ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
LncRNA XR_351665 Contributes to Chronic Pain-Induced Depression by Upregulating DNMT1 via Sponging miR-152-3p

Chronic pain is frequently comorbid with depression. However, the mechanisms underlying chronic pain-induced depression remain unclear. Here, we found that DNA methyltransferase 1 (DNMT1) was upregulated in the central amygdala (CeA) of spared nerve injury (SNI)-induced chronic pain-depression rats, and knockdown of DNMT1 could improve the depression-like behaviors in SNI rats. Additionally, a panel of differentially expressed lncRNAs, including 38 upregulated and 12 downregulated lncRNAs, were identified by microarray analysis. Bioinformatics analysis suggested that the upregulated lncRNA XR_351665 was the upstream molecule to regulate DNMT1 expression. The knockdown of XR_351665 significantly alleviated the depression-like behaviors in SNI rats, whereas overexpression of XR_351665 induced the depression-like behaviors in naïve rats. Further mechanism-related researches uncovered that XR_351665 functioned as a competing endogenous RNA (ceRNA) to upregulate DNMT1 by competitively sponging miR-152-3p, and subsequently promoted the development of chronic pain-induced depression. Our findings suggest that lncRNA XR_351665 is involved in the development of chronic pain-induced depression by upregulating DNMT1 via sponging miR-152-3p. These data provide novel insight into understanding the pathogenesis of chronic pain-induced depression and identify a potential therapeutic target. PERSPECTIVE: LncRNA XR_351665 in CeA functions as a ceRNA to block the inhibitory effect of miR-152-3p on DNMT1 and contributes to the development of chronic pain-induced depression. These data suggest that manipulation of XR_351665/miR-152-3p/DNMT1 axis may be a potential method to attenuate chronic pain-induced depression.
The role of friction in the seismic risk mitigation of freestanding art objects

The problem of reducing the seismic risk for art objects, that are the objects generally contained within Museums, is of great interest. The first studies were performed in Japan and were successively organized in a general framework by a research program performed at Southern California University and sponsored by the Getty Museum at Malibu, California. In these papers and in the following Italian studies, the theoretical models for the problem concerning vases and statues are based on the dynamic behavior of rigid blocks and have been deeply developed. Unfortunately, because of the great lack of experimental data, determinant parameters for the problem characterization (like the friction between two superimposed blocks or between the art object and the support plane) are often assumed without reference to real values derived from laboratory tests. This paper presents the results of a research program containing the experimental determination of the friction coefficient between the art object and the support (by means of a testing apparatus on purpose realized) together with dynamic tests performed on simple-shaped objects made of different materials. The dynamic tests were performed using an unidirectional shaking table and different supporting surfaces, so that the influence of different friction coefficients has been analyzed.
A review of state-of-the-art and proposal for high frequency inductive step-down DC-DC converter in advanced CMOS

This paper reviews the state-of-the-art of high switching frequency, integrated DC-DC converters and presents the main trade-offs and challenges emerging from this review. Various converter structures (1-phase buck, 2-phase buck, 2-phase coupled buck and 3-level converter) are then discussed and analyzed through simulation from a losses point-of-view. Considering the review, the architecture analysis and the technology model, 4 converters are designed for a given set of specifications: 3.3-1.2 V, 280 mA output current at high switching frequency (100-200 MHz) in 40 nm bulk CMOS. A cascode power stage is used in order to enhance power conversion efficiency, and 1-phase and 2-phase structures are designed. Post-layout simulation results are presented, showing an efficiency above 90 % for a 2-phase converter.
Detecting Curvilinear Features Using Structure Tensors

Few published articles on curvilinear structures exist compared with works on detecting lines or corners with high accuracy. In medical ultrasound imaging, the structures that need to be detected appear as a collection of microstructures correlated along a path. In this paper, we investigated techniques that extract meaningful low-level information for curvilinear structures, using techniques based on structure tensor. We proposed a novel structure tensor enhancement inspired by bilateral filtering. We compared the proposed approach with five state-of-the-art curvilinear structure detectors. We tested the algorithms against simulated images with known ground truth and real images from three different domains (medical ultrasound, scanning electron microscope, and astronomy). For the real images, we employed experts to delineate the ground truth for each domain. Techniques borrowed from machine learning robustly assessed the performance of the methods (area under curve and cross validation). As a practical application, we used the proposed method to label a set of 5000 ultrasound images. We conclude that the proposed tensor-based approach outperforms the state-of-the-art methods in providing magnitude and orientation information for curvilinear structures. The evaluation methodology ensures that the employed feature-detection method will yield reproducible performance on new, unseen images. We published all the implemented methods as open-source software.
Novel approach for tracking interdisciplinary research productivity using institutional databases

