ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
The impact of natural resource depletion on energy use and CO2 emission in Belt & Road Initiative countries: A cross-country analysis

We employ STIRPAT (stochastic impact of regression on population, affluence, and technology) model to investigate the impact of natural resource depletion on energy use and carbon dioxide emissions for a panel of 56 "Belt & Road Initiative" (BRI) countries over 1990-2014. We utilize the Augmented Mean Group (AMG) panel estimator and Common Correlated Effects Mean Group (CCEMG) estimator for estimating our model. The results indicate that increasing natural resource depletion increases CO2 emissions and energy use, which shows that a 1% increase in natural resource depletion for our sample of BRI countries will increase CO2 emissions and energy use by 0.0286% and 0.0117%, respectively. To check the causal relationship among our variables, we employ the VECM Granger causality test. The causality results underscore feedback hypothetical links among CO2 emissions, energy use, economic growth, natural resource depletion, urbanization, and trade openness in the long run. Our results show a significant cross-sectional dependence and heterogeneity. (C) 2020 Elsevier Ltd. All rights reserved.
A patient with pulmonary hypertension waiting for donor lungs during the pandemic: 194 days on extracorporeal life support including 143 days on pulmonary artery to left atrium shunt

Patients with pulmonary hypertension and end-stage lung disease are fraught with high mortality while on a waiting list for lung transplant. With sometimes rapid deterioration they may require veno-arterial extracorporeal membrane oxygenation (VA-ECMO) as an immediate life-saving technique, which is a time-limited solution. The technique of pulmonary artery to left atrium (PA-LA) shunt fitted with an oxygenator enables bridging the patient to transplant for a longer time period. This low-resistance paracorporeal pumpless lung assist device allows for de-adaptation of the right ventricle back to lower afterload before the lung transplantation is carried out. The PA-LA shunt with an oxygenator also conveys a risk of multiple complications with reported median of 10-26 days until transplant. We report a case of pulmonary capillary hemangiomatosis in a 35-year-old female who had to wait for donor lungs during the pandemic of SARS-CoV-2 for 143 days on PA-LA shunt with oxygenator following 51 days on VA-ECMO. The extremely long course associated with multiple complications including three cerebral embolisms, episodes of sepsis and ingrowth of the return cannula into the left ventricular wall gives insight into the limits of this bridging technique.
Sub-micro scale cell segmentation using deep learning

Automated cell segmentation is key for rapid and accurate investigation of cell responses. As instrumentation resolving power increases, clear delineation of newly revealed cellular features at the submicron through nanoscale becomes important. Reliance on the manual investigation of myriad small features retards investigation; however, use of deep learning methods has great potential to reveal cell features both at high accuracy and high speed, which may lead to new discoveries in the near term. In this study, semantic cell segmentation systems were investigated by implementing fully convolutional neural networks called U-nets for the segmentation of astrocytes cultured on poly-l-lysine-functionalized planar glass. The network hyperparameters were determined by changing the number of network layers, loss functions, and input image modalities. Atomic force microscopy (AFM) images were selected for investigation as these are inherently nanoscale and are also dimensional. AFM height, deflection, and friction images were used as inputs separately and together, and the segmentation performances were investigated on five-fold cross-validation data. Transfer learning methods, including VGG16, VGG19, and Xception, were used to improve cell segmentation performance. We find that AFM height images inherit more discriminative features than AFM deflection and AFM friction images for cell segmentation. When transfer-learning methods are applied, statistically significant segmentation performance improvements are observed. Segmentation performance was compared to classical image processing algorithms and other algorithms in use by considering both AFM and electron microscopy segmentation. An accuracy of 0.9849, Matthews correlation coefficient of 0.9218, and Dice's similarity coefficient of 0.9306 were obtained on the AFM test images. Performance evaluations show that the proposed system can be successfully used for AFM cell segmentation with high precision.
Impact of institutional quality on environment and energy consumption: evidence from developing world

This study aimed to examine the role of institutional quality on environment and energy consumption for 66 developing countries by using data from 1991 to 2017. Different environmental indicators such as CO2 emissions, CH4 emissions, forest area, organic water pollutants, and energy consumption. The paper constructs institutional quality index by covering three main aspects: political stability, administrative capacity, and democratic accountability. System generalized method of moments results reveal that institutional quality has a positive impact on most of the environmental indicators such as CO2 emissions, CH4 emissions, and forest area. Institutional quality was having a positive impact on energy consumption based on oil and fossil fuel resources. Furthermore, it results in a signal that economic globalization has not increased environmental quality over time in developing countries.
A rare case of acute scrotum in a 12-year old boy: torsion of a paradidymal cystic appendage (organ of Giraldes)

