# on 26-Dec-2019 (Thu)

#### Annotation 4717908200716

 Ions (sodium [Na+], potassium [K+], chloride [Cl-], and calcium [Ca2+]) flow through cardiac membrane channels with pores formed by proteins, with these ion channels encoded by specific genes [3]. The pore-forming protein is called the alpha subunit, which also contains the voltage-dependent sensors and gates. For many ion channels, one or more secondary regulatory subunit proteins are present (usually named beta, gamma, delta, and so on) in association with the alpha subunit, and many ion channel proteins have subunit isoforms adding to their complexity

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Pathophysiology and genetics" and "Brugada syndrome: Epidemiology and pathogenesis" and "Etiology of atrioventricular block", section on 'Familial disease'.) Cardiac ion channels and currents — <span>Ions (sodium [Na+], potassium [K+], chloride [Cl-], and calcium [Ca2+]) flow through cardiac membrane channels with pores formed by proteins, with these ion channels encoded by specific genes [3]. The pore-forming protein is called the alpha subunit, which also contains the voltage-dependent sensors and gates. For many ion channels, one or more secondary regulatory subunit proteins are present (usually named beta, gamma, delta, and so on) in association with the alpha subunit, and many ion channel proteins have subunit isoforms adding to their complexity. The encoding genes, amino acid sequences, and structure-function relationships for many ion channels have been described and are now reasonably well understood (figure 1) [4]. Ion chan

#### Annotation 4717966396684

 The dominant channel types in heart cells are Na+ channels (INa), L-type and T-type Ca2+ channels (ICa-L, ICa-T), and several K+ channels (IK1, Ito1, Ito2, IKr, IKs). The sodium-potassium pump and the sodium-calcium exchanger are not considered channels because they require energy to drive ions across the membrane against their gradients, however they do generate currents (figure 1).

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ng kinetics, or pharmacology (figure 1). For example, the voltage-dependent sodium current (INa) flows through the protein NaV1.5 encoded by the gene SCN5A and similarly for other ion channels. <span>The dominant channel types in heart cells are Na+ channels (INa), L-type and T-type Ca2+ channels (ICa-L, ICa-T), and several K+ channels (IK1, Ito1, Ito2, IKr, IKs). The sodium-potassium pump and the sodium-calcium exchanger are not considered channels because they require energy to drive ions across the membrane against their gradients, however they do generate currents (figure 1). Resting membrane potential — The resting cardiac cell membrane potential is normally polarized between -80 and -95 mV, with the cell interior negative relative to the extracellular spac

#### Annotation 4718746012940

 The resting cardiac cell membrane potential is normally polarized between -80 and -95 mV, with the cell interior negative relative to the extracellular space. The resting membrane potential is determined by the balance of inward (Na+ and Ca2+) and outward (K+) currents and the corresponding equilibrium potentials of these currents

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ger are not considered channels because they require energy to drive ions across the membrane against their gradients, however they do generate currents (figure 1). Resting membrane potential — <span>The resting cardiac cell membrane potential is normally polarized between -80 and -95 mV, with the cell interior negative relative to the extracellular space. The resting membrane potential is determined by the balance of inward (Na+ and Ca2+) and outward (K+) currents and the corresponding equilibrium potentials of these currents. In turn, the equilibrium potential for a given ion is determined by the concentrations of that ion inside and outside the cell. Using these concentrations, the equilibrium potential is

#### Annotation 4719270825228

 In the heart, the resting membrane potential is generated by the inward rectifier current (IK1), which is the predominant open channel at rest. Potassium current IK1 flowing through this channel continues until the interior negative potential is at the same magnitude as the equilibrium potential for potassium. Only small amounts of actual potassium flow are required to maintain this potential. The equilibrium potentials for sodium and calcium are positive (approximately +40 mV and approximately +80 mV, respectively) so that when these channels are open, they tend to depolarize the membrane.

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-95 mV. When potassium channels open, potassium ions flow down their gradient as an outward current, carrying positive ions outside the cell and taking the cell toward more negative potentials. <span>In the heart, the resting membrane potential is generated by the inward rectifier current (IK1), which is the predominant open channel at rest. Potassium current IK1 flowing through this channel continues until the interior negative potential is at the same magnitude as the equilibrium potential for potassium. Only small amounts of actual potassium flow are required to maintain this potential. The equilibrium potentials for sodium and calcium are positive (approximately +40 mV and approximately +80 mV, respectively) so that when these channels are open, they tend to depolarize the membrane. Voltage-sensitive sodium, calcium, and potassium channels play only a small role in the resting state since most of these channels are closed [5,6]. The Na-K-ATPase pump maintains the p

#### Annotation 4719272398092

 Voltage-sensitive sodium, calcium, and potassium channels play only a small role in the resting state since most of these channels are closed [5,6]. The Na-K-ATPase pump maintains the potassium and sodium gradients by pumping potassium into and sodium out of the cells. The Na-Ca exchanger uses the power of the Na gradient to pump Ca out of the cell

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ilibrium potentials for sodium and calcium are positive (approximately +40 mV and approximately +80 mV, respectively) so that when these channels are open, they tend to depolarize the membrane. <span>Voltage-sensitive sodium, calcium, and potassium channels play only a small role in the resting state since most of these channels are closed [5,6]. The Na-K-ATPase pump maintains the potassium and sodium gradients by pumping potassium into and sodium out of the cells. The Na-Ca exchanger uses the power of the Na gradient to pump Ca out of the cell. These and other pumps maintain the ion channel gradient that is important for both excitability and contraction. Action potential in fast response tissues — Tissues that depend upon th

#### Annotation 4719273970956

 #Cardiologie #Médecine #Physiologie #Rythmologie Action potential in fast response tissues — Tissues that depend upon the opening of voltage-sensitive, kinetically rapid (opening in less than a millisecond) sodium channels to initiate depolarization are called fast response tissues [7]. Fast response tissues include the atria, the specialized infranodal conducting system (bundle of His, fascicles and bundle branches, and terminal Purkinje fibers), and the ventricles (figure 2), while the sinoatrial (SA) and atrioventricular (AV) nodes represent slow response tissues. It is important to recognize that accessory AV pathways (ie, bypass tracts) associated with Wolff-Parkinson-White syndrome are derived from the atria and are thus also fast response tissues dependent upon sodium current for depolarization.

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he Na-Ca exchanger uses the power of the Na gradient to pump Ca out of the cell. These and other pumps maintain the ion channel gradient that is important for both excitability and contraction. <span>Action potential in fast response tissues — Tissues that depend upon the opening of voltage-sensitive, kinetically rapid (opening in less than a millisecond) sodium channels to initiate depolarization are called fast response tissues [7]. Fast response tissues include the atria, the specialized infranodal conducting system (bundle of His, fascicles and bundle branches, and terminal Purkinje fibers), and the ventricles (figure 2), while the sinoatrial (SA) and atrioventricular (AV) nodes represent slow response tissues. It is important to recognize that accessory AV pathways (ie, bypass tracts) associated with Wolff-Parkinson-White syndrome are derived from the atria and are thus also fast response tissues dependent upon sodium current for depolarization. (See "Wolff-Parkinson-White syndrome: Anatomy, epidemiology, clinical manifestations, and diagnosis".) The following is a simplified description of the steps involved in the generation

#### Annotation 4719275543820

 Phase 0 — Rapid depolarization (phase 0) occurs when the resting cell is brought to threshold, leading sequentially to activation or opening of voltage-dependent sodium channels, rapid sodium entry into the cells down a favorable concentration gradient, and a cell interior positive potential that can approach +45 mV. The marked depolarization initiates voltage-dependent inactivation of the sodium channels. Calcium channels also open during depolarization, but the inward calcium flux is much slower.

