Differential disease restriction of Moloney and Friend murine leukemia viruses by the mouse Rmcf gene is governed by the viral long terminal repeat.

Neonatal CxD2 (Rmcfr) and Balb/c (Rmcfs) mice inoculated with Moloney murine leukemia virus (M-MuLV) exhibited approximately equivalent time course and pathology for disease. CxD2 mice showed only slightly reduced presence of Moloney mink cell focus-forming virus (M-MCF) provirus as seen by Southern blot analysis compared to Balb/c mice. This lack of restriction for disease and spread of MCF was in sharp contrast to that seen for CxD2 mice inoculated with Friend murine leukemia virus (F-MuLV), where incidence of disease and propagation of MCFs were severely restricted, as previously reported. Inoculation of CxD2 mice with FM-MuLV, a recombinant F-MuLV virus containing M-MuLV LTR sequences (U3 and R), resulted in T cell disease of time course equal to that seen in Balb/c mice; there also was little restriction for propagation of MCFs. This indicated that presence of the M-MuLV long terminal repeat (LTR) was sufficient for propagation of MCFs in CxD2 mice. Differing restriction for F-MuLV vs. M-MuLV in CxD2 mice was explained on the basis of different "MCF propagator cells" for the two viruses. It was suggested that cells propagating F-MCF (e.g., erythroid progenitors) are blocked by endogenous MCF-like gp70env protein, whereas cells propagating M-MCF (e.g., lymphoid) do not express this protein on their surface. F-MuLV disease in CxD2 mice was greatly accelerated when neonates were inoculated with a F-MuLV/F-MCF pseudotypic mixture. However, F-MCF provirus was not detectable or only barely detectable in F-MuLV/F-MCF-induced tumors, suggesting that F-MCF acted indirectly in induction of these tumors

Delay Aware Intelligent Transient Stability Assessment System

Transient stability assessment is a critical tool for power system design and operation. With the emerging advanced synchrophasor measurement techniques, machine learning methods are playing an increasingly important role in power system stability assessment. However, most existing research makes a strong assumption that the measurement data transmission delay is negligible. In this paper, we focus on investigating the influence of communication delay on synchrophasor-based transient stability assessment. In particular, we develop a delay aware intelligent system to address this issue. By utilizing an ensemble of multiple long short-term memory networks, the proposed system can make early assessments to achieve a much shorter response time by utilizing incomplete system variable measurements. Compared with existing work, our system is able to make accurate assessments with a significantly improved efficiency. We perform numerous case studies to demonstrate the superiority of the proposed intelligent system, in which accurate assessments can be developed with time one third less than state-of-the-art methodologies. Moreover, the simulations indicate that noise in the measurements has trivial impact on the assessment performance, demonstrating the robustness of the proposed system.published_or_final_versio

Cooperative output regulation of multi-agent systems coupled by dynamic edges

This paper investigates a new class of linear multi-agent systems, in which nodes are coupled by dynamic edges in the sense that each edge has a dynamic system attached. The outputs of the edge dynamic systems combine to form the external input of the node dynamic system, which is called neighboring input; while the outputs of the node dynamic systems are inputs of the edge dynamic systems that therefore cannot be directly controlled. Distributed controllers for nodes are presented to realize output synchronization and output cooperation. Output cooperation makes the outputs of nodes realize some cooperation that here is specified as making the neighboring input track a predefined trajectory. The controllers depending on local state and neighboring inputs are designed by combining the feedback passivity theory and the internal model principle. A simulation example on the cooperated current control of an electrical network illustrates the potential applications of the analytic results.preprin

Decitabine impact on the endocytosis regulator RhoA, the folate carriers RFC1 and FOLR1, and the glucose transporter GLUT4 in human tumors.

