Summary NLRP3 is a key component of the macromolecular signaling complex called the inflammasome that promotes caspase 1-dependent production of IL-1β. The adapter ASC is necessary for NLRP3-dependent inflammasome function, but it is not known if ASC is a sufficient partner, and whether inflammasome formation occurs in the cytosol or in association with mitochondria is controversial. Here we show that the mitochondria-associated adapter molecule, MAVS, is required for optimal NLRP3 inflammasome activity. MAVS mediates recruitment of NLRP3 to mitochondria, promoting production of IL-1β and the pathophysiologic activity of the NLRP3 inflammasome in vivo. Our data support a more complex model of NLRP3 inflammasome activation than previously appreciated, with at least two adapters required for maximal function. Since MAVS is a mitochondria-associated molecule previously considered to be uniquely involved in type 1 interferon production, these findings also reveal unexpected polygamous involvement of PYD/CARD domain-containing adapters in innate immune signaling events.

Lesion analysis is a classic approach to study brain functions. Because brain function is a result of coherent activations of a collection of functionally related voxels, lesion-symptom relations are generally contributed by multiple voxels simultaneously. Although voxel-based lesion symptom mapping (VLSM) has made substantial contributions to the understanding of brain-behavior relationships, a better understanding of the brain-behavior relationship contributed by multiple brain regions needs a multivariate lesion symptom mapping (MLSM). The purpose of this paper was to develop an MLSM using a machine learning-based multivariate regression algorithm: support vector regression (SVR). In the proposed SVR-LSM, the symptom relation to the entire lesion map as opposed to each isolated voxel is modeled using a non-linear function, so the intervoxel correlations are intrinsically considered, resulting in a potentially more sensitive way to examine lesion-symptom relationships. To explore the relative merits of VLSM and SVR-LSM we used both approaches in the analysis of a synthetic dataset. SVR-LSM showed much higher sensitivity and specificity for detecting the synthetic lesion-behavior relations than VLSM. When applied to lesion data and language measures from patients with brain damages, SVR-LSM reproduced the essential pattern of previous findings identified by VLSM and showed higher sensitivity than VLSM for identifying the lesion-behavior relations. Our data also showed the possibility of using lesion data to predict continuous behavior scores.

In recent years, organometal trihalide perovskites have emerged as promising materials for low-cost, flexible, and highly efficient solar cells. Despite their processing advantages, before the technology can be commercialized the poor stability of the organic-inorganic hybrid perovskite materials with regard to humidity, heat, light, and oxygen has be to overcome. Herein, we distill the current state-of-the-art and highlight recent advances in improving the chemical stability of perovskite materials by substitution of the A-cation and X-anion. Our hope is to pave the way for the rational design of perovskite materials to realize perovskite solar cells with unprecedented improvement in stability.