ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Dynamic multivariate analysis for pollution assessment and river habitat conservation in the Vietnamese La Buong watershed

Analysis of temporal patterns of high-dimensional time-series water quality data is essential for pollution management worldwide. This study has applied dynamic factor analysis (DFA) and cluster analysis (CA) to analyze time-series water quality data monitored at the five stations installed along the La Buong river in Southern Vietnam. Application of the DFA identified two types of temporal patterns, one of the run-off driven parameters (total suspended solid (TSS), turbidity, and iron) and the other of diffuse source pollution. The association of the variables like BOD5 and COD at most stations to the run-off-driven parameters revealed their sharing of drivers. On the contrary, separating variables like phosphate (PO43) at the three upstream stations from the run-off patterns suggested their local point-source origin. The DFA-derived factors were later used in the time-point CA to explore the seasonality of water quality parameters and their pollution intensities compared to regulatory levels. The result suggested intensification in wet season of Fe, TSS, BOD5, and COD concentrations at most sites, which are unobservable in run-off detached parameters like reactive nitrogen, phosphate (PO43-), and E. coli. These findings generated robust insights to support water quality management for river habitat conservation.
Novel Sensor/Access-Point Coverage-Area Maximization for Arbitrary Indoor Polygonal Geometries

Nowadays, sensor/access-point coverage is an essential problem for wireless communication and sensor systems, which will significantly impact the quality of access, monitoring, and surveillance. Indoor sensor/access-point placement still remains very challenging as the regions of interest (ROIs) or underlying geometries may be in an arbitrary polygonal shape. In this work, we would like to study how to place a sensor/access-point to maximize the coverage area within an arbitrary polygonal ROI. Our novel approach is based on finding the maximum-area clique over the visibility graph corresponding to the indoor geometry. According to many examples, our proposed optimal sensor/access-point scheme can lead to larger coverage efficiencies than the existing solution to the art gallery problem (AGP) and the conventional Delaunay triangulation method. Our new scheme is of great practical value as it can be applied for not only both convex and nonconvex simply-connected polygons but also both convex and nonconvex multiply-connected polygons with internal holes.
The Psychological Consequences of Envying Influencers on Instagram

This study examines how being envious toward social media influencers (SMIs) relates to users' affective well-being. An online survey was conducted in which 305 U.S. participants viewed to 20 posts of an SMI and subsequently measured their affective well-being, envy, and inspiration toward the SMI, and self-esteem levels. The results revealed a direct negative relationship between envy and affective well-being, but a positive indirect effect through inspiration. Furthermore, individuals' self-esteem moderates the relationship such that the positive relationship between envy and affective well-being through inspiration is stronger among those with high levels of self-esteem. Moreover, inspiration varies between different influencer categories, that is, participants who viewed fitness influencers reported the greatest amount of inspiration, followed by fashion, beauty, and entertainment influencers.
Examining the Preliminary Efficacy of a Dating Violence Prevention Program for Hispanic Adolescents

The purpose of this study is to evaluate the preliminary efficacy of a dating violence (DV) prevention program for Cuban American adolescents (JOVEN/YOUTH: Juntos Opuestos a la Violence Entre Novios/Together Against Dating Violence). A randomized-controlled experimental design with a delayed condition was used to evaluate the effects on DV victimization and perpetration (N = 82). Self-administrated assessments were completed at baseline, 1 week, 3 months, and 12 months after the intervention to assess for psychological victimization and perpetration and physical and sexual victimization and perpetration. Effect sizes were estimated, and generalized estimating equations were generated to test intervention effects over time and potential gender interactions. The intervention had medium to strong effects on DV victimization and perpetration for male participants but not for females. However, intervention effects were not statistically significant over time. More research is needed to enhance intervention effects of JOVEN on DV outcomes and to evaluate these effects among a larger and more diverse sample.
Two novel mutations in the NR5A1 gene as a cause of disorders of sex development in a Pakistani cohort of 46,XY patients

