ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation

Prostate segmentation in transrectal ultrasound (TRUS) image is an essential prerequisite for many prostate-related clinical procedures, which, however, is also a long-standing problem due to the challenges caused by the low image quality and shadow artifacts. In this paper, we propose a Shadow-consistent Semi-supervised Learning (SCO-SSL) method with two novel mechanisms, namely shadow augmentation (Shadow-AUG) and shadow dropout (Shadow-DROP), to tackle this challenging problem. Specifically, Shadow-AUG enriches training samples by adding simulated shadow artifacts to the images to make the network robust to the shadow patterns. Shadow-DROP enforces the segmentation network to infer the prostate boundary using the neighboring shadow-free pixels. Extensive experiments are conducted on two large clinical datasets (a public dataset containing 1,761 TRUS volumes and an in-house dataset containing 662 TRUS volumes). In the fully-supervised setting, a vanilla U-Net equipped with our Shadow-AUG&Shadow-DROP outperforms the state-of-the-arts with statistical significance. In the semi-supervised setting, even with only 20% labeled training data, our SCO-SSL method still achieves highly competitive performance, suggesting great clinical value in relieving the labor of data annotation. Source code is released at https://github.com/DIAL-RPI/SCO-SSL.
The Effects of Iconicity and Conventionalization on Word Order Preferences

Of the six possible orderings of the three main constituents of language (subject, verb, and object), two-SOV and SVO-are predominant cross-linguistically. Previous research using the silent gesture paradigm in which hearing participants produce or respond to gestures without speech has shown that different factors such as reversibility, salience, and animacy can affect the preferences for different orders. Here, we test whether participants' preferences for orders that are conditioned on the semantics of the event change depending on (i) the iconicity of individual gestural elements and (ii) the prior knowledge of a conventional lexicon. Our findings demonstrate the same preference for semantically conditioned word order found in previous studies, specifically that SOV and SVO are preferred differentially for different types of events. We do not find that iconicity of individual gestures affects participants' ordering preferences; however, we do find that learning a lexicon leads to a stronger preference for SVO-like orders overall. Finally, we compare our findings from English speakers, using an SVO-dominant language, with data from speakers of an SOV-dominant language, Turkish. We find that, while learning a lexicon leads to an increase in SVO preference for both sets of participants, this effect is mediated by language background and event type, suggesting that an interplay of factors together determines preferences for different ordering patterns. Taken together, our results support a view of word order as a gradient phenomenon responding to multiple biases.
Hydroquinone derivatives attenuate biofilm formation and virulence factor production in Vibrio spp

Gram-negative Vibrio parahaemolyticus is a halophilic human pathogen known to be the leading cause of food poisoning associated with consuming uncooked or undercooked seafood. The increasing presence and contamination of seafood have caused serious safety concerns in food facilities. Notably, it can form biofilms on food surfaces that confer resistance to antimicrobial treatments. Therefore, in the present study, the antibacterial, antibiofilm, and antivirulence activities of hydroquinone (HQ) and its 16 derivatives were investigated against V. parahaemolyticus and V. harveyi. Representative active antibacterial and antibiofilm compounds, 2,3-dimethylhydroquinone (2,3-DMHQ) and 2,5-ditert-butylhydroquinone (DBHQ), were further examined using a crystal violet assay, biochemical reactions, live cell imaging, and scanning electron microscopy. 2,3-DMHQ with a minimum inhibitory concentration (MIC) of 20 μg/mL completely inhibited biofilm formation at a sub-MIC of 15 μg/mL. And, DBHQ with an MIC of ˃1000 μg/mL reduced biofilm formation by 70 % at sub-MIC of 25 μg/mL. Both 2,3-DMHQ and DBHQ inhibited protease and indole production as well as motility phenotypes. 2,3-DMHQ decreased fimbriae production and hydrophobicity whereas DBHQ did not. Transcriptomic studies revealed that genes related to biofilm, quorum sensing (QS), and hemolysin were downregulated. In addition, 2,3-DMHQ and DBHQ prevented biofilm formation of V. parahaemolyticus on squid surfaces and 2,3-DMHQ reduced the presence of V. parahaemolyticus in a boiled shrimp model. Toxicity assays using the Caenorhabditis elegans and seed germinations models showed that they were non-to-mildly toxic. These results suggest that 2,3-DMHQ and DBHQ possess the antimicrobial properties required to control V. parahaemolyticus planktonic and biofilm states in food production facilities.
Moving object segmentation in Daubechies complex wavelet domain

