The meta-structure was introduced C646 concentration as a novel concept for protein sequence analysis [34]. In this approach a protein is conceived as a network in which individual amino acids represent the nodes whereas edges connecting two nodes indicate spatial proximity in the 3D structure. Of particular relevance is the fact that in this conceptual view the mutual couplings between individual amino acids and the resulting cooperative character of the protein are retained. It has been shown that the network structure of a protein can be calculated exclusively

based on primary sequence information and statistical distribution functions derived from the PDB database [34]. The meta-structure of the protein is quantified by two sequence-derived parameters, compactness and local secondary structure. Residue-specific compactness values quantify the spatial embedding

of individual residues within the 3D protein structures. Residues in the interior of a structure learn more exhibit large compactness values while residues located on the surface and exposed to the solvent display small (even negative in case of conformationally highly flexible segments) values. The meta-structure derived secondary structure parameter is defined in analogy to the well-established NMR 13Cα chemical shift index, with positive values for α-helices and negative values indicating the presence of an extended conformation. It has already been shown that this novel approach is very useful for the analysis of IDPs [34] and [35]. Firstly, a large scale comparison of calculated compactness values of IDPs (taken from the DisProt database) with well-folded proteins deposited in the PDB database showed that IDPs display significantly smaller compactness values (∼230) compared to their well-folded counter parts (∼330) suggesting Meloxicam that compactness

values are valuable quantitative probes for structural compaction of proteins [34]. Additionally, it was demonstrated that calculated local secondary structure parameters are indicative of α-helices and β-strands [36]. Consistently positive values are found for residues located in α-helical segments while residues populating extended structural elements (β-strands or polyproline II helices) display negative values. A comparison of meta-structure and NMR data for a prototypical IDP is given in Fig. 3. It can be seen that meta-structure values convincingly compare with experimental NMR secondary chemical shifts or NMR-derived secondary structure propensities. Novel applications to large-scale, sequence-based protein analysis and selection (e.g. identification of IDPs displaying significant local α-helical preformation) are feasible and have already been suggested [36]. Here another application of meta-structure data (e.g. compactness) is proposed.

15. The principle dimensions are shown in Table 4. Numerical simulations are conducted

on the three models. The 3-D FE model is made of beam, shell, and point mass elements. It has 14,000 nodes and 40,000 elements. In order to model full-loading conditions, the container mass is modeled by point mass elements and distributed on bulkheads and hulls. In beam modeling, a thin-walled open cross-section and bulkheads necessitate the use of 2-D analysis of the cross-section. The sectional property distribution of the 3-D FE model is calculated by WISH-BSD and plotted in Fig. 16. The accommodation deck and bulkheads induce drastic changes in the sectional properties. Sectional properties are reflected in beam modeling as the solid lines in Fig. 16. The effect of bulkheads is considered by increasing the torsional modulus according to the method by Senjanović et al. (2009b). equation(75) It⁎=(1+al1+4(1+υ)CItl0)ItEq.

Selleckchem MEK inhibitor (75) was derived by Senjanović et al. (2009b). In Eq. (75), the second and third terms are the total bulkhead contribution to hull torsional modulus. The energy coefficients of bulkheads and stools due to warping distortion are calculated using Eqs. (59), (60), (61) and (62) selleck chemical in the paper of Senjanović et al. (2009b). Table 5 and Table 6 show the energy coefficients of bulkhead and stool due to warping. The bulkheads of the shell 3-D model are modified to be stiffer than the original design because the container mass attached to the bulkheads can cause local modes in lower frequency. Consequently, the strain energy becomes larger than that of the original design. Finally, the effect of the bulkheads is considered by increasing the torsional modulus as equation(76) It⁎=(1+0.143+2.160)It=3.303It The effective shear factor is calculated by integrating the shear stress flow.

The shear stress flows evaluated by 2-D analysis are shown as dotted lines in Fig. 17. The distances from the dotted lines to the solid lines show the magnitudes of the shear stresses. Dry mode natural frequencies of the beam models with and without bulkheads and the Cobimetinib mw 3-D FE model are compared. Fig. 18 shows the eigenvectors of the models. The eigenvectors of the beam models are evaluated at the reference axis on the mass center. Table 7 shows the dry mode natural frequencies of the models. Good agreement is obtained in the results of 2-node torsion and 2-node vertical bending. The consideration of the bulkhead plays a role in 2-node torsion. However, the 2-node horizontal bending result shows a difference in the natural frequency and the eigenvectors. Linear simulations are conducted on the three models. Fig. 19 compares RAOs of the models. Heave, roll, and pitch motions at the center of mass are almost the same in all the models, which include only rigid motions. Flexible motions can be compared in modal motions or sectional forces. Small differences between the models are found in flexible motions and sectional forces.

