Firmness loss and IB development were related to firmness at harvest, soft and firm fruit being the most and least damaged, respectively. Soft fruit were characterized by greater IB development during storage along with high soluble solids/acid ratio, which could be used together with firmness to estimate harvest date and storage potential of fruit. Results of this work suggest that the differences in fruit quality traits at harvest could be related to the time that fruit stay on the plant after turning blue, soft fruit being more advanced in maturity. Finally, the observed differences between segregated categories reinforce the importance of analyzing fruit condition for each sorted group separately.Fresh blueberries are very susceptible to mechanical damage, which limits postharvest life and firmness. Softening and susceptibility of cultivars “Duke” and “Brigitta” to developing internal browning (IB) after mechanical impact and subsequent storage was evaluated during a 2-year study . On each season fruit were carefully hand-picked, segregated into soft (<1.60 N), medium (1.61–1.80 N), and firm (1.81–2.00 N) categories, and then either were dropped (32 cm) onto a hard plastic surface or remained non-dropped. All fruit were kept under refrigerated storage (0°C and 85–88% relative humidity) to assess firmness loss and IB after 7, 14, 21, 28, and 35 days. In general, regardless of cultivar or season, high variability in fruit firmness was observed within each commercial harvest, and significant differences in IB and softening rates were found. “Duke” exhibited high softening rates, as well as high and significant  Blueberry production has increased rapidly around the world over the last two decades . Chile iSeveral quality  and condition  traits are evaluated by inspection companies at destination markets. Among them, and regardless of season, dehydration and softening are the most common defects causing shipment rejections .At present, due to low availability and high costs of labor for hand picking, farmers are being forced to invest in the mechanization of this critical production phase . MechaniBlueberries are especially susceptible to mechanical damage, with injured berries resulting in loss of firmness that leads to reduced fruit quality and shelf-life . BruisesUnfortunately, blueberry bruising can be expected to continue occurring, not only because of the use of mechanical/semi-mechanical harvest, or of differences between packing-line designs , but also because of the lack of enough processing facilities during harvest peaks. Because of this, operators are forced to increase the speed at the sorting/packing lines, increasing the risk that fruit develop softening and IB during postharvest.By simulating mechanical impact damage (as for other fruit species such as apples), the resistance of blueberries to IB has been evaluated by dropping fruit from different heights onto diverse surfaces; damage is rated on an internal bruise severity scale (affected area) after a period of cold storage . When beTo the best of our knowledge, there are no previous reports on the implications of firmness segregation at harvest for the development of IB and softening of blueberries maintained under refrigerated conditions. Thus, the objective of this study was to understand how initial firmness and a single mechanical impact could affect the evolution of these traits during postharvest. For this, during two seasons, “Duke” and “Brigitta” fruit were segregated into soft, medium and firm categories at harvest, evaluating firmness loss and IB development of dropped (32 cm) and non-dropped fruit during 35 days under cold storage.Vaccinium corymbosum L.) fruit of cultivars “Duke” and “Brigitta”  were collected at the peak of the commercial harvest from Chilean orchards located in Longaví  and Santa Bárbara , respectively. Both cultivars were planted on raised beds, at 3 m × 1 m in a loam soil. Each bed had two drip irrigation lines (2.4 L h-1 each 50 cm); irrigation frequency and timing were determined according to tensiometers established on each block at 30 and 50 cm depth. Pruning (May to July) was oriented to contribute for light entrance and air circulation, assuring a balance between canes of different ages and a stable production over time; pruning consisted in removing canes either unproductive or causing excessive shade on the plant. Fertigation was applied according to soil/foliar analysis and yield estimations; main nutrients were N , K2O (25–30 kg ha-1), and P2O5 (150–180 kg ha-1). Environmental conditions are summarized in Supplementary Table During two consecutive seasons [2011/2012 (Y1) and 2012/2013 (Y2)], highbush blueberry . Berries were hand-picked by qualified workers belonging to each orchard. To avoid potential differences in sorting and packaging facilities, and to reduce IB damage, fruit were harvested directly into plastic clamshells (125 g). Fruit were immediately transported to the laboratory facilities at Universidad de Talca , for further analysis and treatment establishment.Upon arrival to the research facilities, fruit were initially characterized in terms of firmness and IB, and then subjected to firmness segregation, impact damage simulation, and finally stored under refrigerated conditions as described below.Figure 1).In order to assess firmness and IB variability on commercial fruit coming from the field, a sample of 200 fruit were evaluated on each cultivar and season prior to firmness segregation. Firmness (N) was assessed using a compression device  with the force thresholds set between 200 g (maximum) and 15 g (minimum) . IB was -1 NaOH to an end-point pH of 8.2; (iv) TSS/TA ratio; and (v) IB on slices of five replicates of 20 fruit each.Using the same equipment as for firmness assessments, fruit were assigned to one of three firmness categories: soft (<1.60 N), medium (1.60–1.80 N), and firm (1.81–2.00 N). For each season, this segregation represented 50 clamshells (125 g) per cultivar and category, from which each replicate was withdrawn. Then, for each firmness group, the following traits were assessed as initial condition: (i) firmness on five replicates of 20 fruit each; (ii) total soluble solids  using a digital refractometer , from juice obtained from five replicates of five berries each; (iii) titratable acidity  from five replicates; each one consisted of 10 mL of blueberry juice diluted to 100 mL with distilled water and titrated with 0.1 mol LIn order to study the evolution of IB and softening originated by impact damage, half of the fruit within each firmness category group were dropped from 32 cm onto a 30 cm × 30 cm of a hard plastic surface (6.4 mm-thick plexiglass), while the other half remained non-dropped. Dropping height was selected based on previous findings (data not published), as well as reports on extensive bruising resulting from 15–30 cm drop heights onto hard surfaces . For eacFor each cultivar, firmness category group, and dropping treatment, firmness and IB evaluations were undertaken in samples (five replicates of 20 fruit each) from clamshells removed from cold storage after 7, 14, 21, 28, and 35 days. After each storage removal, fruit were acclimated to room temperature (18°C) for 3 h prior to perform measurements. Individual fruit were first assessed for firmness and then cut transversally for IB rating.p ≤ 0.05). IB data was subjected to non-parametric ANOVA with aligned rank for non-parametric analysis of multifactor designs (p ≤ 0.05) for ranked data.Firmness and IB condition of commercial fruit at harvest (before firmness segregation) was described for each cultivar and season, through box and whisker plots. Quality traits of fruit segregated at harvest were analyzed considering a completely randomized design with factorial arrangement, considering three firmness categories  × two seasons (Y1 and Y2). Data of parametric variables were subjected to analysis of variance (ANOVA), and significance of the differences was determined by Tukey’s test ( designs  and meanr2) and models were fitted for each cultivar, season, firmness category, and drop heights. Additionally, statistical comparisons of slopes and intercepts between models for dropped vs. non-dropped fruit and, between firmness categories of each dropping treatment  were performed. Data were transformed to obtain linearized models between firmness (x) and IB (y). The best-fitted model was 1/x for both cultivars. Analyses were executed using commercial statistical software Statgraphics Centurion XVI  and R 3.0.0 . “Duke” firmness showed similar mean values during seasons 2011/2012 (Y1) and 2012/2013 (Y2) , whereas higher disparity was found on “Brigitta” . Yet, comparison by Kolmogorov–Smirnov test (p ≤ 0.05) evidenced significant differences in frequency distribution between years for both varieties (data not shown). Additionally, on both cultivars, fruit harvested on Y1 had greater variability  than berries picked on Y2. For “Duke,” 55 and 50% of fruit were below 1.6 N (upper threshold of the soft firmness category) for Y1 and Y2, respectively. For “Brigitta” these values reached 60 and 15% for Y1 and Y2, correspondingly. If a threshold of 1.4 N for very soft fruit is considered, 25% (Y1) and 10% (Y2) of “Duke” fruit were below that level, whereas values for “Brigitta” were 42 and 5% for Y1 and Y2, in that order .When commercial fruit sample was assessed for firmness at harvest, both cultivars displayed a wide range of values , with mean IB scores of 0.15–0.19 for “Duke,” and 0.10–1.01 for “Brigitta,” on Y1 and Y2, correspondingly. The most heterogeneous IB values were found for “Brigitta” fruit harvested on Y1. Yet, overall percentages of non-bruised fruit (category 0) at harvest were higher for “Brigitta” (90.0–92.5%) than for “Duke” (83.1–89.8%) (data not shown).Although not subjected to the dropping procedure, fruit displayed some IB at harvest (Table 1). On both cultivars, firmer fruit was related to higher TA but lower TSS/TA and IB; TSS were significant only on “Brigitta,” and higher on the softer group (<1.60 N). Differences between years occurred for TSS, TA, and TTS/TA for “Duke” and for TSS, TA, and IB on “Brigitta,” reinforcing the higher variability found on this last trait during Y1. Significant interactions occurred for TA on “Duke”  and for IB on “Brigitta”  . Between harvest and the end of storage, and for both seasons, firmness of “Duke” blueberries was reduced on average by 39.8, 33.6, and 38.6%  for soft, medium, and firm fruit, respectively (data not shown), whereas firmness loss in “Brigitta” averaged 17.3, 24.4, and 23.8%, correspondingly . When dropped and non-dropped fruit were compared, “Brigitta” fruit appeared to be more sensitive to initial firmness, since significant differences between damaged and non-damaged fruit were found for most of storage evaluations . In contrast, for “Duke” samples consistent differences between dropped and non-dropped fruit along the whole storage period were observed on soft fruit harvested on Y1 uniquely. Additionally, the magnitude of the differences between dropped and non-dropped fruit, as well as between seasons, were higher for “Brigitta.”In comparison to “Brigitta,” “Duke” berries showed lower firmness retention along time, irrespective of firmness category, dropping treatment or season , regardless of cultivar, year, or dropping treatment. “Duke” fruit exhibited relatively low IB values up to 21 days of storage, with the highest IB at 35 days for soft (Y1 and Y2) and medium firmness fruit (Y2) . Similarly to the evolution of firmness in postharvest , “Duke” fruit also developed less IB in response to dropping, given that no significant differences between treatments were found at most of the evaluation dates. “Brigitta,” on the other hand, showed marked differences in IB