ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Lagging X chromatids specify the orientation of asymmetric organelle partitioning in XX spermatocytes of Auanema rhodensis

The unequal partitioning of molecules and organelles during cell division results in daughter cells with different fates. An extreme example is female meiosis, in which consecutive asymmetric cell divisions give rise to 1 large oocyte and 2 small polar bodies with DNA and minimal cytoplasm. Here, we test the hypothesis that during an asymmetric cell division during spermatogenesis of the nematode Auanema rhodensis, the late segregating X chromatids orient the asymmetric partitioning of cytoplasmic components. In previous studies, the secondary spermatocytes of wild-type XO males were found to divide asymmetrically to generate functional spermatids that inherit components necessary for sperm viability and DNA-containing residual bodies that inherit components to be discarded. Here we extend that analysis to 2 novel contexts. First, the isolation and analysis of a strain of mutant XX pseudomales revealed that such animals have highly variable patterns of X-chromatid segregation. The pattern of late segregating X chromatids nevertheless predicted the orientation of organelle partitioning. Second, while wild-type XX hermaphrodites were known to produce both 1X and 2X sperm, here, we show that spermatocytes within specific spermatogonial clusters exhibit 2 different patterns of X-chromatid segregation that correlate with distinct patterns of organelle partitioning. Together this analysis suggests that A. rhodensis has coopted lagging X chromosomes during anaphase II as a mechanism for determining the orientation of organelle partitioning.
Hepatitis B virus (HBV) codon adapts well to the gene expression profile of liver cancer: an evolutionary explanation for HBV's oncogenic role

Due to the evolutionary arms race between hosts and viruses, viruses must adapt to host translation systems to rapidly synthesize viral proteins. Highly expressed genes in hosts have a codon bias related to tRNA abundance, the primary RNA translation rate determinant. We calculated the relative synonymous codon usage (RSCU) of three hepatitis viruses (HAV, HBV, and HCV), SARS-CoV-2, 30 human tissues, and hepatocellular carcinoma (HCC). After comparing RSCU between viruses and human tissues, we calculated the codon adaptation index (CAI) of viral and human genes. HBV and HCV showed the highest correlations with HCC and the normal liver, while SARS-CoV-2 had the strongest association with lungs. In addition, based on HCC RSCU, the CAI of HBV and HCV genes was the highest. HBV and HCV preferentially adapt to the tRNA pool in HCC, facilitating viral RNA translation. After an initial trigger, rapid HBV/HCV translation and replication may change normal liver cells into HCC cells. Our findings reveal a novel perspective on virus-mediated oncogenesis.
Sustainable Management of Contemporary Art Galleries: A Delphi Survey for the Spanish Art Market

The art market operates in a very different way from conventional economic markets, ranging from its behaviors of supply and demand, the trading of goods, and the economic agents intervening in it. In addition, it is a highly unregulated market, with very little standardized information in economic terms. This paper focuses on art galleries, which are the most influential intermediaries in the Spanish primary contemporary fine-art market and perform a role that goes beyond the mere distribution of works of art. This study develops and applies a prospective methodology based on the subjective information compiled by experts, known as the Delphi method, to identify and evaluate the factors that determine the current situation and future outlook for Spanish contemporary art galleries. The results show, on one hand, that the method employed constitutes a valid option to provide reliable information. In addition, they show that the survival of these organizations will depend on their ability to adapt to the changing conditions of the economic environment, reactivating and internationalizing demand, and redirecting their business model towards sustainable management by implementing appropriate business management models and techniques.
Study on Ocean Intelligence Service Security Utilizing Blockchain Technology

The environment of ocean having special conditions is at risk of frequent accidents. For safer use of ocean environment, the whole world is attempting to graft the state-of-the-art technology with the introduction of lots of institutions such as international treaties. In the meantime, with the development of the state-of-the-art IT technology and increase in superhigh speed network establishment, the era of the Internet of Things is opening, and there are increasing cases of fusion with IT in the ocean area. Accordingly, at a time when security for data is especially required, blockchain technology is one of the major alternatives. This study examined application possibility and limits of blockchain technology through ocean space information's security treatment experiment utilizing blockchain technology. For the experiment, GML data which has S-10X electric navigation chart standard structure was used. The experiment results suggested that descrambling of ocean space information utilizing blockchain technology was successfully conducted for safe protection, and it is expected that it would be utilized for ocean space information of other types.
Incorporating Non-Convex Operating Characteristics Into Bi-Level Optimization Electricity Market Models

