ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Sexually mediated phenotypic variation within and between sexes as a continuum structured by ecology: The mosaic nature of skeletal variation across body regions in Threespine stickleback ( Gasterosteus aculeatus L.)

Ecological character displacement between the sexes, and sexual selection, integrate into a convergent set of factors that produce sexual variation. Ecologically modulated, sexually mediated variation within and between sexes may be a major contributor to the amount of total variation that selection can act on in species. Threespine stickleback (Gasterosteus aculeatus) display rapid adaptive responses and sexual variation in many phenotypic traits. We examined phenotypic variation in the skull, pectoral and pelvic girdles of threespine stickleback from two freshwater and two coastal marine sites on the Sunshine Coast of British Columbia, Canada, using an approach that avoids a priori assumptions about bimodal patterns of variation. We quantified shape and size of the cranial, pectoral and pelvic regions of sticklebacks in marine and freshwater habitats using 3D geometric morphometrics and an index of sexually mediated variation. We show that the expression of phenotypic variation is structured in part by the effects of both habitat marine vs freshwater and the effects of individual sites within each habitat. Relative size exerts variable influence, and patterns of phenotypic variation associated with sex vary among body regions. This fine-grained quantification of sexually mediated variation in the context of habitat difference and different anatomical structures indicates a complex relationship between genetically inferred sex and environmental factors, demonstrating that the interplay between shared genetic background and sexually mediated, ecologically based selective pressures structures the phenotypic expression of complex traits.
G-mean based extreme learning machine for imbalance learning

Although extreme learning machine (ELM) provides better generalization performance at a much faster learning speed than traditional learning algorithms, the classical ELM can not obtain ideal results for the imbalanced data problem. In this paper, in order to conquer the learning capability of the classical ELM for an imbalance data learning, we define a new cost function of ELM optimization problem based on G-mean widely used as evaluation metric in imbalance data learning. We perform experiments on standard classification datasets which consist of 58 binary datasets and 11 multi-class datasets with the different degrees of the imbalance ratio. Experimental results show that proposed algorithm can improve the classification performance significantly compared with other state-of-the-art methods. We also demonstrate that our proposed algorithm can achieve high accuracy in representation learning by performing experiments on YouTube-8M with feature representation from convolutional neural networks. Statistical results indicate that the proposed approach not only outperforms the classical ELM, but also yields better or at least competitive results compared with several state-of-the-art class imbalance learning approaches. (C) 2019 Elsevier Inc. All rights reserved.
Community Cooperative: A New Legal Form for Enhancing Social Capital for the Development of Renewable Energy Communities in Italy

This paper investigates the suitability of the community cooperatives (CC) model for the implementation of renewable energy communities (REC), as prescribed by art. 22 of EU Directive 2018/2001, and temporarily transposed into the Italian law by art. 42-bis of the Law Decree n. 162/2019. The hypothesis explored analyses the potential synergies between RECs and CC, based on their similarities. In particular, the article takes into consideration: the actors involved in both the RECs and the CCs; the geographical scope in which they develop, and the purposes that these two legal forms intended to achieve. Through a literature review and the analysis of EU, national and regional legislations, the paper aims at (1) clarifying the main features of RECs and the CCs in Italy; (2) exploring the main differences between CCs and the other legal forms of cooperative (e.g., mutual cooperative, cooperative benefit, etc.) and assessing the extent to which CCs are more suitable to implement renewable energy communities. As a result of the literature and regulatory review, several similarities between CCs and RECs can be detected, particularly, in reference to the strategic valorization of the cooperation between citizens and the local public entities. These similarities allow the authors to provisionally conclude that, in Italy, CCs may be adopted as a tool to implement RECs.
A novel grey prediction model with a feedforward neural network based on a carbon emission dynamic evolution system and its application

