ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Eco-Capabilities as a Pathway to Wellbeing and Sustainability

Eco-Capabilities is an AHRC funded project situated at the intersection of three issues: a concern with children's wellbeing; their disconnect with the environment; and a lack of engagement with arts in school curricula. It builds on Amartya Sen's work on human capabilities as a proxy for wellbeing, developing the term eco-capabilities to describe how children define what they feel they need to live a fully good human life through environmental sustainability, social justice and future economic wellbeing. A total of 101 children aged 7-10 from schools in highly deprived areas participated in eight full days of arts in nature practice. The study drew on arts based research methods, participatory observations, interviews and focus groups with artists, teachers and children. Findings suggest that arts in nature practice contributed towards eight (eco-)capabilities: autonomy; bodily integrity and safety; individuality; mental and emotional wellbeing; relationality: human/nonhuman relations; senses and imagination; and spirituality. This was facilitated through four pedagogical elements: extended and repeated arts in nature sessions; embodiment and engaging children affectively through the senses; 'slowliness', which envelops children with time and space to (re)connect; and thoughtful practice, which facilitates emotional expression. We suggest that, through these elements, arts in nature practice supports children's wellbeing, and guides them towards a more entangled relationship with nature and a clearer understanding of themselves as part of it, thereby motivating them to take better care of it.
Acceleration of Bi-Objective Optimization of Data-Parallel Applications for Performance and Energy on Heterogeneous Hybrid Platforms

Accelerating the bi-objective optimization of applications for performance and energy is crucial to achieving energy efficiency objectives and meeting quality-of-service requirements in modern high-performance computing platforms and cloud computing infrastructures. In this work, we highlight the crucial challenges to accelerate model-based methods proposed for the bi-objective optimization of data parallel applications for performance and energy that employ workload distribution between the executing processors as the decision variable. The methods solve unconstrained bi-objective optimization problems and take input, the processors' performance and energy profiles in the form of discrete functions of workload size, and output Pareto-optimal solutions (workload distributions), minimizing the execution time and the total energy consumption of computations during the parallel execution of the application. One of the challenges is the fast computation of Pareto-optimal solutions. We then formulate the bi-objective optimization problem of data-parallel applications for performance and energy through workload distribution on a cluster of p identical hybrid nodes, each containing h heterogeneous processors. The state-of-the-art algorithm for solving the problem is sequential and takes exorbitant execution times to find Pareto-optimal solutions for even moderate numbers of processors. We propose two algorithms that address this shortcoming. The first algorithm is an exact sequential algorithm that is more efficient and amenable to parallelization and achieves a complexity reduction of O(m x h) over the state-of-the-art sequential algorithm where m is the cardinality of the input discrete execution time and dynamic energy functions. The second algorithm is a parallel algorithm executed by q identical parallel processes that reduces the complexity of our proposed sequential algorithm by O(q). It, therefore, achieves a complexity reduction of O(m x h x q) over the state-of-the-art sequential algorithm. Finally, we experimentally analyze the practical efficacy of our proposed algorithms for two data-parallel applications, matrix multiplication and fast Fourier transform, on a heterogeneous hybrid node containing an Intel Haswell multicore CPU, an Nvidia k40c GPU, and an Nvidia P100 GPU and simulations of clusters of such hybrid nodes. The experiments demonstrate that our proposed algorithms provide tremendous speedups over state-of-the-art solutions.
Human health risk estimation of antibiotics transferred from wastewater and soil to crops