Motion segmentation is a crucial step in video analysis and is associated with a number of computer vision applications. This paper introduces a new method for segmentation of moving object which is based on double change detection technique applied on Daubechies complex wavelet coefficients of three consecutive frames. Daubechies complex wavelet transform for segmentation of moving object has been chosen as it is approximate shift invariant and has a better directional selectivity as compared to real valued wavelet transform. Double change detection technique is used to obtain video object plane by inter-frame difference of three consecutive frames. Double change detection technique also provides automatic detection of appearance of new objects. The proposed method does not require any other parameter except Daubechies complex wavelet coefficients. Results of the proposed method for segmentation of moving objects are compared with results of other state-of-the-art methods in terms of visual performance and a number of quantitative performance metrics viz. Misclassification Penalty, Relative Foreground Area Measure, Pixel Classification Based Measure, Normalized Absolute Error, and Percentage of Correct Classification. The proposed method is found to have high degree of segmentation accuracy than the other state-of-the-art methods.
Fast Superpixel Based Subspace Low Rank Learning Method for Hyperspectral Denoising

Sequential data, such as video frames and event data, have been widely applied in the real-world. As a special kind of sequential data, hyperspectral images (HSIs) can be regarded as a sequence of 2-D images in the spectral dimension, which can be effectively utilized for distinguishing different land-covers according to the spectral sequences. This paper presents a novel noise reduction method based on superpixel-based subspace low rank representation for hyperspectral imagery. First, under the framework of a linear mixture model, the original hyperspectral cube is assumed to be low rank in the spectral domain, which could be represented by decomposing HSI data into two sub-matrices of lower ranks. Meanwhile, due to the high correlation of neighboring pixels, the spectra within each neighborhood would also promote low rankness, and the local spatial low rankness could be exploited by enforcing the nuclear norm within superpixel-based regions in the decomposed subspace. The superpixels are easily and effectively generated by utilizing state-of-the-art superpixel segmentation algorithms in the first principal component of the original HSI. Moreover, benefiting from the subspace decomposition, the proposed method has an overwhelming superiority in computational cost than the state-of-the-art LR-based methods. The final model could be efficiently solved by the augmented Lagrangian method. Experimental results on simulated and real hyperspectral data sets validate that the proposed method produces superior performance than other state-of-the-art denoising methods in terms of quantitative assessment and visual quality.
Dilated Convolutional Pixels Affinity Network for Weakly Supervised Semantic Segmentation

This paper studies semantic segmentation primarily under image-level weak-supervision. Most state-of-the-art technologies have recently used deep classification networks to create small and sparse discriminatory seed regions of each interest target as pseudo-labels for training segmentation networks, which achieve inferior performance compared with the fully supervised setting. We propose a Dilated convolutional pixels affinity network (DCPAN) to localize and expand the seed regions of objects to bridge this gap. Although introduced dilated convolutional units enable capture of additional location information of objects, it falsely highlighted true negative regions as dilated rate enlarge. To address this problem, we properly integrate dilated convolutional units with different dilated rates and self-attention mechanisms to obtain pixel affinity measure matrix for promoting classification network to generate high-quality object seed regions as pseudo-labels; thus, the performance of the segmentation network is boosted. Furthermore, although our approach seems simple, our method obtains a competitive performance, and experiments show that the performance of DCPAN outperforms other state-of-art approaches in weakly-supervised settings, which only use image-level labels on the Pascal VOC 2012 dataset.
Changes in temporomandibular joint morphology in class II patients treated with fixed mandibular repositioning and evaluated through 3D imaging: a systematic review

