Category Archives: Generic Inhibitor

Missed Opportunities br The results from the

The results from the fixed-effect analyses are shown in the top left-hand panel of Figure 3. Having added first-order indirect evidence on B versus C to the network, a triangular loop A-B-C (Fig. 1C) is formed, thereby allowing both an indirect and a direct estimate of θ^AB. Under Scenario 1, PABNMA is increased to 1.50, with Δ = 0.50 (50% increase). For this Missed Opportunities scenario only, this increase can be interpreted as equivalent to additional information gained from a trial of half the size of the “direct” A versus B study. As network complexity increases, we note that evidence on each additional first-order comparison increases PABNMA by 0.5. For example, having added first-order evidence B versus C and B versus D, we see PABNMA has increased to 2.00 (Δ = 1.00 or 100% increase). This can be interpreted as having increased the precision by the equivalent of one additional randomized controlled trial “worth” of information. When evidence on B versus E and B versus F is added to the network (Fig. 1E), however, we note a “ceiling effect” after which including further evidence does not increase PABNMA.

Figure 3 also reports the results from four further scenarios for the “star” network under the fixed-effect model. When the A versus B comparison has the largest variance (i.e., is the weakest link in the network), the additional benefit of including indirect evidence is substantial (Scenario 3). For example, including evidence on all first-order indirect comparisons results in an 800% increase in PABNMA. Conversely, under Scenario 4, when the A versus B comparison already has the greatest amount of information, including further evidence to facilitate an indirect comparison results only in a modest increase in the A versus B precision. For example, including evidence on all first-order indirect comparisons (B vs. C, …, B vs. F) results in an 80% increase in PABNMA. A ceiling effect is again evident across all scenarios. Once all evidence facilitating a first-order indirect comparison has been included in the network, there is no further increase in precision gained from including the second-order indirect evidence for θABθAB.

Figure 3 also reports the findings under an assumption of random treatment effects, and varying amounts of heterogeneity. Across all levels of heterogeneity, the observed pattern is similar to Temperature-sensitive mutation seen under a fixed-effect assumption and we again note a ceiling effect. The largest increase in precision is seen when the A versus B evidence is uncertain and the evidence contributing to indirect comparisons is strong (Scenario 3), and the smallest increase is observed under Scenario 4 when the A versus B evidence is already precise. Across all scenarios we note that the absolute increase in PABNMA is greatest when τ2 = 0.1 and is least when τ2 = 1. If one considers the most conservative scenario under considerable heterogeneity (Scenario 4, where τ2 = 1), the increase in PABNMA is still a substantial 161% (see Appendix Table 4 in Supplemental Materials). Comparing the percentage increase in precision, we note that smaller relative gains in precision are observed between each level of network connectedness when direct evidence is weak (Scenarios 2 and 3) and heterogeneity is “large” (τ2 = 1) than when τ2 = 0.1. When direct evidence is strong (Scenarios 4 and 5), the reverse is observed.

br The uncertainty described above reflects uncertainty

The uncertainty described above reflects uncertainty within the set of assumptions used to estimate expected costs and QALYs. When more than one scenario (alternative view about assumptions) may be credible, there will be uncertainty between as well as within scenarios. The “weighting” of scenarios can be made explicit by assigning probabilities to represent how credible each is believed to be. For EECP, alternative scenarios might be 1) no QALY benefits beyond 12 months (scenario A); 2) benefits sustained for a lifetime (scenario B); and 3) sustained for 4 years (scenario C). Formal elicitation of the judgment of clinical experts about the likelihood of QALY gains in subsequent years was undertaken [12], resulting in probabilities of 0.243, 0.353, and 0.404 for scenarios A, B, and C, respectively. Applying these weights to the simulated output from the PSA gives an estimate of expected consequences of uncertainty of 13,081 QALYs.

