bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phase I study of novel SYK inhibitor TAK‐659 (mivavotinib) in combination with R‐CHOP for front‐line treatment of high‐risk diffuse large B‐cell lymphoma
Authors: Karmali Reem , St‐Pierre Frederique , Ma Shuo , Foster Kelly D. , Kaplan Jason , Mi Xinlei , Pro Barbara , Winter Jane N. , Gordon Leo I. 
Published: December 2022

AbstractBackground: TAK‐659, a novel oral SYK inhibitor, has demonstrated efficacy in heavily pretreated diffuse large B‐cell lymphoma (DLBCL). We report results of a phase I single‐institution escalation study of front‐line treatment with R‐CHOP and TAK‐659 in treatment‐naïve high‐risk DLBCL.Methods: Patients with high‐risk DLBCL were treated with R‐CHOP for 1 cycle, followed by combined R‐CHOP and TAK‐659 for an additional five cycles, with TAK‐659 dosing escalated from 60 mg, to 80 mg, to 100 mg daily, based on a 3 + 3 design. The primary objective was to determine the safety and establish the maximum tolerated dose (MTD) of TAK‐659 in this setting.Results: Twelve patients were enrolled. Dose level 3 (100 mg) was established as the MTD. Dose level 1 (60 mg) maintained a similar area under the curve (AUC) to the MTD. With a median follow‐up of 21 months, 92% of patients achieved complete response (CR). The most common treatment‐emergent adverse events were lymphopenia (100%), infection (50%, n = 3 opportunistic), aspartate aminotransferase elevation (100%), and alanine aminotransferase elevation (83%).Conclusion: A TAK‐659 dose of 60 mg was well tolerated, did not require dose modifications, and maintained a similar AUC to the MTD. The combination of R‐CHOP and TAK‐659 in patients with newly diagnosed high‐risk DLBCL produces promising CR rates.
Dominant role of Atlantic multidecadal oscillation in the tipping point of maximum and minimum temperatures over inner East Asia
Authors: Guo Shuyang , Guan Xiaodan , Gu Tonghui , Shen Xiaohan 
Published: December 2022

AbstractDespite unprecedented heatwave events reported in the past two decades over inner East Asia, the maximum and minimum temperatures of Tmax and Tmin do not always exhibit continuous increasing. After drastic increases in Tmax and Tmin appeared over inner East Asia from 1982, a decreasing trend occurred around 1998, which was closely associated with the Atlantic multidecadal oscillation (AMO). During 1950–2014, the correlation between Tmax (Tmin) and AMO at the decadal time scale reached 0.70 (0.83); and 65% (79%) variance of the decadal variability in Tmax (Tmin) was explained by the AMO and three other classic oceanic modes; the contribution of the AMO was up to 82% (91%) in the decadal modulated oscillation. The declines of Tmin and Tmax synchronized with the downward swing of the AMO since the late‐1990 s, which was because the AMO could affect the vertical motion and wind anomalies of inner East Asia through the cyclonic‐anticyclonic teleconnection wave train in the mid‐latitudes of the Northern Hemisphere. The results of composite analysis show that during the positive phase of the AMO, a local ascending motion was enhanced over inner East Asia and a large‐scale abnormal high was found north of East Asia in favour of cold air intrusion. Meanwhile, the start of growing season was delayed by the positive phase of the AMO over the northern part of inner East Asia due to the high sensitivity of spring phenology to the Tmin controlled by the AMO.
Spatial heterogeneity and driving factors of aerosol in Western China: Analysis on multiangle implementation of atmospheric correction–aerosol optical depth in Xinjiang over 2001–2019
Authors: Ma Wen , Ding Jianli , Jin Xiaoye 
Published: December 2022