To estimate the effects of skeletal class II malocclusion treatment using fixed mandibular repositioning appliances on the position and morphology of the temporomandibular joint (TMJ). Two independent reviewers performed comprehensive electronic searches of MEDLINE, EMBASE, EBM reviews and Scopus (until May 5, 2015). The references of the identified articles were also manually searched. All studies investigating morphological changes of the TMJ articular disc, condyle and glenoid fossa with 3D imaging following non-surgical fixed mandibular repositioning appliances in growing individuals with class II malocclusions were included in the analysis. Of the 269 articles initially reviewed, only 12 articles used magnetic resonance imaging and two articles used computed tomography (CT) or cone-beam CT images. Treatment effect on condyle and glenoid fossa was discussed in eight articles. Treatment effect on TMJ articular disc position and morphology was discussed in seven articles. All articles showed a high risk of bias due to deficient methodology: inadequate consideration of confounding variables, blinding of image assessment, selection or absence of control group and outcome measurement. Reported changes in osseous remodelling, condylar and disc position were contradictory. The selected articles failed to establish conclusive evidence of the exact nature of TMJ tissue response to fixed mandibular repositioning appliances.
Deinococcus betulae sp. nov. and Deinococcus arboris sp. nov., novel bacteria isolated from bark of birch tree ( Betula platyphylla)

Two reddish-coloured bacterial strains (HMF7604T and HMF7620T) were isolated from bark of birch tree (Betula platyphylla) together with two strains (designed as HMF7603 and HMF7618). Cells were observed to be Gram-stain-negative, oval- to short rod-shaped and non-motile. Phylogenetic analysis based on 16S rRNA gene sequences indicated that the four isolates belonged to the genus Deinococcus, family Deinococcaceae. They had the highest similarities (95.4-95.6 %) to Deinococcus multiflagellatus ID1504T, with which they formed a clade in phylogenetic trees. Menaquinone-8 was the only respiratory quinone. The predominant fatty acids were summed feature 3 (C16 : 1  ω7c and/or C16 : 1  ω6c), C15 : 1  ω6c, C17 : 0 and C16 : 0. Strain HMF7604T contained two unidentified phosphoglycolipids, nine unidentified glycolipids, one unidentified aminolipid, three unidentified phospholipids and three unidentified polar lipids, while strain HMF7620T contained one unidentified phosphoglycolipid, four unidentified glycolipids, one unidentified aminophospholipid, one unidentified phospholipid and one unidentified polar lipid. The DNA G+C contents of strains HMF7604T and HMF7620T were 65.6 and 65.7 mol%, respectively. The average nucleotide identity and digital DNA-DNA hybridization values between the two isolates and their close relative D. multiflagellatus were 81.1-95.3 and 24.5-61.6 %, respectively. Based on the results of phenotypic and phylogenetic characterizations, the four isolates are considered to represent two novel species of the genus Deinococcus, for which the names Deinococcus betulae sp. nov. and Deinococcus arboris sp. nov. are proposed. The type strains are HMF7604T (=KCTC 43354T=NBRC 115489T) and HMF7620T (=KCTC 43051T=NBRC 113959T).
Evidence and Impacts of Nanoplastic Accumulation on Crop Grains

