Review: Use the examples below to help you define Polyhedra and Non-Polyhedra. Polyhedra Non-Polyhedra Polyhedra: a solid with flat faces Non-Polyhedra: solid with any surface that is not flat
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Operating Modes C Examples  C – programming msp430x14x.h /************************ * STATUS REGISTER BITS ************************/ #define #define #define #define #define #define #define #define #define C Z N V GIE CPUOFF OSCOFF SCG0 SCG1 0x0001 0x0002 0x0004 0x0100 0x0008 0x0010 0x0020 0x0040 0x0080 /* Low Power Modes coded with Bits 4-7 in SR */ /* Begin #defines for assembler */ #ifndef __IAR_SYSTEMS_ICC #define LPM0 CPUOFF #define LPM1 SCG0+CPUOFF #define LPM2 SCG1+CPUOFF #define LPM3 SCG1+SCG0+CPUOFF #define LPM4 SCG1+SCG0+OSCOFF+CPUOFF /* End #defines for assembler */ #else /* Begin #defines for C */ #define LPM0_bits CPUOFF #define LPM1_bits SCG0+CPUOFF #define LPM2_bits SCG1+CPUOFF #define LPM3_bits SCG1+SCG0+CPUOFF #define LPM4_bits SCG1+SCG0+OSCOFF+CPUOFF  … #include "In430.h“ #define LPM0 _BIS_SR(LPM0_bits) #define LPM0_EXIT _BIC_SR(LPM0_bits) #define LPM1 _BIS_SR(LPM1_bits) #define LPM1_EXIT _BIC_SR(LPM1_bits) #define LPM2 _BIS_SR(LPM2_bits) #define LPM2_EXIT _BIC_SR(LPM2_bits) #define LPM3 _BIS_SR(LPM3_bits) #define LPM3_EXIT _BIC_SR(LPM3_bits) #define LPM4 _BIS_SR(LPM4_bits) #define LPM4_EXIT _BIC_SR(LPM4_bits) #endif /* End #defines for C */ /* /* /* /* /* /* /* /* /* /* Enter LP Mode 0 */ Exit LP Mode 0 */ Enter LP Mode 1 */ Exit LP Mode 1 */ Enter LP Mode 2 */ Exit LP Mode 2 */ Enter LP Mode 3 */ Exit LP Mode 3 */ Enter LP Mode 4 */ Exit LP Mode 4 */ /* - in430.h Intrinsic functions for the MSP430 */ unsigned short _BIS_SR(unsigned short); unsigned short _BIC_SR(unsigned short); CPE 323 23
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24 Functions are not Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods are are are are are are are are are are are are are are are are are are are are are are are are not not not not not not not not not not not not not not not not not not not not not not not not Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods are are are are are are are are are are are are are are are are are are are are are are are are not not not not not not not not not not not not not not not not not not not not not not not not Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods F are are are are are are are are are are are are are are are are are are are are are are are are not not not not not not not not not not not not not not not not not not not not not not not not Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods Methods are are are are are are are are are are are are are are are are are are are are are are are are not not not not not not not not not not not not not not not not not not not not not not not not Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions Functions
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REFLECTIVE JOURNALING TOOLS Reflective J ournalingTools LEARNING: • How is practice different from theory? Did this exercise help you to understand your theory and the application of theory better? How? Why? • Did you learn anything that helped you to better understand a theory, the use of a test that you were taught in lectures/labs? • What did you learn that were not taught in lectures (e.g. communication with patients), and how did you cope or learn more about this to improve your performance? Or how can this be incorporated into lectures? • Did this exercise help you to remember or recall later other aspects of previous experiences that you have forgotten? • Did this exercise help you identify areas that need to be changed, improved etc. in yourself/peers/staff/clinical training etc. Why and how? • What actions did you take you take and what are the results (what did you learn)? SELF ASSESSMENT: • Did you identify areas/issues that you were unclear of, or disagreed with your supervisors/peers, or different from what you have learned in your past lectures? Justify the actions taken. Did this help you in your learning? How? • Have you been open to share with others and to listen what others have to say? • Have you paid attention to both your strong and weak points? Can you identify them? What are you going to do about them? • How did faculty supervision/RW help you in your clinical experiences in relation to your professional growth? (eg. did it encourage you to be more independent, to become more confident in professional activities and behaviors etc) • What have you noted about yourself, your learning altitude, your relationship with peers/supervisors etc. that has changed from doing this exercise? COMMUNICATION: • What have you learned from interacting with others (peers/supervisors/staff etc)? • Did your peers gain anything from YOUR involvement in