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Current artificial photosynthesis (APS) systems are promising for the storage of solar energy via transportable and storable fuels, but the anodic half-reaction of water oxidation is an energy intensive process which in many cases poorly couples with the cathodic half-reaction. Here we demonstrate a self-sustaining microbial photoelectrosynthesis (MPES) system that pairs microbial electrochemical oxidation with photoelectrochemical water reduction for energy efficient H2 generation. MPES reduces the overall energy requirements thereby greatly expanding the range of semiconductors that can be utilized in APS. Due to the recovery of chemical energy from waste organics by the mild microbial process and utilization of cost-effective and stable catalyst/electrode materials, our MPES system produced a stable current of 0.4 mA/cm2 for 24 h without any external bias and ∼10 mA/cm2 with a modest bias under one sun illumination. This system also showed other merits, such as creating benefits of wastewater treatment and facile preparation and scalability. 

Despite the known benefits of data‐driven approaches, the lack of approaches for identifying functional neuroimaging patterns that capture both individual variations and inter‐subject correspondence limits the clinical utility of rsfMRI and its application to single‐subject analyses. Here, using rsfMRI data from over 100k individuals across private and public datasets, we identify replicable multi‐spatial‐scale canonical intrinsic connectivity network (ICN) templates via the use of multi‐model‐order independent component analysis (ICA). We also study the feasibility of estimating subject‐specific ICNs via spatially constrained ICA. The results show that the subject‐level ICN estimations vary as a function of the ICN itself, the data length, and the spatial resolution. In general, large‐scale ICNs require less data to achieve specific levels of (within‐ and between‐subject) spatial similarity with their templates. Importantly, increasing data length can reduce an ICN's subject‐level specificity, suggesting longer scans may not always be desirable. We also find a positive linear relationship between data length and spatial smoothness (possibly due to averaging over intrinsic dynamics), suggesting studies examining optimized data length should consider spatial smoothness. Finally, consistency in spatial similarity between ICNs estimated using the full data and subsets across different data lengths suggests lower within‐subject spatial similarity in shorter data is not wholly defined by lower reliability in ICN estimates, but may be an indication of meaningful brain dynamics which average out as data length increases.