Results and Discussion. Table 1 (top) shows the root mean square error (RMSE) between the three tests for different numbers of topics. These results show that all three tests largely agree with each other but as the sample size (number of topics) decreases, the agreement decreases. In line with the results found for 50 topics, the randomization and bootstrap tests agree more with the t-test than with each other. We looked at pairwise scatterplots of the three tests at the different topic sizes. While there is some disagreement among the tests at large p-values, i.e. those greater than 0.5, none of the tests would predict such a run pair to have a significant difference. More interesting to us is the behavior of the tests for run pairs with lower p-values. ≥ Table 1 (bottom) shows the RMSE among the three tests for run pairs that all three tests agreed had a p-value greater than 0.0001 and less than 0.5. In contrast to all pairs with p-values 0.0001 (Table 1 top), these run pairs are of more importance to the IR researcher since they are the runs that require a statistical test to judge the significance of the per- formance difference. For these run pairs, the randomization and t tests are much more in agreement with each other than the bootstrap is with either of the other two tests. Looking at scatterplots, we found that the bootstrap tracks the t-test very well but shows a systematic bias to produce p-values smaller than the t-test. As the number of topics de- creases, this bias becomes more pronounced. Figure 1 shows a pairwise scatterplot of the three tests when the number of topics is 10. The randomization test also tends to produce smaller p-values than the t-test for run pairs where the t- test estimated a p-value smaller than 0.1, but at the same time, produces some p-values greater than the t-test’s. As Figure 1 shows, the bootstrap consistently gives smaller p- values than the t-test for these smaller p-values. While the bootstrap and the randomization test disagree with each other more than with the t-test, Figure 1 shows that for a low number of topics, the randomization test shows less noise in its agreement with the bootstrap com- Figure 1: A pairwise comparison of the p-values less than 0.25 produced by the randomization, t-test, and the bootstrap tests for pairs of TREC runs with only 10 topics. The small number of topics high- lights the differences between the three tests. pared to the t-test for small p-values.
Results and Discussion. Synthesis and characterization of the [Ru(apy)(tpy)L](ClO4)(2-n) compounds The synthesis of [Ru(apy)(tpy)Cl](ClO4) takes place in a one-pot reaction from the previously synthesized Ru(tpy)Cl3·3H2O and 2,2’-azobispyridine (apy). The presence of both triethylamine and lithium chloride is needed. The first of these compounds acts as a reducing agent of Ru(III) to Ru(II), helping in the dissociation of the chlorido from Ru(tpy)Cl3·3H2O, whereas LiCl is used to prevent any dissociation of Cl- from the product. AgNO3 in an aqueous solution is required to substitute the chlorido ligand, which is filtered off in the form of the insoluble salt AgCl, by an aqua ligand. The latter is easily substituted by acetonitrile by simply refluxing for a short time in that solvent. The possibility to synthesize a complex in which the sixth coordination position can be occupied by ligands with different lability, which also have an influence in the solubility, provides with a choice to fulfill the requirements of each situation. DNA is thought to be the ultimate target of platinum drugs and of some antitumor-active ruthenium compounds.1 The kinetics of the reaction of the complex with DNA are expected to be different in each case. Therefore the kinetics can be optimized by simply tuning the sixth coordination site. Crystallization turned out to be the most appropriate method found for the purification of these three new compounds. For that purpose, perchlorate was found to be the ideal counter ion, which not only allowed obtaining the compounds in high purity, but also crystals suitable for X-ray diffraction analysis. The composition and structures of these three complexes are confirmed by elemental analysis, mass spectrometry, infrared spectroscopy and 1H NMR spectroscopy. The microanalytical data are consistent with the empirical formulas C25H19N7O4RuCl2 (1a), C25 H21N7O9RuCl2·2H2O (1b) and C27H22N8O8RuCl2 (1c). The mass spectrum of 1a reveals the appearance of a molecular peak at m/z (ESIMS) 553.0, which corresponds to the expected cation [Ru(apy)(tpy)Cl]+. In the case of 1b the aqua ligand is dissociated, therefore the molecular peak appears at m/z (ESIMS) 259.2, which corresponds to the species [Ru(apy)(tpy)]2+. This peak was also found in the case of 1c, however the molecular peak was found at m/z (ESIMS) 279.8, corresponding to the cation [Ru(apy)(tpy)(CH3CN)]2+. The infrared spectra of the three complexes are almost identical. The only remarkable difference is the presen...