Come mostrato in Table 3, il gioco può svolgersi anche a 4 giocatori (Wilhelm, 2006), portando a SdE analoghe ma estendendo il tipo di dinamiche

sociali collaborative con alleanze o contrapposizioni fra sottogruppi ( Von Neumann and Morgenstern, 1953). Interpretando la vincita di caramelle in termini economici, la collaborazione in termini sociali e la qualità della vita dell׳orso in termini ambientali, giochi come quelli delle Tables 2 e 3 costituiscono modelli molto semplificati, ma coerenti con la precedente riflessione didattica, dello studio di caso Selleck Ku 0059436 “surriscaldamento globale” (Kyburz-Graber et al., 2010). Il loro obiettivo è infatti spingere i giocatori a scegliere, in base a competenze di analisi e mobilitazione, comportamenti dinamici o stazionari, collaborativi o competitivi, vincolati dalle regole del gioco all׳ordine di criticità in cui le dimensioni fondamentali dell׳ESS sono coinvolte nello studio di caso. Considerare infatti la collaborazione un valore, o voler

salvare l׳orso (ci Pifithrin-�� mw interessa?: competenze di mobilitazione), obbliga a saper trasformare un equilibrio stazionario economico in uno dinamico socioeconomico, o saper trovare un equilibrio dinamico sostenibile (come?: competenze di analisi). Sebbene i giochi descritti in termini di TdG sembrino adatti all׳ESS, solo lo studio sperimentale dei reali processi motivazionali e di apprendimento che innescano può rilevarne l׳efficacia didattica per i giocatori e l׳utilità valutativa per il docente. Le domande di ricerca da porsi sono in particolare: 1. In una vera partita, i giocatori selezionano SdE come previsto dalla TdG? Le domande evidenziano come questo lavoro, pur non focalizzandosi Montelukast Sodium sulle importanti fasi di introduzione

a priori e discussione a posteriori (debriefing) di un gioco, ampiamente trattate in letteratura (Wilhelm, 2014, Morazzi and Valer, 2001, Nicholson, 2012 and Crookall, 2010), voglia stabilire se, come e in quale misura strategie previste dalla TdG possano essere riconosciute e correlate dal docente a competenze e valori richiamati dai giocatori durante le partite, almeno per i giochi utilizzati. Se così fosse, i concetti elementari di TdG introdotti potrebbero essere utili al docente per individuare aspetti realmente vissuti dai giocatori, o progettare addirittura da sé semplici giochi su di essi. A scanso di equivoci, si sottolinea che lo scopo non è controllare il pensiero dei giocatori, ma riconoscerne l׳apprendimento.

In a later study, the authors also noted the decreased expression of Bax and caspase-8 in human small airway epithelial cells exposed to THC, which they suggest could have accounted for the previously observed suppression in Fas-mediated apoptosis ( Sarafian et al., 2005). Although apoptotic pathways were not significantly perturbed following TSC exposure in our present study, Sarafian

et al. and other investigators of tobacco smoke effects have found this to be a commonly disrupted pathway (Jorgensen et al., 2004, Nordskog et al., 2003, Sarafian et al., 2001 and Yauk et al., 2011). It is suspected that the gene expression fold change cutoff of 2 used LY294002 nmr in the present study likely prevented a number of apoptotic genes from being included in the analyses. Cursory analyses with a cutoff of 1.5 shows apoptotic pathways as being significant for TSC exposure as well (data not shown). It is important to note that the marijuana used for this study was obtained from a contracted supplier that provides marijuana for therapeutic use in Canada. It is grown under strictly controlled and documented conditions. Although this study has only examined smoke condensate from a single lot of marijuana, the quality control measures would be expected to minimise differences between marijuana harvests. The TSC used in this study was generated from cigarettes containing Virginia

