bluuebunny/crossref_metadata_2025_split huggingface dataset:
From Synthesis of Amino Acids and Peptides to Enzymatic Catalysis: A Bottom‐Up Approach in Mechanochemistry
Authors: Bolm Carsten , Hernández José G. 
Published: February 2018

AbstractRecently, chemical reactions induced or facilitated by mechanical energy have gained recognition in diverse areas of chemical synthesis. In particular, mechanosyntheses of amino acids and short peptides, along with their applications in catalysis, have revealed the high degree of stability of peptide bonds in environments of harsh mechanical stress. These observations quickly led to the recent interest in developing mechanochemical enzymatic reactions. Experimentally, manual grinding, ball‐milling techniques, and twin‐screw extrusion technology have proven valuable to convey mechanical forces into a chemical synthesis. These practices have enabled the establishment of more sustainable alternatives for chemical synthesis by reducing the use of organic solvents and waste production, thereby having a direct impact on the E‐factor of the chemical process. In this Minireview, the series of events that allowed the development of mechanochemical enzymatic reactions are described from a bottom‐up perspective.
Non‐loadbearing partitions of unfired clay masonry – Fire behaviour tests
Authors: Hartmann Raik , Burkert Toralf 
Published: February 2018

AbstractUnfired clay masonry is the most frequently used construction type for residential buildings worldwide, but the long tradition of building with unfired clay masonry in Germany came to an end with the onset of industrialization.The research project EGsL ”Unfired clay masonry: design and construction principles for a widespread use in residential building taking into account climatic conditions in temperate zones with Germany as example location“ is devoted to the preparation of basic principles based on the current state of knowledge about unfired clay as a building material in order to filter out design and construction principles for residential buildings of modern unfired clay masonry. It is assumed that unfired clay has a much better performance capability than is currently expected from the material.The greatest suspicion about the structural safety of unfired clay buildings is based on the water susceptibility of unfired clay, since unfired clay loses its strength under the action of water. In order to improve confidence in the structural stability of residential buildings of unfired clay masonry, a display at the trade fair BAU 2017 showed the basis for an example application of important constructional joints of a theoretical building of unfired clay masonry.As a follow‐up to this, the EGsL research project now intends to demonstrate the fire protection behaviour of unfired clay internal walls in order to ensure the structural stability of unfired clay buildings. The article reports on a first fire test on non‐loadbearing clay masonry walls and describes an example application of non‐loadbearing clay walls in the new Zinzendorf Gymnasium in Herrnhut.
Depressional wetlands affect watershed hydrological, biogeochemical, and ecological functions
Authors: Evenson Grey R. , Golden Heather E. , Lane Charles R. , McLaughlin Daniel L. , D'Amico Ellen 
Published: February 2018

AbstractDepressional wetlands of the extensive U.S. and Canadian Prairie Pothole Region afford numerous ecosystem processes that maintain healthy watershed functioning. However, these wetlands have been lost at a prodigious rate over past decades due to drainage for development, climate effects, and other causes. Options for management entities to protect the existing wetlands, and their functions, may focus on conserving wetlands based on spatial location vis‐à‐vis a floodplain or on size limitations (e.g., permitting smaller wetlands to be destroyed but not larger wetlands). Yet the effects of such management practices and the concomitant loss of depressional wetlands on watershed‐scale hydrological, biogeochemical, and ecological functions are largely unknown. Using a hydrological model, we analyzed how different loss scenarios by wetland size and proximal location to the stream network affected watershed storage (i.e., inundation patterns and residence times), connectivity (i.e., streamflow contributing areas), and export (i.e., streamflow) in a large watershed in the Prairie Pothole Region of North Dakota, USA. Depressional wetlands store consequential amounts of precipitation and snowmelt. The loss of smaller depressional wetlands (<3.0 ha) substantially decreased landscape‐scale inundation heterogeneity, total inundated area, and hydrological residence times. Larger wetlands act as hydrologic “gatekeepers,” preventing surface runoff from reaching the stream network, and their modeled loss had a greater effect on streamflow due to changes in watershed connectivity and storage characteristics of larger wetlands. The wetland management scenario based on stream proximity (i.e., protecting wetlands 30 m and ~450 m from the stream) alone resulted in considerable landscape heterogeneity loss and decreased inundated area and residence times. With more snowmelt and precipitation available for runoff with wetland losses, contributing area increased across all loss scenarios. We additionally found that depressional wetlands attenuated peak flows; the probability of increased downstream flooding from wetland loss was also consistent across all loss scenarios. It is evident from this study that optimizing wetland management for one end goal (e.g., protection of large depressional wetlands for flood attenuation) over another (e.g., protecting of small depressional wetlands for biodiversity) may come at a cost for overall watershed hydrological, biogeochemical, and ecological resilience, functioning, and integrity.
YKOB: Participatory Sensing‐Based Road Condition Monitoring Using Smartphones Worn by Cyclist
Authors: TAKAHASHI JUNJI , KOBANA YUSUKE , ISOYAMA NAOYA , TOBE YOSHITO , LOPEZ GUILLAUME 
Published: February 2018