development between dropped and non-dropped fruit for all firmness categories . Compared to “Duke” and regardless of dropping treatment, “Brigitta” fruit developed lower IB within medium and firm categories .In general, IB was higher after storage than at harvest, particularly for soft fruit (r2) between IB and firmness  revealed significant effects on dropped and non-dropped fruit for all three firmness categories and for both seasons. Although r2 varied among comparisons, soft and firm fruit showed in general the highest values. In contrast, 9 out of the 12 models fitted for “Brigitta,” which included all non-dropped fruit of both years and dropped fruit of Y2, showed no significant associations. During Y1, the highest r2 values for dropped fruit were found on soft and medium fruit of this cultivar . The comparisons of slopes and intercepts between dropping treatments (Table 2) showed that significant differences for “Duke” were found only between intercepts of firm fruit harvested in Y2. In contrast, equations developed for “Brigitta” differed in slopes (soft and medium fruit of Y1) and intercepts  on five out of the six instances.For “Duke” samples, the regression analyses (Table 3), outcomes varied among seasons. On non-dropped fruit of Y1, three comparisons resulted on different intercepts , but no differences were found between slopes. For the same treatment, differences on Y2 occurred amid slopes of “Duke”  and intercepts of “Brigitta” (soft vs. medium). Within dropped fruit of Y1 no significant differences were found for any comparison on “Duke,” whereas two cases were statistically significant for “Brigitta” . On Y2, differences between intercepts of medium vs. firm, and soft vs. firm occurred for “Duke,” meanwhile for “Brigitta” the only significant difference happened between slopes of soft vs. firm fruit.When firmness categories were contrasted within the same dropping treatment  represent less than 0.5–0.8% , a high Figure 2). Variations in ambient temperature between both seasons . Given the variability found at harvest (box and whisker plots), these dissimilarities would be higher for Y1 “Brigitta” fruit, thus accounting for the greater differences found according to the dropping treatment between fruit within the soft and the medium categories. In fact, according to the Chilean blueberry industry, overall commercial defects  differ between seasons, and the affected produce may account for 10–45% of the fresh fruit reaching final markets , our findings strengthen the idea that mid-to-firm berries can better withstand a long trip to distant markets. Therefore, any strategy oriented to increase the percentage of these firmness classes into the clamshells will assure higher and more homogeneous quality upon arrival to final destination.Figure 4). This finding agrees with the lack of differences between slopes and intercepts of the models fitted for fruit of this cultivar (0 vs. 32 cm drop heights) (Table 2); the only difference was found between intercepts of firm fruit, but not between slopes, which indicates similar rates of change in IB per firmness unit both for dropped and non-dropped fruit . Yet, significant associations between firmness and IB, and generally higher r2 coefficients, both for dropped and non-dropped fruit were obtained for “Duke” as compared to “Brigitta” samples (Table 2). On the other hand, the fact that “Brigitta” fruit did not show significant associations for most of the equations indicates a weak relationship between firmness and IB development for this cultivar, especially for samples harvested in Y2. However, higher IB levels in dropped than in non-dropped fruit, regardless of fruit firmness at harvest should be expected for this cultivar . The analyses undertaken for “Brigitta” samples corresponding to Y1  reveal that differences in slopes and intercepts occurred for all three firmness categories, with different rates of change between dropping treatments. When equations were compared between firmness categories within each dropping treatment (Table 3), variability between seasons became more evident, since significances were not the same in both years considered. Moreover, different slopes (meaning dissimilar rate of change in IB per firmness unit) were found on “Duke” 0 cm and “Brigitta” 32 cm, whereas different intercepts  occurred on “Duke” 32 cm and “Brigitta” 0 cm. Additionally, most of these differences were observed between soft and firm fruit, which emphasizes the negative effects on quality resulting from a high proportion of soft fruit on a particular picking.Dropping the fruit did not always lead to higher IB values, and this observation was more evident for “Duke” samples, in which high softening rates but small differences in IB between dropped and non-dropped fruit occurred , fruit might not necessarily exhibit severe IB symptoms but would probably show different softening patterns. Another important aspect to consider is that sectioning berries through the equator detects bruising caused by impacts occurring onto that area, but this procedure does not take into account damage at or near the calyx or stem ends, and it would hence lead to an underestimation of the actual mechanical damage .The present study demonstrated the different susceptibility to IB development and softening rates in blueberry fruit among different cultivars and firmness categories at harvest, and suggests that fruit displaying firmness lower than 1.6 N at harvest should be avoided if long-term storage is intended. Overall, “Duke” fruit were characterized by high rates of firmness loss, as well as by a strong association between firmness and IB, but little differences were found between dropped and non-dropped fruit. “Brigitta” berries had slower softening rates, and displayed very weak relationships between firmness and IB , but marked differences between dropping treatments were found.Results of this work suggest that the mean firmness value may be not adequate as an indicator of blueberry fruit condition at harvest, and that the differences in fruit quality traits associated to the initial firmness level might be related to the time that fruit stay on the plant after turning blue, softer fruit displaying more advanced maturity. This finding suggests that, during seasons in which adverse environmental events occur (probably associated to high temperatures close to harvest), the proportion and evolution of soft fruit during shipments would enhance rejections at destination markets. Future research should include a more detailed study on potential sources of fruit heterogeneity. Furthermore, more systematic measurements of changes throughout fruit development from early stages, as done for other species, could help in modeling softening and IB during postharvest. Finally, long-time studies are needed to quantify the real genotypic and environmental effects on softening and IB development in blueberries.CM and GL contributed to the conception and design of the work. GG, CM, and GL performed acquisition, analysis, and interpretation of data for the work. CM, GL, JG, and IL collaborated to generate and validate the version to be published.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The speed of visual processing is central to our understanding of face perception. Yet the extent to which early visual processing influences later processing in distributed face processing networks, and the top-down modulation of such bottom-up effects, remains unclear. We used simultaneous EEG-fMRI to investigate cortical activity that showed unique covariation with ERP components of face processing , while manipulating sustained attention and transient cognitive conflict employing an emotional face-word Stroop task.ERP markers of visual processing within 100 ms after stimulus onset showed covariation with brain activation in precuneous, posterior cingulate gyrus, left middle temporal gyrus, left inferior frontal gyrus and frontal operculum, and a left lateral parietal-occipital cluster, illustrating the impact of early stage processing on higher-order mechanisms. Crucially, this covariation depended on sustained attentional focus and was absent for incongruent trials, suggesting flexible top-down gating of bottom-up processing. Processing speed can provide evidence about the number of computational steps that are proposed by theories of visual perception . InvestiA common means of investigating top-down modulation is the examination of task effects on visual perception. ERP research has shown that several top-down factors can influence activity in the visual cortex: Effects of spatial attention are reliably observed within 100 ms post stimulus-onset in the form of increased P1 amplitudes for attended versus unattended locations , indicatA related question is the impact that early stage visual cortex processing has on subsequent processing in more anterior regions of the brain, including temporal, parietal and frontal cortical regions, and how such influences are affected by top-down processes. Behavioural and EEG evidence from tasks such as the attentional blink indicates that processing between 100 ms and 500 ms post-stimulus is particularly sensitive to attentional resource constraints and top-down effects . In fMRITaken together, previous research provides evidence for rapid timing of both bottom-up visual processing and top-down modulation, both being evident within 100 ms after stimulus onset and involving a distributed network of cortical and subcortical brain regions. In the present study we investigated the influence of early information processing in the visual cortex on higher-order processing stages, and its top-down modulation by task-related attention, focusing specifically on the processing of faces. Using simultaneous EEG-fMRI, trial-by-trial ERP amplitudes were used to identify brain regions that showed significant and unique covariation with different visual processing stages, from very early perceptual processing in the striate and extrastriate visual cortex , emotional faces were used as target stimuli to increase the significance of visual stimuli  early stage face processing as indexed by amplitudes in the P1 and possibly C1 time range would show trial-by-trial covariation with fMRI BOLD activation during the Attend Face condition throughout brain regions involved in face processing  and ii) 2The study was reviewed and approved by the University of Reading research ethics committee. Participants provided informed consent prior to taking part in the study.2.118 participants took part in the study; data from 3 participants had to be excluded due to poor EEG data quality. The remaining sample of 15 participants  had a mean age of 23.4 years (SD = 3.8 years). All participants were right handed, had normal or corrected-to-normal vision and did not report any history of neurological or psychiatric disorders. Participants were recruited through the Undergraduate Research Panel and mailing lists; they received £20 or course credit for participation.2.2Stimuli consisted of 216 portraits of unfamiliar females and males with superimposed affective words. The portraits displayed three different facial expressions . The affective words corresponding to these emotion categories were ‘afraid’, ‘happy’, and ‘calm’. All of the pictures were adopted from the Karolinska Directed Emotional Faces set KDEF; . The supTwo comparisons were of particular interest. For the effect of top-down attention on face processing, we compared Attend Face vs. Attend Word in the congruent condition. For the effect of conflict processing on face processing, we compared congruent vs. incongruent trials in the Attend Face condition.2.3The experiment took place in the MRI facility in the Centre for Integrative Neuroscience and Neurodynamics at the University of