Retrograde Amnesia Induced by Drugs Acting on Different Molecular Systems.

The gamma aminobutyric acid-A (GABAA) agonist, muscimol, the glutamate N-methyl-D-aspartate (NMDA) receptor antagonist, D-2-amino-5-phosphonopentanoic acid (AP5), and the inhibitor of the extracellularly regulated kinases (ERKs), UO 126, cause retrograde amnesia when administered to the hippocampus. In the present study, the authors found that they all cause retrograde amnesia for 1-trial inhibitory avoidance, not only when infused into the dorsal CA1 region of the hippocampus, but also when infused into the basolateral amygdala or the entorhinal, parietal, and posterior cingulate cortices. The posttraining time course of the effect of each drug was, however, quite different across brain structures. Thus, in all of them, NMDA receptors and the ERK pathway are indispensable for memory consolidation, and GABAA receptor activation inhibits memory consolidation: but in each case, their influence is interwoven differently. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Conflict structure moderates associations between cardiovascular reactivity and negative marital interaction.

This study examined the role of marital conflict structure--who desires and requests change versus who responds to the change request--in spouses' cardiovascular responses to marital interactions. Forty-one couples discussed 2 marital topics: one in which the wife desired change in the husband, and one in which the husband desired change in the wife. Cardiovascular responses were assessed at 2-min intervals. Results indicated that marital conflict structure moderates cardiovascular reactivity during negative marital interactions: Husbands and wives whose interactions were characterized by high levels of negative behavior showed the most pronounced diastolic blood pressure reactivity, but only when they were in the role of desiring change in their spouses. Implications for gender differences in marital conflict physiology are considered. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

The ontogeny and significance of infants' facial expressions in the first 9 months of life.

Four studies examined aspects of the differential emotions theory (DET) hypothesis of expressive behavior development. In Study 1, facial-expressive movements of 108 2.5–9-mo-old infants were video recorded in positive and negative mother–infant interactions (conditions). As expected, Max-specified full-face and partial expressions of interest, joy, sadness, and anger were morphologically stable between the 2 ages. Studies 1 and 2 confirmed predicted differential responding to mother sadness and anger expressions and to composite positive and negative conditions. Discrete negative expressions exceeded negative blends, and the amount of both expression types remained stable across ages. Studies 3 and 4 provided varying degrees of support for the social validity of Max-specified infant negative affect expressions. Conclusions include revisions and clarifications of DET. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Saturation of subjective reward magnitude as a function of current and pulse frequency.

In 6 rats with electrodes in the medial forebrain bundle, the upper portion of the function relating the experienced magnitude of the reward to pulse frequency was determined at currents ranging from 100 to 1,000 μA. The pulse frequency required to produce an asymptotic level of reward was inversely proportional to current except at the lowest currents and highest pulse frequencies. At a given current, the subjective reward magnitude functions decelerated to an asymptote over an interval in which the pulse frequency doubled or tripled. The asymptotic level of reward was approximately constant for currents between 200 and 1,000 μA but declined substantially at currents at or below 100 μA and pulse frequencies at or above 250 to 400 pulses per second. Results are consistent with the hypothesis that the magnitude of the experienced reward depends only on the number of action potentials generated by the train of pulses in the bundle of reward-relevant axons. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Target Score—A Proteomics Data Selection Tool Applied to Esophageal Cancer Identifies GLUT1-Sialyl Tn Glycoforms as Biomarkers of Cancer Aggressiveness

Esophageal cancer (EC) is a life-threatening disease, demanding the discovery of new biomarkers and molecular targets for precision oncology. Aberrantly glycosylated proteins hold tremendous potential towards this objective. In the current study, a series of esophageal squamous cell carcinomas (ESCC) and EC-derived circulating tumor cells (CTCs) were screened by immunoassays for the sialyl-Tn (STn) antigen, a glycan rarely expressed in healthy tissues and widely observed in aggressive gastrointestinal cancers. An ESCC cell model was glycoengineered to express STn and characterized in relation to cell proliferation and invasion in vitro. STn was found to be widely present in ESCC (70% of tumors) and in CTCs in 20% of patients, being associated with general recurrence and reduced survival. Furthermore, STn expression in ESCC cells increased invasion in vitro, while reducing cancer cells proliferation. In parallel, an ESCC mass spectrometry-based proteomics dataset, obtained from the PRIDE database, was comprehensively interrogated for abnormally glycosylated proteins. Data integration with the Target Score, an algorithm developed in-house, pinpointed the glucose transporter type 1 (GLUT1) as a biomarker of poor prognosis. GLUT1-STn glycoproteoforms were latter identified in tumor tissues in patients facing worst prognosis. Furthermore, healthy human tissues analysis suggested that STn glycosylation provided cancer specificity to GLUT1. In conclusion, STn is a biomarker of worst prognosis in EC and GLUT1-STn glycoforms may be used to increase its specificity on the stratification and targeting of aggressive ESCC forms.     