This study proposes a new practical approach for tracking institutional changes in research teamwork and productivity using commonly available institutional electronic databases such as eCV and grant management systems. We tested several definitions of interdisciplinary collaborations based on number of collaborations and their fields of discipline. We demonstrated that the extent of interdisciplinary collaboration varies significantly by academic unit, faculty appointment and seniority. Interdisciplinary grants constitute 24% of all grants but the trend has significantly increased over the last five years. Departments with more interdisciplinary grants receive more research funding. More research is needed to improve efficiency of interdisciplinary collaborations.
A Single-Center Experience of Internal Pancreatic Fistulas

Background Internal pancreatic fistula (IPF) is a complex disease with different etiologies, varied clinical presentations, and multiple management options. Unlike postoperative pancreatic fistula, IPF lacks guidelines for classification and management. The rarity of the disease makes randomized control studies unlikely and difficult to formulate guidelines. This has resulted in different approaches to managing IPF. IPF associated with both acute and chronic pancreatitis is treated with a step-up approach. Chronic pancreatitis-associated IPF treated with the traditional step-up approach is associated with increased morbidity. Prolonged fasting, drainage of protein-rich pancreatic fluid, and extended hospital stay add to the morbidity. Early surgical intervention in patients with IPF associated with chronic pancreatitis can treat both the fistula and underlying disease processes simultaneously. This may contribute to reduced morbidity and hospital stay. Methodology A retrospective observational study was conducted between June 2018 and May 2019. IPF patients with fluid amylase >1,000 IU/L and fluid albumin >3 g/dL were included in the study. Results In total, 32 patients were included in the study. A total of 13 patients had acute pancreatitis and 19 were associated with chronic pancreatitis. Pseudocyst and walled-off pancreatic necrosis were present in 18 patients. The duration of treatment for the traditional group was 8-14 weeks, and for the early surgery group, it was 8-10 days. Patients were followed up for two years, and none of the patients in the early surgery group had a recurrence. Conclusions The overall mortality of IPF is low but it has high morbidity. The delay in treatment may contribute to high morbidity; hence, early surgical intervention may change the clinical course. The primary pathology of the pancreas can be addressed simultaneously as well. In our study, early surgical intervention was associated with lesser morbidity and decreased duration of hospital stay while recurrence rates and mortality were comparable to the traditional management protocol.
Pseudomonas aeruginosa Quorum Sensing

Pseudomonas aeruginosa, like many bacteria, uses chemical signals to communicate between cells in a process called quorum sensing (QS). QS allows groups of bacteria to sense population density and, in response to changing cell densities, to coordinate behaviors. The P. aeruginosa QS system consists of two complete circuits that involve acyl-homoserine lactone signals and a third system that uses quinolone signals. Together, these three QS circuits regulate the expression of hundreds of genes, many of which code for virulence factors. P. aeruginosa has become a model for studying the molecular biology of QS and the ecology and evolution of group behaviors in bacteria. In this chapter, we recount the history of discovery of QS systems in P. aeruginosa, discuss how QS relates to virulence and the ecology of this bacterium, and explore strategies to inhibit QS. Finally, we discuss future directions for research in P. aeruginosa QS.
When Van Gogh meets Mandelbrot: Multifractal classification of painting's texture

Recently, a growing interest has emerged for examining the potential of Image Processing tools to assist Art Investigation. Simultaneously, several research works showed the interest of using multifractal analysis for the description of homogeneous textures in images. In this context, the goal of the present contribution is to study the benefits of using the wavelet leader based multifractal formalism to characterize paintings. After a brief review of the underlying key theoretical concepts, methods and tools, two sets of digitized paintings are analyzed. The first one, the Princeton Experiment, consists of a set of seven paintings and their replicas, made by the same artist. It enables examination of the potential of multifractal analysis in forgery detection. The second one is composed of paintings by Van Gogh and contemporaries, made available by the Van Gogh and Kroller-Muller Museums (Netherlands) in the framework of the Image processing for Art Investigation research program. It enables us to show various differences in the regularity of textures of Van Gogh's paintings from different periods, or between Van Gogh's and contemporaries' paintings. These preliminary results plead for the constitution of interdisciplinary research teams consisting of experts in art, image processing, mathematics and computer sciences. (C) 2012 Published by Elsevier B.V.
PWNet: An Adaptive Weight Network for the Fusion of Panchromatic and Multispectral Images

