ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Mixed Populations and Co-Infection: Pseudomonas aeruginosa and Staphylococcus aureus

The human pathogens Pseudomonas aeruginosa and Staphylococcus aureus are frequently co-isolated from chronic wounds or cystic fibrosis patient airways. Clinical studies analysing the impact of co-infection on patient clinical outcomes lead to contradictory results. However, laboratory approaches suggest that the two pathogens co-colonize the same infection niches and form a mixed-species biofilm, therefore favouring their resistance to antibiotics and immune response. In parallel, many recent studies have focused on the different interactions between the two bacterial species. It has long been recognized that P. aeruginosa usually outcompetes S. aureus, and the molecular mechanisms involved in this state of bacterial competition are now well understood. However, several recent studies show that interactions between P. aeruginosa and S. aureus can be diverse and evolve over time. Thus, many CF isolates of P. aeruginosa and S. aureus can coexist and develop cooperative behaviours. In this chapter, we will provide an overview of the current knowledge on the mixed populations of P. aeruginosa and S. aureus, from their mechanisms of establishment to their impacts on bacterial physiology and clinical outcomes.
Autoregression and Structured Low-Rank Modeling of Sinogram Neighborhoods

Sinograms are commonly used to represent the raw data from tomographic imaging experiments. Although it is already well-known that sinograms posess some amount of redundancy, in this work, we present novel theory suggesting that sinograms will often possess substantial additional redundancies that have not been explicitly exploited by previous methods. Specifically, we derive that sinograms will often satisfy multiple simple data-dependent autoregression relationships. This kind of autoregressive structure enables missing/degraded sinogram samples to be linearly predicted using a simple shift-invariant linear combination of neighboring samples. Our theory also further implies that if sinogram samples are assembled into a structured Hankel/Toeplitz matrix, then the matrix will be expected to have low-rank characteristics. As a result, sinogram restoration problems can be formulated as structured low-rank matrix recovery problems. Illustrations of this approach are provided using several different (real and simulated) X-ray imaging datasets, including comparisons against a state-of-the-art deep learning approach. Results suggest that structured low-rank matrix methods for sinogram recovery can have comparable performance to state-of-the-art approaches. Although our evaluation focuses on competitive comparisons against other approaches, we believe that autoregressive constraints are actually complementary to existing approaches with strong potential synergies.
Improving Semi-Supervised Learning for Audio Classification with FixMatch

Including unlabeled data in the training process of neural networks using Semi-Supervised Learning (SSL) has shown impressive results in the image domain, where state-of-the-art results were obtained with only a fraction of the labeled data. The commonality between recent SSL methods is that they strongly rely on the augmentation of unannotated data. This is vastly unexplored for audio data. In this work, SSL using the state-of-the-art FixMatch approach is evaluated on three audio classification tasks, including music, industrial sounds, and acoustic scenes. The performance of FixMatch is compared to Convolutional Neural Networks (CNN) trained from scratch, Transfer Learning, and SSL using the Mean Teacher approach. Additionally, a simple yet effective approach for selecting suitable augmentation methods for FixMatch is introduced. FixMatch with the proposed modifications always outperformed Mean Teacher and the CNNs trained from scratch. For the industrial sounds and music datasets, the CNN baseline performance using the full dataset was reached with less than 5% of the initial training data, demonstrating the potential of recent SSL methods for audio data. Transfer Learning outperformed FixMatch only for the most challenging dataset from acoustic scene classification, showing that there is still room for improvement.
RESEARCH ON THE ART OF ENVIRONMENTAL EDUCATION

In this paper, the authors explore preliminarily 'effectiveness environmental education method' through the teaching practice using the search literature, questionnaire interviews, teaching practice and other methods. The whole text is divided into four parts: The first part analyses the important significance of offering environment art education course in university stage against Commercial pop culture. The second part based on interviews with teachers and students in some schools in Suzhou is about the current situation of the effective environment education of environment art education in high school. In the third part, the authors put forward the practical method of improving the effectiveness of the teaching of environment art education, namely, the special teaching method. In this part, the authors try to explore the use of special teaching method, to stimulate students' interest in learning, to carry out reasonable and scientific recombination, the teaching focus and difficult to extract, form a special study to guide students through active research and participation, to break through the key and difficult to understand, so as to effectively improve classroom teaching effect.
Impact of Safe Water Programs on Water Treatment Practices of People Living with Human Immunodeficiency Virus, Ethiopia, 2008

