Cholesterol metabolism during the growth of a rat ascites hepatoma (Yoshida AH-130).

The metabolism of cholesterol has been investigated in tumour cells, ascitic fluid and blood serum during the growth of an ascites hepatoma (Yoshida AH-130) in the rat. High rates of cholesterol synthesis and elevated free and esterified cholesterol content were observed in tumour cells. During tumour growth, the host animals progressively developed marked changes in the level and distribution of serum cholesterol consisting in an increase of total cholesterol and of a marked reduction of HDL cholesterol (HDL2 subfraction in particular). In agreement with previous observations, these findings indicate that a consistent pattern of altered cholesterol homeostasis develops in relation to normal or neoplastic tissue growth. High synthetic rates and intracellular accumulation of cholesterol are observed in the proliferating cells. Moreover, blood serum cholesterol decreases in the HDL fraction while it increases in LDLs, suggesting that during proliferative processes cholesterol fluxes between tissues and serum lipoproteins are markedly perturbed

Efficient numerical integrators for stochastic models

The efficient simulation of models defined in terms of stochastic differential equations (SDEs) depends critically on an efficient integration scheme. In this article, we investigate under which conditions the integration schemes for general SDEs can be derived using the Trotter expansion. It follows that, in the stochastic case, some care is required in splitting the stochastic generator. We test the Trotter integrators on an energy-conserving Brownian model and derive a new numerical scheme for dissipative particle dynamics. We find that the stochastic Trotter scheme provides a mathematically correct and easy-to-use method which should find wide applicability.Comment: v

Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

Regional Gray Matter Atrophy in Patients with Parkinson Disease and Freezing of Gait

BACKGROUND AND PURPOSE: FOG is a troublesome symptom of PD. Despite growing evidence suggesting that FOG in PD may be associated with cognitive dysfunction, the relationship between regional brain atrophy and FOG has been poorly investigated. MATERIALS AND METHODS: Optimized VBM was applied to 3T brain MR images of 24 patients with PD and 12 HC. Patients were classified as either FOG− or FOG+ (n = 12) based on their responses to a validated FOG Questionnaire and clinical observation. All patients with PD also underwent a detailed neuropsychological evaluation. RESULTS: The VBM analysis in patients with FOG+ showed a reduced GM volume in the left cuneus, precuneus, lingual gyrus, and posterior cingulate cortex compared with both patients with FOG− and HC. We did not detect any significant change of GM volume when comparing HC versus all patients with PD (FOG− and FOG+). FOG clinical severity was significantly correlated with GM loss in posterior cortical regions. Finally, patients with FOG+ scored lower on tests of frontal lobe function. CONCLUSIONS: Our findings provide the first evidence that the development of FOG in patients with PD is associated with posterior GM atrophy, which may play a role in the complex pathophysiology of this disabling symptom

Synthetic rubber surface as an alternative to concrete to improve welfare and performance of finishing beef cattle reared on fully slatted flooring

open8noopenBrscic, M.; Ricci, R.; Prevedello, P.; Lonardi, C.; De Nardi, R.; Contiero, B.; Gottardo, F.; Cozzi, G.Brscic, Marta; Ricci, Rebecca; Prevedello, P.; Lonardi, Chiara; DE NARDI, Roberta; Contiero, Barbara; Gottardo, Flaviana; Cozzi, Giuli

Impact of supporting people with advanced Parkinson’s disease on carer’s quality of life and burden