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on of the action potentials also vary in the right and left ventricle, and transmurally across the wall of the heart [9], again depending upon differences in ion channel and current densities. ●<span>Phase 0 — Rapid depolarization (phase 0) occurs when the resting cell is brought to threshold, leading sequentially to activation or opening of voltage-dependent sodium channels, rapid sodium entry into the cells down a favorable concentration gradient, and a cell interior positive potential that can approach +45 mV. The marked depolarization initiates voltage-dependent inactivation of the sodium channels. Calcium channels also open during depolarization, but the inward calcium flux is much slower. ●Phase 1 — Phase 1 repolarization often inscribes a "notch" and is primarily caused by activation of the transient outward potassium currents (Ito) combined with a corresponding rapid d

#### Annotation 4719277116684

 Phase 1 — Phase 1 repolarization often inscribes a "notch" and is primarily caused by activation of the transient outward potassium currents (Ito) combined with a corresponding rapid decay of the sodium current. The degree of repolarization in phase 1 is dependent on the density of Ito and varies between cardiac chambers and regions within chambers.

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+45 mV. The marked depolarization initiates voltage-dependent inactivation of the sodium channels. Calcium channels also open during depolarization, but the inward calcium flux is much slower. ●<span>Phase 1 — Phase 1 repolarization often inscribes a "notch" and is primarily caused by activation of the transient outward potassium currents (Ito) combined with a corresponding rapid decay of the sodium current. The degree of repolarization in phase 1 is dependent on the density of Ito and varies between cardiac chambers and regions within chambers. ●Phase 2 — Following initial repolarization in phase 1, phase 2 represents a plateau that lasts for hundreds of milliseconds and distinguishes the cardiac action potential from nerve an

#### Annotation 4719278689548

 #Cardiologie #Médecine #Physiologie #Rythmologie Phase 2 — Following initial repolarization in phase 1, phase 2 represents a plateau that lasts for hundreds of milliseconds and distinguishes the cardiac action potential from nerve and skeletal muscle action potentials, which are significantly shorter. Late inactivating depolarizing calcium and sodium currents are balanced by activating repolarizing potassium currents to maintain the plateau, which is often down-sloping as repolarizing currents begin to dominate.

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h a corresponding rapid decay of the sodium current. The degree of repolarization in phase 1 is dependent on the density of Ito and varies between cardiac chambers and regions within chambers. ●<span>Phase 2 — Following initial repolarization in phase 1, phase 2 represents a plateau that lasts for hundreds of milliseconds and distinguishes the cardiac action potential from nerve and skeletal muscle action potentials, which are significantly shorter. Late inactivating depolarizing calcium and sodium currents are balanced by activating repolarizing potassium currents to maintain the plateau, which is often down-sloping as repolarizing currents begin to dominate. ●Phases 3 and 4 — The final rapid repolarizing phase 3 is driven by the decay of the calcium current and progressive activation of repolarizing potassium currents (IKr, IKs). Terminal r

#### Annotation 4719280262412

 Phases 3 and 4 — The final rapid repolarizing phase 3 is driven by the decay of the calcium current and progressive activation of repolarizing potassium currents (IKr, IKs). Terminal repolarization toward the potassium equilibrium potential is dominated in phase 3 by IK1, which then maintains the resting membrane potential (phase 4).

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larizing calcium and sodium currents are balanced by activating repolarizing potassium currents to maintain the plateau, which is often down-sloping as repolarizing currents begin to dominate. ●<span>Phases 3 and 4 — The final rapid repolarizing phase 3 is driven by the decay of the calcium current and progressive activation of repolarizing potassium currents (IKr, IKs). Terminal repolarization toward the potassium equilibrium potential is dominated in phase 3 by IK1, which then maintains the resting membrane potential (phase 4). During one cycle of depolarization and repolarization, the voltage-dependent channels cycle through three different kinetic or gating states: ●Resting. ●Open, as the channels open durin

#### Annotation 4719281835276

 In the resting state, the channels can be opened positive to the threshold potential. In comparison, the inactivated channel cannot be activated until it cycles or "recovers" to the resting state. These different states are important clinically, since, for example, some antiarrhythmic drugs (such as the class I antiarrhythmic drugs) preferentially bind to open and inactivated sodium channels

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y in diastole, the channel returns to the resting state. The resting and inactivated states are different physiologically, even though the channel is effectively nonconducting in both settings. <span>In the resting state, the channels can be opened positive to the threshold potential. In comparison, the inactivated channel cannot be activated until it cycles or "recovers" to the resting state. These different states are important clinically, since, for example, some antiarrhythmic drugs (such as the class I antiarrhythmic drugs) preferentially bind to open and inactivated sodium channels. Action potential in slow response tissues — The SA and AV nodes represent slow response tissues, which have different properties from the fast response tissues (table 1). Phase 0 depol

#### Annotation 4719283408140

 Action potential in slow response tissues — The SA and AV nodes represent slow response tissues, which have different properties from the fast response tissues (table 1). Phase 0 depolarization depends on an inward calcium (not sodium) current via L-type calcium channels [10]. These channels are selective for calcium, have a slower conduction velocity than the sodium channels, and take longer to reactivate.In some cases, as with tissue damage or changes in the extracellular milieu, fast response tissues can be converted to slow response tissues. In this setting, sodium channels become inactivated and depolarization is dependent upon the slow calcium channels.

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different states are important clinically, since, for example, some antiarrhythmic drugs (such as the class I antiarrhythmic drugs) preferentially bind to open and inactivated sodium channels. <span>Action potential in slow response tissues — The SA and AV nodes represent slow response tissues, which have different properties from the fast response tissues (table 1). Phase 0 depolarization depends on an inward calcium (not sodium) current via L-type calcium channels [10]. These channels are selective for calcium, have a slower conduction velocity than the sodium channels, and take longer to reactivate. In some cases, as with tissue damage or changes in the extracellular milieu, fast response tissues can be converted to slow response tissues. In this setting, sodium channels become inactivated and depolarization is dependent upon the slow calcium channels. Impulse propagation — When an action potential forms in a patch of membrane (the source), current flows from this patch to neighboring patches (the sink). Gap junctions are the low resi

#### Annotation 4719284981004

 The gap junctions are actually active, opening and closing in response to changes in pH, calcium, and, at times, voltage.

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). Gap junctions are the low resistance structures that allow ions to flow from one cell to another and, if the current flow is sufficient, to cause sequential depolarization from cell to cell. <span>The gap junctions are actually active, opening and closing in response to changes in pH, calcium, and, at times, voltage. In addition to ion flow and gap junction resistance, impulse propagation can also be affected by the orientation of fibers and of the collagen matrix in which the fibers reside. "Fast"

#### Annotation 4719286553868

 In addition to ion flow and gap junction resistance, impulse propagation can also be affected by the orientation of fibers and of the collagen matrix in which the fibers reside

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ow is sufficient, to cause sequential depolarization from cell to cell. The gap junctions are actually active, opening and closing in response to changes in pH, calcium, and, at times, voltage. <span>In addition to ion flow and gap junction resistance, impulse propagation can also be affected by the orientation of fibers and of the collagen matrix in which the fibers reside. "Fast" tissues may conduct very slowly (declining from meters/second to millimeters/second) in a number of circumstances, resulting in prolongation of the QRS and QT intervals on the s

#### Annotation 4719370964236

 Three distinct mechanisms underlie tachyarrhythmia induction: enhanced automaticity, reentry, and triggered activity

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MATION — While the term "arrhythmia" also includes bradyarrhythmias caused by a failure of impulse generation, this section will focus on the cellular and tissue mechanisms of tachyarrhythmias. <span>Three distinct mechanisms underlie tachyarrhythmia induction: enhanced automaticity, reentry, and triggered activity (figure 5) . Enhanced automaticity — Enhanced automaticity refers to abnormal phase 4 diastolic depolarization, and occurs when spontaneous depolarization develops during diastole (figu

#### Annotation 4719372537100

 Enhanced automaticity — Enhanced automaticity refers to abnormal phase 4 diastolic depolarization, and occurs when spontaneous depolarization develops during diastole (figure 5). While this is a normal phenomenon in nodal cells, and with subsidiary pacemakers at slower rates in all myocardial cells, enhanced or abnormal automaticity may lead to tachyarrhythmia. A typical example is automatic (ie, focal) atrial tachycardia. Common automaticity stimulants include excess catecholamine or situations causing hypoxia, acidosis, or ischemic related metabolites.

