Overcoming Challenges to Teamwork in Patient-Centered Medical Homes: A Qualitative Study

There is emerging consensus that enhanced inter-professional teamwork is necessary for the effective and efficient delivery of primary care, but there is less practical information specific to primary care available to guide practices on how to better work as teams. The purpose of this study was to describe how primary care practices have overcome challenges to providing team-based primary care and the implications for care delivery and policy

Maximum Matching in Two, Three, and a Few More Passes Over Graph Streams

We consider the maximum matching problem in the semi-streaming model formalized by Feigenbaum, Kannan, McGregor, Suri, and Zhang that is inspired by giant graphs of today. As our main result, we give a two-pass (1/2 + 1/16)-approximation algorithm for triangle-free graphs and a two-pass (1/2 + 1/32)-approximation algorithm for general graphs; these improve the approximation ratios of 1/2 + 1/52 for bipartite graphs and 1/2 + 1/140 for general graphs by Konrad, Magniez, and Mathieu. In three passes, we achieve approximation ratios of 1/2 + 1/10 for triangle-free graphs and 1/2 + 1/19.753 for general graphs. We also give a multi-pass algorithm where we bound the number of passes precisely - we give a (2/3 - epsilon)-approximation algorithm that uses 2/(3 epsilon) passes for triangle-free graphs and 4/(3 epsilon) passes for general graphs. Our algorithms are simple and combinatorial, use O(n log(n)) space, and have O(1) update time per edge. For general graphs, our multi-pass algorithm improves the best known deterministic algorithms in terms of the number of passes: * Ahn and Guha give a (2/3 - epsilon)-approximation algorithm that uses O(log(1/epsilon)/epsilon^2) passes, whereas our (2/3 - epsilon)-approximation algorithm uses 4/(epsilon) passes; * they also give a (1 - epsilon)-approximation algorithm that uses O(log(n) poly(1/epsilon)) passes, where n is the number of vertices of the input graph; although our algorithm is (2/3 - epsilon)-approximation, our number of passes do not depend on n. Earlier multi-pass algorithms either have a large constant inside big-O notation for the number of passes or the constant cannot be determined due to the involved analysis, so our multi-pass algorithm should use much fewer passes for approximation ratios bounded slightly below 2/3

MUC1 positive, Kras and Pten driven mouse gynecologic tumors replicate human tumors and vary in survival and nuclear grade based on anatomical location

Activating mutations of Kras oncogene and deletions of Pten tumor suppressor gene play important roles in cancers of the female genital tract. We developed here new preclinical models for gynecologic cancers, using conditional (Cre-loxP) mice with floxed genetic alterations in Kras and Pten. The triple transgenic mice, briefly called MUC1KrasPten, express human MUC1 antigen as self and carry a silent oncogenic KrasG12D and Pten deletion mutation. Injection of Cre-encoding adenovirus (AdCre) in the ovarian bursa, oviduct or uterus activates the floxed mutations and initiates ovarian, oviductal, and endometrial cancer, respectively. Anatomical site-specific Cre-loxP recombination throughout the genital tract of MUC1KrasPten mice leads to MUC1 positive genital tract tumors, and the development of these tumors is influenced by the anatomical environment. Endometrioid histology was consistently displayed in all tumors of the murine genital tract (ovaries, oviducts, and uterus). Tumors showed increased expression of MUC1 glycoprotein and triggered de novo antibodies in tumor bearing hosts, mimicking the immunobiology seen in patients. In contrast to the ovarian and endometrial tumors, oviductal tumors showed higher nuclear grade. Survival for oviduct tumors was significantly lower than for endometrial tumors (p = 0.0015), yet similar to survival for ovarian cancer. Oviducts seem to favor the development of high grade tumors, providing preclinical evidence in support of the postulated role of fallopian tubes as the originating site for high grade human ovarian tumors. © 2014 Tirodkar et al

Deterministic Algorithms for Maximum Matching on General Graphs in the Semi-Streaming Model

We present an improved deterministic algorithm for Maximum Cardinality Matching on general graphs in the Semi-Streaming Model. In the Semi-Streaming Model, a graph is presented as a sequence of edges, and an algorithm must access the edges in the given sequence. It can only use O(n polylog n) space to perform computations, where n is the number of vertices of the graph. If the algorithm goes over the stream k times, it is called a k-pass algorithm. In this model, McGregor [McGregor, 2005] gave the currently best known randomized (1+epsilon)-approximation algorithm for maximum cardinality matching on general graphs, that uses (1/epsilon)^{O(1/epsilon)} passes. Ahn and Guha [Ahn and Guha, 2013] later gave the currently best known deterministic (1+epsilon)-approximation algorithms for maximum cardinality matching: one on bipartite graphs that uses O(log log(1/epsilon)/epsilon^2) passes, and the other on general graphs that uses O(log n *poly(1/epsilon)) passes (note that, for general graphs, the number of passes is dependent on the size of the input). We present the first deterministic algorithm that achieves a (1+epsilon)-approximation on general graphs in only a constant number ((1/epsilon)^{O(1/epsilon)}) of passes

On the Approximability and Hardness of the Minimum Connected Dominating Set with Routing Cost Constraint

