Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Reconstruction of Space Objects from Multi-Views by a Visible Sensor

In this paper, a novel 3D reconstruction framework is proposed to recover the 3D structural model of a space object from its multi-view images captured by a visible sensor. Given an image sequence, this framework first estimates the relative camera poses and recovers the depths of the surface points by the structure from motion (SFM) method, then the patch-based multi-view stereo (PMVS) algorithm is utilized to generate a dense 3D point cloud. To resolve the wrong matches arising from the symmetric structure and repeated textures of space objects, a new strategy is introduced, in which images are added to SFM in imaging order. Meanwhile, a refining process exploiting the structural prior knowledge that most sub-components of artificial space objects are composed of basic geometric shapes is proposed and applied to the recovered point cloud. The proposed reconstruction framework is tested on both simulated image datasets and real image datasets. Experimental results illustrate that the recovered point cloud models of space objects are accurate and have a complete coverage of the surface. Moreover, outliers and points with severe noise are effectively filtered out by the refinement, resulting in an distinct improvement of the structure and visualization of the recovered points.
Adverse outcome pathway networks I: Development and applications

Based on the results of a Horizon Scanning exercise sponsored by the Society of Environmental Toxicology and Chemistry that focused on advancing the adverse outcome pathway (AOP) framework, the development of guidance related to AOP network development was identified as a critical need. This not only included questions focusing directly on AOP networks, but also on related topics such as mixture toxicity assessment and the implementation of feedback loops within the AOP framework. A set of two articles has been developed to begin exploring these concepts. In the present article (part I), we consider the derivation of AOP networks in the context of how it differs from the development of individual AOPs. We then propose the use of filters and layers to tailor AOP networks to suit the needs of a given research question or application. We briefly introduce a number of analytical approaches that may be used to characterize the structure of AOP networks. These analytical concepts are further described in a dedicated, complementary article (part II). Finally, we present a number of case studies that illustrate concepts underlying the development, analysis, and application of AOP networks. The concepts described in the present article and in its companion article (which focuses on AOP network analytics) are intended to serve as a starting point for further development of the AOP network concept, and also to catalyze AOP network development and application by the different stakeholder communities. Environ Toxicol Chem 2018;37:1723–1733. © 2018 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals, Inc. on behalf of SETAC.
Characterization of Hot Deformation Behavior of a New Near-β Titanium Alloy: Ti555211

Abstract The deformation behavior of an isothermally compressed Ti555211 titanium alloy was examined by an Arrhenius-type constitutive model using experimental data obtained from hot compression tests; these tests were performed at temperatures and strain rates of 750–950°C and 0.001–1 s−1, respectively. Activation energies of hot deformation were calculated in 0.05 intervals for strains ranging from 0.1 to 0.7. The respective values of were obtained for the (α+β) and β region. In addition, the predictive capability of the model was described by the average absolute relative error (AARE) and the correlation coefficient (R). The simulated values were compared with the experimental values, and R and AARE of 0.99084 and 6.914%, respectively, were obtained for the Arrhenius-type constitutive model. These values were indicative of the good predictive capabilities of the developed strain-compensated constitutive equation. Moreover, in this work isothermal compression tests, scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and transmission electron microscopy (TEM) were used to systematically investigate the high-temperature deformation behavior of Ti555211 alloy under different processing conditions. EBSD and TEM were used to reveal the substructure and grain orientation of samples of the hot-deformed Ti555211 alloy. The phenomenon of discontinuous yielding was also discussed.
Impact of Race and Ethnicity on Outcomes for Children Waitlisted for Pediatric Liver Transplantation

Objective: African Americans and other minorities are known to face barriers to health care influencing their access to organ transplantation but it is not known whether these barriers exist among pediatric liver transplant waitlist candidates. We sought to determine whether outcomes on the waitlist (ie, mortality, deceased donor liver transplantation [DDLT], and living-donor liver transplantation [LDLT]) varied by race/ethnicity. Methods: National registry data were studied to estimate the race/ethnicity-specific risk of waitlist mortality, DDLT and LDLT in children (<18 years) waitlisted between March 2002 and March 2015. Results: There was no evidence of racial/ethnic disparities in waitlist mortality. Compared to Caucasians, LDLT varied by race/ethnicity, with only 6.7% African Americans and 10.3% Hispanic children receiving LDLT compared with 12.4% Caucasian, 13.3% Asian, and 9.4% mix/other children. In an adjusted Cox proportional hazards model, African Americans were half as likely as Caucasians to use LDLT (hazard ratio [HR]: 0.410.550.73) but had similar use of DDLT (HR: 0.981.061.16). In a model that considered mortality, DDLT, and LDLT as competing risks, African Americans had significantly reduced incidence of LDLT (subhazard ratio [sHR]: 0.410.560.75) compared to Caucasians, but increased use of DDLT (sHR: 1.061.161.26). Conclusions: Compared to Caucasian children, African-American children are less likely to use LDLT but have higher rates of DDLT and similar survival on the waitlist. Additional research is necessary to understand the clinical and socioeconomic factors contributing to lower utilization of LDLT among African-American children awaiting transplantation.
Schools, Families, and the Prevention of Child Maltreatment: Lessons That Can Be Learned From a Literature Review

