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The nerve in #17 (the answer to last question) is a(n)  afferent/efferent/mixed nerve. 

An algorithm has a runtime of 99999 + +   What is the runtime when written in Big O notation?

Here is a set of homologous sequences from three different primates for a gene responsible for the higher intelligence observed in primates. We are interested in understanding where transcription factors bind upstream of the gene so that we can attempt to modulate the expression of the gene in individuals with decreased cognition in a highly experimental treatment (i.e., this would never work in reality). We discussed several different approaches for finding motifs in sequences. The sequences are reprinted for each question that requires you to use the sequences. GTTCAG AATCAG TATTCG Use a GreedyMotif search to find motifs for these sequences. Assume you are looking for 4-mers and work your algorithms so there’s never a 4-mer with zero probability in your matrix. If there is a random component, make sure you clearly tell me what the “random” choice is and make sure that it is evident to me that you understand what random means in the context of the greedy motif search (i.e., if there is a random component, explain it to me in words and demonstrate its appropriate use). Make sure you report the score for each set of motifs. If you don’t provide sufficient detail for me to see that you understand what to do, giving you credit will be challenging. I prefer you insert boxes, etc. and type your answer so that it’s clearly legible. However, you may also choose to take a picture of your scratch paper at the very end of the exam and upload it to Canvas.

Given the following DataFrames: Which of the following is the correct syntax to merge these DataFrames?

You need a figure with four subplots: a line chart, a bar chart, a histogram, and a scatter plot. Which tool is more suitable?

Given the following set of paired kmers, reconstruct a genome using a de Bruijn graph. Show all your work, including your de Bruijn graph and the path you traveled in the graph. All nodes should be clearly labeled. Assume an insert size (the textbook called this d) of two and a kmer length of three. Assembling a genome from these reads is Even if correct, the assembled genome will not earn any points without the graph. You may complete this on your scratch paper and take a picture at the very end of your exam to upload here. (30 points)   AAT ATT ATT TTC ATT TTC ATT TTG CAA GAT CAT CTT GAT ATT TCA GCT TCA TGA TGA CAT TTC TGC TTG TCA TTG TCA

How did the 14th amendment change American governance?

Consider the following loop: for (int i = 1; i < n; i++) {    a[i] = a[i-1] * 1.1 + b[i];} Determine whether this loop is parallelizable. Identify the dependency. Suggest one way to restructure the computation.

You are given the following OpenMP program, which approximates π using a parallel loop with reduction: #include #include #include using namespace std; const int N = 100000000; int main(){   int k;   const int NUM_THREADS = 4;   omp_set_num_threads(NUM_THREADS);   double sum = 0.0;    #pragma omp parallel for reduction(+:sum) private(k)   for (k = 0; k < N; k++)   {       double factor = (k % 2 == 0) ? 1.0 : -1.0;       sum += factor / (2 * k + 1);   }    double pi_approx = 4.0 * sum;   cout

You are given the following C++ program that performs naïve matrix multiplication for increasing matrix sizes: // Naive square matrix multiplication: C = A * B (all n x n)void matmul(const std::vector &A,            const std::vector &B,            std::vector &C) {     int n = A.size();     for (int i = 0; i < n; ++i)        for (int j = 0; j < n; ++j)            for (int k = 0; k < n; ++k)                C[i][j] += A[i][k] * B[k][j];}   Assume the main() function measures the runtime for matrix sizes n = 100, 200, 400, 800, 1600.  The computational complexity (i.e. the number of floating-point operations) performed by matmul() is proportional to n3 (written as O(n3)).  Answer the following:   (a) If the time for  n = 200  is measured to be 0.25 seconds, estimate the expected runtime for: n = 400 n = 800 Assume ideal cubic scaling (O(n3)) (b) In reality, the measured execution times for large matrices (e.g., n = 1600 ) are often much worse than the ideal cubic prediction. Explain two reasons related to memory hierarchy or cache behavior that cause this slowdown. (c) Explain why matrix multiplication is embarrassingly parallel at the level of output elements, and briefly describe how OpenMP could parallelize the outer loops. Suppose a student parallelizes the i loop with OpenMP and obtains the following runtimes: threads time (s) 1 8.0 4 2.8 8 1.9 Compute for 8 threads: speedup efficiency Then state one likely bottleneck limiting scalability.