Antibiotics enter into agricultural land, via manure application or wastewater irrigation. The practices of using untreated wastewater in the agricultural system help in the bioaccumulation of antibiotics in vegetables and other crops. Exposure to the bioaccumulated antibiotics poses serious health risks to ecosystem and human. In this study, the prevalence of two fluoroquinolones (levofloxacin and ciprofloxacin), their bioaccumulation in five crops (Daucus carota L., Pisum sativum L., Raphanus raphanistrum L., Lactuca sativa L., Spinacia oleracea L.), and associated human health risks were investigated. Lettuce showed highest bioaccumulation of levofloxacin (LEV) (12.66 μg kg-1) and carrot showed high bioaccumulation of ciprofloxacin (CIP) (13.01 μg kg-1). In roots, bioconcentration factor (BCFroot) was observed to be relatively high in radish (LEV 0.24-0.43, CIP 0.32-0.49) and observed to be lower in spinach (LEV 0.05-0.13, CIP 0.12-0.19). The translocation factor (TF) for LEV and CIP was generally >1 for all five crops under all treatment. The final transfer and distribution of LEV and CIP in the edible parts of the crops were as follows: leaves > shoots > roots for both antibiotics. Risk quotient of both LEV and CIP in current study is found to be in between 0.018 and 0.557 and shows a medium risk (0.1 to 1) to human health due the discharge of untreated wastewater into the fields. However, our study reports that both antibiotics do accumulate in the edible plant parts; therefore, it poses potential risks to human health.
Localization and Segmentation of 3D Intervertebral Discs in MR Images by Data Driven Estimation

This paper addresses the problem of fully-automatic localization and segmentation of 3D intervertebral discs (IVDs) from MR images. Our method contains two steps, where we first localize the center of each IVD, and then segment IVDs by classifying image pixels around each disc center as foreground (disc) or background. The disc localization is done by estimating the image displacements from a set of randomly sampled 3D image patches to the disc center. The image displacements are estimated by jointly optimizing the training and test displacement values in a data-driven way, where we take into consideration both the training data and the geometric constraint on the test image. After the disc centers are localized, we segment the discs by classifying image pixels around disc centers as background or foreground. The classification is done in a similar data-driven approach as we used for localization, but in this segmentation case we are aiming to estimate the foreground/background probability of each pixel instead of the image displacements. In addition, an extra neighborhood smooth constraint is introduced to enforce the local smoothness of the label field. Our method is validated on 3D T2-weighted turbo spin echo MR images of 35 patients from two different studies. Experiments show that compared to state of the art, our method achieves better or comparable results. Specifically, we achieve for localization a mean error of 1.6-2.0 mm, and for segmentation a mean Dice metric of 85%-88% and a mean surface distance of 1.3-1.4 mm.
Mediastinal Lymph Node Detection and Segmentation Using Deep Learning

Automatic lymph node (LN) segmentation and detection for cancer staging are critical. In clinical practice, computed tomography (CT) and positron emission tomography (PET) imaging detect abnormal LNs. Despite its low contrast and variety in nodal size and form, LN segmentation remains a challenging task. Deep convolutional neural networks frequently segment items in medical photographs. Most state-of-the-art techniques destroy image's resolution through pooling and convolution. As a result, the models provide unsatisfactory results. Keeping the issues in mind, a well-established deep learning technique UNet++ was modified using bilinear interpolation and total generalized variation (TGV) based upsampling strategy to segment and detect mediastinal lymph nodes. The modified UNet++ maintains texture discontinuities, selects noisy areas, searches appropriate balance points through backpropagation, and recreates image resolution. Collecting CT image data from TCIA, 5-patients, and ELCAP public dataset, a dataset was prepared with the help of experienced medical experts. The UNet++ was trained using those datasets, and three different data combinations were utilized for testing. Utilizing the proposed approach, the model achieved 94.8% accuracy, 91.9% Jaccard, 94.1% recall, and 93.1% precision on COMBO_3. The performance was measured on different datasets and compared with state-of-the-art approaches. The UNet++ model with hybridized strategy performed better than others.
Deep salient-Gaussian Fisher vector encoding of the spatio-temporal trajectory structures for person re-identification