flue-cured tobacco, VEGFR inhibitor the type of tobacco typically contained in Canadian cigarettes. This is distinct from the mixed tobacco blends (i.e., Virginia, Burley and Oriental) typically found in American cigarettes. Our Erastin cell line earlier toxicogenomic examination of TSC from three Canadian cigarette brands containing either Virginia flue-cured or mixed tobacco blends failed to show any appreciable brand-driven differences in gene expression

profiles elicited by in vitro exposures (Yauk et al., 2011). Therefore, we contend that the similarities and difference between MSC and TSC noted in this study can be cautiously extended to other types of tobacco. Nevertheless, it should also be noted that some toxicogenomic studies have shown that cigarette brand (e.g. full flavor vs. low-tar) can have a significant effect on gene expression signatures elicited by in vitro CSC exposures (Lu et al., 2007 and Pickett et al., 2010), and moreover, many aspects of cigarette design (e.g., rod length, filter presence and type, ventilation, packing density, additives, paper type) and smoking method (e.g., ISO, intense) have been shown to influence the composition and toxicological activity of TSC (Adam et al., 2010, O‘Connor and Hurley, 2008 and Rickert et al., 2007). Our work supports the findings of previous studies, which associate TSC exposure with the expression of genes involved in xenobiotic metabolism, oxidative stress, inflammation, and DNA damage response (i.e., cell cycle arrest, protein unfolding, and transcription regulation).

e. those with the highest t/Z values for the Cyclopamine chemical structure words vs. baseline contrast. As this comparison (words vs. baseline) is orthogonal to both of the variables investigated (lexical category, abstractness), the strategy applied for selecting ROIs follows recent recommendations to avoid “double dipping” ( Kriegeskorte, Simmons, Bellgowan & Baker, 2009). In this data-driven analysis, average activation values within each of these 2 mm-radius spheres for each subject and each of the four word categories were entered into a repeated-measures ANOVA with the factors ROI x lexical category (2) × semantics/abstractness

(2). Note that, because 2 × 2 × 2 mm voxels, 8 mm smoothing kernel and 2 mm ROI radius were chosen, the half maximum width of each ROI was 12 mm. This allowed us to keep overlap between ROIs to a minimum while at the same time compensating for some of the spatial variance caused by the projection of individual brains to the averaged MNI template. Where appropriate, Huynh–Feldt correction was applied to correct for sphericity violations. In this case, epsilon values and corrected p values are reported throughout. Whereas psycholinguistic properties Anti-infection Compound Library solubility dmso were matched between word groups (see Methods, Appendix

B), results of the semantic rating study executed prior to the fMRI experiment revealed significant differences in the semantic variables of imageability, arousal, action-relatedness, concreteness, visual-relatedness, colour-relatedness and form-relatedness (see Appendix B). For all of these features, 2-way ANOVAs revealed significant interaction effects and, in most cases, additional main effects. The interactions of all object-related features, including concreteness, imageability, form- and visual-relatedness, showed, as expected, highest values for concrete nouns towering over

all other word groups. For arousal and action-relatedness, which both reflect semantic action features, concrete verbs achieved the highest ratings and concrete nouns the lowest. In addition, object-related semantic ratings were TCL higher for nouns than for verbs and higher for concrete items than for abstract ones; with regard to action-relatedness, verbs dominated over nouns and, again, concrete over abstract items. Statistical tests for word groups, including interactions and main effects, are displayed in Appendix B. Pairwise comparisons between stimulus groups showed that the abstract noun category was indeed significantly less imageable (t(78) = −14.028, p < .001), less concrete (t(78) = −16.812, p < .001), less related to visual objects (t(78) = −15.145, p < .001), and less form/shape-related (t(78) = −10.443, p < .001) than concrete nouns. Likewise, abstract verbs were significantly less imageable (t(78) = −8.613, p < .001), less concrete (t(78), and less action-related (t(78) = −3.018, p < .005) than concrete verbs.