In the problem of minimum connected dominating set with routing cost constraint, we are given a graph $G=(V,E)$, and the goal is to find the smallest connected dominating set $D$ of $G$ such that, for any two non-adjacent vertices $u$ and $v$ in $G$, the number of internal nodes on the shortest path between $u$ and $v$ in the subgraph of $G$ induced by $D \cup \{u,v\}$ is at most $\alpha$ times that in $G$. For general graphs, the only known previous approximability result is an $O(\log n)$-approximation algorithm ($n=|V|$) for $\alpha = 1$ by Ding et al. For any constant $\alpha > 1$, we give an $O(n^{1-\frac{1}{\alpha}}(\log n)^{\frac{1}{\alpha}})$-approximation algorithm. When $\alpha \geq 5$, we give an $O(\sqrt{n}\log n)$-approximation algorithm. Finally, we prove that, when $\alpha =2$, unless $NP \subseteq DTIME(n^{poly\log n})$, for any constant $\epsilon > 0$, the problem admits no polynomial-time $2^{\log^{1-\epsilon}n}$-approximation algorithm, improving upon the $\Omega(\log n)$ bound by Du et al. (albeit under a stronger hardness assumption)

Improved 3-Dimensional Security in Cloud Computing

Cloud computing is a trending technology in the field of Information Technology as it allows sharing of resources over a network. The reason Cloud computing gained traction so rapidly was because of its performance, availability and low cost among other features. Besides these features, companies are still refraining from binding their business with cloud computing due to the fear of data leakage. The focus of this paper is on the problem of data leakage. It proposes a framework which works in two phases. The first phase consists of data encryption and classification which is performed before storing the data. In this phase, the client may want to encrypt his data prior to uploading. After encryption, data is classified using three parameters namely Confidentiality [C], Integrity [I] and Availability [A]. With the help of proposed algorithm, criticality rating (Cr) of the data is calculated. According to the Cr, security will be provided on the basis of the 3 Dimensions proposed in this paper. The second phase consists of data retrieval by the client. As per the concept of 3D, users who want to access their data need to be authenticated, to avoid data from being compromised. Before every access to data, the users identity is verified for authorization. After the user is authorized for data access, if the data is encrypted, the user can decrypt the same.Comment: 6 Pages, 10 Figures, Published with International Journal of Computer Trends and Technology (IJCTT

ECOLOGICAL OBSERVATIONS AND GC-MS ANALYSIS OF METHANOLIC EXTRACT OF SACOGLOSSAN ELYSIA BANGTAWAENSIS (SWENNEN)

Elysia bangtawaensis Swennen (sacoglossan) was reported from intertidal mangrove habitat from the polyhaline zone of Mandovi estuary, Goa. The detailed ecological survey of the mangrove region of Goa revealed that the density of E. bangtawaensis ranged from 0 (July, August) to 400 (January) individuals m-2 and were found to be mainly depends on phytoplankton population, their exposure to the sunlight (no. of low tides in day time) and moderately on other parameters such as salinity, pH and temperature (air and water). Moreover, GC-MS analysis of methanolic extract of E. bangtawaensis showed major constituents of fatty acids such as myristic acid, oleic acid, stearic acid, arachidic acid etc. The presence of such bioactive compounds (identified as fatty acid) justifies the use of whole animal in pharmaceutical application

2-[2-(Hydroxy­meth­yl)phen­yl]-1-phenyl­ethanol

The title compound, C15H16O2, has a dihedral angle of 19.10 (5)° between the mean planes of the two benzene rings. There is an intra­molecular O—H⋯O hydrogen bond and the C—C—C—C torsion angle across the bridge between the two rings is 173.13 (14)°. The mol­ecules form inter­molecular O—H⋯O hydrogen-bonded chains extending along the a axis. C—H⋯π contacts are also observed between mol­ecules within the chains

A Novel Phenology Based Feature Subset Selection Technique Using Random Forest for Multitemporal PolSAR Crop Classification

Feature selection techniques intent to select a subset of features that minimizes redundancy and maximizes relevancy for classification problems in machine learning. Standard methods for feature selection in machine learning seldom take into account the domain knowledge associated with the data. Multitemporal crop classification studies with full-polarimetric synthetic aperture radar (PolSAR) data ought to consider the changes in the scattering mechanisms with their phenological growth stages. Hence, it is desirable to incorporate these changes while determining a feature subset for classification. In this study, a random forest (RF) based feature selection technique is proposed that takes into account the changes in the physical scattering mechanism with crop phenological stages for multitemporal PolSAR classification. The partial probability plot, which is an attribute of RF, provides information about the marginal effect of a polarimetric parameter on the desired crop class. Moreover, it is used to identify the specific range of a parameter where the probability of the presence of a particular crop class is high. The proposed technique identifies features that change significantly with crop phenology. The selected features are the ones whose ranges show maximum separation amongst crop classes. Additionally, the feature subset is refined by eliminating correlated features. The E-SAR L-band dataset of the AgriSAR-2006 campaign over the Demmin test site in Germany is used in this study. The classification accuracy using the novel feature selection technique is 99.12%. This is nominally better than using the features obtained from a standard feature selection method used in RF, such as mean decrease Gini (98.73%) and mean decrease accuracy (98.68%) that do not take into account the information based on crop phenology.This work was supported in part by the Spanish Ministry of Economy, Industry and Competitiveness, in part by the State Agency of Research (AEI), and in part by the European Funds for Regional Development under Projects TIN2014-55413-C2-2-P and TEC2017-85244-C2-1-P