Reading. After providing informed consent, participants were given information concerning the task and the procedure. After EEG preparation, participants performed 10 practice trials in order to practice the button assignments to affective categories.The experiment employed an event-related paradigm programmed in Matlab (MathWorks) utilizing the Psychophysics Toolbox extensions . Stimuli2.42.4.1Data were collected on a 3T Siemens Trio MRI scanner using the standard 12-channel head matrix coil . Functional images were acquired with a t2*-weighted gradient echo EPI sequence . After functional scans, a high resolution whole-brain structural image was acquired .www.fmrib.ox.ac.uk/fsl). Brain extraction was performed using the FSL Brain Extraction Tool  using an MRI-compatible amplifier system . An additional electrode was placed on the back of the participant, just left of the spinal column, for recording the electrocardiogram (ECG). Data were referenced to electrode Cz; AFz was used as ground. Electrode impedances where kept below 20 kΩ; data were recorded with a sampling rate of 5000 Hz. Optimal synchronisation of EEG recording and MRI slice acquisition was ensured by using a sync box (BrainProducts) which synchronises the scanner and EEG amplifier clocks.Offline, MR gradient artefacts were removed using a modified version of the template subtraction algorithm  as imple2.52.5.1Reaction times (RTs) for correct trials and percentage of correct responses were calculated for each experimental condition and analysed with a mixed effects General Linear Model (GLM) with the fixed factors Attention  and Congruency , and Subject as random factor.Of particular interest were RT differences between the Attend Face and Attend Word condition for congruent  stimuli, which would indicate the direct effect of top-down attentional focus. In addition, differences between congruent and incongruent trials would indicate effects of stimulus conflict.2.5.2A first level GLM was modelled separately for each run of the experiment with separate regressors for congruent and incongruent trials. Regressors were created by convolving the temporal profile of each experimental condition with the double gamma haemodynamic response function in FSL. The contrast of interest compared activation in congruent vs. incongruent trials. Single-level contrasts for each run were combined across all runs in a second level fixed effects analysis for each participant. In this step, Attention , which had been manipulated between runs, was included as an additional contrast.Registration to standard space was performed using FLIRT . In a fi2.5.3Amplitudes of the ERP components  were quantified at specific time windows and regions of interest (ROIs) according to their respective temporal and topographic distribution see : C1 amplFor the analyses of ERPs, component amplitudes of artefact-free trials were averaged per subject and experimental condition (Congruency (2) X Attention (2)) and analysed using a mixed-effects GLM, with the fixed factors Congruency and Attention and Subject as a random factor.2.5.4unique to each ERP component, over and above mean condition-specific BOLD activation. However, since this analysis would not account for any variance that is shared between components, we also included additional contrasts for pairs of components .Normalized (z-scored) single-trial ERP values for each ERP component and Congruency condition were convolved with the FSL canonical hemodynamic response function and included as parametric regressors in the fMRI analyses . GLM analysis of fMRI data was identical to that described above, except for the addition of these parametric regressors. We then identified brain regions showing significant covariation of ERP amplitudes with BOLD response in the Attend Face condition, separately for each congruency condition. Since all ERP regressors were included in the same first level model, identified brain regions show trial-by-trial covariation of BOLD response 33.1F = 29.694, p < 0.001, p2η = 0.678. Furthermore, RTs were longer for incongruent than for congruent trials, F = 103.49, p < 0.001, p2η = 0.878. A significant interaction between Attention and Congruency reflected a larger effect of congruency when participants attended to faces compared to words, F = 51.31, p < 0.001, p2η = 0.778.RT analyses showed longer reaction times for trials where participants attended to faces compared to words, F = 5.59, p < 0.05, p2η = 0.296, and for congruent compared to incongruent trials, F = 37.01, p < 0.001, p2η = 0.721. A significant interaction between Attention and Congruency was based on a larger effect of Congruency in the face condition compared to the word condition, F = 16.19, p < 0.001, p2η = 0.531. Together, RTs and accuracy indicate greater interference of conflicting information for the Attend Face condition than for Attend Word.Accuracy rates were higher in the word condition than in the face condition, F = 7.16, p < 0.05, p2η = 0.34. Responses were slower for faces than for words in the congruent condition , demonstrating the impact of the top-down attention manipulation. For RTs and accuracy rates, see and Importantly for the attentional focus manipulation, there was a significant difference in reaction times between congruent trials in the Attend Face vs. Attend Word condition, 3.2When participants performed the categorisation task on the facial expressions (Attend Face), a number of brain regions showed significantly higher activation for incongruent than for congruent trials see . These r3.3F = 17.748, p < 0.001, p2η = 0.554, based on larger P3 amplitudes when participants attended to faces compared to words  as parametric regressors yielded significant results only for two ERP components, C1 and N170.3.4.1N170 amplitudes in the congruent attend face condition showed significant covariation with a region at the intersection of the lateral occipital cortex and angular gyrus . No signF = 18.42, p < 0.001, and of Congruency, F = 6.14, p = 0.05, and an interaction between the two factors, F = 19.22, p < 0.001, confirming that significant coupling was limited to congruent trials in the Attend Face condition  = 5.28, p < 0.05, p2η = 0.274, and a significant interaction between Attention and Congruency, F = 8.41, p < 0.05, p2η = 0.375, confirming that the coupling was limited to congruent trials in the Attend Face condition  = 6.26, p < 0.05, p2η = 0.309.In order to shed light on relations between ERP-BOLD covariation and BOLD activation itself, we extracted BOLD parameter estimates within brain regions showing significant covariations with ERP amplitudes, separately for Congruency and Attention conditions. We performed a mixed effects GLM with the fixed factors Attention, Congruency and cluster, and Subject as random factor. For the N170 cluster, there were no significant differences in BOLD activation between experimental conditions. For brain regions showing significant coupling with C1 amplitudes in the Attend Face condition, analyses revealed significantly more BOLD activation for incongruent compared to congruent trials, 3.4.4The EEG-fMRI analyses included additional contrasts for pairs of components in order to account for possible shared variance between components. Results showed additional covariations in the lateral parietal-occipital cluster see  between 4Using simultaneous EEG-fMRI during a face-word emotional Stroop task, the present study investigated the influence of visual cortex activity on higher-order face processing stages, and its top-down modulation by task-related attention. We predicted that, in the Attend Face condition, early ERP components would covary from trial to trial with early and later stage face processing regions, and that this covariation would be reduced or absent during the Attend Word condition. We also made a nondirectional prediction of an effect of word-face congruency on ERP-fMRI covariation. We found that trial-by-trial ERP amplitudes within 80 ms of stimulus onset showed significant covariations with a widespread network of brain regions. Importantly, these covariations were limited to congruent trials in the Attend Face condition, showing their dependence on sustained attentional focus and modulation by trial-by-trial conflict processing.When participants performed emotion categorisations on faces while ignoring superimposed words (Attend Face condition), the ERP amplitudes of the C1 window extending from 60 to 80 ms after stimulus onset showed significant covariations with a widespread network of brain regions, including a lateral parietal-occipital cluster and left-lateralized activations of the orbitofrontal cortex, middle temporal gyrus and inferior frontal gyrus. The lateral parietal-occipital cluster was located at the intersection of the superior lateral occipital cortex and angular gyrus, extending into posterior middle temporal gyrus. This region has previously been associated with a number of functions including face processing , semanticoupling of early ERP amplitudes with a more broadly distributed network of brain regions, but not with primary visual cortical regions. This result is consistent with top-down attention acting as a “gate”, permitting or preventing early visual cortical processing (which did not itself show modulation in this task) to flexibly influence higher order processing depending on task demands. Previous ERP research has shown spatial attentional gating of ERPs to emotional faces at frontal cortical sites between 100 and 120 ms post stimulus-onset  words on face processing than vice versa, in line with assumptions that reading occurs automatically whenever a word is presented . SimilarAmplitudes of the N170 component showed specific covariation with activation in the lateral parietal-occipital cluster during congruent trials in the Attend Face condition, a brain region associated with semantic or emotional information about faces . The N17Results of our control analyses on conjoint component pairs were largely in line with results from our main analyses, showing significant covariations in the left lateral parietal-occipital cluster. Furthermore, conjoint N170 + P3 covariations were located bilaterally in the lateral parietal-occipital cluster and in the posterior cingulate cortex. Finally, the latter component pair showed significant covariations with the middle frontal gyrus, which is suggested to play a role in reorienting attention between the dorsal and the ventral attention network . As in punique, i.e. condition-specific, and, more importantly, component-specific covariations, while also allowing for the full use of the high temporal resolution of the EEG signal. At the same time, additional control analyses on conjoint component pairs are employed to reveal potential shared variance between components. Interestingly, and in line with previous literature striate visual cortex up to higher-order stimulus evaluation, with multiple ERP components included in the same GLM. The major advantage of this approach is its power to reveal terature , conditiThe most striking finding of this study was that trial-by-trial variation of visual processing of faces as early as 60–80 ms after stimulus onset showed unique covariation with a widespread network of brain regions, indicating the impact of early stage visual processing on higher-order functions, including semantic and emotional processing. Crucially, this covariation was modulated by the sustained attentional focus (on faces versus words) and depended on trial-by-trial conflict processing, suggesting a flexible and reciprocal link between top-down and bottom-up processing.BBSRC (grant No. BB/H011021/1 to TJ), the Berlin School of Mind and Brain, Humboldt-Universität zu Berlin (MB), and EPSRC (EP/J500501/1 to MTR).This work was supported by