Results and Discussion. Synthesis and characterization of [{Ru(apy)(tpy)}2{µ-H2N(CH2)6NH2}](ClO4)4 The anticancer activity of compounds analogous to 1a-c, 1e and 1f is often hypothesized to be related to their ability to bind to DNA model bases. In order to prove this relation, an additional new compound was synthesized. [{Ru(apy)(tpy)}2{µ-H2N(CH2)6NH2}](ClO4)4 (1g) (see Fig.4.2) was found to be pure by 1H NMR and EA and fully characterized by 2D NMR and ESI Mass spectroscopy. The latter showed the intact dinuclear species and also the mononuclear fragment originating from fragmentation by the electrospray method. The 1H NMR spectrum of 1g was recorded in DMSO-d6 because, although its solubility in water was good enough for cell testing, it was not suitable for 1H NMR spectroscopy. The peak assignment was carried out with the help of 2D NMR spectra (see Table 4.1). The stability of 1g in water was studied by dissolving the compound in this solvent, incubating the solution at 310 K for two weeks, evaporating the water and subsequently recording a 1H NMR in DMSO-d6. The compound was proven to remain unchanged after this time.
Results and Discussion. Kv1.3 is a voltage-gated calcium dependent potassium channel and these channels are known to be present on Jurkat cells (Xxxxx et al., 2000). Kv1.3 ion channel activity can be used as a functional activation marker in T cells (Estes et al., 2008). Such ion channels are critical in maintaining membrane potential and calcium flux, which plays an important role in activating downstream signal transduction pathways. It is known that brevetoxins bind to specific binding sites on voltage-sensitive sodium channels (Baden et al., 2005). Our hypothesis was that brevetoxins would also bind to voltage-gated channels on lymphocytes. Our negative control, 4-AP, decreased protein expression of Kv1.3 as expected (Xxxxxxxx et al., 2003). We did not observe effects on Kv1.3 protein expression in response to brevetoxin treatment, as we had hypothesized. SK2 channel proteins. Jurkat cells were cultured at a concentration of 2.5 x 106 cells/ml, with a total of 10 x 106 Jurkat cells used for each treatment. Control cells were either untreated, stimulated with 2.5 µg/ml PHA or treated with EtOH (0.06% v/v). Brevetoxin treated cells were exposed to 500 or 750 ng PbTx-2/ml. Cells were incubated for 24 h at 37 °C, 5% CO2. Cells were lysed and protein concentrations determined using DC BioRad reagent (BioRad, Richmond, CA). Proteins were separated using 6% SDS-PAGE with 100 µg protein for each treatment. Proteins were transferred to nitrocellulose membranes using standard procedures. Nitrocellulose membranes were blocked with 10% non-fat dry milk in TBS overnight at 4 °C using an orbital shaker. After blocking, membranes were rinsed in TBS and incubated for 2 h at room temperature with a 1:400 dilution of primary SK2 antibody (Sigma Chemical Co., St. Xxxxx, MO), diluted with 1% BSA in TBS. After 4 washes at 5 min each in TBS-T, membranes were incubated for 1 h at room temperature with a 1:2,500 dilution of alkaline phosphatase labled rabbit anti-IgG. Washes were repeated and antibody to protein bands was visualized using BCIP/NBT as substrate. Results from these experiments are shown in Figure 2. Bands indicative of the SK2 channel protein have a molecular weight of 61.3 kD. Compared to untreated control samples, band intensity was increased in samples treated with 2.5 µg PHA/ml or 750 ng PbTx-2/ml. Band intensity did not increase in response to ethanol (EtOH) control or 500 ng PbTx-2/ml. A B C A: EtOH B: 2.5 µg/ml PHA C: Untreated A B C A: 750 ng/ml PbTx-2 B: 500 ng/ml PbTx-2 C: U...
