bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding Grief During the First-Wave of COVID-19 in the United Kingdom—A Hypothetical Approach to Challenges and Support
Authors: Fang Chao , Comery Alastair
Published: March 2021

Purpose:This article develops immediate understandings of loss and grief at both an individual and collective level following the first-wave of COVID-19 in the UK. This allows for insights into the likely challenges and support for loss and grief in facing unprecedented disruption and uncertainty. Ultimately, it explores avenues for the priorities to inform better bereavement support.

Methods:By examining trusted media data and carefully selected academic literature, we analyse both individual and societal responses to loss and grief in the novel context of the first-wave of COVID-19 in the UK. The discussion relocates the ideas of good and bad deaths in the context of increased social constrains and inequalities. Further, two pairs of contrasting hypotheses are proposed to examine how the UK's first-wave outbreak has shaped policy and practical structures and how these have further impacted experiences of loss and grief both at an individual and collective level.

Findings:The discussion captures a mixed picture of loss and grief in the UK, which highlights the importance of timely, holistic, and continuous support both in social policy and care provision. It is found that individuals and collectives express diverse needs in response to deaths and losses as a process of meaning-making. Further, the significance of socio-cultural environments also become evident. These findings highlight community support during the outbreak and further promote a grief literate culture as imperative to support individual and collective needs when confronted with loss and grief.

Conclusion:This article provides a timely and comprehensive account of possible challenges and support both for individual and collective experiences of loss and grief at a time of unprecedented social restrictions and mass deaths in the UK. These understandings provide a base from which we advocate the priorities for future research into the ongoing impacts of COVID-19 on grief and bereavement.
Linear Iterative Procedure to Solve a Rayleigh–Plesset Equation
Authors: Vanhille Christian
Published: March 2021

A nonlinear Rayleigh–Plesset equation for describing the behavior of a gas bubble in an acoustic field written in terms of bubble-volume variation is solved through a linear iterative procedure. The model is validated, and its accuracy and fast convergence are shown through the analysis of several examples of different physical meanings. The simplicity and usefulness of the presented method here in relation to the direct resolution of the whole nonlinear system, which is also discussed, make the method very attractive to solving a problem. This iterative method allows us to solve only linear systems instead of the nonlinear differential problem. Moreover, the implementation of the iterative algorithm includes a tolerance-dependent stopping criterion that is also tested.
Assessing Sustainability of Organic Livestock Farming in Sicily: A Case Study Using the FAO SAFA Framework
Authors: Cammarata Mariarita , Timpanaro Giuseppe , Scuderi Alessandro
Published: March 2021

Greenhouse gas (GHG) emissions and pollutants, soil erosion and groundwater pollution are some of the negative aspects blamed on livestock farming, so their level of sustainability needs to be assessed, taking into account the territory in which they operate. The research focuses on the assessment of sustainability performance in the four dimensions of good governance: environmental integrity, economic resilience and social well-being, considered by the ‘‘Sustainability Assessment of Food and Agriculture Systems’’ (SAFA) tool developed by Food and Agriculture Organization (FAO). The objective of applying this methodology is to highlight the sustainability dimensions in which the ten analyzed farms are weakest and the ones in which they show the most strength, in order to provide farmers a tool to understand the criticalities on which to intervene. The farms considered follow the principles of agroecology and organic farming, which are decisive in the pursuit of sustainable development. The overall results show a satisfactory level of sustainability with high prospects for improvement, in line with the EU commitments undertaken in the Green Deal and the Millennium Development Goals. Livestock farms must, therefore, be encouraged and accompanied with targeted technical assistance strategies and appropriate agroecological protocols.
Strawberry Flavor Is Influenced by the Air Temperature Differential during Fruit Development but Not Management Practices
Authors: Osatuke Anya , Pritts Marvin
Published: March 2021

The majority of cultivated strawberries (Fragaria × ananassa) in the northern United States (US) and Canadian provinces is grown in perennial matted rows across a range of soil types and microclimates. Management practices vary in fertilization rates, intensity of pesticide use, and the source of inputs depending on grower preferences. The objective of this study was to identify environmental and management factors that influence strawberry flavor attributes across a range of production systems. The cultivar Jewel was selected for its popularity in this region and reputation for excellent flavor. “Jewel” was sampled from regional farms and, concurrently, grown in a controlled field study with different inputs over three years. Soluble solids content (SSC) and titratable acidity (TA) across farms was found to be positively associated with the air temperature differential during fruit ripening. In controlled field studies, yield was correlated positively with total N in the form of synthetic urea, but not with the rate of applied organic nitrogen (N). Despite different levels of soil carbon inputs, N rates, pesticides, and microbial supplements, the fruit quality attributes, including SSC, TA, aromatic volatile concentration, and phenolics were not associated with treatment. A human sensory evaluation found no perceptible differences in flavor or aroma among contrasting treatments. Our study concludes that growers should invest in temperature management, rather than agricultural inputs, to influence SSC and TA of strawberry.
Comparative Evaluation of Tomato Hybrids and Inbred Lines for Fruit Quality Traits
Authors: Avdikos Ilias D. , Tagiakas Rafail , Tsouvaltzis Pavlos , Mylonas Ioannis , Xynias Ioannis N. , Mavromatis Athanasios G.
Published: March 2021