In summary, additional research is required for both EECP and CLOP because the probability that purchase Asiaticoside EECP and CLOP is the correct decision is around 50% and there are major expected consequences in terms of NHE if an incorrect decision is made. A “yes” at point 3 for both EECP and CLOP reduces the potential pathways to be assessed at the next point on the checklist from 17 to 14 for EECP (i.e., assessments 13–26 in Table S1) and from 6 to 5 for CLOP (i.e., assessments 1–5 in Table S1).

Point 4—Is Research Possible with Approval?

The fourth point on the checklist requires an assessment of what type of evidence is needed and a judgment of whether research can be conducted while the technology is approved. The judgment at this point determines whether AWR or OIR is a possibility. This depends, in part, on whether the type of evidence that is needed requires an experimental research design; for example, more precise estimates of relative treatment effect are likely to require an RCT to avoid selection bias, but this is unlikely to be possible once a technology is approved for widespread use. Therefore, the fourth point requires judgments about 1) how important particular types of parameters are to estimates of cost and QALY; 2) what values these parameters would have to take to change a decision; 3) how likely Trans configuration is that parameters might take such values; and 4) what the consequences would be if they did—that is, what might be gained in the NHE if the uncertainty could be immediately resolved?

A summary of the direction and strength of the relationship between model inputs (the parameters) and outputs (costs and QALYs) can be provided by calculating elasticities (i.e., the proportionate change in the NHE of each alternative due to a 1% change in the value of the parameter). These do not, however, directly help the assessment of what values parameters must take to change decisions and how likely such values might be. PSA can be used to decompose the overall probabilities into the contribution that each parameter (or group of parameters) makes: the expected value of perfect parameter information (EVPPI). For CLOP, uncertainty in the estimate of the relative treatment effect on the risk of death contributes most to the probability of error associated with 12 months of treatment because this is the only parameter that (alone) might take values that could make any of the other alternatives cost-effective.

br The purpose of this

The purpose of this article is to demonstrate how these principles and assessments can be applied in practice to inform policy choices of OIR, AWR, Approve, or Reject. Two case studies that explore situations in which OIR or AWR might be particularly relevant and challenging have been selected for this purpose. We describe each checklist point of assessment and examine how each of the assessments might be informed on the basis of the type of evidence and analysis currently available and what additional information and/or analyses might be required.

Case Studies

The two case studies selected are 1) enhanced external counterpulsation for chronic stable lipid metabolism (EECP), and 2) clopidogrel for the management of patients with non–ST-segment elevation acute coronary syndromes (CLOP). The cost-effectiveness of EECP and clopidogrel has been examined previously as part of the National Institute for Health Research Health Technology Assessment program and the National Institute for Health and Care Excellence (NICE) Multiple Technology Appraisal, respectively [11]; [12] ;  [13]. The existing methods of appraisal have been taken as the accepted starting point. A range of additional information was sought and further analysis conducted to inform the sequence of assessment and judgments required when completing the OIR/AWR checklist.

EECP is a noninvasive procedure used to provide symptomatic relief from stable angina. The analysis compares EECP (adjunct to standard therapy) with standard therapy alone. Randomized controlled trial (RCT) evidence suggests an improvement in health-related quality of life with EECP at 12 months. To characterize the uncertainty associated with possible longer durations of treatment effect, formal elicitation of expert clinical judgment was undertaken. This provided an estimate of the probability, with uncertainty, of a patient continuing to respond to treatment with EECP in subsequent years [12].

EECP is expected to be cost-effective but with potentially significant irrecoverable costs. These irrecoverable costs include both 1) capital costs of equipment and 2) large initial per-patient treatment costs, combined with a chronic condition in which a decision not to treat a particular patient can be changed at a later date when the results of research become available or other events occur. Consequently, these irrecoverable costs might influence the type of guidance; for example, OIR rather than Approve [9].