Early identification of children and adolescents with type 1 diabetes at high risk for development of complications is important, as early intervention may prevent further deterioration. Here we investigate the applicability of assessing skin advanced glycation end products (sAGEs) by skin autofluorescence (SAF) as a potential surrogate risk marker.This study included a cross-sectional analysis of SAF in 77 patients with type 1 diabetes mellitus and 118 healthy controls across age categories . In patients, the impact of current and historical glycated hemoglobin (HbA1c) values, age, and duration of diabetes on SAF was studied in a retrospective cohort study and analyzed with multivariable analyses.P ≤0.009). For patients, age, duration of diabetes, and current and historical HbA1c were associated with SAF in univariate analysis. Multivariate analysis showed no association between HbA1c and SAF. A subgroup of patients with a HbA1c-within-target (≤7.5 %/59 mmol/mol) were observed to have high SAF.SAF was significantly and similarly higher in patients when compared with controls across all age categories (Children and adolescents with type 1 diabetes show higher SAF than controls. The presumed correlation of high HbA1c with high SAF does not exist in all patients. Thus, use of this non-invasive measure may provide a surrogate marker for diabetic complications, additional to HbA1c. Reactive oxygen species and non-enzymatic reactions between sugars and amino groups of proteins (‘Maillard reaction’) are involved in the formation of advanced glycation end products (AGEs) . AGEs caAs skin collagen has a half-life of 10–15 years , skin AGHere we investigate if SAF reflects glycemic control expressed by HbA1c in a homogeneous study population. SAF in Dutch Caucasian children and adolescents with type 1 diabetes was compared with SAF in healthy Caucasian controls. In patients, associations of SAF with age, diabetes duration, gender, and current and historical (past) HbA1c as a reflection of long-term glycemic control were determined. We hypothesize that SAF will be higher in patients compared with controls and that it will associate with the variables age, historical HbA1c, and diabetes duration.The study design was a retrospective cohort study of patients with type 1 diabetes and healthy controls. Patients aged 11–19 years with type 1 diabetes ≥3 months were recruited between April 2010 and January 2013 while visiting the outpatient clinic of Diabeter, a certified center of reference for diabetes care in Rotterdam, Netherlands. Diabeter provides comprehensive and advanced management for children and adolescents with type 1 diabetes. Patients were only included if they were Caucasian and if measurements of SAF and HbA1c were performed on the same day. Patients with inadequately controlled celiac disease, hypothyroidism, and those using lipid-lowering therapy were excluded.The healthy control group was recruited from a secondary school in Rotterdam in October and November 2011. Controls with missing data, non-Caucasian ethnicity, and concomitant diseases other than attention deficit/hyperactivity disorder were excluded. Study participants and parents of minors provided signed informed consent. The study was approved by the Medical Ethical Board of the Erasmus Medical Center, Rotterdam, Netherlands, and performed in accordance with the Declaration of Helsinki.In controls, information on gender, ethnic background, and concomitant diseases was collected. Anthropometric and laboratory data were not obtained for this group. In patients, information on duration of diabetes, gender, body mass index (BMI), blood pressure, current HbA1c, and HbA1c values in the past  was retrieved from electronic patient charts. Information on BMI and blood pressure was included if measurements were performed within a time interval of 1 month around the SAF measurement. BMI was converted to standard deviation scores (SDS): a high BMI was defined as a BMI ≥+2 SDS. A normal BMI was defined as a BMI <+2 SDS –18. Syst2 with an excitation light source between 300 and 420 nm (peak excitation ∼ 350 nm)  and of patients with a historical HbA1c-above-target (n = 64) 1.40 [IQR 1.22–1.54](P = 0.654). In the same group, median SAF of patients with a historical HbA1c-within-target (n = 19) was 1.36 [IQR 1.23–1.50] and of patients with a historical HbA1c-above-target (n = 54) 1.41 [1.23–1.54](P = 0.580). There were also no statistically significant differences in SAF between the HbA1c-above-target and HbA1c-within-target patients for the various age categories . Figure Figure 2 did not exceed 0.207.Univariate linear regression analyses Table  showed a2 increased to 0.235.For the multiple linear regression analyses Table , Model 1RAGE) gene [NAT2 acetylator [Consistent with previous studies , 14, 15,GE) gene  or the Netylator . Also, oetylator , 27, whietylator . An outsetylator . In addietylator .A strength of this study was that adjustment for skin color was not necessary, as measured patients and controls were from the same ethnic background (Caucasian). Homogeneity of the patient population supports internal validity. However, the results cannot be applied to non-Caucasians and therefore generalizability is lower. Also, we studied the age range 11–19 years in more detail when compared with previous studies , 12, 31,In summary, children and adolescents with type 1 diabetes show higher SAF than controls. Age and duration of diabetes are weakly associated with SAF. In the majority of patients, SAF does not seem to provide information additional to HbA1c. However, in a subgroup of patients with HbA1c-within-target an elevated SAF was observed. For this subgroup, measuring SAF may have added value in identifying patients that are at high risk for complications. Further longitudinal, prospective studies should provide insight into whether SAF measurement in youngsters has predictive value for the development of complications during the disease course of type 1 diabetes and how this is related to glycemic control.


Vitis vinifera) is of considerable commercial importance for both the table grape and processing industries. Studies to date of grape seed development have been made certain progress, but many key genes have yet to be identified and characterized.Seedlessness in grape  were identified and examined from multiple perspectives, including expression patterns, Gene Ontology (GO) annotations, pathway enrichment, inferred hormone influence and epigenetic regulation. The expression data of hormone-related genes and hormone level measurement reveals the differences during seed development between seedless and seeded progeny. Based on both our results and previous studies of A. thaliana seed development, we generated network maps of grape seed-related DEGs, with particular reference to hormone balance, seed coat and endosperm development, and seed identity complexes.In this study we analyzed the seed transcriptomes of progeny derived from the In summary, the major differences identified during seed development of seedless and seeded progeny were associated with hormone and epigenetic regulation, the development of the seed coat and endosperm, and the formation of seed identity complexes. Overall the data provides insights into the possible molecular mechanism controlling grape seed size, which is of great importance for both basic research and future translation applications in the grape industry.The online version of this article (doi:10.1186/s12864-016-3193-1) contains supplementary material, which is available to authorized users. Seed development in angiosperms is initiated by double fertilization, in which two sperm cells separately fuse with the egg and the central cell of the female gametophyte, leading to the formation of a diploid embryo and a triploid endosperm . This prArabidopsis thaliana, where it has been associated with a coordinated series of physiological and biochemical events [A. thaliana ovule development, such as SHOOT MERISTEMLESS (STM), CUP-SHAPED COTYLEDONS1 (CUC1) and CUC2, which are involved in meristematic cell maintenance [AINTEGUMENTA (ANT) is required specifically to promote the growth of the placenta to allow ovule primordium formation [BELL1 (BEL1), which is required for integument morphogenesis, affect ovule identity [AGAMOUS (AG), SEPALLATA , SHATTERPROOF (SHP1 and SHP2) and SEEDSTICK  share a common function in promoting ovule and seed identity [Development of ovule, precursor of seed, has been well studied in the model plant l events . Geneticntenance –8. Thereormation , and mutidentity , 11. Furidentity –15.A. thaliana seed size is affected by both the seed coat and endosperm development [INNER NO OUTER (INO), a member of the YABBY family, is required for outer integument formation, as are the KANADI genes, KAN1 and KAN2 [ABERRANT TESTA SHAPE/KANADI4 (ATS/KAN4) is known to be expressed in the abaxial domain of the inner integument and NOZZLE/SPOROCYTELESS (NZZ/SPL), Knotted1-like Homeobox (KNOX) and PHABULOSA (PHB) have been shown to be important in coordinating inner and outer integument formation [HAIKU2 (IKU2) and MINISEED3 (MINI3) in small seeded mutants provided direct molecular evidence that the regulator of seed size acts solely through the control of endosperm proliferation [Genetic and molecular analyses have also shown that elopment –18. For elopment , 20, andand KAN2 , 22. ABEormation –26, whicferation . In addiferation . In summVitis vinifera L.), an important fruit crop in many parts of the world, and seedless grapes valued as both table grapes and for raisin production. Grape seedlessness is caused by either parthenocarpy or stenospermocarpy. In our study, all the seedless materials used were stenospermocarpy, which means both pollination and fertilization occur but both the seed coat and endosperm cease their normal development at early stages, leaving undeveloped seeds or seed traces [VvCEB1, which encodes a helix–loop–helix transcription factor, affected embryo development and increased cell size [VvAGL11, a MADS-box seed identity gene, has been proposed as playing a role in stenospermocarpy, and has been suggested as a candidate for use in marker-assisted selection [VvMADS28, VvMADS39, VvMADS44, VvMADS45 and VvMADS46) were reported to show different expression patterns during seed development between seeded and seedless grape cultivars, indicating a potential role in seed development [Seed development has also been studied in grapevine  and the seedless paternal parent ‘Centennial seedless’ (V. vinifera); an approach that was designed to minimize genetic background differences. The identification of differentially expressed genes (DEGs), and analyses of their putative biological functions and key pathways that predominated in the different phenotypes, enhances the current understanding of grape seed development and sheds light on the possible mechanism by which grape seed size is controlled.The release of the V. vinifera PN40024 genome 12X) sequence , has sub2X sequenV. vinifera) and the seedless paternal parent, ‘Centennial seedless’ (V. vinifera), were collected at 24, 27, 30, 33, 36, 39 and 42 DAF to characterize changes and differences between the two , a WRKY gene, was found to be related to A. thaliana seed coat and endosperm development [NAM (NO APICAL MERISTEM), CUC1 (CUP-SHAPED COTYLEDON) and CUC2, all NAC genes, have been reported to be involved in regulating secondary cell wall biosynthesis [VvMADS28, VvMADS39 and VvMADS45 in this current study was consistent with previous analysis of seeds from multiple seeded and seedless grape cultivars [Based on seed weight change Fig. , three kelopment , 38. Morynthesis , 40. Manynthesis , 41, 42 ultivars . Additioultivars . In our An analysis using Plant MetGenMAP  indicateTo gain further insight