Video moment retrieval is to search the moment that is most relevant to the given natural language query. Existing methods are mostly trained in a fully-supervised setting, which requires the full annotations of temporal boundary for each query. However, manually labeling the annotations is actually time-consuming and expensive. In this paper, we propose a novel weakly-supervised moment retrieval framework requiring only coarse video-level annotations for training. Specifically, we devise a proposal generation module that aggregates the context information to generate and score all candidate proposals in one single pass. We then devise an algorithm that considers both exploitation and exploration to select top-K proposals. Next, we build a semantic completion module to measure the semantic similarity between the selected proposals and query, compute reward and provide feedbacks to the proposal generation module for scoring refinement. Experiments on the ActivityCaptions and Charades-STA demonstrate the effectiveness of our proposed method.

We tested the possibility that proteasome inhibition may reverse preexisting cardiac hypertrophy and improve remodeling upon pressure overload. Mice were submitted to aortic banding and followed up for 3 wk. The proteasome inhibitor epoxomicin (0.5 mg/kg) or the vehicle was injected daily, starting 2 wk after banding. At the end of the third week, vehicle-treated banded animals showed significant (P<0.05) increase in proteasome activity (PA), left ventricle-to-tibial length ratio (LV/TL), myocyte cross-sectional area (MCA), and myocyte apoptosis compared with sham-operated animals and developed signs of heart failure, including increased lung weight-to-TL ratio and decreased ejection fraction. When compared with that group, banded mice treated with epoxomicin showed no increase in PA, a lower LV/TL and MCA, reduced apoptosis, stabilized ejection fraction, and no signs of heart failure. Because overload-mediated cardiac remodeling largely depends on the activation of the proteasome-regulated transcription factor NF-kappaB, we tested whether epoxomicin would prevent this activation. NF-kappaB activity increased significantly upon overload, which was suppressed by epoxomicin. The expression of NF-kappaB-dependent transcripts, encoding collagen types I and III and the matrix metalloprotease-2, increased (P<0.05) after banding, which was abolished by epoxomicin. The accumulation of collagen after overload, as measured by histology, was 75% lower (P<0.05) with epoxomicin compared with vehicle. Myocyte apoptosis increased by fourfold in hearts submitted to aortic banding compared with sham-operated hearts, which was reduced by half upon epoxomicin treatment. Therefore, we propose that proteasome inhibition after the onset of pressure overload rescues ventricular remodeling by stabilizing cardiac function, suppressing further progression of hypertrophy, repressing collagen accumulation, and reducing myocyte apoptosis.

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Aims/hypothesesIt is now generally accepted that diabetes increases the risk for cognitive impairment, but the precise mechanisms are poorly understood. In recent years, resting-state functional magnetic resonance imaging (rs-fMRI) is increasingly used to investigate the neural basis of cognitive dysfunction in type 2 diabetes (T2D) patients. Alterations in brain functional connectivity may underlie diabetes-related cognitive dysfunction and brain damage. The aim of this study was to investigate the changes in default mode network (DMN) connectivity in different glucose metabolism status and diabetes duration.MethodsWe used a seed-based fMRI analysis to investigate positive and negative DMN connectivity in four groups (39 subjects with normal glucose metabolism [NGM], 23 subjects with impaired glucose metabolism [IGM; i.e., prediabetes], 59 T2D patients with a diabetes duration of <10 years, and 24 T2D patients with a diabetes duration of ≥10 years).ResultsNegative DMN connectivity increased and then regressed with deteriorating glucose metabolism status and extending diabetes duration. DMN connectivity showed a significant correlation with diabetes duration.Conclusion/interpretationThis study suggests that DMN connectivity may exhibit distinct patterns in different glucose metabolism status and diabetes duration, providing some potential neuroimaging evidence for early diagnosis and further understanding of the pathophysiological mechanisms of diabetic brain damage.

Emerging neuroimaging research suggests that antisocial personality disorder (ASPD) may be linked to abnormal brain anatomy, but little is known about possible impairments of white matter microstructure in ASPD, as well as their relationship with impulsivity or risky behaviors. In this study, we systematically investigated white matter abnormalities of ASPD using diffusion tensor imaging (DTI) measures: fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD). Then, we further investigated their correlations with the scores of impulsivity or risky behaviors. ASPD patients showed decreased FA in multiple major white matter fiber bundles, which connect the fronto-parietal control network and the fronto-temporal network. We also found AD/RD deficits in some additional white matter tracts that were not detected by FA. More interestingly, several regions were found correlated with impulsivity or risky behaviors in AD and RD values, although not in FA values, including the splenium of corpus callosum, left posterior corona radiate/posterior thalamic radiate, right superior longitudinal fasciculus, and left inferior longitudinal fasciculus. These regions can be the potential biomarkers, which would be of great interest in further understanding the pathomechanism of ASPD.

BackgroundPrevious functional MRI (fMRI) studies have demonstrated group differences in brain activity between deceptive and honest responses. The functional connectivity network related to lie-telling remains largely uncharacterized.MethodsIn this study, we designed a lie-telling experiment that emphasized strategy devising. Thirty-two subjects underwent fMRI while responding to questions in a truthful, inverse, or deceitful manner. For each subject, whole-brain functional connectivity networks were constructed from correlations among brain regions for the lie-telling and truth-telling conditions. Then, a multivariate pattern analysis approach was used to distinguish lie-telling from truth-telling based on the functional connectivity networks.ResultsThe classification results demonstrated that lie-telling could be differentiated from truth-telling with an accuracy of 82.81% (85.94% for lie-telling, 79.69% for truth-telling). The connectivities related to the fronto-parietal networks, cerebellum and cingulo-opercular networks are most discriminating, implying crucial roles for these three networks in the processing of deception.ConclusionsThe current study may shed new light on the neural pattern of deception from a functional integration viewpoint.Electronic supplementary materialThe online version of this article (doi:10.1186/s12993-014-0046-4) contains supplementary material, which is available to authorized users.

Bus bunching is one of the most serious problems of urban bus systems. Bus bunching increases waiting and travel time of passengers. Many bus systems use schedules to reach equal headways. Compared to the idea of schedules and the target headway introduced later, we propose a new method to improve the efficiency of a bus system and avoid bus bunching by boarding limits. Our solution can be effectively implemented when buses cannot travel as planned because of bad road conditions and dynamic demands at bus stops. Besides, using our method, bus headways reach the state with equal headways dynamically and spontaneously without drivers’ explicit intervention. Moreover, the method can improve the level of the bus service and reduce total travel time of passengers. We verify our method using an ideal bus route and a real bus route, both showing the success of the proposed method.