Miracle-dz/UniFAR-FTUs huggingface dataset:
Changes in Surface Area, Pore Structure and Density during Formation of High-temperature Chars from Representative U.S. Coals

Multiple techniques (CO2 and N2 adsorptions, NMR spin relaxation of adsorbed water, He pycnometry and Hg porosimetry) have been combined in a comprehensive study to determine changes in surface area (CO2 and nitrogen), density (solid, particle and bulk), and pore structure (pore size and volume distributions of micro-, meso- and macro-pores) in high-temperature char formation from rank representative U.S. coals of the ANL and PETC Banks (i.e. Beulah Zap, Dietz, Utah Blind Canyon, Pittsburgh No.8 and Pocahontas No.3). Chars were formed at high heating rates in a flat-frame burner (maximum temperature of 1473 K), a process representative of char formation in pulverized coal combustion. Most of the surface area of the coals was found in micropores with radii less than 1.5 nm, while 95% or more of the pore volume in the coals (85% of that in chars) was contained in mesopores less than 20 nm). During the high-temperature formation of char in a flame: (I) CO2 surface areas (involving mainly micropores, rpore < 1.5 nm) increase two- to three-fold, while N2 surface areas (involving mesopores. 1.5 nm < rpore < 20 nm) increase 20–200-fold; (2) solid densities increase about 25% due to graphitization, while particle densities decrease by about a factor of two due to large increases in particle porosity; (3) pore volumes increase 5–10-fold; and (4) total porosities increase three- to four-fold, most of this increase occurring in the macropore range. The larger surface areas and porosities of chars relative to coals may be explained by (i) the removal by pyrolysis of strongly adsorbed molecules or volatile hydrocarbons from micropores and small mesopores that would otherwise hinder access of CO2 and N2 molecules; (ii) the creation of new pores during the restructuring process involved in charification; and (iii) opening up by gasification with oxygen of new pores previously blocked to gas adsorption. The preparation conditions (e.g. atmosphere, heating rate and temperature) greatly affect the physical properties including the surface area, porosity and density of the resulting chars. The degree of carbon burnout is an important correlating factor affecting these properties.
Was Cinderella just a fairy tale? Survival differences between stepchildren and their half-siblings

The death of a parent, particularly the mother, is linked to a suite of negative outcomes across the life-course. Compounding concerns for child outcomes are expectations of poor treatment by step-parents after parental remarriage. Indeed, folk tales of step-parental abuse abound cross-culturally and are embedded into stories taught to children. To understand why child outcomes might be sensitive to levels of relatedness within the household, evolutionary-oriented research targets patterning in parental expenditure in ways predicted to maximize inclusive fitness. In particular, parents are expected to prioritize investments in their biological children. However, stepfamilies are only formed after children experience multiple unfortunate events (e.g. parental loss, poverty), blurring causal interpretations between step-parental presence and stepchild outcomes. Moreover, stepchildren have been shown to be integral to household functioning, caring for their half-siblings and stabilizing relationships. These results challenge narrow views of adaptive behaviour; specifically, that step-parents, unlike biological parents, do no stand to reap fitness benefits from the care that they provide to their stepchildren. To evaluate these critiques, we analyse the survival outcomes of stepchildren. We include over 400 000 individuals from across a natural fertility period (1847–1940) in the United States state of Utah and examine the consequences of parental loss and step-parental introduction. Our analyses yield three key results: (i) exposure to maternal loss in childhood is associated with elevated mortality risk, (ii) parental remarriage does not increase the risk of mortality among stepchildren compared to non-stepchildren who too had lost a parent, and (iii) stepchildren enjoy higher survival than their half-siblings within the same family. Ultimately, this work contributes to the increasingly recognized importance of cooperative relationships among non-kin for childcare and household functioning. This article is part of the theme issue ‘Multidisciplinary perspectives on social support and maternal–child health’.
Suppressing Residual Acoustic Echo Based on Deep BiGRU with Attention

A residual echo suppressor (RES) aims to suppress the residual echo and nonlinear components in the output of a linear acoustic echo canceler (AEC). Traditional adaptive RES typically estimates the residual acoustic echo path from the inputs that are both the far-end speech and the error signal computed by the AEC, and derives the RES filter coefficients accordingly. However, the near-end speech is severely distorted when the residual echo attenuated in double-talk situations. In this paper, we propose a new supervised learning RES based on deep Bidirectional gated recurrent unity (BiGRU) with attention mechanism (BiGRU-Att). First, the BiGRU can effectively extract the inherent temporal feature of input signals, then attention mechanism assigns the corresponding weight to the extracted feature, taking an ideal ratio mask (IRM) as the learning target of the model. The experimental results show that compared with the deep neural network (DNN) -based RES, the proposed BiGRU-Att can not only suppress residual echo effectively, but also clearly restore near-end speech.
A Content-Driven Architecture for Medical Image Segmentation