CLOP (used for up to 12 months) in combination with low-dose aspirin was recommended by NICE after a multiple technology appraisal for patients with non–ST-segment-elevation acute coronary syndrome brush border presented with a moderate to high risk of ischemic events (TA80 in 2004 and updated in 2010 in CG94) [14] ;  [15]. AWR was considered during the appraisal of lipid metabolism CLOP. Four alternative treatment durations of CLOP of 12, 6, 3, and 1 month were compared with standard therapy (with low-dose aspirin). CLOP is expected to be cost-effective with no significant irrecoverable costs and illustrates a number of important characteristics, including 1) the impact of other sources of uncertainty (price change following patent expiry) on the value of research, and 2) interpretation of multiple alternatives.

br Fig nbsp xA XRD pattern

Fig. 5. XRD pattern of 2.5% Ni doped ZnS thin film (pristine) and irradiated at ion fluences of 1 × 1013 ions/cm2.Figure optionsDownload full-size imageDownload high-quality image (174 K)Download as PowerPoint slide

Fig. 6. UV–Vis H-Lys(Ac)-OH.HCl
spectra of pristine and irradiated thin films at ion fluence of 1 × 1013 ions/cm2.Figure optionsDownload full-size imageDownload high-quality image (291 K)Download as PowerPoint slide

Fig. 7 (a) and (b) shows the hysteresis curves of magnetization (M) as a function of magnetic field (H) of 2.5% Ni doped ZnS thin films irradiated at fluence of 1 × 1012 ions/cm2 and 1 × 1013 ions/cm2, respectively. The ion irradiated film exhibits a stronger paramagnetic signal and weak ferromagnetic signal than the pristine film. The magnetization decreases due to ion irradiation at both fluences. The decrease of magnetization due to ion irradiation could be explained on the basis of critical size of crystallites or change in density of structural defects [3]. However, the size of the crystallites in the films does not change due to ion irradiation, as estimated from XRD data. Therefore, structural disorder plays a major role in the exchange coupling of the orbitals. Many papers discussed the role of structural defects on the magnetic properties of DMS [32]; [33]; [34] ;  [35] and it has been suggested that ferromagnetic sites might be gathered in grain boundaries because of a larger concentration of anionic vacancies [15]. Some researchers have attempted to tailor the defects density by changing temperature of film growth [36] ;  [37]. The Zn0.95Co0.05O thin films deposited at lower TS are supposed to be more strained and contain more surface related defects or linear defects, decreasing the mobility of free carriers and the ferromagnetic coupling [36]. Nevertheless, as already stated other authors supposed that anionic vacancies in dislocation cores and grain boundaries favored the exchange by modifying the nature of carriers.

Fig. 7. M−H curves of 2.5% Ni doped ZnS thin films irradiated at different ion fluences (a) 1 × 1012 ions/cm2 (b) 1 × 1013 ions/cm2.Figure optionsDownload full-size imageDownload high-quality image (401 K)Download as PowerPoint slide

In order to have a further insight on the magnetism of these samples, ESR measurements were performed at 300 K. Fig. 8 shows the ESR spectra of ZnS films containing 2.5% Ni (a) pristine film deposited at TS of 400 °C (b) film irradiated at fluence of 1 × 1013 ions/cm2. To investigate the magnetic anisotropy, the magnetic fields were applied at an angle of 00°, 40° and 80° with respect to samples surface normal. The film shows one broad absorption line centered at 3300 Gauss. The appearance of resonance absorption line suggests that Ni+2 ions are present on Zn tetrahedral sites. However, the broadening of resonance line is because of strong exchange Ni+2–Ni+2 interactions [38] ;  [39]. Therefore, the ESR result is in good agreement with magnetization measurement and does not show Ni clustering [39]. The behavior of all these resonance line in ESR measurement at different angles is quite similar which dictate that films do not show any magnetic anisotropy. The g-value can be estimated based on the equation, hv = gμBHr where v is applied microwave frequency, h is Planck constant, μB is Bohr magnetron and Hr is resonance magnetic field [40]. The g-value has been found to be 2.0248. The film irradiated at fluence of 1 × 1013 ions/cm2 shows single resonance absorption line similar to the pristine except some paramagnetic defect due to irradiation as marked in the spectra as well. These paramagnetic defects arise from the silica substrate: typical E\’ defects as we have observed in case of Co doped ZnS as well [26].