Household water chlorination has been shown to reduce diarrhea incidence among people living with Human Immunodeficiency Virus (PLHIV). Some HIV programs in Ethiopia previously provided a socially marketed chlorination product (brand name WuhaAgar) to prevent diarrhea. To evaluate the program, we compared WuhaAgar use and water treatment practices between 795 clients from 20 antiretroviral therapy (ART) clinics and 795 community members matched by age, sex, and neighborhood. Overall, 19% of study participants reported water treatment with WuhaAgar. Being an ART clinic client was associated with reported treatment of drinking water (matched odds ratios (mOR): 3.8, 95% confidence interval (CI): 2.9-5.0), reported current water treatment with WuhaAgar (mOR: 5.5, 95% CI 3.9-7.7), and bottles of WuhaAgar observed in the home (mOR: 8.8, 95% CI 5.4-14.3). Being an ART clinic client was also associated with reported diarrhea among respondents (mOR: 4.8, 95% CI 2.9-7.9) and household members (mOR:2.8, 95% CI: 1.9-4.2) in the two weeks preceding the survey. Results suggest that promoting and distributing water chlorination products in ART clinics was effective in increasing access to and use of water treatment products among PLHIV. The positive association between ART clinic attendees and diarrhea likely resulted from the immunocompromised status of ART clinic clients.
A study on the effect of the internal exposure to Po-210 on the excretion of urinary proteins in rats

This study was designed to assess the feasibility of a noninvasive urine specimen for the detection of proteins as indicators of internal exposure to ionizing radiation. Three groups of rats (five in each group) were intravenously injected with 1601 +/- A 376, 10,846 +/- A 591 and 48,467 +/- A 2812 Bq of Po-210 in citrate form. A sham-exposed control group of five rats was intravenously injected with sterile physiological saline. Daily urine samples were collected over 4 days following injection. Purification and pre-concentration of urinary proteins were carried out by ultrafiltration using a 3000 Da molecular weight cutoff membrane filter. The concentration of common urinary proteins, namely albumin, alpha-1-acid glycoprotein, immunoglobulins IgA and IgG, was measured by an enzyme-linked immunosorbent assay. Urinary excretion of albumin decreased dose-dependently (p < 0.05) 96 h post-injection relative to the control group. In contrast, no statistically significant effects were observed for other proteins tested. The dose-dependent decrease in urinary excretion of albumin observed in this study underscores the need for further research, which may lead to the discovery of new biomarkers that would reflect the changes in the primary target organs for deposition of Po-210.
Semantic Image Segmentation with Contextual Hierarchical Models

Semantic segmentation is the problem of assigning an object label to each pixel. It unifies the image segmentation and object recognition problems. The importance of using contextual information in semantic segmentation frameworks has been widely realized in the field. We propose a contextual framework, called contextual hierarchical model (CHM), which learns contextual information in a hierarchical framework for semantic segmentation. At each level of the hierarchy, a classifier is trained based on downsampled input images and outputs of previous levels. Our model then incorporates the resulting multi-resolution contextual information into a classifier to segment the input image at original resolution. This training strategy allows for optimization of a joint posterior probability at multiple resolutions through the hierarchy. Contextual hierarchical model is purely based on the input image patches and does not make use of any fragments or shape examples. Hence, it is applicable to a variety of problems such as object segmentation and edge detection. We demonstrate that CHM performs at par with state-of-the-art on Stanford background and Weizmann horse datasets. It also outperforms state-of-the-art edge detection methods on NYU depth dataset and achieves state-of-the-art on Berkeley segmentation dataset (BSDS 500).
Real-time tracking of humans and visualization of their future footsteps in public indoor environments An intelligent interactive system for public entertainment

In this work, an interactive entertainment system which employs multiple-human tracking from a single camera is presented. The proposed system robustly tracks people in an indoor environment and displays their predicted future footsteps in front of them in real-time. The system is composed of a video camera, a computer and a projector. There are three main modules: tracking, analysis and visualization. The tracking module extracts people as moving blobs by using an adaptive background subtraction algorithm. Then, the location and orientation of their next footsteps are predicted. The future footsteps are visualized by a high-paced continuous display of foot images in the predicted location to simulate the natural stepping of a person. To evaluate the performance, the proposed system was exhibited during a public art exhibition in an airport. People showed surprise, excitement, curiosity. They tried to control the display of the footsteps by making various movements.
A novel family of IC-based similarity measures with a detailed experimental survey on WordNet