As the U-Net architecture continues to push the state of the art in medical image segmentation, and keeping track of the multitude of derived models becomes increasingly difficult, opposing trends have emerged that prefer a coherent framework of ancillary processing tasks over the use of highly optimized and sophisticated models. This trend has culminated in the framework nnU-Net, which adapts preprocessing, training and inference to the respective dataset and thus managed to lead the Medical Segmentation Decathlon challenge despite relying on comparably simple models. In this paper, we focus on one ancillary technique that is commonly used but poorly addressed in literature: patchwise training. Since computational costs tend to increase drastically when using naive strategies, we discuss the benefits of content-sensitive sampling for patchwise training of deep learning segmentation models. We adapt this strategy in a content-driven architecture for abdominal aorta and stent-graft segmentation, where we evaluate and compare it with traditional sampling strategies based on a real-world clinical dataset.
Research and Application of Improved RBPF-VSLAM Algorithm

SLAM technology has been widely used in intelligent robots. In order to solve the problems of pose estimation error, poor anti-interference ability and high calculation cost in traditional SLAM method under complex indoor environment, this paper proposes an improved method based on multi-modal sensor fusion, and the work such as feature extraction, data association and down-sampling are concerned in the research. The improved distribution function method based on RBPF and ICP algorithm is presented and applied in simulated experiments. Compared with the traditional RBPF method, which has low robustness and poor performance under complex and dynamic environment, the position estimation accuracy has been improved 43.4% averagely, and the computation consumption has been reduced as 49.1% while the improved algorithm being operated.
The Quant Crisis of 2018–2020: Cornered by Big Growth

This article examines the performance of equity factor portfolios during the quant crisis of 2018–2020. The author finds that there was basically only one way to outperform during this period, namely by investing in the largest and most expensive growth stocks. Other factors were only effective to the extent that they provided implicit exposure to the same large growth stocks. Smaller stock portfolios underperformed across the board. Thus, there were numerous ways to fail during the 2018–2020 period but, essentially, only one way to succeed. Comparing the quant crisis with previous major drawdowns of the value factor, the author finds that these other periods are better characterized as momentum factor rallies with collateral damage for the value factor. Moreover, smaller stocks typically still offered possibilities for outperformance. The author concludes that the 2018–2020 quant crisis posed an exceptional challenge to quantitative managers due to a rare combination of circumstances. TOPICS: Security analysis and valuation, factor-based models, statistical methods, financial crises and financial market history Key Findings ▪ During the quant crisis of 2018–2020 there were many ways to fail but essentially only one way to succeed, namely by investing in the largest and most expensive growth stocks. ▪ Other factors were only effective to the extent that they provided implicit exposure to the same large growth stocks, and smaller stock portfolios underperformed across the board. ▪ Previous major drawdowns of the value factor were less challenging for multifactor investors because profits on the momentum factor exceeded the value losses.
Imaging of T-cell Responses in the Context of Cancer Immunotherapy

Immunotherapy, which promotes the induction of cytotoxic T lymphocytes and enhances their infiltration into and function within tumors, is a rapidly expanding and evolving approach to treating cancer. However, many of the critical denominators for inducing effective anticancer immune responses remain unknown. Efforts are underway to develop comprehensive ex vivo assessments of the immune landscape of patients prior to and during response to immunotherapy. An important complementary approach to these efforts involves the development of noninvasive imaging approaches to detect immune targets, assess delivery of immune-based therapeutics, and evaluate responses to immunotherapy. Herein, we review the merits and limitations of various noninvasive imaging modalities (MRI, PET, and single-photon emission tomography) and discuss candidate targets for cellular and molecular imaging for visualization of T-cell responses at various stages along the cancer–immunity cycle in the context of immunotherapy. We also discuss the potential use of these imaging strategies in monitoring treatment responses and predicting prognosis for patients treated with immunotherapy.
A procedural and flexible approach for specification, modeling, definition, and analysis for self‐adaptive systems

An adaptive system can modify its settings at runtime as a response to changes in its operational environment. To analyse this kind of systems at design time is a difficult task since it requires considering the system together with the adaptation operations, and taking into account how such adaptations act on the system. In order to use simulation‐based techniques for the analysis of such systems, we not only need precise executable models of the systems to be analyzed, but also to capture the semantics of their adaptation mechanisms. Given the wide range and flexibility of adaptation operations, we need ways to allow the definition of new operations. We present a flexible approach for the definition and simulation‐based analysis in design‐time of adaptive component‐based systems. Our approach combines an extension of the Palladio component model in e‐Motions, a model of the adaptation mechanisms, and elastic requirements specification using the SYBL language. From the model of the system, its adaptation mechanisms, and its requirements, an executable Maude specification is generated for simulation. The application of the approach is illustrated on a use case that comprises some components and adaptations rules. The example is then analyzed using simulations. It is also shown that it is indeed possible to define additional metrics, specify adaptation requirements and rules which conduct simulations of the models in a more flexible way, and that the results of the simulation performed from these definitions can be used to carry on a valuable predictive performance analysis.
GABAa receptor subtypes as neuronal substrates for selective actions of benzodiazepines and general anesthetics