SUMMARYWe propose a novel road monitoring system named YKOB (Your Kinetic Observation Bike) based on participatory sensing. YKOB collects acceleration signals using smartphones worn by cyclists, and analyzes the collected signals to investigate road surface condition. When a bicycle passes on a bump or a dimple, its wheels vibrate. The vibrations are transmitted to the smartphone via the bicycle frame or the cyclist body, and registered as acceleration signals. Conversely, by analyzing the acceleration signals we can estimate the road surface condition. There are mainly two research issues in this system. The first issue is that the acceleration registered at the smartphone includes cyclist motion signal as well as road surface signal. The second issue is that it is necessary to distinguish abnormality of road surface from artificial differences in level, such as a difference between streets and sidewalks. We developed a signal separation algorithm based on independent component analysis to solve the first issue. We also developed a bump classification algorithm using real mother wavelet. These two proposed algorithms were evaluated with 640 trials in total of experimental data conducted by eight cyclists. The classification accuracy of 0.68 validates the simultaneous utilization of our proposed algorithms.
Anterior insular volume decrease is associated with dysfunction of the reward system in patients with chronic pain
Authors: Ikeda E. , Li T. , Kobinata H. , Zhang S. , Kurata J. 
Published: February 2018

AbstractBackgroundChronification of pain is associated with both anatomical and functional alterations of the brain. Alteration in regional grey matter volume might potentially be associated with modified activity of specific brain networks. In this cross‐sectional, observational study, we sought to identify brain regions with grey matter volume changes in patients with chronic pain and to reveal its significance by analysing alteration in functional connectivity from those regions. We further explored relevance of such alterations with psychometrics of chronic pain.MethodsWe recruited 23 patients with chronic pain and 17 age‐, gender‐matched healthy control subjects. After completing multiple psychophysical questionnaires, each subject underwent resting‐state functional magnetic resonance imaging and three‐dimensional anatomical imaging on a 3 Tesla magnetic resonance imaging scanner.ResultsPatients with chronic pain showed significant volume decrease at the right anterior insular cortex (p < 0.001) and the left middle cingulate cortex (p < 0.001) compared with healthy controls. They also showed decreased connectivity between the right anterior insular cortex and the left nucleus accumbens in negative association with the Pain Catastrophizing Scale (R2 = 0.20, p = 0.046) and the Beck's Depression Inventory scores (R2 = 0.24, p = 0.017).ConclusionsDecreased grey matter volumes of those core regions for affective processing of pain might be a common cerebral feature shared by, at least some of, different aetiologies of chronic pain. Dysfunctional network between the anterior insular cortex and the nucleus accumbens might reflect affective and motivational disability involved in chronic pain. Such anatomical and functional profiles could potentially be part of a cerebral signature for chronification of pain.SignificanceThis article illustrates decreased network activity of the reward system in association with insular cortical volume decrease in patients with chronic pain, and its close relationships with affective and cognitive morbidity of pain. Attenuation of brain's reward system involving cortical plastic changes might have a key role in chronification of pain.
Randomized double‐blind controlled study of bedtime low‐dose amitriptyline in chronic neck pain
Authors: Maarrawi J. , Abdel Hay J. , Kobaiter‐Maarrawi S. , Tabet P. , Peyron R. , Garcia‐Larrea L. 
Published: February 2018