Nonetheless, the effects of fishing were considered to be currently increasing and driving continuing Deterioration in condition in the Best10% of the SW and Worst10% BMS-754807 datasheet of the E region. About 84% of the scores assigned to the impacts of pressures were considered to have either a High or Medium level of

confidence (Fig. 3b). This was the dominant pattern in the E and SE regions, where no pressure scores were assigned with Low confidence. In contrast, almost half of the pressure estimates assigned for the NW region were graded as Low in confidence. A similar pattern emerged for confidence in the pressure trends, although the trends in the SW were assigned with mainly Medium confidence, and in the N region

with mostly Low confidence (Fig. 3d). Cluster analysis of the full dataset (all regions, all components, all indicator data for condition, trends, confidence and pressures) distinguished the N region from the SE region at a high level in the classification, and these are separate from the E region and from the SW and NW regions (Fig. 4a). This cluster pattern reflects the substantive spatial differences in biodiversity and ecosystem health condition, pressures, information quality (based on confidence grades), learn more and trends across the national jurisdiction. The primary separation of the groups in this cluster is driven by differences in condition and trend in habitats and a number of species groups, and by differences in confidence. The subset of data containing biodiversity and ecosystem health components that occur and were scored in more than one region (21 habitats; 31 species Bay 11-7085 and species groups; 17

ecological processes; 17 physical and chemical processes; and 5 PIDA components – see Supplementary Material) show similar spatial and temporal patterns to those identified in the overall dataset. The uniqueness and group fidelity of conditions and trends for the biodiversity and ecosystem health components from each individual region are highlighted by the cluster analysis (Fig. 4b). The biodiversity and ecosystem health components occurring in 2 or more regions and found to be in worst condition (pooled indicators median score = 5 or less, Poor) include 10 species or species groups, 2 habitats, a physical process (condition of the East Australian Current) and an ecological process (trophic structure and relationships). The Poor condition of 10 of these 14 components is related to fishing or hunting pressures, some of which are historic and date to more than a century ago (such as hunting of fur seals) (Table 5).

41190083). “
“Soil erosion remains one of the biggest environmental problems worldwide, threatening both developed and developing countries (ISCO, 2002). Erosion by rainstorms in agricultural areas not only strips the fertile topsoil on site, but also degrades selleckchem water quality and clogs streams, rivers, and reservoirs off site (Zhu et al., 2013). As a result of increasing population, cultivation has been expanded to steep sloping lands in many developing countries in the world (Liu et al., 1994, Liu et al., 2000, Turkelboom et al., 1997, Rumpel et al., 2006, Podwojewski et al., 2008 and Mugagga et al., 2012), which causes major types of

environmental damage with dramatic consequences in terms of soil fertility decrease and water availability (Lal, find more 1998). This is particularly so in semi-arid areas which are characterized by intense rainstorms and medium to poor soil fertility. The Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978) and its revised version (RUSLE) (Renard

et al., 1997), originally developed in the US, have been employed in many countries for the assessment of soil loss from agriculture because of their simplicity and low requirements for input parameters (Fox and Bryan, 1999). The intimate integration with land use and soil conservation measures in the models can also provide guidance in land use management and planning (Laflen et al., 1978). However, the models are typically applicable to areas with gentle slope gradients between 3% and 18%, a normal probability distribution of annual rainfall, and cropping management systems similar to the US (Wischmeier and Smith, 1978, McCool et al., 1987, Mannaerts and Gabriels, 2000 and Kinnell, 2010). When applied to areas where environmental second conditions and farming techniques, as well as soil conservation practices significantly differ from the U.S., variables in the USLE/RUSLE models need to be modified to accommodate

local characteristics (e.g., Lu and Higgitt, 2001, Hoyos, 2005 and Zhu et al., 2013). In semi-arid areas, most of rainfall events are non-erosive and often relatively few storms generate runoff and cause soil loss each year. Thus it is important to evaluate the relative contributions of large and small storms to total soil loss. From the practical standing point, it is essential to design conservation measures and strategies that are effective in controlling soil losses in those large events. For examples, Larson et al. (1997) suggested that conservation systems should be designed for limiting soil loss (namely, tolerance) to the value corresponding to a return period variable from 10 to 20 years. Mannaerts and Gabriels (2000) emphasized that adding a probability of recurrence to erosion events is essential for successful erosion assessment in semiarid zones.

