TNA) and Serinol Nucleic Acids (SNA). These classes of analogs are discussed in terms of their synthetic methods, the thermal stability of their homo- and hetero-duplexes and their applicability in biological and biochemical research and nanotechnology.
Large Eddy Simulation and Dynamic Mode Decomposition of Turbulent Mixing Layers
Authors: Cheng Yuwei , Chen Qian
Published: December 2021

Turbulent mixing layers are canonical flow in nature and engineering, and deserve comprehensive studies under various conditions using different methods. In this paper, turbulent mixing layers are investigated using large eddy simulation and dynamic mode decomposition. The accuracy of the computations is verified and validated. Standard dynamic mode decomposition is utilized to flow decomposition, reconstruction and prediction. It was found that the dominant-mode selection criterion based on mode amplitude is more suitable for turbulent mixing layer flow compared with the other three criteria based on singular value, modal energy and integral modal amplitude, respectively. For the mixing layer with random disturbance, the standard dynamic mode decomposition method could accurately reconstruct and predict the region before instability happens, but is not qualified in the regions after that, which implies that improved dynamic mode decomposition methods need to be utilized or developed for the future dynamic mode decomposition of turbulent mixing layers.
Dark-Field Hyperspectral Microscopy for Carbon Nanotubes Bioimaging
Authors: Ishmukhametov Ilnur , Fakhrullin Rawil
Published: December 2021

Carbon nanotubes have emerged as a versatile and ubiquitous nanomaterial, finding applications in industry and biomedicine. As a result, biosafety concerns that stimulated the research focused on evaluation of carbon nanotube toxicity. In addition, biomedical applications of carbon nanotubes require their imaging and identification in biological specimens. Among other methods, dark-field microscopy has become a potent tool to visualise and identify carbon nanotubes in cells, tissues, and organisms. Based on the Tyndall effect, dark-field optical microscopy at higher magnification is capable of imaging nanoscale particles in live objects. If reinforced with spectral identification, this technology can be utilised for chemical identification and mapping of carbon nanotubes. In this article we overview the recent advances in dark-field/hyperspectral microscopy for the bioimaging of carbon nanotubes.
Rapid Characterization Method for SMC Materials for a Preliminary Selection
Authors: Pošković Emir , Franchini Fausto , Ferraris Luca , Carosio Federico , Actis Grande Marco
Published: December 2021

In electrical machines, laminated steels are commonly adopted as soft magnetic materials, while for permanent magnets, sintered ferrites and NdFeB are the most common solutions. On the other hand, the growing demand for volume reduction with the increment of efficiency leads to the necessity of exploring other magnetic materials able to face the challenge better than the traditional ones. Bonded magnets have been used to replace sintered magnets, obtaining a better use of space and particular magnetic properties. Instead, for the magnetic circuit, Soft Magnetic Composites (SMC) allow realizing very complex magnetic design (3D path for flux) with iron loss reduction at medium-high frequencies, especially for the eddy currents loss contribution. On the other hand, SMC materials have such drawbacks as low mechanical properties and high hysteresis losses. For this reason, in this work, different studies considering several variables have been carried out. SMCs were produced through a moulding process; inorganic and organic layers to cover ferromagnetic particles were used, adopting different coating processes. Particular tests have been performed for a quicker and more indicative overview of the materials obtained. The single sheet tester (SST) is easier than traditional toroidal methods; on the other hand, the multiplicity of variables affects the SMC materials and their process. For this reason, coercivity and conductibility tests permit rapid measurement and provide a direct classification of the produced SMCs, providing the main information needed to select suitable materials. Results highlighted that choosing the more appropriate SMC material is possible after using these simple preliminary tests. After these tests, it was possible to argue that with 0.2 wt% of phenolic resin as the organic layer (and compaction pressure of 800 MPa), it is possible to produce a good SMC. On the other hand, the SMC with 0.2 wt% of epoxy resin (and compaction pressure of 800 MPa) gives a minor coercivity value. Additionally, despite the SMC with the inorganic layer, 0.2 wt% of nano-ferrites showing the best coercivity values (specifically for vacuum treatment at 600 °C), their resistivity was unsatisfactory.
Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation
Authors: Wang Fei-Xue , Peng Qian , Zang Xin-Liang , Xue Qi-Fan
Published: December 2021