General anesthetics are among the most widely used and important therapeutic agents. The molecular targets mediating different endpoints of the anesthetic state in vivo are currently largely unknown. The analysis of mice carrying point mutations in neurotransmitter receptor subunits is a powerful tool to assess the contribution of the respective receptor subtype to the pharmacological actions of clinically used general anesthetics. We examined the involvement of β3-containing GABAA receptors in the respiratory, cardiovascular, hypothermic and sedative actions of etomidate and propofol using β3(N265M) knock-in mice carrying etomidateand propofol-insensitive β3-containing GABAA receptors. While the respiratory depressant action of etomidate and propofol, as determined by blood gas analysis, was almost absent in β3(N265M) mice, the cardiac depressant and hypothermic effects, as determined by radiotelemetry, and the sedative effect, as determined by decrease of motor activity, were still present. Taken together with previous findings, our results show that both immobilization and respiratory depression are mediated by β3-containing GABAA receptors, hypnosis by both β3and β2-containing GABAA receptors, while the hypothermic, cardiac depressant and sedative actions are largely independent of β3containing GABAA receptors.
Myelin regulatory factor deficiency is associated with the retinal photoreceptor defects in mice

Abstract Previously, we reported the myelin regulatory factor (MYRF) as a candidate gene for nanophthalmos. We have also produced Myrf knockdown (Myrf+/−) mouse strain to investigate the cellular and molecular phenotypes of reduced MYRF expression in the retina. Myrf+/− mouse strain was generated using the CRISPR/Cas9 system. Optomotor response system, electroretinogram (ERG), spectral-domain optical coherence tomography (SD-OCT), histology, and immunohistochemistry were performed to evaluate retinal spatial vision, electrophysiological function, retinal thickness, and pathological changes in cone or rod photoreceptors, respectively. RNA sequencing (RNA-seq) was performed to investigate the underlying molecular mechanism linking Myrf deficiency with photoreceptor defects. The genotype and phenotype of CRISPR/Cas9-induced Myrf+/− mice and their offspring were comprehensively investigated. Photoreceptor defects were detected in the retinas of Myrf+/− mice. Visual acuity and ERG responses were decreased in Myrf+/− mice compared with the control mice (Myrf+/+). The loss of cone and rod neurons was proportional to the decreased outer nuclear layer (ONL) thickness. Moreover, RNA-seq revealed that phototransduction and estrogen signaling pathways played important roles in the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Myrf+/− mouse strain provides a good model to investigate the function of the MYRF gene. Photoreceptor defects with impaired functions of spatial vision and retinal electrophysiology indicate an important role played by MYRF in retinal development. Alterations in phototransduction and estrogen signaling pathways play important roles in linking Myrf deficiency with retinal photoreceptor defects.
Deindustrialization and the Demand for Protection

Abstract Current explanations of demand for anti-dumping protections focus on the role of the business cycle, and fluctuations in real exchange rates. However, empirical evidence supporting these explanations is based primarily on the experience of industrialized countries. Here, we examine anti-dumping petitions in a broader sample of thirty-four industrialized and middle income countries from 1978–2015. We also propose a new determinant of demand for anti-dumping petitions—changes in the pattern of industrial production between developed and developing economies over this period have contributed to deindustrialization in advanced economies and premature industrialization some developing countries. These changes threaten established industries and motivate them to demand protection.
Survival hydrodynamics

Abstract Fish escaping from predators exhibit amazing acceleration. The hydrodynamic mechanisms employed to power this acceleration are highlighted in the paper by Gazzola, van Rees & Koumoutsakos (J. Fluid Mech., this issue, vol. 698, 2012, pp. 5–17), showing that the fish bends its entire body and caudal fin, in order to entrap and then accelerate as large a mass of water as possible. It is also shown that hydrodynamic optimization drives the fast-start kinematics.
Accurate detection of RNA stem-loops in structurome data reveals widespread association with protein binding sites