The deeper nearshore sampling points were located at depths of 7 m and 10 m (Figure 2). The paper includes the results of the grain-size analysis of 263 samples by dry sieving in an Eko-Lab analyser with 0.5 φ mesh sieves (from

4 to 0.004 mm). The lithodynamic indices – mean (MG), sorting (σG), skewness (SkG) and kurtosis (KG) – were calculated using the method of Folk & Ward (1957), which is the most accurate for sandy deposits in the marine coastal zone ( Racinowski et al. 2001) ( Tables 1 and 2). Grain-size values were calculated with the Gradistat program ( Blott & Pye 2001). The lithodynamic interpretation of all grain-size indices was done on the basis of the confidence interval calculated for the standard deviation of the mean (MG), sorting (σG), skewness (SkG) and kurtosis (KG), Endocrinology antagonist with the confidence level of 90%. Passega C/M (1964) and Hjulström (1935) diagrams were constructed. The comparison was carried out on the mean (MG) and sorting (σG) of the samples collected from the shore by two different methods ( Figure 2). Lithological data were interpolated by kriging in Golden Software Surfer see more 8.0. The shore zone of the Vistula Spit consists of one or two (profiles 16p, 5mv, 3mv, 3a, 8a, 9a, 10a) foredunes developed to various degrees (Figure 3). In the north-eastern part of the Vistula Spit, on the 400 m long shore adjacent to the Strait

of Baltiysk, there are no foredunes owing to intensive erosion. In the south-western part of the Spit, the shore is represented by older, afforested dunes, with a relative height of 5.1–14 m Dichloromethane dehalogenase (profiles 6a–10a, Figure 3). In the remaining area, between profiles 5p and 6a, the relative height of the foredune ranges between 4 and 9 m (Figure 3). At the base of the foredune, the 1–3.5 m high initial dunes are formed locally (profiles 16p, 5mv, 3mv, 3a, 8a–10a). The slope of the foredune is 3° near the Strait

of Baltiysk (profile 3p), 9.5°–13° on profiles 6mv, 5mv and 5a, and 24–30° on profiles 10p and 7a. The beach along the Vistula Spit is from 10 m (profile 3p) to 43–45 m (profiles 1mv, 6a) wide and from 1 m (profile 3p) to 3 m (profiles 5p–13p, 1mv, 10a) high. The slope of the beach is from 2.7°–2.9° (profiles 3mv, 4mv, 6a) to 6.4°–6.7° (profiles 13p, 9a). The system of one (profiles 1a–2a and 7a–10a) or two longshore bars is located in the nearshore (Figure 3). One longshore bar with a height from 0.3 m (profile 10p) to 2.6 m (profiles 13p and 1a) is separated from the shore by a trough located 80–100 m from the shoreline, at depths of 3.5–4.8 m (Figure 3). In the nearshore with two 0.5–1.9 m high bars, the trough separating the first bar from the shoreline is located closer to the shore (10–70 m), at depths of 2.2–3.4 m (profiles 3a–6a, Figure 3). The 3.6–5.7 m deep trough that separates the first and the second longshore bar is located 173–280 m from the shoreline (Figure 3).

The contents of each tube were then

diluted 1 in 10 and analysed by ICP-MS as per the method specified above. Five of each sample type were analysed. In order to ascertain whether a worker with a “steady” history of lead exposure would produce differing results to one whose lead exposure had fluctuated, it was necessary to quantify the degree to which each worker’s historical exposure had fluctuated. Over 90% of the lead content of whole blood is contained in the erythrocytes (Goyer, 2001). The average survival time of erythrocytes in the bloodstream is 120 days (Dessypris, 1999). To account for this, the mean of all blood lead values acquired since January 2009, and recorded ≥120 days HSP targets prior to the measurement of the study sample, was calculated for each individual. The difference between the result of the study blood lead value and the mean of the historical observations (Δ) was then calculated. The median Δ was −1 μg/dL, and the 25th and 75th percentiles −2 μg/dL and +1 μg/dL respectively. However, the presence of a small number of large Δ values produced an overall standard deviation Olaparib supplier of 9.49 μg/dL. It was decided to categorise the samples for their exposure history according to the magnitude of Δ. History “1” included all samples where Δ ≤ ± 1 μg/dL; history