Adaptive cruise control (ACC), as a driver assistant system for vehicles, not only relieves the burden of drivers, but also improves driving safety. This paper takes the intelligent pure electric city bus as the research platform, presenting a novel ACC control strategy that could comprehensively address issues of tracking capability, driving safety, energy saving, and driving comfort during vehicle following. A hierarchical control architecture is utilized in this paper. The lower controller is based on the nonlinear vehicle dynamics model and adjusts vehicle acceleration with consideration to the changes of bus mass and road slope by extended Kalman filter (EKF). The upper controller adapts Model Predictive Control (MPC) theory to solve the multi-objective optimal problem in ACC process. Cost functions are developed to balance the tracking distance, driving safety, energy consumption, and driving comfort. The simulations and Hardware-in-the-Loop (HIL) test are implemented; results show that the proposed control strategy ensured the driving safety and tracking ability of the bus, and reduced the vehicle’s maximum impact to 5 m/s3 and the State of Charge (SoC) consumption by 10%. Vehicle comfort and energy economy are improved obviously.
RiIG Modeled WCP Image-Based CNN Architecture and Feature-Based Approach in Breast Tumor Classification from B-Mode Ultrasound
Authors: Kabir Shahriar Mahmud , Bhuiyan Mohammed I. H. , Tanveer Md Sayed , Shihavuddin ASM
Published: December 2021

This study presents two new approaches based on Weighted Contourlet Parametric (WCP) images for the classification of breast tumors from B-mode ultrasound images. The Rician Inverse Gaussian (RiIG) distribution is considered for modeling the statistics of ultrasound images in the Contourlet transform domain. The WCP images are obtained by weighting the RiIG modeled Contourlet sub-band coefficient images. In the feature-based approach, various geometrical, statistical, and texture features are shown to have low ANOVA p-value, thus indicating a good capacity for class discrimination. Using three publicly available datasets (Mendeley, UDIAT, and BUSI), it is shown that the classical feature-based approach can yield more than 97% accuracy across the datasets for breast tumor classification using WCP images while the custom-made convolutional neural network (CNN) can deliver more than 98% accuracy, sensitivity, specificity, NPV, and PPV values utilizing the same WCP images. Both methods provide superior classification performance, better than those of several existing techniques on the same datasets.
Analysis and Evaluation on Residual Strength of Pipelines with Internal Corrosion Defects in Seasonal Frozen Soil Region
Authors: Li Xiaoli , Chen Guitao , Liu Xiaoyan , Ji Jing , Han Lianfu
Published: December 2021

In order to study the residual strength of buried pipelines with internal corrosion defects in seasonally frozen soil regions, we established a thermo-mechanical coupling model of a buried pipeline under differential frost heave by using the finite element elastoplastic analysis method. The material nonlinearity and geometric nonlinearity were considered as the basis of analysis. Firstly, the location of the maximum Mises equivalent stress in the inner wall of the buried non-corroded pipeline was determined. Furthermore, the residual strength of the buried pipeline with corrosion defects and the stress state of internal corrosion area in the pipeline under different defect parameters was analyzed by the orthogonal design method. Based on the data results of the finite element simulation calculation, the prediction formula of residual strength of buried pipelines with internal corrosion defects was obtained by SPSS (Statistical Product and Service Solutions) fitting. The prediction results were analyzed in comparison with the evaluation results of B31G, DNV RP-F101 and the experimental data of hydraulic blasting. The rationality of the finite element model and the accuracy of the fitting formula were verified. The results show that the effect degree of main factors on residual strength was in order of corrosion depth, corrosion length, and corrosion width. when the corrosion length exceeds 600 mm, which affects the influence degree of residual strength will gradually decrease. the prediction error of the fitting formula is small and the distribution is uniform, it can meet the prediction requirements of failure pressure of buried pipelines with internal corrosion defects in seasonally frozen soil regions. This method may provide some useful theoretical reference for the simulation real-time monitoring and safety analysis in the pipeline operation stage.
Biomechanical Analysis of Running in Shoes with Different Heel-to-Toe Drops
Authors: Zhang Masen , Shi Huijuan , Liu Hui , Zhou Xinglong
Published: December 2021