Purpose: The aim of this study was to assess the burden and the quality of life (QoL) perceived by caregivers assisting advanced Parkinson’s disease (PD) patients. Patients and Methods: Consecutive advanced PD patients treated with levodopa/carbidopa intestinal gel (LCIG) or continuous subcutaneous apomorphine infusion (CSAI) or care as usual (CU) and their care partners were recruited during routine visits according to a cross-sectional design. Caregiver’s distress was assessed by Zarit Burden Interview (ZBI) and a QoL survey to evaluate and understand the burden experienced by care partners during family and working activities. Results: A total of 126 patients (53 LCIG, 19 CSAI and 54 CU) and their care partners were enrolled. The ZBI score boxplot showed that LCIG and CU populations have a similar distribution (ZBI inter-quartile range [IQR] values respectively 18–42 for LCIG and 19–43 for CU group), while the CSAI group has a wider score range (IQR 16–52). Caregivers assisting patients in treatment with LCIG have more time to perform family or household duties (p=0.0022), or to engage in leisure activities (p=0.0073) compared to CU, while no difference was found when compared to CSAI group. Approximately 50% of the care partners showed mood changes in the last 6 months and LCIG and CSAI had less impact on caregiver’s mood compared to CU. Patients treated with LCIG were more independent in taking a bath or shower without assistance and were more able to move and walk without assistance. Conclusion: Care partners of advanced PD patients treated with device-aided therapies have more time for their own life and a better perception of their QoL with a tendency to an improvement of mood compared with those of patients treated with CU

MicroRNA expression profiles in pediatric dysembryoplastic neuroepithelial tumors.

© Springer Science+Business Media New York 2015Among noncoding RNAs, microRNAs (miRNAs) have been most extensively studied, and their biology has repeatedly been proven critical for central nervous system pathological conditions. The diagnostic value of several miRNAs was appraised in pediatric dysembryoplastic neuroepithelial tumors (DNETs) using miRNA microarrays and receiving operating characteristic curves analyses. Overall, five pediatric DNETs were studied. As controls, 17 samples were used: the FirstChoice Human Brain Reference RNA and 16 samples from deceased children who underwent autopsy and were not present with any brain malignancy. The miRNA extraction was carried out using the mirVANA miRNA Isolation Kit, while the experimental approach included miRNA microarrays covering 1211 miRNAs. Quantitative real-time polymerase chain reaction was performed to validate the expression profiles of miR-1909* and miR-3138 in all samples initially screened with miRNA microarrays. Our findings indicated that miR-3138 might act as a tumor suppressor gene when down-regulated and miR-1909* as a putative oncogenic molecule when up-regulated in pediatric DNETs compared to the control cohort. Subsequently, both miRNA signatures might serve as putative diagnostic biomarkers for pediatric DNETs.Peer reviewedFinal Accepted Versio

Identifikasi Karakter Siswa Menggunakan Metode K-Means (Studi Kasus Sdn 156 Pekanbaru)

Good character education can have a characteristic impact on students. each student has a different character. Various ways done by the school in character education based on kemendiknas, including State Elementary School 156 Pekanbaru. Problems that arise in the field is there is no method that can determine the character of the students so that the school's special teachers can not understand precisely the characters in the students. The lack of understanding of the character of the students makes the vision of the school mission has not been seen so that character education in SDN 156 Pekanbaru has not been right target. Therefore, it needs to be done grouping student character in SDN 156 Pekanbaru with the aim of school know character owned by students in school. The K-Means algorithm is used to classify the character of the students with the number of clusters as much as 2 using the six attributes of characters studied: Honest, disciplined, confident, caring, creative and responsible with 130 student data. The results of K-Means manual calculation with sample data 10 data from 130 data that is weak character (C1) amounted to 1 student and weak character of 9 students, this result is same with calculation executed by RapidMiner application. Test results with 130 data using RapidMiner resulted in the number of students with weak character 26 students with the average centroid (0.665) with caring and creative characters. While students who have strong character 104 students with average value of centroid (0.900) with honest character, discipline, confidence, and responsibility. The result of character grouping based on class cluster position in RapidMiner is grade 3 which has weak character (C1) 8 students from 35 students, grade 4 is 8 out of 24 students, 5th grade is 1 of 17 students and grade 6 is 9 of 46 students. While clusters with strong characters (C2) class 3 amounted to 27 students, grade 4 amounted to 24 students, class 5 amounted to 16 students, and grade 6 amounted to 37 students. From the results of this study is expected Strong characters can be developed by school continue to perform habits which involves the students so that the characters in the students can be seen while for the caring and creative characters so as not to be weak then the school always provide guidance to the students and give examples of good habits and activities that can be followed by students in school