一等獎

觀察逐時雷達迴波圖之後，我們發現冬末春初時節，從傍晚到凌晨這段時間，在宜蘭外海是一條狀雨帶的「好發期」，這條狀雨帶並非由鋒面或其他已知的天氣系統引起，故我們想研究這條狀雨帶強度與其氣候環境間的關係。前人的研究中，並沒有研究宜蘭外海的條狀雨帶，不過，從其他研究類似的條狀雨帶的參考資料中，我們發現大部分的研究認定條狀雨帶的成因，都是由風場輻合所引起，因此，我們想研究是否還有其他促使條狀雨帶形成的原因。先定義出條狀雨帶常出現的範圍，挑出有出現條狀雨帶的時數，並利用軟體Extract Color擷取每ㄧ張圖片中條狀雨帶的像素，藉此將範圍中的回波訊號量化。整理向中央氣象局收集來的氣象資料(風向、風速、氣溫、海溫)，與回波訊號的量化數據做對照。結果我們發現，以風的輻合輻散來看，當風的輻合量愈大時，此雨帶也愈明顯；以溫差來看，海溫與氣溫的溫差愈大，此雨帶愈明顯。

3C產品及LED燈多以藍光照明，長期照射易導致視網膜黃斑部病變。其作用機轉是藍光引起視網膜細胞過氧化物質 (ROS)增加，造成細胞的氧化壓力進而引發細胞凋亡。幾丁寡醣 (Chitosan oligosaccharides, COS) 藉由減少自由基產生和抑制轉錄因子NF-κB傳導路徑而有抗氧化及抗凋亡的作用。 本研究以ARPE-19細胞株，在添加不同濃度的COS下，經照射LED藍光處理後，分析色素上皮細胞活性、ROS表現量及細胞凋亡情形，並透過PCR、西方墨點法、JC-1及免疫螢光染色分析作用機轉。實驗證實COS能抑制過氧化物質 (ROS)的產生，減少視網膜色素上皮細胞因藍光所造成的細胞凋亡。PCR和西方墨點法的結果顯示COS可以抑制發炎介質HO-1、iNOS、Caspase-3、Bcl-2、ERK，及基因iNOS、MCP-1的產生。免疫螢光染色顯示COS能抑制NF-kB核易位。總結來說，COS能藉由抑制NF-kB的活性，減少其下游基因的表現量，降低細胞氧化壓力和發炎反應來避免視網膜色素上皮細胞損傷。COS在臨床上有潛力成為一種預防LED藍光造成視網膜傷害的有效保健食品。

由於奈米科技與OLED相關工業的蓬勃發展，近來設計出應用於電子元件的有機分子已成為一項熱門的研究主題。本研究合成出兩種分別可以放出藍色與綠色螢光的有機分子，化合物 1 可以放出藍色螢光，而化合物 2 則可以放出綠色螢光。這兩個分子都是由中間的核心共軛分子與兩側的雙尿素辨識基團所組成。 我們所合成出的化合物 1 之放光波長與化合物 2 之吸收波長有重疊，因此可以觀察到兩分子在奈米尺度下之有機溶劑中的能量轉移，即激發化合物 1 使其放出藍光後，能量傳遞至化合物 2 ，使藍光被淬熄並產生綠色螢光。另外，此二分子皆具有π-π作用力、氫鍵作用力與凡德瓦力，而在不同的溶劑下可以強化或弱化這些作用力，從我們的研究成果中，分子可以在四氫呋喃中轉變成直徑約400 nm的均勻奈米球型結構，並且能在顯微鏡下觀察到其奈米尺度下的能量轉移行為。 根據這兩種有機螢光分子的光物理性質與自組裝能力，在未來的發展與應用中，我們希望能使用在OLED顯示器與可撓式面板上。