The heel-to-toe drop of running shoes is a key parameter influencing lower extremity kinematics during running. Previous studies testing running shoes with lower or larger drops generally used minimalist or maximalist shoes, where the factors outside of the drop may lead to the observed changes in running biomechanics. Therefore, our aim was to compare the strike patterns, impact force, and lower extremity biomechanics when running in shoes that varied only in their drops. Eighteen habitual rearfoot strikers performed trials wearing running shoes with four drop conditions: 15 mm, 10 mm, 5 mm, and without a drop. Three-dimensional (3D) tracks of the reflective markers and impact force were synchronously collected using a video graphic acquisition system and two force plates. The biomechanical parameters were compared among the four drop conditions using one-way ANOVA of repeated measures. A greater foot inclination angle (p = 0.001, ηp2 = 0.36) at initial contact and a lower vertical loading rate (p = 0.002, ηp2 = 0.32) during the standing phase were found when running in shoes with large drops compared with running in shoes without a drop. Running in shoes with large drops, as opposed to without, significantly increased the peak knee extension moment (p = 0.002, ηp2 = 0.27), but decreased the peak ankle eversion moment (p = 0.001, ηp2 = 0.35). These findings suggest that the heel-to-toe drop of running shoes significantly influences the running pattern and the loading on lower extremity joints. Running shoes with large drops may be disadvantageous for runners with knee weakness and advantageous for runners with ankle weakness.
Architectural Simulation of Hybrid Energy Harvesting: A Design Experiment in Lanzarote Island
Authors: Choi Ho Soon
Published: December 2021

This study conducts research on an architectural design based on energy harvesting technology. The research subject is a pergola-style structure to be built in a square in Arrecife, the Spanish territory of Lanzarote Island. The architectural design based on the energy harvesting technology developed in this research utilizes solar energy. To install a solar panel on the roof of the pergola, the optimal tilt angle from January to December was derived by using a function that considered the latitude and solar declination value of the study site, and the amount of renewable energy generation was calculated. The architectural design based on energy harvesting also utilizes wind power. To transform wind power into renewable energy, piezoelectric materials that trigger renewable energy with the micro-vibrations generated by wind power are applied to the architectural design. The amount of energy generation was calculated considering the wind power and wind direction in the location where the pergola should be built; in addition, this calculation used information from prior studies on piezoelectric materials. This article is significant, as it has developed an architectural design where hybrid energy harvesting technology that utilizes two types of natural energy (solar and wind) is applied to a building façade.
Experimental Investigation on the Behavior of Gravelly Sand Reinforced with Geogrid under Cyclic Loading
Authors: Wang Jia-Quan , Chang Zhen-Chao , Xue Jian-Feng , Lin Zhi-Nan , Tang Yi
Published: December 2021

In view of the dynamic response of geogrid-reinforced gravel under high-speed train load, this paper explores the dynamic characteristics of geogrid-reinforced gravel under semi-sine wave cyclic loading. A number of large scale cyclic triaxial tests were performed on saturated gravelly soil reinforced with geogrid to study the influence of the number of reinforcement layers and loading frequencies on the dynamic responses of reinforced gravelly sand subgrade for high speed rail track. The variation of cumulative axial and volumetric strains, excess pore pressure and resilient modulus with number of loading cycles, loading frequency, and reinforcement arrangement are analyzed. The test results reveal that the cumulative axial strain decreases as the number of reinforcement layers increases, but increases with loading frequency. The resilience modulus increases with the number of reinforcement layers, but decreases as the loading frequency increases. The addition of geogrid can reduce the excess pore water pressure of the sample, but it can slightly enhance the rubber mold embedding effect of the sand sample. As the loading frequency increases, the rubber mold embedding effect gradually weakens.
Analysis of Long-Term Prestress Loss in Prestressed Concrete (PC) Structures Using Fiber Bragg Grating (FBG) Sensor-Embedded PC Strands
Authors: Kim Sung-Tae , Park Young-Soo , Yoo Chul-Hwan , Shin Soobong , Park Young-Hwan
Published: December 2021

This study aims to develop a prestressed concrete steel (PC) strand with an embedded optical Fiber Bragg Grating (FBG) sensor, which has been developed by the Korea Institute of Civil Engineering and Building Technology since 2013. This new strand is manufactured by replacing the steel core of the normal PC strand with a carbon-fiber-reinforced polymer (CFRP) rod with excellent tensile strength and durability. Because this new strand is manufactured using the pultrusion method, which is a composite material manufacturing process, with an optical fiber sensor embedded in the inner center of the CFRP Rod, it ensures full composite action as well as proper function of the sensor. In this study, a creep test for maintaining a constant load and a relaxation test for maintaining a constant displacement were performed on the proposed sensor-type PC strand. Each of the two tests was conducted for more than 1000 h, and the long-term performance verification of the sensor-type PC strand was only completed by comparing the performance with that of a normal PC strand. The test specimens were fabricated by applying an optical fiber sensor-embedded PC strand, which had undergone long-term performance verification tests, to a reinforced concrete beam. Depending on whether grout was injected in the duct, the specimens were classified into composite and non-composite specimens. A hydraulic jack was used to prestress the fabricated beam specimens, and the long-term change in the prestress force was observed for more than 1600 days using the embedded optical fiber sensor. The experimental results were compared with the analytical results to determine the long-term prestress loss obtained through finite-element analysis based on various international standards.
Performance and Religion: Dancing Bodies in Macedonian Orthodox Fresco Painting
Authors: Zdravkova Djeparoska Sonja
Published: December 2021

