37 research outputs found

    Interference between Sentence Processing and Probabilistic Implicit Sequence Learning

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    During sentence processing we decode the sequential combination of words, phrases or sentences according to previously learned rules. The computational mechanisms and neural correlates of these rules are still much debated. Other key issue is whether sentence processing solely relies on language-specific mechanisms or is it also governed by domain-general principles.In the present study, we investigated the relationship between sentence processing and implicit sequence learning in a dual-task paradigm in which the primary task was a non-linguistic task (Alternating Serial Reaction Time Task for measuring probabilistic implicit sequence learning), while the secondary task were a sentence comprehension task relying on syntactic processing. We used two control conditions: a non-linguistic one (math condition) and a linguistic task (word processing task). Here we show that the sentence processing interfered with the probabilistic implicit sequence learning task, while the other two tasks did not produce a similar effect.Our findings suggest that operations during sentence processing utilize resources underlying non-domain-specific probabilistic procedural learning. Furthermore, it provides a bridge between two competitive frameworks of language processing. It appears that procedural and statistical models of language are not mutually exclusive, particularly for sentence processing. These results show that the implicit procedural system is engaged in sentence processing, but on a mechanism level, language might still be based on statistical computations

    High sensitive troponin T and heart fatty acid binding protein: Novel biomarker in heart failure with normal ejection fraction?: A cross-sectional study

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    Background: High sensitive troponin T (hsTnT) and heart fatty acid binding protein (hFABP) are both markers of myocardial injury and predict adverse outcome in patients with systolic heart failure (SHF). We tested whether hsTnT and hFABP plasma levels are elevated in patients with heart failure with normal ejection fraction (HFnEF). Methods: We analyzed hsTnT, hFABP and N-terminal brain natriuretic peptide in 130 patients comprising 49 HFnEF patients, 51 patients with asymptomatic left ventricular diastolic dysfunction (LVDD), and 30 controls with normal diastolic function. Patients were classified to have HFnEF when the diagnostic criteria as recommended by the European Society of Cardiology were met. Results: Levels of hs TnT and hFABP were significantly higher in patients with asymptomatic LVDD and HFnEF (both p < 0.001) compared to controls. The hsTnT levels were 5.6 [0.0-9.8] pg/ml in LVDD vs. 8.5 [3.9-17.5] pg/ml in HFnEF vs. < 0.03 [< 0.03-6.4] pg/ml in controls; hFABP levels were 3029 [2533-3761] pg/ml in LVDD vs. 3669 [2918-4839] pg/ml in HFnEF vs. 2361 [1860-3081] pg/ml in controls. Furthermore, hsTnT and hFABP levels were higher in subjects with HFnEF compared to LVDD (p = 0.015 and p = 0.022). Conclusion: In HFnEF patients, hsTnT and hFABP are elevated independent of coronary artery disease, suggesting that ongoing myocardial damage plays a critical role in the pathophysiology. A combination of biomarkers and echocardiographic parameters might improve diagnostic accuracy and risk stratification of patients with HFnEF

    Category Theoretic Analysis of Hierarchical Protein Materials and Social Networks

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    Materials in biology span all the scales from Angstroms to meters and typically consist of complex hierarchical assemblies of simple building blocks. Here we describe an application of category theory to describe structural and resulting functional properties of biological protein materials by developing so-called ologs. An olog is like a “concept web” or “semantic network” except that it follows a rigorous mathematical formulation based on category theory. This key difference ensures that an olog is unambiguous, highly adaptable to evolution and change, and suitable for sharing concepts with other olog. We consider simple cases of beta-helical and amyloid-like protein filaments subjected to axial extension and develop an olog representation of their structural and resulting mechanical properties. We also construct a representation of a social network in which people send text-messages to their nearest neighbors and act as a team to perform a task. We show that the olog for the protein and the olog for the social network feature identical category-theoretic representations, and we proceed to precisely explicate the analogy or isomorphism between them. The examples presented here demonstrate that the intrinsic nature of a complex system, which in particular includes a precise relationship between structure and function at different hierarchical levels, can be effectively represented by an olog. This, in turn, allows for comparative studies between disparate materials or fields of application, and results in novel approaches to derive functionality in the design of de novo hierarchical systems. We discuss opportunities and challenges associated with the description of complex biological materials by using ologs as a powerful tool for analysis and design in the context of materiomics, and we present the potential impact of this approach for engineering, life sciences, and medicine.Presidential Early Career Award for Scientists and Engineers (N000141010562)United States. Army Research Office. Multidisciplinary University Research Initiative (W911NF0910541)United States. Office of Naval Research (grant N000141010841)Massachusetts Institute of Technology. Dept. of MathematicsStudienstiftung des deutschen VolkesClark BarwickJacob Luri

