Eigenvalue spectral properties of sparse random matrices obeying Dale's law

Understanding the dynamics of large networks of neurons with heterogeneous connectivity architectures is a complex physics problem that demands novel mathematical techniques. Biological neural networks are inherently spatially heterogeneous, making them difficult to mathematically model. Random recurrent neural networks capture complex network connectivity structures and enable mathematically tractability. Our paper generalises previous classical results to sparse connectivity matrices which have distinct excitatory (E) or inhibitory (I) neural populations. By investigating sparse networks we construct our analysis to examine the impacts of all levels of network sparseness, and discover a novel nonlinear interaction between the connectivity matrix and resulting network dynamics, in both the balanced and unbalanced cases. Specifically, we deduce new mathematical dependencies describing the influence of sparsity and distinct E/I distributions on the distribution of eigenvalues (eigenspectrum) of the networked Jacobian. Furthermore, we illustrate that the previous classical results are special cases of the more general results we have described here. Understanding the impacts of sparse connectivities on network dynamics is of particular importance for both theoretical neuroscience and mathematical physics as it pertains to the structure-function relationship of networked systems and their dynamics. Our results are an important step towards developing analysis techniques that are essential to studying the impacts of larger scale network connectivity on network function, and furthering our understanding of brain function and dysfunction.Comment: 18 pages, 6 figure

Developing a Raspberry Pi magnetometer for schools in the UK

We describe our efforts to build a magnetic field sensor to be deployed in schools across the United Kingdom, adding to the existing variometer network from AuroraWatch set up by the University of Lancaster (Figure 1). The aim is to encourage students from 14-18 years old to look at how sensors can be used to collect geophysical data and integrate it together to give a wider understanding of physical phenomena. A second aim is to provide useful data on the spatial variation of the magnetic field for analysis of geomagnetic storms, alongside data from the BGS observatory and SAMNET variometer network. The system uses a Raspberry Pi computer as a logging and data transfer device, connected to a set of miniature fluxgate magnetometers. The system has a nominal sensitivity of around 1 nT RMS (~1 part in 50,000) in each component and is relatively low-cost at about £250 per unit. We intend to build 10 systems initially. In this poster we show results from the build and testing of the sensor and examples of recorded horizontal field

Experimental verification of the temperature coefficient of resistivity

We have created an experimental procedure for determining the temperature coefficient of resistivity, $\alpha_R$, for introductory physics laboratories. This method examines the relationship between temperature and resistivity to establish $\alpha_R$ within 10% of the accepted value

Determination of the coefficient of thermal expansion by measuring frequency of a heated music wire

Engaging with physical and material properties through empirical observation is a fundamental part of undergraduate physics and engineering education. Several works have proposed experiments to determine thermal physical constants of materials such as finding the coefficient of linear expansion. As Dajbych and Polak et al. have shown, methods for experimentally verifying physical constants can be done by measuring the frequency of a plucked high-carbon steel wire on a guitar. Building upon our previous work, we have extended our method to verify the coefficient of linear thermal expansion through an accessible procedure directed at introductory physics education

Which comorbid conditions should we be analyzing as risk factors for healthcare-associated infections?

OBJECTIVETo determine which comorbid conditions are considered causally related to central-line associated bloodstream infection (CLABSI) and surgical-site infection (SSI) based on expert consensus.DESIGNUsing the Delphi method, we administered an iterative, 2-round survey to 9 infectious disease and infection control experts from the United States.METHODSBased on our selection of components from the Charlson and Elixhauser comorbidity indices, 35 different comorbid conditions were rated from 1 (not at all related) to 5 (strongly related) by each expert separately for CLABSI and SSI, based on perceived relatedness to the outcome. To assign expert consensus on causal relatedness for each comorbid condition, all 3 of the following criteria had to be met at the end of the second round: (1) a majority (&gt;50%) of experts rating the condition at 3 (somewhat related) or higher, (2) interquartile range (IQR)≤1, and (3) standard deviation (SD)≤1.RESULTSFrom round 1 to round 2, the IQR and SD, respectively, decreased for ratings of 21 of 35 (60%) and 33 of 35 (94%) comorbid conditions for CLABSI, and for 17 of 35 (49%) and 32 of 35 (91%) comorbid conditions for SSI, suggesting improvement in consensus among this group of experts. At the end of round 2, 13 of 35 (37%) and 17 of 35 (49%) comorbid conditions were perceived as causally related to CLABSI and SSI, respectively.CONCLUSIONSOur results have produced a list of comorbid conditions that should be analyzed as risk factors for and further explored for risk adjustment of CLABSI and SSI.Infect Control Hosp Epidemiol 2017;38:449–454</jats:sec

Is there a correlation between infection control performance and other hospital quality measures?

Quality measures are increasingly reported by hospitals to the Centers for Medicare and Medicaid Services (CMS), yet there may be tradeoffs in performance between infection control (IC) and other quality measures. Hospitals that performed best on IC measures did not perform well on most CMS non–IC quality measures

Differential effectiveness of berry polyphenols as anti-giardial agents

Following previous work on the anti-giardial effect of blueberry polyphenols, a range of polyphenol-rich extracts from berries and other fruits was screened for their ability to kill Giardia duodenalis, an intestinal parasite of humans. Polyphenol-rich extracts were prepared from berries using solid-phase extraction and applied to trophozoites of Giardia duodenalis grown in vitro. All berry extracts caused inhibition at 166 μg gallic acid equivalents (GAE)/ml phenol content but extracts from strawberry, arctic bramble, blackberry and cloudberry were as effective as the currently used drug, metronidazole, causing complete trophozoite mortality in vitro. Cloudberry extracts were found to be the most effective causing effectively complete trophozoite mortality at 66 μg GAE/ml. The polyphenol composition of the more effective berry extracts suggested that the presence of ellagitannins could be an important factor. However, the potency of cloudberry could be related to high ellagitannin content but also to the presence of substantial amounts of unconjugated p-coumaric acid and benzoic acid. These in vitro effects occur at concentrations easily achievable in the gut after berry ingestion and we discuss the likelihood that berry extracts could be effective anti-giardial agents in vivo