Second language learners face a dual challenge in vocabulary learning: First, they must learn new names for the 100s of common objects that they encounter every day. Second, after some time, they discover that these names do not generalize according to the same rules used in their first language. Lexical categories frequently differ between languages , and suc Second language acquisition research has often highlighted the role of learners’ language history as a strong predictor of ultimate second language (L2) attainment in syntax and phonology e.g., . VariablA closer examination of lexical semantics reveals, though, that the development of the lexicon may be more analogous to that of syntax and phonology than such divergent outcomes suggest. Recent research in lexical categorization has moved beyond the size of learners’ vocabularies and investigated more subtle aspects of word knowledge such as lexical category boundaries in both native and L2 speakers of a language. The studies reviewed below have found significant variation in lexical categorization patterns among native speakers, simultaneous bilinguals, and sequential bilinguals as a function of predictors such as age of onset, language learning experience, and usage patterns. In this paper we examine determinants of L2 lexical acquisition in more detail, with emphases on L2 immersion experience and its interaction with both individual bilinguals’ language histories and the word use patterns of the linguistic communities in which both first and second language are acquired.bottle from the set of 60 common household containers used by Decades of research have indicated differences in lexical categorization across languages  and guàn for shorter, rounder, and more extended in volume, containers (like a 12 oz soft drink), analogous to the English categories bottle and can respectively. However, the relative priority of material  and shape (height and roundness) as defining features differs between Chinese categories and English categories. A tall, metallic container for shaving cream may be called píngzi in Chinese but can in English, violating the ostensive translation relationships for píngzi-bottle and guàn-can.Learners of a second language, including children who acquire two languages simultaneously, are thus faced with a major incongruity between languages. For example, Chinese (referring to Mandarin Chinese throughout this paper) and English differ in the principal features by which containers are categorized. Native Chinese speakers use cup and which similar objects receive a different name (such as mug or glass) by a speaker.Lexical categorization is a valuable tool for identifying variation in lexical semantic mappings among speakers, and with this more sensitive measure of lexical semantic variation, second language lexical proficiency may no longer be sufficiently described by the accumulation of a list of words as tested by most picture naming, lexical decision, and fluency tasks. Instead, lexical semantic mappings are more precisely probed when many similar objects are named, which allows inferences about the boundaries of a given speaker’s lexical category. For instance, the researcher can examine which drinking vessels are named Recent work has investigated whether and how bilinguals can maintain native-like lexical semantic representations in each language despite these differences. These cross-language transfer and convergence effects can be thought of in terms of how exposure to one language might change mappings from objects’ representative features to words in the other language of the bilingual speaker. Theoretical models of lexical semantic representation, such as sunfish belongs to fish, which belongs to animals, all of which differ from plants) as a result of feature overlap between exemplars.At least two computational models have attempted to simulate bilingual lexical categorization , drawingAlthough there are only a few quantitative accounts of bilingual lexical categorization, a number of likely predictors for development of lexical categories are apparent from the broader study of second language acquisition. The extent of L2 immersion, age of second language onset, time spent learning the second language in a formal setting (classroom training), and patterns of language use (the extent to which the languages are intermixed in use) all appear to be involved in non-native learners’ degree of success in learning a second language. Further, because name choice for an object may vary across speakers e.g.,  the cateWhile many studies in second language acquisition explore the influence of language history variables on lexical learning, fewer studies have evaluated a combination of such variables simultaneously and properly controlled for interaction among the variables and statistical obstacles to measuring effects of variables individually, as outlined by The value of L2 immersion is uncontroversial in second language acquisition research with respect to many components of L2 acquisition. Recent findings in lexical categorization suggest that as in other domains of language acquisition, native-like L2 lexical categorization is supported by L2 immersion. chairs each day and drink from a variety of cups. However, classroom learning includes little exposure to the within-category variation necessary to acquire native-like lexical semantics. Consequently, immersed learners are likely to follow different developmental trajectories than non-immersed learners, as their respective language inputs differ fundamentally in the lexical semantic domain. Additionally, aspects of language history interact or confound with immersion experience, as described in Within-category variation arises constantly as part of the natural environment, as one may have occasion to sit in several different Age of second language onset as a predictor of eventual second language attainment remains a controversial topic, as evidence for and against a sensitive period for language acquisition is weighed alongside varying levels of other confounding age-related variables  patterns of the two languages . These eIt is evident that in many instances of simultaneous and sequential bilingualism, the category information provided to bilinguals by the native-speaker communities of each language is variable and yet still bears a significant influence on their production in both languages. With relatively few lexical category stimulus sets normed for native speakers of more than one language and tested on sufficiently advanced bilinguals of both languages, the exact degree and means of this cross-language influence remains to be explored. However, native category norms that represent the full distribution of names produced and thus the degree of name agreement and variation among native speakers may allow an elaborated view of cross-language competition and transfer. The extent to which L1 representations are vulnerable to change may vary as a function of their own entrenchment, with greater native naming agreement representing more robust L1 representations. Conversely, objects named with greater consistency in L2 (high L2 native agreement) could be associated with better learning outcomes as compared to objects for which L2 speakers show little agreement.In the present study, we aim to disentangle the respective roles of four broad categories of individual language history variables in predicting native-likeness of L2 lexical semantics: L2 environment (non-immersion vs. immersion), age of L2 onset, years of L2 classroom study, and L2 usage pattern [code-switching frequency (CSFreq)]. Collinearity between age and immersion predictors has been shown to cause serious confounds in studies of second language acquisition see  and negaBy measuring several language history variables together, accounting for the earliest L2 exposure , and using categorization as a more sensitive measure to inter-personal lexical semantic variation, we aim to make better statistical estimates of each variable’s effect. We offer a simultaneous measure of four variables based primarily on the self-reports of Chinese–English bilinguals resident in Beijing, China and in Pennsylvania, United States.We also introduce linguistic community norms for word use in L1 and L2, derived from native speakers of each language, as possible predictors of bilinguals’ lexical categorization patterns. The contribution of such norms has rarely been considered in predicting L2 performance (except see These non-immersed and immersed participants are compared in an L2 (English) lexical categorization task that has proved highly sensitive to variation in lexical semantic mapping for other populations of bilinguals. Based on the simultaneous evaluation of all four language history variables and the linguistic community norms, we evaluate participants’ English native-likeness on the lexical categorization task. We offer an interactive account of how various aspects of one’s native language, second language, and language learning history jointly influence the lexical semantic mappings that defines object naming, a behavior that occurs often in our daily experience.Two groups of bilingual students, one in the United States and one in China, participated in this study. In the U.S., Chinese–English bilingual undergraduate and graduate students were recruited from the Introduction to Psychology subject pool and through posters around the campus community at Penn State University . In China, Chinese–English bilingual undergraduate and graduate students were recruited through an online campus message board (BBS) and through personal referrals at Beijing Normal University . Generally speaking, the students at Penn State were slightly younger (mostly undergraduates) than those at Beijing Normal (mostly graduate students), were first exposed to English at a slightly earlier age, and had higher self-rated proficiencies in English.Although many of the bilingual participants reported some degree of training in a third language, most rated themselves at very low proficiency. Participants who self-reported a proficiency of 2.5 or greater in the third language on a 7-point scale  or failed to provide a proficiency rating in their third language were not included in the data. In total, 57 participants from Beijing Normal and 68 participants from Penn State met the inclusion criterion. Third languages included French, German, Russian, Mongolian, Japanese, Korean, Taiwanese, and Cantonese.M= 19.5, SD = 1.2). They were first exposed to English between ages 1 and 16 , and self-rated their English proficiency between 2.5 and 7.0 . The Penn State students had resided in the United States for 0–19 years . Students at Beijing Normal University had ages ranging 18 to 28 , and age of earliest English exposure was 5–15 . Their self-rated English proficiency varied between 1.3 and 5.5 , as some were studying English while others majored in different subjects. None of the participants at Beijing Normal University reported living in or visiting an English-speaking country for an extended period of time.Penn State students ranged in age from 18 to 23  underestimate the true rate of code-switching in cultural environments that stigmatize language mixing. An additional code-switching questionnaire (CSQ) was added to subsequent sessions to specifically probe participants’ code-switching and was administered according to the dominant language environment. A single item on the CSQ was used to obtain a point-estimate of participants’ overall CSFreq: “Do you use English words when speaking Chinese, or do you use Chinese words when speaking English?” rated on a five-point ordinal scale with response options from “never” to “very often.”Figure 1). Photographs were displayed at 480 × 360 pixels on a personal computer equipped for digital recording. Each voice response was recorded through a standard omni-directional consumer microphone to the computer’s sound card and encoded