Child maltreatment is a worldwide social problem that receives considerable attention. However, prevention efforts remain rare, allowing the phenomenon to continue and spread. The aim of the current article is to systematically review evidence-based prevention efforts that address schools and families as key stakeholders for preventing child maltreatment. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a thorough literature review revealed that only five programs matched the inclusion criteria for the current article. These programs were analyzed for several domains, including level of prevention, target population, participants, and the programs’ outcomes. The current review highlights the urgent needs to develop, modify, and further evaluate prevention programs for child maltreatment in the context of the ecological model. More specifically, it illuminates the need to create and champion programs that enhance the collaboration between families and schools, both of which are key stakeholders within the phenomenon of child maltreatment. Collaboration between policymakers, researchers, and practitioners should guide future efforts by promoting cultural adaptation to such programs and by integrating children’s perceptions to improve these efforts and to benefit everyone involved.
Strong Stability of Neutral Equations with an Arbitrary Delay Dependency Structure

The stability theory for linear neutral equations subjected to delay perturbations is addressed. It is assumed that the delays cannot necessarily vary independently of each other, but depend on a possibly smaller number of independent parameters. As a main result, necessary and sufficient conditions for strong stability are derived along with bounds on the spectrum, which take into account the precise dependency structure of the delays. In the derivation of the stability theory, results from realization theory and determinantal representations of multivariable polynomials play an important role. The observations and results obtained in the paper are first illustrated and validated with a numerical example. Next, the effects of small feedback delays on the stability of a boundary controlled hyperbolic partial differential equation and of a control system involving state derivative feedback are analyzed.
Toward Unconstrained Fingerprint Recognition: A Fully Touchless 3-D System Based on Two Views on the Move

Touchless fingerprint recognition systems do not require contact of the finger with any acquisition surface and thus provide an increased level of hygiene, usability, and user acceptability of fingerprint-based biometric technologies. The most accurate touchless approaches compute 3-D models of the fingertip. However, a relevant drawback of these systems is that they usually require constrained and highly cooperative acquisition methods. We present a novel, fully touchless fingerprint recognition system based on the computation of 3-D models. It adopts an innovative and less-constrained acquisition setup compared with other previously reported 3-D systems, does not require contact with any surface or a finger placement guide, and simultaneously captures multiple images while the finger is moving. To compensate for possible differences in finger placement, we propose novel algorithms for computing 3-D models of the shape of a finger. Moreover, we present a new matching strategy based on the computation of multiple touch-compatible images. We evaluated different aspects of the biometric system: acceptability, usability, recognition performance, robustness to environmental conditions and finger misplacements, and compatibility and interoperability with touch-based technologies. The proposed system proved to be more acceptable and usable than touch-based techniques. Moreover, the system displayed satisfactory accuracy, achieving an equal error rate of 0.06% on a dataset of 2368 samples acquired in a single session and 0.22% on a dataset of 2368 samples acquired over the course of one year. The system was also robust to environmental conditions and to a wide range of finger rotations. The compatibility and interoperability with touch-based technologies was greater or comparable to those reported in public tests using commercial touchless devices.
Dissociable signatures of visual salience and behavioral relevance across attentional priority maps in human cortex

Computational models posit that visual attention is guided by activity within spatial maps that index the image-computable salience and the behavioral relevance of objects in the scene. However, the simultaneous influence of these factors on putative neural ‘attentional priority maps’ in human cortex is not well understood. We tested the hypothesis that visual salience and behavioral relevance independently impact the activation profile across retinotopically-organized cortical regions by quantifying attentional priority maps measured in human brains using functional MRI while participants attended one of two differentially-salient stimuli. We find that the topography of activation in priority maps, as reflected in the modulation of region-level patterns of population activity, independently indexed the physical salience and behavioral relevance of each scene element. Moreover, salience strongly impacted activation patterns in early visual areas, whereas later visual areas were dominated by relevance. This suggests that prioritizing spatial locations relies on distributed neural codes containing graded representations of salience and relevance across the visual hierarchy. Significance Statement Often, it is necessary to orient towards bright, unique, or sudden events in the environment – that is, salient stimuli. However, we can focus processing resources on less salient visual information if it is relevant to the task at hand. We tested a theory which supposes that we represent different scene elements according to both their salience and their relevance in a series of ‘priority maps’ by measuring fMRI activation patterns across the human brain and reconstructing spatial maps of the visual scene under different task conditions. We found that different regions indexed either the salience or the relevance of scene items, but not their interaction, suggesting an evolving representation of salience and relevance across different visual areas.
Predicting outcome of Morris water maze test in vascular dementia mouse model with deep learning

