bluuebunny/crossref_metadata_2025 huggingface dataset:
Applying High-Resolution UAV-LiDAR and Quantitative Structure Modelling for Estimating Tree Attributes in a Crop-Livestock-Forest System
Authors: Dalla Corte Ana Paula , de Vasconcellos Bruna Nascimento , Rex Franciel Eduardo , Sanquetta Carlos Roberto , Mohan Midhun , Silva Carlos Alberto , Klauberg Carine , de Almeida Danilo Roberti Alves , Zambrano Angelica Maria Almeyda , Trautenmüller Jonathan William , Leite Rodrigo Vieira , do Amaral Cibele Hummel , Veras Hudson Franklin Pessoa , da Silva Rocha Karla , de Moraes Anibal , Karasinski Mauro Alessandro , Sanquetta Matheus Niroh Inoue , Broadbent Eben North
Published: April 2022

Individual tree attributes, such as stem volume and biomass, are usually predicted by using traditional field-derived allometric models. However, these models are derived from data collected from small areas and lack a level of detail of tree components (e.g., stem, branches, and leaves). Remote sensing techniques such as the Quantitative Structure Modelling (QSM) applied on high-density LiDAR data emerge as a promising solution for obtaining extensive and detailed tree attribute estimates. We used a high-density LiDAR data on board of a Unmanned Aerial Vehicle (UAV) to evaluate the performance of the QSM approach in estimating field-derived individual tree attributes such as the diameter at breast height (dbh), tree height (ht), and volume (v), as well as the stem (SAGB), branch (BAGB), and total (TAGB) aboveground biomass of eucalyptus trees. QSM was used in two different approaches: (i) using dbh and h derived from QSM and then applied into the field-based equations for estimation of volume and (ii) deriving tree volume directly from QSM. In general, all fitted models using the QSM approach were satisfactory, but with a slight tendency of over-estimation of dbh (9.33%), ht (12.40%), v-QSM1 (26.35%), v-QSM2 (26.66%), TAGB (27.08%), SAGB (25.57%), and BAGB (20.08%). Non-significant differences were noticed when estimating the dbh, tree volume, stem, and aboveground biomass. Despite the overestimation, this study indicates that using the QSM approach to estimate individual tree attributes from UAV-LiDAR is a promising alternative to support the decision-making process regarding forest management activities, especially when considering tree architecture and biomass components.
Inter-Provincial Differences in Potential Obstacles to Land Transfer Income to Support Rural Revitalization in China
Authors: Wang Yubo , Xue Yuyu , Xi Jianchao
Published: April 2022

This study analyzes the triple path obstacles of land transfer income in supporting rural revitalization under the premise of existing policy support. It is innovative to try to de-qualitatively analyze the obstacle factors and quantitatively measure the degree of obstacles. We examined the potential obstacles to land transfer income supporting rural revitalization in China, measured the potential comprehensive obstacle degree of 31 provincial-level administrative divisions’ land transfer income supporting rural revitalization, and constructed a way to resolve the differences. We employed inter alia the entropy method, ideal solution similarity ranking method, and cluster analyses. Potential obstacles were divided into nine types. The resolution path was constructed by combining the original and weighted values of various obstacle factors in the top three obstacle factors. It is of great significance to ensure the effective implementation of China’s “opinions on adjusting and improving the scope of land transfer income to give priority to supporting rural revitalization” (2020). This study addresses the theoretical gap in the analysis of possible paths needed to support rural revitalization in China.
Compound-Internal Language Mixing in American Norwegian
Authors: Eik Ragnhild , Riksem Brita Ramsevik
Published: April 2022

This paper investigates cases of compounding in the heritage language American Norwegian (AmNo), where elements from Norwegian and English are mixed word-internally, e.g., hoste-candy ‘cough candy’, where the Norwegian item hoste ‘cough’ is combined with the English item candy. Norwegian and English create compounds in similar ways, but with certain important differences, e.g., the use of linking elements. Based on data from the Corpus of American Nordic Speech, we investigate the encounter of these two languages within one word and find that both Norwegian and English lexical items occur as both left-hand and right-hand members of mixed compounds. Moreover, these mixed compounds are generally accompanied by Norwegian functional items. Hence, we argue that the overall structure of mixed compounds in AmNo is Norwegian, and English lexical items may be inserted into specific positions. This is successfully analyzed in a DM/exoskeletal model of grammar. We show that our results are in line with what we expect based on previous accounts of AmNo language mixing and Norwegian compounds, and our specific focus on compound-internal mixing provides a novel perspective and new insights into both the structure of compounds and the nature of language mixing.
Radiomics-Based Artificial Intelligence Differentiation of Neurodegenerative Diseases with Reference to the Volumetry
Authors: Cheung Eva Y. W. , Chau Anson C. M. , Tang Fuk Hay ,   on behalf of the Alzheimer’s Disease Neuroimaging Initiative
Published: April 2022