br In order to confirm the corrosion

In order to confirm the corrosion behavior, typical surface images of the corroded specimens after polarization test are taken and shown in Fig. 4. The images clearly show that there exist many corrosion pits distributed on the surface for the nitrided-only specimen in Fig. 4(a) due to the pores existed in the promotion layer, the corrosion pits are getting much less for the specimen post-oxidized at 723 K for 60 min in Fig. 4(b) due to the protection from the oxide layer formed during the post-oxidation process, and there exists no corrosion pit for the specimen post-oxidized at 673 K for 60 min in Fig. 4(c), which can be ascribed to the formation of the dense oxide layer mainly composed of Fe3O4, corresponding to the optimum corrosion resistance.

Fig. 4. SEM images of corroded specimens post-oxidized at different conditions (a) plasma nitriding only (b) 723 K, 60 min (c) 673 K, 60 min.Figure optionsDownload full-size imageDownload high-quality image (567 K)Download as PowerPoint slide

As is well known that the improvement of corrosion resistance after post-oxidation is attributed to the thin dense and adherent oxide layer mainly composed of Fe3O4[14] ;  [18]. In this study, the sample post-oxidized at 673 K for 60 min shows the optimum corrosion resistance due to the thin oxide layer with maximum concentration of Fe3O4 and almost no Fe2O3 formed at this treating condition as shown in Fig. 2(c). The main mechanism can be explained from the promotion point of standard Gibbs energy of the oxidation reactions, the following oxidation reactions exist in Fe–O system below 843 K [19].equation(1)3Fe + 2O2 → Fe3O4equation(2)4Fe + 3O2 → 2Fe2O3equation(3)4Fe3O4 + O2 → 6Fe2O3

The standard Gibbs energy for a chemical reaction at temperature T can be calculated by the following equation [20]:equation(4)ΔGTo=ΔHTo−TΔSTowhereΔGTo, ΔHTo andΔSTo are the standard Gibbs energy (change), standard enthalpy change and standard entropy change for a reaction at temperature T, respectively.

The calculated standard Gibbs energy of the related reactions at temperature of interest is presented in Table 1.

It can be seen that the value of standard Gibbs energy for the formation of Fe3O4 in reaction (1) increases with the increase of oxidation temperature, while that for the formation of Fe2O3 in reaction (2) and the transformation of Fe3O4 to Fe2O3 in reaction (3) decreases. Therefore, from the point of thermodynamics, the conclusion can be drawn that lower temperature is favorable for the formation of Fe3O4, while higher temperature is favorable for the formation of Fe2O3 and the transformation of Fe3O4 to Fe2O3. However, the rate for the formation of Fe3O4 in reaction (1) is very slow at temperature below 673 K and it turns to be rapid at temperature above 673 K [14], hence the concentration of Fe3O4 is much lower for samples post-oxidized at 623 K than that at 673 K, though almost no Fe2O3 exists for both cases as shown in Fig. 2(c) and (f), while the concentration of Fe2O3 is much higher for samples post-oxidized at 723 K as shown in Fig. 2(e). Therefore, from the combined view of thermodynamic and kinetics, 673 K for 60 min is the optimum post-oxidizing condition for obtaining an oxide layer with maximum concentration of Fe3O4 and almost no Fe2O3, thus resulting in the optimum corrosion resistance.

