bluuebunny/crossref_metadata_2025_split huggingface dataset:
The determination of Plutella xylostella (L.) GSTs (PxGSTs) involved in the detoxification metabolism of Tolfenpyrad
Authors: Li Yifan , Sun Hong , Tian Zhen , Su Xinxin , Li Yue , Ye Xuan , Zhou Yifei , Zheng Shengli , Liu Jiyuan , Zhang Yalin 
Published: June 2020

AbstractBACKGROUNDInsect glutathione S‐transferases (GSTs) play a crucial role in insecticide detoxification. However, there remains a distinct lack of information regarding the role of GSTs in the detoxification of Tolfenpyrad (TFP) in insects.RESULTSReal‐time quantitative PCR showed significant upregulation of PxGSTs after exposure to TFP for 6 h. An in vitro inhibition assay showed that TFP could inhibit PxGSTδ, PxGSTε and PxGSTσ, and the most pronounced inhibitory effect was on PxGSTσ. Metabolism assays displayed that PxGSTσ was superior to other test PxGSTs in metabolizing TFP. The molecular docking of TFP and PxGSTσ revealed that the H‐bond provided by the sidechains of Tyr107 and Tyr162 were key to the detoxification of TFP by PxGSTσ. Further tests using mutant PxGSTσ proteins at the sites of Tyr107 (PxGSTσY107A) and Tyr162 (PxGSTσY162A) corroborated that the individual replacement of Tyr107 and Tyr162 could greatly weaken the binding and metabolic abilities to TFP.CONCLUSIONMetabolic interactions between the Plutella xylostella (L.) GSTs (PxGSTs) and TFP were deciphered. This study illustrates the molecular metabolism mechanism of PxGSTσ towards TFP and provides theoretical underpinnings for the design and optimization of novel TFP‐like insecticides. © 2020 Society of Chemical Industry
Epidemiology of cancer‐related weight loss and sarcopenia in the UK and Ireland: incidence, prevalence, and clinical impact
Authors: Sullivan Erin S. , Daly Louise E. , Power Derek G. , Ryan Aoife M. 
Published: June 2020

AbstractBackgroundWeight loss (WL) and sarcopenia are associated with negative oncological outcomes including poor treatment tolerance, decreased quality of life, and reduced survival. The number of patients affected by sarcopenia and WL in Ireland and the UK is unknown.MethodsA systematic review was undertaken to determine median rate of WL > 5% and computed tomography‐diagnosed sarcopenia in oncology populations. Gaps in the literature were supplemented using local data, collected as part of a 5 year prospective study. Rates of WL and sarcopenia in the population were extrapolated from these data based on incidence and prevalence of each cancer as per national cancer registries.ResultsWe estimated that across Ireland and the UK, 128 892 cancer patients (34%) are affected by WL > 5% annually (121 641 UK; 7251 Ireland) and there are 133 707 annual cases of sarcopenia in cancer patients (35%) (126 265 UK; 7442 Ireland). Furthermore, we estimate that there are 716 124 and 771 589 cancer survivors with history of WL > 5% or sarcopenia, respectively.ConclusionsLarge numbers of patients are affected by cancer‐related malnutrition. Given the impact of malnutrition on oncological outcomes and long‐term frailty, there is an urgent need to improve access to cancer nutrition care.
Stratigraphic flux—A method for determining preferential pathways for complex sites
Authors: Curry Patrick J. , Welty Nicklaus R. H. , Yanites Allison J. , Varley Catharine , Quinnan Joseph A. 
Published: June 2020