This study aimed to build automated detection models—one by brain regional volume (V-model), and the other by radiomics features of the whole brain (R-model)—to differentiate mild cognitive impairment (MCI) from cognitive normal (CN), and Alzheimer’s Disease (AD) from mild cognitive impairment (MCI). The objectives are to compare the models and identify whether radiomics or volumetry can provide a better prediction for differentiating different types of dementia. Method: 582 MRI T1-weighted images were retrieved from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, which is a multicenter operating open source database for AD. In total, 97 images of AD, 293 images of MCI patient and 192 images of cognitive normal were divided into a training, a validation and a test group at a ratio of 70:15:15. For each T1-weighted image, volumetric segmentation was performed with the image analysis software FreeSurfer, and radiomics features were retrieved by imaging research software 3D slicers. Brain regional volume and radiomics features were used to build the V-model and R-model, respectively, using the random forest algorithm by R. The receiver operating characteristics (ROC) curve of both models were used to evaluate their diagnostic accuracy and reliability to differentiate AD, MCI and CN. Results: To differentiate MCI and CN, both V-model and R-model achieved excellent performance, with an AUC of 0.9992 ± 0.0022 and 0.9850 ± 0.0032, respectively. No significant difference was found between the two AUCs, indicating both models attained similar good performance. In MCI and AD differentiation, the V-model and R-model yielded AUC of 0.9986 ± 0.0013 and 0.9714 ± 0.0175, respectively. The best performance was to differentiate AD from CN, where the V-model and R-model yielded AUC of 0.9994 ± 0.0019 and 0.9830 ± 0.009, respectively. The results suggested that both volumetry and radiomics approaches could be used in differentiating AD, MCI and CN, based on T1 weighted MR images using random forest algorithm successfully. Conclusion: This study showed that the radiomics features from T1-weighted MR images achieved excellence performance in differentiating AD, MCI and CN. Compared to the volumetry method, the accuracy, sensitivity and specificity are slightly lower in using radiomics, but still attained very good and reliable classification of the three stages of neurodegenerations. In view of the convenience and operator independence in feature extraction, radiomics can be a quantitative biomarker to differentiate the disease groups.
Comparative Efficacy and Safety of Antifungal Agents in the Prophylaxis of Oropharyngeal Candidiasis among HIV-Infected Adults: A Systematic Review and Network Meta-Analysis
Authors: Rajadurai Shamala Gopal , Maharajan Mari Kannan , Veettil Sajesh K. , Gopinath Divya
Published: April 2022

The objective of the study was to compare the efficacy and safety of antifungal agents used in the prevention of oropharyngeal candidiasis among HIV-infected adults. A systematic search was conducted in four databases (MEDLINE, Scopus, CENTRAL, and Embase) for eligible randomized control trials (RCTs). The network meta-analyses (NMA) were performed using a random-effects model. Interventions were ranked based on the efficacy and safety using the surface under the cumulative ranking curve (SUCRA). The quality of evidence was assessed using the GRADE approach. From a total of 1574 studies screened, 7 RCTs comprising 959 participants were included in NMA. The use of fluconazole as a prophylactic agent was associated with a significant reduction in incidence of OPC compared to placebo (RR, 0.45 (95% CI: 0.27–0.77)) in HIV-infected adults. The overall quality of evidence was graded as moderate. Fluconazole was ranked the best antifungal for efficacy (SUCRA—95.6%) as well as safety (SUCRA—39.3%) in HIV-infected adults. Overall, the quality of evidence was graded as moderate. Fluconazole can be considered as an effective agent with a better safety profile for the prophylaxis of OPC in HIV-infected adults. However, similar to any other antimicrobial agent, the risk of possibility of resistance must be weighed against the benefits.
The Final Step in the Application of Perovskite Solar Cells
Authors: Feng Jiangshan
Published: April 2022

Since 2009, there has been renewed interest in perovskite materials due to the rise of metal halide perovskite photoelectric materials, especially in the field of perovskite solar cells (PSCs) [...]
Counterfactual Models for Fair and Adequate Explanations
Authors: Asher Nicholas , De Lara Lucas , Paul Soumya , Russell Chris
Published: April 2022

Recent efforts have uncovered various methods for providing explanations that can help interpret the behavior of machine learning programs. Exact explanations with a rigorous logical foundation provide valid and complete explanations, but they have an epistemological problem: they are often too complex for humans to understand and too expensive to compute even with automated reasoning methods. Interpretability requires good explanations that humans can grasp and can compute. We take an important step toward specifying what good explanations are by analyzing the epistemically accessible and pragmatic aspects of explanations. We characterize sufficiently good, or fair and adequate, explanations in terms of counterfactuals and what we call the conundra of the explainee, the agent that requested the explanation. We provide a correspondence between logical and mathematical formulations for counterfactuals to examine the partiality of counterfactual explanations that can hide biases; we define fair and adequate explanations in such a setting. We provide formal results about the algorithmic complexity of fair and adequate explanations. We then detail two sophisticated counterfactual models, one based on causal graphs, and one based on transport theories. We show transport based models have several theoretical advantages over the competition as explanation frameworks for machine learning algorithms.
Ramadan Intermittent Fasting Is Associated with Changes in Circulating Proprotein Convertase Subtilisin/Kexin Type 9 (PCSK9) in Metabolically Healthy Obese Subjects
Authors: Hasan Hayder , Madkour Mohamed , Awadallah Samir , Hassanein Mohamed , Jahrami Haitham , Faris MoezAlIslam
Published: April 2022