In this paper, we propose a deep spatio-temporal appearance (DSTA) descriptor for person re-identification (re-ID). The proposed descriptor is based on the deep Fisher vector (FV) encoding of the trajectory spatio-temporal structures. These have the advantage of robustly handling the misalignment in the pedestrian tracklets. The deep encoding exploits the richness of the spatio-temporal structural information around the trajectories. This is achieved by hierarchically encoding the trajectory structures leveraging a larger tracklet neighborhood scale when moving from one layer to the next one. In order to eliminate the noisy background located around the pedestrian and model the uniqueness of its identity, the deep FV encoder is further enriched towards the deep Salient-Gaussian weighted FV (deepSGFV) encoder by integrating the pedestrian Gaussian and saliency templates in the encoding process, respectively. The proposed descriptor produces competitive accuracy with respect to state-of-the art methods and especially the deep CNN ones without necessitating either pre-training or data augmentation on four challenging pedestrian video datasets: PRID2011, i-LIDS-VID, Mars and LPW. The further combination of DSTA with deep CNN boosts the current state-of-the-art methods and demonstrates their complementarity.
The impact of age on the morphology of the 12th thoracic vertebral endplates

The current article explores the aging effects on the overall morphology of the endplates of the 12th thoracic vertebra (T12), while screening for sex differences. It further evaluates the suitability of T12 for estimating age-at-death in bioarcheaological contexts. We captured the morphology of the vertebral endplates, including the formation of osteophytes, in a novel continuous quantitative manner using digital photography. 168 Greek adults from the Athens Collection were used for modeling the aging effects and another 107 individuals from two Danish archaeological assemblages for evaluation. Regression analysis is based on generalized additive models for correlating age-at-death and morphological variation. Our proposed measurement method is highly reliable (R>0.98) and the main differences observed between sexes are size related. Aging has considerable effect on the endplate morphology of the T12 with the total area of the endplate, the area of the epiphyseal rim, and the shape irregularities of the endplate's external boundary being mostly affected. Multivariate regression shows that aging effects account up to 46% of the observed variation, although with differential expression between sexes. Correct age prediction on archaeological remains reached 33% with a prominent tendency for overestimation. The morphology of the T12 endplates is influenced by age and it can provide some insight with respect to the age-at-death of unidentified individuals, especially when other skeletal age markers are unavailable. Our proposed method provides an age-estimation framework for bioarchaeological settings, especially for estimating broader age ranges, such as discriminating between young and old adults.
Hybrid Physics-Based Adaptive Kalman Filter State Estimation Framework

State-of-the art physics-model based dynamic state estimation generally relies on the assumption that the system's transition matrix is always correct, the one that relates the states in two different time instants, which might not hold always on real-life applications. Further, while making such assumptions, state-of-the-art dynamic state estimation models become unable to discriminate among different types of anomalies, as measurement gross errors and sudden load changes, and thus automatically leads the state estimator framework to inaccuracy. Towards the solution of this important challenge, in this work, a hybrid adaptive dynamic state estimator framework is presented. Based on the Kalman Filter formulation, measurement innovation analytical-based tests are presented and integrated into the state estimator framework. Gross measurement errors and sudden load changes are automatically detected, identified, and corrected, providing continuous updating of the state estimator. Towards such, the asymmetry index applied to the measurement innovation is introduced, as an anomaly discrimination method, which assesses the physics-model-based dynamic state estimation process in different piece-wise stationary levels. Comparative tests with the state-of-the-art are presented, considering the IEEE 14, IEEE 30, and IEEE 118 test systems. Easy-to-implement-model, without hard-to-design parameters, build-on the classical Kalman Filter solution, highlights potential aspects towards real-life applications.
Partial Regret After Gender Affirmation Surgery of a 35-Year-Old Taiwanese Transgender Woman

