High-efficiency robust perovskite solar cells on ultrathin flexible substrates.

Wide applications of personal consumer electronics have triggered tremendous need for portable power sources featuring light-weight and mechanical flexibility. Perovskite solar cells offer a compelling combination of low-cost and high device performance. Here we demonstrate high-performance planar heterojunction perovskite solar cells constructed on highly flexible and ultrathin silver-mesh/conducting polymer substrates. The device performance is comparable to that of their counterparts on rigid glass/indium tin oxide substrates, reaching a power conversion efficiency of 14.0%, while the specific power (the ratio of power to device weight) reaches 1.96 kW kg(-1), given the fact that the device is constructed on a 57-μm-thick polyethylene terephthalate based substrate. The flexible device also demonstrates excellent robustness against mechanical deformation, retaining >95% of its original efficiency after 5,000 times fully bending. Our results confirmed that perovskite thin films are fully compatible with our flexible substrates, and are thus promising for future applications in flexible and bendable solar cells

MiR130b from Schlafen4+ MDSCs stimulates epithelial proliferation and correlates with preneoplastic changes prior to gastric cancer

The myeloid differentiation factor Schlafen4 (Slfn4) marks a subset of myeloid-derived suppressor cells (MDSCs) in the stomach during Helicobacter-induced spasmolytic polypeptide-expressing metaplasia (SPEM). OBJECTIVE: To identify the gene products expressed by Slfn4+-MDSCs and to determine how they promote SPEM. DESIGN: We performed transcriptome analyses for both coding genes (mRNA by RNA-Seq) and non-coding genes (microRNAs using NanoString nCounter) using flow-sorted SLFN4+ and SLFN4- cells from Helicobacter-infected mice exhibiting metaplasia at 6 months postinfection. Thioglycollate-elicited myeloid cells from the peritoneum were cultured and treated with IFNα to induce the T cell suppressor phenotype, expression of MIR130b and SLFN4. MIR130b expression in human gastric tissue including gastric cancer and patient sera was determined by qPCR and in situ hybridisation. Knockdown of MiR130b in vivo in Helicobacter-infected mice was performed using Invivofectamine. Organoids from primary gastric cancers were used to generate xenografts. ChIP assay and Western blots were performed to demonstrate NFκb p65 activation by MIR130b. RESULTS: MicroRNA analysis identified an increase in MiR130b in gastric SLFN4+ cells. Moreover, MIR130b colocalised with SLFN12L, a human homologue of SLFN4, in gastric cancers. MiR130b was required for the T-cell suppressor phenotype exhibited by the SLFN4+ cells and promoted Helicobacter-induced metaplasia. Treating gastric organoids with the MIR130b mimic induced epithelial cell proliferation and promoted xenograft tumour growth. CONCLUSION: Taken together, MiR130b plays an essential role in MDSC function and supports metaplastic transformation.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Multi‐objective evolutionary optimization for hardware‐aware neural network pruning

Neural network pruning is a popular approach to reducing the computational complexity of deep neural networks. In recent years, as growing evidence shows that conventional network pruning methods employ inappropriate proxy metrics, and as new types of hardware become increasingly available, hardware-aware network pruning that incorporates hardware characteristics in the loop of network pruning has gained growing attention. Both network accuracy and hardware efficiency (latency, memory consumption, etc.) are critical objectives to the success of network pruning, but the conflict between the multiple objectives makes it impossible to find a single optimal solution. Previous studies mostly convert the hardware-aware network pruning to optimization problems with a single objective. In this paper, we propose to solve the hardware-aware network pruning problem with Multi-Objective Evolutionary Algorithms (MOEAs). Specifically, we formulate the problem as a multi-objective optimization problem, and propose a novel memetic MOEA, namely HAMP, that combines an efficient portfolio-based selection and a surrogate-assisted local search, to solve it. Empirical studies demonstrate the potential of MOEAs in providing simultaneously a set of alternative solutions and the superiority of HAMP compared to the state-of-the-art hardware-aware network pruning method