Pressure-induced delocalization of photoexcited states in a semiconducting polymer.

We present broadband transient absorption spectroscopy on the fluorescent copolymer poly(9,9-dioctylfluorene-co-benzothiadiazole) under hydrostatic pressure of up to 75 kbar. We observe a strong reduction of the stimulated emission intensity under pressure, coupled with slower decay kinetics and reduced fluorescence intensity. These observations indicate increased delocalization of photogenerated singlet excitons, facilitated by an increased dielectric constant at high pressure. Spin triplet excitons, generated via an iridium complex-F8BT oligomer, show reduced lifetimes under pressure

Local majority dynamics on preferential attachment graphs

Suppose in a graph $G$ vertices can be either red or blue. Let $k$ be odd. At each time step, each vertex $v$ in $G$ polls $k$ random neighbours and takes the majority colour. If it doesn't have $k$ neighbours, it simply polls all of them, or all less one if the degree of $v$ is even. We study this protocol on the preferential attachment model of Albert and Barab\'asi, which gives rise to a degree distribution that has roughly power-law $P(x) \sim \frac{1}{x^{3}}$, as well as generalisations which give exponents larger than $3$. The setting is as follows: Initially each vertex of $G$ is red independently with probability $\alpha < \frac{1}{2}$, and is otherwise blue. We show that if $\alpha$ is sufficiently biased away from $\frac{1}{2}$, then with high probability, consensus is reached on the initial global majority within $O(\log_d \log_d t)$ steps. Here $t$ is the number of vertices and $d \geq 5$ is the minimum of $k$ and $m$ (or $m-1$ if $m$ is even), $m$ being the number of edges each new vertex adds in the preferential attachment generative process. Additionally, our analysis reduces the required bias of $\alpha$ for graphs of a given degree sequence studied by the first author (which includes, e.g., random regular graphs)

Augmentation of Urinary Lactoferrin Enhances Host Innate Immune Clearance of Uropathogenic Escherichia coli.

Urinary tract infection (UTI) is a prominent global health care burden. Although UTI is readily treated with antibiotics in healthy adults, complicated cases in immune-compromised individuals and the emerging antibiotic resistance of several uropathogens have accelerated the need for new treatment strategies. Here, we surveyed the composition of urinary exosomes in a mouse model of uropathgenic Escherichia coli (UPEC) UTI to identify specific urinary tract defense constituents for therapeutic development. We found an enrichment of the iron-binding glycoprotein lactoferrin in the urinary exosomes of infected mice. In subsequent in vitro studies, we identified human bladder epithelial cells as a source of lactoferrin during UPEC infection. We further established that exogenous treatment with human lactoferrin (hLf) reduces UPEC epithelial adherence and enhances neutrophil antimicrobial functions including bacterial killing and extracellular trap production. Notably, a single intravesicular dose of hLf drastically reduced bladder bacterial burden and neutrophil infiltration in our murine UTI model. We propose that lactoferrin is an important modulator of innate immune responses in the urinary tract and has potential application in novel therapeutic design for UTI

Charge transport at the protein-electrode interface in the emerging field of biomolecular electronics

The emerging field of BioMolecular Electronics aims to unveil the charge transport characteristics of biomolecules with two primary outcomes envisioned. The first is to use nature's efficient charge transport mechanisms as an inspiration to build the next generation of hybrid bioelectronic devices towards a more sustainable, biocompatible and efficient technology. The second is to understand this ubiquitous physicochemical process in life, exploited in many fundamental biological processes such as cell signalling, respiration, photosynthesis or enzymatic catalysis, leading us to a better understanding of disease mechanisms connected to charge diffusion. Extracting electrical signatures from a protein requires optimised methods for tethering the molecules to an electrode surface, where it is advantageous to have precise electrochemical control over the energy levels of the hybrid protein-electrode interface. Here, we review recent progress towards understanding the charge transport mechanisms through protein-electrode-protein junctions, which has led to the rapid development of the new BioMolecular Electronics field. The field has brought a new vision into the molecular electronics realm, wherein complex supramolecular structures such as proteins can efficiently transport charge over long distances when placed in a hybrid bioelectronic device. Such anomalous long-range charge transport mechanisms acutely depend on specific chemical modifications of the supramolecular protein structure and on the precisely engineered protein-electrode chemical interactions. Key areas to explore in more detail are parameters such as protein stiffness (dynamics) and intrinsic electrostatic charge and how these influence the transport pathways and mechanisms in such hybrid devices

Medication Regimen Complexity and Readmissions after Hospitalization for Heart Failure, Acute Myocardial Infarction, Pneumonia, and Chronic Obstructive Pulmonary Disease