The Morris water maze test (MWM) is one of the most popular and established behavioral tests to evaluate rodents’ spatial learning ability. The conventional training period is around 5 days, but there is no clear evidence or guidelines about the appropriate duration. In many cases, the final outcome of the MWM seems predicable from previous data and their trend. So, we assumed that if we can predict the final result with high accuracy, the experimental period could be shortened and the burden on testers reduced. An artificial neural network (ANN) is a useful modeling method for datasets that enables us to obtain an accurate mathematical model. Therefore, we constructed an ANN system to estimate the final outcome in MWM from the previously obtained 4 days of data in both normal mice and vascular dementia model mice. Ten-week-old male C57B1/6 mice (wild type, WT) were subjected to bilateral common carotid artery stenosis (WT-BCAS) or sham-operation (WT-sham). At 6 weeks after surgery, we evaluated their cognitive function with MWM. Mean escape latency was significantly longer in WT-BCAS than in WT-sham. All data were collected and used as training data and test data for the ANN system. We defined a multiple layer perceptron (MLP) as a prediction model using an open source framework for deep learning, Chainer. After a certain number of updates, we compared the predicted values and actual measured values with test data. A significant correlation coefficient was derived form the updated ANN model in both WT-sham and WT-BCAS. Next, we analyzed the predictive capability of human testers with the same datasets. There was no significant difference in the prediction accuracy between human testers and ANN models in both WT-sham and WT-BCAS. In conclusion, deep learning method with ANN could predict the final outcome in MWM from 4 days of data with high predictive accuracy in a vascular dementia model.
Neisseria meningitidis ST11 Complex Isolates Associated with Nongonococcal Urethritis, Indiana, USA, 2015–2016

At a clinic in Indianapolis, Indiana, USA, we observed an increase in Neisseria gonorrhoeae–negative men with suspected gonococcal urethritis who had urethral cultures positive for N. meningitidis. We describe genomes of 2 of these N. meningitidis sequence type 11 complex urethritis isolates. Clinical evidence suggests these isolates may represent an emerging urethrotropic clade.
Outer Membrane Proteins Ail and OmpF of Yersinia pestis Are Involved in the Adsorption of T7-Related Bacteriophage Yep-phi

ABSTRACT Yep-phi is a T7-related bacteriophage specific to Yersinia pestis, and it is routinely used in the identification of Y. pestis in China. Yep-phi infects Y. pestis grown at both 20°C and 37°C. It is inactive in other Yersinia species irrespective of the growth temperature. Based on phage adsorption, phage plaque formation, affinity chromatography, and Western blot assays, the outer membrane proteins of Y. pestis Ail and OmpF were identified to be involved, in addition to the rough lipopolysaccharide, in the adsorption of Yep-phi. The phage tail fiber protein specifically interacts with Ail and OmpF proteins, and residues 518N, 519N, and 523S of the phage tail fiber protein are essential for the interaction with OmpF, whereas residues 518N, 519N, 522C, and 523S are essential for the interaction with Ail. This is the first report to demonstrate that membrane-bound proteins are involved in the adsorption of a T7-related bacteriophage. The observations highlight the importance of the tail fiber protein in the evolution and function of various complex phage systems and provide insights into phage-bacterium interactions.
Forest bat population dynamics over 14 years at a climate refuge: Effects of timber harvesting and weather extremes