Extended bisection method for parameter identification of the transient heat conduction equation for thermo-elastic deformations during drilling

A new interdisciplinary approach is discribed to identifying unknown parameters using an extended version of the known interval bisection method. This developed method is based on the use of finite elements for calibrating the simulation calculation. The resulting thermo-elastic deformations which occur in drilling processes with impaired cooling lubrication are to be used as correction values for tool positioning in the NC control. Based on the strong impact on workpiece temperature of machining, a simulation approach is presented for calculating the temperature fields and their thermo-elastic consequences. In addition, methods are presented to correct these effects. This paper particularly deals with the temperature fields of drilling operations. Special attention is paid to the technique employed for iterative numerical determination of the unknown heat flux η _w and heat transfer coefficient \(\bar {\gamma }\) values. Finally, the data obtained from experiments are compared with those achieved by numerical simulation in order to verify the efficiency of simulation and determination of parameters.     

Biomic river restoration: A new focus for river management

River management based solely on physical science has proven to be unsustainable and unsuccessful, evidenced by the fact that the problems this approach intended to solve (e.g., flood hazards, water scarcity, and channel instability) have not been solved and long‐term deterioration in river environments has reduced the capacity of rivers to continue meeting the needs of society. In response, there has been a paradigm shift in management over the past few decades, towards river restoration. But the ecological, morphological, and societal benefits of river restoration have, on the whole, been disappointing. We believe that this stems from the fact that restoration overrelies on the same physical analyses and approaches, with flowing water still regarded as the universally predominant driver of channel form and structural intervention seen as essential to influencing fluvial processes. We argue that if river restoration is to reverse long‐standing declines in river functions, it is necessary to recognize the influence of biology on river forms and processes and re‐envisage what it means to restore a river. This entails shifting the focus of river restoration from designing and constructing stable channels that mimic natural forms to reconnecting streams within balanced and healthy biomes, and so levering the power of biology to influence river processes. We define this new approach as biomic river restoration.     

Feature-based statistical analysis of combustion simulation data

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing and reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.     

Gene expression changes in a transgenic mouse model overexpressing human wildtype and mutant torsinA

Primary torsion dystonia is an autosomal-dominantly inherited, neurodevelopmental movement disorder caused by a GAG deletion (ΔGAG) in the DYT1 gene, encoding torsinA. This mutation is responsible for approximately 70% of cases of early-onset primary torsion dystonia. The function of wildtype torsinA is still unknown, and it is unsolved how the deletion in the DYT1 gene contributes to the development of the disease. To better understand the molecular processes involved in torsinA pathology, we used genome-wide oligonucleotide microarrays to characterize gene expression patterns in the striatum of mouse models overexpressing the human wildtype and mutant torsinA. By this approach we were able to detect gene expression changes that seem to be specific for torsinA pathology. We found an impact of torsinA, independent from genotype, on vesicle trafficking, exocytosis, and neurotransmitter release in our mouse model. In addition, we were able to identify several new pathways and processes involved in the development of the nervous system that are affected by wildtype and mutant torsinA. Furthermore, we have striking evidence from our gene expression data that glutamate receptor mediated synaptic plasticity in the striatum is the affected underlying cellular process for impaired motor learning in human ΔGAG torsinA transgenic mice.     

A Convergent-Divergent Approach to Context Processing, General Intellectual Functioning, and the Genetic Liability to Schizophrenia.

Convergent and divergent validity are critically important in developing psychological measures that reveal interpretable deficits in disordered populations. This article reports on 2 studies that evaluated the validity of context processing measures. In Experiment 1, a confirmatory factor analysis of data from 481 healthy adults established the convergent validity of 2 context processing measures and showed that context processing accounted for significant amounts of variance in standard IQ and working memory measures. In Experiment 2, 20 schizophrenia patients, 16 of their healthy siblings, and 28 controls were evaluated using a novel, short context processing measure, the dot pattern expectancy (DPX) task. The DPX was sensitive to specific deficits in schizophrenia patients and their healthy siblings. These findings support the construct validity of context processing measures, suggest context processing is a component of intellectual functioning, and demonstrate that brief context processing measures remain sensitive to psychopathological deficits. (PsycINFO Database Record (c) 2010 APA, all rights reserved)