this exercise and vice versa? • Did this exercise encourage and facilitate communication? • Did you clarify with your supervisors/peers about problematic issues identified? Why (not)? What are the results? • How could you/your peers/staff help you overcome negative emotions arising from your work? Did your show empathy for your peers? PROFESSIONALISM: • Did you learn that different situations call for different strategies in management? • What are the good and bad practices that you have identified? How would you suggest to handle the bad/poor practices identified (if any)? • Did you learn to accept and use constructive criticism? • Did you accept responsibility for your own actions? • Did you try to maintain high standard of performance? • Did you display a generally positive altitude and demonstrate self-confidence? • Did you demonstrate knowledge of the legal boundaries and ethics of contact lens practice? EMOTION & PERSONAL GROWTH: • Did you reflect on your feelings when dealing with the case/peers/supervisor (eg. frustration, embarrassment, fear) for this exercise? If not, why not? If yes, who should be responsible — you, your patient or your supervisor? Why? • Did you find reflection (as required for this exercise) helpful, challenging, and enjoyable, change the way you learn? How? Why (not)? • How and what did you do to handle negative emotions arising from doing this subject? How could these feelings be minimized? • Did you try to find out if your feelings were different from your peers? Why? What did you do to help your peers? • Did you reflect on your learning altitude? How was it? Is there room for improvement? How? Why (not)? • What did you learn about your relationship with your peers/supervisors? What did you learn about working with others? Ideas for Reflective Journaling Writing Contributor(s): Dr. Michael Ying and Dr. Pauline Cho
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Trademark Information Xilinx is disclosing this Document and Intellectual Propery (hereinafter “the Design”) to you for use in the development of designs to operate on, or interface with Xilinx FPGAs. Except as stated herein, none of the Design may be copied, reproduced, distributed, republished, downloaded, displayed, posted, or transmitted in any form or by any means including, but not limited to, electronic, mechanical, photocopying, recording, or otherwise, without the prior written consent of Xilinx. Any unauthorized use of the Design may violate copyright laws, trademark laws, the laws of privacy and publicity, and communications regulations and statutes. Xilinx does not assume any liability arising out of the application or use of the Design; nor does Xilinx convey any license under its patents, copyrights, or any rights of others. You are responsible for obtaining any rights you may require for your use or implementation of the Design. Xilinx reserves the right to make changes, at any time, to the Design as deemed desirable in the sole discretion of Xilinx. Xilinx assumes no obligation to correct any errors contained herein or to advise you of any correction if such be made. Xilinx will not assume any liability for the accuracy or correctness of any engineering or technical support or assistance provided to you in connection with the Design. THE DESIGN IS PROVIDED “AS IS" WITH ALL FAULTS, AND THE ENTIRE RISK AS TO ITS FUNCTION AND IMPLEMENTATION IS WITH YOU. YOU ACKNOWLEDGE AND AGREE THAT YOU HAVE NOT RELIED ON ANY ORAL OR WRITTEN INFORMATION OR ADVICE, WHETHER GIVEN BY XILINX, OR ITS AGENTS OR EMPLOYEES. XILINX MAKES NO OTHER WARRANTIES, WHETHER EXPRESS, IMPLIED, OR STATUTORY, REGARDING THE DESIGN, INCLUDING ANY WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE, AND NONINFRINGEMENT OF THIRD-PARTY RIGHTS. IN NO EVENT WILL XILINX BE LIABLE FOR ANY CONSEQUENTIAL, INDIRECT, EXEMPLARY, SPECIAL, OR INCIDENTAL DAMAGES, INCLUDING ANY LOST DATA AND LOST PROFITS, ARISING FROM OR RELATING TO YOUR USE OF THE DESIGN, EVEN IF YOU HAVE BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. THE TOTAL CUMULATIVE LIABILITY OF XILINX IN CONNECTION WITH YOUR USE OF THE DESIGN, WHETHER IN CONTRACT OR TORT OR OTHERWISE, WILL IN NO EVENT EXCEED THE AMOUNT OF FEES PAID BY YOU TO XILINX HEREUNDER FOR USE OF THE DESIGN. YOU ACKNOWLEDGE THAT THE FEES, IF ANY, REFLECT THE ALLOCATION OF RISK SET FORTH IN THIS AGREEMENT AND THAT XILINX WOULD NOT MAKE AVAILABLE THE DESIGN TO YOU WITHOUT THESE LIMITATIONS OF LIABILITY. The Design is not designed or intended for use in the development of on-line control equipment in hazardous environments requiring fail-safe controls, such as in the operation of nuclear facilities, aircraft navigation or communications systems, air traffic control, life support, or weapons systems (“High-Risk Applications”). Xilinx specifically disclaims any express or implied warranties of fitness for such High-Risk Applications. You represent that use of the Design in such High-Risk Applications is fully at your risk. © 2009 Xilinx, Inc. All rights reserved. XILINX, the Xilinx logo, and other designated brands included herein are trademarks of Xilinx, Inc. All other trademarks are the property of their respective owners. FPGA and ASIC Technology Comparison - 23 © 2009 2007 Xilinx, Inc. All Rights Reserved