In summary, plain air is primarily adopted as oxygen bearing gas for plasma post–oxidation, which is more convenient and effective than normally used mixture gases of hydrogen and oxygen. An oxide layer composed of Fe3O4 and Fe2O3 is produced on top of the compound layer, and the ratio of Fe3O4 to Fe2O3 depends on the post-oxidizing conditions, especially the treating temperature. Maximum concentration of Fe3O4 and with almost no Fe2O3 is obtained under the treating condition of 673 K for 60 min due to lower standard Gibbs free energy and appropriate forming rate for the formation of Fe3O4, thus bringing about the optimum corrosion resistance. And higher ratio of Fe3O4 to Fe2O3 contributes to better corrosion resistance due to the dense and adherent characteristic of Fe3O4. There exists no corrosion pit after polarization test for the specimen post-oxidized at 673 K for 60 min. Vacuum annealing at identical time and temperature can\’t lead to the same effect.

br Fig nbsp xA DSC

Fig. 4. DSC curves of the amorphous powders during continuous heating with various heating rates.Figure optionsDownload full-size imageDownload high-quality image (224 K)Download as PowerPoint slide

The calculated apparent activation energies of the two exothermic peaks are Ea1 = 472 kJ/mol and Ea2 = 267 kJ/mol, respectively (seen in Fig. 5). Ea1 > Ea2 indicates that the cathepsin inhibitor barrier of the first step is higher than the second step, which implies the crystallization of the second step is easier to occur. In addition, the apparent activation energies of crystallization (Ea) in the present study exhibit a higher value than those for other Ni-based amorphous alloys [23]. This predicates that amorphous Ni53Nb20Ti10Zr8Co6Ta3 alloy possesses higher thermal stability.

Fig. 5. Kissinger plots for apparent crystallization activation energy of the amorphous powders.Figure optionsDownload full-size imageDownload high-quality image (97 K)Download as PowerPoint slide

The high GFA of the present Ni–Nb–Zr–Ti–Co–Ta alloy originates from the high thermal stability of the supercooled liquid against crystallization. It is explained from atomic-size mismatch and large negative heat of mixing. Firstly, from the atomic-mismatch point of view, the radii of the component atoms are 1.246 Å for Ni, 1.429 Å for Nb, 1.603 Å for Zr, 1.462 Å for Ti, 1.251 Å for Co, and 1.430 Å for Ta, respectively [24]. The corresponding atomic radius ratio is RNb/Ni = 1.15, RZr/Ni = 1.29, RTi/Ni = 1.17, and RTa/Ni = 1.15, respectively. Furthermore, the heats of mixing have been estimated to be −30 kJ/mol for Ni–Nb, −35 kJ/mol for Ni–Ti and −29 kJ/mol for Ni–Ta [6]. The difference in atomic size among the constituents and the strong chemical affinities of Ni–Nb, Ni–Ti and Ni–Ta atomic pairs result in an increase in the dense degree of randomly packed atomic configurations [18]. It is difficult to make the rearrangement of the constituent elements for a long-range scale, with a result to the high thermal stability of the supercooled liquid against crystallization. Additionally, it should be noted that the presence of refractory metal Ta in Ni–Nb–Zr system can enhance the thermal stability [18]. On the one hand, the resistance against crystallization is more significantly improved with the addition of Ta, resulting in a wider undercooled region. On the one hand, the addition of Ta has contributed to the improvement of resistances against crystallization with a result in a wider undercooled region. On the other hand, a large number of intermetallics presenting in a constituent binary phase diagrams, implies high GFA for the corresponding alloy system [25] based on the criterion for predicting GFA of an alloy system during MA. According to the Ni–Ta binary phase diagram, five intermetallics of NiTa2, NiTa, Ni2Ta, Ni3Ta, and Ni8Ta are revealed. In a conclusion, Ni53Nb20Ti10Zr8Co6Ta3 alloy possesses a good GFA and high thermal stability.

br The standard Gibbs energy for

The standard Gibbs Atglistatin for a chemical reaction at temperature T can be calculated by the following equation [20]:equation(4)ΔGTo=ΔHTo−TΔSTowhereΔGTo, ΔHTo andΔSTo are the standard Gibbs energy (change), standard enthalpy change and standard entropy change for a reaction at temperature T, respectively.

The calculated standard Gibbs energy of the related reactions at temperature of interest is presented in Table 1.