as 10 s uncompressed WAV files. Each photograph was accompanied by the written prompt: “What is this?” or  according to the task language.Sixty-seven photographs of common household objects were used to elicit category names from monolingual and bilingual participants. These objects were drawn from a stimulus set  used by An Operation-Span (O-Span) test was also used to screen the bilingual participant groups for systematic differences in working memory, a cognitive factor that might be confounded with language proficiency or language transfer. The O-Span includes mathematical and verbal components : ParticiAfter giving informed consent in the local language, participants completed the LHQ, also in the local language (Chinese or English). They then completed an unrelated English receptive vocabulary task (results not presented here) to establish an English language mode to the extent possible in both the Chinese- and English-immersed participants. After the vocabulary test, all participants performed the English picture naming task. The Chinese O-Span was then completed and used to shift participants into a Chinese language mode before naming the objects again in Chinese. Finally, the CSQ was completed last. Participants in the US completed English and Chinese tasks on separate days, 1–2 weeks apart  and counter-balanced for order. Sessions in China could not be scheduled separately and all tasks were completed on the same day, with English first, followed by Chinese. We reasoned that the English task was less likely to influence Chinese naming in a Chinese immersion environment, and intervening Chinese tasks  would help to reduce any language priming effects.bag) and a trash can full of paper . These instructions were provided in written form on the computer screen according to the language of the task. Participants were verbally encouraged to name every object and to always make a guess if unsure. Participants were also provided two practice naming trials for photographs of unrelated bottle-like stimuli, followed by the most dominant monolingual name for each stimulus  of participants’ responses were digitally recorded by the computer for each stimulus.In the picture naming tasks, participants were instructed to name aloud photographs of objects depicted on the computer. They were asked not to name the objects’ contents, as illustrated by two photographic examples: a grocery bag full of vegetables  for comparison with the native norming data. Skipped trials, inaudible responses, and irrelevant responses  were entered as blanks and treated as missing data.Four biographical variables were included for each subject: Age of first exposure to English (AOEE), LOR in the English immersion environment (LOR), self-reported frequency of code-switching between Chinese and English (CSFreq) and the total number of years spent learning English . For participants who failed to complete some language history and code-switching questions, missing data for the CSFreq variable were replaced with the sample mean . Participants who did not report AOEE were excluded from the analysis (eight participants), and an additional set of early childhood bilinguals  were removed from the US participant data to maintain comparability with the sample in China (AOEE range 5–15 years).Table 1). Results from 20 participants in the US sample and 20 in the China sample were discarded due to recording equipment failure (no audio data recorded). Participants were encouraged to speak loudly and clearly directly into a desktop microphone; however, additional participants (not excluded due to recording failure) periodically produced inaudible responses or no response, decreasing their total response rate1. Some participants may have chosen not to respond to stimuli when uncertain about those objects’ names, a possibility we tested by correlating response rate to self-rated English proficiency. Indeed, response rate in English was weakly correlated to English proficiency  while response rate in Chinese was not . Non-response as a predictor of name uncertainty is preserved in the remaining participants (50% or higher response rate) insofar as all trials are included for analysis, with non-response trials counted as incorrect names.Given the above exclusion/inclusion criteria for data analysis, our data analyses presented in the Results section were based on a total of 30 participants from China and 33 from State College  The group-wise analysis compares the overall patterns of transfer and convergence between Chinese and English as spoken by the bilingual participants. This analysis looks at the overall trends in naming distributions generated by sub-groups, which is defined by their degrees of L2 immersion (see details below). This analysis allows direct correlations of the bilinguals’ overall patterns with the monolingual norms. (2) The subject-wise analysis focuses on individual bilingual participants’ language histories and how these variables predict their individual differences in L2 naming patterns. (3) The item-wise analysis examines naming performance on each object of the stimulus set, controlling for variation in individuals’ language histories and examining the impact of linguistic community norms on the bilinguals’ accuracy in producing the native preferred L2 names.For group-wise comparison, participants were organized by three discrete values of LOR to describe three types of immersion conditions observed in our sample: No Immersion, Short-term, and Long-term. No Immersion was defined by LOR = 0, describing participants who have never lived in an English immersion environment. English-immersed participants were divided into two groups by a median split (median non-zero LOR = 1.3 years). Short- and Long-term Immersion were defined as the samples below and above the median, respectively.Figure 2 provides these correlation matrices for each immersion group, compared with the monolingual speakers of Chinese and English and between the Chinese and English patterns produced by the bilinguals, according to the convention of A cross-language correlation matrix was calculated for each bilingual and monolingual group according to the method of r = 0.64 . This value serves as the baseline correlation against which bilinguals’ Chinese and English categorization patterns can be compared. All of the bilinguals showed a highly convergent pattern of naming between languages, correlating their Chinese and English word use around 0.92–0.95 , strongly suggesting that they relied on a single set of mappings (with varying degrees of influence from each language). Correlation of the bilinguals’ English naming with the monolingual norms  was compared using p < 0.01 in both cases). The bilinguals’ English categorization also decreased in its dependence on Chinese norms with increased immersion .The cross-language correlations revealed that native, monolingual speakers of Chinese and English correlate in their categorization of this set of objects at p < 0.001) and greater Chinese resemblance to the English patterns . This effect may be attributable to differences in the administration of the Chinese naming task, in which the No Immersion group completed Chinese naming shortly after the English naming task. Henceforth, we will examine English naming only, as English names were not subject to priming across tasks because English naming occurred either first or in a separate session for all participants, and L2 acquisition is the focus of the current study.Surprisingly, the No Immersion group showed the highest convergence between their two languages (0.95), a relatively low correlation with the monolingual Chinese (0.80) compared to their recently immersed peers .Participants’ picture naming responses were compared to a set of English native norms to generate a score for each participant describing the English native-likeness of their lexical categories. Each of a participant’s responses was awarded a score based on the proportion of native monolingual speakers who produced that same response in the norms  = 2.14, p = 0.02, Adjusted R2 = 0.22].We estimated a linear regression model for the English native-likeness scores over participants’ language histories to determine the relationships between language background and attained L2 lexical category proficiency. Previous analyses from smaller datasets showed several two-way interactions between the language history variables and an inter-dependency of the significance of these interactions in the model (see AIC) stepwise procedure was adopted which started with all possible interactions and systematically excluded and re-included variables to find the best-fitting model with the lowest AIC score  = 2.27, p = 0.02, Adjusted R2 = 0.23] with slightly improved parsimony . Ultimately, all predictors were included in one or more significant interactions.In an attempt to improve the parsimony of this model without discarding important interaction effects, an automatic Akiake information criterion . To further simplify the plots, we again used the three discrete values of LOR to describe three types of immersion conditions observed in our sample: No Immersion (LOR = 0), Short- (LOR < 1.3 years), and Long-term (LOR > 1.3 years). Short and Long-term Immersion were represented by the mean LOR values for each of these two groups: 0.5 and 4.7 years, respectively. Figure 3 shows plots for the interactions between LOR and each of the remaining predictors: AOEE, CSFreq, and YrsLearn.To understand the highly interactive terms of the subject-wise model, we generated several estimated marginal means plots based on the model’s predicted English native-likeness scores across a range of values for the two-way interactions between L2 immersion (LOR) and each remaining predictor . These two-way interactions were all highly significant  contrasts years of English study with the duration of English immersion, which are not independent predictors. That is, as the duration of immersion (LOR) increases, so do the years of English study (YrsLearn). Conversely, however, YrsLearn may increase without immersion experience. Therefore these variables contrast the predicted English native-likeness associated with varying durations of study when the amount of that time spent in an Immersion environment is held constant . In all three LOR conditions, the relationship between YrsLearn and English native-likeness is negatively sloped, indicating a relative disadvantage for years of English study after controlling for years of English immersion. In other words, every additional year of English study in China beyond a participant’s immersion experience reduced the native-likeness of their English categorization patterns. For example, a learner with 15 years of study and almost 5 years of immersion (LOR = 4.7) has had over 10 years of English study in China, and they are predicted to perform worse (on average) than somebody with fewer years of English study and the same amount of (or even less) immersion.The first plot  also displayed a negative relationship with English native-likeness. When controlling for the other variables, later ages of English onset generally result in poorer performance in English categorization. Interestingly, however, the interaction with LOR did not appear to be large  indicating a largely additive effect of these two variables. The relative weight of each variable was approximately balanced such that the negative effect of being exposed to English 1 year later is offset by the benefit of 1 year of immersion experience.Age of earliest English exposure , while all other trials were scored as 0 (incorrect). Next, we performed two binomial logistic regressions to estimate the probability that a participant would produce the dominant name for any given object.In this analysis looking at how native naming consensus for objects impacts the likelihood of naming objects correctly, we compared each response by the participants to the single