Nanoplastics are emerging pollutants of global concern. Agricultural soil is becoming a primary sink for nanoplastics generated from plastic debris. The uptake and accumulation of nanoplastics by crops contaminate the food chain and pose unexpected risks to human health. However, whether nanoplastics can enter grains and their impact on the grains of crop grown in contaminated soil is still unknown. Here, the translocation of polystyrene nanoplastics (PS-NPs) in crops, including peanut (Arachis hypogaea L.) and rice (Oryza sativa L.) is investigated. It is demonstrated PS-NPs translocation from the root and accumulation in the grains at the maturation stage. The treatment with PS-NPs (250 mg kg-1 ) increases the empty-shell numbers of rice grain by 35.45%, thereby decreasing the seed-setting rate of rice by 3.02%, and also decreases the average seed weight of peanuts by 3.45%. Moreover, PS-NPs exerted adverse effects on nutritional quality, such as decreasing the content of mineral elements, amino acids, and unsaturated fatty acids. To the knowledge, this is the first report of the presence of nanoplastics in the grains of crop plants grown in soil containing nanoplastics, and the results highlight the impact of nanoplastics on the yield and nutritional quality of crop grains.
Yellow-Green Luminescence Due to Polarity-Dependent Incorporation of Carbon Impurities in Self-Assembled GaN Microdisk

Yellow-green luminescence (YGL) competes with near-bandgap emission (NBE) for carrier recombination channels, thereby reducing device efficiency; yet uncovering the origin of YGL remains a major challenge. In this paper, nearly stress-free and low dislocation density self-assembled GaN microdisks were synthesized by Na-flux method. The YGL of GaN microdisks highly depend on their polar facets. Variable accelerating voltage/power CL, variable temperature PL, and Raman spectroscopy are further performed to clarify the origin of polarity dependence of GaN microdisk YGL behavior, which indicates its independence of dislocations, surface effects, stress, crystalline quality, and gallium vacancies. It was found that the incorporation ability of carbon impurities in the polar (0001) facet is greater than that in the semipolar (101̅1) facets, producing higher content of CN or CNON defects, resulting in a more pronounced YGL in the polar (0001) facet of GaN.
Multimodal Single-Cell Translation and Alignment with Semi-Supervised Learning

Single-cell multi-omics technologies enable comprehensive interrogation of cellular regulation, yet most single-cell assays measure only one type of activity-such as transcription, chromatin accessibility, DNA methylation, or 3D chromatin architecture-for each cell. To enable a multimodal view for individual cells, we propose Polarbear, a semi-supervised machine learning framework that facilitates missing modality profile prediction and single-cell cross-modality alignment. Polarbear learns to translate between modalities by using data from co-assay measurements coupled with the large quantity of single-assay data available in public databases. This semi-supervised scheme mitigates issues related to low cell quantities and high sparsity in co-assay data. Polarbear first pre-trains a beta-variational autoencoder for each modality using both co-assay and single-assay profiles to learn robust representations of individual cells, and it then uses the co-assay labels to train a translator between these cell representations. This semi-supervised framework enables us to predict missing modality profiles and match single cells across modalities with improved accuracy compared with fully supervised methods, thus facilitating multimodal data integration.
Understanding Performance Limitations of Cu(In,Ga)Se-2 Solar Cells due to Metastable Defects-A Route toward Higher Efficiencies

Thin-film Cu(In,Ga)Se-2 solar cells reach power conversion efficiencies exceeding 23% and nonradiative recombination in the bulk is reported to limit device performance. The diode factor has not received much attention, although it limits the fill factor, and therefore the efficiency, for state-of-the-art solar cells. Herein, the diode factor of Cu(In,Ga)Se-2 absorbers, measured by photoluminescence spectroscopy, and of solar cells, measured by current-voltage and capacitance-voltage characteristics, are compared, supported by simulations using rate equations of generation and recombination. It is found that the diode factor is already increased in the neutral zone of the absorber due to metastable defects, such as the V-Se-V-Cu defect found in Cu(In,Ga)Se-2, because of an increased net acceptor density upon minority-carrier injection. The metastable and persistent increase of the net acceptor density has a detrimental effect on the device performance. Diode factors of 1 and efficiencies exceeding 24% are expected when, in current state-of-the-art Cu(In,Ga)Se-2 solar cells, the formation of metastable defects is suppressed.
Visibility enhancement of fog degraded images using adaptive defogging function