Results and Discussion. The execution of ECP involves local detection of convergence and the transition across phases. Phases transition towards the explicit global agreement in the ECP is illustrated in Figure 1. Figure 1.a shows the percentage of nodes in each phase over time. The figure also illustrates the smooth transition from a phase to the successive. Figure 1.b shows the average of estimates in each phase. It is clear that estimates in each phase converge to the same approximation value at all nodes. In the Aggregation phase, local estimates converge to 1 which is the correct average of spreading vdˆı = n over n nodes. Estimates in Convergence and Agreement phases converge to n as expected. In Figure 1.c, the variance of estimates over all nodes is tending towards very small value indicating the reduction in estimation error and the reach of convergence among nodes in each phase. Results in Figure 1 validates the ability of ECP to locally detect convergence, makes the transition in phases and attain the certainty of the explicit global agreement on the outcome of the global averaging. The internal performance of the ECP is examined by varying one of the associated parameters in each experiment. The error thresholds ε1 and ε2 are used in different phases, the effect of each one can be recognised by monitoring the corresponding phase, and hence both parameters are set to the same value. Figure 2 shows linear rising in the completion times of each phase when error thresholds are set for a higher accuracy. The values of ε1 and ε2 can be tuned to trade-off between accuracy and speed. For instance, a small error threshold can be used in Aggregation phase whilst using bigger one in Convergenc and Agreement phases to speed up convergence. On another hand, the use of higher values of Υ significantly slows the detection of convergence in each phase. Thereby, Υ is set to 5 which allows feasible convergence speed for large network sizes up to one million nodes. Also, a small delay in the completion time of Aggregation phase is noticed when the length of history queue H increases as shown in Figure 2.b. and thus the use of minimum reasonable length is preferable for faster convergence and less execution load.
Results and Discussion. A. Demographic Analysis Most of the respondents are male, 80 students out of 105 which represent 76% of the respondents where the rest of the respondent are female with approximately 24% as it can been seen in figure 3. Figure 3. Information on respondent's gender Figure 4 shows the level of education of the respondents, the highest number of the respondents are Masters students which represent 69% of the total respondents , where 18% of them are bachelor degree students and 10% are Phd students. While the rest are 1.9% are new undergrad students. As mentioned earlier the scope of the respondents was limited in student of faculty of computing, in order to make sure the answers are accurate regarding the parameters which can be used in ecommerce cloud environment. Figure 4: Education level of the respondents Figure 5 explains the information of respondent about how frequently they buy online. Based on the graph below, the major number of the respondent is buying once a month which represent 33.3%. The second is 28% which buy online once in every year and 22.8% represent the frequency of buying once in sex month, while 11.4% of the respondents buy once in every week. The less number represent 3.8% who are buy every day.
Results and Discussion. Participants liked the robot more in the functional noise conditions, instead of a constant noise conditions, F(1,39)=3.844, p<0.05. A main effect was found for functional noise on perceived helpfulness: participants rated the functional noise conditions (M=3.35, sd=1.089), as being significantly more helpful than the constant noise conditions (M=2.70, sd=1.081), U=135.5, p<.05. When we combined this dataset with the one in [6], we found a significant main effect of functional noise on helpfulness. Participants found an intentional noise pattern (M=3.35, sd=1.122) significantly more helpful a constant functional noise pattern (M=2.73, sd=.987), U=546.00, Z=-2.546, p<0.05. Furthermore, we found significant (2-tailed) main effects for functional noise on all Godspeed scales: anthropomorphism (F(1,73)=7.685, p<0.01), animacy (F(1,75)=7.474, p<0.01), likeability (F(1,75)=9.336, p<0.01), perceived intelligence (U=520.00, Z=0.10, p<0.01) and perceived safety (U=607.50, Z=0.059, p<0.05). For the above scales the intentional noise conditions were rated more positively than the constant noise conditions as can be seen in Figure 2. No significant effects were found between size of the robots. Both short and tall robots were simple-looking robotic devices without moveable arms. It could be that a robot with a more anthropomorphic, or sophisticated shape, yields different results. We are aware that we have introduced limitations towards the validity of our work. Previous work in HRI has found that full- frontal robot approaches are not necessarily the most comfortable. The experiment procedure perhaps made participants unnaturally well aware of the approaching robot; participants were focused on the robot from start to finish. In conclusion, we found that a robot approaching with intentional noise (increasing in volume when the robot accelerated and decreasing in volume when the robot decelerated) was perceived more helpful, and was regarded more positively. Our study shows that functional noise could be a powerful tool to convey a robot's intentions when approaching a user.