Objectives: Readmission rate is increasingly being viewed as a key indicator of health system performance. Medication regimen complexity index scores may be predictive of readmissions; however, few studies have examined this potential association. The primary objective of this study was to determine whether medication regimen complexity index is associated with all-cause 30-day readmission after admission for heart failure, acute myocardial infarction, pneumonia, or chronic obstructive pulmonary disease. Methods: This study was an institutional review board–approved, multi-center, case–control study. Patients admitted with a primary diagnosis of heart failure, acute myocardial infarction, pneumonia, or chronic obstructive pulmonary disease were randomly selected for inclusion. Patients were excluded if they discharged against medical advice or expired during their index visit. Block randomization was utilized for equal representation of index diagnosis and site. Discharge medication regimen complexity index scores were compared between subjects with readmission versus those without. Medication regimen complexity index score was then used as a predictor in logistic regression modeling for readmission. Results: Seven hundred and fifty-six patients were randomly selected for inclusion, and 101 (13.4%) readmitted within 30days. The readmission group had higher medication regimen complexity index scores than the no-readmission group (p\u3c0.01). However, after controlling for demographics, disease state, length of stay, site, and medication count, medication regimen complexity index was no longer a significant predictor of readmission (odds ratio 0.99, 95% confidence interval 0.97–1.01) or revisit (odds ratio 0.99, 95% confidence interval 0.98–1.02). Conclusion: There is little evidence to support the use of medication regimen complexity index in readmission prediction when other measures are available. Medication regimen complexity index may lack sufficient sensitivity to capture an effect of medication regimen complexity on all-cause readmission

Using the Electronic Medical Record to Examine Racial and Ethnic Differences in Depression Diagnosis and Treatment in a Primary Care Population

Objective: We assessed racial and ethnic differences in depression diagnosis and treatment in a primary care population. Methods: A sample of primary care outpatients in 2007 was generated using the electronic medical record (EMR). Patients were considered depressed if their providers billed for depression-related codes; they were considered prescribed antidepressants if any antidepressants were on their medication list. Rates of diagnosis and medication prescription were estimated using a generalized linear model with a Poisson distribution, adjusting for covariates. Results: In the resulting sample (n=85,790), all minority groups were less likely to be diagnosed with depression as compared to Whites (p<0.05); 11.36% of Whites had a depression diagnosis, as compared to 6.44% of Asian Americans, 7.55% of African Americans, and 10.18% of Latino Americans. Among those with a depression diagnosis (n=11,096), 54.07% of African Americans were prescribed antidepressant medications, as compared to 63.19% Whites (p<0.05); Asian Americans and Latino Americans showed a trend of being less likely to be prescribed antidepressant medications. Conclusions: Our study illustrates differences in diagnosis and treatment for minority primary care patients, and is innovative in using the EMR to probe these differences. Further research is needed to understand the underlying reasons for these observed differences

The Web of Human Sexual Contacts

Many ``real-world'' networks are clearly defined while most ``social'' networks are to some extent subjective. Indeed, the accuracy of empirically-determined social networks is a question of some concern because individuals may have distinct perceptions of what constitutes a social link. One unambiguous type of connection is sexual contact. Here we analyze data on the sexual behavior of a random sample of individuals, and find that the cumulative distributions of the number of sexual partners during the twelve months prior to the survey decays as a power law with similar exponents $\alpha \approx 2.4$ for females and males. The scale-free nature of the web of human sexual contacts suggests that strategic interventions aimed at preventing the spread of sexually-transmitted diseases may be the most efficient approach.Comment: 7 pages with 2 eps figures. Latex file. For more details or for downloading the PDF file of the published article see http://polymer.bu.edu/~amaral/WebofContacts.html . For more results on teh structure of complex networks see http://polymer.bu.edu/~amaral/Networks.htm

Modeling the scaling properties of human mobility

While the fat tailed jump size and the waiting time distributions characterizing individual human trajectories strongly suggest the relevance of the continuous time random walk (CTRW) models of human mobility, no one seriously believes that human traces are truly random. Given the importance of human mobility, from epidemic modeling to traffic prediction and urban planning, we need quantitative models that can account for the statistical characteristics of individual human trajectories. Here we use empirical data on human mobility, captured by mobile phone traces, to show that the predictions of the CTRW models are in systematic conflict with the empirical results. We introduce two principles that govern human trajectories, allowing us to build a statistically self-consistent microscopic model for individual human mobility. The model not only accounts for the empirically observed scaling laws but also allows us to analytically predict most of the pertinent scaling exponents