Schizophrenia is a severe mental disorder that impairs a person's mental, social, and emotional faculties gradually. Detection in the early stages with an accurate diagnosis is crucial to remedying the patients. This study proposed a new method to classify schizophrenia disease in the rest state based on neurologic signals achieved from the brain by electroencephalography (EEG). The datasets used consisted of 28 subjects, 14 for each group, which are schizophrenia and healthy control. The data was collected from the scalps with 19 EEG channels using a 250 Hz frequency. Due to the brain signal variation, we have decomposed the EEG signals into five sub-bands using a band-pass filter, ensuring the best signal clarity and eliminating artifacts. This work was performed with several scenarios: First, traditional techniques were applied. Secondly, augmented data (additive white Gaussian noise and stretched signals) were utilized. Additionally, we assessed Minimum Redundancy Maximum Relevance (MRMR) as the features reduction method. All these data scenarios are applied with three different window sizes (epochs): 1, 2, and 5 s, utilizing six algorithms to extract features: Fast Fourier Transform (FFT), Approximate Entropy (ApEn), Log Energy entropy (LogEn), Shannon Entropy (ShnEn), and kurtosis. The L2-normalization method was applied to the derived features, positively affecting the results. In terms of classification, we applied four algorithms: K-nearest neighbor (KNN), support vector machine (SVM), quadratic discriminant analysis (QDA), and ensemble classifier (EC). From all the scenarios, our evaluation showed that SVM had remarkable results in all evaluation metrics with LogEn features utilizing a 1-s window size, impacting the diagnosis of Schizophrenia disease. This indicates that an accurate diagnosis of schizophrenia can be achieved through the right features and classification model selection. Finally, we contrasted our results to recently published works using the same and a different dataset, where our method showed a notable improvement.

Gonadal dysgenesis is the name given to any of a multitude of conditions that can cause impaired development of the gonads, i.e., the testes or ovaries [1]. The most notable of these conditions is Turner syndrome, a disorder affecting 1 in every 2500 live female births, with an array of associated symptoms and complications [2]. Although there are many syndromes of which gonadal dysgenesis is a component, this article will focus on Turner syndrome, 46, XX, 46, XY, and mosaic forms of gonadal dysgenesis. Understanding the genetics behind gonadal dysgenesis allows clinicians to better predict the disorder’s phenotypic presentation, in turn, improving both screening methods for associated medical problems and the ongoing care of those medical problems. This article will explore the cellular, biochemical, and molecular basis behind the genetic processes that cause gonadal dysgenesis and study the clinical implications of this subset of disorders.

Puberty is the process of physical maturation where an adolescent reaches sexual maturity and becomes capable of reproduction. On average, puberty typically begins between 8 and 13 in females and 9 and 14 in males. Puberty is associated with emotional and hormonal changes, as well as physical changes such as breast development in females (thelarche), pubic hair development (pubarche), genital changes in males, voice changes, an increase in height, and the onset of menstruation (menarche). Puberty proceeds through five stages, termed Tanner stages, ranging from prepubertal, to full maturity.

The discrimination of non-focal class (NFC) and focal class (FC), is vital in localizing the epileptogenic zone (EZ) during neurosurgery. In the conventional diagnosis method, the neurologist has to visually examine the long hour electroencephalogram (EEG) signals, which consumes time and is prone to error. Hence, in this present work, automated diagnosis of FC EEG signals from NFC EEG signals is developed using the Fast Walsh-Hadamard Transform (FWHT) method, entropies, and artificial neural network (ANN). The FWHT analyzes the EEG signals in the frequency domain and decomposes it into the Hadamard coefficients. Five different nonlinear features, namely approximate entropy (ApEn), log-energy entropy (LogEn), fuzzy entropy (FuzzyEn), sample entropy (SampEn), and permutation entropy (PermEn) are extracted from the decomposed Hadamard coefficients. The extracted features detail the nonlinearity in the NFC and the FC EEG signals. The judicious entropy features are supplied to the ANN classifier, with a 10-fold cross-validation method to classify the NFC and FC classes. Two publicly available datasets such as the University of Bonn and Bern-Barcelona dataset are used to evaluate the proposed approach. A maximum sensitivity of 99.70%, the accuracy of 99.50%, and specificity of 99.30% with the 3750 pairs of NFC and FC signal are achieved using the Bern-Barcelona dataset, while the accuracy of 92.80%, the sensitivity of 91%, and specificity of 94.60% is achieved using University of Bonn dataset. Compared to the existing technique, the proposed approach attained a maximum classification performance in both the dataset.

