長期服用安非他命對小鼠腦部紋狀體內蛋白質表
安非他命的濫用在台灣是非常嚴重的公眾健康及社會問題。安非他命會導致一連串的行為異常，包括在中腦紋狀體內釋放多巴胺及阻止多巴胺回收來增加使用者的活動力。由於安非他命會對腦細胞造成傷害，本研究的目的為探討低劑量、無立即毒性之安非他命(類似於人類使用習慣)長期施打下，是否會對C57BL6 小鼠大腦紋狀體內的蛋白質表現有影響。因此利用西方點墨法分析施打低劑量安非他命(2 到6 mg/kg) 約一星期之後，C57BL6 小鼠的大腦紋狀體中一些重要蛋白質(包括腺.酸受體A2A-R、第五亞型腺.酸環化.AC5、caspase-8 及PARP) 的表現是否有改變。實驗結果顯示，低劑量安非他命處理對這些蛋白質的表現並沒有明顯的差異。但利用二維電泳法可看到有少許蛋白質，在經過安非他命處理下有顯著的差別，如KIAA0193 homolog 、GOS-28、gammacrystallin A、malate dehydrogenase 和phosphoglycerate mutase isozyme B (PGAM-B)。這些蛋白質中，malate dehydrogenase 和PGAM-B 與代謝和產生ATP 有關，但前者是增加的，而後者減少，推測安非他命會影響神經細胞的能量代謝，因此長期施打安非他命對紋狀體造成的影響值得進一步探討。;The wide spreading use of amphetamine (AMPH) in Taiwan has become a serious public health and social problem. AMPH evokes a series of behavior abnormality including enhanced locomotor behavior by releasing dopamine and inhibiting dopamine-uptake in the striatum. Since AMPH is known to cause brain damage, the purpose of this study is to investigate the expression of several important proteins in the striatum of C57BL6 mice after chronic treatment with low and non-toxic dosages of AMPH (mimicking the common usage pattern of AMPH addict). C57BL6 mice were daily IP-injected with various dosages of AMPH (0 to 6 mg/kg) for one week. Expression levels of A2A adenosine receptor (A2A-R), adenylyl cyclase type V (AC5), caspase-8 and PARP in the striatum were analyzed by Western blotting analysis. Most proteins examined were not affected by this 1-week AMPH treatment. By the aid of two-dimensional gel electrophoresis, expressions of a few striatal proteins (such as KIAA0193 homolog, GOS-28, gammacrystallin A, malate dehydrogenase and phosphoglycerate mutase isozyme B (PGAM-B) in AMPH-treated mice were altered. Note that malate dehydrogenase and PGAM-B are two enzymes involved in energy metabolism and ATP generation. Interestingly, the former was increased and while the latter was decreased in AMPH-treated mice. Collectively AMPH may affect the energy metabolism in neuronal cells. These results suggest that the injury induced by long-term AMPH exposure warrants our further concerns and investigation.
Generalized Quantum Tic-Tac-Toe
Early physicists such as Newton thought that all objects have definite positions. For example, they thought that an apple is either inside a fruit bowl, or outside of it. The advent of quantum physics in the early 20th century proved this viewpoint wrong. There is an uncertainty in the position of any object; we can find a set of possible locations where the object might be. This concept was termed superposition. Quantum tic-tac-toe (QT3) elegantly extends the popular game of tic-tac-toe by adding this quantum physics concept of superposition. Each turn, 1 piece is simultaneously played into 2 distinct squares of a 3-by-3 grid. Eventually, however, every piece will occupy exactly one square, like in tic-tac-toe. Yet, despite this intriguing addition, not much research has been done on the game. Hence in this paper we explore the game in terms of extension, analysis and solution. Firstly, we note that the quantum extension proposed by Alan Goff in QT3 is incomplete. In reality, there can be more than 2 possible locations for any object. Unfortunately, the QT3 game rules do not allow for this extension. Thus we non-trivially generalize the game (GQT3) by proposing a new set of rules. We show that the original QT3 is a subset of GQT3 and prove that our generalized game can always be successfully played from start to finish in a finite number of moves. Then, we begin our analysis of GQT3. Firstly, we investigate the game tree complexity, state space complexity and computational complexity of the game; indicators of how complicated the game is. Notably, we find here that QT3 has a total of about 18 trillion possible games, which is substantially higher than tic-tac-toe’s 400 thousand. Then we examine the Nash Equilibrium of the game; the result if two ‘Gods’ play the game against each other. We find that in this scenario, the first player will win by 0.5 points. To make the game fairer, we suggest minor variations on the scoring, which make the Nash Equilibrium a draw. Note that standard methods to analyze all of these would take at least a year, but we bring down the time to about a minute using symmetry considerations and other optimizations. Finally, we extend our programs into an artificial intelligence that is a perfect solution to the game. We then supplement this with a utility function to make the run-time performance pragmatic for more time-consuming versions of GQT3. Ultimately, GQT3 is a challenging and unique game with myriads of exploration possibilities; we have only scratched the surface here.