Background and Objectives: Dietary modification is the principal approach to the management of hyperlipidemia in adults. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is a key regulator of plasma cholesterol and a target for novel lipid-lowering pharmacotherapies. This study aimed to explore how circulating levels of PCSK9 changed during Ramadan intermittent fasting in metabolically healthy obese subjects. Materials and Methods: This cross-sectional study used convenience sampling to recruit 55 overweight and obese participants (22 females and 33 males) who observed Ramadan fasting. Body weight and composition, glucoregulatory factors, serum PCSK9 concentration, dietary intake, and physical activity were assessed 1 week before and at the end of Ramadan fasting. Results: The median (interquartile range) age was 35 (22) years, and body mass index was 30.2 (5.4). We found significant (p < 0.05) increases in serum levels of PCSK9, serum insulin, insulin resistance, and leptin at the end of Ramadan compared with pre-fasting levels. Significant (p < 0.05) reductions in body weight, waist circumference, systolic and diastolic blood pressure, total cholesterol, triglycerides, high-density lipoprotein cholesterol, and adiponectin were also observed at the end of Ramadan. Conclusions: Observing Ramadan fasting was associated with increased PCSK9 levels in metabolically healthy obese subjects. The complex relationships between PCSK9 and insulin resistance and dysregulation of adipokine secretion in relation to dietary and lifestyle modifications during Ramadan warrant further research.
A Systematic Review of Artificial Intelligence Applications Used for Inherited Retinal Disease Management
Authors: Esengönül Meltem , Marta Ana , Beirão João , Pires Ivan Miguel , Cunha António
Published: April 2022

Nowadays, Artificial Intelligence (AI) and its subfields, Machine Learning (ML) and Deep Learning (DL), are used for a variety of medical applications. It can help clinicians track the patient’s illness cycle, assist with diagnosis, and offer appropriate therapy alternatives. Each approach employed may address one or more AI problems, such as segmentation, prediction, recognition, classification, and regression. However, the amount of AI-featured research on Inherited Retinal Diseases (IRDs) is currently limited. Thus, this study aims to examine artificial intelligence approaches used in managing Inherited Retinal Disorders, from diagnosis to treatment. A total of 20,906 articles were identified using the Natural Language Processing (NLP) method from the IEEE Xplore, Springer, Elsevier, MDPI, and PubMed databases, and papers submitted from 2010 to 30 October 2021 are included in this systematic review. The resultant study demonstrates the AI approaches utilized on images from different IRD patient categories and the most utilized AI architectures and models with their imaging modalities, identifying the main benefits and challenges of using such methods.
Poly(ethylene-co-vinyl alcohol) Electrospun Nanofiber Membranes for Gravity-Driven Oil/Water Separation
Authors: Shah Aatif Ali , Yoo Youngmin , Park Ahrumi , Cho Young Hoon , Park You-In , Park Hosik
Published: April 2022

Fabrication of highly efficient oil/water separation membranes is attractive and challenging work for the actual application of the membranes in the treatment of oily wastewater and cleaning up oil spills/oil leakage accidents. In this study, hydrophilic poly(ethylene-co-polyvinyl alcohol) (EVOH) nanofiber membranes were made using an electrospinning technique for oil/water separation. The as-prepared EVOH electrospun nanofiber membranes (ENMs) exhibited a super-hydrophilic property (water contact angle 33.74°) without further treatment. As prepared, ENMs can provide continuous separation of surfactant-free and surfactant-stabilized water-in-oil emulsions with high efficiency (i.e., flux 8200 L m−2 h−1 (LMH), separation efficiency: >99.9%). In addition, their high stability (i.e., reusable, mechanically robust) would broaden the conditions under which they can be employed in the real field oil/water separation applications. Various characterization techniques (including morphology investigation, pore size, porosity, mechanical properties, and performance test) for gravity-driven oil/water separation were employed to evaluate the newly prepared EVOH ENMs.
The Application of a Modified Polyacrylonitrile Porous Membrane in Vanadium Flow Battery
Authors: Qiao Lin , Liu Shumin , Cheng Haodong , Ma Xiangkun
Published: April 2022

Vanadium flow battery (VFB) is one of the most promising candidates for large-scale energy storage. A modified polyacrylonitrile (PAN) porous membrane is successfully applied in VFB. Herein, a simple solvent post-processing method is presented to modify PAN porous membranes prepared by the traditional nonsolvent induced phase separation (NIPS) method. In the design, polymer PAN is chosen as the membrane material owing to its low cost and high stability. The large-size pores from NIPS method are well optimized by the solvent swelling and shrinking during the solvent post-processing. Meanwhile, the interconnectivity of pores is maintained well. As a result, the ion selectivity of PAN porous membranes is dramatically improved, and the CE of a VFB with PAN porous membranes rises from 68% to 93% after the solvent post-processing process. A VFB with the modified PAN porous membranes is capable of delivering a limiting current density of 900 mA cm−2, and a high peak power density of 650 mW cm−2, which is very competitive among the various flow batteries.
Exploration of the Effects of Metallic Powder Handling and Storage Conditions on Flowability and Moisture Content for Additive Manufacturing Applications
Authors: Grubbs Jack , Sousa Bryer C. , Cote Danielle
Published: April 2022

Metal powder-based additive manufacturing (AM) relies on consistently successful processing of feedstock powder, necessitating through-process predictability in powder properties and behavior. However, routine powder handling and storage may degrade powder performance by influencing flowability and moisture content through exposure to ambient conditions. Therefore, this study aimed to evaluate the effects of repeated environmental exposure on the flowability and moisture content of Al 5056 and Ta powders for AM applications. Using Carney Funnel flow tests, thermogravimetric analysis, and particle size/shape analysis, powder characterization helped elucidate powder property and behavioral changes with exposure. Results indicated inconsistent flowability and moisture content changes for both material types when exposure conditions were altered. Correlational statistics highlighted the most influential particle characteristics on powder behavior after exposure; particle morphology was most impactful for the semi-spherical Al 5056, whereas moisture content and particle size were most significant for the angular Ta. While exposure to laboratory conditions minimally changed powder performance in this study, caution is advised when handling and storing powders in more “extreme” environments. Powder users are urged to implement quality controls alongside powder characterization to pinpoint how specific powders should be treated, handled, and stored in a given environment for successful processing in AM.
Solidification Simulation of Al-Si Alloys with Dendrite Tip Undercooling
Authors: Wang Hongda , Hamed Mohamed S. , Shankar Sumanth
Published: April 2022

