ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Cleaning of Oil Fouling with Water Enabled by Zwitterionic Polyelectrolyte Coatings: Overcoming the Imperative Challenge of Oil-Water Separation Membranes

Herein we report a self-cleaning coating derived from zwitterionic poly(2-methacryloyloxylethyl phosphorylcholine) (PMPC) brushes grafted on a solid substrate. The PMPC surface not only exhibits complete oil repellency in a water-wetted state (i.e., underwater superoleophobicity), but also allows effective cleaning of oil fouled on dry surfaces by water alone. The PMPC surface was compared with typical underwater superoleophobic surfaces realized with the aid of surface roughening by applying hydrophilic nanostructures and those realized by applying smooth hydrophilic polyelectrolyte multilayers. We show that underwater superoleophobicity of a surface is not sufficient to enable water to clean up oil fouling on a dry surface, because the latter circumstance demands the surface to be able to strongly bond water not only in its pristine state but also in an oil-wetted state. The PMPC surface is unique with its described self-cleaning performance because the zwitterionic phosphorylcholine groups exhibit exceptional binding affinity to water even when they are already wetted by oil. Further, we show that applying this PMPC coating onto steel meshes produces oil-water separation membranes that are resilient to oil contamination with simply water rinsing. Consequently, we provide an effective solution to the oil contamination issue on the oil-water separation membranes, which is an imperative challenge in this field. Thanks to the self-cleaning effect of the PMPC surface, PMPC-coated steel meshes can not only separate oil from oil-water mixtures in a water-wetted state, but also can lift oil out from oil-water mixtures even in a dry state, which is a very promising technology for practical oil-spill remediation. In contrast, we show that oil contamination on conventional hydrophilic oil-water separation membranes would permanently induce the loss of oil-water separation function, and thus they have to be always used in a completely water-wetted state, which significantly restricts their application in practice.
Entropy-assisted approach to determine priorities in water quality monitoring process

Effective determination of water quality and water pollution assessment is crucial and challenging processes. Evaluating water quality in rivers, researchers have referred to various statistical, probabilistic and stochastic methods to obtain efficient information from the monitoring network. As data are greatly random, the information content can be obtained by utilizing various methods including but not limited to the "entropy." Monitoring is a difficult process due to high measurement costs, while it is also difficult to optimize the network in terms of time, space, and especially the variable to be monitored. In the presented study, it is aimed to create an effective approach to be used in optimizing the monitoring network by determining the "prior" variables by entropy that measures the uncertainty by using all the data without time difference. The presented study proposes an alternative method to define the water quality variables that should be monitored much more frequently. Study is exemplified for demonstrating its potential use in a case study level, Grand River in Canada, by assessing water quality data obtained from 15 water quality monitoring stations. Results showed that BOD, Cl, and NO2-N among examined 8 different variables are as the "prior" variables should be monitored. It is being proven that the prior variable that should be monitored for optimization of the network can be easily determined with the information obtained from the data statistically evaluated with entropy, and it can be stated as an effective method for managers to use in the decision-making process.
Durable viral suppression among persons with HIV in the deep south: an observational study

This study assessed predictors of stable HIV viral suppression in a racially diverse sample of persons living with HIV (PWH) in the southern US. A total of 700 PWH were recruited from one of four HIV clinics in Metro Atlanta, GA. Data were collected from September 2012 to July 2017, and HIV viral loads were retrieved from EMR for 18 months. The baseline visits and EMR data were used for current analyses. Durable viral suppression was categorized as 1. Remain suppressed, 2. Remain unsuppressed, and 3. Unstable suppression. The number of antiretroviral medications and age were significantly associated with durable viral suppression. Older age, fewer ART medications and availability of social support were positively associated with durable viral suppression over the 18-month observation period. Findings suggest that regimen complexity is potentially a better predictor of viral suppression than self-reported medication adherence. The need for consensus on the definition of durable viral suppression is also urged.
Extrinsic Calibration of Camera Networks Based on Pedestrians

In this paper, we propose a novel extrinsic calibration method for camera networks by analyzing tracks of pedestrians. First of all, we extract the center lines of walking persons by detecting their heads and feet in the camera images. We propose an easy and accurate method to estimate the 3D positions of the head and feet w.r.t. a local camera coordinate system from these center lines. We also propose a RANSAC-based orthogonal Procrustes approach to compute relative extrinsic parameters connecting the coordinate systems of cameras in a pairwise fashion. Finally, we refine the extrinsic calibration matrices using a method that minimizes the reprojection error. While existing state-of-the-art calibration methods explore epipolar geometry and use image positions directly, the proposed method first computes 3D positions per camera and then fuses the data. This results in simpler computations and a more flexible and accurate calibration method. Another advantage of our method is that it can also handle the case of persons walking along straight lines, which cannot be handled by most of the existing state-of-the-art calibration methods since all head and feet positions are co-planar. This situation often happens in real life.
Assessing the potential of steel as a substrate for building integrated photovoltaic applications