AbstractBackgroundAmitriptyline has well‐established efficacy in several chronic pain conditions. While optimal treatment for chronic neck pain (CNP) remains controversial, amitriptyline was not tested for CNP. We evaluated the effect of bedtime amitriptyline in the management of CNP.MethodsA total of 220 patients suffering from idiopathic CNP were randomized to receive either placebo pill (n = 108) or 5 mg of amitriptyline (n = 112) at bedtime for 2 months. Primary outcome measure was visual analog scale (VAS) for pain. Secondary outcome measures were neck pain disability index (NPDI), Bergen Insomnia Score (BIS) and Hospital Anxiety and Depression Scale (HAD), measured before and at the end of 2 months of treatment, with the percentage of patient satisfaction measured at the end of follow‐up only.ResultsEight of 112 patients (7.14%) in the amitriptyline group withdrew from the study because of intolerance. Amitriptyline group showed significantly lower VAS scores than placebo group (3.34 ± 1.45 vs. 6.12 ± 0.92; p < 0.0001), which corresponds to a 53.06 ± 20.29% of improvement from baseline pain as compared to 14.41 ± 11.05%, respectively (p < 0.0001). Similar significant improvements were observed with lesser extents for secondary outcome measures: NPDI, BIS, HAD‐A, HAD‐D and percentage of patient satisfaction.ConclusionLow‐dose amitriptyline is effective for the management of idiopathic CNP with few side effects and high patients’ satisfaction.SignificanceThis randomized controlled trial is the first to show the effectiveness and tolerance of a medication, low‐dose amitriptyline, in managing idiopathic chronic neck pain and its related comorbidities. The optimal treatment of this condition was still controversial in the literature. It extends the indication of low‐dose amitriptyline to another chronic pain condition.
Self‐Humidified Pt Electrocatalyst Fabricated from Hydrophilic Molecules Coating with Enhanced Fuel Cell Performance
Authors: Xu Zejun , Ling Ying , Yang Zehui , Zhang Quan , Zhang Yunfeng , Cai Weiwei , Zhang Daohong 
Published: February 2018

AbstractCatalyst layer is essential for fuel cell performance of polymer electrolyte fuel cells (PEFCs) since electrochemical reactions occur and triple phase boundary forms in catalyst layer. Here, we report a self‐humidified platinum (Pt) electrocatalyst, in which the Pt nanoparticles were coated by 2,7‐bis(3,4‐dihydroxyphenethyl)benzo[lmn][3,8]phenanthroline‐1,3,6,8‐tetraone (NPDA) and the surfaces of Pt nanoparticles are tuned to be hydrophilic, which could adsorb the generated water and enhanced the fuel cell performance under non‐humidified operation condition. Due to the hydrophilic surfaces of Pt nanoparticles, power density of self‐humidified Pt electrocatalyst rapidly reached the maximum power density (700 mW cm−2) without any humidification; in contrast, with hydrophobic surface, power density was only (525 mW cm−2). Meanwhile, the fabricated electrocatalyst exhibits higher durability and could work under real fuel cell operation condition for 18,000 h with only 9 % decay in electrochemical surface area.
Application of the patient rule induction method to detect hydrologic model behavioural parameters and quantify uncertainty
Authors: Shokri Ashkan , Walker Jeffrey P. , van Dijk Albert I. J. M. , Wright Ashley J. , Pauwels Valentijn R. N. 
Published: February 2018