into the functional significance of the identified DEGs, they were classified according to the GO terms in the ‘biological process’ , ‘molecular function’, and ‘cellular component’ categories  during seed development are shown in Fig. Seed development is known to result from the coordinated regulatory actions of multiple hormones . We meas3 was measured and found to be higher in seedless progeny than seeded progeny  and VvMADS39 (GSVIVT01008139001) were found to be consistent with those presented in a previously published study [To validate the RNA-Seq based DEG data, we quantified the expression of 30 DEGs related to seed development using quantitative real-time PCR (qRT-PCR) analysis in samples ‘Seeded’ and ‘Seedless’ which were previously used for RNA-Seq , MYB46 and MYB83 [Pathways and regulators affecting either ovule or seed development in  studied  and prov studied , 41, 45,nd MYB83 –54, werend MYB83 , 38, 50,nd MYB83 . MoreoveA. thaliana epigenetic regulation related genes. In our results, we observed that most VvPcG genes were expressed at lower levels (SL/S) during seed development in the seedless progeny, especially at stage 2 and 3, than in the seeded progeny, while conversely, the expression of endosperm-related VvIKU1/2, VvTTG2 and VvTTG1 was higher (SL/S). From previous reported A. thaliana epigenetic regulation mechanism[PHE1, while DECREASE IN DNA METHYLATION1 (DDM1) act antagonistically. And the high PHE1 expression in A. thaliana is primarily responsible for the mea seed-abortion phenotype [VvPHE1 was up-regulated (SL/S), probably resulting from the combination of down-regulated expression of VvPcG and up-regulated expression of VvDDM1 (SL/S)  Fig. . In thisVvSHP1/2, VvSTK, VvBEL1 and VvSEP1/2, was lower during seed development in the seedless progeny than in the seeded progeny  and the seedless paternal parent ‘Centennial seedless’ (V. vinifera) were developed in 2009 and planted in 2011 as family groups at the Zhengzhou Fruit Institute, Zhengzhou, China. A total of 65 progeny were obtained: 31 seedless and 34 seeded. In 2014, 9 seedless progeny and 9 seeded ones, selected at random from the populations, were grown under similar growth condition (based on previous observation) and used as experimental material.Cross-progeny populations from the seeded maternal parent ‘Red Globe’  with Accu Bond C18SPE cartridge , and then dried using nitrogen gas . The resP < 0.05).One way analysis of variance (ANOVA) on ranks followed by a Dunn’s test was performed using the SPSS 18.0 software . Different letters above each bar represent statistically significant differences  [Two biological replicates of each pooled seeds sample at each stage were used for the RNA-Seq experiments. Total RNA was extracted using the E.Z.N.A.™ Plant RNA Kit , according to the manufacturer’s instructions. The quality and quantity of RNA was assessed by electrophoresis on 1 % agarose gels and by a NanoDrop1000 spectrophotometer , respectively . Strand-4 genome  using To4 genome . After a4 genome  with the4 genome , 67. Gen4 genome . To revead.html) .ACTIN gene (GenBank Accession number NC_012010) and EF1-α gene (GenBank Accession number NC_012012) as internal reference genes. All primers were designed using Primer5 software and can be found in Additional file Quantitative real-time RT-PCR was carried out using a Bio-Rad iQ5 thermo cycler . For each sample, 1 μg of total RNA was converted into cDNA using PrimeScript™ RTase and an oligo dT primer  and was subsequently diluted six times with sterile water. Each reaction was carried out in triplicate with a reaction volume of 20 μl containing 0.8 μl each primer (1.0 μM), 1.0 μl of cDNA, 10 μl of SYBR green (TaKaRa Bio Inc.), and 7.4 μl sterile distilled water. The PCR parameters were 95 °C for 30s, followed by 40 cycles of 95 °C for 5 s and 60 °C for 30s. Melt-curve analyses were performed at 95 °C for 15 s and then a constant increase from 60 °C to 95 °C. The grape


Porphyromonas gingivalis, because it is a prominent component of the oral microbiome and a successful colonizer of the oral epithelium. Here, we focus our attention on the study of the carbonic anhydrases  encoded in the genome of this pathogen as a possible drug target. Carbonic anhydrases are a superfamily of metalloenzymes, which catalyze the simple but physiologically crucial reaction of carbon dioxide hydration to bicarbonate and protons. Bacterial CAs have attracted significant attention for affecting the survival, invasion, and pathogenicity of many microorganisms. The P. gingivalis genome encodes for two CAs belonging to β-CA (PgiCAβ) and γ-CA (PgiCAγ) families. These two enzymes were cloned, heterologously expressed in Escherichia coli, and purified to homogeneity. Moreover, they were subject to extensive inhibition studies using the classical CA inhibitors (sulfonamides and anions) with the aim of identifying selective inhibitors of PgiCAβ and PgiCAγ to be used as pharmacological tools for P. gingivalis eradication.Periodontitis originates from a microbial synergy causing the development of a mouth microbial imbalance (dysbiosis), consisting of a microbial community composed of anaerobic bacteria. Most studies concerning the treatment of periodontitis have primarily take into account the Gram-negative bacterium  Porphyromonas gingivalis, Treponema denticola, and Tannerella forsythia were considered the main candidates for the clinical destruction of the gingiva, periodontal ligament, and alveolar bone, which are elements of the teeth-supporting tissues .,25.2 hyd1–24 and AAZ-HCT are either simple aromatic/heterocyclic sulfonamides widely used as building blocks for obtaining new families of such pharmacological agents, or they are clinically used agents, among which acetazolamide (AAZ), methazolamide (MZA), ethoxzolamide (EZA), and dichlorophenamide (DCP) are the classical, systemically acting antiglaucoma CAIs. Dorzolamide (DZA) and brinzolamide (BRZ) are topically acting antiglaucoma agents; benzolamide (BZA) is an orphan drug belonging to this class of pharmacological agents; topiramate (TPM), zonisamide (ZNS), and sulthiame (SLT) are widely used antiepileptic drugs. Sulpiride (SLP) and indisulam (IND) were also shown by our group to belong to this class of pharmacological agents, together with the COX2 selective inhibitors celecoxib (CLX) and valdecoxib (VLX). Saccharin (SAC) and the diuretic hydrochlorothiazide (HCT) are also known to act as CAIs.A library of 40 compounds, 39 sulfonamides and one sulfamate, were used in this study  4,9,11,,77,78,79vice versa [ZNS with a KI of 345 nM against PgiCAβ and 157 nM against PgiCAγ was intriguing. In fact, compounds 1–7, MZA, and BRZ had inhibition constants ranging between 345 and 818 nM towards PgiCAβ, but were rather ineffective as PgiCAγ inhibitors. Moreover, AAZ and EZA were the most effective PgiCAβ inhibitors, while compounds 9, 10, 16, ZNS, and IND were very effective against PgiCAγ. P. gingivalis infection, it is necessary to use a drug efficient for both bacterial enzymes in vivo. Thus, good candidates could be AAZ, MZA, and ZNS, which have KI < 400 nM against both enzymes. Of course, in the future, we will investigate other classes of CAIs for their inhibitory action against these enzymes.In general, the two recombinant PgiCAβ and PgiCAγ were antithetic to the sulfonamides inhibitors because the effective inhibitors of PgiCAβ were ineffective towards PgiCAγ, and ce versa ,20. HoweI of 5.4–21.4 mM) [I of 7.5–7.8 mM), whereas the heavier halogenides are weaker inhibitors (KI of 15.9–21.4 mM). The most efficient PgiCAβ inhibitors detected so far are sulfamide, sulfamate, phenylboronic acid, and phenylarsonic acid, with KI ranging between 60 and 78 µM  ,21. Intend 78 µM . It is and 78 µM . Thus, ePorphyromonas gingivalis is the major pathogen responsible for the development of chronic periodontitis. The P. gingivalis genome encodes for two CAs, one belonging to the β-CA class (PgiCAβ) and the other to the γ-CA class (PgiCAγ). These two enzymes were cloned, heterologously expressed in Escherichia coli, and purified to homogeneity. PgiCAβ and PgiCAγ showed significant catalytic activity for the hydration of CO2 to bicarbonate and protons. Several in vitro and in vivo inhibition studies with various classes of inhibitors, such as anions, sulfonamides, and sulfamates, have been reported for bacterial CAs. Efficient in vitro inhibitors have been discovered for many such enzymes, but only for Neisseria spp., Helicobacter pylori, Brucella suis, Streptococcus pneumoniae, and Mycobacterium tuberculosis was the CA inhibition reported to lead to the inhibition of bacterial growth in vivo. It is thus obvious that PgiCAβ and PgiCAγ inhibition studies are needed in order to detect even stronger inhibitors, and elucidate the role that these enzymes play in the pathogenesis of P. gingivalis infection. In this review, the anion and sulfonamide inhibition profiles of PgiCAβ and PgiCAγ were reported. The inhibition profile of Porphyromonas gingivalis was very different from that of the human isozymes (h CA I and hCA II). Interestingly, the PgiCAγ enzyme was much more sensitive to sulfonamide inhibitors compared to the PgiCAβ enzyme. Moreover, PgiCAβ and PgiCAγ were antithetic towards sulfonamides. In fact, the effective inhibitors of PgiCAβ were ineffective towards PgiCAγ, and vice versa. PgiCAβ and PgiCAγ represent promising targets for obtaining anti-bacterials devoid of the resistance problems of the clinically used agents, but further studies are needed for the identification of potent and possibly selective inhibitors of PgiCAβ and PgiCAγ, which may lead to the development of pharmacological tools helpful in the eradication of Porphyromonas gingivalis that is present in chronic periodontitis.The microbiota of the human oral mucosa consists of a myriad of bacterial species. Among them,


With the development of biotechnology, high-throughput studies on protein-protein, protein-gene, and gene-gene interactions become possible and attract remarkable attention. To explore the interactions in dynamic gene regulatory networks, we propose a single-index ordinary differential equation (ODE) model and develop a variable selection procedure. We employ the smoothly clipped absolute deviation penalty (SCAD) penalized function for variable selection. We analyze a yeast cell cycle gene expression data set to illustrate the usefulness of the single-index ODE model. In real data analysis, we group genes into functional modules using the smoothing spline clustering approach. We estimate state functions and their first derivatives for functional modules using penalized spline-based nonparametric mixed-effects models and the spline method. We substitute the estimates into the single-index ODE models, and then use the penalized profile least-squares procedure to identify network structures among the models. The results indicate that our model fits the data better than linear ODE models and our variable selection procedure identifies the interactions that may be missed by linear ODE models but confirmed in biological studies. In addition, Monte Carlo simulation studies are used to evaluate and compare the methods. Gene regulatory networks (GRN) are complex and dynamic systems in nature. They are composed of genes that interact with each other and with other substances inside cells, such as RNAs and proteins. Over the past few decades, a variety of methods have been proposed to model GRN. Commonly used models include information theory models, Boolean networks, ordinary differential equation (ODE) models, and Bayesian networks . InformaX(t) = (X1(t), ⋯, Xp(t))T represents gene expression levels at the time t of the p genes; F is a function which can be linear or nonlinear; and θ is an unknown parameter vector which quantifies the