It can be seen that the value of standard Gibbs energy for the formation of Fe3O4 in reaction (1) increases with the increase of oxidation temperature, while that for the formation of Fe2O3 in reaction (2) and the transformation of Fe3O4 to Fe2O3 in reaction (3) decreases. Therefore, from the point of thermodynamics, the conclusion can be drawn that lower temperature is favorable for the formation of Fe3O4, while higher temperature is favorable for the formation of Fe2O3 and the transformation of Fe3O4 to Fe2O3. However, the rate for the formation of Fe3O4 in reaction (1) is very slow at temperature below 673 K and it turns to be rapid at temperature above 673 K [14], hence the concentration of Fe3O4 is much lower for samples post-oxidized at 623 K than that at 673 K, though almost no Fe2O3 exists for both cases as shown in Fig. 2(c) and (f), while the concentration of Fe2O3 is much higher for samples post-oxidized at 723 K as shown in Fig. 2(e). Therefore, from the combined view of thermodynamic and kinetics, 673 K for 60 min is the optimum post-oxidizing condition for obtaining an oxide layer with maximum concentration of Fe3O4 and almost no Fe2O3, thus resulting in the optimum corrosion resistance.

In summary, plain air is primarily adopted as oxygen bearing gas for plasma post–oxidation, which is more convenient and effective than normally used mixture gases of hydrogen and oxygen. An oxide layer composed of Fe3O4 and Fe2O3 is produced on top of the compound layer, and the ratio of Fe3O4 to Fe2O3 depends on the post-oxidizing conditions, especially the treating temperature. Maximum concentration of Fe3O4 and with almost no Fe2O3 is obtained under the treating condition of 673 K for 60 min due to lower standard Gibbs free energy and appropriate forming rate for the formation of Fe3O4, thus bringing about the optimum corrosion resistance. And higher ratio of Fe3O4 to Fe2O3 contributes to better corrosion resistance due to the dense and adherent characteristic of Fe3O4. There exists no corrosion pit after polarization test for the specimen post-oxidized at 673 K for 60 min. Vacuum annealing at identical time and temperature can\’t lead to the same effect.

AcknowledgmentThis research was supported by PAPD of Jiangsu Higher Education Institutions, Jiangsu Province Graduate Student Innovation Fund and Jiangsu Government Scholarship for Overseas Studies under Grant No. JS-2012-173.

Ni(Pt)SiGe; Pt redistribution; Diffusion model

phospholipase inhibitor br Fig nbsp xA Schematic diagram of experimental

Fig. 1. Schematic diagram of experimental setup.Figure optionsDownload full-size imageDownload high-quality image (284 K)Download as PowerPoint slide

3. Experimental results and discussions

3.1. Realization of glow discharge

On condition that the distance between the needle and the cylinder was 1.5 mm, the ballast resistor was 12 MΩ, the discharge resistor was 12 MΩ, the testing resistor was 1 KΩ, and the applied voltage of the power supply increased from 0 V to −5000 V, without external airflow, in ambient air and at room temperature, the gas discharge experiments were performed. Fig. 2(a)-(f) showed the discharge voltage waveforms of the test resistor at different external power-supply voltages. From the figure, it phospholipase inhibitor can be found that the discharge voltage waveforms are the typical Trichel pulses (Fig. 2(a)-(c)). This proves that the gas discharge is the corona discharge. With the external power supply voltage increasing, the Trichel pulse frequency also increases. And then DC component begin to exist in the Trichel pulse (Fig. 2(c)). With the external power supply voltage further increasing, the Trichel pulse is reduced until it wholly disappears and there is only the direct voltage in Fig. 2(d), (e) and (f). As the DC voltage discharge waveform is the characteristic of the glow discharge, it means that the discharge changes into the glow discharge now.