Long-term data are needed to explore the interaction of weather extremes with habitat alteration; in particular, can ‘refugia’ buffer population dynamics against climate change and are they robust to disturbances such as timber harvesting. Because forest bats are good indicators of ecosystem health, we used 14 years (1999–2012) of mark-recapture data from a suite of small tree-hollow roosting bats to estimate survival, abundance and body condition in harvested and unharvested forest and over extreme El Niño and La Niña weather events in southeastern Australia. Trapping was replicated within an experimental forest, located in a climate refuge, with different timber harvesting treatments. We trapped foraging bats and banded 3043 with a 32% retrap rate. Mark-recapture analyses allowed for dependence of survival on time, species, sex, logging treatment and for transients. A large portion of the population remained resident, with a maximum time to recapture of nine years. The effect of logging history (unlogged vs 16–30 years post-logging regrowth) on apparent survival was minor and species specific, with no detectable effect for two species, a positive effect for one and negative for the other. There was no effect of logging history on abundance or body condition for any of these species. Apparent survival of residents was not strongly influenced by weather variation (except for the smallest species), unlike previous studies outside of refugia. Despite annual variation in abundance and body condition across the 14 years of the study, no relationship with extreme weather was evident. The location of our study area in a climate refuge potentially buffered bat population dynamics from extreme weather. These results support the value of climate refugia in mitigating climate change impacts, though the lack of an external control highlights the need for further studies on the functioning of climate refugia. Relatively stable population dynamics were not compromised by timber harvesting, suggesting ecologically sustainable harvesting may be compatible with climate refugia.
Neural Mechanisms of Updating under Reducible and Irreducible Uncertainty

Adaptive decision making depends on an agent's ability to use environmental signals to reduce uncertainty. However, because of multiple types of uncertainty, agents must take into account not only the extent to which signals violate prior expectations but also whether uncertainty can be reduced in the first place. Here we studied how human brains of both sexes respond to signals under conditions of reducible and irreducible uncertainty. We show behaviorally that subjects' value updating was sensitive to the reducibility of uncertainty, and could be quantitatively characterized by a Bayesian model where agents ignore expectancy violations that do not update beliefs or values. Using fMRI, we found that neural processes underlying belief and value updating were separable from responses to expectancy violation, and that reducibility of uncertainty in value modulated connections from belief-updating regions to value-updating regions. Together, these results provide insights into how agents use knowledge about uncertainty to make better decisions while ignoring mere expectancy violation. SIGNIFICANCE STATEMENT To make good decisions, a person must observe the environment carefully, and use these observations to reduce uncertainty about consequences of actions. Importantly, uncertainty should not be reduced purely based on how surprising the observations are, particularly because in some cases uncertainty is not reducible. Here we show that the human brain indeed reduces uncertainty adaptively by taking into account the nature of uncertainty and ignoring mere surprise. Behaviorally, we show that human subjects reduce uncertainty in a quasioptimal Bayesian manner. Using fMRI, we characterize brain regions that may be involved in uncertainty reduction, as well as the network they constitute, and dissociate them from brain regions that respond to mere surprise.
CIDExtractor: A chemical-induced disease relation extraction system for biomedical literature

Adverse drug reactions between chemicals and diseases make chemical-disease relations (CDR) become a research focus. In this paper, we present a chemical-induced disease (CID) relation extraction system, CIDExtractor, to extract CID relations from biomedical literature. CIDExtractor first employs a sentence-level classifier to extract the CID relations located in the same sentence. To construct the classifier, a sentence-level training set is manually annotated and then Co-Training algorithm is used to exploit the unlabeled data with the feature kernel and graph kernel as two independent views. Then CIDExtractor uses a document-level classifier to extract the CID relations spanning multiple sentences. The classifier utilizes the document level information (features) of the chemical and disease pair. Finally, some post-processing rules are applied to the union set of two classifiers and generate the final outputs. Experimental results on the test set of BioCreative V CDR CID subtask show that CIDExtractor can achieve better performance (an F-score of 67.72%) than the state-of-the-art methods. The online CIDExtractor demonstration system is available at http://202.118. 75.18:8888/cdr-dut-ir/cid.html.
Boosting Docking-Based Virtual Screening with Deep Learning

In this work, we propose a deep learning approach to improve docking-based virtual screening. The deep neural network that is introduced, DeepVS, uses the output of a docking program and learns how to extract relevant features from basic data such as atom and residues types obtained from protein-ligand complexes. Our approach introduces the use of atom and amino acid embeddings and implements an effective way of creating distributed vector representations of protein-ligand complexes by modeling the compound as a set of atom contexts that is further processed by a convolutional layer. One of the main advantages of the proposed method is that it does not require feature engineering. We evaluate DeepVS on the Directory of Useful Decoys (DUD), using the output of two docking programs: Autodock Vina1.1. 2 and Dock 6.6. Using a strict evaluation with leave-one-out cross-validation, DeepVS outperforms the docking programs, with regard to both AUC ROC and enrichment factor. Moreover, using the output of Autodock Vina1.1. 2, DeepVS achieves an AUC ROC of 0.81, which, to the best of our knowledge, is the best AUC reported so far for virtual screening using the 40 receptors from the DUD.