bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation of a similarity anisotropic diffusion denoising approach for improving in vivo CEST‐MRI tumor pH imaging
Authors: Romdhane Feriel , Villano Daisy , Irrera Pietro , Consolino Lorena , Longo Dario Livio 
Published: January 2021

PurposeChemical exchange saturation transfer MRI provides new approaches for investigating tumor microenvironment, including tumor acidosis that plays a key role in tumor progression and resistance to therapy. Following iopamidol injection, the detection of the contrast agent inside the tumor tissue allows measurements of tumor extracellular pH. However, accurate tumor pH quantifications are hampered by the low contrast efficiency of the CEST technique and by the low SNR of the acquired CEST images, hence in a reduced detectability of the injected agent. This work aims to investigate a novel denoising method for improving both tumor pH quantification and accuracy of CEST‐MRI pH imaging.MethodsAn hybrid denoising approach was investigated for CEST‐MRI pH imaging based on the combination of the nonlocal mean filter and the anisotropic diffusion tensor method. The denoising approach was tested in simulated and in vitro data and compared with previously reported methods for CEST imaging and with established denoising approaches. Finally, it was validated with in vivo data to improve the accuracy of tumor pH maps.ResultsThe proposed method outperforms current denoising methods in CEST contrast quantification and detection of the administered contrast agent at several increasing noise levels with simulated data. In addition, it achieved a better pH quantification in in vitro data and demonstrated a marked improvement in contrast detection and a substantial improvement in tumor pH accuracy in in vivo data.ConclusionThe proposed approach effectively reduces the noise in CEST images and increases the sensitivity detection in CEST‐MRI pH imaging.
Changes in participant behaviour and attitudes are associated with knowledge and skills gained by using a turtle conservation citizen science app
Authors: Santori Claudia , Keith Ryan J. , Whittington Camilla M. , Thompson Michael B. , Van Dyke James U. , Spencer Ricky‐John 
Published: January 2021

Abstract

Citizen science has become a popular way to collect biodiversity data and engage the wider public in scientific research. It has the potential to improve the knowledge and skills of participants, and positively change their behaviour and attitude towards the environment. Citizen science outcomes are particularly valuable for wildlife conservation, as they could help alleviate human impacts on the environment.

We used an online questionnaire to investigate the consequences of participating in an Australian turtle mapping app, TurtleSAT, on skills and knowledge gain, and test for any association between these gains and behavioural or attitudinal changes reported by the participants.

One hundred and forty‐eight citizen scientists completed our questionnaire, mostly from the states of New South Wales and Victoria. TurtleSAT was the third most common source of correct answers about turtle ecology and conservation, after a talk about turtles and personal observations/research. Citizen scientists who participated more often were more knowledgeable about turtles than infrequent users. Self‐reported gains in knowledge and skills were positively linked to attitudinal and behavioural changes, such as being more aware of turtles on roads. However, behaviour and attitude changes were not related to participation rate. Respondents also reported that after learning about the current decline in turtle populations, they adopted several turtle‐friendly practices, such as habitat restoration or moving turtles out of harm's way, underlining the importance of increasing people's awareness on species declines.

The reported changes in attitudes and behaviours are likely to positively impact the conservation of Australian freshwater turtles. Engagement with citizen science projects like TurtleSAT may result in participants being more interested in the natural world, by learning more about it and being more exposed to it, and therefore contributing more actively to its protection.

A free Plain Language Summary can be found within the Supporting Information of this article.
The Lys 280 → Gln mutation mimicking disease‐linked acetylation of Lys 280 in tau extends the structural core of fibrils and modulates their catalytic properties
Authors: Kumar Harish , Udgaonkar Jayant B. 
Published: January 2021