AbstractKnowledge of aerosol dynamics is essential to combating atmospheric aerosol pollution, and there is a growing interest in aerosol changes and their drivers. However, the effects of interactions between natural and anthropogenic drivers of aerosol are not well understood. Here, we analyse changes in aerosol optical depth (AOD) in Xinjiang, China using the multiangle implementation of atmospheric correction aerosol products over 2001–2019 and investigate the driving factors of aerosol dynamics using a random forest (RF) and a geographical detector. The results show the dominant AOD changes in the quasi‐period of 3.21 months, 7.86 months, 1.19 years, for seasonal, half‐year, and interannual variations of aerosols. The increasing and then decreasing nonlinear trends were observed in the interannual AOD variation during 19 years period. The importance ranking results of the two models indicated that meteorological factors dominated the spatial distribution of AOD in Xinjiang (72.73% for RF and 65.78% for the geographic detector), and that interactions between factors enhanced the explanatory power in AOD changes. In addition, the influence of anthropogenic factors on AOD was increasing in North Xinjiang, and the influence of precipitation and population on AOD was increasing in East Xinjiang. However, the influence of drivers on AOD in South Xinjiang was basically constant over time, showing the spatially heterogeneous relationship between AOD and drivers. This study emphasized the spatial heterogeneity and driving factors of small‐scale aerosols in arid regions and so can guide targeted air pollution prevention and control in local areas.
Continuity properties of the shearlet transform and the shearlet synthesis operator on the Lizorkin type spaces
Authors: Bartolucci Francesca , Pilipović Stevan , Teofanov Nenad 
Published: December 2022

AbstractWe develop a distributional framework for the shearlet transform and the shearlet synthesis operator , where is the Lizorkin test function space and is the space of highly localized test functions on the standard shearlet group . These spaces and their duals are called Lizorkin type spaces of test functions and distributions. We analyze the continuity properties of these transforms when the admissible vector ψ belongs to . Then, we define the shearlet transform and the shearlet synthesis operator of Lizorkin type distributions as transpose mappings of the shearlet synthesis operator and the shearlet transform, respectively. They yield continuous mappings from to and from to . Furthermore, we show the consistency of our definition with the shearlet transform defined by direct evaluation of a distribution on the shearlets. The same can be done for the shearlet synthesis operator. Finally, we give a reconstruction formula for Lizorkin type distributions, from which follows that the action of such generalized functions can be written as an absolutely convergent integral over the standard shearlet group.
Analysis of carbohydrates and glycoconjugates by matrix‐assisted laser desorption/ionization mass spectrometry: An update for 2019–2020
Authors: Harvey David J. 
Published: December 2022

AbstractThis review is the tenth update of the original article published in 1999 on the application of matrix‐assisted laser desorption/ionization (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2020. Also included are papers that describe methods appropriate to analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. The review is basically divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of arrays. (2) Applications to various structural types such as oligo‐ and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other areas such as medicine, industrial processes and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. The reported work shows increasing use of incorporation of new techniques such as ion mobility and the enormous impact that MALDI imaging is having. MALDI, although invented nearly 40 years ago is still an ideal technique for carbohydrate analysis and advancements in the technique and range of applications show little sign of diminishing.
Characterizing viral samples using machine learning for Raman and absorption spectroscopy
Authors: Boodaghidizaji Miad , Milind Athalye Shreya , Thakur Sukirt , Esmaili Ehsan , Verma Mohit S. , Ardekani Arezoo M. 
Published: December 2022

AbstractMachine learning methods can be used as robust techniques to provide invaluable information for analyzing biological samples in pharmaceutical industries, such as predicting the concentration of viral particles of interest in biological samples. Here, we utilized both convolutional neural networks (CNNs) and random forests (RFs) to predict the concentration of the samples containing measles, mumps, rubella, and varicella‐zoster viruses (ProQuad®) based on Raman and absorption spectroscopy. We prepared Raman and absorption spectra data sets with known concentration values, then used the Raman and absorption signals individually and together to train RFs and CNNs. We demonstrated that both RFs and CNNs can make predictions with R2 values as high as 95%. We proposed two different networks to jointly use the Raman and absorption spectra, where our results demonstrated that concatenating the Raman and absorption data increases the prediction accuracy compared to using either Raman or absorption spectrum alone. Additionally, we further verified the advantage of using joint Raman‐absorption with principal component analysis. Furthermore, our method can be extended to characterize properties other than concentration, such as the type of viral particles.
Measuring the effects of information exchange on incentives to collude using calibrated simulations (with an example of the South African oil industry)
Authors: Bageri Vasiliki , Katsoulacos Yannis 
Published: December 2022