Gender-affirming surgery (GAS) is often sought after to alleviate the distress of those who suffer from gender dysphoria (GD). While many studies have shown that a significant percentage of people benefit from this procedure, a number of individuals later regret their decision of undergoing surgery. Studies have illustrated what regret depicts, categorizing regret based on intensity, persistency, and sources, in the hopes to prevent an unwanted irreversible intervention. Here, an in-depth interview with a 35-year-old transwoman from Taiwan who underwent feminizing GAS at the age of 31 illustrates her unique cultural upbringing and the course of her regret. Her experience best matches the characteristics of true regret and major regret based on the classifications of Pfäfflin and Wiepjes, respectively, indicating that she expected GAS to be the solution to her personal acceptance issue, but, in retrospect, regretted the diagnosis and treatment as her problems were not solved and worsened to the extent of secondary dysphoria. This case report hopes to shed light on the complexity of GD and regret after GAS, while encouraging the pre-surgical evaluation of psychological comorbidities and post-surgical psychotherapy, and ensuring that patients are informed and give full consent. In addition, more elaborate, long-term, large-scale qualitative research, especially within more conservative cultural settings, is needed.
Ageing behaviour and analytical pyrolysis characterisation of diterpenic resins used as art materials: Manila copal and sandarac

Artificial indoor and outdoor exposure conditions have been applied in order to investigate the photo-ageing behaviour of two natural resins once used as art materials. Copals and sandarac consist of free labdane diterpenoids and of a highly cross-linked fraction of polycommunic acid. To determine the nature and the composition of the cross-linked fraction, pyrolysis is required. Thermally assisted hydrolysis and methylation, coupled with gas chromatography-mass spectrometry (THM-GOMS), has been used to identify the acidic compounds. Many secondary pyrolysis products have been recognised and distinguished from the original resin components. Further details on the composition and ageing behaviour of Manila copal and sandarac have been obtained from Fourier transform infrared spectroscopy (FTIR), size exclusion chromatography (SEC) and direct temperature-resolved mass spectrometry (DTMS). During ageing, cross-linking and cleavage reactions were found to affect largely the chemical structure of the two resins, together with minor degradation processes such as isomerisation, defunctionalisation and oxidation. (C) 2003 Elsevier Science B.V. All rights reserved.
Endophilin A2 protects against renal fibrosis by targeting TGF-β/Smad signaling

Renal fibrosis underlies all forms of end-stage kidney disease. Endophilin A2 (EndoA2) plays a role in nephrotic syndrome; however, its effect on renal fibrosis remains unknown. Here, we demonstrate that EndoA2 protects against kidney interstitial fibrosis via the transforming growth factor-β (TGF-β)/Smad signaling pathway. Mouse kidneys with fibrosis or kidney biopsy specimens from patients with fibrotic nephropathy had lower levels of EndoA2 protein expression than that in kidneys without fibrosis. In vivo overexpression of EndoA2 with the endophilin A2 transgene (EndoA2Tg ) notably prevented renal fibrosis, decreased the protein expression of profibrotic molecules, suppressed tubular injury, and reduced apoptotic tubular cells in the obstructed kidney cortex of mice with unilateral ureteral obstruction (UUO). In vivo and in vitro overexpression of EndoA2 markedly inhibited UUO- or TGF-β1-induced phosphorylation of Smad2/3 and tubular epithelial cells dedifferentiation. Furthermore, EndoA2 was co-immunoprecipitated with the type II TGF-β receptor (TβRII), thus inhibiting the binding of the type I TGF-β receptor (TβRI) to TβRII. These findings indicate that EndoA2 mitigates renal fibrosis, at least partially, via modulating the TGF-β/Smad signaling. Targeting EndoA2 may be a new potential therapeutic strategy for treatment of renal fibrosis.
2-Methoxyjuglone, a Promising Bioactive Compound for Pharmaceutical and Agricultural Purposes: A Review

