Iowa Influenza Surveillance Network: Weekly Activity Report Week 47, November 28, 2015

The Iowa Influenza Surveillance Network (IISN) was established in 2004, though surveillance has been conducted at the Iowa Department of Public Health. Schools and long-term care facilities report data weekly into a Web-based reporting system. Schools report the number of students absent due to illness and the total enrolled. Long-term care facilities report cases of influenza and vaccination status of each case. Both passively report outbreaks of illness, including influenza, to IDPH.

Iowa Influenza Surveillance Network: Weekly Activity Report Week 48, December 5, 2015

The Iowa Influenza Surveillance Network (IISN) was established in 2004, though surveillance has been conducted at the Iowa Department of Public Health. Schools and long-term care facilities report data weekly into a Web-based reporting system. Schools report the number of students absent due to illness and the total enrolled. Long-term care facilities report cases of influenza and vaccination status of each case. Both passively report outbreaks of illness, including influenza, to IDPH.

Iowa Influenza Surveillance Network: Weekly Activity Report Week 49, December 12, 2015

The Iowa Influenza Surveillance Network (IISN) was established in 2004, though surveillance has been conducted at the Iowa Department of Public Health. Schools and long-term care facilities report data weekly into a Web-based reporting system. Schools report the number of students absent due to illness and the total enrolled. Long-term care facilities report cases of influenza and vaccination status of each case. Both passively report outbreaks of illness, including influenza, to IDPH.

Iowa Influenza Surveillance Network: Weekly Activity Report Week 50, December 19, 2015

The Iowa Influenza Surveillance Network (IISN) was established in 2004, though surveillance has been conducted at the Iowa Department of Public Health. Schools and long-term care facilities report data weekly into a Web-based reporting system. Schools report the number of students absent due to illness and the total enrolled. Long-term care facilities report cases of influenza and vaccination status of each case. Both passively report outbreaks of illness, including influenza, to IDPH.

Iowa Influenza Surveillance Network: Weekly Activity Report Week 51, December 26, 2015

The Iowa Influenza Surveillance Network (IISN) was established in 2004, though surveillance has been conducted at the Iowa Department of Public Health. Schools and long-term care facilities report data weekly into a Web-based reporting system. Schools report the number of students absent due to illness and the total enrolled. Long-term care facilities report cases of influenza and vaccination status of each case. Both passively report outbreaks of illness, including influenza, to IDPH.

Iowa Influenza Surveillance Network: Weekly Activity Report Week 52, January 2, 2016

The Iowa Influenza Surveillance Network (IISN) was established in 2004, though surveillance has been conducted at the Iowa Department of Public Health. Schools and long-term care facilities report data weekly into a Web-based reporting system. Schools report the number of students absent due to illness and the total enrolled. Long-term care facilities report cases of influenza and vaccination status of each case. Both passively report outbreaks of illness, including influenza, to IDPH.

Inventory of sparse forest populations using adaptive cluster sampling

Abstract

Cab45 is required for Ca(2+)-dependent secretory cargo sorting at the trans-Golgi network

Ca(2+) import into the lumen of the trans-Golgi network (TGN) by the secretory pathway calcium ATPase1 (SPCA1) is required for the sorting of secretory cargo. How is Ca(2+) retained in the lumen of the Golgi, and what is its role in cargo sorting? We show here that a soluble, lumenal Golgi resident protein, Cab45, is required for SPCA1-dependent Ca(2+) import into the TGN; it binds secretory cargo in a Ca(2+)-dependent reaction and is required for its sorting at the TGN.

Kinesin-5/Eg5 is important for transport of CARTS from the trans-Golgi network to the cell surface

Here we report that the kinesin-5 motor Klp61F, which is known for its role in bipolar spindle formation in mitosis, is required for protein transport from the Golgi complex to the cell surface in Drosophila S2 cells. Disrupting the function of its mammalian orthologue, Eg5, in HeLa cells inhibited secretion of a protein called pancreatic adenocarcinoma up-regulated factor (PAUF) but, surprisingly, not the trafficking of vesicular stomatitis virus G protein (VSV-G) to the cell surface. We have previously reported that PAUF is transported from the trans-Golgi network (TGN) to the cell surface in specific carriers called CARTS that exclude VSV-G. Inhibition of Eg5 function did not affect the biogenesis of CARTS; however, their migration was delayed and they accumulated near the Golgi complex. Altogether, our findings reveal a surprising new role of Eg5 in nonmitotic cells in the facilitation of the transport of specific carriers, CARTS, from the TGN to the cell surface.