Government edicts and national time bound policy directives are shaping the drive toward cost effective renewables such as photovoltaics (PV). Building Integrated Photovoltaics (BIPV) has the potential to provide significant energy generation by utilising the existing building infrastructure as a power generator, engendering a transformation shift from traditional energy sources. This research presents an innovative study on the industrial viability of utilising "rough" low carbon steel integrated with an Intermediate Layer (IL) to develop lower cost thin film BIPV products and is compared to existing commercial products. Consideration of the final product cost is given and potential business models to enter the BIPV are identified. The lab scale and upscaling elements of the research support the significant benefits of an approach that extends beyond the use of expensive solar grade steel. A state-of-the-art review of existing steel-based BIPV products is given and used as a benchmark to compare the new products. The results demonstrate that a competitively commercial product is viable and also highlight the strong potential for the adoption of a "rough" steel + IL focused approach to BIPV manufacture and a potential new direction to develop cost efficiencies in an increasingly competitive market.
Learning Hierarchical Attention for Weakly-Supervised Chest X-Ray Abnormality Localization and Diagnosis

We consider the problem of abnormality localization for clinical applications. While deep learning has driven much recent progress in medical imaging, many clinical challenges are not fully addressed, limiting its broader usage. While recent methods report high diagnostic accuracies, physicians have concerns trusting these algorithm results for diagnostic decision-making purposes because of a general lack of algorithm decision reasoning and interpretability. One potential way to address this problem is to further train these models to localize abnormalities in addition to just classifying them. However, doing this accurately will require a large amount of disease localization annotations by clinical experts, a task that is prohibitively expensive to accomplish for most applications. In this work, we take a step towards addressing these issues by means of a new attention-driven weakly supervised algorithm comprising a hierarchical attention mining framework that unifies activation- and gradient-based visual attention in a holistic manner. Our key algorithmic innovations include the design of explicit ordinal attention constraints, enabling principled model training in a weakly-supervised fashion, while also facilitating the generation of visual-attention-driven model explanations by means of localization cues. On two large-scale chest X-ray datasets (NIH ChestX-ray14 and CheXpert), we demonstrate significant localization performance improvements over the current state of the art while also achieving competitive classification performance.
ART: An Attack-Resistant Trust Management Scheme for Securing Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) have the potential to transform the way people travel through the creation of a safe interoperable wireless communications network that includes cars, buses, traffic signals, cell phones, and other devices. However, VANETs are vulnerable to security threats due to increasing reliance on communication, computing, and control technologies. The unique security and privacy challenges posed by VANETs include integrity (data trust), confidentiality, nonrepudiation, access control, real-time operational constraints/demands, availability, and privacy protection. The trustworthiness of VANETs could be improved by addressing holistically both data trust, which is defined as the assessment of whether or not and to what extent the reported traffic data are trustworthy, and node trust, which is defined as how trustworthy the nodes in VANETs are. In this paper, an attack-resistant trust management scheme (ART) is proposed for VANETs that is able to detect and cope with malicious attacks and also evaluate the trustworthiness of both data and mobile nodes in VANETs. Specially, data trust is evaluated based on the data sensed and collected from multiple vehicles; node trust is assessed in two dimensions, i.e., functional trust and recommendation trust, which indicate how likely a node can fulfill its functionality and how trustworthy the recommendations from a node for other nodes will be, respectively. The effectiveness and efficiency of the proposed ART scheme is validated through extensive experiments. The proposed trust management theme is applicable to a wide range of VANET applications to improve traffic safety, mobility, and environmental protection with enhanced trustworthiness.
Microsatellite instability profiles of gastrointestinal cancers: comparison between non-colorectal and colorectal origin