Tomato is one of the most consumed fruit vegetables globally and is a high dietary source of minerals, fiber, carotenoids, and vitamin C. The tomato is also well known for its nutraceutical chemical content which strengthens human immune systems and is protective against infectious and degenerative diseases. For this reason, there has been recent emphasis on breeding new tomato cultivars with nutraceutical value. Most of the modern tomato cultivars are F1 hybrids, and many of the characteristics associated with fruit quality have additive gene action; so, in theory, inbred vigor could reach hybrid vigor. A sum of 20 recombinant lines was released from the commercial single-cross hybrids Iron, Sahara, Formula, and Elpida, through a breeding process. Those recombinant lines were evaluated during spring–summer 2015 under organic farming conditions in a randomized complete block design (RCBD) experimental design with three replications. A sum of eleven qualitative characteristics of the fruit was recorded on an individual plant basis. Results from this study indicated that the simultaneous selection of individual tomato plants, both in terms of their high yield and desired fruit quality characteristics, can lead to highly productive recombinant lines with integrated quality characteristics. So, inbred vigor can reach and even surpass hybrid vigor. The response to selection for all characteristics evaluated shows additive gene action of all characteristics measured. These recombinant lines can fulfill this role as alternatives to hybrid cultivars and those that possess high nutritional values to function as functional-protective food.
Outcomes and Risk Factors in Prosthetic Joint Infections by multidrug-resistant Gram-negative Bacteria: A Retrospective Cohort Study
Authors: da Silva Raquel Bandeira , Salles Mauro José
Published: March 2021

Gram-negative bacteria (GNB), including multidrug-resistant (MDR) pathogens, are gaining importance in the aetiology of prosthetic joint infection (PJI). This retrospective observational study identified independent risk factors (RFs) associated with MDR-GNB PJI and their influence on treatment outcomes. We assessed MDR bacteria causing hip and knee PJIs diagnosed at a Brazilian tertiary hospital from January 2014 to July 2018. RFs associated with MDR-GNB PJI were estimated by bivariate and multivariate analyses using prevalence ratios (PRs) with significance at p < 0.05. Kaplan–Meier analysis was performed to evaluate treatment outcomes. Overall, 98 PJI patients were analysed, including 56 with MDR-GNB and 42 with other bacteria. Independent RFs associated with MDR-GNB PJI were revision arthroplasty (p = 0.002), postoperative hematoma (p < 0.001), previous orthopaedic infection (p = 0.002) and early infection (p = 0.001). Extensively drug-resistant GNB (p = 0.044) and comorbidities (p = 0.044) were independently associated with MDR-GNB PJI treatment failure. In sum, MDR-GNB PJI was independently associated with previous orthopaedic surgery, postoperative local complications and pre-existing infections and was possibly related to selective pressure on bacterial skin colonisation by antibiotics prescribed for early PJI. Infections due to MDR-GNB and comorbidities were associated with higher treatment failure rates.
Characterization of NADPH Oxidase Expression and Activity in Acute Myeloid Leukemia Cell Lines: A Correlation with the Differentiation Status
Authors: Dakik Hassan , El Dor Maya , Leclerc Joan , Kouzi Farah , Nehme Ali , Deynoux Margaux , Debeissat Christelle , Khamis Georges , Ducrocq Elfi , Ibrik Aida , Stasia Marie-José , Raad Houssam , Rezvani Hamid Reza , Gouilleux Fabrice , Zibara Kazem , Herault Olivier , Mazurier Frédéric
Published: March 2021

In acute myeloid leukemia (AML), a low level of reactive oxygen species (ROS) is associated with leukemic stem cell (LSC) quiescence, whereas a high level promotes blast proliferation. ROS homeostasis relies on a tightly-regulated balance between the antioxidant and oxidant systems. Among the oxidants, NADPH oxidases (NOX) generate ROS as a physiological function. Although it has been reported in AML initiation and development, the contribution of NOX to the ROS production in AML remains to be clarified. The aim of this study was to investigate the NOX expression and function in AML, and to examine the role of NOX in blast proliferation and differentiation. First, we interrogated the NOX expression in primary cells from public datasets, and investigated their association with prognostic markers. Next, we explored the NOX expression and activity in AML cell lines, and studied the impact of NOX knockdown on cell proliferation and differentiation. We found that NOX2 is ubiquitously expressed in AML blasts, and particularly in cells from the myelomonocytic (M4) and monocytic (M5) stages; however, it is less expressed in LSCs and in relapsed AML. This is consistent with an increased expression throughout normal hematopoietic differentiation, and is reflected in AML cell lines. Nevertheless, no endogenous NOX activity could be detected in the absence of PMA stimulation. Furthermore, CYBB knockdown, although hampering induced NOX2 activity, did not affect the proliferation and differentiation of THP-1 and HL-60 cells. In summary, our data suggest that NOX2 is a marker of AML blast differentiation, while AML cell lines lack any NOX2 endogenous activity.
Comprehensive Phenolic and Free Amino Acid Analysis of Rosemary Infusions: Influence on the Antioxidant Potential
Authors: Peixoto Juliana A. Barreto , Álvarez-Rivera Gerardo , Alves Rita C. , Costa Anabela S. G. , Machado Susana , Cifuentes Alejandro , Ibáñez Elena , Oliveira M. Beatriz P. P.
Published: March 2021