AbstractExperience with groundwater remediation over several decades has demonstrated that successful outcomes depend on quantitative conceptual site models (CSMs). Over the last 30 years, we have progressed from groundwater pump‐and‐treat remedies, which were largely designed based on a water supply perspective, to in situ and combined remedy strategies, which are only beginning to benefit from understanding the aquifer architecture and distribution of contaminant mass to assess plume maturity, mass flux, and more reliable means of fate and transport assessment. The U.S. Air Force funded the development of the Stratigraphic Flux approach to provide a framework for understanding contaminant transport pathways at its complex sites and enable more reliable and cost‐effective remediation. Stratigraphic Flux enables the development of quantitative, flux‐based CSMs that are founded in sequence stratigraphy, and high‐resolution hydraulic conductivity and contaminant distribution measurements. The result is a three‐dimensional graphical mapping of relative contaminant flux and classification of transport potential that is easy for all stakeholders to understand. The Stratigraphic Flux graphical model is based on a hydrofacies classification system that describes transport potential in three segments of the aquifer: transport zones—where the majority of groundwater flow occurs and transport rates are measured in feet per day; slow advection zones—where transport rates are measured in feet per year; and storage zones—where typically less than 1% of flow occurs, and diffusion dominates contaminant transport. The hydrofacies architectures are based on stratigraphy and transport potential is defined by grouping facies by orders of magnitude classes in hydraulic conductivity. By combining the hydrofacies architecture with contaminant concentration distributions, one can map relative contaminant flux to define and target the complex pathways that control contaminant transport and cleanup behavior. In this article, we describe the Stratigraphic Flux framework, focusing on the key information needed and the methods of analysis. We illustrate the results of its application to evaluate migration pathways for trichlorethylene and chromium at a former chrome pit at Air Force Plant 4 in Fort Worth, Texas. A comprehensive guidance document that describes the approach with a broad spectrum of tools and several site examples can be requested from the authors.
Vapor intrusion risk evaluation using automated continuous chemical and physical parameter monitoring
Authors: Kram Mark , Hartman Blayne , Frescura Cliff , Negrao Paulo , Egelton Dane 
Published: June 2020

AbstractVapor intrusion risk characterization efforts are challenging due to complexities associated with background indoor air constituents, preferential subsurface migration pathways, and representativeness limitations associated with traditional randomly timed time‐integrated sampling methods that do not sufficiently account for factors controlling concentration dynamics. The U.S. Environmental Protection Agency recommends basing risk related decisions on the reasonable maximum exposure (RME). However, with very few exceptions, practitioners have not been applying this criterion. The RME will most likely occur during upward advective flux conditions. As such, for RME determinations, it is important to sample when upward advective flux conditions are occurring. The most common vapor intrusion assessment efforts include randomly timed sample collection events, and therefore do not accurately yield RME estimates. More specifically, researchers have demonstrated that randomly timed sampling schemes can result in false negative determinations of potential risk corresponding to RMEs. For sites experiencing trichloroethylene (TCE) vapor intrusion, the potential for acute risks poses additional challenges, as there is a critical need for rapid response to exposure exceedances to minimize health risks and liabilities. To address these challenges, continuous monitoring platforms have been deployed to monitor indoor concentrations of key volatile constituents, atmospheric pressure, and pressure differential conditions that can result in upward toxic vapor transport and entry into overlying buildings. This article demonstrates how vapor intrusion RME‐based risks can be successfully and efficiently determined using continuous monitoring of concentration and parameters indicating upward advective chemical flux. Time series analyses from multiple selected 8‐ and 24‐hr time increments during upward advective TCE flux conditions were performed to simulate results expected from the most commonly employed sampling methods. These analyses indicate that, although most of the selected time increments overlap within the same 24‐hr window, results and conclusions vary. As such, these findings demonstrate that continuous monitoring of concentration and parameters such as differential pressure and determination of a time‐weighted concentration average over a selected duration when upward advective flux is occurring can allow for a realistic RME‐based risk estimate.
Developing an Electrochemical Sensor Based on Modified Siliceous Mesocellular Foam for Efficient and Easy Monitoring of Cadmium Ions
Authors: Bojdi Majid K. , Behbahani Mohammad , Ranjbari Somaye 
Published: June 2020