NR5A1 plays a central role in gonadal development and regulation by transcriptional regulation of key modulators involved in steroidogenesis. Mutations in human NR5A1 are frequently associated with 46,XY disorders of sex development (DSD). We analysed a Pakistani cohort of patients with 46,XY DSD, presenting with variable degrees of gonadal dysgenesis, for NR5A1 mutations. The study identified three mutations (p.Tyr03X, p.Glu07X and p.Gln299HisfsX386), of which two are novel, in these patients with 46,XY DSD. The mutations, p.Tyr03X and novel p.Glu07X, are located in the coding region of the gene, corresponding to DNA-binding domain of the predicted protein. In silico analysis for the novel homozygous p.Gln299HisfsX386 mutation in ligand-binding domain of NR5A1 revealed subtle changes in overall tertiary conformation which is predicted to affect the normal physiology of this mutant protein. This study reveals two novel mutations with altered NR5A1 protein in twenty patients with 46,XY DSD, highlighting the critical role of NR5A1 protein in gonadal development and differentiation. In conclusion, the current and previous studies suggest that the NR5A1 mutations are present in around 8-15% of patients with 46,XY DSD presenting with gonadal dysgenesis. For the clinical utility of NR5A1 gene mutations, more comprehensive studies with large 46,XY DSD patient series in different populations are suggested.
Variation consistency of attributes-based postverification method for copy image retrieval

The state-of-the-art approaches of copy image retrieval are based on the bag-of-visual-words model, which represents an image with a set of visual words obtained by quantizing local features. However, the quantization process reduces local features' discriminative power and thus causes many false matches of local features between images. As a consequence, this brings down the effectiveness of copy image retrieval in large-scale image dataset. In order to handle this problem, postverification methods have been proposed to reject false matches. Previous works of the postverification method focused mainly on geometric relationship consistency among matches of local feature between query image and its candidate for rejecting false candidates. The variation consistency of local feature's attributes is proposed to verify if two pairs of matches are consistent. The matching reliability of local features can be measured by a voting-based method, which is based on the number of consistent matches between two images. This method can easily integrate more attributes of local feature, such as dominant orientation, position, and scale, rather than position of local feature. Experiments on the large-scale datasets demonstrate the effectiveness and efficiency of the proposed approach and show it outperforms the state-of-the-art postverification approaches. (C) 2018 SPIE and IS&T
Circular economy-driven ammonium recovery from municipal wastewater: State of the art, challenges and solutions forward

In current biological nitrogen removal (BNR) processes, most of ammonium in municipal wastewater is biologically transformed to nitrogen gas, making ammonium recovery impossible. Thus, this article aims to provide a holistic review with in-depth discussions on (i) current BNR processes for municipal wastewater treatment, (ii) environmental and economic costs behind ammonium in municipal wastewater, (iii) state of the art of ammonium recovery from municipal wastewater including anaerobic membrane bioreactor turning municipal wastewater to a liquid fertilizer, capturing ammonium in phototrophic biomass, waste activated sludge for land application, bioelectrochemical systems, biological conversion of ammonium to nitrous oxide as a fuel oxidizer, and adsorption, (iv) feasibility and challenge of adsorption for ammonium recovery from municipal wastewater and (v) innovative municipal wastewater reclamation processes coupled with ammonium recovery. Moving forward, municipal wastewater reclamation and resource recovery should be addressed under the framework of circular economy.
Learning Rotation Domain Deep Mutual Information Using Convolutional LSTM for Unsupervised PolSAR Image Classification

Deep learning can archive state-of-the-art performance in polarimetric synthetic aperture radar (PolSAR) image classification with plenty of labeled data. However, obtaining large number of accurately labeled samples of PolSAR data is very hard, which limits the practical use of deep learning. Therefore, unsupervised PolSAR image classification is worthy of further investigation that is based on deep learning. Inspired by the superior performance of deep mutual information in natural image feature learning and clustering, an end-to-end Convolutional Long Short Term Memory (ConvLSTM) network is used in order to learn the deep mutual information of polarimetric coherent matrices in the rotation domain with different polarimetric orientation angles (POAs) for unsupervised PolSAR image classification. First, for each pixel, paired "POA-spatio" samples are generated from the polarimetric coherent matrices with different POAs. Second, a special designed ConvLSTM network, along with deep mutual information losses, is used in order to learn the discriminative deep mutual information feature representation of the paired data. Finally, the classification results can be output directly from the trained network model. The proposed method is trained in an end-to-end manner and does not have cumbersome pipelines. Experiments on four real PolSAR datasets show that the performance of proposed method surpasses some state-of-the-art deep learning unsupervised classification methods.
An enhanced fast mode decision model for spatial enhancement layers in scalable video coding