2-Methoxyjuglone, a member of the 1,4-naphthoquinone family, was first obtained through semi-synthesis based on 2-hydroxyjuglone as the precursor in 1966. It has been isolated and identified from different plant species of the Juglandaceae, Sterculiaceae, and Proteaceae families. 2-Methoxyjuglone has been demonstrated to possess a wide range of biological activities, including antitumor, antifungal, and antibacterial activities; in addition, it has been shown to poison fish and inhibit seed germination. These properties make 2-methoxyjuglone a promising bioactive compound for pharmaceutical and agricultural purposes. This review article provides an overview of the current research progress on 2-methoxyjuglone for the first time, with a primary focus on the plant sources, extraction, identification, synthesis, and biological activities associated with this compound for further development.
N-acetylcysteine prevents stress-induced anxiety behavior in zebrafish

Despite the recent advances in understanding the pathophysiology of anxiety disorders, the pharmacological treatments currently available are limited in efficacy and induce serious side effects. A possible strategy to achieve clinical benefits is drug repurposing, i.e., discovery of novel applications for old drugs, bringing new treatment options to the market and to the patients who need them. N-acetylcysteine (NAC), a commonly used mucolytic and paracetamol antidote, has emerged as a promising molecule for the treatment of several neuropsychiatric disorders. The mechanism of action of this drug is complex, and involves modulation of antioxidant, inflammatory, neurotrophic and glutamate pathways. Here we evaluated the effects of NAC on behavioral parameters relevant to anxiety in zebrafish. NAC did not alter behavioral parameters in the novel tank test, prevented the anxiety-like behaviors induced by an acute stressor (net chasing), and increased the time zebrafish spent in the lit side in the light/dark test. These data may indicate that NAC presents an anti-stress effect, with the potential to prevent stress-induced psychiatric disorders such as anxiety and depression. The considerable homology between mammalian and zebrafish genomes invests the current data with translational validity for the further clinical trials needed to substantiate the use of NAC in anxiety disorders.
Family Physician Racial Identity: An Analysis of "Other" Race Selection and Implications for Future Data Collection

Family physicians who report their race as "Other" in a single best option question find the existing categories and forced choice of one category to be problematic. Our analysis of open-text responses in the "Other" race category supports a modification in the way these data are collected to provide more accurate and meaningful ways to understand the workforce and move toward more diverse, equitable, and inclusive policies in family medicine.
Effect of protein supplementation on ruminal parameters and microbial community fingerprint of Nellore steers fed tropical forages

In tropical regions, protein supplementation is a common practice in dairy and beef farming. However, the effect of highly degradable protein in ruminal fermentation and microbial community composition has not yet been investigated in a systematic manner. In this work, we aimed to investigate the impact of casein supplementation on volatile fatty acids (VFA) production, specific activity of deamination (SAD), ammonia concentration and bacterial and archaeal community composition. The experimental design was a 4×4 Latin square balanced for residual effects, with four animals (average initial weight of 280±10 kg) and four experimental periods, each with duration of 29 days. The diet comprised Tifton 85 (Cynodon sp.) hay with an average CP content of 9.8%, on a dry matter basis. Animals received basal forage (control) or infusions of pure casein (230 g) administered direct into the rumen, abomasum or divided (50 : 50 ratio) in the rumen/abomasum. There was no differences (P>0.05) in ruminal pH and microbial protein concentration between supplemented v. non-supplemented animals. However, in steers receiving ruminal infusion of casein the SAD and ruminal ammonia concentration increased 33% and 76%, respectively, compared with the control. The total concentration of VFA increased (P0.05) in species richness and diversity of γ-proteobacteria, firmicutes and archaea between non-supplemented Nellore steers and steers receiving casein supplementation in the rumen. However, species richness and the Shannon-Wiener index were lower (P<0.05) for the phylum bacteroidetes in steers supplemented with casein in the rumen compared with non-supplemented animals. Venn diagrams indicated that the number of unique bands varied considerably among individual animals and was usually higher in number for non-supplemented steers compared with supplemented animals. These results add new knowledge about the effects of ruminal and postruminal protein supplementation on metabolic activities of rumen microbes and the composition of bacterial and archaeal communities in the rumen of steers.