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17-The Y-Chromosome DNA testing helps in the examination of the male specific portion of the biological evidence. This is useful where small amount of male DNA is found amidst a large amount of female DNA , as in the sexual assault cases.It can also help in the missing person investigation as it extends the range of potential reference samples. Sine the Y chromosome is passed on from the fathers to their sone (unchanged, except in the case of some mutations), therefore all the males of the same paternal lineage will possess common Y chromosome haplotype. A set of Short tandem repeats (STR) are found on the male specific Y chromosome.The coding genes found on the short arm of the Ychromosome are vital to male sex determination, spermatogenesis andother male related functions. The Y-STRs are polymorphic among the nonrelated males. Sometimes when sepoarating the mal ecomponent and the female component becomes difficult, then the YSTRs help in the determination. Since The Y-STRs are not present in the females, therefore in a case where the male and female are involved, it becomes clear that as the Y-STRs are present the male component is easily detected as only this part will be amplified in the PCR. The YSTRs are also helpful when a number of males are involved. As regular STRs cause masking effect the YSTR will help to identify all the males who have contributed to the evidence. ADVANTAGES- 1. extremely discriminating. 2. Extremely sensitive.e. low amounts of DNA can be used for determination. 3. Not onlu body fluids, but alsp touched materials or handled surfaces, where skinn cells ahev been left can be used for DNA profiling. 4. Multiple testing systems ar available, which increases the likelyhood of successfully generating DNA profiles. 5. Apart from standart=d STR enem mt DNA or YSTRs are used for profiling. 6. With the presence of the current technologies, from amidst mixed DNA samples, the DNA of a particular individual can be profiled. LIMITATIONS 1. Random match probability cannot be excluded. 2. Sometimes even when crime is not committed, we do get ,low levels of DNA on surfaces. 3.The important question while dealing with low DNA quantity is that whether the DNA that was deposited was deposited during the crime or at some other time. 4.Searching evidence is challenging. 5.Sometimes finding the evidence is altogether impossibl. 6. In case of mtDNA /T STRs , as they are passed on from generation to generation either from fathers to sons or mothers to daughters and son, therefore sometimes unrelated persons are also share the same profile. 7. DNA mixtures are very complex. 8.Interpretation is required in many cases. 16- from Cyanobacterial to humans, many terristrial organisms have acquired the circadian rhythms to adapt to sunlight to increase their survival rate.Circadian Rhythm is a biological clock that displays an endogenous, entrainable oscillation of 24 hours.Thesse oscillations are driven by circadian clocks. PULSE CHASE EXPERIME.

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#includestdio.h#includestring.h#includestdlib.h#define M.pdf

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The pH and pOH of a solution are defined as: pH = -log[H+] pOH = -log[OH-] and pH + pOH = 14.00 [H+] = 0.055 Conversions: [OH-] = 1.00 x 10^-14 / [H+] = 1.00 x 10-14 /0.055= 1.8x10^-13 pOH = -log[OH-]=12.74 Solution The pH and pOH of a solution are defined as: pH = -log[H+] pOH = -log[OH-] and pH + pOH = 14.00 [H+] = 0.055 Conversions: [OH-] = 1.00 x 10^-14 / [H+] = 1.00 x 10-14 /0.055= 1.8x10^-13 pOH = -log[OH-]=12.74.

