The long reach of DNA sequence heterogeneity in diffusive processes

Many biological processes involve one dimensional diffusion over a correlated inhomogeneous energy landscape with a correlation length $\xi_c$. Typical examples are specific protein target location on DNA, nucleosome repositioning, or DNA translocation through a nanopore, in all cases with $\xi_c\approx$ 10 nm. We investigate such transport processes by the mean first passage time (MFPT) formalism, and find diffusion times which exhibit strong sample to sample fluctuations. For a a displacement $N$, the average MFPT is diffusive, while its standard deviation over the ensemble of energy profiles scales as $N^{3/2}$ with a large prefactor. Fluctuations are thus dominant for displacements smaller than a characteristic $N_c \gg \xi_c$: typical values are much less than the mean, and governed by an anomalous diffusion rule. Potential biological consequences of such random walks, composed of rapid scans in the vicinity of favorable energy valleys and occasional jumps to further valleys, is discussed

Apex Exponents for Polymer--Probe Interactions

We consider self-avoiding polymers attached to the tip of an impenetrable probe. The scaling exponents $\gamma_1$ and $\gamma_2$, characterizing the number of configurations for the attachment of the polymer by one end, or at its midpoint, vary continuously with the tip's angle. These apex exponents are calculated analytically by $\epsilon$-expansion, and numerically by simulations in three dimensions. We find that when the polymer can move through the attachment point, it typically slides to one end; the apex exponents quantify the entropic barrier to threading the eye of the probe

Subject–ventilator synchrony during neural versus pneumatically triggered non-invasive helmet ventilation

OBJECTIVE: Patient-ventilator synchrony during non-invasive pressure support ventilation with the helmet device is often compromised when conventional pneumatic triggering and cycling-off were used. A possible solution to this shortcoming is to replace the pneumatic triggering with neural triggering and cycling-off-using the diaphragm electrical activity (EA(di)). This signal is insensitive to leaks and to the compliance of the ventilator circuit. DESIGN: Randomized, single-blinded, experimental study. SETTING: University Hospital. PARTICIPANTS AND SUBJECTS: Seven healthy human volunteers. INTERVENTIONS: Pneumatic triggering and cycling-off were compared to neural triggering and cycling-off during NIV delivered with the helmet. MEASUREMENTS AND RESULTS: Triggering and cycling-off delays, wasted efforts, and breathing comfort were determined during restricted breathing efforts (<20% of voluntary maximum EA(di)) with various combinations of pressure support (PSV) (5, 10, 20 cm H(2)O) and respiratory rates (10, 20, 30 breath/min). During pneumatic triggering and cycling-off, the subject-ventilator synchrony was progressively more impaired with increasing respiratory rate and levels of PSV (p < 0.001). During neural triggering and cycling-off, effect of increasing respiratory rate and levels of PSV on subject-ventilator synchrony was minimal. Breathing comfort was higher during neural triggering than during pneumatic triggering (p < 0.001). CONCLUSIONS: The present study demonstrates in healthy subjects that subject-ventilator synchrony, trigger effort, and breathing comfort with a helmet interface are considerably less impaired during increasing levels of PSV and respiratory rates with neural triggering and cycling-off, compared to conventional pneumatic triggering and cycling-off

Identification of long-duration noise transients in LIGO and Virgo

The LIGO and Virgo detectors are sensitive to a variety of noise sources, such as instrumental artifacts and environmental disturbances. The Stochastic Transient Analysis Multi-detector Pipeline (STAMP) has been developed to search for long-duration (t$\gtrsim$1s) gravitational-wave (GW) signals. This pipeline can also be used to identify environmental noise transients. Here we present an algorithm to determine when long-duration noise sources couple into the interferometers, as well as identify what these noise sources are. We analyze the cross-power between a GW strain channel and an environmental sensor, using pattern recognition tools to identify statistically significant structure in cross-power time-frequency maps. We identify interferometer noise from airplanes, helicopters, thunderstorms and other sources. Examples from LIGO's sixth science run, S6, and Virgo's third scientific run, VSR3, are presented.Comment: 10 pages, 7 figures, Gravitational-wave Physics & Astronomy Worksho