A novel solution approach is proposed for the numerical simulation of the solidification process of binary Al-Si hypoeutectic alloys during upward and downward solidification modes. Undercooling is always observed during solidification, but the phenomenon could not be considered in the present-day numerical solution. In this approach, the temperature distribution in the mushy zone was used to define the fraction of solid, which enabled the evaluation of the effect of dendrite tip undercooling on the characteristics of the binary alloy solidification. The present numerical algorithm was found to significantly reduce the computation time. Transient temperature distribution and solidification time from the numerical analysis, with consideration of natural convection due to temperature and concentration gradients, have been successfully simulated and validated with experiment results. Numerical results with consideration of dendrite tip undercooling have better agreement with experimental results. The effect of dendrite tip undercooling on the fluid flow (velocity profile), G, R and λ1 for both upward and downward solidification modes of Al-Si alloys have been investigated and discussed. Consideration of undercooling was found to increase G and reduce R in both solidification modes. During downward solidification, considering undercooling significantly increased flow velocity and decreased λ1. The primary dendrite arm spacing could be validated with results from uni-directional solidification experiments only when dendrite tip undercooling was considered.
Influence of Nickel on Niobium Nitride Formation in As-Cast Stainless Steels
Authors: Bajželj Anže , Šetina Batič Barbara , Medved Jožef , Burja Jaka
Published: April 2022

The influence of the nickel content and cooling rate on niobium nitride precipitation in as-cast stainless steels were analysed. Niobium microalloying is important for mechanical properties and the prevention of intergranular corrosion in stainless steels. However, coarse precipitates can negatively affect steel properties. The precipitation of NbN depends on thermodynamic conditions, which are dictated by the chemical composition and temperature. The thermodynamic computations were used to estimate niobium nitride precipitation. Additionally, segregation models were used to predict precipitation. Three steel batches with different nickel contents (9 wt.%, 4.7 wt.%, and 0.16 wt.%) were prepared in an induction furnace and cast into sand moulds. The polished and etched samples were examined with an optical microscope, followed by a more detailed examination using a scanning electron microscope. An automatic scanning electron microscope analysis of the niobium particles was performed to obtain particle number and size distribution. Primary niobium carbonitrides, eutectic phases, and heterogenous nucleations on MnS inclusions were observed. As the proportion of nickel in the solution decreased, the solubility of nitrogen in the melt increased, which is manifested by a lower formation of primary and eutectic niobium carbonitrides, while MnS non-metallic inclusions played an important role in the heterogeneous nucleation.
NMR Metabolomics Reveal Urine Markers of Microbiome Diversity and Identify Benzoate Metabolism as a Mediator between High Microbial Alpha Diversity and Metabolic Health
Authors: Hertel Johannes , Fässler Daniel , Heinken Almut , Weiß Frank , Rühlemann Malte , Bang Corinna , Franke Andre , Budde Kathrin , Henning Ann-Kristin , Petersmann Astrid , Völker Uwe , Völzke Henry , Thiele Ines , Grabe Hans-Jörgen , Lerch Markus , Nauck Matthias , Friedrich Nele , Frost Fabian
Published: April 2022

Microbial metabolites measured using NMR may serve as markers for physiological or pathological host–microbe interactions and possibly mediate the beneficial effects of microbiome diversity. Yet, comprehensive analyses of gut microbiome data and the urine NMR metabolome from large general population cohorts are missing. Here, we report the associations between gut microbiota abundances or metrics of alpha diversity, quantified from stool samples using 16S rRNA gene sequencing, with targeted urine NMR metabolites measures from 951 participants of the Study of Health in Pomerania (SHIP). We detected significant genus–metabolite associations for hippurate, succinate, indoxyl sulfate, and formate. Moreover, while replicating the previously reported association between hippurate and measures of alpha diversity, we identified formate and 4-hydroxyphenylacetate as novel markers of gut microbiome alpha diversity. Next, we predicted the urinary concentrations of each metabolite using genus abundances via an elastic net regression methodology. We found profound associations of the microbiome-based hippurate prediction score with markers of liver injury, inflammation, and metabolic health. Moreover, the microbiome-based prediction score for hippurate completely mediated the clinical association pattern of microbial diversity, hinting at a role of benzoate metabolism underlying the positive associations between high alpha diversity and healthy states. In conclusion, large-scale NMR urine metabolomics delivered novel insights into metabolic host–microbiome interactions, identifying pathways of benzoate metabolism as relevant candidates mediating the beneficial health effects of high microbial alpha diversity.
Untargeted Metabolomics Reveals Metabolic Stress Alleviation by Prepartum Exercise in Transition Dairy Cows
Authors: Luo Zhengzhong , Huang Yixin , Ma Li , Jiang Jing , Luo Qiao , Yang Zhuo , Yong Kang , Shen Liuhong , Yu Shumin , Yao Xueping , Tao Jinzhong , Cao Suizhong
Published: April 2022

