ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Scientific evidence invalidates health assumptions underlying the FCC and ICNIRP exposure limit determinations for radiofrequency radiation: implications for 5G

In the late-1990s, the FCC and ICNIRP adopted radiofrequency radiation (RFR) exposure limits to protect the public and workers from adverse effects of RFR. These limits were based on results from behavioral studies conducted in the 1980s involving 40-60-minute exposures in 5 monkeys and 8 rats, and then applying arbitrary safety factors to an apparent threshold specific absorption rate (SAR) of 4 W/kg. The limits were also based on two major assumptions: any biological effects were due to excessive tissue heating and no effects would occur below the putative threshold SAR, as well as twelve assumptions that were not specified by either the FCC or ICNIRP. In this paper, we show how the past 25 years of extensive research on RFR demonstrates that the assumptions underlying the FCC's and ICNIRP's exposure limits are invalid and continue to present a public health harm. Adverse effects observed at exposures below the assumed threshold SAR include non-thermal induction of reactive oxygen species, DNA damage, cardiomyopathy, carcinogenicity, sperm damage, and neurological effects, including electromagnetic hypersensitivity. Also, multiple human studies have found statistically significant associations between RFR exposure and increased brain and thyroid cancer risk. Yet, in 2020, and in light of the body of evidence reviewed in this article, the FCC and ICNIRP reaffirmed the same limits that were established in the 1990s. Consequently, these exposure limits, which are based on false suppositions, do not adequately protect workers, children, hypersensitive individuals, and the general population from short-term or long-term RFR exposures. Thus, urgently needed are health protective exposure limits for humans and the environment. These limits must be based on scientific evidence rather than on erroneous assumptions, especially given the increasing worldwide exposures of people and the environment to RFR, including novel forms of radiation from 5G telecommunications for which there are no adequate health effects studies.
Bias-Dependent Small-Signal Modeling Based on Neuro-Space Mapping for MOSFET

In this article, bias-dependent small-signal modeling approach based on neuro-space mapping is proposed for MOSFET. Good agreement is obtained between the simulated and measured results for a 130 nm MOSFET in the frequency range of 100 MHz-40 GHz confirming the validity and effectiveness of our approach. In addition, higher accuracy is achieved by our approach in contrast to conventional empirical model. (C) 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE 21:182-189, 2011.
Navigating Environmental Transitions: the Role of Phenotypic Variation in Bacterial Responses

The ability of bacteria to respond to changes in their environment is critical to their survival, allowing them to withstand stress, form complex communities, and induce virulence responses during host infection. A remarkable feature of many of these bacterial responses is that they are often variable across individual cells, despite occurring in an isogenic population exposed to a homogeneous environmental change, a phenomenon known as phenotypic heterogeneity. Phenotypic heterogeneity can enable bet-hedging or division of labor strategies that allow bacteria to survive fluctuating conditions. Investigating the significance of phenotypic heterogeneity in environmental transitions requires dynamic, single-cell data. Technical advances in quantitative single-cell measurements, imaging, and microfluidics have led to a surge of publications on this topic. Here, we review recent discoveries on single-cell bacterial responses to environmental transitions of various origins and complexities, from simple diauxic shifts to community behaviors in biofilm formation to virulence regulation during infection. We describe how these studies firmly establish that this form of heterogeneity is prevalent and a conserved mechanism by which bacteria cope with fluctuating conditions. We end with an outline of current challenges and future directions for the field. While it remains challenging to predict how an individual bacterium will respond to a given environmental input, we anticipate that capturing the dynamics of the process will begin to resolve this and facilitate rational perturbation of environmental responses for therapeutic and bioengineering purposes.
Tuning space mapping: The state of the art

The electromagnetic (EM)-simulator-based tuning process for rapid microwave design can combine EM accuracy with circuit-design speed. Our own approach is based on the intuitive engineering idea of space mapping. In this article, we explain the art of microwave design optimization through tuning space mapping procedures. We list various appropriate types of models (called surrogates). We demonstrate the implementation of these surrogates through a simple bandstop filter. We provide application examples using commercial simulation software. Our purpose is to help microwave engineers understand the tuning space mapping methodology and to inspire new implementations and applications. (C) 2012 Wiley Periodicals, Inc. Int J RF and Microwave CAE 22: 639651, 2012.
Indoor air quality at Salarjung Museum, Hyderabad, India

