## MEASURING DISTANCES IN ASTRONOMY Basic Principles: Geometric methods St in addition to ard ca

Swift, Ron, Freelance Columnist has reference to this Academic Journal, PHwiki organized this Journal MEASURING DISTANCES IN ASTRONOMY Basic Principles: Geometric methods St in addition to ard c in addition to les St in addition to ard rulers [the last two methods relate quantities that are independent of distance to quantities that depend on distance] Parallax in addition to Proper Motion Angular size: degree [º], arcminute [‘], arcsecond [“] [in arcseconds] = 206265 (L/D) where: = angular size; L = linear (or true) size; D = distance Definitions: parallax (p), Astronomical Unit [AU], parsec [pc] D [in parsec] = 1/p [in arcseconds] where: 1 pc = 206265 AU = 3.26 light yr Parallax can only be used on nearby stars (D < 100 pc) [Atmospheric blurring (seeing); Hipparcos satellite; Hubble Space Telescope]

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Motion of stars within a cluster Proper motion [arcsec/s] = change of angular position Line-of-sight motion [km/s] – measured via Doppler shift Comparison of average stellar proper motion in cluster with average line-of-sight speed yields distance to cluster Luminosity in addition to Flux Inverse square law: f = L / (4D2) where: f = flux [erg/s/cm2]; L = luminosity [erg/s]; D = distance [cm] Magnitude scale: brightnesses of astronomical sources

St in addition to ard C in addition to les in addition to Rulers Variable stars: Cepheids in addition to RR Lyrae stars Period-luminosity relation; measure P & infer L; measure f & infer D Other st in addition to ard c in addition to les: brightest red giants, HII regions, planetary nebulae, supernovae, globular cluster luminosity Galaxies: Luminosity is seen to be correlated with the typical speed of internal motion of stars in addition to gas [Tully-Fisher relation: rotation of disks of spiral galaxies] [Faber-Jackson relation: r in addition to om stellar motion in elliptical galaxies] Galaxies: Size correlated with typical speed of (r in addition to om) stellar motion [Dn- relation as long as elliptical galaxies] Redshift as Distance Indicator Expansion of the Universe Hubble’s law: v = H0 D where: H0 = Hubble constant [km/s/Mpc] Doppler shift used to measure recession velocity: v c ( / ) where: / = fractional change in wavelength Astronomical Distance Ladder

Special Theory of Relativity (STR) Speed of light (in vacuum): c = 300,000 km/s Constancy of the speed of light: Michelson & Morley experiment No signal or object can travel faster than c [The ultimate speed limit!] Special Theory of Relativity (STR) Basic Principles The speed of light is the same to all observers The laws of physics are the same to all observers Observable Consequences Simultaneity is a relative concept Length contraction: moving rulers appear to be short Time dilation: moving clocks appear to run slow The apparent mass (inertia) of an object increases as its speed increases (impossible to accelerate it up to c) Equivalence of mass in addition to energy: E = mc2 Special relativistic effects are important when the SPEED of an object is CLOSE TO THE SPEED OF LIGHT: v c

Simultaneity in addition to time are relative, not absolute Marion Jones sees A flash be as long as e B Marion Jones sees A in addition to B flash simultaneously Measuring the length of a moving object: Length Contraction The apparent (i.e., measured) length of a moving object is shorter than the true length (measured when the object is at rest) Measuring time on a moving clock: Time Dilation A moving clock runs slower than its counterpart at rest Stationary Clock Moving Clock

A Thought Experiment: Length Contraction in addition to an Apparent Paradox The Garage Attendants Perspective A Thought Experiment: Length Contraction in addition to an Apparent Paradox The Drivers Perspective Solution: The driver in addition to garage attendant do not agree on the question of whether the two doors were closed simultaneously A Real Laboratory Experiment: Direct Verification of Time Dilation in addition to Length Contraction as Predicted by the Special Theory of Relativity The scientist in the laboratory witnesses time dilation, while the Uranium atoms witness length contraction Beam of fast-moving Uranium atoms Suitably placed Geiger counter Nuclear fission of Uranium atoms

General Theory of Relativity (GTR) Principle of Equivalence All objects experience the same motion in a given gravitational field, irrespective of their mass [Galileo’s experiment at the leaning tower of Pisa] Gravitational field <===> Accelerated reference frame Gravity can be thought of as a distortion of space-time Observable Consequences of GTR Perihelion precession of Mercury Light bending: Solar eclipse experiment