Pansharpening is a typical image fusion problem, which aims to produce ahigh resolution multispectral(HRMS) image by integrating a high spatial resolutionpanchromatic(PAN) image with a low spatial resolutionmultispectral(MS) image. Prior arts have used eithercomponent substitution(CS)-based methods ormultiresolution analysis(MRA)-based methods for this propose. Although they are simple and easy to implement, they usually suffer from spatial or spectral distortions and could not fully exploit the spatial and/or spectral information existed in PAN and MS images. By considering their complementary performances and with the goal of combining their advantages, we propose apansharpening weight network(PWNet) to adaptively average the fusion results obtained by different methods. The proposed PWNet works by learning adaptive weight maps for different CS-based and MRA-based methods through an end-to-end trainableneural network(NN). As a result, the proposed PWN inherits the data adaptability or flexibility of NN, while maintaining the advantages of traditional methods. Extensive experiments on data sets acquired by three different kinds of satellites demonstrate the superiority of the proposed PWNet and its competitiveness with the state-of-the-art methods.
"A whole new perspective on how the body fits together"-An evaluation of a cadaver laboratory experience for high school students

The Center for Anatomy and Physiology Education has hosted interactive human cadaver laboratory tours for local high schools (ages 14-18) and undergraduate university students since 2014 to expose students to healthcare careers. Students receive information on the history of body donation and healthcare careers and observe human anatomy on prosections and with isolated organs. The goal of this study was to evaluate students' perceptions of the anatomy laboratory tours and their impact on students' interests in healthcare careers. Students completed pre- and post-tour questionnaires. Responses were analyzed using thematic analysis and linguistic inquiry. Of the 261 students who completed pre-tour questionnaires, 204 (78%) completed the post-tour questionnaire. Before the tour, students anticipated learning about human anatomy and expected to only see but not touch a cadaver. Most students expressed excitement and/or nervousness. A few students viewed the laboratory tour as an opportunity to test if they could see themselves in a healthcare career. After the tour, most students indicated that the tour either met or exceeded their expectations. Students found the laboratory tour to be educational and interesting and were surprised by the opportunity to interact with the donor. Numerous students expressed an increased interest in healthcare careers after the tour. Overall, students perceived the tour as an engaging experience that improved their anatomical knowledge and reinforced/increased their interest in healthcare careers. Academic institutions can positively impact local students by implementing an anatomy tour, sharing access to their in-house human cadaver laboratory, and recruiting instructors to share their anatomy expertise.
Bilateral Parainfectious Optic Neuritis in Young Patient

Parainfectious optic neuritis arises from infectious aetiology either from pathogen direct invasion or after an infectious disease which can be immunologically mediated demyelination of optic nerve or, from inflammation of optic disc vasculature. We report a case of bilateral optic neuritis in a young patient. A 13-year-old boy presented with painless profound vision loss in both eyes preceded by an episode of fever two weeks prior. Visual acuity in both eyes was a perception of light. Fundoscopy showed a bilateral hyperemic swollen disc. Blood investigations were normal except for C-reactive protein and ESR was elevated. CSF analysis was also normal with no growth of micro-organisms. Both CT scans and MRIs of the brain and orbit showed normal findings. The patient was diagnosed to have parainfectious optic neuritis. He was started on intravenous methylprednisolone for five days followed by a tapering dose of oral prednisolone for a total of one month. His final visual acuity improved to 6/6 in both eyes with a normal optic disc appearance.
Optimization of CMOS-integrated LC oscillators using the genetic algorithm

This paper reports on state-of-the-art power-frequency-normalized phase noise of 21 dB, obtained from a CMOS-integrated LC oscillator with a measured phase noise of -112 dBc/Hz at 100 kHz from the 5.3-GHz center frequency and with power consumption of 15 mW. The excellent performance (if the oscillator is attributed to the careful selection of the oscillator-circuit parameters, which determined using the genetic algorithm. (C) 2004 Wiley Periodicals, Inc.
FAT: Frequency-Aware Transformation for Bridging Full-Precision and Low-Precision Deep Representations

Learning low-bitwidth convolutional neural networks (CNNs) is challenging because performance may drop significantly after quantization. Prior arts often quantize the network weights by carefully tuning hyperparameters such as nonuniform stepsize and layerwise bitwidths, which are complicated since the full-and low-precision representations have large discrepancies. This work presents a novel quantization pipeline, named frequency-aware transformation (FAT), that features important benefits: 1) instead of designing complicated quantizers, FAT learns to transform network weights in the frequency domain to remove redundant information before quantization, making them amenable to training in low bitwidth with simple quantizers; 2) FAT readily embeds CNNs in low bitwidths using standard quantizers without tedious hyperparameter tuning and theoretical analyses show that FAT minimizes the quantization errors in both uniform and nonuniform quantizations; and 3) FAT can be easily plugged into various CNN architectures. Using FAT with a simple uniform/logarithmic quantizer can achieve the state-of-the-art performance in different bitwidths on various model architectures. Consequently, FAT serves to provide a novel frequency-based perspective for model quantization.
DLTTA: Dynamic Learning Rate for Test-Time Adaptation on Cross-Domain Medical Images