dominant nameR2 = 0.02, indicating that subjects’ language backgrounds could account for overall trends in the native-likeness of their English categorization but not for most of the variation trial-to-trial. This result points to the importance of considering variation in the learner’s input across objects (such as the native norms) as a predictor of success in naming individual objects.In the first logistic regression, we entered the same language history variables used in the subject-wise analysis to determine how adequate these variables were for identifying variation in native-like categorization for different objects. The logistic regression model including only participants’ language history information contained several statistically significant predictors, but offered a very poor fit to the data .In the next analysis, we added four language variables which described the native speaker norms for every given object: naming agreement in Chinese (L1), naming agreement in English (L2), number of alternative names produced by the Chinese norming group, and number of alternative names produced by the English norming group. Due to computational limitations, this model was estimated with up to four-way interactions and reduced using the same As in the subject-wise analysis, the model contained many interaction terms that impeded interpretation without isolating a few of the variables. Again, we sought to describe how immersion experience affected the role of these language variables in predicting the participants’ success in producing native-like English names for objects. A binomial logistic regression predicts the probability that an outcome will occur, in this case the probability that the participant will produce the English native-like dominant name for a given object. Again, we estimated plots in which the individual variables  interacted with three levels of immersion while holding all other variables constant at a mean value.Figure 4 presents plots of each linguistic community variable against the three levels of English immersion . These plots revealed that English native-likeness in the learners was more likely at higher levels of English norm agreement , while the inverse was true for Chinese: There was less English native-likeness with higher agreement in the Chinese norm (4B). An opposing relationship was also observed for the number of alternative names available from the norming sample. Having a greater number of English names available in the norm actually increased the predicted probability that the learners would produce the dominant English name (4C), but having many possible Chinese names for an object decreased the predicted probability of the participants producing an English native-like name (4D). The apparent advantage for a greater number of English names is explored in the next section.In a follow-up analysis, we asked how L1 and L2 norms might interact with one another in predicting a learner’s success in producing the L2 dominant name. Several interaction terms between these norming variables were highly significant, so we examined the cross-language relationships between L1 and L2 agreement and number of alternate L1 and L2 names. This analysis offers a closer examination of two interesting effects from the preceding results: (1) native speaker agreement in each language appears to compete in predicting L2 native-likeness and (2) an increasing number of names in English seems to be associated with greater L2 native-likeness.Figure 5 depicts both the observed item-wise accuracies (A and C) and the estimated marginal mean accuracy at varying levels of the predictors using the logistic regression model. In Figure 5A, the average response accuracy across participants is plotted over both English and Chinese norm agreement levels for each object in the stimulus set. This plot shows the empirical effects observed in our sample and is generally consistent with the competitive account of L1 and L2 agreement estimated by the regression model. Among the objects in the experimental stimulus set, best performance (depicted by blue-colored dots) is observed when both languages have high agreement levels. Items in this set are not limited to one-to-one translation pairs, as cup, mug, and glass were all represented in this set .bēizi  category, highlighting learners’ weakness with the sub-divided English categories it includes , but several cross-cutting categories that did not have clear one-to-one translations also appeared: gāng, pénzi, and wăn are approximately translated as decreasing sizes of bowls but are translated as dish for some items according to the monolingual norms.This performance diminishes as English agreement decreases. In general, high levels of L2 (English) agreement are associated with successful learning across varying levels of L1 (Chinese) agreement. However, the worst performance by the learners occurs when Chinese agreement is high and English agreement is low, confirming that L1 patterns can have a strong negative effect on L2 native-likeness when L2 input is inconsistent. Many of these items came from the Figure 5B plots the model’s estimated accuracy levels, generalized to all levels of agreement in each language. While Figure 5B covers a broader range of potential values , it generally fits the patterns established by the empirical data. Further, the observed data  do not control for confounding variables . The regression model estimates both predictors and thus isolates the effect of agreement while holding the number of names constant, resulting in the smoother contours along values of L1 and L2 agreement depicted in Figure 5B.For comparison, Figure 5C depicts the accuracy rate for each object in the stimulus set across varying numbers of alternate names in each language. This plot indicates that when learners had only one L1 name for an object in each language, performance was highest. The worst performance was observed when exactly two competing names for an object were available in either language. The model’s prediction that more competing L2 names improve the probability of learners producing the L2 dominant name  is consistent with the latter observation that objects with three or more competing names were named more accurately than those with two names, but it overlooks the advantage for objects with only one name. Again, in the observed data , the number of names and agreement level are confounded, but the regression model isolates these effects and controls for agreement in its estimations of the effects of L1 and L2 names . It is not clear that these representations disagree, per se. Rather, Figure 5C represents the objects provided in the stimulus set, while Figure 5D provides a controlled, parametric representation over many values of each variable. The discrepancy between these representations is further discussed below.Participants’ observed performance across the different numbers of alternative names in the English and Chinese norms, however, differed significantly from the regression model’s estimated accuracy rates. In this study we examined the relative effects of four language history variables in predicting learners’ outcomes in L2 lexical categorization native-likeness. Highly significant interactions were found among these variables, supporting the idea that language history  variables should not be evaluated in isolation from other variables. Significant age of L2 onset effects were observed, but these effects were tempered by the positive contribution of increased immersion experience. A surprising observation was that increased experience with L2 prior to immersion was actually associated with reduced native-likeness of L2 lexical categorization. Finally, we found that for bilinguals with long-term L2 immersion, patterns of language use  were a significant predictor of L2 native-likeness, but for learners with less immersion experience (including no immersion experience), language use was a less important predictor of L2 native-likeness.We further explored how the naming norms of the linguistic communities of both languages influenced the learners’ success in acquiring native-like L2 lexical semantic mappings. Both L1 (Chinese) and L2 (English) norms were significant predictors of the learners’ L2 native-likeness, consistent with previous findings in other domains of second language acquisition, such as phonology. Further, we identified unique effects for agreement among native speakers and the number of alternate names produced in the norming samples. The result of an item-wise analysis revealed that a large amount of the between-object variation in naming was captured by these native speaker naming norms, indicating both the lasting impact of L1 mappings on L2 production and the sensitivity of L2 learners to the native speaker norms of the L2. Below we present a more detailed discussion of how L2 naming patterns are influenced by the learner variables and input (linguistic community norm) variables.The most surprising finding of this study was that the number of years spent studying English outside an immersion environment was negatively related to L2 native-likeness in the lexical categorization task, even after controlling for the length of eventual L2 immersion. This outcome was not predicted by any past research nor intuition. This novel contrast between years of non-immersed and immersed learning in learners who have significant experience in both environments suggests that L2 training outside of an immersion environment may ultimately reinforce lexical semantic mappings that significantly differ from those of L2 native speakers. There is little doubt that immersion experience is beneficial to second language learning, and second language acquisition research has long promoted this view, but the present study adds the unique corollary that L2 learning without immersion may, in fact, hinder native-likeness. This effect may be due to the entrenchment of L1 structures in learners’ L2 as a result of impoverished input. Common classroom techniques for learning translation equivalents or naming highly prototypical objects encourage learners to export their inferences about object categories from L1 to L2 by way of one-to-one translation. However, native-like L2 mappings only become available to the learner with more diverse input from an immersion environment or  another immersive instructional setting such as the highly enriched virtual environments that may be simulated in computer games . In a later section on implications for L2 instruction, we address these issues in further detail.Whereas age effects and training effects focus specifically on the conditions under which learners begin acquiring a new language, eventual native-likeness may just as well depend on how that language is used at later stages, such as in an L2 immersion environment. Switching from one language to another may be common, even difficult to avoid, in bilingual environments, but considerably more variation in individual CSFreq could be observed among bilinguals in relatively monolingual-like environments. While some bilinguals may use each language in a distinct context , others may switch frequently. Research in first language lexical attrition has highlighted the role of bilinguals’ specific language use patterns in re-shaping L1  and offeFigure 2). For learners with significantly less immersion  code-switching behavior had no strong effect on L2 native-likeness, both emphasizing the importance of prolonged L2 exposure for the acquisition of these lexical semantic mappings and perhaps mitigating a belief that frequent switching between languages significantly impedes native-like acquisition of an L2.In the present study we observe a complementary effect. Increased code-switching is associated with greater L2 native-likeness. However, this effect interacts with L2 immersion such that it applies only after a significant period of immersion  and increase their rate of