This research is aimed to make an in-depth exploration into Raoul’s Law and Henry’s Law by using an accurate but simple vapor pressure gauge. The gauge is constructed from non-complicated electronics components- electronics IC PCB, film resistor, digital multi-meter, and battery. In the first step, we measured the vapor pressure of six kinds of liquids and 3 liquid mixtures- water, ethanol, chloroform, acetone, benzene, toluene, mixture of water and ethanol, mixture of chloroform and acetone, mixture benzene and toluene. From the results of this experiments, the vapor pressures of water and ethanol liquid mixture, and chloroform and acetone liquid mixture were slightly lower than their theoretic values-called negative deviation solution, while the vapor pressure of the benzene and toluene liquid mixture was quite close to its theoretic value-near an ideal solution. In the second step, the individual vapor pressures of water, ethanol, and chloroform were measured at various temperatures; the vapor heat(ΔH) were calculated by using the lausius-Clapeyron equation. In the final step, we used the gauge and other non-commercial instruments to measure the B.O.D. values of water from the Kaohsiung Love River, found the P-T correlation using Gay-Lussac’s Law, and calculated the absolute zero temperature value by extrapolation. 本研究是利用一些簡易的電子元件－電路IC板、電阻膜、數位三用電表和電瓶來組裝一 個準確、簡易的氣壓量測器。我們將此量測器用來深入探討「拉午耳定律」及「亨利定律」 。 首先，我們測量了水、乙醇、氯仿、丙酮、苯、甲苯等六種純液體的蒸氣壓，並測量了(水＋乙醇)、(氯仿＋丙酮)、(苯＋甲苯)等兩成份系溶液的混合蒸氣壓。結果發現(水＋乙醇) 、(氯仿＋丙酮)的混合蒸氣壓都比理論值低了一些，此稱為負偏差溶液；(苯＋甲苯)的混合蒸氣壓與理論值差不多，較接近理想溶液。 接下來，我們還測量了不同溫度下水、酒精及丙酮的蒸氣壓，並利用clausius－clapeyron equation求出液體純質的汽化熱( H Δ )。 最後，我們還搭配了自製的儀器裝置，來測定愛河水質的B.O.D.值(生化需氧量)以及探討氣體的給呂薩克定律(P~T關係)，並利用外差法來推求絕對零度。

Despite the multitude of biomedical scans conducted, there is still relatively low accuracy and standardization of diagnoses from these images. In both the fields of computer science and medicine there is very strong interest in developing personalized treatment policies for patients who have variable responses to treatments. The aim of my research was automatic segmentation of brain MRI scans to better analyze patients with tumors, multiple sclerosis, ALS, or Alzheimer’s. In particular, I aim to use this information, along with novel artificial intelligence algorithms, to find an optimal personalized treatment policy which is a non-deterministic function of the patient specific covariate data that maximizes the expected survival time or clinical outcome. The result of the research was IlluminaMed, a biomedical image analysis toolkit that relies on the development of new artificial neural networks and training algorithms and novel research in fuzzy logic. The networks can detect patterns more complex than humans can identify and create patterns over long periods of time. IlluminaMed was trained by a dataset of professionally and manually segmented MRI scans from several prestigious hospitals and universities. I then developed an algorithmic framework to solve multistage decision problem with a varying number of stages that are subject to censoring in which the “rewards” are expected survival times. In specific, I developed a novel Q-learning algorithm that dynamically adjusts for these parameters. Furthermore, I found finite upper bounds on the generalized error of the treatment paths constructed by this algorithm. I have also shown that when the optimal Q-function is an element of the approximation space, the anticipated survival times for the treatment regime constructed by the algorithm will converge to the optimal treatment path. I demonstrated the performance of the proposed algorithmic framework via simulation studies and through the analysis of chronic depression data and a hypothetical clinical trial. IlluminaMed can automatically segment the scans with 98% accuracy, find tumors with 96% accuracy and approximate their volume within a 2% margin of error. It can also find lesions in MS and ALS, distinguishing them from tumors with 94% accuracy. IlluminaMed can, in addition, determine the tendency of a patient to develop Alzheimer’s several months before patients develop symptoms correlating the brain structure and its fluctuations. Lastly, the censored Q-learning algorithm I developed is more effective than the state of the art clinical decision support systems and is able to operate in environments when many covariate parameters may be unobtainable or censored. IlluminaMed is the only fully automatic biomedical image analysis toolkit and personalized medicine engine. The personalized medicine engine runs at a level that is comparable to the best physicians. It is less computationally complex than similar software and is unique in the fact that it can find new patterns in the brain with possible future diagnoses. IlluminaMed’s implications are not only great in terms of the biomedical field, but also in the field of artificial intelligence with new findings in neural networks and the relationships of fuzzy extensional subsets.