AbstractInformation sharing may help firms solve the coordination and monitoring problems that threaten the sustainability of concerted practices or tacit collusion. This paper models the impact of information sharing on the incentives of firms in an oligopolistic market to sustain a concerted practice, taking into account recent theoretical developments and empirical evidence on tacit collusion. We then calibrate and simulate the effect of information sharing, mediated by the industry association, on each firm in the South African oil industry. Our analysis offers a rigorous, but also practical, framework for facilitating Competition Authorities' decision making in investigations of information sharing.
An untrained deep learning method for reconstructing dynamic MR images from accelerated model‐based data
Authors: Slavkova Kalina P. , DiCarlo Julie C. , Wadhwa Viraj , Kumar Sidharth , Wu Chengyue , Virostko John , Yankeelov Thomas E. , Tamir Jonathan I. 
Published: December 2022

PurposeTo implement physics‐based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data.MethodsThe ConvDecoder (CD) neural network was trained with a physics‐based regularization term incorporating the spoiled gradient echo equation that describes variable‐flip angle data. Fully‐sampled variable‐flip angle k‐space data were retrospectively accelerated by factors of R = {8, 12, 18, 36} and reconstructed with CD, CD with the proposed regularization (CD + r), locally low‐rank (LR) reconstruction, and compressed sensing with L1‐wavelet regularization (L1). Final images from CD + r training were evaluated at the “argmin” of the regularization loss; whereas the CD, LR, and L1 reconstructions were chosen optimally based on ground truth data. The performance measures used were the normalized RMS error, the concordance correlation coefficient, and the structural similarity index.ResultsThe CD + r reconstructions, chosen using the stopping condition, yielded structural similarity indexs that were similar to the CD (p = 0.47) and LR structural similarity indexs (p = 0.95) across R and that were significantly higher than the L1 structural similarity indexs (p = 0.04). The concordance correlation coefficient values for the CD + r T1 maps across all R and subjects were greater than those corresponding to the L1 (p = 0.15) and LR (p = 0.13) T1 maps, respectively. For R ≥ 12 (≤4.2 min scan time), L1 and LR T1 maps exhibit a loss of spatially refined details compared to CD + r.ConclusionThe use of an untrained neural network together with a physics‐based regularization loss shows promise as a measure for determining the optimal stopping point in training without relying on fully‐sampled ground truth data.
Red cell exchange transfusions increase cerebral capillary transit times and may alter oxygen extraction in sickle cell disease
Authors: DeBeer Tonner , Jordan Lori C. , Waddle Spencer , Lee Chelsea , Patel Niral J. , Garza Maria , Davis Taylor , Pruthi Sumit , Jones Sky , Donahue Manus J. 
Published: December 2022