code-switching as a result. Future research could investigate the short-term effects of code-switching in an experimental procedure, but the long-term causal relationship between these variables remains unknown.Although the learner-oriented variables as discussed above proved useful in predicting overall performance in lexical categorization, they were rather inadequate in predicting native-like naming for individual objects. Language-specific variation, on the other hand, proved extremely important in predicting trial-by-trial accuracy of participants’ object naming, even after controlling for inter-participant differences in the learner variables. These effects have been revealed by our item-wise analyses. One lesson from these effects is that any kind of overall attainment score in lexical categorization masks significant variation in mastery for individual words, with some words posing much greater challenges for the learner see also . The curWe found a competing relationship between the level of native-speaker agreement in L1 and L2 in predicting the native-likeness of learners’ L2 responses. The role of L2 agreement in learners’ responses indicates that these learners are sensitive to variation in native speakers’ lexical categories for these objects. In the alternative case, where learners rely only on a general majority name for objects, we should see little effect of the L2 agreement variable, as learners would be more consistent than native speakers. Instead, learners respond proportionally to native speakers in their level of naming agreement. Further, the interaction between immersion and L2 agreement demonstrates that the advantage for high L2 agreement increased with greater immersion: These objects show greater improvement than low L2 agreement objects, which did not improve much even with almost 5 years of immersion.Conversely, agreement among native-speakers of the L1 significantly impeded native-like naming in the L2, indicating that L1 learners were more resistant to revising their lexical semantic mappings in L2 when L1 native speakers were more consistent, likely showing a higher degree of confidence about the object’s category membership. The interaction between immersion and L1 agreement demonstrates that this L1 disadvantage predicts learners’ improvement with greater immersion experience. Low L1 agreement words significantly increase in their native-likeness with longer periods of immersion, while higher L1 agreement words show less improvement, highlighting these lexical semantic mappings’ resistance to restructuring.Re-examining the observed accuracy rates for the learners across both L1 and L2 agreement levels, we found an antagonistic interaction between these variables. When L1 agreement was especially high, learners struggled to produce native-like L2 names, even at relatively high levels of L2 agreement. However, when L1 agreement was relatively low, L2 agreement was a better predictor of L2 learners’ native-likeness, apparently becoming more salient in the absence of strong L1 cues. The statistical model did not find such a strong interaction, instead identifying the same opposing main effects of L1 and L2 agreement but without an effect of the very small (though significant) interaction term. It remains to be seen whether the observed interaction is a byproduct of the objects in our particular task or whether the model simply underestimates the importance of this interaction. In either case, the important roles of L1 and L2 agreement norms are apparent, either independently or interactively.The number of alternate names for an object produced by native speakers in L1 and L2 were also significant predictors of L2 learners’ native-likeness and were highly interactive with one another. If learners have only one name for an object in their native language, the model indicated that they would be equally likely to produce the dominant L2 name, regardless of alternatives. However, the observed naming behavior indicated that this trend overlooked a significant variation from L2 norms in the learners’ naming. For this subset of objects with only one name in L1, the lowest probability of producing a native-like L2 name occurred when the L2 provided two name alternatives, with greater L2 native-likeness occurring when only one L2 name was available or when three or more L2 names were available. This pattern suggests two mechanisms: (1) the attraction of the 1-to-1 translation, as learners struggle with competing pairs of L2 names, and (2) the competition within a distribution of L2 names, as learners’ performance improves with a greater number of name alternatives, showing some indifference to the L2 alternatives when there are several.In the remaining conditions, when learners have multiple L1 words for an object, a greater number of L2 names appears to offer an advantage in selecting the dominant name. One potential explanation for this effect is the proportion of input that each alternative name comprises for L2 learners. Because the present model looks at both agreement in the dominant name and the number of alternatives, the latter provides an indirect measure of the native-speaker agreement levels for each alternate (non-dominant) name. As the number of alternative names increases, the remaining portion of the norm is divided into smaller parts relative to the dominant name, and thus each alternative name becomes a less salient competitor.and split with only one alternate name which shares all of the remaining native speaker agreement, e.g., 60% of native speakers name an object plate while 40% call the object dish. As new name alternatives are introduced, the second- through nth-most dominant names fall off in agreement, e.g., 60% of native speakers call an object plate, while 20% call the object dish, and 20% call it platter. This account of agreement and alternate names emphasizes the competition between names, and successful native-like naming is supported by greater agreement in the dominant name relative to the alternate names available. In our sample of objects, naming agreement and number of alternate names are correlated such that higher agreement is associated with fewer alternate names, producing the low performance effect at two alternate names . The logistic model, on the other hand, dissociates these two variables and thus does not show this effect in either variable independently .Under the foregoing explanation, we would expect the lowest L2 learner performance to occur when naming agreement is low As the number of names in the sample of L1 native speakers increased, L2 learners’ native-likeness declined, suggesting that the relative frequency of the dominant name was less important for L1 than the full array of available names. This observation makes intuitive sense, as we would expect the learners to have a more stable, entrenched knowledge of their native language. In the case of L1, participants may simply be sensitive to the presence of names regardless of agreement level, or alternate names in L1 may reflect a more general uncertainty about the identity of an object and, apart from language, its membership in semantic categories with other objects. If the function of an entirely novel object is unknown, even native speakers will have a difficult time settling on the best name for that object because lexical categorization does not strictly adhere to similarity of physical features like size and shape.Finally, performance on six objects that had two competing names in both L1 and L2 was observed to be the worst overall among the stimuli. This effect is not replicated in the modeled plots because, again, it depends not only on the number of names but on the combination of name agreement and number of names, while the model parametrically varies each of these factors. Indeed, the item-wise observations are consistent with the proposal that the distribution of naming agreement between the two objects in L2 drives the general disadvantage for two-named objects.bottle with feature sets describing its exemplars).In the introduction, we explored how theories of lexical and semantic representation could be extended to understanding patterns of lexical categorization. The present study does not directly implement any specific theoretical model, as we observe only the naturally occurring shifts in lexical categorization by Chinese–English bilinguals over their varying language learning and language immersion experiences. Nonetheless, connectionist theories such as the Distributed Feature Model  and compSpecifically, important factors in connectionist training paradigms, such as amount, frequency, and consistency of input are readily translated into the lexical categorization terms used in this study. We quantify the amount of L2 experience (LOR), frequency of the dominant name relative to other names (naming agreement), and alternate names, finding compelling parallels between the associative learning principles that underlie connectionist models and the estimated effects of these variables on L2 categorization. For example, the (weak) age of onset effect observed in the present study concurs with entrenchment accounts of age effects in models of lexical acquisition e.g., .Entrenchment also provides some explanation for the relative disadvantage in re-mapping L2 categories for objects with high L1 agreement, as high agreement confers greater training frequency for the dominant L1 name (for a given object presentation). The role of L2 linguistic community variables in predicting learners’ native-likeness confirms that learners are sensitive to the relative frequency of several alternate names, showing improved performance when the agreement for the dominant name increases and decreased performance when alternate name competitors increase in frequency . We also found support for the interactive relationship between L1 and L2 mappings, as suggested by the models proposed by The present study offers several new insights into the role of language history, language training, and language use in second language lexical semantic learning. Most importantly, we find that greater time spent studying a second language before immersion predicted lower levels of eventual L2 native-likeness, likely due to the entrenchment of L1-like lexical-semantic mappings. Although we do find an age of onset effect, even after controlling for immersion and duration of language training, the magnitude of this age effect is proportional to the benefits of immersion, and the benefits to L2 native-likeness from early age of onset are small relative to the effects of more pre-immersion training.On the extreme end, one might propose that pre-immersion language instruction is actually counter-productive to native-like lexical semantic development, and second language education would be best postponed until immersion opportunities arise. However, this viewpoint is impractical for most non-immigrant learners, and likely over-stated, as our analysis of language-specific variables  show that learners are, in fact, highly sensitive to the inconsistent input that describes native-like lexical categorization. Lexical semantic learning in non-immersion environments might therefore be improved by introducing learners to a greater variety of referents and the naturally diverse naming patterns associated with those referents, allowing them to develop more native-like intuitions about the relationships between objects that define lexical categories. The method of using a diverse set of naming patterns in second language instruction clearly contradicts the traditional classroom teaching method, in which training focuses primarily on one-to-one translations; such a focus underestimates cross-languages differences, and by our findings, encourages the use L1 patterns for L2 words and therefore impedes learners’ later ability to acquire native-like lexical semantic mappings.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.