This paper introduces a novel family of ontology-based similarity measures based on the Information Content (IC) theory, a detailed state of the art, a large experimental survey into ontology-based similarity measures on WordNet, and a new comparison between intrinsic and corpus-based IC models. Our experiments are based on our implementation of a large set of similarity measures, intrinsic and corpus-based IC models, which are evaluated on two known datasets and three different WordNet versions. The new measures are called weighted Jiang-Conrath distance (wJ&Cdist) and similarity (wJ&Csim), cosine-normalized Jiang-Conrath similarity (cosJ&Csim) and cosine-normalized weighted Jiang-Conrath similarity (coswl&Csim). Two of our similarity measures outperform the state-of-the-art measures on the RG65 dataset, and one of them obtains the third overall score on all the datasets and evaluated WordNet versions. The cosine-normalized similarity measures are a non-linear normalization of the classic Jiang-Conrath (J&C) distance and the new wJ&C distance. On the other hand, the wJ&C distance is a generalization of the classic J&C distance which is based on the length of the shortest path between concepts within an IC-based weighted graph. Our measures are based on two not previously considered notions: (1) a generalization of the classic J&C distance to any type of taxonomy, based on an IC-based weighted graph derived from the conditional probabilities between child and parent concepts, and (2) a non-linear normalization function that converts the ontology-based semantic distances into similarity functions. Finally, the corpus-based IC models based on the Resnik method obtain rivaling results as regards the state-of-the-art intrinsic IC models, when they are used with some unexplored WordNet-based frequency files. Therefore, this latter fact allows us to reconsider some previous conclusions about the outperformance of the intrinsic IC models over the corpus-based ones. (C) 2015 Elsevier Ltd. All rights reserved.
Robust long-term correlation tracking with multiple models

To address the challenge of repetitive target appearance variation and frequent occlusion, existing visual tracking methods either handle corrupted samples or correct the appearance model. In this study, the authors propose a novel framework that successfully combines these two strategies. In their method, the base tracker is an improved discriminative correlation filter-based tracker, in which an independent classifier is employed to alleviate the problem of corrupted samples; the best model is selected for improvement from a group of models, which they call a 'model colony'. The model colony is composed of models updated via different processes. The correlation output and the peak-to-sidelobe ratio are used to evaluate each model in the model colony. In addition, they propose a novel criterion called the maximum-to-others ratio for superior model selection. Experiments on 80 challenging sequences show that their tracker outperforms state-of-the-art trackers. In addition, experimental results demonstrate that their formulation significantly improves the performance of their base tracker.
High-Performance Thin-Film Transistor with Atomic Layer Deposition (ALD)-Derived Indium-Gallium Oxide Channel for Back-End-of-Line Compatible Transistor Applications: Cation Combinatorial Approach

In this paper, the feasibility of an indium-gallium oxide (In2(1-x)Ga2xOy) film through combinatorial atomic layer deposition (ALD) as an alternative channel material for back-end-of-line (BEOL) compatible transistor applications is studied. The microstructure of random polycrystalline In2Oy with a bixbyite structure was converted to the amorphous phase of In2(1-x)Ga2xOy film under thermal annealing at 400 °C when the fraction of Ga is ≥29 at. %. In contrast, the enhancement in the orientation of the (222) face and subsequent grain size was observed for the In1.60Ga0.40Oy film with the intermediate Ga fraction of 20 at. %. The suitability as a channel layer was tested on the 10-nm-thick HfO2 gate oxide where the natural length was designed to meet the requirement of short channel devices with a smaller gate length (<100 nm). The In1.60Ga0.40Oy thin-film transistors (TFTs) exhibited the high field-effect mobility (μFE) of 71.27 ± 0.98 cm2/(V s), low subthreshold gate swing (SS) of 74.4 mV/decade, threshold voltage (VTH) of -0.3 V, and ION/OFF ratio of >108, which would be applicable to the logic devices such as peripheral circuit of heterogeneous DRAM. The in-depth origin for this promising performance was discussed in detail, based on physical, optical, and chemical analysis.
THz Properties of Typical Woods Important for European Forestry

Terahertz (THz) properties of seventeen types of wood typically growing within the European area are investigated. The samples include both coniferous and deciduous timbers. The determined properties can be useful for further THz studies on samples consisting completely or partially of wood, for example, art objects. Especially in the field of art conservation, THz spectroscopy is an excellent non-destructive evaluation method, which shows considerable analysis potential.
Predictability of Music Descriptor Time Series and its Application to Cover Song Detection

Intuitively, music has both predictable and unpredictable components. In this paper, we assess this qualitative statement in a quantitative way using common time series models fitted to state-of-the-art music descriptors. These descriptors cover different musical facets and are extracted from a large collection of real audio recordings comprising a variety of musical genres. Our findings show that music descriptor time series exhibit a certain predictability not only for short time intervals, but also for mid-term and relatively long intervals. This fact is observed independently of the descriptor, musical facet and time series model we consider. Moreover, we show that our findings are not only of theoretical relevance but can also have practical impact. To this end we demonstrate that music predictability at relatively long time intervals can be exploited in a real-world application, namely the automatic identification of cover songs (i.e., different renditions or versions of the same musical piece). Importantly, this prediction strategy yields a parameter-free approach for cover song identification that is substantially faster, allows for reduced computational storage and still maintains highly competitive accuracies when compared to state-of-the-art systems.
Intravenous immunoglobulin therapy in children with severe atopic dermatitis