Acute scrotum from testicular and epididymal cyst is relatively uncommon, whereas torsion of a cystic paradidymis is exceedingly rare. In this paper, we present the case of a healthy 12-year-old male patient admitted in our emergency room for acute scrotum, in whom diagnosis of torsion of a cystic paradidymis was suggested at color-Doppler US. Surgical exploration after ultrasound examination revealed a cystic paradidymis (Giraldes' organ) (PC) torsion. Immunohistochemical investigations were also performed after cyst excision to confirm the diagnosis. In our experience this is a very rare condition and appears to be only the third case reported in literature and the only one with pre-operative ultrasound (US) images demonstrating the presence of the cyst and its twisted pedicle.
Automated detection of diabetic retinopathy using custom convolutional neural network

Diabetic retinopathy is an eye deficiency that affects retina as a result of the patient having diabetes mellitus caused by high sugar levels, which may eventually lead to macular edema. The objective of this study is to design and compare several deep learning models that detect severity of diabetic retinopathy, determine risk of leading to macular edema, and segment different types of disease patterns using retina images. Indian Diabetic Retinopathy Image Dataset (IDRiD) dataset was used for disease grading and segmentation. Since images of the dataset have different brightness and contrast, we employed three techniques for generating processed images from the original images, which include brightness, color and, contrast (BCC) enhancing, color jitters (CJ), and contrast limited adaptive histogram equalization (CLAHE). After image preporcessing, we used pre-trained ResNet50, VGG16, and VGG19 models on these different preprocessed images both for determining the severity of the retinopathy and also the chances of macular edema. UNet was also applied to segment different types of diseases. To train and test these models, image dataset was divided into training, testing, and validation data at 70%, 20%, and 10% ratios, respectively. During model training, data augmentation method was also applied to increase the number of training images. Study results show that for detecting the severity of retinopathy and macular edema, ResNet50 showed the best accuracy using BCC and original images with an accuracy of 60.2% and 82.5%, respectively, on validation dataset. In segmenting different types of diseases, UNet yielded the highest testing accuracy of 65.22% and 91.09% for microaneurysms and hard exudates using BCC images, 84.83% for optic disc using CJ images, 59.35% and 89.69% for hemorrhages and soft exudates using CLAHE images, respectively. Thus, image preprocessing can play an important role to improve efficacy and performance of deep learning models.
Revisiting the Environmental Kuznets Curve in Malaysia: The role of globalization in sustainable environment

The present study examines the presence of Environmental Kuznets Curve (EKC) in Malaysia by investigating impact of globalization (including overall, social, economic and political) on ecological footprint in the Malaysian economy using quarterly data from 1970 to 2018. This research utilizes the Quantile Autoregressive Distributed Lag (QARDL) method. This approach is useful as it depicts the causality patterns through various quantiles of the globalization and environmental degradation by providing more comprehensive explanations of the over-all dependence among the aforementioned variables almost ignored by the traditional approach (for instance ARDL and OLS). The outcomes of QARDL showed that the error correction coefficient is significant with the anticipated negative sign for all quantiles. This outcome provides evidence on the presence of considerable return to the long-run equilibrium association between the factors of interest and environmental degradation. Overall, the results recommended that overall globalization and economic globalization enhance the level of environmental degradation in the long run, however, political and social globalization help to reduce the level of environmental degradation in the long term. Moreover, the results of QARDL also confirmed the existence of inverted UShape curve in the Malaysian economy. In the shorter-run period, this study found a significant negative impact of overall globalization and political globalization on environmental degradation. The results recommended that the Malaysian government and policymakers need to give more attention to social and political globalization to achieve the goals of sustainable environment in the long-run equilibrium. (C) 2020 Elsevier Ltd. All rights reserved.
Global Indicators of Sustainable Development: Evaluation of the Influence of the Human Development Index on Consumption and Quality of Energy

The article explores the impact of the quality and volume of energy consumption of the population on the human development index using a sample of a number of countries as an example. The hypothesis concerning the relationship between the amount of energy consumed, the human development index (HDI), and the environment (CO2 emissions into the atmosphere) has been verified. The study results show that the size and rating of the HDI are influenced by such factors as urbanization growth, gross domestic product (GDP), gross national income (GNI) per capita, the share of "clean" energy consumption by the population and business in total energy consumption, the level of socio-economic development, and R&D expenses. In the course of building the model, the recommendations by the United Nations (UN) and the Organization for Economic Co-operation and Development (OECD) were used. The results show that the volume of energy consumption not only affects the human development index in a particular country, but is also an important factor in determining the level of sustainable development. The results, obtained in the course of the study and described in the article, may be applicable in the practice of research related to the assessment of human development and sustainable development.
Developments of Economic Growth and Employment in Bioeconomy Sectors across the EU