We present the results of time-series photometric analysis of 15106 A-K type stars observed by the Kepler space mission. We identified 513 new rotational variables and measured their starspot rotation periods as a function of spectral type and discuss the distribution of their amplitudes. We examined the well-established period-color relationship that applies to stars of spectral types F5-K for all of these rotational variables and, interestingly, found that a similar period-color relationship appears to extend to stars of spectral types A7 to early-F too. This result is not consistent with the very foundation of the period-color relationship. We have characterized 350 new non-radial pulsating variables such as A- and F-type candidate δ Scuti, γ Doradus and hybrid stars, which increases the known candidate non-radial pulsators in the Kepler field significantly, by ∼20%. The relationship between two recently constructed observables, Energy and Efficiency , was also studied for the large sample of non-radial pulsators, which shows that the distribution in the logarithm of Energy (log (En)) can be used as a potential tool to distinguish between the non-radial pulsators, to some extent. Through visual inspection of the light curves and their corresponding frequency spectra, we found 23 new candidate red giant solar-like oscillators not previously reported in the literature. The basic physical parameters such as masses, radii and luminosities of these solar-like oscillators were also derived using asteroseismic relations.

In this study, normal EEG series recorded from healthy volunteers and epileptic EEG series recorded from patients within and without seizure are classified by using Multilayer Neural Network (MLNN) architectures with respect to several time domain entropy measures such as Shannon Entropy (ShanEn), Log Energy Entropy (LogEn), and Sample Entropy (Sampen). In tests, the MLNN is performed with several numbers of neurons for both one hidden layer and two hidden layers. The results show that segments in seizure have significantly lower entropy values than normal EEG series. This result indicates an important increase of EEG regularity in epilepsy patients. The LogEn approach, which has not been experienced in EEG classification yet, provides the most reliable features into the EEG classification with very low absolute error as 0.01. In particular, the MLNN can be proposed to distinguish the seizure activity from the seizure-free epileptic series where the LogEn values are considered as signal features that characterize the degree of EEG complexity. The highest classification accuracy is obtained for one hidden layer architecture.

Important differential diagnoses to epicondylitis humeri radialis are the nerve compression syndromes of the elbow. After a long period of conservative treatment, paresis of the motor branch of nervus radialis in this case led to the diagnosis and surgical treatment showing an unknown submuscular lipoma as the cause of a supinator syndrome with paresis of the finger extensors. In cases of therapy-resistant pain of the elbow, especially resistant to the conservative therapy of an epicondylitis humeri radialis, a nontraumatic supinator syndrome should be considered as a differential diagnosis. A tumor as the cause of a compression syndrome of the motor branch of the nervus radialis is very rare.

At the department of neurosurgery and pediatrics of the University Hospital of Hamburg 18 children with pineal region tumors were treated by surgical removal and craniospinal axis radiation. Total or nearly total removal was achieved in 13 cases. In case of astrocytoma and pineocytoma no further treatment was performed. Children with germinoma, pinealoblastoma and teratoma underwent craniospinal axis radiation postoperatively. All children thus treated, except one with a highly malignant pinealoblastoma, are so far relapse-free. Two patients with germinoma and two patients with malignant mixed teratoma, which showed extensive growth and/or primary metastases already at admission died. Subject of this report is an assessment of quality of live and a comprehensive follow-up of neurological, endocrinological and psychointellectual conditions of the surviving children. Parent assessment of quality of life indicate for all but two children life quality as excellent or reasonable. One child was disabled by severe motor dysfunction, visual defects and convulsions. In two children minor neurological handicap was found. Minor oculomotor dysfunction was the most frequent neurological finding. Four children showed endocrinopathies. In three cases tumor associated panhypopituitarism persisted after treatment. Only one child developed growth hormone and ADH deficit succeeding surgery and craniospinal axis radiation. The children were examined with a neuropsychological test-battery. In four children severe neuropsychological deterioration was found. Three children showed no remarkable deterioration, five only minor or moderate psychological deficits. Generally verbal intelligence seemed to be on average, non-verbal intelligence and ability for concentration showed decreased scores.