A computational study of fluid transport characteristics in the brain parenchyma of dementia subtypes

The cerebral environment is a complex system consisting of parenchymal tissue and multiple fluids. Dementia is a common class of neurodegenerative diseases, caused by structural damages and functional deficits in the cerebral environment. In order to better understand the pathology of dementia from a cerebral fluid transport angle and provide clearer evidence that could help differentiate between dementia subtypes, such as Alzheimer's disease and vascular dementia, we conducted fluid–structure interaction modelling of the brain using a multiple-network poroelasticity model, which considers both neuropathological and cerebrovascular factors. The parenchyma was further subdivided and labelled into parcellations to obtain more localised and detailed data. The numerical results were converted to computed functional images by an in-house workflow. Different cerebral blood flow (CBF) and cerebrospinal fluid (CSF) clearance abnormalities were identified in the modelling results, when comparing Alzheimer's disease and vascular dementia. This paper presents our preliminary results as a proof of concept for a novel clinical diagnostic tool, and paves the way for a larger clinical study

An iterated greedy heuristic for no-wait flow shops with sequence dependent setup times, learning and forgetting effects

[EN] This paper addresses a sequence dependent setup times no-wait flowshop with learning and forgetting effects to minimize total flowtime. This problem is NP-hard and has never been considered before. A position-based learning and forgetting effects model is constructed. Processing times of operations change with the positions of corresponding jobs in a schedule. Objective increment properties are deduced and based on them three accelerated neighbourhood construction heuristics are presented. Because of the simplicity and excellent performance shown in flowshop scheduling problems, an iterated greedy heuristic is proposed. The proposed iterated greedy algorithm is compared with some existing algorithms for related problems on benchmark instances. Comprehensive computational and statistical tests show that the presented method obtains the best performance among the compared methods. (C) 2018 Elsevier Inc. All rights reserved.This work is supported by the National Natural Science Foundation of China (Nos. 61572127, 61272377), the Collaborative Innovation Center of Wireless Communications Technology and the Key Natural Science Fund for Colleges and Universities in Jiangsu Province (No. 12KJA630001). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness(MINECO), under the project "SCHEYARD - Optimization of Scheduling Problems in Container Yards" with reference DPI2015-65895-R.Li, X.; Yang, Z.; Ruiz García, R.; Chen, T.; Sui, S. (2018). An iterated greedy heuristic for no-wait flow shops with sequence dependent setup times, learning and forgetting effects. Information Sciences. 453:408-425. https://doi.org/10.1016/j.ins.2018.04.038S40842545

An anterior-posterior axis within the ventromedial prefrontal cortex separates self and reward.

Although theoretical discourse and experimental studies on the self- and reward-biases have a long tradition, currently we have only a limited understanding of how the biases are represented in the brain and, more importantly, how they relate to each other. We used multi-voxel pattern analysis to test for common representations of self and reward in perceptual matching in healthy human subjects. Voxels across an anterior-posterior axis in ventromedial prefrontal cortex (vmPFC) distinguished (i) self-others and (ii) high-low reward, but cross-generalization between these dimensions decreased from anterior to posterior vmPFC. The vmPFC is characterized by a shift from a common currency for value to independent, distributed representations of self and reward across an anterior-posterior axis. This shift reflected changes in functional connectivity between the posterior part of the vmPFC and the frontal pole when processing self-associated stimuli, and the middle frontal gyrus when processing stimuli associated with high reward. The changes in functional connectivity were correlated with behavioral biases, respectively, to the self and reward. The distinct representations of self and reward in the posterior vmPFC are associated with self- and reward-biases in behavior

Spatiotemporal double-phase hologram for complex-amplitude holographic displays

This Letter describes an approach to encode complex-amplitude light waves with spatiotemporal double-phase holograms (DPHs) for overcoming the limit of the space-bandwidth product (SBP) delivered by existing methods. To construct DPHs, two spatially macro-pixel encoded phase components are employed in the SBP-preserved resampling of complex holograms. Four generated sub-DPHs are displayed sequentially in time for high-quality holographic image reconstruction without reducing the image size or discarding any image terms when the DPHs are interweaved. The reconstructed holographic images contain more details and less speckle noise, with their signal-to-noise ratio and structure similarity index being improved by 14.64% and 78.79%, respectively

Failure Assessment for the High-Strength Pipelines with Constant-Depth Circumferential Surface Cracks

In the oil and gas transportation system over long distance, application of high-strength pipeline steels can efficiently reduce construction and operation cost by increasing operational pressure and reducing the pipe wall thickness. Failure assessment is an important issue in the design, construction, and maintenance of the pipelines. The small circumferential surface cracks with constant depth in the welded pipelines are of practical interest. This work provides an engineering estimation procedure based upon the GE/EPRI method to determine the J-integral for the thin-walled pipelines with small constant-depth circumferential surface cracks subject to tension and bending loads. The values of elastic influence functions for stress intensity factor and plastic influence functions for fully plastic J-integral estimation are derived in tabulated forms through a series of three-dimensional finite element calculations for different crack geometries and material properties. To check confidence of the J-estimation solution in practical application, J-integral values obtained from detailed finite element (FE) analyses are compared with those estimated from the new influence functions. Excellent agreement of FE results with the proposed J-estimation solutions for both tension and bending loads indicates that the new solutions can be applied for accurate structural integrity assessment of high-strength pipelines with constant-depth circumferential surface cracks