AbstractIn response to a mandatory demand to introduce sensitive technique to measure cadmium ion in ultra‐trace concentration, an electrochemical sensor by applying modified siliceous mesocellular foam (S‐MCF) with diphenylthiocarbazone as electrode material is developed. Several instrumental analysis including Fourier transform infrared spectrometry, nitrogen adsorption‐desorption examination, scanning electron microscopy, thermal evaluation and elemental analysis were applied to characterize the prepared electrode material. In the work, the prepared electrode with introduced mesoporous material is conjugated with differential pulse anodic stripping voltammetry (DP‐ASV) for ultra‐trace measurement of Cd2+ ions. Cadmium ions interact strongly with active sites of diphenylthiocarbazone and a negative potential (‐0.9 V) was applied to reduce these ions. Based on the specific characteristic of the electrochemical sensor, this electrode material was applied to pre‐concentrate target ions on the modified electrode by the prepared mesoporous material. In order to measure Cd2+ at ultra‐trace concentrations, differential pulse‐ASV was utilized to oxidize the reduced Cd ions. Two dynamic linear range (DLR) for measurement of cadmium ions at ultra‐trace concentrations were seen within ranges of 0.01 to 1.0 nmol L−1 (with sensitivity of 13.23 μA/nmol L−1) and 1.0 nmol L−1 to 10.0 nmol L−1 (with sensitivity of 0.23 μA/nmol L−1). The method detection limit (MDL) of 2.5 pmol L−1 was acquired.
Optimal design of an electrochemical reactor for blackwater treatment
Authors: Varigala Siva , Krishnaswamy Srinivas , Lohia Chandra P. , Hegarty‐Craver Meghan , Grego Sonia , Luettgen Michael , Cid Clement A. 
Published: June 2020

AbstractElectrolysis of blackwater for disinfection and nutrient removal is a portable and scalable technology that can lessen the need for cities to construct large‐scale wastewater treatment infrastructure and enable the safe onsite reuse of blackwater. Several systems for treating wastewater from single toilets are described in the literature, but there are few examples of systems designed to use electrolysis to treat blackwater from nearby toilets, which is a situation more common in densely packed urban living environments. In order to scale a single toilet electrolysis system to one that could service multiple toilets, computational fluid dynamic analysis was used to optimize the electrochemical reactor design, and laboratory and field‐testing were used to confirm results. Design efforts included optimization of the reactor shape and mixing to improve treatment efficiency, as well as automated cleaning and salt injection to reduce maintenance and service requirements.Practitioner points

Design of a reverse polarity mechanism to enable in situ electrode cleaning and improve long‐term electrode performance.

Optimization of a hopper design and drainpipe location to collect and remove flaking precipitates and mitigate maintenance issues.

Design of an automated salt injection system to guarantee sufficient chloride levels for producing adequate chlorine residuals for consistent disinfection.
Deep learning: Computational aspects
Authors: Polson Nicholas , Sokolov Vadim 
Published: June 2020

AbstractIn this article, we review computational aspects of deep learning (DL). DL uses network architectures consisting of hierarchical layers of latent variables to construct predictors for high‐dimensional input–output models. Training a DL architecture is computationally intensive, and efficient linear algebra library is the key for training and inference. Stochastic gradient descent (SGD) optimization and batch sampling are used to learn from massive datasets.This article is categorized under:Statistical Learning and Exploratory Methods of the Data Sciences > Deep LearningStatistical Learning and Exploratory Methods of the Data Sciences > Modeling MethodsStatistical Learning and Exploratory Methods of the Data Sciences > Neural Networks
A Statistical and Biophysical Toolbox to Elucidate Structure and Formation of Stress Fibers
Authors: Eltzner Benjamin , Hauke Lara , Huckemann Stephan , Rehfeldt Florian , Wollnik Carina 
Published: June 2020

AbstractWe are concerned with statistically validated early mechanically guided differentiation of human mesenchymal stem cells (hMSCs). This chapter reviews and extends methods of fixed and live imaging of hMSCs, automated reliable and unbiased near real-time filament extraction and digitization for massive data via the FilamentSensor, suitable aggregation of simple (area, mean orientation, aspect ratio and order parameter) and advanced (orientation mode persistence and orientation fields) data descriptors and methods of their non-euclidean inferential statistics. Exemplary, we study the morphology of stress fibers in fixed and live hMSCs within 24 h post seeding on elastic matrices exhibiting Young’s moduli of 1 kPa (soft, brain-like elasticity), 11 kPa (intermediate, muscle-like stiffness) and 30 kPa (hard, pre-calcified bone rigidity). The combination of these methods constitutes a novel integrated toolbox, where for instance, statistical insight may be used to guide experimental design.
Photonic Imaging with Statistical Guarantees: From Multiscale Testing to Multiscale Estimation
Authors: Munk Axel , Proksch Katharina , Li Housen , Werner Frank 
Published: June 2020