Prepartum exercise (PA) has been proposed as a strategy for the peripartum management of dairy cows; however, the mechanism by which PA affects metabolism has not been elucidated. Here, we investigated the metabolic changes in transition dairy cows with PA. Holstein transition multiparous dairy cows were assigned to an exercise (n = 12) or a control (n = 12) group; the cows in the exercise group walked for a targeted 45 min at 3.25 km/h, two times a day. Plasma non-esterified fatty acid (NEFA), β-hydroxybutyric acid (BHBA), glucose, and triglyceride levels were measured, and metabolic profiles were analyzed using untargeted mass spectrometry. Compared with those in the control group, the concentrations of NEFA at −7 d, glucose at 0 d, and BHBA at +7 d relative to calving were considerably decreased in the exercise group. Untargeted metabolomics analysis revealed differences in the levels of key metabolites, including kynurenine, tryptophan, homovanillic acid, dopamine, cis-9-palmitoleic acid, and palmitic acid, between the exercise and control group cows. This study suggests that PA may decrease homovanillic acid and cis-9-palmitoleic acid levels and increase tryptophan levels to alleviate the metabolic stress in dairy cows during calving, thereby improving postpartum health.
Dual-Arm Robot Trajectory Planning Based on Deep Reinforcement Learning under Complex Environment
Authors: Tang Wanxing , Cheng Chuang , Ai Haiping , Chen Li
Published: April 2022

In this article, the trajectory planning of the two manipulators of the dual-arm robot is studied to approach the patient in a complex environment with deep reinforcement learning algorithms. The shape of the human body and bed is complex which may lead to the collision between the human and the robot. Because the sparse reward the robot obtains from the environment may not support the robot to accomplish the task, a neural network is trained to control the manipulators of the robot to prepare to hold the patient up by using a proximal policy optimization algorithm with a continuous reward function. Firstly, considering the realistic scene, the 3D simulation environment is built to conduct the research. Secondly, inspired by the idea of the artificial potential field, a new reward and punishment function was proposed to help the robot obtain enough rewards to explore the environment. The function is consisting of four parts which include the reward guidance function, collision detection, obstacle avoidance function, and time function. Where the reward guidance function is used to guide the robot to approach the targets to hold the patient, the collision detection and obstacle avoidance function are complementary to each other and are used to avoid obstacles, and the time function is used to reduce the number of training episode. Finally, after the robot is trained to reach the targets, the training results are analyzed. Compared with the DDPG algorithm, the PPO algorithm reduces about 4 million steps for training to converge. Moreover, compared with the other reward and punishment functions, the function used in this paper will obtain many more rewards at the same training time. Apart from that, it will take much less time to converge, and the episode length will be shorter; so, the advantage of the algorithm used in this paper is verified.
Intestinal Microbial Diversity of Free-Range and Captive Yak in Qinghai Province
Authors: Wen Ying , Li Shaofei , Wang Zishuo , Feng Hao , Yao Xiaoting , Liu Mingjie , Chang Jianjun , Ding Xiaoyu , Zhao Huiying , Ma Wentao
Published: April 2022

Background: The gut microbiome is a large and complex organic assemblage with subtle and close relationships with the host. This symbiotic mechanism is important for the health and adaptability of the host to the environment. Compared with other ruminants, there are few studies on yak intestinal microbes. The study of the gut microbiota of the yak will help us better understand the correlation between the microbiota and the environmental adaptability of the host. In this study, we adapted 16S rDNA sequencing technology to investigate the diversity and composition of the intestinal microbial community in free-range yaks and captive yaks living on the Qinghai–Tibet Plateau (QTP). Results: Sequencing results showed that the intestinal microbial community diversity was significantly different between free-range yaks and captive yaks. Firmicutes and Bacteroidetes were the dominant bacteria in both free-range and captive yaks. However, there were differences between the microbes of the two analyzed feeding styles in different classification levels. Compared with the captive type, free-range yaks had a higher abundance of Ruminococcaceae, Eubacteriaceae, Desulfovibrionaceae, Elusimicrobium, and Oscillibacter, while the abundance of Succinivibrionaceae, Clostridiales, Lachnospiraceae, Prevotellaceae, Roseburia, and Barnesiella was relatively low. The feeding method may be the key factor for the formation of intestinal flora differences in yaks, while altitude did not significantly affect Qinghai yak. Conclusions: In this study, we used 16S rDNA sequencing technology to investigate the composition of intestinal flora in free-range and captive yaks living on the QTP. The exploration of dietary factors can provide a theoretical basis for scientifically and rationally breeding yaks and provides a new direction for the development of prebiotics and microecological agents.
Evaluation of the Antimicrobial and Antivirulent Potential of Essential Oils Isolated from Juniperus oxycedrus L. ssp. macrocarpa Aerial Parts
Authors: Spengler Gabriella , Gajdács Márió , Donadu Matthew Gavino , Usai Marianna , Marchetti Mauro , Ferrari Marco , Mazzarello Vittorio , Zanetti Stefania , Nagy Fruzsina , Kovács Renátó
Published: April 2022