Although dance as a topic has been explored through various theoretical and thematic discourse, little attention has been paid to the presence of dance motifs in Christian imagery. An examination of Orthodox Macedonian medieval fresco painting provides a fascinating point of entry into this overlooked subject. Analysis reveals the presence of two dominant approaches, conditioned primarily by the position of dancing in the philosophical-ethical discourse present in the Bible and other late antique and medieval theological texts. Some frescoes and icons show the body as a channel through which the Lord is glorified. Others show it as an instrument and reflection of immorality instigated by demonic powers. As in each approach, the bodies have differing semantic qualities, valuable information can be obtained about the performing practices present in this historical period.
Toward the Estimation of All-Weather Daytime Downward Longwave Radiation over the Tibetan Plateau
Authors: Long Zhiyong , Ding Lirong , Zhou Ji , Zhou Tianhao
Published: December 2021

Downward longwave radiation (DLR) is a critical parameter for radiation balance, energy budget, and water cycle studies at regional and global scales. Accurate estimation of the all-weather DLR with a high temporal resolution is important for the estimation of the surface net radiation and evapotranspiration. However, most DLR products involve instantaneous DLR estimates based on polar orbiting satellite data under clear-sky conditions. To obtain an in-depth understanding of the performances of different models in the estimation of DLR over the Tibetan Plateau, which is a focus area of climate change study, this study tests eight methods for clear-sky conditions and six methods for cloudy conditions based on ground-measured data. It is found that the Dilley and O’Brien model and the Lhomme model are most suitable for clear-sky conditions and cloudy conditions, respectively. For the Dilley and O’Brien model, the average root mean square error (RMSE) of DLR under clear-sky conditions is approximately 22.5 W/m2 for nine ground sites; for the Lhomme model, the average RMSE is approximately 23.2 W/m2. Based on the estimated cloud fraction and meteorological data provided by the China Land Surface Data Assimilation System (CLDAS), hourly all-weather daytime DLR with a 0.0625° resolution over the Tibetan Plateau is estimated. Results demonstrate that the average RMSE of the estimated hourly all-weather DLR is approximately 26.4 W/m2. With the combined all-weather DLR model, the hourly all-weather daytime DLR dataset with a 0.0625° resolution from 2008 to 2016 over the Tibetan Plateau is generated. This dataset can contribute to studies associated with the radiation balance and energy budget, water cycle, and climate change over the Tibetan Plateau.
Comparison of Volatile Organic Compound Concentrations in Ambient Air among Different Source Areas around Khon Kaen, Thailand
Authors: Sakunkoo Pornpun , Phonphinyo Saksit , Maneenin Naowarat , Jirapornkul Chananya , Limmongkon Yuparat , Rayubkul Jetnapis , Thongtip Sakesun , Sangkham Sarawut
Published: December 2021

Volatile organic compounds (VOCs) are a complex group of chemicals that pose a direct risk to human health. They also lead to the formation of other air pollution constituents, including fine particulate matter (PM2.5) and ground level ozone (O₃). The ambient air concentrations of 19 VOCs were measured using multi-day 24 h sampling at two urban sites and two rural sites in the area of Khon Kaen, Thailand. Results showed that most VOCs were at concentrations considered acceptable according to the 24 h average standards established by the Thai Pollution Control Department. The VOC acrolein, however, was detected at concentrations (0.69–1.15 μg/m3) in excess of the 24 h average standard (0.55 μg/m3). Two other VOCs, benzene and 1,3-butadiene, were also detected at elevated levels (1.73–2.75 and 0.18–0.40 μg/m3, respectively) that indicated the potential to exceed the 1-year average standard. VOC concentrations were highest in the urban market monitoring site, suggesting that vehicle exhaust and food preparation using cooking oil at high temperatures may have been potential sources of the elevated VOCs.
Is Hadley Cell Expanding?
Authors: Xian Tao , Xia Jingwen , Wei Wei , Zhang Zehua , Wang Rui , Wang Lian-Ping , Ma Yong-Feng
Published: December 2021

