Distributed Submodular Minimization over Networks: a Greedy Column Generation Approach

Submodular optimization is a special class of combinatorial optimization arising in several machine learning problems, but also in cooperative control of complex systems. In this paper, we consider agents in an asynchronous, unreliable and time-varying directed network that aim at cooperatively solving submodular minimization problems in a fully distributed way. The challenge is that the (submodular) objective set-function is only partially known by agents, that is, each one is able to evaluate the function only for subsets including itself. We propose a distributed algorithm based on a proper linear programming reformulation of the combinatorial problem. Our algorithm builds on a column generation approach in which each agent maintains a local candidate basis and locally generates columns with a suitable greedy inner routine. A key interesting feature of the proposed algorithm is that the pricing problem, which involves an exponential number of constraints, is solved by the agents through a polynomial time greedy algorithm. We prove that the proposed distributed algorithm converges in finite time to an optimal solution of the submodular minimization problem and we corroborate the theoretical results by performing numerical computations on instances of the $s$--$t$ minimum graph cut problem.Comment: 12 pages, 4 figures, 57th IEEE Conference on Decision and Contro

Expression of high- and low-affinity epidermal growth factor receptors in human hepatoma cell lines

AbstractData are presented from a comparative research on expression of epidermal growth factor (EGF) receptors and response to EGF of six independently established cell lines derived from human hepatoma. These lines differ in terms of the degree of differentiation, presence of hepatitis B virus (HBV) DNA copies in integrated form and expression of HBV genes. Our results indicate differential expression of membrane EGF receptors and differential response to EGF under serum- and hormone-free culture conditions. Furthermore, a significant difference in affinity could be detected between EGF receptors of the two highly dedifferentiated cell lines (HA22T/VGH and Li7A) whose replication is inhibited by EGF concentrations capable of stimulating more differentiated phenotypes

Efficacy of Mesoglycan in Pain Control after Excisional Hemorrhoidectomy: A Pilot Comparative Prospective Multicenter Study

Introduction. Various pain management strategies for patients undergoing open excisional hemorrhoidectomy have been proposed, yet postoperative pain remains a frequent complaint. Objective. To determine whether mesoglycan (30 mg two vials i.m. once/day for the first 5 days postoperative, followed by 50 mg 1 oral tablet twice/day for 30 days) would reduce the edema of the mucocutaneous bridges and thus improve postoperative pain symptoms. Patients and Methods. For this prospective observational multicenter study, 101 patients undergoing excisional diathermy hemorrhoidectomy for III-IV degree hemorrhoidal disease were enrolled at 5 colorectal referral centers. Patients were assigned to receive either mesoglycan (study group SG) or a recommended oral dose of ketorolac tromethamine of 10 mg every 4–6 hours, not exceeding 40 mg per day and not exceeding 5 postoperative days according to the indications for short-term management of moderate/severe acute postoperative pain, plus stool softeners (control group CG). Results. Postoperative thrombosis (SG 1/48 versus CG 5/45) (p<0.001) and pain after rectal examination (p<0.001) were significantly reduced at 7–10 days after surgery in the mesoglycan-treated group, permitting a faster return to work (p<0.001); however, in the same group, the incidence of postoperative bleeding, considered relevant when needing a readmission or an unexpected outpatient visit, was higher, possibly owing to the drug’s antithrombotic properties. Conclusions. The administration of mesoglycan after an open diathermy excisional hemorrhoidectomy can reduce postoperative thrombosis and pain at 7–10 days after surgery, permitting a faster return to normal activities

Mitochondrial DNA Backgrounds Might Modulate Diabetes Complications Rather than T2DM as a Whole

Mitochondrial dysfunction has been implicated in rare and common forms of type 2 diabetes (T2DM). Additionally, rare mitochondrial DNA (mtDNA) mutations have been shown to be causal for T2DM pathogenesis. So far, many studies have investigated the possibility that mtDNA variation might affect the risk of T2DM, however, when found, haplogroup association has been rarely replicated, even in related populations, possibly due to an inadequate level of haplogroup resolution. Effects of mtDNA variation on diabetes complications have also been proposed. However, additional studies evaluating the mitochondrial role on both T2DM and related complications are badly needed. To test the hypothesis of a mitochondrial genome effect on diabetes and its complications, we genotyped the mtDNAs of 466 T2DM patients and 438 controls from a regional population of central Italy (Marche). Based on the most updated mtDNA phylogeny, all 904 samples were classified into 57 different mitochondrial sub-haplogroups, thus reaching an unprecedented level of resolution. We then evaluated whether the susceptibility of developing T2DM or its complications differed among the identified haplogroups, considering also the potential effects of phenotypical and clinical variables. MtDNA backgrounds, even when based on a refined haplogroup classification, do not appear to play a role in developing T2DM despite a possible protective effect for the common European haplogroup H1, which harbors the G3010A transition in the MTRNR2 gene. In contrast, our data indicate that different mitochondrial haplogroups are significantly associated with an increased risk of specific diabetes complications: H (the most frequent European haplogroup) with retinopathy, H3 with neuropathy, U3 with nephropathy, and V with renal failure

Distributed Submodular Minimization Over Networks: A Greedy Column Generation Approach

Submodular optimization is a special class of combinatorial optimization arising in several machine learning problems, but also in cooperative control of complex systems. In this paper, we consider agents in an asynchronous, unreliable and time-varying directed network that aim at cooperatively solving submodular minimization problems in a fully dis- tributed way. The challenge is that the (submodular) objective set-function is only partially known by agents, that is, each one is able to evaluate the function only for subsets including itself. We propose a distributed algorithm based on a proper linear programming reformulation of the combinatorial problem. Our algorithm builds on a column generation approach in which each agent maintains a local candidate basis and locally generates columns with a suitable greedy inner routine. A key interesting feature of the proposed algorithm is that the pricing problem, which involves an exponential number of constraints, is solved by the agents through a polynomial time greedy algorithm. We prove that the proposed distributed algorithm converges in finite time to an optimal solution of the submodular minimization problem and we corroborate the theoretical results by performing numerical computations on instances of the s\u2013t minimum graph cut proble

Determination of Vanillylmandelic, 5-Hydroxyindoleacetic and Homovanillic Acid in Urine by Isocratic Liquid Chromatography

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