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A Graph Kernel Method for DNA-Binding Site Prediction Changhui Yan* and Yingfeng Wang CS, Utah State U, Logan, UT ABSTRACT: This paper presents a graph kernel method for predicting DNA-binding sites on protein structures. Surface patches are represented using labeled graphs. Then, the graph kernel method is used to calculate the similarities between graphs. A new surface patch is predicted to be interface or non-interface patch based on its similarities to known DNA-binding patches and non-DNA-binding patches. The proposed method achieves 88.7% accuracy, 89.7% specificity, and 87.7% sensitivity when tested on a representative set of 146 protein-DNA complexes using leave-one-out cross-validation. Then, the method is applied to identify DNA-binding sties on 13 unbound structures of DNA-binding proteins. In each of the unbound structure, the top 1 patch predicted by the proposed method precisely indicates the location of the DNA-binding site. Comparisons with other methods confirm the effectiveness of the method. Introduction Structural genomics projects are yielding an increasingly large number of protein structures with unknown function. As a result, computational methods for predicting functional sites on these structures are in urgent demand. There has been significant interest in developing computational methods for identifying amino acid residues that participate in protein-DNA interactions based on combinations of sequence, structure, evolutionary information, and chemical or physical properties. For example, Jones et al. (2003) analyzed residue patches on the surface of DNA-binding proteins and used electrostatic potentials of residues to predict DNA-binding sites. Later, they extended that method by including DNA-binding structural motifs (Shanahan, et al., 2004). In related studies, Tsuchiya et al. (2004) used a structure-based method to identify protein-DNA binding sites based on electrostatic potentials and surface shape, and Keil et al. (2004) trained a neural network classifier to identify patches likely to be DNA-binding sites based on physical and chemical properties of the patches. Neural network classifiers have also been used to identify protein-DNA interface residues based on a combination of sequence and structure information (Ahmad, et al., 2004). Recently, Tjong and Zhou (2007) developed a neural network method for predicting whether a surface residue is in the DNA-binding sites based on the sequence profile of that residue and its structural neighbors. On another track, several methods have been developed for predicting DNA-binding sites using only protein sequence-derived information as input (Ahmad and Sarai, 2005; Wang and Brown, 2006; Yan, et al., 2006). To date, the methods that take the advantage of structure-derived information achieve better results than those using only sequence-derived information. One common limitation of the above-mentioned methods is that the sequence and structural properties of a surface patch are input to machinelearning methods in the form of vectors. When the properties of a surface patch are encoded as a vector, the information of how these properties distribute over the surface is lost. For example, if a surface patch includes five amino acid residues, the above-mentioned methods will encode the amino acid identities of this surface patch as five independent values in a vector. In this representation, the spatial arrangement of these five residues on the surface patch is not encoded. Unfortunately, the spatial arrangement of properties on a surface patch plays a crucial role in determining the function of the surface patch. To overcome this limitation, this paper presents a graphical approach for DNA-binding site prediction. In this study, graphs are used to represent surface patches, such that the spatial arrangement of various properties on the surface is explicitly encoded. The similarities between surface patches are then computed using a graph kernel method. A voting strategy is then used to classify surface patches into DNA-binding sites versus non-binding sites. The proposed method achieves 88.7% accuracy, 89.7% specificity, and 87.7% sensitivity in leave-one-out crossvalidation. When applied to set of unbound structures of DNA-binding proteins, the proposed method can precisely identify the locations of DNA-binding sites.
