Guidance for Industry PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance pdf

... Pharmaceutical CGMPs September 2004 Guidance for Industry PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance Additional ... Standard Practice for Applying Statistical Quality Assurance Techniques to Evaluate Analytical Measurement System Performance. E 456-02: Standard Terminology Relating to Quality and Statistics ... http://www.fda.gov/cder/OPS /PAT. htm. 16 Guidance for Industry PAT — A Framework for Innovative Pharmaceutical Development, Manufacturing, and Quality Assurance U.S....

... and to some extent have a physical appearance similar to theparadigm. However, the similarities are slightly less for these advancedfetuses than for infants because infants have been born and ... language – concepts such as consciousness and self. A paradigmexample of a self-conscious individual would be a normal adult human being.By contrast, lower animals that lack concepts and language ... answersare to these main questions, we can give arguments for and against diVerentanswers, and we can try to decide what answers are best supported byarguments. That is what ethics is all about...

... without permission. A framework for analysing manufacturing flexibilityAdegoke OkeInternational Journal of Operations & Production Management; 2005; 25, 9/10; ABI/INFORM Globalpg. 973Reproduced...

... successful exchange of IK. Application: Transfer of the Washambaa agricultural system to Rwanda adaptation and re-transfer. The Washambaa of the Usambara Mountains in Tanzania had developed a land use ... handbooks how torecord and document indigenousknowledge by means ofparticipatory approaches areavailable for manual and computeruse prepared by IIRR and CIKARD§ case studies are available ... maintain and preserve IK§ capturing and documenting IK§ transfer locally and regionally§ disseminate§ advocacy§ provide and operate documentation and access space(libraries, databases, info...

... Kawabata and Zeki (2004) found activity associatedmostly with beautiful images, was most active after a reward and decreased itsactivity after punishment, whereas the activity in the lateral ... termrepresentation while maintaining the idea of a stage of higher-level visual processing. Abbreviations — V & G: Vartanian and Goel (2004); K & Z: Kawabata and Zeki (2004); C-C et al: Cela-Conde ... and negatively valued stimuli.Kawabata and Zeki (2004) and Vartanian and Goel (2004) obtained interestingresults when comparing brain activity before different categories of stimuli, such asabstract...

... Adequate mechanical translation can be based only on adequate structural descrip- tions of the languages involved and on an adequate statement of equivalences. Translation is conceived of as a ... 'had to.' If we want to indicate the connection in meaning (parallel- ing a similarity in distribution) between 'can' and 'is able to' and between 'must' and ... 'to be' followed by an adjective which takes the infinitive with 'to.' Again the auxil- iary 'must' has no past tense and again one uses a circumlocution — 'had...

... interpreted as a metagrammar, i.e a grammar for generating the actual CF-PS grammar. A GPSG can be defined as a two-leveJ grammar containing a metagrammar and an object grammar. The object grammar combines ... German belongs to the class of natural-language phenomena that require a closer interaction of syntax and pragmatics than is usually accounted for in formal linguistic frameworks. Computational ... Semantics (Barwise and Perry. 1981) narrows the gap between .,,.'mantics and pragmatics. If we as.~ume that a language generation system should be able to generate all grammatical word...