AbstractAmyloid fibrillar aggregates isolated from the brains of patients with neurodegenerative diseases invariably have post‐translational modifications (PTMs). The roles that PTMs play in modulating the structures and polymorphism of amyloid aggregates, and hence their ability to catalyze the conversion of monomeric protein to their fibrillar structure is, however, poorly understood. This is particularly true in the case of tau aggregates, where specific folds of fibrillar tau have been implicated in specific tauopathies. Several PTMs, including acetylation at Lys 280, increase aggregation of tau in the brain, and increase neurodegeneration. In this study, tau‐K18 K280Q, in which the Lys 280 → Gln mutation is used to mimic acetylation at Lys 280, is shown, using HX‐MS measurements, to form fibrils with a structural core that is longer than that of tau‐K18 fibrils. Measurements of critical concentrations show that the binding affinity of monomeric tau‐K18 for its fibrillar counterpart is only marginally more than that of monomeric tau‐K18 K280Q for its fibrillar counterpart. Quantitative analysis of the kinetics of seeded aggregation, using a simple Michaelis–Menten‐like model, in which the monomer first binds and then undergoes conformational conversion to β‐strand, shows that the fibrils of tau‐K18 K280Q convert monomeric protein more slowly than do fibrils of tau‐K18. In contrast, monomeric tau‐K18 K280Q is converted faster to fibrils than is monomeric tau‐K18. Thus, the effect of Lys 280 acetylation on tau aggregate propagation in brain cells is expected to depend on the amount of acetylated tau present, and on whether the propagating seed is acetylated at Lys 280 or not.
Identification of the metabolites of ivermectin in humans
Authors: Tipthara Phornpimon , Kobylinski Kevin C. , Godejohann Markus , Hanboonkunupakarn Borimas , Roth Alison , Adams John H. , White Nicholas J. , Jittamala Podjanee , Day Nicholas P. J. , Tarning Joel 
Published: January 2021

AbstractMass drug administration of ivermectin has been proposed as a possible malaria elimination tool. Ivermectin exhibits a mosquito‐lethal effect well beyond its biological half‐life, suggesting the presence of active slowly eliminated metabolites. Human liver microsomes, primary human hepatocytes, and whole blood from healthy volunteers given oral ivermectin were used to identify ivermectin metabolites by ultra‐high performance liquid chromatography coupled with high‐resolution mass spectrometry. The molecular structures of metabolites were determined by mass spectrometry and verified by nuclear magnetic resonance. Pure cytochrome P450 enzyme isoforms were used to elucidate the metabolic pathways. Thirteen different metabolites (M1‐M13) were identified after incubation of ivermectin with human liver microsomes. Three (M1, M3, and M6) were the major metabolites found in microsomes, hepatocytes, and blood from volunteers after oral ivermectin administration. The chemical structure, defined by LC‐MS/MS and NMR, indicated that M1 is 3″‐O‐demethyl ivermectin, M3 is 4‐hydroxymethyl ivermectin, and M6 is 3″‐O‐demethyl, 4‐hydroxymethyl ivermectin. Metabolic pathway evaluations with characterized cytochrome P450 enzymes showed that M1, M3, and M6 were produced primarily by CYP3A4, and that M1 was also produced to a small extent by CYP3A5. Demethylated (M1) and hydroxylated (M3) ivermectin were the main human in vivo metabolites. Further studies are needed to characterize the pharmacokinetic properties and mosquito‐lethal activity of these metabolites.
Characterizing the environmental drivers of the abundance and distribution of Alopecurus myosuroides on a national scale
Authors: Hicks Helen , Lambert James , Pywell Richard , Hulmes Lucy , Hulmes Sarah , Walker Kevin , Childs Dylan Z , Freckleton Robert P 
Published: January 2021