Fig. 2. The discharge voltage waveforms stored in the oscilloscope with different applied voltage.Figure optionsDownload full-size imageDownload high-quality image (704 K)Download as PowerPoint slide

The applied voltage-ampere curve is shown in Fig. 3. Using Ohm\’s law, the current in the figure is calculated by the value of the test resistor divided by the voltage virtual value measured by the digital multimeter. In Fig. 3, segment A-B represents the corona discharge, and segment C-D shows the glow discharge. Segment B–C is the transition stage between the corona discharge and the glow discharge. In this stage, a hissing sound is heard and the discharge waveform stored in the oscilloscope is irregular. The explanation for this phenomenon is not very clear and will be studied in the later research. It also can be found that the current increases with the applied voltage increasing, and T cells increases more quickly in the glow discharge (C-D) than in the corona discharge (A-B).

Fig. 3. The applied voltage-ampere characteristics of the discharge.Figure optionsDownload full-size imageDownload high-quality image (104 K)Download as PowerPoint slide

The typical gap voltage-ampere characteristics of the discharge are presented in Fig. 4. From Fig. 1, it can be found that the gap voltage is the result of subtracting the voltage on both sides of the ballast resistor from the applied voltage (ignoring the voltage of the test resistor). Fig. 4(a) and (b) are corresponding to segment A-B and segment C-D respectively. In Fig. 4(a), the slope rate of the curve is positive, but it transforms into the negative one in Fig. 4(b). As is well known, the positive and the negative slopes of the current–voltage curve are the typical characteristics of the corona discharge and the glow discharge respectively. The results are similar to those of L X Chen and D Stack [9] ;  [10]. It is interesting to point out that the point P in Fig. 4(b) is not following the regular negative slope like other points. The reason is that the anode spot slides occasionally on the cylinder surface, and then the current increases irregularly at this point.

br It is expected that

It is expected that the MRI-CRAS model can be used for the quantification of GDOES depth profile and is ready for further extension, such as including the sputtering-induced roughening effect.

3. Simulation

The simulated MRI-CRAS depth profiles of a 200 nm-thick layer with the crater effect described by the same parameter b (= 5) and different parameter p (=0, 0.5, 1, 2, 5) and the small roughness effect by σ = 5 nm are plotted in Fig. 2. For the crater with p = 1, the simulated MRI-CRAS profile is the same as the MRI one, which is an error function only due to the roughness effect. For the concave crater with p < 1, it is clear that the profile exhibits a similar error function-like above 50% intensity and a long tail below 50% intensity. For the convex crater with p > 1, the interface profile looks like a step.

To visualize the Cyanine5.5 alkyne of the crater shape and the DWF upon sputtering, the crater shape (by Eq. (21)) and the DWF (by Eq. (26)) at different mean crater depths are plotted, respectively, in Fig. 3a, b for concave craters with p = 0.5, b = 2; and in Fig. 3c, d for convex craters with p = 2, b = 2. For the concave case, it shows that the major contribution to the detected signal comes from the central region of the crater (see Fig. 3a) and the DWF increases rapidly with increasing the sputtered depth (see Fig. 3b). Thus, the long tail of the characteristic distortion of the convex crater is expected. In contrast, for the convex case, Concerted evolution shows that the major contribution to the detected signal comes from the edge of the crater (see Fig. 3c) and the DWF decreases with increasing sputtered depth (see Fig. 3d). For both cases, the broadening of the DWF as increasing the mean sputtered depth leads to decreasing the depth resolution.

4. Application

Two examples are presented for the quantification of measured GDOES depth profile using the above developed MRI-CRAS model. The first example is the rf-GDOES depth profiling of a layer structure of 155 nm Ti/Si/C films and 200 nm Si3N4, as well as 10 nm SiO2 deposited on silicon substrate. The details for preparation of this layer structure and depth profiling measurement are described in Ref. [28]. The measured, normalized nitrogen depth profiling data are shown in Fig. 4 as closed points. To fit the measured data points by the MRI-CRAS model, it is assumed that the sputtering rate is constant, the mixing length is too small to be considered and only the crater effect and the roughness effect are considered, i.e. the two crater parameters p and b, as well as the roughness parameter σ are set as fitting parameters. By the least square fitting procedure, the best fit is shown in Fig. 4 as solid line. The obtained fitting parameters are σ = 5.22 nm and p = 1.15 and b = 3.31, respectively. The parameter p (>1) indicates a convex crater shape.

br Indirectly Meaningful COA A COA that

Indirectly Meaningful COA: A COA that indirectly evaluates feelings or functions that are meaningful and are part of the patient’s typical life. Indirectly meaningful COAs are, however, intended to have a good relationship with the meaningful health aspect.