Microsatellite instability (MSI) is a major carcinogenic pathway with prognostic and predictive implications. The validity of polymerase chain reaction (PCR)-based MSI testing is well established in colorectal cancer; however, the data are limited in non-colorectal gastrointestinal cancers. The aim of this study is to clarify the detailed MSI profiles of non-colorectal gastrointestinal cancers and to investigate the differences from those of colorectal cancers. MSI testing was performed using paired tumour/normal tissues of 123 mismatch repair-deficient cancers detected by immunohistochemistry including 80 non-colorectal cancers (eight oesophagogastric junction (EGJ), 57 gastric and 15 small intestine) and 43 colorectal cancers. Fragment size analysis revealed that the mean nucleotide shifts of five markers (Promega panel) were the highest in the stomach (6.4), followed by colorectum (5.7), small intestine (5.0) and EGJ cancers (mean = 4.0; P = 0.015, versus stomach). All cases showed ≥ 1 nucleotide shift in ≥ 2 markers and were considered as MSI-high. However, when the cut-off was set to ≥ 3 nucleotide shifts in ≥ 2 markers, three EGJ (37.5%), two small intestine (13.3%) and two gastric (3.5%) cancers showed false-negative results. In addition, cases with isolated loss of MSH6 or PMS2 showed smaller nucleotide shifts than those in others. MSI testing is applicable to non-colorectal gastrointestinal cancers; however, a subset can yield false-negative results due to subtle nucleotide shift in multiple markers. Analysis of paired tumour/normal tissues and careful interpretation is necessary to avoid false-negative results and ensure appropriate treatment.
BSUV-Net 2.0: Spatio-Temporal Data Augmentations for Video-Agnostic Supervised Background Subtraction

Background subtraction (BGS) is a fundamental video processing task which is a key component of many applications. Deep learning-based supervised algorithms achieve very good performance in BGS, however, most of these algorithms are optimized for either a specific video or a group of videos, and their performance decreases dramatically when applied to unseen videos. Recently, several papers addressed this problem and proposed video-agnostic supervised BGS algorithms. However, nearly all of the data augmentations used in these algorithms are limited to the spatial domain and do not account for temporal variations that naturally occur in video data. In this work, we introduce spatio-temporal data augmentations and apply them to one of the leading video-agnostic BGS algorithms, BSUV-Net. We also introduce a new cross-validation training and evaluation strategy for the CDNet-2014 dataset that makes it possible to fairly and easily compare the performance of various video-agnostic supervised BGS algorithms. Our new model trained using the proposed data augmentations, named BSUV-Net 2.0, significantly outperforms state-of-the-art algorithms evaluated on unseen videos of CDNet-2014. We also evaluate the cross-dataset generalization capacity of BSUV-Net 2.0 by training it solely on CDNet-2014 videos and evaluating its performance on LASIESTA dataset. Overall, BSUV-Net 2.0 provides a similar to 5% improvement in the F-score over state-of-the-art methods on unseen videos of CDNet-2014 and LASIESTA datasets. Furthermore, we develop a real-time variant of our model, that we call Fast BSUV-Net 2.0, whose performance is close to the state of the art.
Alternative mammalian strategies leading towards gastrulation: losing polar trophoblast (Rauber's layer) or gaining an epiblast cavity

Using embryological data from 14 mammalian orders, the hypothesis is presented that in placental mammals, epiblast cavitation and polar trophoblast loss are alternative developmental solutions to shield the central epiblast from extraembryonic signalling. It is argued that such reciprocal signalling between the edge of the epiblast and the adjoining polar trophoblast or edge of the mural trophoblast or with the amniotic ectoderm is necessary for the induction of gastrulation. This article is part of the theme issue 'Extraembryonic tissues: exploring concepts, definitions and functions across the animal kingdom'.
RelaHash: Deep Hashing With Relative Position

Deep hashing has been widely used as a solution to encoding binary hash code for approximating nearest neighbor problem. It has been showing superior performance in terms of its ability to index high-level features by learning compact binary code. Many recent state-of-the-art deep hashing methods often use multiple loss terms at once, thus introducing optimization difficulty and may result in sub-optimal hash codes. OrthoHash was proposed to replace those losses with just a single loss function. However, the quantization error minimization problem in OrthoHash is still not addressed effectively. In this paper, we take one step further - propose a single-loss model that can effectively minimize the quantization error without explicit loss terms. Specifically, we introduce a new way to measure the similarity between the relaxed codes with centroids, called relative similarity. The relative similarity is the similarity between the relative position representation of continuous codes and the normalized centroids. The resulting model outperforms many state-of-the-art deep hashing models on popular benchmark datasets.
Secretory pattern and regulatory mechanism of growth hormone in cattle

The ultradian rhythm of growth hormone (GH) secretion has been known in several animal species for years and has recently been observed in cattle. Although the physiological significance of the rhythm is not yet fully understood, it appears essential for normal growth. In this review, previous studies concerning the GH secretory pattern in cattle, including its ultradian rhythm, are introduced and the regulatory mechanism is discussed on the basis of recent findings.
Architecture of the chikungunya virus replication organelle