Children with severe atopic dermatitis (AD) can benefit from intravenous immunoglobulin (IVIG) therapy. This study aimed to identify the efficacy and safety of IVIG therapy in children with severe AD. Twenty pediatric AD patients were enrolled in this study. Patients with an Investigator's Global Assessment score of 0 or 1 or a reduction of 2 points after treatment were defined as high-responders (HRs), otherwise, they were defined as low-responders (LRs). Twelve patients (60%) achieved an excellent treatment response after 2 months, while eight (40%) had a low response. The Scoring Atopic Dermatitis index had improved significantly at 2 months post-treatment compared with baseline (p < 0.001). Baseline total serum IgE levels and eosinophil counts were elevated in all subjects and decreased significantly at 2 months post-treatment (p = 0.004 and 0.021, respectively). Baseline IgE levels were significantly higher in the HR group compared with the LR group (p = 0.020). The treatment was well tolerated. Fever was the most common adverse event and occurred in five patients (25%). In conclusion, IVIG could be a safe and effective therapy for children with severe AD and may be more effective in patients with higher IgE levels. Further studies are needed to investigate the different therapeutic responses in patients with different AD phenotypes.
A 3D Tubular Flux Model for Centerline Extraction in Neuron Volumetric Images

Digital morphology reconstruction from neuron volumetric images is essential for computational neuroscience. The centerline of the axonal and dendritic tree provides an effective shape representation and serves as a basis for further neuron reconstruction. However, it is still a challenge to directly extract the accurate centerline from the complex neuron structure with poor image quality. In this paper, we propose a neuron centerline extraction method based on a 3D tubular flux model via a two-stage CNN framework. In the first stage, a 3D CNN is used to learn the latent neuron structure features, namely flux features, from neuron images. In the second stage, a light-weight U-Net takes the learned flux features as input to extract the centerline with a spatial weighted average strategy to constrain the multi-voxel width response. Specifically, the labels of flux features in the first stage are generated by the 3D tubular model which calculates the geometric representations of the flux between each voxel in the tubular region and the nearest point on the centerline ground truth. Compared with self-learned features by networks, flux features, as a kind of prior knowledge, explicitly take advantage of the contextual distance and direction distribution information around the centerline, which is beneficial for the precise centerline extraction. Experiments on two challenging datasets demonstrate that the proposed method outperforms other state-of-the-art methods by 18% and 35.1% in F1-measurement and average distance scores at the most, and the extracted centerline is helpful to improve the neuron reconstruction performance.
Convolutional neural network with joint stepwise character/word modeling based system for scene text recognition

Text recognition in the wild is a challenging task in the field of computer vision and machine learning. Existing optical character recognition engines cannot perform well in the natural scene. In this context, deep learning models have emerged as a powerful state-of-the-art technique in the classification and recognition process. This study proposes a new Convolutional Neural Network based system for scene text reading. We investigate how to combine the character recognition module followed by the word recognition module to achieve the overall system goal. The first module analyzes characters within multi-scale images by relaying on the power of the convolutional network and the fully connected network for character recognition. The second module relies on the Viterbi search to find the closest word to a given characters sequence. For the sake of more precision, a bigram based linguistic module is applied. The proposed system achieves the state-of-the-art performance on three standard scene text recognition benchmarks: chars74k, ICDAR 2003 and ICDAR 2013. In particular, this performance is proven on both of character and word recognition accuracy as well as speed aspects via a comparative study between different deep learning architectures.
Low-Dose CBCT Reconstruction Using Hessian Schatten Penalties

Cone-beam computed tomography (CBCT) has been widely used in radiation therapy. For accurate patient setup and treatment target localization, it is important to obtain high-quality reconstruction images. The total variation (TV) penalty has shown the state-of-the-art performance in suppressing noise and preserving edges for statistical iterative image reconstruction, but it sometimes leads to the so-called staircase effect. In this paper, we proposed to use a new family of penalties-the Hessian Schatten (HS) penalties-for the CBCT reconstruction. Consisting of the second-order derivatives, the HS penalties are able to reflect the smooth intensity transitions of the underlying image without introducing the staircase effect. We discussed and compared the behaviors of several convex HS penalties with orders 1, 2, and +infinity for CBCT reconstruction. We used the majorization-minimization approach with a primal-dual formulation for the corresponding optimization problem. Experiments on two digital phantoms and two physical phantoms demonstrated the proposed penalty family's outstanding performance over TV in suppressing the staircase effect, and the HS penalty with order 1 had the best performance among the HS penalties tested.