Recently, the H.264/AVC standard has been extended to incorporate Scalable Video Coding (SVC). SVC offers the advantage of scalable (layered) coding, but has the disadvantage of a highly increased computational complexity at the encoder side when dealing with spatial scalability. To restrict the increase in required processing power, fast mode decision models for spatial enhancement layers have been proposed in literature. We propose a novel generic fast mode decision model for spatial enhancement layers for both P and B frames based on both the quantization of the enhancement layer and the correlation between the macroblock type in the enhancement layer and the co-located macroblock in the reference layer. In this paper, an evaluation of the proposed model and comparison with a state-of-the-art model is given. Results show that the proposed technique performs exceptionally well for spatial scalability. For both dyadic and non-dyadic spatial scalability, we achieve an average time saving of 75%, while only a slight bit rate increase of 2.23% and a minor PSNR decrease of 0.46 dB are measured. Compared with state of the art techniques, we further halve the complexity while having comparable rate-distortion results.
Motion Estimation by Deep Learning in 2D Echocardiography: Synthetic Dataset and Validation

Motion estimation in echocardiography plays an important role in the characterization of cardiac function, allowing the computation of myocardial deformation indices. However, there exist limitations in clinical practice, particularly with regard to the accuracy and robustness of measurements extracted from images. We therefore propose a novel deep learning solution for motion estimation in echocardiography. Our network corresponds to a modified version of PWC-Net which achieves high performance on ultrasound sequences. In parallel, we designed a novel simulation pipeline allowing the generation of a large amount of realistic B-mode sequences. These synthetic data, together with strategies during training and inference, were used to improve the performance of our deep learning solution, which achieved an average endpoint error of 0.07 +/- 0.06 mm per frame and 1.20 +/- 0.67 mm between ED and ES on our simulated dataset. The performance of our method was further investigated on 30 patients from a publicly available clinical dataset acquired from a GE system. The method showed promise by achieving a mean absolute error of the global longitudinal strain of 2.5 +/- 2.1% and a correlation of 0.77 compared to GLS derived from manual segmentation, much better than one of the most efficient methods in the state-of-the-art (namely the FFT-Xcorr block-matching method). We finally evaluated our method on an auxiliary dataset including 30 patients from another center and acquired with a different system. Comparable results were achieved, illustrating the ability of our method to maintain high performance regardless of the echocardiographic data processed.
Effect of Natural Forest Fires on Regional Weather Conditions in Siberia

Effects of forest fires on regional weather conditions were analyzed for Central and Eastern Siberia after warm and dry weather conditions in summer 2019 using COSMO-Ru (COnsortium for Small-scale MOdeling; Ru-Russia) and COSMO-RuART (ART-Aerosols and Reactive Trace gases) model systems. Four series of numerical experiments were conducted (one control experiment and three forest fire experiments assuming total vegetation destruction within the burned areas) to evaluate possible effects of forest fires on surface albedo and vegetation properties as well as their influence on air chemistry and aerosol concentration in the atmosphere. The modeling results showed significant influence of forest fires on regional weather conditions that occurred over large areas situated even away from burnt regions. Decreased surface albedo and reduced latent heat fluxes due to fire-induced destruction of forest cover lead to higher near-surface air temperature and lower air humidity in both burned and surrounding unburned forest areas. On the other hand, reduced incoming solar radiation due to smoke from forest fire plumes decreased land surface temperatures and increased thermal atmospheric stability resulting in reduced regional precipitation.
Recurrent pleomorphic adenoma: unusual cause of isolated sphenoid sinus lesion

Pleomorphic adenoma is the most common benign salivary gland tumour of the head and neck region, most commonly seen in parotid glands. These may arise also from minor salivary glands of the upper aerodigestive tract comprises the upper lip, cheek, floor of the mouth and rarely from mucoserous glands in the nasal cavity and paranasal sinuses. A middle-aged man, who had undergone surgery for a nasal mass 14 years ago, presented with headache and visual complaints from a recurrent mass originating from the sphenoid sinus. Isolated sphenoid sinus space-occupying lesions have always been a diagnostic challenge. The mass was biopsied and diagnosed as pleomorphic adenoma on histopathology, and then excised endoscopically. Early and accurate diagnosis on a biopsy may result in complete excision of the tumour and prevent recurrence. The endoscopic route is preferred for surgical excision and the patient should be followed up clinically and radiologically to detect early recurrence.
A phosphate starvation response-regulated receptor-like kinase, OsADK1, is required for mycorrhizal symbiosis and phosphate starvation responses