regulations or interactions among the genes in GRN.The Bayesian networks –12 integX(t), the gene expression levels which should be included in the ODE model (n) of measurements for individual genes is much smaller than the number (p) of genes, traditional statistical methods face significant challenges in developing statistical procedures and deriving theory i can be X(t) and its first derivative X′(t) for each functional module. For notation simplicity, we consider the estimation of the state function and its first derivative for module 1 (k = 1) and denote them by X(t) and X′(t) respectively. Suppose the number of genes in module 1 is m, and the number of measurements collected from each gene is mi. The NPME model can be described asgi(t) is the observed gene expression level for the ith gene; X(t) presents the fixed-effect or population curve which reflects an overall time-related trend of the gene expression level for module 1; vi(t) describe individual curve variations; εi(t) are measurement errors; and vi(t) and εi(t) are assumed to be independent.After grouping genes into functional modules, we apply NPME models to estimate the state function X(t). For presentation completeness, we briefly summarize the estimation procedure. We first approximate X(t) and vi(t) by l ≥ 1 is an integer, ζ1 < ⋯ < ζR are fixed knots, ur(t − ζr)+ = max, α = , u = , bi = , and wi = . Letα, b, u and w can be estimated by using the function lme (available in the R package nlme). Substituting the estimated X(t) for module 1. After estimating X(t), we apply the spline method (available in the R package splines) to estimate the first derivative of We can combine the penalized spline –36 with of model  can be eerred to  and 39]X(t). Forp modules; Xj(t), j = 1, ⋯, p are the population mean curves estimated by NPME models; and dXk(t)/dt for the k-th module. Substituting Xj(t), j = 1, ⋯, p and the first derivative k-th module in model  = (X1(t), …, Xp(t))T, ε is the sum of numerical errors due to integration and estimation. This complexity ε makes it challenging to study the properties of the proposed estimator for β′s, For simplicity, we adopt an additive error model used in the literature [(⋅) is a penalty function with a regularization parameter λk][. The PPrLS estimator of βk][ is the minimizer of λk][ by minimizing βk][. By determining non-zero βk][ by minimizing the following objective function:λk][ is the number of nonzero coefficients of λk]+ε.We also Applying the procedure to all functional modules, we constructed a regulatory network among modules Figs  and 3 anTo compare the results provided by the single-index ODE and linear ODE models, we summarized the inward (significantly impact on) and outward (impacted by) regulatory relationships between modules in In this part we conducted Monte Carlo simulation studies to validate the proposed procedure for the single-index ODE models. Due to the intensive computational cost, we designed a system with 7 ODEs, which include following linear and nonlinear forms. The simulation settings are data-driven because the gene expression pattern show sine and cosines patterns dX1(t)dtXp0, p = 1, …, 7, we can numerically solve the above ODE system and obtain the numerical solution Xp(t), p = 1, …, 7. In this simulation study, we first generated initial values Xp(0), p = 1, …, 7 by usingep follows N and X0 = . We then numerically solved the ODE system (Xp(t), p = 1, …, 7, using three different schedules: equally spaced time points on the ranges of , but three different intervals between time points. As a result, we simulated seven population means Xp(ti), p = 1, …, 7, i = 1, …, N with sample sizes N = 180, 288, 360. After generating the population mean curves, we used the spline method to estimate the first derivatives, which are denoted by p = 1, …, 7. For notation simplicity, we gave the model structure and estimation procedure for the first ODE (k = 1). The same procedure can be applied to the rest of the ODEs. Substituting the generated Xp(t), p = 1, …, 7 and estimated X(ti) = (X1(it), ⋯, X7(it))T, i = 1, ⋯, N. Applying the procedure given in Step 3, we selected X(t) and estimated k = 2, …, 7. As a result, we constructed GRN for the simulated functional modules. We repeated the same procedure 100 time and summarized the βq for jth iteration. In Given initial values Saccharomyces cerevisiae. In addition, there is physical and genetic evidence that MBP1 interacts with SKN7, a transcription factor [In this paper, we have proposed single-index ODE models and developed a procedure to select variables and estimate parameters. The procedure has further been used to analyze a time-course data set with the aim of exploring the module based and regulator-regulator interactions. We found the interactions identified by using single-index ODE were more accurate, i.e., the linear ODE models overlooked some confirmed regulator-regulator interactions . We tookn factor . These tThe advantages of our method include (i) single-index ODE models can fit the data better than linear ODE models; (ii) the interactions found by single-index ODE models can cover most of the interactions identified by linear ODE models for some of the modules; and (iii) our method is computationally efficient because we can select significant modules and estimate index coefficients simultaneously.Similar to the linear ODE model, our method needs estimated population means and their corresponding first derivatives, which may be treated as the limitation of the proposed procedure. The PPrLS estimator has good performance in identifying significant modules. But new stable techniques are still needed to group genes to reduce the gene cluster uncertainty because cluster assignment still plays an important role in enhancing the usefulness of this research. It is worth noting that the PPrLS estimates may not be most efficient in terms of estimation accuracy because PPrLS estimation is a nonparametric method and inherits error if the data contain a large noise. Regulator-regulator interaction exploration depends on the knowledge of gene-regulator relationship, which we study. So the proposed method may provide valuable insights into complicated biological processes with understanding gene-gene and gene-regulator relationships. Overall, our procedure is useful to single out high level (module based) and potential regulator-regulator interactions which are helpful to provide guidance for tedious and costly experiments.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.S2 File(TXT)Click here for additional data file.S3 File(TXT)Click here for additional data file.S4 File(TXT)Click here for additional data file.S5 File(XLS)Click here for additional data file.S6 File(R)Click here for additional data file.S7 File(R)Click here for additional data file.


The Proxy Mobile IPv6 (PMIPv6) is a network-based mobility management protocol that allows a Mobile Node(MN) connected to the PMIPv6 domain to move from one network to another without changing the assigned IPv6 address. The user authentication procedure in this protocol is not standardized, but many smartcard based authentication schemes have been proposed. Recently, Alizadeh et al. proposed an authentication scheme for the PMIPv6. However, it could allow an attacker to derive an encryption key that must be securely shared between MN and the Mobile Access Gate(MAG). As a result, outsider adversary can derive MN’s identity, password and session key. In this paper, we analyze Alizadeh et al.’s scheme regarding security and propose an enhanced authentication scheme that uses a dynamic identity to satisfy anonymity. Furthermore, we use BAN logic to show that our scheme can successfully generate and communicate with the inter-entity session key. In recent years, the mobile-device market has grown rapidly, and with the increasing availability of wireless Internet access, various services including browsing, file-sharing, and shopping are becoming increasingly available regardless of the time and place. The Internet Engineering Task Force (IETF) has been developing the Internet standards, and after more than 20 releases, the standardization of IPv6-based mobility has been discussed as “Mobility Support in IPv6 (MIPv6)” since the late 1990s; the standardization to the proposed standard “RFC 3775” was completed in June 2004 [However, the MIPv6 imposes a burden on the mobile terminal by increasing the resource usage, and this is due to the signaling between the mobile terminal and the access router and the implementation of a complicated standard specification in a mobile terminal with limited resources. Thus, telecommunication operator were not satisfied. To solve this problem, the IETF proposed the Proxy Mobile IPv6 (PMIPv6) technology, and various research institutes are actively conducting the corresponding research. With the adoption of the PMIPv6, the complicated specification and signaling problems that are highlighted in the existing MIPv6 have been solved. However, it is still necessary to continue research because the technology cannot significantly reduce the handover-delay time that can occur with the movement of the Mobile Node (MN) , 3. AddiIn this circumstance, a smartcard can be used as an authentication method between MN and MAG. Because of high potability and low cost, authentication schemes using smartcard have been proposed over the past few years. Since Lamport proposed the first password-based authentication scheme in 1981. Smartcard-based authentication has been applied to numerous protocols, such as the session initiation protocol , mobile In 2013, Chuang et al. proposed a new authentication mechanism using smartcard called “SPAM”. SPAM offers a low packet loss and low latency rates compared with the other PMIPv6 mechanisms . HoweverMan in the middle attack: an adversary can interrupt between two entities during authentication. Thus, the adversary can intercept, modify, or drop the packets sourced by or destined to the MNImpersonation attack: an adversary can impersonate a user to the MN or MAG through inspection and discovery of the authentication information.Replay attack: an adversary can resend the legal message sent earlier in order to disorder the traffic flow or impersonate.Verifier impersonation: impersonation attack that the adversary creates independent connection with the victims and sends messages between them, causing them to think that they can directly communicate to each other.Modification attack: an adversary may try to change the authentication message of the MAG or the MN.Stolen-verifier: an adversary may thieve verification table if the scheme of authentication saves this table with LMA or MAG.The following paper is organized as follows. Section 2 concisely introduces the requisite preliminary knowledge for an improved comprehension of this paper, including the PMIPv6, hash function, and bio-hash function. Section 3 is a review of Alizadeh et al.’s scheme. Section 4 is an analysis of Alizadeh et al.’s scheme and shows its security vulnerabilities. Section 5 describes the proposed scheme that protects against the attacks shown in Section 4. In Section 6, the proposed scheme is analyzed using a formal security analysis with Burrows-Abadi-Needham (BAN) logic and an informal security analysis. Section 7 presents a comparison of the performances of the prior schemes with that of the proposed scheme, and Section 8 concludes this paper.In this section, we introduce some preliminaries, including the structure of PMIPv6, the hash function based on both Alizadeh et al.’s and our proposed scheme.The basic method for the provision of Internet protocol (IP) mobility to a mobile terminal involves the use of the mobile IP. But, the mobile IP manages the binding information on the MN’s location information by exchanging