As a consequence of the worsening situation with multidrug-resistant (MDR) pathogens and a disparity in the commercialization of novel antimicrobial agents, scientists have been prompted to seek out new compounds with antimicrobial activity from a wide range of sources, including medicinal plants. In the present study, the antibacterial, antifungal, anti-virulence, and resistance-modulating properties of the essential oil from the Sardinian endemic Juniperus oxycedrus L. ssp. macrocarpa aerial parts were evaluated. The GC/MS analysis showed that the main compounds in the oil were α-pinene (56.63 ± 0.24%), limonene (14.66 ± 0.11%), and β-pinene (13.42 ± 0.09%). The essential oil showed potent antibacterial activity against Gram-positive bacteria (0.25–2 v/v%) and Salmonella spp. (4 v/v%). The strongest fungicidal activity was recorded against Candida auris sessile cells (median FICI was 0.088) but not against C. albicans biofilms (median FICI was 1). The oil showed potent efflux pump inhibitory properties in the case of Staphylococcus aureus and Escherichia coli. The therapeutic potential of Juniperus may be promising for future more extensive research and in vivo tests to develop new drugs against antibiotic and antifungal resistance.
Biofilm Growth on Simulated Fracture Fixation Plates Using a Customized CDC Biofilm Reactor for a Sheep Model of Biofilm-Related Infection
Authors: Kay Walker , Hunt Connor , Nehring Lisa , Barnum Brian , Ashton Nicholas , Williams Dustin
Published: April 2022

Most animal models of infection utilize planktonic bacteria as initial inocula. However, this may not accurately mimic scenarios where bacteria in the biofilm phenotype contaminate a site at the point of injury. We developed a modified CDC biofilm reactor in which biofilms can be grown on the surface of simulated fracture fixation plates. Multiple reactor runs were performed and demonstrated that monomicrobial biofilms of a clinical strain of methicillin-resistant Staphylococcus aureus, S. aureus ATCC 6538, and Pseudomonas aeruginosa ATCC 27853 consistently developed on fixation plates. We also identified a method by which to successfully grow polymicrobial biofilms of S. aureus ATCC 6538 and P. aeruginosa ATCC 27853 on fixation plates. This customized reactor can be used to grow biofilms on simulated fracture fixation plates that can be inoculated in animal models of biofilm implant-related infection that, for example, mimic open fracture scenarios. The reactor provides a method for growing biofilms that can be used as initial inocula and potentially improve the testing and development of antibiofilm technologies.
Abiotic Depletion of Boron: An Update Characterization Factors for CML 2002 and ReCiPe
Authors: Çolak Ayşenur , Laratte Bertrand , Elevli Birol , Çoruh Semra
Published: April 2022

The risk of resource depletion for future generations of humanity is often cited as an important issue. The choice of impact categories and characterization models for resource extraction in LCA is no more precise than other impact categories and models. This means that more discussion is needed on the use of resources. In this article, the potential depletion of Boron and Boron minerals (Colemanite, Ulexite, Tincal) are studied. These minerals have a big role for the world and for Turkey; however, this resource is limited. Using the life cycle assessment methodology, one can estimate the resource depletion through the indicator “abiotic resource depletion”. Several models can evaluate this indicator, but the most used models are ReCiPe and CML (that is the previous attempt of ReCiPe) methods. Here, we estimated the damage that is done to natural resource scarcity. The values that are calculated by these two methods were compared to identify the potential evolution of the model and to observe the gap between these two models. The ReCiPe method refers to the average amount of extra ore that is produced in the future to extract 1 kg of boron ore or boron minerals resource. On the other hand, The CML method depends on the final reserve amount in terms of depletion. The results show no depletion shortly for boron ore and boron minerals. Correlation coefficients were calculated in the ReCiPe method, and ‘high uncertainty’ was estimated since R2 < 0.8. This research highlights the fact that there is the necessity to propose different impact factors for the various minerals and not only for boron (that is done today).
Synthesis, Characterization, and Optimization Studies of Starch/Chicken Gelatin Composites for Food-Packaging Applications
Authors: Castro Jorge Iván , Navia-Porras Diana Paola , Arbeláez Cortés Jaime Andrés , Mina Hernández José Herminsul , Grande-Tovar Carlos David
Published: April 2022

The indiscriminate use of plastic in food packaging contributes significantly to environmental pollution, promoting the search for more eco-friendly alternatives for the food industry. This work studied five formulations (T1–T5) of biodegradable cassava starch/gelatin films. The results showed the presence of the starch/gelatin functional groups by FT-IR spectroscopy. Differential scanning calorimetry (DSC) showed a thermal reinforcement after increasing the amount of gelatin in the formulations, which increased the crystallization temperature (Tc) from 190 °C for the starch-only film (T1) to 206 °C for the film with 50/50 starch/gelatin (T3). It also exhibited a homogeneous surface morphology, as evidenced by scanning electron microscopy (SEM). However, an excess of gelatin showed low compatibility with starch in the 25/75 starch/gelatin film (T4), evidenced by the low Tc definition and very rough and fractured surface morphology. Increasing gelatin ratio also significantly increased the strain (from 2.9 ± 0.5% for T1 to 285.1 ± 10.0% for T5) while decreasing the tensile strength (from 14.6 ± 0.5 MPa for T1 to 1.5 ± 0.3 MPa for T5). Water vapor permeability (WVP) increased, and water solubility (WS) also decreased with gelatin mass rising in the composites. On the other hand, opacity did not vary significantly due to the films’ cassava starch and gelatin ratio. Finally, optimizing the mechanical and water barrier properties resulted in a mass ratio of 53/47 cassava starch/gelatin as the most appropriate for their application in food packaging, indicating their usefulness in the food-packaging industry.
Identification and Validation of a New Peptide Targeting Pancreatic Beta Cells
Authors: Wang Qianwen , Zheng Lei , Wu Kangze , Zhang Bo
Published: April 2022

