Parameterized Inapproximability of Target Set Selection and Generalizations

In this paper, we consider the Target Set Selection problem: given a graph and a threshold value $thr(v)$ for any vertex $v$ of the graph, find a minimum size vertex-subset to "activate" s.t. all the vertices of the graph are activated at the end of the propagation process. A vertex $v$ is activated during the propagation process if at least $thr(v)$ of its neighbors are activated. This problem models several practical issues like faults in distributed networks or word-to-mouth recommendations in social networks. We show that for any functions $f$ and $\rho$ this problem cannot be approximated within a factor of $\rho(k)$ in $f(k) \cdot n^{O(1)}$ time, unless FPT = W[P], even for restricted thresholds (namely constant and majority thresholds). We also study the cardinality constraint maximization and minimization versions of the problem for which we prove similar hardness results

Intersectionality: Social Marginalisation and Self-Reported Health Status in Young People.

BACKGROUND:The aim of this study was to measure young people's health status and explore associations between health status and belonging to one or more socio-culturally marginalised group. METHODS:part of the Access 3 project, this cross-sectional survey of young people aged 12-24 years living in New South Wales, Australia, oversampled young people from one or more of the following groups: Aboriginal and or Torres Strait Islander; living in rural and remote areas; homeless; refugee; and/or, sexuality and/or gender diverse. This paper reports on findings pertaining to health status, presence of chronic health conditions, psychological distress, and wellbeing measures. RESULTS:1416 participants completed the survey; 897 (63.3%) belonged to at least one marginalised group; 574 (40.5%) to one, 281 (19.8%) to two and 42 (3.0%) to three or four groups. Belonging to more marginalised groups was significantly associated with having more chronic health conditions (p = 0.001), a greater likelihood of high psychological distress (p = 0.001) and of illness or injury related absence from school or work (p < 0.05). CONCLUSIONS:increasing marginalisation is associated with decreasing health status. Using an intersectional lens can to be a useful way to understand disadvantage for young people belonging to multiple marginalised groups

Highly purified extracellular vesicles from human cardiomyocytes demonstrate preferential uptake by human endothelial cells

Extracellular vesicles (EVs) represent a promising cell-free alternative for treatment of cardiovascular diseases. Nevertheless, the lack of standardised and reproducible isolation methods capable of recovering pure, intact EVs presents a significant obstacle. Additionally, there is significant interest in investigating the interactions of EVs with different cardiac cell types. Here we established a robust technique for the production and isolation of EVs harvested from an enriched (>97% purity) population of human induced pluripotent stem cell (iPSC)-derived cardiomyocytes (CMs) with size exclusion chromatography. Utilizing an advanced fluorescence labelling strategy, we then investigated the interplay of the CM-EVs with the three major cellular components of the myocardium (fibroblasts, cardiomyocytes and endothelial cells) and identified that cardiac endothelial cells show preferential uptake of these EVs. Overall, our findings provide a great opportunity to overcome the translational hurdles associated with the isolation of intact, non-aggregated human iPSC-CM EVs at high purity. Furthermore, understanding in detail the interaction of the secreted EVs with their surrounding cells in the heart may open promising new avenues in the field of EV engineering for targeted delivery in cardiac regeneration

Highly purified extracellular vesicles from human cardiomyocytes demonstrate preferential uptake by human endothelial cells

Extracellular vesicles (EVs) represent a promising cell-free alternative for treatment of cardiovascular diseases. Nevertheless, the lack of standardised and reproducible isolation methods capable of recovering pure, intact EVs presents a significant obstacle. Additionally, there is significant interest in investigating the interactions of EVs with different cardiac cell types. Here we established a robust technique for the production and isolation of EVs harvested from an enriched (>97% purity) population of human induced pluripotent stem cell (iPSC)-derived cardiomyocytes (CMs) with size exclusion chromatography. Utilizing an advanced fluorescence labelling strategy, we then investigated the interplay of the CM-EVs with the three major cellular components of the myocardium (fibroblasts, cardiomyocytes and endothelial cells) and identified that cardiac endothelial cells show preferential uptake of these EVs. Overall, our findings provide a great opportunity to overcome the translational hurdles associated with the isolation of intact, non-aggregated human iPSC-CM EVs at high purity. Furthermore, understanding in detail the interaction of the secreted EVs with their surrounding cells in the heart may open promising new avenues in the field of EV engineering for targeted delivery in cardiac regeneration

Influence Diffusion in Social Networks under Time Window Constraints

We study a combinatorial model of the spread of influence in networks that generalizes existing schemata recently proposed in the literature. In our model, agents change behaviors/opinions on the basis of information collected from their neighbors in a time interval of bounded size whereas agents are assumed to have unbounded memory in previously studied scenarios. In our mathematical framework, one is given a network $G=(V,E)$, an integer value $t(v)$ for each node $v\in V$, and a time window size $\lambda$. The goal is to determine a small set of nodes (target set) that influences the whole graph. The spread of influence proceeds in rounds as follows: initially all nodes in the target set are influenced; subsequently, in each round, any uninfluenced node $v$ becomes influenced if the number of its neighbors that have been influenced in the previous $\lambda$ rounds is greater than or equal to $t(v)$. We prove that the problem of finding a minimum cardinality target set that influences the whole network $G$ is hard to approximate within a polylogarithmic factor. On the positive side, we design exact polynomial time algorithms for paths, rings, trees, and complete graphs.Comment: An extended abstract of a preliminary version of this paper appeared in: Proceedings of 20th International Colloquium on Structural Information and Communication Complexity (Sirocco 2013), Lectures Notes in Computer Science vol. 8179, T. Moscibroda and A.A. Rescigno (Eds.), pp. 141-152, 201