AbstractFinding an operational parameter vector is always challenging in the application of hydrologic models, with over‐parameterization and limited information from observations leading to uncertainty about the best parameter vectors. Thus, it is beneficial to find every possible behavioural parameter vector. This paper presents a new methodology, called the patient rule induction method for parameter estimation (PRIM‐PE), to define where the behavioural parameter vectors are located in the parameter space. The PRIM‐PE was used to discover all regions of the parameter space containing an acceptable model behaviour. This algorithm consists of an initial sampling procedure to generate a parameter sample that sufficiently represents the response surface with a uniform distribution within the “good‐enough” region (i.e., performance better than a predefined threshold) and a rule induction component (PRIM), which is then used to define regions in the parameter space in which the acceptable parameter vectors are located. To investigate its ability in different situations, the methodology is evaluated using four test problems. The PRIM‐PE sampling procedure was also compared against a Markov chain Monte Carlo sampler known as the differential evolution adaptive Metropolis (DREAMZS) algorithm. Finally, a spatially distributed hydrological model calibration problem with two settings (a three‐parameter calibration problem and a 23‐parameter calibration problem) was solved using the PRIM‐PE algorithm. The results show that the PRIM‐PE method captured the good‐enough region in the parameter space successfully using 8 and 107 boxes for the three‐parameter and 23‐parameter problems, respectively. This good‐enough region can be used in a global sensitivity analysis to provide a broad range of parameter vectors that produce acceptable model performance. Moreover, for a specific objective function and model structure, the size of the boxes can be used as a measure of equifinality.
Investigating adoption factors of 3G services: An empirical study in Vietnam
Authors: Tapanainen Tommi J. , Dao Kien T. , Nguyen Hai T. T. 
Published: February 2018

AbstractThis study aimed to examine the factors that affect the 3G mobile services adoption. The technology acceptance model was used, and structural equation modeling was applied to the data sample obtained from a survey in Hanoi, the capital of Vietnam. The research found that perceived usefulness has the strongest effect on intention and attitude to adopt. Social influence does not affect 3G services adoption. The result also underlines the variable of attitude that it should not be eliminated from technology acceptance models. Based on the findings, this research proposes practical recommendations to 3G providers, such as enhancing the service usefulness and focusing on features developed via collaborations between telecommunication carriers and content developers. Furthermore, 3G providers are recommended to focus on understanding their customers' needs better rather than relying on peer and social advertising in promoting 3G services. Finally, future research is proposed to study the effect of different combinations of perceived enjoyment, usefulness, and ease of use, and the relationship between perceived cost, social influence, and intention on 3G services adoption.
Environmental mixture with estrogenic activity increases Hsd3b1 expression through estrogen receptors in immature rat granulosa cells
Authors: Samardzija Nenadov Dragana , Pogrmic‐Majkic Kristina , Fa Svetlana , Stanic Bojana , Tubic Aleksandra , Andric Nebojsa 
Published: February 2018

AbstractHumans are exposed not only to single endocrine disruptors, but also to chemical mixtures that can adversely affect their reproductive health. Steroidogenesis in reproductive tissues is emerging as the key target of endocrine disruptor action. Here, we analyzed the effect of environmental chemical mixtures with estrogenic activity on steroidogenic processes in immature rat granulosa cells and whether the observed steroidogenic effects were mediated through estrogen receptors. Extracts from untreated wastewater were prepared by solid‐phase extraction and silica gel fractionation. ER‐CALUX assay showed that the polar fractions of wastewater exerted different levels of estrogenic activity. Exposure of immature granulosa cells to the polar fraction exerting 9 ng of 17β‐estradiol equivalents per liter of water of estrogenic activity increased mRNA expression of the key enzymes of progesterone biosynthetic pathway Star and Hsd3b1, but did not alter the level of Cyp19a1 and Lhr. Addition of estrogen receptor inhibitor ICI 182 780 prevented the estrogenic mixture‐induced increase in Hsd3b1, but not Star mRNA level in immature granulosa cells. These results indicate that the environmental chemical mixtures with estrogenic activity exert endocrine disrupting effects by augmenting the progesterone biosynthetic pathway in immature rat granulosa cells, which is an effect achieved in part through activation of the estrogen receptors.
High temporal resolution motion estimation using a self‐navigated simultaneous multi‐slice echo planar imaging acquisition
Authors: Teruel Jose R. , Kuperman Joshua M. , Dale Anders M. , White Nathan S. 
Published: February 2018

