Berpetualang ke Karawang Yuuk!

Dalam buku Seri Jejak Purbakala kali ini, Kak Arki akan mengajak kalian berpetualang ke masa lalu di daerah Karawang. Kalian akan Kak Arki ajak memecahkan misteri tentang asal usul leluhur kita yang berada di Karawan

A γ-secretase inhibitor, but not a γ-secretase modulator, induced defects in BDNF axonal trafficking and signaling: evidence for a role for APP.

Clues to Alzheimer disease (AD) pathogenesis come from a variety of different sources including studies of clinical and neuropathological features, biomarkers, genomics and animal and cellular models. An important role for amyloid precursor protein (APP) and its processing has emerged and considerable interest has been directed at the hypothesis that Aβ peptides induce changes central to pathogenesis. Accordingly, molecules that reduce the levels of Aβ peptides have been discovered such as γ-secretase inhibitors (GSIs) and modulators (GSMs). GSIs and GSMs reduce Aβ levels through very different mechanisms. However, GSIs, but not GSMs, markedly increase the levels of APP CTFs that are increasingly viewed as disrupting neuronal function. Here, we evaluated the effects of GSIs and GSMs on a number of neuronal phenotypes possibly relevant to their use in treatment of AD. We report that GSI disrupted retrograde axonal trafficking of brain-derived neurotrophic factor (BDNF), suppressed BDNF-induced downstream signaling pathways and induced changes in the distribution within neuronal processes of mitochondria and synaptic vesicles. In contrast, treatment with a novel class of GSMs had no significant effect on these measures. Since knockdown of APP by specific siRNA prevented GSI-induced changes in BDNF axonal trafficking and signaling, we concluded that GSI effects on APP processing were responsible, at least in part, for BDNF trafficking and signaling deficits. Our findings argue that with respect to anti-amyloid treatments, even an APP-specific GSI may have deleterious effects and GSMs may serve as a better alternative

Modulation of γ-Secretase Reduces β-Amyloid Deposition in a Transgenic Mouse Model of Alzheimer's Disease

SummaryAlzheimer's disease (AD) is characterized pathologically by the abundance of senile plaques and neurofibrillary tangles in the brain. We synthesized over 1200 novel gamma-secretase modulator (GSM) compounds that reduced Aβ42 levels without inhibiting epsilon-site cleavage of APP and Notch, the generation of the APP and Notch intracellular domains, respectively. These compounds also reduced Aβ40 levels while concomitantly elevating levels of Aβ38 and Aβ37. Immobilization of a potent GSM onto an agarose matrix quantitatively recovered Pen-2 and to a lesser degree PS-1 NTFs from cellular extracts. Moreover, oral administration (once daily) of another potent GSM to Tg 2576 transgenic AD mice displayed dose-responsive lowering of plasma and brain Aβ42; chronic daily administration led to significant reductions in both diffuse and neuritic plaques. These effects were observed in the absence of Notch-related changes (e.g., intestinal proliferation of goblet cells), which are commonly associated with repeated exposure to functional gamma-secretase inhibitors (GSIs)

An unsupervised deep learning technique for susceptibility artifact correction in reversed phase-encoding EPI images

© 2020 Elsevier Inc. Echo planar imaging (EPI) is a fast and non-invasive magnetic resonance imaging technique that supports data acquisition at high spatial and temporal resolutions. However, susceptibility artifacts, which cause the misalignment to the underlying structural image, are unavoidable distortions in EPI. Traditional susceptibility artifact correction (SAC) methods estimate the displacement field by optimizing an objective function that involves one or more pairs of reversed phase-encoding (PE) images. The estimated displacement field is then used to unwarp the distorted images and produce the corrected images. Since this conventional approach is time-consuming, we propose an end-to-end deep learning technique, named S-Net, to correct the susceptibility artifacts the reversed-PE image pair. The proposed S-Net consists of two components: (i) a convolutional neural network to map a reversed-PE image pair to the displacement field; and (ii) a spatial transform unit to unwarp the input images and produce the corrected images. The S-Net is trained using a set of reversed-PE image pairs and an unsupervised loss function, without ground-truth data. For a new image pair of reversed-PE images, the displacement field and corrected images are obtained simultaneously by evaluating the trained S-Net directly. Evaluations on three different datasets demonstrate that S-Net can correct the susceptibility artifacts in the reversed-PE images. Compared with two state-of-the-art SAC methods (TOPUP and TISAC), the proposed S-Net runs significantly faster: 20 times faster than TISAC and 369 times faster than TOPUP, while achieving a similar correction accuracy. Consequently, S-Net accelerates the medical image processing pipelines and makes the real-time correction for MRI scanners feasible. Our proposed technique also opens up a new direction in learning-based SAC

