Future impacts of fresh water resource management: sensitivity of coastal deltas

We present an assessment of contemporary and future effective sealevel rise (ESLR) using a sample of 40 deltas distributed worldwide. For any delta, ESLR is a net rate defined by eustatic sea-level rise, natural gross rates of fluvial sediment deposition and subsidence, and accelerated subsidence due to groundwater and hydrocarbon extraction. Present-day ESLR, estimated from geospatial data and a simple model of deltaic dynamics, ranges from 0.5 to 12.5 mm year-1. Reduced accretion of fluvial sediment from upstream siltation of reservoirs and freshwater consumptive irrigation losses are primary determinants of ESLR in nearly 70% of the deltas, while for only 12% eustatic sea-level rise predominates. Future scenarios indicate a much larger impact on deltas than previously estimated. Serious challenges to human occupancy of deltas worldwide are conveyed by upland watershed factors, which have been studied less comprehensively than the climate change and sea-level rise question

The Genetics of Axonal Transport and Axonal Transport Disorders

Neurons are specialized cells with a complex architecture that includes elaborate dendritic branches and a long, narrow axon that extends from the cell body to the synaptic terminal. The organized transport of essential biological materials throughout the neuron is required to support its growth, function, and viability. In this review, we focus on insights that have emerged from the genetic analysis of long-distance axonal transport between the cell body and the synaptic terminal. We also discuss recent genetic evidence that supports the hypothesis that disruptions in axonal transport may cause or dramatically contribute to neurodegenerative diseases

Boosting Image Forgery Detection using Resampling Features and Copy-move analysis

Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. While resampling detection algorithms are effective in detecting splicing and resampling, copy-move detection algorithms excel in detecting cloning and region removal. In this paper, we combine these complementary approaches in a way that boosts the overall accuracy of image manipulation detection. We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework. Experimental results on various datasets including the 2017 NIST Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and tampered images shows that there is a consistent increase of 8%-10% in detection rates, when copy-move algorithm is combined with different resampling detection algorithms

Inadvertent Lead Placement In The Left Ventricle: A Case Report And Brief Review

Inadvertent lead placement in the left ventricle (LV) is an uncommon and often under-diagnosed complication of cardiac device implantation. Thromboembolic (TE) events are common and usually secondary to fibrosis or thrombus formation on or around the lead. Anticoagulation can prevent TE events. Percutaneous and surgical LV lead extractions have been performed successfully, but the risks of percutaneous lead removal are not well-defined. In this report, we describe a case of inadvertent LV lead placement and briefly review the contemporary literature

Helping to Support CPC+ Initiative to Integrate Behavioral Health Within Primary Care: A Team-Based Approach to Improving Depression Management

AIM: The objective of this project is to increase the rate of documented successful treatment of depression for both new and established diagnoses of depression at Jefferson Internal Medicine Associates (JIMA) from 29% to 50% over 12 months.https://jdc.jefferson.edu/patientsafetyposters/1027/thumbnail.jp

Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis

The amount of digital imagery recorded has recently grown exponentially, and with the advancement of software, such as Photoshop or Gimp, it has become easier to manipulate images. However, most images on the internet have not been manipulated and any automated manipulation detection algorithm must carefully control the false alarm rate. In this paper we discuss a method to automatically detect local resampling using deep learning while controlling the false alarm rate using a-contrario analysis. The automated procedure consists of three primary steps. First, resampling features are calculated for image blocks. A deep learning classifier is then used to generate a heatmap that indicates if the image block has been resampled. We expect some of these blocks to be falsely identified as resampled. We use a-contrario hypothesis testing to both identify if the patterns of the manipulated blocks indicate if the image has been tampered with and to localize the manipulation. We demonstrate that this strategy is effective in indicating if an image has been manipulated and localizing the manipulations.Comment: arXiv admin note: text overlap with arXiv:1802.0315

Peroxisome proliferator-activated receptor delta limits the expansion of pathogenic Th cells during central nervous system autoimmunity.

Peroxisome proliferator-activated receptors (PPARs; PPAR-alpha, PPAR-delta, and PPAR-gamma) comprise a family of nuclear receptors that sense fatty acid levels and translate this information into altered gene transcription. Previously, it was reported that treatment of mice with a synthetic ligand activator of PPAR-delta, GW0742, ameliorates experimental autoimmune encephalomyelitis (EAE), indicating a possible role for this nuclear receptor in the control of central nervous system (CNS) autoimmune inflammation. We show that mice deficient in PPAR-delta (PPAR-delta(-/-)) develop a severe inflammatory response during EAE characterized by a striking accumulation of IFN-gamma(+)IL-17A(-) and IFN-gamma(+)IL-17A(+) CD4(+) cells in the spinal cord. The preferential expansion of these T helper subsets in the CNS of PPAR-delta(-/-) mice occurred as a result of a constellation of immune system aberrations that included higher CD4(+) cell proliferation, cytokine production, and T-bet expression and enhanced expression of IL-12 family cytokines by myeloid cells. We also show that the effect of PPAR-delta in inhibiting the production of IFN-gamma and IL-12 family cytokines is ligand dependent and is observed in both mouse and human immune cells. Collectively, these findings suggest that PPAR-delta serves as an important molecular brake for the control of autoimmune inflammation

Using Perturbation Theory to Compute the Morphological Similarity of Diffusion Tensors

Computing the morphological similarity of diffusion tensors (DTs) at neighboring voxels within a DT image, or at corresponding locations across different DT images, is a fundamental and ubiquitous operation in the postprocessing of DT images. The morphological similarity of DTs typically has been computed using either the principal directions (PDs) of DTs (i.e., the direction along which water molecules diffuse preferentially) or their tensor elements. Although comparing PDs allows the similarity of one morphological feature of DTs to be visualized directly in eigenspace, this method takes into account only a single eigenvector, and it is therefore sensitive to the presence of noise in the images that can introduce error in to the estimation of that vector. Although comparing tensor elements, rather than PDs, is comparatively more robust to the effects of noise, the individual elements of a given tensor do not directly reflect the diffusion properties of water molecules. We propose a measure for computing the morphological similarity of DTs that uses both their eigenvalues and eigenvectors, and that also accounts for the noise levels present in DT images. Our measure presupposes that DTs in a homogeneous region within or across DT images are random perturbations of one another in the presence of noise. The similarity values that are computed using our method are smooth (in the sense that small changes in eigenvalues and eigenvectors cause only small changes in similarity), and they are symmetric when differences in eigenvalues and eigenvectors are also symmetric. In addition, our method does not presuppose that the corresponding eigenvectors across two DTs have been identified accurately, an assumption that is problematic in the presence of noise. Because we compute the similarity between DTs using their eigenspace components, our similarity measure relates directly to both the magnitude and the direction of the diffusion of water molecules. The favorable performance characteristics of our measure offer the prospect of substantially improving additional postprocessing operations that are commonly performed on DTI datasets, such as image segmentation, fiber tracking, noise filtering, and spatial normalization