The development of the bioeconomy-or the substitution of fossil-based materials and energy by bio-based solutions-is considered a strategic economic orientation by the European Commission and its Green Deal. This paper presents a methodology to monitor the contribution of the bioeconomy to jobs and growth within the European Union (EU) and its Member States. Classified as an ''output-based'' approach, the methodology relies on expert estimations of the biomass content of the bio-based materials produced in the EU and the subsequent calculation of ''sectoral'' bio-based shares by using Eurostat statistics on the production of manufactured goods (prom). Sectoral shares are applied to indicators of employment, and value added is reported in Eurostat-Structural business statistics. This paper updates the methodology and time series presented in 2018. The bioeconomy of the EU (post-Brexit composition) employed around 17.5 million people and generated Euro614 billion of value added in 2017. The study evidences structural differences between EU national bioeconomies, which become more pronounced over time, especially in terms of the level of apparent labour productivity of national bioeconomies. Finally, this paper describes cases of transition over the 2008-2017 period.
Transport CO(2)emissions, drivers, and mitigation: an empirical investigation in India

India has one of the world's largest transport networks, with the dominant share being road transportation. India's transport sector consumes oil products for more than 95% of its energy needs and is a significant contributor to environmental deterioration in urban areas. Therefore, we examine the driving factors of transport CO(2)emissions in India, incorporating road transport energy, economic growth, industrialization, urbanization, oil prices, and road infrastructure, over the period 1980 to 2015. The findings indicate that economic growth and road sector energy use cause an upsurge in transport emissions. Likewise, industrialization and road infrastructure stimulate transport emissions. Urbanization mitigates transport emissions, while oil prices have no influence on transport emissions. One-way causality is shown from economic growth, urbanization, and road sector energy to transport emissions. Finally, suitable policy implications are proposed to reduce transport CO(2)emissions for a sustainable environment.
Pathways for Low-Carbon Transition of the Steel Industry-A Swedish Case Study

The concept of techno-economic pathways is used to investigate the potential implementation of CO(2)abatement measures over time towards zero-emission steelmaking in Sweden. The following mitigation measures are investigated and combined in three pathways: top gas recycling blast furnace (TGRBF); carbon capture and storage (CCS); substitution of pulverized coal injection (PCI) with biomass; hydrogen direct reduction of iron ore (H-DR); and electric arc furnace (EAF), where fossil fuels are replaced with biomass. The results show that CCS in combination with biomass substitution in the blast furnace and a replacement primary steel production plant with EAF with biomass (Pathway 1) yield CO(2)emission reductions of 83% in 2045 compared to CO(2)emissions with current steel process configurations. Electrification of the primary steel production in terms of H-DR/EAF process (Pathway 2), could result in almost fossil-free steel production, and Sweden could achieve a 10% reduction in total CO(2)emissions. Finally, (Pathway 3) we show that increased production of hot briquetted iron pellets (HBI), could lead to decarbonization of the steel industry outside Sweden, assuming that the exported HBI will be converted via EAF and the receiving country has a decarbonized power sector.
Aggression, rank and power: why hens (and other animals) do not always peck according to their strength

Thorlief Schjelderup-Ebbe's seminal paper on the 'pecking' order of chickens inspired numerous ethologists to research and debate the phenomenon of dominance. The expansion of dominance to the broader concept of power facilitated disentangling aggression, strength, rank and power. Aggression is only one means of coercing other individuals, and can sometimes highlight a lack of power. The fitness advantages of aggression may only outweigh the costs during periods of uncertainty. Effective instruments of power also include incentives and refusals to act. Moreover, the stability of the power relationship might vary with the instruments used if different means of power vary in the number and types of outcomes achieved, as well as the speed of accomplishing those outcomes. In well-established relationships, actions or physiological responses in the subordinate individual may even be the only indicator of a power differential. A focus on strength, aggression and fighting provides an incomplete understanding of the power landscape that individuals actually experience. Multiple methods for constructing hierarchies exist but greater attention to the implications of the types of data used in these constructions is needed. Many shifts in our understanding of power were foreshadowed in Schjelderup-Ebbe's discussion about deviations from the linear hierarchy in chickens. This article is part of the theme issue 'The centennial of the pecking order: current state and future prospects for the study of dominance hierarchies'.
An Argument for Simple Tests of Treatment of Alzheimer's Disease

