Abstract:
Epilepsy surgery is effective in reducing both the number and frequency of seizures, particularly in temporal lobe epilepsy (TLE). Nevertheless, a significant proportion of these patients continue suffering seizures after surgery. Here we used a machine learning approach to predict the outcome of epilepsy surgery based on supervised classification data mining taking into account not only the common clinical variables, but also pathological and neuropsychological evaluations. We have generated models capable of predicting whether a patient with TLE secondary to hippocampal sclerosis will fully recover from epilepsy or not. The machine learning analysis revealed that outcome could be predicted with an estimated accuracy of almost 90% using some clinical and neuropsychological features. Importantly, not all the features were needed to perform the prediction; some of them proved to be irrelevant to the prognosis. Personality style was found to be one of the key features to predict the outcome. Although we examined relatively few cases, findings were verified across all data, showing that the machine learning approach described in the present study may be a powerful method. Since neuropsychological assessment of epileptic patients is a standard protocol in the pre-surgical evaluation, we propose to include these specific psychological tests and machine learning tools to improve the selection of candidates for epilepsy surgery.

Abstract:
Almost a 20% of the residues of tau protein are phosphorylatable amino acids: serine, threonine, and tyrosine. In this paper we comment on the consequences for tau of being a phosphoprotein. We will focus on serine/threonine phosphorylation. It will be discussed that, depending on the modified residue in tau molecule, phosphorylation could be protective, in processes like hibernation, or toxic like in development of those diseases known as tauopathies, which are characterized by an hyperphosphorylation and aggregation of tau. 1. Introduction Tau protein was discovered as one of the brain microtubule-associated proteins bound to in vitro assembled tubulin [1]. Tau protein appears as a series of different polypeptides on gel electrophoresis. These different forms are generated by alternative RNA splicing [2] or by different phosphorylation levels [3]. In every tau form, four different regions could be identified: the amino terminal region, the proline-rich region, the microtubule binding region, and the carboxy terminal region. Here, we will mainly comment on the tau molecule of the largest tau form present in the central nervous system (CNS) [4]. This molecule contains 441 residues. The N-terminal region (1–150 residues) and the proline-rich region (151–239 residues) have been involved in the interaction of tau protein with cell membranes [5, 6]. The microtubule-binding region (residues 240–367) contains four similar but not identical sequences involved in the interaction with tubulin [7], the main component of microtubules. The last part of the molecule is the C-terminal region (residues 368 to 441). Curiously, for a specific residue, threonine, there is a decreasing gradient in its presence from the N-terminal to the C-terminal region in human tau. Threonine is particularly abundant at the N-terminal region [7]. Some of these threonines, residues 17, 30, 39, 50, 52, or 95, present in human tau are not always conserved in other species and may have appeared during evolution in human tau. None of these human tau threonines have been found to be phosphorylated, but we cannot rule out that some of these sites could be modified with a very fast turnover. 2. Tau Functions Tau is a neuronal microtubule-associated protein, and some of its functions are related to that association that may result in microtubule stabilization or the regulation of axonal transport, but tau protein is a “sticky” protein that could bind to other proteins, apart from tubulin (the main component of microtubules), which could also facilitate its subcellular localization at the

Abstract:
In this paper, we will derive the following formula for the value of the gravitational constant G: (1). This equation has only 0.81% error compared to the common accepted value [1]. The parameters in the equation are the following: the fine structure constant, qthe elementary charge, the mass of the electron, the permittivity of the free space, ethe exponential function and the relation between a circumference and its diameter. Values attached:[2],

Abstract:
The fine-structure constant α [1] is a constant in physics that plays a fundamental role in the electromagnetic interaction. It is a dimensionless constant, defined as: (1)
being q the elementary charge, ε0 the vacuum permittivity, h the Planck constant and c the speed of light in vacuum. The value shown in (1) is according CODATA 2014 [2].
In this paper, it will be explained that the fine-structure constant is one of the roots of the following equation: (2)
being e the mathematical constant e (the base of the natural logarithm). One of the solutions of this equation is: (3)
This means that it is equal to the CODATA value in nine decimal digits (or the seven most significant ones if you prefer). And therefore, the difference between both values is: (4)
This coincidence is higher in orders of magnitude than the commonly accepted necessary to validate a theory towards experimentation.
As the cosine function is periodical, the Equation (2) has infinite roots and could seem the coincidence is just by chance. But as it will be shown in the paper, the separation among the different solutions is sufficiently high to disregard this possibility.
It will also be shown that another elegant way to show Equation (2) is the following (being i the imaginary unit): (5)
having of course the same root (3). The possible meaning of this other representation (5) will be explained.

Abstract:
In the history of mathematics
different methods have been used to detect if a number is prime or not. In this
paper a new one will be shown. It will be demonstrated that if the following
equation is zero for a certain number p,
this number p would be prime. And
being m an integer number higher than (the lowest, the most efficient the operation). . If the result is an integer, this result will tell
us how many permutations of two divisors, the input number has. As you can
check, no recurrent division by odd or prime numbers is done, to check if the
number is prime or has divisors. To get to this point, we will do the
following. First, we will create a domain with all the composite numbers. This
is easy, as you can just multiply one by one all the integers (greater or equal
than 2) in that domain. So, you will get all the composite numbers (not getting
any prime) in that domain. Then, we will use the Fourier transform to change
from this original domain (called discrete time domain in this regards) to the
frequency domain. There, we can check, using Parseval’s theorem, if a certain
number is there or not. The use of Parseval’s theorem leads to the above
integral. If the number p that we
want to check is not in the domain, the result of the integral is zero and the
number is a prime. If instead, the result is an integer, this integer will tell
us how many permutations of two divisors the number p has. And, in consequence information how many factors, the number p has. So, for any number p lower than 2m？- 1, you can check if it is prime or not, just making the
numerical definite integration. We will apply this integral in a computer
program to check the efficiency of the operation. We will check, if no further
developments are done, the numerical integration is inefficient computing-wise
compared with brute-force checking. To be added, is the question regarding the
level of accuracy needed (number of decimals and number of steps in the
numerical integration) to have a reliable result for large numbers. This will
be commented on the paper, but a separate study will be needed to have detailed
conclusions. Of course,

Abstract:
The tremendous expansion and the differentiation of the neocortex constitute two major events in the evolution of the mammalian brain. The increase in size and complexity of our brains opened the way to a spectacular development of cognitive and mental skills. This expansion during evolution facilitated the addition of microcircuits with a similar basic structure, which increased the complexity of the human brain and contributed to its uniqueness. However, fundamental differences even exist between distinct mammalian species. Here, we shall discuss the issue of our humanity from a neurobiological and historical perspective.