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ocus on the cellular and tissue mechanisms of tachyarrhythmias. Three distinct mechanisms underlie tachyarrhythmia induction: enhanced automaticity, reentry, and triggered activity (figure 5) . <span>Enhanced automaticity — Enhanced automaticity refers to abnormal phase 4 diastolic depolarization, and occurs when spontaneous depolarization develops during diastole (figure 5). While this is a normal phenomenon in nodal cells, and with subsidiary pacemakers at slower rates in all myocardial cells, enhanced or abnormal automaticity may lead to tachyarrhythmia. A typical example is automatic (ie, focal) atrial tachycardia. Common automaticity stimulants include excess catecholamine or situations causing hypoxia, acidosis, or ischemic related metabolites. (See "Focal atrial tachycardia" and "Enhanced cardiac automaticity".) Reentry — Reentry is the most commonly encountered arrhythmia mechanism and refers to any arrhythmia dependent on a

#### Annotation 4719374109964

 #Cardiologie #Médecine #Physiologie #Rythmologie Critical components for reentry include both of the following: ● The presence of fast and slow conduction with varying refractory/recovery periods ● A fixed or functional core about which the circuit moves

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nced cardiac automaticity".) Reentry — Reentry is the most commonly encountered arrhythmia mechanism and refers to any arrhythmia dependent on an electrical circuit within the heart (figure 5). <span>Critical components for reentry include both of the following: ●The presence of fast and slow conduction with varying refractory/recovery periods ●A fixed or functional core about which the circuit moves Initiation of reentry requires a unidirectional block within the reentrant path, such that one arm of the circuit conducts the approaching electrical wave front and the blocks it in the

#### Annotation 4719375682828

 Interventions to terminate reentrant arrhythmias differ from other mechanisms and are generally geared to modify the critical components of the reentrant circuit. Blocking Na+ or Ca2+ channels can slow or block conduction, while blocking K+ channels prolongs the action potential and therefore increases refractoriness

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slow conducting limb is the normal AV node. An example of fixed reentry arrhythmia is ventricular tachycardia with a fixed myocardial scar and variable conduction in the surrounding myocardium. <span>Interventions to terminate reentrant arrhythmias differ from other mechanisms and are generally geared to modify the critical components of the reentrant circuit. Blocking Na+ or Ca2+ channels can slow or block conduction, while blocking K+ channels prolongs the action potential and therefore increases refractoriness. Another approach is to improve functional properties such as ischemia in an area of functional block that can terminate the arrhythmia. Interventions that electrically interrupt the re

#### Annotation 4719377255692

 Triggered activity — Triggered activity refers to a depolarization that occurs after the initial depolarization wavefront and comes in two forms, either early or late. Secondary depolarizations that occur before the action potential has fully repolarized are early afterdepolarizations (EADs) (figure 5). Those that occur after the action potential has fully repolarized are delayed afterdepolarizations (DADs) (figure 5). Both EADs and DADs depend on the previous action potential to trigger them, hence an afterdepolarization is said to be a triggered arrhythmia. However, it is important to understand that DADs and EADs differ in mechanism.

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nt loop (ie, cardioversion), or ablating tissue critical to the reentrant loop. (See "Ventricular arrhythmias during acute myocardial infarction: Incidence, mechanisms, and clinical features".) <span>Triggered activity — Triggered activity refers to a depolarization that occurs after the initial depolarization wavefront and comes in two forms, either early or late. Secondary depolarizations that occur before the action potential has fully repolarized are early afterdepolarizations (EADs) (figure 5). Those that occur after the action potential has fully repolarized are delayed afterdepolarizations (DADs) (figure 5). Both EADs and DADs depend on the previous action potential to trigger them, hence an afterdepolarization is said to be a triggered arrhythmia. However, it is important to understand that DADs and EADs differ in mechanism. ●EADs — EADs are triggered during prolonged action potentials. A prolonged action potential allows a longer window for reopening of L-type Ca2+ channels during phase 2 (or occasionally

#### Annotation 4719378828556

 #Cardiologie #Médecine #Physiologie #Rythmologie EADs — EADs are triggered during prolonged action potentials. A prolonged action potential allows a longer window for reopening of L-type Ca2+ channels during phase 2 (or occasionally phase 3) of the action potential. L-type Ca2+ current depolarizes the membrane before repolarization, triggering an afterdepolarization. Due to L-type Ca2+ channel time and voltage dependence, EADs occur at slow stimulation rates or after a ventricular pause when action potential duration (phase 2) is prolonged and they are suppressed with faster heart rates. EADs are thought to initiate the polymorphic ventricular arrhythmias torsades de pointes (TdP) found in inherited and acquired long QT syndrome (LQTS), for example drug-induced LQTS

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the previous action potential to trigger them, hence an afterdepolarization is said to be a triggered arrhythmia. However, it is important to understand that DADs and EADs differ in mechanism. ●<span>EADs — EADs are triggered during prolonged action potentials. A prolonged action potential allows a longer window for reopening of L-type Ca2+ channels during phase 2 (or occasionally phase 3) of the action potential. L-type Ca2+ current depolarizes the membrane before repolarization, triggering an afterdepolarization. Due to L-type Ca2+ channel time and voltage dependence, EADs occur at slow stimulation rates or after a ventricular pause when action potential duration (phase 2) is prolonged and they are suppressed with faster heart rates. EADs are thought to initiate the polymorphic ventricular arrhythmias torsades de pointes (TdP) found in inherited and acquired long QT syndrome (LQTS), for example drug-induced LQTS. A point of distinction to be made here is that triggered activity can initiate TdP, but TdP may be a re-entrant mechanism at the organ level with a functional (spiral reentry) rather t

#### Annotation 4719380401420

 DADs — DADs, which result from intracellular Ca2+ overload, are triggered after the action potential is fully repolarized. Under conditions of Ca2+ overload, Ca2+ taken back up by the sarcoplasmic reticulum is then transiently re-released into the cytoplasm. This in turn causes a transient rise in cytoplasmic Ca2+ activating Ca2+-dependent depolarizing membrane current mostly through the Na+-Ca2+ exchanger. The exchange of three Na+ for two Ca2+ produces a net inward and transient depolarization or a DAD.

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be a re-entrant mechanism at the organ level with a functional (spiral reentry) rather than fixed anatomical core. (See "Acquired long QT syndrome: Definitions, causes, and pathophysiology".) ●<span>DADs — DADs, which result from intracellular Ca2+ overload, are triggered after the action potential is fully repolarized. Under conditions of Ca2+ overload, Ca2+ taken back up by the sarcoplasmic reticulum is then transiently re-released into the cytoplasm. This in turn causes a transient rise in cytoplasmic Ca2+ activating Ca2+-dependent depolarizing membrane current mostly through the Na+-Ca2+ exchanger. The exchange of three Na+ for two Ca2+ produces a net inward and transient depolarization or a DAD. If the DAD reaches threshold voltage, it can initiate an action potential. Conditions which enhance cellular Ca2+ loading, such as rapid heart rates, enhance DAD susceptibility. DADs ma

#### Annotation 4719381974284

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a+-Ca2+ exchanger. The exchange of three Na+ for two Ca2+ produces a net inward and transient depolarization or a DAD. If the DAD reaches threshold voltage, it can initiate an action potential. <span>Conditions which enhance cellular Ca2+ loading, such as rapid heart rates, enhance DAD susceptibility. DADs may be important in myocardial ischemia, digoxin toxicity, and in some inherited arrhythmia syndromes such as catecholaminergic polymorphic ventricular tachycardia. (See "Cardiac

#### Annotation 4719384595724

 Certain arrhythmogenic substrates are common, such as those induced by ischemia or infarction. In this setting, a certain effect of a drug becomes predominant and predictable, as with class I activity in ischemia, and a drug classification appears accurate. However, the major drug effect may be quite different if a different proarrhythmic substrate exists. Consider, for example, the differences in digitalis action in hypokalemia and hyperkalemia.

