ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Human action recognition by means of subtensor projections and dense trajectories

In last years, most human action recognition works have used dense trajectories features, to achieve state-of-the-art results. Histograms of Oriented Gradients (HOG), Histogram of Optical Flow (HOF) and Motion Boundary Histograms (MBH) features are extracted from regions and being tracked across the frames. The goal of this paper is to improve the performance obtained by means of Improved Dense Trajectories (IDTs), adding new features based on temporal templates. We construct these templates considering a video sequence as a third-order tensor and computing three different projections. We use several functions for projecting the fibers from the video sequences, and combined them by means of sum pooling. As a first contribution of our work, we present in detail the method based on tensor projections. First, we have assessed the results obtained using only template based action recognition. Next, in order to achieve state-of-art recognition rates, we have fused our features with those of IDTs. This is the second contribution of the article. Experiments on four different public datasets have shown that this technique improves IDTs performance and that the results outperform the ones obtained by most of the state-of-the-art techniques for action recognition. (C) 2018 Elsevier Ltd. All rights reserved.
Association of environmental exposure to perchlorate, nitrate, and thiocyanate with overweight/obesity and central obesity among children and adolescents in the United States of America using data from the National Health and Nutrition Examination Survey (NHANES) 2005-2016

The association of overweight/obesity, and central obesity with thiocyanate (SCN), perchlorate (CIO), and nitrate (NO) in childhood and adolescence is unclear. Therefore, this study aimed to explore this association in 4447 participants comprising children and adolescents (aged 6-19 years) using data from the United States National Health and Nutrition Examination Survey 2005-2016. SCN level was positively associated with overweight/obesity in both children and adolescents, while CIO level was negatively associated with overweight/obesity only in children; however, no significant association was found for NO level. Similar associations were found between SCN level and central obesity. Thus, our results suggest that SCN exposure was associated with overweight/obesity and central obesity in both children and adolescents, while a negative association was observed for CIO in children. Strategies to monitor the exposure levels and the mechanisms underlying the relationship between exposure and the weight parameters are recommended.
Automatic Parameter Selection for Multimodal Image Registration

Over the past ten years similarity measures based on intensity distributions have become state-of-the-art in automatic multimodal image registration. An implementation for clinical usage has to support a plurality of images. However, a generally applicable parameter configuration for the number and sizes of histogram bins, optimal Parzen-window kernel widths or background thresholds cannot be found. This explains why various research groups present partly contradictory empirical proposals for these parameters. This paper proposes a set of data-driven estimation schemes for a parameter-free implementation that eliminates major caveats of heuristic trial and error. We present the following novel approaches: a new coincidence weighting scheme to reduce the influence of background noise on the similarity measure in combination with Max-Lloyd requantization, and a tradeoff for the automatic estimation of the number of histogram bins. These methods have been integrated into a state-of-the-art rigid registration that is based on normalized mutual information and applied to CT-MR, PET-MR, and MR-MR image pairs of the RIRE 2.0 database. We compare combinations of the proposed techniques to a standard implementation using default parameters, which can be found in the literature, and to a manual registration by a medical expert. Additionally, we analyze the effects of various histogram sizes, sampling rates, and error thresholds for the number of histogram bins. The comparison of the parameter selection techniques yields 25 approaches in total, with 114 registrations each. The number of bins has no significant influence on the proposed implementation that performs better than both the manual and the standard method in terms of acceptance rates and target registration error (TRE). The overall mean TRE is 2.34 mm compared to 2.54 mm for the manual registration and 6.48 mm for a standard implementation. Our results show a significant TRE reduction for distortion-corrected magnetic resonance images.
Collective response of fish to combined manipulations of illumination and flow

Collective behavior is ubiquitous among fish, yet, its hows and whys are yet to be completely elucidated. It is known that several environmental factors can dramatically influence collective behavior, by eliciting behavioral adaptations in the individuals and altering physical pathways of social interactions in the group. Yet, empirical research has mostly focused on the quantification of the role of one factor at a time, with a paucity of studies designed to explore the multi-sensory basis of collective behavior. We investigated collective behavior of zebrafish (Danio rerio) pairs swimming in a water channel under combined manipulations of illumination (bright and dark) and flow conditions (absence and presence). The ability of the pair to orient and school increased in the presence of the flow and when fish were allowed to visually interact under bright illumination. Shoaling, instead, was only modulated by the illumination, so that fish swam at higher relative distances in the dark, irrespective of the flow. We also found evidence in favor of a modulatory effect of flow and illumination on the formation of the pair. Specifically, in the bright illumination, fish swam more side-by-side against a flow than in placid water; likewise, in the presence of a flow, they spent more time side-by-side in the bright illumination than in the dark. These findings point at a rich interplay between flow and illumination, whose alterations have profound effects on collective behavior.
Design and Properties of Antimicrobial Biomaterials Surfaces

