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The chance that a person will eventually be able to discontinue medication varies depending on the person's age and his or her type of epilepsy. More than half of children who go into remission with medication can eventually s their medication without having new seizures. One study showed that 68 percent of adults who had been seizure-free for 2 years before stopping medication were able to do so without having more seizures and 75 percent could successfully discontinue medication if they had been seizure-free for 3 years. However, the odds of successfully sping medication are not as good for people with a family history of epilepsy, those who need multiple medications, those with focal seizures, and those who continue to have abnormal EEG results while on medication.

While surgery can significantly reduce or even halt seizures for many people, any kind of surgery involves some level of risk. Surgery for epilepsy does not always successfully reduce seizures and it can result in cognitive or personality changes as well as physical disability, even in people who are excellent candidates for it. Nonetheless, when medications fail, several studies have shown that surgery is much more likely to make someone seizure-free compared to attempts to use other medications. Anyone thinking about surgery for epilepsy should be assessed at an epilepsy center experienced in surgical techniques and should discuss with the epilepsy specialists the balance between the risks of surgery and desire to become seizure-free.

Of all epileptic patients, 70% are expected to enter remission, defined as five or more years of remaining seizure-free with medication.9 Seventy-five percent of seizure-free people taking medication for two to five years can be successfully withdrawn from pharmacotherapy. Despite optimal medical management, 10% of new patients are not treated successfully.

Cognitive science has a longstanding interest in the ways that people acquire and use abstract vs. concrete words (e.g., truth vs. piano). One dominant theory holds that abstract and concrete words are subserved by two parallel semantic systems. We recently proposed an alternative account of abstract-concrete word representation premised upon a unitary, high dimensional semantic space wherein word meaning is nested. We hypothesize that a range of cognitive and perceptual dimensions (e.g., emotion, time, space, color, size, visual form) bound this space, forming a conceptual topography. Here we report a normative study where we examined the clustering properties of a sample of English words (N = 750) spanning a spectrum of concreteness in a continuous manner from highly abstract to highly concrete. Participants (N = 328) rated each target word on a range of 14 cognitive dimensions (e.g., color, emotion, valence, polarity, motion, space). The dimensions reduced to three factors: Endogenous factor, Exogenous factor, and Magnitude factor. Concepts were plotted in a unified, multimodal space with concrete and abstract concepts along a continuous continuum. We discuss theoretical implications and practical applications of this dataset. These word norms are freely available for download and use at -coglab.com/data/. PMID:29075224

Cognitive science has a longstanding interest in the ways that people acquire and use abstract vs. concrete words (e.g., truth vs. piano). One dominant theory holds that abstract and concrete words are subserved by two parallel semantic systems. We recently proposed an alternative account of abstract-concrete word representation premised upon a unitary, high dimensional semantic space wherein word meaning is nested. We hypothesize that a range of cognitive and perceptual dimensions (e.g., emotion, time, space, color, size, visual form) bound this space, forming a conceptual topography. Here we report a normative study where we examined the clustering properties of a sample of English words ( N = 750) spanning a spectrum of concreteness in a continuous manner from highly abstract to highly concrete. Participants ( N = 328) rated each target word on a range of 14 cognitive dimensions (e.g., color, emotion, valence, polarity, motion, space). The dimensions reduced to three factors: Endogenous factor, Exogenous factor, and Magnitude factor. Concepts were plotted in a unified, multimodal space with concrete and abstract concepts along a continuous continuum. We discuss theoretical implications and practical applications of this dataset. These word norms are freely available for download and use at -coglab.com/data/.

The function of a non-protein-coding RNA is often determined by its structure. Since experimental determination of RNA structure is time-consuming and expensive, its computational prediction is of great interest, and efficient solutions based on thermodynamic parameters are known. Frequently, however, the predicted minimum free energy structures are not the native ones, leading to the necessity of generating suboptimal solutions. While this can be accomplished by a number of programs, the user is often confronted with large outputs of similar structures, although he or she is interested in structures with more fundamental differences, or, in other words, with different abstract shapes. Here, we formalize the concept of abstract shapes and introduce their efficient computation. Each shape of an RNA molecule comprises a class of similar structures and has a representative structure of minimal free energy within the class. Shape analysis is implemented in the program RNAshapes. We applied RNAshapes to the prediction of optimal and suboptimal abstract shapes of several RNAs. For a given energy range, the number of shapes is considerably smaller than the number of structures, and in all cases, the native structures were among the top shape representatives. This demonstrates that the researcher can quickly focus on the structures of interest, without processing up to thousands of near-optimal solutions. We complement this study with a large-scale analysis of the growth behaviour of structure and shape spaces. RNAshapes is available for download and as an online version on the Bielefeld Bioinformatics Server. 2b1af7f3a8