Gravitational lensing: Multiple images, image distortion Gravitational Redshift [Extreme case: light is trapped in a black hole] General relativistic effects are important in a STRONG GRAVITATIONAL FIELD

## Swift, Ron Freelance Columnist

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Putting it all together: The Bloch equation Sums of the phenomena transverse magnetization precession, RF excitation longitudinal magnetization Changes the direction of , but not the length. These change the length of only, not the direction. includes Bo, B1, in addition to Review: Cosine Theorem (What is a mixer ) – Consider a pulse A(t) that is multiplied by cos(ot). This is called modulation . A(t) is called the envelope function. o is the carrier frequency. Mixer cos(ot) A(t) cos(ot) A(t) Frequency response of RF pulse o = 2fo -fo fo f B1(f) Selective Slice Excitation Lets consider 2D imaging Slice excitation or selective excitation We first spoke of B1(t) as a rectangle, an on/off pulse, a) Now modulate the carrier cos(ot) by B1(t) where B1(t) is a sinc . B1(t) F.T. Mixer cos(ot) B1(t) cos(ot) B1(t) fo f B1(f) -fo 0 Create a circularly polarized field

Slice Selection Simultaneously, we apply a gradient Gz. This creates a mapping along z such that only a subset of spins will be within the b in addition to width of the RF pulse. Set amplitude of Gz such that b in addition to width of where is the desired slice thickness. B1(t) cos(ot) F.T. o Gradient Fields: In Plane Encoding x Bz Bo slope = Gx Apply Gx during a FID Frequency is proportional to

Gradient Fields: In Plane Encoding The Fourier trans as long as m of the signal gives us the projection of the object. The signal detected by the coil, a function of time (t),has spatial in as long as mation encoded into it. fo f t Sr Signal(t) x Bz Bo slope = Gx so, Gradient Fields: Example Example: Assume a 20 cm wide head. Let Gx = 2 G/cm What is the frequency range across the head Gradient Fields: Example Example: Assume a 40 cm wide object. Gx = 0.5 G/cm At 1 T, B in addition to width of interest is small compared to 42.57 MHz.

Gradient Fields: Matlab example x m(x) Gradient Fields Gy changes field strength of B field in z direction as a function of y Gz changes field strength of B field in z direction as a function of z Generalized Gradient Fields We can write this all together by looking at G as a vector. Each element refers to the gradient field present at any time ( current in the coil at that time).

Basic Procedure 1) Selectively excite a slice (z) – time .4 ms to 4 ms – thickness 2 mm to 1 cm 2) Record FID, control Gx in addition to Gy – time 1 ms to 50 ms 3) Wait as long as recovery – time 5 ms to 3s 4) Repeat as long as next measurement. – measurements 128 to 512 – in just 1 flip 5) Next: More on spatial encoding

## Nieder, Alison Executive Editor

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## Validation of Rapid Microbiology Systems What Validation is Not Validation Definition Intentions of Validation Demonstration data which shows:

Salazar, Angela, Editorial Assistant has reference to this Academic Journal, PHwiki organized this Journal Validation of Rapid Microbiology Systems Based upon PDA Technical Report No. 33 Task Force Members Brian Bauer, Ph.D., Merck & Co. Mark Claerbout, Lilly Research Laboratories Warren Casey, Ph.D., GlaxoWellcome R&D Anthony M. Cundell, Ph.D., Wyeth-Ayerst Pharmaceuticals, Pearl River, New York (Chair) Martin Easter, Ph.D., Celsis Ltd., Edward Fitzgerald, Ph.D. Consultant, (USP Microbiology Subcommittee) Carol Gravens, BioMerieux, Inc. David Hussong, Ph.D., CDER, FDA Michael Korcynzski, Ph.D., PDA Training Institute (USP Microbiology Subcommittee) Robin Lerchen, American Pharmaceutical Partners Frederic J. Marsik, Ph.D. CDER, FDA Amy Meszaros, StatProbe Inc Jeanne Moldenhauer, Ph.D., Jordan Pharmaceuticals, Inc. Manju Sethi, Qualicon Scott Sutton, Ph.D., Alcon Laboratories (USP Microbiology Subcommitee) Martin Tricarico, Chemunex (USA) Am in addition to a Turton, Millipore Corp Christine Vojt, Johnson & Johnson Diagnostics Inc. Kirsty Wills, Celsis Ltd. Jon Wuannlund, Becton Dickinson Microbiology Systems Disclaimer Presentation represents the views published in the PDA Technical Report May not in all ways be consistent with my personal views regarding the validation methods