Deterioration of art objects at Salarjung Museum has been noticed such as blackening of white and pink pigments of Indian miniature paintings and other objects like pigments, paints, varnishes, coatings, silver ware, zari works, textiles, which are displayed in museum galleries. The cause of deterioration of the artifacts is attributed to air pollution. The outdoor air pollution levels with respect to suspended particulate matter, sulphur dioxide, oxides of nitrogen, ammonia, aldehydes and oxidants are observed to be high when compared with background environment and ambient air quality standards for sensitive areas. The indoor air quality levels in terms of various parameters including temperature and relative humidity (RH) observed to be more than the threshold limits. The climatic conditions coupled with polluted indoor air are the main causes for the deterioration of art objects. Hence remedial measures are suggested to avoid further deterioration of objects.
Surgical Outcomes of Congenital Heart Disease in Down Syndrome: Tertiary Center Experience-Focus on the Electrical Conduction System

To document outcomes of cardiac surgical repair in Down syndrome (DS) patients with specific focus on the associated electrical conduction morbidities, ultimately leading to a higher incidence of pacemaker implantation (PMI). A retrospective study conducted between 2011 and 2020. A total of 167 DS patients undergoing 204 surgeries were included. The mean gestational age (GA) and mean weight were 37.3 weeks and 5.5 kg, respectively. Complete atrioventricular septal defect (AVSD) was the most common diagnosis. Pre-operative ECG revealed superior axis deviation (SAD) in 92 and 32% of patients with AVSD and isolated perimembranous ventricular septal defect (VSD), respectively (p < 0.01). Postoperative right bundle branch block (RBBB) was observed in 83 and 55% of patients with AVSD and following perimembranous VSD repair, respectively (p = 0.04). Ten patients underwent post-operative pacemaker implantation (PMI). Reintervention rate was around 8.9%. Three mortalities were encountered throughout the study period, 2 of which were in-hospital deaths. Low mortality was observed, however, a higher rate of PMI requirements noted with risk factors including lower age and weight.
Identify Representative Samples by Conditional Random Field of Cancer Histology Images

Pathologyanalysis is crucial to precise cancer diagnoses and the succeeding treatment plan as well. To detect abnormality in histopathology images with prevailing patch-based convolutional neural networks (CNNs), contextual information often serves as a powerful cue. However, aswhole-slide images (WSIs) are characterized by intense morphological heterogeneity and extensive tissue scale, a straightforward visual span to a larger context may not well capture the information closely associated with the focal patch. In this paper, we propose a novel pixel-offset based patch-locationmethod to identify high-representative tissues, with a CNN backbone. Pathology Deformable Conditional Random Field (PDCRF) is proposed to learn the offsets and weights of neighboring contexts in a spatialadaptivemanner, to search for high-representative patches. A CNN structure with the localized patches as training input is then capable of consistently reaching superior classification outcomes for histology images. Overall, the proposed method has achieved state-of-the-art performance, in terms of the test classification accuracy improvement to the baseline by 1.15-2.60%, 0.78-1.78%, and 1.47-2.18% on TCGA public datasets of TCGA-STAD, TCGA-COAD, and TCGA-READ respectively. It also achieves 88.95% test accuracy and 0.920 test AUC on Camelyon 16. To show the effectiveness of the proposed framework on downstream tasks, we take a further step by incorporating an active learning model, which noticeably reduces the number of manual annotations by PDCRF to reach a parallel patchbased histology classifier.
Hyperspectral image classification based on spectral and spatial information using ResNet with channel attention

Classification of hyperspectral image (HSI) is widely used for the study of remotely sensed images. Convolutional Neural Networks (CNNs) are one of the most commonly chosen deep learning algorithms for visual data analysis. The HSI classification framework based on the CNN is presented in this paper. Since the imbalance between the high dimension of HSI input data and the limited amount of labeled training data would induce overfitting, current convolutional networks are fairly superficial for HSI classification. To stop the limited efficiency of feature learning, a new HSI classification network called Residual Spectral Spatial-Channel Attention Network (RSS-CAN) is proposed. By utilizing the "shortcut connection" framework, RSS-CAN can use deeper layers to extract more succinct and efficient features. Furthermore, attention mechanism is used to emphasize meaningful features. In addition, we revised an HSI dataset called Shandong Feicheng. The resolution and pixel quantity of this dataset are significantly greater. In order to check its variety, it has been contrasted with state-of-the-art approaches. Experimental results with widely used hyperspectral image datasets demonstrate that, our proposed method has achieved better performance in comparison with state-of-the-art classifiers and conventional deep learning-based classifiers.
App design of distance art education platform under internet ecological environment

With the rapid development of computer and network technology, the application of mobile app has been involved in all walks of life, especially the traditional art teaching mode. Therefore, it is of great significance to design an app with art learning and education as the main content based on the concept of young people's cognitive psychology and art training. This paper first analyzes the current situation and development trend of art education app at home and abroad, then analyzes and studies the design and application needs of education app at home, clarifies the needs of students and teachers, analyzes the visual and psychological characteristics of teenagers and their impact on interface and interaction design, and finally designs an art education app that meets the domestic needs. The results show that the app can enhance the interest of young people in art learning.
Plasma Functionalization of Silica Bilayer Polymorphs