Local optima networks of hard combinatorial landscapes

Combinatorial optimization involves finding an optimal solution in a finite set of options; many everyday life problems are of this kind. However, the number of options grows exponentially with the size of the problem, such that an exhaustive search for the best solution is practically infeasible beyond a certain problem size. When efficient algorithms are not available, a practical approach to obtain an approximate solution to the problem at hand, is to start with an educated guess and gradually refine it until we have a good-enough solution. Roughly speaking, this is how local search heuristics work. These stochastic algorithms navigate the problem search space by iteratively turning the current solution into new candidate solutions, guiding the search towards better solutions. The search performance, therefore, depends on structural aspects of the search space, which in turn depend on the move operator being used to modify solutions. A common way to characterize the search space of a problem is through the study of its fitness landscape, a mathematical object comprising the space of all possible solutions, their value with respect to the optimization objective, and a relationship of neighborhood defined by the move operator. The landscape metaphor is used to explain the search dynamics as a sort of potential function. The concept is indeed similar to that of potential energy surfaces in physical chemistry. Borrowing ideas from that field, we propose to extend to combinatorial landscapes the notion of the inherent network formed by energy minima in energy landscapes. In our case, energy minima are the local optima of the combinatorial problem, and we explore several definitions for the network edges. At first, we perform an exhaustive sampling of local optima basins of attraction, and define weighted transitions between basins by accounting for all the possible ways of crossing the basins frontier via one random move. Then, we reduce the computational burden by only counting the chances of escaping a given basin via random kick moves that start at the local optimum. Finally, we approximate network edges from the search trajectory of simple search heuristics, mining the frequency and inter-arrival time with which the heuristic visits local optima. Through these methodologies, we build a weighted directed graph that provides a synthetic view of the whole landscape, and that we can characterize using the tools of complex networks science. We argue that the network characterization can advance our understanding of the structural and dynamical properties of hard combinatorial landscapes. We apply our approach to prototypical problems such as the Quadratic Assignment Problem, the NK model of rugged landscapes, and the Permutation Flow-shop Scheduling Problem. We show that some network metrics can differentiate problem classes, correlate with problem non-linearity, and predict problem hardness as measured from the performances of trajectory-based local search heuristics.

Comparison of randomized branch sampling with and without replacement at the first stage

Abstract

APTIC : a social network to improve the quality of life of members of patients' associations

Peer-reviewed

History of cholelithiasis and cancer risk in a network of case-control studies.

Background We analyzed the relationship between cholelithiasis and cancer risk in a network of case-control studies conducted in Italy and Switzerland in 1982-2009. Methods The analyses included 1997 oropharyngeal, 917 esophageal, 999 gastric, 23 small intestinal, 3726 colorectal, 684 liver, 688 pancreatic, 1240 laryngeal, 6447 breast, 1458 endometrial, 2002 ovarian, 1582 prostate, 1125 renal cell, 741 bladder cancers, and 21 284 controls. The odds ratios (ORs) were estimated by multiple logistic regression models. Results The ORs for subjects with history of cholelithiasis compared with those without were significantly elevated for small intestinal (OR = 3.96), prostate (OR = 1.36), and kidney cancers (OR = 1.57). These positive associations were observed ≥10 years after diagnosis of cholelithiasis and were consistent across strata of age, sex, and body mass index. No relation was found with the other selected cancers. A meta-analysis including this and three other studies on the relation of cholelithiasis with small intestinal cancer gave a pooled relative risk of 2.35 [95% confidence interval (CI) 1.82-3.03]. Conclusion In subjects with cholelithiasis, we showed an appreciably increased risk of small intestinal cancer and suggested a moderate increased risk of prostate and kidney cancers. We found no material association with the other cancers considered.

2

The quality of environmental data analysis and propagation of errors are heavily affected by the representativity of the initial sampling design [CRE 93, DEU 97, KAN 04a, LEN 06, MUL07]. Geostatistical methods such as kriging are related to field samples, whose spatial distribution is crucial for the correct detection of the phenomena. Literature about the design of environmental monitoring networks (MN) is widespread and several interesting books have recently been published [GRU 06, LEN 06, MUL 07] in order to clarify the basic principles of spatial sampling design (monitoring networks optimization) based on Support Vector Machines was proposed. Nonetheless, modelers often receive real data coming from environmental monitoring networks that suffer from problems of non-homogenity (clustering). Clustering can be related to the preferential sampling or to the impossibility of reaching certain regions.