the signaling message between the MN and the Home Agent (HA). The PMIPv6 does not need a separate protocol stack for mobility management because the network elements handle the exchange of the binding-related messages instead of the MN. The components of the PMIPv6 are shown in The PMIPv6 domain refers to a network that manages the movement of the MN using the PMIPv6. Domains require the new functional elements the MAG and the LMA. The MAG monitors the movement of the MN on the access link and transmits the MN’s mobile signaling message to the LMA instead of the MN, while the LMA acts as the HA for the MN in the PMIPv6 domain. The LMA is an anchor point on the topology of the home-network prefix that is allocated to the MN and serves to manage the reachability state of the MN in the domain. In general, the function of the MAG can be implemented in the access router, and the LMA can be located in the gateway of the domain.Between the LMA and the MAG, there is an IP tunnel for the transmission of signaling messages and the data packets for sending and receiving the MN. The MAG can support different IP prefixes for terminals receiving mobility-support services and general terminals using the PMIPv6. The previous MAG (PMAG) detected by the MN is a detached event wherein the MN is not present on its access link, and it notifies the LMA of the detachment of the MN using a Proxy Binding Update (PBU) message. The LMA performs an operation to delete the binding entry associated with the MN and transmits the PBA.When the MN is connected to a new MAG (NMAG), the NMAG performs the initial access procedure of the MN, and it transmits the home-network-prefix information that the MN has allocated in the initial access through the Router Solicitation/Router Advertisement that is sent to the MN. Therefore, the MN can use the initially assigned address. “dactylogram” of data. Also hash function can be any function that is used to map data of an arbitrary size to data of a fixed size. Furthermore, There are three main conditions of hash function that are defined as y = h(x) [A cryptographic hash function can support confidence of data integrity. Hash function is used to construct a short y = h(x) , 16 as fh(x) is given, find x′ such that h(x) = h(x′) is infeasible.Preimage Resistance: When x and h(x) are given, find x′ ≠ x such that h(x) = h(x′) is infeasible.Second Preimage Resistance: When x′ ≠ x such that h(x) = h(x′) is infeasible.Collision Resistance: Find H(⋅) also has features of one-way hash function as mentioned previously.Recently, a three-factor authentication scheme that adds user’s biometric information to a two-factor authentication scheme using identity, password for growth security was widely proposed –19. To aIn This section, we review the Alizadeh et al.’s secure password authentication mechanism in 2015. Alizadeh et al.’s scheme consists of following phases: registration, mutual authentication, password change phase. The notation utilized in Alizadeh et al.’s and our proposed scheme is summarized as The MN proceeds the registration phase using the Authentication, Authorization, and Accounting (AAA), which is the authentication server, before it commences the mutual authentication phase. In a typical authentication scheme, the registration phase communicates via a secure channel between the user and the server. It is assumed that the communication on this channel is not vulnerable to eavesdropping.IDMN, PWMN and extra value RMN.Mobile user selects his/her identity and password RPWMN = h(PWMN||RMN). Then, sends < IDMN, RPWMN > via a secure channel.MN → AAA: Mobile Node(MN) computes S1 = h(IDMN||sv), S2 = h(RPWMN) ⊕ S1, S3 = EPSK(IDMN||sv||aMN) where aMN is random nonce generated by AAA. Then, sends < S1, S2, S3, h(⋅) > via a secure channel.AAA → MN: AAA computes S4 = h(IDMN||PWMN) ⊕ S1, S5 = RMN ⊕ S1, S6 = S3 ⊕ S1. Then, issues a new smartcard and writes S2, S4, S5, S6 into smartcard’s memory.MN computes In the mutual-authentication phase, the MN checks the authenticity of the user data, such as the user identity or password, and sends an authentication request message to the MAG. The MAG also authenticates the MN, generates a session key when the authentication is passed, and transmits the authentication confirmation message to the MN again. Lastly, the MN generates a session key using the received message, and the session key is finally shared between the MN and the MAG.S2. If this satisfies, proceeds with the next step.Mobile user inserts his/her smartcard and inputs N1, calculates AIDMN = S1 ⊕ S6, AUTHMN = h(S1||N1). Then, sends < AIDMN, ES1 > to the MAG via public channel.MN → MAG: Smartcard generates random nonce AIDMN using pre-shared Key(PSK) and obtains . Then, calculates S1 = h(IDMN||sv) and decrypts ES1.MAG decrypts h(S1||N1) is equal to AUTHMN. If this holds, proceeds with the next stepMAG verifies N2, computes h(N2||IDMAG), SKMN−MAG = h(N1||N2). Then sends ES1 to MN.MAG → MN: MAG generates random nonce S1. Checks N1 + 1 and h(N2||IDMAG). MN calculates SKMN−MAG = h(N1||N2). Then, sends (ESKMN−MAG(N2 + 1)) to MAG.MN → MAG: MN decrypts message using SKMN−MAG. Then, checks N2 + 1.MAG decrypts message using The password change phase is performed when the user wants to change his/her password. Primarily, the smartcard first verifies the authenticity and the user then inputs his/her new password. Based on the new password, the smartcard replaces the existing values with the new password based values.IDMN, PWMN, RMN.Mobile user inputs his/her original S1 = h(IDMN||PWMN) ⊕ S4, RMN = S1 ⊕ S5, RPWMN = h(RMN||PWMN). Then, checks S2 is same as h(RPWMN) ⊕ S1. If holds, password change phase proceeds with the next step.Smartcard computes User inputs his/her new password and extra value Smartcard computes S2, S4, S5, S6 new values Smartcard replaces In this section, we point out security drawbacks of Alizadeh et al.’s scheme. Before showing the security weakness, we discuss some widely accepted threat model concerning user authentication and key agreement scheme –23.The smartcard contains the MN and AAA’s information in plaintext form. Therefore, an adversary can extract the smartcard information by monitoring the diffrential power analysis .An adversary can eavesdrop all the message between the entities via to public channel. Additionally, He/She can modify, delete, resend the eavesdropped message.An adversary can guess low entropy password and identity individually easily but guessing two secret parameters are computationally infeasible in polynomial time , 26.An adversary may be a valid user or with the order reversed.An adversary already knows all authentication scheme between MN, AAA and MAG.Under these threat models, this study shows that Alizadeh et al.’s scheme is unable to resist against various attacks, including the offline password guessing and session-key-derived attacks.Most significant weakness of Alizadeh et al.’s scheme is leak of symmetric encryption key by following steps:S6 which in the smartcard and AIDMN which in the login message via to public channel.Adversary can extract S1 = S6 ⊕ AIDMN.Adversary computes S1 is the symmetric encryption key from all of the messages communicated between the MN and the MAG. Therefore, an adversary can easily encrypt or decrypt every message and attack using various security threats.Computing value Ua successfully derives symmetric key S1. Ua can perform offline password guessing attack by following steps:If an outsider adversary Ua derives RMN = S5 ⊕ S1, which S5 is in the smartcard.Ua selects random password candidate S2 which is in the smartcard, adversary infers that it has guessed the MN’s password accurately.If Ua chooses another password nominee and performs same steps just before discover password.Otherwise, Ua successfully derives MN’s password by offline password guessing attack, Ua also can do offline identity guessing attack by following steps:If an outsider adversary Ua selects random identity candidate S4 which is in the smartcard, adversary infers that it has guessed the MN’s identity accurately.If Otherwise, adversary chooses another identity nominee and repeats the same steps that precede the discovery of the identity.Ua has made a fake login request message that it sends to the MAG. However, MAG cannot identify it, and accepts it as a legal login request message. In Alizadeh et al.’s scheme, an adversary can make a fake login request message using the following steps:The MN impersonation attack means a outsider adversary Ua eavesdrops AIDMN beforehand because AIDMN is always same as EPSK. So, adversary can reuse it.Adversary Ua selects random nonce Ua makes login request message MAG decrypts message then obtains Ua.MAG checks Ua makes fake authentication message and sends it to the MN. Also, MN can not attention it, then MN accept it is legal authentication message. MAG impersonation attack is performed by following steps:Similar with MN impersonation attack, MAG impersonation attack means outsider adversary Ua eavesdrops ES1 then, acquire IDMAG. In the same way, acquire N1 from ES1Adversary Ua selects random nonce Ua makes authentication request message MN decrypts message then obtains Ua.MN successfully accepts authentication request message which made by Session key derive attack means adversary can compute session key and then use it after communication between MN and MAG. According to Alizadeh et al.’s scheme, adversary can derive session key between legal entities by following steps:Ua eavesdrops ES1) and ES1.Adversary Ua can derive N1, N2 by using symmetric key S1.Ua computes session key SKMN−MAG = h(N1||N2).Since then, adversary can communicate using derived session key either MN or MAG without registration or login.In this section, the scheme that is an improvement compared with Alizadeh et al.’s scheme is proposed. The proposed enhancements are described, as follows:Ua cannot identify the initiation of two different sessions by the same user.Use of a dynamic identity to satisfy the MN anonymity. The main idea is the changing of the dynamic identity to another value upon the completion of the authentication phase. Therefore, the Ua cannot derive without the legal user’s information.Use of an encryption key that the Use of biometric information with Bio-hashing to protect the MN’s information more securely.Our proposed scheme consists of following phases: registration, mutual authentication and password change phase.DIDMN is created based on the random number generated by the AAA. The dynamic identity provides the MN anonymity because it is continuously changed in a mutual authentication phase that is performed later. Details procedure of registration phase is in We designed a 3-factor authentication scheme by registering the user’s bio information in order to enhance safety. Also, at this phase, the dynamic identity IDMN, PWMN and imprints his/her biometrics BMN.Mobile user selects his/her identity and password RPWMN = h(PWMN||H(BMN)). Then, sends < IDMN, RPWMN > via a secure channel.MN → AAA: Mobile Node(MN) computes S1 = h(IDMN||RPWMN), S2 = h(aMN||sv), DIDMN = EPSK, S3 = EPSK ⊕ S2 where aMN is random nonce generated by AAA. Then, AAA sends < S1, S2, S3, DIDMN, h(.) > via a secure channel.AAA → MN: AAA computes S4 = S2 ⊕ h(RPWMN||IDMN), S5 = S3 ⊕ RPWMN. Then, issues a new smartcard and writes < S1, S4, S5, DIDMN, H(.), h(.) > into smartcard.MN computes DIDMN value in the smartcard. Thereby, an outsider adversary can not infer that same user performs mutual authentication several times. Details procedure of mutual authentication phase is in When an MN joins a localized mobility domain, it must pass a mutual authentication step with the MAG. To enhance the safety of the proposed method, this process prevents an attacker from deriving an encryption key even if he/she eavesdrops