This review provides a comprehensive coverage of changes of the Hadley Cell extent and their impacts on the weather, climate, and society. The theories predicting the Hadley Cell width are introduced as a background for the understanding of the circulation changes and the metrics used for detection. A variety of metrics derived from various data sources have been used to quantify the Hadley Cell width. These metrics can be classified as dynamical, hydrological, thermal, and chemical metrics, based on the properties of the variables used. The dynamical metrics have faster trends than those based on thermal or hydrological metrics, with the values exceeding 1 degree per decade. The hydrological metric edge poleward trends were found a slightly faster expansion in the Northern Hemisphere than its southern counterpart. The chemical metrics show a poleward trend of more than 1 degree per decade in both hemispheres. We also suggest a few reasons for the discrepancy among trends in Hadley Cell expansion found in previous studies. Multiple forcings have been found responsible for the expansion, which seems to be more attributed to the natural variability than anthropogenic forcing. Validation of the scaling theories by the trends in Hadley Cell width suggests that theories considering the extratropical factor would be better models for predicting the Hadley Cell width changes. The Hadley Cell has an impact on different atmospheric processes on varying spatio-temporal scales, ranging from weather to climate, and finally on society. The remaining questions regarding Hadley Cell climate are briefly summarized at the end.
A Satellite Data Based Detailed Study of the Aerosol Emitted from Open Biomass Burning in Northeast China
Authors: Shi Shuaiyi , Ma Yanjun , Bao Fangwen , Mumtaz Faisal
Published: December 2021

Due to its unique natural conditions and agricultural tradition, northeast China (NEC) has formed a distinctive open biomass burning habit with local-specific biomass burning aerosol features. In this research, with the help of a newly optimized biomass burning aerosol identification method, which combines satellite aerosol and fire observational products with the HYSPLIT model forward trajectories, a systematic and quantitative analysis of aerosol emitted from open biomass burning in the NEC region are conducted to determine in detail its local-specific features, such as influence region, aging characteristics, and seasonal variation. During the 72-h aging process after biomass burning emission, aerosol particle size growth found with the Angstrom exponent declines from 1.6 to 1.54. Additionally, the volume fraction of black carbon decreases from 4.5% to 3.1%, leading to the Single Scattering Albedo (SSA) increasing from the fresh state of 0.84 to the aged state of 0.89. The cooling effect at TOA, due to the existence of aerosol, is enhanced by more than 70%, indicating its severe and dynamic influence on climate change. The average AOD in spring is 0.63, which is higher than autumn’s value of 0.52, indicating that biomass burning is more intensive in spring. Compared to autumn, aerosols emitted from spring biomass burning in the NEC region have lower sphere fraction, smaller particle size, higher volume fraction of black carbon, higher absorbability, and weaker cooling effect at TOA, which can be partly explained by the drier ambient environment and lower water content of the burned crop straw in spring.
Heterologous Expression and Rational Design of l-asparaginase from Rhizomucor miehei to Improve Thermostability
Authors: Zhang Xian , Wang Zhi , Wang Yimai , Li Xu , Zhu Manchi , Zhang Hengwei , Xu Meijuan , Yang Taowei , Rao Zhiming
Published: December 2021

l-asparaginase (EC 3.5.1.1) hydrolyzes l-asparagine to produce l-aspartate and ammonia and is widely found in microorganisms, plants, and some rodent sera. l-asparaginase used for industrial production should have good thermostability. We heterologously expressed l-asparaginase from Rhizomucor miehei, selected nine loci for site-directed mutagenesis by rational design, and obtained two mutants with significantly improved thermostability. The optimal temperature of mutants S302I and S302M was 50 °C. After incubating the mutant and wild-type enzymes at 45 °C for 35 h, the residual activity of the wild-type enzyme (WT) was only about 10%. In contrast, the residual activity of S302I and S302M was more than 50%. After combination mutagenesis, Bacillus subtilis 168-pMA5-A344E/S302I was constructed using the food-safe host strain B. subtilis 168. Additionally, a 5′ untranslated region (UTR) modification strategy was adopted to enhance the expression level of R. miehei-derived l-asparaginase in B. subtilis. In a 5-L fermenter scale-up experiment, the enzyme activity of recombinant B. subtilis 168-pMA5-UTR-A344E/S302I reached 521.9 U·mL−1 by fed-batch fermentation.
BreastNet18: A High Accuracy Fine-Tuned VGG16 Model Evaluated Using Ablation Study for Diagnosing Breast Cancer from Enhanced Mammography Images
Authors: Montaha Sidratul , Azam Sami , Rafid Abul Kalam Muhammad Rakibul Haque , Ghosh Pronab , Hasan Md. Zahid , Jonkman Mirjam , De Boer Friso
Published: December 2021

