FAT10 Haplotypes as a Potential Biomarker for Cancer
Cancer is the second leading cause of death today[1], accounting for nearly 1 in 6 deaths worldwide. Despite this, diagnosis and treatment models for cancer are limited and as such, new methods to identify and treat susceptible patients are required urgently. HLAF- adjacent transcript 10 (FAT10) is an oncogene that is strongly implicated in the development of inflammation-associated cancers[2]. Previous research on this highly polymorphic gene has identified 2 haplotypes – the reference haplotype, which is found in both cancer patients and healthy individuals, as well as an additional haplotype that is occurs at higher frequency in cancer patients and is associated with higher odds of cancer. In this study, it was hypothesised that the cancer-associated FAT10 haplotype can better promote tumorigenicity and could thereby serve as a useful biomarker for cancer. Here, we functionally characterize the 2 FAT10 haplotypes to understand how they influence some of the hallmarks of cancer. The cancer-exclusive haplotype was observed to enhance hallmarks of cancer, namely uncontrolled cell growth, resisting cell death and anchorage-independent growth as compared to the reference haplotype. Moreover, we uncovered the differential gene expression patterns induced by each haplotype. Molecules involved in cell adhesion and proliferation, as well as transcription were upregulated by the cancer-associated haplotype and hence could have contributed to the increased tumourigenic potential of the cancer haplotype.
連續函數與多倍角公式推廣研究
本研究考慮的主要問題: 若非常數之連續函數f滿足∀m∈N,∃P(x)∈C[x] s.t.f(mx)=P(f(x)),其形式應為何? (一)、若考慮函數範圍為解析函數,則f(x)的形式必為下列三者之一: (1).axn+b (2). akx^n+b (3). acos(kxn)+b ,其中a,b,k∈C、n∈N (二)、若將考慮函數範圍改為:連續函數f:[0,∞)→C,則f(x)之形式必為下列三者之一: (1).axk'+b (2). akx^n+b (3). acos(kxn)+b ,其中a,b,k,k'∈C、n∈N、Re(k' )>0 (三)、若將考慮函數範圍改為:連續函數f:(0,∞)→C,則f(x)之形式必為下列四者之一: (1).alogx+b (2).axk'+b (3). akx^n+b (4). acos(kxn)+b ,其中a,b,k,k'∈C、n∈N 在本篇的最後,我們也將N的角色以其他正實數子集取代掉以推廣結果。
Satellite Modeling of Wildfire Susceptibility in California Using Artificial Neural Networking
Wildfires have become increasingly frequent and severe due to global climatic change, demanding improved methodologies for wildfire modeling. Traditionally, wildfire severities are assessed through post-event, in-situ measurements. However, developing a reliable wildfire susceptibility model has been difficult due to failures in accounting for the dynamic components of wildfires (e.g. excessive winds). This study examined the feasibility of employing satellite observation technology in conjunction with artificial neural networking to devise a wildfire susceptibility modeling technique for two regions in California. Timeframes of investigation were July 16 to August 24, 2017, and June 25 to December 8, 2017, for the Detwiler and Salmon August Complex wildfires, respectively. NASA’s MODIS imagery was utilized to compute NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), land surface temperature, net evapotranspiration, and elevation values. Neural network and linear regression modeling were then conducted between these variables and ∆NBR (Normalized Burn Ratio), a measure of wildfire burn severity. The neural network model generated from the Detwiler wildfire region was subsequently applied to the Salmon August Complex wildfire. Results suggest that a significant degree of variability in ∆NBR can be attributed to variation in the tested environmental factors. Neural networking also proved to be significantly superior in modeling accuracy as compared to the linear regression. Furthermore, the neural network model generated from the Detwiler data predicted ∆NBR for the Salmon August Complex with high accuracy, suggesting that if fires share similar environmental conditions, one fire’s model can be applied to others without the need for localized training.