BackgroundIn 31 solid tumor patients treated with the demethylating agent decitabine, we performed tumor biopsies before and after the first cycle of decitabine and used immunohistochemistry (IHC) to assess whether decitabine increased expression of various membrane transporters. Resistance to chemotherapy may arise due to promoter methylation/downregulation of expression of transporters required for drug uptake, and decitabine can reverse resistance in vitro. The endocytosis regulator RhoA, the folate carriers FOLR1 and RFC1, and the glucose transporter GLUT4 were assessed.ResultsPre-decitabine RhoA was higher in patients who had received their last therapy >3 months previously than in patients with more recent prior therapy (P = 0.02), and varied inversely with global DNA methylation as assessed by LINE1 methylation (r = -0.58, P = 0.006). Tumor RhoA scores increased with decitabine (P = 0.03), and RFC1 also increased in patients with pre-decitabine scores ≤150 (P = 0.004). Change in LINE1 methylation with decitabine did not correlate significantly with change in IHC scores for any transporter assessed. We also assessed methylation of the RFC1 gene (alias SLC19A1). SLC19A1 methylation correlated with tumor LINE1 methylation (r = 0.45, P = 0.02). There was a small (statistically insignificant) decrease in SLC19A1 methylation with decitabine, and there was a trend towards change in SLC19A1 methylation with decitabine correlating with change in LINE1 methylation (r = 0.47, P <0.15). While SLC19A1 methylation did not correlate with RFC1 scores, there was a trend towards an inverse correlation between change in SLC19A1 methylation and change in RFC1 expression (r = -0.45, P = 0.19).ConclusionsIn conclusion, after decitabine administration, there was increased expression of some (but not other) transporters that may play a role in chemotherapy uptake. Larger patient numbers will be needed to define the extent to which this increased expression is associated with changes in DNA methylation

Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking

Discriminative Correlation Filters (DCF) have demonstrated excellent performance for visual object tracking. The key to their success is the ability to efficiently exploit available negative data by including all shifted versions of a training sample. However, the underlying DCF formulation is restricted to single-resolution feature maps, significantly limiting its potential. In this paper, we go beyond the conventional DCF framework and introduce a novel formulation for training continuous convolution filters. We employ an implicit interpolation model to pose the learning problem in the continuous spatial domain. Our proposed formulation enables efficient integration of multi-resolution deep feature maps, leading to superior results on three object tracking benchmarks: OTB-2015 (+5.1% in mean OP), Temple-Color (+4.6% in mean OP), and VOT2015 (20% relative reduction in failure rate). Additionally, our approach is capable of sub-pixel localization, crucial for the task of accurate feature point tracking. We also demonstrate the effectiveness of our learning formulation in extensive feature point tracking experiments. Code and supplementary material are available at http://www.cvl.isy.liu.se/research/objrec/visualtracking/conttrack/index.html.Comment: Accepted at ECCV 201

A New Methodology for Generalizing Unweighted Network Measures

Several important complex network measures that helped discovering common patterns across real-world networks ignore edge weights, an important information in real-world networks. We propose a new methodology for generalizing measures of unweighted networks through a generalization of the cardinality concept of a set of weights. The key observation here is that many measures of unweighted networks use the cardinality (the size) of some subset of edges in their computation. For example, the node degree is the number of edges incident to a node. We define the effective cardinality, a new metric that quantifies how many edges are effectively being used, assuming that an edge's weight reflects the amount of interaction across that edge. We prove that a generalized measure, using our method, reduces to the original unweighted measure if there is no disparity between weights, which ensures that the laws that govern the original unweighted measure will also govern the generalized measure when the weights are equal. We also prove that our generalization ensures a partial ordering (among sets of weighted edges) that is consistent with the original unweighted measure, unlike previously developed generalizations. We illustrate the applicability of our method by generalizing four unweighted network measures. As a case study, we analyze four real-world weighted networks using our generalized degree and clustering coefficient. The analysis shows that the generalized degree distribution is consistent with the power-law hypothesis but with steeper decline and that there is a common pattern governing the ratio between the generalized degree and the traditional degree. The analysis also shows that nodes with more uniform weights tend to cluster with nodes that also have more uniform weights among themselves.Comment: 23 pages, 10 figure

Causes of death in the decade after hospitalisation for injury during adolescence: a study using linked hospital admissions and death registrations data