費瑪也瘋狂-平面上存在障礙時連接三定點的最佳網絡問題
在一個有障礙的平面上，給三個定點，我們探討連接此三點的最佳網絡。我們討論了諸如直線、射線、線段、圓、網格狀、三角形……等類的障礙，當網絡每穿越障礙一次，就必須付出代價，例如「拖延5 分鐘」。所以，設網絡穿越障礙的次數為y ，則網絡除了原本的總長度之外，還額外加入y 倍某固定數值的損耗。我們以費瑪點的各種性質及三角形不等式等方法為工具，就不同的穿越障礙次數綜合比較，而找出最佳網絡。在某些情況下，最佳網絡不是以費瑪點來連接三點，而是在障礙(如：直線)上找出符合某種與餘弦值相關特殊性質的點，以該點來連接三點，而此網絡可用GSP 軟體相當精確地作出。另外，我們也探討在考慮障礙造成損耗的情況下，兩點間的「實際距離」為何。 最後，我們考慮「混合障礙」問題。在此類問題中，除了前面所討論的障礙，還另加了如同「河流」的兩平行直線間區域之障礙，在這種障礙區域中，網絡的長度要乘以數倍來計算。我們發現，此類問題的最佳網絡也可用特定的正弦條件配合GSP 而相當精確地作出來。;Considering various kinds of obstacles in a plane, such as a line, a segment, a ray, a circle, a triangle or chessboard grids, which function like a red light, we research into the problem of finding the optimal network connecting three given points A, B, C in the plane amidst obstacles described above. Each time when the network crosses an obstacle, it will cause losses, such as five minute’s delay or a loss of one hundred dollars. Taking advantage of Fermat points, some basic inequalities concerning triangles and some special qualities about sine or cosine functions, we obtain the optimal networks in different situations. Besides, we consider what the “real distance” between two points is when there are obstacles in a plane. We also put another obstacle, including a line and a weighted region between two parallel lines, into consideration. In the region, like a river or a muddy ground in real life, the length of the network should be multiplied by a fixed time. Furthermore, we can use GSP to make the networks very accurately.
Bezier曲線與蚶線間之關聯性的探討與推廣
在這篇報告中，我們以貝斯曲線的做圖原理建立出一種新的曲線－環狀貝斯曲線，進而得到不少有趣的結果。我們發現有名的古典曲線－蚶線，也是屬於二次環狀貝斯曲線。軌跡方程式為：，此時，係數恰符合二項式定理。之後我們推廣至n次環狀貝斯曲線的軌跡方程式：，也符合二項式定理。
在複數平面上，給定z0、z1、z2三點，我們定義出一個二次變換 ，若，，可映射成蚶線的圖形；若z∈實數，則可映射成拋物線。利用此結果類推我們找到一個複數平面上由 z0、z1、...、zn 所決定的n次變換將以原點為圓心的單位圓，映射成n次環狀Bezier曲線。
In this essay, we use the method of forming a Bezier Curve to establish a new curve, circular Bezier Curve, and find a lot of interesting results. We discover the famous classical curve ＂limacon＂, which belongs to the Quadratic Circular Bezier Curve. The locus of Quadratic Circular Bezier Curve is, where. Its coefficients match the binomial theorem. Then we apply it to the locus of nth-circular Bezier Curve:, and it also matches the binomial theorem.On the complex plane, we define a quadratic transformation corresponding to three points—z0,z1 and z2 as .If , where , a limacon is mapped. If z is a real number, a parabola is mapped. With this result, we will find a nth transformation defined by z0、z1、...、zn on the complex plane. It will form a nth-circular Bezier Curve with unit circle centering on the origin.
Do SAT Problems Have Boiling Points？
The Boolean Satisfiability problem, called SAT for short, is the problem of determining if a set of constraints involving Boolean (True/False) variables can be simultaneously satisfied. SAT solvers have become an integral part in many computations that involve making choices subject to constraints, such as scheduling software, chip design, decision making for robots (and even Sudoku!). Given their practical applications, one question is when SAT problems become hard to solve. The problem difficulty depends on the constrainedness of the SAT instance, which is defined as the ratio of the number of constraints to the number of variables. Research in the early 90’s showed that SAT problems are easy to solve both when the constrainedness is low and when it is high, abruptly transitioning (“boiling over” ) from easy to hard in a very narrow region in the middle. My project is aimed at verifying this surprising finding. I wrote a basic SAT solver in Python and used it to solve a large number of randomly generated 3SAT problems with given level of constrainedness. My experimental results showed that the percentage of problems with satisfying assignment transitions sharply from 100% to 0% as constrainedness varies between 4 and 5. Right at this point, the time taken to solve the problems peaks sharply. Similar behavior also holds for 2SAT and 4SAT. Thus, SAT problems do seem to exhibit phase transition behavior; my experimental data supported my hypothesis.