Test-time adaptation (TTA) has increasingly been an important topic to efficiently tackle the cross-domain distribution shift at test time for medical images from different institutions. Previous TTA methods have a common limitation of using a fixed learning rate for all the test samples. Such a practice would be sub-optimal for TTA, because test data may arrive sequentially therefore the scale of distribution shift would change frequently. To address this problem, we propose a novel dynamic learning rate adjustment method for test-time adaptation, called DLTTA, which dynamically modulates the amount of weights update for each test image to account for the differences in their distribution shift. Specifically, our DLTTA is equipped with a memory bank based estimation scheme to effectively measure the discrepancy of a given test sample. Based on this estimated discrepancy, a dynamic learning rate adjustment strategy is then developed to achieve a suitable degree of adaptation for each test sample. The effectiveness and general applicability of our DLTTA is extensively demonstrated on three tasks including retinal optical coherence tomography (OCT) segmentation, histopathological image classification, and prostate 3D MRI segmentation. Our method achieves effective and fast test-time adaptation with consistent performance improvement over current state-of-the-art test-time adaptation methods. Code is available at https://github.com/med-air/DLTTA.
Somatostatin-Positive Neurons in the Rostral Zona Incerta Modulate Innate Fear-Induced Defensive Response in Mice

Defensive behaviors induced by innate fear or Pavlovian fear conditioning are crucial for animals to avoid threats and ensure survival. The zona incerta (ZI) has been demonstrated to play important roles in fear learning and fear memory, as well as modulating auditory-induced innate defensive behavior. However, whether the neuronal subtypes in the ZI and specific circuits can mediate the innate fear response is largely unknown. Here, we found that somatostatin (SST)-positive neurons in the rostral ZI of mice were activated by a visual innate fear stimulus. Optogenetic inhibition of SST-positive neurons in the rostral ZI resulted in reduced flight responses to an overhead looming stimulus. Optogenetic activation of SST-positive neurons in the rostral ZI induced fear-like defensive behavior including increased immobility and bradycardia. In addition, we demonstrated that manipulation of the GABAergic projections from SST-positive neurons in the rostral ZI to the downstream nucleus reuniens (Re) mediated fear-like defensive behavior. Retrograde trans-synaptic tracing also revealed looming stimulus-activated neurons in the superior colliculus (SC) that projected to the Re-projecting SST-positive neurons in the rostral ZI (SC-ZIrSST-Re pathway). Together, our study elucidates the function of SST-positive neurons in the rostral ZI and the SC-ZIrSST-Re tri-synaptic circuit in mediating the innate fear response.
Current State-of-the-Art and New Directions in Strategic Environmental Noise Mapping

Environmental noise mapping has the potential to act as a powerful resource for policymakers as a decision support tool for the mitigation of the negative effects of environmental noise pollution and its impact on public health. The aim of this paper is to review current state-of-the-art developments in how the strategic noise mapping (SNM) process has progressed at the EU level since the introduction of the Environmental Noise Directive (END) in 2002. Reviewing such developments is important because of the relevance of SNM to public health. In this regard, the development of a new standardized noise calculation method (i.e. CNOSSOS-EU) is also considered, as well as the future potential for noise mapping and the impact of technology on the development of noise pollution assessment.
Elections have (health) consequences: Depression, anxiety, and the 2020 presidential election

In this paper, we examine the effect of the 2020 presidential election on anxiety and depression among Americans. We use data from the 2020 Household Pulse Survey (HPS), a nationally representative rapid response survey conducted weekly from April to July of 2020 and then bi-weekly until December of 2021. The high-frequency nature of the survey implies that we can identify week-to-week changes in mental health outcomes. We find that self-reported symptoms of moderate to severe anxiety and depression increased steadily up to the presidential election and declined after the election. The anxiety and depression levels are significantly higher around the 2020 election than in April 2020, when most of the U.S. was under mandatory or advisory stay-at-home orders due to the COVID-19 pandemic. Furthermore, anxiety and depression-specific office visits and usage of mental-health-specific prescription drugs show similar patterns. Robustness checks rule out alternative explanations such as a COVID-19 surge or vaccine development.
Improving Human Pose Estimation With Self-Attention Generative Adversarial Networks

Human pose estimation in images is challenging and important for many computer vision applications. Large improvements in human pose estimation have been achieved with the development of convolutional neural networks. Even though, when encountered some difficult cases even the state-of-the-art models may fail to predict all the body joints correctly. Some recent works try to refine the pose estimator. GAN (Generative Adversarial Networks) has been proved to be efficient to improve human pose estimation. However, GAN can only learn local body joints structural constrains. In this paper, we propose to apply Self-Attention GAN to further improve the performance of human pose estimation. With attention mechanism in the framework of GAN, we can learn long-range body joints dependencies, therefore enforce the entire body joints structural constrains to make all the body joints to be consistent. Our method outperforms other state-of-the-art methods on two standard benchmark datasets MPII and LSP for human pose estimation.