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Document Contents Part I : Selection of New Microbiological Methods 1.0 Introduction 2.0 Technology Overview Generic Description of Types of Methods Technology Review Growth-based technologies Viability-based technologies Artifact based technologies Nucleic Acid-based technologies 3.0 Regulatory Review Document Contents Part II: How to Validate in addition to Implement New Microbiological Methods 4.0 The Validation Process Equipment Model Validation Criteria Special Considerations as long as Microbiology 5.0 Glossary 6.0 References Implementation Requires Validation May require regulatory prior approval Many are not equivalent to traditional methods May be little or no guidance available as long as validation of microbial methods Many have associated computer-related systems in addition to 21 CFR Part 11 issues New technology frequently requires new learning curves Reluctance to change

What Validation is Not Method/technology selection Evaluation of feasibility of the method Proof of concept testing Vendor selection Doing it differently as long as economic considerations Typical Flow as long as Selection, Evaluation & Validation as long as a Rapid Method Is current compendial or industry st in addition to ard method meeting all of your companys needs Determine what test requirements in addition to specifications are (faster, less labor intensive, etc.) Look at alternate methods in addition to see if the method can meet the specified requirements Per as long as m sufficient feasibility proof of concept testing Does testing yield acceptable results Plan in addition to execute validation protocol. Were results acceptable Submit regulatory supplement if required Implement test (after approval) Evaluate other alternate methods, rejecting the unacceptable method Continue using existing method No No No No Yes Yes Yes Validation Definition Established documented evidence which provides a high degree of assurance that a specific process will consistently produce a product meeting pre-determined specifications in addition to quality attributes

The Equipment Validation Model Method/Vendor Requirements (Defined) Validation Plan Design Installation Qualification (IQ) Operational Qualification (OQ) Per as long as mance Qualification (PQ) Verify in addition to document the system per as long as ms as specified using selected compendial organisms in addition to environmental isolates as long as routine testing of batches of product. Intentions of Validation Demonstration data which shows: Accuracy of method (appropriateness) Reproducibility of method (continuity) Unique Concerns as long as Developing Validation Requirements as long as Rapid Microbiological Methods What type of validation support does the vendor have (e.g., DMF, sample protocols) Is the new method equivalent to existing method If side-by-side studies are used, what is the impact of the rapid method being out-of-specification, when the traditional method is within limits Is the new technology/method subject to different variabilities than traditional methods Can the method h in addition to le the expected laboratory throughput

Unique Concerns as long as Developing Validation Requirements as long as Rapid Microbiological Methods (continued) USP definitions as long as validation terms (e.g., accuracy, precision, ruggedness ) are written specifically as long as chemistry applications while automated microbiological methods probably should take these definitions into account {USP has drafted a chapter to resolve this} Are there other human interface concerns that are not present in the compendial methods Difficulties in setting limits as long as the unknown Establishing Validation Criteria USP Chapter <1225> Validation of Compendial Methods gives criteria, but definitions are in terms of chemical methods Draft revision in Pharmacopeial Forum with criteria as long as Alternative Methods. PDA Technical Report 33 has issued microbiological definitions of these same criteria Since some tests are quantitative in addition to others are qualitative (e.g., limits tests), not all criteria are applicable to each test type Validation Criteria

Accuracy Definition: A measure of the exactness of the microbiological method that is true as long as all practical purposes Determination: Analyze at least five suspensions across the range of test. Calculate each suspension as a percentage dilution of the original. Acceptance criteria: Should be equivalent or better than existing methods (±30% are considered equivalent) Precision Definition: Precision may be a measure of either the degree of reproducibility of the microbiological method under normal operating conditions Repeatability refers to use of the method within the same laboratory over a short period of time using the same analysts in addition to same equipment Reproducibility uses different analysts with the same equipment Precision (continued) Determination: at least five suspensions across the range of the test should be analyzed. Each suspension should be analyzed as long as at least ten replicates. St in addition to ard deviation or coefficient of variation should be calculated. Acceptance criteria: generally, a coefficient of variation in the 15-30% range is acceptable