The phenolics profile, free amino acids composition, and antioxidant potential of rosemary infusions were studied. Forty-four compounds belonging to nine different groups (hydroxybenzoic acids, hydroxycinnamic acids, flavan-3-ols, flavanones, flavones, phenolic diterpenes, hydroxybenzaldehydes, coumarins, and pyranochromanones) were identified by UHPLC-ESI-Q-TOF-MS. Of these, seven were firstly described in rosemary infusions: a rosmanol derivative, two dihydroxycoumarin hexosides, a hydroxybenzaldehyde, a dihydroxybenzoic acid hexoside, coumaric acid hexoside, and isocalolongic acid. The free amino acid profile of the beverages was also reported by the first time with seven amino acids found (asparagine, threonine, alanine, tyrosine, phenylalanine, isoleucine, and proline). Furthermore, DPPH• scavenging ability, Ferric Reducing Antioxidant Power and Oxygen Radical Absorbance Capacity, as well as total phenolics and flavonoids contents, were assessed. Overall, rosemary infusions showed to be a very good source of antioxidants. A 200 mL cup of this infusion contributes to the ingestion of ~30 mg of phenolic compounds and about 0.5–1.1 μg of free amino acids. This type of beverages may present a positive impact on the maintenance of the body antioxidant status and contribute to the prevention of oxidative stress related diseases.
Psychological Stress Detection According to ECG Using a Deep Learning Model with Attention Mechanism
Authors: Zhang Pengfei , Li Fenghua , Du Lidong , Zhao Rongjian , Chen Xianxiang , Yang Ting , Fang Zhen
Published: March 2021

To satisfy the need to accurately monitor emotional stress, this paper explores the effectiveness of the attention mechanism based on the deep learning model CNN (Convolutional Neural Networks)-BiLSTM (Bi-directional Long Short-Term Memory) As different attention mechanisms can cause the framework to focus on different positions of the feature map, this discussion adds attention mechanisms to the CNN layer and the BiLSTM layer separately, and to both the CNN layer and BiLSTM layer simultaneously to generate different CNN–BiLSTM networks with attention mechanisms. ECG (electrocardiogram) data from 34 subjects were collected on the server platform created by the Institute of Psychology of the Chinese Academy of Science and the researches. It verifies that the average accuracy of CNN–BiLSTM is up to 0.865 without any attention mechanism, while the highest average accuracy of 0.868 is achieved using the CNN–attention–based BiLSTM.
An MRI-Based Patient-Specific Computational Framework for the Calculation of Range of Motion of Total Hip Replacements
Authors: Kebbach Maeruan , Schulze Christian , Meyenburg Christian , Kluess Daniel , Sungu Mevluet , Hartmann Albrecht , Günther Klaus-Peter , Bader Rainer
Published: March 2021

The calculation of range of motion (ROM) is a key factor during preoperative planning of total hip replacements (THR), to reduce the risk of impingement and dislocation of the artificial hip joint. To support the preoperative assessment of THR, a magnetic resonance imaging (MRI)-based computational framework was generated; this enabled the estimation of patient-specific ROM and type of impingement (bone-to-bone, implant-to-bone, and implant-to-implant) postoperatively, using a three-dimensional computer-aided design (CAD) to visualize typical clinical joint movements. Hence, patient-specific CAD models from 19 patients were generated from MRI scans and a conventional total hip system (Bicontact® hip stem and Plasmacup® SC acetabular cup with a ceramic-on-ceramic bearing) was implanted virtually. As a verification of the framework, the ROM was compared between preoperatively planned and the postoperatively reconstructed situations; this was derived based on postoperative radiographs (n = 6 patients) during different clinically relevant movements. The data analysis revealed there was no significant difference between preoperatively planned and postoperatively reconstructed ROM (∆ROM) of maximum flexion (∆ROM = 0°, p = 0.854) and internal rotation (∆ROM = 1.8°, p = 0.917). Contrarily, minor differences were observed for the ROM during maximum external rotation (∆ROM = 9°, p = 0.046). Impingement, of all three types, was in good agreement with the preoperatively planned and postoperatively reconstructed scenarios during all movements. The calculated ROM reached physiological levels during flexion and internal rotation movement; however, it exceeded physiological levels during external rotation. Patients, where implant-to-implant impingement was detected, reached higher ROMs than patients with bone-to-bone impingement. The proposed framework provides the capability to predict postoperative ROM of THRs.
Uncovering Hidden Community Structure in Multi-Layer Networks
Authors: Khawaja Faiza Riaz , Sheng Jinfang , Wang Bin , Memon Yumna
Published: March 2021

Community detection, also known as graph clustering, in multi-layer networks has been extensively studied in the literature. The goal of community detection is to partition vertices in a network into densely connected components so called communities. Networks contain a set of strong, dominant communities, which may interfere with the detection of weak, natural community structure. When most of the members of the weak communities also belong to stronger communities, they are extremely hard to be uncovered. We call the weak communities the hidden or disguised community structure. In this paper, we present a method to uncover weak communities in a network by weakening the strength of the dominant structure. With the aim to detect the weak communities, through experiments, we observe real-world networks to answer the question of whether real-world networks have hidden community structure or not. Results of the hidden community detection (HCD) method showed the great variation in the number of communities detected in multiple layers when compared with the results of other community detection methods.
Design of a Computed Tomography Automation Architecture
Authors: Hashem Nicholas , Pryor Mitchell , Haas Derek , Hunter James
Published: March 2021