Noninvasive targeted visualization of pancreatic beta cells or islets is becoming the focus of molecular imaging application in diabetes and islet transplantation studies. In this study, we aimed to produce the beta-cell-targeted peptide for molecular imaging of islet. We used phage display libraries to screen a beta-cell-targeted peptide, LNTPLKS, which was tagged with fluorescein isothiocyanate (FITC). This peptide was validated for targeting beta-cell with in vitro and in vivo studies. Immunocytochemistry (ICC) and fluorescence-activated cell sorting (FACS) analysis were used to validate the target specificity of the peptide. FITC-LNTPLKS displayed much higher fluorescence in beta cells vs. control cells in ICC. This discrimination was consistently observed using primary rodent islet. FACS analysis showed right shift of peak point in beta cells compared to control cells. The specific bind to in situ islet was verified by in vitro experiments using rodent and human pancreatic slices. The peptide also showed high affinity of islet grafts under the renal capsule. In the insulinoma animal model, we could find FITC-LNTPLKS accumulated specifically to the tumor, thus indicating a potential clinical application of molecular imaging of insulinoma. In conclusion, LNTPLKS showed a specific probe for beta-cells, which might be further utilized in targeted imaging/monitoring beta cells and theragnosis for beta-cells-related disease (diabetes, insulinoma, etc.).
Ligand and Structure-Based In Silico Determination of the Most Promising SARS-CoV-2 nsp16-nsp10 2′-o-Methyltransferase Complex Inhibitors among 3009 FDA Approved Drugs
Authors: Eissa Ibrahim H. , Alesawy Mohamed S. , Saleh Abdulrahman M. , Elkaeed Eslam B. , Alsfouk Bshra A. , El-Attar Abdul-Aziz M. M. , Metwaly Ahmed M.
Published: April 2022

As a continuation of our earlier work against SARS-CoV-2, seven FDA-approved drugs were designated as the best SARS-CoV-2 nsp16-nsp10 2′-o-methyltransferase (2′OMTase) inhibitors through 3009 compounds. The in silico inhibitory potential of the examined compounds against SARS-CoV-2 nsp16-nsp10 2′-o-methyltransferase (PDB ID: (6W4H) was conducted through a multi-step screening approach. At the beginning, molecular fingerprints experiment with SAM (S-Adenosylmethionine), the co-crystallized ligand of the targeted enzyme, unveiled the resemblance of 147 drugs. Then, a structural similarity experiment recommended 26 compounds. Therefore, the 26 compounds were docked against 2′OMTase to reveal the potential inhibitory effect of seven promising compounds (Protirelin, (1187), Calcium folinate (1913), Raltegravir (1995), Regadenoson (2176), Ertapenem (2396), Methylergometrine (2532), and Thiamine pyrophosphate hydrochloride (2612)). Out of the docked ligands, Ertapenem (2396) showed an ideal binding mode like that of the co-crystallized ligand (SAM). It occupied all sub-pockets of the active site and bound the crucial amino acids. Accordingly, some MD simulation experiments (RMSD, RMSF, Rg, SASA, and H-bonding) have been conducted for the 2′OMTase—Ertapenem complex over 100 ns. The performed MD experiments verified the correct binding mode of Ertapenem against 2′OMTase exhibiting low energy and optimal dynamics. Finally, MM-PBSA studies indicated that Ertapenem bonded advantageously to the targeted protein with a free energy value of −43 KJ/mol. Furthermore, the binding free energy analysis revealed the essential amino acids of 2′OMTase that served positively to the binding. The achieved results bring hope to find a treatment for COVID-19 via in vitro and in vivo studies for the pointed compounds.
Rutin Promotes Pancreatic Cancer Cell Apoptosis by Upregulating miRNA-877-3p Expression
Authors: Huo Mingxing , Xia Aowen , Cheng Wenwen , Zhou Mengjie , Wang Jiankang , Shi Tiantian , Cai Cifeng , Jin Wenqi , Zhou Meiliang , Liao Yueling , Liao Zhiyong
Published: April 2022

(1) Background: pancreatic cancer is one of the most serious cancers due to its rapid and inevitable fatality, which has been proved very difficult to treat, compared with many other common cancers. Thus, developing an effective therapeutic strategy, especially searching for potential drugs, is the focus of current research. The exact mechanism of rutin in pancreatic cancer remains unknown. (2) Method: three pancreatic cancer cell lines were used to study the anti-pancreatic cancer effect of rutin. The potent anti-proliferative, anti-migration and pro-apoptotic properties of rutin were uncovered by cell viability, a wound-healing migration assay, and a cell apoptosis assay. High-throughput sequencing technology was used to detect the change of miRNAs expression. Immunoblotting analysis was used to detect the expression of apoptotic proteins. (3) Results: CCK-8 and EDU assays revealed that rutin significantly inhibited pancreatic cancer cells’ proliferation (p < 0.05). A wound-healing assay showed that rutin significantly suppressed pancreatic cancer cells’ migration (p < 0.05). A flow cytometric assay showed that rutin could promote pancreatic cancer cells’ apoptosis. Intriguingly, rutin significantly upregulated miR-877-3p expression to repress the transcription of Bcl-2 and to induce pancreatic cancer cell apoptosis. Accordingly, rutin and miR-877-3p mimics could promote apoptotic protein expression. (4) Conclusions: our findings indicate that rutin plays an important role in anti-pancreatic cancer effects through a rutin-miR-877-3p-Bcl-2 axis and suggests a potential therapeutic strategy for pancreatic cancer.
Electrochemical Sensor Nanoarchitectonics for Sensitive Detection of Uric Acid in Human Whole Blood Based on Screen-Printed Carbon Electrode Equipped with Vertically-Ordered Mesoporous Silica-Nanochannel Film
Authors: Ma Kai , Yang Luoxing , Liu Jun , Liu Jiyang
Published: April 2022

