Cypin: A novel target for traumatic brain injury.

Cytosolic PSD-95 interactor (cypin), the primary guanine deaminase in the brain, plays key roles in shaping neuronal circuits and regulating neuronal survival. Despite this pervasive role in neuronal function, the ability for cypin activity to affect recovery from acute brain injury is unknown. A key barrier in identifying the role of cypin in neurological recovery is the absence of pharmacological tools to manipulate cypin activity in vivo. Here, we use a small molecule screen to identify two activators and one inhibitor of cypin's guanine deaminase activity. The primary screen identified compounds that change the initial rate of guanine deamination using a colorimetric assay, and secondary screens included the ability of the compounds to protect neurons from NMDA-induced injury and NMDA-induced decreases in frequency and amplitude of miniature excitatory postsynaptic currents. Hippocampal neurons pretreated with activators preserved electrophysiological function and survival after NMDA-induced injury in vitro, while pretreatment with the inhibitor did not. The effects of the activators were abolished when cypin was knocked down. Administering either cypin activator directly into the brain one hour after traumatic brain injury significantly reduced fear conditioning deficits 5 days after injury, while delivering the cypin inhibitor did not improve outcome after TBI. Together, these data demonstrate that cypin activation is a novel approach for improving outcome after TBI and may provide a new pathway for reducing the deficits associated with TBI in patients.

Variable specificity of memory trace reactivation during hippocampal sharp wave ripples.

Hippocampal sharp wave-ripples (SWR) are thought to mediate brain-wide reactivation of memory traces in service of memory consolidation. However, rather than the faithful replay of neural activity observed during a specific experience, reactivation in both the hippocampus and downstream regions is more variable. We suggest that variable reactivation is a unifying feature of recurrent brain circuits. In the hippocampus, self-organized activation during offline states is constrained by existing attractor manifolds, or maps, and may be biased toward particular mapped locations by salient experience, which results in the appearance of experience-specific replay. Similarly, the impact of SWR-associated reactivation on downstream regions is not a simple transfer of hippocampal representational content. Rather, the response of downstream regions depends on a transformation function, defined by both the feedforward and local circuit architecture, as well as the 'listening state' of the downstream region. We hypothesize that SWRs act as a multiplexed signal, the mnemonic specificity of which is largely determined by this transformation function, and discuss the implications of this framing for theories of systems consolidation.

A cross-sectional study of the temporal evolution of electricity consumption of six commercial buildings.

Current approaches to building efficiency diagnoses include conventional energy audit techniques that can be expensive and time consuming. In contrast, virtual energy audits of readily available 15-minute-interval building electricity consumption are being explored to provide quick, inexpensive, and useful insights into building operation characteristics. A cross sectional analysis of six buildings in two different climate zones provides methods for data cleaning, population-based building comparisons, and relationships (correlations) of weather and electricity consumption. Data cleaning methods have been developed to categorize and appropriately filter or correct anomalous data including outliers, missing data, and erroneous values (resulting in < 0.5% anomalies). The utility of a cross-sectional analysis of a sample set of building's electricity consumption is found through comparisons of baseload, daily consumption variance, and energy use intensity. Correlations of weather and electricity consumption 15-minute interval datasets show important relationships for the heating and cooling seasons using computed correlations of a Time-Specific-Averaged-Ordered Variable (exterior temperature) and corresponding averaged variables (electricity consumption)(TSAOV method). The TSAOV method is unique as it introduces time of day as a third variable while also minimizing randomness in both correlated variables through averaging. This study found that many of the pair-wise linear correlation analyses lacked strong relationships, prompting the development of the new TSAOV method to uncover the causal relationship between electricity and weather. We conclude that a combination of varied HVAC system operations, building thermal mass, plug load use, and building set point temperatures are likely responsible for the poor correlations in the prior studies, while the correlation of time-specific-averaged-ordered temperature and corresponding averaged variables method developed herein adequately accounts for these issues and enables discovery of strong linear pair-wise correlation R values. TSAOV correlations lay the foundation for a new approach to building studies, that mitigates plug load interferences and identifies more accurate insights into weather-energy relationship for all building types. Over all six buildings analyzed the TSAOV method reported very significant average correlations per building of 0.94 to 0.82 in magnitude. Our rigorous statistics-based methods applied to 15-minute-interval electricity data further enables virtual energy audits of buildings to quickly and inexpensively inform energy savings measures.