Susceptibility artifact correction for sub-millimeter fMRI using inverse phase encoding registration and T1 weighted regularization

© 2020 Elsevier B.V. Background: Functional magnetic resonance imaging (fMRI) enables non-invasive examination of both the structure and the function of the human brain. The prevalence of high spatial-resolution (sub-millimeter) fMRI has triggered new research on the intra-cortex, such as cortical columns and cortical layers. At present, echo-planar imaging (EPI) is used exclusively to acquire fMRI data; however, susceptibility artifacts are unavoidable. These distortions are especially severe in high spatial-resolution images and can lead to misrepresentation of brain function in fMRI experiments. New method: This paper presents a new method for correcting susceptibility artifacts by combining a T1-weighted (T1w) image and inverse phase-encoding (PE) based registration. The latter uses two EPI images acquired using identical sequences but with inverse-PE directions. In the proposed method, the T1w image is used to regularize the registration, and to select the regularization parameters automatically. The motivation is that the T1w image is considered to reflect the anatomical structure of the brain. Results: Our proposed method is evaluated on two sub-millimeter EPI-fMRI datasets, acquired using 3T and 7T scanners. Experiments show that the proposed method provides improved corrections that are well-aligned to the T1w image. Comparison with existing methods: The proposed method provides more robust and sharper corrections and runs faster compared with two other state-of-the-art inverse-PE based correction methods, i.e. HySCO and TOPUP. Conclusions: The proposed correction method used the T1w image as a reference in the inverse-PE registration. Results show its promising performance. Our proposed method is timely, as sub-millimeter fMRI has become increasingly popular

Proses pengolahan sampah dan kebersihan lingkungan di wilayah perkotaan ( Studi implementasi kebijakan berdasarkan peraturan daerah nomor 03 tahun 2006 tentang kebersihan lingkungan di Kota Malang)

The city of Palangkaraya implement waste management policies based on local reguation Palangkaraya nomor 03 of 2006 on waste management which is a refinement of the law ..

Human Brain Tissue Segmentation in fMRI using Deep Long-Term Recurrent Convolutional Network

Accurate segmentation of different brain tissue types is an important step in the study of neuronal activities using functional magnetic resonance imaging (fMRI). Traditionally, due to the low spatial resolution of fMRI data and the absence of an automated segmentation approach, human experts often resort to superimposing fMRI data on high resolution structural MRI images for analysis. The recent advent of fMRI with higher spatial resolutions offers a new possibility of differentiating brain tissues by their spatio-temporal characteristics, without relying on the structural MRI images. In this paper, we propose a patch-wise deep learning method for segmenting human brain tissues into five types, which are gray matter, white matter, blood vessel, non-brain and cerebrospinal fluid. The proposed method achieves a classification rate of 84.04% and a Dice similarity coefficient of 76.99%, which exceed those by several other methods

Anatomy-Guided Inverse-Gradient Susceptibility Artifact Correction Method for High-Resolution FMRI

Functional Magnetic Resonance Imaging (fMRI) is a widely used and non-invasive technique for recording changes in brain activity. However, susceptibility artifacts are ubiquitous distortions in fMRI, especially strong in high-resolution images, causing the misrepresentation of brain function and structure in the affected regions. Here, we present a novel method for correcting these distortions in high-resolution fMRI images based on the hyper-elastic susceptibility artifact correction (HySCO) method. The novelty of the proposed method is the utilization of the easily-acquired T1-weighted ({T}{1w}) anatomy image as a ground-truth measurement to regularize deformations, thereby obtaining meaningful corrections. The performance of the new method is compared to that of HySCO. Results from high-resolution (1mm) EPI data are presented demonstrating the robustness of the new method for image correction and its suitability for subsequent fMRI analysis