This paper presents a literature review on techniques related to the computed tomography procedure that incorporate automation elements in their research investigations or industrial applications. Computed tomography (CT) is a non-destructive testing (NDT) technique in that the imaging and inspection are performed without damaging the sample, allowing for additional or repeated analysis if necessary. The reviewed literature is organized based on the steps associated with a general NDT task in order to define an end-to-end computed tomography automation architecture. The process steps include activities prior to image collection, during the scan, and after the data are collected. It further reviews efforts related to repeating this process based on a previous scan result. By analyzing the multiple existing but disparate efforts found in the literature, we present a framework for fully automating NDT procedures and discuss the remaining technical gaps in the developed framework.
Efficiency of Machine Sanding of Wood
Authors: Sydor Maciej , Mirski Radosław , Stuper-Szablewska Kinga , Rogoziński Tomasz
Published: March 2021

We hypothesized that the type of wood, in combination with the grit size of sandpapers, would affect sanding efficiency. Fixed factors were used in the experiment (a belt sander with pressure p = 3828 Pa, and a belt speed of vs = 14.5 m/s) as well as variable factors (three sand belts (P60, P120, P180), six hardwood species (beech, oak, ash, hornbeam, alder, walnut) and three softwood species (pine, spruce, larch)). The masses of the test samples were measured until they were completely sanded. The sanding efficiency of hardwood species is less variable than for softwood species. Maximum sanding efficiency for the softwood ranged from 1 to 2 min, while for the hardwood species, it ranged from 2 to 4.5 min at the start of sanding and then decreased. The average time for complete sanding of the softwood samples was: 87 s (P60), 150 s (P120), and 188 s (P180). For hardwood, these times were 2.4, 1.5, and 1.8 times longer. The results indicate that the factors determining sanding efficiency are the type of wood, and, secondly, the grit size of sanding belts. In the first phase of blunting with the sanding belts, the sanding processes of hardwood and softwood are significantly different. In the second phase of blunting, sanding belts with higher grit numbers (P120 and P180) behaved similarly while sanding hardwood and softwood.
Marine Gas Hydrate Geohazard Assessment on the European Continental Margins. The Impact of Critical Knowledge Gaps
Authors: León Ricardo , Llorente Miguel , Giménez-Moreno Carmen Julia
Published: March 2021

This paper presents a geohazard assessment along the European continental margins and adjacent areas. This assessment is understood in the framework of the seafloor’s susceptibility to (i.e., likelihood of) being affected by the presence of hydrate deposits and the subsequent hazardous dissociation processes (liquefaction, explosion, collapse, crater-like depressions or submarine landslides). Geological and geophysical evidence and indicators of marine gas hydrates in the theoretical gas hydrate stability zone (GHSZ) were taken into account as the main factors controlling the susceptibility calculation. Svalbald, the Barents Sea, the mid-Norwegian margin-northwest British Islands, the Gulf of Cádiz, the eastern Mediterranean and the Black Sea have the highest susceptibility. Seafloor areas outside the theoretical GHSZ were excluded from this geohazard assessment. The uncertainty analysis of the susceptibility inference shows extensive seafloor areas with no data and a very low density of data that are defined as critical knowledge gaps.
Structural Crack Detection and Recognition Based on Deep Learning
Authors: Yang Cheng , Chen Jingjie , Li Zhiyuan , Huang Yi
Published: March 2021

The detection and recognition of surface cracks are of great significance for structural safety. This paper is based on a deep-learning methodology to detect and recognize structural cracks. First, a training dataset of the model is built. Then, three neural networks, AlexNet, VGGNet13, and ResNet18, are employed to recognize and classify crack images. The tests indicate that the ResNet18 model generates the most satisfactory results. It is also found that the trained YOLOv3 model detects the crack area with satisfactory accuracy. This study also confirms that the proposed deep learning as a novel technology has the potential to be an efficient and robust tool for crack detection and recognition to replace traditional methods.
Turbine Blade Temperature Field Prediction Using the Numerical Methods
Authors: Spodniak Miroslav , Semrád Karol , Draganová Katarína
Published: March 2021

Nowadays, material science and stress characteristics are crucial in the field of jet engines. There are methods for fatigue life, stress, and temperature prediction; however, the conventional methods are ineffective and time-consuming. The article is devoted to the research in the field of application of the numerical methods in order to develop an innovative methodology for the temperature fields prediction based on the integration of the finite element methods and artificial neural networks, which leads to the creation of the novel methodology for the temperature field prediction. The proposed methodology was applied to the temperature field prediction on the surface blades of the experimental iSTC-21v jet engine turbine. The results confirmed the correctness of the new methodology, which is able to predict temperatures at the specific points on the surface of a turbine blade immediately. Moreover, the proposed methodology is able to predict temperatures at specific points on the turbine blade during the engine runs, even for the multiple operational regimes of the jet engine. Thanks to this new unique methodology, it is possible to increase the reliability and lifetime of turbines and hot parts of any jet engine and to reduce not only the maintenance but also the research and development costs due to the significantly lower time demands. The main advantage is to predict temperature fields much faster in comparison to the methods available today (computational fluid dynamics (CFD), etc.), and the major aim of the proposed article is to predict temperatures using a neural network. Apart from the above-mentioned advantages, the article’s main purpose is devoted to the artificial neural networks, which have been until now used for many applications, but in our case, the neural network was for the first time applied for the temperature field prediction on the turbine blade.
Frequency-Based Haze and Rain Removal Network (FHRR-Net) with Deep Convolutional Encoder-Decoder
Authors: Kim Dong Hwan , Ahn Woo Jin , Lim Myo Taeg , Kang Tae Koo , Kim Dong Won
Published: March 2021