RNA molecules are known to fold into specific structures which often play a central role in their functions and regulation. In silico folding of RNA transcripts, especially when assisted with structure profiling (SP) data, is capable of accurately elucidating relevant structural conformations. However, such methods scale poorly to the swaths of SP data generated by transcriptome-wide experiments, which are becoming more commonplace and advancing our understanding of RNA structure and its regulation at global and local levels. This has created a need for tools capable of rapidly deriving structural assessments from SP data in a scalable manner. One such tool we previously introduced that aims to process such data is patteRNA, a statistical learning algorithm capable of rapidly mining big SP datasets for structural elements. Here, we present a reformulation of patteRNA’s pattern recognition scheme that sees significantly improved precision without major compromises to computational overhead. Specifically, we developed a data-driven logistic classifier which interprets patteRNA’s statistical characterizations of SP data in addition to local sequence properties as measured with a nearest neighbor thermodynamic model. Application of the classifier to human structurome data reveals a marked association between detected stem-loops and RNA binding protein (RBP) footprints. The results of our application demonstrate that upwards of 30% of RBP footprints occur within loops of stable stem-loop elements. Overall, our work arrives at a rapid and accurate method for automatically detecting families of RNA structure motifs and demonstrates the functional relevance of identifying them transcriptome-wide.
Modeling intracranial electrodes

Background Intracranial electrodes are implanted in patients with drug-resistant epilepsy as part of their pre-surgical evaluation. This allows investigation of normal and pathological brain functions with excellent spatial and temporal resolution. The spatial resolution relies on methods that precisely localize the implanted electrodes in the cerebral cortex, which is critical for drawing valid anatomical inferences about brain function. Multiple methods have been developed to localize implanted electrodes, mainly relying on pre-implantation MRI and post-implantation CT images. However, there is no standard approach to quantify the performance of these methods systematically. The purpose of our work is to model intracranial electrodes to simulate realistic implantation scenarios, thereby providing methods to optimize localization algorithm performance. Results We implemented novel methods to model the coordinates of implanted grids, strips, and depth electrodes, as well as the CT artifacts produced by these. We successfully modeled a large number of realistic implantation “scenarios”, including different sizes, inter-electrode distances, and brain areas. In total, more than 3300 grids and strips were fitted over the brain surface, and more than 850 depth electrode arrays penetrating the cortical tissue were modeled. More than 37000 simulations of electrode array CT artifacts were performed in these “scenarios”, mimicking the intensity profile and orientation of real artifactual voxels. Realistic artifacts were simulated by introducing different noise levels, as well as overlapping electrodes. Conclusions We successfully developed the first platform to model implanted intracranial grids, strips, and depth electrodes and realistically simulate CT artifacts and noise. These methods set the basis for developing more complex models, while simulations allow the performance evaluation of electrode localization techniques systematically. The methods described in this article, and the results obtained from the simulations, are freely available via open repositories. A graphical user interface implementation is also accessible via the open-source iElectrodes toolbox.
Expression of a CO2-permeable aquaporin enhances mesophyll conductance in the C4 species Setaria viridis

A fundamental limitation of photosynthetic carbon fixation is the availability of CO4. In C4 plants, primary carboxylation occurs in mesophyll cytosol, and little is known about the role of CO2 diffusion in facilitating C4 photosynthesis. We have examined the expression, localization, and functional role of selected plasma membrane intrinsic aquaporins (PIPs) from Setaria italica (foxtail millet) and discovered that SiPIP2;7 is CO2-permeable. When ectopically expressed in mesophyll cells of S. viridis (green foxtail), SiPIP2;7 was localized to the plasma membrane and caused no marked changes in leaf biochemistry. Gas-exchange and C18O16O discrimination measurements revealed that targeted expression of SiPIP2;7 enhanced the conductance to CO2 diffusion from the intercellular airspace to the mesophyll cytosol. Our results demonstrate that mesophyll conductance limits C4 photosynthesis at low pCO2 and that SiPIP2;7 is a functional CO2 permeable aquaporin that can improve CO2 diffusion at the airspace/mesophyll interface and enhance C4 photosynthesis.
Development of a 3D atlas of the embryonic pancreas for topological and quantitative analysis of heterologous cell interactions

Generating comprehensive image maps, while preserving spatial 3D context, is essential to quantitatively assess and locate specific cellular features and cell-cell interactions during organ development. Despite the recent advances in 3D imaging approaches, our current knowledge of the spatial organization of distinct cell types in the embryonic pancreatic tissue is still largely based on 2D histological sections. Here, we present a light-sheet fluorescence microscopy approach to image the pancreas in 3D and map tissue interactions at key development time points in the mouse embryo. We used transgenic mouse models and antibodies to visualize the three main cellular components within the developing pancreas, including epithelial, mesenchymal and endothelial cell populations. We demonstrated the utility of the approach by providing volumetric data, 3D distribution of distinct progenitor populations and quantification of relative cellular abundance within the tissue. Lastly, our image data were combined in an open source online repository (referred to as Pancreas Embryonic Cell Atlas). This image dataset will serve the scientific community by enabling further investigation on pancreas organogenesis but also for devising strategies for the in vitro generation of transplantable pancreatic tissue for regenerative therapies.