Precision (continued) For comparing precision of two methods, the F-test is recommended. If the critical value exceeds the statistical table, a significant difference exists between the methods. Specificity Definition: the ability of a method to detect a range of microorganisms which demonstrate that the method is fit as long as use Determination: screen against representative range of microorganisms in addition to sample types Acceptance criteria: all selected organisms are isolated in addition to enumerated from sample matrices Limit of Detection Definition: a parameter of a limit test which identifies the lowest number of microbes which can be detected, but not necessarily quantified in the test conditions Determination: multiple replicates, greater than or equal to five, should be tested, since one cannot reliably obtain samples with a count of one

Limit of Detection (continued) Acceptance criteria: ideally, if one organism is tested, it will be detected in the time frame of the assay (use of CHI-square test) Limit of Quantification Definition: the lowest number of microbes which can be determined with acceptable precision in addition to accuracy under the slated experimental conditions Determination: since there are no reliable methods to get a sample count of one, greater than or equal to five replicates should be used Acceptance criteria: if a single organism is present, it will be quantified in the time frame of the assay Linearity Definition: the ability to elicit results that are proportional to the concentration of microbes present in the sample within a given range Determination: at least five replicates across range of assay Acceptance criteria: correlation coefficient r2 = 0.9, or better with a slope not diverging more than 20% from 1.0, i.e., r2 = 0.8 – 1.2

Range Definition: the interval between the upper in addition to lower levels of microbes that have demonstrated to be determined with precision, accuracy in addition to linearity, using the method as written Determination: the range of the method is validated by verifying that the analytical method provides acceptable precision, accuracy in addition to linearity, when applied to samples at the extreme of the range in addition to within the range Acceptance criteria: depends upon per as long as mance characteristics of the method Ruggedness Definition: degree of reproducibility of test results obtained by samples under variety of normal test conditions, e.g., different analysts, instruments, lots of reagents Determination: prepare a suspension of microbes in addition to test at least ten replicates against each assay variable. Calculate st in addition to ard deviation or coefficient of variation. Acceptance criteria: generally, a coefficient of variation in the 10-15% range is acceptable Robustness Definition: a measure of its capacity to remain unaffected by small but deliberate variations in method parameters in addition to provides an indication of its reliability during normal usage Determination: the manufacturer investigates changes up to 20% on the critical reagent concentrations, instrument operation parameters in addition to incubation temperatures Acceptance criteria: review results need to be reviewed against the manufacturers quality requirements in addition to instructions as long as use

Equivalence Definition: a measure of how similar the test results are to the method it is intended to replace Determination: should first be demonstrated in pure culture work. Requires the two methods be run in parallel as long as comparison. Its important to have r in addition to omized experimental design. A large number of replicates should be used. Acceptance criteria: generally, a statistical test is used Microbiological Reference St in addition to ards Issues associated with identification in addition to determination of material to be used as a reference st in addition to ard as long as this type of testing. TR33 highlights the need to show equivalence to existing methods, but the technology as long as many of these systems is superior to the old method in addition to may not show equivalence. Provides some guidance on preparing in-house st in addition to ards. Method Validation Variability of Microbiological Methods Sample Distribution Error Cell Morphology Metabolic Activity

Other Issues in Validation Use of multiple identical equipment within the laboratory in addition to company Whether or not you are the first or fiftieth company to submit this test method Companys regulatory compliance status Unique issues with microbiology equipment Regulatory requirements, e.g., LAL Impact on other system, e.g., sterility testing Etc.

## Salazar, Angela Editorial Assistant

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## TCP/IP Internal TCP/IP Learning outcome Application layer HTTP, FTP, TELNET

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## TCP/IP Internal TCP/IP Learning outcome Application layer HTTP, FTP, TELNET

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Reading List in consideration of this chapter Julian Moss, ?understanding TCP/IP? (parts 2-4, October 1997-March 1998) William Buchanan, Mastering the Internet, Ch. 4 Joe Casad, Teach Teach Yourself TCP/IP, chs. 4-6 Liam Proven, ?understanding TCP/IP? [details of IP addressing] ( January 2001) or or in addition to pcnetworkadvisor TCP/IP TCP/IP Reference Model HTTP TELNET FTP SMTP SNMP Protocols TCP UDP IP ICMP ETHERNET PACKET RADIO TCP/IP TCP/IP Protocol Suite is a four-layered protocol suite. The location of the important protocols within the TCP/IP layers is showed below * TCP/IP

