Dual Targeting of 3-Hydroxy-3-methylglutaryl Coenzyme A Reductase and Histone Deacetylase as a Therapy for Colorectal Cancer

AbstractStatins are 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase (HMGR) inhibitors decreasing serum cholesterol and have shown promise in cancer prevention. In this study, we demonstrated the oncogenic role of HMGR in colorectal cancer (CRC) by disclosing increased HMGR activity in CRC patients and its enhancement of anti-apoptosis and stemness. Our previous studies showed that statins containing carboxylic acid chains possessed activity against histone deacetylases (HDACs), and strengthened their anti-HDAC activity through designing HMGR-HDAC dual inhibitors, JMF compounds. These compounds exerted anti-cancer effect in CRC cells as well as in AOM-DSS and ApcMin/+ CRC mouse models. JMF mostly regulated the genes related to apoptosis and inflammation through genome-wide ChIP-on-chip analysis, and Ingenuity Pathways Analysis (IPA) predicted their respective regulation by NR3C1 and NF-κB. Furthermore, JMF inhibited metastasis, angiogenesis and cancer stemness, and potentiated the effect of oxaliplatin in CRC mouse models. Dual HMGR-HDAC inhibitor could be a potential treatment for CRC

Rationalization and Design of the Complementarity Determining Region Sequences in an Antibody-Antigen Recognition Interface

Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes

The study on Empirical Bayes Procedure for Selecting the Best Normal Population

碩士論文[[abstract]]於本文中，我們在貝氏的架構下，利用經驗貝氏法來解決擇優問題。我們所探討的問題是：假設k 個母體 ，而 ，之分配為常態分配 ，其參數 及 是未知的，且具有某種事前機率分配，在給定兩個控制值 與 之情況下，我們想從合格子集中，選出平均數最接近目標值的母體。而所謂「合格子集」，即此k個母體中其相關參數 且 介於 與 之間所有母體所成之集合。明顯地，在論文中我們所要探討的擇優問題會涉及兩個參數及三項準則。而如何找到一個平均數最接近目標值，但變異數不至於過大的母體將是我們的目標。 相關的經驗貝氏擇優規則已被提出，我們也證明其漸進最適性。並且我們也從事小樣本的模擬研究，來探討經驗貝氏擇優規則之行為表現，並獲得不錯的結果。[[abstract]]In this paper, we use empirical Bayes approach to solve the selecting problem under Bayes framework. We focus on selecting the best population from k Normal population with some prior. Consider k population .Each population has a normal distribution , whose parameters and are unknown. For given control values and ,we are interested in selecting some population whose mean is the closest to the target in the qualified subset in which each parameter and is between and .Clearly ,in this paper , it involves two parameter and three criteria in the selecting problem. In this paper, an empirical Bayes rule would be proposed, and the asymptotical optimality of the proposed would be proved. At the same time, some Monte Carlo simulation will be done to discover the behavior of the proposed rule and by the approach we get well result

Improvement of p-electrode structures for 280 nm AlGaN LED applications

An improvement of Ni/Au/p+-GaN p-electrode for AlGaN deep-ultraviolet light-emitting diodes (DUV LEDs) with the emission wavelength of 280 nm is proposed for both p-side-up and flip-chip structures. An interdigitated multi-finger Ni/Au was employed in p-side-up DUV LED, where the p-GaN contact layer was partially removed to improve the light extraction efficiency without a serious current-crowding effect. The 9- and 12-finger LEDs were determined to have higher thermal dissipation and lower surface temperatures and correlated well with the theoretical simulation. For the comparison of p-side-up emission LEDs, the output power of 9-finger LED is 172% higher than that of conventional LED at the current injection of 350 mA. The optimum p-electrode pattern was further applied to the flip-chip LED structure. It is determined that the output power of 9-finger flip-chip LED at 350 mA is still 14.6% higher than that of a conventional flip-chip LED. The higher output power of 9-finger flip-chip LED with a wall-plug efficiency of 1.05% is attributed to the combination of the improved current-spreading path and the higher reflection through the moderate removal of partial p+-GaN absorbing layer

