From Micro to Macro: Uncovering and Predicting Information Cascading Process with Behavioral Dynamics

Cascades are ubiquitous in various network environments. How to predict these cascades is highly nontrivial in several vital applications, such as viral marketing, epidemic prevention and traffic management. Most previous works mainly focus on predicting the final cascade sizes. As cascades are typical dynamic processes, it is always interesting and important to predict the cascade size at any time, or predict the time when a cascade will reach a certain size (e.g. an threshold for outbreak). In this paper, we unify all these tasks into a fundamental problem: cascading process prediction. That is, given the early stage of a cascade, how to predict its cumulative cascade size of any later time? For such a challenging problem, how to understand the micro mechanism that drives and generates the macro phenomenons (i.e. cascading proceese) is essential. Here we introduce behavioral dynamics as the micro mechanism to describe the dynamic process of a node's neighbors get infected by a cascade after this node get infected (i.e. one-hop subcascades). Through data-driven analysis, we find out the common principles and patterns lying in behavioral dynamics and propose a novel Networked Weibull Regression model for behavioral dynamics modeling. After that we propose a novel method for predicting cascading processes by effectively aggregating behavioral dynamics, and propose a scalable solution to approximate the cascading process with a theoretical guarantee. We extensively evaluate the proposed method on a large scale social network dataset. The results demonstrate that the proposed method can significantly outperform other state-of-the-art baselines in multiple tasks including cascade size prediction, outbreak time prediction and cascading process prediction.Comment: 10 pages, 11 figure

Motion Based Event Recognition Using HMM

Motion is an important cue for video understanding and is widely used in many semantic video analyses. We present a new motion representation scheme in which motion in a video is represented by the responses of frames to a set of motion filters. Each of these filters is designed to be most responsive to a type of dominant motion. Then we employ hidden Markov models (HMMs) to characterize the motion patterns based on these features and thus classify basketball video into 16 events. The evaluation by human satisfaction rate to classification result is 75%, demonstrating effectiveness of the proposed approach to recognizing semantic events in video

An HMM-Based Framework for Video Semantic Analysis

Video semantic analysis is essential in video indexing and structuring. However, due to the lack of robust and generic algorithms, most of the existing works on semantic analysis are limited to specific domains. In this paper, we present a novel hidden Markove model (HMM)-based framework as a general solution to video semantic analysis. In the proposed framework, semantics in different granularities are mapped to a hierarchical model space, which is composed of detectors and connectors. In this manner, our model decomposes a complex analysis problem into simpler subproblems during the training process and automatically integrates those subproblems for recognition. The proposed framework is not only suitable for a broad range of applications, but also capable of modeling semantics in different semantic granularities. Additionally, we also present a new motion representation scheme, which is robust to different motion vector sources. The applications of the proposed framework in basketball event detection, soccer shot classification, and volleyball sequence analysis have demonstrated the effectiveness of the proposed framework on video semantic analysis

A HMM Based Semantic Analysis Framework for Sports Game Event Detection

Video events detection or recognition is one of important tasks in semantic understanding of video content. Sports game video should be considered as a rule-based sequential signal. Therefore, it is reasonable to model sports events using hidden Markov models. In this paper, we present a generic, scalable and multilayer framework based on HMMs, called SG-HMMs (sports game HMMs), for sports game event detection. At the bottom layer of this framework, event HMMs output basic hypotheses based on low-level features. The upper layers are composed of composition HMMs, which add constraints on those hypotheses of the lower layer. Instead of isolated event recognition, the hypotheses at different layers are optimized in a bottom-up manner and the optimal semantics are determined by top-down process. The experimental results on basketball and volleyball videos have demonstrated the effectiveness of the proposed framework for sports game analysis

Modified Glucose-Insulin-Potassium Regimen Provides Cardioprotection With Improved Tissue Perfusion in Patients Undergoing Cardiopulmonary Bypass Surgery

