Development of Compact and High-efficient Scroll Compressor with Novel Bearing Structure

High-Side Shell(HSS) scroll compressors have been widely used for Variable Refrigerant Flow(VRF) system which is a powerful solution for the cooling and heating of commercial buildings. In order to improve the characteristics of the VRF system, a new HSS scroll compressor has been developed with a novel bearing structure. The core elements of the novel bearing structure are an outer-type bearing mounted on an orbiting scroll and a female-type eccentric journal inside of a shaft. The outer-type bush bearing which is made of engineering plastic without a back steel layer has been newly developed. The new HSS scroll compressor employing the novel bearing structure has a compact size, high efficiency, and low noise level compared to a conventional HSS scroll compressor. In order to confirm the advantages of the new HSS scroll compressor, basic tests and theoretical analysis have been performed in this study

Genetic Algorithm for the Optimization of a Building Power Consumption Prediction Model

Accurately predicting power consumption is essential to ensure a safe power supply. Various technologies have been studied to predict power consumption, but the prediction of power consumption using deep learning models has been quite successful. However, in order to predict power consumption by utilizing deep learning models, it is necessary to find an appropriate set of hyper-parameters. This introduces the problem of complexity and wide search areas. The power consumption field should be accurately predicted in various distributed areas. To this end, a customized consumption prediction deep learning model is needed, which is essential for optimizing the hyper-parameters that are suitable for the environment. However, typical deep learning model users lack the knowledge needed to find the optimal values of parameters. To solve this problem, we propose a method for finding the optimal values of parameters for learning. In addition, the layer parameters of deep learning models are optimized by applying genetic algorithms. In this paper, we propose a hyper-parameter optimization method that solves the time and cost problems that depend on existing methods or experiences. We derive a hyper-parameter optimization plan that solves the existing method or experience-dependent time and cost problems. As a result, the RNN model achieved a 30% and 21% better mean squared error and mean absolute error, respectively, than did the arbitrary deep learning model, and the LSTM model was able to achieve 9% and 5% higher performance

Genetic Algorithm for the Optimization of a Building Power Consumption Prediction Model

Accurately predicting power consumption is essential to ensure a safe power supply. Various technologies have been studied to predict power consumption, but the prediction of power consumption using deep learning models has been quite successful. However, in order to predict power consumption by utilizing deep learning models, it is necessary to find an appropriate set of hyper-parameters. This introduces the problem of complexity and wide search areas. The power consumption field should be accurately predicted in various distributed areas. To this end, a customized consumption prediction deep learning model is needed, which is essential for optimizing the hyper-parameters that are suitable for the environment. However, typical deep learning model users lack the knowledge needed to find the optimal values of parameters. To solve this problem, we propose a method for finding the optimal values of parameters for learning. In addition, the layer parameters of deep learning models are optimized by applying genetic algorithms. In this paper, we propose a hyper-parameter optimization method that solves the time and cost problems that depend on existing methods or experiences. We derive a hyper-parameter optimization plan that solves the existing method or experience-dependent time and cost problems. As a result, the RNN model achieved a 30% and 21% better mean squared error and mean absolute error, respectively, than did the arbitrary deep learning model, and the LSTM model was able to achieve 9% and 5% higher performance

Epigenetic Modification of CFTR in Head and Neck Cancer

Cystic fibrosis transmembrane conductance regulator (CFTR), a cyclic AMP (cAMP)-regulated chloride channel, is critical for secretion and absorption across diverse epithelia. Mutations or absence of CFTR result in pathogeneses, including cancer. While CFTR has been proposed as a tumor suppressing gene in tumors of the intestine, lung, and breast cancers, its effects in head and neck cancer (HNC) have yet to be investigated. This study aimed to define expression patterns and epigenetic modifications of CFTR in HNC. CFTR was expressed in normal but not in HNC cells and tissues. Treatment with 5-aza-2 ' -deoxycytidine (5-Aza-CdR) was associated with rescued expression of CFTR, whose function was confirmed by patch clamp technique. Further experiments demonstrated that CFTR CpG islands were hypermethylated in cancer cells and tissues and hypomethylated in normal cells and tissue. Our results suggest that CFTR epigenetic modifications are critical in both down-regulation and up-regulation of CFTR expression in HNC and normal cells respectively. We then investigated the impact of CFTR on expressions and functions of cancer-related genes. CFTR silencing was closely associated with changes to other cancer-related genes, suppressing apoptosis while enhancing proliferation, cell motility, and invasion in HNC. Our findings demonstrate that hypermethylation of CFTR CpG islands and CFTR deficiency is closely related to HNC.Y