Two potential disease-modifying approaches for dementia are being vigorously tested: the early targeting of the neuropathology of Alzheimer's disease (AD) and multi-domain lifestyle interventions to promote resilience to neuropathology. We apply the "web of information" model of clinical translation to both approaches to argue firstly that tests of treatments aiming to achieve clinically meaningful outcomes should remain simple, and secondly, that building clinically-meaningful treatments should be kept separate from public health policy which means promoting wide-reaching action against risk factors now with available information.
A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making

Urban planners, local authorities, and energy policymakers often develop strategic sustainable energy plans for the urban building stock in order to minimize overall energy consumption and emissions. Planning at such scales could be informed by building stock modeling using existing building data and Geographic Information System-based mapping. However, implementing these processes involves several issues, namely, data availability, data inconsistency, data scalability, data integration, geocoding, and data privacy. This research addresses the aforementioned information challenges by proposing a generalized integrated methodology that implements bottom-up, data-driven, and spatial modeling approaches for multi-scale Geographic Information System mapping of building energy modeling. This study uses the Irish building stock to map building energy performance at multiple scales. The generalized data-driven methodology uses approximately 650,000 Irish Energy Performance Certificates buildings data to predict more than 2 million buildings' energy performance. In this case, the approach delivers a prediction accuracy of 88% using deep learning algorithms. These prediction results are then used for spatial modeling at multiple scales from the individual building level to a national level. Furthermore, these maps are coupled with available spatial resources (social, economic, or environmental data) for energy planning, analysis, and support decision-making. The modeling results identify clusters of buildings that have a significant potential for energy savings within any specific region. Geographic Information System-based modeling aids stakeholders in identifying priority areas for implementing energy efficiency measures. Furthermore, the stakeholders could target local communities for retrofit campaigns, which would enhance the implementation of sustainable energy policy decisions.
Variability and specificity of bone cutting mark properties in cases involving stabbing with knives

Sharp force traumas are frequently encountered in stabbing crime victims. During an examination, the properties of cutting marks in bones are compared with the properties of suspect tools, particularly knives. Therefore, the variation and specificity of knife and cutting mark properties must be known. This article provides the variability and specificity of a set of knife blade and cutting mark properties. Plain and serrated knives are used to create experimental cutting marks in porcine ribs, knife properties are derived from surface acquisitions of the blades and mark properties from Micro-CT data. We consider two conditions, automated stabbing using a motorized stage and manual stabbing. In addition, we study the influence of maceration on marks. For knives, the blade edge angle, blade thickness, and bevel height, and for cutting marks, the shape, the wall angle, the width, and the bevel height are determined and compared. The results show that the relationship between corresponding properties of blades and marks depends on the knife type. For plain knives, the width and wall angle of the marks are dependent on the mark depth and are significantly smaller than the blade properties edge angle and width. For serrated knives, this is not the case. The mark shape only provides slight support for a knife type for marks deeper than the blade bevel height. In conclusion, mark properties are only specific for a particular knife brand and model if the blade properties differ significantly and assuming a specific knife type.
Pulmonary Nontuberculous Mycobacterial Infection in Infants: A Systematic Review

Limited information and literature exist examining pulmonary infections caused by nontuberculous mycobacterial specifically in an infant population. The objective of our study was to summarize clinical characteristics and outcomes of infant patients with nontuberculous mycobacterial pulmonary infection via systematic literature review to identify common diagnostic and treatment regimens for this infection in infants. A search of MEDLINE and PubMed databases in October 2019 using MeSH search terms "infant," "NTM," "pulmonary," and "Mycobacterium abscessus" yielded 139 articles. Inclusion criteria were i) English-language studies including cases and case series with ii) established nontuberculous mycobacterial pulmonary infection in iii) a patient population of infants no older than 24 months. Patients with cystic fibrosis and any study which did not contain relevant information such as infection and age were excluded. This yielded data on 37 patients extracted from 28 studies analyzed. The most common strain was Mycobacterium avium complex, isolated in 56.8% of patient diagnoses. Bronchoscopy/thoracoscopy with a subsequent culture were the most common diagnostic techniques, utilized in 64.9% of cases. Drug therapeutic treatment was utilized in 86% of cases, with a median of three drugs administered. Notable limitations of this study are the small sample size and its retrospective nature, which relies on information reported in previous case studies. Although there is limited formal clinician consensus on the treatment of NTM pulmonary infection and how it may differ in an infant population, our findings indicate an informal consensus typically involving diagnostic lung specimen culture and antibiotic therapy.