The objective and accurate prediction of carbon dioxide emissions holds great significance for improving governmental energy policies and plans. Therefore, starting from an evolutionary system of carbon emissions, this paper studies the evolution of the system, establishes a grey model of the system, and expands the modeling structure of this model. The modeling mechanism of the classical feedforward neural network model is organically combined with the function of the external influencing factors of carbon emissions, and the grey model of the carbon emission dynamic system is established with a neural network. Then, the properties of the model are studied, the parameters of the model are optimized, and the modeling steps are obtained. Finally, the validity of the model is analyzed by using the carbon emissions of Beijing from 2009 to 2018. The results of the four cases show that the simulation and prediction errors of the new model are all less than 10%, and case 1 shows the best results of 1.56% and 2.07%, respectively, which are used to predict the carbon dioxide emissions in the next 5 years in Beijing. The prediction results are in accordance with the actual trend, which indicates the effectiveness and feasibility of the model.
Father's involvement and its effect on early breastfeeding practices in Viet Nam

Fathers have an important but often neglected role in the promotion of healthy breastfeeding practices in developing countries. A community-based education intervention was designed to mobilize fathers' support for early breastfeeding. This study aimed to evaluate an education intervention targeting fathers to increase the proportion of early breastfeeding initiation and to reduce prelacteal feeding. Quasi-experimental study design was used to compare intervention and control areas located in two non-adjacent rural districts that shared similar demographic and health service characteristics in northern Viet Nam. Fathers and expectant fathers with pregnant wives from 7 to 30 weeks gestational age were recruited. Fathers in the intervention area received breastfeeding education materials, counselling services at a commune health centre and household visits. They were also invited to participate in a breastfeeding promotion social event. After intervention, early breastfeeding initiation rate was 81.2% in the intervention area and 39.6% in the control area (P < 0.001). Babies in the intervention area were more likely to be breastfed within the first hour after birth [odds ratio (OR) 7.64, 95% confidence interval (CI) 4.81-12.12] and not to receive any prelacteal feeding (OR 4.43, 95% CI 2.88-6.82) compared with those in the control area. Fathers may positively influence the breastfeeding practices of mothers, and as a resource for early childcare, they can be mobilized in programmes aimed at improving the early initiation of breastfeeding.
Image Reconstruction in Electrical Impedance Tomography Based on Structure-Aware Sparse Bayesian Learning

Electrical impedance tomography (EIT) is developed to investigate the internal conductivity changes of an object through a series of boundary electrodes, and has become increasingly attractive in a broad spectrum of applications. However, the design of optimal tomography image reconstruction algorithms has not achieved the adequate level of progress and matureness. In this paper, we propose an efficient and high-resolution EIT image reconstruction method in the framework of sparse Bayesian learning. Significant performance improvement is achieved by imposing structure-aware priors on the learning process to incorporate the prior knowledge that practical conductivity distribution maps exhibit clustered sparsity and intracluster continuity. The proposed method not only achieves high-resolution estimation and preserves the shape information even in low signal-to-noise ratio scenarios but also avoids the time-consuming parameter tuning process. The effectiveness of the proposed algorithm is validated through comparisons with state-of-the-art techniques using extensive numerical simulation and phantom experiment results. Index
Assessing Team Effectiveness by How Players Structure Their Search in a First-Person Multiplayer Video Game

People working as a team can achieve more than when working alone due to a team's ability to parallelize the completion of tasks. In collaborative search tasks, this necessitates the formation of effective division of labor strategies to minimize redundancies in search. For such strategies to be developed, team members need to perceive the task's relevant components and how they evolve over time, as well as an understanding of what others will do so that they can structure their own behavior to contribute to the team's goal. This study explored whether the capacity for team members to coordinate effectively can be related to how participants structure their search behaviors in an online multiplayer collaborative search task. Our results demonstrated that the structure of search behavior, quantified using detrended fluctuation analysis, was sensitive to contextual factors that limit a participant's ability to gather information. Further, increases in the persistence of movement fluctuations during search behavior were found as teams developed more effective coordinative strategies and were associated with better task performance.
Immune function of the serosa in hemimetabolous insect eggs