Ultrathin silica films are considered suitable two-dimensional model systems for the study of fundamental chemical and physical properties of all-silica zeolites and their derivatives, as well as novel supports for the stabilization of single atoms. In the present work, we report the creation of a new model catalytic support based on the surface functionalization of different silica bilayer (BL) polymorphs with well-defined atomic structures. The functionalization is carried out by means of in situ H-plasma treatments at room temperature. Low energy electron diffraction and microscopy data indicate that the atomic structure of the films remains unchanged upon treatment. Comparing the experimental results (photoemission and infrared absorption spectra) with density functional theory simulations shows that H2 is added via the heterolytic dissociation of an interlayer Si-O-Si siloxane bond and the subsequent formation of a hydroxyl and a hydride group in the top and bottom layers of the silica film, respectively. Functionalization of the silica films constitutes the first step into the development of a new type of model system of single-atom catalysts where metal atoms with different affinities for the functional groups can be anchored in the SiO2 matrix in well-established positions. In this way, synergistic and confinement effects between the active centers can be studied in a controlled manner.
Timing is everything: Transcriptional repression is not the default mode for regulating Hedgehog signaling

Hedgehog (HH) signaling is a conserved pathway that drives developmental growth and is essential for the formation of most organs. The expression of HH target genes is regulated by a dual switch mechanism where GLI proteins function as bifunctional transcriptional activators (in the presence of HH signaling) and transcriptional repressors (in the absence of HH signaling). This results in a tight control of GLI target gene expression during rapidly changing levels of pathway activity. It has long been presumed that GLI proteins also repress target genes prior to the initial expression of HH in a given tissue. This idea forms the basis for the limb bud pre-patterning model for regulating digit number. Recent findings indicate that GLI repressor proteins are indeed present prior to HH signaling but contrary to this model, GLI proteins are inert as they do not regulate transcriptional responses or enhancer chromatin modifications at this time. These findings suggest that GLI transcriptional repressor activity is not a default state as assumed, but is itself regulated in an unknown fashion. We discuss these findings and their implications for understanding pre-patterning, digit regulation, and HH-driven disease.
Capsicum annuum L. and its bioactive constituents: A critical review of a traditional culinary spice in terms of its modern pharmacological potentials with toxicological issues

Capsicum annuum L., commonly known as chili pepper, is used as an important spice globally and as a crude drug in many traditional medicine systems. The fruits of C. annuum have been used as a tonic, antiseptic, and stimulating agent, to treat dyspepsia, appetites, and flatulence, and to improve digestion and circulation. The article aims to critically review the phytochemical and pharmacological properties of C. annuum and its major compounds. Capsaicin, dihydrocapsaicin, and some carotenoids are reported as the major active compounds with several pharmacological potentials especially as anticancer and cardioprotectant. The anticancer effect of capsaicinoids is mainly mediated through mechanisms involving the interaction of Ca2+ -dependent activation of the MAPK pathway, suppression of NOX-dependent reactive oxygen species generation, and p53-mediated activation of mitochondrial apoptosis in cancer cells. Similarly, the cardioprotective effects of capsaicinoids are mediated through their interaction with cellular transient receptor potential vanilloid 1 channel, and restoration of calcitonin gene-related peptide via Ca2+ -dependent release of neuropeptides and suppression of bradykinin. In conclusion, this comprehensive review presents detailed information about the traditional uses, phytochemistry, and pharmacology of major bioactive principles of C. annuum with special emphasis on anticancer, cardioprotective effects, and plausible toxic adversities along with food safety.
DENAO: Monocular Depth Estimation Network With Auxiliary Optical Flow

Estimating depth from multi-view images captured by a localized monocular camera is an essential task in computer vision and robotics. In this study, we demonstrate that learning a convolutional neural network (CNN) for depth estimation with an auxiliary optical flow network and the epipolar geometry constraint can greatly benefit the depth estimation task and in turn yield large improvements in both accuracy and speed. Our architecture is composed of two tightly-coupled encoder-decoder networks, i.e., an optical flow net and a depth net, the core part being a list of exchange blocks between the two nets and an epipolar feature layer in the optical flow net to improve predictions of both depth and optical flow. Our architecture allows to input arbitrary number of multiview images with a linearly growing time cost for optical flow and depth estimation. Experimental result on five public datasets demonstrates that our method, named DENAO, runs at 38.46fps on a single Nvidia TITAN Xp GPU which is 5.15X similar to 142X faster than the state-of-the-art depth estimation methods Meanwhile, our DENAO can concurrently output predictions of both depth and optical flow, and performs on par with or outperforms the state-of-the-art depth estimation methods and optical flow methods.
Ultrawideband Conical Log-Spiral Circularly Polarized Feed for Radio Astronomy