... src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABDUAAAXeCAIAAADw5oNDAAAACXBIWXMAABYlAAAWJQFJUiTwAAAgAElEQVR42uzdu1Oc63 3A8 YddQCBNJgWoScGWEnGTGbtaLvWZyeD1Uecz0DBnzvkLAoVqFeAqpTy2UsA4nc7s0LjmVjlNCiMVKZYijVCVQSDQsil+4tGrd2ElHSEuOp/PZOJlry9rFXz93PrGfv80AQAAn+7vv/rel3CxKr4CAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAABAnwAAAOgTAAAAfQIAAOgTAAAAfQIAAOgTAAAAfQIAAOgTAAAAfQIAAOgTAACAz9DvKwAAgK9VdbxRGb3Xbm2e7G7rEwAA4MpUxupDD56klFJa3H80cjOu2X9tAADwVarWJvPtgakFfQIAAKBPAACAgnZrU58AAADXgvXxAADAFYilJsUBk6P1pcpYPZajHG8s6xMAAOCLN8ng9GK+52ClPKErHo3/f7S+dD03HdYnAABwU8WoSE6Oo/WlruGRt8VysrudtxgemFqo1ibyq67ViIo+AQCAa10gg9OLh6uNM++v1ibbrc2DlZmPHwmpjNWPN5aPN96GyuD04uD04nmVMjTbbLe2LjNgrI8HAIBrWiZDs83hubWj9aXSQwNTC8Nzaymlg5WZw9XGeXHSfX9ehRKON5b3H40crMxUaxN3Hr6sjNVLzz9cbQxOLw7NNrsf+kKMnwAAwHWMkyiQo/WlYmbkYZNPGjNJp3O62q2tMzPmcLURzdNubZbGag5WZobn1obnPvkTf+Yv7r97AAC4nnHSbm0W51bl+/cfjfRIhYGphaHZZveB8bFSZXB68c7Dl92PxlhKSqk0WnKyu32wMpNSGp5bu4RRFH0CAADXNE6KQxkx1ysmdPXIkjsPX8YAS+9PiVlb3fcfrjbara3hubW+0XtXkij6BAAArlh1/F1y3PrmDymlo/WlYofc+vbPfbdHXz2udw+bVMcbtx486c6Sk73nKaVOpxM/xvDI4dP5Nztvs6Ramxz+YSs6pG/0Xr6G443lV4/rt/7130uJ8upx/eTFs1vf/CHur443ik/QJwAA8DWojNUHfj0ft4dmm5W79w9WZorTugamFvr/+XcHf5zo7D0vvfDOw5ed/Rf9441 2a7 O4jP5gZaa900wp9fX1FV/S3mm+fjofgyEppcrd+7e+WU4p3f5xuzJ6L4+NdPaed/7vf2//uF0cLensPT/440Tn1V68pDJ6L27oEwAA+HoMz63FsvXYXCuWoQ9MLUQbxIL4V4/rpRUjsTXw/qORGDPJp6C0W5u9V6eklE52t/Pp8vHyo/WlwenFmFcWmXS0vhQr40sTumJUJzYprtYmu9ex6BMAALipSn/9R1oMzTartYmT3e1Yi3K0vtTZe368sZxjIJaORCoUz4yPJSsDUwsDUwt3Hr4sfVBunpRScSOvYmPEpsbxiZEx+f3zmvvixLPip+sTAAC42d5bMbK7HVO2qrXJmKw1OL1Y3MIrEiX6Ie4spkW8JNIlb8ZVfPN 2a7 Nam4zNu/L4SUkM5uThl0iR+JQYY4ndveIJ8YkXu2Le+ScAAHDFIjaGZpuRK3mKV7U2WcqMOMOke/pWu7VZrU2ct7VXTpST3e3jjeWYM/b +a9 9lUkRR/oij9aU4FyV28eo+C6Vam7zAc1GMnwAAwJUpzp4qxkk8VDo5Po+cxIBGZazebm0erMzE4vjecVIKldKPpeGUvBAlPxrXWdpoOLKqWpu4wC9EnwAAwGUrzstqtzZjZXzERsRDTMEqHc6Y0yLfH8viD1cbnzSCkVsoPr10I9x68CTfPlxtVGuT+QJyoqTC2MtFLZTXJwAAcNnen161FROoipnRPXgyPLdWGiEZnF784Jyuo/WlvFgllsjH+pZie8Q9h6uNYqL0jzeKC0ti8UnczokyNNs8b529PgEAgJshbxycM6N0VHwMnhSHRKrjjXxoSfFNinlwpjiNMbJkeG6ttN3WwcrMwNRCtEdlrH642ihGUXGWV4yT5GuORKnWJouTuy5kopc+AQCAS5VPLEmns6qK22SdFst74VEZvVfMlVjgfrjaKG46XBJNcvvH7cHpxe5