Bi-level optimization constitutes the most popular mathematical methodology for modeling the deregulated electricity market. However, state-of-the-art models neglect the physical non-convex operating characteristics of market participants, due to their inherent inability to capture binary decision variables in their representation of the market clearing process, rendering them problematic in modeling markets with complex bidding and unit commitment (UC) clearing mechanisms. This paper addresses this fundamental limitation by proposing a novel modeling approach enabling incorporation of these non-convexities into bi-level optimization market models, which is based on the relaxation and primal-dual reformulation of the original, non-convex lower level problem and the penalization of the associated duality gap. Case studies demonstrate the ability of the proposed approach to closely approximate the market clearing solution of the actual UC clearing algorithm and devise more profitable bidding decisions for strategic producers than the state-of-the-art bi-level optimization approach, and reveal the potential of strategic behavior in terms of misreporting non-convex operating characteristics.
State of the Art in Holographic Displays: A Survey

True-3D imaging and display systems are based on physical duplication of light distribution. Holography is a true-3D technique. There are significant developments in electro-holographic displays in recent years. Liquid crystal, liquid crystal on silicon, optically addressed, mirror-based, holographic polymer-dispersed, and acousto-optic devices are used as holographic displays. There are complete electro-holographic display systems and some of them are already commercialized.
Estimating healthcare expenditures after becoming divorced or widowed using propensity score matching

Becoming divorced or widowed are stressful life events experienced by a substantial part of the population. While marital status is a significant predictor in many studies on healthcare expenditures, effects of a change in marital status, specifically becoming divorced or widowed, are less investigated. This study combines individual health claims data and registered sociodemographic characteristics from all Dutch inhabitants (about 17 million) to estimate the differences in healthcare expenditure for individuals whose marital status changed (n = 469,901) compared to individuals who remained married, using propensity score matching and generalized linear models. We found that individuals who were (long-term) divorced or widowed had 12-27% higher healthcare expenditures (RR = 1.12, 95% CI 1.11-1.14; RR = 1.27, 95% CI 1.26-1.29) than individuals who remained married. Foremost, this could be attributed to higher spending on mental healthcare and home care. Higher healthcare expenditures are observed for both divorced and widowed individuals, both recently and long-term divorced/widowed individuals, and across all age groups, income levels and educational levels.
Experiences of romantic relationships among early adults who do not turn to their long-term partner when in need of love and support

As most early adults in long-term romantic relationships rely on their partner to fulfill their relational needs, relationships with romantic partners are very important to many people at this time of life. However, there is a group of individuals for whom their long-term partner is not the most important person when they need love and support. This study explored experiences of romantic relationships among early adults who do not turn to their long-term partner to meet these needs. Twelve individuals in early adulthood (Mage = 33.3 years; SD = 0.54) were selected from a larger community sample (N = 124) based on their answers on a questionnaire about who they turn to when in need of love and support. A thematic analysis of their answers in interviews about romantic relationships resulted in three main themes: Distancing attitudes toward romantic relationships, Desirable aspects of romantic relationships, and Not thinking about romantic relationships. The results show that these early adults' reflections on romantic relationships were characterized by ambivalence, striving toward independence, and relating to their partner as a person to have fun with rather than someone to share a deep connection with. Participants also expressed disagreement with what they perceived as norms concerning romantic relationships. Taken together, this study illustrates that the experiences of early adults who do not turn to their long-term partner to meet needs of love and support are characterized by a complex interaction between contradictory feelings, values, and behaviors.
Fast Blind Image Deblurring Using Smoothing-Enhancing Regularizer

Blind deconvolution is a highly ill-posed problem for the restoration of degraded images and requires prior knowledge or regularization. Recently, various priors have been proposed and the models based on these priors have achieved state-of-the-art performances. In this paper, we present a blind image deblurring method based on a computationally efficient and effective image regularizer. The proposed regularizer is motivated by the fact that the success of recent priors mainly stems from their properties, which implicitly generate an unnatural latent image suppressing insignificant structures and preserving only salient edges. These salient edges guide the models to estimate an accurate kernel. In this paper, the proposed regularizer termed smoothing-enhancing regularizer, not only assures that only salient structures in the image are preserved but also enhances these salient structures to help the model estimate the more accurate kernel. To efficiently solve the proposed model, we develop an efficient numerical approach based on the half-quadratic splitting algorithm and the lagged-fixed-point iteration scheme. The optimization scheme only requires a few additional shrinkage operations compared with the original half-quadratic splitting algorithm, making our method much faster than recent leading methods. The qualitative and quantitative experimental results show that our algorithm achieves the state-of-the-art results and can be extended to other challenging deblurring tasks, such as those involving text, face, and low-illuminated images. Furthermore, the proposed method is much more computationally efficient than the recent state-of-the-art algorithms with up to more than 10x faster execution time.
Semi-supervised robust deep neural networks for multi-label image classification