In the field of image processing, analyzing fog-affected images is challenging, as their visibility is degraded. In the absence of state-of-the-art image processing techniques to mitigate the impact of high-density fog, an adaptive-function-based image-defogging technique is proposed in this paper. The proposed technique accurately enhances such degraded images by adjusting the contrast and brightness based on a suitable threshold operator. The images are subsequently characterized as foggy or non-foggy on the basis of objective evaluation. The experimental results have proven that the proposed method achieves superior performance in terms of qualitative evaluation on non-reference metric (i.e., in terms of e = 0.468, sigma = 0, r = 1.8857) and reference metric (i.e. in terms of MSE = 1580, PSNR = 19.2126, NCC = 0.4873, SC = 0.4684, MD = 60, NAE = 0.2229) compared with nine state-of-the-art dehazing methods. Furthermore, based on the average computational time achieved by the proposed method (0.36 s using a test set of 2000 images), it can be highly suitable for real-time applications.
3D facial expression recognition using kernel methods on Riemannian manifold

Automatic human Facial Expressions Recognition (FER) is becoming of increased interest. FER finds its applications in many emerging areas such as affective computing and intelligent human computer interaction. Most of the existing work on FER has been done using 2D data which suffers from inherent problems of illumination changes and pose variations. With the development of 3D image capturing technologies, the acquisition of 3D data is becoming a more feasible task. The 3D data brings a more effective solution in addressing the issues raised by its 2D counterpart. State-of-the-art 3D FER methods are often based on a single descriptor which may fail to handle the large inter-class and intra-class variability of the human facial expressions. In this work, we explore, for the first time, the usage of covariance matrices of descriptors, instead of the descriptors themselves, in 3D FER. Since covariance matrices are elements of the non-linear manifold of Symmetric Positive Definite (SPD) matrices, we particularly look at the application of manifold-based classification to the problem of 3D FER. We evaluate the performance of the proposed framework on the BU-3DFE and the Bosphorus datasets, and demonstrate its superiority compared to the state-of-the-art methods. (C) 2017 Elsevier Ltd. All rights reserved.
Laypeople's perceptions of the effects of event repetition, reporting delay, and emotion on children's and adults' memory

For crimes such as child abuse and family violence, jurors' assessments of memory reports from key witnesses are vital to case outcomes in court. Since jurors are not experts on memory, the present research measured laypeople's (i.e., non-experts') beliefs about how three key factors affect witnesses' memory reports for an experienced event: how frequently an event was experienced (repeated, single), the delay between experiencing and reporting the event, and the emotional valence of the event. Across two studies, lay participants completed an online survey that measured their beliefs about each factor. In Study 1, 51 participants completed a survey about how the three factors affect children's memory. In Study 2, another 51 participants completed a survey about how the three factors affect adult's memory. Across both studies, delays were believed to worsen memory, and emotion was believed to improve memory. Beliefs about single and repeated events showed different patterns across the studies. In Study 1, participants' beliefs about children's memory for repeated experience were variable. In Study 2, participants believed that adults' memory was worse for repeated events than single events. Overall, laypeople demonstrated many accurate beliefs about memory, but showed some confusion about children's memory for repeated events.
Mexicanolide limonoids from the seeds of Khaya ivorensis with antimicrobial activity

The methanol extract of the seeds of Khaya ivorensis afforded two new mexicanolide limonoids, ivorensines A and B (1 and 2), together with one known compound, ruageanin D (3). The structures of the isolated compounds were established based on 1 D and 2 D (1H-1H COSY, HMQC, and HMBC) NMR spectroscopy, in addition to high resolution mass spectrometry. The isolated limonoids were tested in vitro for antimicrobial potentials against 5 pathogenic microorganisms. As a result, compounds 1-3 exhibited antimicrobial activity against the tested Gram negative bacteria at the minimum inhibitory concentration values less than 50 μg/ml.