dgGMuWUwqO95YzsMjsfFXXpQfu4GlwvZf+f6cKHmrseKwjD4BAICbp93aynOlcnukwgqTM0WcxO3SuSh5Etd5J5PEwYvx8jxK033YYgy8FEd12q2t/J7x5DzR6wLZXxgAAK5MPsYkD0TEmEbxOQNTCyd7z/OPlbF6aXQlEqVam4hhkO5xjHjn4pyx4mr7OM8k9g7OzzlaX+q7PZKPOolPiWlgJ7vb8UHx5AvcXDgZPwEAgKsVk7VyGHRP7qrWJto7zfzj8Nxa9+jKmXFytL706nF9/9HI8cby8cZyMU6Ka0tyoqTCwv3O3vOj9aVS6uQpXnEBscvwBX8b/kEAAMBliuGRXAJxo3gWeyk/irlSGauX9vVKKcUa91Q4jb7d2owsGZxevPPwZSosys/n08dgSDw/4iTWk+RZXie726VJYsUpXnnZiT4BAICbqdKf3j8esTreqNYmD5/Ox53dU7NKk7sGfvP9m8JYSkqpb/Re5e79k93tgd98/7YrXjw7XG1Uxxspper938adw3Nr1fFGDJsMTi/2jzeqtcnhubU7D192Xr3MQfJmp1m5ez+/eemzilcSq+qrtcn4oO4n6xMAALjeBm7Hf+YxkKEHT9qtzeL0rdLwSGlyV9/tkU4hElJK/eON47/9KW7kTqiM1YcePEkp9fX15WcOFY5cLEZI/3jj3ZjJi2cnL569C5IXz947SnKnmQo7Ix9vLLdbmwO/nn/749/+VPwd9QkAAFx3p+eWbObbh6uNylg9bncfIVIcUeleGZ9SGpxejHUgeVV9tTYR2wF/5CXFBsSx8Vcqnx15xpvEJeWLLx4tn1LqG/pHfQIAADdD/PWfF6OXDg/pfYRI96N5V+JqbTKnS7U2OTTb7H2u/JkX1m5txv/lO3vszZWXr+SNv04L47O2CNYnAABwefJoQyzeiKXwPbKkezV8MU7yC6u1ieKq+kiUT7qww9VGbBbc+0z6Yr0UWytedd5hkR/P+ScAAHCp8tHvMZur9Dd9tTaR0kK+XUyX09lfC2c9NFkZq8dQRr6n+Lb7j0YGphbO226r3dosnl5frU3kSim9JA/UlPbyijoanF5889//qU8AAOAmePM6t0SccpirI49+tFtb+fbxRhqYWigMjCzkZw5Ov4ztgHMkDM+tHazM7D8aKY6rZEOzzXZr67xHS5PBShsc9zjMPi4vnhDjQp3jV5/zDZnfBQAAl6X9Ov6zODPqQ2tOJs74I/50NXypHIbn1gamFg5XG/uPRvLEsHw0yuD04tBs82h9qcecsfShI016PPr2oeMDfQIAADdDroXiyes9giHXSxwzH42RF9bnl+dlIbFsfWi2eWbYxJknsS9w8f47D1/GMY4lPdaT5Ald+fLeXqrxEwAAuClyG8Rf86UA 6A6 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 3a7 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 0a+ QzH6nhj6MGT7ilVxZKJl8dxiu/+3O 8a3 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 ... src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABDUAAAXeCAIAAADw5oNDAAAACXBIWXMAABYlAAAWJQFJUiTwAAAgAElEQVR42uzdu1Oc63 3A8 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 2a7 O4jP5gZaa900wp9fX1FV/S3mm+fjofgyEppcrd+7e+WU4p3f5xuzJ6L4+NdPaed/7vf2//uF0cLensPT/440Tn1V68pDJ6L27oEwAA+HoMz63FsvXYXCuWoQ9MLUQbxIL4V4/rpRUjsTXw/qORGDPJp6C0W5u9V6eklE52t/Pp8vHyo/WlwenFmFcWmXS0vhQr40sTumJUJzYprtYmu9ex6BMAALipSn/9R1oMzTartYmT3e1Yi3K0vtTZe368sZxjIJaORCoUz4yPJSsDUwsDUwt3Hr4sfVBunpRScSOvYmPEpsbxiZEx+f3zmvvixLPip+sTAAC42d5bMbK7HVO2qrXJmKw1OL1Y3MIrEiX6Ie4spkW8JNIlb8ZVfPN 