Insects comprise more than a million species and many authors have attempted to explain this success by evolutionary innovations. A much overlooked evolutionary novelty of insects is the serosa, an extraembryonic epithelium around the yolk and embryo. We have shown previously that this epithelium provides innate immune protection to eggs of the beetle Tribolium castaneum. It remained elusive, however, whether this immune competence evolved in the Tribolium lineage or is ancestral to all insects. Here, we expand our studies to two hemimetabolous insects, the bug Oncopeltus fasciatus and the swarming grasshopper Locusta migratoria. For Oncopeltus, RNA sequencing reveals an extensive response upon infection, including the massive upregulation of antimicrobial peptides (AMPs). We demonstrate antimicrobial activity of these peptides using in vitro bacterial growth assays and describe two novel AMP families called Serosins and Ovicins. For both insects, quantitative polymerase chain reaction shows immune competence of the eggs when the serosa is present, and in situ hybridizations demonstrate that immune gene expression is localized in the serosa. This first evidence from hemimetabolous insect eggs suggests that immune competence is an ancestral property of the serosa. The evolutionary origin of the serosa with its immune function might have facilitated the spectacular radiation of the insects. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'.
Comparison of reconstruction algorithm for compressive sensing magnetic resonance imaging

Compressed sensing can reconstruct the undersampled image. The combination of compressed sensing and magnetic resonance imaging is a potential future fast imaging method in hospitals. This study investigated five state-of-the-art reconstruction approaches: iterative shrinkage/threshold algorithm (ISTA), fast ISTA, subband-adaptive ISTA, exponential wavelet transform ISTA, and exponential wavelet ISTA with random search (EWISTARS). The simulation results compared the five algorithms over hand image and shoulder image. Finally, we can observe the EWISTARS obtains the best result.
Honeycomb-Type TiO2 Films Toward a High Tolerance to Optical Paths for Perovskite Solar Cells

Given the advantages of high power conversion efficiencies (PCEs), antisolvent-step free production, and suitability for device production in ambient conditions, perovskite solar cells (PSCs) based on ionic-liquid solvents have attained particular research interest. To further improve device performance, light management could be optimized to increase light harvesting in the perovskite layer. Here, ordered honeycomb-like TiO2 (Hc-TiO2 ) structures with a periodicity of around 450 nm were fabricated through a sacrificial template method. With this photonic crystal structure, the control to light flow and the confinement effect for perovskite growth were achieved simultaneously in the Hc-TiO2 , leading to improved light absorption as well as preferred crystal orientation. Furthermore, a reduced trap-state density and a well-aligned energy level induced by the perovskite/pore interlayer facilitated the charge-carrier extraction from the perovskite layer to electron transport layer. As a result, the structured devices performed better than the planar cells. And the angular dependent J-V sweeps show that the structured device reserved 76 % of its initial short circuit current density (Jsc ), whereas the planar cell showed more than a half loss under the incident light of 40°, demonstrating a reduced downward trend in Jsc with the presence of photonic crystal structures. This occurrence also suggests that the structured PSCs in this work have a high tolerance to optical path changes.
Brain Metabolic DNA: A Long Story and Some Conclusions