BackgroundSubject motion is known to produce spurious covariance among time‐series in functional connectivity that has been reported to induce distance‐dependent spurious correlations.PurposeTo present a feasibility study for applying the extended Kalman filter (EKF) framework for high temporal resolution motion correction of resting state functional MRI (rs‐fMRI) series using each simultaneous multi‐slice (SMS) echo planar imaging (EPI) shot as its own navigator.Study TypeProspective feasibility study.Population/SubjectsThree human volunteers.Field Strength/Sequence3T GE DISCOVERY MR750 scanner using a 32‐channel head coil. Simultaneous multi‐slice rs‐fMRI sequence with repetition time (TR)/echo time (TE) = 800/30 ms, and SMS factor 6.AssessmentMotion estimates were computed using two techniques: a conventional rigid‐body volume‐wise registration; and a high‐temporal resolution motion estimation rigid‐body approach. The reference image was resampled using the estimates obtained from both approaches and the difference between these predicted volumes and the original moving series was summarized using the normalized mean squared error (NMSE).Statistical TestsDirect comparison of NMSE values.ResultsHigh‐temporal motion estimation was always superior to volume‐wise motion estimation for the sample presented. For staged continuous rotations, the NMSE using high‐temporal resolution motion estimates ranged between [0.130, 0.150] for the first volunteer (in‐plane rotations), between [0.060, 0.068] for the second volunteer (in‐plane rotations), and between [0.063, 0.080] for the third volunteer (through‐plane rotations). These values went up to [0.384, 0.464]; [0.136, 0.179]; and [0.080, 0.096], respectively, when using volume‐wise motion estimates.Data ConclusionAccurate high‐temporal rigid‐body motion estimates can be obtained for rs‐fMRI taking advantage of simultaneous multi‐slice EPI sub‐TR shots.Level of Evidence: 2Technical Efficacy: Stage 1J. Magn. Reson. Imaging 2018;48:780–787.
Deep learning enables reduced gadolinium dose for contrast‐enhanced brain MRI
Authors: Gong Enhao , Pauly John M. , Wintermark Max , Zaharchuk Greg 
Published: February 2018

BackgroundThere are concerns over gadolinium deposition from gadolinium‐based contrast agents (GBCA) administration.PurposeTo reduce gadolinium dose in contrast‐enhanced brain MRI using a deep learning method.Study typeRetrospective, crossover.PopulationSixty patients receiving clinically indicated contrast‐enhanced brain MRI.Sequence3D T1‐weighted inversion‐recovery prepped fast‐spoiled‐gradient‐echo (IR‐FSPGR) imaging was acquired at both 1.5T and 3T. In 60 brain MRI exams, the IR‐FSPGR sequence was obtained under three conditions: precontrast, postcontrast images with 10% low‐dose (0.01mmol/kg) and 100% full‐dose (0.1 mmol/kg) of gadobenate dimeglumine. We trained a deep learning model using the first 10 cases (with mixed indications) to approximate full‐dose images from the precontrast and low‐dose images. Synthesized full‐dose images were created using the trained model in two test sets: 20 patients with mixed indications and 30 patients with glioma.AssessmentFor both test sets, low‐dose, true full‐dose, and the synthesized full‐dose postcontrast image sets were compared quantitatively using peak‐signal‐to‐noise‐ratios (PSNR) and structural‐similarity‐index (SSIM). For the test set comprised of 20 patients with mixed indications, two neuroradiologists scored blindly and independently for the three postcontrast image sets, evaluating image quality, motion‐artifact suppression, and contrast enhancement compared with precontrast images.Statistical AnalysisResults were assessed using paired t‐tests and noninferiority tests.ResultsThe proposed deep learning method yielded significant (n = 50, P < 0.001) improvements over the low‐dose images (>5 dB PSNR gains and >11.0% SSIM). Ratings on image quality (n = 20, P = 0.003) and contrast enhancement (n = 20, P < 0.001) were significantly increased. Compared to true full‐dose images, the synthesized full‐dose images have a slight but not significant reduction in image quality (n = 20, P = 0.083) and contrast enhancement (n = 20, P = 0.068). Slightly better (n = 20, P = 0.039) motion‐artifact suppression was noted in the synthesized images. The noninferiority test rejects the inferiority of the synthesized to true full‐dose images for image quality (95% CI: –14–9%), artifacts suppression (95% CI: –5–20%), and contrast enhancement (95% CI: –13–6%).Data ConclusionWith the proposed deep learning method, gadolinium dose can be reduced 10‐fold while preserving contrast information and avoiding significant image quality degradation.Level of Evidence: 3Technical Efficacy: Stage 5J. MAGN. RESON. IMAGING 2018;48:330–340.