AbstractBACKGROUNDArable weeds threaten farming and food production, impacting on productivity. Large‐scale data on weed populations are typically lacking, and changes are frequently undocumented until they reach problem levels. Managing the future spread of weeds requires that we understand the factors that influence current densities and distributions. In doing so, one of the challenges is to measure populations at a large enough scale to be able to accurately measure changes in densities and distributions. Here we analyse the density and distribution of a major weed (Alopecurus myosuroides) on a large scale. Our objectives were to (i) develop a methodology for rapid measurement of occurrence and abundance, (ii) test hypotheses about the roles of soils and climate variation in determining densities, and (iii) use this information to identify areas in which occurrence could increase in the future.RESULTSPopulations were mapped through England over 4 years in 4631 locations. We also analysed UK atlas data published over the past 50 years. Densities of populations show significant interannual variability, but historical data show that the species has spread. We find significant impacts of soil and rainfall on densities, which increase with the proportion of heavy soils, but decrease with increasing rainfall. Compared with independent atlas data we found that our statistical models provide good predictions of large‐scale occupancy and we provide maps of current and potential densities.CONCLUSIONModels of spread highlight the localised nature of colonisation, and this emphasises the need for management to limit dispersal. Comparisons of current, historical and potential distributions suggest sizeable habitable areas in which increases in abundance are still possible.© 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
Comprehensive two‐dimensional gas chromatography‐high resolution mass spectrometry with complementary ionization methods in the study of 5000‐year‐old mummy
Authors: Lebedev Albert T. , Polyakova Olga V. , Artaev Viatcheslav B. , Mednikova Maria B. , Anokhina Eugenia A. 
Published: January 2021

RationaleMummification is one of the defining customs of ancient Egypt. The nuances of the embalming procedure and the composition of the embalming mixtures have attracted the attention of scientists and laypeople for a long time. Modern analytical tools make mummy studies more efficient.MethodsComprehensive two‐dimensional gas chromatography‐high resolution mass spectrometry (GCxGC/HRMS) with complementary ionization methods (electron ionization, positive chemical ionization, and electron capture negative ionization [ECNI]) with a Pegasus GC‐HRT+4D instrument was used to identify embalming components in the mummy from the Pushkin Museum of Fine Arts acquired in 1913 in London at the de Rustafjaell sale. The mummy dates back to the late Predynastic period (direct accelerator mass spectrometry‐dating 3356–3098bc), being one of the oldest in the world.ResultsThe results showed the complexity of the embalming mixtures that were already in use 5000 years ago. Several hundred organic compounds were identified in the mummy samples. Various types of hydrocarbons (triterpanes, steranes, isoprenoid, and polycyclic aromatic hydrocarbons) prove the presence of petroleum products. Iodinated compounds detected using ECNI define oils of marine origin, whereas esters of palmitic acid indicate the use of beeswax. The nature of the discovered components of conifer tar proves that the preliminary processing of conifer resins involved heating. GCxGC/HRMS also allowed a number of modern contaminants (phthalates, organophosphates, and even DDT) to be identified.ConclusionsApplication of a powerful GCxGC/HRMS technique with complementary ionization methods allowed significant widening of the range of organic compounds used for mummification that could be identified. The complexity of the embalming mixtures supports the hypothesis of the high social status of the child made on the basis of the preliminary study of the mummy.
Extending deep learning approaches for forest disturbance segmentation on very high‐resolution satellite images
Authors: Kislov Dmitry E. , Korznikov Kirill A. , Altman Jan , Vozmishcheva Anna S. , Krestov Pavel V. 
Published: January 2021

AbstractAccurate remote detection of various forest disturbances is a challenge in global environmental monitoring. Addressing this issue is crucial for forest health assessment, planning salvage logging operations, modeling stand dynamics, and estimating forest carbon stocks and uptake. Substantial progress on this problem has been achieved owing to the rapid development of remote sensing devices that provide very high‐resolution images. Concurrently, image processing algorithms have witnessed rapid development owing to the extensive use of artificial neural networks with complex architectures and deep learning approaches. This opens new opportunities and perspectives for applying deep learning methods to solving various problems in environmental sciences. In this study, we used deep convolutional neural networks (DCNNs) to recognize forest damage induced by windthrows and bark beetles. We used satellite imagery of very high resolution in visual spectra represented as pansharpened images (RGB channels). When predicting forest damage, we obtained accuracies higher than 90% on test data for recognition of both windthrow areas and damaged trees impacted by bark beetles. A comparative analysis indicated that the DCNN‐based approach outperforms traditional pixel‐based classification methods (AdaBoost, random forest, support vector machine, quadratic discrimination) by at least several percentage points. DCNNs can learn a specific pattern of the area of interest and thus yield fewer false positive decisions than pixel‐based algorithms. The ability of DCNNs to generalize makes them a good tool for delineating smooth and ill‐defined boundaries of damaged forest areas, such as windthrow patches.
Synthesis of Copper(II) Coordination Complex, Its Molecular Docking and Computational Exploration for Novel Functional Properties: A Dual Approach
Authors: Mohan Bharti , Muhammad Shabbir , Al‐Sehemi Abdullah G. , Bharti Sulakshna , Kumar Santosh , Choudhary Mukesh 
Published: January 2021