Emergence of antibiotic-resistance pathogens has caused serious health issues and if the current trend is to continue, treatment of the infection will become complicated and even unsuccessful due to new antimicrobial resistance (AMR). Therefore, there is a global drive to identify new methods to treat infection and develop better antibacterial materials and therapy. Although new and more potent antibiotics have aided the fight against microbes, they only offer a temporary solution because future bacteria strains may become resistant to these antibiotics and drugs. Recently, application of non-biological methods such as, electrical currents and photothermal/dynamic therapies to kill bacteria, reveal new approaches to design antimicrobial biomaterials, as complications stemming from drug-resistant bacteria can be obviated. Furthermore, recent research has focused on mimicking the surface patterns on plants and insects such as lotus leaves and dragonfly wings. Bio-inspired micro/nano patterns have been replicated on a variety of biomaterials to improve the bacterial resistance and other properties with good success. This is an exciting research area with immense practical and clinical potentials. In this review, recent advances in the application of chemical/biological approaches to combat bacterial infection and AMR are summarized and the related mechanisms are discussed.
Outcomes of Robot-Assisted Laparoscopic Pyeloplasty Based on Degree of Obstruction on Preoperative Tc-99 MAG-3 Renal Scintigraphy

Objective: Management of symptomatic ureteropelvic junction (UPJ) obstruction with hydronephrosis and discordant Tc-99 mercaptoacetyltriglycine (MAG-3) renal scintigraphy is challenging. In this study we describe long-term outcomes of patients who underwent robot-assisted laparoscopic pyeloplasty for the correction of symptomatic UPJ obstruction with discordant preoperative Tc-99m MAG-3 renal scintigraphy. Methods: Patients undergoing robot-assisted laparoscopic pyeloplasty for symptomatic UPJ obstruction at a single academic center from 2009 to 2021 were retrospectively reviewed. Patients were categorized into three groups with varying degrees of obstruction based on preoperative MAG-3 imaging: Group 1: no obstruction (Lasix T1/2 clearance <10 minutes), Group 2: equivocal obstruction (Lasix T1/2 clearance 10-20 minutes), and Group 3: obstruction (Lasix T1/2 clearance >20 minutes. Pyeloplasty success was defined as resolution of symptoms and improvement/stable computed tomography (CT) imaging or MAG-3 scintigraphy. Failure was defined as persistence of symptoms with either obstruction on functional imaging, worsening hydronephrosis, or subsequent intervention. Results: A total of 125 cases were identified, with a median patient age of 35 years. Dismembered pyeloplasty technique was performed in 98.4% of cases. Median preoperative split renal function on MAG-3 scintigraphy was the only statistically significant (p = 0.003) difference in preoperative characteristics between the three groups. There were 15 postoperative complications, with a rate of Clavien-Dindo grade 3 or higher complications of 4.8%. Overall pyeloplasty success was 92.8%, with success rates of 100% (15/15) and 97% (32/33) in the no obstruction and equivocal obstruction groups, respectively. Median time to pyeloplasty failure was 20.4 months. Conclusion: Robot-assisted laparoscopic pyeloplasty is a safe and effective surgical intervention for correcting UPJ obstruction. Patients with symptoms of UPJ obstruction and discordant functional imaging studies demonstrate similar or improved success rates after pyeloplasty compared with patients with documented high-grade obstruction. Based on these findings preoperative renal scan may not be reliable in appropriate selection of candidacy for pyeloplasty.
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era

Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human-computer interaction research. In recent years, machines have managed to master the art of generating speech that is understandable by humans. However, the linguistic content of an utterance encompasses only a part of its meaning. Affect, or expressivity, has the capacity to turn speech into a medium capable of conveying intimate thoughts, feelings, and emotions-aspects that are essential for engaging and naturalistic interpersonal communication. While the goal of imparting expressivity to synthesized utterances has so far remained elusive, following recent advances in text-to-speech synthesis, a paradigm shift is well under way in the fields of affective speech synthesis and conversion as well. Deep learning, as the technology that underlies most of the recent advances in artificial intelligence, is spearheading these efforts. In this overview, we outline ongoing trends and summarize state-of-the-art approaches in an attempt to provide a broad overview of this exciting field.
Towards Personalized Statistical Deformable Model and Hybrid Point Matching for Robust MR-TRUS Registration