Background: Identification and treatment of breast cancer at an early stage can reduce mortality. Currently, mammography is the most widely used effective imaging technique in breast cancer detection. However, an erroneous mammogram based interpretation may result in false diagnosis rate, as distinguishing cancerous masses from adjacent tissue is often complex and error-prone. Methods: Six pre-trained and fine-tuned deep CNN architectures: VGG16, VGG19, MobileNetV2, ResNet50, DenseNet201, and InceptionV3 are evaluated to determine which model yields the best performance. We propose a BreastNet18 model using VGG16 as foundational base, since VGG16 performs with the highest accuracy. An ablation study is performed on BreastNet18, to evaluate its robustness and achieve the highest possible accuracy. Various image processing techniques with suitable parameter values are employed to remove artefacts and increase the image quality. A total dataset of 1442 preprocessed mammograms was augmented using seven augmentation techniques, resulting in a dataset of 11,536 images. To investigate possible overfitting issues, a k-fold cross validation is carried out. The model was then tested on noisy mammograms to evaluate its robustness. Results were compared with previous studies. Results: Proposed BreastNet18 model performed best with a training accuracy of 96.72%, a validating accuracy of 97.91%, and a test accuracy of 98.02%. In contrast to this, VGGNet19 yielded test accuracy of 96.24%, MobileNetV2 77.84%, ResNet50 79.98%, DenseNet201 86.92%, and InceptionV3 76.87%. Conclusions: Our proposed approach based on image processing, transfer learning, fine-tuning, and ablation study has demonstrated a high correct breast cancer classification while dealing with a limited number of complex medical images.
Use of Collagen Membrane in the Treatment of Periodontal Defects Distal to Mandibular Second Molars Following Surgical Removal of Impacted Mandibular Third Molars: A Comparative Clinical Study
Authors: Kumari C. Burnice Nalina , Ramakrishnan Thiagarajan , Devadoss Pradeep , Vijayalakshmi Rajaram , Alzahrani Khalid J. , Almasri Mazen A. , Al-Ahmari Manea Musa. , Al Dira Hajar Saeed , Suhluli Malath , Bhati Ashok Kumar , Ahmad Zeeshan Heera , Raj A. Thirumal , Bhandi Shilpa , Patil Shankargouda
Published: December 2021

The study aims to assess the efficacy of using collagen membrane in the treatment of distal periodontal defects of mandibular second molars following the removal of mesioangularly or horizontally impacted mandibular third molars surgically. Forty sites in twenty patients with bilaterally impacted mandibular third molars (mesioangular or horizontal) were considered for the study. In 20 test sites (Group A), after surgical removal of the mandibular third molar, a resorbable collagen membrane barrier was placed on the distal aspect of the mandibular second molar to cover the post-surgical bone defect. In the other control 20 sites (Group B), the same surgical procedure was repeated without placing any membrane barrier. The clinical parameters recorded were Oral Hygiene Index Simplified (OHI-S), Probing pocket depth (PPD), Clinical attachment level (CAL), and radiographic assessment of alveolar bone level (ABL). OHI-S score of most of the patients was observed to be satisfactory. Group A was observed to achieve a statistically significant reduction in PPD, CAL, and ABL gain compared to Group B. The improvements indicated that the use of collagen membrane facilitates early wound stabilization and promotes primary closure of the defect. This recovery is achieved through its unique property to assist fibrinogenesis over osteoconduction. Further longitudinal studies are needed to confirm the present findings.
Nutritional and Bioactive Characterization of Sicana odorifera Naudim Vell. Seeds By-Products and Its Potential Hepatoprotective Properties in Swiss Albino Mice
Authors: Caballero Silvia , Mereles Laura , Burgos-Edwards Alberto , Alvarenga Nelson , Coronel Eva , Villalba Rocío , Heinichen Olga
Published: December 2021

The “Kurugua” (Sicana odorifera) is a native fruit that demonstrates attractive nutritional, coloring, flavoring, and antioxidant properties. The main by-products from the processing and consumption of kurugua fruit are epicarp and seeds. In this work, the properties of the seeds of S. odorifera were evaluated. The nutritional composition of the fruit seeds was determined through AOAC official methods and UHPLC-ESI-MS/MS profiling. The antioxidant activities were determined using in vitro methods, and the acute toxicity and hepatoprotective properties were investigated in Swiss albino mice. Quercetin derivatives and cucurbitacins were the main phytochemicals in the seeds’ methanolic extract and demonstrated some biological activities. GC-MS analysis revealed the essential fatty acids linolenic and linoleic as the main compounds present in seeds oil. The methanolic extract significantly reduced the serum levels of glutamic-pyruvic transaminase (GPT) and glutamic-oxaloacetic transaminase (GOT) in mice with induced hepatotoxicity (GPT p < 0.05; GOT p < 0.001) at the minor concentration tested (100 mg/kg EMSo). The results suggest that the S. odorifera seeds as by-products show potential use as a source of phytochemicals and in the production of oils with application in food supplements and nutraceuticals. Their integral use could contribute to waste reduction from kurugua fruits processing within the food safety and environmental sustainability framework.
Culture-Independent Survey of Thermophilic Microbial Communities of the North Caucasus
Authors: Toshchakov Stepan V. , Izotova Anna O. , Vinogradova Elizaveta N. , Kachmazov Gennady S. , Tuaeva Albina Y. , Abaev Vladimir T. , Evteeva Martha A. , Gunitseva Natalia M. , Korzhenkov Aleksei A. , Elcheninov Alexander G. , Patrushev Maxim V. , Kublanov Ilya V.
Published: December 2021