The suite of Protocols in consideration of TCP/IP ICMP The application layer Handles high-level protocols, issues of representation, encoding, in addition to dialog control.ÿ The TCP/IP combines all application-related issues into one layer, in addition to assures this data is properly packaged in consideration of the next layer. FTP, HTTP, SMNP, DNS . Format of data, data structure, encode ? Dialog control, session management ? TCP/IP suite Application Protocols TCP/IP suite

TCP/IP Internal TCP/IP Learning outcome Application layer HTTP, FTP, TELNET

Application Protocols TCP/IP suite Application Protocols (cont?d) TCP/IP suite The transport layer Transport layer Transport protocols UDP TCP TCP AND UDP segments TCP/IP suite

Transport Protocols TCP/IP suite Orientation Transport layer protocols are end-to-end protocols They are only implemented at the hosts TCP/IP suite Transport Protocols in the Internet UDP – User Datagram Protocol datagram oriented unreliable, connectionless No acknowledgment simple unicast in addition to multicast useful only in consideration of few applications, e.g., multimedia applications used a lot in consideration of services network management (SNMP), routing (RIP), naming (DNS), etc. TCP – Transmission Control Protocol stream oriented reliable, connection-oriented complex only unicast used in consideration of most Internet applications: web (HTTP), email (SMTP), file transfer (FTP), terminal (TELNET), etc. The Internet supports 2 transport protocols TCP/IP suite

Position of UDP in the TCP/IP protocol suite User Datagram Protocol Uses IP so that transport message from source so that destination Unreliable, connectionless datagram delivery No acknowledgements Messages can be lost, duplicated, or arrive out of order TCP/IP suite User Datagram Protocol Adds ability so that distinguish from multiple destinations on target Applications responsibility so that handle unreliability TCP/IP suite

User Datagram Protocol TCP/IP User Datagram Protocol Source port (optional – zero if not used) Length – Count of octets including header in addition to data (minimum is 8) Checksum (optional – zero if not used) UDP Message Length UDP Destination Port Data . . . UDP Source Port UDP Checksum TCP/IP User Datagram Protocol IP checksum does not include data UDP checksum is only way so that guarantee that data is correct UDP checksum includes pseudo-header UDP Header UDP Data Pseudo Header TCP/IP

UDP Pseudo-Header UDP Length Source IP Address Destination Address Zero Protocol UDP Message Length UDP Destination Port Data . . . UDP Source Port UDP Checksum TCP/IP UDP Pseudo-Header Prefixed so that the front of datagram Verifies that datagram reached correct destination UDP header only includes port numbers Pseudo-header includes IP addresses TCP/IP TCP/IP Layers Conceptual Layers are independent Network Interface Internet (IP) User Datagram (UDP) Application TCP/IP

TCP/IP Layers Each layer interacts alongside layer above or below No details passed between the two layers Messages encapsulated as they are passed down TCP/IP TCP/IP Layers IP Datagram Header IP Datagram Data Area Frame Header Frame Data Area UDP Header UDP Data TCP/IP TCP/IP Layers UDP checksum includes pseudo-header which includes source in addition to destination IP address Source IP address depends on route chosen (multiple interfaces) UDP layer builds IP datagram TCP/IP

User Datagram Protocol Summary Uses ports on source in addition to target Does not add significantly so that IP Unreliable connectionless packet delivery Interacts strongly alongside IP layer Low overhead TCP/IP Transport Control Protocol TCP/IP TCP Lingo When a client requests a connection, it sends a ?SYN? segment (a special TCP segment) so that the server port. SYN stands in consideration of synchronize. The SYN message includes the client?s ISN. ISN is Initial Sequence Number. TCP/IP

RARP conversation SUMMARY Transport layer UDP Connectionless Unreliable transmission Less overheat TCP CONNECTION ORIENTED Reliable Transmission More overheat so that deal alongside ack?s Internet layer IP Connectonless IP routing (next-hop using routing table) Unreliable ICMP (information control message protocol) ARP (IP so that MAC) RARP (MAC TO IP)

## Eldred, Doug Editorial Board

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