Dielectric, Piezoelectric, and Vibration Properties of the LiF-Doped (Ba0.95Ca0.05)(Ti0.93Sn0.07)O3 Lead-Free Piezoceramic Sheets

By the conventional solid state reaction method, a small amount of lithium fluoride (LiF) was used as the sintering promoter to improve the sintering and piezoelectric characteristics of (Ba0.95Ca0.05)(Ti0.93Sn0.07)O3 (BCTS) lead-free piezoceramic sheets. Using X-ray diffraction (XRD) and a scanning electron microscope (SEM), the inferences of the crystalline and surface microstructures were obtained and analyzed. Then, the impedance analyzer and d33-meter were used to measure the dielectric and piezoelectric characteristics. In this study, the optimum sintering temperature of the BCTS sheets decreased from 1450 °C to 1390 °C due to LiF doping. For the 0.07 wt % LiF-doped BCTS sheets sintered at 1390 °C, the piezoelectric constant (d33) is 413 pC/N, the electric–mechanical coupling coefficient (kp) is 47.5%, the dielectric loss (tan δ) is 3.9%, and the dielectric constant (εr) is 8100, which are all close to or even better than that of the pure undoped BCTS ceramics. The Curie temperature also improved, from 85 °C for pure BCTS to 140 °C for BCTS–0.07 LiF sheets. Furthermore, by using the vibration system and fixing 1.5 g tip mass at the end of the sheets, as the vibration frequency is 20 Hz, the proposed piezoelectric ceramic sheets also reveal a good energy harvesting performance at the maximum output peak voltage of 4.6 V, which is large enough and can be applied in modern low-power electronic products

Application of Data-Independent Acquisition Approach to Study the Proteome Change from Early to Later Phases of Tomato Pathogenesis Responses

Plants and pathogens are entangled in a continual arms race. Plants have evolved dynamic defence and immune mechanisms to resist infection and enhance immunity for second wave attacks from the same or different types of pathogenic species. In addition to evolutionarily and physiological changes, plant-pathogen interaction is also highly dynamic at the molecular level. Recently, an emerging quantitative mass spectrometry-based proteomics approach named data-independent acquisition (DIA), has been developed for the analysis of the proteome in a high-throughput fashion. In this study, the DIA approach was applied to quantitatively trace the change in the plant proteome from the early to the later stage of pathogenesis progression. This study revealed that at the early stage of the pathogenesis response, proteins directly related to the chaperon were regulated for the defence proteins. At the later stage, not only the defence proteins but also a set of the pathogen-associated molecular pattern-triggered immunity (PTI) and effector triggered immunity (ETI)-related proteins were highly induced. Our findings show the dynamics of the plant regulation of pathogenesis at the protein level and demonstrate the potential of using the DIA approach for tracing the dynamics of the plant proteome during pathogenesis responses

Semantic Proximity Update of GNSS/INS/VINS for Seamless Vehicular Navigation Using Smartphone Sensors

International audienc

Deep Learning Applied to Defect Detection in Powder Spreading Process of Magnetic Material Additive Manufacturing

Due to its advantages of high customization and rapid production, metal laser melting manufacturing (MAM) has been widely applied in the medical industry, manufacturing, aerospace and boutique industries in recent years. However, defects during the selective laser melting (SLM) manufacturing process can result from thermal stress or hardware failure during the selective laser melting (SLM) manufacturing process. To improve the product’s quality, the use of defect detection during manufacturing is necessary. This study uses the process images recorded by powder bed fusion equipment to develop a detection method, which is based on the convolutional neural network. This uses three powder-spreading defect types: powder uneven, powder uncovered and recoater scratches. This study uses a two-stage convolutional neural network (CNN) model to finish the detection and segmentation of defects. The first stage uses the EfficientNet B7 to classify the images with/without defects, and then to locate the defects by evaluating three different instance segmentation networks in second stage. Experimental results show that the accuracy and Dice measurement of Mask-R-CNN network with ResNet 152 backbone can reach 0.9272 and 0.9438. The computational time of an image only takes approximately 0.2197 sec. The used CNN model meets the requirements of the early detected defects, regarding the SLM manufacturing process