OBJECTIVES: To quantify risks of cause-specific death up to ten years after discharge from an emergency admission to hospital for violent, self-inflicted, or drug/alcohol-related injury, during adolescence. To compare these risks by type of original injury, and with risks after accident-related injury. APPROACH: We used admissions data for England linked to death registrations from 1997 to 2012. We identified emergency admissions for injury in 10-19y olds and categorised type of injury as either violent, self-inflicted, drug/alcohol-related, or accident-related (no record of violent, self-inflicted, or drug/alcohol-related injury, but record of an accident), using ICD-10 codes in admission records. We categorised causes of death as homicide, suicide, drug/alcohol-related, accidental (excluding drug/alcohol-related accidents), or ‘other’ (remaining causes), using ICD-9 and ICD-10 codes from death registration records. We estimated cumulative risks of cause-specific death in the ten years after discharge, by sex and type of original injury. We used time-to-event regression models to estimate risks of cause-specific death, after violent, self-inflicted or drug/alcohol-related injury (relative to those after accident-related injury), adjusted for age-group (10-15, 16-17, 18-19y) and chronic condition status (yes/no; indicated by ICD-10 codes in past year admission records), and stratified by sex. RESULTS: There were 333,009 adolescents admitted for violent, self-inflicted, or drug/alcohol-related injury (girls 181,926, boys 181,053), and 649,818 for accident-related injury (girls 166,462, boys 483,356). There were 4,782 deaths in the ten years after discharge: 2,415 after violent, self-inflicted or drug/alcohol-related injury (girls 873, boys 1,542) and 2,367 after accident-related injury (girls 439, boys 1,928). Deaths after violent, self-inflicted or drug/alcohol-related injury injury were mostly accounted for by suicide (girls 35.8% of all deaths, boys 34.2%) or drug/alcohol-related death (girls 31.7%, boys 35.6%). Risks of suicide were similar to those for drug/alcohol-related death, regardless of the type of original injury. Adjusted risks of death were 1.4 to 6.8 times greater than after accident-related injury (by cause and sex). CONCLUSION: Adolescent girls and boys discharged after violent, self-inflicted, or drug/alcohol-related injury had similar risks of suicide and drug/alcohol related death, regardless of the category of the original injury. These adolescents also had increased risks of cause-specific deaths compared to those discharged after accident-related injury. Current practice to assess and reduce risks of future harm after self-inflicted injury should be extended to adolescents discharged after violent or drug/alcohol-related injury. Preventive strategies should address risks of drug/alcohol-related death as well as risks of suicide

Attention model of binocular rivalry

This is the final version of the article. Available from National Academy of Sciences from the DOI in this record.When the corresponding retinal locations in the two eyes are presented with incompatible images, a stable percept gives way to perceptual alternations in which the two images compete for perceptual dominance. As perceptual experience evolves dynamically under constant external inputs, binocular rivalry has been used for studying intrinsic cortical computations and for understanding how the brain regulates competing inputs. Converging behavioral and EEG results have shown that binocular rivalry and attention are intertwined: binocular rivalry ceases when attention is diverted away from the rivalry stimuli. In addition, the competing image in one eye suppresses the target in the other eye through a pattern of gain changes similar to those induced by attention. These results require a revision of the current computational theories of binocular rivalry, in which the role of attention is ignored. Here, we provide a computational model of binocular rivalry. In the model, competition between two images in rivalry is driven by both attentional modulation and mutual inhibition, which have distinct selectivity (feature vs. eye of origin) and dynamics (relatively slow vs. relatively fast). The proposed model explains a wide range of phenomena reported in rivalry, including the three hallmarks: (i) binocular rivalry requires attention; (ii) various perceptual states emerge when the two images are swapped between the eyes multiple times per second; (iii) the dominance duration as a function of input strength follows Levelt’s propositions. With a bifurcation analysis, we identified the parameter space in which the model’s behavior was consistent with experimental results.This work was supported by NIH National Eye Institute Grants R01-EY019693 (to M.C. and D.J.H.) and R01-EY025673 (to D.J.H.). H.-H.L. was supported by NIH Grant R90DA043849. J. Rankin was supported by the Swartz Foundation

Vertex importance extension of betweenness centrality algorithm

Variety of real-life structures can be simplified by a graph. Such simplification emphasizes the structure represented by vertices connected via edges. A common method for the analysis of the vertices importance in a network is betweenness centrality. The centrality is computed using the information about the shortest paths that exist in a graph. This approach puts the importance on the edges that connect the vertices. However, not all vertices are equal. Some of them might be more important than others or have more significant influence on the behavior of the network. Therefore, we introduce the modification of the betweenness centrality algorithm that takes into account the vertex importance. This approach allows the further refinement of the betweenness centrality score to fulfill the needs of the network better. We show this idea on an example of the real traffic network. We test the performance of the algorithm on the traffic network data from the city of Bratislava, Slovakia to prove that the inclusion of the modification does not hinder the original algorithm much. We also provide a visualization of the traffic network of the city of Ostrava, the Czech Republic to show the effect of the vertex importance adjustment. The algorithm was parallelized by MPI (http://www.mpi-forum.org/) and was tested on the supercomputer Salomon (https://docs.it4i.cz/) at IT4Innovations National Supercomputing Center, the Czech Republic.808726