We have previously outlined the main properties of brain metabolic DNA (BMD) and its involvement in circadian oscillations, learning, and post-trial sleep. The presence of BMD in certain subcellular fractions and their behavior in cesium gradients have suggested that BMD originates from cytoplasmic reverse transcription and subsequently acquires a double-stranded configuration. More recently, it has been reported that some DNA sequences of cytoplasmic BMD in learning mice are different from that of the control animals. Furthermore, BMD is located in vicinity of the genes involved in different modifications of synaptic activity, suggesting that BMD may contribute to the brain's response to the changing environment. The present review outlines recent data with a special emphasis on reverse transcription of BMD that may recapitulate the molecular events at the time of the "RNA world" by activating mitochondrial telomerase and generating RNA templates from mitochondrial transcripts. The latter unexpected role of mitochondria is likely to promote a better understanding of mitochondrial contribution to cellular interactions and eukaryotic evolution. An initial step regards the role of human mitochondria in embryonic BMD synthesis, which is exclusively of maternal origin. In addition, mitochondrial transcripts involved in reverse transcription of BMD might possibly reveal unexpected features elucidating mitochondrial involvement in cancer events and neurodegenerative disorders.
Malaria metrics distribution under global warming: assessment of the VECTRI malaria model over Cameroon

Malaria is a critical health issue across the world and especially in Africa. Studies based on dynamical models helped to understand inter-linkages between this illness and climate. In this study, we evaluated the ability of the VECTRI community vector malaria model to simulate the spread of malaria in Cameroon using rainfall and temperature data from FEWS-ARC2 and ERA-interim, respectively. In addition, we simulated the model using five results of the dynamical downscaling of the regional climate model RCA4 within two time frames named near future (2035-2065) and far future (2071-2100), aiming to explore the potential effects of global warming on the malaria propagation over Cameroon. The evaluated metrics include the risk maps of the entomological inoculation rate (EIR) and the parasite ratio (PR). During the historical period (1985-2005), the model satisfactorily reproduces the observed PR and EIR. Results of projections reveal that under global warming, heterogeneous changes feature the study area, with localized increases or decreases in PR and EIR. As the level of radiative forcing increases (from 2.6 to 8.5 W.m-2), the magnitude of change in PR and EIR also gradually intensifies. The occurrence of transmission peaks is projected in the temperature range of 26-28 °C. Moreover, PR and EIR vary depending on the three agro-climatic regions of the study area. VECTRI still needs to integrate other aspects of disease transmission, such as population mobility and intervention strategies, in order to be more relevant to support actions of decision-makers and policy makers.
A Universal Framework of Spatiotemporal Bias Block for Long-Term Traffic Forecasting

Recent studies have demonstrated the great success of graph convolutional networks in short-term traffic forecasting (e.g., 15-30 min ahead) tasks by capturing dependencies in road network structure. Based on these models, long-term forecasting can be achieved by two approaches: (1) recursively generating a one-step-ahead prediction and (2) adapting the models to sequence-to-sequence (seq2seq) learning. However, in practice, these two approaches often show poor performance in long-term forecasting tasks. The recursive approach suffers from the error accumulation problem, as the model is trained based on one-step-ahead loss. On the other hand, seq2seq shows convergence issues that limit its application. To address the issues for long-term forecasting, in this paper, we propose a universal framework that directly transforms any existing state-of-the-art models for one-step-ahead prediction to achieve more accurate long-term forecasting. The proposed framework consists of two components--a base model and a bias block. The base model is assumed to be a well-trained state-of-the-art one-step-ahead forecasting model, and the bias block is constructed by a spatiotemporal graph neural network composed of gated temporal convolution layers and graph convolution layers. The base model and the bias block are residually-connected so that we can substantially reduce the training complexity. Extensive experiments are conducted on existing benchmark datasets. We experiment with several state-of-the-art models in the literature as base models, and our results demonstrate the ability of the proposed universal framework to greatly improve the long-term prediction accuracy for all models.
Leptospirosis Infection Misdiagnosed as COVID-19: A Rare Case Report

