Corrigendum to "Neural distinctiveness and discriminability underlying unitization and associative memory in aging" [Aging Brain 4 (2023) 100097].

[This corrects the article DOI: 10.1016/j.nbas.2023.100097.].

Neural distinctiveness and discriminability underlying unitization and associative memory in aging.

Previous work has suggested unitized pairs behave as a single unit and more critically, are processed neurally different than those of associative memories. The current works examines the neural differences between unitization and non-unitized memory using fMRI and multivoxel analyses. Specifically, we examined the differences across face-occupation pairings as a function of whether the pairing was viewed as a person performing the given job (unitized binding) or a person saying they knew someone who had a particular job (non-unitized binding). The results show that at encoding and retrieval, the angular gyrus can discriminate between unitized and non-unitized target trials. Additionally, during encoding, the medial temporal lobe (hippocampus and perirhinal cortex), frontal parietal regions (angular gyrus and medial frontal gyrus) and visual regions (middle occipital cortex) exhibit distinct neural patterns to recollected unitized and non-unitized targets. Furthermore, the perirhinal cortex and medial frontal gyrus show greater neural similarity within subsequently recollected unitized trials compared to non-unitized trials. We conclude that an encoding based strategy to elicit unitization can produce greater associative memory compared to non-unitized trials in older adults. Additionally, when unitized trials are subsequently recollected in the perirhinal cortex older adults show greater neural similarity within unitized trials compared to non-unitized trials.

Neural distinctiveness and reinstatement of hippocampal representations support unitization for associations.

The medial temporal lobe (MTL) is critical to associative memory success, yet not all types of associations may be processed in a similar manner within MTL subregions. In particular, previous work suggests intra- and inter-item associations not only exhibit differences in overall rates of recollection, but also recruit different MTL subregions. Whereas intra-item associations, akin to unitization, take advantage of associations between within-item features, inter-item associations form links across discrete items. The current work examines the neural differences between these two types of associations using fMRI and multivoxel analyses. Specifically, the current study examines differences across face-occupation as a function of whether the pairing was viewed as a person performing the given job (intra-item binding) or a person saying they knew someone who had a particular job (inter-item binding). The results show that at encoding, successfully recollected neural patterns related to intra- and inter-item associations are distinct from one another in the hippocampus, parahippocampal and perirhinal cortex. Additionally, the two trial types are reinstated distinctly such that inter-item trials have higher neural reinstatement from encoding to retrieval compared to intra-item trials in the hippocampus. We conclude that intra- and inter- associative pairs may utilize similar neural regions that represent patterns of activation differentially at encoding. However, to reinstate information to the same degree (i.e., subsequently successfully recollected) inter-item associations, that are all encoded in the same manner, may be reinstated more similarly compared to intra-item associations that are encoded by imagining pairs differently and occupation specific. This may indicate that intra-item associations promote more efficient reinstatement.

Within-category similarity negatively affects associative memory performance in both younger and older adults.

Associative memory involves the ability to encode and remember the relationship between individual items. This ability can become diminished when there is a high degree of similarity between stimuli that are being learned. Associative memory errors often stem from the fact that lures include a high degree of item familiarity as well as mnemonic similarity with the original associative episode. The current set of experiments examined how this overlap, in the form of within-category similarity, affects veridical and false retrieval in both younger and older adults. Across three experiments, results suggest that mnemonic overlap between targets and lures is detrimental to the ability to discriminate between highly similar information. Specifically, shared category membership for targets and lures led to increased false associative memories across age groups. These results have implications for scenarios where there is a high degree of overlap between target and lure events and indicate that these types of associative memory distinctions are difficult irrespective of age.

Combining genetic and physiological data to identify predictors of lifetime reproductive success and the effect of selection on these predictors on underlying fertility traits.

