Publications

This study was designed to clarify the association between inter-episode bipolar disorder (BD) and sleep architecture. Participants completed a baseline symptom and sleep assessment and, 3 months later, an assessment of symptoms and impairment. The effects of psychiatric medications on sleep architecture were also considered. Participants included 22 adults with BD I or II (inter-episode) and 22 non-psychiatric controls. The sleep assessment was conducted at the Sleep and Psychological Disorders Laboratory at the University of California, Berkeley. Follow-up assessments 3 months later were conducted over the phone. Results indicate that, at the sleep assessment, BD participants exhibited greater rapid eye movement sleep (REM) density than control participants with no other group differences in sleep architecture. Sleep architecture was not correlated with concurrent mood symptoms in either group. In the BD group, duration of the first REM period and slow-wave sleep (SWS) amount were positively correlated with manic symptoms and impairment at 3 months, while REM density was positively correlated with depressive symptoms and impairment at 3 months. The amount of Stage 2 sleep was negatively correlated with manic symptoms and impairment at 3 months. In contrast, for the control group, REM density was negatively correlated with impairment at 3 months. SWS and Stage 2 sleep were not correlated with symptoms or impairment. Study findings suggest that inter-episode REM sleep, SWS and Stage 2 sleep are correlated with future manic and depressive symptoms and impairment in BD. This is consistent with the proposition that sleep architecture may be a mechanism of illness maintenance in BD.

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

The electrochemical reduction of CO 2 (CO 2 R) to value-added products is an attractive technology for tackling the rising atmospheric CO 2 levels and storing intermittent renewable energy into chemical bonds. Fundamental understanding of CO 2 R has progressed significantly in recent years and is critical in the development of CO 2 R to liquid-fuel electrolyzers, where gas-diffusion electrodes (GDEs) have been shown to be key enabling architectures. Various designs have been proposed and studied in the literature to enhance overall selectivity, rates, and maximize the conversion of CO 2 , the latter of which is only now being recognized as a critical issue. In this respect, there is a need to explore the governing phenomena inherent in these architectures to enable optimization. Mathematical modeling is ideally suited to tackle and explore these multiphysics interactions and provide virtual design analysis. In this talk, we discuss modeling methodologies and physics inherent in these devices and present our recent modeling of GDEs for CO 2 reduction. We specifically examine the impacts of multiphase flow and related phenomena on overall cell performance. We then explore the performance and limitations of various cell designs guided by simulation results and examine potential methods for improving water management and tuning catalyst selectivity including the use of different anion-exchange and bipolar membranes. Finally, we discuss the disparities in local environments between aqueous and GDE devices and propose strategies to reduce the gap in knowledge between the two systems. Acknowledgements This material is based upon work performed by the Joint Center for Artificial Photosynthesis, a DOE Energy Innovation Hub, supported through the Office of Science of the U.S. Department of Energy under Award Number DE-SC0004993.

Nebular-phase spectra of SN 2006aj, discovered in coincidence with XRF 060218, were obtained with Keck in 2006 July and VLT in 2006 September. At the latter epoch spectropolarimetry was also attempted, yielding a polarization upper limit of ~2%. The spectra show strong [O I] and Mg I] emission lines, as expected in Type Ic supernovae, but weak Ca II lines. The [Fe II] lines that were strong in SN 1998bw are much weaker in SN 2006aj, consistent with its lower luminosity. The outer velocity of the line-emitting region is ~8000 km s-1 in July and ~7400 km s-1 in September, consistent with the relatively low expansion kinetic energy of SN 2006aj. All lines have similar width, and their profiles indicate that no major asymmetries are present in the ejecta at velocities below v < 8000 km s-1, except perhaps in the innermost part. The spectra were modeled with a non-LTE code. The mass of 56Ni required to power the emission is ~0.20 M☉, confirming earlier results based on the light curve. The oxygen mass is ~1.5 M☉, again much less than in SN 1998bw, but ~0.7 M☉ larger than the value derived from the early-time modeling. The ejected mass below 8000 km s-1 is ~2 M☉, confirming that SN 2006aj was only about twice as massive and energetic as the normal Type Ic SN 1994I. The presence of a dense inner core, containing ~1 M☉ of mostly oxygen and carbon, is inferred, as in all broad-lined SNe Ic. This core, which may be disklike, is too deep to influence the early light curve and too small to affect the late polarization spectrum.

This work presents a novel mem-computing paradigm for the Internet-of-Things era. The paradigm revolves around the peculiar feature of memristors to process information and store data in the same physical location in order to enhance the performance of Cellular Nonlinear Networks, especially in view of a future hardware implementation and its integration with state-of-the-art high-resolution visual sensor arrays. A bistable-like non-volatile memristor is used in each dynamic processor of the cellular network in place for the linear resistor of a standard realization. The resulting cell is then endowed with a couple of possible Dynamic Route Maps, one for each of the two memristor states. This peculiar property endows the network with new functionalities as compared to a standard nonlinear dynamic array.