Background Laboratory studies demonstrate glucose-insulin-potassium (GIK) as a potent cardioprotective intervention, but clinical trials have yielded mixed results, likely because of varying formulas and timing of GIK treatment and different clinical settings. This study sought to evaluate the effects of modified GIK regimen given perioperatively with an insulin-glucose ratio of 1:3 in patients undergoing cardiopulmonary bypass surgery. Methods and Results In this prospective, randomized, double-blinded trial with 930 patients referred for cardiac surgery with cardiopulmonary bypass, GIK (200 g/L glucose, 66.7 U/L insulin, and 80 mmol/L KCl) or placebo treatment was administered intravenously at 1 mL/kg per hour 10 minutes before anesthesia and continuously for 12.5 hours. The primary outcome was the incidence of in-hospital major adverse cardiac events including all-cause death, low cardiac output syndrome, acute myocardial infarction, cardiac arrest with successful resuscitation, congestive heart failure, and arrhythmia. GIK therapy reduced the incidence of major adverse cardiac events and enhanced cardiac function recovery without increasing perioperative blood glucose compared with the control group. Mechanistically, this treatment resulted in increased glucose uptake and less lactate excretion calculated by the differences between arterial and coronary sinus, and increased phosphorylation of insulin receptor substrate-1 and protein kinase B in the hearts of GIK-treated patients. Systemic blood lactate was also reduced in GIK-treated patients during cardiopulmonary bypass surgery. Conclusions A modified GIK regimen administered perioperatively reduces the incidence of in-hospital major adverse cardiac events in patients undergoing cardiopulmonary bypass surgery. These benefits are likely a result of enhanced systemic tissue perfusion and improved myocardial metabolism via activation of insulin signaling by GIK. Clinical Trial Registration URL: clinicaltrials.gov. Identifier: NCT01516138

Cardiac-derived CTRP9 protects against myocardial ischemia/reperfusion injury via calreticulin-dependent inhibition of apoptosis.

Cardiokines play an essential role in maintaining normal cardiac functions and responding to acute myocardial injury. Studies have demonstrated the heart itself is a significant source of C1q/TNF-related protein 9 (CTRP9). However, the biological role of cardiac-derived CTRP9 remains unclear. We hypothesize cardiac-derived CTRP9 responds to acute myocardial ischemia/reperfusion (MI/R) injury as a cardiokine. We explored the role of cardiac-derived CTRP9 in MI/R injury via genetic manipulation and a CTRP9-knockout (CTRP9-KO) animal model. Inhibition of cardiac CTRP9 exacerbated, whereas its overexpression ameliorated, left ventricular dysfunction and myocardial apoptosis. Endothelial CTRP9 expression was unchanged while cardiomyocyte CTRP9 levels decreased after simulated ischemia/`reperfusion (SI/R) in vitro. Cardiomyocyte CTRP9 overexpression inhibited SI/R-induced apoptosis, an effect abrogated by CTRP9 antibody. Mechanistically, cardiac-derived CTRP9 activated anti-apoptotic signaling pathways and inhibited endoplasmic reticulum (ER) stress-related apoptosis in MI/R injury. Notably, CTRP9 interacted with the ER molecular chaperone calreticulin (CRT) located on the cell surface and in the cytoplasm of cardiomyocytes. The CTRP9-CRT interaction activated the protein kinase A-cAMP response element binding protein (PKA-CREB) signaling pathway, blocked by functional neutralization of the autocrine CTRP9. Inhibition of either CRT or PKA blunted cardiac-derived CTRP9\u27s anti-apoptotic actions against MI/R injury. We further confirmed these findings in CTRP9-KO rats. Together, these results demonstrate that autocrine CTRP9 of cardiomyocyte origin protects against MI/R injury via CRT association, activation of the PKA-CREB pathway, ultimately inhibiting cardiomyocyte apoptosis

Mitochondrial DNA Sequence Variation and Haplogroup Distribution in Chinese Patients with LHON and m.14484T>C