Removing haze or rain is one of the difficult problems in computer vision applications. On real-world road images, haze and rain often occur together, but traditional methods cannot solve this imaging problem. To address rain and haze problems simultaneously, we present a robust network-based framework consisting of three steps: image decomposition using guided filters, a frequency-based haze and rain removal network (FHRR-Net), and image restoration based on an atmospheric scattering model using predicted transmission maps and predicted rain-removed images. We demonstrate FHRR-Net’s capabilities with synthesized and real-world road images. Experimental results show that our trained framework has superior performance on synthesized and real-world road test images compared with state-of-the-art methods. We use PSNR (peak signal-to-noise) and SSIM (structural similarity index) indicators to evaluate our model quantitatively, showing that our methods have the highest PSNR and SSIM values. Furthermore, we demonstrate through experiments that our method is useful in real-world vision applications.
Fault Current and Voltage Estimation for Pole-to-Pole Faults in Modular Multilevel Converter Based DC Grids Considering AC Active Power
Authors: Cai Hui , Zhang Junli , Yu Jingqiu , Zhang Zheren
Published: March 2021

DC short-circuit faults are one of the challenges for modular multilevel converter (MMC) based DC grid. It is vital for proper design of protection system to estimate the fault currents and voltages. The existing calculation methods based on RLC equivalent model of MMC have enough accuracy in estimating the branch currents but suffer from poor accuracy in estimating the node voltages. To better reflect the dynamics of MMC control during the fault, MMC is equivalent to a RLC series circuit in parallel with a variable controlled current source. This model not only considers the discharge of sub-module capacitors but also the AC active power and MMC control. Then, based on the discrete adjoint model of the equivalent MMC model and the RL series equivalent model of DC lines, the fault voltages and currents for the pre-fault and faulted DC grids could be easily obtained. From the aspect of power balance, the importance of AC active power on estimating the fault currents and voltages is discussed then. At last, based on the Zhangbei bipolar DC grid, comparisons are conducted between the simulations on PSCAD, the numerical calculation under the proposed method and the existing methods. The results show that the proposed method and the existing methods are both able to accurately estimate the fault currents within a relative error of 1%. However, compared with the error of the existing methods in calculating the fault voltages, the relative error for the proposed method is limited to less than 5% for the whole DC gird.
Development of the Global to Mesoscale Air Quality Forecast and Analysis System (GMAF) and Its Application to PM2.5 Forecast in Korea
Authors: Cho SeogYeon , Park HyeonYeong , Son JeongSeok , Chang LimSeok
Published: March 2021

This paper presents the development of the global to mesoscale air quality forecast and analysis system (GMAF) and its application to particulate matter under 2.5 μm (PM2.5) forecast in Korea. The GMAF combined a mesoscale model with a global data assimilation system by the grid nudging based four-dimensional data assimilation (FDDA). The grid nudging based FDDA developed for weather forecast and analysis was extended to air quality forecast and analysis for the first time as an alternative to data assimilation of surface monitoring data. The below cloud scavenging module and the secondary organic formation module of the community multiscale air quality model (CMAQ) were modified and subsequently verified by comparing with the PM speciation observation from the PM supersite. The observation data collected from the criteria air pollutant monitoring networks in Korea were used to evaluate forecast performance of GMAF for the year of 2016. The GMAF showed good performance in forecasting the daily mean PM2.5 concentrations at Seoul; the correlation coefficient between the observed and forecasted PM2.5 concentrations was 0.78; the normalized mean error was 25%; the probability of detection for the events exceeding the national PM2.5 standard was 0.81 whereas the false alarm rate was only 0.38. Both the hybrid bias correction technique and the Kalman filter bias adjustment technique were implemented into the GMAF as postprocessors. For the continuous and the categorical performance metrics examined, the Kalman filter bias adjustment technique performed better than the hybrid bias correction technique.
SCRaMbLE: A Study of Its Robustness and Challenges through Enhancement of Hygromycin B Resistance in a Semi-Synthetic Yeast
Authors: Ong Jun Yang , Swidah Reem , Monti Marco , Schindler Daniel , Dai Junbiao , Cai Yizhi
Published: March 2021