Most land plants associate with arbuscular mycorrhizal (AM) fungi to secure mineral nutrient acquisition, especially that of phosphorus. A phosphate starvation response (PHR)-centered network regulates AM symbiosis. Here, we identified 520 direct target genes for the rice transcription factor OsPHR1/2/3 during AM symbiosis using transcriptome deep sequencing and DNA affinity purification sequencing. These genes were involved in strigolactone biosynthesis, transcriptional reprogramming, and bidirectional nutrient exchange. Moreover, we identified the receptor-like kinase, Arbuscule Development Kinase 1 (OsADK1), as a new target of OsPHR1/2/3. Electrophoretic mobility shift assays and transactivation assays showed that OsPHR2 can bind directly to the P1BS elements within the OsADK1 promoter to activate its transcription. OsADK1 appeared to be required for mycorrhizal colonization and arbuscule development. In addition, hydroponic experiments suggested that OsADK1 may be involved in plant Pi starvation responses. Our findings validate a role for OsPHR1/2/3 as master regulators of mycorrhizal-related genes involved in various stages of symbiosis, and uncover a new RLK involved in AM symbiosis and plant Pi starvation responses.
A new dominance-relation metric balancing convergence and diversity in multi- and many-objective optimization

Maintaining a good balance between convergence and diversity in many-objective optimization is a key challenge for most Pareto dominance-based multi-objective evolutionary algorithms. In most existing multi-objective evolutionary algorithms, a certain fixed metric is used in the selection operation, no matter how far the solutions are from the Pareto front. Such a selection scheme directly affects the performance of the algorithm, such as its convergence, diversity or computational complexity. In this paper, we use a more structured metric, termed augmented penalty boundary intersection, which acts differently on each of the non-dominated fronts in the selection operation, to balance convergence and diversity in many-objective optimization problems. In diversity maintenance, we apply a distance-based selection scheme to each non-dominated front. The performance of our proposed algorithm is evaluated on a variety of benchmark problems with 3 to 15 objectives and compared with five state-of-the-art multi-objective evolutionary algorithms. The empirical results demonstrate that our proposed algorithm has highly competitive performance on almost all test instances considered. Furthermore, the combination of a special mate selection scheme and a clustering-based selection scheme considerably reduces the computational complexity compared to most state-of-the-art multi-objective evolutionary algorithms. (C) 2019 Elsevier Ltd. All rights reserved.
A hybrid ART-GRNN online learning neural network with a epsilon-insensitive loss function

In this brief, a new neural network model called generalized adaptive resonance theory (GART) is introduced. GART is a hybrid model that comprises a modified Gaussian adaptive resonance theory (MGA) and the generalized regression neural network (GRNN). It is an enhanced version of the GRNN, which preserves the online learning properties of adaptive resonance theory (ART). A series of empirical studies to assess the effectiveness of GART in classification, regression, and time series prediction tasks is conducted. The results demonstrate that GART is able to produce good performances as compared with those of other methods, including the online sequential extreme learning machine (OSELM) and sequential learning radial basis function (RBF) neural network models.
A Gentle Introduction to Bayesian Network Meta-Analysis Using an Automated R Package

Network meta-analysis is an extension of standard meta-analysis. It allows researchers to build a network of evidence to compare multiple interventions that may have not been compared directly in existing publications. With a Bayesian approach, network meta-analysis can be used to obtain a posterior probability distribution of all the relative treatment effects, which allows for the estimation of relative treatment effects to quantify the uncertainty of parameter estimates, and to rank all the treatments in the network. Ranking treatments using both direct and indirect evidence can provide guidance to policy makers and clinicians for making decisions. The purpose of this paper is to introduce fundamental concepts of Bayesian network meta-analysis (BNMA) to researchers in psychology and social sciences. We discuss several essential concepts of BNMA, including the assumptions of homogeneity and consistency, the fixed and random effects models, prior specification, and model fit evaluation strategies, while pointing out some issues and areas where researchers should use caution in the application of BNMA. Additionally, using an automated R package, we provide a step-by-step demonstration on how to conduct and report the findings of BNMA with a real dataset of psychological interventions extracted from PubMed.
Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images

Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. However, generating exhaustive and accurate annotations is labor-intensive, challenging, and costly. Drawing only coarse and approximate annotations is a much easier task, less costly, and it alleviates pathologists' workload. In this paper, we study the problem of refining these approximate annotations in digital pathology to obtain more accurate ones. Some previous works have explored obtaining machine learning models from these inaccurate annotations, but few of them tackle the refinement problemwhere themislabeled regions should be explicitly identified and corrected, and all of them require a - often very large - number of training samples. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need for external training data. Patches cropped from a WSI with inaccurate labels are processed jointly within a multiple instance learning framework, mitigating their impact on the predictive model and refining the segmentation. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming state-of-the-art alternatives, even while learning from a single slide. Moreover, we demonstrate how real annotations drawn by pathologists can be efficiently refined and improved by the proposed approach. All these results demonstrate that LC-MIL is a promising, lightweight tool to provide fine-grained annotations from coarsely annotated pathology sets.