The pH and pOH of a solution are defined as pH .pdf

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goal_state = [1, 8, 7, 2, 0, 6, 3, 4, 5] #goal_state = [1, 0, 7, 2, 8, 6, 3, 4, 5] import sys global count def display_board( state ): #print \"-------------\" print \" %i %i %i \" % (state[0], state[3], state[6]) #print \"-------------\" print \" %i %i %i \" % (state[1], state[4], state[7]) #print \"-------------\" print \" %i %i %i \" % (state[2], state[5], state[8]) #print \"-------------\" def move_up( state ): new_state = state[:] index = new_state.index( 0 ) if index not in [0, 3, 6]: temp = new_state[index - 1] new_state[index - 1] = new_state[index] new_state[index] = temp return new_state else: return None def move_down( state ): new_state = state[:] index = new_state.index( 0 ) if index not in [2, 5, 8]: temp = new_state[index + 1] new_state[index + 1] = new_state[index] new_state[index] = temp return new_state else: return None def move_left( state ): new_state = state[:] index = new_state.index( 0 ) if index not in [0, 1, 2]: temp = new_state[index - 3] new_state[index - 3] = new_state[index] new_state[index] = temp return new_state else: return None def move_right( state ): new_state = state[:] index = new_state.index( 0 ) if index not in [6, 7, 8]: temp = new_state[index + 3] new_state[index + 3] = new_state[index] new_state[index] = temp return new_state else: return None def create_node( state, parent, operator, depth, cost ): return Node( state, parent, operator, depth, cost ) def expand_node( node, nodes ): expanded_nodes = [] expanded_nodes.append( create_node( move_up( node.state ), node, \"u\", node.depth + 1, 0 ) ) expanded_nodes.append( create_node( move_down( node.state ), node, \"d\", node.depth + 1, 0 ) ) expanded_nodes.append( create_node( move_left( node.state ), node, \"l\", node.depth + 1, 0 ) ) expanded_nodes.append( create_node( move_right( node.state), node, \"r\", node.depth + 1, 0 ) ) # Filter the list and remove the nodes that are impossible (move function returned None) expanded_nodes = [node for node in expanded_nodes if node.state != None] #list comprehension! expanded_nodes = [node for node in expanded_nodes if node not in nodes] #list comprehension! #expanded_nodes = [node for node in expanded_nodes if node.state!= ((node.parent).parent).state] #list comprehension! expanded_nodes1 = [] for node in expanded_nodes: temp=node temp=temp.parent temp=temp.parent if temp==None: expanded_nodes1.extend(expanded_nodes) break if node.state !=temp.state: expanded_nodes1.append(node) return expanded_nodes1 def bfs( start, goal ): nodes = [] count=0 # Create the queue with the root node in it. nodes.append( create_node( start, None, None, 0, 0 ) ) while True: # We\'ve run out of states, no solution. if len( nodes ) == 0: return None # take the node from the front of the queue node = nodes.pop(0) count=count+1 #display_board(node.state) # Append the move we made to moves # if this node is the goal, return the moves it took to get here. if node.state == goal: #display_board(node.state) moves = [] temp = node print cou.

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Use Dalton\'s Law of partial pressures. P(Total)=P(H2)+P(H20) p(H2)=P(Total)- P(H20) Look up the partial pressure of water at the given temperature. It comes out to be 0.0351 atm. P(H2)=1-0.0351 = 0.9649 atm Solution Use Dalton\'s Law of partial pressures. P(Total)=P(H2)+P(H20) p(H2)=P(Total)- P(H20) Look up the partial pressure of water at the given temperature. It comes out to be 0.0351 atm. P(H2)=1-0.0351 = 0.9649 atm.

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The Br was originally neutral, but picks up an extra electron from the N and becomes Br-. The Br- leaves the molecule The O was originally negative, but gives up an electron to form a double bond with C1 and becomes neutral. Leaving O=C=N-C as a neutral molecule. Keeping the labeling of the two carbons it would be O=C1=N-C2. With the double bonds on both sides of C1, the O, C1, N backbone is linear. Solution The Br was originally neutral, but picks up an extra electron from the N and becomes Br-. The Br- leaves the molecule The O was originally negative, but gives up an electron to form a double bond with C1 and becomes neutral. Leaving O=C=N-C as a neutral molecule. Keeping the labeling of the two carbons it would be O=C1=N-C2. With the double bonds on both sides of C1, the O, C1, N backbone is linear..

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Year 2000 is the period when the productivity growth rate was at minimum ie., 0.7. Degrowth refers to those years where productivity growth rate was in negative. Minimum growth rate refers to the minimum positive growth rate achieved. Solution Year 2000 is the period when the productivity growth rate was at minimum ie., 0.7. Degrowth refers to those years where productivity growth rate was in negative. Minimum growth rate refers to the minimum positive growth rate achieved..

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