Polymer Adsorption on Disordered Substrate

We analyze the recently proposed "pattern-matching" phase of a Gaussian random heteropolymer adsorbed on a disordered substrate [S. Srebnik, A.K. Chakraborty and E.I. Shakhnovich, Phys. Rev. Lett. 77, 3157 (1996)]. By mapping the problem to that of a directed homopolymer in higher-dimensional random media, we show that the pattern-matching phase is asymptotically weakly unstable, and the large scale properties of the system are given by that of an adsorbed homopolymer.Comment: 5 pages, RevTeX, text also available at http://matisse.ucsd.edu/~hw

Unofficial policy: access to housing, housing information and social services among homeless drug users in Hartford, Connecticut

BACKGROUND: Much research has shown that the homeless have higher rates of substance abuse problems than housed populations and that substance abuse increases individuals' vulnerability to homelessness. However, the effects of housing policies on drug users' access to housing have been understudied to date. This paper will look at the "unofficial" housing policies that affect drug users' access to housing. METHODS: Qualitative interviews were conducted with 65 active users of heroin and cocaine at baseline, 3 and 6 months. Participants were purposively sampled to reflect a variety of housing statuses including homeless on the streets, in shelters, "doubled-up" with family or friends, or permanently housed in subsidized, unsubsidized or supportive housing. Key informant interviews and two focus group interviews were conducted with 15 housing caseworkers. Data were analyzed to explore the processes by which drug users receive information about different housing subsidies and welfare benefits, and their experiences in applying for these. RESULTS: A number of unofficial policy mechanisms limit drug users' access to housing, information and services, including limited outreach to non-shelter using homeless regarding housing programs, service provider priorities, and service provider discretion in processing applications and providing services. CONCLUSION: Unofficial policy, i.e. the mechanisms used by caseworkers to ration scarce housing resources, is as important as official housing policies in limiting drug users' access to housing. Drug users' descriptions of their experiences working with caseworkers to obtain permanent, affordable housing, provide insights as to how access to supportive and subsidized housing can be improved for this population

epoxy simulations

2019 raw results for epoxy simulation

The association between ABO blood group and obstetric hemorrhage.

Whether intra- and early post-partum hemorrhage is influenced by ABO blood groups remains unknown. Therefore, we compared women with O to non-O blood groups with regard to maternal post-partum hemorrhage and transfusion need. This retrospective study was conducted in a single tertiary center between 2005 and 2014. For the purpose of the study, parturients were categorized as O and non-O blood groups. Data included all deliveries but excluded patients with missing blood grouping or hemoglobin values, and/or stillbirth. Drop in hemoglobin was defined as hemoglobin concentration at admission for delivery minus lowest hemoglobin concentration post-delivery. Study outcomes were postpartum hemorrhage, hemoglobin drop >2-7 g/dL inclusive, and packed red blood cells transfusion. STATISTICS: descriptive, χ(2) (p < 0.05 significant) and multivariable regression models [odds ratio (OR), 95 % confidence interval (CI), p value]. 125,768 deliveries were included. After multivariable analysis, women with O blood type relative to women with non-O blood type had significantly higher odds of postpartum hemorrhage (OR 1.14; 95 % CI 1.05-1.23, p < 0.001), higher odds of statistically significant hemoglobin decreases of >2, 3, or 4 g/dL (OR 1.07; 95 % CI 1.04-1.11, p < 0.001, OR 1.08; 95 % CI 1.03-1.14, p = 0.002, OR 1.14; 95 % CI 1.05-1.23, p = 0.001; respectively), and higher odds, albeit not statistically significant of 5, 6, or 7 g/dL decreases in hemoglobin (OR 1.13; 95 % CI 1.00-1.29, p = 0.055, OR 1.05; 95 % CI 0.84-1.32, p = 0.66, OR 1.15; 95 % CI 0.79-1.68, p = 0.46; respectively), but no difference in blood products transfusion (OR 1.03; 95 % CI 0.92-1.16, p = 0.58). In conclusion, women with blood type O may be at greater risk of obstetrical hemorrhage

NICU Admission for Term Neonates in a Large Single-Center Population: A Comprehensive Assessment of Risk Factors Using a Tandem Analysis Approach

Objective: Neonatal intensive care unit (NICU) admission among term neonates is associated with significant morbidity and mortality, as well as high healthcare costs. A comprehensive NICU admission risk assessment using an integrated statistical approach for this rare admission event may be used to build a risk calculation algorithm for this group of neonates prior to delivery. Methods: A single-center case–control retrospective study was conducted between August 2005 and December 2019, including in-hospital singleton live born neonates, born at ≥37 weeks’ gestation. Analyses included univariate and multivariable models combined with the machine learning gradient-boosting model (GBM). The primary aim of the study was to identify and quantify risk factors and causes of NICU admission of term neonates. Results: During the study period, 206,509 births were registered at the Shaare Zedek Medical Center. After applying the study exclusion criteria, 192,527 term neonates were included in the study; 5292 (2.75%) were admitted to the NICU. The NICU admission risk was significantly higher (ORs [95%CIs]) for offspring of nulliparous women (1.19 [1.07, 1.33]), those with diabetes mellitus or hypertensive complications of pregnancy (2.52 [2.09, 3.03] and 1.28 [1.02, 1.60] respectively), and for those born during the 37th week of gestation (2.99 [2.63, 3.41]; p < 0.001 for all), adjusted for congenital malformations and genetic syndromes. A GBM to predict NICU admission applied to data prior to delivery showed an area under the receiver operating characteristic curve of 0.750 (95%CI 0.743–0.757) and classified 27% as high risk and 73% as low risk. This risk stratification was significantly associated with adverse maternal and neonatal outcomes. Conclusion: The present study identified NICU admission risk factors for term neonates; along with the machine learning ranking of the risk factors, the highly predictive model may serve as a basis for individual risk calculation algorithm prior to delivery. We suggest that in the future, this type of planning of the delivery will serve different health systems, in both high- and low-resource environments, along with the NICU admission or transfer policy