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rmed by arrhythmogenic factors interacts with antiarrhythmic drugs. Depending upon the substrate encountered, the resulting substrate may be antiarrhythmic, antifibrillatory, or proarrhythmic. ●<span>Certain arrhythmogenic substrates are common, such as those induced by ischemia or infarction. In this setting, a certain effect of a drug becomes predominant and predictable, as with class I activity in ischemia, and a drug classification appears accurate. However, the major drug effect may be quite different if a different proarrhythmic substrate exists. Consider, for example, the differences in digitalis action in hypokalemia and hyperkalemia. Class 0 — Drugs in the newly proposed Class 0 modulate the pacemaker channel HCN4, affecting the pacemaker current If [11]. The blocker ivabradine slows heart rate. Class I — The class

#### Annotation 4719386168588

 Class 0 — Drugs in the newly proposed Class 0 modulate the pacemaker channel HCN4, affecting the pacemaker current If [11]. The blocker ivabradine slows heart rate.

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However, the major drug effect may be quite different if a different proarrhythmic substrate exists. Consider, for example, the differences in digitalis action in hypokalemia and hyperkalemia. <span>Class 0 — Drugs in the newly proposed Class 0 modulate the pacemaker channel HCN4, affecting the pacemaker current If [11]. The blocker ivabradine slows heart rate. Class I — The class I drugs act by modulating or blocking the sodium channels, thereby inhibiting phase 0 depolarization. They are all at least in part positively charged and presumably

#### Annotation 4719387741452

 #Cardiologie #Médecine #Physiologie #Rythmologie Class I — The class I drugs act by modulating or blocking the sodium channels, thereby inhibiting phase 0 depolarization. They are all at least in part positively charged and presumably interact with specific amino acid residues in the internal pore of the sodium channel.

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ypokalemia and hyperkalemia. Class 0 — Drugs in the newly proposed Class 0 modulate the pacemaker channel HCN4, affecting the pacemaker current If [11]. The blocker ivabradine slows heart rate. <span>Class I — The class I drugs act by modulating or blocking the sodium channels, thereby inhibiting phase 0 depolarization. They are all at least in part positively charged and presumably interact with specific amino acid residues in the internal pore of the sodium channel. Three different subgroups (table 2 and table 3) have been identified because their mechanism or duration of action is somewhat different due to variable rates of drug binding to and dis

#### Annotation 4719389314316

 ● The class Ic agents have the slowest binding and dissociation from the binding site. ● The class Ib agents have the most rapid binding and dissociation from the binding site. ● The class Ia agents are intermediate in terms of the speed of binding and dissociation from the binding site.

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2 and table 3) have been identified because their mechanism or duration of action is somewhat different due to variable rates of drug binding to and dissociation from the channel receptor [14]: <span>●The class Ic agents have the slowest binding and dissociation from the binding site. ●The class Ib agents have the most rapid binding and dissociation from the binding site. ●The class Ia agents are intermediate in terms of the speed of binding and dissociation from the binding site. During faster heart rates, less time exists for the drug to dissociate from the receptor, resulting in an increased number of blocked channels and enhanced blockade. These pharmacologic

#### Annotation 4719390887180

 During faster heart rates, less time exists for the drug to dissociate from the receptor, resulting in an increased number of blocked channels and enhanced blockade. These pharmacologic effects may cause a progressive decrease in impulse conduction velocity and a widening of the QRS complex. This property is known as "use-dependence" and is seen most frequently with the class Ic agents, less frequently with the class Ia drugs, and rarely with the class Ib agents

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lass Ib agents have the most rapid binding and dissociation from the binding site. ●The class Ia agents are intermediate in terms of the speed of binding and dissociation from the binding site. <span>During faster heart rates, less time exists for the drug to dissociate from the receptor, resulting in an increased number of blocked channels and enhanced blockade. These pharmacologic effects may cause a progressive decrease in impulse conduction velocity and a widening of the QRS complex. This property is known as "use-dependence" and is seen most frequently with the class Ic agents, less frequently with the class Ia drugs, and rarely with the class Ib agents [15]. ●Class Ia drugs (quinidine, procainamide, and disopyramide) depress phase 0 (sodium-dependent) depolarization, thereby slowing conduction. They also have moderate potassium channe

#### Annotation 4719392460044

 Class Ia drugs (quinidine, procainamide, and disopyramide) depress phase 0 (sodium-dependent) depolarization, thereby slowing conduction. They also have moderate potassium channel blocking activity (which tends to slow the rate of repolarization and prolong action potential duration [APD])

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QRS complex. This property is known as "use-dependence" and is seen most frequently with the class Ic agents, less frequently with the class Ia drugs, and rarely with the class Ib agents [15]. ●<span>Class Ia drugs (quinidine, procainamide, and disopyramide) depress phase 0 (sodium-dependent) depolarization, thereby slowing conduction. They also have moderate potassium channel blocking activity (which tends to slow the rate of repolarization and prolong action potential duration [APD]), and quinidine in particular also blocks potassium current ITo, which is useful for suppressing certain ventricular arrhythmias such as those found in the Brugada syndrome. Class Ia age

#### Annotation 4719394032908

 Class Ia agents also have anticholinergic activity and tend to depress myocardial contractility. At slower heart rates, when use-dependent blockade of the sodium current is not significant, potassium channel blockade may become predominant (reverse use-dependence), leading to prolongation of the APD and QT interval and increased automaticity

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tial duration [APD]), and quinidine in particular also blocks potassium current ITo, which is useful for suppressing certain ventricular arrhythmias such as those found in the Brugada syndrome. <span>Class Ia agents also have anticholinergic activity and tend to depress myocardial contractility. At slower heart rates, when use-dependent blockade of the sodium current is not significant, potassium channel blockade may become predominant (reverse use-dependence), leading to prolongation of the APD and QT interval and increased automaticity. One difference between the drugs is that quinidine and procainamide generally decrease vascular resistance, whereas disopyramide increases vascular resistance. In addition, N-acetyl-pr

#### Annotation 4719395605772

 One difference between the drugs is that quinidine and procainamide generally decrease vascular resistance, whereas disopyramide increases vascular resistance.

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the sodium current is not significant, potassium channel blockade may become predominant (reverse use-dependence), leading to prolongation of the APD and QT interval and increased automaticity. <span>One difference between the drugs is that quinidine and procainamide generally decrease vascular resistance, whereas disopyramide increases vascular resistance. In addition, N-acetyl-procainamide (NAPA), a metabolite of procainamide, has little sodium current blocking activity, while retaining potassium current blocking activity. Thus, NAPA beh

#### Annotation 4719397178636

 The class Ib drugs (lidocaine and mexiletine) have less prominent sodium channel blocking activity at rest, but effectively block the sodium channel in depolarized tissues.

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metabolite of procainamide, has little sodium current blocking activity, while retaining potassium current blocking activity. Thus, NAPA behaves like a class III drug. (See 'Class III' below.) ●<span>The class Ib drugs (lidocaine and mexiletine) have less prominent sodium channel blocking activity at rest, but effectively block the sodium channel in depolarized tissues. They tend to bind in the inactivated state (which is induced by depolarization) and dissociate from the sodium channel more rapidly than other class I drugs. As a result, they are more

#### Annotation 4719398751500

 #Cardiologie #Médecine #Physiologie #Rythmologie Class Ic drugs (flecainide and propafenone) primarily block open sodium channels and slow conduction. They dissociate slowly from the sodium channels during diastole, resulting in increased effect at a more rapid rate (use-dependence). This characteristic is the basis for their antiarrhythmic efficacy, especially against supraventricular arrhythmias. Use-dependence may also contribute to the proarrhythmic activity of these drugs, especially in the diseased myocardium, resulting in incessant ventricular tachycardia

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induced by depolarization) and dissociate from the sodium channel more rapidly than other class I drugs. As a result, they are more effective with tachyarrhythmias than with slow arrhythmias. ●<span>Class Ic drugs (flecainide and propafenone) primarily block open sodium channels and slow conduction. They dissociate slowly from the sodium channels during diastole, resulting in increased effect at a more rapid rate (use-dependence). This characteristic is the basis for their antiarrhythmic efficacy, especially against supraventricular arrhythmias. Use-dependence may also contribute to the proarrhythmic activity of these drugs, especially in the diseased myocardium, resulting in incessant ventricular tachycardia. Flecainide and propafenone also have potassium channel blocking activity and can increase the APD in ventricular myocytes. Propafenone has significant beta blocking activity. Another r

#### Annotation 4719400324364

 Flecainide and propafenone also have potassium channel blocking activity and can increase the APD in ventricular myocytes. Propafenone has significant beta blocking activity.