Recent advances in synthetic genomics launched the ambitious goal of generating the first synthetic designer eukaryote, based on the model organism Saccharomyces cerevisiae (Sc2.0). Excitingly, the Sc2.0 project is now nearing its completion and SCRaMbLE, an accelerated evolution tool implemented by the integration of symmetrical loxP sites (loxPSym) downstream of almost every non-essential gene, is arguably the most applicable synthetic genome-wide alteration to date. The SCRaMbLE system offers the capability to perform rapid genome diversification, providing huge potential for targeted strain improvement. Here we describe how SCRaMbLE can evolve a semi-synthetic yeast strain housing the synthetic chromosome II (synII) to generate hygromycin B resistant genotypes. Exploiting long-read nanopore sequencing, we show that all structural variations are due to recombination between loxP sites, with no off-target effects. We also highlight a phenomenon imposed on SCRaMbLE termed “essential raft”, where a fragment flanked by a pair of loxPSym sites can move within the genome but cannot be removed due to essentiality restrictions. Despite this, SCRaMbLE was able to explore the genomic space and produce alternative structural compositions that resulted in an increased hygromycin B resistance in the synII strain. We show that among the rearrangements generated via SCRaMbLE, deletions of YBR219C and YBR220C contribute to hygromycin B resistance phenotypes. However, the hygromycin B resistance provided by SCRaMbLEd genomes showed significant improvement when compared to corresponding single deletions, demonstrating the importance of the complex structural variations generated by SCRaMbLE to improve hygromycin B resistance. We anticipate that SCRaMbLE and its successors will be an invaluable tool to predict and evaluate the emergence of antibiotic resistance in yeast.
Quantitative Proteomics Analysis of Berberine-Treated Colon Cancer Cells Reveals Potential Therapy Targets
Authors: Li Pengfei , Hao Zhifang , Liu Huanhuan , Zhu Bojing , Dang Liuyi , Ma Chen , Xu Yintai , Zhang Yiyan , Fan Daidi , Sun Shisheng
Published: March 2021

Colon cancer is one of the most lethal malignancies worldwide. Berberine has been found to exert potential anti-colon cancer activity in vitro and in vivo, although the detailed regulatory mechanism is still unclear. This study aims to identify the underlying crucial proteins and regulatory networks associated with berberine treatment of colon cancer by using proteomics as well as publicly available transcriptomics and tissue array data. Proteome profiling of berberine-treated colon cancer cells demonstrated that among 5130 identified proteins, the expression of 865 and 675 proteins were changed in berberine-treated HCT116 and DLD1 cells, respectively. Moreover, 54 differently expressed proteins that overlapped in both cell lines were mainly involved in mitochondrial protein synthesis, calcium mobilization, and metabolism of fat-soluble vitamins. Finally, GTPase ERAL1 and mitochondrial ribosomal proteins including MRPL11, 15, 30, 37, 40, and 52 were identified as hub proteins of berberine-treated colon cancer cells. These proteins have higher transcriptional and translational levels in colon tumor samples than that of colon normal samples, and were significantly down-regulated in berberine-treated colon cancer cells. Genetic dependency analysis showed that silencing the gene expression of seven hub proteins could inhibit the proliferation of colon cancer cells. This study sheds a light for elucidating the berberine-related regulatory signaling pathways in colon cancer, and suggests that ERAL1 and several mitochondrial ribosomal proteins might be promising therapeutic targets for colon cancer.
A Novel Graph Neural Network Methodology to Investigate Dihydroorotate Dehydrogenase Inhibitors in Small Cell Lung Cancer
Authors: Zhi Hong-Yi , Zhao Lu , Lee Cheng-Chun , Chen Calvin Yu-Chian
Published: March 2021

Small cell lung cancer (SCLC) is a particularly aggressive tumor subtype, and dihydroorotate dehydrogenase (DHODH) has been demonstrated to be a therapeutic target for SCLC. Network pharmacology analysis and virtual screening were utilized to find out related proteins and investigate candidates with high docking capacity to multiple targets. Graph neural networks (GNNs) and machine learning were used to build reliable predicted models. We proposed a novel concept of multi-GNNs, and then built three multi-GNN models called GIAN, GIAT, and SGCA, which achieved satisfactory results in our dataset containing 532 molecules with all R^2 values greater than 0.92 on the training set and higher than 0.8 on the test set. Compared with machine learning algorithms, random forest (RF), and support vector regression (SVR), multi-GNNs had a better modeling effect and higher precision. Furthermore, the long-time 300 ns molecular dynamics simulation verified the stability of the protein–ligand complexes. The result showed that ZINC8577218, ZINC95618747, and ZINC4261765 might be the potentially potent inhibitors for DHODH. Multi-GNNs show great performance in practice, making them a promising field for future research. We therefore suggest that this novel concept of multi-GNNs is a promising protocol for drug discovery.
MRAP2 Interaction with Melanocortin-4 Receptor in SnakeHead (Channa argus)
Authors: Wen Zheng-Yong , Liu Ting , Qin Chuan-Jie , Zou Yuan-Chao , Wang Jun , Li Rui , Tao Ya-Xiong
Published: March 2021