Leptospirosis is an acute, febrile, systemic, and zoonotic infectious disease characterized by widespread vasculitis caused by Leptospira interrogans from the leptospira family. It can be in the form of asymptomatic infection; it can also progress with severe symptomatic forms characterized by multiorgan involvement such as aseptic meningitis as well as liver and kidney failure. Leptospirosis is transmitted to humans through water, soil, and food contaminated with the urine of infected mice or other mammals. COVID-19 is a newly detected coronavirus that causes pneumonia. The disease has led to a pandemic all over the world. In this case report, we aimed to draw attention to leptospirosis infection in the presence of a case who was followed up with the differential diagnosis of COVID-19 and diagnosed with leptospirosis during the COVID-19 pandemic. Leptospirosis is one of the diagnoses that should be kept in mind in especially developing countries in patients presenting with findings that may be confused with COVID-19 during the pandemic period.
Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units

Image segmentation is an important application of polarimetric synthetic aperture radar. This study aimed to create an 11-layer deep convolutional neural network for this task. The Pauli decomposition formed the RGB image and was used as the input. We created an 11-layer convolutional neural network (CNN). L-band data over the San Francisco bay area and C-band data over Flevoland area were employed as the dataset. For the San Francisco bay PSAR image, our method achieved an overall accuracy of 97.32%, which was at least 2% superior to four state-of-the-art approaches. We provided the confusion matrix over test area, and the kernel visualization. We compared the max pooling and average pooling. We validated by experiment that four convolution layers perform the best. Besides, our method gave better results than AlexNet. The GPU yields a 173x acceleration on the training samples, and a 181x acceleration on the test samples, compared to standard CPU. For the Flevoland PSAR image, our 11-layer CNN also gives better overall accuracy than five state-of-the-art approaches. The convolutional neural network is better than traditional classifiers and is effective in remote sensing image segmentation.
Discovery of a new genus of anaerobic ammonium oxidizing bacteria with a mechanism for oxygen tolerance

In the past 20 years, there has been a major stride in understanding the core mechanism of anaerobic ammonium-oxidizing (anammox) bacteria, but there are still several discussion points on their survival strategies. Here, we discovered a new genus of anammox bacteria in a full-scale wastewater-treating biofilm system, tentatively named "Candidatus Loosdrechtia aerotolerans". Next to genes of all core anammox metabolisms, it encoded and transcribed genes involved in the dissimilatory nitrate reduction to ammonium (DNRA), which coupled to oxidation of small organic acids, could be used to replenish ammonium and sustain their metabolism. Surprisingly, it uniquely harbored a new ferredoxin-dependent nitrate reductase, which has not yet been found in any other anammox genome and might confer a selective advantage to it in nitrate assimilation. Similar to many other microorganisms, superoxide dismutase and catalase related to oxidative stress resistance were encoded and transcribed by "Ca. Loosdrechtia aerotolerans". Interestingly, bilirubin oxidase (BOD), likely involved in oxygen resistance of anammox bacteria under fluctuating oxygen concentrations, was identified in "Ca. Loosdrechtia aerotolerans" and four Ca. Brocadia genomes, and its activity was demonstrated using purified heterologously expressed proteins. A following survey of oxygen-active proteins in anammox bacteria revealed the presence of other previously undetected oxygen defense systems. The novel cbb3-type cytochrome c oxidase and bifunctional catalase-peroxidase may confer a selective advantage to Ca. Kuenenia and Ca. Scalindua that face frequent changes in oxygen concentrations. The discovery of this new genus significantly broadens our understanding of the ecophysiology of anammox bacteria. Furthermore, the diverse oxygen tolerance strategies employed by distinct anammox bacteria advance our understanding of their niche adaptability and provide valuable insight for the operation of anammox-based wastewater treatment systems.
[Research and Application of Seismic Performance of Medical Imaging Equipment]

In view of the shortage of research on the seismic performance of medical imaging equipment, this paper investigates and summarizes the seismic regulatory requirements and seismic tests of medical imaging equipment, and focuses on the parameter selection, detection steps, result evaluation and detection equipment requirements of seismic detection of medical imaging equipment. The seismic test data of medical imaging equipment with various installation modes are analyzed, and the seismic performance of medical imaging equipment is analyzed and summarized.