利用共生菌與小球藻建構不須添加培養基且能日夜發電的長效生物光伏電池
¬生物光伏電池(BPV)是一種利用光合自營生物進行光電能量轉換的發電裝置。本研究利用共生菌G76創造不須補充培養基的固態複合型BPV。我們以陽極只含小球藻的BPV為控制組(X-C),發現在實驗開始24小時之後,BPV電壓開始隨著光照週期產生規律變化,前三個光週期電壓高峰平均值為116.23±2.92 mV, 谷底平均值為87.96±4.48 mV,波動幅度28.27 mV。實驗組為陽極有小球藻與共生菌G76的複合型BPV (X-CG),同時期電壓高峰平均值為109.23±2.45 mV, 谷底平均值為100.63±0.9 mV,波動幅度8.6 mV。與X-C相比,添加G76會使電壓高峰下降6.02%,但提高谷底電壓14.4%且縮小電壓波動幅度69.58%。目前X-CG已運轉超過1032小時,電壓高峰為95.2mV,衰減幅度19.35%。同時期控制組X-C電壓高峰已下降至61.1mV,衰減幅度90.22%。實驗過程中我們發現在X-C及X-CG組別運作73小時後, 在陰極區都出現了紫黑色微生物(PB1),同時這些被汙染的BPV的電壓明顯比其他組別更高,將BP1單獨培養並引入陰極後(P-CG), 此一BPV的電壓高峰平均值高達179.3±3.66 mV,谷底平均值則為162.37±1.38 mV,都比X-CG組提高近六成。更重要的是X-CG與P-CG分別能保持日間電壓的92.13%與90.56%,都是非常穩定的BPV。 由以上結果可知, 將共生菌G76加入BPV陽極能提高日夜間的供電穩定度並延長裝置使用壽命,而將PB1引入陰極則能使BPV電壓提高六成以上。若能進一步優化應用這些共生菌, 此種低成本複合型生物光電轉換裝置將有潛力建構出一套不須儲電系統的太陽能發電系統。
The change in NaCl crystals from cubic to octahedral~Sodium polyacrylate stabilizes the {111} face of Miller indices~
When adding 2% or 4% sodium polyacrylate as habit modifier, standard milky-white octahedral NaCl crystals grew gradually in saturated NaCl solution on the bottom of the container. [1] [2] Sodium polyacrylate is well known as a highly water-absorbable polymer with many carboxylate anions. In the case of low concentration (0.01%, 0.02%, 0.05%, 0.1% and 0.5%) sodium polyacrylate many small or microscopic crystals whose shapes were nearly octahedrons and had {111} faces were observed with an optical microscope on the bottoms of the solution containers. In low concentration sodium polyacrylate, octahedral NaCl crystals made up of electrostatically unstable {111} faces grew similarly to crystals in high concentrations of 2% or 4% NaCl. Therefore, by adding sodium polyacrylate to saturated NaCl solution, cleaved rock salt crystals in this sol were observed to find out whether or not a change in crystal morphology from cuboids of {100} faces to octahedrons of {111} faces would occur. Regardless of the sodium polyacrylate concentrations of 0.01%, 0.02%, 0.05%, 0.1%, 0.5% and 2%, all cuboid crystals changed into a pyramidal shape in which four of the side surfaces formed an equilateral triangle. When one side of each equilateral triangle face was rotated so the square face of the crystal was soaked in the NaCl sol, all crystals grew into octahedrons of high transparency. Sodium polyacrylate, even under a low concentration, caused morphological change in the NaCl crystals. Many carboxylate anions in the sodium polyacrylate attracted sodium ions and the repulsive force between the carboxylate anions became weak, excluding the water in the internal space of the polymer. We considered that the stabilizing {111} faces of gathered sodium ions attached to carboxylate anions. Chloride and sodium ions coordinated continuously to minimize the NaCl surface area, growing into an octahedral and lowering the surface energy of the NaCl crystal. [3]