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Resting state functional connectomes are massive and complex. It is an open question, however, whether connectomes differ across individuals in a correspondingly massive number of ways, or whether most differences take a small number of characteristic forms. We systematically investigated this question and found clear evidence of low-rank structure in which a modest number of connectomic components, around 50–150, account for a sizable portion of inter-individual connectomic variation. This number was convergently arrived at with multiple methods including estimation of intrinsic dimensionality and assessment of reconstruction of out-of-sample data. In addition, we show that these connectomic components enable prediction of a broad array of neurocognitive and clinical symptom variables at levels comparable to a leading method that is trained on the whole connectome. Qualitative observation reveals that these connectomic components exhibit extensive community structure reflecting interrelationships between intrinsic connectivity networks. We provide quantitative validation of this observation using novel stochastic block model-based methods. We propose that these connectivity components form an effective basis set for quantifying and interpreting inter-individual connectomic differences, and for predicting behavioral/clinical phenotypes.

Monodisperse nanoparticles with antimicrobial polymer shells were fabricated using a seeded copolymerization; they exhibited excellent antibacterial activities against gram-positive bacteria as well as gram-negative bacteria.

In this study, we investigated an energy harvesting effect of tensile stress using piezoelectric polymers and flexible electrodes. A chemical-vapor-deposition grown graphene film was transferred onto both sides of the PVDF and P(VDF-TrFE) films simultaneously by means of a conventional wet chemical method. Output voltage induced by sound waves was measured and analyzed when a mechanical tension was applied to the device. Another energy harvester was made with a metallic electrode, where Al and Ag were deposited by using an electron-beam evaporator. When acoustic vibrations (105 dB) were applied to the graphene/PVDF/graphene device, an induced voltage of 7.6 Vpp was measured with a tensile stress of 1.75 MPa, and this was increased up to 9.1 Vpp with a stress of 2.18 MPa for the metal/P(VDF-TrFE)/metal device. The 9 metal/PVDF/metal layers were stacked as an energy harvester, and tension was applied by using springs. Also, we fabricated a full-wave rectifying circuit to store the electrical energy in a 100 μF capacitor, and external vibration generated the electrical charges. As a result, the stored voltage at the capacitor, obtained from the harvester via a bridge diode rectifier, was saturated to ~7.04 V after 180 s charging time.

Owing to its high sensitivity and high selectivity along with rapid response time, plasmonic detection has gained considerable interest in a wide variety of sensing applications. To improve the fieldwork applicability and reliability of plasmonic detection, the integration of plasmonic nanoparticles into optical devices is desirable. Herein, we propose an integrated label-free detection platform comprising a plasmonic cavity that allows sensitive molecular detection via either surface-enhanced Raman scattering (SERS) or plasmon resonance energy transfer (PRET). A small droplet of metal ion solution spontaneously produces a plasmonic cavity on the surface of uncured poly(dimethylsiloxane) (PDMS), and as PDMS is cured, the metal ions are reduced to form a plasmonic antennae array on the cavity surface. Unique spherical feature and the integrated metallic nanoparticles of the cavity provide excellent optical functions to focus the incident light in the cavity and to rescatter the light absorbed by the nanoparticles. The optical properties of the plasmonic cavity for SERS or PRET are optimized by controlling the composition, size, and density of the metal nanoparticles. By using the cavity, we accomplish both 1000-fold sensitive detection and real-time monitoring of reactive oxygen species secreted by live cells via PRET. In addition, we achieve sensitive detection of trace amounts of toxic environmental molecules such as 5-chloro-2-methyl-4-isothiazolin-3-one/2-methyl-4-isothiazol-3-one (CMIT/MIT) and bisphenol A, as well as several small biomolecules such as glucose, adenine, and tryptophan, via SERS.

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