Fertility of the dairy cow relies on complex interactions between genetics, physiology, and management. Mathematical modeling can combine a range of information sources to facilitate informed predictions of cow fertility in scenarios that are difficult to evaluate empirically. We have developed a stochastic model that incorporates genetic and physiological data from more than 70 published reports on a wide range of fertility-related traits in dairy cattle. The model simulates pedigree, random mating, genetically correlated traits (in the form of breeding values for traits such as hours in estrus, estrous cycle length, age at puberty, milk yield, and so on), and interacting environmental variables. This model was used to generate a large simulated data set (200,000 cows replicated 100 times) of herd records within a seasonal dairy production system (based on an average New Zealand system). Using these simulated data, we investigated the genetic component of lifetime reproductive success (LRS), which, in reality, would be impractical to assess empirically. We defined LRS as the total number of times, during her lifetime, a cow calved within the first 42 d of the calving season. Sire estimated breeding values for LRS and other traits were calculated using simulated daughter records. Daughter pregnancy rate in the first lactation (PD_1) was the strongest single predictor of a sire's genetic merit for LRS (R2 = 0.81). A simple predictive model containing PD_1, calving date for the second season and calving rate in the first season provided a good estimate of sire LRS (R2 = 0.97). Daughters from sires with extremely high (n = 99,995 daughters, sire LRS = +0.70) or low (n = 99,635 daughters, sire LRS = -0.73) LRS estimated breeding values were compared over a single generation. Of the 14 underlying component traits of fertility, 12 were divergent between the 2 lines. This suggests that genetic variation in female fertility has a complex and multifactorial genetic basis. When simulated phenotypes were compared, daughters of the high LRS sires (HiFERT) reached puberty 44.5 d younger and calved ∼14 d younger at each parity than daughters from low LRS sires (LoFERT). Despite having a much lower genetic potential for milk production (-400 L/lactation) than LoFERT cows, HiFERT cows produced 33% more milk over their lifetime due to additional lactations before culling. In summary, this simulation model suggests that LRS contributes substantially to cow productivity, and novel selection criteria would facilitate a more accurate prediction at a younger age.

Predicting Positive and Negative Relationships in Large Social Networks.

In a social network, users hold and express positive and negative attitudes (e.g. support/opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship between them deteriorates. Accordingly, in the training phase, we propose a segment-based training framework to divide the training data into two subsets according to the number of common neighbors between users, and build a prediction model for each subset based on support vector machine (SVM). Moreover, to deal with large-scale social network data, we employ a sampling strategy that selects small amount of training data while maintaining high accuracy of prediction. We compare our algorithm with traditional algorithms and adaptive boosting of them. Experimental results of typical data sets show that our algorithm can deal with large social networks and consistently outperforms other methods.

Enhancing free-recall rates of individuals with mental retardation.

Generative encoding contexts promote activation of multiple retrieval routes and have been shown to enhance free-recall rates of individuals without mental retardation. The present extension to individuals with mental retardation involved a comparison of two encoding conditions: (a) fade-in, initially presenting pictures out of focus then slowly fading them into focus, and (b) fade-out, presenting pictures clearly then slowly blurring them. Results indicated that free-recall rates were greater for the fade-in items for the individuals with mental retardation and CA-matched comparisons, but not for the MA-matched group. These findings demonstrate the utility of a generative encoding context that does not involve verbal instruction for individuals with and without mental retardation.

Dissolution fractions and half-times of single source yellowcake in simulated lung fluids.

Thirty-five samples of yellowcake, the primary product of uranium milling, were obtained from industry. Samples were selected from the archives of 6 yr of yellowcake production from a single uranium milling operation. Ten representative samples were selected on the basis of color variation and chemical composition. Dissolution fractions and half-times were obtained using both simulated interstitial and simulated surfactant lung fluids maintained in vials submerged in a shaker water bath at 37 degrees C for 26 days. Samples were collected at 12 different time intervals and fresh simulant replaced. Uranium concentrations were analyzed by fluorometric methods using ASTM techniques. The fraction of the uranium undissolved, F, can be well modelled by the sum of three exponential terms: (See formula in text). Dissolution fractions and half-times in the two simulated lung fluids were found to be not significantly different. The point estimates for the six parameters (three fractions of the amount of uranium and the three associated half-times for dissolution) based on the analyses are: f1 equals 16.0% and t1/2(1) equals 3.5 hr, f2 equals 16.5% and t1/2(2) equals 4.4 days, and f3 equals 67.5% and t1/2(3) equals 224 days. The final characterization of yellowcake resulting from this investigation strongly suggests that dissolution rates of yellowcake from a single source may be individually process specific or site specific.