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Question: you gave me five different equations for electric flux. Which one do I need to use? Answer: use the simplest (easiest!) one that works.  E EA Flat surface, E  A, E constant over surface. Easy!  E EA cos  Flat surface, E not  A, E constant over surface.    E E A    E E dA    E E dA Flat surface, E not  A, E constant over surface. Surface not flat, E not uniform. Avoid, if This is the definition of electric flux, so it is on your equation sheet. possible. Closed surface. The circle on the integral just reminds you to integrate over a closed surface. If the surface is closed, you may be able to “break it up” into simple segments and still use E=E·A for each segment.
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Important Legal Information for Adolescents and Parents According to Iowa law, a minor (an individual younger than 18 years) may seek medical care for the following without the permission or knowledge of his parents: • Substance abuse treatment; • Sexually Transmitted Infection(STI) testing and treatment; • HIV testing – if test is positive, Iowa law requires parent notification; • Contraceptive care and counseling, including emergency contraception; and Even though teenagers young • Blood donation if 17and years of age or adults can receive these treatments older. without their parent’s knowledge, it is important to remember parents are a key part of all aspects of your life. We encourage parents and teens to be open and honest with each other when it comes to health care decisions. It is important for teens to know that if they are covered by their parents’ medical insurance and want it to cover their treatment, they will need to consent to their medical records being shared – possibly even with parents. A minor may also consent for evaluation and treatment in a medical emergency or following a sexual assault. However, treatment information can not be kept confidential from parents. Bill of Rights for Teens and Young Adults • The things you tell us in confidence will be kept private. • We will speak and write respectfully about your teen and family. • We will honor your privacy. YOU HAVE THE RIGHT TO: Emotional Support • Care that respects your teen’s growth and development. • We will consider all of your teen’s interests and needs, not just those related to illness or disability. Respect and Personal Dignity • You are important. We want to get to know you. • We will tell you who we are, and we will call you by your name. We will take time to listen to you. • We will honor your privacy. Care that Supports You and Your Family • All teens are different. We want to learn what is important to you and your family. Information You Can Understand • We will explain things to you. We will speak in ways you can understand. You can ask about what is happening to you and why. Care that Respects Your Need to Grow and Learn • We will consider all your interests and needs, not just those related to your illness or disability. Make Choices and Decisions • Your ideas and feelings about how you want to be cared for are important. • You can tell us how we can help you feel more comfortable. • You can tell us how you want to take part in your care. • You can make choices whenever possible like when and where you YOU HAVE THE RIGHT TO: receive your treatments. Bill of Rights for Parents Respect and Personal Dignity • You and your teen will be treated with courtesy and respect. Make Decisions About Your Teen’s Care • We will work in partnership with you and your teen to make decisions about his care. • You can ask for a second opinion from another healthcare provider. Family Responsibilities YOU HAVE THE RESPONSIBILITY TO: Provide Information • You have important information about your teen’s health. We need to know about symptoms, treatments, medicines, and other illnesses. • You should tell us what you want for your child. It is important for you to tell us how you want to take part in your teen’s care. • You should tell us if you don’t understand something about your teen’s care. • If you are not satisfied with your teen’s care, please tell us. Provide Appropriate Care • You and the other members of the health care team work together to plan your teen’s care. • You are responsible for doing the things you agreed to do in this plan
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