Registration and fusion of magnetic resonance (MR) and 3D transrectal ultrasound (TRUS) images of the prostate gland can provide high-quality guidance for prostate interventions. However, accurate MR-TRUS registration remains a challenging task, due to the great intensity variation between two modalities, the lack of intrinsic fiducials within the prostate, the large gland deformation caused by the TRUS probe insertion, and distinctive biomechanical properties in patients and prostate zones. To address these challenges, a personalized model-to-surface registration approach is proposed in this study. The main contributions of this paper can be threefold. First, a new personalized statistical deformable model (PSDM) is proposed with the finite element analysis and the patient-specific tissue parameters measured from the ultrasound elastography. Second, a hybrid point matching method is developed by introducing the modality independent neighborhood descriptor (MIND) to weight the Euclidean distance between points to establish reliable surface point correspondence. Third, the hybrid point matching is further guided by the PSDM for more physically plausible deformation estimation. Eighteen sets of patient data are included to test the efficacy of the proposed method. The experimental results demonstrate that our approach provides more accurate and robust MR-TRUS registration than state-of-the-art methods do. The averaged target registration error is 1.44 mm, which meets the clinical requirement of 1.9 mm for the accurate tumor volume detection. It can be concluded that the presented method can effectively fuse the heterogeneous image information in the elastography, MR, and TRUS to attain satisfactory image alignment performance.
An improved reversible watermarking scheme using weighted prediction and watermarking simulation

For the reversible watermarking scheme using the prediction error expansion and histogram shifting (PEE-HS), improving the prediction accuracy facilitates performance enhancement, which still remains a challenging problem in this field. To this end, the paper improves the state-of-the-art local predictor (LP) by designing the following approaches: 1) enlarging the prediction context; 2) partitioning the prediction block surrounding the target pixel into the watermarked and original regions, and imposing different weights on prediction values from these two regions to generate the final prediction for the target pixel; and 3) conducting watermarking simulation on the original region via random noises to further enhance the prediction performance. These three approaches are then integrated to result in an improved LP using weighted prediction and watermarking simulation (LP-WPWS). By exploiting the LP-WPWS for prediction error generation, we thus construct a new PEE-HS-based reversible watermarking scheme. Extensive simulation shows that the proposed scheme outperforms the state-of-the-art LP and is comparable to the excellent methods exploiting the sorting, multiple histograms modification, and hybrid dimensional histogram generation with adaptive mapping selection.
Discovering Hidden Topical Hubs and Authorities Across Multiple Online Social Networks

Finding influential users in online social networks (OSNs) is an important problem with many possible useful applications. Many methods have been proposed to identify influential users in OSNs. PageRank and HITs are two well known examples that determine influential users through link analysis. In recent years, new models that consider both content and social network links have been developed. The Hub and Authority Topic (HAT) model is one that extends HITS to identify topic-specific hubs and authorities by jointly learning hubs, authorities, and topical interests from users' relationship and textual content. However, many of the previous works are confined to identifying influential users within a single OSN. These models, when applied to multiple OSNs, could not learn influential users under a common set of topics nor address platform preferences. In this paper, we therefore propose the MPHAT model, an extension of HAT, to jointly model the topic-specific hub users, authority users, their topical interests and platform preferences. We evaluate MPHAT against several existing state-of-the-art methods in three tasks: (i) modeling of topics, (ii) platform choice prediction, and (iii) link recommendation. Based on our extensive experiments in multiple OSNs settings using synthetic datasets and real-world datasets from Twitter and Instagram, we show that MPHAT is comparable to state-of-the-art topic models in learning topics but outperforms the state-of-the-art models in platform prediction and link recommendation tasks. We also empirically demonstrate the ability of MPHAT to determine influential users within and across multiple OSNs.
Intramolecular Cyclization of Carbonate and Thiocarbonate Derivatives of myo-Inositol in the Solid State: Implications for Acyl Group Transfer Reactions in Molecular Crystals

Racemic 4-O-phenoxycarbonyl and 4-O-phenoxythiocarbonyl derivatives of myo-inositol orthoformate undergo thermal intramolecular cyclization in the solid state to yield the corresponding 4,6-bridged carbonates and thiocarbonates, respectively. The thermal cyclization also occurs in the solution and molten states, but less efficiently, suggesting that these cyclization reactions are aided by molecular pre-organization, although not strictly topochemically controlled. Crystal structures of two carbonates and a thiocarbonate clearly revealed that the relative orientation of the electrophile and the nucleophile in the crystal lattice facilitates the intramolecular cyclization reaction and forbids the intermolecular reaction. The correlation observed between the chemical reactivity and the non-covalent interactions in the crystal of the reactants provides a way to estimate the chemical stability of analogous molecules in the solid state.
Antecedent blood pressure as a predictor of cardiovascular disease