The melanocortin-4 receptor (MC4R) plays an important role in the regulation of food intake and energy expenditure. Melanocortin-2 receptor accessory protein 2 (MRAP2) modulates trafficking, ligand binding, and signaling of MC4R. The Northern snakehead (Channa argus) is an economically important freshwater fish native to East Asia. To explore potential interaction between snakehead MC4R and MRAP2, herein we cloned snakehead mc4r and mrap2. The snakehead mc4r consisted of a 984 bp open reading frame encoding a protein of 327 amino acids, while snakehead mrap2 contained a 693 bp open reading frame encoding a protein of 230 amino acids. Synteny analysis indicated that mc4r was highly conserved with similar gene arrangement, while mrap2 contained two isoforms in teleost with different gene orders. Snakehead mc4r was primarily expressed in the brain, whereas mrap2 was expressed in the brain and intestine. Snakehead mc4r and mrap2 expression was modulated by fasting and refeeding. Further pharmacological experiments showed that the cloned snakehead MC4R was functional, capable of binding to peptide agonists and increasing intracellular cAMP production in a dose-dependent manner. Snakehead MC4R exhibited high constitutive activity. MRAP2 significantly decreased basal and agonist-stimulated cAMP signaling. These findings suggest that snakehead MC4R might be involved in energy balance regulation by interacting with MRAP2. Further studies are needed to elucidate MC4R in regulating diverse physiological processes in snakehead.
MiR-7 in Cancer Development
Authors: Korać Petra , Antica Mariastefania , Matulić Maja
Published: March 2021

MicroRNAs (miRNAs) are short non-coding RNA involved in the regulation of specific mRNA translation. They participate in cellular signaling circuits and can act as oncogenes in tumor development, so-called oncomirs, as well as tumor suppressors. miR-7 is an ancient miRNA involved in the fine-tuning of several signaling pathways, acting mainly as tumor suppressor. Through downregulation of PI3K and MAPK pathways, its dominant role is the suppression of proliferation and survival, stimulation of apoptosis and inhibition of migration. Besides these functions, it has numerous additional roles in the differentiation process of different cell types, protection from stress and chromatin remodulation. One of the most investigated tissues is the brain, where its downregulation is linked with glioblastoma cell proliferation. Its deregulation is found also in other tumor types, such as in liver, lung and pancreas. In some types of lung and oral carcinoma, it can act as oncomir. miR-7 roles in cell fate determination and maintenance of cell homeostasis are still to be discovered, as well as the possibilities of its use as a specific biotherapeutic.
Factorial Study on the Impact of Climate Change on Freeze-Thaw Damage, Mould Growth and Wood Decay in Solid Masonry Walls in Brussels
Authors: Vandemeulebroucke Isabeau , Caluwaerts Steven , Van Den Bossche Nathan
Published: March 2021

Previous studies show that climate change has an impact on the damage risks in solid masonry facades. To conserve these valuable buildings, it is important to determine the projected change in damages for the original and internally insulated cases. Since historical masonry covers a wide range of properties, it is unknown how sensitive the climate change impact is to variations in different parameters, such as wall thickness, brick type, etc. A factorial study is performed to determine the climate change impact on freeze-thaw risk, mould growth and wood decay in solid masonry in Brussels, Belgium. It is found that the critical orientation equals the critical wind-driven rain orientation and does not change over time. Further, the freeze-thaw risk is generally decreasing, whereas the change in mould growth and wood decay depends on the climate scenario. Knowing the brick type and rain exposure coefficient is most important when assessing the climate change impact. For freeze-thaw risk and wood decay, it is found that simulating one wall thickness for the uninsulated and one insulated case is sufficient to represent the climate change impact. Finally, the effects of climate change generally do not compensate for the increase in damage after the application of internal insulation.
Hydrogen Production via Pd-TiO2 Photocatalytic Water Splitting under Near-UV and Visible Light: Analysis of the Reaction Mechanism
Authors: Rusinque Bianca , Escobedo Salvador , de Lasa Hugo
Published: March 2021

Photocatalytic hydrogen production via water splitting using a noble metal on a TiO2 is a technology that has developed rapidly over the past few years. Specifically, palladium doped TiO2 irradiated with near-UV or alternatively with visible light has shown promising results. With this end in mind, strategically designed experiments were developed in the Photo-CREC Water-II (PCW-II) Reactor using a 0.25 wt.% Pd-TiO2 under near-UV and visible light, and ethanol as an organic scavenger. Acetaldehyde, carbon monoxide, carbon dioxide, methane, ethane, ethylene, and hydrogen peroxide together with hydrogen were the main chemical species observed. A Langmuir adsorption isotherm was also established for hydrogen peroxide. On this basis, it is shown that pH variations, hydrogen peroxide formation/adsorption, and the production of various redox chemical species provide an excellent carbon element balance, as well as OH• and H• radicals balances. Under near-UV irradiation, 113 cm3 STP of H2 is produced after 6 h, reaching an 99.8% elemental carbon balance and 99.2% OH• and H• and radical balance. It is also proven that a similar reaction network can be considered adequate for the photoreduced Pd-TiO2 photocatalyst yielding 29 cm3 STP of H2 with 95.4% carbon and the 97.5% OH•–H• radical balance closures. It is shown on this basis that a proposed “series-parallel” reaction network describes the water splitting reaction using the mesoporous Pd-TiO2 and ethanol as organic scavenger.
Elucidating the Influence of the d-Band Center on the Synthesis of Isobutanol
Authors: Häusler Johannes , Pasel Joachim , Woltmann Friederike , Everwand Andreas , Meledina Maria , Valencia Helen , Lipińska-Chwałek Marta , Mayer Joachim , Peters Ralf
Published: March 2021