This paper introduces a robust method for semi-supervised training of deep neural networks for multi-label image classification. To this end, a ramp loss is utilized since it is more robust against noisy and incomplete image labels compared to the classic hinge loss. The proposed method allows for learning from both labeled and unlabeled data in a semi-supervised setting. This is achieved by propagating labels from the labeled images to their unlabeled neighbors in the feature space. Using a robust loss function becomes crucial here, as the initial label propagations may include many errors, which degrades the performance of non-robust loss functions. In contrast, the proposed robust ramp loss restricts extreme penalties from the samples with incorrect labels, and the label assignment improves in each iteration and contributes to the learning process. The proposed method achieves state-of-the-art results in semi-supervised learning experiments on the CIFAR-10 and STL-10 datasets, and comparable results to the state-of the-art in supervised learning experiments on the NUS-WIDE and MS-COCO datasets. Experimental results also verify that our proposed method is more robust against noisy image labels as expected. (C) 2019 Elsevier Ltd. All rights reserved.
COVID-19 Vaccination-Induced Cholangiopathy and Autoimmune Hepatitis: A Series of Two Cases

The coronavirus disease 2019 (COVID-19) pandemic has been associated with significant morbidity and mortality. Following the introduction of vaccines, various side effects have been reported. Whilst those reported may be attributed to the vaccine itself, at times, it may simply incite an immunological phenomenon. We present a case series of two patients who presented with symptoms of yellowing of the eyes and the skin along with fatigue, and tiredness, following vaccination for COVID-19. The diagnosis of post COVID-19-vaccination related hepatitis is one of the fewer, less understood, yet reported side effects associated with significant morbidity. The diagnosis of COVID-19 vaccination-related cholangitis is an outcome reported here for the first time to the best of our knowledge. It was alarming that both patients did not have any significant past history of medical ailments. A prompt assessment followed by investigations including liver biopsy assisted in a timely understanding of the phenomenon with complete resolution of the symptoms.
Super-resolution using neural networks based on the optimal recovery theory

An optimal recovery based neural-network Super Resolution algorithm is developed. The proposed method is computationally less expensive and outputs images with high subjective quality, compared with previous neural-network or optimal recovery algorithms. It is evaluated on classical SR test images with both generic and specialized training sets, and compared with other state-of-the-art methods. Results show that our algorithm is among the state-of-the-art, both in quality and efficiency.
Graph-Based Intercategory and Intermodality Network for Multilabel Classification and Melanoma Diagnosis of Skin Lesions in Dermoscopy and Clinical Images

The identification of melanoma involves an integrated analysis of skin lesion images acquired using clinical and dermoscopy modalities. Dermoscopic images provide a detailed view of the subsurface visual structures that supplement the macroscopic details from clinical images. Visual melanoma diagnosis is commonly based on the 7-point visual category checklist (7PC), which involves identifying specific characteristics of skin lesions. The 7PC contains intrinsic relationships between categories that can aid classification, such as shared features, correlations, and the contributions of categories towards diagnosis. Manual classification is subjective and prone to intra- and interobserver variability. This presents an opportunity for automated methods to aid in diagnostic decision support. Current state-of-the-art methods focus on a single image modality (either clinical or dermoscopy) and ignore information from the other, or do not fully leverage the complementary information from both modalities. Furthermore, there is not a method to exploit the 'intercategory' relationships in the 7PC. In this study, we address these issues by proposing a graph-based intercategory and intermodality network (GIIN) with two modules. A graph-based relational module (GRM) leverages intercategorical relations, intermodal relations, and prioritises the visual structure details from dermoscopy by encoding category representations in a graph network. The category embedding learning module (CELM) captures representations that are specialised for each category and support the GRM. We show that our modules are effective at enhancing classification performance using three public datasets (7PC, ISIC 2017, and ISIC 2018), and that our method outperforms state-of-the-art methods at classifying the 7PC categories and diagnosis.
Identification of the novel KIR3DL1*00703 allele in a Chinese Han individual

The novel KIR3DL1*00703 allele differs from the closest allele KIR3DL1*00701 by a single silent mutation.
Attention and boundary guided salient object detection