AbstractPersons with sickle cell disease (SCD) suffer from chronic hemolytic anemia, reduced blood oxygen content, and lifelong risk of silent and overt stroke. Major conventional stroke risk factors are absent in most individuals with SCD, yet nearly 50% have evidence of brain infarcts by the age of 30 years, indicating alternative etiologies for ischemia. We investigated whether radiological evidence of accelerated blood water transit through capillaries, visible on arterial spin labeling (ASL) magnetic resonance imaging, reduces following transfusion‐induced increases in hemoglobin and relates to oxygen extraction fraction (OEF). Neurological evaluation along with anatomical and hemodynamic imaging with cerebral blood flow (CBF)‐weighted pseudocontinuous ASL and OEF imaging with T2‐relaxation‐under‐spin‐tagging were applied in sequence before and after blood transfusion therapy (n = 32) and in a comparator cohort of nontransfused SCD participants on hydroxyurea therapy scanned at two time points to assess stability without interim intervention (n = 13). OEF was calculated separately using models derived from human hemoglobin‐F, hemoglobin‐A, and hemoglobin‐S. Gray matter CBF and dural sinus signal, indicative of rapid blood transit, were evaluated at each time point and compared with OEF using paired statistical tests (significance: two‐sided p < 0.05). No significant change in sinus signal was observed in nontransfused participants (p = 0.650), but a reduction was observed in transfused participants (p = 0.034), consistent with slower red cell transit following transfusion. The dural sinus signal intensity was inversely associated with OEF pretransfusion (p = 0.011), but not posttransfusion. Study findings suggest that transfusion‐induced increases in total hemoglobin may lengthen blood transit times through cerebral capillaries and alter cerebral OEF in SCD.
Application of a global–local full‐waveform inversion of Rayleigh wave to estimate the near‐surface shear wave velocity model
Authors: Lamuraglia Salvatore , Stucchi Eusebio , Aleardi Mattia 
Published: December 2022

AbstractWe present a global–local full‐waveform inversion (FWI) of surface waves to estimate a high‐resolution shear wave velocity model of the near‐surface at the site of Grenoble (France). The seismic data we use have been acquired in the framework of the InterPACIFIC project. A first attempt is made by employing the multichannel analysis of surface waves (MASW) to invert the fundamental modes of Rayleigh waves. This inversion is solved through a global optimization driven by the neighbourhood algorithm. However, the limiting 1D assumption, together with the severely ill‐posedness of the MASW inversion motivate us to exploit all the information content of the recorded Rayleigh waves (i.e., travel‐times and amplitudes) by implementing a global–local FWI approach. We first perform a global FWI in which a genetic algorithm (GA) is used to minimize the L2 norm difference between observed and simulated seismic waveforms up to 30 Hz. This inversion employs a two‐grid scheme in which the subsurface is described by a fine grid input to the forward modelling, whereas the unknowns are defined on a coarse grid. A bilinear interpolation brings the coarse‐grid model into the fine grid. To accelerate the convergence, two strategies are considered; offset marching and frequency marching. Then, the long‐wavelength velocity profile provided by the GA inversion is used as the starting point for a local FWI for further refinement of the predicted model. In this case, we employ a frequency‐marching strategy up to a maximum frequency of 60 Hz. Both the global and local approaches use a finite difference code for the forward computation. Despite the limited a priori information infused into the inversion framework, the presented global–local FWI scheme provides reliable results as demonstrated by the good match between recorded and predicted seismograms and by the agreement of the estimated velocity with both nearby borehole data and stratigraphic information on the study site. Our global–local strategy also achieves better data fitting and improved matching with the available borehole data with respect to the velocity profile estimated when the local FWI is started from the MASW predictions.
The impact of microfinance on poverty reduction in Vietnam: A dynamic panel‐data analysis
Authors: Duong Hoai A. , Nghiem Hong S. 
Published: December 2022

AbstractThe current study uses the Generalised Method of Moments to examine the dynamic relationship between access to microfinance and poverty reduction. The panel‐data set is constructed from the Vietnam Household Living Standard Surveys during 2002–2008. The results show that access to microfinance in the previous period significantly improves per adult‐equivalent income and consumption of the households in the current period. However, previous participation in microfinance programs is not sufficient to help households escape from poverty. In addition, both private and government bank loans significantly help improve household per adult‐equivalent income, consumption, and poverty rates. The empirical results imply that a larger loan size, diversified loans for different consumption purposes, and improvement in infrastructure and facilities, especially in rural areas, are believed to help reduce poverty.
Isotopic labeling of metabolic water with 18O2
Authors: Koch George W. , Schwartz Egbert 
Published: December 2022