AbstractThe present investigation uses a dual approach to study the copper (II) complex [Cu(phen)3]. (ClO4)2.HL.CH3CN (1) and its cationic complex‐ [Cu(II)(phen)3]2+ (1 a), where, HL = 4‐Bromo‐2((Z) ‐(naphthalene‐4‐ylimino)methyl)phenol, phen=1,10‐phenanthroline. The complex (1) crystallized in the triclinic system of the space group P‐1 with two molecules in the unit cell and reveals a distorted octahedral geometry. Inspiring by recent developments to find a potential inhibitor for the COVID‐19 virus, we have also performed molecular docking study of [Cu(phen)3]+2 to see if our novel complex shows an affinity for the main protease (Mpro) of COVID‐19 spike protein. Interestingly, the results are found quite encouraging where the binding affinity and inhibition constant were found to be −8.400 kcal/mol and 0.661 μM, respectively, for the best‐docked confirmation of [Cu(II)(Phen)3]+2 complex with Mpro protein. This binding affinity is reasonably well as compared to recently known antiviral drugs. For instance, the binding affinity of [Cu(II)(Phen)3]+2 complex is found to be better than recently docking results of chloroquine (−6.293 kcal/mol), hydroxychloroquine (−5.573 kcal/mol) and remdesivir (−6.352 kcal/mol) with Mpro protein. Thus, we believe the broad‐spectrum functional properties of our complex will provoke not only the interest of material chemists in materials designing but also incite the drug designing community.
Chemical Sensors for Water Detection in Organic Solvents and their Applications
Authors: Kumar Pawan , Ghosh Amrita , Jose D. Amilan 
Published: January 2021

AbstractMoisture or trace water detection at a very low level in organic solvents and other chemical reagents has great significance in the chemical and industrial process. At present, reports on chemical sensors for moisture are still in the early development phase and need continuous improvements. Recently, several approaches have been proposed for the easier detection of water content in the organic solvents. In this review article, we have summarized recent reports on chemical sensors used to detect water in organic solvents. The discussion is focused on optical methods, including fluorescent and colorimetric probes exploring solvatochromism, displacement of ligand or metal and water‐induced protonation. Besides, water detection in organic solvents by NMR spectroscopy is also discussed. Probe structure, sensitivity with organic solvents, spectroscopy detection limits, and possible application in various other research areas, including ink‐less writing, sweat pore mapping, food products and engineering materials are described in detail.
Phenanthridine‐Based Donor/Acceptor Fluorescent Dyes: Synthesis, Photophysical Properties and Fluorometric Sensing of Biogenic Primary Amines
Authors: Sawminathan Sathish , Munusamy Sathishkumar , Jothi Dhanapal , Iyer Sathiyanarayanan Kulathu 
Published: January 2021

AbstractSynthesis of two new and straight forward phenanthridine based fluorophores (PA1 and PA2) have been described. The molecular structure of PA1 and PA2 was confirmed by spectroscopic studies (1H NMR, 13C NMR and HRMS). The dyes are further characterized by comprehensive photophysical study, and their data are critically analyzed. The fluorescence properties of these D−A chromophores are highly influenced by the position of the hydroxyl substitution and the solvent polarity. These probes exhibited an arsenal of advantageous features such as higher Stokes shift values and appreciable photostability. Compounds PA1 and PA2 are found to be indicating fluorescent ratiometric detection towards primary amines (PAs) with high selectivity and sensitivity. With this background, sensing of PAs was extended to biogenic amines as they have primary amine functionality. As expected, these fluorophores have excellent detection ability towards neurotransmitters, such as dopamine (DA) and serotonin (SN) with a low limit of detection (LOD) such as 1.61 nM and 0.31 nM respectively. The sensing mechanism of the probe PA1 with PAs are based on the nucleophilic addition and confirmed through investigation by 1H NMR titration, mass analysis and theoretical calculations. Moreover, sensor probe PA1 based test paper strip was developed, which could detect DA conveniently when visualized under 365 nm UV lamp.
Floating platform effects on power generation in spar and semisubmersible wind turbines
Authors: Johlas Hannah M. , Martínez‐Tossas Luis A. , Churchfield Matthew J. , Lackner Matthew A. , Schmidt David P. 
Published: January 2021