Arabidopsis thaliana and in the model legume species Medicago truncatula to highlight and compare the regulation of sulfate transporter genes under drought and salt stress. We also discuss their possible function in the plant’s response and adaptation to abiotic stresses and present prospects about the potential benefits of mycorrhizal associations, which by facilitating sulfate uptake may assist plants to cope with abiotic stresses. Several transporters are highlighted in this review that appear promising targets for improving sulfate transport capacities of crops under fluctuating environmental conditions.Drought and salinity are two frequently combined abiotic stresses that affect plant growth, development, and crop productivity. Sulfate, and molecules derived from this anion such as glutathione, play important roles in the intrinsic responses of plants to such abiotic stresses. Therefore, understanding how plants facing environmental constraints re-equilibrate the flux of sulfate between and within different tissues might uncover perspectives for improving tolerance against abiotic stresses. In this review, we took advantage of genomics and post-genomics resources available in  Arabidopsis that may help to combat environmental stresses. Such metabolic adjustments undoubtedly rely on the plant’s ability to absorb and distribute sulfate to the different organs in amounts sufficient to fulfill requirements.Drought, the incidence of which is expected to increase with climatic changes, is one of the major abiotic constraints on agricultural productivity. Because drought is often associated with salinity, one challenge for sustainable agriculture is to breed crops for enhanced tolerance to both stresses. This requires an understanding of the adaptive mechanisms allowing plants to survive in low-water and high-salt environments. Sulfur is a key component in helping plants to cope with such abiotic stresses  fungi to improve sulfate uptake.Major advances have been made toward identifying and characterizing the transporters involved in the uptake, distribution, or eﬄux of sulfate from the vacuoles, especially in salinity . After aMedicago truncatula is an annual forage species adopted in 2001 as a model for legumes because of its small genome, compared to crop legumes such as pea, and its ability to perform symbiotic interactions with nitrogen-fixing rhizobia and AM fungi, like most legume species (M. truncatula genome with that of pea (Pisum sativum L.) facilitates the transfer of information to the crop, and molecular markers have been developed for translational genomics between the two species . Fourteen genes homologous to the Arabidopsis SULTR genes (AtSULTR) were identified in the last Medicago genome version 4.0v11. Phylogenetic analysis using SULTR full length amino-acid sequences allowed us to re-annotate the MtSULTRs and to refine their phylogenetic relationship with AtSULTRs . The corresponding neighbor-joining tree divided into four clusters matching the four groups described in Arabidopsis . Expression of three of the 14 MtSULTR genes  could not be investigated as there was no corresponding probe set in the Affymetrix chip used to build the MtGEA. To compare SULTR gene regulation between M. truncatula and Arabidopsis, we used transcriptomic data available in Arabidopsis for drought and salt stress experiments  and co-localizes with quantitative trait loci (QTL) regions for salt tolerance  is co-expressed in roots with SULTR3;1 in response to drought in the two species and in response to salt stress in Arabidopsis (Table 1). The reduced ABA content in seedlings for the two mutants Atsultr3;1 and Atsultr3;4 suggests a role for both genes in relation to ABA production. The subcellular localization of SULTR3;4 is unknown. Investigating spatial and subcellular localizations in roots for both transporters might help to decipher whether they can have a coordinated function or a functional redundancy in this tissue. It should be noted that in contrast to Arabidopsis, MtSULTR3;1 and MtSULTR3;4a are differentially regulated in response to salt stress  and that a second MtSULTR3;4 gene  exists whose response to salt stress is currently unknown.Of particular interest is the up-regulation of the ynthesis . Cysteinynthesis . The cysynthesis , it is tolerance , as alsoSULTR3;1 . This main roots , suggestbidopsis . AnotherM. truncatula, the expression of another group 3 SULTR  is strongly up-regulated in roots subjected to salt stress . Its closest Arabidopsis homolog, AtSULTR3;5, shows opposite trends of expression in roots with a consistent down-regulation in response to salinity. This suggests distinct roles or transcriptional regulation of SULTR3;5 between the two species. In the legume species Lotus japonicus, the SULTR3;5 homolog SST1 (Symbiotic Sulfate Transporter 1) is necessary for nodule formation and essential for the symbiotic supply of sulfur to the bacteria . Owing to the co-activator function of AtSULTR3;5, this may slow the allocation of sulfate to aerial parts. It is therefore possible that Arabidopsis adjusts the level of sulfate in roots under salt stress by modulating AtSULTR3;5 expression. This could be part of the adaptive mechanisms used by Arabidopsis to load sulfate into xylem vessels while ensuring that sufficient sulfate remains in roots when uptake is limited due to high salt concentrations in soils. In M. truncatula, the SULTR2;1 gene is not significantly regulated in roots in response to salt stress, but down-regulated in this tissue at early stages of water stress. The function of this transporter has not been reported yet, but if we assume a similar role to its Arabidopsis homolog, the down-regulation observed is likely to reflect a need to maintain sulfate in roots at these stages.In contrast to the functional SST1 , AtSULTRal parts . Interesal parts  and undeSULTR genes in Table 1 that are regulated in shoots or leaves are good candidates for re-equilibrating the flux of sulfate in aerial parts in response to abiotic stresses. First, SULTR2;1 is significantly down-regulated in leaves of Arabidopsis and M. truncatula subjected to drought. AtSULTR2;1 has been shown to be not only expressed in the xylem parenchyma cells but also in the phloem cells of mature leaves, where it participates in the translocation of sulfate to young leaves . It would be of particular interest to investigate whether this transporter could play a role in leaves in controlling their early response to water stress in strong connection with ABA biosynthesis. In Arabidopsis, AtSULTR3;1 and 3;4 are both significantly up-regulated in leaves subjected to salt stress, reinforcing the hypothesis raised in the previous section that both transporters could act in concert to mitigate the effect of salt stress.A continued loading of sulfate into xylem vessels is of paramount importance for maintaining the synthesis of sulfur molecules in aerial parts. Moreover, sulfate from the xylem acts as a chemical signal for ABA-dependent stomatal closure in leaves during early stages of water stress when ABA biosynthesis is restricted to leaves . Severalg leaves . Hence, AtSULTR4 genes is significantly enhanced in leaves by drought and salinity. Moreover, AtSULTR4;1 and AtSULTR4;2 fall in QTL regions for tolerance to both stresses . MtSULTR1;1 appeared down-regulated in roots subjected to both abiotic stresses, whereas MtSULTR1;2 and AtSULTR1;2 were up-regulated in response to salinity and drought, respectively. SULTR1;1 and SULTR1;2 display unequal functional redundancy in Arabidopsis and left open the possibility for the SULTR1;1 gene to display an additional function besides its role in sulfate membrane transport. Recent findings also proposed a supplementary role for AtSULTR1;2 in the regulatory or sensing/signaling pathways related to sulfur metabolism (The capacity of roots to take up nutrients generally declines in salt- and water-stressed plants, which may explain the changes in expression of SULTR genes belonging to groups 2, 3, and 4 under these conditions to rebalance sulfate flux between affected tissues. By examining the regulation of the two tabolism . FurtherArabidopsis, is their ability to perform symbiotic interactions with AM fungi. This mutualistic association is known to increase plant tolerance to drought (M. truncatula revealed that AM fungi improve sulfur nutrition in low-sulfate environments (SULTR genes up-regulated at low sulfate concentrations in roots colonized with AM fungi (MtSULTR1;1 and MtSULTR1;2, both up-regulated in roots of AM symbiotic plants, especially at low sulfate concentrations (LjSULTR1;2 gene during the Lotus japonicus/Rhizophagus irregularis mutualistic interaction, and the specific expression of this transporter in arbuscule-containing cells, strongly suggesting AM-specific sulfate transport. Investigating the regulation of such genes during AM symbiosis in response to abiotic stresses might help to decipher the roles played by these transporters in fluctuating environments.The emerging role of sulfate in plant adaptation to abiotic stresses reinforces the need to sustain proper sulfate uptake and use in cultures that face environmental stresses. One specific feature of legumes, compared to  drought , an abioronments , probablronments . To dateAM fungi  might hetrations . RecentlSULTR genes regulated by drought and/or salinity were highlighted in this review that may contribute to adjust sulfur distribution in plants subjected to abiotic stresses. We discussed their possible roles using information available in Arabidopsis, for which considerable advances have been made in the last two decades toward understanding SULTR functions, more recently in response to salinity (SULTR genes similarly regulated in Arabidopsis and M. truncatula are promising targets for improving sulfate transport capacities under fluctuating environmental conditions. Among these are group 3 SULTR, also in the list of abiotic stress-responsive genes shared between Arabidopsis and M. truncatula of M. truncatula and in the pea crop (SULTR genes as relevant candidates for discovering favorable alleles for abiotic stress tolerance.Several salinity . SULTR gpea crop  that canThe 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.


Currently, hematopoietic stem cell transplantation (HSCT) is the only curative option for patients with beta-thalassemia major, but liver iron overload in these patients will not decrease and hepatic fibrosis may still progress despite successful HSCT.Liver biopsy samples were taken from 14 patients (Out of 25 patients) who underwent HSCT. All patients met three criteria: negative HCV antibody, liver fibrosis in samples before HSCT and lack of regular treatment for iron overload after HSCT (Because patients did not consent to phlebotomy or they had not regular follow-up). We evaluated liver fibrosis and liver iron overload by a semi quantitative method, Perls’ Prussian blue staining, before and after HSCT. HSCT was successful in all the patients. Liver iron overload did not change after transplant (P=0.61), but hepatic fibrosis progressed after transplant (P=0.01).In patients with beta thalassemia major who previously had some degree of liver fibrosis, HSCT alone cannot reduce liver iron overload and liver fibrosis will increase. We recommend that regardless of the amount of iron overload in patients with beta thalassemia major that have shown some degree of fibrosis in their liver biopsy before transplantation, appropriate steps should be taken to reduce iron overload as soon as possible after successful transplantation. Based on three criteria, Lucarelli et al. classified patients with thalassemia in terms of HSCT risks: the presence of hepatomegaly, the presence of liver fibrosis in the pretransplantation liver biopsy and a history of inadequate chelation therapy before HSCT. These three factors stratify patients into three groups: class 1, with none of the risk factors; class 2, having one or two of the risk factors; and class 3, with all three risk factors present. Event-free survival rates after transplantation are 90%, 81%, and 54% in classes 1, 2, and 3, respectively.8 Beta-thalassemia is very common in Iran and is considered to be an endemic disease. Beta Thalassemia can be clinically divided into three forms: 1) a minor or trait form, 2) a major form that is dependent on blood transfusion, and 3) an intermediate form in which the patient has anemia, but usually becomes dependent on blood