RationaleWater is the medium of life, is involved in biochemical reactions, and is exchanged among internal pools and with the water in the external environment of organisms. Understanding these processes can be improved by isotopically labeling the metabolic water that is produced inside the cells of organisms during aerobic respiration.MethodsHere we describe a new method for isotopically labeling cellular water by incubating microbes and plant tissues in air enriched in 18O2. As oxygen gas is reduced during respiration, H218O is produced. The rate of H218O production and the synthesis of biomolecules that incorporate 18O from H218O can be quantified using cavity ringdown spectrometry and isotope ratio mass spectrometry.ResultsFor Escherichia coli in solution culture, soil microbial communities, and respiring tissues of plants, the amount of H218O produced was strongly correlated with that of 18O2 consumed during incubations. Measurements of 18O in DNA, microbial biomass, and CO2 showed that metabolic water was an important substrate in biosynthesis reactions.ConclusionsAny organism with aerobic respiration is amenable to labeling with 18O2, and the method described here enables a new approach to investigate questions regarding plant and microbial physiology. In plants, 18O introduced as metabolic water could be tracked as it moves between living cells and exchanges with external water. For probing soil microbial physiology, the method described here has the advantage over the application of exogenous H218O of not increasing the soil moisture, a disturbance that can affect microbial metabolism.
Investigating scientific modeling practices in U.S. and German elementary science classrooms: A comparative, cross‐national video study
Authors: Böschl Florian , Forbes Cory , Lange‐Schubert Kim 
Published: December 2022

AbstractScience education reform efforts have emphasized the incorporation of more authentic and meaningful scientific practices into formal learning environments. With this focus on practices, such as scientific modeling, a powerful tool for sense‐making and reasoning, comes a need to observe and understand if and how it is actually occurring, particularly in elementary science learning environments. To address this need, and moving forward an international, collaborative research effort centered around scientific modeling, we undertook a cross‐national observational study in which we used an observation protocol to characterize scientific modeling practices in elementary classrooms in both the United States and Germany. Drawing on existing video‐based datasets from multiple projects, this study's findings provide empirical evidence for model‐based teaching and learning and explore the relative frequencies of relevant modeling subpractices (i.e., construction, use, evaluation, and revision of scientific models) in regular, day‐to‐day elementary science classrooms. This study also illustrates the differences and similarities between German and American science instruction. In general, students in both countries experience model construction more often than other modeling practices. German teachers more consistently foreground anchoring phenomena and students’ questions/preconceptions as part of the modeling process, thus leading to slightly higher overall modeling scores—despite a complete lack of model evaluation and revision in any of the observed German classrooms. Ultimately, however, young learners in both nations seem to experience alarmingly little opportunities to engage in any of the modeling subpractices. Particularly in terms of modeling as a means to make sense of phenomena, constrained by teachers most commonly using finished representations (i.e., models) of phenomena and processes to teach science/deliver content. Our results indicate the need for a greater focus on all scientific modeling subprocesses, particularly the integration of scientific modeling with already established, more general (e.g., inquiry‐based) strategies for effective science teaching and an emphasis on actualizing modeling as an authentic sense‐making endeavor.
Game of tax: Rethinking the relationship between redistribution and reciprocity through a Georgian tax lottery
Authors: Björklund Larsen Lotta 
Published: December 2022

ABSTRACTThis article addresses a failed tax lottery in the country of Georgia's rapid yet shaky political and economic development. The purpose of a tax lottery is to formalize transactions and increase tax compliance. It aims to motivate consumers in any commercial transaction to ask for a receipt qua lottery ticket and ensure that businesses pay taxes due. Tax lotteries thus have a dual function: more revenue is collected from businesses, and consumers do soft policing work while also having a chance to win. Taxation and gambling are two very different ways of exchanging. Gambling is mostly voluntary, effortless, and playful, whereas taxation is dull government revenue collection. Yet both gambling and taxation are ways to understand any society and its political life through its reciprocal qualities and redistributive effects. Drawing on anthropological research studying taxation and gambling, this article is a Schumpeterian analysis, as it provides an inlet into how Georgians regard their society. Studying this tax lottery provides an opportunity to rethink the relation between redistribution and reciprocity, and I argue that to understand how citizens accept the redistribution of taxation, we have to attend to its reciprocal qualities.