Micro-Segregated Liquid Crystal Haze Films for Photovoltaic Applications: A Novel Strategy to Fabricate Haze Films Employing Liquid Crystal Technology

Herein, a novel strategy to fabricate haze films employing liquid crystal (LC) technology for photovoltaic (PV) applications is reported. We fabricated a high optical haze film composed of low-molecular LCs and polymer and applied the film to improve the energy conversion efficiency of PV module. The technique utilized to fabricate our haze film is based on spontaneous polymerization-induced phase separation between LCs and polymers. With optimized fabrication conditions, the haze film exhibited an optical haze value over 95% at 550 nm. By simply attaching our haze film onto the front surface of a silicon-based PV module, an overall average enhancement of 2.8% in power conversion efficiency was achieved in comparison with a PV module without our haze film

Selenoprotein gene nomenclature

The human genome contains 25 genes coding for selenocysteine-containing proteins (selenoproteins). These proteins are involved in a variety of functions, most notably redox homeostasis. Selenoprotein enzymes with known functions are designated according to these functions: TXNRD1, TXNRD2, and TXNRD3 (thioredoxin reductases), GPX1, GPX2, GPX3, GPX4 and GPX6 (glutathione peroxidases), DIO1, DIO2, and DIO3 (iodothyronine deiodinases), MSRB1 (methionine-R-sulfoxide reductase 1) and SEPHS2 (selenophosphate synthetase 2). Selenoproteins without known functions have traditionally been denoted by SEL or SEP symbols. However, these symbols are sometimes ambiguous and conflict with the approved nomenclature for several other genes. Therefore, there is a need to implement a rational and coherent nomenclature system for selenoprotein-encoding genes. Our solution is to use the root symbol SELENO followed by a letter. This nomenclature applies to SELENOF (selenoprotein F, the 15 kDa selenoprotein, SEP15), SELENOH (selenoprotein H, SELH, C11orf31), SELENOI (selenoprotein I, SELI, EPT1), SELENOK (selenoprotein K, SELK), SELENOM (selenoprotein M, SELM), SELENON (selenoprotein N, SEPN1, SELN), SELENOO (selenoprotein O, SELO), SELENOP (selenoprotein P, SeP, SEPP1, SELP), SELENOS (selenoprotein S, SELS, SEPS1, VIMP), SELENOT (selenoprotein T, SELT), SELENOV (selenoprotein V, SELV) and SELENOW (selenoprotein W, SELW, SEPW1). This system, approved by the HUGO Gene Nomenclature Committee, also resolves conflicting, missing and ambiguous designations for selenoprotein genes and is applicable to selenoproteins across vertebrates

TonEBP recognizes R-loops and initiates m6A RNA methylation for R-loop resolution

R-loops are three-stranded, RNA???DNA hybrid, nucleic acid structures produced due to inappropriate processing of newly transcribed RNA or transcription-replication collision (TRC). Although R-loops are important for many cellular processes, their accumulation causes genomic instability and malignant diseases, so these structures are tightly regulated. It was recently reported that R-loop accumulation is resolved by methyltransferase-like 3 (METTL3)-mediated m6A RNA methylation under physiological conditions. However, it remains unclear how R-loops in the genome are recognized and induce resolution signals. Here, we demonstrate that tonicity-responsive enhancer binding protein (TonEBP) recognizes R-loops generated by DNA damaging agents such as ultraviolet (UV) or camptothecin (CPT). Single-molecule imaging and biochemical assays reveal that TonEBP preferentially binds a R-loop via both 3D collision and 1D diffusion along DNA in vitro. In addition, we find that TonEBP recruits METTL3 to R-loops through the Rel homology domain (RHD) for m6A RNA methylation. We also show that TonEBP recruits RNaseH1 to R-loops through a METTL3 interaction. Consistent with this, TonEBP or METTL3 depletion increases R-loops and reduces cell survival in the presence of UV or CPT. Collectively, our results reveal an R-loop resolution pathway by TonEBP and m6A RNA methylation by METTL3 and provide new insights into R-loop resolution processes