transfusion in the second decade of life. The main treatment for thalassemia major is regular blood transfusion with the major side effect of iron overload leading to severe complications within a few years and eventually to death if not treated. The mechanism of secondary cell damage due to intracellular iron overload is not clear, but it is believed to produce hydroxyl free radicals that lead to damage to cell lysosomes and mitochondria.6,9,10 Another method is Hepatic Iron Concentration (HIC) expressed as milligrams of iron per gram of dry liver weight; it can also be expressed as µmole of iron per gram of dry liver weight, with normal values under 25 µmole/gr or 1.6 mg/gr. Values over 71 µmole/gr indicate liver iron overload.3,6,12,13,14 Liver biopsy and the semiquantitative measurement of liver iron concentration through histochemical staining methods such as Perls’ Prussian Blue Staining are the methods used for the assessment of the liver iron status. The patients with thalassemia major who had degrees of liver fibrosis in their liver biopsies before transplantation (Class 2 or 3) and had not received iron reduction treatments after successful transplantation were selected and invited for a second biopsy; all of them were negative for the HCV antibody test. The objectives of this study included determination of the degree of liver fibrosis and liver iron storage before and after HSCT. Out of 25 patients meeting the above-mentioned criteria, 4 patients with active evidence of chronic graft-versus-host disease (GVHD) and 7 patients who were not satisfied were excluded. The 14 remaining patients gave valid consent for liver biopsy. In 9 and 5 patients, stem cells were isolated from the peripheral blood and bone marrow donors, respectively. Informed consent was obtained from all patients and/or parents. Patients were admitted to the ward and liver biopsy was performed under ultrasound guidance. Information about patients is shown in 9,10 Iron is not demonstrable in the normal liver using standard histochemical  methods and the Perl’s histochemical method is widely used for this purpose because of its high sensitivity and specificity. This method also has the advantage of being relatively simple to perform. Hepatocytes with no increase in iron storage produce a pale blue color, but those with iron overload appear to have dark blue pigments in their cytoplasm, nuclear membrane, nucleolus, or sinusoidal spaces. Intensity of iron deposition in the liver is expressed as the grade or amount of the stained iron, and depends on the presence of iron deposits in acinar zones and portal structures, and also on the presence of fibrosis in the liver. The most common grading method of tissue iron overload is from 0 to 4+, but there are other methods in which the grade ranges from 0 to 5+. Post-transplant liver biopsy slides were stained. The patients’ pre-transplant liver biopsy slides were then retrieved from the pathology archives. All slides were coded with numbers before being submitted to thepathologist for review of iron and fibrosis status . The mean time interval between bone marrow transplantation and liver biopsy after transplantation was 47.2 months (range: 9 to 102 months). In this study, 14 patients  with a mean age of 11±6.2 years were investigated (age range: 4 to 25 years old). Of them, 6 were under and 8 were over 10 years of age. The source of stem cells was bone marrow in 5 and peripheral blood in 9 patients. The increasing fibrotic changes were significant (P=0.01), but iron variations did not show a statistically significant difference (P=0.61).The comparison was performed using the Wilcoxon signed ranks test. Changes in fibrosis were directly correlated with the time of biopsy, but the correlation was not statistically significant. The later the biopsy was performed, the more fibrosis was reported but the correlation was not significant due to the small sample size of the study . However, the Spearman correlation test showed no linear correlation between iron changes and the time of biopsy . The Mann-Whitney test showed no significant difference in iron changes between males and females .  Additionally, according to the Mann-Whitney test, there was no significant difference between patients <10 and those ≥10 years of age in terms of iron changes . The Mann-Whitney test showed no significant difference between transplantation using bone marrow and transplantation using the peripheral blood in terms of iron and fibrosis changes . 5 The use of iron chelators such as Deferoxamine after HSCT has the same effect.15 Deferoxamine should not be administered immediately after transplantation because it can interfere with engraftment. Iron depletion by phlebotomy after HSCT can cause cirrhosis regression in patients with iron overload.8 In this study, fibrosis and iron overload before HSCT were not compared with the values after HSCT (prior to phlebotomy). In one study in Pesaro, 48 patients who had been regularly followed-up after transplantation for homozygous beta thalassemia were enrolled in a program of regular phlebotomy. Patients were eligible for this study if they were in prognostic classes 2 or 3 before transplantation; the 2-year follow-up examinations showed serum ferritin levels greater than 2,000 μg/L and the 2-year post-transplant liver biopsies showed moderate or severe iron overload without phlebotomy. In this study thirty-five (85%) patients were hepatitis C seropositive. The results of the study showed that serum ferritin and HIC were reduced after repeated phlebotomy , but the degree of hepatic fibrosis was not significantly changed .In our center, most patients with beta thalassemia major were treated with phlebotomy after successful transplantation, but some of them had not received this treatment, because did not consent to phlebotomy or they had not regular follow-up. The main selection criterion in our study was liver fibrosis before transplantation (prognostic classes 2 or 3) regardless of the severity of iron accumulation in the liver or serum ferritin level. In this study we have shown that liver fibrosis alone before HSCT (with any degree of hepatic iron overload) can cause liver fibrosis progression after HSCT.16, but this did not occur in patients with severe iron overload at the time of transplant; in these patients, there may be progression of liver damage after HSCT.17,18 Our investigation has shown, however, that liver fibrosis before HSCT in patients with β-thalassemia is a more important factor.In patients with mild to moderate iron overload, a gradual spontaneous decrease of iron overload after HSCT was almost sufficient to normalize iron levels8 and the progression of liver fibrosis after transplantation shown in our study, highlight the importance of preventive phlebotomy. A lack of regression of hepatic fibrosis after phlebotomyCombining the four criteria in all patients  limited the number of samples available for this study.6,7,8,9, 12 Another point that should be considered in the study was the assessment of iron overload using Perl’s histochemical tissue staining. We used this method because quantitative measurement (HIC) was not available in our country. However, the results of our study are supported by studies that have used HIC. In other words, non-depletion of the excess iron by phlebotomy or iron chelators after HSCT will result in stabilization of the amount of liver iron. According to our results, patients who had received transplantation at least 9 months earlier had liver fibrosis progression.  We recommend that if HSCT is successful in a patient with any degree of liver fibrosis before transplantation (irrespective of the severity of iron overload), the total body iron load should be reduced with phlebotomy as soon as possible.


TM is a T cell antigen discovery platform in which putative antigens are expressed as individual clones that can be processed by any subject's antigen presenting cells and presented as peptide epitopes in the context of their own MHC class I or II molecules. If autologous CD4+ or CD8+ T cells are added that are specific for a given clone in a given well, a readout of activation can be measured. We hypothesized that the ATLAS™ technology could be applied to characterize and profile the T cell responses to tumor-associated antigens (TAA) of diverse human subjects undergoing CPI therapy. As a proof of concept, an expression library containing 23 full-length melanoma TAA was constructed and used to interrogate memory CD4+ and CD8+ T cell responses from of a cohort of melanoma patients who have undergone treatment with pembrolizumab. All 23 TAA were cloned and sequence verified; 96% and 91% were successfully expressed in the CD4-specific and CD8-specific library, respectively. Peripheral blood mononuclear cells (PBMC) were collected from ten patients who had undergone immunotherapy with pembrolizumab; CD4+ and CD8+ T cells were sorted and non-specifically expanded, and monocytes differentiated into dendritic cells (MDDC) in vitro. PBMC yields were comparable to historical assay data with the exception of lower viability of MDDC. Memory T cell responses to TAA were detectable and this study suggests that T cell responses to TAA can be measured in multiple, HLA-diverse subjects during CPI therapy, without the need to derive cell lines or use predictive algorithms. Work is currently underway to increase both the number of antigens and participants evaluated. Analyses will include the frequency and breadth of responses and the relationship between CD4+ and CD8+ T cell responses in subjects who benefit from CPI therapy compared with those who do not. This work has implications for both patient stratification and identification of novel immunotherapies.Successful treatment of melanoma patients with checkpoint inhibitors (CPI) has reinforced the importance of T cells in anti-tumor efficacy. Despite significant progress, CPI therapy is effective in only 40-50% of treated subjects, with substantial toxicity. As a result, it is imperative to understand the profile of T cell responses to tumor antigens, to determine if patterns of responsiveness can be identified for those subjects who respond to immunotherapy. ATLAS


The Bradykinesia Akinesia Incoordination (BRAIN) test is a computer keyboard-tapping task that was developed for use in assessing the effect of symptomatic treatment on motor function in Parkinson's disease (PD). An online version has now been designed for use in a wider clinical context and the research setting.Validation of the online BRAIN test was undertaken in 58 patients with Parkinson's disease (PD) and 93 age-matched, non-neurological controls. Kinesia scores , akinesia times , incoordination scores  and dysmetria scores  were compared between groups. These parameters were correlated against total motor scores and sub-scores from the Unified Parkinson's Disease Rating Scale (UPDRS).Mean KS30, AT30 and IS30 were significantly different between PD patients and controls (p≤0.0001). Sensitivity for 85% specificity was 50% for KS30, 40% for AT30 and 29% for IS30. KS30, AT30 and IS30 correlated significantly with UPDRS total motor scores  and motor UPDRS sub-scores. The reliability of KS30, AT30 and DS30 was good on repeated testing.The BRAIN test is a reliable, convenient test of upper limb motor function that can be used routinely in the outpatient clinic, at home and in clinical trials. In addition, it can be used as an objective longitudinal measurement of emerging motor dysfunction for the prediction of PD in at-risk cohorts. The Bradykinesia Akinesia Incoordination (BRAIN) test has been validated as a sensitive software tool for detecting signs of neurological disease, including Parkinson's disease (PD) and cerebellar dysfunction The traditional alternate finger tap test, in which the patient is observed tapping two mounted counters 15 cm apart as fast as possible has been used for many years to measure response to symptomatic drug treatment The original version of the BRAIN test was programmed to run in MS-DOS mode on an IBM-compatible personal computer The normal range of scores in healthy individuals and what influences these,The difference in scores between PD patients and healthy individuals,The reliability of the BRAIN test and its utility in repeat measurement.Ethics statement: All participants gave written informed consent via the BRAIN test website. The web-based consent form lists relevant consent statements and each statement has a