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aventricular arrhythmias. Use-dependence may also contribute to the proarrhythmic activity of these drugs, especially in the diseased myocardium, resulting in incessant ventricular tachycardia. <span>Flecainide and propafenone also have potassium channel blocking activity and can increase the APD in ventricular myocytes. Propafenone has significant beta blocking activity. Another recognized target for antiarrhythmic action is the late sodium current, which is enhanced in both acquired and inherited arrhythmias. When enhanced, it lengthens the APD and can

#### Annotation 4719401897228

 Class II — Class II drugs act by inhibiting sympathetic activity, primarily by causing beta blockade. They may also have a mild inhibitory effect on the sodium channels.

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ing drug is ranolazine, a drug approved for the treatment of chronic angina, but which may have antiarrhythmic activity [16]. This target has been proposed as a new sub-classification, Id [11]. <span>Class II — Class II drugs act by inhibiting sympathetic activity, primarily by causing beta blockade. They may also have a mild inhibitory effect on the sodium channels. Sympathetic stimulation has the following potential proarrhythmic actions [17]: ●An increase in automaticity due to enhancement of phase 4 spontaneous depolarization (see "Enhanced card

#### Annotation 4719403470092

 Sympathetic stimulation has the following potential proarrhythmic actions [ 17]: ● An increase in automaticity due to enhancement of phase 4 spontaneous depolarization (see "Enhanced cardiac automaticity"). ● An increase in membrane excitability due to shortening in refractoriness (phases 2 and 3 of the action potential). ● An increase in the rate of impulse conduction through the myocardial membrane, resulting from acceleration of phase 0 upstroke velocity or the rate of membrane depolarization. ● An increase in delayed afterpotentials, especially when the cell is calcium loaded, such as in digoxin toxicity.

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lassification, Id [11]. Class II — Class II drugs act by inhibiting sympathetic activity, primarily by causing beta blockade. They may also have a mild inhibitory effect on the sodium channels. <span>Sympathetic stimulation has the following potential proarrhythmic actions [17]: ●An increase in automaticity due to enhancement of phase 4 spontaneous depolarization (see "Enhanced cardiac automaticity"). ●An increase in membrane excitability due to shortening in refractoriness (phases 2 and 3 of the action potential). ●An increase in the rate of impulse conduction through the myocardial membrane, resulting from acceleration of phase 0 upstroke velocity or the rate of membrane depolarization. ●An increase in delayed afterpotentials, especially when the cell is calcium loaded, such as in digoxin toxicity. By blocking catecholamine and sympathetically mediated actions, beta blockers slow the rate of discharge of the sinus and ectopic pacemakers, and increase the effective refractory perio

#### Annotation 4719405042956

 #Cardiologie #Médecine #Physiologie #Rythmologie By blocking catecholamine and sympathetically mediated actions, beta blockers slow the rate of discharge of the sinus and ectopic pacemakers, and increase the effective refractory period of the AV node. They also slow both antegrade and retrograde conduction in anomalous pathways [18]. Carvedilol is a beta-blocker with unique additional properties. In addition to beta- and alpha-adrenergic blockade, carvedilol can also block potassium (KCNH2, formerly HERG), calcium, and sodium currents and modestly prolong APD. However, when administered chronically, carvedilol increases the number of these channels, which is probably a favorable effect in diseased hearts [19].

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celeration of phase 0 upstroke velocity or the rate of membrane depolarization. ●An increase in delayed afterpotentials, especially when the cell is calcium loaded, such as in digoxin toxicity. <span>By blocking catecholamine and sympathetically mediated actions, beta blockers slow the rate of discharge of the sinus and ectopic pacemakers, and increase the effective refractory period of the AV node. They also slow both antegrade and retrograde conduction in anomalous pathways [18]. Carvedilol is a beta-blocker with unique additional properties. In addition to beta- and alpha-adrenergic blockade, carvedilol can also block potassium (KCNH2, formerly HERG), calcium, and sodium currents and modestly prolong APD. However, when administered chronically, carvedilol increases the number of these channels, which is probably a favorable effect in diseased hearts [19]. The most recent classification (table 2 and table 3) expands the definition of class II to include "autonomic inhibitors and activators," with subclass IIa as beta adrenergic blockers s

#### Annotation 4728154361100

 #continuum-mechanics The reader is encouraged to pay special attention to the distinctions between the different concepts introduced here. These concepts include the notions of a body,a configuration of the body,a reference configuration of the body,the region occupied by the body in some configuration,a particle (or material point),the location of a particle in some configuration,a deformation,a motion,Eulerian and Lagrangian descriptions of a physical quantity,Eulerian and Lagrangian spatial derivatives, andEulerian and Lagrangian time derivatives (including the material time derivative).

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#### Annotation 4728570121484

 #deep-learning For a future or current practitioner of machine learning, it’s important to be able to recognize the signal in the noise so that you can tell world-changing developments from overhyped press releases.

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#### Flashcard 4730524929292

Question
Give a concise definition of artificial intelligence.
A concise definition of the field would be as follows: the effort to automate intellectual tasks normally performed by humans.

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#### Annotation 4730526764300

 #deep-learning Early chess programs, for instance, only involved hardcoded rules crafted by programmers, and didn’t qualify as machine learning.

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Question
[...], for instance, only involved hardcoded rules crafted by programmers, and didn’t qualify as machine learning.
Early chess programs

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Early chess programs, for instance, only involved hardcoded rules crafted by programmers, and didn’t qualify as machine learning.

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#### Annotation 4730533055756

 #deep-learning For a fairly long time, many experts believed that human-level artificial intelligence could be achieved by having programmers handcraft a sufficiently large set of explicit rules for manipulating knowledge. This approach is known as symbolic AI, and it was the dominant paradigm in AI from the 1950s to the late 1980s.

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Question
For a fairly long time, many experts believed that human-level artificial intelligence could be achieved by having programmers handcraft a sufficiently large set of explicit rules for manipulating knowledge. This approach is known as [...], and it was the dominant paradigm in AI from the 1950s to the late 1980s.
symbolic AI

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ieved that human-level artificial intelligence could be achieved by having programmers handcraft a sufficiently large set of explicit rules for manipulating knowledge. This approach is known as <span>symbolic AI, and it was the dominant paradigm in AI from the 1950s to the late 1980s. <span>

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Question
For a fairly long time, many experts believed that human-level artificial intelligence could be achieved by having programmers handcraft a sufficiently large set of explicit rules for manipulating knowledge. This approach is known as symbolic AI, and it was the dominant paradigm in AI from the [...].
1950s to the late 1980s

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ved by having programmers handcraft a sufficiently large set of explicit rules for manipulating knowledge. This approach is known as symbolic AI, and it was the dominant paradigm in AI from the <span>1950s to the late 1980s. <span>

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#### Annotation 4730536725772

 #deep-learning Although symbolic AI proved suitable to solve well-defined, logical problems, such as playing chess, it turned out to be intractable to figure out explicit rules for solving more complex, fuzzy problems, such as image classification, speech recognition, and language translation. A new approach arose to take symbolic AI’s place: machine learning.

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Question
Although symbolic AI proved suitable to solve well-defined, logical problems, such as playing chess, it turned out to be intractable [...] A new approach arose to take symbolic AI’s place: machine learning.
to figure out explicit rules for solving more complex, fuzzy problems, such as image classification, speech recognition, and language translation.

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Although symbolic AI proved suitable to solve well-defined, logical problems, such as playing chess, it turned out to be intractable to figure out explicit rules for solving more complex, fuzzy problems, such as image classification, speech recognition, and language translation. A new approach arose to take symbolic AI’s place: machine learning.

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#### Flashcard 4730540133644

Question
Although symbolic AI proved suitable to solve well-defined, logical problems, such as playing chess, it turned out to be intractable to figure out explicit rules for solving more complex, fuzzy problems, such as image classification, speech recognition, and language translation. A new approach arose to take symbolic AI’s place: [...].
machine learning

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figure out explicit rules for solving more complex, fuzzy problems, such as image classification, speech recognition, and language translation. A new approach arose to take symbolic AI’s place: <span>machine learning. <span>

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#### Annotation 4730543803660

 #deep-learning Machine learning arises from this question: could a computer go beyond “what we know how to order it to perform” and learn on its own how to perform a specified task? Could a computer surprise us? Rather than programmers crafting data-processing rules by hand, could a computer automatically learn these rules by looking at data?