Screen-printed carbon electrodes (SPCEs) bear great potential in the detection of biomarker in clinical samples with low sample consumption. However, modification of electrode surfaces to improve the anti-interference ability and sensitivity is highly desirable for direct electroanalysis of whole blood samples. Here, a reliable and miniaturized electrochemical sensor is demonstrated based on SPCE equipped with vertically-ordered mesoporous silica-nanochannel film (VMSF). To achieve stable binding of VMSF and improve the electrocatalytic performance, electrochemically reduced graphene oxide (ErGO) is applied as a conductive adhesion layer, that is in situ reduced from GO nanosheets during fast growth (less than 10 s) of amino groups modified VMSF (NH2-VMSF) using electrochemically assisted self-assembly (EASA). In comparison with bare SPCE, NH2-VMSF/ErGO/SPCE exhibits decreased oxidation potential of uric acid (UA) by 147 mV owing to significant electrocatalytic ability of ErGO. The dual signal amplification based on electrocatalysis of ErGO and enrichment of nanochannels leads to enhanced peak current by 3.9 times. Thus, the developed NH2-VMSF/ErGO/SPCE sensor enables sensitive detection of UA in the range from 0.5 μM to 180 μM with a low limit of detection (LOD, 129 nM, S/N = 3). Owing to good anti-fouling ability and high selectivity of the sensor, direct and rapid detection of UA in human whole blood is realized with very low sample consumption (50 μL).
Simulated Cropping Season Effects on N Mineralization from Accumulated No-Till Crop Residues
Authors: Alghamdi Rashad S. , Cihacek Larry , Wen Qian
Published: April 2022

The adoption of no-till management practices has increased in the United States over the last decade. In the state of North Dakota, approximately 5.7 million hectares of cropland is managed under no-till or conservation tillage management practices. Even though conservation tillage is known for building soil health, increasing soil organic matter, capturing soil moisture, and reducing wind and water erosion, it also presents a unique best management practice since an increased mass of crop residue remains on the soil surface. Producers are concerned about whether plant needs are being met by nitrogen fertilizer that is currently being applied based on current North Dakota recommendations for long-term no-till systems. A Forman clay loam soil (fine-loamy, mixed, superactive, frigid Calcic Argiudolls) was used in this study, as it represented glacial till soils of the region. We examined whether N mineralization from surface-applied crop residue would result in similar or different results when compared to crop residue mixed into the soil. Soil freeze-thaw contribution to soil N mineralization was also evaluated. Six residue treatments with different C/N ratios including corn (Zea mays L.), soybean (Glycine max L.), forage radish (Raphanus sativus L.), winter pea (Pisum sativum L.), spring wheat (Triticum aestivum L.), and winter wheat (Triticum aestivum L.) were used. Five 10–14-week cycles with a three-week freeze period between each cycle at 0 ºC were evaluated for NO3-N production. Crop residues with a narrow C/N ratio contributed to greater instances of N mineralization during each incubation cycle, and the accumulation of crop residues with a wide C/N ratio over each incubation cycle following the first incubation did not offset the immobilization trends observed in the first incubation. A change in N mineralized in the untreated control soil during the last two incubation cycles may have been caused by freeze-thaw effects or a shift in microbial population due to a lack of fresh C inputs.
Sparse-Input Neural Networks to Differentiate 32 Primary Cancer Types on the Basis of Somatic Point Mutations
Authors: Dikaios Nikolaos
Published: April 2022

Background and Objective: This paper aimed to differentiate primary cancer types from primary tumor samples on the basis of somatic point mutations (SPMs). Primary cancer site identification is necessary to perform site-specific and potentially targeted treatment. Current methods such as histopathology and lab tests cannot accurately determine cancer origin, which results in empirical patient treatment and poor survival rates. The availability of large deoxyribonucleic acid sequencing datasets has allowed scientists to examine the ability of somatic mutations to classify primary cancer sites. These datasets are highly sparse since most genes will not be mutated, have a low signal-to-noise ratio, and are often imbalanced since rare cancers have fewer samples. Methods: To overcome these limitations a sparse-input neural network (SPINN) is suggested that projects the input data in a lower-dimensional space, where the more informative genes are used for learning. To train and evaluate SPINN, an extensive dataset for SPM was collected from the cancer genome atlas containing 7624 samples spanning 32 cancer types. Different sampling strategies were performed to balance the dataset. SPINN was further validated on an independent ICGC dataset that contained 226 samples spanning four cancer types. Results and Conclusions: SPINN consistently outperformed classification algorithms such as extreme gradient boosting, deep neural networks, and support vector machines, achieving an accuracy up to 73% on independent testing data. Certain primary cancer types/subtypes (e.g., lung, brain, colon, esophagus, skin, and thyroid) were classified with an F-score > 0.80.