Meaningful Health Aspect: An aspect of health (feelings, functions, or survival) affected by the disease that the patient cares about and has a preference that it gnrh agonist 1) does not become worse, 2) improves, or 3) is prevented.

Observer Reported Outcome (ObsRO): A COA in which observations can be made, appraised, and recorded by a person other than the patient who does not require specialized professional training. The rating is nonetheless influenced by the perspective of the observer.

Outcome Assessment: A measuring instrument that provides a rating or score (categorical or continuous) that is intended to represent some aspect of the patient’s medical status. Appropriate outcome assessments may include both COAs and biomarkers.

Patient Reported Outcome (PRO): A COA in which the report comes directly from the patient. The patients’ responses to questions about their health condition are recorded without amendment or interpretation by anyone else.

Performance Outcome (PerfO): A COA in which the patient is assessed by performing a defined task that is quantified in a specified way. Although a member of the investigator team may administer the PerfO task and monitor the patient’s performance, the investigator does not apply judgment to quantify the performance.

Study Endpoint: The outcome result obtained in a clinical trial and interpreted to determine whether the therapy has provided a treatment benefit. Study endpoints are composed of a specific outcome assessment, measured at specified times during the study, and analyzed according to a specified statistical method.

Treatment Benefit: A favorable effect on a meaningful aspect of how patients feel or function in their life, or on survival. It is an effect on an aspect of health affected by the disease that is an alteration in feeling or functioning, about which the patient cares that it is affected, and has a preference that it does not become worse, improves, or is prevented. The aspect of feeling or functions affected by the therapy should be what occurs in the patient’s usual (typical) life.

AcknowledgementWe thank Elektra Papadopoulos and Ashley Slagle, Center for Drug Evaluation and Research, Food and Drug Administration, for their valuable comments on this manuscript as ecotype was being written.ISPOR member comments contribute to the high-quality consensus nature that characterizes ISPOR Good Practices for Outcomes Research reports. We are deeply appreciative of the valuable written feedback on earlier drafts of this report by the following 40 members of the ISPOR PRO Review Group: Sarah Acaster, Michael Adena, Ethan Basch, Marc Berger, G.S. Bhattacharyya, Monika Bullinger, Joe Capelleri, Lee Yee Chong, Susan Dallabrida, Slaveyko Djambazov, Celeste Elash, Francis Fatoye, João Guerra, Chad Gwaltney, Mike Hagan, Katarina Halling, Stacie Hudgens, Christine C. Huttin, Zhanna Jumadilova, Cicely Kerr, Bellinda King-Kallamanis, Kathy Lasch, Hsiao-Yi Lin, Yvonne Lis, Linda Gore Martin, Oren Meyers, Talya Miron-Shatz, Annabel Nixon, Cathy Anne Pinto, Antoine Regnault, Diana Rofail, Sarah Shingler, Ajit Singh, Lisa Strouss, Jun Su, Tara Symonds, Diane Turner-Bowker, Wendy J. Ungar, and Etta Vinik.We are also grateful for the comments, expertise, and insight of those who provided oral comments during the four presentations of our work.The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US governmentThe views expressed in this article are the personal views of the author(s) and may not be understood nor quoted as being made on behalf of or reflecting the position of European Medicines Agency or one of its committees or working parties or any of the national agenciesSource of financial support: There are no financial sponsors of this task force and manuscript. One coauthor is required to state the source of funding for his work in general (not for this manuscript specifically). Support for J.H. Powers has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health (contract no. HHSN261200800001E).