The Greater Caucasus is a part of seismically active Alpine–Himalayan orogenic belt and has been a center of significant volcanic activity during the Quaternary period. That led to the formation of the number of hydrothermal habitats, including subterranean thermal aquifers and surface hot springs. However, there are only a limited number of scientific works reporting on the microbial communities of these habitats. Moreover, all these reports concern only studies of specific microbial taxa, carried out using classical cultivation approaches. In this work, we present first culture-independent study of hydrotherms in the Republic of North Ossetia-Alania, located in the southern part of the North Caucasus. Using 16S metabarcoding, we analyzed the composition of the microbial communities of two subterranean thermal aquifers and terrestrial hot springs of the Karmadon valley. Analysis of correlations between the chemical composition of water and the representation of key taxa allowed us to identify the key factors determining the formation of microbial communities. In addition, we were able to identify a significant number of highly abundant deep phylogenetic lineages. Our study represents a first glance on the thermophilic microbial communities of the North Caucasus and may serve as a basis for further microbiological studies of the extreme habitats of this region.
Fluorescent Soybean Hairy Root Construction and Its Application in the Soybean—Nematode Interaction: An Investigation
Authors: Yang Ruowei , Li Shuang , Yang Xiaowen , Zhu Xiaofeng , Fan Haiyan , Xuan Yuanhu , Chen Lijie , Liu Xiaoyu , Wang Yuanyuan , Duan Yuxi
Published: December 2021

Background: The yield of soybean is limited by the soybean cyst nematode (SCN, Heterodera glycines). Soybean transformation plays a key role in gene function research but the stable genetic transformation of soybean usually takes half a year. Methods: Here, we constructed a vector, pNI-GmUbi, in an Agrobacterium rhizogenes-mediated soybean hypocotyl transformation to induce fluorescent hairy roots (FHRs). Results: We describe the operation of FHR-SCN, a fast, efficient and visual operation pathosystem to study the gene functions in the soybean-SCN interaction. With this method, FHRs were detected after 25 days in 4 cultivars (Williams 82, Zhonghuang 13, Huipizhiheidou and Peking) and at least 66.67% of the composite plants could be used to inoculate SCNs. The demographics of the SCN could be started 12 days post-SCN inoculation. Further, GmHS1pro-1 was overexpressed in the FHRs and GmHS1pro-1 provided an additional resistance in Williams 82. In addition, we found that jasmonic acid and JA-Ile increased in the transgenic soybean, implying that the resistance was mainly caused by affecting the content of JA and JA-Ile. Conclusions: In this study, we established a pathosystem, FHR-SCN, to verify the functional genes in soybeans and the SCN interaction. We also verified that GmHS1pro-1 provides additional resistance in both FHRs and transgenic soybeans, and the resistance may be caused by an increase in JA and JA-Ile contents.
A Multifactorial Approach for Sarcopenia Assessment: A Literature Review
Authors: Supriya Rashmi , Singh Kumar Purnendu , Gao Yang , Li Feifei , Dutheil Frédéric , Baker Julien S.
Published: December 2021