AbstractIn this chapter we discuss how to obtain statistical guarantees in photonic imaging. We start with an introduction to hypothesis testing in the context of imaging, more precisely we describe how to test if there is signal in a specific region of interest (RoI) or just noise. Afterwards we extend this approach to a family of RoIs and examine the occurring problems such as inflation of type I error and dependency issues. We discuss how to control the family-wise error rate by different modifications, and provide a connection to extreme value theory. Afterwards we present possible extension to inverse problems. Moving from testing to estimation, we finally introduce a method which constructs an estimator of the desired quantity of interest with automatic smoothness guarantees.
Efficient, Quantitative Numerical Methods for Statistical Image Deconvolution and Denoising
Authors: Luke D. Russell , Charitha C. , Shefi Ron , Malitsky Yura 
Published: June 2020

AbstractWe review the development of efficient numerical methods for statistical multi-resolution estimation of optical imaging experiments. In principle, this involves constrained linear deconvolution and denoising, and so these types of problems can be formulated as convex constrained, or even unconstrained, optimization. We address two main challenges: first of these is to quantify convergence of iterative algorithms; the second challenge is to develop efficient methods for these large-scale problems without sacrificing the quantification of convergence. We review the state of the art for these challenges.
Single Particle Imaging with FEL Using Photon Correlations
Authors: von Ardenne Benjamin , Grubmüller Helmut 
Published: June 2020

AbstractScattering experiments with femtosecond high-intensity free-electron laser pulses provide a new route to macromolecular structure determination without the need for crystallization at low material usage. In these experiments, the X-ray pulses are scattered with high repetition on a stream of identical single biomolecules and the scattered photons are recorded on a pixelized detector. The main challenges are the unknown random orientation of the molecule in each shot and the extremely low signal to noise ratio due to the very low expected photon count per scattering image, typically well below the number of over 100 photons required by available analysis methods. The latter currently limits the scattering experiments to nano-crystals or larger virus particles, but the ultimate goal remains to retrieve the atomic structure of single biomolecules. Here, we use photon correlations to overcome the issue with low photon counts and present an approach that can determine the molecular structure de novo from as few as three coherently scattered photons per image. We further validate the method with a small protein (46 residues), show that near-atomic resolution of $$3.3~{\AA }$$

3.3

Å

is within experimental reach and demonstrate structure determination in the presence of isotropic noise from various sources, indicating that the number of disordered solvent molecules attached to the macromolecular surface should be kept at a minimum. Our correlation method allows to infer structure from images containing multiple particles, potentially opening the method to other types of experiments such as fluctuation X-ray scattering (FXS).
Wavefront and Coherence Characteristics of Extreme UV and Soft X-ray Sources
Authors: Schäfer Bernd , Flöter Bernhard , Mey Tobias , Mann Klaus 
Published: June 2020

AbstractThe first part of this chapter comprises setups and results of the determination of wavefront and beam parameters for different EUV sources (free-electron lasers, HHG-sources, synchrotron radiation) by self supporting Hartmann-Sensors. We present here i.a. a sensor applied for alignment of the ellipsodial mirror at FLASH beamline 2, yielding a reduction of the rms-wavefront aberrations by more than a factor of 3. In the second part we report on the characterization of the Free-Electron-Laser FLASH at DESY by a quantitative determination of the Wigner distribution function. The setup, comprising an ellipsodial mirror and a moveable extreme UV sensitive CCD detector, enables the mapping of two-dimensional phase space corresponding to the horizontal and vertical coordinate axes, respectively. Furthermore, an extended setup utilizing a torodial mirror for complete 4D-Wigner reconstruction has been accomplished and tested using radiation from a multimode Nd:VO4 laser.
Multilayer Zone Plates for Hard X-ray Imaging
Authors: Osterhoff Markus , Krebs Hans-Ulrich 
Published: June 2020