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#### Annotation 4730545376524

 #deep-learning A machine-learning system is trained rather than explicitly programmed.

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A machine-learning system is [...] rather than explicitly programmed.
trained

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A machine-learning system is trained rather than explicitly programmed.

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#### Annotation 4730550357260

 #deep-learning if you wished to automate the task of tagging your vacation pictures, you could present a machine-learning system with many examples of pictures already tagged by humans, and the system would learn statistical rules for associating specific pictures to specific tags.

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Question
if you wished to automate the task of tagging your vacation pictures, you could [...].
present a machine-learning system with many examples of pictures already tagged by humans, and the system would learn statistical rules for associating specific pictures to specific tags

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if you wished to automate the task of tagging your vacation pictures, you could present a machine-learning system with many examples of pictures already tagged by humans, and the system would learn statistical rules for associating specific pictures to specific tags.

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#### Annotation 4730553502988

 #deep-learning So, to do machine learning, we need three things: Input data points—For instance, if the task is speech recognition, these data points could be sound files of people speaking. If the task is image tagging, they could be pictures.Examples of the expected output—In a speech-recognition task, these could be human-generated transcripts of sound files. In an image task, expected outputs could be tags such as “dog,” “cat,” and so on.A way to measure whether the algorithm is doing a good job—This is necessary in order to determine the distance between the algorithm’s current output and its expected output. The measurement is used as a feedback signal to adjust the way the algorithm works. This adjustment step is what we call learning.

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#### Annotation 4730563988748

 Class III — The class III drugs (eg, amiodarone, dronedarone, ibutilide, dofetilide, sotalol, vernakalant) block the potassium channels to inhibit IKr, IKs, IK1, and IKUR, thereby prolonging repolarization, the APD, and the refractory period

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on the electrophysiology of the sinoatrial (SA) node and AV node. These additional classifications bring drugs that were previously outside the Vaughan Williams classification into the scheme. <span>Class III — The class III drugs (eg, amiodarone, dronedarone, ibutilide, dofetilide, sotalol, vernakalant) block the potassium channels to inhibit IKr, IKs, IK1, and IKUR, thereby prolonging repolarization, the APD, and the refractory period. The relative potency of these drugs for specific potassium currents may account for atrial selectivity, for example IKUR is only known to be in the atria [19]. Blockage of ventricular

#### Annotation 4730565561612

 Blockage of ventricular potassium currents is manifested on the surface ECG by prolongation of the QT interval, providing the substrate for torsades de pointes, a polymorphic ventricular tachycardia. Amiodarone and dronedarone are exceptions with very little proarrhythmic activity, perhaps because of a balance of offsetting actions.

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e APD, and the refractory period. The relative potency of these drugs for specific potassium currents may account for atrial selectivity, for example IKUR is only known to be in the atria [19]. <span>Blockage of ventricular potassium currents is manifested on the surface ECG by prolongation of the QT interval, providing the substrate for torsades de pointes, a polymorphic ventricular tachycardia. Amiodarone and dronedarone are exceptions with very little proarrhythmic activity, perhaps because of a balance of offsetting actions. Additionally, there are more atrial-specific agents such as vernakalant that block primarily IKUR. These drugs also have other antiarrhythmic effects: ●Sotalol has beta blocking activit

#### Annotation 4730567134476

 #Cardiologie #Médecine #Physiologie #Rythmologie Class IV — The class IV drugs are calcium channel blockers. Verapamil has a more pronounced inhibitory effect on the slow response SA and AV nodes than diltiazem.

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iarrhythmic activity [11]. Subclass IIIc (transmitter dependent K channel blockers) such as acetylcholine activate potassium channels, with no clinically available drugs yet having this action. <span>Class IV — The class IV drugs are calcium channel blockers. Verapamil has a more pronounced inhibitory effect on the slow response SA and AV nodes than diltiazem. In comparison, the dihydropyridines, such as nifedipine, have little electrophysiologic effect on the heart. Verapamil and diltiazem can slow the sinus rate (usually in the presence of

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Tags
#has-images
Question
Represent, graphically, the new programming paradigm of machine learning.
[unknown IMAGE 4730570804492]

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Tags
#has-images
[unknown IMAGE 4730602786060]
Question
Define a new representation which separates the white points from the black points.
[unknown IMAGE 4730600951052]

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#### Annotation 4730621922572

 [unknown IMAGE 4730600951052] #deep-learning #has-images In this case, we defined the coordinate change by hand. But if instead we tried systematically searching for different possible coordinate changes, and used as feedback the percentage of points being correctly classified, then we would be doing machine learning. Learning, in the context of machine learning, describes an automatic search process for better representations.

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Tags
#has-images
[unknown IMAGE 4730600951052]
Question
In this case, we defined the coordinate change by hand. But if instead [...], then we would be doing machine learning. Learning, in the context of machine learning, describes an automatic search process for better representations.
we tried systematically searching for different possible coordinate changes, and used as feedback the percentage of points being correctly classified

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In this case, we defined the coordinate change by hand. But if instead we tried systematically searching for different possible coordinate changes, and used as feedback the percentage of points being correctly classified, then we would be doing machine learning. Learning, in the context of machine learning, describes an automatic search process for better representations.

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Tags
#has-images
[unknown IMAGE 4730600951052]
Question
In this case, we defined the coordinate change by hand. But if instead we tried systematically searching for different possible coordinate changes, and used as feedback the percentage of points being correctly classified, then we would be doing [...]. Learning, in the context of machine learning, describes an automatic search process for better representations.
machine learning

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and. But if instead we tried systematically searching for different possible coordinate changes, and used as feedback the percentage of points being correctly classified, then we would be doing <span>machine learning. Learning, in the context of machine learning, describes an automatic search process for better representations. <span>

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Tags
#has-images
[unknown IMAGE 4730600951052]
Question
In this case, we defined the coordinate change by hand. But if instead we tried systematically searching for different possible coordinate changes, and used as feedback the percentage of points being correctly classified, then we would be doing machine learning. Learning, in the context of machine learning, describes [...].
an automatic search process for better representations

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le coordinate changes, and used as feedback the percentage of points being correctly classified, then we would be doing machine learning. Learning, in the context of machine learning, describes <span>an automatic search process for better representations. <span>

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#### Annotation 4730632146188

 #deep-learning All machine-learning algorithms consist of automatically finding such transformations that turn data into more-useful representations for a given task. These operations can be coordinate changes, as you just saw, or linear projections (which may destroy information), translations, nonlinear operations (such as “select all points such that x > 0”), and so on. Machine-learning algorithms aren’t usually creative in finding these transformations; they’re merely searching through a predefined set of operations, called a hypothesis space.

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Question
All machine-learning algorithms consist of automatically finding such transformations that turn data into more-useful representations for a given task. These operations can be coordinate changes, as you just saw, or linear projections (which may destroy information), translations, nonlinear operations (such as “select all points such that x > 0”), and so on. Machine-learning algorithms aren’t usually creative in finding these transformations; they’re merely searching through a predefined set of operations, called a [...].
hypothesis space

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such that x > 0”), and so on. Machine-learning algorithms aren’t usually creative in finding these transformations; they’re merely searching through a predefined set of operations, called a <span>hypothesis space. <span>

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#### Annotation 4730636078348

 #deep-learning So that’s what machine learning is, technically: searching for useful representations of some input data, within a predefined space of possibilities, using guidance from a feedback signal. This simple idea allows for solving a remarkably broad range of intellectual tasks, from speech recognition to autonomous car driving.

