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Aims: The estimated incidence of bile duct injuries (BDIs) has increased ranging from 0.4% to 0.7% since global recognition of laparoscopic cholecystectomy as a standard procedure. Bile duct injuries are associated with increased morbidity and mortality, as well as substantial health care costs, and malpractice litigation claims. This study aims to determine the quality of life (QoL) of Filipino patients after repair of BDIs incurred from their previous laparoscopic or open cholecystectomies. Methods: A total of 22 patients post-repair of BDIs and 22 age- and sex-matched patients who had unremarkable cholecystectomies from 1997 to 2017 were recruited for this study. The patients were requested to answer a survey on QoL using RAND 36-Item Health Survey 1.0, a validated survey which includes a multi-item scale that assesses eight health concepts. Results: Patients with BDIs had lower scores in all domains compared to non-BDI group. Physical functioning and role limitations due to physical health are statistically significant (p values 0.0473 and 0.0025). Conclusion: The effect of BDI is considerable and bears an impact on a patient’s health and well-being. Physical functioning and role limitations due to physical health were identified to be impaired among these patients and these factors should be assessed during long-term follow-up.

Breast cancer is the most frequent cancer among women. Common sites of metastatic involvement of breast cancer, in order of frequency, are bone, lung, pleura, soft tissues, and liver. Isolated metastasis to the pancreas from a primary breast cancer is a rare occurrence. Presented herein is a case of a 55-year-old female patient postmastectomy for breast carcinoma who developed signs and symptoms of obstructive jaundice secondary to a pancreatic head mass. It was documented to be metastatic from the primary breast cancer and was managed surgically to relieve the biliary obstruction.

Les capitalistes sociaux sont des utilisateurs de m��dias sociaux tels que Twitter, appliquant diverses techniques pour obtenir un maximum de visibilit��. Ils peuvent ��tre n��fastes �� l'��quilibre du service, dans la mesure o�� leurs comptes, en gagnant en importance sans r��elle raison de contenu, rendent difficile l'acc��s �� un contenu pertinent. Dans ce travail, nous nous int��ressons �� leur caract��risation d'un point de vue purement topologique, i.e. sans consid��rer la nature des contenus partag��s. Nous utilisons pour cela la notion de r��le communautaire, qui est bas��e sur la structure de communaut��s du r��seau ��tudi��. Nous apportons des modifications �� des mesures pr��c��demment d��finies �� cet effet, et proposons une m��thode objective de d��tection des r��les. Nous appliquons ensuite notre m��thode �� l'analyse d'un r��seau repr��sentant Twitter. Nos r��sultats montrent que les r��les que nous identifions via nos mesures se r��v��lent particuli��rement coh��rents par rapport aux capitalistes sociaux du r��seau Twitter, dont le comportement est clairement identifi��---Social capitalists are social media users taking advantage of various methods to maximize their visibility. This results in artificially important accounts, in the sense this importance is not backed by any real content. The risk is then to see those accounts hiding relevant contents and therefore preventing other users to access them. In this work, we want to characterize social capitalists from a purely topological perspective, i.e. without considering the nature of the shared contents. For this purpose, we use the notion of community role, based on the community structure of the studied network. We modify some measures previously designed for this matter, and propose an objective method to determine roles. We then apply this method to the analysis of a Twitter network. Our results show the roles identified through our measures are particularly consistent with Twitter's social capitalists, whose behavior was clearly identified.

The notion of community structure is particularly useful when analyzing complex networks, because it provides an intermediate level, compared to the more classic global (whole network) and local (node neighborhood) approaches. The concept of community role of a node was derived from this base, in order to describe the position of a node in a network depending on its connectivity at the community level. However, the existing approaches are restricted to undirected networks, use topological measures which do not consider all aspects of community-related connectivity, and their role identification methods are not generalizable to all networks. We tackle these limitations by generalizing and extending the measures, and using an unsupervised approach to determine the roles. We then illustrate the applicability of our method by analyzing a Twitter network.We show how our modifications allow discovering the fact some particular users called social capitalists occupy very specific roles in this system. --- La notion de structure de communaut��s est particuli��rement utile pour ��tudier les r��seaux complexes, car elle am��ne un niveau d'analyse interm��diaire, par opposition aux plus classiques niveaux local (voisinage des noeuds) et global (r��seau entier). Le concept de r��le communautaire permet de d��crire le positionnement d'un noeud en fonction de sa connectivit�� communautaire. Cependant, les approches existantes sont restreintes aux r��seaux non-orient��s, utilisent des mesures topologiques ne consid��rant pas tous les aspects de la connectivit�� communautaire, et des m��thodes d'identification des r��les non-g��n��ralisables �� tous les r��seaux. Nous proposons de r��soudre ces probl��mes en g��n��ralisant les mesures existantes, et en utilisant une m��thode non-supervis��e pour d��terminer les r��les. Nous illustrons l'int��r��t de notre m��thode en l'appliquant au r��seau de Twitter. Nous montrons que nos modifications mettent en ��vidence les r��les sp��cifiques d'utilisateurs particuliers du r��seau, nomm��s capitalistes sociaux., arXiv admin note: substantial text overlap with arXiv:1309.2132

We develop a technique that we call Conflict Packing in the context of kernelization, obtaining (and improving) several polynomial kernels for editing problems on dense instances. We apply this technique on several well-studied problems: Feedback Arc Set in (Bipartite) Tournaments, Dense Rooted Triplet Inconsistency and Betweenness in Tournaments. For the former, one is given a (bipartite) tournament $T = (V,A)$ and seeks a set of at most $k$ arcs whose reversal in $T$ results in an acyclic (bipartite) tournament. While a linear vertex-kernel is already known for the first problem, using the Conflict Packing allows us to find a so-called safe partition, the central tool of the kernelization algorithm in, with simpler arguments. For the case of bipartite tournaments, the same technique allows us to obtain a quadratic vertex-kernel. Again, such a kernel was already known to exist, using the concept of so-called bimodules. We believe however that providing an unifying technique to cope with such problems is interesting. Regarding Dense Rooted Triplet Inconsistency, one is given a set of vertices $V$ and a dense collection $\mathcal{R}$ of rooted binary trees over three vertices of $V$ and seeks a rooted tree over $V$ containing all but at most $k$ triplets from $\mathcal{R}$. As a main consequence of our technique, we prove that the Dense Rooted Triplet Inconsistency problem admits a linear vertex-kernel. This result improves the best known bound of $O(k^2)$ vertices for this problem. Finally, we use this technique to obtain a linear vertex-kernel for Betweenness in Tournaments, where one is given a set of vertices $V$ and a dense collection $\mathcal{R}$ of so-called betweenness triplets and seeks a linear ordering of the vertices containing all but at most $k$ triplets from $\mathcal{R}$.