AbstractThis chapter reviews progress both in the fabrication of multilayer zone plate optics for focusing X-rays, as well as in imaging experiments using these optics. The fabrication based on pulsed laser deposition is accompanied by analytical and numerical treatment of X-ray propagation to control volume diffraction effects. On the imaging side, different schemes are presented; these include scanning-scattering with focused X-rays, holography, as well as recent advances in lens-enhanced phase-reconstruction.
One-Dimensional Discrete-Time Phase Retrieval
Authors: Beinert Robert , Plonka Gerlind 
Published: June 2020

AbstractThe phase retrieval problem has a long and rich history with applications in physics and engineering such as crystallography, astronomy, and laser optics. Usually, the phase retrieval consists in recovering a real-valued or complex-valued signal from the intensity measurements of its Fourier transform. If the complete phase information in frequency domain is lost then the problem of signal reconstruction is severelly ill-posed and possesses many non-trivial ambiguities. Therefore, it can only be solved using appropriate additional signal information. We restrict ourselves to one-dimensional discrete-time phase retrieval from Fourier intensities and particularly consider signals with finite support. In the first part of this section, we study the structure of the arising ambiguities of the phase retrieval problem and show how they can be characterized using the given Fourier intensity. Employing these observations, in the second part, we study different kinds of a priori assumptions on the signal, where we are especially interested in their ability to reduce the non-trivial ambiguities or even to ensure uniqueness of the solution. In particular, we consider the assumption of non-negativity of the solution signal, additional magnitudes or phases of some signal components in time domain, or additional intensities of interference measurements in frequency domain. Finally, we transfer our results to phase retrieval problems where the intensity measurements arise, for example, from the Fresnel or fractional Fourier transform.
X-ray Focusing and Optics
Authors: Salditt Tim , Osterhoff Markus 
Published: June 2020

AbstractThis chapter addresses fundamental concepts of X-ray optics and X-ray coherence, in view of the increasing number of X-ray applications requiring nano-focused X-ray beams. The chapter is meant as a tutorial to facilitate the understanding of later chapters of this book. After the introduction and an overview over focusing optics and recent benchmarks in X-ray focusing, we present refractive, reflective and diffractive X-ray optics in more detail. Particular emphasis is given to two kinds of X-ray optics which are particularly relevant for later chapters in this book, namely X-ray waveguides (XWG) and multilayer zone plates (MZP). Both are geared towards ultimate confinement and focusing, respectively, i.e. applications at the forefront of what is currently possible for multi-keV radiation. Since optics must be designed in view of coherence properties, we include a basic treatment of coherence theory and simulation for X-ray optics. Finally, the chapter closes with a brief outlook on compound (combined) optical schemes for hard X-ray microscopy.
Inverse Problems
Authors: Hohage Thorsten , Sprung Benjamin , Weidling Frederic 
Published: June 2020

AbstractGenerally speaking, inverse problems typically consist in the reconstruction of causes for observed effects. In imaging applications the cause is usually a probe and the effect are observed data. The corresponding forward problems then consists in predicting experimental data given perfect knowledge of the probe. In some sense solving an inverse problems means “computing backwards”, which is usually more difficult then solving the forward problem.
Integrating Climate Adaptation, Poverty Reduction, and Environmental Conservation in Kwale County, Kenya
Authors: Ambrosino Chiara , Hufton Ben , Nyawade Benson Okinyi , Osimbo Harriet , Owiti Phanuel 
Published: June 2020

AbstractShoreline erosion, flood surges, river sediments, and water pollution are only a few of the common threats to many coastal areas, with extreme climate-related events exacerbating the intensity and urgency of the resulting negative impacts. In addition, some coastal areas are excessively mined for sand, protective mangroves are destroyed, and coastal waters are overfished, affecting the well-being, safety, and livelihoods of local communities. These threats disproportionally affect the poorest and most marginalized groups, including women and children, leading to their increased vulnerability to climate change and adoption of negative coping mechanisms.This chapter proposes an integrated people-centered approach, with a particular focus on women, to address the triple crisis – poverty, climate change, and nature – at the local level. Findings will be shared from a 2-year project implemented in the southernmost coastal region of Kwale County in Kenya, which aimed to achieve beneficial and interconnected social, environmental, and climate outcomes. The chapter discusses findings, successes, and lessons learned from the action and the requirement to position vulnerable groups at the center of initiatives designed to address the triple crisis. Limitations of the study and main recommendations for future programming in similar contexts are also shared.