“2” all samples where Δ ≤ ± 2 μg/dL; history “3” all samples where Δ ≤ ± 3 μg/dL. Samples where Δ > ± 3 μg/dL were categorised as “fluctuating history”. Samples with no blood lead values recorded ≥120 days prior to the measurement of the study sample were categorised as “no sample history”. Neither the blood lead nor the salivary lead data were normally distributed, with the salivary

lead data more skewed than the blood ADP ribosylation factor lead data. Both datasets could be much more closely approximated to a log-normal distribution; therefore the relationship between log(saliva lead) and log(blood lead) was investigated. Log(saliva lead) was plotted against log(blood lead) and the Pearson’s correlation coefficient (r) was calculated, for the entire dataset and for the various history categories. Multiple regression analyses were also carried out to investigate whether smoking status or the age of the participant had any effect on the saliva or blood lead levels, or on the relationship between the two. For the blood lead analysis, all CRM results were within the certified range. Values obtained for the CRMs were as follows: level 1 lot 36741 (certified range 9.39–14.1 μg/dL): n = 91 mean 11.1 μg/dL, standard deviation (SD) 0.63 μg/dL; level 3 lot 36743 (certified range 43.7–65.5 μg/dL): n = 91, mean 52.5 μg/dL, SD 2.81 μg/dL. The limit of detection (LOD) for the saliva analysis for the study was 0.011 μg/L, based on the mean of all the blank samples, plus three times the standard deviation of the mean (McNaught and Wilkinson, 1997). All results were greater than the LOD and therefore no non-detects were observed.

8/97.8% vs. 93.1/93.1%, p = 0.006) ( Fig. 1). The Gleason pattern 3 patients also trended toward a higher 10- and 14-year CSS (99.3/99.3% vs. 96.9/96.9%, p = 0.058) ( Fig. 2). OS was not statistically different between the two Gleason 7 cohorts (78.2/70.7% vs. 76.0/56.9%, p = 0.198) ( Fig. 3). Subset analyses were performed to control for imbalances in PSA and PPC between the two study groups. In the subset of patients with PSA ≤10, primary Gleason pattern 3 patients maintained a significantly higher 10-

and 14-year bPFS (98.7/98.7% vs. 94.8/94.8%, p = 0.009) and CSS (100/100% vs. 97.0/97.0%, p = 0.013). ZD1839 nmr In those patients with PSA >10, the bPFS (93.0/93.0% vs. 90.0/90.0%, p = 0.52) and CSS (96.2/96.2% vs. 96.2/96.2%, p = 0.95) did not differ according to primary Gleason pattern. In the subset of patients with PPC ≤50%, there was a trend toward improved bPFS (97.5/97.5% vs. 94.3/94.3%, p = 0.14) and CSS (99.8/99.8% vs. 97.5/97.5%, p = 0.066) for Gleason pattern 3, but this did not reach statistical significance. In those patients with PPC >50%, there was a superior bPFS among

primary Gleason pattern 3 patients (97.7/97.7% vs. 90.5/90.5%, p = 0.018), but this did not translate into an improved CSS (97.9/97.9% vs. 96.4/96.4%, p = 0.69). Univariate and multivariate analyses were performed to identify the strongest predictors of bPFS, CSS, and OS (Table 2). Primary Gleason pattern was predictive of bPFS on both univariate (relative risk, 2.73; p = 0.005) and multivariate (relative risk, 2.265; p = 0.024) analyses. Primary Gleason pattern also trended toward predicting CSS (p = 0.081) on univariate analysis although STAT inhibitor this did not reach statistical significance. Gleason score is an important prognostic factor having been shown to predict for bPFS and CSS after definitive treatment of prostate cancer [1], [2], [3], [4] and [5]. Gleason 7 prostate cancer represents one of the most common histologic patterns. Some studies indicate that within the Gleason 7 stratum, a primary pattern 4 carries a less

favorable prognosis than a primary pattern 3, although conflicting results have been reported [5], [6], [7], [8], [14], [15], [16] and [17]. In a prior publication, we reported our outcome data for Gleason Thalidomide 7 patients treated with LDR interstitial brachytherapy. At that time, there were no statistically significant differences observed between primary Gleason pattern 3 and 4 (8). In this updated analysis, which includes a larger study population and longer median followup, we are now seeing a trend in outcome that favors primary Gleason pattern 3. The primary Gleason 3 cohort exhibited a superior bPFS and a nonsignificant trend toward improved CSS. One notable limitation of the present study is an imbalance in prognostic factors between the two study arms. The primary Gleason 4 population had a statistically higher PSA and PPC, which in itself would portend a less favorable outcome.