    Current clinical applications of spectral tissue Doppler echocardiography (E/E' ratio) as a noninvasive surrogate for left ventricular diastolic pressures in the diagnosis of heart failure with preserved left ventricular systolic function

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    Congestive heart failure with preserved left ventricular systolic function has emerged as a growing epidemic medical syndrome in developed countries, which is characterized by high morbidity and mortality rates. Rapid and accurate diagnosis of this condition is essential for optimizing the therapeutic management. The diagnosis of congestive heart failure is challenging in patients presenting without obvious left ventricular systolic dysfunction and additional diagnostic information is most commonly required in this setting. Comprehensive Doppler echocardiography is the single most useful diagnostic test recommended by the ESC and ACC/AHA guidelines for assessing left ventricular ejection fraction and cardiac abnormalities in patients with suspected congestive heart failure, and non-invasively determined basal or exercise-induced pulmonary capillary hypertension is likely to become a hallmark of congestive heart failure in symptomatic patients with preserved left ventricular systolic function. The present review will focus on the current clinical applications of spectral tissue Doppler echocardiography used as a reliable noninvasive surrogate for left ventricular diastolic pressures at rest as well as during exercise in the diagnosis of heart failure with preserved left ventricular systolic function. Chronic congestive heart failure, a disease of exercise, and acute heart failure syndromes are characterized by specific pathophysiologic and diagnostic issues, and these two clinical presentations will be discussed separately

    Phonemes:Lexical access and beyond

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    Unconscious learning processes: mental integration of verbal and pictorial instructional materials

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    A stochastic context free grammar based framework for analysis of protein sequences

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    <p>Abstract</p> <p>Background</p> <p>In the last decade, there have been many applications of formal language theory in bioinformatics such as RNA structure prediction and detection of patterns in DNA. However, in the field of proteomics, the size of the protein alphabet and the complexity of relationship between amino acids have mainly limited the application of formal language theory to the production of grammars whose expressive power is not higher than stochastic regular grammars. However, these grammars, like other state of the art methods, cannot cover any higher-order dependencies such as nested and crossing relationships that are common in proteins. In order to overcome some of these limitations, we propose a Stochastic Context Free Grammar based framework for the analysis of protein sequences where grammars are induced using a genetic algorithm.</p> <p>Results</p> <p>This framework was implemented in a system aiming at the production of binding site descriptors. These descriptors not only allow detection of protein regions that are involved in these sites, but also provide insight in their structure. Grammars were induced using quantitative properties of amino acids to deal with the size of the protein alphabet. Moreover, we imposed some structural constraints on grammars to reduce the extent of the rule search space. Finally, grammars based on different properties were combined to convey as much information as possible. Evaluation was performed on sites of various sizes and complexity described either by PROSITE patterns, domain profiles or a set of patterns. Results show the produced binding site descriptors are human-readable and, hence, highlight biologically meaningful features. Moreover, they achieve good accuracy in both annotation and detection. In addition, findings suggest that, unlike current state-of-the-art methods, our system may be particularly suited to deal with patterns shared by non-homologous proteins.</p> <p>Conclusion</p> <p>A new Stochastic Context Free Grammar based framework has been introduced allowing the production of binding site descriptors for analysis of protein sequences. Experiments have shown that not only is this new approach valid, but produces human-readable descriptors for binding sites which have been beyond the capability of current machine learning techniques.</p
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