BACKGROUND: Leber hereditary optic neuropathy (LHON, MIM 535000) is one of the most common mitochondrial genetic disorders caused by three primary mtDNA mutations (m.3460G>A, m.11778G>A and m. 14484T>C). The clinical expression of LHON is affected by many additional factors, e.g. mtDNA background, nuclear genes, and environmental factors. Hitherto, there is no comprehensive study of Chinese LHON patients with m.14484T>C. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we analyzed the mtDNA sequence variations and haplogroup distribution pattern of the largest number of Chinese LHON patients with m.14484T>C to date. We first determined the complete mtDNA sequences in eleven LHON probands with m.14484T>C, to discern the potentially pathogenic mutations that co-segregate with m.14484T>C. We then dissected the matrilineal structure of 52 patients with m.14484T>C (including 14 from unrelated families and 38 sporadic cases) and compared it with the reported Han Chinese from general populations. Complete mtDNA sequencing showed that the eleven matrilines belonged to nine haplogroups including Y2, C4a, M8a, M10a1a, G1a1, G2a1, G2b2, D5a2a1, and D5c. We did not identify putatively pathogenic mutation that was co-segregated with m.14484T>C in these lineages based on the evolutionary analysis. Compared with the reported Han Chinese from general populations, the LHON patients with m.14484T>C had significantly higher frequency of haplogroups C, G, M10, and Y, but a lower frequency of haplogroup F. Intriguingly, we also observed a lower prevalence of F lineages in LHON subjects with m.11778G>A in our previous study, suggesting that this haplogroup may enact similar role during the onset of LHON in the presence of m.14484T>C or m.11778G>A. CONCLUSIONS/SIGNIFICANCE: Our current study provided a comprehensive profile regarding the mtDNA variation and background of Chinese patients with LHON and m.14484T>C. Matrilineal background might affect the expression of LHON in Chinese patients with m.14484T>C

Patterns and Distributions of Urban Expansion in Global Watersheds

Abstract Understanding urban expansion at the watershed scale is important because watersheds are important carriers of ecological and environmental impacts. However, current analyses are mainly restricted to administrative units only. Here, we used a long‐term multitemporal data set of urban land to quantify the spatiotemporal trends in the extent and form of urban expansion from 1992 to 2016 in endorheic and exoreic watersheds, globally. Overall, urban expansion in 70% of watersheds (154/220) showed a decelerating trend. The average urban expansion speed of these watersheds in the last 6 years was approximately half of that in the last 24 years. Urban expansion speed in endorheic watersheds lagged behind the counterparts in exoreic watersheds, with the former approximately 1/4 of the latter. More importantly, the pattern of urban expansion in endorheic watersheds was following the low‐density and sprawling trend in exoreic watersheds, which could exert far‐reaching impacts on the sustainability of endorheic watersheds located in arid lands. These findings suggest the need to look beyond administrative city boundaries for land use planning and policies in the context of watershed management

EXPERIMENTAL STUDY OF OPEN CHANNEL FLOWS WITH TWO LAYERS VEGETATION

Vegetation in river bed has a great impact on flow characteristics in rivers, especially during floods. Understanding the structure of flow in vegetated open channels, in both submerged and emergent conditions, would provide valuable scientific basis for evaluating the effect of vegetation on river flows. This paper studies the structure of the open channel flows with two layers vegetation through experiments. The experiments were conducted at Nanjing Hydraulic Research Institute (NHRI), in a 12 m long by 0.4 m wide straight flume with a rectangular cross-section at a constant slope of 0.004. The vegetation was modeled by dowels with 6.35 mm diameter at two different heights of 100 mm and 200 mm, which was configured with different patterns and placed over a 10 mm thick plate on the bed of the flume. The artificial vegetation covered a 7 m long portion of the flume. In our study, various flow depths were taken to cover both emergent and submerged flow conditions. The measurement locations have been chosen in certain key sections of the vegetation region such as free flow region, behind short and tall dowels. The experimental data showed that the velocity profile is mostly uniform over the depth in the both emergent and submerged cases, except location 1 (directly behind tall dowel) in a submerged condition. The flow velocity in the vegetation layer is significantly smaller than that in the surface layer (i.e. non-vegetation flow layer). A near-constant velocity dominates in the vegetation layer and increases close to the interface at the top of short vegetation. There is a sudden change in the shape of the velocity profile near the top edge of vegetation. The results also showed that the flow velocity is strongly dependent on measurement locations