Alphaviruses are mosquito-borne viruses that cause serious disease in humans and other mammals. Along with its mosquito vector, the Alphavirus chikungunya virus (CHIKV) has spread explosively in the last 20 years, and there is no approved treatment for chikungunya fever. On the plasma membrane of the infected cell, CHIKV generates dedicated organelles for viral RNA replication, so-called spherules. Whereas structures exist for several viral proteins that make up the spherule, the architecture of the full organelle is unknown. Here, we use cryo-electron tomography to image CHIKV spherules in their cellular context. This reveals that the viral protein nsP1 serves as a base for the assembly of a larger protein complex at the neck of the membrane bud. Biochemical assays show that the viral helicase-protease nsP2, while having no membrane affinity on its own, is recruited to membranes by nsP1. The tomograms further reveal that full-sized spherules contain a single copy of the viral genome in double-stranded form. Finally, we present a mathematical model that explains the membrane remodeling of the spherule in terms of the pressure exerted on the membrane by the polymerizing RNA, which provides a good agreement with the experimental data. The energy released by RNA polymerization is found to be sufficient to remodel the membrane to the characteristic spherule shape.
3D Reconstruction of "In-the-Wild" Faces in Images and Videos

3D Morphable Models (3DMMs) are powerful statistical models of 3D facial shape and texture, and are among the state-of-the-art methods for reconstructing facial shape from single images. With the advent of new 3D sensors, many 3D facial datasets have been collected containing both neutral as well as expressive faces. However, all datasets are captured under controlled conditions. Thus, even though powerful 3D facial shape models can be learnt from such data, it is difficult to build statistical texture models that are sufficient to reconstruct faces captured in unconstrained conditions ("in-the-wild"). In this paper, we propose the first "in-the-wild" 3DMM by combining a statistical model of facial identity and expression shape with an "in-the-wild" texture model. We show that such an approach allows for the development of a greatly simplified fitting procedure for images and videos, as there is no need to optimise with regards to the illumination parameters. We have collected three new benchmarks that combine "in-the-wild" images and video with ground truth 3D facial geometry, the first of their kind, and report extensive quantitative evaluations using them that demonstrate our method is state-of-the-art.
On favourable conditions for adaptive random testing

Recently, adaptive random testing ( ART) has been developed to enhance the fault-detection effectiveness of random testing (RT). It has been known in general that the fault-detection effectiveness of ART depends on the distribution of failure-causing inputs, yet this understanding is in coarse terms without precise details. In this paper, we conduct an in-depth investigation into the factors related to the distribution of failure-causing inputs that have an impact on the fault-detection effectiveness of ART. This paper gives a comprehensive analysis of the favourable conditions for ART. Our study contributes to the knowledge of ART and provides useful information for testers to decide when it is more cost-effective to use ART.
Adaptive Image Enhancement Based on Guide Image and Fraction-Power Transformation for Wireless Capsule Endoscopy

Good image quality of the wireless capsule endoscopy (WCE) is the key for doctors to diagnose gastrointestinal (GI) tract diseases. However, the poor illumination, limited performance of the camera in WCE, and complex environment in the GI tract usually result in low-quality endoscopic images. Existing image enhancement methods only use the information of the image itself or multiple images of the same scene to accomplish the enhancement. In this paper, we propose an adaptive image enhancement method based on guide image and fraction-power transformation. First, intensities of endoscopic images are analyzed to assess the illumination conditions. Second, images captured under poor illumination conditions are enhanced by a brand-new image enhancement method called adaptive guide image based enhancement (AGIE). AGIE enhances low-quality images by using the information of a good quality image of the similar scene. Otherwise, images are enhanced by the proposed adaptive fraction-power transformation. Experimental results show that the proposed method improves the average intensity of endoscopic images by 64.20% and the average local entropy by 31.25%, which outperforms the state-of-art methods.
Crane: Mitigating Accelerator Under-utilization Caused by Sparsity Irregularities in CNNs

Convolutional neural networks (CNNs) have achieved great success in numerous AI applications. To improve inference efficiency of CNNs, researchers have proposed various pruning techniques to reduce both computation intensity and storage overhead. These pruning techniques result in multi-level sparsity irregularities in CNNs. Together with that in activation matrices, which is induced by employment of ReLU activation function, all these sparsity irregularities cause a serious problem of computation resource under-utilization in sparse CNN accelerators. To mitigate this problem, we propose a method of load-balancing based on a workload stealing technique. We demonstrate that this method can be applied to two major inference data-flows, which cover all state-of-the-art sparse CNN accelerators. Based on this method, we present an accelerator, called Crane, which addresses all kinds of sparsity irregularities in CNNs. We perform a fair comparison between Crane and state-of-the-art prior approaches. Experimental results show that Crane improves performance by 27% similar to 88% and reduces energy consumption by 16% similar to 48%, respectively, compared to the counterparts.