Sarcopenia refers to a progressive and generalized weakness of skeletal muscle as individuals age. Sarcopenia usually occurs after the age of 60 years and is associated with a persistent decline in muscle strength, function, and quality. A comparison of the risk factors associated with sarcopenia based on the European Working Group on Sarcopenia (1 and 2) in Older People, the Asian Working Group for Sarcopenia (1 and 2), the International Working Group on Sarcopenia, and the Foundation for the National Institutes of Health revealed no consistent patterns. Accordingly, the identification of a single risk factor for sarcopenia is unpredictable due to its “multifactorial” pathogenesis, with the involvement of a multitude of factors. Therefore, the first aim of this review was to outline and propose that the multiple factors associated with sarcopenia need to be considered in combination in the design of new experimentation in this area. A secondary aim was to highlight the biochemical risk factors that are already identified in subjects with sarcopenia to assist scientists in understanding the biology of the pathophysiological mechanisms affecting the old people with sarcopenia. We also briefly discuss primary outcomes (physical) and secondary outcomes (social and financial) of sarcopenia. For future investigative purposes, this comprehensive review may be useful in considering important risk factors in the utilization of a panel of biomarkers emanating from all pathways involved in the pathogenesis of this disease. This may help to establish a uniform consensus for screening and defining this disease. Considering the COVID-19 pandemic, its impact may be exacerbated in older populations, which requires immediate attention. Here, we briefly suggest strategies for advancing the development of smart technologies to deliver exercise in the COVID-19 era in an attempt regress the onset of sarcopenia. These strategies may also have an impact on sarcopenia’s primary and secondary outcomes.
Correction: Fernandes et al. Stress Granule Assembly Can Facilitate but Is Not Required for TDP-43 Cytoplasmic Aggregation. Biomolecules 2020, 10, 1367
Authors: Fernandes Nikita , Nero Luke , Lyons Shawn M. , Ivanov Pavel , Mittelmeier Telsa M. , Bolger Timothy A. , Buchan J. Ross
Published: December 2021

In the original article [...]
A Deep Convolutional Neural Network for Prediction of Peptide Collision Cross Sections in Ion Mobility Spectrometry
Authors: Samukhina Yulia V. , Matyushin Dmitriy D. , Grinevich Oksana I. , Buryak Aleksey K.
Published: December 2021

Most frequently, the identification of peptides in mass spectrometry-based proteomics is carried out using high-resolution tandem mass spectrometry. In order to increase the accuracy of analysis, additional information on the peptides such as chromatographic retention time and collision cross section in ion mobility spectrometry can be used. An accurate prediction of the collision cross section values allows erroneous candidates to be rejected using a comparison of the observed values and the predictions based on the amino acids sequence. Recently, a massive high-quality data set of peptide collision cross sections was released. This opens up an opportunity to apply the most sophisticated deep learning techniques for this task. Previously, it was shown that a recurrent neural network allows for predicting these values accurately. In this work, we present a deep convolutional neural network that enables us to predict these values more accurately compared with previous studies. We use a neural network with complex architecture that contains both convolutional and fully connected layers and comprehensive methods of converting a peptide to multi-channel 1D spatial data and vector. The source code and pre-trained model are available online.
Expression, Regulation, and Functions of the Galectin-16 Gene in Human Cells and Tissues
Authors: Kaminker Jennifer D. , Timoshenko Alexander V.
Published: December 2021

Galectins comprise a family of soluble β-galactoside-binding proteins, which regulate a variety of key biological processes including cell growth, differentiation, survival, and death. This paper aims to address the current knowledge on the unique properties, regulation, and expression of the galectin-16 gene (LGALS16) in human cells and tissues. To date, there are limited studies on this galectin, with most focusing on its tissue specificity to the placenta. Here, we report the expression and 8-Br-cAMP-induced upregulation of LGALS16 in two placental cell lines (BeWo and JEG-3) in the context of trophoblastic differentiation. In addition, we provide the results of a bioinformatics search for LGALS16 using datasets available at GEO, Human Protein Atlas, and prediction tools for relevant transcription factors and miRNAs. Our findings indicate that LGALS16 is detected by microarrays in diverse human cells/tissues and alters expression in association with cancer, diabetes, and brain diseases. Molecular mechanisms of the transcriptional and post-transcriptional regulation of LGALS16 are also discussed based on the available bioinformatics resources.
Prototype Foamy Virus Integrase Displays Unique Biochemical Activities among Retroviral Integrases
Authors: Rabe Anthony J. , Tan Yow Yong , Larue Ross C. , Yoder Kristine E.
Published: December 2021

Integrases of different retroviruses assemble as functional complexes with varying multimers of the protein. Retroviral integrases require a divalent metal cation to perform one-step transesterification catalysis. Tetrameric prototype foamy virus (PFV) intasomes assembled from purified integrase and viral DNA oligonucleotides were characterized for their activity in the presence of different cations. While most retroviral integrases are inactive in calcium, PFV intasomes appear to be uniquely capable of catalysis in calcium. The PFV intasomes also contrast with other retroviral integrases by displaying an inverse correlation of activity with increasing manganese beginning at relatively low concentrations. The intasomes were found to be significantly more active in the presence of chloride co-ions compared to acetate. While HIV-1 integrase appears to commit to a target DNA within 20 s, PFV intasomes do not commit to target DNA during their reaction lifetime. Together, these data highlight the unique biochemical activities of PFV integrase compared to other retroviral integrases.