In order to meet the requirements of the new generation of radio telescopes, we have developed a new topology called DYQSA, which stands for DYson Quad-Spiral Array. The design exhibits dual circular polarization contrary to dual linear polarization of state-of-the-art feeds. It covers the required ultrawideband (UWB) from 2 to 14 GHz with an almost constant and real input impedance which facilitates the design of the feeding structure and the low-noise amplifiers (LNAs). Different versions are investigated for enhancing feed performance, ensuring higher aperture efficiencies and mechanical stability. The simulation results of the reflector loaded by the proposed feed show an aperture efficiency of 65% +/- 5% with a noise antenna temperature around 14 K and a system equivalent flux density (SEFD) of about 1300 Jy, both averaged over the required bandwidth at zenith. Measurements of the single-element and the four-element feeds are presented. Comparisons with other state-of-the-art feeds are shown in terms of total aperture efficiencies, design adaptability to different reflectors, calibration signal injection, and the required number of LNAs per feed, cost, and physical volume.
Workload control in the semiconductor industry

The state-of-the-art in published research on workload control as applied to the semiconductor industry is described. The focus is on examining the concepts behind workload control heuristics and evaluating their effectiveness, overhead requirements, and implementability. Individual sections provide an overview of the distinctive elements in a semiconductor manufacturing environment, general dispatching and order release methods, workload control strategies developed for semiconductor manufacturing, and future directions.
Affinity learning via a diffusion process for subspace clustering

Subspace clustering refers to the problem of finding low-dimensional subspaces (clusters) for high-dimensional data. Current state-of-the-art subspace clustering methods are usually based on spectral clustering, where an affinity matrix is learned by the self-expressive model, i.e., reconstructing every data point by a linear combination of all other points while regularizing the coefficients using the l(1) norm. The sparsity nature of l(1) norm guarantees the subspace-preserving property (i.e., no connection between clusters) of affinity matrix under certain condition, but the connectedness property (i.e., fully connected within clusters) is less considered. In this paper, we propose a novel affinity learning method by incorporating the sparse representation and diffusion process. Instead of using sparse coefficients directly as the affinity values, we apply the l(1) norm as a neighborhood selection criterion, which could capture the local manifold structure. An effective diffusion process is then deployed to spread such local information along with the global geometry of data manifold. Each pairwise affinity is augmented and re-evaluated by the context of data point pair, yielding significant enhancements of within-cluster connectivity. Extensive experiments on synthetic data and real-world data have demonstrated the effectiveness of the proposed method in comparison to other state-of-the-art methods. (C) 2018 Elsevier Ltd. All rights reserved.
Changes in heart rate variability of healthy subjects shortly exposed to printing shop particles and the effect of air purifier intervention

Particulate matter (PM) released by printers may cause airway inflammation and cardiac electrophysiological changes. We conducted a two-stage crossover study to examine the association between short-term exposure to printing shop particles (PSPs) and the heart rate variability (HRV) among healthy volunteers, as well as to evaluate the effect of air purifier intervention. The on-site assessments of PSPs and individual HRV parameters of the volunteers were used to analyze the influence of size-fractionated PSPs and air purifier intervention on HRV at different lag times after PSPs exposure (0 min, 5 min, 15 min, and 30 min) by using the linear mixed-effects models. We observed that changes in 6 HRV parameters were associated with particle mass concentration (PMC) of PSPs, and changes in 8 HRV parameters were associated with particle number concentration (PNC) of PSPs. The sensitive HRV parameters were the square root of the mean of the sum of the squares of differences between adjacent NN intervals (rMSSD), NN50 count divided by the total number of all NN intervals (pNN50), normalized high frequency power (nHF), very high frequency power (VHF), normalized low frequency power (nLF), and the ratio of low frequency power to high frequency power (LF/HF). Most HRV parameters exhibited the strongest effect associated with PMC and PNC at a lag time of 30 min. The air purifier intervention markedly reduced the PNC and PMC of size-fractionated PSPs, enhanced 5 HRV parameters, and decreased the nLF and LF/HF. Our study suggests that short-term exposure to PSPs can affect HRV parameters, reflecting changes in cardiac autonomic nervous activity, and the use of an air purifier can reduce the concentration of PSPs and improve the autonomic nervous system activity of the exposed individuals.