check box. Participants must check each box before clicking the submit button on screen. The Queen Square Research Ethics Committee approved the entire study and the specific method of obtaining written consent.3Patients who fulfilled the Queen Square Brain Bank for Neurological Disorders criteria for the clinical diagnosis of PD and age-matched non-neurological controls were recruited from the outpatient department at the Royal London Hospital and the National Hospital for Neurology and Neurosurgery.Participants undertook the test seated at a desktop computer and keyboard. They followed on-screen test instructions and received no assistance during the test. Participants were allowed to choose which hand was tested first in acknowledgement of the fact that future use may frequently be unobserved. Each participant undertook two tests (one for each hand). Preliminary study established that a 30 second time period gave adequate information and would lead to greater compliance than testing for one minute For reliability testing, 17 of the controls were asked to repeat the test multiple times for each hand. This had the secondary advantage of being able to investigate the possibility of a learning effect. Due to the tendency of motor features of PD to change in relation to medication and possible time of day, PD patients were not included in tests measuring reliability. However, six patients with PD and known motor fluctuations were invited to undertake the test on several occasions during the day, before and after medication in order to evaluate the BRAIN test in monitoring motor fluctuations.The BRAIN test reports four variables calculated from raw data generated from key taps: kinesia score (KS30), the number of key taps in 30 seconds; akinesia time (AT30), the mean dwell time on each key in milliseconds (msec); dysmetria score (DS30), a weighted index using the number of incorrectly hit keys scored in a target fashion  then divided by the total number of key taps ; and incoordination (or arrhythmia) score (IS30), the variance of the time interval in msec between keystrokes.Descriptive statistics were calculated for all four variables. For continuous variables, means were reported if the data were normally distributed (assessed using the Shapiro-Wilks test) and medians were reported if not normally distributed. Within-group (PD or control) comparisons were performed using the paired t-test for normal distributed data or Wilcoxon-signed rank test for non-normally distributed data. Between-group comparisons were made using the unpaired t-test or Wilcoxon rank sum test. The sensitivity and specificity of test parameters separately and together were determined using logistic regression and receiver operated characteristic (ROC) curves. Associations between UPDRS and BRAIN test parameters were estimated using Spearman's rank correlation coefficient for non-normally distributed data. Coefficients of variation were calculated to determine reliability of test parameters in control subjects undertaking the test multiple times. The pre-determined significance level for all calculations was p = 0.05. All analyses were performed using Stata version 12 and GraphPad Prism version 6 for Mac.There were 58 PD patients and 93 non-neurological controls included in the main analysis  and controls (n = 93) were compared using averages of the scores from each hand; KS30, AT30 and IS30 discriminated between groups, but DS30 did not see . KS30 shHands were compared in patients and controls see . In bothBRAIN test scores in PD patients only were compared to total motor UPDRS scores and sub-scores see  using avIn PD patients there was a difference of borderline significance with lower KS30 in the more affected hand when compared to the less affected hand . There was no difference in AT30, IS30 and DS30 between the two hands . Duration of PD in years did not correlate with any of the four parameters (data not shown).Seventeen of the controls repeated the BRAIN test five times for each hand to estimate the reliability of KS30, AT30, IS30 and DS30. The coefficient of variation for KS30 was 6.0%, for AT30 was 7.3%, and for DS30 was 3.4%. IS30 had a high coefficient of variation reflecting the fact that pauses in the test (even in control subjects) magnify the variance of travelling time significantly, decreasing the reliability of IS30 overall. There was a mild learning effect that saw KS30 increase by 1.2 taps per attempt (p = 0.002) but no learning effect for AT30  or for IS30 and DS30 .Finally, using KS30 and AT30 the effects of medication were assessed in a small number of PD patients with predictable motor fluctuations see  and alsoThe BRAIN test has previously been shown to differentiate individuals with PD from healthy controls and also to correlate with PD severity measured by disease-specific rating scales In PD patients, KS30 correlated significantly with total motor UPDRS score, and limb specific sub-scores, as clinical indicators of motor disease severity. AT30 and IS30 also correlated significantly, albeit less strongly than KS30, with the total motor UPDRS score and some of the limb specific sub-scores.Our data for PD and non-neurologic controls shows wide intra-group variability and yet the differences between KS30, AT30 and IS30 are highly significant and of clinically relevant magnitude, enabling cut-offs for sensitivity and specificity to be determined. We present results to optimize specificity, with resulting moderate to low sensitivity, thereby reducing the false-positive rate in acknowledgment that the test will often be performed remotely. Of course sensitivity and specificity operate on a continuum and much higher sensitivity can be achieved if the cut-offs are altered to accept a higher false positive rate.Age clearly affected most of BRAIN test parameters and should be taken in consideration in future studies using the test. Education also influenced parameters perhaps reflecting an effect of computer literacy. However, this was not further reflected when examining occupation, with which strong relationships could not be found. This perhaps reflects widespread uptake of computers regardless of whether use is work-related or recreational. Patients that were tested in whilst ‘On’ performed better than those that were ‘Off’. When PD patients that were ‘Off’ were compared to controls the discriminative ability of KS30, AT30 and IS30 improved significantly. Comparison of most and least affected sides in PD patients only showed trends for poorer scores on the most affected side, perhaps reflecting bilateral involvement in 80% of the patient group (Hoehn and Yahr score 2 & 3).Whilst not seemingly useful for differentiating PD from healthy controls, the dysmetria score (DS30) does provides a useful reference for judging whether tests have been completed properly, which is particularly valuable for remote testing. For example, the mean KS30 in controls is 60.3 and has a standard deviation of 13.1. Using three standard deviations as a cut-off, it is unlikely that an individual can exceed 100 alternate taps in 30 seconds without a dramatic loss of accuracy as reflected by the DS30. Occasionally very high scores have been seen in remote tests, such as those performed in the PREDICT-PD study (described further below), with perfect or near-perfect accuracy (e.g. KS30 of 200 and DS30 of 1.0) Some highly specialized tools have been developed that can accurately measure the specific motor deficit that occurs in PD and some have the capacity to differentiate the sequential tapping abnormalities in PD (true bradykinesia) from that seen in progressive supranuclear palsy and atypical tremors One criticism leveled at the BRAIN test during preliminary testing was that not all keyboards have identical characters, particularly outside the US and UK. Furthermore, the position of the ‘S’ and ‘;’ keys on the keyboard can vary between countries. These keys were originally chosen because they are 15 cm apart on a US/UK English standard desktop computer keyboard and most laptop computers. If one considers a standard keyboard divided in two halves by an imaginary line down the center, then these keys occupy a central position on their respective sides. In countries where US/UK English keyboards are not standard, the test can still be used with the keys that correspond to the position of ‘S’ and ‘;’ and has been implemented successfully by groups in Italy, Norway and the Netherlands .During testing we have found no evidence that use of different keyboards results in significant differences in results between subjects. However, use on tablet computers may be limited by the availability of the ‘;’ key and the different nature of touching a screen rather than pressing a key. As such we do not advise use of the BRAIN test on tablet computers or smart phones. In addition, use of sterile covers for keyboards in clinical settings may impair key presses. This might conceivably result in loss of accuracy (DS30) but is unlikely to affect the other three parameters .Undoubtedly the greatest value of the BRAIN test in established PD may come in the longitudinal monitoring of individual patients throughout the duration of their disease, including response to treatment and monitoring motor fluctuations. Repeat testing in controls suggests good reliability for three of the four parameters and only a minimal learning effect. In this analysis, control subjects repeated the tapping tests back-to-back and the fact that only a minimal learning effect was noted makes it unlikely that improvements due to learning would be seen were serial tests separated by days or weeks.The BRAIN test has been successfully implemented as a motor assessment in our longitudinal online Parkinson’s risk study, PREDICT-PD, which began recruiting participants in April 2011 www.braintaptest.com. Tokens for individual use can be requested by clinicians and researchers via the website.The online BRAIN test represents a simple, validated, objective tool to longitudinally monitor motor function not only in established PD but also in studies seeking to identify those at high risk of future PD. The BRAIN test can be accessed at


Diabetes mellitus (DM) refers to a group of common metabolic disorders, with multiple causes, that share a common phenotype: hyperglycemia . The concept of multiple etiologies is recent and a third group of DM is proposed today, a monogenic form with autosomal dominant inheritance: MODY. There are 13 subtypes described for MODY, which in MODY2 is one of the most common, although the relative frequency varies according to the study population . MODY2 is provoked by mutations in the glucokinase gene (GCK) located on human chromosome 7p15.3-p15.1, which consists of 10 exons that span 45.169 bp and encode a 465-amino-acid protein .To identify single base mutations in the GCK gene of patients with diagnosis of MODY2 and evaluate whether these mutations are related to the disease.This study was approved by Ethics Committee under no 923744. Patients who provided blood samples signed informed consent. DNA was extracted with Genomic DNA mini kit (Invitrogen). Primers for amplification of exon 10 are described in the literature  and PCR was performed with GoTaq kit (Promega). The amplicons were sequenced with BigDye Terminator v.3.1 kit (Life Technologies). Sequences obtained were analyzed by SeqManTM II program (DNA Star Inc.). Comparisons of the files were held with the bank sequences of the human genome plus the bank transcripts (NCBI) using BLAST program .The case study was conducted with a female proband, 40 yrs., with MODY2 diagnosis. Santos et al (2014) identified 3 mutations in the upstream promoter GCK gene for this proband and hypothesized if mutations in the promoter could be responsible for the disease. To test this hypothesis, we started sequencing all exons to exclude the possibility if other mutations are responsible for the phenotype. So far, we haven't found SNPs in sequenced exons  and from 2 to 6. Few studies have reported mutations in GCK promoter, but these can be connected with some form of diabetes, such as gestational diabetes .In this study, we sequenced 70% of the exons of the GCK gene and SNPs were not identified or any other type of mutation that corroborate the hypothesis of MODY2. However, these Results added to the previous GCK promoter data of this proband strengthens the hypothesis that the reported mutations interfere with the ideal regulation of gene expression.