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#### Annotation 4730642369804

 #body #configuration #continuum-mechanics #particle #position #region We shall use the term “body” to be a mathematical abstraction of an “object that occurs in nature”. A body $$\mathcal B$$ is composed of a set of particles $$p$$ (or material points). In a given configuration of the body, each particle is located at some definite point $$\mathbf{y}$$ in three-dimensional space. The set of all the points in space, corresponding to the locations of all the particles, is the region $$\mathcal R$$ occupied by the body in that configuration. A particular body, composed of a particular set of particles, can adopt different configurations under the action of different stimuli (forces, heating etc.) and therefore occupy different regions of space under different conditions. Note the distinction between the body, a configuration of the body, and the region the body occupies in that configuration; we make these distinctions rigorous in what follows. Similarly note the distinction between a particle and the position in space it occupies in some configuration. In order to appreciate the difference between a configuration and the region occupied in that configuration, consider the following example: suppose that a body, in a certain configuration, occupies a circular cylindrical region of space. If the object is “twisted” about its axis (as in torsion), it continues to occupy this same (circular cylindrical) region of space. Thus the region occupied by the body has not changed even though we would say that the body is in a different “configuration”.

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#### Annotation 4730647874828

 [unknown IMAGE 4730646564108] #continuum-mechanics #has-images More formally, in continuum mechanics a body $$\mathcal B$$ is a collection of elements which can be put into one-to-one correspondence with some region $$\mathcal R$$ of Euclidean point space. An element $$p \in \mathcal B$$ is called a particle (or material point). Thus, given a body $$\mathcal B$$, there is necessarily a mapping $$\chi$$ that takes particles $$p \in \mathcal B$$ into their geometric locations $$y \in \mathcal R$$ in three-dimensional Euclidean space: $$y = \chi(p) \quad \textrm{where} \quad p \in \mathcal B, \mathbf{y} \in \mathcal R.$$ The mapping $$\chi$$ is called a configuration of the body $$\mathcal B$$; $$\mathbf y$$ is the position occupied by the particle $$p$$ in the configuration $$\chi$$; and $$\mathcal R$$ is the region occupied by the body in the configuration $$\chi$$. Often, we write $$\mathcal R = \chi \left( \mathcal B \right)$$.

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#### Annotation 4730734906636

 First, injections of che- moconvulsants into this region produced bilateral clonic sei- zures at much lower concentrations than required when applied to other brain regions.

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#### Annotation 4730739363084

 #deep-learning Deep learning is a specific subfield of machine learning: a new take on learning representations from data that puts an emphasis on learning successive layers of increasingly meaningful representations. The deep in deep learning isn’t a reference to any kind of deeper understanding achieved by the approach; rather, it stands for this idea of successive layers of representations. How many layers contribute to a model of the data is called the depth of the model. Other appropriate names for the field could have been layered representations learning and hierarchical representations learning. Modern deep learning often involves tens or even hundreds of successive layers of representations—and they’re all learned automatically from exposure to training data. Meanwhile, other approaches to machine learning tend to focus on learning only one or two layers of representations of the data; hence, they’re sometimes called shallow learning.

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#### Annotation 4730741722380

 #deep-learning Deep learning is a specific subfield of machine learning: a new take on learning representations from data that puts an emphasis on learning successive layers of increasingly meaningful representations.

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Deep learning is a specific subfield of machine learning: a new take on learning representations from data that puts an emphasis on learning successive layers of increasingly meaningful representations. The deep in deep learning isn’t a reference to any kind of deeper understanding achieved by the approach; rather, it stands for this idea of successive layers of representations. How ma

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#### Annotation 4730744868108

 #deep-learning How many layers contribute to a model of the data is called the depth of the model.

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Question
How many layers contribute to a model of the data is called the [...] of the model.
depth

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How many layers contribute to a model of the data is called the depth of the model.

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#deep-learning
Question
[...] is called the depth of the model.
How many layers contribute to a model of the data

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How many layers contribute to a model of the data is called the depth of the model.

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#### Annotation 4730751945996

 #deep-learning #neural-network In deep learning, these layered representations are (almost always) learned via models called neural networks, structured in literal layers stacked on top of each other. The term neural network is a reference to neurobiology, but although some of the central concepts in deep learning were developed in part by drawing inspiration from our understanding of the brain, deep-learning models are not models of the brain. There’s no evidence that the brain implements anything like the learning mechanisms used in modern deep-learning models.

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#### Annotation 4730758237452

 [unknown IMAGE 4730756926732] #deep-learning #has-images As you can see in figure 1.6, the network transforms the digit image into representations that are increasingly different from the original image and increasingly informative about the final result. You can think of a deep network as a multistage information-distillation operation, where information goes through successive filters and comes out increasingly purified (that is, useful with regard to some task).

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#### Annotation 4730780257548

 [unknown IMAGE 4730789694732] #deep-learning #has-images The specification of what a layer does to its input data is stored in the layer’s weights, which in essence are a bunch of numbers. In technical terms, we’d say that the transformation implemented by a layer is parameterized by its weights (see figure 1.7). (Weights are also sometimes called the parameters of a layer.) In this context, learning means finding a set of values for the weights of all layers in a network, such that the network will correctly map example inputs to their associated targets. But here’s the thing: a deep neural network can contain tens of millions of parameters. Finding the correct value for all of them may seem like a daunting task, especially given that modifying the value of one parameter will affect the behavior of all the others!

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#### Annotation 4730783403276

 #deep-learning Learning means finding a set of values for the weights of all layers in a network, such that the network will correctly map example inputs to their associated targets.

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terms, we’d say that the transformation implemented by a layer is parameterized by its weights (see figure 1.7). (Weights are also sometimes called the parameters of a layer.) In this context, <span>learning means finding a set of values for the weights of all layers in a network, such that the network will correctly map example inputs to their associated targets. But here’s the thing: a deep neural network can contain tens of millions of parameters. Finding the correct value for all of them may seem like a daunting task, especially given that mo

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#deep-learning
Question
[...] means finding a set of values for the weights of all layers in a network, such that the network will correctly map example inputs to their associated targets.
Learning

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Learning means finding a set of values for the weights of all layers in a network, such that the network will correctly map example inputs to their associated targets.

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#### Annotation 4730793889036

 [unknown IMAGE 4730792578316] #deep-learning #has-images #loss-function #objective-function To control something, first you need to be able to observe it. To control the output of a neural network, you need to be able to measure how far this output is from what you expected. This is the job of the loss function of the network, also called the objective function. The loss function takes the predictions of the network and the true target (what you wanted the network to output) and computes a distance score, capturing how well the network has done on this specific example (see figure 1.8)

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#### Annotation 4730798607628

 [unknown IMAGE 4730797296908] #backpropagation #deep-learning #has-images #optimizer The fundamental trick in deep learning is to use this score as a feedback signal to adjust the value of the weights a little, in a direction that will lower the loss score for the current example (see figure 1.9). This adjustment is the job of the optimizer, which implements what’s called the Backpropagation algorithm: the central algorithm in deep learning.

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#backpropagation #deep-learning #has-images #optimizer
[unknown IMAGE 4730797296908]
Question
The fundamental trick in deep learning is to use this score as a feedback signal to adjust the value of the weights a little, in a direction that will lower the loss score for the current example (see figure 1.9). This adjustment is the job of the optimizer, which implements what’s called the [...]: the central algorithm in deep learning.
Backpropagation algorithm

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of the weights a little, in a direction that will lower the loss score for the current example (see figure 1.9). This adjustment is the job of the optimizer, which implements what’s called the <span>Backpropagation algorithm: the central algorithm in deep learning. <span>

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#backpropagation #deep-learning #has-images #optimizer
[unknown IMAGE 4730797296908]
Question
The fundamental trick in deep learning is to [...] (see figure 1.9). This adjustment is the job of the optimizer, which implements what’s called the Backpropagation algorithm: the central algorithm in deep learning.
use this score as a feedback signal to adjust the value of the weights a little, in a direction that will lower the loss score for the current example

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The fundamental trick in deep learning is to use this score as a feedback signal to adjust the value of the weights a little, in a direction that will lower the loss score for the current example (see figure 1.9). This adjustment is the job of the optimizer, which implements what’s called the Backpropagation algorithm: the central algorithm in deep learning.