給定一個簡單圖G（simple graph ），令V(G)、E(G)分別為G的頂點與邊所形成的集合。對於兩個不同的頂點u, v∈V(G)，若存在一條邊連結頂點u, v，則將此邊記為uv，以uv∈E(G)表示。給定函數f: V(G)→ℕ，若對任意的邊uv∈E(G)，函數f皆滿足f(u)≠f(v)，則稱函數f為圖G的一個著色函數（proper coloring）。對於圖G，任意給定每個頂點v∈V(G)一個可用的顏色清單L(v)⊂ℕ，其中清單內各有四個可用顏色（|L(v)|=4 ，每個頂點的顏色清單可以不相同），若總是存在一個著色函數f，滿足∀v∈V(G)，f(v)∈L(v)，則稱圖G為『可四元列表著色（4-choosable ）』。對於平面圖（ plane graph），針對長度較小的圈（cycle）進行限制，我們設計充分條件，使得滿足條件的平面圖為可四元列表著色。

本研究主要利用最大概似法及動態規劃演算法來嘗試縮短生醫領域在疫苗研發的時程。透過序列比較的計算方式加速找出病毒序列具有專一性的有效區段。使科學家可以減少盲目測試的實驗。我們期望找出經過電泳之後，可以判斷具有可製造疫苗的最佳生物序列區段。藉由已知流感病毒的基因序列來分析現有流感病毒的演化親緣關係。嘗試由已知流感病毒疫苗來設計未知的流感病毒疫苗之建議(結果如下圖)。

NO donors are an emerging class of pharmaceutical compounds, with many important functions in the cardiovascular, nervous and immune systems. With great therapeutic potential, the development of new NO donor compounds would be of great medicinal value, potentially opening a whole class of drugs to be used to treat various ailments. This project studies a specific class of compounds, substituted cyclic oxime ethers, which have proven to be useful intermediates in fields such as medicine and biochemistry. The cyclic structure along with a determinable substitutable group at the C3 position is highly valuable, as it allows the oxime ether to act as a convenient precursor for a variety of useful products, playing key components in many drugs. And with a substituted nitrate group, which is an O-nucleophile, the oxime ether has the potential to become an NO-donor, and hence become a possible intermediate in a wide array of NO donor drugs. Co(NO)3 was used in the synthesis of the cyclic oxime ether, directly from a phenyl substituted bis(oxy)enamine intermediate, producing an entirely new compound: α-hydroxyoxime nitrates, the oxime ether being substituted with a nitrate group. This new reaction of the synthesis of α-hydroxyoxime nitrates was further studied for optimization purposes, in order to open a new class of NO donor precursors. In addition, other nucleophiles were also explored in this class of reactions, forming important bonds such as C-N and C-S bonds, with key structures for other types of synthesis intermediates and precursors. Different metal nitrates, or various other nucleophiles in place of the nitrates, were used in reaction with bis(oxy)enamine, and the yield and structure of the final products were determined by NMR spectra. Successful optimization of the synthesis of α-hydroxyoxime nitrates has been achieved, where the conditions for optimum synthesis involve using Cr(NO3)3•9H2O which achieved a high yield of 76%, dissolved in THF with the bis(oxy)enamine starting compound. It has been determined that the metal in the salt affects the reaction pathway, as the nature of the metal cation affects its efficiency to cleave the N-O bond in the starting compound (with d-block elements being the best performing), and H+ ions can promote the reaction as well. Also, the reaction proceeds with different types of bis(oxy)enamines, meaning the substrate scope can be expanded to give a variety of products. The reaction can also proceed to form other products with different nucleophiles other than the nitrate group, where the C-N and C-S bonds were successfully formed in the reactions from bis(oxy)enamine to oxime ether. Thus, this class of reaction in converting the bis(oxy)enamine to a cyclic oxime ether has potentially opened a new class of NO donor compounds, and further possesses the potential to form a wide variety of products to be used in other important synthesis procedures.

外來入侵種小花蔓澤蘭繁殖速度太快，使台灣本土生態系受到嚴重破壞。我們研究發現，小花蔓澤蘭葉和根莖具有保護自由基誘發劑AAPH誘導紅血球溶血及清除DPPH自由基的能力。小花蔓澤蘭葉和根莖總多酚含量可達51.6及20.9 mg/g，推測其所含多酚化合物可能是抗氧化活性的來源。抗癌研究發現，小花蔓澤蘭葉和根莖會誘導人類急性骨髓血癌(HL-60)細胞毒性(24小時IC50為129及203 μg/mL)。顯微鏡觀察下，血癌細胞有減少和皺縮現象; TUNEL試驗法發現，血癌細胞DNA斷裂; 西方墨點法發現，Caspase-3蛋白增加、Bcl-2蛋白減少、Bax蛋白增加及PARP蛋白裂解; 推論小花蔓澤蘭可能誘發血癌細胞凋亡。裸鼠移植腫瘤模式證實，小花蔓澤蘭確實具有抑制活體血癌腫瘤及誘導血癌細胞凋亡之功效。總結，小花蔓澤蘭具抗氧化及抗血癌功效，可開發成為預防自由基疾病及抗癌的保健食品。