As the search for carbon-efficient synthesis pathways for green alternatives to fossil fuels continues, an expanding class of catalysts have been developed for the upgrading of lower alcohols. Understanding of the acid base functionalities has greatly influenced the search for new materials, but the influence of the metal used in catalysts cannot be explained in a broader sense. We address this herein and correlate our findings with the most fundamental understanding of chemistry to date by applying it to d-band theory as part of an experimental investigation. The commercial catalysts of Pt, Rh, Ru, Cu, Pd, and Ir on carbon as a support have been characterized by means of SEM, EDX-mapping, STEM, XRD, N2-physisorption, and H2-chemisorption. Their catalytic activity has been established by means of c-methylation of ethanol with methanol. For all catalysts, the TOF with respect to i-butanol was examined. The Pt/C reached the highest TOF with a selectivity towards i-butanol of 89%. The trend for the TOFs could be well correlated with the d-band centers of the metal, which formed a volcano curve. Therefore, this study is another step towards the rationalization of catalyst design for the upgrading of alcohols into carbon-neutral fuels or chemical feedstock.
Investigations on the Ethylene Polymerization with Bisarylimine Pyridine Iron (BIP) Catalysts
Authors: Schoeneberger Elsa M. , Luinstra Gerrit A.
Published: March 2021

The kinetics and terminations of ethylene polymerization, mediated by five bisarylimine pyridine (BIP) iron dichloride precatalysts, and activated by large amounts of methyl aluminoxane (MAO) was studied. Narrow distributed paraffins from initially formed aluminum polymeryls and broader distributed 1-polyolefins and (bimodal) mixtures, thereof, were obtained after acidic workup. The main pathway of olefin formation is beta-hydrogen transfer to ethylene. The rate of polymerization in the initial phase is inversely proportional to the co-catalyst concentration for all pre-catalysts; a first-order dependence was found on ethylene and catalyst concentrations. The inhibition by aluminum alkyls is released to some extent in a second phase, which arises after the original methyl groups are transformed into n-alkyl entities and the aluminum polymeryls partly precipitate in the toluene medium. The catalysis is interpretable in a mechanism, wherein, the relative rate of chain shuttling, beta-hydrogen transfer and insertion of ethylene are determining the outcome. Beta-hydrogen transfer enables catalyst mobility, which leads to a (degenerate) chain growth of already precipitated aluminum alkyls. Stronger Lewis acidic centers of the single site catalysts, and those with smaller ligands, are more prone to yield 1-olefins and to undergo a faster reversible alkyl exchange between aluminum and iron.
Advances in Electromagnetic Piezoelectric Acoustic Sensor Technology for Biosensor-Based Detection
Authors: Mészáros Gábor , Akbarzadeh Sanaz , De La Franier Brian , Keresztes Zsófia , Thompson Michael
Published: March 2021

The ultra-high frequency EMPAS (electromagnetic piezoelectric acoustic sensor) device is composed of an electrode-less quartz disc in which shear oscillation is induced by an AC-powered magnetic coil located 30 μm below the substrate. This configuration allows the instigation of high acoustic harmonics (in the region of 49th–53rd), with the resulting enhanced analytical sensitivity for biosensor purposes compared to the conventional thickness-shear mode device. In this paper, we introduce significant improvements to the operation of the system with respect to sensing applications. This includes a new interface program and the capability to measure the acoustic quality factor not available in the prototype version. The enhanced configuration is subject to testing through biosensor detection of surface adsorption of biological macromolecules, which include β-casein, and a gelsolin-actin complex.
Preliminary Evaluation of the Influence of Surface and Tooth Root Damage on the Stress and Strain State of a Planetary Gearbox: An Innovative Hybrid Numerical–Analytical Approach for Further Development of Structural Health Monitoring Models
Authors: Concli Franco , Kolios Athanasios
Published: March 2021

Wind turbine gearboxes are known to be among the weakest components in the system and the possibility to study and understand the behavior of geared transmissions when subject to several types of faults might be useful to plan maintenance and eventually reduce the costs by preventing further damage. The aim of this work is to develop a high-fidelity numerical model of a single-stage planetary gearbox selected as representative and to evaluate its behavior in the presence of surface fatigue and tooth-root bending damage, i.e., pits and cracks. The planetary gearbox is almost entirely modelled, including shafts, gears as well as bearings with all the rolling elements. Stresses and strains in the most critical areas are analyzed to better evaluate if the presence of such damage can be somehow detected using strain gauges and where to place them to maximize the sensitivity of the measures to the damage. Several simulations with different levels, types and positions of the damage were performed to better understand the mutual relations between the damaged and the stress state. The ability to introduce the effect of the damage in the model of a gearbox represents the first indispensable step of a Structural Health Monitoring (SHM) strategy. The numerical activity was performed taking advantage of an innovative hybrid numerical–analytical approach that ensures a significant reduction of the computational effort. The developed model shows good sensitivity to the presence, type and position of the defects. For the studied configuration, the numerical results show clearly show a relation between the averaged rim stress and the presence of root cracks. Moreover, the presence of surface defects seems to produce local stress peaks (when the defects pass through the contact) in the instantaneous rim stress.