Elevated blood pressure (BP) is associated with greater risk of cardiovascular disease (CVD), and evidence suggests that prior BP levels may be at least as important as current BP in prediction models. We analyzed the determinants of CVD risk in Offspring Framingham Heart Study participants (n = 3344). The baseline Cox model included the traditional risk factors and current systolic BP to predict 20-year risk of CVD (643 events). Current systolic BP was significant, and the associated hazard ratio was 1.09 for 10 mm Hg (confidence interval [CI] 95%: 1.04-1.15). A second model used the traditional risk factors plus antecedent BP (hazard ratio [HR] = 1.19; CI 95%: 1.10-1.24). In a third model that included traditional risk factors and both current and antecedent BP, the antecedent BP was significant (HR = 1.18; CI 95%: 1.08-1.23), but the current BP was not statistically significant (HR = 1.01; CI 95%: 0.97-1.09). Antecedent BP showed a significantly stronger effect on risk of CVD than current BP.
Recognition of Chinese artists via windowed and entropy balanced fusion in classification of their authored ink and wash paintings (IWPs)

As one of the most important cultural heritages, ink and wash paintings (IWPs) play an important role in the world of traditional Chinese arts. In comparison with western arts, the Chinese IWPs have the unique feature that the art form is primarily populated with limited number of content elements, such as stones, mountains, flowers, and animals etc. and hence most likely different art pieces share similar content, making it difficult to differentiate in terms of content alone. In this paper, we propose to extract histogram-based local feature and global feature to characterize different aspects of art styles, and such features are applied to drive neural networks to complete the classification of IWPs in terms of individual artistic descriptors. We then propose a windowed and entropy balanced fusion scheme to make integrated decisions to optimize the final classification and recognition results. Extensive evaluation via experiments is also reported, which supports that the proposed algorithm achieves good performances, outperforming the existing benchmark techniques and hence providing an excellent potential for computerized analysis and management of traditional Chinese IWPs. Crown Copyright (C) 2013 Published by Elsevier Ltd. All rights reserved.
Scalable Probabilistic Similarity Ranking in Uncertain Databases

This paper introduces a scalable approach for probabilistic top-k similarity ranking on uncertain vector data. Each uncertain object is represented by a set of vector instances that is assumed to be mutually exclusive. The objective is to rank the uncertain data according to their distance to a reference object. We propose a framework that incrementally computes for each object instance and ranking position, the probability of the object falling at that ranking position. The resulting rank probability distribution can serve as input for several state-of-the-art probabilistic ranking models. Existing approaches compute this probability distribution by applying the Poisson binomial recurrence technique of quadratic complexity. In this paper, we theoretically as well as experimentally show that our framework reduces this to a linear-time complexity while having the same memory requirements, facilitated by incremental accessing of the uncertain vector instances in increasing order of their distance to the reference object. Furthermore, we show how the output of our method can be used to apply probabilistic top-k ranking for the objects, according to different state-of-the-art definitions. We conduct an experimental evaluation on synthetic and real data, which demonstrates the efficiency of our approach.
Identification of polymorphisms in GDF9 and BMP15 genes in Jamunapari and crossbred goats in Bangladesh

Polymorphisms in growth differentiation factor 9 (GDF9) and bone morphogenetic protein 15 (BMP15) genes have been found to be associated with litter size in goats across the globe. Our previous study detected single-nucleotide polymorphisms (SNPs) in GDF9 and BMP15 genes associated with litter size in Black Bengal, Bangladesh's primary native goat breed. However, Jamunapari and crossbred goats in Bangladesh are yet to be investigated for litter size-associated polymorphisms. In this study, we screened Jamunapari and crossbred (50% Black Bengal × 50% Jamunapari) goats to identify polymorphisms in the GDF9 and BMP15 genes and to assess the association between identified SNPs and litter size. The genomic DNA from 100 female goats (50 Jamunapari and 50 crossbred) was used in polymerase chain reactions (PCRs) to amplify exon 2 of the GDF9 and exon 2 of the BMP15 genes. PCR products were sequenced employing the BigDye Terminator cycle sequencing protocol to identify SNPs. We used a generalized linear model to perform the association analysis for identified SNPs and litter size. Seven SNPs were identified, of which four, C818CT, G1073A, G1189A, and G1330T, were in the GDF9 gene and three, G616T, G735A, and G811A, were in the BMP15 gene. G735A was a synonymous SNP, whereas the remaining were non-synonymous SNPs. Identified SNP loci in GDF9 were low polymorphic (PIC < 0.25), while loci in BMP15 were moderately polymorphic (PIC ≥ 0.25). The genotypes at the G1330T locus had a significant (p < 0.05) difference in litter size in Jamunapari goats, but no significant difference was observed for all genotypes at other loci. Therefore, the G1330T loci could be useful as a marker in marker-assisted selection for litter size traits in goats of Bangladesh.