In recent years, fully convolutional neural network (FCN) has broken all records in various vision task. It also achieves great performance in salient object detection. However, most of the state-of-the-art methods have suffered from the challenge of precisely segmenting the entire salient object with uniform region and explicit boundary and effectively suppressing the backgrounds on complex images. There is still a large room for improvement over the FCN-based saliency detection approaches. In this paper, we propose an attention and boundary guided deep neural network for salient object detection to better locate and segment the salient objects with uniform interior and explicit boundary. A channel-wise attention module is utilized to emphasize the important regions, which selects the important feature channels and assigns large weights to them. A boundary information localization module is proposed for suppressing the irrelevant boundary information to better locate and explore the useful structure of objects. The proposed approach achieves state-of-the-art performance on four well-known benchmark datasets. (C) 2020 Elsevier Ltd. All rights reserved.
Development of a Punch-O-Meter for Sport Karate Training

In karate sparring (kumite), punches are used more than kicks to score points. Among these punches, gyaku tsuki is a very commonly used punch. The objective of the punch is to hit the target at a medium range in a very short time, producing a maximum force. In this study, we proposed the development of a novel standalone Punch-O-Meter system to measure the speed and the force generated by a punch.
Lesion-Harvester: Iteratively Mining Unlabeled Lesions and Hard-Negative Examples at Scale

The acquisition of large-scale medical image data, necessary for training machine learning algorithms, is hampered by associated expert-driven annotation costs. Mining hospital archives can address this problem, but labels often incomplete or noisy, e.g., 50% of the lesions in DeepLesion are left unlabeled. Thus, effective label harvesting methods are critical. This is the goal of our work, where we introduce Lesion-Harvester-a powerful system to harvest missing annotations from lesion datasets at high precision. Accepting the need for some degree of expert labor, we use a small fully-labeled image subset to intelligently mine annotations from the remainder. To do this, we chain together a highly sensitive lesion proposal generator (LPG) and a very selective lesion proposal classifier (LPC). Using a new hard negative suppression loss, the resulting harvested and hard-negative proposals are then employed to iteratively finetune our LPG. While our framework is generic, we optimize our performance by proposing a new 3D contextual LPG and by using a global-local multi-view LPC. Experiments on DeepLesion demonstrate that Lesion-Harvester can discover an additional 9,805 lesions at a precision of 90%. We publicly release the harvested lesions, along with a new test set of completely annotated DeepLesion volumes. We also present a pseudo 3D IoU evaluation metric that corresponds much better to the real 3D IoU than current DeepLesion evaluation metrics. To quantify the downstream benefits of Lesion-Harvester we show that augmenting the DeepLesion annotations with our harvested lesions allows state-of-the-art detectors to boost their average precision by 7 to 10%.
A Free-Standing Polyaniline/Silicon Nanowire Forest as the Anode for Lithium-ion Batteries

Despite its high theoretical capacity, silicon anode has limited intrinsic conductivity and experiences significant volume changes during charge-discharge. To overcome these issues, facile metal-assisted chemical etching and in-situ polymerization of aniline are employed to produce a dense 1D polyaniline/silicon nanowire forest without noticeable agglomeration as a free-standing anode for lithium-ion batteries. This hybrid electrode possesses high cycling performance, delivering a stable capacity capped at 2 mAh cm-2 for 346 cycles of charge-discharge. Maximum capacity of 2 mAh cm-2 is also achievable at high-rate cell testing of 2 mA cm-2 , which cannot be obtained by the anode with plain silicon wafer and silicon nanowire only. The introduction of polyaniline on the silicon nanowire is shown to reduce the solid electrolyte interface (SEI) resistance, stabilize the SEI layer, further alleviate the effect of volume changes, and boost the conductivity of the hybrid anode, resulting in the high electrochemical performance of the anode.
State of the art and future perspectives of thermophilic anaerobic digestion

The slate of the art of thermophilic digestion is discussed. Thermophilic digestion is a well established technology in Europe for treatment of mixtures of waste in common large scale biogas plants or for treatment of the organic fraction of municipal solid waste. Due to a large number of failures over time with thermophilic digestion of sewage sludge this process has lost its appeal in the USA. New demands on sanitation of biosolids before land use will, however, bring the attention back to the use of elevated temperatures during sludge stabilization. In the paper we show how the use of a start-up strategy based on the actual activity of key microbes can be used to ensure proper and fast transfer of mesophilic digesters into thermophilic operation. Extreme thermophilic temperatures of 65degreesC or more may be necessary in the future to meet the demands for full sanitation of the waste material before final disposal. We show data of anaerobic digestion at extreme thermophilic temperatures.