An intensity-modulated proton therapy (IMPT) patient-specific quality assurance (PSQA) program based on measurement alone can be very time consuming due to the highly modulated dose distributions of IMPT fields. Incorporating independent dose calculation and treatment log file analysis could reduce the time required for measurements. In this article, we summarize our effort to develop an efficient and effective PSQA program that consists of three components: measurements, independent dose calculation, and analysis of patient-specific treatment delivery log files. Measurements included two-dimensional (2D) measurements using an ionization chamber array detector for each field delivered at the planned gantry angles with the electronic medical record (EMR) system in the QA mode and the accelerator control system (ACS) in the treatment mode, and additional measurements at depths for each field with the ACS in physics mode and without the EMR system. Dose distributions for each field in a water phantom were calculated independently using a recently developed in-house pencil beam algorithm and compared with those obtained using the treatment planning system (TPS). The treatment log file for each field was analyzed in terms of deviations in delivered spot positions from their planned positions using various statistical methods. Using this improved PSQA program, we were able to verify the integrity of the data transfer from the TPS to the EMR to the ACS, the dose calculation of the TPS, and the treatment delivery, including the dose delivered and spot positions. On the basis of this experience, we estimate that the in-room measurement time required for each complex IMPT case  is less than 1 h using the improved PSQA program. Our experience demonstrates that it is possible to develop an efficient and effective PSQA program for IMPT with the equipment and resources available in the clinic. Proton therapy can be delivered using either scattering or scanning beam methods ,2. CurreThere are two general approaches to optimizing a spot scanning proton therapy (SSPT) plan. The first approach is single-field optimization (SFO), in which the spot weights are optimized on a field-by-field basis; that is, each field is optimized individually to deliver a fraction of the prescribed doses to the entire target volume(s). SFO is most commonly used to produce a uniform dose over the entire target volume by each field, known as single-field uniform dose (SFUD); it is also used to create a simultaneously integrated boost by each field, called single-field integrated boost (SFIB) . The seci.e., the monitor unit (MU)).One of the most important elements of a successful SSPT program is patient-specific quality assurance (PSQA). A PSQA program should be able to verify the dose calculated by the treatment planning system (TPS) and the correctness of treatment delivery. This verification is commonly achieved by comparing the calculated and measured dose distributions in a phantom. For SSPT, treatment delivery includes the variables specifying the spot position , spot size, and amount of dose delivered by the spot (We previously reported our PSQA experience for SFUD patients that was based on measurement only and derived from a PSQA program for intensity-modulated radiation therapy (IMRT) with photons . The incThe limited time available for PSQA merits the development of PSQA programs that can be executed efficiently. However, knowledge in this area is lacking, as few reports have comprehensively described PSQA programs for patients receiving particle therapy ,8,9. Mos2 in water [2/g at the distal end of the target), a 6.7-cm range shifter made of acrylonitrile butadiene styrene resin is used to bring the Bragg peak as shallow as 0.3 cm2/g. A schematic of the M. D. Anderson scanning nozzle and its major PSQA-related components is shown in The SSPT beam delivery system at the University of Texas M. D. Anderson Cancer Center consists of a synchrotron  with 94 energies ranging from 72.5 MeV to 221.8 MeV; these energies correspond to proton ranges of 4.0–30.6 g/cmin water . The maxWe used the Eclipse TPS , which has a proton module for double scattering and SSPT delivery, in this report. The TPS uses a pencil beam algorithm. The commissioning of the TPS’s dose models in water for SSPT has been described previously [As of April 2014, more than 1200 patients had been treated with SSPT at our proton therapy center. These patients’ disease sites included the prostate (64% of patients), central nervous system and pediatric (14%), head and neck (15%), and lung (6%). About 16% of these patients were treated with IMPT, 11% were treated with SFIB, and the remaining 73% were treated with SFUD.While it was initially developed based on photon IMRT , our PSQAs shown in For the first 249 prostate cancer patients we treated with SFUD using this PSQA program , the difTM, ScanditronixWellhofer, Schwarzenbruck, Germany) mounted either to a couch attachment or in the gantry for fields with a range shifter  and range shifter (94.8% vs. 99.0% with vs. without range shifter) dependent but independent of the optimization methods SFO vs. MFO. The steep dose gradients perpendicular to the beam measurement plane were the major source of low γ-index scores. The authors also confirmed that γ-index passing rate 90% with 3%/3 mm criteria was a reasonable clinical action level for 2-dimensional comparisons of dose planes even for more complex dose distributions encountered in SFIB and IMPT.In a follow up paper, Mackin et al.  retrospeWe recently reported that treatment delivery log files containing delivered spot positions and MUs could be used as part of a PSQA program . We founThe lack of treatment room availability for PSQA measurements can cause a bottleneck in starting new patients’ treatment. We reviewed the amount of time the treatment room was used for PSQA for 10 oropharynx patients with bilateral target volumes, including supra-clavicle nodes, treated with IMPT at three different dose levels ) to the CTV1, CTV2 and CTV3, respectively). All patients were treated with a technique involving three fields: the right superior-inferior anterior oblique, the left superior-inferior anterior oblique, and the posterior to anterior fields. The time (mean ± 1 standard deviation) required for each measurement, which included the beam-on time, the time spent waiting for the beam to become available, and the time spent changing certain machine settings and/or QA setup as necessary, was 6.7 ± 1.7 min. The time for each measurement also depended on the performance of the machine (the number of times beam delivery was paused or aborted during beam delivery). The time required to set up and calibrate the 2D-ICAD was 33 ± 3 min. This time did not include the time required to warm up the detector, which was done before the treatment room became available.As mentioned above, to obtain the required magnetic bending strength for each energy type, our SSPT delivery system requires each treatment field to be delivered through the EMR system in the QA mode and through the ACS in the treatment mode at least once. Given these circumstances, the minimum time required for use of the treatment room for PSQA measurement for one oropharynx patient is about 50 min. This time would be reduced to about 30 min per patient if three patients were measured during the same session because the time required to set up and calibrate the detector is reduced on a per-patient basis. Because we perform two additional measurements in the physics mode for each field, the treatment room time required for PSQA for one oropharynx patient would be about 100 min. This time would be reduced to approximately 70 min per patient if three patients were measured per session. Although the time is still long to use the treatment room, it represents significant progress compared to the PSQA time required for the first group of 10 oropharynx IMPT patients we treated (approximately 200 min for one patient only and about 170 min per patient if three patients were measured per session), for whom we took up to seven additional measurements for each field in the physics mode. Eliminating the measurements in the physics mode would reduce the treatment room time required for each patient to approximately 30–50 min depending on the number of patients measured per session. These results are specific to oropharynx patients with bilateral target volumes treated with a three-field IMPT technique. For other types of patients, these numbers may vary significantly depending on the complexity of the target volumes and the numbers of fields used.We have demonstrated that it is possible to develop an effective and efficient PSQA program for SSPT patients using the resources available to us in the clinic at M. D. Anderson.The purpose of PSQA program was to ensure the planned dose was delivered to patient accurately. The ideal PSQA program for SSPT should be based on reliable and accurate 3D dosimeters to perform end-to-end test measurements for each field through the EMR in QA mode and ACS in clinical mode. However, 3D dosimeters are not mature enough to be used for routine QA, especially for proton beam therapy, despite active research in several different fronts, including liquid ,23 and sFor dose distributions measured with 2D dosimeters, 3D γ-analysis may be necessary, especially for the measurement plane near the steep dose gradient region such as the distal region in the example shown in et al. [et al. [et al. [et al. [et al. [et al. [The literature on comprehensive PSQA programs for patients receiving particle therapy is limited ,8,9. Somet al.  develope [et al.  reported [et al.  reported [et al.  publishe [et al.  summarizIndependent dose calculations have been used as part of PSQA for IMRT with photons since the early days of IMRT ,36,37,38et al. reported using a similar approach for carbon ion beam therapy, in which a multiwire proportional chamber was used to record the fluence on a layer-by-layer basis [The use of treatment delivery log files as part of PSQA for photon IMRT has been studied extensively ,45,46,47er basis .Our PSQA program for SSPT combining measurements , indepen