Auto-tracking system for human lumbar motion analysis

Previous lumbar motion analyses suggest the usefulness of quantitatively characterizing spine motion. However, the application of such measurements is still limited by the lack of user-friendly automatic spine motion analysis systems. This paper describes an automatic analysis system to measure lumbar spine disorders that consists of a spine motion guidance device, an X-ray imaging modality to acquire digitized video fluoroscopy (DVF) sequences and an automated tracking module with a graphical user interface (GUI). DVF sequences of the lumbar spine are recorded during flexion-extension under a guidance device. The automatic tracking software utilizing a particle filter locates the vertebra-of-interest in every frame of the sequence, and the tracking result is displayed on the GUI. Kinematic parameters are also extracted from the tracking results for motion analysis. We observed that, in a bone model test, the maximum fiducial error was 3.7%, and the maximum repeatability error in translation and rotation was 1.2% and 2.6%, respectively. In our simulated DVF sequence study, the automatic tracking was not successful when the noise intensity was greater than 0.50. In a noisy situation, the maximal difference was 1.3 mm in translation and 1° in the rotation angle. The errors were calculated in translation (fiducial error: 2.4%, repeatability error: 0.5%) and in the rotation angle (fiducial error: 1.0%, repeatability error: 0.7%). However, the automatic tracking software could successfully track simulated sequences contaminated by noise at a density ≤ 0.5 with very high accuracy, providing good reliability and robustness. A clinical trial with 10 healthy subjects and 2 lumbar spondylolisthesis patients were enrolled in this study. The measurement with auto-tacking of DVF provided some information not seen in the conventional X-ray. The results proposed the potential use of the proposed system for clinical applications. © 2011 - IOS Press and the authors. All rights reserved.postprin

Familial Clustering For Weakly-labeled Android Malware Using Hybrid Representation Learning

IEEE Labeling malware or malware clustering is important for identifying new security threats, triaging and building reference datasets. The state-of-the-art Android malware clustering approaches rely heavily on the raw labels from commercial AntiVirus (AV) vendors, which causes misclustering for a substantial number of weakly-labeled malware due to the inconsistent, incomplete and overly generic labels reported by these closed-source AV engines, whose capabilities vary greatly and whose internal mechanisms are opaque (i.e., intermediate detection results are unavailable for clustering). The raw labels are thus often used as the only important source of information for clustering. To address the limitations of the existing approaches, this paper presents ANDRE, a new ANDroid Hybrid REpresentation Learning approach to clustering weakly-labeled Android malware by preserving heterogeneous information from multiple sources (including the results of static code analysis, the metainformation of an app, and the raw-labels of the AV vendors) to jointly learn a hybrid representation for accurate clustering. The learned representation is then fed into our outlieraware clustering to partition the weakly-labeled malware into known and unknown families. The malware whose malicious behaviours are close to those of the existing families on the network, are further classified using a three-layer Deep Neural Network (DNN). The unknown malware are clustered using a standard density-based clustering algorithm. We have evaluated our approach using 5,416 ground-truth malware from Drebin and 9,000 malware from VIRUSSHARE (uploaded between Mar. 2017 and Feb. 2018), consisting of 3324 weakly-labeled malware. The evaluation shows that ANDRE effectively clusters weaklylabeled malware which cannot be clustered by the state-of-theart approaches, while achieving comparable accuracy with those approaches for clustering ground-truth samples

A modified protocol for the detection of three different mRNAs with a new-generation in situ hybridization chain reaction on frozen sections

A new multiple fluorescence in situ hybridization method based on hybridization chain reaction was recently reported, enabling simultaneous mapping of multiple target mRNAs within intact zebrafish and mouse embryos. With this approach, DNA probes complementary to target mRNAs trigger chain reactions in which metastable fluorophore-labeled DNA hairpins self-assemble into fluorescent amplification polymers. The formation of the specific polymers enhances greatly the sensitivity of multiple fluorescence in situ hybridization. In this study we describe the optimal parameters (hybridization chain reaction time and temperature, hairpin and salt concentration) for multiple fluorescence in situ hybridization via amplification of hybridization chain reaction for frozen tissue sections. The combined use of fluorescence in situ hybridization and immunofluorescence, together with other control experiments (sense probe, neutralization and competition, RNase treatment, and anti-sense probe without initiator) confirmed the high specificity of the fluorescence in situ hybridization used in this study. Two sets of three different mRNAs for oxytocin, vasopressin and somatostatin or oxytocin, vasopressin and thyrotropin releasing hormone were successfully visualized via this new method. We believe that this modified protocol for multiple fluorescence in situ hybridization via hybridization chain reaction would allow researchers to visualize multiple target nucleic acids in the future

Language-Led Visual Grounding and Future Possibilities

In recent years, with the rapid development of computer vision technology and the popularity of intelligent hardware, as well as the increasing demand for human–machine interaction in intelligent products, visual localization technology can help machines and humans to recognize and locate objects, thereby promoting human–machine interaction and intelligent manufacturing. At the same time, human–machine interaction is constantly evolving and improving, becoming increasingly intelligent, humanized, and efficient. In this article, a new visual localization model is proposed, and a language validation module is designed to use language information as the main information to increase the model’s interactivity. In addition, we also list the future possibilities of visual localization and provide two examples to explore the application and optimization direction of visual localization and human–machine interaction technology in practical scenarios, providing reference and guidance for relevant researchers and promoting the development and application of visual localization and human–machine interaction technology