a public channel or extracts a smartcard’s contents. In addition, once the authentication is completed, the MAG issues new dynamic identity value, S1. If this satisfies, proceeds with the next step.Mobile user inserts his/her smartcard and inputs N1, calculates AIDMN, AUTHMN, TN1 > to the MAG via public channel.MN → MAG: Smartcard generates random nonce AIDMN) using Pre-Shared Key(PSK) and obtains . Then MAG decrypts DIDMN using PSK once again and obtains IDMN, aMN. Then, MAG calculates MAG decrypts AUTHMN. If this holds, proceeds with the next step.MAG verifies N2, MAG → MN: MAG generates random nonces N1 + 1 and h(N2||IDMAG). Then, MN calculates SKMN−MAG = h(N1||N2), DIDMN with S5 with MN decrypts message using ESKMN−MAG(N2 + 1)) to MAG.MN → MAG: MN sends ) checks S1 is same as h(IDMN||RPWMN). If holds, password change phase proceeds with the next step.Smartcard computes User inputs his/her new password Smartcard computes S1, S4, S5 new values Smartcard replaces In this section, the proposed scheme is analyzed using the following two methods: informal analysis and formal analysis. The informal analysis proves that the proposed scheme is secure against many security threats compared with the other existing schemes. On the other side, using BAN logic, the formal analysis shows the proposed scheme’s generation of the session key’s legality to the entities who take part in the proposed scheme.In this subsection, we check our proposed scheme is safe with various secure threat, and satisfies some basic requirements to design authentication scheme.RPWMN = h(PWMN||H(BMN)). In this case, server’s insider is not able to guess password because password is protected with bio-hash value based on user’s biometric.The insider attack is performed by someone who is in the server’s side and then guesses the user’s password from the registration message. However in our proposed scheme, MN sends user’s password to server in a form of DIDMN = EPSK. Additionally, after a authentication phase, MAG computes new dynamic identity S2 known only MAG and MN. Then, MN replaces the previous DIDMN with received An authentication scheme is said to satisfy anonymity if it can satisfy two main conditions: (1) User’s identity is not disclose to adversary and (2) the adversary cannot find out two different sessions are initiated by same user , 28. In MN and MAG. Mutual authentication means there are processes that each entity completes to authenticate the other party during the progression of the protocol. In our proposed scheme MAG checks MN’s legality by checking derived AUTHMN is equal to receiving value. The other way, MN checks MAG’s legality by checking derived h(N2||IDMAG) is equal to receiving value. Additionally, MN can check MAG’s legality by N1 + 1 whether MAG can derive MN generated nonce N1.Our proposed scheme provides mutual authentication between Several authentication schemes comprise a verification table that stores some of the user information. However, the use of a verification table can cause overhead problems in the server’s side and a vulnerability to the stolen-verifier attack. However, the proposed scheme does not need to store any information during the entire phase, and this means it prevents not only the AAA overhead but also the stolen-verifier attack.MN impersonation attack, adversary need to make AIDMN, AUTHMN, TN1. However AIDMN is encrypted text with pre-shared-key, AUTHMN is mixed IDMN, TN1 is mixed with AAA’s secret key sv and AAA generated random nonce aMN. So, even though adversary Ua generates his/her own random nonce Ua can not make any require value which sends to MAG. Therefore, our proposed scheme prevents MN impersonation attack.To do MAG impersonation attack, adversary needs to make S2 to encrypt message. However S2 is mixed with AAA’s secret key sv and AAA generated random nonce aMN. Like the preceding attack, even though adversary Ua can not derive MAG impersonation attack.To do MN and MAG generate random nonce N1, N2 during our proposed scheme process to resist replay attack. When adversary Ua eavesdrops login message < AIDMN, AUTHMN, TN1 > then resends it. In this case Ua’s login request is rejected by MAG, because our proposed scheme can expose an wrong number by contrasting AUTHMN. Supplementary, our proposed scheme uses various numbers when each session begins. Therefore, our proposed scheme can resist replay attack.MN’s identity, password verification process is in the MAG’s side, adversary inputs wrong identity and password in succession. In this circumstance, MAG is received a lot of login request message. As a result, MAG is overloaded by adversary. To prevent this attack, our proposed scheme checks MN’s identity and password in MN’s smartcard side. So, when adversary inputs wrong information, smartcard rejects login request in MN’s side quickly. As a result, our proposed scheme resists Denial-of-service attack.Denial-of-service(DOS) attack is occurred by adversary’s continuous wrong login requests. If MN’s password/identity must using S1’s value. Nevertheless, S1 has 3 MN’s information, identity, password and biometric. Even if adversary can guess user’s identity and password at same time in polynomial time, there is a precondition that adversary already knows MN’s biometric information. But, it is not possible to know MN’s biometric information in our scheme. Therefore, our scheme resist MN guessing attack.According to our proposed scheme, adversary who guess MN and MAG have to synchronize there clock beforehand. In the synchronization process, there is possibility that time synchronization error. To prevent this problem, our proposed scheme only use random nonce based authentication instead timestamp.Several authentication scheme using timestamp to resist replay attack. However, using timestamp in authentication scheme, MN user always chooses his/her password without any restriction in registration phase. Additionally, when MN changes his/her password in password change phase, smartcard checks the original password’s legality at first. Then, MN can change password. In this process, the MN only needs to communicate with the smartcard and not with the MAG.In our proposed scheme, Also, the proposed scheme is compared with two existing schemes regarding the PMIPv6 user authentication, as shown in Formal security analysis is usually used to analyse and judge various authentication schemes’ performance –32. TherIn this subsection, BAN logic is used to analyze the proposed scheme. BAN logic helps to prove whether or not a protocol does or does not meet its security goals. Also, BAN logic contributes to the improvement of the efficiency of a protocol by eliminating messages, message content, or message encryptions. The BAN-logic notation is defined in In order to achieve the reasonable result of BAN logic, we define some rules about introduction and elimination as follows:P sees a message which is encrypted with the shared key of P and Q, than P believes that Q has sent the message. As the secret key only is known to P and Q, only P or Q are able to produce the message and P knows what it has said.Message-meaning rule: P believes that X is a fresh message, and P believes that it was said by Q than P believes that Q still believes the message X.Nonce-verification rule: Believe rule(1): Believe rule(2): Freshness-conjuncatenation rule: P believes that the principal Q jurisdiction has over the formula X. This means that Q is trusted to make statements over X.Jurisdiction rule: The major objective of our proposed scheme is mutual authentication between the MN and MAG with shared key. Our objectives symbolized by BAN logic are as follows:Objective 1. Objective 2. After establishing the main objectives, convert the message between MN and MAG to the idealized form.MN → MAG: < IDMN >S2, < N1 >IDMN, < N1 >S2Message 1. MAG → MN: < N2 >S2, < N2 >IDMNMessage 2. Also there are some assumptions of our proposed scheme to derive proper objective.MAG ∣ ≡ ♯(N1)A1: MN ∣ ≡ ♯(N2)A2: MAG ∣≡ MN ⇒ N1A3: MN ∣≡ MAG ⇒ N2A4: A5: A6: Now, we describe our main proof as follows. According to Message 1, we could get:MAG⊲ < IDMN >S2, < IDMN >N1, < N1 >S2V1: A6, we apply the message meaning rule to obtain V2 and V3.According to assumption MAG ∣≡ MN ∣∼ IDMNV2: MAG ∣≡ MN ∣∼ N1V3: A1, we apply the freshness conjuncatenation rule to obtain V4.According to assumption MAG ∣≡ MN ∣≡ N1V4: A3 and V4, we apply the jurisdiction rule to obtain V5.According to assumption MAG ∣≡ N1V5: sk = h(N1||N2), V5 and assumption A3, we derive:According to V6: According to Message 2, we could get:MN⊲ < N2 >S2, < N2 >IDMNV7: A5, we apply the message meaning rule to obtain V8.According to assumption MN ∣≡ MAG ∣∼ N2V8: A2, we apply the freshness conjuncatenation rule to obtain V9.According to assumption MN ∣≡ N2V9: sk = h(N1||N2), V9 and assumption A4, we derive:According to V10: MN and MAG achieve mutual authentication, and based on  and , MN and MAG trust that the session key sk is securely shared between them.The preceding discussion clearly shows that In this section, we measure our proposed scheme’s performance and compare with those of existing schemes. The notations used in this measurement are described as follows:Th: the time of executing a one-way hash function/bio-hash function.Tx: the time of executing a XOR operation.Ts: the time of executing a symmetric encryption or decryption.Th + 4Tx + 8Ts, Alizadeh et al.’s scheme is 14Th + 9Tx + 8Ts, and our proposed scheme is 17Th + 7Tx + 10Ts. The totals of the hash-function and XOR-operation executions that were recorded for the proposed scheme are similar to those of the two existing schemes. The proposed scheme implements the dynamic identity to satisfy the user anonymity, and it needs two further symmetric-encryption and symmetric-decryption operationsBased on the results in Tx is not counted because it is too petty compared with other operations such as symmetric encryption or hash function.Under this simulation environment, the value of each cryptographic operation time was measured. Th + 10Ts ≈ 17 × 0.48ms + 10 × 0.73ms). The execution times for Chuang et al.’s and Alizadeh et al.’s schemes are 13.52ms (16Th + 8Ts ≈ 16 × 0.48ms + 8 × 0.73ms) and 12.56ms(14Th + 8Ts ≈ 14 × 0.48ms + 8 × 0.73ms), respectively. The results show that our proposed scheme’s execution time is more than those of the other schemes. However, in terms of security, the other schemes show has several vulnerabilities. Contrarily, our proposed scheme implements the dynamic identity at a relatively low additional cost, to satisfy MN anonymity and provide protection against various secure attacks. Thus, our proposed scheme also takes into account the necessary efficiency.As shown in This paper shows that Chuang et al.’s scheme, which was proposed as the authentication scheme for the PMIPv6, is vulnerable to an attacker who can derive the symmetric key that is used in overall communication, and the execution of this attack is relatively simple. Then, we demonstrate how an outsider adversary can execute various security threats, such as the offline password guessing, MN impersonation, and MAG impersonation attacks, on Alizadeh et al.’s scheme. Accordingly, we propose an improved and efficient scheme using the MN user’s biometric information and a dynamic identity that provide protection against the previous security drawbacks. As a result, this paper shows that the proposed scheme can prevent attacks such as the MN guessing, MAG impersonation, and session key derived attacks, and its effectiveness is also due to the fact that it does not use timestamps or verification tables. Furthermore, BAN logic shows that the proposed scheme exhibited successful and stable session-key sharing between the MN and the MAG, and it is more efficient in terms of the computational-time cost.