AbstractThe design and financing of commercial‐scale floating offshore wind projects require a better understanding of how power generation differs between newer floating turbines and well‐established fixed‐bottom turbines. In floating turbines, platform mobility causes additional rotor motion that can change the time‐averaged power generation. In this work, OpenFAST simulations examine the power generated by the National Renewable Energy Laboratory's 5‐MW reference turbine mounted on the OC3‐UMaine spar and OC4‐DeepCWind semisubmersible floating platforms, subjected to extreme irregular waves and below‐rated turbulent inflow wind from large‐eddy simulations of a neutral atmospheric boundary layer. For these below‐rated conditions, average power generation in floating turbines is most affected by two types of turbine displacements: an average rotor pitch angle that reduces power, caused by platform pitch; and rotor motion upwind‐downwind that increases power, caused by platform surge and pitch. The relative balance between these two effects determines whether a floating platform causes power gains or losses compared to a fixed‐bottom turbine; for example, the spar creates modest (3.1%–4.5%) power gains, whereas the semisubmersible creates insignificant (0.1%–0.2%) power gains for the simulated conditions. Furthermore, platform surge and pitch motions must be analyzed concurrently to fully capture power generation in floating turbines, which is not yet universal practice. Finally, a simple analytical model for predicting average power in floating turbines under below‐rated wind speeds is proposed, incorporating effects from both the time‐averaged pitch displacement and the dynamic upwind‐downwind displacements.
From object detection to text detection and recognition: A brief evolution history of optical character recognition
Authors: Wang Haifeng , Pan Changzai , Guo Xiao , Ji Chunlin , Deng Ke 
Published: January 2021

AbstractText detection and recognition, which is also known as optical character recognition (OCR), is an active research area under quick development with a lot of exciting applications. Deep‐learning‐based methods represent the state‐of‐art of this area. However, these methods are largely deterministic: they give a deterministic output for each input. For both statisticians and general users, methods supporting uncertainty inference are of great appeal, leaving rich research opportunities to incorporate statistical models and methods with the established deep‐learning‐based approaches. In this paper, we provide a comprehensive review of the evolution history of research development on OCR with discussions on the statistical insights behind these developments and potential directions to enhance the current methods with statistical approaches. We hope this article can serve as a useful guidebook for statisticians who are seeking for a path toward edge‐cutting research in this exciting area.This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Deep LearningData: Types and Structure > Image and Spatial Data
Financial Competence on the Board Case
Authors: Leibfried Peter 
Published: January 2021

AbstractWhile accounting at first sight always seems to be about numbers only, in reality, it does heavily involve people and culture. Thus, also general management should be open to the respective issues, and not push it into a technical, sometimes even “nerdy” corner. Very often, discussions about numbers are in reality discussions about underlying business or cultural issues. In order to bring them up, the board needs at least one member (financial expert) who is on a level playing field with the CFO and the auditor and will not be distracted by (presumably) complicated technical questions.
Fraud Governance Case
Authors: van Eenennaam Fred , Michel Hagar 
Published: January 2021

AbstractThe Imtech case is about a Dutch listed company that tried a restart and failed at that restart.A couple of spinning questions are in the case among which.•	Why did the supervisory board and the management board dynamics prevent from taking appropriate actions?•	Why did the decentral local entrepreneurial model fail and to what extend were missing Corp governance systems to blame?•	What should the supervisory board have done to mitigate their company and personal risks?