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#### Flashcard 4730804112652

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#backpropagation #deep-learning #has-images #optimizer
[unknown IMAGE 4730797296908]
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The fundamental trick in deep learning is to use this score as a feedback signal to adjust the value of the weights a little, in a direction that will lower the loss score for the current example (see figure 1.9). This adjustment is the job of the [...], which implements what’s called the Backpropagation algorithm: the central algorithm in deep learning.
optimizer

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score as a feedback signal to adjust the value of the weights a little, in a direction that will lower the loss score for the current example (see figure 1.9). This adjustment is the job of the <span>optimizer, which implements what’s called the Backpropagation algorithm: the central algorithm in deep learning. <span>

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#### Flashcard 4730806471948

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#deep-learning #has-images
[unknown IMAGE 4730806996236]
[unknown IMAGE 4730797296908]

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 [unknown IMAGE 4730797296908] #deep-learning #has-images #training Initially, the weights of the network are assigned random values, so the network merely implements a series of random transformations. Naturally, its output is far from what it should ideally be, and the loss score is accordingly very high. But with every example the network processes, the weights are adjusted a little in the correct direction, and the loss score decreases. This is the training loop, which, repeated a sufficient number of times (typically tens of iterations over thousands of examples), yields weight values that minimize the loss function. A network with a minimal loss is one for which the outputs are as close as they can be to the targets: a trained network. Once again, it’s a simple mechanism that, once scaled, ends up looking like magic.

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#### Flashcard 4730813811980

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#deep-learning #has-images #training
[unknown IMAGE 4730797296908]
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Initially, the weights of the network are assigned random values, so the network merely implements a series of random transformations. Naturally, its output is far from what it should ideally be, and the loss score is accordingly very high. But with every example the network processes, the weights are adjusted a little in the correct direction, and the loss score decreases. This is the [...], which, repeated a sufficient number of times (typically tens of iterations over thousands of examples), yields weight values that minimize the loss function. A network with a minimal loss is one for which the outputs are as close as they can be to the targets: a trained network. Once again, it’s a simple mechanism that, once scaled, ends up looking like magic.
training loop

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and the loss score is accordingly very high. But with every example the network processes, the weights are adjusted a little in the correct direction, and the loss score decreases. This is the <span>training loop, which, repeated a sufficient number of times (typically tens of iterations over thousands of examples), yields weight values that minimize the loss function. A network with a minimal l

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#### Flashcard 4730816957708

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#deep-learning #has-images #training
[unknown IMAGE 4730797296908]
Question
Initially, the weights of the network are assigned random values, so the network merely implements a series of random transformations. Naturally, its output is far from what it should ideally be, and the loss score is accordingly very high. But with every example the network processes, the weights are adjusted a little in the correct direction, and the loss score decreases. This is the training loop, which, repeated a sufficient number of times (typically tens of iterations over thousands of examples), yields [...]. A network with a minimal loss is one for which the outputs are as close as they can be to the targets: a trained network. Once again, it’s a simple mechanism that, once scaled, ends up looking like magic.
weight values that minimize the loss function

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in the correct direction, and the loss score decreases. This is the training loop, which, repeated a sufficient number of times (typically tens of iterations over thousands of examples), yields <span>weight values that minimize the loss function. A network with a minimal loss is one for which the outputs are as close as they can be to the targets: a trained network. Once again, it’s a simple mechanism that, once scaled, ends up

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#### Flashcard 4730824297740

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#deep-learning #has-images #training
[unknown IMAGE 4730797296908]
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Initially, the weights of the network are assigned random values, so the network merely implements a series of random transformations. Naturally, its output is far from what it should ideally be, and the loss score is accordingly very high. But with every example the network processes, the weights are adjusted a little in the correct direction, and the loss score decreases. This is the training loop, which, repeated a sufficient number of times (typically tens of iterations over thousands of examples), yields weight values that minimize the loss function. A network with a minimal loss is [...]. Once again, it’s a simple mechanism that, once scaled, ends up looking like magic.
one for which the outputs are as close as they can be to the targets: a trained network

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p, which, repeated a sufficient number of times (typically tens of iterations over thousands of examples), yields weight values that minimize the loss function. A network with a minimal loss is <span>one for which the outputs are as close as they can be to the targets: a trained network. Once again, it’s a simple mechanism that, once scaled, ends up looking like magic. <span>

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#### Flashcard 4730832162060

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#deep-learning #has-images
[unknown IMAGE 4730832686348]
[unknown IMAGE 4730797296908]

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#### Flashcard 4730846842124

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#continuum-mechanics #reference-configuration
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In order to identify a particle of a body, we must label the particles. The abstract particle label $$p$$, while perfectly acceptable in principle and intuitively clear, [...]. It is more convenient to pick some arbitrary configuration of the body, say $$\chi_\textrm{ref}$$ , and use the (unique) position $$\mathbf x = \chi_\textrm{ref} (p)$$ of a particle in that configuration to label it instead. Such a configuration $$\chi_\textrm{ref}$$ is called a reference configuration of the body. It simply provides a convenient way in which to label the particles of a body. The particles are now labeled by $$\mathbf x$$ instead of $$p$$.
is not convenient for carrying out calculations

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In order to identify a particle of a body, we must label the particles. The abstract particle label $$p$$, while perfectly acceptable in principle and intuitively clear, is not convenient for carrying out calculations. It is more convenient to pick some arbitrary configuration of the body, say $$\chi_\textrm{ref}$$ , and use the (unique) position $$\mathbf x = \chi_\textrm{ref} (p)$$ of a particle in

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#### Flashcard 4730848414988

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#continuum-mechanics #reference-configuration
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In order to identify a particle of a body, we must label the particles. The abstract particle label $$p$$, while perfectly acceptable in principle and intuitively clear, is not convenient for carrying out calculations. It is more convenient to pick some arbitrary configuration of the body, say $$\chi_\textrm{ref}$$ , and use [...] of a particle in that configuration to label it instead. Such a configuration $$\chi_\textrm{ref}$$ is called a reference configuration of the body. It simply provides a convenient way in which to label the particles of a body. The particles are now labeled by $$\mathbf x$$ instead of $$p$$.
the (unique) position $$\mathbf x = \chi_\textrm{ref} (p)$$

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in principle and intuitively clear, is not convenient for carrying out calculations. It is more convenient to pick some arbitrary configuration of the body, say $$\chi_\textrm{ref}$$ , and use <span>the (unique) position $$\mathbf x = \chi_\textrm{ref} (p)$$ of a particle in that configuration to label it instead. Such a configuration $$\chi_\textrm{ref}$$ is called a reference configuration of the body. It simply provides a convenient way

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#### Flashcard 4730849987852

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#continuum-mechanics #reference-configuration
Question
In order to identify a particle of a body, we must label the particles. The abstract particle label $$p$$, while perfectly acceptable in principle and intuitively clear, is not convenient for carrying out calculations. It is more convenient to pick some arbitrary configuration of the body, say $$\chi_\textrm{ref}$$ , and use the (unique) position $$\mathbf x = \chi_\textrm{ref} (p)$$ of a particle in that configuration to label it instead. Such a configuration $$\chi_\textrm{ref}$$ is called a [...] of the body. It simply provides a convenient way in which to label the particles of a body. The particles are now labeled by $$\mathbf x$$ instead of $$p$$.
reference configuration

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xtrm{ref}\) , and use the (unique) position $$\mathbf x = \chi_\textrm{ref} (p)$$ of a particle in that configuration to label it instead. Such a configuration $$\chi_\textrm{ref}$$ is called a <span>reference configuration of the body. It simply provides a convenient way in which to label the particles of a body. The particles are now labeled by $$\mathbf x$$ instead of $$p$$. <span>

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#### Flashcard 4730851560716

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#continuum-mechanics #reference-configuration
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In order to identify a particle of a body, we must label the particles. The abstract particle label $$p$$, while perfectly acceptable in principle and intuitively clear, is not convenient for carrying out calculations. It is more convenient to pick some arbitrary configuration of the body, say $$\chi_\textrm{ref}$$ , and use the (unique) position $$\mathbf x = \chi_\textrm{ref} (p)$$ of a particle in that configuration to label it instead. Such a configuration $$\chi_\textrm{ref}$$ is called a reference configuration of the body. It simply provides a convenient way in which to label the particles of a body. The particles are now labeled by [...].
$$\mathbf x$$ instead of $$p$$
figuration $$\chi_\textrm{ref}$$ is called a reference configuration of the body. It simply provides a convenient way in which to label the particles of a body. The particles are now labeled by <span>$$\mathbf x$$ instead of $$p$$. <span>