Novel electric field effects on Landau levels in Graphene

A single graphene layer exhibits an anomalous Landau level spectrum. A massless Dirac like low energy electronic spectrum underlies this anomaly. We study, analytically and numerically, the effect of a uniform electric field $(E)$ on the anomalous Landau levels. We solve the problem exactly within the Dirac cone approximation and find an interesting scaling of the spectrum, leading to the collapse of the Landau levels at a critical $E_c(B)$, for a given magnetic field $B$. We offer a physical interpretation of our result, which uses `graphene relativity' and the boost operation. Electric fields, non-uniform at nanoscopic ($\sim l_c$, magnetic) length scales, produce local collapse at $E < E_c$. We expect an anomalous breakdown of quantum Hall states in real graphene, induced by large Hall currents.Comment: 4 pages, 3 figure

Linear predictor and autocorrelation for noisy and delayed digital signal

This paper deals with the association between the linear prediction and digital signal modeling and ends up with the suitable ways to predict the signal by considering a stationary signal $y_{n}$. The linear prediction of signal modeling based on the finite past and the solutions are arrived in a recursive manner. Further we analyzed the wiener filter along with spectral theorem and autocorrelation in terms ofpredictive analysis.This estimates the gap function along with delay and noise. The delayed signal’sproperties are analyzed like causal, stability and applied these into optimum filtering. Finally the predicted error is compared with linear predictor and Wiener filter. Then transfer function is applied to estimate the interval function and gap function along with delay

A clinicopathological and immunohistochemical study of malignant peripheral nerve sheath tumors

Background:Malignant Peripheral Nerve Sheath Tumor (MPNST) is a rare aggressive sarcoma that develops within a peripheral nerve and forms a diagnostic challenge in view of its varied histomorphology. This short series highlights the clinicopathological spectrum of 11 cases of MPNST and the incidence of neurofibromatosis 1 (NF1) association.Methods:This retrospective and descriptive study on MPNST was done in the department of pathology, Kasturba medical college Mangalore (ManipalUniversity),India over a period of three years from January 2008 to December 2010. Cases which were histopathologically diagnosed as MPNST were reviewed & immunostains was done where ever indicated to rule out the differentials.Results:A total of 11 cases of MPNST were documented with a wide age range of 17-85 years. Male:female ratio was 2.6:1. Extremities (63.64%) were found to be the most common site. Location wise most of the tumors were deep seated (63.64%) and maximum cases were high grade (54.55%). NF1 association was seen in 2 cases. Heterologous elements in the form of chondroid differentiation was seen in one case. Immunostain with S-100 was focally positive in all the cases.Conclusion:MPNST is a highly aggressive sarcoma with poor prognosis characterized by a challenge in its diagnosis as it has several mimics. Its diagnosis necessitates the incorporation of clinicopathological features and IHC with S-100 protein.

Evaluation of Shear Strain Distribution In Polypropylene Fiber Reinforced Cement Concrete Moderate Deep Beams

ABSTRACT This paper describes a study on evaluation of shear strain and its variation as well as distribution across the width and depths in Polypropylene Fiber Reinforced Cement Concrete (PPFRC) moderate deep beams having span-to-depth ratio 2.0, 2.4, 3.0 and 4.0. Using micro mechanical measuring device a complete shear strain distribution-variation along with its response to the load deflection, crack patterns and modes of failures were studied experimentally. Twelve beams were tested and results of shear strain variation along vertical axis were plotted at various sections. Parabolic nature of shear stain distribution was observed along the vertical axis of plains situated at various depths

MRI Kidney Tumor Image Classification with SMOTE Preprocessing and SIFT-tSNE Features using CNN

Kidney tumor detection is a challenging task due to the complexity of tumor characteristics and variability in imaging modalities. In this paper, we propose a deep learning-based approach for detecting kidney tumors with 98.5% accuracy. Our method addresses the issue of an imbalanced dataset by applying the Synthetic Minority Over-sampling Technique (SMOTE) to balance the distribution of images. SMOTE generates synthetic samples of the minority class to increase the number of samples, thus providing a balanced dataset. We utilize a convolutional neural network (CNN) architecture that is trained on this balanced dataset of kidney tumor images. The CNN can learn and extract relevant features from the images, resulting in precise tumor classification. We evaluated our approach on a separate dataset and compared it with state-of-the-art methods. The results demonstrate that our method not only outperforms other methods but also shows robustness in detecting kidney tumors with a high degree of accuracy. By enabling early detection and diagnosis of kidney tumors, our proposed method can potentially improve patient outcomes. Additionally, addressing the imbalance in the dataset using SMOTE demonstrates the usefulness of this technique in improving the performance of deep learning-based image classification systems

Associations between neuropsychiatric symptoms and ADRD serum biomarkers in Mexican American and non-Hispanic white adults with mild cognitive impairment

Background: Mild cognitive impairment (MCI) is a heterogenous diagnostic category with trajectories ranging from reversion to unimpaired cognition to progression to dementia. Neuropsychiatric symptoms such as depression and irritability are common and influence quality of life of patients and caregivers. The role of neuropsychiatric symptoms on disease biology, presentation, and course remains poorly understood. The goal of this study was to evaluate the associations between neuropsychiatric symptoms and serum ADRD biomarkers in Mexican American and non-Hispanic white participants diagnosed with MCI. Method: Participants from the Texas Alzheimer’s Research and Care Consortium underwent a blood draw and clinical evaluation, including psychopathological and cognitive assessments. Diagnoses of MCI were adjudicated in consensus reviews. The presence and severity of neuropsychiatric symptoms were assessed by informant report using the Neuropsychiatric Inventory (NPI). Serum levels of total tau, neurofilament light (NfL), and glial fibrillary acidic protein (GFAP) were assessed using Simoa HD-X Analyzer. Associations between NPI total score and individual items with serum biomarker levels were assessed using linear regression adjusted for age and sex. Result: A total of 425 participants (mean age: 71 ± 9 years, 62% female, 74% Mexican American) had a diagnosis of MCI and serum ADRD biomarkers (Table 1). Total NPI score was not associated with total tau (ß=0.002, p=0.609), NfL (ß=0.001, p=0.658), or GFAP (ß=0.001, p=0.777). However, endorsement of appetite changes was associated with higher NfL (ß=0.077, p=0.006) and GFAP (ß=0.088, p=0.002) levels. Stratified analyses indicated associations of appetite changes with serum NfL (ß=0.108, p=0.002) and GFAP (ß=0.095, p=0.003) in Mexican Americans, but not in non-Hispanic whites (NfL: ß=0.022, p=0.633, GFAP: ß=0.102, p=0.066).There were no other significant associations between individual items on the NPI with serum biomarkers (p\u3e0.05, Bonferroni adjustment p±0.003). Conclusion: Within Mexican American adults with MCI, changes in appetite were associated with higher serum NFL and GFAP levels. As elevations in circulating NfL and GFAP levels are associated with ADRD pathology and accelerated disease progression, appetite changes, a non-invasive and easily discernible behavioral phenotype, may predict higher likelihood of worsening cognitive course. Future longitudinal studies will be necessary to confirm predictive utility of appetite changes for disease progression