2a7 Nam4zNu/L4SUkM5uThl0iR+JQYY4ndveIJ8YkXu2Le+ScAAHDFIjaGZpuRK3mKV7U2WcqMOMOke/pWu7VZrU2ct7VXTpST3e3jjeWYM/b +a9 9lUkRR/oij9aU4FyV28eo+C6Vam7zAc1GMnwAAwJUpzp4qxkk8VDo5Po+cxIBGZazebm0erMzE4vjecVIKldKPpeGUvBAlPxrXWdpoOLKqWpu4wC9EnwAAwGUrzstqtzZjZXzERsRDTMEqHc6Y0yLfH8viD1cbnzSCkVsoPr10I9x68CTfPlxtVGuT+QJyoqTC2MtFLZTXJwAAcNnen161FROoipnRPXgyPLdWGiEZnF784Jyuo/WlvFgllsjH+pZie8Q9h6uNYqL0jzeKC0ti8UnczokyNNs8b529PgEAgJshbxycM6N0VHwMnhSHRKrjjXxoSfFNinlwpjiNMbJkeG6ttN3WwcrMwNRCtEdlrH642ihGUXGWV4yT5GuORKnWJouTuy5kopc+AQCAS5VPLEmns6qK22SdFst74VEZvVfMlVjgfrjaKG46XBJNcvvH7cHpxe5dgGMuWUwqO95YzsMjsfFXXpQfu4GlwvZf+f6cKHmrseKwjD4BAICbp93aynOlcnukwgqTM0WcxO3SuSh5Etd5J5PEwYvx8jxK033YYgy8FEd12q2t/J7x5DzR6wLZXxgAAK5MPsYkD0TEmEbxOQNTCyd7z/OPlbF6aXQlEqVam4hhkO5xjHjn4pyx4mr7OM8k9g7OzzlaX+q7PZKPOolPiWlgJ7vb8UHx5AvcXDgZPwEAgKsVk7VyGHRP7qrWJto7zfzj8Nxa9+jKmXFytL706nF9/9HI8cby8cZyMU6Ka0tyoqTCwv3O3vOj9aVS6uQpXnEBscvwBX8b/kEAAMBliuGRXAJxo3gWeyk/irlSGauX9vVKKcUa91Q4jb7d2owsGZxevPPwZSosys/n08dgSDw/4iTWk+RZXie726VJYsUpXnnZiT4BAICbqdKf3j8esTreqNYmD5/Ox53dU7NKk7sGfvP9m8JYSkqpb/Re5e79k93tgd98/7YrXjw7XG1Uxxspper938adw3Nr1fFGDJsMTi/2jzeqtcnhubU7D192Xr3MQfJmp1m5ez+/eemzilcSq+qrtcn4oO4n6xMAALjeBm7Hf+YxkKEHT9qtzeL0rdLwSGlyV9/tkU4hElJK/eON47/9KW7kTqiM1YcePEkp9fX15WcOFY5cLEZI/3jj3ZjJi2cnL569C5IXz947SnKnmQo7Ix9vLLdbmwO/nn/749/+VPwd9QkAAFx3p+eWbObbh6uNylg9bncfIVIcUeleGZ9SGpxejHUgeVV9tTYR2wF/5CXFBsSx8Vcqnx15xpvEJeWLLx4tn1LqG/pHfQIAADdD/PWfF6OXDg/pfYRI96N5V+JqbTKnS7U2OTTb7H2u/JkX1m5txv/lO3vszZWXr+SNv04L47O2CNYnAABwefJoQyzeiKXwPbKkezV8MU7yC6u1ieKq+kiUT7qww9VGbBbc+0z6Yr0UWytedd5hkR/P+ScAAHCp8tHvMZur9Dd9tTaR0kK+XUyX09lfC2c9NFkZq8dQRr6n+Lb7j0YGphbO226r3dosnl5frU3kSim9JA/UlPbyijoanF5889//qU8AAOAmePM6t0SccpirI49+tFtb+fbxRhqYWigMjCzkZw5Ov4ztgHMkDM+tHazM7D8aKY6rZEOzzXZr67xHS5PBShsc9zjMPi4vnhDjQp3jV5/zDZnfBQAAl6X9Ov6zODPqQ2tOJs74I/50NXypHIbn1gamFg5XG/uPRvLEsHw0yuD04tBs82h9qcecsfShI016PPr2oeMDfQIAADdDroXiyes9giHXSxwzH42RF9bnl+dlIbFsfWi2eWbYxJknsS9w8f47D1/GMY4lPdaT5Ald+fLeXqrxEwAAuClyG8Rf86UA 6A6 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 3a7 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 0a+ QzH6nhj6MGT7ilVxZKJl8dxiu/+3O 8a3 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 ... src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABDUAAAXeCAIAAADw5oNDAAAACXBIWXMAABYlAAAWJQFJUiTwAAAgAElEQVR42uzdu1Oc63 3A8 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 2a7 O4jP5gZaa900wp9fX1FV/S3mm+fjofgyEppcrd+7e+WU4p3f5xuzJ6L4+NdPaed/7vf2//uF0cLensPT/440Tn1V68pDJ6L27oEwAA+HoMz63FsvXYXCuWoQ9MLUQbxIL4V4/rpRUjsTXw/qORGDPJp6C0W5u9V6eklE52t/Pp8vHyo/WlwenFmFcWmXS0vhQr40sTumJUJzYprtYmu9ex6BMAALipSn/9R1oMzTartYmT3e1Yi3K0vtTZe368sZxjIJaORCoUz4yPJSsDUwsDUwt3Hr4sfVBunpRScSOvYmPEpsbxiZEx+f3zmvvixLPip+sTAAC42d5bMbK7HVO2qrXJmKw1OL1Y3MIrEiX6Ie4spkW8JNIlb8ZVfPN 2a7 Nam4zNu/L4SUkM5uThl0iR+JQYY4ndveIJ8YkXu2Le+ScAAHDFIjaGZpuRK3mKV7U2WcqMOMOke/pWu7VZrU2ct7VXTpST3e3jjeWYM/b +a9 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 6A6 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 3a7 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 0a+ QzH6nhj6MGT7ilVxZKJl8dxiu/+3O 8a3 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...

... and site-specific data on achievementrelated data are collected and disaggregated11.3 Standards and competency-related data arecollected and disaggregated11.4 Counselors are accountable for ... time each studentleaves the school system. The ASCA National Standards domain areas serve as the foundational goals for theschool counseling program: academic, career and personal/social development. ... guidance program audit, a summary of the programresults reports and makes appropriaterecommendationsProgram Audit 116 THE ASCA NATIONAL MODEL: A FRAMEWORK FOR SCHOOL COUNSELING PROGRAMSAPPENDIX • SAMPLE...

... detectionof figuratevely used verbs and, whereas we onlyrefer to the concepts and ideas of WSD, they adapt a particular existing one-word disambiguationmethod.As we have already said, we deal with ... figurative language (metaphors,metonymies etc.). However, there are some worksaimed at extracting particular types of figura-tive language. For example, Nissim and Mark-ert (2003) proposed a machine ... DifferentiationWe could treat a figurative meaning of a word asan additional, not common meaning of this word.Actually, some metaphors are quite common (e.g.eye of a needle, leg of a table, etc.) and...