Epigenetic Modification of CFTR in Head and Neck Cancer

Cystic fibrosis transmembrane conductance regulator (CFTR), a cyclic AMP (cAMP)-regulated chloride channel, is critical for secretion and absorption across diverse epithelia. Mutations or absence of CFTR result in pathogeneses, including cancer. While CFTR has been proposed as a tumor suppressing gene in tumors of the intestine, lung, and breast cancers, its effects in head and neck cancer (HNC) have yet to be investigated. This study aimed to define expression patterns and epigenetic modifications of CFTR in HNC. CFTR was expressed in normal but not in HNC cells and tissues. Treatment with 5-aza-2′-deoxycytidine (5-Aza-CdR) was associated with rescued expression of CFTR, whose function was confirmed by patch clamp technique. Further experiments demonstrated that CFTR CpG islands were hypermethylated in cancer cells and tissues and hypomethylated in normal cells and tissue. Our results suggest that CFTR epigenetic modifications are critical in both down-regulation and up-regulation of CFTR expression in HNC and normal cells respectively. We then investigated the impact of CFTR on expressions and functions of cancer-related genes. CFTR silencing was closely associated with changes to other cancer-related genes, suppressing apoptosis while enhancing proliferation, cell motility, and invasion in HNC. Our findings demonstrate that hypermethylation of CFTR CpG islands and CFTR deficiency is closely related to HNC

R-Spondin 2 and WNT/CTNNB1 Signaling Pathways Are Required for Porcine Follicle Development and In Vitro Maturation

The secretion of oocyte-derived paracrine factors, such as R-spondin2, is an essential mechanism for follicle growth by promoting the proliferation and differentiation of cumulus cells around oocytes. In the present study, we aimed to identify the effect of R-spondin2 during follicular development. First, R-spondin2-related factors (R-spondin2, CTNNB1, LGR4, and LGR5) were identified through immunofluorescence in porcine ovarian tissue. CTNNB1 was expressed in ooplasm, and CTNNB1 and LGR4 were expressed in granulosa cells. In addition, R-spondin2, LGR4, and LGR5 were expressed in the theca interna. These results imply that these proteins play a major role in porcine follicular development. In addition, the effects of R-spondin2 on the in vitro maturation process of porcine cumulus oocyte complexes and subsequent embryonic development were confirmed. A treatment of 100 ng/mL R-spondin2 in the in vitro maturation (IVM) process increased nuclear maturation and increased the expression of EGFR mRNA in cumulus cells. The EGFR-ERK signal is essential for oocyte maturation, ovulation, and luteinization. R-spondin2 treatment also increased the expression of CTNNB1 and EGFR in primary cultured cumulus cells. In conclusion, RSPO2 and WNT/CTNNB1 signaling pathways are required for porcine follicle development and are predicted to be involved in the EGFR-ERK signaling pathway

Establishment of 3D Neuro-Organoids Derived from Pig Embryonic Stem-Like Cells

Although the human brain would be an ideal model for studying human neuropathology, it is difficult to perform in vitro culture of human brain cells from genetically engineered healthy or diseased brain tissue. Therefore, a suitable model for studying the molecular mechanisms responsible for neurological diseases that can appropriately mimic the human brain is needed. Somatic cell nuclear transfer (SCNT) was performed using an established porcine Yucatan EGFP cell line and whole seeding was performed using SCNT blastocysts. Two Yucatan EGFP porcine embryonic stem-like cell (pESLC) lines were established. These pESLC lines were then used to establish an in vitro neuro-organoids. Aggregates were cultured in vitro until 61 or 102 days after neural induction, neural patterning, and neural expansion. The neuro-organoids were sampled at each step and the expression of the dopaminergic neuronal marker (TH) and mature neuronal marker (MAP2) was confirmed by reverse transcription-PCR. Expression of the neural stem cell marker (PAX6), neural precursor markers (S100 and SOX2), and early neural markers (MAP2 and Nestin) were confirmed by immunofluorescence staining. In conclusion, we successfully established neuro-organoids derived from pESLCs in vitro. This protocol can be used as a tool to develop in vitro models for drug development, patient-specific chemotherapy, and human central nervous system disease studies