DeepParcellation: A novel deep learning method for robust brain magnetic resonance imaging parcellation in older East Asians

Accurate parcellation of cortical regions is crucial for distinguishing morphometric changes in aged brains, particularly in degenerative brain diseases. Normal aging and neurodegeneration precipitate brain structural changes, leading to distinct tissue contrast and shape in people aged >60 years. Manual parcellation by trained radiologists can yield a highly accurate outline of the brain; however, analyzing large datasets is laborious and expensive. Alternatively, newly-developed computational models can quickly and accurately conduct brain parcellation, although thus far only for the brains of Caucasian individuals. To develop a computational model for the brain parcellation of older East Asians, we trained magnetic resonance images of dimensions 256 × 256 × 256 on 5,035 brains of older East Asians (Gwangju Alzheimer’s and Related Dementia) and 2,535 brains of Caucasians. The novel N-way strategy combining three memory reduction techniques inception blocks, dilated convolutions, and attention gates was adopted for our model to overcome the intrinsic memory requirement problem. Our method proved to be compatible with the commonly used parcellation model for Caucasians and showed higher similarity and robust reliability in older aged and East Asian groups. In addition, several brain regions showing the superiority of the parcellation suggest that DeepParcellation has a great potential for applications in neurodegenerative diseases such as Alzheimer’s disease

Assessment of Hybrid RANS/LES Models in Heat and Fluid Flows around Staggered Pin-Fin Arrays

In the present work, the three-dimensional heat and fluid flows around staggered pin-fin arrays are predicted using two hybrid RANS/LES models (an improved delayed detached eddy simulation (IDDES) model and a stress-blended eddy simulation (SBES) model), and one transitional unsteady Reynolds averaged Navier-Stokes (URANS) model, called k-ω SSTLM. The periodic segment geometry with a total of nine pins is considered with a channel height of 2D and a distance of 2.5D between each pin. The corresponding Reynolds number based on the pin diameter and the maximum velocity between pins is 10,000. The two hybrid RANS/LES results show the superior prediction of the mean velocity profiles around the pins, pressure distributions on the pin wall, and Nusselt number distributions. However, the transitional model, k-ω SSTLM, shows large discrepancies except in front of the pins where the flow is not fully developed. The vortical structures are well resolved by the two hybrid RANS/LES models. The SBES model is particularly adept at capturing the 3-D vortex structures after the pins. The effects of the blending function switching between RANS and LES mode of the two hybrid RANS/LES models are also investigated

Bayesian Inference of Cavitation Model Coefficients and Uncertainty Quantification of a Venturi Flow Simulation

In the present work, uncertainty quantification of a venturi tube simulation with the cavitating flow is conducted based on Bayesian inference and point-collocation nonintrusive polynomial chaos (PC-NIPC). A Zwart–Gerber–Belamri (ZGB) cavitation model and RNG k-ε turbulence model are adopted to simulate the cavitating flow in the venturi tube using ANSYS Fluent, and the simulation results, with void fractions and velocity profiles, are validated with experimental data. A grid convergence index (GCI) based on the SLS-GCI method is investigated for the cavitation area, and the uncertainty error (UG) is estimated as 1.12 × 10−5. First, for uncertainty quantification of the venturi flow simulation, the ZGB cavitation model coefficients are calibrated with an experimental void fraction as observation data, and posterior distributions of the four model coefficients are obtained using MCMC. Second, based on the calibrated model coefficients, the forward problem with two random inputs, an inlet velocity, and wall roughness, is conducted using PC-NIPC for the surrogate model. The quantities of interest are set to the cavitation area and the profile of the velocity and void fraction. It is confirmed that the wall roughness with a Sobol index of 0.72 has a more significant effect on the uncertainty of the cavitating flow simulation than the inlet velocity of 0.52