Results and Discussion. Figure 7 shows the results of our experiments on hecs without repairs, where we estimated ψ = unrel750. Figure 8 shows the results for hecs with repairs, where we estimated ϕ = unrel1000. We ran on a computer with a CPU Intel® Xeon® E7-8890 v4 @ 2.20 GHz and 2 TB of RAM DDR4 @ 1866 MHz, run- ning Linux x64 (Ubuntu, kernel 3.13.0-168). We use whisker-bar plots to show the width CMC FT MCS MCS-PR MCS-N MCS-PR-N Fig. 6. Color legend of bar plots of the 95% cis estimated for each instance. An instance is a combination of a res algorithm, a model, and an importance function—e.g. fee=8, hecs3, and MCS—represented in Figs. 7 and 8 by one bar in one plot. Each instance was repeated 13 times. The height of a bar represents the result- ing average ci width: achieved by that algorithm, in that model, via that importance function (we I removed outliers using a Z-scorem=2 [11]). The whiskers on top of the bar rep- resent the variance of these widths. The number at the base of a bar indicates how many out of the 13 repetitions of that instance yielded valid results—if no rare event is observed, estimation. outputs the “null ci” [0, 0] to indicate an invalid I
Results and Discussion. 1H NMR studies of the interaction between three ruthenium polypyridyl complexes and 9-ethylguanine 3A(1d) t = 24 hours 6A´(1d) 6A(1d) t = 5 hours H8(1d) 6A´(1b) t = 15 min 6A(1b) 3A(1b) The reaction between the ruthenium polypyridyl complex [Ru(apy)(tpy)(H2O)]2+ and the DNA model base 9-ethylguanine was studied by 1H NMR at a 1:2 ratio (see Fig.3.2). The conditions of the experiment were chosen to be as close as possible to physiological conditions, using D2O as a solvent and a temperature of 310 K. The reaction was studied for 24 hours, during which the pH was seen to remain neutral. H8(9-EtGua) 3A(1d) t = 24 hours 6A´(1d) 6A(1d) t = 5 hours H8(1d) 6A´(1b) t = 15 min 6A(1b) 3A(1b)
Results and Discussion. Table 1: PCA coefficients for different assumptions and the (%) of total variance for the first three components. Coefficient relating to the different assumptions Total Va- riance (%) Mean Inte- gral RMS Weighted average time CP-50 %time- RULA =4 %time- RULA =6 %time- RULA=7 First Com- po- nent 29.57 0.03 0.03 0.03 0.003 0.99 -0.007 0.03 0.01 Se- cond Com- po- nent 25.67 0.25 0.25 0.26 0.10 -0.05 -0.29 0.73 0.05 Third Com- po- nent 23.81 0.11 0.11 0.08 0.03 0.06 0.82 -0.04 -0.03 Table 1 shows the results of the three first components of the PCA that described 79.05 % of the total variance. The first component with 29.57 % of the total variance was mainly based on CP-50 (99%). All the postural scores derived from RULA appear in the second and third components with 25 % for the mean RULA score value, 26% for the root mean square RULA score value. The percentage of time spent at specific RULA levels (%time RULA=1, 2, 3 and 5) were not significant in these three first components and therefore not reported here. We noticed a few negative correlations especially with the %time spent at RULA score equal to 4. The PCA results reveal that no correlation exists between the CP-50 and the postural scores derived from RULA. The ICC for all subjects and all trials was equal to 0.326, demonstrating a poor con- sistency of the intra and inter-subjects' ratings (CP-50 answers) with regard to the ex- perimental conditions (bottle location on the shelf). We found poor correlation between CP-50 and postural scores, in agreement with the PCA analysis. Hence, although RULA scores enabled us to distinguish differences between the postures, our results show that discomfort reported by the subjects where inconsistent. Subject did not feel enough changes in the experimental conditions to clearly identify higher levels of discomfort for extreme postures (highest and lowest levels of the shelf for example). The CP-50 scale may also be too complex for such a task and may had resulted in a larger disper- sion of the results. Concurrently, the scores derived from RULA logically reached higher levels for - assumed- more extreme postures. In the literature [16] showed a small agreement between CP-50 and RULA scores for handling task with 0 to 10 Kg load. Similarly, [19] showed an agreement between RPE and physiological parameters for handling tasks with 23Kg load. Ours results seems to indicate that this agreement vanishes for the lowest load levels (1kg). This result is in accordance wi...
